Study 1 Analyses

DATA PREP

Data & libraries

library(psych)
library(tidyverse)
library(knitr)
library(lme4)
library(lmerTest)
library(stringr)
library(FactoMineR)
library(factoextra)

d <- read.csv("CarnegieProject_study1.csv")
nrow(d) #654

## [1] 654

1. Cleaning Data

d <- d[d$Consent == 1,]
nrow(d) #634 - 19 did not consent
d <- d[d$DistributionChannel == "anonymous",]
nrow(d) #634
d <- d[d$age > 0,]
nrow(d) #634
d <- d[-grep("Black Tea", d$persRes1_comments),] # cleaning - omit nonsense response
nrow(d) #n = 633

a. Constructing variables

## Partisan ID
# Creating continuous measure of party from branching ANES question

d$partyCont <- NA
d$partyCont[d$demStrength == 1] <- -3
d$partyCont[d$demStrength == 2] <- -2
d$partyCont[d$partyClose == 1] <- -1
d$partyCont[d$partyClose == 3] <- 0
d$partyCont[d$partyClose == 2] <- 1
d$partyCont[d$repStrength == 2] <- 2
d$partyCont[d$repStrength == 1] <- 3

## Creating factor measure of party from continuous measure
# leaners leave Independent
d$party_factor <- NA
d$party_factor[d$partyCont == -3 | d$partyCont == -2] <- "Democrat"
d$party_factor[d$partyCont == 3 | d$partyCont == 2 ] <- "Republican"
d$party_factor[d$partyCont == 0 | d$partyCont == -1 | d$partyCont == 1] <- "Independent"

# leaners group with party
d$party_factor2 <- NA
d$party_factor2[d$partyCont == -3 | d$partyCont == -2 | d$partyCont == -1] <- "Democrat"
d$party_factor2[d$partyCont == 3 | d$partyCont == 2 | d$partyCont == 1] <- "Republican"
d$party_factor2[d$partyCont == 0] <- "Independent"

## Creating condition measure
d$cond <- NA
d$cond[d$FL_42_DO_Climate_Intro == 1] <- "climate"
d$cond[d$FL_42_DO_Ctrl_Intro == 1] <- "ctrl"

## Actions measures

d <- d %>% dplyr::mutate(
  across(
    .cols = behavs_1:behavs_30,
    .names = '{.col}x'
  )
)

behavs <- grep("behavs", colnames(d) )

names(d)[behavs[61:90]] <- paste0("act", substr(names(d[,behavs[1:30]]), start = 8, stop = 9))


d <- d %>%
   dplyr::mutate(across(act1:act30, ~na_if(., -98)))

d <- d %>%
   dplyr::mutate(across(act1:act30, ~na_if(., -99)))

## Checks
#addmargins(table(d$behavs_1, d$act1, exclude = F))
#addmargins(table(d$behavs_30, d$act30, exclude = F))

d <- d %>%
  dplyr::mutate(ideology = recode(ideology,
                           `-2` = "Strong Liberal",
                           `-1` = "Liberal",
                           `0` = "Moderate",
                           `1` = "Conservative",
                           `2` = "Strong Conservative"
     ))

d$ideology <- factor(d$ideology,
                     levels = c("Strong Liberal",
                           "Liberal",
                           "Moderate",
                           "Conservative",
                           "Strong Conservative"))

d <- d %>%
  dplyr::mutate(gend = recode(gender,
                           `1` = "Female",
                           `2` = "Male",
                           `3` = "Other"
     ))

b. Checks for missing data

d <- d[!is.na(d$cond),]

d %>% dplyr::summarise(across(everything(), ~ sum(is.na(.)))) 

psych::describe(d[,19:ncol(d)])[c('n','min','max','mean','skew','kurtosis')]

c. Survey timing

mins <- d$Duration..in.seconds./60
round(mean(mins, na.rm = T),2)

## [1] 8.44

median(mins, na.rm = T)

## [1] 6.75

ACTIONS ANALYSES

2. Descriptive Stats

a. Demographics

gender	n	proportion
Female	403	0.74
Male	140	0.26
Other	2	0.00

race	n	proportion
Asian, Asian-American	18	0.03
Black, African-American	76	0.14
Hispanic, Latino-American	24	0.04
Native American	7	0.01
Native Pacific Islander	2	0.00
Other	5	0.01
White, Caucasian-American	412	0.76
NA	1	0.00

party_factor	n	proportion
Democrat	179	0.33
Independent	191	0.35
Republican	172	0.32
NA	3	0.01

ideology	n	proportion
Strong Liberal	47	0.09
Liberal	77	0.14
Moderate	229	0.42
Conservative	118	0.22
Strong Conservative	74	0.14

education	n	proportion
Elementary/Grammar School	1	0.00
Middle School	4	0.01
High School or Equivalent	133	0.24
Vocational/Technical School	56	0.10
Some College	138	0.25
College or University	148	0.27
Masters Degree	45	0.08
Doctoral Degree	6	0.01
Professional Degree	9	0.02
Other	3	0.01
NA	2	0.00

income	n	proportion
Prefer not to say	28	0.05
≤ $10,000	31	0.06
$10,000 - $19,999	52	0.10
$20,000 - $29,999	64	0.12
$30,000 - $39,999	68	0.12
$40,000 - $49,999	44	0.08
$50,000 - $74,999	109	0.20
$75,000 - $99,999	53	0.10
$100,000 - $149,999	59	0.11
≥ $150,000	36	0.07
NA	1	0.00

	n	min	max	mean	sd	median
X1	545	18	95	47.84	17.77	49

	Female	Male	Other
Strong Liberal	33	13	1
Liberal	58	19	0
Moderate	175	53	1
Conservative	87	31	0
Strong Conservative	50	24	0

	%Female	%Male	%Other
Strong Liberal	70.2	27.7	2.1
Liberal	75.3	24.7	0.0
Moderate	76.4	23.1	0.4
Conservative	73.7	26.3	0.0
Strong Conservative	67.6	32.4	0.0

b. M, SD of all Action questions

acts <- d %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

acts$name <- factor(acts$name, levels = c(paste0("act", 1:30)))
acts <- acts[order(acts$name),]

kable(acts)

name	value_mean	value_SD	value_n
act1	-0.08	2.17	449
act2	1.14	1.81	290
act3	-0.22	2.03	444
act4	-0.02	2.17	371
act5	0.99	1.88	297
act6	1.25	1.81	359
act7	-0.12	2.09	415
act8	-0.40	2.18	481
act9	0.03	2.00	449
act10	0.08	2.12	477
act11	0.93	1.89	314
act12	-0.14	2.19	385
act13	-0.09	2.09	443
act14	0.46	2.03	288
act15	0.14	2.06	385
act16	0.93	1.83	332
act17	0.47	2.01	417
act18	0.66	1.85	354
act19	1.17	1.80	275
act20	1.22	1.79	324
act21	-0.07	2.08	403
act22	0.32	2.00	404
act23	0.17	2.27	293
act24	-0.25	2.10	416
act25	0.74	1.87	446
act26	1.49	1.82	227
act27	0.74	1.89	319
act28	1.05	1.82	374
act29	1.11	1.76	370
act30	1.08	1.87	354

i. By Condition

1. Control

ctrl <- d[d$cond == "ctrl",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrl$name <- factor(ctrl$name, levels = c(paste0("act", 1:30)))
ctrl <- ctrl[order(ctrl$name),]

kable(ctrl)

name	value_mean	value_SD	value_n
act1	0.04	2.21	227
act2	1.31	1.73	154
act3	-0.28	2.07	228
act4	0.01	2.25	190
act5	1.16	1.84	145
act6	1.34	1.85	185
act7	-0.24	2.13	210
act8	-0.34	2.15	239
act9	0.02	2.04	231
act10	0.17	2.11	240
act11	1.17	1.90	162
act12	-0.10	2.21	196
act13	-0.01	2.07	219
act14	0.37	2.09	148
act15	0.25	2.09	193
act16	0.85	1.83	168
act17	0.53	1.98	209
act18	0.63	1.90	189
act19	1.44	1.72	124
act20	1.49	1.59	167
act21	-0.19	2.05	200
act22	0.34	2.02	207
act23	0.35	2.35	151
act24	-0.05	2.11	210
act25	0.70	1.86	226
act26	1.47	1.87	114
act27	0.96	1.80	163
act28	1.08	1.77	197
act29	1.11	1.83	186
act30	1.02	1.94	174

2. Climate

climate <- d[d$cond=="climate",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climate$name <- factor(climate$name, levels = c(paste0("act", 1:30)))
climate <- climate[order(climate$name),]

kable(climate)

name	value_mean	value_SD	value_n
act1	-0.22	2.13	222
act2	0.96	1.88	136
act3	-0.16	1.99	216
act4	-0.05	2.09	181
act5	0.84	1.90	152
act6	1.17	1.77	174
act7	0.01	2.04	205
act8	-0.45	2.21	242
act9	0.04	1.95	218
act10	-0.01	2.14	237
act11	0.68	1.85	152
act12	-0.19	2.18	189
act13	-0.18	2.11	224
act14	0.55	1.97	140
act15	0.04	2.03	192
act16	1.01	1.83	164
act17	0.40	2.04	208
act18	0.70	1.79	165
act19	0.95	1.83	151
act20	0.94	1.93	157
act21	0.05	2.10	203
act22	0.30	1.98	197
act23	-0.01	2.16	142
act24	-0.45	2.08	206
act25	0.77	1.88	220
act26	1.50	1.78	113
act27	0.50	1.96	156
act28	1.01	1.89	177
act29	1.11	1.69	184
act30	1.13	1.80	180

ii. By Party ID

1. Democrats

dem <- d[d$party_factor == "Democrat",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

dem$name <- factor(dem$name, levels = c(paste0("act", 1:30)))
dem <- dem[order(dem$name),]

kable(dem)

name	value_mean	value_SD	value_n
act1	0.28	2.15	148
act2	1.28	1.88	100
act3	0.13	2.05	151
act4	0.30	2.11	132
act5	1.12	1.93	103
act6	1.20	1.89	114
act7	0.10	2.02	141
act8	0.23	2.14	159
act9	0.26	1.97	149
act10	0.59	1.98	155
act11	1.08	1.81	106
act12	0.32	2.15	120
act13	0.19	2.12	143
act14	0.55	2.04	104
act15	0.53	2.07	122
act16	1.06	1.82	109
act17	0.65	2.01	140
act18	0.84	1.83	121
act19	1.13	1.92	87
act20	1.10	1.92	110
act21	0.01	2.08	141
act22	0.80	1.88	133
act23	0.16	2.36	100
act24	-0.21	2.12	141
act25	1.11	1.86	148
act26	1.40	1.92	78
act27	1.14	1.86	114
act28	1.23	1.74	128
act29	1.32	1.78	125
act30	1.06	2.02	117

2. Republicans

rep <- d[d$party_factor == "Republican",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

rep$name <- factor(rep$name, levels = c(paste0("act", 1:30)))
rep <- rep[order(rep$name),]

kable(rep)

name	value_mean	value_SD	value_n
act1	-0.58	2.16	154
act2	0.98	1.71	91
act3	-0.69	1.99	138
act4	-0.21	2.20	116
act5	0.96	1.81	96
act6	1.11	1.78	117
act7	-0.50	2.08	131
act8	-1.13	2.05	158
act9	-0.44	2.02	143
act10	-0.78	2.08	154
act11	0.69	1.97	97
act12	-0.90	2.13	131
act13	-0.56	2.01	136
act14	0.32	2.07	82
act15	-0.20	2.07	123
act16	0.92	1.82	107
act17	0.30	2.03	131
act18	0.42	1.88	106
act19	1.00	1.82	90
act20	1.26	1.62	103
act21	-0.50	2.07	128
act22	-0.29	2.02	126
act23	-0.08	2.28	85
act24	-0.50	2.11	133
act25	0.24	1.84	146
act26	1.57	1.73	67
act27	0.43	1.89	97
act28	0.95	1.81	122
act29	0.93	1.74	117
act30	0.97	1.86	114

3. Independents

ind <- d[d$party_factor == "Independent",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ind$name <- factor(ind$name, levels = c(paste0("act", 1:30)))
ind <- ind[order(ind$name),]

kable(ind)

name	value_mean	value_SD	value_n
act1	0.05	2.12	144
act2	1.10	1.83	96
act3	-0.17	1.96	152
act4	-0.23	2.16	120
act5	0.89	1.86	95
act6	1.40	1.77	125
act7	-0.01	2.09	140
act8	-0.33	2.12	161
act9	0.20	1.92	154
act10	0.35	2.06	165
act11	0.98	1.88	108
act12	0.16	2.10	131
act13	0.02	2.06	161
act14	0.44	1.99	99
act15	0.07	2.00	137
act16	0.82	1.83	113
act17	0.42	1.98	143
act18	0.69	1.80	124
act19	1.34	1.67	95
act20	1.31	1.77	108
act21	0.22	2.02	131
act22	0.39	1.96	142
act23	0.38	2.15	105
act24	-0.07	2.04	139
act25	0.84	1.80	149
act26	1.52	1.80	79
act27	0.56	1.85	105
act28	0.94	1.90	121
act29	1.07	1.72	125
act30	1.19	1.70	120

iii. By ideology

1. Strong Liberals

Slibs <- d[d$ideology == "Strong Liberal",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

Slibs$name <- factor(Slibs$name, levels = c(paste0("act", 1:30)))
Slibs <- Slibs[order(Slibs$name),]

kable(Slibs)

name	value_mean	value_SD	value_n
act1	0.17	2.50	35
act2	1.04	2.18	24
act3	0.37	2.02	35
act4	0.48	2.29	29
act5	0.42	2.19	24
act6	1.19	2.05	32
act7	0.37	2.17	35
act8	0.62	2.14	40
act9	0.19	2.25	36
act10	1.15	2.03	40
act11	1.54	1.91	24
act12	0.33	2.29	27
act13	0.26	2.50	35
act14	0.09	2.27	22
act15	0.31	2.38	32
act16	1.03	1.94	29
act17	1.21	2.03	34
act18	0.76	2.12	29
act19	1.30	2.33	27
act20	1.00	2.20	27
act21	0.42	2.21	33
act22	1.14	1.80	37
act23	0.56	2.75	25
act24	-0.08	2.18	37
act25	1.34	2.03	35
act26	0.95	2.07	19
act27	1.39	2.01	31
act28	1.19	1.87	31
act29	1.48	1.86	29
act30	1.14	2.29	28

2. Moderate Liberals

libs <- d[d$ideology == "Liberal",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

libs$name <- factor(libs$name, levels = c(paste0("act", 1:30)))
libs <- libs[order(libs$name),]

kable(libs)

name	value_mean	value_SD	value_n
act1	0.65	2.05	65
act2	1.57	1.71	42
act3	-0.19	2.12	68
act4	0.38	2.14	58
act5	1.40	1.82	42
act6	1.43	1.76	46
act7	-0.03	2.03	59
act8	0.48	2.13	63
act9	0.58	1.72	64
act10	0.68	2.04	65
act11	1.66	1.54	41
act12	0.74	2.12	53
act13	0.34	1.98	64
act14	0.95	1.93	39
act15	0.89	2.13	45
act16	1.07	1.65	43
act17	0.55	2.17	60
act18	0.91	1.71	57
act19	1.44	1.54	32
act20	1.20	1.88	46
act21	0.44	2.09	59
act22	0.59	2.05	56
act23	0.62	2.22	39
act24	-0.14	2.03	58
act25	1.13	1.86	63
act26	1.26	2.16	31
act27	1.18	1.77	45
act28	1.00	1.93	55
act29	0.98	1.86	55
act30	1.18	2.05	51

3. Moderates

mod <- d[d$ideology == "Moderate",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

mod$name <- factor(mod$name, levels = c(paste0("act", 1:30)))
mod <- mod[order(mod$name),]

kable(mod)

name	value_mean	value_SD	value_n
act1	-0.04	2.08	184
act2	1.22	1.64	113
act3	-0.02	1.96	184
act4	-0.21	2.09	160
act5	1.09	1.73	109
act6	1.31	1.80	142
act7	-0.13	2.02	178
act8	-0.44	2.04	201
act9	0.20	1.88	190
act10	0.08	2.07	199
act11	0.73	1.85	135
act12	0.00	2.07	158
act13	-0.09	2.02	188
act14	0.42	1.94	128
act15	0.10	1.91	166
act16	0.96	1.75	140
act17	0.48	1.91	173
act18	0.82	1.72	141
act19	1.23	1.71	113
act20	1.36	1.61	129
act21	-0.06	1.98	167
act22	0.29	1.92	168
act23	0.11	2.18	123
act24	-0.23	2.11	174
act25	0.89	1.81	186
act26	1.62	1.65	89
act27	0.55	1.89	127
act28	0.97	1.77	147
act29	1.14	1.70	147
act30	1.04	1.72	138

4. Moderate Conservatives

cons <- d[d$ideology == "Conservative",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

cons$name <- factor(cons$name, levels = c(paste0("act", 1:30)))
cons <- cons[order(cons$name),]

kable(cons)

name	value_mean	value_SD	value_n
act1	-0.49	2.10	100
act2	0.89	1.86	65
act3	-0.42	2.00	96
act4	-0.29	2.24	73
act5	0.97	1.83	73
act6	1.33	1.69	85
act7	-0.31	2.10	86
act8	-0.71	2.15	108
act9	-0.25	1.97	99
act10	-0.26	2.03	105
act11	0.82	1.86	65
act12	-0.60	2.17	90
act13	-0.22	2.13	92
act14	0.32	2.11	59
act15	0.11	2.01	83
act16	0.99	1.84	72
act17	0.17	2.04	89
act18	0.49	1.92	74
act19	1.05	1.84	60
act20	1.13	1.85	69
act21	-0.27	2.06	82
act22	0.34	1.99	82
act23	-0.18	2.19	65
act24	-0.50	2.14	92
act25	0.50	1.70	102
act26	1.57	1.98	51
act27	0.72	1.82	64
act28	1.32	1.76	84
act29	1.02	1.82	83
act30	1.01	1.95	80

Above 0: 2; 5; 6; 11; 14; 15;16; 17; 18; 19; 20; 22; 25; 26; 27; 28; 29; 30

5. Strong Conservatives

Scons <- d[d$ideology == "Strong Conservative",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

Scons$name <- factor(Scons$name, levels = c(paste0("act", 1:30)))
Scons <- Scons[order(Scons$name),]

kable(Scons)

name	value_mean	value_SD	value_n
act1	-0.46	2.33	65
act2	0.96	1.99	46
act3	-0.89	2.04	61
act4	0.20	2.23	51
act5	0.73	2.10	49
act6	0.87	1.94	54
act7	-0.16	2.27	57
act8	-1.14	2.28	69
act9	-0.72	2.27	60
act10	-0.62	2.21	68
act11	0.73	2.14	49
act12	-0.84	2.27	57
act13	-0.58	2.04	64
act14	0.50	2.15	40
act15	-0.36	2.22	59
act16	0.56	2.14	48
act17	0.38	2.01	61
act18	0.19	2.02	53
act19	0.91	1.78	43
act20	1.15	1.82	53
act21	-0.58	2.17	62
act22	-0.33	2.13	61
act23	0.27	2.33	41
act24	-0.13	2.07	55
act25	-0.12	1.98	60
act26	1.54	1.57	37
act27	0.44	1.92	52
act28	0.79	1.91	57
act29	1.09	1.69	56
act30	1.14	1.77	57

Above 0: 2; 4; 5; 6; 11; 14; 16; 17; 18; 19; 20; 23; 26; 27; 28; 29; 30

iv. By Condition x Party ID

1. Control: Republicans

ctrlR <- d[d$cond == "ctrl" & d$party_factor == "Republican",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlR$name <- factor(ctrlR$name, levels = c(paste0("act", 1:30)))
ctrlR <- ctrlR[order(ctrlR$name),]

kable(ctrlR)

name	value_mean	value_SD	value_n
act1	-0.59	2.16	71
act2	1.21	1.63	43
act3	-0.75	2.05	64
act4	-0.19	2.38	54
act5	1.15	1.74	41
act6	1.17	1.82	52
act7	-0.66	2.14	62
act8	-0.84	2.12	70
act9	-0.48	2.13	65
act10	-0.59	2.08	70
act11	0.74	2.08	46
act12	-0.78	2.17	64
act13	-0.25	2.09	61
act14	0.27	2.14	37
act15	-0.21	2.13	58
act16	0.98	1.76	50
act17	0.44	2.02	57
act18	0.29	1.99	52
act19	1.54	1.40	35
act20	1.51	1.52	49
act21	-0.71	2.15	56
act22	-0.31	2.05	58
act23	-0.22	2.30	40
act24	-0.36	2.18	58
act25	0.34	1.84	64
act26	1.69	1.69	32
act27	0.73	1.73	44
act28	1.14	1.80	58
act29	0.96	1.77	55
act30	0.86	1.84	51

2. Control: Democrats

ctrlD <- d[d$cond == "ctrl" & d$party_factor == "Democrat",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlD$name <- factor(ctrlD$name, levels = c(paste0("act", 1:30)))
ctrlD <- ctrlD[order(ctrlD$name),]

kable(ctrlD)

name	value_mean	value_SD	value_n
act1	0.42	2.19	72
act2	1.38	1.75	50
act3	0.01	2.11	77
act4	0.12	2.04	67
act5	1.36	1.89	53
act6	1.22	1.88	60
act7	-0.03	1.98	68
act8	0.10	2.13	80
act9	0.23	1.93	75
act10	0.58	1.94	80
act11	1.29	1.75	58
act12	0.33	2.16	63
act13	0.38	2.01	71
act14	0.54	2.03	54
act15	0.70	2.03	60
act16	1.07	1.79	57
act17	0.68	1.98	71
act18	0.78	1.69	64
act19	1.28	1.95	39
act20	1.62	1.50	56
act21	0.06	1.93	72
act22	0.93	1.72	67
act23	0.39	2.52	51
act24	-0.04	2.12	72
act25	0.92	1.87	76
act26	1.28	2.05	36
act27	1.30	1.77	61
act28	1.31	1.60	68
act29	1.30	1.82	61
act30	1.00	2.12	57

3. Control: Independents

ctrlI <- d[d$cond == "ctrl" & d$party_factor == "Independent",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlI$name <- factor(ctrlI$name, levels = c(paste0("act", 1:30)))
ctrlI <- ctrlI[order(ctrlI$name),]

kable(ctrlI)

name	value_mean	value_SD	value_n
act1	0.23	2.18	83
act2	1.28	1.80	60
act3	-0.22	2.00	86
act4	0.00	2.34	68
act5	0.92	1.87	50
act6	1.53	1.85	72
act7	-0.14	2.20	79
act8	-0.38	2.11	88
act9	0.18	2.03	90
act10	0.36	2.13	89
act11	1.35	1.89	57
act12	0.10	2.17	68
act13	-0.20	2.07	86
act14	0.23	2.11	56
act15	0.20	2.05	74
act16	0.50	1.91	60
act17	0.44	1.96	80
act18	0.72	1.99	72
act19	1.45	1.77	49
act20	1.33	1.75	61
act21	-0.07	2.04	71
act22	0.30	2.09	81
act23	0.66	2.19	59
act24	0.13	2.03	79
act25	0.74	1.86	85
act26	1.44	1.88	45
act27	0.75	1.84	57
act28	0.79	1.87	70
act29	1.03	1.89	69
act30	1.14	1.85	65

4. Climate: Republicans

climateR <- d[d$cond == "climate" & d$party_factor=="Republican",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateR$name <- factor(climateR$name, levels = c(paste0("act", 1:30)))
climateR <- climateR[order(climateR$name),]

kable(climateR)

name	value_mean	value_SD	value_n
act1	-0.58	2.18	83
act2	0.77	1.78	48
act3	-0.64	1.95	74
act4	-0.23	2.04	62
act5	0.82	1.86	55
act6	1.06	1.76	65
act7	-0.35	2.04	69
act8	-1.35	1.98	88
act9	-0.41	1.94	78
act10	-0.94	2.07	84
act11	0.65	1.88	51
act12	-1.01	2.09	67
act13	-0.81	1.92	75
act14	0.36	2.04	45
act15	-0.18	2.02	65
act16	0.86	1.88	57
act17	0.19	2.04	74
act18	0.56	1.77	54
act19	0.65	1.97	55
act20	1.04	1.69	54
act21	-0.33	2.01	72
act22	-0.26	2.01	68
act23	0.04	2.29	45
act24	-0.61	2.05	75
act25	0.16	1.84	82
act26	1.46	1.77	35
act27	0.19	1.99	53
act28	0.78	1.82	64
act29	0.90	1.72	62
act30	1.06	1.88	63

5. Climate: Democrats

climateD <- d[d$cond == "climate" & d$party_factor=="Democrat",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateD$name <- factor(climateD$name, levels = c(paste0("act", 1:30)))
climateD <- climateD[order(climateD$name),]

kable(climateD)

name	value_mean	value_SD	value_n
act1	0.14	2.11	76
act2	1.18	2.01	50
act3	0.26	1.99	74
act4	0.49	2.19	65
act5	0.86	1.95	50
act6	1.19	1.92	54
act7	0.22	2.06	73
act8	0.37	2.16	79
act9	0.30	2.03	74
act10	0.61	2.04	75
act11	0.81	1.88	48
act12	0.30	2.16	57
act13	0.00	2.23	72
act14	0.56	2.06	50
act15	0.37	2.11	62
act16	1.04	1.87	52
act17	0.62	2.05	69
act18	0.91	1.98	57
act19	1.00	1.90	48
act20	0.56	2.15	54
act21	-0.04	2.24	69
act22	0.68	2.03	66
act23	-0.08	2.17	49
act24	-0.39	2.12	69
act25	1.32	1.84	72
act26	1.50	1.82	42
act27	0.96	1.96	53
act28	1.13	1.91	60
act29	1.34	1.75	64
act30	1.12	1.94	60

6. Climate: Independents

climateI <- d[d$cond == "climate" & d$party_factor=="Independent",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateI$name <- factor(climateI$name, levels = c(paste0("act", 1:30)))
climateI <- climateI[order(climateI$name),]

kable(climateI)

name	value_mean	value_SD	value_n
act1	-0.20	2.02	61
act2	0.81	1.86	36
act3	-0.11	1.93	66
act4	-0.54	1.86	52
act5	0.87	1.88	45
act6	1.23	1.66	53
act7	0.16	1.95	61
act8	-0.27	2.14	73
act9	0.23	1.79	64
act10	0.33	1.98	76
act11	0.57	1.80	51
act12	0.22	2.05	63
act13	0.27	2.03	75
act14	0.72	1.80	43
act15	-0.08	1.95	63
act16	1.19	1.68	53
act17	0.40	2.03	63
act18	0.63	1.51	52
act19	1.22	1.56	46
act20	1.30	1.82	47
act21	0.57	1.95	60
act22	0.51	1.77	61
act23	0.02	2.05	46
act24	-0.33	2.04	60
act25	0.97	1.72	64
act26	1.62	1.71	34
act27	0.33	1.85	48
act28	1.16	1.93	51
act29	1.12	1.51	56
act30	1.25	1.52	55

v. By Condition x ideology

1. Control: Strong liberal

ctrlSL <- d[d$cond == "climate" & d$ideology=="Strong Liberal",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.)))))

ctrlSL$name <- factor(ctrlSL$name, levels = c(paste0("act", 1:30)))
ctrlSL <- ctrlSL[order(ctrlSL$name),]

kable(ctrlSL)

name	value_mean	value_SD	value_n
act1	-0.31	2.50	13
act2	1.09	2.26	11
act3	0.25	1.91	12
act4	0.40	2.63	10
act5	0.40	2.22	10
act6	1.08	2.10	13
act7	-0.07	2.40	15
act8	1.12	2.03	16
act9	-0.08	2.47	12
act10	1.38	1.96	16
act11	1.20	2.15	10
act12	0.60	2.07	10
act13	0.14	2.80	14
act14	0.50	2.00	8
act15	0.53	2.33	15
act16	0.70	2.00	10
act17	1.71	1.86	14
act18	0.82	2.32	11
act19	1.23	2.28	13
act20	0.69	2.29	13
act21	0.47	2.20	15
act22	0.93	1.94	14
act23	1.22	2.39	9
act24	-0.13	2.03	15
act25	1.79	1.76	14
act26	1.22	2.17	9
act27	1.64	1.86	14
act28	1.33	2.06	12
act29	1.45	2.07	11
act30	1.25	2.22	12

2. Control: Liberal

ctrlL <- d[d$cond == "ctrl" & d$ideology == "Liberal",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlL$name <- factor(ctrlL$name, levels = c(paste0("act", 1:30)))
ctrlL <- ctrlL[order(ctrlL$name),]

kable(ctrlL)

name	value_mean	value_SD	value_n
act1	0.55	2.20	33
act2	1.62	1.61	24
act3	-0.64	1.97	39
act4	0.03	1.98	33
act5	1.82	1.62	22
act6	1.50	1.79	26
act7	-0.61	1.87	33
act8	0.20	2.17	35
act9	0.58	1.57	36
act10	0.46	2.18	37
act11	1.67	1.62	27
act12	0.43	2.05	30
act13	0.41	1.94	34
act14	0.74	1.94	23
act15	1.56	1.69	25
act16	1.00	1.68	25
act17	0.45	2.17	33
act18	0.71	1.81	31
act19	1.67	1.53	18
act20	1.52	1.50	25
act21	0.34	1.91	32
act22	0.44	2.05	32
act23	1.13	2.16	23
act24	-0.06	2.00	34
act25	0.72	1.90	32
act26	1.27	2.37	15
act27	1.36	1.64	28
act28	0.91	1.71	32
act29	0.87	1.93	31
act30	1.14	2.01	29

3. Control: Moderates

ctrlM <- d[d$cond == "ctrl" & d$ideology == "Moderate",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlM$name <- factor(ctrlM$name, levels = c(paste0("act", 1:30)))
ctrlM <- ctrlM[order(ctrlM$name),]

kable(ctrlM)

name	value_mean	value_SD	value_n
act1	0.23	2.09	98
act2	1.38	1.64	65
act3	-0.06	2.03	96
act4	-0.11	2.29	85
act5	1.26	1.58	58
act6	1.40	1.83	77
act7	-0.27	2.10	92
act8	-0.50	2.01	105
act9	0.13	2.05	102
act10	0.17	2.02	103
act11	1.03	1.79	67
act12	0.11	2.08	82
act13	-0.25	1.97	97
act14	0.37	1.94	67
act15	0.15	1.94	89
act16	0.83	1.73	70
act17	0.62	1.83	92
act18	0.83	1.73	77
act19	1.48	1.53	52
act20	1.59	1.43	73
act21	-0.16	1.94	87
act22	0.31	1.95	89
act23	0.31	2.25	64
act24	0.08	2.12	92
act25	0.93	1.78	101
act26	1.59	1.67	46
act27	0.82	1.85	67
act28	0.88	1.81	78
act29	1.19	1.78	78
act30	1.09	1.83	68

4. Control: Conservatives

ctrlC <- d[d$cond == "ctrl" & d$ideology == "Conservative",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlC$name <- factor(ctrlC$name, levels = c(paste0("act", 1:30)))
ctrlC <- ctrlC[order(ctrlC$name),]

kable(ctrlC)

name	value_mean	value_SD	value_n
act1	-0.39	2.07	44
act2	1.15	1.72	33
act3	-0.45	2.01	42
act4	-0.20	2.46	30
act5	1.03	1.78	31
act6	1.36	1.72	39
act7	-0.42	2.30	38
act8	-0.13	2.18	45
act9	-0.05	1.98	43
act10	-0.02	1.91	46
act11	1.23	1.77	30
act12	-0.33	2.24	39
act13	0.27	2.10	41
act14	0.48	2.23	27
act15	0.22	1.99	36
act16	0.76	2.00	34
act17	0.67	1.96	36
act18	0.58	2.10	38
act19	1.58	1.64	24
act20	1.34	1.77	32
act21	-0.46	2.16	35
act22	0.38	2.00	34
act23	-0.04	2.33	28
act24	-0.29	2.13	38
act25	0.62	1.64	45
act26	1.52	2.06	29
act27	0.86	1.64	29
act28	1.45	1.85	42
act29	1.06	1.86	34
act30	1.14	1.87	36

5. Control: Strong Conservatives

ctrlSC <- d[d$cond == "ctrl" & d$ideology == "Strong Conservative",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ctrlSC$name <- factor(ctrlSC$name, levels = c(paste0("act", 1:30)))
ctrlSC <- ctrlSC[order(ctrlSC$name),]

kable(ctrlSC)

name	value_mean	value_SD	value_n
act1	-0.80	2.38	30
act2	1.11	1.94	19
act3	-0.82	2.26	28
act4	0.22	2.35	23
act5	0.85	2.39	20
act6	0.96	2.07	24
act7	-0.15	2.33	27
act8	-1.23	2.28	30
act9	-1.35	2.12	26
act10	-0.57	2.40	30
act11	0.54	2.54	24
act12	-1.11	2.35	28
act13	-0.38	2.26	26
act14	0.12	2.45	17
act15	-0.54	2.40	26
act16	0.55	2.09	20
act17	-0.07	2.18	28
act18	-0.04	2.09	25
act19	0.88	1.96	16
act20	1.48	1.65	23
act21	-0.93	2.12	28
act22	-0.45	2.23	29
act23	0.25	2.43	20
act24	-0.17	2.12	24
act25	-0.33	1.98	27
act26	1.79	1.42	14
act27	0.86	1.81	22
act28	1.27	1.54	26
act29	0.92	1.91	25
act30	0.52	1.98	25

6. Climate: Strong liberal

climateSL <- d[d$cond == "climate" & d$ideology == "Strong Liberal",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateSL$name <- factor(climateSL$name, levels = c(paste0("act", 1:30)))
climateSL <- climateSL[order(climateSL$name),]

kable(climateSL)

name	value_mean	value_SD	value_n
act1	-0.31	2.50	13
act2	1.09	2.26	11
act3	0.25	1.91	12
act4	0.40	2.63	10
act5	0.40	2.22	10
act6	1.08	2.10	13
act7	-0.07	2.40	15
act8	1.12	2.03	16
act9	-0.08	2.47	12
act10	1.38	1.96	16
act11	1.20	2.15	10
act12	0.60	2.07	10
act13	0.14	2.80	14
act14	0.50	2.00	8
act15	0.53	2.33	15
act16	0.70	2.00	10
act17	1.71	1.86	14
act18	0.82	2.32	11
act19	1.23	2.28	13
act20	0.69	2.29	13
act21	0.47	2.20	15
act22	0.93	1.94	14
act23	1.22	2.39	9
act24	-0.13	2.03	15
act25	1.79	1.76	14
act26	1.22	2.17	9
act27	1.64	1.86	14
act28	1.33	2.06	12
act29	1.45	2.07	11
act30	1.25	2.22	12

7. Climate: Liberal

climateL <- d[d$cond == "climate" & d$ideology == "Liberal",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateL$name <- factor(climateL$name, levels = c(paste0("act", 1:30)))
climateL <- climateL[order(climateL$name),]

kable(climateL)

name	value_mean	value_SD	value_n
act1	0.75	1.92	32
act2	1.50	1.89	18
act3	0.41	2.20	29
act4	0.84	2.30	25
act5	0.95	1.96	20
act6	1.35	1.76	20
act7	0.69	2.02	26
act8	0.82	2.07	28
act9	0.57	1.91	28
act10	0.96	1.84	28
act11	1.64	1.45	14
act12	1.13	2.20	23
act13	0.27	2.05	30
act14	1.25	1.95	16
act15	0.05	2.37	20
act16	1.17	1.65	18
act17	0.67	2.22	27
act18	1.15	1.59	26
act19	1.14	1.56	14
act20	0.81	2.23	21
act21	0.56	2.33	27
act22	0.79	2.08	24
act23	-0.12	2.16	16
act24	-0.25	2.11	24
act25	1.55	1.75	31
act26	1.25	2.02	16
act27	0.88	2.00	17
act28	1.13	2.24	23
act29	1.12	1.80	24
act30	1.23	2.14	22

8. Climate: Moderates

climateM <- d[d$cond == "climate" & d$ideology == "Moderate",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateM$name <- factor(climateM$name, levels = c(paste0("act", 1:30)))
climateM <- climateM[order(climateM$name),]

kable(climateM)

name	value_mean	value_SD	value_n
act1	-0.35	2.03	86
act2	1.00	1.62	48
act3	0.03	1.89	88
act4	-0.32	1.84	75
act5	0.90	1.88	51
act6	1.20	1.76	65
act7	0.01	1.94	86
act8	-0.39	2.07	96
act9	0.28	1.67	88
act10	-0.01	2.13	96
act11	0.43	1.86	68
act12	-0.12	2.06	76
act13	0.09	2.06	91
act14	0.48	1.96	61
act15	0.05	1.88	77
act16	1.10	1.77	70
act17	0.32	2.00	81
act18	0.80	1.72	64
act19	1.02	1.84	61
act20	1.05	1.79	56
act21	0.05	2.02	80
act22	0.25	1.90	79
act23	-0.10	2.11	59
act24	-0.57	2.04	82
act25	0.85	1.85	85
act26	1.65	1.65	43
act27	0.25	1.90	60
act28	1.07	1.74	69
act29	1.09	1.62	69
act30	1.00	1.62	70

9. Climate: Conservatives

climateC <- d[d$cond == "climate" & d$ideology == "Conservative",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateC$name <- factor(climateC$name, levels = c(paste0("act", 1:30)))
climateC <- climateC[order(climateC$name),]

kable(climateC)

name	value_mean	value_SD	value_n
act1	-0.57	2.13	56
act2	0.62	2.00	32
act3	-0.39	2.01	54
act4	-0.35	2.10	43
act5	0.93	1.89	42
act6	1.30	1.67	46
act7	-0.23	1.96	48
act8	-1.13	2.05	63
act9	-0.41	1.96	56
act10	-0.44	2.12	59
act11	0.46	1.88	35
act12	-0.80	2.12	51
act13	-0.61	2.09	51
act14	0.19	2.04	32
act15	0.02	2.05	47
act16	1.18	1.69	38
act17	-0.17	2.04	53
act18	0.39	1.73	36
act19	0.69	1.89	36
act20	0.95	1.93	37
act21	-0.13	1.98	47
act22	0.31	2.00	48
act23	-0.30	2.11	37
act24	-0.65	2.16	54
act25	0.40	1.76	57
act26	1.64	1.92	22
act27	0.60	1.97	35
act28	1.19	1.69	42
act29	1.00	1.81	49
act30	0.91	2.02	44

10. Climate: Strong Conservatives

climateSC <- d[d$cond == "climate" & d$ideology == "Strong Conservative",] %>%
  select(act1:act30) %>%
  pivot_longer(act1:act30) %>%
  group_by(name) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 SD = ~round(sd(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

climateSC$name <- factor(climateSC$name, levels = c(paste0("act", 1:30)))
climateSC <- climateSC[order(climateSC$name),]

kable(climateSC)

name	value_mean	value_SD	value_n
act1	-0.17	2.28	35
act2	0.85	2.05	27
act3	-0.94	1.87	33
act4	0.18	2.16	28
act5	0.66	1.91	29
act6	0.80	1.86	30
act7	-0.17	2.26	30
act8	-1.08	2.30	39
act9	-0.24	2.30	34
act10	-0.66	2.07	38
act11	0.92	1.71	25
act12	-0.59	2.20	29
act13	-0.71	1.89	38
act14	0.78	1.91	23
act15	-0.21	2.09	33
act16	0.57	2.22	28
act17	0.76	1.80	33
act18	0.39	1.97	28
act19	0.93	1.71	27
act20	0.90	1.94	30
act21	-0.29	2.20	34
act22	-0.22	2.06	32
act23	0.29	2.31	21
act24	-0.10	2.07	31
act25	0.06	1.98	33
act26	1.39	1.67	23
act27	0.13	1.98	30
act28	0.39	2.11	31
act29	1.23	1.50	31
act30	1.62	1.43	32

vi. By ideology x gender

ideogend <- d[d$gend != "Other",] %>%
  select(c(act1:act30, gender, ideology)) %>%
  dplyr::mutate(gender = recode(gender, `1` = "female",
                `2` = "male")) %>%
  pivot_longer(act1:act30) %>%
  group_by(name, ideology, gender) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

ideogend$name <- factor(ideogend$name, levels = c(paste0("act", 1:30)))
ideogend <- ideogend[order(ideogend$name),]

kable(ideogend)

name	ideology	gender	value_mean	value_n
act1	Strong Liberal	female	0.65	23
act1	Strong Liberal	male	-1.00	11
act1	Liberal	female	0.68	50
act1	Liberal	male	0.53	15
act1	Moderate	female	-0.23	137
act1	Moderate	male	0.50	46
act1	Conservative	female	-0.26	74
act1	Conservative	male	-1.15	26
act1	Strong Conservative	female	-0.62	42
act1	Strong Conservative	male	-0.17	23
act2	Strong Liberal	female	1.13	15
act2	Strong Liberal	male	0.75	8
act2	Liberal	female	1.44	32
act2	Liberal	male	2.00	10
act2	Moderate	female	1.22	87
act2	Moderate	male	1.16	25
act2	Conservative	female	1.17	42
act2	Conservative	male	0.39	23
act2	Strong Conservative	female	0.59	27
act2	Strong Conservative	male	1.47	19
act3	Strong Liberal	female	0.58	24
act3	Strong Liberal	male	-0.10	10
act3	Liberal	female	-0.28	50
act3	Liberal	male	0.06	18
act3	Moderate	female	0.04	136
act3	Moderate	male	-0.17	47
act3	Conservative	female	-0.42	69
act3	Conservative	male	-0.41	27
act3	Strong Conservative	female	-1.16	38
act3	Strong Conservative	male	-0.43	23
act4	Strong Liberal	female	0.94	18
act4	Strong Liberal	male	-0.30	10
act4	Liberal	female	0.29	42
act4	Liberal	male	0.62	16
act4	Moderate	female	-0.20	125
act4	Moderate	male	-0.24	34
act4	Conservative	female	-0.10	52
act4	Conservative	male	-0.76	21
act4	Strong Conservative	female	0.04	28
act4	Strong Conservative	male	0.39	23
act5	Strong Liberal	female	0.46	13
act5	Strong Liberal	male	0.30	10
act5	Liberal	female	1.30	30
act5	Liberal	male	1.67	12
act5	Moderate	female	1.06	81
act5	Moderate	male	1.11	27
act5	Conservative	female	1.21	47
act5	Conservative	male	0.54	26
act5	Strong Conservative	female	0.96	26
act5	Strong Conservative	male	0.48	23
act6	Strong Liberal	female	1.55	22
act6	Strong Liberal	male	0.33	9
act6	Liberal	female	1.24	33
act6	Liberal	male	1.92	13
act6	Moderate	female	1.25	109
act6	Moderate	male	1.47	32
act6	Conservative	female	1.43	60
act6	Conservative	male	1.08	25
act6	Strong Conservative	female	1.03	33
act6	Strong Conservative	male	0.62	21
act7	Strong Liberal	female	0.68	25
act7	Strong Liberal	male	-0.44	9
act7	Liberal	female	-0.11	44
act7	Liberal	male	0.20	15
act7	Moderate	female	-0.16	134
act7	Moderate	male	-0.05	43
act7	Conservative	female	-0.20	64
act7	Conservative	male	-0.64	22
act7	Strong Conservative	female	-0.31	35
act7	Strong Conservative	male	0.09	22
act8	Strong Liberal	female	0.93	28
act8	Strong Liberal	male	-0.09	11
act8	Liberal	female	0.41	49
act8	Liberal	male	0.71	14
act8	Moderate	female	-0.49	153
act8	Moderate	male	-0.28	47
act8	Conservative	female	-0.69	80
act8	Conservative	male	-0.79	28
act8	Strong Conservative	female	-1.53	47
act8	Strong Conservative	male	-0.32	22
act9	Strong Liberal	female	0.46	24
act9	Strong Liberal	male	-0.55	11
act9	Liberal	female	0.57	47
act9	Liberal	male	0.59	17
act9	Moderate	female	0.20	144
act9	Moderate	male	0.13	45
act9	Conservative	female	-0.29	72
act9	Conservative	male	-0.15	27
act9	Strong Conservative	female	-0.65	37
act9	Strong Conservative	male	-0.83	23
act10	Strong Liberal	female	1.59	29
act10	Strong Liberal	male	-0.10	10
act10	Liberal	female	0.43	49
act10	Liberal	male	1.44	16
act10	Moderate	female	0.07	153
act10	Moderate	male	0.11	46
act10	Conservative	female	-0.19	77
act10	Conservative	male	-0.43	28
act10	Strong Conservative	female	-0.78	45
act10	Strong Conservative	male	-0.30	23
act11	Strong Liberal	female	1.56	16
act11	Strong Liberal	male	1.43	7
act11	Liberal	female	1.54	28
act11	Liberal	male	1.92	13
act11	Moderate	female	0.75	99
act11	Moderate	male	0.69	35
act11	Conservative	female	1.00	43
act11	Conservative	male	0.45	22
act11	Strong Conservative	female	0.22	27
act11	Strong Conservative	male	1.36	22
act12	Strong Liberal	female	0.56	16
act12	Strong Liberal	male	-0.20	10
act12	Liberal	female	0.41	41
act12	Liberal	male	1.83	12
act12	Moderate	female	0.13	119
act12	Moderate	male	-0.45	38
act12	Conservative	female	-0.36	67
act12	Conservative	male	-1.30	23
act12	Strong Conservative	female	-1.14	35
act12	Strong Conservative	male	-0.36	22
act13	Strong Liberal	female	0.61	23
act13	Strong Liberal	male	-0.36	11
act13	Liberal	female	0.24	50
act13	Liberal	male	0.71	14
act13	Moderate	female	-0.02	142
act13	Moderate	male	-0.28	46
act13	Conservative	female	-0.19	69
act13	Conservative	male	-0.30	23
act13	Strong Conservative	female	-1.19	42
act13	Strong Conservative	male	0.59	22
act14	Strong Liberal	female	0.57	14
act14	Strong Liberal	male	-0.86	7
act14	Liberal	female	0.75	28
act14	Liberal	male	1.45	11
act14	Moderate	female	0.46	95
act14	Moderate	male	0.31	32
act14	Conservative	female	0.78	41
act14	Conservative	male	-0.72	18
act14	Strong Conservative	female	0.26	23
act14	Strong Conservative	male	0.82	17
act15	Strong Liberal	female	0.55	20
act15	Strong Liberal	male	-0.08	12
act15	Liberal	female	0.66	32
act15	Liberal	male	1.46	13
act15	Moderate	female	0.31	120
act15	Moderate	male	-0.44	45
act15	Conservative	female	0.30	56
act15	Conservative	male	-0.30	27
act15	Strong Conservative	female	-0.57	35
act15	Strong Conservative	male	-0.04	24
act16	Strong Liberal	female	1.00	19
act16	Strong Liberal	male	0.89	9
act16	Liberal	female	1.19	31
act16	Liberal	male	0.75	12
act16	Moderate	female	0.84	107
act16	Moderate	male	1.36	33
act16	Conservative	female	1.14	50
act16	Conservative	male	0.64	22
act16	Strong Conservative	female	0.24	29
act16	Strong Conservative	male	1.05	19
act17	Strong Liberal	female	1.39	23
act17	Strong Liberal	male	0.80	10
act17	Liberal	female	0.37	46
act17	Liberal	male	1.14	14
act17	Moderate	female	0.48	130
act17	Moderate	male	0.49	43
act17	Conservative	female	0.27	63
act17	Conservative	male	-0.08	26
act17	Strong Conservative	female	0.34	38
act17	Strong Conservative	male	0.43	23
act18	Strong Liberal	female	0.58	19
act18	Strong Liberal	male	1.00	9
act18	Liberal	female	0.86	43
act18	Liberal	male	1.07	14
act18	Moderate	female	0.82	107
act18	Moderate	male	0.73	33
act18	Conservative	female	0.55	53
act18	Conservative	male	0.33	21
act18	Strong Conservative	female	-0.10	30
act18	Strong Conservative	male	0.57	23
act19	Strong Liberal	female	1.53	19
act19	Strong Liberal	male	0.75	8
act19	Liberal	female	1.38	24
act19	Liberal	male	1.62	8
act19	Moderate	female	1.30	84
act19	Moderate	male	0.96	28
act19	Conservative	female	1.16	37
act19	Conservative	male	0.87	23
act19	Strong Conservative	female	0.65	26
act19	Strong Conservative	male	1.29	17
act20	Strong Liberal	female	1.12	17
act20	Strong Liberal	male	0.67	9
act20	Liberal	female	1.12	34
act20	Liberal	male	1.42	12
act20	Moderate	female	1.40	98
act20	Moderate	male	1.20	30
act20	Conservative	female	1.39	46
act20	Conservative	male	0.61	23
act20	Strong Conservative	female	1.03	29
act20	Strong Conservative	male	1.29	24
act21	Strong Liberal	female	0.30	23
act21	Strong Liberal	male	0.56	9
act21	Liberal	female	0.41	44
act21	Liberal	male	0.53	15
act21	Moderate	female	-0.16	125
act21	Moderate	male	0.20	41
act21	Conservative	female	-0.14	58
act21	Conservative	male	-0.58	24
act21	Strong Conservative	female	-0.45	40
act21	Strong Conservative	male	-0.82	22
act22	Strong Liberal	female	1.15	26
act22	Strong Liberal	male	1.00	10
act22	Liberal	female	0.56	43
act22	Liberal	male	0.69	13
act22	Moderate	female	0.16	129
act22	Moderate	male	0.63	38
act22	Conservative	female	0.49	57
act22	Conservative	male	0.00	25
act22	Strong Conservative	female	-0.64	39
act22	Strong Conservative	male	0.23	22
act23	Strong Liberal	female	0.81	16
act23	Strong Liberal	male	-0.25	8
act23	Liberal	female	0.71	24
act23	Liberal	male	0.47	15
act23	Moderate	female	0.01	97
act23	Moderate	male	0.50	26
act23	Conservative	female	-0.02	44
act23	Conservative	male	-0.52	21
act23	Strong Conservative	female	-0.32	22
act23	Strong Conservative	male	0.95	19
act24	Strong Liberal	female	0.07	27
act24	Strong Liberal	male	-0.50	10
act24	Liberal	female	-0.23	44
act24	Liberal	male	0.14	14
act24	Moderate	female	-0.16	127
act24	Moderate	male	-0.50	46
act24	Conservative	female	-0.36	64
act24	Conservative	male	-0.82	28
act24	Strong Conservative	female	-0.58	33
act24	Strong Conservative	male	0.55	22
act25	Strong Liberal	female	1.28	25
act25	Strong Liberal	male	1.44	9
act25	Liberal	female	1.04	47
act25	Liberal	male	1.38	16
act25	Moderate	female	0.88	138
act25	Moderate	male	0.89	47
act25	Conservative	female	0.60	75
act25	Conservative	male	0.22	27
act25	Strong Conservative	female	-0.21	39
act25	Strong Conservative	male	0.05	21
act26	Strong Liberal	female	0.50	10
act26	Strong Liberal	male	1.44	9
act26	Liberal	female	1.00	24
act26	Liberal	male	2.14	7
act26	Moderate	female	1.66	67
act26	Moderate	male	1.43	21
act26	Conservative	female	1.67	33
act26	Conservative	male	1.39	18
act26	Strong Conservative	female	1.50	20
act26	Strong Conservative	male	1.59	17
act27	Strong Liberal	female	1.26	19
act27	Strong Liberal	male	1.45	11
act27	Liberal	female	1.18	33
act27	Liberal	male	1.17	12
act27	Moderate	female	0.45	96
act27	Moderate	male	0.80	30
act27	Conservative	female	0.68	41
act27	Conservative	male	0.78	23
act27	Strong Conservative	female	0.00	28
act27	Strong Conservative	male	0.96	24
act28	Strong Liberal	female	1.39	23
act28	Strong Liberal	male	0.57	7
act28	Liberal	female	1.07	43
act28	Liberal	male	0.75	12
act28	Moderate	female	0.92	113
act28	Moderate	male	1.09	33
act28	Conservative	female	1.43	61
act28	Conservative	male	1.04	23
act28	Strong Conservative	female	0.81	36
act28	Strong Conservative	male	0.76	21
act29	Strong Liberal	female	1.53	19
act29	Strong Liberal	male	1.33	9
act29	Liberal	female	0.80	41
act29	Liberal	male	1.50	14
act29	Moderate	female	1.14	111
act29	Moderate	male	1.09	35
act29	Conservative	female	1.07	59
act29	Conservative	male	0.92	24
act29	Strong Conservative	female	1.00	35
act29	Strong Conservative	male	1.24	21
act30	Strong Liberal	female	1.35	17
act30	Strong Liberal	male	0.80	10
act30	Liberal	female	1.08	38
act30	Liberal	male	1.46	13
act30	Moderate	female	1.07	107
act30	Moderate	male	0.90	30
act30	Conservative	female	1.18	57
act30	Conservative	male	0.61	23
act30	Strong Conservative	female	1.35	34
act30	Strong Conservative	male	0.83	23

vii. By Partisan ID x gender

party.gend <- d[d$gend != "Other" & !is.na(d$party_factor),] %>%
  select(c(act1:act30, gender, party_factor)) %>%
  dplyr::mutate(gender = recode(gender, `1` = "female",
                `2` = "male")) %>%
  pivot_longer(act1:act30) %>%
  group_by(name, party_factor, gender) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

party.gend$name <- factor(party.gend$name, levels = c(paste0("act", 1:30)))
party.gend <- party.gend[order(party.gend$name),]

kable(party.gend)

name	party_factor	gender	value_mean	value_n
act1	Democrat	female	0.24	102
act1	Democrat	male	0.32	44
act1	Independent	female	0.01	105
act1	Independent	male	0.15	39
act1	Republican	female	-0.45	117
act1	Republican	male	-1.00	37
act2	Democrat	female	1.29	65
act2	Democrat	male	1.18	33
act2	Independent	female	1.06	70
act2	Independent	male	1.23	26
act2	Republican	female	1.08	66
act2	Republican	male	0.72	25
act3	Democrat	female	0.18	103
act3	Democrat	male	0.02	46
act3	Independent	female	-0.18	110
act3	Independent	male	-0.14	42
act3	Republican	female	-0.67	102
act3	Republican	male	-0.75	36
act4	Democrat	female	0.28	87
act4	Democrat	male	0.37	43
act4	Independent	female	-0.22	91
act4	Independent	male	-0.28	29
act4	Republican	female	-0.06	85
act4	Republican	male	-0.61	31
act5	Democrat	female	1.17	64
act5	Democrat	male	0.97	37
act5	Independent	female	0.78	68
act5	Independent	male	1.19	27
act5	Republican	female	1.25	63
act5	Republican	male	0.39	33
act6	Democrat	female	1.34	76
act6	Democrat	male	0.86	36
act6	Independent	female	1.28	92
act6	Independent	male	1.73	33
act6	Republican	female	1.22	87
act6	Republican	male	0.80	30
act7	Democrat	female	0.17	95
act7	Democrat	male	-0.02	44
act7	Independent	female	-0.05	106
act7	Independent	male	0.12	34
act7	Republican	female	-0.44	99
act7	Republican	male	-0.66	32
act8	Democrat	female	0.16	113
act8	Democrat	male	0.45	44
act8	Independent	female	-0.34	121
act8	Independent	male	-0.30	40
act8	Republican	female	-1.11	121
act8	Republican	male	-1.19	37
act9	Democrat	female	0.36	101
act9	Democrat	male	-0.04	46
act9	Independent	female	0.22	112
act9	Independent	male	0.14	42
act9	Republican	female	-0.39	109
act9	Republican	male	-0.59	34
act10	Democrat	female	0.54	112
act10	Democrat	male	0.71	42
act10	Independent	female	0.37	121
act10	Independent	male	0.27	44
act10	Republican	female	-0.70	118
act10	Republican	male	-1.03	36
act11	Democrat	female	0.95	66
act11	Democrat	male	1.29	38
act11	Independent	female	1.05	77
act11	Independent	male	0.81	31
act11	Republican	female	0.66	68
act11	Republican	male	0.76	29
act12	Democrat	female	0.42	83
act12	Democrat	male	0.00	35
act12	Independent	female	0.16	95
act12	Independent	male	0.17	36
act12	Republican	female	-0.76	98
act12	Republican	male	-1.33	33
act13	Democrat	female	0.08	102
act13	Democrat	male	0.50	40
act13	Independent	female	0.19	119
act13	Independent	male	-0.48	42
act13	Republican	female	-0.70	103
act13	Republican	male	-0.12	33
act14	Democrat	female	0.46	65
act14	Democrat	male	0.73	37
act14	Independent	female	0.52	75
act14	Independent	male	0.21	24
act14	Republican	female	0.68	59
act14	Republican	male	-0.61	23
act15	Democrat	female	0.53	78
act15	Democrat	male	0.56	43
act15	Independent	female	0.30	96
act15	Independent	male	-0.46	41
act15	Republican	female	-0.06	87
act15	Republican	male	-0.53	36
act16	Democrat	female	1.10	70
act16	Democrat	male	0.92	38
act16	Independent	female	0.69	87
act16	Independent	male	1.27	26
act16	Republican	female	0.95	77
act16	Republican	male	0.83	30
act17	Democrat	female	0.54	96
act17	Democrat	male	0.88	43
act17	Independent	female	0.43	108
act17	Independent	male	0.40	35
act17	Republican	female	0.44	94
act17	Republican	male	-0.05	37
act18	Democrat	female	0.74	82
act18	Democrat	male	0.97	37
act18	Independent	female	0.77	92
act18	Independent	male	0.44	32
act18	Republican	female	0.39	76
act18	Republican	male	0.50	30
act19	Democrat	female	1.08	59
act19	Democrat	male	1.15	27
act19	Independent	female	1.35	65
act19	Independent	male	1.30	30
act19	Republican	female	1.17	64
act19	Republican	male	0.58	26
act20	Democrat	female	0.99	70
act20	Democrat	male	1.26	38
act20	Independent	female	1.38	80
act20	Independent	male	1.14	28
act20	Republican	female	1.51	72
act20	Republican	male	0.68	31
act21	Democrat	female	-0.15	100
act21	Democrat	male	0.31	39
act21	Independent	female	0.24	96
act21	Independent	male	0.17	35
act21	Republican	female	-0.35	92
act21	Republican	male	-0.89	36
act22	Democrat	female	0.69	93
act22	Democrat	male	1.00	38
act22	Independent	female	0.34	104
act22	Independent	male	0.53	38
act22	Republican	female	-0.24	95
act22	Republican	male	-0.42	31
act23	Democrat	female	0.08	64
act23	Democrat	male	0.23	35
act23	Independent	female	0.28	78
act23	Independent	male	0.67	27
act23	Republican	female	-0.07	59
act23	Republican	male	-0.12	26
act24	Democrat	female	-0.34	98
act24	Democrat	male	0.00	42
act24	Independent	female	0.13	100
act24	Independent	male	-0.59	39
act24	Republican	female	-0.53	95
act24	Republican	male	-0.45	38
act25	Democrat	female	1.02	103
act25	Democrat	male	1.28	43
act25	Independent	female	0.92	108
act25	Independent	male	0.63	41
act25	Republican	female	0.32	111
act25	Republican	male	0.00	35
act26	Democrat	female	1.24	49
act26	Democrat	male	1.61	28
act26	Independent	female	1.44	57
act26	Independent	male	1.73	22
act26	Republican	female	1.76	46
act26	Republican	male	1.14	21
act27	Democrat	female	0.95	75
act27	Democrat	male	1.43	37
act27	Independent	female	0.48	75
act27	Independent	male	0.77	30
act27	Republican	female	0.40	65
act27	Republican	male	0.50	32
act28	Democrat	female	1.22	93
act28	Democrat	male	1.21	33
act28	Independent	female	0.99	92
act28	Independent	male	0.79	29
act28	Republican	female	1.04	89
act28	Republican	male	0.70	33
act29	Democrat	female	1.29	87
act29	Democrat	male	1.33	36
act29	Independent	female	1.06	89
act29	Independent	male	1.11	36
act29	Republican	female	0.93	87
act29	Republican	male	0.93	30
act30	Democrat	female	1.18	79
act30	Democrat	male	0.75	36
act30	Independent	female	1.17	89
act30	Independent	male	1.26	31
act30	Republican	female	1.12	83
act30	Republican	male	0.58	31

viii. By gender

gender <- d[d$gender != 3,] %>%
  select(c(act1:act30, gender)) %>%
  dplyr::mutate(gender = recode(gender, `1` = "female",
                `2` = "male")) %>%
  pivot_longer(act1:act30) %>%
  group_by(name, gender) %>% 
  dplyr::summarise(across(everything(),
            list(mean = ~round(mean(.x, na.rm = TRUE),2), 
                 n = ~sum(!is.na(.))))) 

gender$name <- factor(gender$name, levels = c(paste0("act", 1:30)))
gender <- gender[order(gender$name),]

kable(gender)

name	gender	value_mean	value_n
act1	female	-0.08	326
act1	male	-0.12	121
act2	female	1.15	203
act2	male	1.08	85
act3	female	-0.21	317
act3	male	-0.23	125
act4	female	0.00	265
act4	male	-0.08	104
act5	female	1.08	197
act5	male	0.80	98
act6	female	1.29	257
act6	male	1.15	100
act7	female	-0.11	302
act7	male	-0.14	111
act8	female	-0.44	357
act8	male	-0.27	122
act9	female	0.07	324
act9	male	-0.11	123
act10	female	0.08	353
act10	male	0.07	123
act11	female	0.90	213
act11	male	1.00	99
act12	female	-0.08	278
act12	male	-0.33	105
act13	female	-0.12	326
act13	male	-0.01	116
act14	female	0.55	201
act14	male	0.25	85
act15	female	0.25	263
act15	male	-0.09	121
act16	female	0.89	236
act16	male	1.01	95
act17	female	0.47	300
act17	male	0.46	116
act18	female	0.64	252
act18	male	0.68	100
act19	female	1.22	190
act19	male	1.05	84
act20	female	1.29	224
act20	male	1.06	98
act21	female	-0.07	290
act21	male	-0.10	111
act22	female	0.27	294
act22	male	0.44	108
act23	female	0.11	203
act23	male	0.28	89
act24	female	-0.24	295
act24	male	-0.31	120
act25	female	0.74	324
act25	male	0.70	120
act26	female	1.46	154
act26	male	1.53	72
act27	female	0.62	217
act27	male	0.95	100
act28	female	1.08	276
act28	male	0.93	96
act29	female	1.08	265
act29	male	1.16	103
act30	female	1.15	253
act30	male	0.88	99

c. By Action Summaries

Action 1

# by condition
kable(aggregate(d$act1, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.22
ctrl	0.04

# by ideology
kable(aggregate(d$act1, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.17
Liberal	0.65
Moderate	-0.04
Conservative	-0.49
Strong Conservative	-0.46

# by party
kable(aggregate(d$act1, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.28
Independent	0.05
Republican	-0.58

# by gender
kable(aggregate(d$act1, list(d$gend), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Female	-0.08
Male	-0.12
Other	1.50

# by condition, ideology
kable(aggregate(d$act1, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	-0.31
Liberal	climate	0.75
Moderate	climate	-0.35
Conservative	climate	-0.57
Strong Conservative	climate	-0.17
Strong Liberal	ctrl	0.45
Liberal	ctrl	0.55
Moderate	ctrl	0.23
Conservative	ctrl	-0.39
Strong Conservative	ctrl	-0.80

# by condition, party
kable(aggregate(d$act1, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.14
Independent	climate	-0.20
Republican	climate	-0.58
Democrat	ctrl	0.42
Independent	ctrl	0.23
Republican	ctrl	-0.59

# by gender, condition
kable(aggregate(d$act1, list(d$gend, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Female	climate	-0.21
Male	climate	-0.26
Other	climate	1.00
Female	ctrl	0.04
Male	ctrl	0.03
Other	ctrl	2.00

Action 2

# by condition
kable(aggregate(d$act2, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.96
ctrl	1.31

# by ideology
kable(aggregate(d$act2, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.04
Liberal	1.57
Moderate	1.22
Conservative	0.89
Strong Conservative	0.96

# by party
kable(aggregate(d$act2, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.28
Independent	1.10
Republican	0.98

# by condition, ideology
kable(aggregate(d$act2, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.09
Liberal	climate	1.50
Moderate	climate	1.00
Conservative	climate	0.62
Strong Conservative	climate	0.85
Strong Liberal	ctrl	1.00
Liberal	ctrl	1.62
Moderate	ctrl	1.38
Conservative	ctrl	1.15
Strong Conservative	ctrl	1.11

# by condition, party
kable(aggregate(d$act2, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.18
Independent	climate	0.81
Republican	climate	0.77
Democrat	ctrl	1.38
Independent	ctrl	1.28
Republican	ctrl	1.21

Action 3

# by condition
kable(aggregate(d$act3, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.16
ctrl	-0.28

# by ideology
kable(aggregate(d$act3, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.37
Liberal	-0.19
Moderate	-0.02
Conservative	-0.42
Strong Conservative	-0.89

# by party
kable(aggregate(d$act3, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.13
Independent	-0.17
Republican	-0.69

# by condition, ideology
kable(aggregate(d$act3, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.25
Liberal	climate	0.41
Moderate	climate	0.03
Conservative	climate	-0.39
Strong Conservative	climate	-0.94
Strong Liberal	ctrl	0.43
Liberal	ctrl	-0.64
Moderate	ctrl	-0.06
Conservative	ctrl	-0.45
Strong Conservative	ctrl	-0.82

# by condition, party
kable(aggregate(d$act3, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.26
Independent	climate	-0.11
Republican	climate	-0.64
Democrat	ctrl	0.01
Independent	ctrl	-0.22
Republican	ctrl	-0.75

Action 4

# by condition
kable(aggregate(d$act4, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.05
ctrl	0.01

# by ideology
kable(aggregate(d$act4, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.48
Liberal	0.38
Moderate	-0.21
Conservative	-0.29
Strong Conservative	0.20

# by party
kable(aggregate(d$act4, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.30
Independent	-0.23
Republican	-0.21

# by condition, ideology
kable(aggregate(d$act4, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.40
Liberal	climate	0.84
Moderate	climate	-0.32
Conservative	climate	-0.35
Strong Conservative	climate	0.18
Strong Liberal	ctrl	0.53
Liberal	ctrl	0.03
Moderate	ctrl	-0.11
Conservative	ctrl	-0.20
Strong Conservative	ctrl	0.22

# by condition, party
kable(aggregate(d$act4, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.49
Independent	climate	-0.54
Republican	climate	-0.23
Democrat	ctrl	0.12
Independent	ctrl	0.00
Republican	ctrl	-0.19

Action 5

# by condition
kable(aggregate(d$act5, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.84
ctrl	1.16

# by ideology
kable(aggregate(d$act5, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.42
Liberal	1.40
Moderate	1.09
Conservative	0.97
Strong Conservative	0.73

# by party
kable(aggregate(d$act5, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.12
Independent	0.89
Republican	0.96

# by condition, ideology
kable(aggregate(d$act5, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.40
Liberal	climate	0.95
Moderate	climate	0.90
Conservative	climate	0.93
Strong Conservative	climate	0.66
Strong Liberal	ctrl	0.43
Liberal	ctrl	1.82
Moderate	ctrl	1.26
Conservative	ctrl	1.03
Strong Conservative	ctrl	0.85

# by condition, party
kable(aggregate(d$act5, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.86
Independent	climate	0.87
Republican	climate	0.82
Democrat	ctrl	1.36
Independent	ctrl	0.92
Republican	ctrl	1.15

Action 6

# by condition
kable(aggregate(d$act6, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	1.17
ctrl	1.34

# by ideology
kable(aggregate(d$act6, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.19
Liberal	1.43
Moderate	1.31
Conservative	1.33
Strong Conservative	0.87

# by party
kable(aggregate(d$act6, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.20
Independent	1.40
Republican	1.11

# by condition, ideology
kable(aggregate(d$act6, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.08
Liberal	climate	1.35
Moderate	climate	1.20
Conservative	climate	1.30
Strong Conservative	climate	0.80
Strong Liberal	ctrl	1.26
Liberal	ctrl	1.50
Moderate	ctrl	1.40
Conservative	ctrl	1.36
Strong Conservative	ctrl	0.96

# by condition, party
kable(aggregate(d$act6, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.19
Independent	climate	1.23
Republican	climate	1.06
Democrat	ctrl	1.22
Independent	ctrl	1.53
Republican	ctrl	1.17

Action 7

# by condition
kable(aggregate(d$act7, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.01
ctrl	-0.24

# by ideology
kable(aggregate(d$act7, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.37
Liberal	-0.03
Moderate	-0.13
Conservative	-0.31
Strong Conservative	-0.16

# by party
kable(aggregate(d$act7, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.10
Independent	-0.01
Republican	-0.50

# by condition, ideology
kable(aggregate(d$act7, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	-0.07
Liberal	climate	0.69
Moderate	climate	0.01
Conservative	climate	-0.23
Strong Conservative	climate	-0.17
Strong Liberal	ctrl	0.70
Liberal	ctrl	-0.61
Moderate	ctrl	-0.27
Conservative	ctrl	-0.42
Strong Conservative	ctrl	-0.15

# by condition, party
kable(aggregate(d$act7, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.22
Independent	climate	0.16
Republican	climate	-0.35
Democrat	ctrl	-0.03
Independent	ctrl	-0.14
Republican	ctrl	-0.66

Action 8

# by condition
kable(aggregate(d$act8, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.45
ctrl	-0.34

# by ideology
kable(aggregate(d$act8, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.62
Liberal	0.48
Moderate	-0.44
Conservative	-0.71
Strong Conservative	-1.14

# by party
kable(aggregate(d$act8, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.23
Independent	-0.33
Republican	-1.13

# by condition, ideology
kable(aggregate(d$act8, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.12
Liberal	climate	0.82
Moderate	climate	-0.39
Conservative	climate	-1.13
Strong Conservative	climate	-1.08
Strong Liberal	ctrl	0.29
Liberal	ctrl	0.20
Moderate	ctrl	-0.50
Conservative	ctrl	-0.13
Strong Conservative	ctrl	-1.23

# by condition, party
kable(aggregate(d$act8, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.37
Independent	climate	-0.27
Republican	climate	-1.35
Democrat	ctrl	0.10
Independent	ctrl	-0.38
Republican	ctrl	-0.84

Action 9

# by condition
kable(aggregate(d$act9, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.04
ctrl	0.02

## by ideology
kable(aggregate(d$act9, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.19
Liberal	0.58
Moderate	0.20
Conservative	-0.25
Strong Conservative	-0.72

# by party
kable(aggregate(d$act9, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.26
Independent	0.20
Republican	-0.44

# by condition, ideology
kable(aggregate(d$act9, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	-0.08
Liberal	climate	0.57
Moderate	climate	0.28
Conservative	climate	-0.41
Strong Conservative	climate	-0.24
Strong Liberal	ctrl	0.33
Liberal	ctrl	0.58
Moderate	ctrl	0.13
Conservative	ctrl	-0.05
Strong Conservative	ctrl	-1.35

# by condition, party
kable(aggregate(d$act9, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.30
Independent	climate	0.23
Republican	climate	-0.41
Democrat	ctrl	0.23
Independent	ctrl	0.18
Republican	ctrl	-0.48

Action 10

# by condition
kable(aggregate(d$act10, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.01
ctrl	0.17

# by ideology
kable(aggregate(d$act10, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.15
Liberal	0.68
Moderate	0.08
Conservative	-0.26
Strong Conservative	-0.62

# by party
kable(aggregate(d$act10, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.59
Independent	0.35
Republican	-0.78

# by condition, ideology
kable(aggregate(d$act10, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.38
Liberal	climate	0.96
Moderate	climate	-0.01
Conservative	climate	-0.44
Strong Conservative	climate	-0.66
Strong Liberal	ctrl	1.00
Liberal	ctrl	0.46
Moderate	ctrl	0.17
Conservative	ctrl	-0.02
Strong Conservative	ctrl	-0.57

# by condition, party
kable(aggregate(d$act10, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.61
Independent	climate	0.33
Republican	climate	-0.94
Democrat	ctrl	0.58
Independent	ctrl	0.36
Republican	ctrl	-0.59

Action 11

# by condition
kable(aggregate(d$act11, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.68
ctrl	1.17

# by ideology
kable(aggregate(d$act11, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.54
Liberal	1.66
Moderate	0.73
Conservative	0.82
Strong Conservative	0.73

# by party
kable(aggregate(d$act11, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.08
Independent	0.98
Republican	0.69

# by condition, ideology
kable(aggregate(d$act11, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.20
Liberal	climate	1.64
Moderate	climate	0.43
Conservative	climate	0.46
Strong Conservative	climate	0.92
Strong Liberal	ctrl	1.79
Liberal	ctrl	1.67
Moderate	ctrl	1.03
Conservative	ctrl	1.23
Strong Conservative	ctrl	0.54

# by condition, party
kable(aggregate(d$act11, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.81
Independent	climate	0.57
Republican	climate	0.65
Democrat	ctrl	1.29
Independent	ctrl	1.35
Republican	ctrl	0.74

Action 12

# by condition
kable(aggregate(d$act12, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.19
ctrl	-0.10

# by ideology
kable(aggregate(d$act12, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.33
Liberal	0.74
Moderate	0.00
Conservative	-0.60
Strong Conservative	-0.84

# by party
kable(aggregate(d$act12, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.32
Independent	0.16
Republican	-0.90

# by condition, ideology
kable(aggregate(d$act12, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.60
Liberal	climate	1.13
Moderate	climate	-0.12
Conservative	climate	-0.80
Strong Conservative	climate	-0.59
Strong Liberal	ctrl	0.18
Liberal	ctrl	0.43
Moderate	ctrl	0.11
Conservative	ctrl	-0.33
Strong Conservative	ctrl	-1.11

# by condition, party
kable(aggregate(d$act12, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.30
Independent	climate	0.22
Republican	climate	-1.01
Democrat	ctrl	0.33
Independent	ctrl	0.10
Republican	ctrl	-0.78

Action 13

# by condition
kable(aggregate(d$act13, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.18
ctrl	-0.01

# by ideology
kable(aggregate(d$act13, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.26
Liberal	0.34
Moderate	-0.09
Conservative	-0.22
Strong Conservative	-0.58

# by party
kable(aggregate(d$act13, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.19
Independent	0.02
Republican	-0.56

# by condition, ideology
kable(aggregate(d$act13, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.14
Liberal	climate	0.27
Moderate	climate	0.09
Conservative	climate	-0.61
Strong Conservative	climate	-0.71
Strong Liberal	ctrl	0.33
Liberal	ctrl	0.41
Moderate	ctrl	-0.25
Conservative	ctrl	0.27
Strong Conservative	ctrl	-0.38

# by condition, party
kable(aggregate(d$act13, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.00
Independent	climate	0.27
Republican	climate	-0.81
Democrat	ctrl	0.38
Independent	ctrl	-0.20
Republican	ctrl	-0.25

Action 14

# by condition
kable(aggregate(d$act14, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.55
ctrl	0.37

# by ideology
kable(aggregate(d$act14, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.09
Liberal	0.95
Moderate	0.42
Conservative	0.32
Strong Conservative	0.50

# by party
kable(aggregate(d$act14, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.55
Independent	0.44
Republican	0.32

# by condition, ideology
kable(aggregate(d$act14, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.50
Liberal	climate	1.25
Moderate	climate	0.48
Conservative	climate	0.19
Strong Conservative	climate	0.78
Strong Liberal	ctrl	-0.14
Liberal	ctrl	0.74
Moderate	ctrl	0.37
Conservative	ctrl	0.48
Strong Conservative	ctrl	0.12

# by condition, party
kable(aggregate(d$act14, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.56
Independent	climate	0.72
Republican	climate	0.36
Democrat	ctrl	0.54
Independent	ctrl	0.23
Republican	ctrl	0.27

Action 15

# by condition
kable(aggregate(d$act15, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.04
ctrl	0.25

# by ideology
kable(aggregate(d$act15, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.31
Liberal	0.89
Moderate	0.10
Conservative	0.11
Strong Conservative	-0.36

# by party
kable(aggregate(d$act15, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.53
Independent	0.07
Republican	-0.20

# by condition, ideology
kable(aggregate(d$act15, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.53
Liberal	climate	0.05
Moderate	climate	0.05
Conservative	climate	0.02
Strong Conservative	climate	-0.21
Strong Liberal	ctrl	0.12
Liberal	ctrl	1.56
Moderate	ctrl	0.15
Conservative	ctrl	0.22
Strong Conservative	ctrl	-0.54

# by condition, party
kable(aggregate(d$act15, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.37
Independent	climate	-0.08
Republican	climate	-0.18
Democrat	ctrl	0.70
Independent	ctrl	0.20
Republican	ctrl	-0.21

Action 16

# by condition
kable(aggregate(d$act16, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	1.01
ctrl	0.85

# by ideology
kable(aggregate(d$act16, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.03
Liberal	1.07
Moderate	0.96
Conservative	0.99
Strong Conservative	0.56

# by party
kable(aggregate(d$act16, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.06
Independent	0.82
Republican	0.92

# by condition, ideology
kable(aggregate(d$act16, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.70
Liberal	climate	1.17
Moderate	climate	1.10
Conservative	climate	1.18
Strong Conservative	climate	0.57
Strong Liberal	ctrl	1.21
Liberal	ctrl	1.00
Moderate	ctrl	0.83
Conservative	ctrl	0.76
Strong Conservative	ctrl	0.55

# by condition, party
kable(aggregate(d$act16, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.04
Independent	climate	1.19
Republican	climate	0.86
Democrat	ctrl	1.07
Independent	ctrl	0.50
Republican	ctrl	0.98

Action 17

# by condition
kable(aggregate(d$act17, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.40
ctrl	0.53

# by ideology
kable(aggregate(d$act17, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.21
Liberal	0.55
Moderate	0.48
Conservative	0.17
Strong Conservative	0.38

# by party
kable(aggregate(d$act17, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.65
Independent	0.42
Republican	0.30

# by condition, ideology
kable(aggregate(d$act17, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.71
Liberal	climate	0.67
Moderate	climate	0.32
Conservative	climate	-0.17
Strong Conservative	climate	0.76
Strong Liberal	ctrl	0.85
Liberal	ctrl	0.45
Moderate	ctrl	0.62
Conservative	ctrl	0.67
Strong Conservative	ctrl	-0.07

# by condition, party
kable(aggregate(d$act17, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.62
Independent	climate	0.40
Republican	climate	0.19
Democrat	ctrl	0.68
Independent	ctrl	0.44
Republican	ctrl	0.44

Action 18

# by condition
kable(aggregate(d$act18, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.70
ctrl	0.63

# by ideology
kable(aggregate(d$act18, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.76
Liberal	0.91
Moderate	0.82
Conservative	0.49
Strong Conservative	0.19

# by party
kable(aggregate(d$act18, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.84
Independent	0.69
Republican	0.42

# by condition, ideology
kable(aggregate(d$act18, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.82
Liberal	climate	1.15
Moderate	climate	0.80
Conservative	climate	0.39
Strong Conservative	climate	0.39
Strong Liberal	ctrl	0.72
Liberal	ctrl	0.71
Moderate	ctrl	0.83
Conservative	ctrl	0.58
Strong Conservative	ctrl	-0.04

# by condition, party
kable(aggregate(d$act18, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.91
Independent	climate	0.63
Republican	climate	0.56
Democrat	ctrl	0.78
Independent	ctrl	0.72
Republican	ctrl	0.29

Action 19

# by condition
kable(aggregate(d$act19, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.95
ctrl	1.44

# by ideology
kable(aggregate(d$act19, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.30
Liberal	1.44
Moderate	1.23
Conservative	1.05
Strong Conservative	0.91

# by party
kable(aggregate(d$act19, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.13
Independent	1.34
Republican	1.00

# by condition, ideology
kable(aggregate(d$act19, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.23
Liberal	climate	1.14
Moderate	climate	1.02
Conservative	climate	0.69
Strong Conservative	climate	0.93
Strong Liberal	ctrl	1.36
Liberal	ctrl	1.67
Moderate	ctrl	1.48
Conservative	ctrl	1.58
Strong Conservative	ctrl	0.88

# by condition, party
kable(aggregate(d$act19, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.00
Independent	climate	1.22
Republican	climate	0.65
Democrat	ctrl	1.28
Independent	ctrl	1.45
Republican	ctrl	1.54

Action 20

# by condition
kable(aggregate(d$act20, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.94
ctrl	1.49

# by ideology
kable(aggregate(d$act20, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.00
Liberal	1.20
Moderate	1.36
Conservative	1.13
Strong Conservative	1.15

# by party
kable(aggregate(d$act20, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.10
Independent	1.31
Republican	1.26

# by condition, ideology
kable(aggregate(d$act20, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.69
Liberal	climate	0.81
Moderate	climate	1.05
Conservative	climate	0.95
Strong Conservative	climate	0.90
Strong Liberal	ctrl	1.29
Liberal	ctrl	1.52
Moderate	ctrl	1.59
Conservative	ctrl	1.34
Strong Conservative	ctrl	1.48

# by condition, party
kable(aggregate(d$act20, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.56
Independent	climate	1.30
Republican	climate	1.04
Democrat	ctrl	1.62
Independent	ctrl	1.33
Republican	ctrl	1.51

Action 21

# by condition
kable(aggregate(d$act21, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.05
ctrl	-0.19

# by ideology
kable(aggregate(d$act21, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.42
Liberal	0.44
Moderate	-0.06
Conservative	-0.27
Strong Conservative	-0.58

# by party
kable(aggregate(d$act21, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.01
Independent	0.22
Republican	-0.50

# by condition, ideology
kable(aggregate(d$act21, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.47
Liberal	climate	0.56
Moderate	climate	0.05
Conservative	climate	-0.13
Strong Conservative	climate	-0.29
Strong Liberal	ctrl	0.39
Liberal	ctrl	0.34
Moderate	ctrl	-0.16
Conservative	ctrl	-0.46
Strong Conservative	ctrl	-0.93

# by condition, party
kable(aggregate(d$act21, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	-0.04
Independent	climate	0.57
Republican	climate	-0.33
Democrat	ctrl	0.06
Independent	ctrl	-0.07
Republican	ctrl	-0.71

Action 22

# by condition
kable(aggregate(d$act22, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.30
ctrl	0.34

# by ideology
kable(aggregate(d$act22, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.14
Liberal	0.59
Moderate	0.29
Conservative	0.34
Strong Conservative	-0.33

# by party
kable(aggregate(d$act22, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.80
Independent	0.39
Republican	-0.29

# by condition, ideology
kable(aggregate(d$act22, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	0.93
Liberal	climate	0.79
Moderate	climate	0.25
Conservative	climate	0.31
Strong Conservative	climate	-0.22
Strong Liberal	ctrl	1.26
Liberal	ctrl	0.44
Moderate	ctrl	0.31
Conservative	ctrl	0.38
Strong Conservative	ctrl	-0.45

# by condition, party
kable(aggregate(d$act22, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.68
Independent	climate	0.51
Republican	climate	-0.26
Democrat	ctrl	0.93
Independent	ctrl	0.30
Republican	ctrl	-0.31

Action 23

# by condition
kable(aggregate(d$act23, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.01
ctrl	0.35

# by ideology
kable(aggregate(d$act23, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.56
Liberal	0.62
Moderate	0.11
Conservative	-0.18
Strong Conservative	0.27

# by party
kable(aggregate(d$act23, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	0.16
Independent	0.38
Republican	-0.08

# by condition, ideology
kable(aggregate(d$act23, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.22
Liberal	climate	-0.12
Moderate	climate	-0.10
Conservative	climate	-0.30
Strong Conservative	climate	0.29
Strong Liberal	ctrl	0.19
Liberal	ctrl	1.13
Moderate	ctrl	0.31
Conservative	ctrl	-0.04
Strong Conservative	ctrl	0.25

# by condition, party
kable(aggregate(d$act23, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	-0.08
Independent	climate	0.02
Republican	climate	0.04
Democrat	ctrl	0.39
Independent	ctrl	0.66
Republican	ctrl	-0.22

Action 24

# by condition
kable(aggregate(d$act24, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	-0.45
ctrl	-0.05

# by ideology
kable(aggregate(d$act24, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	-0.08
Liberal	-0.14
Moderate	-0.23
Conservative	-0.50
Strong Conservative	-0.13

# by party
kable(aggregate(d$act24, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	-0.21
Independent	-0.07
Republican	-0.50

# by condition, ideology
kable(aggregate(d$act24, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	-0.13
Liberal	climate	-0.25
Moderate	climate	-0.57
Conservative	climate	-0.65
Strong Conservative	climate	-0.10
Strong Liberal	ctrl	-0.05
Liberal	ctrl	-0.06
Moderate	ctrl	0.08
Conservative	ctrl	-0.29
Strong Conservative	ctrl	-0.17

# by condition, party
kable(aggregate(d$act24, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	-0.39
Independent	climate	-0.33
Republican	climate	-0.61
Democrat	ctrl	-0.04
Independent	ctrl	0.13
Republican	ctrl	-0.36

Action 25

# by condition
kable(aggregate(d$act25, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.77
ctrl	0.70

# by ideology
kable(aggregate(d$act25, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.34
Liberal	1.13
Moderate	0.89
Conservative	0.50
Strong Conservative	-0.12

# by party
kable(aggregate(d$act25, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.11
Independent	0.84
Republican	0.24

# by condition, ideology
kable(aggregate(d$act25, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.79
Liberal	climate	1.55
Moderate	climate	0.85
Conservative	climate	0.40
Strong Conservative	climate	0.06
Strong Liberal	ctrl	1.05
Liberal	ctrl	0.72
Moderate	ctrl	0.93
Conservative	ctrl	0.62
Strong Conservative	ctrl	-0.33

# by condition, party
kable(aggregate(d$act25, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.32
Independent	climate	0.97
Republican	climate	0.16
Democrat	ctrl	0.92
Independent	ctrl	0.74
Republican	ctrl	0.34

Action 26

# by condition
kable(aggregate(d$act26, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	1.50
ctrl	1.47

# by ideology
kable(aggregate(d$act26, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	0.95
Liberal	1.26
Moderate	1.62
Conservative	1.57
Strong Conservative	1.54

# by party
kable(aggregate(d$act26, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.40
Independent	1.52
Republican	1.57

# by condition, ideology
kable(aggregate(d$act26, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.22
Liberal	climate	1.25
Moderate	climate	1.65
Conservative	climate	1.64
Strong Conservative	climate	1.39
Strong Liberal	ctrl	0.70
Liberal	ctrl	1.27
Moderate	ctrl	1.59
Conservative	ctrl	1.52
Strong Conservative	ctrl	1.79

# by condition, party
kable(aggregate(d$act26, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.50
Independent	climate	1.62
Republican	climate	1.46
Democrat	ctrl	1.28
Independent	ctrl	1.44
Republican	ctrl	1.69

Action 27

# by condition
kable(aggregate(d$act27, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	0.50
ctrl	0.96

# by ideology
kable(aggregate(d$act27, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.39
Liberal	1.18
Moderate	0.55
Conservative	0.72
Strong Conservative	0.44

# by party
kable(aggregate(d$act27, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.14
Independent	0.56
Republican	0.43

# by condition, ideology
kable(aggregate(d$act27, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.64
Liberal	climate	0.88
Moderate	climate	0.25
Conservative	climate	0.60
Strong Conservative	climate	0.13
Strong Liberal	ctrl	1.18
Liberal	ctrl	1.36
Moderate	ctrl	0.82
Conservative	ctrl	0.86
Strong Conservative	ctrl	0.86

# by condition, party
kable(aggregate(d$act27, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	0.96
Independent	climate	0.33
Republican	climate	0.19
Democrat	ctrl	1.30
Independent	ctrl	0.75
Republican	ctrl	0.73

Action 28

# by condition
kable(aggregate(d$act28, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	1.01
ctrl	1.08

# by ideology
kable(aggregate(d$act28, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.19
Liberal	1.00
Moderate	0.97
Conservative	1.32
Strong Conservative	0.79

# by party
kable(aggregate(d$act28, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.23
Independent	0.94
Republican	0.95

# by condition, ideology
kable(aggregate(d$act28, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.33
Liberal	climate	1.13
Moderate	climate	1.07
Conservative	climate	1.19
Strong Conservative	climate	0.39
Strong Liberal	ctrl	1.11
Liberal	ctrl	0.91
Moderate	ctrl	0.88
Conservative	ctrl	1.45
Strong Conservative	ctrl	1.27

# by condition, party
kable(aggregate(d$act28, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.13
Independent	climate	1.16
Republican	climate	0.78
Democrat	ctrl	1.31
Independent	ctrl	0.79
Republican	ctrl	1.14

Action 29

# by condition
kable(aggregate(d$act29, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	1.11
ctrl	1.11

# by ideology
kable(aggregate(d$act29, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.48
Liberal	0.98
Moderate	1.14
Conservative	1.02
Strong Conservative	1.09

# by party
kable(aggregate(d$act29, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.32
Independent	1.07
Republican	0.93

# by condition, ideology
kable(aggregate(d$act29, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.45
Liberal	climate	1.12
Moderate	climate	1.09
Conservative	climate	1.00
Strong Conservative	climate	1.23
Strong Liberal	ctrl	1.50
Liberal	ctrl	0.87
Moderate	ctrl	1.19
Conservative	ctrl	1.06
Strong Conservative	ctrl	0.92

# by condition, party
kable(aggregate(d$act29, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.34
Independent	climate	1.12
Republican	climate	0.90
Democrat	ctrl	1.30
Independent	ctrl	1.03
Republican	ctrl	0.96

Action 30

# by condition
kable(aggregate(d$act30, list(d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
climate	1.13
ctrl	1.02

# by ideology
kable(aggregate(d$act30, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Strong Liberal	1.14
Liberal	1.18
Moderate	1.04
Conservative	1.01
Strong Conservative	1.14

# by party
kable(aggregate(d$act30, list(d$party_factor), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	x
Democrat	1.06
Independent	1.19
Republican	0.97

# by condition, ideology
kable(aggregate(d$act30, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Strong Liberal	climate	1.25
Liberal	climate	1.23
Moderate	climate	1.00
Conservative	climate	0.91
Strong Conservative	climate	1.62
Strong Liberal	ctrl	1.06
Liberal	ctrl	1.14
Moderate	ctrl	1.09
Conservative	ctrl	1.14
Strong Conservative	ctrl	0.52

# by condition, party
kable(aggregate(d$act30, list(d$party_factor, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	1.12
Independent	climate	1.25
Republican	climate	1.06
Democrat	ctrl	1.00
Independent	ctrl	1.14
Republican	ctrl	0.86

3. Inferential stats

a. codes

### Condition

## contrast code
d$cond.c <- NA
d$cond.c[d$cond == "ctrl"] <- -1/2
d$cond.c[d$cond == "climate"] <- 1/2

## dummies
d$clim.d <- ifelse(d$cond == "climate", 0, 1)
d$ctrl.d <- ifelse(d$cond == "ctrl", 0, 1)


### Gender

## contrast codes, omit "other"
d$gend.mf <- NA
d$gend.mf[d$gend == "Other"] <- NA
d$gend.mf[d$gend == "Female"] <- -1/2
d$gend.mf[d$gend == "Male"] <- 1/2

## dummmies

# male
d$gend.m <- NA
d$gend.m[d$gend == "Other"] <- NA
d$gend.m[d$gend == "Female"] <- 1
d$gend.m[d$gend == "Male"] <- 0

# female
d$gend.f <- NA
d$gend.f[d$gend == "Other"] <- NA
d$gend.f[d$gend == "Female"] <- 0
d$gend.f[d$gend == "Male"] <- 1

### Ideology 

## Dummies

# Strong Liberals
d$SlibsSC.d <- NA
d$SlibsSC.d[d$ideology == "Strong Conservative"] <- 1
d$SlibsSC.d[d$ideology == "Conservative"] <- 0
d$SlibsSC.d[d$ideology == "Moderate"] <- 0
d$SlibsSC.d[d$ideology == "Liberal"] <- 0
d$SlibsSC.d[d$ideology == "Strong Liberal"] <- 0

d$SlibsC.d <- NA
d$SlibsC.d[d$ideology == "Strong Conservative"] <- 0
d$SlibsC.d[d$ideology == "Conservative"] <- 1
d$SlibsC.d[d$ideology == "Moderate"] <- 0
d$SlibsC.d[d$ideology == "Liberal"] <- 0
d$SlibsC.d[d$ideology == "Strong Liberal"] <- 0

d$SlibsM.d <- NA
d$SlibsM.d[d$ideology == "Strong Conservative"] <- 0
d$SlibsM.d[d$ideology == "Conservative"] <- 0
d$SlibsM.d[d$ideology == "Moderate"] <- 1
d$SlibsM.d[d$ideology == "Liberal"] <- 0
d$SlibsM.d[d$ideology == "Strong Liberal"] <- 0

d$SlibsL.d <- NA
d$SlibsL.d[d$ideology == "Strong Conservative"] <- 0
d$SlibsL.d[d$ideology == "Conservative"] <- 0
d$SlibsL.d[d$ideology == "Moderate"] <- 0
d$SlibsL.d[d$ideology == "Liberal"] <- 1
d$SlibsL.d[d$ideology == "Strong Liberal"] <- 0

# Liberals
d$LibsSC.d <- NA
d$LibsSC.d[d$ideology == "Strong Conservative"] <- 1
d$LibsSC.d[d$ideology == "Conservative"] <- 0
d$LibsSC.d[d$ideology == "Moderate"] <- 0
d$LibsSC.d[d$ideology == "Liberal"] <- 0
d$LibsSC.d[d$ideology == "Strong Liberal"] <- 0

d$LibsM.d <- NA
d$LibsM.d[d$ideology == "Strong Conservative"] <- 0
d$LibsM.d[d$ideology == "Conservative"] <- 0
d$LibsM.d[d$ideology == "Moderate"] <- 1
d$LibsM.d[d$ideology == "Liberal"] <- 0
d$LibsM.d[d$ideology == "Strong Liberal"] <- 0

d$LibsSL.d <- NA
d$LibsSL.d[d$ideology == "Strong Conservative"] <- 0
d$LibsSL.d[d$ideology == "Conservative"] <- 0
d$LibsSL.d[d$ideology == "Moderate"] <- 0
d$LibsSL.d[d$ideology == "Liberal"] <- 0
d$LibsSL.d[d$ideology == "Strong Liberal"] <- 1

d$LibsC.d <- NA
d$LibsC.d[d$ideology == "Strong Conservative"] <- 0
d$LibsC.d[d$ideology == "Conservative"] <- 1
d$LibsC.d[d$ideology == "Moderate"] <- 0
d$LibsC.d[d$ideology == "Liberal"] <- 0
d$LibsC.d[d$ideology == "Strong Liberal"] <- 0


# Moderates
d$ModsSC.d <- NA
d$ModsSC.d[d$ideology == "Strong Conservative"] <- 1
d$ModsSC.d[d$ideology == "Conservative"] <- 0
d$ModsSC.d[d$ideology == "Moderate"] <- 0
d$ModsSC.d[d$ideology == "Liberal"] <- 0
d$ModsSC.d[d$ideology == "Strong Liberal"] <- 0

d$ModsC.d <- NA
d$ModsC.d[d$ideology == "Strong Conservative"] <- 0
d$ModsC.d[d$ideology == "Conservative"] <- 1
d$ModsC.d[d$ideology == "Moderate"] <- 0
d$ModsC.d[d$ideology == "Liberal"] <- 0
d$ModsC.d[d$ideology == "Strong Liberal"] <- 0

d$ModsL.d <- NA
d$ModsL.d[d$ideology == "Strong Conservative"] <- 0
d$ModsL.d[d$ideology == "Conservative"] <- 0
d$ModsL.d[d$ideology == "Moderate"] <- 0
d$ModsL.d[d$ideology == "Liberal"] <- 1
d$ModsL.d[d$ideology == "Strong Liberal"] <- 0

d$ModsSL.d <- NA
d$ModsSL.d[d$ideology == "Strong Conservative"] <- 0
d$ModsSL.d[d$ideology == "Conservative"] <- 0
d$ModsSL.d[d$ideology == "Moderate"] <- 0
d$ModsSL.d[d$ideology == "Liberal"] <- 0
d$ModsSL.d[d$ideology == "Strong Liberal"] <- 1


# Conservatives 
d$ConsSC.d <- NA
d$ConsSC.d[d$ideology == "Strong Conservative"] <- 1
d$ConsSC.d[d$ideology == "Conservative"] <- 0
d$ConsSC.d[d$ideology == "Moderate"] <- 0
d$ConsSC.d[d$ideology == "Liberal"] <- 0
d$ConsSC.d[d$ideology == "Strong Liberal"] <- 0

d$ConsM.d <- NA
d$ConsM.d[d$ideology == "Strong Conservative"] <- 0
d$ConsM.d[d$ideology == "Conservative"] <- 0
d$ConsM.d[d$ideology == "Moderate"] <- 1
d$ConsM.d[d$ideology == "Liberal"] <- 0
d$ConsM.d[d$ideology == "Strong Liberal"] <- 0

d$ConsL.d <- NA
d$ConsL.d[d$ideology == "Strong Conservative"] <- 0
d$ConsL.d[d$ideology == "Conservative"] <- 0
d$ConsL.d[d$ideology == "Moderate"] <- 0
d$ConsL.d[d$ideology == "Liberal"] <- 1
d$ConsL.d[d$ideology == "Strong Liberal"] <- 0

d$ConsSL.d <- NA
d$ConsSL.d[d$ideology == "Strong Conservative"] <- 0
d$ConsSL.d[d$ideology == "Conservative"] <- 0
d$ConsSL.d[d$ideology == "Moderate"] <- 0
d$ConsSL.d[d$ideology == "Liberal"] <- 0
d$ConsSL.d[d$ideology == "Strong Liberal"] <- 1

# Strong Conservatives
d$SconsC.d <- NA
d$SconsC.d[d$ideology == "Strong Conservative"] <- 0
d$SconsC.d[d$ideology == "Conservative"] <- 1
d$SconsC.d[d$ideology == "Moderate"] <- 0
d$SconsC.d[d$ideology == "Liberal"] <- 0
d$SconsC.d[d$ideology == "Strong Liberal"] <- 0

d$SconsM.d <- NA
d$SconsM.d[d$ideology == "Strong Conservative"] <- 0
d$SconsM.d[d$ideology == "Conservative"] <- 0
d$SconsM.d[d$ideology == "Moderate"] <- 1
d$SconsM.d[d$ideology == "Liberal"] <- 0
d$SconsM.d[d$ideology == "Strong Liberal"] <- 0

d$SconsL.d <- NA
d$SconsL.d[d$ideology == "Strong Conservative"] <- 0
d$SconsL.d[d$ideology == "Conservative"] <- 0
d$SconsL.d[d$ideology == "Moderate"] <- 0
d$SconsL.d[d$ideology == "Liberal"] <- 1
d$SconsL.d[d$ideology == "Strong Liberal"] <- 0

d$SconsSL.d <- NA
d$SconsSL.d[d$ideology == "Strong Conservative"] <- 0
d$SconsSL.d[d$ideology == "Conservative"] <- 0
d$SconsSL.d[d$ideology == "Moderate"] <- 0
d$SconsSL.d[d$ideology == "Liberal"] <- 0
d$SconsSL.d[d$ideology == "Strong Liberal"] <- 1


## Contrast Codes

d$Libs_Cons.c <- NA
d$Libs_Cons.c[d$ideology == "Strong Liberal"| d$ideology == "Liberal"] <- -1/2
d$Libs_Cons.c[d$ideology == "Moderate"] <- 0
d$Libs_Cons.c[d$ideology == "Conservative"| d$ideology == "Strong Conservative"] <- 1/2

d$Mods_Ideo.c <- NA
d$Mods_Ideo.c[d$ideology == "Strong Liberal"| d$ideology == "Liberal"] <- 1/3
d$Mods_Ideo.c[d$ideology == "Moderate"] <- -2/3
d$Mods_Ideo.c[d$ideology == "Conservative"| d$ideology == "Strong Conservative"] <- 1/3

### Party ID

## Dummies

#  Republicans
d$RepD.d <- NA
d$RepD.d[d$party_factor == "Democrat"] <- 1
d$RepD.d[d$party_factor == "Independent"] <- 0
d$RepD.d[d$party_factor == "Republican"] <- 0

d$RepI.d <- NA
d$RepI.d[d$party_factor == "Democrat"] <- 0
d$RepI.d[d$party_factor == "Independent"] <- 1
d$RepI.d[d$party_factor == "Republican"] <- 0

# Democrats
d$DemR.d <- NA
d$DemR.d[d$party_factor == "Democrat"] <- 0
d$DemR.d[d$party_factor == "Independent"] <- 0
d$DemR.d[d$party_factor == "Republican"] <- 1

d$DemI.d <- NA
d$DemI.d[d$party_factor == "Democrat"] <- 0
d$DemI.d[d$party_factor == "Independent"] <- 1
d$DemI.d[d$party_factor == "Republican"] <- 0

# Independents
d$IndD.d <- NA
d$IndD.d[d$party_factor == "Democrat"] <- 1
d$IndD.d[d$party_factor == "Independent"] <- 0
d$IndD.d[d$party_factor == "Republican"] <- 0

d$IndR.d <- NA
d$IndR.d[d$party_factor == "Democrat"] <- 0
d$IndR.d[d$party_factor == "Independent"] <- 0
d$IndR.d[d$party_factor == "Republican"] <- 1

## Contrast codes

#  Republicans v. Dems
d$Dem_Rep <- NA
d$Dem_Rep[d$party_factor == "Democrat"] <- -1/2
d$Dem_Rep[d$party_factor == "Independent"] <- 0
d$Dem_Rep[d$party_factor == "Republican"] <- 1/2

# Independents vs. Partisans
d$Ind_Parties <- NA
d$Ind_Parties[d$party_factor == "Democrat"] <- 1/3
d$Ind_Parties[d$party_factor == "Independent"] <- -2/3
d$Ind_Parties[d$party_factor == "Republican"] <- 1/3

b. Actions by Ideology

i. Strong Liberals

# Action 1
slib.b1 <- lm(act1 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b1)

## 
## Call:
## lm(formula = act1 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.646 -1.962  0.038  2.038  3.490 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.1714     0.3635   0.472    0.637
## SlibsSC.d    -0.6330     0.4509  -1.404    0.161
## SlibsC.d     -0.6614     0.4224  -1.566    0.118
## SlibsM.d     -0.2095     0.3966  -0.528    0.598
## SlibsL.d      0.4747     0.4509   1.053    0.293
## 
## Residual standard error: 2.151 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.02106 
## F-statistic: 3.409 on 4 and 444 DF,  p-value: 0.00923

# Action 2
slib.b2 <- lm(act2 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b2)

## 
## Call:
## lm(formula = act2 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.2212  0.1077  1.7788  2.1077 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  1.04167    0.36851   2.827  0.00504 **
## SlibsSC.d   -0.08514    0.45459  -0.187  0.85156   
## SlibsC.d    -0.14936    0.43121  -0.346  0.72932   
## SlibsM.d     0.17957    0.40576   0.443  0.65842   
## SlibsL.d     0.52976    0.46195   1.147  0.25243   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 285 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01519,    Adjusted R-squared:  0.001372 
## F-statistic: 1.099 on 4 and 285 DF,  p-value: 0.3572

# Action 3
slib.b3 <- lm(act3 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b3)

## 
## Call:
## lm(formula = act3 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.9837  0.0163  1.4167  3.8852 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.3714     0.3397   1.093  0.27483   
## SlibsSC.d    -1.2567     0.4262  -2.949  0.00336 **
## SlibsC.d     -0.7881     0.3968  -1.986  0.04766 * 
## SlibsM.d     -0.3877     0.3706  -1.046  0.29605   
## SlibsL.d     -0.5626     0.4181  -1.346  0.17911   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 439 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02777,    Adjusted R-squared:  0.01891 
## F-statistic: 3.134 on 4 and 439 DF,  p-value: 0.01467

# Action 4
slib.b4 <- lm(act4 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b4)

## 
## Call:
## lm(formula = act4 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4828 -1.7937  0.2063  2.0051  3.2877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.4828     0.4014   1.203    0.230
## SlibsSC.d    -0.2867     0.5027  -0.570    0.569
## SlibsC.d     -0.7704     0.4745  -1.624    0.105
## SlibsM.d     -0.6890     0.4363  -1.579    0.115
## SlibsL.d     -0.1034     0.4916  -0.210    0.833
## 
## Residual standard error: 2.162 on 366 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.006351 
## F-statistic: 1.591 on 4 and 366 DF,  p-value: 0.176

# Action 5
slib.b5 <- lm(act5 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b5)

## 
## Call:
## lm(formula = act5 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4048 -1.0917  0.2653  1.5952  2.5833 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4167     0.3820   1.091   0.2763  
## SlibsSC.d     0.3180     0.4662   0.682   0.4957  
## SlibsC.d      0.5559     0.4403   1.263   0.2077  
## SlibsM.d      0.6751     0.4219   1.600   0.1107  
## SlibsL.d      0.9881     0.4788   2.064   0.0399 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 292 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.01867,    Adjusted R-squared:  0.005229 
## F-statistic: 1.389 on 4 and 292 DF,  p-value: 0.2378

# Action 6
slib.b6 <- lm(act6 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b6)

## 
## Call:
## lm(formula = act6 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4348 -1.3099  0.6706  1.6706  2.1296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1875     0.3205   3.705 0.000245 ***
## SlibsSC.d    -0.3171     0.4044  -0.784 0.433482    
## SlibsC.d      0.1419     0.3760   0.377 0.706075    
## SlibsM.d      0.1224     0.3547   0.345 0.730361    
## SlibsL.d      0.2473     0.4173   0.593 0.553852    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 354 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.00896,    Adjusted R-squared:  -0.002238 
## F-statistic: 0.8001 on 4 and 354 DF,  p-value: 0.5257

# Action 7
slib.b7 <- lm(act7 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b7)

## 
## Call:
## lm(formula = act7 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.8652  0.1348  1.6286  3.3140 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.3714     0.3531   1.052    0.293
## SlibsSC.d    -0.5293     0.4486  -1.180    0.239
## SlibsC.d     -0.6854     0.4188  -1.636    0.103
## SlibsM.d     -0.5063     0.3862  -1.311    0.191
## SlibsL.d     -0.4053     0.4457  -0.909    0.364
## 
## Residual standard error: 2.089 on 410 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.006798,   Adjusted R-squared:  -0.002892 
## F-statistic: 0.7016 on 4 and 410 DF,  p-value: 0.5912

# Action 8
slib.b8 <- lm(act8 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b8)

## 
## Call:
## lm(formula = act8 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.625 -1.855 -0.287  1.713  4.145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6250     0.3354   1.864  0.06298 .  
## SlibsSC.d    -1.7699     0.4215  -4.199  3.2e-05 ***
## SlibsC.d     -1.3380     0.3926  -3.408  0.00071 ***
## SlibsM.d     -1.0678     0.3672  -2.908  0.00381 ** 
## SlibsL.d     -0.1488     0.4288  -0.347  0.72872    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 476 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.06121,    Adjusted R-squared:  0.05332 
## F-statistic: 7.759 on 4 and 476 DF,  p-value: 4.609e-06

# Action 9
slib.b9 <- lm(act9 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b9)

## 
## Call:
## lm(formula = act9 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5781 -1.5781  0.2525  1.7167  3.7167 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.194444   0.327704   0.593   0.5532  
## SlibsSC.d   -0.911111   0.414516  -2.198   0.0285 *
## SlibsC.d    -0.446970   0.382676  -1.168   0.2434  
## SlibsM.d     0.005556   0.357404   0.016   0.9876  
## SlibsL.d     0.383681   0.409630   0.937   0.3494  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.966 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03759,    Adjusted R-squared:  0.02892 
## F-statistic: 4.336 on 4 and 444 DF,  p-value: 0.00189

# Action 10
slib.b10 <- lm(act10 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b10)

## 
## Call:
## lm(formula = act10 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1500     0.3281   3.505 0.000500 ***
## SlibsSC.d    -1.7676     0.4135  -4.275 2.31e-05 ***
## SlibsC.d     -1.4071     0.3855  -3.650 0.000292 ***
## SlibsM.d     -1.0696     0.3595  -2.975 0.003081 ** 
## SlibsL.d     -0.4731     0.4170  -1.135 0.257147    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

# Action 11
slib.b11 <- lm(act11 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b11)

## 
## Call:
## lm(formula = act11 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6585 -0.8154  0.2741  1.3415  2.2741 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.5417     0.3812   4.045 6.62e-05 ***
## SlibsSC.d    -0.8070     0.4652  -1.735   0.0838 .  
## SlibsC.d     -0.7263     0.4460  -1.628   0.1045    
## SlibsM.d     -0.8157     0.4136  -1.972   0.0495 *  
## SlibsL.d      0.1169     0.4799   0.244   0.8078    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 309 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03501,    Adjusted R-squared:  0.02252 
## F-statistic: 2.803 on 4 and 309 DF,  p-value: 0.02604

# Action 12
slib.b12 <- lm(act12 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b12)

## 
## Call:
## lm(formula = act12 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.736 -2.158  0.000  2.000  3.842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3333     0.4129   0.807   0.4200  
## SlibsSC.d    -1.1754     0.5013  -2.345   0.0195 *
## SlibsC.d     -0.9333     0.4708  -1.982   0.0482 *
## SlibsM.d     -0.3333     0.4468  -0.746   0.4561  
## SlibsL.d      0.4025     0.5073   0.793   0.4280  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.146 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0525, Adjusted R-squared:  0.04253 
## F-statistic: 5.264 on 4 and 380 DF,  p-value: 0.0003892

# Action 13
slib.b13 <- lm(act13 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b13)

## 
## Call:
## lm(formula = act13 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3438 -1.9149  0.0851  1.6562  3.5781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.25714    0.35140   0.732   0.4647  
## SlibsSC.d   -0.83527    0.43705  -1.911   0.0566 .
## SlibsC.d    -0.47453    0.41287  -1.149   0.2510  
## SlibsM.d    -0.34225    0.38272  -0.894   0.3717  
## SlibsL.d     0.08661    0.43705   0.198   0.8430  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.079 on 438 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01713,    Adjusted R-squared:  0.008155 
## F-statistic: 1.909 on 4 and 438 DF,  p-value: 0.108

# Action 14
slib.b14 <- lm(act14 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b14)

## 
## Call:
## lm(formula = act14 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9487 -1.4219 -0.0198  1.6780  2.9091 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.09091    0.43309   0.210    0.834
## SlibsSC.d    0.40909    0.53920   0.759    0.449
## SlibsC.d     0.23112    0.50746   0.455    0.649
## SlibsM.d     0.33097    0.46884   0.706    0.481
## SlibsL.d     0.85781    0.54165   1.584    0.114
## 
## Residual standard error: 2.031 on 283 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.01158,    Adjusted R-squared:  -0.002388 
## F-statistic: 0.829 on 4 and 283 DF,  p-value: 0.5076

# Action 15
slib.b15 <- lm(act15 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b15)

## 
## Call:
## lm(formula = act15 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8889 -2.1024  0.1111  1.8916  3.3559 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.3125     0.3621   0.863    0.389
## SlibsSC.d    -0.6684     0.4497  -1.487    0.138
## SlibsC.d     -0.2041     0.4262  -0.479    0.632
## SlibsM.d     -0.2101     0.3954  -0.531    0.596
## SlibsL.d      0.5764     0.4736   1.217    0.224
## 
## Residual standard error: 2.048 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.02508,    Adjusted R-squared:  0.01482 
## F-statistic: 2.444 on 4 and 380 DF,  p-value: 0.0462

# Action 16
slib.b16 <- lm(act16 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b16)

## 
## Call:
## lm(formula = act16 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0698 -0.9861  0.0357  1.4375  2.4375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  1.03448    0.34080   3.035  0.00259 **
## SlibsSC.d   -0.47198    0.43164  -1.093  0.27499   
## SlibsC.d    -0.04837    0.40364  -0.120  0.90468   
## SlibsM.d    -0.07020    0.37443  -0.187  0.85140   
## SlibsL.d     0.03528    0.44099   0.080  0.93628   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 327 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.007238,   Adjusted R-squared:  -0.004905 
## F-statistic: 0.5961 on 4 and 327 DF,  p-value: 0.6657

# Action 17
slib.b17 <- lm(act17 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b17)

## 
## Call:
## lm(formula = act17 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2059 -1.4798 -0.1685  1.6230  2.8315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.2059     0.3430   3.515 0.000488 ***
## SlibsSC.d    -0.8288     0.4281  -1.936 0.053539 .  
## SlibsC.d     -1.0373     0.4033  -2.572 0.010452 *  
## SlibsM.d     -0.7261     0.3752  -1.935 0.053664 .  
## SlibsL.d     -0.6559     0.4294  -1.528 0.127391    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 412 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.006815 
## F-statistic: 1.714 on 4 and 412 DF,  p-value: 0.146

# Action 18
slib.b18 <- lm(act18 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b18)

## 
## Call:
## lm(formula = act18 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1196  0.1844  1.5135  2.8113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.75862    0.34198   2.218   0.0272 *
## SlibsSC.d   -0.56994    0.42538  -1.340   0.1812  
## SlibsC.d    -0.27213    0.40347  -0.674   0.5004  
## SlibsM.d     0.05698    0.37551   0.152   0.8795  
## SlibsL.d     0.15366    0.42007   0.366   0.7147  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01769,    Adjusted R-squared:  0.006435 
## F-statistic: 1.572 on 4 and 349 DF,  p-value: 0.1814

# Action 19
slib.b19 <- lm(act19 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b19)

## 
## Call:
## lm(formula = act19 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.438 -1.050  0.093  1.704  2.093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.29630    0.34665   3.740 0.000225 ***
## SlibsSC.d   -0.38932    0.44229  -0.880 0.379510    
## SlibsC.d    -0.24630    0.41742  -0.590 0.555654    
## SlibsM.d    -0.06621    0.38585  -0.172 0.863887    
## SlibsL.d     0.14120    0.47069   0.300 0.764416    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 270 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.00789,    Adjusted R-squared:  -0.006807 
## F-statistic: 0.5368 on 4 and 270 DF,  p-value: 0.7088

# Action 20
slib.b20 <- lm(act20 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b20)

## 
## Call:
## lm(formula = act20 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3566 -1.1957  0.6434  1.6434  2.0000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   1.0000     0.3450   2.898  0.00401 **
## SlibsSC.d     0.1509     0.4239   0.356  0.72203   
## SlibsC.d      0.1304     0.4070   0.320  0.74881   
## SlibsM.d      0.3566     0.3794   0.940  0.34805   
## SlibsL.d      0.1957     0.4347   0.450  0.65293   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.793 on 319 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.004413,   Adjusted R-squared:  -0.008071 
## F-statistic: 0.3535 on 4 and 319 DF,  p-value: 0.8415

# Action 21
slib.b21 <- lm(act21 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b21)

## 
## Call:
## lm(formula = act21 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -1.9401  0.0599  1.5806  3.5806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.42424    0.35863   1.183   0.2375  
## SlibsSC.d   -1.00489    0.44392  -2.264   0.0241 *
## SlibsC.d    -0.69254    0.42470  -1.631   0.1038  
## SlibsM.d    -0.48412    0.39246  -1.234   0.2181  
## SlibsL.d     0.01644    0.44783   0.037   0.9707  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 398 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02474,    Adjusted R-squared:  0.01494 
## F-statistic: 2.524 on 4 and 398 DF,  p-value: 0.04049

# Action 22
slib.b22 <- lm(act22 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b22)

## 
## Call:
## lm(formula = act22 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1351 -1.3415 -0.1351  1.7143  3.3279 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1351     0.3247   3.496 0.000525 ***
## SlibsSC.d    -1.4630     0.4115  -3.555 0.000423 ***
## SlibsC.d     -0.7937     0.3911  -2.029 0.043089 *  
## SlibsM.d     -0.8494     0.3586  -2.369 0.018335 *  
## SlibsL.d     -0.5458     0.4184  -1.305 0.192756    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.975 on 399 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03383,    Adjusted R-squared:  0.02414 
## F-statistic: 3.492 on 4 and 399 DF,  p-value: 0.008087

# Action 23
slib.b23 <- lm(act23 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b23)

## 
## Call:
## lm(formula = act23 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6154 -2.1138  0.1846  2.1846  3.1846 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.56000    0.45302   1.236    0.217
## SlibsSC.d   -0.29171    0.57477  -0.508    0.612
## SlibsC.d    -0.74462    0.53307  -1.397    0.164
## SlibsM.d    -0.44618    0.49693  -0.898    0.370
## SlibsL.d     0.05538    0.58033   0.095    0.924
## 
## Residual standard error: 2.265 on 288 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.01368,    Adjusted R-squared:  -2.033e-05 
## F-statistic: 0.9985 on 4 and 288 DF,  p-value: 0.4087

# Action 24
slib.b24 <- lm(act24 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b24)

## 
## Call:
## lm(formula = act24 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9189 -1.8727  0.1273  1.6453  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08108    0.34610  -0.234    0.815
## SlibsSC.d   -0.04619    0.44762  -0.103    0.918
## SlibsC.d    -0.41892    0.40983  -1.022    0.307
## SlibsM.d    -0.14880    0.38112  -0.390    0.696
## SlibsL.d    -0.05685    0.44294  -0.128    0.898
## 
## Residual standard error: 2.105 on 411 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.004608,   Adjusted R-squared:  -0.005079 
## F-statistic: 0.4757 on 4 and 411 DF,  p-value: 0.7536

# Action 25
slib.b25 <- lm(act25 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b25)

## 
## Call:
## lm(formula = act25 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3429 -0.8925  0.1167  1.5000  3.1167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3429     0.3099   4.333 1.82e-05 ***
## SlibsSC.d    -1.4595     0.3900  -3.743 0.000206 ***
## SlibsC.d     -0.8429     0.3592  -2.347 0.019380 *  
## SlibsM.d     -0.4504     0.3378  -1.333 0.183136    
## SlibsL.d     -0.2159     0.3865  -0.559 0.576784    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 441 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.04037 
## F-statistic:  5.68 on 4 and 441 DF,  p-value: 0.0001824

# Action 26
slib.b26 <- lm(act26 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b26)

## 
## Call:
## lm(formula = act26 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5686 -1.2581  0.4595  1.4314  2.0526 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.9474     0.4193   2.259   0.0248 *
## SlibsSC.d     0.5932     0.5158   1.150   0.2514  
## SlibsC.d      0.6213     0.4912   1.265   0.2073  
## SlibsM.d      0.6706     0.4619   1.452   0.1480  
## SlibsL.d      0.3107     0.5325   0.583   0.5602  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.828 on 222 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01216,    Adjusted R-squared:  -0.005638 
## F-statistic: 0.6832 on 4 and 222 DF,  p-value: 0.6042

# Action 27
slib.b27 <- lm(act27 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b27)

## 
## Call:
## lm(formula = act27 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3871 -0.9483  0.2813  1.5577  2.5577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3871     0.3372   4.113 4.99e-05 ***
## SlibsSC.d    -0.9448     0.4261  -2.217   0.0273 *  
## SlibsC.d     -0.6683     0.4109  -1.627   0.1048    
## SlibsM.d     -0.8359     0.3762  -2.222   0.0270 *  
## SlibsL.d     -0.2093     0.4383  -0.478   0.6333    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 314 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02704,    Adjusted R-squared:  0.01464 
## F-statistic: 2.182 on 4 and 314 DF,  p-value: 0.07092

# Action 28
slib.b28 <- lm(act28 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b28)

## 
## Call:
## lm(formula = act28 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3214 -0.9728  0.2105  1.6786  2.2105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1935     0.3277   3.642 0.000309 ***
## SlibsSC.d    -0.4041     0.4072  -0.992 0.321683    
## SlibsC.d      0.1279     0.3834   0.334 0.738946    
## SlibsM.d     -0.2208     0.3606  -0.612 0.540805    
## SlibsL.d     -0.1935     0.4098  -0.472 0.636987    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 369 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.009436,   Adjusted R-squared:  -0.001302 
## F-statistic: 0.8787 on 4 and 369 DF,  p-value: 0.4767

# Action 29
slib.b29 <- lm(act29 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b29)

## 
## Call:
## lm(formula = act29 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4828 -1.0893  0.0182  1.5172  2.0182 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.4828     0.3277   4.525 8.19e-06 ***
## SlibsSC.d    -0.3935     0.4037  -0.975    0.330    
## SlibsC.d     -0.4587     0.3807  -1.205    0.229    
## SlibsM.d     -0.3399     0.3586  -0.948    0.344    
## SlibsL.d     -0.5009     0.4050  -1.237    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.765 on 365 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.005014,   Adjusted R-squared:  -0.00589 
## F-statistic: 0.4598 on 4 and 365 DF,  p-value: 0.7652

# Action 30
slib.b30 <- lm(act30 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, data = d)

summary(slib.b30)

## 
## Call:
## lm(formula = act30 ~ SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1765 -1.0435  0.8235  1.8235  1.9875 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  1.142857   0.354557   3.223  0.00139 **
## SlibsSC.d   -0.002506   0.432970  -0.006  0.99538   
## SlibsC.d    -0.130357   0.411958  -0.316  0.75186   
## SlibsM.d    -0.099379   0.388866  -0.256  0.79844   
## SlibsL.d     0.033613   0.441280   0.076  0.93933   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.001091,   Adjusted R-squared:  -0.01036 
## F-statistic: 0.09525 on 4 and 349 DF,  p-value: 0.9839

Significantly higher than 0: 2, 6, 10, 11, 16, 17, 18, 19, 20, 22, 25, 26, 27, 28, 29, 30 Not different from 0: 1, 3, 4, 5, 7, 8, 9, 12, 13, 14, 15, 21, 23, 24 Significantly lower than 0: None

1. Condition Differences?

# Action 1
slib.c.b1 <- lm(act1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b1) # no

## 
## Call:
## lm(formula = act1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.750 -2.200  0.250  1.765  3.800 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.07343    0.37571   0.195    0.845
## SlibsSC.d        -0.55914    0.46104  -1.213    0.226
## SlibsC.d         -0.55232    0.43355  -1.274    0.203
## SlibsM.d         -0.13050    0.40785  -0.320    0.749
## SlibsL.d          0.57430    0.46060   1.247    0.213
## cond.c           -0.76224    0.75142  -1.014    0.311
## SlibsSC.d:cond.c  1.39081    0.92209   1.508    0.132
## SlibsC.d:cond.c   0.57717    0.86711   0.666    0.506
## SlibsM.d:cond.c   0.17871    0.81569   0.219    0.827
## SlibsL.d:cond.c   0.96678    0.92121   1.049    0.295
## 
## Residual standard error: 2.148 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04314,    Adjusted R-squared:  0.02353 
## F-statistic: 2.199 on 9 and 439 DF,  p-value: 0.02116

# Action 2
slib.c.b2 <- lm(act2 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b2) #no

## 
## Call:
## lm(formula = act2 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -1.068  0.375  1.615  2.375 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       1.04545    0.37117   2.817   0.0052 **
## SlibsSC.d        -0.06690    0.45975  -0.146   0.8844   
## SlibsC.d         -0.15720    0.43393  -0.362   0.7174   
## SlibsM.d          0.14685    0.40926   0.359   0.7200   
## SlibsL.d          0.51705    0.46645   1.108   0.2686   
## cond.c            0.09091    0.74234   0.122   0.9026   
## SlibsSC.d:cond.c -0.34432    0.91950  -0.374   0.7083   
## SlibsC.d:cond.c  -0.61742    0.86785  -0.711   0.4774   
## SlibsM.d:cond.c  -0.47552    0.81852  -0.581   0.5617   
## SlibsL.d:cond.c  -0.21591    0.93289  -0.231   0.8171   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.812 on 280 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -0.006049 
## F-statistic: 0.8069 on 9 and 280 DF,  p-value: 0.6102

# Action 3
slib.c.b3 <- lm(act3 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b3) #no

## 
## Call:
## lm(formula = act3 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9375  0.0625  1.6410  3.9394 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.34239    0.35791   0.957  0.33928   
## SlibsSC.d        -1.22280    0.44135  -2.771  0.00584 **
## SlibsC.d         -0.76303    0.41335  -1.846  0.06558 . 
## SlibsM.d         -0.35660    0.38743  -0.920  0.35787   
## SlibsL.d         -0.45601    0.43455  -1.049  0.29459   
## cond.c           -0.18478    0.71583  -0.258  0.79642   
## SlibsSC.d:cond.c  0.06682    0.88270   0.076  0.93970   
## SlibsC.d:cond.c   0.24827    0.82670   0.300  0.76408   
## SlibsM.d:cond.c   0.28137    0.77486   0.363  0.71669   
## SlibsL.d:cond.c   1.23960    0.86911   1.426  0.15450   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03846,    Adjusted R-squared:  0.01852 
## F-statistic: 1.929 on 9 and 434 DF,  p-value: 0.04628

# Action 4
slib.c.b4 <- lm(act4 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b4) # no

## 
## Call:
## lm(formula = act4 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8400 -1.8941  0.1059  2.0378  3.3488 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.46316    0.42370   1.093    0.275
## SlibsSC.d        -0.26518    0.52218  -0.508    0.612
## SlibsC.d         -0.73758    0.49607  -1.487    0.138
## SlibsM.d         -0.67610    0.45721  -1.479    0.140
## SlibsL.d         -0.02801    0.51207  -0.055    0.956
## cond.c           -0.12632    0.84741  -0.149    0.882
## SlibsSC.d:cond.c  0.08750    1.04436   0.084    0.933
## SlibsC.d:cond.c  -0.02252    0.99214  -0.023    0.982
## SlibsM.d:cond.c  -0.08780    0.91443  -0.096    0.924
## SlibsL.d:cond.c   0.93601    1.02414   0.914    0.361
## 
## Residual standard error: 2.169 on 361 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0238, Adjusted R-squared:  -0.0005384 
## F-statistic: 0.9779 on 9 and 361 DF,  p-value: 0.4579

# Action 8
slib.c.b8 <- lm(act8 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b8) # yes, cond difference (negative)

## 
## Call:
## lm(formula = act8 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.7083     0.3409   2.078 0.038259 *  
## SlibsSC.d         -1.8635     0.4266  -4.368 1.54e-05 ***
## SlibsC.d          -1.3385     0.3984  -3.360 0.000843 ***
## SlibsM.d          -1.1487     0.3721  -3.087 0.002141 ** 
## SlibsL.d          -0.1976     0.4335  -0.456 0.648692    
## cond.c             0.8333     0.6818   1.222 0.222206    
## SlibsSC.d:cond.c  -0.6769     0.8532  -0.793 0.427957    
## SlibsC.d:cond.c   -1.8270     0.7967  -2.293 0.022285 *  
## SlibsM.d:cond.c   -0.7235     0.7442  -0.972 0.331434    
## SlibsL.d:cond.c   -0.2119     0.8670  -0.244 0.807017    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

# Action 9
slib.c.b9 <- lm(act9 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b9) # no

## 
## Call:
## lm(formula = act9 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.5893  0.0465  1.4286  4.3462 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.12500    0.34711   0.360   0.7189  
## SlibsSC.d        -0.91572    0.43117  -2.124   0.0342 *
## SlibsC.d         -0.35361    0.40014  -0.884   0.3773  
## SlibsM.d          0.08077    0.37535   0.215   0.8297  
## SlibsL.d          0.45238    0.42624   1.061   0.2891  
## cond.c           -0.41667    0.69422  -0.600   0.5487  
## SlibsSC.d:cond.c  1.52753    0.86233   1.771   0.0772 .
## SlibsC.d:cond.c   0.05246    0.80028   0.066   0.9478  
## SlibsM.d:cond.c   0.57331    0.75070   0.764   0.4455  
## SlibsL.d:cond.c   0.40476    0.85248   0.475   0.6352  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.964 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.05102,    Adjusted R-squared:  0.03157 
## F-statistic: 2.623 on 9 and 439 DF,  p-value: 0.00582

# Action 10
slib.c.b10 <- lm(act10 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b10) # no

## 
## Call:
## lm(formula = act10 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3750 -1.9896  0.0217  1.8350  3.6579 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.1875     0.3357   3.538 0.000444 ***
## SlibsSC.d         -1.7998     0.4209  -4.276 2.31e-05 ***
## SlibsC.d          -1.4187     0.3931  -3.609 0.000340 ***
## SlibsM.d          -1.1102     0.3667  -3.028 0.002600 ** 
## SlibsL.d          -0.4756     0.4249  -1.119 0.263546    
## cond.c             0.3750     0.6713   0.559 0.576709    
## SlibsSC.d:cond.c  -0.4662     0.8419  -0.554 0.579986    
## SlibsC.d:cond.c   -0.7939     0.7862  -1.010 0.313080    
## SlibsM.d:cond.c   -0.5505     0.7333  -0.751 0.453245    
## SlibsL.d:cond.c    0.1298     0.8498   0.153 0.878642    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.08 on 467 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05853,    Adjusted R-squared:  0.04038 
## F-statistic: 3.226 on 9 and 467 DF,  p-value: 0.000826

# Action 11
slib.c.b11 <- lm(act11 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b11) # no

## 
## Call:
## lm(formula = act11 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -1.0299  0.3452  1.5429  2.5735 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.49286    0.38503   3.877  0.00013 ***
## SlibsSC.d        -0.76202    0.46784  -1.629  0.10439    
## SlibsC.d         -0.64762    0.44921  -1.442  0.15042    
## SlibsM.d         -0.76470    0.41698  -1.834  0.06765 .  
## SlibsL.d          0.16190    0.49199   0.329  0.74232    
## cond.c           -0.58571    0.77007  -0.761  0.44749    
## SlibsSC.d:cond.c  0.96405    0.93568   1.030  0.30368    
## SlibsC.d:cond.c  -0.19048    0.89841  -0.212  0.83224    
## SlibsM.d:cond.c  -0.01767    0.83397  -0.021  0.98311    
## SlibsL.d:cond.c   0.56190    0.98398   0.571  0.56838    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 304 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.0581, Adjusted R-squared:  0.03021 
## F-statistic: 2.083 on 9 and 304 DF,  p-value: 0.03077

# Action 12
slib.c.b12 <- lm(act12 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b12) # no

## 
## Call:
## lm(formula = act12 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1304 -1.8929 -0.1098  1.8696  4.1071 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.38824    0.42814   0.907   0.3651  
## SlibsSC.d        -1.23491    0.51412  -2.402   0.0168 *
## SlibsC.d         -0.95686    0.48531  -1.972   0.0494 *
## SlibsM.d         -0.39257    0.46105  -0.851   0.3951  
## SlibsL.d          0.39365    0.52149   0.755   0.4508  
## cond.c            0.42353    0.85628   0.495   0.6212  
## SlibsSC.d:cond.c  0.09741    1.02825   0.095   0.9246  
## SlibsC.d:cond.c  -0.89412    0.97063  -0.921   0.3576  
## SlibsM.d:cond.c  -0.65171    0.92210  -0.707   0.4802  
## SlibsL.d:cond.c   0.27357    1.04299   0.262   0.7932  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.149 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0624, Adjusted R-squared:  0.0399 
## F-statistic: 2.773 on 9 and 375 DF,  p-value: 0.003732

# Action 13
slib.c.b13 <- lm(act13 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b13) # yes, condition difference

## 
## Call:
## lm(formula = act13 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.23810    0.35832   0.664   0.5067  
## SlibsSC.d        -0.78567    0.44527  -1.764   0.0784 .
## SlibsC.d         -0.40787    0.41934  -0.973   0.3313  
## SlibsM.d         -0.31785    0.38906  -0.817   0.4144  
## SlibsL.d          0.10112    0.44280   0.228   0.8195  
## cond.c           -0.19048    0.71665  -0.266   0.7905  
## SlibsSC.d:cond.c -0.13543    0.89053  -0.152   0.8792  
## SlibsC.d:cond.c  -0.68566    0.83869  -0.818   0.4141  
## SlibsM.d:cond.c   0.52581    0.77812   0.676   0.4996  
## SlibsL.d:cond.c   0.04538    0.88559   0.051   0.9592  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

# Action 14
slib.c.b14 <- lm(act14 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b14) # no

## 
## Call:
## lm(formula = act14 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.3731  0.1429  1.6269  3.1429 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0.1786     0.4521   0.395    0.693
## SlibsSC.d          0.2716     0.5576   0.487    0.627
## SlibsC.d           0.1559     0.5249   0.297    0.767
## SlibsM.d           0.2457     0.4869   0.505    0.614
## SlibsL.d           0.8160     0.5610   1.455    0.147
## cond.c             0.6429     0.9043   0.711    0.478
## SlibsSC.d:cond.c   0.0221     1.1152   0.020    0.984
## SlibsC.d:cond.c   -0.9368     1.0498  -0.892    0.373
## SlibsM.d:cond.c   -0.5406     0.9737  -0.555    0.579
## SlibsL.d:cond.c   -0.1320     1.1220  -0.118    0.906
## 
## Residual standard error: 2.04 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.02046,    Adjusted R-squared:  -0.01125 
## F-statistic: 0.6452 on 9 and 278 DF,  p-value: 0.7579

# Action 15
slib.c.b15 <- lm(act15 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b15) # no

## 
## Call:
## lm(formula = act15 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.32549    0.36180   0.900    0.369  
## SlibsSC.d        -0.70078    0.45015  -1.557    0.120  
## SlibsC.d         -0.20374    0.42670  -0.477    0.633  
## SlibsM.d         -0.22648    0.39518  -0.573    0.567  
## SlibsL.d          0.47951    0.47411   1.011    0.312  
## cond.c            0.41569    0.72361   0.574    0.566  
## SlibsSC.d:cond.c -0.08935    0.90029  -0.099    0.921  
## SlibsC.d:cond.c  -0.61663    0.85340  -0.723    0.470  
## SlibsM.d:cond.c  -0.50981    0.79036  -0.645    0.519  
## SlibsL.d:cond.c  -1.92569    0.94822  -2.031    0.043 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

# Action 16
slib.c.b16 <- lm(act16 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b16) # no

## 
## Call:
## lm(formula = act16 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2105 -1.1000  0.2034  1.4500  2.4500 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.955263   0.359998   2.654  0.00836 **
## SlibsSC.d        -0.394549   0.449865  -0.877  0.38112   
## SlibsC.d          0.019195   0.420615   0.046  0.96363   
## SlibsM.d          0.009023   0.392248   0.023  0.98166   
## SlibsL.d          0.128070   0.459059   0.279  0.78044   
## cond.c           -0.510526   0.719997  -0.709  0.47880   
## SlibsSC.d:cond.c  0.531955   0.899730   0.591  0.55478   
## SlibsC.d:cond.c   0.930031   0.841230   1.106  0.26974   
## SlibsM.d:cond.c   0.781955   0.784497   0.997  0.31963   
## SlibsL.d:cond.c   0.677193   0.918117   0.738  0.46130   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.843 on 322 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.01422,    Adjusted R-squared:  -0.01334 
## F-statistic: 0.5159 on 9 and 322 DF,  p-value: 0.8629

# Action 17
slib.c.b17 <- lm(act17 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b17) # marginal

## 
## Call:
## lm(formula = act17 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.28214    0.34678   3.697 0.000248 ***
## SlibsSC.d        -0.93907    0.43086  -2.180 0.029865 *  
## SlibsC.d         -1.03372    0.40799  -2.534 0.011661 *  
## SlibsM.d         -0.81187    0.37848  -2.145 0.032540 *  
## SlibsL.d         -0.72154    0.43238  -1.669 0.095933 .  
## cond.c            0.86429    0.69357   1.246 0.213428    
## SlibsSC.d:cond.c -0.03528    0.86172  -0.041 0.967361    
## SlibsC.d:cond.c  -1.70076    0.81598  -2.084 0.037754 *  
## SlibsM.d:cond.c  -1.16286    0.75697  -1.536 0.125264    
## SlibsL.d:cond.c  -0.65216    0.86475  -0.754 0.451189    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

# Action 18
slib.c.b18 <- lm(act18 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.1538  0.2031  1.4211  3.0400 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.77020    0.35405   2.175   0.0303 *
## SlibsSC.d        -0.59377    0.43606  -1.362   0.1742  
## SlibsC.d         -0.28628    0.41430  -0.691   0.4900  
## SlibsM.d          0.04382    0.38709   0.113   0.9099  
## SlibsL.d          0.16156    0.43113   0.375   0.7081  
## cond.c            0.09596    0.70810   0.136   0.8923  
## SlibsSC.d:cond.c  0.33690    0.87212   0.386   0.6995  
## SlibsC.d:cond.c  -0.28602    0.82860  -0.345   0.7302  
## SlibsM.d:cond.c  -0.13025    0.77418  -0.168   0.8665  
## SlibsL.d:cond.c   0.34821    0.86227   0.404   0.6866  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.85 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.0227, Adjusted R-squared:  -0.002867 
## F-statistic: 0.8879 on 9 and 344 DF,  p-value: 0.5362

# Action 19
slib.c.b19 <- lm(act19 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b19) # marginal

## 
## Call:
## lm(formula = act19 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.29396    0.34618   3.738 0.000227 ***
## SlibsSC.d        -0.39349    0.44749  -0.879 0.380015    
## SlibsC.d         -0.15507    0.41945  -0.370 0.711907    
## SlibsM.d         -0.04537    0.38551  -0.118 0.906394    
## SlibsL.d          0.11081    0.47161   0.235 0.814428    
## cond.c           -0.12637    0.69236  -0.183 0.855309    
## SlibsSC.d:cond.c  0.17730    0.89498   0.198 0.843115    
## SlibsC.d:cond.c  -0.76252    0.83890  -0.909 0.364203    
## SlibsM.d:cond.c  -0.33800    0.77102  -0.438 0.661465    
## SlibsL.d:cond.c  -0.39744    0.94322  -0.421 0.673835    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

# Action 20
slib.c.b20 <- lm(act20 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b20) # no

## 
## Call:
## lm(formula = act20 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.589 -1.054  0.411  1.411  2.308 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.98901    0.34388   2.876   0.0043 **
## SlibsSC.d         0.20012    0.42365   0.472   0.6370   
## SlibsC.d          0.15584    0.40584   0.384   0.7012   
## SlibsM.d          0.33230    0.37869   0.877   0.3809   
## SlibsL.d          0.17575    0.43370   0.405   0.6856   
## cond.c           -0.59341    0.68776  -0.863   0.3889   
## SlibsSC.d:cond.c  0.01515    0.84730   0.018   0.9857   
## SlibsC.d:cond.c   0.19560    0.81168   0.241   0.8097   
## SlibsM.d:cond.c   0.05794    0.75738   0.076   0.9391   
## SlibsL.d:cond.c  -0.11707    0.86740  -0.135   0.8927   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.786 on 314 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.028,  Adjusted R-squared:  0.0001354 
## F-statistic: 1.005 on 9 and 314 DF,  p-value: 0.4359

# Action 21
slib.c.b21 <- lm(act21 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b21) # no

## 
## Call:
## lm(formula = act21 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5556 -1.8723  0.1609  1.9286  3.9286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.42778    0.36123   1.184   0.2370  
## SlibsSC.d        -1.03912    0.44723  -2.323   0.0207 *
## SlibsC.d         -0.72018    0.42861  -1.680   0.0937 .
## SlibsM.d         -0.48324    0.39510  -1.223   0.2220  
## SlibsL.d          0.02187    0.45099   0.049   0.9613  
## cond.c            0.07778    0.72246   0.108   0.9143  
## SlibsSC.d:cond.c  0.55668    0.89446   0.622   0.5341  
## SlibsC.d:cond.c   0.25171    0.85721   0.294   0.7692  
## SlibsM.d:cond.c   0.13314    0.79019   0.168   0.8663  
## SlibsL.d:cond.c   0.13403    0.90197   0.149   0.8820  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.067 on 393 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.03105,    Adjusted R-squared:  0.008857 
## F-statistic: 1.399 on 9 and 393 DF,  p-value: 0.1863

# Action 23
slib.c.b23 <- lm(act23 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b23) # no

## 
## Call:
## lm(formula = act23 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.222 -2.250  0.125  1.870  3.297 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.7049     0.4716   1.495   0.1361  
## SlibsSC.d         -0.4370     0.5895  -0.741   0.4591  
## SlibsC.d          -0.8714     0.5503  -1.584   0.1144  
## SlibsM.d          -0.5995     0.5140  -1.166   0.2444  
## SlibsL.d          -0.2021     0.5985  -0.338   0.7358  
## cond.c             1.0347     0.9432   1.097   0.2736  
## SlibsSC.d:cond.c  -0.9990     1.1789  -0.847   0.3975  
## SlibsC.d:cond.c   -1.2963     1.1005  -1.178   0.2398  
## SlibsM.d:cond.c   -1.4489     1.0279  -1.410   0.1598  
## SlibsL.d:cond.c   -2.2902     1.1970  -1.913   0.0567 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.264 on 283 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.03197,    Adjusted R-squared:  0.001189 
## F-statistic: 1.039 on 9 and 283 DF,  p-value: 0.4092

# Action 24
slib.c.b24 <- lm(act24 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b24) # no

## 
## Call:
## lm(formula = act24 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      -0.08939    0.35250  -0.254    0.800
## SlibsSC.d        -0.04233    0.45407  -0.093    0.926
## SlibsC.d         -0.37942    0.41707  -0.910    0.364
## SlibsM.d         -0.15915    0.38706  -0.411    0.681
## SlibsL.d         -0.06502    0.45059  -0.144    0.885
## cond.c           -0.08788    0.70500  -0.125    0.901
## SlibsSC.d:cond.c  0.15777    0.90815   0.174    0.862
## SlibsC.d:cond.c  -0.27080    0.83413  -0.325    0.746
## SlibsM.d:cond.c  -0.56138    0.77413  -0.725    0.469
## SlibsL.d:cond.c  -0.10330    0.90117  -0.115    0.909
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

# Action 25
slib.c.b25 <- lm(act25 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b25) # no

## 
## Call:
## lm(formula = act25 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -0.9307  0.1529  1.3778  3.3333 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.41667    0.31603   4.483 9.44e-06 ***
## SlibsSC.d        -1.55303    0.39544  -3.927 9.98e-05 ***
## SlibsC.d         -0.90380    0.36502  -2.476   0.0137 *  
## SlibsM.d         -0.52779    0.34359  -1.536   0.1252    
## SlibsL.d         -0.28310    0.39135  -0.723   0.4698    
## cond.c            0.73810    0.63207   1.168   0.2435    
## SlibsSC.d:cond.c -0.34416    0.79088  -0.435   0.6637    
## SlibsC.d:cond.c  -0.95681    0.73004  -1.311   0.1907    
## SlibsM.d:cond.c  -0.82173    0.68718  -1.196   0.2324    
## SlibsL.d:cond.c   0.09154    0.78271   0.117   0.9069    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 436 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.06134,    Adjusted R-squared:  0.04197 
## F-statistic: 3.166 on 9 and 436 DF,  p-value: 0.001022

# Action 28
slib.c.b28 <- lm(act28 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b28) # no

## 
## Call:
## lm(formula = act28 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.219298   0.336662   3.622 0.000334 ***
## SlibsSC.d        -0.391134   0.415083  -0.942 0.346662    
## SlibsC.d          0.102130   0.391200   0.261 0.794186    
## SlibsM.d         -0.240759   0.368931  -0.653 0.514436    
## SlibsL.d         -0.200956   0.419089  -0.480 0.631866    
## cond.c            0.228070   0.673325   0.339 0.735014    
## SlibsSC.d:cond.c -1.110204   0.830166  -1.337 0.181951    
## SlibsC.d:cond.c  -0.489975   0.782399  -0.626 0.531546    
## SlibsM.d:cond.c  -0.040222   0.737862  -0.055 0.956558    
## SlibsL.d:cond.c  -0.003885   0.838178  -0.005 0.996304    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

# Action 30
slib.c.b30 <- lm(act30 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * cond.c, data = d)

summary(slib.c.b30) # no

## 
## Call:
## lm(formula = act30 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       1.15625    0.35805   3.229  0.00136 **
## SlibsSC.d        -0.08375    0.43685  -0.192  0.84808   
## SlibsC.d         -0.13226    0.41545  -0.318  0.75041   
## SlibsM.d         -0.11213    0.39203  -0.286  0.77503   
## SlibsL.d          0.02635    0.44551   0.059  0.95287   
## cond.c            0.18750    0.71611   0.262  0.79361   
## SlibsSC.d:cond.c  0.91750    0.87370   1.050  0.29440   
## SlibsC.d:cond.c  -0.41730    0.83091  -0.502  0.61584   
## SlibsM.d:cond.c  -0.27574    0.78407  -0.352  0.72530   
## SlibsL.d:cond.c  -0.09816    0.89101  -0.110  0.91234   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

Climate > Control: none Control > Climate: none

2. Gender effects?

# Action 1
slib.g.b1 <- lm(act1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b1) 

## 
## Call:
## lm(formula = act1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8261  0.2263  2.1538  4.1538 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        -0.1739     0.3913  -0.444   0.6570   
## SlibsSC.d          -0.2226     0.4794  -0.464   0.6427   
## SlibsC.d           -0.5314     0.4608  -1.153   0.2495   
## SlibsM.d            0.3108     0.4316   0.720   0.4718   
## SlibsL.d            0.7806     0.5019   1.555   0.1206   
## gend.mf            -1.6522     0.7827  -2.111   0.0353 * 
## SlibsSC.d:gend.mf   2.0973     0.9588   2.187   0.0292 * 
## SlibsC.d:gend.mf    0.7551     0.9217   0.819   0.4131   
## SlibsM.d:gend.mf    2.3785     0.8631   2.756   0.0061 **
## SlibsL.d:gend.mf    1.5055     1.0038   1.500   0.1344   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.135 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.05646,    Adjusted R-squared:  0.03703 
## F-statistic: 2.906 on 9 and 437 DF,  p-value: 0.002374

# Action 2
slib.g.b2 <- lm(act2 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b2) 

## 
## Call:
## lm(formula = act2 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4375 -1.2184  0.5263  1.5625  2.6087 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.94167    0.39471   2.386   0.0177 *
## SlibsSC.d          0.09147    0.47821   0.191   0.8484  
## SlibsC.d          -0.16268    0.45879  -0.355   0.7232  
## SlibsM.d           0.24753    0.44458   0.557   0.5781  
## SlibsL.d           0.77708    0.51233   1.517   0.1305  
## gend.mf           -0.38333    0.78942  -0.486   0.6276  
## SlibsSC.d:gend.mf  1.26442    0.95642   1.322   0.1872  
## SlibsC.d:gend.mf  -0.39203    0.91758  -0.427   0.6695  
## SlibsM.d:gend.mf   0.32494    0.88916   0.365   0.7151  
## SlibsL.d:gend.mf   0.94583    1.02466   0.923   0.3568  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.03738,    Adjusted R-squared:  0.006217 
## F-statistic: 1.199 on 9 and 278 DF,  p-value: 0.2951

# Action 3
slib.g.b3 <- lm(act3 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b3) 

## 
## Call:
## lm(formula = act3 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.8390 -0.0368  1.4167  4.1579 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.2417     0.3798   0.636   0.5249  
## SlibsSC.d          -1.0380     0.4640  -2.237   0.0258 *
## SlibsC.d           -0.6555     0.4435  -1.478   0.1401  
## SlibsM.d           -0.3084     0.4164  -0.741   0.4593  
## SlibsL.d           -0.3539     0.4703  -0.753   0.4521  
## gend.mf            -0.6833     0.7595  -0.900   0.3688  
## SlibsSC.d:gend.mf   1.4064     0.9280   1.516   0.1304  
## SlibsC.d:gend.mf    0.6962     0.8870   0.785   0.4329  
## SlibsM.d:gend.mf    0.4764     0.8328   0.572   0.5676  
## SlibsL.d:gend.mf    1.0189     0.9405   1.083   0.2793  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.03535,    Adjusted R-squared:  0.01525 
## F-statistic: 1.759 on 9 and 432 DF,  p-value: 0.07405

# Action 4
slib.g.b4 <- lm(act4 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b4) 

## 
## Call:
## lm(formula = act4 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.944 -1.904  0.200  2.056  3.762 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.3222     0.4279   0.753    0.452
## SlibsSC.d          -0.1087     0.5257  -0.207    0.836
## SlibsC.d           -0.7513     0.5116  -1.468    0.143
## SlibsM.d           -0.5399     0.4766  -1.133    0.258
## SlibsL.d            0.1331     0.5336   0.250    0.803
## gend.mf            -1.2444     0.8558  -1.454    0.147
## SlibsSC.d:gend.mf   1.6000     1.0513   1.522    0.129
## SlibsC.d:gend.mf    0.5787     1.0233   0.566    0.572
## SlibsM.d:gend.mf    1.2092     0.9532   1.269    0.205
## SlibsL.d:gend.mf    1.5837     1.0671   1.484    0.139
## 
## Residual standard error: 2.17 on 359 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02849,    Adjusted R-squared:  0.004137 
## F-statistic:  1.17 on 9 and 359 DF,  p-value: 0.3132

# Action 5
slib.g.b5 <- lm(act5 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b5) # nothing

## 
## Call:
## lm(formula = act5 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3000 -1.2128  0.4615  1.7000  2.7000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.3808     0.3953   0.963   0.3362  
## SlibsSC.d           0.3391     0.4781   0.709   0.4787  
## SlibsC.d            0.4948     0.4572   1.082   0.2800  
## SlibsM.d            0.7057     0.4470   1.579   0.1156  
## SlibsL.d            1.1026     0.5092   2.165   0.0312 *
## gend.mf            -0.1615     0.7905  -0.204   0.8382  
## SlibsSC.d:gend.mf  -0.3217     0.9562  -0.336   0.7368  
## SlibsC.d:gend.mf   -0.5128     0.9143  -0.561   0.5754  
## SlibsM.d:gend.mf    0.2109     0.8941   0.236   0.8137  
## SlibsL.d:gend.mf    0.5282     1.0183   0.519   0.6044  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 285 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  -0.0005149 
## F-statistic: 0.9832 on 9 and 285 DF,  p-value: 0.454

# Action 6
slib.g.b6 <- lm(act6 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b6) 

## 
## Call:
## lm(formula = act6 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5455 -1.2424  0.5667  1.5667  2.6667 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         0.9394     0.3588   2.618  0.00923 **
## SlibsSC.d          -0.1147     0.4391  -0.261  0.79404   
## SlibsC.d            0.3173     0.4187   0.758  0.44912   
## SlibsM.d            0.4188     0.4024   1.041  0.29873   
## SlibsL.d            0.6434     0.4657   1.381  0.16803   
## gend.mf            -1.2121     0.7176  -1.689  0.09208 . 
## SlibsSC.d:gend.mf   0.8009     0.8782   0.912  0.36242   
## SlibsC.d:gend.mf    0.8588     0.8374   1.026  0.30584   
## SlibsM.d:gend.mf    1.4332     0.8049   1.781  0.07586 . 
## SlibsL.d:gend.mf    1.8928     0.9314   2.032  0.04290 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 347 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -1.033e-06 
## F-statistic:     1 on 9 and 347 DF,  p-value: 0.4397

# Action 7
slib.g.b7 <- lm(act7 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b7) 

## 
## Call:
## lm(formula = act7 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8433  0.1567  1.6364  3.6364 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.1178     0.4077   0.289    0.773
## SlibsSC.d          -0.2295     0.4976  -0.461    0.645
## SlibsC.d           -0.5375     0.4831  -1.113    0.267
## SlibsM.d           -0.2194     0.4472  -0.491    0.624
## SlibsL.d           -0.0746     0.5143  -0.145    0.885
## gend.mf            -1.1244     0.8153  -1.379    0.169
## SlibsSC.d:gend.mf   1.5296     0.9952   1.537    0.125
## SlibsC.d:gend.mf    0.6912     0.9662   0.715    0.475
## SlibsM.d:gend.mf    1.2347     0.8944   1.380    0.168
## SlibsL.d:gend.mf    1.4381     1.0286   1.398    0.163
## 
## Residual standard error: 2.097 on 403 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  -0.006717 
## F-statistic: 0.6946 on 9 and 403 DF,  p-value: 0.714

# Action 8
slib.g.b8 <- lm(act8 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b8)

## 
## Call:
## lm(formula = act8 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9286 -1.9091 -0.2143  1.6875  4.5319 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         0.4188     0.3772   1.110  0.26735   
## SlibsSC.d          -1.3439     0.4661  -2.883  0.00411 **
## SlibsC.d           -1.1554     0.4432  -2.607  0.00942 **
## SlibsM.d           -0.8022     0.4165  -1.926  0.05471 . 
## SlibsL.d            0.1424     0.4954   0.287  0.77391   
## gend.mf            -1.0195     0.7543  -1.352  0.17718   
## SlibsSC.d:gend.mf   2.2332     0.9321   2.396  0.01697 * 
## SlibsC.d:gend.mf    0.9213     0.8864   1.039  0.29917   
## SlibsM.d:gend.mf    1.2331     0.8330   1.480  0.13949   
## SlibsL.d:gend.mf    1.3256     0.9908   1.338  0.18157   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 469 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.07584,    Adjusted R-squared:  0.05811 
## F-statistic: 4.276 on 9 and 469 DF,  p-value: 2.361e-05

# Action 9
slib.g.b9 <- lm(act9 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b9)

## 
## Call:
## lm(formula = act9 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5882 -1.5882  0.1481  1.5417  3.8261 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -0.04356    0.35872  -0.121    0.903
## SlibsSC.d         -0.69381    0.44398  -1.563    0.119
## SlibsC.d          -0.17635    0.42203  -0.418    0.676
## SlibsM.d           0.21092    0.39622   0.532    0.595
## SlibsL.d           0.62491    0.45435   1.375    0.170
## gend.mf           -1.00379    0.71745  -1.399    0.162
## SlibsSC.d:gend.mf  0.82635    0.88796   0.931    0.353
## SlibsC.d:gend.mf   1.14731    0.84407   1.359    0.175
## SlibsM.d:gend.mf   0.93573    0.79245   1.181    0.238
## SlibsL.d:gend.mf   1.01756    0.90869   1.120    0.263
## 
## Residual standard error: 1.97 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.04187,    Adjusted R-squared:  0.02214 
## F-statistic: 2.122 on 9 and 437 DF,  p-value: 0.02658

# Action 10
slib.g.b10 <- lm(act10 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b10)

## 
## Call:
## lm(formula = act10 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5862 -2.0719  0.1948  1.5714  3.7778 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         0.7431     0.3793   1.959  0.05069 . 
## SlibsSC.d          -1.2842     0.4628  -2.775  0.00574 **
## SlibsC.d           -1.0548     0.4427  -2.383  0.01759 * 
## SlibsM.d           -0.6528     0.4173  -1.564  0.11839   
## SlibsL.d            0.1899     0.4823   0.394  0.69388   
## gend.mf            -1.6862     0.7586  -2.223  0.02671 * 
## SlibsSC.d:gend.mf   2.1596     0.9255   2.333  0.02005 * 
## SlibsC.d:gend.mf    1.4524     0.8854   1.640  0.10158   
## SlibsM.d:gend.mf    1.7230     0.8346   2.065  0.03951 * 
## SlibsL.d:gend.mf    2.6951     0.9645   2.794  0.00542 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 466 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07046,    Adjusted R-squared:  0.05251 
## F-statistic: 3.925 on 9 and 466 DF,  p-value: 7.905e-05

# Action 11
slib.g.b11 <- lm(act11 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b11)

## 
## Call:
## lm(formula = act11 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5625 -1.0000  0.2834  1.4375  2.7778 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.49554    0.42349   3.531 0.000478 ***
## SlibsSC.d         -0.70261    0.50138  -1.401 0.162142    
## SlibsC.d          -0.76826    0.48924  -1.570 0.117384    
## SlibsM.d          -0.77894    0.46165  -1.687 0.092576 .  
## SlibsL.d           0.23386    0.52699   0.444 0.657527    
## gend.mf           -0.13393    0.84698  -0.158 0.874464    
## SlibsSC.d:gend.mf  1.27534    1.00277   1.272 0.204415    
## SlibsC.d:gend.mf  -0.41153    0.97847  -0.421 0.674360    
## SlibsM.d:gend.mf   0.07217    0.92329   0.078 0.937750    
## SlibsL.d:gend.mf   0.52129    1.05397   0.495 0.621244    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 302 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.05336,    Adjusted R-squared:  0.02515 
## F-statistic: 1.891 on 9 and 302 DF,  p-value: 0.05276

# Action 12
slib.g.b12 <- lm(act12 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b12)

## 
## Call:
## lm(formula = act12 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5625 -1.8571 -0.1345  1.8655  4.3043 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.1812     0.4290   0.422   0.6729  
## SlibsSC.d          -0.9345     0.5176  -1.805   0.0718 .
## SlibsC.d           -1.0125     0.5002  -2.024   0.0437 *
## SlibsM.d           -0.3377     0.4726  -0.714   0.4754  
## SlibsL.d            0.9427     0.5533   1.704   0.0892 .
## gend.mf            -0.7625     0.8581  -0.889   0.3748  
## SlibsSC.d:gend.mf   1.5417     1.0352   1.489   0.1373  
## SlibsC.d:gend.mf   -0.1836     1.0005  -0.184   0.8545  
## SlibsM.d:gend.mf    0.1807     0.9453   0.191   0.8485  
## SlibsL.d:gend.mf    2.1812     1.1065   1.971   0.0494 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.129 on 373 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.08175,    Adjusted R-squared:  0.0596 
## F-statistic:  3.69 on 9 and 373 DF,  p-value: 0.0001907

# Action 13
slib.g.b13 <- lm(act13 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b13)

## 
## Call:
## lm(formula = act13 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.8095  0.1884  1.9558  3.3636 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         0.1225     0.3776   0.324  0.74573   
## SlibsSC.d          -0.4223     0.4648  -0.908  0.36412   
## SlibsC.d           -0.3689     0.4518  -0.817  0.41462   
## SlibsM.d           -0.2744     0.4161  -0.659  0.50994   
## SlibsL.d            0.3546     0.4895   0.724  0.46918   
## gend.mf            -0.9723     0.7552  -1.287  0.19862   
## SlibsSC.d:gend.mf   2.7537     0.9297   2.962  0.00323 **
## SlibsC.d:gend.mf    0.8564     0.9035   0.948  0.34376   
## SlibsM.d:gend.mf    0.7109     0.8322   0.854  0.39346   
## SlibsL.d:gend.mf    1.4466     0.9790   1.478  0.14022   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.04764,    Adjusted R-squared:  0.0278 
## F-statistic: 2.401 on 9 and 432 DF,  p-value: 0.01156

# Action 14
slib.g.b14 <- lm(act14 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b14)

## 
## Call:
## lm(formula = act14 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8235 -1.2778  0.2195  1.5455  3.7222 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.14286    0.46673  -0.306   0.7598  
## SlibsSC.d          0.68506    0.56731   1.208   0.2282  
## SlibsC.d           0.17199    0.54691   0.314   0.7534  
## SlibsM.d           0.53069    0.51020   1.040   0.2992  
## SlibsL.d           1.24513    0.58869   2.115   0.0353 *
## gend.mf           -1.42857    0.93346  -1.530   0.1271  
## SlibsSC.d:gend.mf  1.99123    1.13461   1.755   0.0804 .
## SlibsC.d:gend.mf  -0.07414    1.09382  -0.068   0.9460  
## SlibsM.d:gend.mf   1.27791    1.02040   1.252   0.2115  
## SlibsL.d:gend.mf   2.13312    1.17739   1.812   0.0711 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.017 on 276 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.04977,    Adjusted R-squared:  0.01878 
## F-statistic: 1.606 on 9 and 276 DF,  p-value: 0.1132

# Action 15
slib.g.b15 <- lm(act15 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b15)

## 
## Call:
## lm(formula = act15 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6562 -1.7674  0.4444  1.6917  3.5714 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0.23333    0.37240   0.627    0.531
## SlibsSC.d         -0.53988    0.46015  -1.173    0.241
## SlibsC.d          -0.22970    0.44247  -0.519    0.604
## SlibsM.d          -0.30139    0.41287  -0.730    0.466
## SlibsL.d           0.82556    0.50119   1.647    0.100
## gend.mf           -0.63333    0.74480  -0.850    0.396
## SlibsSC.d:gend.mf  1.16310    0.92030   1.264    0.207
## SlibsC.d:gend.mf   0.03347    0.88494   0.038    0.970
## SlibsM.d:gend.mf  -0.11944    0.82575  -0.145    0.885
## SlibsL.d:gend.mf   1.43862    1.00239   1.435    0.152
## 
## Residual standard error: 2.04 on 374 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02548 
## F-statistic: 2.113 on 9 and 374 DF,  p-value: 0.0277

# Action 16
slib.g.b16 <- lm(act16 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b16)

## 
## Call:
## lm(formula = act16 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3636 -1.1400  0.1589  1.6364  2.7586 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.94444    0.37065   2.548   0.0113 *
## SlibsSC.d         -0.29744    0.45877  -0.648   0.5172  
## SlibsC.d          -0.05626    0.43852  -0.128   0.8980  
## SlibsM.d           0.15793    0.41309   0.382   0.7025  
## SlibsL.d           0.02733    0.48411   0.056   0.9550  
## gend.mf           -0.11111    0.74130  -0.150   0.8809  
## SlibsSC.d:gend.mf  0.92236    0.91754   1.005   0.3155  
## SlibsC.d:gend.mf  -0.39253    0.87703  -0.448   0.6548  
## SlibsM.d:gend.mf   0.63363    0.82619   0.767   0.4437  
## SlibsL.d:gend.mf  -0.33244    0.96822  -0.343   0.7316  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 321 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.02518,    Adjusted R-squared:  -0.002148 
## F-statistic: 0.9214 on 9 and 321 DF,  p-value: 0.5066

# Action 17
slib.g.b17 <- lm(act17 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b17)

## 
## Call:
## lm(formula = act17 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.391 -1.477 -0.033  1.630  3.077 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         1.0957     0.3803   2.881  0.00418 **
## SlibsSC.d          -0.7072     0.4637  -1.525  0.12799   
## SlibsC.d           -0.9992     0.4466  -2.238  0.02579 * 
## SlibsM.d           -0.6130     0.4193  -1.462  0.14455   
## SlibsL.d           -0.3394     0.4884  -0.695  0.48747   
## gend.mf            -0.5913     0.7606  -0.777  0.43738   
## SlibsSC.d:gend.mf   0.6840     0.9274   0.738  0.46121   
## SlibsC.d:gend.mf    0.2445     0.8931   0.274  0.78437   
## SlibsM.d:gend.mf    0.6028     0.8387   0.719  0.47273   
## SlibsL.d:gend.mf    1.3646     0.9769   1.397  0.16320   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.008 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.02291,    Adjusted R-squared:  0.001247 
## F-statistic: 1.058 on 9 and 406 DF,  p-value: 0.3934

# Action 18
slib.g.b18 <- lm(act18 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b18) 

## 
## Call:
## lm(formula = act18 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.1369  0.1776  1.4528  3.1000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.78947    0.37407   2.110   0.0355 *
## SlibsSC.d         -0.55686    0.45340  -1.228   0.2202  
## SlibsC.d          -0.34922    0.44356  -0.787   0.4316  
## SlibsM.d          -0.01462    0.41691  -0.035   0.9720  
## SlibsL.d           0.17647    0.46994   0.376   0.7075  
## gend.mf            0.42105    0.74814   0.563   0.5739  
## SlibsSC.d:gend.mf  0.24416    0.90680   0.269   0.7879  
## SlibsC.d:gend.mf  -0.63489    0.88712  -0.716   0.4747  
## SlibsM.d:gend.mf  -0.51621    0.83381  -0.619   0.5363  
## SlibsL.d:gend.mf  -0.21009    0.93988  -0.224   0.8233  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.02394,    Adjusted R-squared:  -0.001748 
## F-statistic: 0.9319 on 9 and 342 DF,  p-value: 0.4973

# Action 19
slib.g.b19 <- lm(act19 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b19) 

## 
## Call:
## lm(formula = act19 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1622  0.3462  1.6250  2.3462 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        1.138158   0.380634   2.990  0.00305 **
## SlibsSC.d         -0.164176   0.473531  -0.347  0.72909   
## SlibsC.d          -0.122294   0.449877  -0.272  0.78596   
## SlibsM.d          -0.007206   0.428628  -0.017  0.98660   
## SlibsL.d           0.361842   0.529926   0.683  0.49532   
## gend.mf           -0.776316   0.761269  -1.020  0.30877   
## SlibsSC.d:gend.mf  1.416587   0.947062   1.496  0.13591   
## SlibsC.d:gend.mf   0.483719   0.899753   0.538  0.59130   
## SlibsM.d:gend.mf   0.442982   0.857257   0.517  0.60577   
## SlibsL.d:gend.mf   1.026316   1.059852   0.968  0.33375   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.806 on 264 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.0208, Adjusted R-squared:  -0.01258 
## F-statistic: 0.6232 on 9 and 264 DF,  p-value: 0.7769

# Action 20
slib.g.b20 <- lm(act20 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b20) 

## 
## Call:
## lm(formula = act20 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.398 -1.200  0.602  1.602  2.391 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.8922     0.3710   2.405   0.0168 *
## SlibsSC.d           0.2709     0.4465   0.607   0.5444  
## SlibsC.d            0.1078     0.4364   0.247   0.8050  
## SlibsM.d            0.4068     0.4158   0.978   0.3287  
## SlibsL.d            0.3750     0.4785   0.784   0.4338  
## gend.mf            -0.4510     0.7420  -0.608   0.5438  
## SlibsSC.d:gend.mf   0.7082     0.8929   0.793   0.4283  
## SlibsC.d:gend.mf   -0.3316     0.8729  -0.380   0.7043  
## SlibsM.d:gend.mf    0.2530     0.8316   0.304   0.7611  
## SlibsL.d:gend.mf    0.7500     0.9570   0.784   0.4338  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.8 on 312 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.01743,    Adjusted R-squared:  -0.01091 
## F-statistic: 0.615 on 9 and 312 DF,  p-value: 0.7842

# Action 21
slib.g.b21 <- lm(act21 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b21) 

## 
## Call:
## lm(formula = act21 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.556 -1.840  0.160  1.696  3.818 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.42995    0.40660   1.057   0.2910  
## SlibsSC.d         -1.06404    0.49058  -2.169   0.0307 *
## SlibsC.d          -0.79058    0.47783  -1.655   0.0988 .
## SlibsM.d          -0.41239    0.44717  -0.922   0.3570  
## SlibsL.d           0.04126    0.51081   0.081   0.9357  
## gend.mf            0.25121    0.81320   0.309   0.7576  
## SlibsSC.d:gend.mf -0.61939    0.98116  -0.631   0.5282  
## SlibsC.d:gend.mf  -0.69661    0.95566  -0.729   0.4665  
## SlibsM.d:gend.mf   0.10391    0.89435   0.116   0.9076  
## SlibsL.d:gend.mf  -0.12697    1.02162  -0.124   0.9012  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.068 on 391 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.0295, Adjusted R-squared:  0.007162 
## F-statistic: 1.321 on 9 and 391 DF,  p-value: 0.2241

# Action 22
slib.g.b22 <- lm(act22 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b22)

## 
## Call:
## lm(formula = act22 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.4912 -0.1538  1.6410  3.6410 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         1.0769     0.3671   2.933  0.00355 **
## SlibsSC.d          -1.2838     0.4516  -2.843  0.00471 **
## SlibsC.d           -0.8313     0.4368  -1.903  0.05775 . 
## SlibsM.d           -0.6797     0.4098  -1.659  0.09798 . 
## SlibsL.d           -0.4517     0.4820  -0.937  0.34923   
## gend.mf            -0.1538     0.7342  -0.210  0.83414   
## SlibsSC.d:gend.mf   1.0221     0.9033   1.132  0.25850   
## SlibsC.d:gend.mf   -0.3374     0.8736  -0.386  0.69956   
## SlibsM.d:gend.mf    0.6226     0.8196   0.760  0.44790   
## SlibsL.d:gend.mf    0.2880     0.9639   0.299  0.76526   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.973 on 392 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.04639,    Adjusted R-squared:  0.02449 
## F-statistic: 2.119 on 9 and 392 DF,  p-value: 0.02709

# Action 23
slib.g.b23 <- lm(act23 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b23) 

## 
## Call:
## lm(formula = act23 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9474 -2.0103  0.1875  2.0227  3.5238 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.28125    0.48926   0.575   0.5658  
## SlibsSC.d          0.03334    0.60381   0.055   0.9560  
## SlibsC.d          -0.55452    0.57374  -0.966   0.3346  
## SlibsM.d          -0.02610    0.54921  -0.048   0.9621  
## SlibsL.d           0.30625    0.61455   0.498   0.6186  
## gend.mf           -1.06250    0.97851  -1.086   0.2785  
## SlibsSC.d:gend.mf  2.32805    1.20763   1.928   0.0549 .
## SlibsC.d:gend.mf   0.56142    1.14748   0.489   0.6250  
## SlibsM.d:gend.mf   1.55219    1.09842   1.413   0.1587  
## SlibsL.d:gend.mf   0.82083    1.22910   0.668   0.5048  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.26 on 282 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03359,    Adjusted R-squared:  0.002749 
## F-statistic: 1.089 on 9 and 282 DF,  p-value: 0.3707

# Action 24
slib.g.b24 <- lm(act24 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b24) 

## 
## Call:
## lm(formula = act24 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5455 -1.8425  0.1575  1.8214  3.8214 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        -0.2130     0.3883  -0.548   0.5837  
## SlibsSC.d           0.1978     0.4839   0.409   0.6829  
## SlibsC.d           -0.3774     0.4553  -0.829   0.4076  
## SlibsM.d           -0.1158     0.4282  -0.270   0.7870  
## SlibsL.d            0.1708     0.5044   0.339   0.7351  
## gend.mf            -0.5741     0.7766  -0.739   0.4602  
## SlibsSC.d:gend.mf   1.6953     0.9678   1.752   0.0806 .
## SlibsC.d:gend.mf    0.1120     0.9105   0.123   0.9021  
## SlibsM.d:gend.mf    0.2316     0.8564   0.270   0.7870  
## SlibsL.d:gend.mf    0.9442     1.0087   0.936   0.3498  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.098 on 405 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.02029,    Adjusted R-squared:  -0.001484 
## F-statistic: 0.9318 on 9 and 405 DF,  p-value: 0.4971

# Action 25
slib.g.b25 <- lm(act25 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b25) 

## 
## Call:
## lm(formula = act25 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -0.8936  0.1642  1.4389  3.2051 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         1.3622     0.3580   3.805 0.000162 ***
## SlibsSC.d          -1.4410     0.4362  -3.303 0.001035 ** 
## SlibsC.d           -0.9511     0.4134  -2.301 0.021872 *  
## SlibsM.d           -0.4770     0.3903  -1.222 0.222329    
## SlibsL.d           -0.1535     0.4463  -0.344 0.731164    
## gend.mf             0.1644     0.7160   0.230 0.818450    
## SlibsSC.d:gend.mf   0.0883     0.8724   0.101 0.919427    
## SlibsC.d:gend.mf   -0.5422     0.8267  -0.656 0.512263    
## SlibsM.d:gend.mf   -0.1476     0.7806  -0.189 0.850081    
## SlibsL.d:gend.mf    0.1680     0.8927   0.188 0.850803    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.05138,    Adjusted R-squared:  0.03171 
## F-statistic: 2.612 on 9 and 434 DF,  p-value: 0.006027

# Action 26
slib.g.b26 <- lm(act26 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b26) 

## 
## Call:
## lm(formula = act26 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0000  0.5278  1.3433  2.5000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.9722     0.4213   2.307    0.022 *
## SlibsSC.d           0.5719     0.5187   1.103    0.271  
## SlibsC.d            0.5556     0.4997   1.112    0.267  
## SlibsM.d            0.5704     0.4797   1.189    0.236  
## SlibsL.d            0.5992     0.5768   1.039    0.300  
## gend.mf             0.9444     0.8427   1.121    0.264  
## SlibsSC.d:gend.mf  -0.8562     1.0374  -0.825    0.410  
## SlibsC.d:gend.mf   -1.2222     0.9995  -1.223    0.223  
## SlibsM.d:gend.mf   -1.1726     0.9594  -1.222    0.223  
## SlibsL.d:gend.mf    0.1984     1.1536   0.172    0.864  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 216 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.02923,    Adjusted R-squared:  -0.01122 
## F-statistic: 0.7225 on 9 and 216 DF,  p-value: 0.688

# Action 27
slib.g.b27 <- lm(act27 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b27) 

## 
## Call:
## lm(formula = act27 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4545 -1.1667  0.2174  1.5521  3.0000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.35885    0.35584   3.819 0.000162 ***
## SlibsSC.d         -0.87969    0.44146  -1.993 0.047182 *  
## SlibsC.d          -0.62608    0.43185  -1.450 0.148142    
## SlibsM.d          -0.73489    0.40647  -1.808 0.071587 .  
## SlibsL.d          -0.18461    0.47631  -0.388 0.698592    
## gend.mf            0.19139    0.71169   0.269 0.788171    
## SlibsSC.d:gend.mf  0.76695    0.88292   0.869 0.385717    
## SlibsC.d:gend.mf  -0.09171    0.86370  -0.106 0.915511    
## SlibsM.d:gend.mf   0.16070    0.81294   0.198 0.843432    
## SlibsL.d:gend.mf  -0.20654    0.95261  -0.217 0.828498    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 307 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.03928,    Adjusted R-squared:  0.01112 
## F-statistic: 1.395 on 9 and 307 DF,  p-value: 0.1895

# Action 28
slib.g.b28 <- lm(act28 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b28) 

## 
## Call:
## lm(formula = act28 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4262 -0.9511  0.1944  1.5738  2.4286 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.98137    0.39567   2.480   0.0136 *
## SlibsSC.d         -0.19764    0.46893  -0.421   0.6737  
## SlibsC.d           0.25349    0.45481   0.557   0.5776  
## SlibsM.d           0.02427    0.43526   0.056   0.9556  
## SlibsL.d          -0.07148    0.49609  -0.144   0.8855  
## gend.mf           -0.81988    0.79134  -1.036   0.3009  
## SlibsSC.d:gend.mf  0.77622    0.93787   0.828   0.4084  
## SlibsC.d:gend.mf   0.43712    0.90963   0.481   0.6311  
## SlibsM.d:gend.mf   0.99043    0.87051   1.138   0.2560  
## SlibsL.d:gend.mf   0.50011    0.99218   0.504   0.6145  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 362 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.01604,    Adjusted R-squared:  -0.008426 
## F-statistic: 0.6556 on 9 and 362 DF,  p-value: 0.749

# Action 29
slib.g.b29 <- lm(act29 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b29) 

## 
## Call:
## lm(formula = act29 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1003  0.1951  1.5000  2.1951 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.42982    0.35890   3.984 8.21e-05 ***
## SlibsSC.d         -0.31078    0.43445  -0.715    0.475    
## SlibsC.d          -0.43759    0.41823  -1.046    0.296    
## SlibsM.d          -0.31490    0.39796  -0.791    0.429    
## SlibsL.d          -0.27739    0.45187  -0.614    0.540    
## gend.mf           -0.19298    0.71780  -0.269    0.788    
## SlibsSC.d:gend.mf  0.43108    0.86889   0.496    0.620    
## SlibsC.d:gend.mf   0.04185    0.83647   0.050    0.960    
## SlibsM.d:gend.mf   0.13455    0.79592   0.169    0.866    
## SlibsL.d:gend.mf   0.88810    0.90374   0.983    0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.774 on 358 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01019,    Adjusted R-squared:  -0.0147 
## F-statistic: 0.4093 on 9 and 358 DF,  p-value: 0.93

# Action 30
slib.g.b30 <- lm(act30 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)

summary(slib.g.b30) 

## 
## Call:
## lm(formula = act30 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3529 -1.0654  0.5928  1.6471  2.3913 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        1.07647    0.37504   2.870  0.00436 **
## SlibsSC.d          0.01304    0.45300   0.029  0.97705   
## SlibsC.d          -0.18440    0.44124  -0.418  0.67627   
## SlibsM.d          -0.09376    0.42244  -0.222  0.82448   
## SlibsL.d           0.19377    0.48175   0.402  0.68777   
## gend.mf           -0.55294    0.75008  -0.737  0.46152   
## SlibsSC.d:gend.mf  0.02609    0.90600   0.029  0.97705   
## SlibsC.d:gend.mf  -0.01380    0.88249  -0.016  0.98753   
## SlibsM.d:gend.mf   0.38752    0.84487   0.459  0.64676   
## SlibsL.d:gend.mf   0.93553    0.96350   0.971  0.33225   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.882 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.01188,    Adjusted R-squared:  -0.01412 
## F-statistic: 0.4569 on 9 and 342 DF,  p-value: 0.9027

a. Gender means

aggregate(d$act1[d$ideology == "Strong Liberal"], list(d$gend.mf[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1    -0.5  0.65
## 2     0.5 -1.00

table(d$ideology, d$gend.mf)

##                      
##                       -0.5 0.5
##   Strong Liberal        33  13
##   Liberal               58  19
##   Moderate             175  53
##   Conservative          87  31
##   Strong Conservative   50  24

aggregate(d$act10[d$ideology == "Strong Liberal"], list(d$gend.mf[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1     x
## 1    -0.5  1.59
## 2     0.5 -0.10

table(d$ideology, d$gend.mf)

##                      
##                       -0.5 0.5
##   Strong Liberal        33  13
##   Liberal               58  19
##   Moderate             175  53
##   Conservative          87  31
##   Strong Conservative   50  24

3. Gender x Condition

Actions 1, 13, 16, 17, 18

# Action 1
summary(lm(act1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.960  0.080  2.000  5.000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               -0.2729     0.3968  -0.688  0.49199   
## SlibsSC.d                 -0.1292     0.4838  -0.267  0.78950   
## SlibsC.d                  -0.4012     0.4698  -0.854  0.39355   
## SlibsM.d                   0.3818     0.4364   0.875  0.38212   
## SlibsL.d                   0.8674     0.5056   1.716  0.08694 . 
## gend.mf                   -1.2542     0.7937  -1.580  0.11480   
## cond.c                     0.1208     0.7937   0.152  0.87906   
## SlibsSC.d:gend.mf          1.7554     0.9676   1.814  0.07033 . 
## SlibsC.d:gend.mf           0.4194     0.9396   0.446  0.65552   
## SlibsM.d:gend.mf           1.9564     0.8728   2.241  0.02551 * 
## SlibsL.d:gend.mf           1.0831     1.0111   1.071  0.28469   
## SlibsSC.d:cond.c           0.6304     0.9676   0.652  0.51503   
## SlibsC.d:cond.c           -0.3231     0.9396  -0.344  0.73112   
## SlibsM.d:cond.c           -0.8230     0.8728  -0.943  0.34625   
## SlibsL.d:cond.c           -0.2469     1.0111  -0.244  0.80720   
## gend.mf:cond.c             4.1583     1.5873   2.620  0.00911 **
## SlibsSC.d:gend.mf:cond.c  -3.6003     1.9351  -1.860  0.06350 . 
## SlibsC.d:gend.mf:cond.c   -4.5643     1.8791  -2.429  0.01555 * 
## SlibsM.d:gend.mf:cond.c   -4.5940     1.7457  -2.632  0.00880 **
## SlibsL.d:gend.mf:cond.c   -5.3705     2.0222  -2.656  0.00821 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.124 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.0873, Adjusted R-squared:  0.04669 
## F-statistic:  2.15 on 19 and 427 DF,  p-value: 0.003504

# Action 2
summary(lm(act2 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6000 -1.0000  0.3485  1.5192  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.80833    0.40542   1.994   0.0472 *
## SlibsSC.d                 0.24138    0.48909   0.494   0.6220  
## SlibsC.d                 -0.01212    0.46800  -0.026   0.9794  
## SlibsM.d                  0.35234    0.45446   0.775   0.4389  
## SlibsL.d                  0.86250    0.52519   1.642   0.1017  
## gend.mf                  -0.55000    0.81085  -0.678   0.4982  
## cond.c                    0.61667    0.81085   0.761   0.4476  
## SlibsSC.d:gend.mf         1.33693    0.97818   1.367   0.1728  
## SlibsC.d:gend.mf         -0.19394    0.93599  -0.207   0.8360  
## SlibsM.d:gend.mf          0.56200    0.90891   0.618   0.5369  
## SlibsL.d:gend.mf          1.12500    1.05038   1.071   0.2851  
## SlibsSC.d:cond.c         -0.76723    0.97818  -0.784   0.4335  
## SlibsC.d:cond.c          -1.34242    0.93599  -1.434   0.1527  
## SlibsM.d:cond.c          -0.77610    0.90891  -0.854   0.3939  
## SlibsL.d:cond.c          -0.62500    1.05038  -0.595   0.5523  
## gend.mf:cond.c            2.23333    1.62169   1.377   0.1696  
## SlibsSC.d:gend.mf:cond.c -1.15947    1.95636  -0.593   0.5539  
## SlibsC.d:gend.mf:cond.c  -3.81212    1.87199  -2.036   0.0427 *
## SlibsM.d:gend.mf:cond.c  -1.24780    1.81783  -0.686   0.4930  
## SlibsL.d:gend.mf:cond.c  -1.38333    2.10076  -0.658   0.5108  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 268 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.07459,    Adjusted R-squared:  0.008984 
## F-statistic: 1.137 on 19 and 268 DF,  p-value: 0.3138

# Action 3
summary(lm(act3 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7059 -1.8261  0.0278  1.7143  3.9412 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.19790    0.39142   0.506   0.6134  
## SlibsSC.d                -0.98812    0.47401  -2.085   0.0377 *
## SlibsC.d                 -0.52429    0.45873  -1.143   0.2537  
## SlibsM.d                 -0.26264    0.42709  -0.615   0.5389  
## SlibsL.d                 -0.26040    0.48312  -0.539   0.5902  
## gend.mf                  -0.59580    0.78284  -0.761   0.4470  
## cond.c                    0.08992    0.78284   0.115   0.9086  
## SlibsSC.d:gend.mf         1.28987    0.94801   1.361   0.1744  
## SlibsC.d:gend.mf          0.80413    0.91747   0.876   0.3813  
## SlibsM.d:gend.mf          0.38471    0.85418   0.450   0.6527  
## SlibsL.d:gend.mf          0.86366    0.96624   0.894   0.3719  
## SlibsSC.d:cond.c         -0.09281    0.94801  -0.098   0.9221  
## SlibsC.d:cond.c          -0.40936    0.91747  -0.446   0.6557  
## SlibsM.d:cond.c          -0.02496    0.85418  -0.029   0.9767  
## SlibsL.d:cond.c           0.67794    0.96624   0.702   0.4833  
## gend.mf:cond.c            1.02017    1.56568   0.652   0.5150  
## SlibsSC.d:gend.mf:cond.c -0.24165    1.89602  -0.127   0.8986  
## SlibsC.d:gend.mf:cond.c  -2.60350    1.83494  -1.419   0.1567  
## SlibsM.d:gend.mf:cond.c  -1.16457    1.70836  -0.682   0.4958  
## SlibsL.d:gend.mf:cond.c  -2.27017    1.93247  -1.175   0.2408  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.05725,    Adjusted R-squared:  0.01481 
## F-statistic: 1.349 on 19 and 422 DF,  p-value: 0.1485

# Action 4
summary(lm(act4 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2000 -1.8551  0.1449  2.0909  3.5000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.36154    0.44910   0.805    0.421
## SlibsSC.d                -0.16167    0.54610  -0.296    0.767
## SlibsC.d                 -0.72009    0.53416  -1.348    0.179
## SlibsM.d                 -0.57411    0.49649  -1.156    0.248
## SlibsL.d                  0.15322    0.55658   0.275    0.783
## gend.mf                  -1.32308    0.89820  -1.473    0.142
## cond.c                    0.07692    0.89820   0.086    0.932
## SlibsSC.d:gend.mf         1.69607    1.09219   1.553    0.121
## SlibsC.d:gend.mf          0.85749    1.06833   0.803    0.423
## SlibsM.d:gend.mf          1.31072    0.99299   1.320    0.188
## SlibsL.d:gend.mf          1.69356    1.11316   1.521    0.129
## SlibsSC.d:cond.c         -0.08628    1.09219  -0.079    0.937
## SlibsC.d:cond.c          -0.60084    1.06833  -0.562    0.574
## SlibsM.d:cond.c          -0.41964    0.99299  -0.423    0.673
## SlibsL.d:cond.c           0.68303    1.11316   0.614    0.540
## gend.mf:cond.c           -0.55385    1.79640  -0.308    0.758
## SlibsSC.d:gend.mf:cond.c  0.11802    2.18438   0.054    0.957
## SlibsC.d:gend.mf:cond.c  -1.26369    2.13665  -0.591    0.555
## SlibsM.d:gend.mf:cond.c   0.11428    1.98597   0.058    0.954
## SlibsL.d:gend.mf:cond.c   0.23394    2.22632   0.105    0.916
## 
## Residual standard error: 2.183 on 349 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.04372,    Adjusted R-squared:  -0.008345 
## F-statistic: 0.8397 on 19 and 349 DF,  p-value: 0.6588

# Action 5
summary(lm(act5 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4800 -1.2500  0.1622  1.5200  3.2500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                0.2708     0.4153   0.652   0.5149  
## SlibsSC.d                  0.4588     0.4988   0.920   0.3585  
## SlibsC.d                   0.6892     0.4787   1.440   0.1511  
## SlibsM.d                   0.8033     0.4652   1.727   0.0853 .
## SlibsL.d                   1.2006     0.5279   2.274   0.0237 *
## gend.mf                   -0.1250     0.8307  -0.150   0.8805  
## cond.c                     0.1250     0.8307   0.150   0.8805  
## SlibsSC.d:gend.mf         -0.3570     0.9977  -0.358   0.7208  
## SlibsC.d:gend.mf          -0.3441     0.9574  -0.359   0.7196  
## SlibsM.d:gend.mf           0.1855     0.9305   0.199   0.8421  
## SlibsL.d:gend.mf           0.6108     1.0558   0.579   0.5634  
## SlibsSC.d:cond.c          -0.3930     0.9977  -0.394   0.6939  
## SlibsC.d:cond.c           -0.4474     0.9574  -0.467   0.6407  
## SlibsM.d:cond.c           -0.5124     0.9305  -0.551   0.5823  
## SlibsL.d:cond.c           -0.9469     1.0558  -0.897   0.3706  
## gend.mf:cond.c             1.5833     1.6613   0.953   0.3414  
## SlibsSC.d:gend.mf:cond.c  -2.0018     1.9954  -1.003   0.3166  
## SlibsC.d:gend.mf:cond.c   -3.3699     1.9148  -1.760   0.0795 .
## SlibsM.d:gend.mf:cond.c   -1.5338     1.8609  -0.824   0.4105  
## SlibsL.d:gend.mf:cond.c   -1.0824     2.1115  -0.513   0.6086  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.884 on 275 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.0598, Adjusted R-squared:  -0.005162 
## F-statistic: 0.9205 on 19 and 275 DF,  p-value: 0.5577

# Action 6
summary(lm(act6 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.0816  0.5333  1.5294  2.7500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.93036    0.36597   2.542   0.0115 *
## SlibsSC.d                -0.08673    0.44631  -0.194   0.8460  
## SlibsC.d                  0.38266    0.42696   0.896   0.3708  
## SlibsM.d                  0.42020    0.40941   1.026   0.3055  
## SlibsL.d                  0.63888    0.47304   1.351   0.1777  
## gend.mf                  -1.21071    0.73194  -1.654   0.0990 .
## cond.c                    0.03929    0.73194   0.054   0.9572  
## SlibsSC.d:gend.mf         0.73255    0.89262   0.821   0.4124  
## SlibsC.d:gend.mf          0.98469    0.85392   1.153   0.2497  
## SlibsM.d:gend.mf          1.44686    0.81882   1.767   0.0781 .
## SlibsL.d:gend.mf          1.83415    0.94608   1.939   0.0534 .
## SlibsSC.d:cond.c         -0.08060    0.89262  -0.090   0.9281  
## SlibsC.d:cond.c          -0.42015    0.85392  -0.492   0.6230  
## SlibsM.d:cond.c          -0.19210    0.81882  -0.235   0.8147  
## SlibsL.d:cond.c          -0.48728    0.94608  -0.515   0.6069  
## gend.mf:cond.c            0.22143    1.46388   0.151   0.8799  
## SlibsSC.d:gend.mf:cond.c  1.07939    1.78524   0.605   0.5458  
## SlibsC.d:gend.mf:cond.c  -1.85969    1.70783  -1.089   0.2770  
## SlibsM.d:gend.mf:cond.c   0.07635    1.63764   0.047   0.9628  
## SlibsL.d:gend.mf:cond.c  -1.51591    1.89216  -0.801   0.4236  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 337 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.04582,    Adjusted R-squared:  -0.00798 
## F-statistic: 0.8517 on 19 and 337 DF,  p-value: 0.6439

# Action 7
summary(lm(act7 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0625 -1.8571  0.0625  1.7143  3.6000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              -0.02604    0.42698  -0.061   0.9514  
## SlibsSC.d                -0.07571    0.51252  -0.148   0.8826  
## SlibsC.d                 -0.31041    0.50245  -0.618   0.5371  
## SlibsM.d                 -0.06978    0.46432  -0.150   0.8806  
## SlibsL.d                  0.10660    0.53132   0.201   0.8411  
## gend.mf                  -1.11458    0.85395  -1.305   0.1926  
## cond.c                   -0.11458    0.85395  -0.134   0.8933  
## SlibsSC.d:gend.mf         1.49990    1.02504   1.463   0.1442  
## SlibsC.d:gend.mf          0.89463    1.00489   0.890   0.3739  
## SlibsM.d:gend.mf          1.22830    0.92864   1.323   0.1867  
## SlibsL.d:gend.mf          1.34236    1.06263   1.263   0.2073  
## SlibsSC.d:cond.c          0.24631    1.02504   0.240   0.8102  
## SlibsC.d:cond.c          -0.20832    1.00489  -0.207   0.8359  
## SlibsM.d:cond.c           0.52944    0.92864   0.570   0.5689  
## SlibsL.d:cond.c           0.97014    1.06263   0.913   0.3618  
## gend.mf:cond.c            1.89583    1.70790   1.110   0.2677  
## SlibsSC.d:gend.mf:cond.c -0.70474    2.05008  -0.344   0.7312  
## SlibsC.d:gend.mf:cond.c  -4.03575    2.00978  -2.008   0.0453 *
## SlibsM.d:gend.mf:cond.c  -1.42684    1.85728  -0.768   0.4428  
## SlibsL.d:gend.mf:cond.c  -3.71806    2.12526  -1.749   0.0810 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.081 on 393 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.05477,    Adjusted R-squared:  0.009077 
## F-statistic: 1.199 on 19 and 393 DF,  p-value: 0.2548

# Action 8
summary(lm(act8 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4000 -1.6786 -0.1429  1.5466  4.8421 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                0.4333     0.3797   1.141  0.25442   
## SlibsSC.d                 -1.3833     0.4678  -2.957  0.00326 **
## SlibsC.d                  -0.8960     0.4510  -1.987  0.04755 * 
## SlibsM.d                  -0.8194     0.4183  -1.959  0.05074 . 
## SlibsL.d                   0.1500     0.4961   0.302  0.76252   
## gend.mf                   -1.2000     0.7595  -1.580  0.11480   
## cond.c                     1.2000     0.7595   1.580  0.11480   
## SlibsSC.d:gend.mf          2.4636     0.9355   2.633  0.00874 **
## SlibsC.d:gend.mf           1.6106     0.9019   1.786  0.07480 . 
## SlibsM.d:gend.mf           1.4087     0.8366   1.684  0.09290 . 
## SlibsL.d:gend.mf           1.4619     0.9922   1.473  0.14133   
## SlibsSC.d:cond.c          -1.0760     0.9355  -1.150  0.25068   
## SlibsC.d:cond.c           -2.8752     0.9019  -3.188  0.00153 **
## SlibsM.d:cond.c           -0.9625     0.8366  -1.151  0.25053   
## SlibsL.d:cond.c           -0.6048     0.9922  -0.610  0.54249   
## gend.mf:cond.c             0.9333     1.5190   0.614  0.53923   
## SlibsSC.d:gend.mf:cond.c  -1.7267     1.8711  -0.923  0.35657   
## SlibsC.d:gend.mf:cond.c   -3.5010     1.8039  -1.941  0.05289 . 
## SlibsM.d:gend.mf:cond.c   -0.4481     1.6732  -0.268  0.78896   
## SlibsL.d:gend.mf:cond.c   -0.9810     1.9844  -0.494  0.62131   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.102 on 459 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.07388 
## F-statistic: 3.007 on 19 and 459 DF,  p-value: 2.47e-05

# Action 9
summary(lm(act9 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8000 -1.5455  0.0455  1.5000  4.4545 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -0.066667   0.377581  -0.177    0.860
## SlibsSC.d                -0.732955   0.460320  -1.592    0.112
## SlibsC.d                  0.002554   0.443179   0.006    0.995
## SlibsM.d                  0.233734   0.413408   0.565    0.572
## SlibsL.d                  0.629762   0.471382   1.336    0.182
## gend.mf                  -0.966667   0.755161  -1.280    0.201
## cond.c                   -0.033333   0.755161  -0.044    0.965
## SlibsSC.d:gend.mf         0.861364   0.920640   0.936    0.350
## SlibsC.d:gend.mf          1.428225   0.886358   1.611    0.108
## SlibsM.d:gend.mf          0.891426   0.826816   1.078    0.282
## SlibsL.d:gend.mf          0.926190   0.942765   0.982    0.326
## SlibsSC.d:cond.c          1.155303   0.920640   1.255    0.210
## SlibsC.d:cond.c          -0.853062   0.886358  -0.962    0.336
## SlibsM.d:cond.c           0.237922   0.826816   0.288    0.774
## SlibsL.d:cond.c          -0.140476   0.942765  -0.149    0.882
## gend.mf:cond.c            0.266667   1.510323   0.177    0.860
## SlibsSC.d:gend.mf:cond.c -0.101515   1.841280  -0.055    0.956
## SlibsC.d:gend.mf:cond.c  -2.271655   1.772717  -1.281    0.201
## SlibsM.d:gend.mf:cond.c   0.023761   1.653633   0.014    0.989
## SlibsL.d:gend.mf:cond.c  -0.947619   1.885530  -0.503    0.616
## 
## Residual standard error: 1.968 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.06597,    Adjusted R-squared:  0.02441 
## F-statistic: 1.587 on 19 and 427 DF,  p-value: 0.05552

# Action 10
summary(lm(act10 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.8630  0.1739  1.7370  4.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                0.7303     0.3816   1.914  0.05626 . 
## SlibsSC.d                 -1.2680     0.4651  -2.726  0.00665 **
## SlibsC.d                  -0.9277     0.4483  -2.069  0.03907 * 
## SlibsM.d                  -0.6446     0.4192  -1.538  0.12486   
## SlibsL.d                   0.2354     0.4845   0.486  0.62725   
## gend.mf                   -1.6606     0.7632  -2.176  0.03007 * 
## cond.c                     1.1939     0.7632   1.564  0.11841   
## SlibsSC.d:gend.mf          2.1298     0.9302   2.290  0.02249 * 
## SlibsC.d:gend.mf           1.6554     0.8966   1.846  0.06549 . 
## SlibsM.d:gend.mf           1.7065     0.8385   2.035  0.04240 * 
## SlibsL.d:gend.mf           2.6041     0.9690   2.687  0.00746 **
## SlibsSC.d:cond.c          -1.2597     0.9302  -1.354  0.17634   
## SlibsC.d:cond.c           -1.9943     0.8966  -2.224  0.02661 * 
## SlibsM.d:cond.c           -1.1111     0.8385  -1.325  0.18580   
## SlibsL.d:cond.c           -1.0254     0.9690  -1.058  0.29051   
## gend.mf:cond.c             2.8121     1.5263   1.842  0.06607 . 
## SlibsSC.d:gend.mf:cond.c  -2.8019     1.8604  -1.506  0.13273   
## SlibsC.d:gend.mf:cond.c   -4.4114     1.7931  -2.460  0.01426 * 
## SlibsM.d:gend.mf:cond.c   -1.8474     1.6769  -1.102  0.27119   
## SlibsL.d:gend.mf:cond.c   -3.8992     1.9380  -2.012  0.04481 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 456 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.0938, Adjusted R-squared:  0.05604 
## F-statistic: 2.484 on 19 and 456 DF,  p-value: 0.0005392

# Action 11
summary(lm(act11 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9000 -1.0292  0.3902  1.5200  3.4167 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.43333    0.42829   3.347 0.000925 ***
## SlibsSC.d                -0.64300    0.50466  -1.274 0.203635    
## SlibsC.d                 -0.65924    0.49312  -1.337 0.182306    
## SlibsM.d                 -0.72081    0.46566  -1.548 0.122718    
## SlibsL.d                  0.33869    0.53665   0.631 0.528457    
## gend.mf                  -0.03333    0.85658  -0.039 0.968985    
## cond.c                   -0.36667    0.85658  -0.428 0.668924    
## SlibsSC.d:gend.mf         1.17994    1.00933   1.169 0.243343    
## SlibsC.d:gend.mf         -0.43152    0.98624  -0.438 0.662043    
## SlibsM.d:gend.mf         -0.04203    0.93131  -0.045 0.964031    
## SlibsL.d:gend.mf          0.22738    1.07330   0.212 0.832371    
## SlibsSC.d:cond.c          0.77950    1.00933   0.772 0.440560    
## SlibsC.d:cond.c          -0.68514    0.98624  -0.695 0.487795    
## SlibsM.d:cond.c          -0.28426    0.93131  -0.305 0.760415    
## SlibsL.d:cond.c          -0.01071    1.07330  -0.010 0.992042    
## gend.mf:cond.c            1.06667    1.71316   0.623 0.534013    
## SlibsSC.d:gend.mf:cond.c -0.80143    2.01865  -0.397 0.691647    
## SlibsC.d:gend.mf:cond.c  -2.79638    1.97249  -1.418 0.157348    
## SlibsM.d:gend.mf:cond.c  -1.28770    1.86263  -0.691 0.489905    
## SlibsL.d:gend.mf:cond.c  -3.12143    2.14660  -1.454 0.146986    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.858 on 292 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.09531,    Adjusted R-squared:  0.03644 
## F-statistic: 1.619 on 19 and 292 DF,  p-value: 0.05071

# Action 12
summary(lm(act12 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.000 -1.875  0.125  1.635  4.235 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.24091    0.44000   0.548   0.5844  
## SlibsSC.d                -0.99544    0.52593  -1.893   0.0592 .
## SlibsC.d                 -0.81211    0.51760  -1.569   0.1175  
## SlibsM.d                 -0.40457    0.48224  -0.839   0.4020  
## SlibsL.d                  0.91289    0.56140   1.626   0.1048  
## gend.mf                  -0.88182    0.88001  -1.002   0.3170  
## cond.c                    0.71818    0.88001   0.816   0.4150  
## SlibsSC.d:gend.mf         1.66361    1.05186   1.582   0.1146  
## SlibsC.d:gend.mf          0.47066    1.03520   0.455   0.6496  
## SlibsM.d:gend.mf          0.31441    0.96447   0.326   0.7446  
## SlibsL.d:gend.mf          2.24089    1.12280   1.996   0.0467 *
## SlibsSC.d:cond.c         -0.08286    1.05186  -0.079   0.9373  
## SlibsC.d:cond.c          -1.90077    1.03520  -1.836   0.0672 .
## SlibsM.d:cond.c          -0.67375    0.96447  -0.699   0.4853  
## SlibsL.d:cond.c          -0.20225    1.12280  -0.180   0.8572  
## gend.mf:cond.c            0.16364    1.76002   0.093   0.9260  
## SlibsSC.d:gend.mf:cond.c  0.74753    2.10372   0.355   0.7225  
## SlibsC.d:gend.mf:cond.c  -3.19131    2.07041  -1.541   0.1241  
## SlibsM.d:gend.mf:cond.c   0.90539    1.92895   0.469   0.6391  
## SlibsL.d:gend.mf:cond.c  -0.52883    2.24559  -0.235   0.8140  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.117 on 363 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.06946 
## F-statistic: 2.501 on 19 and 363 DF,  p-value: 0.0005499

# Action 13
summary(lm(act13 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1333 -1.8000  0.1852  1.8235  4.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              -0.05208    0.38224  -0.136  0.89168   
## SlibsSC.d                -0.24876    0.46992  -0.529  0.59683   
## SlibsC.d                 -0.03630    0.45985  -0.079  0.93712   
## SlibsM.d                 -0.09578    0.41982  -0.228  0.81964   
## SlibsL.d                  0.55369    0.49335   1.122  0.26238   
## gend.mf                  -0.86250    0.76448  -1.128  0.25987   
## cond.c                    0.56250    0.76448   0.736  0.46226   
## SlibsSC.d:gend.mf         2.64600    0.93983   2.815  0.00510 **
## SlibsC.d:gend.mf          1.03927    0.91970   1.130  0.25911   
## SlibsM.d:gend.mf          0.59301    0.83963   0.706  0.48041   
## SlibsL.d:gend.mf          1.40096    0.98671   1.420  0.15639   
## SlibsSC.d:cond.c         -0.69146    0.93983  -0.736  0.46231   
## SlibsC.d:cond.c          -1.83018    0.91970  -1.990  0.04724 * 
## SlibsM.d:cond.c          -0.26422    0.83963  -0.315  0.75316   
## SlibsL.d:cond.c          -0.35737    0.98671  -0.362  0.71739   
## gend.mf:cond.c            4.14167    1.52895   2.709  0.00703 **
## SlibsSC.d:gend.mf:cond.c -4.42921    1.87967  -2.356  0.01891 * 
## SlibsC.d:gend.mf:cond.c  -5.60631    1.83939  -3.048  0.00245 **
## SlibsM.d:gend.mf:cond.c  -4.30345    1.67926  -2.563  0.01073 * 
## SlibsL.d:gend.mf:cond.c  -2.96859    1.97342  -1.504  0.13325   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.08224,    Adjusted R-squared:  0.04091 
## F-statistic:  1.99 on 19 and 422 DF,  p-value: 0.008085

# Action 14
summary(lm(act14 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1818 -1.2411  0.3129  1.5849  3.9000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               -0.2083     0.4911  -0.424   0.6718  
## SlibsSC.d                  0.7100     0.5913   1.201   0.2310  
## SlibsC.d                   0.2525     0.5698   0.443   0.6581  
## SlibsM.d                   0.5939     0.5339   1.112   0.2670  
## SlibsL.d                   1.3464     0.6121   2.200   0.0287 *
## gend.mf                   -1.6667     0.9822  -1.697   0.0909 .
## cond.c                     1.4167     0.9822   1.442   0.1504  
## SlibsSC.d:gend.mf          2.2777     1.1827   1.926   0.0552 .
## SlibsC.d:gend.mf           0.1784     1.1396   0.157   0.8757  
## SlibsM.d:gend.mf           1.4988     1.0678   1.404   0.1616  
## SlibsL.d:gend.mf           2.2905     1.2242   1.871   0.0624 .
## SlibsSC.d:cond.c          -0.8277     1.1827  -0.700   0.4847  
## SlibsC.d:cond.c           -1.7231     1.1396  -1.512   0.1317  
## SlibsM.d:cond.c           -1.2750     1.0678  -1.194   0.2335  
## SlibsL.d:cond.c           -1.1111     1.2242  -0.908   0.3649  
## gend.mf:cond.c             2.3333     1.9645   1.188   0.2360  
## SlibsSC.d:gend.mf:cond.c  -3.1399     2.3654  -1.327   0.1855  
## SlibsC.d:gend.mf:cond.c   -2.5204     2.2793  -1.106   0.2698  
## SlibsM.d:gend.mf:cond.c   -2.2674     2.1356  -1.062   0.2893  
## SlibsL.d:gend.mf:cond.c   -3.1446     2.4484  -1.284   0.2001  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.033 on 266 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.06875,    Adjusted R-squared:  0.002233 
## F-statistic: 1.034 on 19 and 266 DF,  p-value: 0.4224

# Action 15
summary(lm(act15 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5294 -1.7357  0.3167  1.4947  4.2000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.13899    0.37519   0.370   0.7113  
## SlibsSC.d                -0.48125    0.46232  -1.041   0.2986  
## SlibsC.d                 -0.04163    0.44684  -0.093   0.9258  
## SlibsM.d                 -0.21834    0.41487  -0.526   0.5990  
## SlibsL.d                  0.86628    0.50579   1.713   0.0876 .
## gend.mf                  -0.76369    0.75039  -1.018   0.3095  
## cond.c                    0.81131    0.75039   1.081   0.2803  
## SlibsSC.d:gend.mf         1.36488    0.92464   1.476   0.1408  
## SlibsC.d:gend.mf          0.38662    0.89369   0.433   0.6656  
## SlibsM.d:gend.mf          0.02121    0.82973   0.026   0.9796  
## SlibsL.d:gend.mf          1.57815    1.01158   1.560   0.1196  
## SlibsSC.d:cond.c         -0.49583    0.92464  -0.536   0.5921  
## SlibsC.d:cond.c          -1.35505    0.89369  -1.516   0.1303  
## SlibsM.d:cond.c          -0.67558    0.82973  -0.814   0.4161  
## SlibsL.d:cond.c          -1.95518    1.01158  -1.933   0.0540 .
## gend.mf:cond.c            2.20595    1.50078   1.470   0.1425  
## SlibsSC.d:gend.mf:cond.c -3.00357    1.84928  -1.624   0.1052  
## SlibsC.d:gend.mf:cond.c  -4.28318    1.78738  -2.396   0.0171 *
## SlibsM.d:gend.mf:cond.c  -1.37070    1.65947  -0.826   0.4094  
## SlibsL.d:gend.mf:cond.c  -0.76821    2.02316  -0.380   0.7044  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.021 on 364 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.09033,    Adjusted R-squared:  0.04285 
## F-statistic: 1.902 on 19 and 364 DF,  p-value: 0.013

# Action 16
summary(lm(act16 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -0.9811  0.2963  1.4800  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.51667    0.41233   1.253  0.21113   
## SlibsSC.d                 0.11717    0.49442   0.237  0.81282   
## SlibsC.d                  0.38239    0.47520   0.805  0.42161   
## SlibsM.d                  0.58469    0.45061   1.298  0.19541   
## SlibsL.d                  0.50636    0.51741   0.979  0.32852   
## gend.mf                   0.13333    0.82465   0.162  0.87166   
## cond.c                   -0.03333    0.82465  -0.040  0.96778   
## SlibsSC.d:gend.mf         0.71010    0.98885   0.718  0.47323   
## SlibsC.d:gend.mf         -0.61521    0.95040  -0.647  0.51790   
## SlibsM.d:gend.mf          0.38454    0.90123   0.427  0.66990   
## SlibsL.d:gend.mf         -0.67939    1.03482  -0.657  0.51197   
## SlibsSC.d:cond.c          0.09899    0.98885   0.100  0.92032   
## SlibsC.d:cond.c           0.29368    0.95040   0.309  0.75753   
## SlibsM.d:cond.c           0.28234    0.90123   0.313  0.75427   
## SlibsL.d:cond.c          -0.26272    1.03482  -0.254  0.79976   
## gend.mf:cond.c            3.73333    1.64931   2.264  0.02429 * 
## SlibsSC.d:gend.mf:cond.c -4.08687    1.97769  -2.066  0.03961 * 
## SlibsC.d:gend.mf:cond.c  -4.73265    1.90079  -2.490  0.01330 * 
## SlibsM.d:gend.mf:cond.c  -3.79017    1.80246  -2.103  0.03629 * 
## SlibsL.d:gend.mf:cond.c  -6.14123    2.06964  -2.967  0.00324 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 311 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.06263,    Adjusted R-squared:  0.005359 
## F-statistic: 1.094 on 19 and 311 DF,  p-value: 0.3561

# Action 17
summary(lm(act17 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4286 -1.3889 -0.0571  1.6000  4.2500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                0.9229     0.3880   2.379  0.01785 * 
## SlibsSC.d                 -0.5687     0.4694  -1.212  0.22641   
## SlibsC.d                  -0.6462     0.4595  -1.406  0.16039   
## SlibsM.d                  -0.4398     0.4257  -1.033  0.30227   
## SlibsL.d                  -0.1534     0.4931  -0.311  0.75587   
## gend.mf                   -0.9292     0.7760  -1.197  0.23187   
## cond.c                     1.6958     0.7760   2.185  0.02945 * 
## SlibsSC.d:gend.mf          1.1148     0.9389   1.187  0.23581   
## SlibsC.d:gend.mf           0.8897     0.9190   0.968  0.33358   
## SlibsM.d:gend.mf           0.9564     0.8515   1.123  0.26205   
## SlibsL.d:gend.mf           1.6759     0.9862   1.699  0.09004 . 
## SlibsSC.d:cond.c          -0.9042     0.9389  -0.963  0.33613   
## SlibsC.d:cond.c           -2.8032     0.9190  -3.050  0.00244 **
## SlibsM.d:cond.c           -2.1360     0.8515  -2.509  0.01252 * 
## SlibsL.d:cond.c           -1.7777     0.9862  -1.803  0.07221 . 
## gend.mf:cond.c             3.4417     1.5520   2.218  0.02715 * 
## SlibsSC.d:gend.mf:cond.c  -3.9038     1.8778  -2.079  0.03827 * 
## SlibsC.d:gend.mf:cond.c   -4.6992     1.8380  -2.557  0.01094 * 
## SlibsM.d:gend.mf:cond.c   -4.0030     1.7030  -2.351  0.01923 * 
## SlibsL.d:gend.mf:cond.c   -4.4208     1.9724  -2.241  0.02556 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 396 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.0642, Adjusted R-squared:  0.0193 
## F-statistic:  1.43 on 19 and 396 DF,  p-value: 0.1086

# Action 18
summary(lm(act18 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.2632  0.2372  1.3333  3.3846 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.62660    0.38506   1.627  0.10463   
## SlibsSC.d                -0.40547    0.46318  -0.875  0.38200   
## SlibsC.d                 -0.13953    0.45400  -0.307  0.75878   
## SlibsM.d                  0.14388    0.42704   0.337  0.73639   
## SlibsL.d                  0.36032    0.47907   0.752  0.45251   
## gend.mf                   0.49679    0.77012   0.645  0.51932   
## cond.c                    0.58013    0.77012   0.753  0.45181   
## SlibsSC.d:gend.mf         0.21245    0.92637   0.229  0.81875   
## SlibsC.d:gend.mf         -0.63760    0.90800  -0.702  0.48305   
## SlibsM.d:gend.mf         -0.58555    0.85407  -0.686  0.49344   
## SlibsL.d:gend.mf         -0.32778    0.95814  -0.342  0.73249   
## SlibsSC.d:cond.c         -0.08657    0.92637  -0.093  0.92560   
## SlibsC.d:cond.c          -1.05427    0.90800  -1.161  0.24644   
## SlibsM.d:cond.c          -0.59552    0.85407  -0.697  0.48612   
## SlibsL.d:cond.c          -0.43367    0.95814  -0.453  0.65112   
## gend.mf:cond.c            3.33974    1.54024   2.168  0.03084 * 
## SlibsSC.d:gend.mf:cond.c -3.35716    1.85274  -1.812  0.07089 . 
## SlibsC.d:gend.mf:cond.c  -4.72480    1.81601  -2.602  0.00969 **
## SlibsM.d:gend.mf:cond.c  -3.15455    1.70814  -1.847  0.06567 . 
## SlibsL.d:gend.mf:cond.c  -4.48981    1.91628  -2.343  0.01972 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.05189,    Adjusted R-squared:  -0.002371 
## F-statistic: 0.9563 on 19 and 332 DF,  p-value: 0.5132

# Action 19
summary(lm(act19 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6364 -1.0465  0.3693  1.4390  2.8750 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               1.08617    0.39139   2.775  0.00593 **
## SlibsSC.d                -0.17645    0.48824  -0.361  0.71810   
## SlibsC.d                  0.03500    0.46199   0.076  0.93967   
## SlibsM.d                  0.06703    0.43994   0.152  0.87901   
## SlibsL.d                  0.45827    0.54643   0.839  0.40244   
## gend.mf                  -0.83902    0.78279  -1.072  0.28481   
## cond.c                    0.20265    0.78279   0.259  0.79593   
## SlibsSC.d:gend.mf         1.64457    0.97648   1.684  0.09338 . 
## SlibsC.d:gend.mf          0.66016    0.92398   0.714  0.47559   
## SlibsM.d:gend.mf          0.53795    0.87987   0.611  0.54149   
## SlibsL.d:gend.mf          1.28346    1.09285   1.174  0.24133   
## SlibsSC.d:cond.c         -0.13321    0.97648  -0.136  0.89160   
## SlibsC.d:cond.c          -1.20474    0.92398  -1.304  0.19346   
## SlibsM.d:cond.c          -0.63903    0.87987  -0.726  0.46834   
## SlibsL.d:cond.c          -0.98043    1.09285  -0.897  0.37050   
## gend.mf:cond.c            0.92803    1.56557   0.593  0.55386   
## SlibsSC.d:gend.mf:cond.c -2.31692    1.95296  -1.186  0.23659   
## SlibsC.d:gend.mf:cond.c  -1.90797    1.84796  -1.032  0.30283   
## SlibsM.d:gend.mf:cond.c  -0.77186    1.75975  -0.439  0.66131   
## SlibsL.d:gend.mf:cond.c  -1.63914    2.18570  -0.750  0.45399   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.809 on 254 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.05536,    Adjusted R-squared:  -0.0153 
## F-statistic: 0.7834 on 19 and 254 DF,  p-value: 0.7262

# Action 20
summary(lm(act20 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7000 -1.1616  0.4211  1.4211  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.70476    0.38682   1.822   0.0695 .
## SlibsSC.d                 0.49915    0.46120   1.082   0.2800  
## SlibsC.d                  0.32008    0.45035   0.711   0.4778  
## SlibsM.d                  0.56138    0.43018   1.305   0.1929  
## SlibsL.d                  0.54568    0.49072   1.112   0.2670  
## gend.mf                  -0.57619    0.77364  -0.745   0.4570  
## cond.c                    0.04286    0.77364   0.055   0.9559  
## SlibsSC.d:gend.mf         0.75170    0.92240   0.815   0.4157  
## SlibsC.d:gend.mf         -0.16580    0.90070  -0.184   0.8541  
## SlibsM.d:gend.mf          0.38319    0.86036   0.445   0.6564  
## SlibsL.d:gend.mf          0.90865    0.98143   0.926   0.3553  
## SlibsSC.d:cond.c         -0.58957    0.92240  -0.639   0.5232  
## SlibsC.d:cond.c          -0.49318    0.90070  -0.548   0.5844  
## SlibsM.d:cond.c          -0.71452    0.86036  -0.830   0.4069  
## SlibsL.d:cond.c          -0.91040    0.98143  -0.928   0.3543  
## gend.mf:cond.c            2.91429    1.54728   1.883   0.0606 .
## SlibsSC.d:gend.mf:cond.c -2.65418    1.84479  -1.439   0.1513  
## SlibsC.d:gend.mf:cond.c  -3.39826    1.80140  -1.886   0.0602 .
## SlibsM.d:gend.mf:cond.c  -3.39239    1.72072  -1.972   0.0496 *
## SlibsL.d:gend.mf:cond.c  -3.51253    1.96286  -1.789   0.0745 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.795 on 302 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.05394,    Adjusted R-squared:  -0.00558 
## F-statistic: 0.9063 on 19 and 302 DF,  p-value: 0.5755

# Action 21
summary(lm(act21 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1250 -1.7424  0.0508  1.7059  4.2727 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.359615   0.408961   0.879   0.3798  
## SlibsSC.d                -1.010394   0.492997  -2.049   0.0411 *
## SlibsC.d                 -0.621275   0.489360  -1.270   0.2050  
## SlibsM.d                 -0.338507   0.449283  -0.753   0.4517  
## SlibsL.d                  0.106754   0.512993   0.208   0.8353  
## gend.mf                   0.180769   0.817922   0.221   0.8252  
## cond.c                    0.680769   0.817922   0.832   0.4058  
## SlibsSC.d:gend.mf        -0.515575   0.985994  -0.523   0.6013  
## SlibsC.d:gend.mf         -0.396946   0.978721  -0.406   0.6853  
## SlibsM.d:gend.mf          0.169871   0.898567   0.189   0.8502  
## SlibsL.d:gend.mf         -0.131364   1.025987  -0.128   0.8982  
## SlibsSC.d:cond.c         -0.003717   0.985994  -0.004   0.9970  
## SlibsC.d:cond.c          -0.739803   0.978721  -0.756   0.4502  
## SlibsM.d:cond.c          -0.523834   0.898567  -0.583   0.5603  
## SlibsL.d:cond.c          -0.956364   1.025987  -0.932   0.3519  
## gend.mf:cond.c            2.438462   1.635845   1.491   0.1369  
## SlibsSC.d:gend.mf:cond.c -1.974383   1.971989  -1.001   0.3174  
## SlibsC.d:gend.mf:cond.c  -4.370814   1.957441  -2.233   0.0261 *
## SlibsM.d:gend.mf:cond.c  -2.538047   1.797134  -1.412   0.1587  
## SlibsL.d:gend.mf:cond.c  -4.422985   2.051974  -2.155   0.0318 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 381 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05638,    Adjusted R-squared:  0.009323 
## F-statistic: 1.198 on 19 and 381 DF,  p-value: 0.2555

# Action 22
summary(lm(act22 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2222 -1.4571 -0.0508  1.7429  3.7778 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                1.0850     0.3729   2.909  0.00383 **
## SlibsSC.d                 -1.2967     0.4568  -2.839  0.00477 **
## SlibsC.d                  -0.7975     0.4442  -1.795  0.07340 . 
## SlibsM.d                  -0.6922     0.4152  -1.667  0.09631 . 
## SlibsL.d                  -0.4517     0.4878  -0.926  0.35500   
## gend.mf                   -0.1699     0.7459  -0.228  0.81990   
## cond.c                    -0.5477     0.7459  -0.734  0.46320   
## SlibsSC.d:gend.mf          1.0480     0.9135   1.147  0.25201   
## SlibsC.d:gend.mf          -0.1717     0.8884  -0.193  0.84682   
## SlibsM.d:gend.mf           0.6475     0.8304   0.780  0.43601   
## SlibsL.d:gend.mf           0.1653     0.9756   0.169  0.86552   
## SlibsSC.d:cond.c           0.8111     0.9135   0.888  0.37518   
## SlibsC.d:cond.c            0.1727     0.8884   0.194  0.84596   
## SlibsM.d:cond.c            0.5498     0.8304   0.662  0.50829   
## SlibsL.d:cond.c            0.2256     0.9756   0.231  0.81721   
## gend.mf:cond.c            -1.3046     1.4917  -0.875  0.38238   
## SlibsSC.d:gend.mf:cond.c   1.3233     1.8270   0.724  0.46932   
## SlibsC.d:gend.mf:cond.c   -0.2788     1.7768  -0.157  0.87542   
## SlibsM.d:gend.mf:cond.c    1.7214     1.6608   1.036  0.30063   
## SlibsL.d:gend.mf:cond.c   -1.2418     1.9511  -0.636  0.52487   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.976 on 382 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.0681, Adjusted R-squared:  0.02175 
## F-statistic: 1.469 on 19 and 382 DF,  p-value: 0.0928

# Action 23
summary(lm(act23 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -2.000  0.125  1.920  4.500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.36136    0.50085   0.722   0.4712  
## SlibsSC.d                -0.05657    0.61276  -0.092   0.9265  
## SlibsC.d                 -0.59612    0.58472  -1.019   0.3089  
## SlibsM.d                 -0.14023    0.56027  -0.250   0.8025  
## SlibsL.d                  0.15838    0.62625   0.253   0.8005  
## gend.mf                  -1.22273    1.00170  -1.221   0.2233  
## cond.c                    1.67727    1.00170   1.674   0.0952 .
## SlibsSC.d:gend.mf         2.46869    1.22552   2.014   0.0450 *
## SlibsC.d:gend.mf          0.83112    1.16944   0.711   0.4779  
## SlibsM.d:gend.mf          1.64713    1.12055   1.470   0.1427  
## SlibsL.d:gend.mf          0.95110    1.25250   0.759   0.4483  
## SlibsSC.d:cond.c         -1.62323    1.22552  -1.325   0.1864  
## SlibsC.d:cond.c          -2.21110    1.16944  -1.891   0.0597 .
## SlibsM.d:cond.c          -2.24453    1.12055  -2.003   0.0462 *
## SlibsL.d:cond.c          -3.12946    1.25250  -2.499   0.0131 *
## gend.mf:cond.c            1.64545    2.00339   0.821   0.4122  
## SlibsSC.d:gend.mf:cond.c -0.26465    2.45103  -0.108   0.9141  
## SlibsC.d:gend.mf:cond.c  -3.18890    2.33889  -1.363   0.1739  
## SlibsM.d:gend.mf:cond.c  -2.24426    2.24110  -1.001   0.3175  
## SlibsL.d:gend.mf:cond.c  -3.70538    2.50500  -1.479   0.1402  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.253 on 272 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.07369,    Adjusted R-squared:  0.008984 
## F-statistic: 1.139 on 19 and 272 DF,  p-value: 0.3118

# Action 24
summary(lm(act24 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8750 -0.0429  1.6429  4.6667 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              -0.243056   0.397695  -0.611   0.5414  
## SlibsSC.d                 0.210975   0.491805   0.429   0.6682  
## SlibsC.d                 -0.194048   0.466426  -0.416   0.6776  
## SlibsM.d                 -0.082903   0.436364  -0.190   0.8494  
## SlibsL.d                  0.217792   0.517330   0.421   0.6740  
## gend.mf                  -0.597222   0.795391  -0.751   0.4532  
## cond.c                    0.152778   0.795391   0.192   0.8478  
## SlibsSC.d:gend.mf         1.752292   0.983609   1.781   0.0756 .
## SlibsC.d:gend.mf          0.504762   0.932851   0.541   0.5887  
## SlibsM.d:gend.mf          0.305957   0.872727   0.351   0.7261  
## SlibsL.d:gend.mf          1.047749   1.034659   1.013   0.3118  
## SlibsSC.d:cond.c          0.006838   0.983609   0.007   0.9945  
## SlibsC.d:cond.c          -1.084127   0.932851  -1.162   0.2459  
## SlibsM.d:cond.c          -1.029370   0.872727  -1.179   0.2389  
## SlibsL.d:cond.c          -0.123304   1.034659  -0.119   0.9052  
## gend.mf:cond.c            0.527778   1.590781   0.332   0.7402  
## SlibsSC.d:gend.mf:cond.c -0.847009   1.967219  -0.431   0.6670  
## SlibsC.d:gend.mf:cond.c  -3.398413   1.865702  -1.822   0.0693 .
## SlibsM.d:gend.mf:cond.c  -1.388230   1.745454  -0.795   0.4269  
## SlibsL.d:gend.mf:cond.c   0.213275   2.069319   0.103   0.9180  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.083 on 395 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.05818,    Adjusted R-squared:  0.01287 
## F-statistic: 1.284 on 19 and 395 DF,  p-value: 0.1894

# Action 25
summary(lm(act25 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6087 -0.8846  0.2222  1.3913  3.4000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               1.21078    0.37976   3.188  0.00154 **
## SlibsSC.d                -1.30477    0.45440  -2.871  0.00429 **
## SlibsC.d                 -0.69049    0.43662  -1.581  0.11452   
## SlibsM.d                 -0.32414    0.41036  -0.790  0.43003   
## SlibsL.d                  0.00389    0.46358   0.008  0.99331   
## gend.mf                  -0.25490    0.75953  -0.336  0.73733   
## cond.c                    1.24510    0.75953   1.639  0.10189   
## SlibsSC.d:gend.mf         0.49741    0.90879   0.547  0.58444   
## SlibsC.d:gend.mf          0.10319    0.87324   0.118  0.90599   
## SlibsM.d:gend.mf          0.27691    0.82072   0.337  0.73599   
## SlibsL.d:gend.mf          0.57555    0.92715   0.621  0.53508   
## SlibsSC.d:cond.c         -0.73895    0.90879  -0.813  0.41661   
## SlibsC.d:cond.c          -1.81561    0.87324  -2.079  0.03820 * 
## SlibsM.d:cond.c          -1.44338    0.82072  -1.759  0.07935 . 
## SlibsL.d:cond.c          -0.69075    0.92715  -0.745  0.45667   
## gend.mf:cond.c            1.84314    1.51906   1.213  0.22567   
## SlibsSC.d:gend.mf:cond.c -1.14635    1.81759  -0.631  0.52858   
## SlibsC.d:gend.mf:cond.c  -3.36878    1.74648  -1.929  0.05441 . 
## SlibsM.d:gend.mf:cond.c  -2.20381    1.64145  -1.343  0.18012   
## SlibsL.d:gend.mf:cond.c  -2.95183    1.85431  -1.592  0.11216   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.837 on 424 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.07813,    Adjusted R-squared:  0.03682 
## F-statistic: 1.891 on 19 and 424 DF,  p-value: 0.0133

# Action 26
summary(lm(act26 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2857 -1.1563  0.5577  1.3947  3.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                0.8036     0.4554   1.764   0.0791 .
## SlibsSC.d                  0.8333     0.5551   1.501   0.1348  
## SlibsC.d                   0.8324     0.5320   1.565   0.1192  
## SlibsM.d                   0.7499     0.5131   1.462   0.1454  
## SlibsL.d                   0.8964     0.6259   1.432   0.1536  
## gend.mf                    0.8929     0.9108   0.980   0.3281  
## cond.c                     0.2262     0.9108   0.248   0.8041  
## SlibsSC.d:gend.mf         -1.0000     1.1102  -0.901   0.3688  
## SlibsC.d:gend.mf          -1.2399     1.0640  -1.165   0.2453  
## SlibsM.d:gend.mf          -1.1152     1.0261  -1.087   0.2784  
## SlibsL.d:gend.mf           0.5071     1.2519   0.405   0.6858  
## SlibsSC.d:cond.c          -0.6190     1.1102  -0.558   0.5777  
## SlibsC.d:cond.c           -0.3649     1.0640  -0.343   0.7320  
## SlibsM.d:cond.c           -0.2244     1.0261  -0.219   0.8271  
## SlibsL.d:cond.c           -0.8262     1.2519  -0.660   0.5100  
## gend.mf:cond.c             1.8810     1.8217   1.033   0.3030  
## SlibsSC.d:gend.mf:cond.c  -0.7619     2.2205  -0.343   0.7319  
## SlibsC.d:gend.mf:cond.c   -4.2536     2.1281  -1.999   0.0469 *
## SlibsM.d:gend.mf:cond.c   -2.1152     2.0522  -1.031   0.3039  
## SlibsL.d:gend.mf:cond.c   -3.0810     2.5037  -1.231   0.2199  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.844 on 206 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.06431,    Adjusted R-squared:  -0.02199 
## F-statistic: 0.7452 on 19 and 206 DF,  p-value: 0.7689

# Action 27
summary(lm(act27 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2143  0.1667  1.4167  3.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.35606    0.35596   3.810 0.000169 ***
## SlibsSC.d                -0.87689    0.44361  -1.977 0.048997 *  
## SlibsC.d                 -0.52789    0.43317  -1.219 0.223945    
## SlibsM.d                 -0.73480    0.40620  -1.809 0.071467 .  
## SlibsL.d                 -0.23864    0.47840  -0.499 0.618271    
## gend.mf                   0.12121    0.71192   0.170 0.864920    
## cond.c                    0.62121    0.71192   0.873 0.383591    
## SlibsSC.d:gend.mf         0.83712    0.88722   0.944 0.346173    
## SlibsC.d:gend.mf          0.18276    0.86635   0.211 0.833071    
## SlibsM.d:gend.mf          0.28805    0.81240   0.355 0.723164    
## SlibsL.d:gend.mf         -0.02273    0.95679  -0.024 0.981065    
## SlibsSC.d:cond.c         -1.24621    0.88722  -1.405 0.161175    
## SlibsC.d:cond.c          -1.25375    0.86635  -1.447 0.148904    
## SlibsM.d:cond.c          -1.30996    0.81240  -1.612 0.107924    
## SlibsL.d:cond.c          -0.96212    0.95679  -1.006 0.315441    
## gend.mf:cond.c            0.42424    1.42383   0.298 0.765944    
## SlibsSC.d:gend.mf:cond.c -1.67424    1.77443  -0.944 0.346173    
## SlibsC.d:gend.mf:cond.c  -2.79408    1.73269  -1.613 0.107901    
## SlibsM.d:gend.mf:cond.c  -0.60033    1.62479  -0.369 0.712034    
## SlibsL.d:gend.mf:cond.c   0.25758    1.91358   0.135 0.893016    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 297 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.0834, Adjusted R-squared:  0.02476 
## F-statistic: 1.422 on 19 and 297 DF,  p-value: 0.1144

# Action 28
summary(lm(act28 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6250 -1.1321  0.2667  1.5522  3.2000 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.993750   0.403019   2.466   0.0141 *
## SlibsSC.d                -0.175568   0.475408  -0.369   0.7121  
## SlibsC.d                  0.293396   0.465317   0.631   0.5288  
## SlibsM.d                  0.003158   0.442139   0.007   0.9943  
## SlibsL.d                 -0.121607   0.504288  -0.241   0.8096  
## gend.mf                  -0.904167   0.806038  -1.122   0.2627  
## cond.c                    0.387500   0.806038   0.481   0.6310  
## SlibsSC.d:gend.mf         0.840530   0.950815   0.884   0.3773  
## SlibsC.d:gend.mf          0.629875   0.930634   0.677   0.4990  
## SlibsM.d:gend.mf          1.032573   0.884278   1.168   0.2437  
## SlibsL.d:gend.mf          0.388452   1.008576   0.385   0.7004  
## SlibsSC.d:cond.c         -1.301136   0.950815  -1.368   0.1720  
## SlibsC.d:cond.c          -0.754385   0.930634  -0.811   0.4181  
## SlibsM.d:cond.c          -0.515907   0.884278  -0.583   0.5600  
## SlibsL.d:cond.c          -0.831786   1.008576  -0.825   0.4101  
## gend.mf:cond.c            0.058333   1.612075   0.036   0.9712  
## SlibsSC.d:gend.mf:cond.c -0.285606   1.901631  -0.150   0.8807  
## SlibsC.d:gend.mf:cond.c  -0.724564   1.861268  -0.389   0.6973  
## SlibsM.d:gend.mf:cond.c  -1.112631   1.768555  -0.629   0.5297  
## SlibsL.d:gend.mf:cond.c  -2.426905   2.017152  -1.203   0.2297  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 352 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  -0.006203 
## F-statistic: 0.8796 on 19 and 352 DF,  p-value: 0.6089

# Action 29
summary(lm(act29 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6923 -1.0447  0.3077  1.4286  2.4286 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.3522436  0.3729183   3.626 0.000331 ***
## SlibsSC.d                -0.2638237  0.4485709  -0.588 0.556818    
## SlibsC.d                 -0.4061968  0.4395428  -0.924 0.356056    
## SlibsM.d                 -0.2423829  0.4114592  -0.589 0.556189    
## SlibsL.d                 -0.1898661  0.4652998  -0.408 0.683487    
## gend.mf                  -0.1544872  0.7458365  -0.207 0.836027    
## cond.c                    0.2621795  0.7458365   0.352 0.725408    
## SlibsSC.d:gend.mf         0.4503746  0.8971417   0.502 0.615978    
## SlibsC.d:gend.mf         -0.1073544  0.8790855  -0.122 0.902874    
## SlibsM.d:gend.mf          0.1047004  0.8229183   0.127 0.898831    
## SlibsL.d:gend.mf          0.8297323  0.9305995   0.892 0.373218    
## SlibsSC.d:cond.c         -0.0009241  0.8971417  -0.001 0.999179    
## SlibsC.d:cond.c          -0.1266996  0.8790855  -0.144 0.885484    
## SlibsM.d:cond.c          -0.3307898  0.8229183  -0.402 0.687952    
## SlibsL.d:cond.c          -0.0743293  0.9305995  -0.080 0.936385    
## gend.mf:cond.c            1.5756410  1.4916731   1.056 0.291568    
## SlibsSC.d:gend.mf:cond.c -2.2436064  1.7942835  -1.250 0.211987    
## SlibsC.d:gend.mf:cond.c  -0.8718109  1.7581710  -0.496 0.620305    
## SlibsM.d:gend.mf:cond.c  -1.3338452  1.6458366  -0.810 0.418243    
## SlibsL.d:gend.mf:cond.c  -1.6656270  1.8611990  -0.895 0.371448    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 348 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01904,    Adjusted R-squared:  -0.03452 
## F-statistic: 0.3554 on 19 and 348 DF,  p-value: 0.995

# Action 30
summary(lm(act30 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.545 -1.059  0.400  1.466  3.250 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.94886    0.38564   2.461   0.0144 *
## SlibsSC.d                 0.09508    0.46253   0.206   0.8373  
## SlibsC.d                 -0.06822    0.45439  -0.150   0.8808  
## SlibsM.d                  0.01995    0.43233   0.046   0.9632  
## SlibsL.d                  0.31913    0.49071   0.650   0.5159  
## gend.mf                  -0.64773    0.77127  -0.840   0.4016  
## cond.c                    0.60227    0.77127   0.781   0.4354  
## SlibsSC.d:gend.mf         0.13561    0.92505   0.147   0.8835  
## SlibsC.d:gend.mf          0.05310    0.90879   0.058   0.9534  
## SlibsM.d:gend.mf          0.46123    0.86466   0.533   0.5941  
## SlibsL.d:gend.mf          0.94508    0.98142   0.963   0.3363  
## SlibsSC.d:cond.c          0.57652    0.92505   0.623   0.5336  
## SlibsC.d:cond.c          -0.66241    0.90879  -0.729   0.4666  
## SlibsM.d:cond.c          -0.53877    0.86466  -0.623   0.5336  
## SlibsL.d:cond.c          -0.92992    0.98142  -0.948   0.3441  
## gend.mf:cond.c            2.29545    1.54255   1.488   0.1377  
## SlibsSC.d:gend.mf:cond.c -1.13788    1.85011  -0.615   0.5390  
## SlibsC.d:gend.mf:cond.c  -1.84184    1.81758  -1.013   0.3116  
## SlibsM.d:gend.mf:cond.c  -1.68943    1.72932  -0.977   0.3293  
## SlibsL.d:gend.mf:cond.c  -3.97348    1.96285  -2.024   0.0437 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.04439,    Adjusted R-squared:  -0.0103 
## F-statistic: 0.8117 on 19 and 332 DF,  p-value: 0.6931

a. Gender x Cond means

# Action 1
aggregate(d$act1[d$ideology == "Strong Liberal"], list(d$gend[d$ideology == "Strong Liberal"], d$cond[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.62
## 2    Male climate  0.20
## 3  Female    ctrl  1.33
## 4    Male    ctrl -2.00
## 5   Other    ctrl  2.00

# Action 13
aggregate(d$act13[d$ideology == "Strong Liberal"], list(d$gend[d$ideology == "Strong Liberal"], d$cond[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.38
## 2    Male climate  0.83
## 3  Female    ctrl  1.13
## 4    Male    ctrl -1.80
## 5   Other    ctrl -1.00

# Action 16
aggregate(d$act16[d$ideology == "Strong Liberal"], list(d$gend[d$ideology == "Strong Liberal"], d$cond[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.50
## 2    Male climate  1.50
## 3  Female    ctrl  1.40
## 4    Male    ctrl -0.33
## 5   Other    ctrl  3.00

# Action 17
aggregate(d$act17[d$ideology == "Strong Liberal"], list(d$gend[d$ideology == "Strong Liberal"], d$cond[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate  1.38
## 2    Male climate  2.17
## 3  Female    ctrl  1.40
## 4    Male    ctrl -1.25
## 5   Other    ctrl  1.00

# Action 18
aggregate(d$act18[d$ideology == "Strong Liberal"], list(d$gend[d$ideology == "Strong Liberal"], d$cond[d$ideology == "Strong Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.17
## 2    Male climate  2.00
## 3  Female    ctrl  0.92
## 4    Male    ctrl -0.25
## 5   Other    ctrl  2.00

ii. Liberals

# Action 1
lib.b1 <- lm(act1 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b1)

## 
## Call:
## lm(formula = act1 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.646 -1.962  0.038  2.038  3.490 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6462     0.2668   2.422 0.015825 *  
## LibsSC.d     -1.1077     0.3773  -2.936 0.003495 ** 
## LibsC.d      -1.1362     0.3427  -3.316 0.000989 ***
## LibsM.d      -0.6842     0.3103  -2.205 0.027980 *  
## LibsSL.d     -0.4747     0.4509  -1.053 0.292995    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.151 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.02106 
## F-statistic: 3.409 on 4 and 444 DF,  p-value: 0.00923

# Action 2
lib.b2 <- lm(act2 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b2)

## 
## Call:
## lm(formula = act2 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.2212  0.1077  1.7788  2.1077 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.5714     0.2786   5.641 4.07e-08 ***
## LibsSC.d     -0.6149     0.3853  -1.596   0.1116    
## LibsC.d      -0.6791     0.3574  -1.900   0.0584 .  
## LibsM.d      -0.3502     0.3263  -1.073   0.2840    
## LibsSL.d     -0.5298     0.4620  -1.147   0.2524    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 285 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01519,    Adjusted R-squared:  0.001372 
## F-statistic: 1.099 on 4 and 285 DF,  p-value: 0.3572

# Action 3
lib.b3 <- lm(act3 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b3) 

## 
## Call:
## lm(formula = act3 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.9837  0.0163  1.4167  3.8852 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.1912     0.2437  -0.784   0.4332  
## LibsSC.d     -0.6941     0.3544  -1.958   0.0508 .
## LibsC.d      -0.2255     0.3185  -0.708   0.4794  
## LibsM.d       0.1749     0.2852   0.613   0.5401  
## LibsSL.d      0.5626     0.4181   1.346   0.1791  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 439 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02777,    Adjusted R-squared:  0.01891 
## F-statistic: 3.134 on 4 and 439 DF,  p-value: 0.01467

# Action 4
lib.b4 <- lm(act4 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b4) 

## 
## Call:
## lm(formula = act4 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4828 -1.7938  0.2062  2.0051  3.2877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3793     0.2838   1.336   0.1822  
## LibsSC.d     -0.1832     0.4149  -0.442   0.6590  
## LibsC.d      -0.6670     0.3802  -1.754   0.0802 .
## LibsM.d      -0.5856     0.3313  -1.767   0.0780 .
## LibsSL.d      0.1034     0.4916   0.210   0.8334  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.162 on 366 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.006351 
## F-statistic: 1.591 on 4 and 366 DF,  p-value: 0.176

# Action 5
lib.b5 <- lm(act5 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b5) 

## 
## Call:
## lm(formula = act5 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4048 -1.0917  0.2653  1.5952  2.5833 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.4048     0.2888   4.865 1.88e-06 ***
## LibsSC.d     -0.6701     0.3935  -1.703   0.0897 .  
## LibsC.d      -0.4322     0.3624  -1.192   0.2341    
## LibsM.d      -0.3130     0.3399  -0.921   0.3578    
## LibsSL.d     -0.9881     0.4788  -2.064   0.0399 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 292 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.01867,    Adjusted R-squared:  0.005229 
## F-statistic: 1.389 on 4 and 292 DF,  p-value: 0.2378

# Action 6
lib.b6 <- lm(act6 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b6)

## 
## Call:
## lm(formula = act6 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4348 -1.3099  0.6706  1.6706  2.1296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.4348     0.2673   5.368 1.44e-07 ***
## LibsSC.d     -0.5644     0.3637  -1.552    0.122    
## LibsC.d      -0.1054     0.3318  -0.318    0.751    
## LibsM.d      -0.1249     0.3076  -0.406    0.685    
## LibsSL.d     -0.2473     0.4173  -0.593    0.554    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 354 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.00896,    Adjusted R-squared:  -0.002238 
## F-statistic: 0.8001 on 4 and 354 DF,  p-value: 0.5257

# Action 7
lib.b7 <- lm(act7 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b7) 

## 
## Call:
## lm(formula = act7 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.8652  0.1348  1.6286  3.3140 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.0339     0.2720  -0.125    0.901
## LibsSC.d     -0.1240     0.3880  -0.320    0.749
## LibsC.d      -0.2801     0.3531  -0.793    0.428
## LibsM.d      -0.1009     0.3138  -0.322    0.748
## LibsSL.d      0.4053     0.4457   0.909    0.364
## 
## Residual standard error: 2.089 on 410 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.006798,   Adjusted R-squared:  -0.002892 
## F-statistic: 0.7016 on 4 and 410 DF,  p-value: 0.5912

# Action 8
lib.b8 <- lm(act8 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b8) 

## 
## Call:
## lm(formula = act8 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.625 -1.855 -0.287  1.713  4.145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.4762     0.2672   1.782 0.075384 .  
## LibsSC.d     -1.6211     0.3696  -4.386 1.42e-05 ***
## LibsC.d      -1.1892     0.3362  -3.537 0.000445 ***
## LibsM.d      -0.9190     0.3062  -3.001 0.002834 ** 
## LibsSL.d      0.1488     0.4288   0.347 0.728718    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 476 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.06121,    Adjusted R-squared:  0.05332 
## F-statistic: 7.759 on 4 and 476 DF,  p-value: 4.609e-06

# Action 9
lib.b9 <- lm(act9 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b9)

## 
## Call:
## lm(formula = act9 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5781 -1.5781  0.2525  1.7167  3.7167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.5781     0.2458   2.352 0.019097 *  
## LibsSC.d     -1.2948     0.3533  -3.665 0.000278 ***
## LibsC.d      -0.8307     0.3154  -2.634 0.008736 ** 
## LibsM.d      -0.3781     0.2842  -1.331 0.183999    
## LibsSL.d     -0.3837     0.4096  -0.937 0.349447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.966 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03759,    Adjusted R-squared:  0.02892 
## F-statistic: 4.336 on 4 and 444 DF,  p-value: 0.00189

# Action 10
lib.b10 <- lm(act10 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b10)

## 
## Call:
## lm(formula = act10 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6769     0.2574   2.630 0.008812 ** 
## LibsSC.d     -1.2946     0.3599  -3.597 0.000356 ***
## LibsC.d      -0.9341     0.3275  -2.852 0.004531 ** 
## LibsM.d      -0.5965     0.2964  -2.012 0.044752 *  
## LibsSL.d      0.4731     0.4170   1.135 0.257147    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

# Action 11
lib.b11 <- lm(act11 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b11)

## 
## Call:
## lm(formula = act11 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6585 -0.8154  0.2741  1.3415  2.2741 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.6585     0.2916   5.687 2.99e-08 ***
## LibsSC.d     -0.9238     0.3952  -2.338  0.02005 *  
## LibsC.d      -0.8432     0.3724  -2.264  0.02426 *  
## LibsM.d      -0.9326     0.3330  -2.801  0.00542 ** 
## LibsSL.d     -0.1169     0.4799  -0.244  0.80776    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 309 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03501,    Adjusted R-squared:  0.02252 
## F-statistic: 2.803 on 4 and 309 DF,  p-value: 0.02604

# Action 12
lib.b12 <- lm(act12 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b12)

## 
## Call:
## lm(formula = act12 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.736 -2.158  0.000  2.000  3.842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.7358     0.2947   2.497 0.012959 *  
## LibsSC.d     -1.5780     0.4094  -3.854 0.000136 ***
## LibsC.d      -1.3358     0.3715  -3.596 0.000366 ***
## LibsM.d      -0.7358     0.3406  -2.160 0.031360 *  
## LibsSL.d     -0.4025     0.5073  -0.793 0.428037    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.146 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0525, Adjusted R-squared:  0.04253 
## F-statistic: 5.264 on 4 and 380 DF,  p-value: 0.0003892

# Action 13
lib.b13 <- lm(act13 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b13)

## 
## Call:
## lm(formula = act13 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3437 -1.9149  0.0851  1.6563  3.5781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.34375    0.25987   1.323   0.1866  
## LibsSC.d    -0.92188    0.36751  -2.508   0.0125 *
## LibsC.d     -0.56114    0.33839  -1.658   0.0980 .
## LibsM.d     -0.42886    0.30087  -1.425   0.1548  
## LibsSL.d    -0.08661    0.43705  -0.198   0.8430  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.079 on 438 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01713,    Adjusted R-squared:  0.008155 
## F-statistic: 1.909 on 4 and 438 DF,  p-value: 0.108

# Action 14
lib.b14 <- lm(act14 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b14) 

## 
## Call:
## lm(formula = act14 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9487 -1.4219 -0.0198  1.6780  2.9091 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.9487     0.3253   2.917  0.00382 **
## LibsSC.d     -0.4487     0.4571  -0.982  0.32714   
## LibsC.d      -0.6267     0.4192  -1.495  0.13607   
## LibsM.d      -0.5268     0.3715  -1.418  0.15730   
## LibsSL.d     -0.8578     0.5416  -1.584  0.11438   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.031 on 283 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.01158,    Adjusted R-squared:  -0.002388 
## F-statistic: 0.829 on 4 and 283 DF,  p-value: 0.5076

# Action 15
lib.b15 <- lm(act15 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b15) 

## 
## Call:
## lm(formula = act15 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8889 -2.1024  0.1111  1.8916  3.3559 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.8889     0.3053   2.911  0.00381 **
## LibsSC.d     -1.2448     0.4054  -3.071  0.00229 **
## LibsC.d      -0.7805     0.3792  -2.058  0.04024 * 
## LibsM.d      -0.7865     0.3442  -2.285  0.02288 * 
## LibsSL.d     -0.5764     0.4736  -1.217  0.22437   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.048 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.02508,    Adjusted R-squared:  0.01482 
## F-statistic: 2.444 on 4 and 380 DF,  p-value: 0.0462

# Action 16
lib.b16 <- lm(act16 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b16) 

## 
## Call:
## lm(formula = act16 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0698 -0.9861  0.0357  1.4375  2.4375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.06977    0.27987   3.822 0.000158 ***
## LibsSC.d    -0.50727    0.38535  -1.316 0.188973    
## LibsC.d     -0.08366    0.35371  -0.237 0.813182    
## LibsM.d     -0.10548    0.31998  -0.330 0.741874    
## LibsSL.d    -0.03528    0.44099  -0.080 0.936276    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 327 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.007238,   Adjusted R-squared:  -0.004905 
## F-statistic: 0.5961 on 4 and 327 DF,  p-value: 0.6657

# Action 17
lib.b17 <- lm(act17 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b17) 

## 
## Call:
## lm(formula = act17 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2059 -1.4798 -0.1685  1.6230  2.8315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.55000    0.25823   2.130   0.0338 *
## LibsSC.d    -0.17295    0.36369  -0.476   0.6347  
## LibsC.d     -0.38146    0.33412  -1.142   0.2542  
## LibsM.d     -0.07023    0.29968  -0.234   0.8148  
## LibsSL.d     0.65588    0.42937   1.528   0.1274  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 412 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.006815 
## F-statistic: 1.714 on 4 and 412 DF,  p-value: 0.146

# Action 18
lib.b18 <- lm(act18 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b18)

## 
## Call:
## lm(formula = act18 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1196  0.1844  1.5135  2.8113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.91228    0.24393   3.740 0.000215 ***
## LibsSC.d    -0.72360    0.35142  -2.059 0.040228 *  
## LibsC.d     -0.42579    0.32455  -1.312 0.190403    
## LibsM.d     -0.09668    0.28906  -0.334 0.738237    
## LibsSL.d    -0.15366    0.42007  -0.366 0.714736    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01769,    Adjusted R-squared:  0.006435 
## F-statistic: 1.572 on 4 and 349 DF,  p-value: 0.1814

# Action 19
lib.b19 <- lm(act19 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b19) 

## 
## Call:
## lm(formula = act19 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.438 -1.050  0.093  1.704  2.093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.4375     0.3184   4.515 9.49e-06 ***
## LibsSC.d     -0.5305     0.4205  -1.262    0.208    
## LibsC.d      -0.3875     0.3943  -0.983    0.327    
## LibsM.d      -0.2074     0.3607  -0.575    0.566    
## LibsSL.d     -0.1412     0.4707  -0.300    0.764    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 270 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.00789,    Adjusted R-squared:  -0.006807 
## F-statistic: 0.5368 on 4 and 270 DF,  p-value: 0.7088

# Action 20
lib.b20 <- lm(act20 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b20) 

## 
## Call:
## lm(formula = act20 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3566 -1.1957  0.6434  1.6434  2.0000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.19565    0.26435   4.523 8.61e-06 ***
## LibsSC.d    -0.04471    0.36130  -0.124    0.902    
## LibsC.d     -0.06522    0.34128  -0.191    0.849    
## LibsM.d      0.16094    0.30790   0.523    0.602    
## LibsSL.d    -0.19565    0.43467  -0.450    0.653    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.793 on 319 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.004413,   Adjusted R-squared:  -0.008071 
## F-statistic: 0.3535 on 4 and 319 DF,  p-value: 0.8415

# Action 21
lib.b21 <- lm(act21 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b21) 

## 
## Call:
## lm(formula = act21 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -1.9401  0.0599  1.5806  3.5806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.44068    0.26821   1.643   0.1012   
## LibsSC.d    -1.02132    0.37469  -2.726   0.0067 **
## LibsC.d     -0.70897    0.35170  -2.016   0.0445 * 
## LibsM.d     -0.50056    0.31201  -1.604   0.1094   
## LibsSL.d    -0.01644    0.44783  -0.037   0.9707   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 398 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02474,    Adjusted R-squared:  0.01494 
## F-statistic: 2.524 on 4 and 398 DF,  p-value: 0.04049

# Action 22
lib.b22 <- lm(act22 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b22) 

## 
## Call:
## lm(formula = act22 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1351 -1.3415 -0.1351  1.7143  3.3279 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.5893     0.2639   2.233   0.0261 *
## LibsSC.d     -0.9172     0.3655  -2.509   0.0125 *
## LibsC.d      -0.2478     0.3423  -0.724   0.4696  
## LibsM.d      -0.3036     0.3047  -0.996   0.3197  
## LibsSL.d      0.5458     0.4184   1.305   0.1928  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.975 on 399 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03383,    Adjusted R-squared:  0.02414 
## F-statistic: 3.492 on 4 and 399 DF,  p-value: 0.008087

# Action 23
lib.b23 <- lm(act23 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b23) 

## 
## Call:
## lm(formula = act23 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6154 -2.1138  0.1846  2.1846  3.1846 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.61538    0.36271   1.697   0.0908 .
## LibsSC.d    -0.34709    0.50665  -0.685   0.4938  
## LibsC.d     -0.80000    0.45879  -1.744   0.0823 .
## LibsM.d     -0.50156    0.41625  -1.205   0.2292  
## LibsSL.d    -0.05538    0.58033  -0.095   0.9240  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.265 on 288 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.01368,    Adjusted R-squared:  -2.033e-05 
## F-statistic: 0.9985 on 4 and 288 DF,  p-value: 0.4087

# Action 24
lib.b24 <- lm(act24 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b24) 

## 
## Call:
## lm(formula = act24 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9189 -1.8727  0.1273  1.6453  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13793    0.27643  -0.499    0.618
## LibsSC.d     0.01066    0.39623   0.027    0.979
## LibsC.d     -0.36207    0.35297  -1.026    0.306
## LibsM.d     -0.09195    0.31920  -0.288    0.773
## LibsSL.d     0.05685    0.44294   0.128    0.898
## 
## Residual standard error: 2.105 on 411 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.004608,   Adjusted R-squared:  -0.005079 
## F-statistic: 0.4757 on 4 and 411 DF,  p-value: 0.7536

# Action 25
lib.b25 <- lm(act25 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b25) 

## 
## Call:
## lm(formula = act25 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3429 -0.8925  0.1167  1.5000  3.1167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1270     0.2310   4.879 1.49e-06 ***
## LibsSC.d     -1.2437     0.3307  -3.760 0.000193 ***
## LibsC.d      -0.6270     0.2938  -2.134 0.033383 *  
## LibsM.d      -0.2345     0.2673  -0.877 0.380715    
## LibsSL.d      0.2159     0.3865   0.559 0.576784    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 441 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.04037 
## F-statistic:  5.68 on 4 and 441 DF,  p-value: 0.0001824

# Action 26
lib.b26 <- lm(act26 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b26) 

## 
## Call:
## lm(formula = act26 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5686 -1.2581  0.4595  1.4314  2.0526 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.2581     0.3283   3.832 0.000165 ***
## LibsSC.d      0.2825     0.4450   0.635 0.526245    
## LibsC.d       0.3106     0.4162   0.746 0.456391    
## LibsM.d       0.3599     0.3812   0.944 0.346080    
## LibsSL.d     -0.3107     0.5325  -0.583 0.560184    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.828 on 222 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01216,    Adjusted R-squared:  -0.005638 
## F-statistic: 0.6832 on 4 and 222 DF,  p-value: 0.6042

# Action 27
lib.b27 <- lm(act27 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b27) 

## 
## Call:
## lm(formula = act27 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3871 -0.9483  0.2813  1.5577  2.5577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1778     0.2799   4.208 3.37e-05 ***
## LibsSC.d     -0.7355     0.3823  -1.924   0.0553 .  
## LibsC.d      -0.4590     0.3653  -1.257   0.2098    
## LibsM.d      -0.6266     0.3258  -1.924   0.0553 .  
## LibsSL.d      0.2093     0.4383   0.478   0.6333    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 314 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02704,    Adjusted R-squared:  0.01464 
## F-statistic: 2.182 on 4 and 314 DF,  p-value: 0.07092

# Action 28
lib.b28 <- lm(act28 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b28) 

## 
## Call:
## lm(formula = act28 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3214 -0.9728  0.2105  1.6786  2.2105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.00000    0.24604   4.064 5.89e-05 ***
## LibsSC.d    -0.21053    0.34488  -0.610    0.542    
## LibsC.d      0.32143    0.31649   1.016    0.310    
## LibsM.d     -0.02721    0.28841  -0.094    0.925    
## LibsSL.d     0.19355    0.40979   0.472    0.637    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 369 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.009436,   Adjusted R-squared:  -0.001302 
## F-statistic: 0.8787 on 4 and 369 DF,  p-value: 0.4767

# Action 29
lib.b29 <- lm(act29 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b29) 

## 
## Call:
## lm(formula = act29 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4828 -1.0893  0.0182  1.5172  2.0182 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.98182    0.23796   4.126 4.58e-05 ***
## LibsSC.d     0.10747    0.33502   0.321    0.749    
## LibsC.d      0.04228    0.30683   0.138    0.890    
## LibsM.d      0.16104    0.27894   0.577    0.564    
## LibsSL.d     0.50094    0.40499   1.237    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.765 on 365 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.005014,   Adjusted R-squared:  -0.00589 
## F-statistic: 0.4598 on 4 and 365 DF,  p-value: 0.7652

# Action 30
lib.b30 <- lm(act30 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, data = d)

summary(lib.b30) 

## 
## Call:
## lm(formula = act30 ~ LibsSC.d + LibsC.d + LibsM.d + LibsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1765 -1.0435  0.8235  1.8235  1.9875 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.17647    0.26271   4.478 1.02e-05 ***
## LibsSC.d    -0.03612    0.36162  -0.100    0.920    
## LibsC.d     -0.16397    0.33618  -0.488    0.626    
## LibsM.d     -0.13299    0.30745  -0.433    0.666    
## LibsSL.d    -0.03361    0.44128  -0.076    0.939    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.001091,   Adjusted R-squared:  -0.01036 
## F-statistic: 0.09525 on 4 and 349 DF,  p-value: 0.9839

Significantly higher than 0: 1, 2, 5, 6, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 22, 25, 26, 27, 28, 29, 30 Not different from 0: 3, 4, 7, 8, 13, 21, 23, 24 Significantly lower than 0: None

1. Condition Differences?

# Action 1
lib.c.b1 <- lm(act1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b1) 

## 
## Call:
## lm(formula = act1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.750 -2.200  0.250  1.765  3.800 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.6477     0.2665   2.431  0.01546 * 
## LibsSC.d         -1.1334     0.3774  -3.004  0.00282 **
## LibsC.d          -1.1266     0.3432  -3.282  0.00111 **
## LibsM.d          -0.7048     0.3101  -2.273  0.02353 * 
## LibsSL.d         -0.5743     0.4606  -1.247  0.21312   
## cond.c            0.2045     0.5329   0.384  0.70129   
## LibsSC.d:cond.c   0.4240     0.7547   0.562  0.57452   
## LibsC.d:cond.c   -0.3896     0.6865  -0.568  0.57063   
## LibsM.d:cond.c   -0.7881     0.6203  -1.271  0.20456   
## LibsSL.d:cond.c  -0.9668     0.9212  -1.049  0.29454   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04314,    Adjusted R-squared:  0.02353 
## F-statistic: 2.199 on 9 and 439 DF,  p-value: 0.02116

# Action 2
lib.c.b2 <- lm(act2 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b2) 

## 
## Call:
## lm(formula = act2 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -1.068  0.375  1.615  2.375 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.5625     0.2825   5.531 7.31e-08 ***
## LibsSC.d         -0.5839     0.3917  -1.491   0.1371    
## LibsC.d          -0.6742     0.3610  -1.868   0.0629 .  
## LibsM.d          -0.3702     0.3310  -1.119   0.2643    
## LibsSL.d         -0.5170     0.4664  -1.108   0.2686    
## cond.c           -0.1250     0.5650  -0.221   0.8251    
## LibsSC.d:cond.c  -0.1284     0.7834  -0.164   0.8699    
## LibsC.d:cond.c   -0.4015     0.7220  -0.556   0.5786    
## LibsM.d:cond.c   -0.2596     0.6619  -0.392   0.6952    
## LibsSL.d:cond.c   0.2159     0.9329   0.231   0.8171    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.812 on 280 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -0.006049 
## F-statistic: 0.8069 on 9 and 280 DF,  p-value: 0.6102

# Action 3
lib.c.b3 <- lm(act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b3) 

## 
## Call:
## lm(formula = act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9375  0.0625  1.6410  3.9394 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.11362    0.24645  -0.461   0.6450  
## LibsSC.d        -0.76679    0.35697  -2.148   0.0323 *
## LibsC.d         -0.30702    0.32170  -0.954   0.3404  
## LibsM.d          0.09941    0.28764   0.346   0.7298  
## LibsSL.d         0.45601    0.43455   1.049   0.2946  
## cond.c           1.05482    0.49289   2.140   0.0329 *
## LibsSC.d:cond.c -1.17278    0.71393  -1.643   0.1012  
## LibsC.d:cond.c  -0.99133    0.64341  -1.541   0.1241  
## LibsM.d:cond.c  -0.95823    0.57528  -1.666   0.0965 .
## LibsSL.d:cond.c -1.23960    0.86911  -1.426   0.1545  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03846,    Adjusted R-squared:  0.01852 
## F-statistic: 1.929 on 9 and 434 DF,  p-value: 0.04628

# Action 4
lib.c.b4 <- lm(act4 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b4) 

## 
## Call:
## lm(formula = act4 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8400 -1.8941  0.1059  2.0378  3.3488 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.43515    0.28756   1.513   0.1311  
## LibsSC.d        -0.23717    0.41933  -0.566   0.5720  
## LibsC.d         -0.70957    0.38633  -1.837   0.0671 .
## LibsM.d         -0.64809    0.33498  -1.935   0.0538 .
## LibsSL.d         0.02801    0.51207   0.055   0.9564  
## cond.c           0.80970    0.57512   1.408   0.1600  
## LibsSC.d:cond.c -0.84852    0.83865  -1.012   0.3123  
## LibsC.d:cond.c  -0.95853    0.77266  -1.241   0.2156  
## LibsM.d:cond.c  -1.02381    0.66996  -1.528   0.1273  
## LibsSL.d:cond.c -0.93601    1.02414  -0.914   0.3614  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.169 on 361 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0238, Adjusted R-squared:  -0.0005384 
## F-statistic: 0.9779 on 9 and 361 DF,  p-value: 0.4579

# Action 8
lib.c.b8 <- lm(act8 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b8) 

## 
## Call:
## lm(formula = act8 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.5107     0.2678   1.907 0.057116 .  
## LibsSC.d         -1.6658     0.3708  -4.492 8.87e-06 ***
## LibsC.d          -1.1409     0.3380  -3.376 0.000797 ***
## LibsM.d          -0.9510     0.3065  -3.103 0.002033 ** 
## LibsSL.d          0.1976     0.4335   0.456 0.648692    
## cond.c            0.6214     0.5356   1.160 0.246529    
## LibsSC.d:cond.c  -0.4650     0.7416  -0.627 0.530950    
## LibsC.d:cond.c   -1.6151     0.6759  -2.389 0.017264 *  
## LibsM.d:cond.c   -0.5116     0.6131  -0.835 0.404412    
## LibsSL.d:cond.c   0.2119     0.8670   0.244 0.807017    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

# Action 9
lib.c.b9 <- lm(act9 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b9) 

## 
## Call:
## lm(formula = act9 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.5893  0.0465  1.4286  4.3462 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.5774     0.2474   2.334 0.020048 *  
## LibsSC.d         -1.3681     0.3558  -3.845 0.000139 ***
## LibsC.d          -0.8060     0.3175  -2.538 0.011483 *  
## LibsM.d          -0.3716     0.2857  -1.301 0.193980    
## LibsSL.d         -0.4524     0.4262  -1.061 0.289126    
## cond.c           -0.0119     0.4948  -0.024 0.980815    
## LibsSC.d:cond.c   1.1228     0.7117   1.578 0.115368    
## LibsC.d:cond.c   -0.3523     0.6351  -0.555 0.579351    
## LibsM.d:cond.c    0.1685     0.5713   0.295 0.768126    
## LibsSL.d:cond.c  -0.4048     0.8525  -0.475 0.635164    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.964 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.05102,    Adjusted R-squared:  0.03157 
## F-statistic: 2.623 on 9 and 439 DF,  p-value: 0.00582

# Action 10
lib.c.b10 <- lm(act10 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b10)

## 
## Call:
## lm(formula = act10 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3750 -1.9896  0.0217  1.8350  3.6579 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.7119     0.2605   2.733 0.006521 ** 
## LibsSC.d         -1.3242     0.3638  -3.639 0.000304 ***
## LibsC.d          -0.9431     0.3312  -2.847 0.004604 ** 
## LibsM.d          -0.6346     0.2994  -2.120 0.034574 *  
## LibsSL.d          0.4756     0.4249   1.119 0.263546    
## cond.c            0.5048     0.5210   0.969 0.333083    
## LibsSC.d:cond.c  -0.5961     0.7277  -0.819 0.413146    
## LibsC.d:cond.c   -0.9238     0.6625  -1.394 0.163842    
## LibsM.d:cond.c   -0.6803     0.5988  -1.136 0.256482    
## LibsSL.d:cond.c  -0.1298     0.8498  -0.153 0.878642    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.08 on 467 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05853,    Adjusted R-squared:  0.04038 
## F-statistic: 3.226 on 9 and 467 DF,  p-value: 0.000826

# Action 11
lib.c.b11 <- lm(act11 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b11)

## 
## Call:
## lm(formula = act11 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -1.0299  0.3452  1.5429  2.5735 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.65476    0.30627   5.403 1.33e-07 ***
## LibsSC.d        -0.92393    0.40549  -2.279  0.02339 *  
## LibsC.d         -0.80952    0.38384  -2.109  0.03576 *  
## LibsM.d         -0.92660    0.34558  -2.681  0.00773 ** 
## LibsSL.d        -0.16190    0.49199  -0.329  0.74232    
## cond.c          -0.02381    0.61254  -0.039  0.96902    
## LibsSC.d:cond.c  0.40214    0.81099   0.496  0.62035    
## LibsC.d:cond.c  -0.75238    0.76769  -0.980  0.32784    
## LibsM.d:cond.c  -0.57957    0.69116  -0.839  0.40238    
## LibsSL.d:cond.c -0.56190    0.98398  -0.571  0.56838    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 304 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.0581, Adjusted R-squared:  0.03021 
## F-statistic: 2.083 on 9 and 304 DF,  p-value: 0.03077

# Action 12
lib.c.b12 <- lm(act12 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b12) 

## 
## Call:
## lm(formula = act12 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1304 -1.8929 -0.1098  1.8696  4.1071 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.7819     0.2977   2.626 0.008993 ** 
## LibsSC.d         -1.6286     0.4119  -3.954  9.2e-05 ***
## LibsC.d          -1.3505     0.3753  -3.598 0.000363 ***
## LibsM.d          -0.7862     0.3434  -2.290 0.022599 *  
## LibsSL.d         -0.3936     0.5215  -0.755 0.450814    
## cond.c            0.6971     0.5955   1.171 0.242487    
## LibsSC.d:cond.c  -0.1762     0.8238  -0.214 0.830788    
## LibsC.d:cond.c   -1.1677     0.7507  -1.556 0.120662    
## LibsM.d:cond.c   -0.9253     0.6868  -1.347 0.178699    
## LibsSL.d:cond.c  -0.2736     1.0430  -0.262 0.793237    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.149 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0624, Adjusted R-squared:  0.0399 
## F-statistic: 2.773 on 9 and 375 DF,  p-value: 0.003732

# Action 13
lib.c.b13 <- lm(act13 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b13) 

## 
## Call:
## lm(formula = act13 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.33922    0.26014   1.304   0.1929  
## LibsSC.d        -0.88679    0.37086  -2.391   0.0172 *
## LibsC.d         -0.50899    0.33930  -1.500   0.1343  
## LibsM.d         -0.41897    0.30107  -1.392   0.1648  
## LibsSL.d        -0.10112    0.44280  -0.228   0.8195  
## cond.c          -0.14510    0.52028  -0.279   0.7805  
## LibsSC.d:cond.c -0.18081    0.74172  -0.244   0.8075  
## LibsC.d:cond.c  -0.73104    0.67860  -1.077   0.2820  
## LibsM.d:cond.c   0.48043    0.60214   0.798   0.4254  
## LibsSL.d:cond.c -0.04538    0.88559  -0.051   0.9592  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

# Action 14
lib.c.b14 <- lm(act14 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b14) 

## 
## Call:
## lm(formula = act14 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.3731  0.1429  1.6269  3.1429 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.9946     0.3321   2.995  0.00299 **
## LibsSC.d         -0.5444     0.4656  -1.169  0.24326   
## LibsC.d          -0.6601     0.4259  -1.550  0.12230   
## LibsM.d          -0.5703     0.3780  -1.509  0.13252   
## LibsSL.d         -0.8160     0.5610  -1.455  0.14693   
## cond.c            0.5109     0.6642   0.769  0.44247   
## LibsSC.d:cond.c   0.1541     0.9312   0.165  0.86868   
## LibsC.d:cond.c   -0.8049     0.8517  -0.945  0.34551   
## LibsM.d:cond.c   -0.4086     0.7560  -0.540  0.58932   
## LibsSL.d:cond.c   0.1320     1.1220   0.118  0.90644   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.02046,    Adjusted R-squared:  -0.01125 
## F-statistic: 0.6452 on 9 and 278 DF,  p-value: 0.7579

# Action 15
lib.c.b15 <- lm(act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b15) 

## 
## Call:
## lm(formula = act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.8050     0.3064   2.627  0.00896 **
## LibsSC.d         -1.1803     0.4070  -2.900  0.00395 **
## LibsC.d          -0.6833     0.3809  -1.794  0.07362 . 
## LibsM.d          -0.7060     0.3452  -2.045  0.04152 * 
## LibsSL.d         -0.4795     0.4741  -1.011  0.31248   
## cond.c           -1.5100     0.6128  -2.464  0.01418 * 
## LibsSC.d:cond.c   1.8363     0.8139   2.256  0.02463 * 
## LibsC.d:cond.c    1.3091     0.7617   1.719  0.08652 . 
## LibsM.d:cond.c    1.4159     0.6904   2.051  0.04097 * 
## LibsSL.d:cond.c   1.9257     0.9482   2.031  0.04298 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

# Action 16
lib.c.b16 <- lm(act16 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b16) 

## 
## Call:
## lm(formula = act16 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2105 -1.1000  0.2034  1.4500  2.4500 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.0833     0.2848   3.803 0.000171 ***
## LibsSC.d         -0.5226     0.3923  -1.332 0.183762    
## LibsC.d          -0.1089     0.3584  -0.304 0.761494    
## LibsM.d          -0.1190     0.3246  -0.367 0.714085    
## LibsSL.d         -0.1281     0.4591  -0.279 0.780436    
## cond.c            0.1667     0.5697   0.293 0.770048    
## LibsSC.d:cond.c  -0.1452     0.7846  -0.185 0.853266    
## LibsC.d:cond.c    0.2528     0.7168   0.353 0.724524    
## LibsM.d:cond.c    0.1048     0.6493   0.161 0.871921    
## LibsSL.d:cond.c  -0.6772     0.9181  -0.738 0.461301    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.843 on 322 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.01422,    Adjusted R-squared:  -0.01334 
## F-statistic: 0.5159 on 9 and 322 DF,  p-value: 0.8629

# Action 17
lib.c.b17 <- lm(act17 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b17) 

## 
## Call:
## lm(formula = act17 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.56061    0.25825   2.171   0.0305 *
## LibsSC.d        -0.21753    0.36342  -0.599   0.5498  
## LibsC.d         -0.31218    0.33599  -0.929   0.3534  
## LibsM.d         -0.09033    0.29947  -0.302   0.7631  
## LibsSL.d         0.72154    0.43238   1.669   0.0959 .
## cond.c           0.21212    0.51649   0.411   0.6815  
## LibsSC.d:cond.c  0.61688    0.72684   0.849   0.3965  
## LibsC.d:cond.c  -1.04860    0.67198  -1.560   0.1194  
## LibsM.d:cond.c  -0.51070    0.59894  -0.853   0.3943  
## LibsSL.d:cond.c  0.65216    0.86475   0.754   0.4512  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

# Action 18
lib.c.b18 <- lm(act18 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b18) 

## 
## Call:
## lm(formula = act18 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.1538  0.2031  1.4211  3.0400 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.93176    0.24602   3.787  0.00018 ***
## LibsSC.d        -0.75533    0.35401  -2.134  0.03358 *  
## LibsC.d         -0.44784    0.32683  -1.370  0.17150    
## LibsM.d         -0.11774    0.29157  -0.404  0.68660    
## LibsSL.d        -0.16156    0.43113  -0.375  0.70809    
## cond.c           0.44417    0.49204   0.903  0.36731    
## LibsSC.d:cond.c -0.01131    0.70802  -0.016  0.98726    
## LibsC.d:cond.c  -0.63423    0.65367  -0.970  0.33260    
## LibsM.d:cond.c  -0.47846    0.58314  -0.820  0.41250    
## LibsSL.d:cond.c -0.34821    0.86227  -0.404  0.68659    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.85 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.0227, Adjusted R-squared:  -0.002867 
## F-statistic: 0.8879 on 9 and 344 DF,  p-value: 0.5362

# Action 19
lib.c.b19 <- lm(act19 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b19) 

## 
## Call:
## lm(formula = act19 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.40476    0.32028   4.386 1.67e-05 ***
## LibsSC.d        -0.50430    0.42777  -1.179    0.239    
## LibsC.d         -0.26587    0.39834  -0.667    0.505    
## LibsM.d         -0.15618    0.36243  -0.431    0.667    
## LibsSL.d        -0.11081    0.47161  -0.235    0.814    
## cond.c          -0.52381    0.64056  -0.818    0.414    
## LibsSC.d:cond.c  0.57474    0.85554   0.672    0.502    
## LibsC.d:cond.c  -0.36508    0.79668  -0.458    0.647    
## LibsM.d:cond.c   0.05943    0.72486   0.082    0.935    
## LibsSL.d:cond.c  0.39744    0.94322   0.421    0.674    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

# Action 20
lib.c.b20 <- lm(act20 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b20) 

## 
## Call:
## lm(formula = act20 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.589 -1.054  0.411  1.411  2.308 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.16476    0.26428   4.407 1.44e-05 ***
## LibsSC.d         0.02437    0.36203   0.067    0.946    
## LibsC.d         -0.01991    0.34102  -0.058    0.953    
## LibsM.d          0.15654    0.30821   0.508    0.612    
## LibsSL.d        -0.17575    0.43370  -0.405    0.686    
## cond.c          -0.71048    0.52855  -1.344    0.180    
## LibsSC.d:cond.c  0.13222    0.72407   0.183    0.855    
## LibsC.d:cond.c   0.31267    0.68204   0.458    0.647    
## LibsM.d:cond.c   0.17501    0.61642   0.284    0.777    
## LibsSL.d:cond.c  0.11707    0.86740   0.135    0.893    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.786 on 314 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.028,  Adjusted R-squared:  0.0001354 
## F-statistic: 1.005 on 9 and 314 DF,  p-value: 0.4359

# Action 21
lib.c.b21 <- lm(act21 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b21) 

## 
## Call:
## lm(formula = act21 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5556 -1.8723  0.1609  1.9286  3.9286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.449653   0.270007   1.665  0.09664 . 
## LibsSC.d        -1.060997   0.377403  -2.811  0.00518 **
## LibsC.d         -0.742054   0.355137  -2.089  0.03731 * 
## LibsM.d         -0.505112   0.313879  -1.609  0.10836   
## LibsSL.d        -0.021875   0.450987  -0.049  0.96134   
## cond.c           0.211806   0.540014   0.392  0.69511   
## LibsSC.d:cond.c  0.422648   0.754806   0.560  0.57584   
## LibsC.d:cond.c   0.117678   0.710273   0.166  0.86849   
## LibsM.d:cond.c  -0.000886   0.627759  -0.001  0.99887   
## LibsSL.d:cond.c -0.134028   0.901974  -0.149  0.88195   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.067 on 393 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.03105,    Adjusted R-squared:  0.008857 
## F-statistic: 1.399 on 9 and 393 DF,  p-value: 0.1863

# Action 23
lib.c.b23 <- lm(act23 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b23) 

## 
## Call:
## lm(formula = act23 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.222 -2.250  0.125  1.870  3.297 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.5027     0.3685   1.364   0.1735  
## LibsSC.d         -0.2349     0.5107  -0.460   0.6460  
## LibsC.d          -0.6692     0.4649  -1.439   0.1511  
## LibsM.d          -0.3973     0.4213  -0.943   0.3465  
## LibsSL.d          0.2021     0.5985   0.338   0.7358  
## cond.c           -1.2554     0.7369  -1.704   0.0896 .
## LibsSC.d:cond.c   1.2911     1.0214   1.264   0.2072  
## LibsC.d:cond.c    0.9939     0.9298   1.069   0.2861  
## LibsM.d:cond.c    0.8412     0.8426   0.998   0.3190  
## LibsSL.d:cond.c   2.2902     1.1970   1.913   0.0567 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.264 on 283 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.03197,    Adjusted R-squared:  0.001189 
## F-statistic: 1.039 on 9 and 283 DF,  p-value: 0.4092

# Action 24
lib.c.b24 <- lm(act24 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b24) 

## 
## Call:
## lm(formula = act24 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)     -0.15441    0.28066  -0.550    0.583
## LibsSC.d         0.02269    0.40087   0.057    0.955
## LibsC.d         -0.31440    0.35841  -0.877    0.381
## LibsM.d         -0.09413    0.32301  -0.291    0.771
## LibsSL.d         0.06502    0.45059   0.144    0.885
## cond.c          -0.19118    0.56133  -0.341    0.734
## LibsSC.d:cond.c  0.26107    0.80174   0.326    0.745
## LibsC.d:cond.c  -0.16750    0.71682  -0.234    0.815
## LibsM.d:cond.c  -0.45808    0.64601  -0.709    0.479
## LibsSL.d:cond.c  0.10330    0.90117   0.115    0.909
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

# Action 25
lib.c.b25 <- lm(act25 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b25) 

## 
## Call:
## lm(formula = act25 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -0.9307  0.1529  1.3778  3.3333 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.13357    0.23083   4.911 1.28e-06 ***
## LibsSC.d        -1.26993    0.33133  -3.833 0.000145 ***
## LibsC.d         -0.62070    0.29435  -2.109 0.035540 *  
## LibsM.d         -0.24469    0.26732  -0.915 0.360505    
## LibsSL.d         0.28310    0.39135   0.723 0.469834    
## cond.c           0.82964    0.46165   1.797 0.073012 .  
## LibsSC.d:cond.c -0.43570    0.66265  -0.658 0.511203    
## LibsC.d:cond.c  -1.04835    0.58871  -1.781 0.075646 .  
## LibsM.d:cond.c  -0.91327    0.53463  -1.708 0.088307 .  
## LibsSL.d:cond.c -0.09154    0.78271  -0.117 0.906949    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 436 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.06134,    Adjusted R-squared:  0.04197 
## F-statistic: 3.166 on 9 and 436 DF,  p-value: 0.001022

# Action 28
lib.c.b28 <- lm(act28 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b28) 

## 
## Call:
## lm(formula = act28 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.018342   0.249588   4.080 5.53e-05 ***
## LibsSC.d        -0.190179   0.348205  -0.546    0.585    
## LibsC.d          0.303086   0.319358   0.949    0.343    
## LibsM.d         -0.039803   0.291655  -0.136    0.892    
## LibsSL.d         0.200956   0.419089   0.480    0.632    
## cond.c           0.224185   0.499176   0.449    0.654    
## LibsSC.d:cond.c -1.106319   0.696410  -1.589    0.113    
## LibsC.d:cond.c  -0.486090   0.638717  -0.761    0.447    
## LibsM.d:cond.c  -0.036336   0.583310  -0.062    0.950    
## LibsSL.d:cond.c  0.003885   0.838178   0.005    0.996    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

# Action 30
lib.c.b30 <- lm(act30 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * cond.c, data = d)

summary(lib.c.b30) 

## 
## Call:
## lm(formula = act30 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.18260    0.26509   4.461 1.11e-05 ***
## LibsSC.d        -0.11010    0.36457  -0.302    0.763    
## LibsC.d         -0.15861    0.33863  -0.468    0.640    
## LibsM.d         -0.13848    0.30945  -0.448    0.655    
## LibsSL.d        -0.02635    0.44551  -0.059    0.953    
## cond.c           0.08934    0.53018   0.169    0.866    
## LibsSC.d:cond.c  1.01566    0.72914   1.393    0.165    
## LibsC.d:cond.c  -0.31914    0.67727  -0.471    0.638    
## LibsM.d:cond.c  -0.17758    0.61890  -0.287    0.774    
## LibsSL.d:cond.c  0.09816    0.89101   0.110    0.912    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

Climate > Control: 3 Control > Climate: 15

a. Means for condition diffs

# Action 3
aggregate(d$act3[d$ideology == "Liberal"], list(d$cond[d$ideology == "Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1 climate  0.41
## 2    ctrl -0.64

summary(lm(d$act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * ctrl.d, data = d)) # Opposed in control condition

## 
## Call:
## lm(formula = d$act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9375  0.0625  1.6410  3.9394 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.6410     0.3219  -1.991   0.0471 *
## LibsSC.d         -0.1804     0.4979  -0.362   0.7173  
## LibsC.d           0.1886     0.4470   0.422   0.6732  
## LibsM.d           0.5785     0.3817   1.516   0.1303  
## LibsSL.d          1.0758     0.5285   2.036   0.0424 *
## ctrl.d            1.0548     0.4929   2.140   0.0329 *
## LibsSC.d:ctrl.d  -1.1728     0.7139  -1.643   0.1012  
## LibsC.d:ctrl.d   -0.9913     0.6434  -1.541   0.1241  
## LibsM.d:ctrl.d   -0.9582     0.5753  -1.666   0.0965 .
## LibsSL.d:ctrl.d  -1.2396     0.8691  -1.426   0.1545  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03846,    Adjusted R-squared:  0.01852 
## F-statistic: 1.929 on 9 and 434 DF,  p-value: 0.04628

summary(lm(d$act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * clim.d, data = d)) # Neutral in climate condition

## 
## Call:
## lm(formula = d$act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9375  0.0625  1.6410  3.9394 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.4138     0.3733   1.109  0.26824   
## LibsSC.d         -1.3532     0.5116  -2.645  0.00847 **
## LibsC.d          -0.8027     0.4628  -1.734  0.08354 . 
## LibsM.d          -0.3797     0.4304  -0.882  0.37816   
## LibsSL.d         -0.1638     0.6900  -0.237  0.81246   
## clim.d           -1.0548     0.4929  -2.140  0.03291 * 
## LibsSC.d:clim.d   1.1728     0.7139   1.643  0.10117   
## LibsC.d:clim.d    0.9913     0.6434   1.541  0.12411   
## LibsM.d:clim.d    0.9582     0.5753   1.666  0.09650 . 
## LibsSL.d:clim.d   1.2396     0.8691   1.426  0.15450   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03846,    Adjusted R-squared:  0.01852 
## F-statistic: 1.929 on 9 and 434 DF,  p-value: 0.04628

# Action 15
aggregate(d$act15[d$ideology == "Liberal"], list(d$cond[d$ideology == "Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1    x
## 1 climate 0.05
## 2    ctrl 1.56

summary(lm(d$act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * ctrl.d, data = d)) # Supported in control condition

## 
## Call:
## lm(formula = d$act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.5600     0.4085   3.819 0.000157 ***
## LibsSC.d         -2.0985     0.5722  -3.668 0.000280 ***
## LibsC.d          -1.3378     0.5318  -2.516 0.012300 *  
## LibsM.d          -1.4139     0.4624  -3.058 0.002388 ** 
## LibsSL.d         -1.4424     0.6421  -2.246 0.025274 *  
## ctrl.d           -1.5100     0.6128  -2.464 0.014184 *  
## LibsSC.d:ctrl.d   1.8363     0.8139   2.256 0.024633 *  
## LibsC.d:ctrl.d    1.3091     0.7617   1.719 0.086517 .  
## LibsM.d:ctrl.d    1.4159     0.6904   2.051 0.040967 *  
## LibsSL.d:ctrl.d   1.9257     0.9482   2.031 0.042977 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

summary(lm(d$act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * clim.d, data = d)) # Neutral in climate condition

## 
## Call:
## lm(formula = d$act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.050000   0.456754   0.109   0.9129  
## LibsSC.d        -0.262121   0.578846  -0.453   0.6509  
## LibsC.d         -0.028723   0.545344  -0.053   0.9580  
## LibsM.d          0.001948   0.512652   0.004   0.9970  
## LibsSL.d         0.483333   0.697703   0.693   0.4889  
## clim.d           1.510000   0.612799   2.464   0.0142 *
## LibsSC.d:clim.d -1.836340   0.813905  -2.256   0.0246 *
## LibsC.d:clim.d  -1.309054   0.761709  -1.719   0.0865 .
## LibsM.d:clim.d  -1.415881   0.690357  -2.051   0.0410 *
## LibsSL.d:clim.d -1.925686   0.948224  -2.031   0.0430 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

2. Gender effects?

# Action 1
lib.g.b1 <- lm(act1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b1) 

## 
## Call:
## lm(formula = act1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8261  0.2263  2.1538  4.1538 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.6067     0.3143   1.930  0.05420 . 
## LibsSC.d          -1.0031     0.4189  -2.395  0.01704 * 
## LibsC.d           -1.3120     0.3975  -3.301  0.00104 **
## LibsM.d           -0.4698     0.3631  -1.294  0.19642   
## LibsSL.d          -0.7806     0.5019  -1.555  0.12062   
## gend.mf           -0.1467     0.6285  -0.233  0.81560   
## LibsSC.d:gend.mf   0.5918     0.8377   0.706  0.48029   
## LibsC.d:gend.mf   -0.7504     0.7950  -0.944  0.34571   
## LibsM.d:gend.mf    0.8729     0.7262   1.202  0.23002   
## LibsSL.d:gend.mf  -1.5055     1.0038  -1.500  0.13439   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.135 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.05646,    Adjusted R-squared:  0.03703 
## F-statistic: 2.906 on 9 and 437 DF,  p-value: 0.002374

# Action 2
lib.g.b2 <- lm(act2 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b2) 

## 
## Call:
## lm(formula = act2 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4375 -1.2184  0.5263  1.5625  2.6087 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.7188     0.3266   5.262 2.85e-07 ***
## LibsSC.d          -0.6856     0.4238  -1.618    0.107    
## LibsC.d           -0.9398     0.4017  -2.339    0.020 *  
## LibsM.d           -0.5296     0.3854  -1.374    0.171    
## LibsSL.d          -0.7771     0.5123  -1.517    0.130    
## gend.mf            0.5625     0.6533   0.861    0.390    
## LibsSC.d:gend.mf   0.3186     0.8475   0.376    0.707    
## LibsC.d:gend.mf   -1.3379     0.8034  -1.665    0.097 .  
## LibsM.d:gend.mf   -0.6209     0.7708  -0.805    0.421    
## LibsSL.d:gend.mf  -0.9458     1.0247  -0.923    0.357    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.03738,    Adjusted R-squared:  0.006217 
## F-statistic: 1.199 on 9 and 278 DF,  p-value: 0.2951

# Action 3
lib.g.b3 <- lm(act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b3) 

## 
## Call:
## lm(formula = act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.8390 -0.0368  1.4167  4.1579 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.1122     0.2773  -0.405    0.686  
## LibsSC.d          -0.6841     0.3847  -1.778    0.076 .
## LibsC.d           -0.3016     0.3597  -0.839    0.402  
## LibsM.d            0.0455     0.3257   0.140    0.889  
## LibsSL.d           0.3539     0.4703   0.753    0.452  
## gend.mf            0.3356     0.5547   0.605    0.546  
## LibsSC.d:gend.mf   0.3876     0.7694   0.504    0.615  
## LibsC.d:gend.mf   -0.3227     0.7194  -0.449    0.654  
## LibsM.d:gend.mf   -0.5425     0.6514  -0.833    0.405  
## LibsSL.d:gend.mf  -1.0189     0.9405  -1.083    0.279  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.03535,    Adjusted R-squared:  0.01525 
## F-statistic: 1.759 on 9 and 432 DF,  p-value: 0.07405

# Action 4
lib.g.b4 <- lm(act4 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b4) 

## 
## Call:
## lm(formula = act4 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.944 -1.904  0.200  2.056  3.762 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.4554     0.3187   1.429   0.1540  
## LibsSC.d          -0.2419     0.4414  -0.548   0.5841  
## LibsC.d           -0.8844     0.4246  -2.083   0.0380 *
## LibsM.d           -0.6730     0.3816  -1.764   0.0787 .
## LibsSL.d          -0.1331     0.5336  -0.250   0.8031  
## gend.mf            0.3393     0.6375   0.532   0.5949  
## LibsSC.d:gend.mf   0.0163     0.8827   0.018   0.9853  
## LibsC.d:gend.mf   -1.0050     0.8492  -1.184   0.2374  
## LibsM.d:gend.mf   -0.3746     0.7632  -0.491   0.6239  
## LibsSL.d:gend.mf  -1.5837     1.0671  -1.484   0.1387  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.17 on 359 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02849,    Adjusted R-squared:  0.004137 
## F-statistic:  1.17 on 9 and 359 DF,  p-value: 0.3132

# Action 5
lib.g.b5 <- lm(act5 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b5) 

## 
## Call:
## lm(formula = act5 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3000 -1.2128  0.4615  1.7000  2.7000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.4833     0.3210   4.621 5.78e-06 ***
## LibsSC.d          -0.7634     0.4188  -1.823   0.0694 .  
## LibsC.d           -0.6077     0.3947  -1.540   0.1247    
## LibsM.d           -0.3969     0.3829  -1.037   0.3008    
## LibsSL.d          -1.1026     0.5092  -2.165   0.0312 *  
## gend.mf            0.3667     0.6419   0.571   0.5683    
## LibsSC.d:gend.mf  -0.8499     0.8376  -1.015   0.3111    
## LibsC.d:gend.mf   -1.0410     0.7894  -1.319   0.1883    
## LibsM.d:gend.mf   -0.3173     0.7659  -0.414   0.6790    
## LibsSL.d:gend.mf  -0.5282     1.0183  -0.519   0.6044    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 285 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  -0.0005149 
## F-statistic: 0.9832 on 9 and 285 DF,  p-value: 0.454

# Action 6
lib.g.b6 <- lm(act6 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b6) 

## 
## Call:
## lm(formula = act6 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5455 -1.2424  0.5667  1.5667  2.6667 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.5828     0.2969   5.331 1.77e-07 ***
## LibsSC.d          -0.7581     0.3902  -1.943   0.0528 .  
## LibsC.d           -0.3261     0.3671  -0.888   0.3750    
## LibsM.d           -0.2245     0.3484  -0.644   0.5197    
## LibsSL.d          -0.6434     0.4657  -1.381   0.1680    
## gend.mf            0.6807     0.5938   1.146   0.2525    
## LibsSC.d:gend.mf  -1.0919     0.7803  -1.399   0.1626    
## LibsC.d:gend.mf   -1.0340     0.7342  -1.408   0.1599    
## LibsM.d:gend.mf   -0.4596     0.6968  -0.660   0.5100    
## LibsSL.d:gend.mf  -1.8928     0.9314  -2.032   0.0429 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 347 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -1.033e-06 
## F-statistic:     1 on 9 and 347 DF,  p-value: 0.4397

# Action 7
lib.g.b7 <- lm(act7 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b7) 

## 
## Call:
## lm(formula = act7 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8433  0.1567  1.6364  3.6364 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.04318    0.31356   0.138    0.891
## LibsSC.d         -0.15487    0.42395  -0.365    0.715
## LibsC.d          -0.46293    0.40681  -1.138    0.256
## LibsM.d          -0.14480    0.36346  -0.398    0.691
## LibsSL.d          0.07460    0.51431   0.145    0.885
## gend.mf           0.31364    0.62711   0.500    0.617
## LibsSC.d:gend.mf  0.09156    0.84790   0.108    0.914
## LibsC.d:gend.mf  -0.74688    0.81362  -0.918    0.359
## LibsM.d:gend.mf  -0.20343    0.72692  -0.280    0.780
## LibsSL.d:gend.mf -1.43808    1.02862  -1.398    0.163
## 
## Residual standard error: 2.097 on 403 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  -0.006717 
## F-statistic: 0.6946 on 9 and 403 DF,  p-value: 0.714

# Action 8
lib.g.b8 <- lm(act8 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b8) 

## 
## Call:
## lm(formula = act8 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9286 -1.9091 -0.2143  1.6875  4.5319 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.56122    0.32120   1.747 0.081243 .  
## LibsSC.d         -1.48627    0.42206  -3.521 0.000471 ***
## LibsC.d          -1.29783    0.39665  -3.272 0.001147 ** 
## LibsM.d          -0.94462    0.36662  -2.577 0.010284 *  
## LibsSL.d         -0.14239    0.49539  -0.287 0.773907    
## gend.mf           0.30612    0.64240   0.477 0.633917    
## LibsSC.d:gend.mf  0.90761    0.84412   1.075 0.282829    
## LibsC.d:gend.mf  -0.40434    0.79330  -0.510 0.610509    
## LibsM.d:gend.mf  -0.09252    0.73325  -0.126 0.899642    
## LibsSL.d:gend.mf -1.32560    0.99079  -1.338 0.181569    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 469 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.07584,    Adjusted R-squared:  0.05811 
## F-statistic: 4.276 on 9 and 469 DF,  p-value: 2.361e-05

# Action 9
lib.g.b9 <- lm(act9 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b9) 

## 
## Call:
## lm(formula = act9 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5882 -1.5882  0.1481  1.5417  3.8261 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.58135    0.27883   2.085 0.037654 *  
## LibsSC.d         -1.31872    0.38234  -3.449 0.000617 ***
## LibsC.d          -0.80126    0.35662  -2.247 0.025151 *  
## LibsM.d          -0.41399    0.32567  -1.271 0.204329    
## LibsSL.d         -0.62491    0.45435  -1.375 0.169709    
## gend.mf           0.01377    0.55767   0.025 0.980316    
## LibsSC.d:gend.mf -0.19121    0.76468  -0.250 0.802668    
## LibsC.d:gend.mf   0.12975    0.71324   0.182 0.855732    
## LibsM.d:gend.mf  -0.08182    0.65133  -0.126 0.900088    
## LibsSL.d:gend.mf -1.01756    0.90869  -1.120 0.263414    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.97 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.04187,    Adjusted R-squared:  0.02214 
## F-statistic: 2.122 on 9 and 437 DF,  p-value: 0.02658

# Action 10
lib.g.b10 <- lm(act10 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b10) 

## 
## Call:
## lm(formula = act10 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5862 -2.0719  0.1948  1.5714  3.7778 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.9330     0.2978   3.133 0.001840 ** 
## LibsSC.d          -1.4741     0.3987  -3.697 0.000244 ***
## LibsC.d           -1.2447     0.3752  -3.317 0.000980 ***
## LibsM.d           -0.8427     0.3449  -2.444 0.014914 *  
## LibsSL.d          -0.1899     0.4823  -0.394 0.693876    
## gend.mf            1.0089     0.5956   1.694 0.090959 .  
## LibsSC.d:gend.mf  -0.5355     0.7975  -0.672 0.502227    
## LibsC.d:gend.mf   -1.2427     0.7505  -1.656 0.098412 .  
## LibsM.d:gend.mf   -0.9721     0.6898  -1.409 0.159396    
## LibsSL.d:gend.mf  -2.6951     0.9645  -2.794 0.005415 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 466 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07046,    Adjusted R-squared:  0.05251 
## F-statistic: 3.925 on 9 and 466 DF,  p-value: 7.905e-05

# Action 11
lib.g.b11 <- lm(act11 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b11) 

## 
## Call:
## lm(formula = act11 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5625 -1.0000  0.2834  1.4375  2.7778 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.7294     0.3136   5.514 7.54e-08 ***
## LibsSC.d          -0.9365     0.4128  -2.269  0.02400 *  
## LibsC.d           -1.0021     0.3980  -2.518  0.01232 *  
## LibsM.d           -1.0128     0.3635  -2.786  0.00567 ** 
## LibsSL.d          -0.2339     0.5270  -0.444  0.65753    
## gend.mf            0.3874     0.6273   0.618  0.53735    
## LibsSC.d:gend.mf   0.7541     0.8256   0.913  0.36180    
## LibsC.d:gend.mf   -0.9328     0.7959  -1.172  0.24213    
## LibsM.d:gend.mf   -0.4491     0.7270  -0.618  0.53721    
## LibsSL.d:gend.mf  -0.5213     1.0540  -0.495  0.62124    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 302 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.05336,    Adjusted R-squared:  0.02515 
## F-statistic: 1.891 on 9 and 302 DF,  p-value: 0.05276

# Action 12
lib.g.b12 <- lm(act12 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b12)

## 
## Call:
## lm(formula = act12 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5625 -1.8571 -0.1345  1.8655  4.3043 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.1240     0.3493   3.218  0.00141 ** 
## LibsSC.d          -1.8772     0.4537  -4.137 4.35e-05 ***
## LibsC.d           -1.9553     0.4338  -4.507 8.80e-06 ***
## LibsM.d           -1.2804     0.4017  -3.188  0.00156 ** 
## LibsSL.d          -0.9427     0.5533  -1.704  0.08922 .  
## gend.mf            1.4187     0.6986   2.031  0.04300 *  
## LibsSC.d:gend.mf  -0.6395     0.9075  -0.705  0.48145    
## LibsC.d:gend.mf   -2.3648     0.8676  -2.726  0.00672 ** 
## LibsM.d:gend.mf   -2.0005     0.8034  -2.490  0.01320 *  
## LibsSL.d:gend.mf  -2.1812     1.1065  -1.971  0.04944 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.129 on 373 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.08175,    Adjusted R-squared:  0.0596 
## F-statistic:  3.69 on 9 and 373 DF,  p-value: 0.0001907

# Action 13
lib.g.b13 <- lm(act13 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b13) 

## 
## Call:
## lm(formula = act13 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.8095  0.1884  1.9558  3.3636 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.4771     0.3115   1.532   0.1263  
## LibsSC.d          -0.7769     0.4129  -1.882   0.0606 .
## LibsC.d           -0.7235     0.3981  -1.817   0.0699 .
## LibsM.d           -0.6290     0.3571  -1.761   0.0789 .
## LibsSL.d          -0.3546     0.4895  -0.724   0.4692  
## gend.mf            0.4743     0.6229   0.761   0.4468  
## LibsSC.d:gend.mf   1.3071     0.8258   1.583   0.1142  
## LibsC.d:gend.mf   -0.5902     0.7963  -0.741   0.4590  
## LibsM.d:gend.mf   -0.7358     0.7143  -1.030   0.3035  
## LibsSL.d:gend.mf  -1.4466     0.9790  -1.478   0.1402  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.04764,    Adjusted R-squared:  0.0278 
## F-statistic: 2.401 on 9 and 432 DF,  p-value: 0.01156

# Action 14
lib.g.b14 <- lm(act14 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b14) 

## 
## Call:
## lm(formula = act14 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8235 -1.2778  0.2195  1.5455  3.7222 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        1.1023     0.3588   3.072  0.00234 **
## LibsSC.d          -0.5601     0.4824  -1.161  0.24665   
## LibsC.d           -1.0731     0.4583  -2.342  0.01990 * 
## LibsM.d           -0.7144     0.4138  -1.727  0.08533 . 
## LibsSL.d          -1.2451     0.5887  -2.115  0.03532 * 
## gend.mf            0.7045     0.7176   0.982  0.32703   
## LibsSC.d:gend.mf  -0.1419     0.9648  -0.147  0.88319   
## LibsC.d:gend.mf   -2.2073     0.9165  -2.408  0.01668 * 
## LibsM.d:gend.mf   -0.8552     0.8275  -1.033  0.30229   
## LibsSL.d:gend.mf  -2.1331     1.1774  -1.812  0.07111 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.017 on 276 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.04977,    Adjusted R-squared:  0.01878 
## F-statistic: 1.606 on 9 and 276 DF,  p-value: 0.1132

# Action 15
lib.g.b15 <- lm(act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b15) 

## 
## Call:
## lm(formula = act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6562 -1.7674  0.4444  1.6917  3.5714 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        1.0589     0.3354   3.157  0.00172 **
## LibsSC.d          -1.3654     0.4308  -3.170  0.00165 **
## LibsC.d           -1.0553     0.4118  -2.562  0.01079 * 
## LibsM.d           -1.1269     0.3799  -2.967  0.00320 **
## LibsSL.d          -0.8256     0.5012  -1.647  0.10036   
## gend.mf            0.8053     0.6709   1.200  0.23075   
## LibsSC.d:gend.mf  -0.2755     0.8616  -0.320  0.74930   
## LibsC.d:gend.mf   -1.4052     0.8237  -1.706  0.08885 . 
## LibsM.d:gend.mf   -1.5581     0.7597  -2.051  0.04098 * 
## LibsSL.d:gend.mf  -1.4386     1.0024  -1.435  0.15207   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 374 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02548 
## F-statistic: 2.113 on 9 and 374 DF,  p-value: 0.0277

# Action 16
lib.g.b16 <- lm(act16 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b16) 

## 
## Call:
## lm(formula = act16 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3636 -1.1400  0.1589  1.6364  2.7586 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.97177    0.31142   3.120  0.00197 **
## LibsSC.d         -0.32477    0.41240  -0.788  0.43156   
## LibsC.d          -0.08359    0.38974  -0.214  0.83031   
## LibsM.d           0.13060    0.36090   0.362  0.71767   
## LibsSL.d         -0.02733    0.48411  -0.056  0.95502   
## gend.mf          -0.44355    0.62284  -0.712  0.47689   
## LibsSC.d:gend.mf  1.25480    0.82479   1.521  0.12916   
## LibsC.d:gend.mf  -0.06009    0.77948  -0.077  0.93860   
## LibsM.d:gend.mf   0.96606    0.72180   1.338  0.18171   
## LibsSL.d:gend.mf  0.33244    0.96822   0.343  0.73156   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 321 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.02518,    Adjusted R-squared:  -0.002148 
## F-statistic: 0.9214 on 9 and 321 DF,  p-value: 0.5066

# Action 17
lib.g.b17 <- lm(act17 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b17) 

## 
## Call:
## lm(formula = act17 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.391 -1.477 -0.033  1.630  3.077 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.7562     0.3065   2.468   0.0140 *
## LibsSC.d          -0.3678     0.4053  -0.907   0.3648  
## LibsC.d           -0.6598     0.3856  -1.711   0.0879 .
## LibsM.d           -0.2736     0.3537  -0.773   0.4397  
## LibsSL.d           0.3394     0.4884   0.695   0.4875  
## gend.mf            0.7733     0.6129   1.262   0.2078  
## LibsSC.d:gend.mf  -0.6806     0.8106  -0.840   0.4016  
## LibsC.d:gend.mf   -1.1201     0.7712  -1.452   0.1472  
## LibsM.d:gend.mf   -0.7618     0.7074  -1.077   0.2822  
## LibsSL.d:gend.mf  -1.3646     0.9769  -1.397   0.1632  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.008 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.02291,    Adjusted R-squared:  0.001247 
## F-statistic: 1.058 on 9 and 406 DF,  p-value: 0.3934

# Action 18
lib.g.b18 <- lm(act18 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b18) 

## 
## Call:
## lm(formula = act18 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.1369  0.1776  1.4528  3.1000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.9659     0.2845   3.396 0.000765 ***
## LibsSC.d          -0.7333     0.3828  -1.916 0.056250 .  
## LibsC.d           -0.5257     0.3711  -1.417 0.157538    
## LibsM.d           -0.1911     0.3388  -0.564 0.573118    
## LibsSL.d          -0.1765     0.4699  -0.376 0.707504    
## gend.mf            0.2110     0.5689   0.371 0.711000    
## LibsSC.d:gend.mf   0.4543     0.7656   0.593 0.553377    
## LibsC.d:gend.mf   -0.4248     0.7422  -0.572 0.567482    
## LibsM.d:gend.mf   -0.3061     0.6776  -0.452 0.651735    
## LibsSL.d:gend.mf   0.2101     0.9399   0.224 0.823258    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.02394,    Adjusted R-squared:  -0.001748 
## F-statistic: 0.9319 on 9 and 342 DF,  p-value: 0.4973

# Action 19
lib.g.b19 <- lm(act19 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b19) 

## 
## Call:
## lm(formula = act19 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1622  0.3462  1.6250  2.3462 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.5000     0.3687   4.068 6.26e-05 ***
## LibsSC.d          -0.5260     0.4640  -1.134    0.258    
## LibsC.d           -0.4841     0.4398  -1.101    0.272    
## LibsM.d           -0.3690     0.4181  -0.883    0.378    
## LibsSL.d          -0.3618     0.5299  -0.683    0.495    
## gend.mf            0.2500     0.7374   0.339    0.735    
## LibsSC.d:gend.mf   0.3903     0.9280   0.421    0.674    
## LibsC.d:gend.mf   -0.5426     0.8796  -0.617    0.538    
## LibsM.d:gend.mf   -0.5833     0.8361  -0.698    0.486    
## LibsSL.d:gend.mf  -1.0263     1.0599  -0.968    0.334    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.806 on 264 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.0208, Adjusted R-squared:  -0.01258 
## F-statistic: 0.6232 on 9 and 264 DF,  p-value: 0.7769

# Action 20
lib.g.b20 <- lm(act20 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b20) 

## 
## Call:
## lm(formula = act20 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.398 -1.200  0.602  1.602  2.391 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.26716    0.30219   4.193 3.59e-05 ***
## LibsSC.d         -0.10408    0.39115  -0.266    0.790    
## LibsC.d          -0.26716    0.37966  -0.704    0.482    
## LibsM.d           0.03182    0.35579   0.089    0.929    
## LibsSL.d         -0.37500    0.47850  -0.784    0.434    
## gend.mf           0.29902    0.60439   0.495    0.621    
## LibsSC.d:gend.mf -0.04184    0.78230  -0.053    0.957    
## LibsC.d:gend.mf  -1.08163    0.75933  -1.424    0.155    
## LibsM.d:gend.mf  -0.49698    0.71158  -0.698    0.485    
## LibsSL.d:gend.mf -0.75000    0.95700  -0.784    0.434    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.8 on 312 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.01743,    Adjusted R-squared:  -0.01091 
## F-statistic: 0.615 on 9 and 312 DF,  p-value: 0.7842

# Action 21
lib.g.b21 <- lm(act21 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b21) 

## 
## Call:
## lm(formula = act21 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.556 -1.840  0.160  1.696  3.818 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.47121    0.30919   1.524  0.12832   
## LibsSC.d         -1.10530    0.41346  -2.673  0.00783 **
## LibsC.d          -0.83184    0.39825  -2.089  0.03737 * 
## LibsM.d          -0.45365    0.36089  -1.257  0.20949   
## LibsSL.d         -0.04126    0.51081  -0.081  0.93566   
## gend.mf           0.12424    0.61839   0.201  0.84087   
## LibsSC.d:gend.mf -0.49242    0.82692  -0.595  0.55186   
## LibsC.d:gend.mf  -0.56964    0.79649  -0.715  0.47492   
## LibsM.d:gend.mf   0.23088    0.72178   0.320  0.74923   
## LibsSL.d:gend.mf  0.12697    1.02162   0.124  0.90116   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.068 on 391 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.0295, Adjusted R-squared:  0.007162 
## F-statistic: 1.321 on 9 and 391 DF,  p-value: 0.2241

# Action 22
lib.g.b22 <- lm(act22 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b22)

## 
## Call:
## lm(formula = act22 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.4912 -0.1538  1.6410  3.6410 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.6252     0.3123   2.002   0.0460 *
## LibsSC.d          -0.8321     0.4083  -2.038   0.0422 *
## LibsC.d           -0.3796     0.3918  -0.969   0.3332  
## LibsM.d           -0.2280     0.3615  -0.631   0.5285  
## LibsSL.d           0.4517     0.4820   0.937   0.3492  
## gend.mf            0.1342     0.6245   0.215   0.8300  
## LibsSC.d:gend.mf   0.7341     0.8166   0.899   0.3692  
## LibsC.d:gend.mf   -0.6254     0.7836  -0.798   0.4253  
## LibsM.d:gend.mf    0.3346     0.7230   0.463   0.6437  
## LibsSL.d:gend.mf  -0.2880     0.9639  -0.299   0.7653  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.973 on 392 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.04639,    Adjusted R-squared:  0.02449 
## F-statistic: 2.119 on 9 and 392 DF,  p-value: 0.02709

# Action 23
lib.g.b23 <- lm(act23 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b23) 

## 
## Call:
## lm(formula = act23 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9474 -2.0103  0.1875  2.0227  3.5238 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.5875     0.3719   1.580   0.1153  
## LibsSC.d          -0.2729     0.5133  -0.532   0.5954  
## LibsC.d           -0.8608     0.4776  -1.802   0.0726 .
## LibsM.d           -0.3323     0.4478  -0.742   0.4586  
## LibsSL.d          -0.3063     0.6146  -0.498   0.6186  
## gend.mf           -0.2417     0.7438  -0.325   0.7455  
## LibsSC.d:gend.mf   1.5072     1.0267   1.468   0.1432  
## LibsC.d:gend.mf   -0.2594     0.9552  -0.272   0.7861  
## LibsM.d:gend.mf    0.7314     0.8957   0.817   0.4149  
## LibsSL.d:gend.mf  -0.8208     1.2291  -0.668   0.5048  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.26 on 282 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03359,    Adjusted R-squared:  0.002749 
## F-statistic: 1.089 on 9 and 282 DF,  p-value: 0.3707

# Action 24
lib.g.b24 <- lm(act24 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b24)

## 
## Call:
## lm(formula = act24 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5455 -1.8425  0.1575  1.8214  3.8214 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      -0.04221    0.32187  -0.131    0.896
## LibsSC.d          0.02706    0.43239   0.063    0.950
## LibsC.d          -0.54819    0.40011  -1.370    0.171
## LibsM.d          -0.28653    0.36903  -0.776    0.438
## LibsSL.d         -0.17076    0.50436  -0.339    0.735
## gend.mf           0.37013    0.64374   0.575    0.566
## LibsSC.d:gend.mf  0.75108    0.86477   0.869    0.386
## LibsC.d:gend.mf  -0.83218    0.80022  -1.040    0.299
## LibsM.d:gend.mf  -0.71265    0.73806  -0.966    0.335
## LibsSL.d:gend.mf -0.94420    1.00873  -0.936    0.350
## 
## Residual standard error: 2.098 on 405 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.02029,    Adjusted R-squared:  -0.001484 
## F-statistic: 0.9318 on 9 and 405 DF,  p-value: 0.4971

# Action 25
lib.g.b25 <- lm(act25 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b25) 

## 
## Call:
## lm(formula = act25 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -0.8936  0.1642  1.4389  3.2051 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.2088     0.2666   4.535 7.47e-06 ***
## LibsSC.d          -1.2875     0.3649  -3.528 0.000463 ***
## LibsC.d           -0.7977     0.3373  -2.365 0.018475 *  
## LibsM.d           -0.3236     0.3086  -1.048 0.295014    
## LibsSL.d           0.1535     0.4463   0.344 0.731164    
## gend.mf            0.3325     0.5331   0.624 0.533214    
## LibsSC.d:gend.mf  -0.0797     0.7299  -0.109 0.913098    
## LibsC.d:gend.mf   -0.7102     0.6746  -1.053 0.293008    
## LibsM.d:gend.mf   -0.3156     0.6172  -0.511 0.609336    
## LibsSL.d:gend.mf  -0.1680     0.8927  -0.188 0.850803    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.05138,    Adjusted R-squared:  0.03171 
## F-statistic: 2.612 on 9 and 434 DF,  p-value: 0.006027

# Action 26
lib.g.b26 <- lm(act26 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b26)

## 
## Call:
## lm(formula = act26 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0000  0.5278  1.3433  2.5000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.57143    0.39392   3.989 9.08e-05 ***
## LibsSC.d         -0.02731    0.49667  -0.055    0.956    
## LibsC.d          -0.04365    0.47683  -0.092    0.927    
## LibsM.d          -0.02878    0.45581  -0.063    0.950    
## LibsSL.d         -0.59921    0.57680  -1.039    0.300    
## gend.mf           1.14286    0.78783   1.451    0.148    
## LibsSC.d:gend.mf -1.05462    0.99334  -1.062    0.290    
## LibsC.d:gend.mf  -1.42063    0.95367  -1.490    0.138    
## LibsM.d:gend.mf  -1.37100    0.91162  -1.504    0.134    
## LibsSL.d:gend.mf -0.19841    1.15360  -0.172    0.864    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 216 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.02923,    Adjusted R-squared:  -0.01122 
## F-statistic: 0.7225 on 9 and 216 DF,  p-value: 0.688

# Action 27
lib.g.b27 <- lm(act27 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b27)

## 
## Call:
## lm(formula = act27 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4545 -1.1667  0.2174  1.5521  3.0000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.17424    0.31661   3.709 0.000247 ***
## LibsSC.d         -0.69508    0.41049  -1.693 0.091419 .  
## LibsC.d          -0.44147    0.40014  -1.103 0.270764    
## LibsM.d          -0.55028    0.37261  -1.477 0.140744    
## LibsSL.d          0.18461    0.47631   0.388 0.698592    
## gend.mf          -0.01515    0.63323  -0.024 0.980926    
## LibsSC.d:gend.mf  0.97348    0.82099   1.186 0.236639    
## LibsC.d:gend.mf   0.11483    0.80028   0.143 0.885997    
## LibsM.d:gend.mf   0.36723    0.74522   0.493 0.622515    
## LibsSL.d:gend.mf  0.20654    0.95261   0.217 0.828498    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 307 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.03928,    Adjusted R-squared:  0.01112 
## F-statistic: 1.395 on 9 and 307 DF,  p-value: 0.1895

# Action 28
lib.g.b28 <- lm(act28 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b28)

## 
## Call:
## lm(formula = act28 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4262 -0.9511  0.1944  1.5738  2.4286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.90988    0.29925   3.041  0.00253 **
## LibsSC.d         -0.12615    0.39102  -0.323  0.74717   
## LibsC.d           0.32497    0.37397   0.869  0.38544   
## LibsM.d           0.09575    0.34993   0.274  0.78453   
## LibsSL.d          0.07148    0.49609   0.144  0.88551   
## gend.mf          -0.31977    0.59851  -0.534  0.59348   
## LibsSC.d:gend.mf  0.27612    0.78205   0.353  0.72424   
## LibsC.d:gend.mf  -0.06298    0.74795  -0.084  0.93294   
## LibsM.d:gend.mf   0.49032    0.69985   0.701  0.48400   
## LibsSL.d:gend.mf -0.50011    0.99218  -0.504  0.61453   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 362 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.01604,    Adjusted R-squared:  -0.008426 
## F-statistic: 0.6556 on 9 and 362 DF,  p-value: 0.749

# Action 29
lib.g.b29 <- lm(act29 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b29) 

## 
## Call:
## lm(formula = act29 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1003  0.1951  1.5000  2.1951 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.15244    0.27455   4.198 3.41e-05 ***
## LibsSC.d         -0.03339    0.36785  -0.091    0.928    
## LibsC.d          -0.16021    0.34855  -0.460    0.646    
## LibsM.d          -0.03751    0.32394  -0.116    0.908    
## LibsSL.d          0.27739    0.45187   0.614    0.540    
## gend.mf           0.69512    0.54909   1.266    0.206    
## LibsSC.d:gend.mf -0.45703    0.73569  -0.621    0.535    
## LibsC.d:gend.mf  -0.84625    0.69710  -1.214    0.226    
## LibsM.d:gend.mf  -0.75355    0.64788  -1.163    0.246    
## LibsSL.d:gend.mf -0.88810    0.90374  -0.983    0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.774 on 358 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01019,    Adjusted R-squared:  -0.0147 
## F-statistic: 0.4093 on 9 and 358 DF,  p-value: 0.93

# Action 30
lib.g.b30 <- lm(act30 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)

summary(lib.g.b30) 

## 
## Call:
## lm(formula = act30 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3529 -1.0654  0.5928  1.6471  2.3913 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.2702     0.3024   4.201  3.4e-05 ***
## LibsSC.d          -0.1807     0.3949  -0.458    0.648    
## LibsC.d           -0.3782     0.3814  -0.992    0.322    
## LibsM.d           -0.2875     0.3595  -0.800    0.424    
## LibsSL.d          -0.1938     0.4818  -0.402    0.688    
## gend.mf            0.3826     0.6047   0.633    0.527    
## LibsSC.d:gend.mf  -0.9094     0.7899  -1.151    0.250    
## LibsC.d:gend.mf   -0.9493     0.7628  -1.245    0.214    
## LibsM.d:gend.mf   -0.5480     0.7190  -0.762    0.446    
## LibsSL.d:gend.mf  -0.9355     0.9635  -0.971    0.332    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.882 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.01188,    Adjusted R-squared:  -0.01412 
## F-statistic: 0.4569 on 9 and 342 DF,  p-value: 0.9027

a. Means for gender

# Action 12
aggregate(d$act12[d$ideology == "Liberal"], list(d$gend[d$ideology == "Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1    x
## 1  Female 0.41
## 2    Male 1.83

3. Gender x condition

# Action 1
summary(lm(act1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.960  0.080  2.000  5.000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.59446    0.31323   1.898  0.05839 . 
## LibsSC.d                -0.99661    0.41794  -2.385  0.01754 * 
## LibsC.d                 -1.26860    0.40166  -3.158  0.00170 **
## LibsM.d                 -0.48556    0.36207  -1.341  0.18061   
## LibsSL.d                -0.86738    0.50556  -1.716  0.08694 . 
## gend.mf                 -0.17107    0.62645  -0.273  0.78492   
## cond.c                  -0.12607    0.62645  -0.201  0.84060   
## LibsSC.d:gend.mf         0.67233    0.83589   0.804  0.42165   
## LibsC.d:gend.mf         -0.66366    0.80332  -0.826  0.40919   
## LibsM.d:gend.mf          0.87326    0.72414   1.206  0.22851   
## LibsSL.d:gend.mf        -1.08310    1.01111  -1.071  0.28469   
## LibsSC.d:cond.c          0.87733    0.83589   1.050  0.29450   
## LibsC.d:cond.c          -0.07618    0.80332  -0.095  0.92450   
## LibsM.d:cond.c          -0.57612    0.72414  -0.796  0.42671   
## LibsSL.d:cond.c          0.24690    1.01111   0.244  0.80720   
## gend.mf:cond.c          -1.21214    1.25291  -0.967  0.33386   
## LibsSC.d:gend.mf:cond.c  1.77022    1.67177   1.059  0.29025   
## LibsC.d:gend.mf:cond.c   0.80618    1.60664   0.502  0.61608   
## LibsM.d:gend.mf:cond.c   0.77652    1.44828   0.536  0.59212   
## LibsSL.d:gend.mf:cond.c  5.37048    2.02222   2.656  0.00821 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.124 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.0873, Adjusted R-squared:  0.04669 
## F-statistic:  2.15 on 19 and 427 DF,  p-value: 0.003504

# Action 2
summary(lm(act2 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6000 -1.0000  0.3485  1.5192  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.670833   0.333849   5.005 1.01e-06 ***
## LibsSC.d                -0.621117   0.431620  -1.439   0.1513    
## LibsC.d                 -0.874621   0.407564  -2.146   0.0328 *  
## LibsM.d                 -0.510165   0.391941  -1.302   0.1942    
## LibsSL.d                -0.862500   0.525189  -1.642   0.1017    
## gend.mf                  0.575000   0.667698   0.861   0.3899    
## cond.c                  -0.008333   0.667698  -0.012   0.9901    
## LibsSC.d:gend.mf         0.211932   0.863241   0.246   0.8063    
## LibsC.d:gend.mf         -1.318939   0.815128  -1.618   0.1068    
## LibsM.d:gend.mf         -0.563004   0.783881  -0.718   0.4732    
## LibsSL.d:gend.mf        -1.125000   1.050378  -1.071   0.2851    
## LibsSC.d:cond.c         -0.142235   0.863241  -0.165   0.8693    
## LibsC.d:cond.c          -0.717424   0.815128  -0.880   0.3796    
## LibsM.d:cond.c          -0.151099   0.783881  -0.193   0.8473    
## LibsSL.d:cond.c          0.625000   1.050378   0.595   0.5523    
## gend.mf:cond.c           0.850000   1.335397   0.637   0.5250    
## LibsSC.d:gend.mf:cond.c  0.223864   1.726481   0.130   0.8969    
## LibsC.d:gend.mf:cond.c  -2.428788   1.630255  -1.490   0.1374    
## LibsM.d:gend.mf:cond.c   0.135531   1.567763   0.086   0.9312    
## LibsSL.d:gend.mf:cond.c  1.383333   2.100757   0.658   0.5108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 268 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.07459,    Adjusted R-squared:  0.008984 
## F-statistic: 1.137 on 19 and 268 DF,  p-value: 0.3138

# Action 3
summary(lm(act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7059 -1.8261  0.0278  1.7143  3.9412 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.062500   0.283185  -0.221   0.8254  
## LibsSC.d                -0.727718   0.389443  -1.869   0.0624 .
## LibsC.d                 -0.263889   0.370705  -0.712   0.4769  
## LibsM.d                 -0.002246   0.330741  -0.007   0.9946  
## LibsSL.d                 0.260399   0.483118   0.539   0.5902  
## gend.mf                  0.267857   0.566369   0.473   0.6365  
## cond.c                   0.767857   0.566369   1.356   0.1759  
## LibsSC.d:gend.mf         0.426216   0.778886   0.547   0.5845  
## LibsC.d:gend.mf         -0.059524   0.741410  -0.080   0.9360  
## LibsM.d:gend.mf         -0.478946   0.661482  -0.724   0.4694  
## LibsSL.d:gend.mf        -0.863655   0.966235  -0.894   0.3719  
## LibsSC.d:cond.c         -0.770754   0.778886  -0.990   0.3230  
## LibsC.d:cond.c          -1.087302   0.741410  -1.467   0.1432  
## LibsM.d:cond.c          -0.702904   0.661482  -1.063   0.2886  
## LibsSL.d:cond.c         -0.677941   0.966235  -0.702   0.4833  
## gend.mf:cond.c          -1.250000   1.132739  -1.104   0.2704  
## LibsSC.d:gend.mf:cond.c  2.028520   1.557772   1.302   0.1936  
## LibsC.d:gend.mf:cond.c  -0.333333   1.482820  -0.225   0.8222  
## LibsM.d:gend.mf:cond.c   1.105601   1.322965   0.836   0.4038  
## LibsSL.d:gend.mf:cond.c  2.270168   1.932470   1.175   0.2408  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.05725,    Adjusted R-squared:  0.01481 
## F-statistic: 1.349 on 19 and 422 DF,  p-value: 0.1485

# Action 4
summary(lm(act4 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2000 -1.8551  0.1449  2.0909  3.5000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.514760   0.328771   1.566   0.1183  
## LibsSC.d                -0.314893   0.452349  -0.696   0.4868  
## LibsC.d                 -0.873312   0.437869  -1.994   0.0469 *
## LibsM.d                 -0.727331   0.391029  -1.860   0.0637 .
## LibsSL.d                -0.153221   0.556580  -0.275   0.7833  
## gend.mf                  0.370481   0.657542   0.563   0.5735  
## cond.c                   0.759954   0.657542   1.156   0.2486  
## LibsSC.d:gend.mf         0.002514   0.904698   0.003   0.9978  
## LibsC.d:gend.mf         -0.836069   0.875737  -0.955   0.3404  
## LibsM.d:gend.mf         -0.382838   0.782059  -0.490   0.6248  
## LibsSL.d:gend.mf        -1.693557   1.113160  -1.521   0.1291  
## LibsSC.d:cond.c         -0.769313   0.904698  -0.850   0.3957  
## LibsC.d:cond.c          -1.283876   0.875737  -1.466   0.1435  
## LibsM.d:cond.c          -1.102669   0.782059  -1.410   0.1594  
## LibsSL.d:cond.c         -0.683031   1.113160  -0.614   0.5399  
## gend.mf:cond.c          -0.319908   1.315083  -0.243   0.8079  
## LibsSC.d:gend.mf:cond.c -0.115920   1.809396  -0.064   0.9490  
## LibsC.d:gend.mf:cond.c  -1.497632   1.751474  -0.855   0.3931  
## LibsM.d:gend.mf:cond.c  -0.119662   1.564118  -0.077   0.9391  
## LibsSL.d:gend.mf:cond.c -0.233938   2.226320  -0.105   0.9164  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.183 on 349 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.04372,    Adjusted R-squared:  -0.008345 
## F-statistic: 0.8397 on 19 and 349 DF,  p-value: 0.6588

# Action 5
summary(lm(act5 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4800 -1.2500  0.1622  1.5200  3.2500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.4714     0.3258   4.516 9.35e-06 ***
## LibsSC.d                 -0.7418     0.4272  -1.736   0.0836 .  
## LibsC.d                  -0.5114     0.4035  -1.267   0.2061    
## LibsM.d                  -0.3972     0.3874  -1.025   0.3061    
## LibsSL.d                 -1.2006     0.5279  -2.274   0.0237 *  
## gend.mf                   0.4858     0.6516   0.746   0.4566    
## cond.c                   -0.8219     0.6516  -1.261   0.2083    
## LibsSC.d:gend.mf         -0.9677     0.8544  -1.133   0.2583    
## LibsC.d:gend.mf          -0.9548     0.8070  -1.183   0.2377    
## LibsM.d:gend.mf          -0.4253     0.7748  -0.549   0.5835    
## LibsSL.d:gend.mf         -0.6108     1.0558  -0.579   0.5634    
## LibsSC.d:cond.c           0.5539     0.8544   0.648   0.5173    
## LibsC.d:cond.c            0.4995     0.8070   0.619   0.5364    
## LibsM.d:cond.c            0.4345     0.7748   0.561   0.5754    
## LibsSL.d:cond.c           0.9469     1.0558   0.897   0.3706    
## gend.mf:cond.c            0.5010     1.3032   0.384   0.7010    
## LibsSC.d:gend.mf:cond.c  -0.9194     1.7087  -0.538   0.5910    
## LibsC.d:gend.mf:cond.c   -2.2876     1.6139  -1.417   0.1575    
## LibsM.d:gend.mf:cond.c   -0.4514     1.5496  -0.291   0.7710    
## LibsSL.d:gend.mf:cond.c   1.0824     2.1115   0.513   0.6086    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.884 on 275 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.0598, Adjusted R-squared:  -0.005162 
## F-statistic: 0.9205 on 19 and 275 DF,  p-value: 0.5577

# Action 6
summary(lm(act6 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.0816  0.5333  1.5294  2.7500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.56924    0.29972   5.236  2.9e-07 ***
## LibsSC.d                -0.72561    0.39382  -1.842   0.0663 .  
## LibsC.d                 -0.25622    0.37174  -0.689   0.4911    
## LibsM.d                 -0.21868    0.35145  -0.622   0.5342    
## LibsSL.d                -0.63888    0.47304  -1.351   0.1777    
## gend.mf                  0.62343    0.59945   1.040   0.2991    
## cond.c                  -0.44799    0.59945  -0.747   0.4554    
## LibsSC.d:gend.mf        -1.10159    0.78764  -1.399   0.1629    
## LibsC.d:gend.mf         -0.84946    0.74349  -1.143   0.2540    
## LibsM.d:gend.mf         -0.38729    0.70290  -0.551   0.5820    
## LibsSL.d:gend.mf        -1.83415    0.94608  -1.939   0.0534 .  
## LibsSC.d:cond.c          0.40668    0.78764   0.516   0.6060    
## LibsC.d:cond.c           0.06713    0.74349   0.090   0.9281    
## LibsM.d:cond.c           0.29518    0.70290   0.420   0.6748    
## LibsSL.d:cond.c          0.48728    0.94608   0.515   0.6069    
## gend.mf:cond.c          -1.29449    1.19890  -1.080   0.2810    
## LibsSC.d:gend.mf:cond.c  2.59531    1.57527   1.648   0.1004    
## LibsC.d:gend.mf:cond.c  -0.34378    1.48698  -0.231   0.8173    
## LibsM.d:gend.mf:cond.c   1.59227    1.40579   1.133   0.2582    
## LibsSL.d:gend.mf:cond.c  1.51591    1.89216   0.801   0.4236    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 337 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.04582,    Adjusted R-squared:  -0.00798 
## F-statistic: 0.8517 on 19 and 337 DF,  p-value: 0.6439

# Action 7
summary(lm(act7 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0625 -1.8571  0.0625  1.7143  3.6000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.08056    0.31621   0.255   0.7990  
## LibsSC.d                -0.18230    0.42468  -0.429   0.6680  
## LibsC.d                 -0.41701    0.41247  -1.011   0.3126  
## LibsM.d                 -0.17638    0.36506  -0.483   0.6293  
## LibsSL.d                -0.10660    0.53132  -0.201   0.8411  
## gend.mf                  0.22778    0.63242   0.360   0.7189  
## cond.c                   0.85556    0.63242   1.353   0.1769  
## LibsSC.d:gend.mf         0.15754    0.84936   0.185   0.8529  
## LibsC.d:gend.mf         -0.44774    0.82494  -0.543   0.5876  
## LibsM.d:gend.mf         -0.11406    0.73013  -0.156   0.8759  
## LibsSL.d:gend.mf        -1.34236    1.06263  -1.263   0.2073  
## LibsSC.d:cond.c         -0.72383    0.84936  -0.852   0.3946  
## LibsC.d:cond.c          -1.17845    0.82494  -1.429   0.1539  
## LibsM.d:cond.c          -0.44070    0.73013  -0.604   0.5465  
## LibsSL.d:cond.c         -0.97014    1.06263  -0.913   0.3618  
## gend.mf:cond.c          -1.82222    1.26483  -1.441   0.1505  
## LibsSC.d:gend.mf:cond.c  3.01331    1.69873   1.774   0.0769 .
## LibsC.d:gend.mf:cond.c  -0.31769    1.64988  -0.193   0.8474  
## LibsM.d:gend.mf:cond.c   2.29122    1.46026   1.569   0.1174  
## LibsSL.d:gend.mf:cond.c  3.71806    2.12526   1.749   0.0810 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.081 on 393 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.05477,    Adjusted R-squared:  0.009077 
## F-statistic: 1.199 on 19 and 393 DF,  p-value: 0.2548

# Action 8
summary(lm(act8 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4000 -1.6786 -0.1429  1.5466  4.8421 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.58333    0.31923   1.827 0.068306 .  
## LibsSC.d                -1.53331    0.42014  -3.650 0.000293 ***
## LibsC.d                 -1.04595    0.40134  -2.606 0.009454 ** 
## LibsM.d                 -0.96938    0.36425  -2.661 0.008058 ** 
## LibsSL.d                -0.15000    0.49610  -0.302 0.762518    
## gend.mf                  0.26190    0.63847   0.410 0.681845    
## cond.c                   0.59524    0.63847   0.932 0.351677    
## LibsSC.d:gend.mf         1.00168    0.84027   1.192 0.233843    
## LibsC.d:gend.mf          0.14871    0.80268   0.185 0.853097    
## LibsM.d:gend.mf         -0.05323    0.72851  -0.073 0.941788    
## LibsSL.d:gend.mf        -1.46190    0.99221  -1.473 0.141333    
## LibsSC.d:cond.c         -0.47126    0.84027  -0.561 0.575177    
## LibsC.d:cond.c          -2.27048    0.80268  -2.829 0.004880 ** 
## LibsM.d:cond.c          -0.35778    0.72851  -0.491 0.623581    
## LibsSL.d:cond.c          0.60476    0.99221   0.610 0.542488    
## gend.mf:cond.c          -0.04762    1.27694  -0.037 0.970269    
## LibsSC.d:gend.mf:cond.c -0.74578    1.68055  -0.444 0.657414    
## LibsC.d:gend.mf:cond.c  -2.52003    1.60536  -1.570 0.117160    
## LibsM.d:gend.mf:cond.c   0.53284    1.45701   0.366 0.714749    
## LibsSL.d:gend.mf:cond.c  0.98095    1.98442   0.494 0.621311    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.102 on 459 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.07388 
## F-statistic: 3.007 on 19 and 459 DF,  p-value: 2.47e-05

# Action 9
summary(lm(act9 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8000 -1.5455  0.0455  1.5000  4.4545 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.56310    0.28220   1.995 0.046633 *  
## LibsSC.d                -1.36272    0.38596  -3.531 0.000459 ***
## LibsC.d                 -0.62721    0.36534  -1.717 0.086747 .  
## LibsM.d                 -0.39603    0.32859  -1.205 0.228784    
## LibsSL.d                -0.62976    0.47138  -1.336 0.182264    
## gend.mf                 -0.04048    0.56439  -0.072 0.942861    
## cond.c                  -0.17381    0.56439  -0.308 0.758263    
## LibsSC.d:gend.mf        -0.06483    0.77191  -0.084 0.933110    
## LibsC.d:gend.mf          0.50203    0.73068   0.687 0.492409    
## LibsM.d:gend.mf         -0.03476    0.65719  -0.053 0.957837    
## LibsSL.d:gend.mf        -0.92619    0.94276  -0.982 0.326450    
## LibsSC.d:cond.c          1.29578    0.77191   1.679 0.093949 .  
## LibsC.d:cond.c          -0.71259    0.73068  -0.975 0.329998    
## LibsM.d:cond.c           0.37840    0.65719   0.576 0.565063    
## LibsSL.d:cond.c          0.14048    0.94276   0.149 0.881620    
## gend.mf:cond.c          -0.68095    1.12878  -0.603 0.546654    
## LibsSC.d:gend.mf:cond.c  0.84610    1.54382   0.548 0.583938    
## LibsC.d:gend.mf:cond.c  -1.32404    1.46137  -0.906 0.365434    
## LibsM.d:gend.mf:cond.c   0.97138    1.31437   0.739 0.460286    
## LibsSL.d:gend.mf:cond.c  0.94762    1.88553   0.503 0.615523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.06597,    Adjusted R-squared:  0.02441 
## F-statistic: 1.587 on 19 and 427 DF,  p-value: 0.05552

# Action 10
summary(lm(act10 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.8630  0.1739  1.7370  4.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.96573    0.29854   3.235 0.001305 ** 
## LibsSC.d                -1.50338    0.39978  -3.761 0.000192 ***
## LibsC.d                 -1.16312    0.38010  -3.060 0.002343 ** 
## LibsM.d                 -0.88001    0.34536  -2.548 0.011159 *  
## LibsSL.d                -0.23543    0.48449  -0.486 0.627249    
## gend.mf                  0.94353    0.59707   1.580 0.114740    
## cond.c                   0.16853    0.59707   0.282 0.777867    
## LibsSC.d:gend.mf        -0.47430    0.79956  -0.593 0.553343    
## LibsC.d:gend.mf         -0.94875    0.76019  -1.248 0.212657    
## LibsM.d:gend.mf         -0.89760    0.69072  -1.299 0.194429    
## LibsSL.d:gend.mf        -2.60414    0.96898  -2.687 0.007462 ** 
## LibsSC.d:cond.c         -0.23426    0.79956  -0.293 0.769664    
## LibsC.d:cond.c          -0.96887    0.76019  -1.275 0.203132    
## LibsM.d:cond.c          -0.08567    0.69072  -0.124 0.901348    
## LibsSL.d:cond.c          1.02540    0.96898   1.058 0.290512    
## gend.mf:cond.c          -1.08707    1.19415  -0.910 0.363129    
## LibsSC.d:gend.mf:cond.c  1.09731    1.59912   0.686 0.492937    
## LibsC.d:gend.mf:cond.c  -0.51225    1.52038  -0.337 0.736330    
## LibsM.d:gend.mf:cond.c   2.05177    1.38145   1.485 0.138173    
## LibsSL.d:gend.mf:cond.c  3.89919    1.93797   2.012 0.044807 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 456 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.0938, Adjusted R-squared:  0.05604 
## F-statistic: 2.484 on 19 and 456 DF,  p-value: 0.0005392

# Action 11
summary(lm(act11 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9000 -1.0292  0.3902  1.5200  3.4167 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.77202    0.32336   5.480  9.2e-08 ***
## LibsSC.d                -0.98169    0.41930  -2.341  0.01989 *  
## LibsC.d                 -0.99793    0.40534  -2.462  0.01440 *  
## LibsM.d                 -1.05950    0.37144  -2.852  0.00465 ** 
## LibsSL.d                -0.33869    0.53665  -0.631  0.52846    
## gend.mf                  0.19405    0.64672   0.300  0.76435    
## cond.c                  -0.37738    0.64672  -0.584  0.55999    
## LibsSC.d:gend.mf         0.95256    0.83860   1.136  0.25694    
## LibsC.d:gend.mf         -0.65890    0.81068  -0.813  0.41700    
## LibsM.d:gend.mf         -0.26942    0.74287  -0.363  0.71712    
## LibsSL.d:gend.mf        -0.22738    1.07330  -0.212  0.83237    
## LibsSC.d:cond.c          0.79022    0.83860   0.942  0.34682    
## LibsC.d:cond.c          -0.67443    0.81068  -0.832  0.40612    
## LibsM.d:cond.c          -0.27354    0.74287  -0.368  0.71297    
## LibsSL.d:cond.c          0.01071    1.07330   0.010  0.99204    
## gend.mf:cond.c          -2.05476    1.29344  -1.589  0.11323    
## LibsSC.d:gend.mf:cond.c  2.32000    1.67720   1.383  0.16764    
## LibsC.d:gend.mf:cond.c   0.32505    1.62135   0.200  0.84124    
## LibsM.d:gend.mf:cond.c   1.83373    1.48575   1.234  0.21812    
## LibsSL.d:gend.mf:cond.c  3.12143    2.14660   1.454  0.14699    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.858 on 292 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.09531,    Adjusted R-squared:  0.03644 
## F-statistic: 1.619 on 19 and 292 DF,  p-value: 0.05071

# Action 12
summary(lm(act12 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.000 -1.875  0.125  1.635  4.235 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.1538     0.3487   3.309 0.001029 ** 
## LibsSC.d                 -1.9083     0.4523  -4.219  3.1e-05 ***
## LibsC.d                  -1.7250     0.4426  -3.898 0.000116 ***
## LibsM.d                  -1.3175     0.4006  -3.288 0.001106 ** 
## LibsSL.d                 -0.9129     0.5614  -1.626 0.104796    
## gend.mf                   1.3591     0.6973   1.949 0.052067 .  
## cond.c                    0.5159     0.6973   0.740 0.459854    
## LibsSC.d:gend.mf         -0.5773     0.9046  -0.638 0.523760    
## LibsC.d:gend.mf          -1.7702     0.8851  -2.000 0.046253 *  
## LibsM.d:gend.mf          -1.9265     0.8013  -2.404 0.016706 *  
## LibsSL.d:gend.mf         -2.2409     1.1228  -1.996 0.046702 *  
## LibsSC.d:cond.c           0.1194     0.9046   0.132 0.895067    
## LibsC.d:cond.c           -1.6985     0.8851  -1.919 0.055779 .  
## LibsM.d:cond.c           -0.4715     0.8013  -0.588 0.556610    
## LibsSL.d:cond.c           0.2023     1.1228   0.180 0.857150    
## gend.mf:cond.c           -0.3652     1.3946  -0.262 0.793581    
## LibsSC.d:gend.mf:cond.c   1.2764     1.8091   0.706 0.480947    
## LibsC.d:gend.mf:cond.c   -2.6625     1.7703  -1.504 0.133457    
## LibsM.d:gend.mf:cond.c    1.4342     1.6026   0.895 0.371403    
## LibsSL.d:gend.mf:cond.c   0.5288     2.2456   0.235 0.813955    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.117 on 363 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.06946 
## F-statistic: 2.501 on 19 and 363 DF,  p-value: 0.0005499

# Action 13
summary(lm(act13 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1333 -1.8000  0.1852  1.8235  4.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.50160    0.31192   1.608   0.1086  
## LibsSC.d                -0.80244    0.41474  -1.935   0.0537 .
## LibsC.d                 -0.58999    0.40329  -1.463   0.1442  
## LibsM.d                 -0.64946    0.35698  -1.819   0.0696 .
## LibsSL.d                -0.55369    0.49335  -1.122   0.2624  
## gend.mf                  0.53846    0.62383   0.863   0.3885  
## cond.c                   0.20513    0.62383   0.329   0.7425  
## LibsSC.d:gend.mf         1.24504    0.82948   1.501   0.1341  
## LibsC.d:gend.mf         -0.36169    0.80659  -0.448   0.6541  
## LibsM.d:gend.mf         -0.80795    0.71395  -1.132   0.2584  
## LibsSL.d:gend.mf        -1.40096    0.98671  -1.420   0.1564  
## LibsSC.d:cond.c         -0.33408    0.82948  -0.403   0.6873  
## LibsC.d:cond.c          -1.47280    0.80659  -1.826   0.0686 .
## LibsM.d:cond.c           0.09315    0.71395   0.130   0.8963  
## LibsSL.d:cond.c          0.35737    0.98671   0.362   0.7174  
## gend.mf:cond.c           1.17308    1.24767   0.940   0.3476  
## LibsSC.d:gend.mf:cond.c -1.46062    1.65895  -0.880   0.3791  
## LibsC.d:gend.mf:cond.c  -2.63772    1.61318  -1.635   0.1028  
## LibsM.d:gend.mf:cond.c  -1.33486    1.42790  -0.935   0.3504  
## LibsSL.d:gend.mf:cond.c  2.96859    1.97342   1.504   0.1333  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.08224,    Adjusted R-squared:  0.04091 
## F-statistic:  1.99 on 19 and 422 DF,  p-value: 0.008085

# Action 14
summary(lm(act14 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1818 -1.2411  0.3129  1.5849  3.9000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              1.138102   0.365312   3.115  0.00204 **
## LibsSC.d                -0.636453   0.491871  -1.294  0.19681   
## LibsC.d                 -1.093963   0.465777  -2.349  0.01957 * 
## LibsM.d                 -0.752582   0.421068  -1.787  0.07503 . 
## LibsSL.d                -1.346435   0.612090  -2.200  0.02869 * 
## gend.mf                  0.623797   0.730624   0.854  0.39399   
## cond.c                   0.305615   0.730624   0.418  0.67607   
## LibsSC.d:gend.mf        -0.012808   0.983741  -0.013  0.98962   
## LibsC.d:gend.mf         -2.112074   0.931554  -2.267  0.02418 * 
## LibsM.d:gend.mf         -0.791661   0.842137  -0.940  0.34804   
## LibsSL.d:gend.mf        -2.290463   1.224180  -1.871  0.06244 . 
## LibsSC.d:cond.c          0.283396   0.983741   0.288  0.77351   
## LibsC.d:cond.c          -0.612074   0.931554  -0.657  0.51172   
## LibsM.d:cond.c          -0.163956   0.842137  -0.195  0.84578   
## LibsSL.d:cond.c          1.111052   1.224180   0.908  0.36492   
## gend.mf:cond.c          -0.811230   1.461249  -0.555  0.57925   
## LibsSC.d:gend.mf:cond.c  0.004637   1.967483   0.002  0.99812   
## LibsC.d:gend.mf:cond.c   0.624149   1.863107   0.335  0.73789   
## LibsM.d:gend.mf:cond.c   0.877118   1.684274   0.521  0.60296   
## LibsSL.d:gend.mf:cond.c  3.144563   2.448360   1.284  0.20014   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.033 on 266 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.06875,    Adjusted R-squared:  0.002233 
## F-statistic: 1.034 on 19 and 266 DF,  p-value: 0.4224

# Action 15
summary(lm(act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5294 -1.7357  0.3167  1.4947  4.2000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               1.0053     0.3392   2.964  0.00324 **
## LibsSC.d                 -1.3475     0.4336  -3.108  0.00203 **
## LibsC.d                  -0.9079     0.4171  -2.177  0.03013 * 
## LibsM.d                  -1.0846     0.3826  -2.835  0.00484 **
## LibsSL.d                 -0.8663     0.5058  -1.713  0.08761 . 
## gend.mf                   0.8145     0.6784   1.201  0.23069   
## cond.c                   -1.1439     0.6784  -1.686  0.09262 . 
## LibsSC.d:gend.mf         -0.2133     0.8672  -0.246  0.80588   
## LibsC.d:gend.mf          -1.1915     0.8341  -1.428  0.15402   
## LibsM.d:gend.mf          -1.5569     0.7652  -2.035  0.04262 * 
## LibsSL.d:gend.mf         -1.5782     1.0116  -1.560  0.11961   
## LibsSC.d:cond.c           1.4593     0.8672   1.683  0.09328 . 
## LibsC.d:cond.c            0.6001     0.8341   0.719  0.47232   
## LibsM.d:cond.c            1.2796     0.7652   1.672  0.09535 . 
## LibsSL.d:cond.c           1.9552     1.0116   1.933  0.05404 . 
## gend.mf:cond.c            1.4377     1.3568   1.060  0.28999   
## LibsSC.d:gend.mf:cond.c  -2.2354     1.7345  -1.289  0.19829   
## LibsC.d:gend.mf:cond.c   -3.5150     1.6683  -2.107  0.03581 * 
## LibsM.d:gend.mf:cond.c   -0.6025     1.5305  -0.394  0.69406   
## LibsSL.d:gend.mf:cond.c   0.7682     2.0232   0.380  0.70438   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.021 on 364 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.09033,    Adjusted R-squared:  0.04285 
## F-statistic: 1.902 on 19 and 364 DF,  p-value: 0.013

# Action 16
summary(lm(act16 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -0.9811  0.2963  1.4800  3.3333 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              1.02303    0.31257   3.273  0.00118 **
## LibsSC.d                -0.38919    0.41490  -0.938  0.34895   
## LibsC.d                 -0.12397    0.39179  -0.316  0.75190   
## LibsM.d                  0.07833    0.36158   0.217  0.82864   
## LibsSL.d                -0.50636    0.51741  -0.979  0.32852   
## gend.mf                 -0.54605    0.62513  -0.873  0.38307   
## cond.c                  -0.29605    0.62513  -0.474  0.63613   
## LibsSC.d:gend.mf         1.38949    0.82979   1.675  0.09504 . 
## LibsC.d:gend.mf          0.06417    0.78358   0.082  0.93478   
## LibsM.d:gend.mf          1.06393    0.72316   1.471  0.14224   
## LibsSL.d:gend.mf         0.67939    1.03482   0.657  0.51197   
## LibsSC.d:cond.c          0.36171    0.82979   0.436  0.66321   
## LibsC.d:cond.c           0.55639    0.78358   0.710  0.47819   
## LibsM.d:cond.c           0.54506    0.72316   0.754  0.45158   
## LibsSL.d:cond.c          0.26272    1.03482   0.254  0.79976   
## gend.mf:cond.c          -2.40789    1.25027  -1.926  0.05503 . 
## LibsSC.d:gend.mf:cond.c  2.05436    1.65958   1.238  0.21670   
## LibsC.d:gend.mf:cond.c   1.40858    1.56715   0.899  0.36945   
## LibsM.d:gend.mf:cond.c   2.35105    1.44631   1.626  0.10506   
## LibsSL.d:gend.mf:cond.c  6.14123    2.06964   2.967  0.00324 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 311 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.06263,    Adjusted R-squared:  0.005359 
## F-statistic: 1.094 on 19 and 311 DF,  p-value: 0.3561

# Action 17
summary(lm(act17 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4286 -1.3889 -0.0571  1.6000  4.2500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.76951    0.30430   2.529   0.0118 *
## LibsSC.d                -0.41534    0.40303  -1.031   0.3034  
## LibsC.d                 -0.49282    0.39139  -1.259   0.2087  
## LibsM.d                 -0.28635    0.35115  -0.815   0.4153  
## LibsSL.d                 0.15341    0.49309   0.311   0.7559  
## gend.mf                  0.74670    0.60859   1.227   0.2206  
## cond.c                  -0.08187    0.60859  -0.135   0.8931  
## LibsSC.d:gend.mf        -0.56110    0.80606  -0.696   0.4868  
## LibsC.d:gend.mf         -0.78619    0.78278  -1.004   0.3158  
## LibsM.d:gend.mf         -0.71951    0.70231  -1.024   0.3062  
## LibsSL.d:gend.mf        -1.67587    0.98618  -1.699   0.0900 .
## LibsSC.d:cond.c          0.87353    0.80606   1.084   0.2792  
## LibsC.d:cond.c          -1.02547    0.78278  -1.310   0.1909  
## LibsM.d:cond.c          -0.35825    0.70231  -0.510   0.6103  
## LibsSL.d:cond.c          1.77770    0.98618   1.803   0.0722 .
## gend.mf:cond.c          -0.97912    1.21719  -0.804   0.4216  
## LibsSC.d:gend.mf:cond.c  0.51700    1.61211   0.321   0.7486  
## LibsC.d:gend.mf:cond.c  -0.27842    1.56556  -0.178   0.8589  
## LibsM.d:gend.mf:cond.c   0.41781    1.40462   0.297   0.7663  
## LibsSL.d:gend.mf:cond.c  4.42079    1.97237   2.241   0.0256 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 396 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.0642, Adjusted R-squared:  0.0193 
## F-statistic:  1.43 on 19 and 396 DF,  p-value: 0.1086

# Action 18
summary(lm(act18 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.2632  0.2372  1.3333  3.3846 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.9869     0.2850   3.463 0.000605 ***
## LibsSC.d                 -0.7658     0.3841  -1.994 0.046981 *  
## LibsC.d                  -0.4999     0.3729  -1.340 0.181062    
## LibsM.d                  -0.2164     0.3396  -0.637 0.524340    
## LibsSL.d                 -0.3603     0.4791  -0.752 0.452512    
## gend.mf                   0.1690     0.5700   0.296 0.767035    
## cond.c                    0.1465     0.5700   0.257 0.797393    
## LibsSC.d:gend.mf          0.5402     0.7681   0.703 0.482360    
## LibsC.d:gend.mf          -0.3098     0.7459  -0.415 0.678133    
## LibsM.d:gend.mf          -0.2578     0.6792  -0.380 0.704535    
## LibsSL.d:gend.mf          0.3278     0.9581   0.342 0.732493    
## LibsSC.d:cond.c           0.3471     0.7681   0.452 0.651656    
## LibsC.d:cond.c           -0.6206     0.7459  -0.832 0.405985    
## LibsM.d:cond.c           -0.1618     0.6792  -0.238 0.811798    
## LibsSL.d:cond.c           0.4337     0.9581   0.453 0.651122    
## gend.mf:cond.c           -1.1501     1.1401  -1.009 0.313828    
## LibsSC.d:gend.mf:cond.c   1.1326     1.5363   0.737 0.461473    
## LibsC.d:gend.mf:cond.c   -0.2350     1.4918  -0.158 0.874923    
## LibsM.d:gend.mf:cond.c    1.3353     1.3584   0.983 0.326336    
## LibsSL.d:gend.mf:cond.c   4.4898     1.9163   2.343 0.019720 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.05189,    Adjusted R-squared:  -0.002371 
## F-statistic: 0.9563 on 19 and 332 DF,  p-value: 0.5132

# Action 19
summary(lm(act19 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6364 -1.0465  0.3693  1.4390  2.8750 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.5444     0.3813   4.050  6.8e-05 ***
## LibsSC.d                 -0.6347     0.4802  -1.322    0.187    
## LibsC.d                  -0.4233     0.4535  -0.933    0.352    
## LibsM.d                  -0.3912     0.4310  -0.908    0.365    
## LibsSL.d                 -0.4583     0.5464  -0.839    0.402    
## gend.mf                   0.4444     0.7626   0.583    0.561    
## cond.c                   -0.7778     0.7626  -1.020    0.309    
## LibsSC.d:gend.mf          0.3611     0.9604   0.376    0.707    
## LibsC.d:gend.mf          -0.6233     0.9069  -0.687    0.493    
## LibsM.d:gend.mf          -0.7455     0.8620  -0.865    0.388    
## LibsSL.d:gend.mf         -1.2835     1.0929  -1.174    0.241    
## LibsSC.d:cond.c           0.8472     0.9604   0.882    0.379    
## LibsC.d:cond.c           -0.2243     0.9069  -0.247    0.805    
## LibsM.d:cond.c            0.3414     0.8620   0.396    0.692    
## LibsSL.d:cond.c           0.9804     1.0929   0.897    0.370    
## gend.mf:cond.c           -0.7111     1.5252  -0.466    0.641    
## LibsSC.d:gend.mf:cond.c  -0.6778     1.9208  -0.353    0.724    
## LibsC.d:gend.mf:cond.c   -0.2688     1.8139  -0.148    0.882    
## LibsM.d:gend.mf:cond.c    0.8673     1.7239   0.503    0.615    
## LibsSL.d:gend.mf:cond.c   1.6391     2.1857   0.750    0.454    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.809 on 254 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.05536,    Adjusted R-squared:  -0.0153 
## F-statistic: 0.7834 on 19 and 254 DF,  p-value: 0.7262

# Action 20
summary(lm(act20 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7000 -1.1616  0.4211  1.4211  3.3333 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.25044    0.30195   4.141 4.49e-05 ***
## LibsSC.d                -0.04652    0.39274  -0.118   0.9058    
## LibsC.d                 -0.22560    0.37994  -0.594   0.5531    
## LibsM.d                  0.01571    0.35580   0.044   0.9648    
## LibsSL.d                -0.54568    0.49072  -1.112   0.2670    
## gend.mf                  0.33246    0.60389   0.551   0.5824    
## cond.c                  -0.86754    0.60389  -1.437   0.1519    
## LibsSC.d:gend.mf        -0.15695    0.78548  -0.200   0.8418    
## LibsC.d:gend.mf         -1.07444    0.75989  -1.414   0.1584    
## LibsM.d:gend.mf         -0.52546    0.71160  -0.738   0.4608    
## LibsSL.d:gend.mf        -0.90865    0.98143  -0.926   0.3553    
## LibsSC.d:cond.c          0.32083    0.78548   0.408   0.6832    
## LibsC.d:cond.c           0.41722    0.75989   0.549   0.5834    
## LibsM.d:cond.c           0.19588    0.71160   0.275   0.7833    
## LibsSL.d:cond.c          0.91040    0.98143   0.928   0.3543    
## gend.mf:cond.c          -0.59825    1.20779  -0.495   0.6207    
## LibsSC.d:gend.mf:cond.c  0.85835    1.57096   0.546   0.5852    
## LibsC.d:gend.mf:cond.c   0.11427    1.51977   0.075   0.9401    
## LibsM.d:gend.mf:cond.c   0.12014    1.42321   0.084   0.9328    
## LibsSL.d:gend.mf:cond.c  3.51253    1.96286   1.789   0.0745 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.795 on 302 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.05394,    Adjusted R-squared:  -0.00558 
## F-statistic: 0.9063 on 19 and 302 DF,  p-value: 0.5755

# Action 21
summary(lm(act21 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1250 -1.7424  0.0508  1.7059  4.2727 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.46637    0.30970   1.506  0.13293   
## LibsSC.d                -1.11715    0.41438  -2.696  0.00733 **
## LibsC.d                 -0.72803    0.41005  -1.775  0.07662 . 
## LibsM.d                 -0.44526    0.36127  -1.232  0.21853   
## LibsSL.d                -0.10675    0.51299  -0.208  0.83526   
## gend.mf                  0.04940    0.61940   0.080  0.93647   
## cond.c                  -0.27560    0.61940  -0.445  0.65661   
## LibsSC.d:gend.mf        -0.38421    0.82876  -0.464  0.64320   
## LibsC.d:gend.mf         -0.26558    0.82009  -0.324  0.74623   
## LibsM.d:gend.mf          0.30124    0.72255   0.417  0.67698   
## LibsSL.d:gend.mf         0.13136    1.02599   0.128  0.89819   
## LibsSC.d:cond.c          0.95265    0.82876   1.149  0.25108   
## LibsC.d:cond.c           0.21656    0.82009   0.264  0.79187   
## LibsM.d:cond.c           0.43253    0.72255   0.599  0.54978   
## LibsSL.d:cond.c          0.95636    1.02599   0.932  0.35185   
## gend.mf:cond.c          -1.98452    1.23879  -1.602  0.10999   
## LibsSC.d:gend.mf:cond.c  2.44860    1.65752   1.477  0.14043   
## LibsC.d:gend.mf:cond.c   0.05217    1.64018   0.032  0.97464   
## LibsM.d:gend.mf:cond.c   1.88494    1.44510   1.304  0.19290   
## LibsSL.d:gend.mf:cond.c  4.42299    2.05197   2.155  0.03175 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 381 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05638,    Adjusted R-squared:  0.009323 
## F-statistic: 1.198 on 19 and 381 DF,  p-value: 0.2555

# Action 22
summary(lm(act22 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2222 -1.4571 -0.0508  1.7429  3.7778 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.633254   0.314407   2.014   0.0447 *
## LibsSC.d                -0.845014   0.410364  -2.059   0.0402 *
## LibsC.d                 -0.345754   0.396341  -0.872   0.3836  
## LibsM.d                 -0.240467   0.363540  -0.661   0.5087  
## LibsSL.d                 0.451713   0.487784   0.926   0.3550  
## gend.mf                 -0.004603   0.628814  -0.007   0.9942  
## cond.c                  -0.322063   0.628814  -0.512   0.6088  
## LibsSC.d:gend.mf         0.882670   0.820729   1.075   0.2828  
## LibsC.d:gend.mf         -0.337063   0.792683  -0.425   0.6709  
## LibsM.d:gend.mf          0.482187   0.727081   0.663   0.5076  
## LibsSL.d:gend.mf        -0.165331   0.975568  -0.169   0.8655  
## LibsSC.d:cond.c          0.585411   0.820729   0.713   0.4761  
## LibsC.d:cond.c          -0.052937   0.792683  -0.067   0.9468  
## LibsM.d:cond.c           0.324179   0.727081   0.446   0.6559  
## LibsSL.d:cond.c         -0.225649   0.975568  -0.231   0.8172  
## gend.mf:cond.c          -2.546349   1.257628  -2.025   0.0436 *
## LibsSC.d:gend.mf:cond.c  2.565108   1.641457   1.563   0.1190  
## LibsC.d:gend.mf:cond.c   0.963016   1.585365   0.607   0.5439  
## LibsM.d:gend.mf:cond.c   2.963171   1.454161   2.038   0.0423 *
## LibsSL.d:gend.mf:cond.c  1.241774   1.951137   0.636   0.5249  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.976 on 382 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.0681, Adjusted R-squared:  0.02175 
## F-statistic: 1.469 on 19 and 382 DF,  p-value: 0.0928

aggregate(d$act22[d$ideology == "Liberal"], list(d$gend[d$ideology == "Liberal"], d$cond[d$ideology == "Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate  1.11
## 2    Male climate -0.17
## 3  Female    ctrl  0.16
## 4    Male    ctrl  1.43

# Action 23
summary(lm(act23 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -2.000  0.125  1.920  4.500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.5197     0.3760   1.382   0.1680  
## LibsSC.d                 -0.2149     0.5157  -0.417   0.6772  
## LibsC.d                  -0.7545     0.4821  -1.565   0.1187  
## LibsM.d                  -0.2986     0.4521  -0.660   0.5095  
## LibsSL.d                 -0.1584     0.6263  -0.253   0.8005  
## gend.mf                  -0.2716     0.7519  -0.361   0.7182  
## cond.c                   -1.4522     0.7519  -1.931   0.0545 .
## LibsSC.d:gend.mf          1.5176     1.0314   1.471   0.1424  
## LibsC.d:gend.mf          -0.1200     0.9641  -0.124   0.9011  
## LibsM.d:gend.mf           0.6960     0.9042   0.770   0.4421  
## LibsSL.d:gend.mf         -0.9511     1.2525  -0.759   0.4483  
## LibsSC.d:cond.c           1.5062     1.0314   1.460   0.1454  
## LibsC.d:cond.c            0.9184     0.9641   0.953   0.3417  
## LibsM.d:cond.c            0.8849     0.9042   0.979   0.3286  
## LibsSL.d:cond.c           3.1295     1.2525   2.499   0.0131 *
## gend.mf:cond.c           -2.0599     1.5038  -1.370   0.1719  
## LibsSC.d:gend.mf:cond.c   3.4407     2.0629   1.668   0.0965 .
## LibsC.d:gend.mf:cond.c    0.5165     1.9283   0.268   0.7890  
## LibsM.d:gend.mf:cond.c    1.4611     1.8084   0.808   0.4198  
## LibsSL.d:gend.mf:cond.c   3.7054     2.5050   1.479   0.1402  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.253 on 272 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.07369,    Adjusted R-squared:  0.008984 
## F-statistic: 1.139 on 19 and 272 DF,  p-value: 0.3118

# Action 24
summary(lm(act24 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8750 -0.0429  1.6429  4.6667 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.025263   0.330860  -0.076   0.9392  
## LibsSC.d                -0.006817   0.439521  -0.016   0.9876  
## LibsC.d                 -0.411840   0.410925  -1.002   0.3168  
## LibsM.d                 -0.300695   0.376457  -0.799   0.4249  
## LibsSL.d                -0.217792   0.517330  -0.421   0.6740  
## gend.mf                  0.450526   0.661720   0.681   0.4964  
## cond.c                   0.029474   0.661720   0.045   0.9645  
## LibsSC.d:gend.mf         0.704544   0.879042   0.801   0.4233  
## LibsC.d:gend.mf         -0.542987   0.821851  -0.661   0.5092  
## LibsM.d:gend.mf         -0.741791   0.752914  -0.985   0.3251  
## LibsSL.d:gend.mf        -1.047749   1.034659  -1.013   0.3118  
## LibsSC.d:cond.c          0.130142   0.879042   0.148   0.8824  
## LibsC.d:cond.c          -0.960823   0.821851  -1.169   0.2431  
## LibsM.d:cond.c          -0.906066   0.752914  -1.203   0.2295  
## LibsSL.d:cond.c          0.123304   1.034659   0.119   0.9052  
## gend.mf:cond.c           0.741053   1.323440   0.560   0.5758  
## LibsSC.d:gend.mf:cond.c -1.060283   1.758084  -0.603   0.5468  
## LibsC.d:gend.mf:cond.c  -3.611688   1.643702  -2.197   0.0286 *
## LibsM.d:gend.mf:cond.c  -1.601505   1.505829  -1.064   0.2882  
## LibsSL.d:gend.mf:cond.c -0.213275   2.069319  -0.103   0.9180  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.083 on 395 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.05818,    Adjusted R-squared:  0.01287 
## F-statistic: 1.284 on 19 and 395 DF,  p-value: 0.1894

# Action 25
summary(lm(act25 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6087 -0.8846  0.2222  1.3913  3.4000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.21467    0.26586   4.569 6.44e-06 ***
## LibsSC.d                -1.30866    0.36461  -3.589  0.00037 ***
## LibsC.d                 -0.69437    0.34220  -2.029  0.04307 *  
## LibsM.d                 -0.32803    0.30799  -1.065  0.28745    
## LibsSL.d                -0.00389    0.46358  -0.008  0.99331    
## gend.mf                  0.32065    0.53173   0.603  0.54680    
## cond.c                   0.55435    0.53173   1.043  0.29775    
## LibsSC.d:gend.mf        -0.07814    0.72922  -0.107  0.91472    
## LibsC.d:gend.mf         -0.47236    0.68440  -0.690  0.49045    
## LibsM.d:gend.mf         -0.29865    0.61599  -0.485  0.62805    
## LibsSL.d:gend.mf        -0.57555    0.92715  -0.621  0.53508    
## LibsSC.d:cond.c         -0.04820    0.72922  -0.066  0.94733    
## LibsC.d:cond.c          -1.12486    0.68440  -1.644  0.10100    
## LibsM.d:cond.c          -0.75263    0.61599  -1.222  0.22245    
## LibsSL.d:cond.c          0.69075    0.92715   0.745  0.45667    
## gend.mf:cond.c          -1.10870    1.06345  -1.043  0.29775    
## LibsSC.d:gend.mf:cond.c  1.80549    1.45843   1.238  0.21641    
## LibsC.d:gend.mf:cond.c  -0.41695    1.36879  -0.305  0.76081    
## LibsM.d:gend.mf:cond.c   0.74802    1.23197   0.607  0.54406    
## LibsSL.d:gend.mf:cond.c  2.95183    1.85431   1.592  0.11216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.837 on 424 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.07813,    Adjusted R-squared:  0.03682 
## F-statistic: 1.891 on 19 and 424 DF,  p-value: 0.0133

# Action 26
summary(lm(act26 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2857 -1.1562  0.5577  1.3947  3.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.70000    0.42940   3.959 0.000104 ***
## LibsSC.d                -0.06310    0.53399  -0.118 0.906057    
## LibsC.d                 -0.06399    0.50992  -0.125 0.900260    
## LibsM.d                 -0.14650    0.49011  -0.299 0.765314    
## LibsSL.d                -0.89643    0.62594  -1.432 0.153619    
## gend.mf                  1.40000    0.85881   1.630 0.104593    
## cond.c                  -0.60000    0.85881  -0.699 0.485562    
## LibsSC.d:gend.mf        -1.50714    1.06797  -1.411 0.159688    
## LibsC.d:gend.mf         -1.74702    1.01983  -1.713 0.088208 .  
## LibsM.d:gend.mf         -1.62239    0.98022  -1.655 0.099420 .  
## LibsSL.d:gend.mf        -0.50714    1.25187  -0.405 0.685819    
## LibsSC.d:cond.c          0.20714    1.06797   0.194 0.846399    
## LibsC.d:cond.c           0.46131    1.01983   0.452 0.651502    
## LibsM.d:cond.c           0.60175    0.98022   0.614 0.539966    
## LibsSL.d:cond.c          0.82619    1.25187   0.660 0.510014    
## gend.mf:cond.c          -1.20000    1.71761  -0.699 0.485562    
## LibsSC.d:gend.mf:cond.c  2.31905    2.13594   1.086 0.278869    
## LibsC.d:gend.mf:cond.c  -1.17262    2.03967  -0.575 0.565982    
## LibsM.d:gend.mf:cond.c   0.96574    1.96044   0.493 0.622809    
## LibsSL.d:gend.mf:cond.c  3.08095    2.50375   1.231 0.219899    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.844 on 206 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.06431,    Adjusted R-squared:  -0.02199 
## F-statistic: 0.7452 on 19 and 206 DF,  p-value: 0.7689

# Action 27
summary(lm(act27 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2143  0.1667  1.4167  3.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.11742    0.31962   3.496 0.000544 ***
## LibsSC.d                -0.63826    0.41502  -1.538 0.125134    
## LibsC.d                 -0.28925    0.40384  -0.716 0.474404    
## LibsM.d                 -0.49616    0.37476  -1.324 0.186540    
## LibsSL.d                 0.23864    0.47840   0.499 0.618271    
## gend.mf                  0.09848    0.63924   0.154 0.877662    
## cond.c                  -0.34091    0.63924  -0.533 0.594221    
## LibsSC.d:gend.mf         0.85985    0.83003   1.036 0.301080    
## LibsC.d:gend.mf          0.20548    0.80769   0.254 0.799355    
## LibsM.d:gend.mf          0.31078    0.74952   0.415 0.678709    
## LibsSL.d:gend.mf         0.02273    0.95679   0.024 0.981065    
## LibsSC.d:cond.c         -0.28409    0.83003  -0.342 0.732393    
## LibsC.d:cond.c          -0.29163    0.80769  -0.361 0.718304    
## LibsM.d:cond.c          -0.34783    0.74952  -0.464 0.642934    
## LibsSL.d:cond.c          0.96212    0.95679   1.006 0.315441    
## gend.mf:cond.c           0.68182    1.27848   0.533 0.594221    
## LibsSC.d:gend.mf:cond.c -1.93182    1.66006  -1.164 0.245478    
## LibsC.d:gend.mf:cond.c  -3.05166    1.61537  -1.889 0.059847 .  
## LibsM.d:gend.mf:cond.c  -0.85790    1.49905  -0.572 0.567553    
## LibsSL.d:gend.mf:cond.c -0.25758    1.91358  -0.135 0.893016    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 297 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.0834, Adjusted R-squared:  0.02476 
## F-statistic: 1.422 on 19 and 297 DF,  p-value: 0.1144

# Action 28
summary(lm(act28 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6250 -1.1321  0.2667  1.5522  3.2000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.87214    0.30312   2.877  0.00426 **
## LibsSC.d                -0.05396    0.39430  -0.137  0.89122   
## LibsC.d                  0.41500    0.38207   1.086  0.27814   
## LibsM.d                  0.12476    0.35348   0.353  0.72433   
## LibsSL.d                 0.12161    0.50429   0.241  0.80958   
## gend.mf                 -0.51571    0.60624  -0.851  0.39553   
## cond.c                  -0.44429    0.60624  -0.733  0.46414   
## LibsSC.d:gend.mf         0.45208    0.78860   0.573  0.56683   
## LibsC.d:gend.mf          0.24142    0.76414   0.316  0.75224   
## LibsM.d:gend.mf          0.64412    0.70695   0.911  0.36285   
## LibsSL.d:gend.mf        -0.38845    1.00858  -0.385  0.70036   
## LibsSC.d:cond.c         -0.46935    0.78860  -0.595  0.55211   
## LibsC.d:cond.c           0.07740    0.76414   0.101  0.91938   
## LibsM.d:cond.c           0.31588    0.70695   0.447  0.65528   
## LibsSL.d:cond.c          0.83179    1.00858   0.825  0.41009   
## gend.mf:cond.c          -2.36857    1.21248  -1.953  0.05155 . 
## LibsSC.d:gend.mf:cond.c  2.14130    1.57719   1.358  0.17544   
## LibsC.d:gend.mf:cond.c   1.70234    1.52828   1.114  0.26609   
## LibsM.d:gend.mf:cond.c   1.31427    1.41390   0.930  0.35325   
## LibsSL.d:gend.mf:cond.c  2.42690    2.01715   1.203  0.22973   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 352 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  -0.006203 
## F-statistic: 0.8796 on 19 and 352 DF,  p-value: 0.6089

# Action 29
summary(lm(act29 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6923 -1.0447  0.3077  1.4286  2.4286 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.16238    0.27827   4.177 3.74e-05 ***
## LibsSC.d                -0.07396    0.37361  -0.198    0.843    
## LibsC.d                 -0.21633    0.36272  -0.596    0.551    
## LibsM.d                 -0.05252    0.32813  -0.160    0.873    
## LibsSL.d                 0.18987    0.46530   0.408    0.683    
## gend.mf                  0.67525    0.55655   1.213    0.226    
## cond.c                   0.18785    0.55655   0.338    0.736    
## LibsSC.d:gend.mf        -0.37936    0.74722  -0.508    0.612    
## LibsC.d:gend.mf         -0.93709    0.72544  -1.292    0.197    
## LibsM.d:gend.mf         -0.72503    0.65625  -1.105    0.270    
## LibsSL.d:gend.mf        -0.82973    0.93060  -0.892    0.373    
## LibsSC.d:cond.c          0.07341    0.74722   0.098    0.922    
## LibsC.d:cond.c          -0.05237    0.72544  -0.072    0.942    
## LibsM.d:cond.c          -0.25646    0.65625  -0.391    0.696    
## LibsSL.d:cond.c          0.07433    0.93060   0.080    0.936    
## gend.mf:cond.c          -0.08999    1.11309  -0.081    0.936    
## LibsSC.d:gend.mf:cond.c -0.57798    1.49444  -0.387    0.699    
## LibsC.d:gend.mf:cond.c   0.79382    1.45088   0.547    0.585    
## LibsM.d:gend.mf:cond.c   0.33178    1.31250   0.253    0.801    
## LibsSL.d:gend.mf:cond.c  1.66563    1.86120   0.895    0.371    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 348 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01904,    Adjusted R-squared:  -0.03452 
## F-statistic: 0.3554 on 19 and 348 DF,  p-value: 0.995

# Action 30
summary(lm(act30 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.545 -1.059  0.400  1.466  3.250 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.2680     0.3035   4.179 3.75e-05 ***
## LibsSC.d                 -0.2241     0.3966  -0.565   0.5725    
## LibsC.d                  -0.3873     0.3871  -1.001   0.3177    
## LibsM.d                  -0.2992     0.3609  -0.829   0.4078    
## LibsSL.d                 -0.3191     0.4907  -0.650   0.5159    
## gend.mf                   0.2973     0.6069   0.490   0.6245    
## cond.c                   -0.3277     0.6069  -0.540   0.5896    
## LibsSC.d:gend.mf         -0.8095     0.7932  -1.020   0.3082    
## LibsC.d:gend.mf          -0.8920     0.7742  -1.152   0.2501    
## LibsM.d:gend.mf          -0.4838     0.7219  -0.670   0.5032    
## LibsSL.d:gend.mf         -0.9451     0.9814  -0.963   0.3363    
## LibsSC.d:cond.c           1.5064     0.7932   1.899   0.0584 .  
## LibsC.d:cond.c            0.2675     0.7742   0.346   0.7299    
## LibsM.d:cond.c            0.3912     0.7219   0.542   0.5883    
## LibsSL.d:cond.c           0.9299     0.9814   0.948   0.3441    
## gend.mf:cond.c           -1.6780     1.2138  -1.382   0.1678    
## LibsSC.d:gend.mf:cond.c   2.8356     1.5864   1.787   0.0748 .  
## LibsC.d:gend.mf:cond.c    2.1316     1.5484   1.377   0.1695    
## LibsM.d:gend.mf:cond.c    2.2841     1.4437   1.582   0.1146    
## LibsSL.d:gend.mf:cond.c   3.9735     1.9628   2.024   0.0437 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.04439,    Adjusted R-squared:  -0.0103 
## F-statistic: 0.8117 on 19 and 332 DF,  p-value: 0.6931

a. Gender x Cond means

# Action 22
aggregate(d$act22[d$ideology == "Liberal"], list(d$cond[d$ideology == "Liberal"], d$gend[d$ideology == "Liberal"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1 climate  Female  1.11
## 2    ctrl  Female  0.16
## 3 climate    Male -0.17
## 4    ctrl    Male  1.43

iii. Moderates

# Action 1
mod.b1 <- lm(act1 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b1) # yes, higher than 0

## 
## Call:
## lm(formula = act1 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.646 -1.962  0.038  2.038  3.490 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.03804    0.15855  -0.240   0.8105  
## ModsSC.d    -0.42349    0.31032  -1.365   0.1730  
## ModsC.d     -0.45196    0.26720  -1.691   0.0914 .
## ModsL.d      0.68420    0.31032   2.205   0.0280 *
## ModsSL.d     0.20947    0.39660   0.528   0.5976  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.151 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.02106 
## F-statistic: 3.409 on 4 and 444 DF,  p-value: 0.00923

# Action 2
mod.b2 <- lm(act2 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b2) # yes, higher than 0

## 
## Call:
## lm(formula = act2 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.2212  0.1077  1.7788  2.1077 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.2212     0.1698   7.191 5.68e-12 ***
## ModsSC.d     -0.2647     0.3157  -0.838    0.403    
## ModsC.d      -0.3289     0.2810  -1.170    0.243    
## ModsL.d       0.3502     0.3263   1.073    0.284    
## ModsSL.d     -0.1796     0.4058  -0.443    0.658    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 285 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01519,    Adjusted R-squared:  0.001372 
## F-statistic: 1.099 on 4 and 285 DF,  p-value: 0.3572

# Action 3
mod.b3 <- lm(act3 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b3) # nothing

## 
## Call:
## lm(formula = act3 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.9837  0.0163  1.4167  3.8852 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.0163     0.1482  -0.110  0.91242   
## ModsSC.d     -0.8689     0.2969  -2.926  0.00361 **
## ModsC.d      -0.4004     0.2530  -1.582  0.11431   
## ModsL.d      -0.1749     0.2852  -0.613  0.54012   
## ModsSL.d      0.3877     0.3706   1.046  0.29605   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 439 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02777,    Adjusted R-squared:  0.01891 
## F-statistic: 3.134 on 4 and 439 DF,  p-value: 0.01467

# Action 4
mod.b4 <- lm(act4 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b4) # nothing

## 
## Call:
## lm(formula = act4 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4828 -1.7937  0.2062  2.0051  3.2877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.20625    0.17089  -1.207    0.228  
## ModsSC.d     0.40233    0.34759   1.157    0.248  
## ModsC.d     -0.08142    0.30530  -0.267    0.790  
## ModsL.d      0.58556    0.33130   1.767    0.078 .
## ModsSL.d     0.68901    0.43626   1.579    0.115  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.162 on 366 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.006351 
## F-statistic: 1.591 on 4 and 366 DF,  p-value: 0.176

# Action 5
mod.b5 <- lm(act5 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b5) # yes, higher than 0

## 
## Call:
## lm(formula = act5 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4048 -1.0917  0.2653  1.5952  2.5833 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0917     0.1792   6.091 3.53e-09 ***
## ModsSC.d     -0.3570     0.3219  -1.109    0.268    
## ModsC.d      -0.1191     0.2830  -0.421    0.674    
## ModsL.d       0.3130     0.3399   0.921    0.358    
## ModsSL.d     -0.6751     0.4219  -1.600    0.111    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 292 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.01867,    Adjusted R-squared:  0.005229 
## F-statistic: 1.389 on 4 and 292 DF,  p-value: 0.2378

# Action 6
mod.b6 <- lm(act6 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b6) # yes, higher than 0

## 
## Call:
## lm(formula = act6 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4348 -1.3099  0.6706  1.6706  2.1296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.30986    0.15213   8.610 2.44e-16 ***
## ModsSC.d    -0.43949    0.28984  -1.516    0.130    
## ModsC.d      0.01955    0.24861   0.079    0.937    
## ModsL.d      0.12492    0.30755   0.406    0.685    
## ModsSL.d    -0.12236    0.35475  -0.345    0.730    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 354 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.00896,    Adjusted R-squared:  -0.002238 
## F-statistic: 0.8001 on 4 and 354 DF,  p-value: 0.5257

# Action 7
mod.b7 <- lm(act7 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b7) # nothing

## 
## Call:
## lm(formula = act7 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.8652  0.1348  1.6286  3.3140 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13483    0.15657  -0.861    0.390
## ModsSC.d    -0.02306    0.31790  -0.073    0.942
## ModsC.d     -0.17912    0.27432  -0.653    0.514
## ModsL.d      0.10093    0.31380   0.322    0.748
## ModsSL.d     0.50626    0.38624   1.311    0.191
## 
## Residual standard error: 2.089 on 410 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.006798,   Adjusted R-squared:  -0.002892 
## F-statistic: 0.7016 on 4 and 410 DF,  p-value: 0.5912

# Action 8
mod.b8 <- lm(act8 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b8) # marginally higher than 0

## 
## Call:
## lm(formula = act8 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.625 -1.855 -0.287  1.713  4.145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.4428     0.1496  -2.960  0.00323 **
## ModsSC.d     -0.7021     0.2959  -2.373  0.01806 * 
## ModsC.d      -0.2702     0.2531  -1.068  0.28621   
## ModsL.d       0.9190     0.3062   3.001  0.00283 **
## ModsSL.d      1.0678     0.3672   2.908  0.00381 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 476 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.06121,    Adjusted R-squared:  0.05332 
## F-statistic: 7.759 on 4 and 476 DF,  p-value: 4.609e-06

# Action 9
mod.b9 <- lm(act9 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b9) # yes, higher than 0

## 
## Call:
## lm(formula = act9 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5781 -1.5781  0.2525  1.7167  3.7167 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.200000   0.142645   1.402  0.16159   
## ModsSC.d    -0.916667   0.291172  -3.148  0.00175 **
## ModsC.d     -0.452525   0.243718  -1.857  0.06401 . 
## ModsL.d      0.378125   0.284173   1.331  0.18400   
## ModsSL.d    -0.005556   0.357404  -0.016  0.98760   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.966 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03759,    Adjusted R-squared:  0.02892 
## F-statistic: 4.336 on 4 and 444 DF,  p-value: 0.00189

# Action 10
mod.b10 <- lm(act10 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b10) # yes, higher than 0

## 
## Call:
## lm(formula = act10 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.0804     0.1471   0.547  0.58490   
## ModsSC.d     -0.6980     0.2915  -2.395  0.01701 * 
## ModsC.d      -0.3375     0.2503  -1.349  0.17809   
## ModsL.d       0.5965     0.2964   2.012  0.04475 * 
## ModsSL.d      1.0696     0.3595   2.975  0.00308 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

# Action 11
mod.b11 <- lm(act11 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b11) # yes, higher than 0

## 
## Call:
## lm(formula = act11 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6585 -0.8154  0.2741  1.3415  2.2741 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.725926   0.160708   4.517 8.93e-06 ***
## ModsSC.d    0.008768   0.311421   0.028  0.97756    
## ModsC.d     0.089459   0.281900   0.317  0.75120    
## ModsL.d     0.932611   0.332967   2.801  0.00542 ** 
## ModsSL.d    0.815741   0.413647   1.972  0.04949 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 309 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03501,    Adjusted R-squared:  0.02252 
## F-statistic: 2.803 on 4 and 309 DF,  p-value: 0.02604

# Action 12
mod.b12 <- lm(act12 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b12) # yes, higher than 0

## 
## Call:
## lm(formula = act12 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.736 -2.158  0.000  2.000  3.842 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  1.584e-16  1.707e-01   0.000   1.0000  
## ModsSC.d    -8.421e-01  3.315e-01  -2.540   0.0115 *
## ModsC.d     -6.000e-01  2.834e-01  -2.117   0.0349 *
## ModsL.d      7.358e-01  3.406e-01   2.160   0.0314 *
## ModsSL.d     3.333e-01  4.468e-01   0.746   0.4561  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.146 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0525, Adjusted R-squared:  0.04253 
## F-statistic: 5.264 on 4 and 380 DF,  p-value: 0.0003892

# Action 13
mod.b13 <- lm(act13 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b13) # nothing

## 
## Call:
## lm(formula = act13 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3438 -1.9149  0.0851  1.6562  3.5781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08511    0.15162  -0.561    0.575
## ModsSC.d    -0.49302    0.30087  -1.639    0.102
## ModsC.d     -0.13228    0.26451  -0.500    0.617
## ModsL.d      0.42886    0.30087   1.425    0.155
## ModsSL.d     0.34225    0.38272   0.894    0.372
## 
## Residual standard error: 2.079 on 438 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01713,    Adjusted R-squared:  0.008155 
## F-statistic: 1.909 on 4 and 438 DF,  p-value: 0.108

# Action 14
mod.b14 <- lm(act14 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b14) # yes, higher than 0

## 
## Call:
## lm(formula = act14 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9487 -1.4219 -0.0198  1.6780  2.9091 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.42188    0.17955   2.350   0.0195 *
## ModsSC.d     0.07812    0.36797   0.212   0.8320  
## ModsC.d     -0.09984    0.31966  -0.312   0.7550  
## ModsL.d      0.52684    0.37155   1.418   0.1573  
## ModsSL.d    -0.33097    0.46884  -0.706   0.4808  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.031 on 283 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.01158,    Adjusted R-squared:  -0.002388 
## F-statistic: 0.829 on 4 and 283 DF,  p-value: 0.5076

# Action 15
mod.b15 <- lm(act15 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b15) # yes, higher than 0

## 
## Call:
## lm(formula = act15 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8889 -2.1024  0.1111  1.8916  3.3559 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.102410   0.158970   0.644   0.5198  
## ModsSC.d    -0.458342   0.310442  -1.476   0.1407  
## ModsC.d      0.006024   0.275344   0.022   0.9826  
## ModsL.d      0.786479   0.344232   2.285   0.0229 *
## ModsSL.d     0.210090   0.395433   0.531   0.5955  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.048 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.02508,    Adjusted R-squared:  0.01482 
## F-statistic: 2.444 on 4 and 380 DF,  p-value: 0.0462

# Action 16
mod.b16 <- lm(act16 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b16) # yes, higher than 0

## 
## Call:
## lm(formula = act16 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0698 -0.9861  0.0357  1.4375  2.4375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.96429    0.15511   6.217 1.55e-09 ***
## ModsSC.d    -0.40179    0.30696  -1.309    0.191    
## ModsC.d      0.02183    0.26615   0.082    0.935    
## ModsL.d      0.10548    0.31998   0.330    0.742    
## ModsSL.d     0.07020    0.37443   0.187    0.851    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 327 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.007238,   Adjusted R-squared:  -0.004905 
## F-statistic: 0.5961 on 4 and 327 DF,  p-value: 0.6657

# Action 17
mod.b17 <- lm(act17 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b17) # yes, higher than 0

## 
## Call:
## lm(formula = act17 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2059 -1.4798 -0.1685  1.6230  2.8315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.47977    0.15208   3.155  0.00172 **
## ModsSC.d    -0.10272    0.29785  -0.345  0.73037   
## ModsC.d     -0.31123    0.26092  -1.193  0.23363   
## ModsL.d      0.07023    0.29968   0.234  0.81483   
## ModsSL.d     0.72611    0.37524   1.935  0.05366 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 412 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.006815 
## F-statistic: 1.714 on 4 and 412 DF,  p-value: 0.146

# Action 18
mod.b18 <- lm(act18 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b18) # yes, higher than 0

## 
## Call:
## lm(formula = act18 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1196  0.1844  1.5135  2.8113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.81560    0.15509   5.259 2.53e-07 ***
## ModsSC.d    -0.62692    0.29673  -2.113   0.0353 *  
## ModsC.d     -0.32912    0.26436  -1.245   0.2140    
## ModsL.d      0.09668    0.28906   0.334   0.7382    
## ModsSL.d    -0.05698    0.37551  -0.152   0.8795    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01769,    Adjusted R-squared:  0.006435 
## F-statistic: 1.572 on 4 and 349 DF,  p-value: 0.1814

# Action 19
mod.b19 <- lm(act19 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b19) # yes, higher than 0

## 
## Call:
## lm(formula = act19 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.438 -1.050  0.093  1.704  2.093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.23009    0.16945   7.259 4.15e-12 ***
## ModsSC.d    -0.32311    0.32274  -1.001    0.318    
## ModsC.d     -0.18009    0.28773  -0.626    0.532    
## ModsL.d      0.20741    0.36069   0.575    0.566    
## ModsSL.d     0.06621    0.38585   0.172    0.864    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 270 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.00789,    Adjusted R-squared:  -0.006807 
## F-statistic: 0.5368 on 4 and 270 DF,  p-value: 0.7088

# Action 20
mod.b20 <- lm(act20 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b20) # yes, higher than 0

## 
## Call:
## lm(formula = act20 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3566 -1.1957  0.6434  1.6434  2.0000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3566     0.1579   8.594 3.82e-16 ***
## ModsSC.d     -0.2056     0.2925  -0.703    0.483    
## ModsC.d      -0.2262     0.2674  -0.846    0.398    
## ModsL.d      -0.1609     0.3079  -0.523    0.602    
## ModsSL.d     -0.3566     0.3794  -0.940    0.348    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.793 on 319 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.004413,   Adjusted R-squared:  -0.008071 
## F-statistic: 0.3535 on 4 and 319 DF,  p-value: 0.8415

# Action 21
mod.b21 <- lm(act21 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b21) # nothing

## 
## Call:
## lm(formula = act21 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -1.9401  0.0599  1.5806  3.5806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.05988    0.15942  -0.376    0.707  
## ModsSC.d    -0.52076    0.30638  -1.700    0.090 .
## ModsC.d     -0.20841    0.27780  -0.750    0.454  
## ModsL.d      0.50056    0.31201   1.604    0.109  
## ModsSL.d     0.48412    0.39246   1.234    0.218  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 398 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02474,    Adjusted R-squared:  0.01494 
## F-statistic: 2.524 on 4 and 398 DF,  p-value: 0.04049

# Action 22
mod.b22 <- lm(act22 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b22) # yes, higher than 0

## 
## Call:
## lm(formula = act22 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1351 -1.3415 -0.1351  1.7143  3.3279 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.28571    0.15236   1.875   0.0615 .
## ModsSC.d    -0.61358    0.29520  -2.079   0.0383 *
## ModsC.d      0.05575    0.26603   0.210   0.8341  
## ModsL.d      0.30357    0.30472   0.996   0.3197  
## ModsSL.d     0.84942    0.35863   2.369   0.0183 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.975 on 399 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03383,    Adjusted R-squared:  0.02414 
## F-statistic: 3.492 on 4 and 399 DF,  p-value: 0.008087

# Action 23
mod.b23 <- lm(act23 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b23) # marginally higher than 0

## 
## Call:
## lm(formula = act23 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6154 -2.1138  0.1846  2.1846  3.1846 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.1138     0.2042   0.557    0.578
## ModsSC.d      0.1545     0.4085   0.378    0.706
## ModsC.d      -0.2984     0.3473  -0.859    0.391
## ModsL.d       0.5016     0.4163   1.205    0.229
## ModsSL.d      0.4462     0.4969   0.898    0.370
## 
## Residual standard error: 2.265 on 288 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.01368,    Adjusted R-squared:  -2.033e-05 
## F-statistic: 0.9985 on 4 and 288 DF,  p-value: 0.4087

# Action 24
mod.b24 <- lm(act24 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b24) # nothing

## 
## Call:
## lm(formula = act24 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9189 -1.8727  0.1273  1.6453  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.22989    0.15960  -1.440    0.151
## ModsSC.d     0.10261    0.32566   0.315    0.753
## ModsC.d     -0.27011    0.27138  -0.995    0.320
## ModsL.d      0.09195    0.31920   0.288    0.773
## ModsSL.d     0.14880    0.38112   0.390    0.696
## 
## Residual standard error: 2.105 on 411 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.004608,   Adjusted R-squared:  -0.005079 
## F-statistic: 0.4757 on 4 and 411 DF,  p-value: 0.7536

# Action 25
mod.b25 <- lm(act25 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b25) # yes, higher than 0

## 
## Call:
## lm(formula = act25 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3429 -0.8925  0.1167  1.5000  3.1167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8925     0.1344   6.639 9.31e-11 ***
## ModsSC.d     -1.0091     0.2722  -3.707 0.000236 ***
## ModsC.d      -0.3925     0.2259  -1.737 0.083010 .  
## ModsL.d       0.2345     0.2673   0.877 0.380715    
## ModsSL.d      0.4504     0.3378   1.333 0.183136    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 441 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.04037 
## F-statistic:  5.68 on 4 and 441 DF,  p-value: 0.0001824

# Action 26
mod.b26 <- lm(act26 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b26) # yes, higher than 0

## 
## Call:
## lm(formula = act26 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5686 -1.2581  0.4595  1.4314  2.0526 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.61798    0.19374   8.351 7.29e-15 ***
## ModsSC.d    -0.07744    0.35752  -0.217    0.829    
## ModsC.d     -0.04935    0.32099  -0.154    0.878    
## ModsL.d     -0.35991    0.38117  -0.944    0.346    
## ModsSL.d    -0.67061    0.46190  -1.452    0.148    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.828 on 222 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01216,    Adjusted R-squared:  -0.005638 
## F-statistic: 0.6832 on 4 and 222 DF,  p-value: 0.6042

# Action 27
mod.b27 <- lm(act27 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b27) # yes, higher than 0

## 
## Call:
## lm(formula = act27 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3871 -0.9483  0.2813  1.5577  2.5577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.5512     0.1666   3.308  0.00105 **
## ModsSC.d     -0.1089     0.3091  -0.352  0.72494   
## ModsC.d       0.1676     0.2878   0.582  0.56088   
## ModsL.d       0.6266     0.3258   1.924  0.05532 . 
## ModsSL.d      0.8359     0.3762   2.222  0.02698 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 314 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02704,    Adjusted R-squared:  0.01464 
## F-statistic: 2.182 on 4 and 314 DF,  p-value: 0.07092

# Action 28
mod.b28 <- lm(act28 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b28) # yes, higher than 0

## 
## Call:
## lm(formula = act28 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3214 -0.9728  0.2105  1.6786  2.2105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.97279    0.15049   6.464 3.23e-10 ***
## ModsSC.d    -0.18332    0.28471  -0.644    0.520    
## ModsC.d      0.34864    0.24957   1.397    0.163    
## ModsL.d      0.02721    0.28841   0.094    0.925    
## ModsSL.d     0.22076    0.36062   0.612    0.541    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 369 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.009436,   Adjusted R-squared:  -0.001302 
## F-statistic: 0.8787 on 4 and 369 DF,  p-value: 0.4767

# Action 29
mod.b29 <- lm(act29 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b29) # yes, higher than 0

## 
## Call:
## lm(formula = act29 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4828 -1.0893  0.0182  1.5172  2.0182 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.14286    0.14555   7.852 4.61e-14 ***
## ModsSC.d    -0.05357    0.27713  -0.193    0.847    
## ModsC.d     -0.11876    0.24230  -0.490    0.624    
## ModsL.d     -0.16104    0.27894  -0.577    0.564    
## ModsSL.d     0.33990    0.35858   0.948    0.344    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.765 on 365 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.005014,   Adjusted R-squared:  -0.00589 
## F-statistic: 0.4598 on 4 and 365 DF,  p-value: 0.7652

# Action 30
mod.b30 <- lm(act30 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, data = d)

summary(mod.b30) # yes, higher than 0

## 
## Call:
## lm(formula = act30 ~ ModsSC.d + ModsC.d + ModsL.d + ModsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1765 -1.0435  0.8235  1.8235  1.9875 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.04348    0.15971   6.534 2.27e-10 ***
## ModsSC.d     0.09687    0.29540   0.328    0.743    
## ModsC.d     -0.03098    0.26364  -0.118    0.907    
## ModsL.d      0.13299    0.30745   0.433    0.666    
## ModsSL.d     0.09938    0.38887   0.256    0.798    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.001091,   Adjusted R-squared:  -0.01036 
## F-statistic: 0.09525 on 4 and 349 DF,  p-value: 0.9839

Significantly higher than 0: 2, 5, 6, 11, 14, 16, 17, 18, 19, 20, 25, 26, 27, 28, 29, 30 Not different from 0: 1, 3, 4, 7, 9, 10, 12, 13, 15, 21, 22, 23, 24 Significantly lower than 0: 8

1. Condition Differences?

# Action 1
mod.c.b1 <- lm(act1 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b1) # no

## 
## Call:
## lm(formula = act1 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.750 -2.200  0.250  1.765  3.800 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.05707    0.15869  -0.360   0.7193  
## ModsSC.d        -0.42864    0.31078  -1.379   0.1685  
## ModsC.d         -0.42182    0.26832  -1.572   0.1166  
## ModsL.d          0.70480    0.31013   2.273   0.0235 *
## ModsSL.d         0.13050    0.40785   0.320   0.7491  
## cond.c          -0.58353    0.31738  -1.839   0.0666 .
## ModsSC.d:cond.c  1.21210    0.62157   1.950   0.0518 .
## ModsC.d:cond.c   0.39847    0.53663   0.743   0.4582  
## ModsL.d:cond.c   0.78808    0.62026   1.271   0.2046  
## ModsSL.d:cond.c -0.17871    0.81569  -0.219   0.8267  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04314,    Adjusted R-squared:  0.02353 
## F-statistic: 2.199 on 9 and 439 DF,  p-value: 0.02116

# Action 2
mod.c.b2 <- lm(act2 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b2) #no

## 
## Call:
## lm(formula = act2 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -1.068  0.375  1.615  2.375 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.1923     0.1724   6.915 3.17e-11 ***
## ModsSC.d         -0.2138     0.3215  -0.665    0.507    
## ModsC.d          -0.3041     0.2833  -1.073    0.284    
## ModsL.d           0.3702     0.3310   1.119    0.264    
## ModsSL.d         -0.1469     0.4093  -0.359    0.720    
## cond.c           -0.3846     0.3448  -1.115    0.266    
## ModsSC.d:cond.c   0.1312     0.6429   0.204    0.838    
## ModsC.d:cond.c   -0.1419     0.5666  -0.250    0.802    
## ModsL.d:cond.c    0.2596     0.6619   0.392    0.695    
## ModsSL.d:cond.c   0.4755     0.8185   0.581    0.562    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.812 on 280 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -0.006049 
## F-statistic: 0.8069 on 9 and 280 DF,  p-value: 0.6102

# Action 3
mod.c.b3 <- lm(act3 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b3) #no

## 
## Call:
## lm(formula = act3 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9375  0.0625  1.6410  3.9394 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -0.01420    0.14833  -0.096  0.92375   
## ModsSC.d        -0.86621    0.29781  -2.909  0.00382 **
## ModsC.d         -0.40643    0.25448  -1.597  0.11097   
## ModsL.d         -0.09941    0.28764  -0.346  0.72980   
## ModsSL.d         0.35660    0.38743   0.920  0.35787   
## cond.c           0.09659    0.29666   0.326  0.74489   
## ModsSC.d:cond.c -0.21456    0.59562  -0.360  0.71886   
## ModsC.d:cond.c  -0.03310    0.50896  -0.065  0.94818   
## ModsL.d:cond.c   0.95823    0.57528   1.666  0.09650 . 
## ModsSL.d:cond.c -0.28137    0.77486  -0.363  0.71669   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03846,    Adjusted R-squared:  0.01852 
## F-statistic: 1.929 on 9 and 434 DF,  p-value: 0.04628

# Action 4
mod.c.b4 <- lm(act4 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b4) # no

## 
## Call:
## lm(formula = act4 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8400 -1.8941  0.1059  2.0378  3.3488 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.21294    0.17181  -1.239   0.2160  
## ModsSC.d         0.41092    0.35024   1.173   0.2415  
## ModsC.d         -0.06148    0.30997  -0.198   0.8429  
## ModsL.d          0.64809    0.33498   1.935   0.0538 .
## ModsSL.d         0.67610    0.45721   1.479   0.1401  
## cond.c          -0.21412    0.34363  -0.623   0.5336  
## ModsSC.d:cond.c  0.17530    0.70047   0.250   0.8025  
## ModsC.d:cond.c   0.06528    0.61993   0.105   0.9162  
## ModsL.d:cond.c   1.02381    0.66996   1.528   0.1273  
## ModsSL.d:cond.c  0.08780    0.91443   0.096   0.9236  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.169 on 361 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0238, Adjusted R-squared:  -0.0005384 
## F-statistic: 0.9779 on 9 and 361 DF,  p-value: 0.4579

# Action 8
mod.c.b8 <- lm(act8 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b8) # yes, cond difference (negative)

## 
## Call:
## lm(formula = act8 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -0.44033    0.14915  -2.952  0.00331 **
## ModsSC.d        -0.71480    0.29671  -2.409  0.01637 * 
## ModsC.d         -0.18983    0.25445  -0.746  0.45600   
## ModsL.d          0.95104    0.30653   3.103  0.00203 **
## ModsSL.d         1.14866    0.37209   3.087  0.00214 **
## cond.c           0.10982    0.29829   0.368  0.71292   
## ModsSC.d:cond.c  0.04659    0.59341   0.079  0.93746   
## ModsC.d:cond.c  -1.10347    0.50889  -2.168  0.03063 * 
## ModsL.d:cond.c   0.51161    0.61306   0.835  0.40441   
## ModsSL.d:cond.c  0.72351    0.74418   0.972  0.33143   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

# Action 9
mod.c.b9 <- lm(act9 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b9) # no

## 
## Call:
## lm(formula = act9 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.5893  0.0465  1.4286  4.3462 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.20577    0.14284   1.441 0.150415    
## ModsSC.d        -0.99649    0.29296  -3.402 0.000732 ***
## ModsC.d         -0.43438    0.24501  -1.773 0.076936 .  
## ModsL.d          0.37161    0.28566   1.301 0.193980    
## ModsSL.d        -0.08077    0.37535  -0.215 0.829720    
## cond.c           0.15664    0.28568   0.548 0.583757    
## ModsSC.d:cond.c  0.95422    0.58591   1.629 0.104116    
## ModsC.d:cond.c  -0.52084    0.49002  -1.063 0.288414    
## ModsL.d:cond.c  -0.16854    0.57132  -0.295 0.768126    
## ModsSL.d:cond.c -0.57331    0.75070  -0.764 0.445458    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.964 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.05102,    Adjusted R-squared:  0.03157 
## F-statistic: 2.623 on 9 and 439 DF,  p-value: 0.00582

# Action 10
mod.c.b10 <- lm(act10 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b10) # no

## 
## Call:
## lm(formula = act10 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3750 -1.9896  0.0217  1.8350  3.6579 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.07732    0.14754   0.524   0.6005   
## ModsSC.d        -0.68960    0.29375  -2.348   0.0193 * 
## ModsC.d         -0.30852    0.25222  -1.223   0.2219   
## ModsL.d          0.63456    0.29939   2.120   0.0346 * 
## ModsSL.d         1.11018    0.36666   3.028   0.0026 **
## cond.c          -0.17547    0.29508  -0.595   0.5524   
## ModsSC.d:cond.c  0.08424    0.58750   0.143   0.8860   
## ModsC.d:cond.c  -0.24347    0.50444  -0.483   0.6296   
## ModsL.d:cond.c   0.68029    0.59878   1.136   0.2565   
## ModsSL.d:cond.c  0.55047    0.73332   0.751   0.4532   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.08 on 467 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05853,    Adjusted R-squared:  0.04038 
## F-statistic: 3.226 on 9 and 467 DF,  p-value: 0.000826

# Action 11
mod.c.b11 <- lm(act11 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b11) # no

## 
## Call:
## lm(formula = act11 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -1.0299  0.3452  1.5429  2.5735 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.728161   0.160078   4.549 7.81e-06 ***
## ModsSC.d         0.002673   0.310242   0.009  0.99313    
## ModsC.d          0.117077   0.281354   0.416  0.67762    
## ModsL.d          0.926601   0.345581   2.681  0.00773 ** 
## ModsSL.d         0.764696   0.416985   1.834  0.06765 .  
## cond.c          -0.603380   0.320157  -1.885  0.06043 .  
## ModsSC.d:cond.c  0.981713   0.620485   1.582  0.11465    
## ModsC.d:cond.c  -0.172810   0.562708  -0.307  0.75897    
## ModsL.d:cond.c   0.579571   0.691161   0.839  0.40238    
## ModsSL.d:cond.c  0.017666   0.833969   0.021  0.98311    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 304 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.0581, Adjusted R-squared:  0.03021 
## F-statistic: 2.083 on 9 and 304 DF,  p-value: 0.03077

# Action 12
mod.c.b12 <- lm(act12 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b12) # no

## 
## Call:
## lm(formula = act12 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1304 -1.8929 -0.1098  1.8696  4.1071 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.004332   0.171058  -0.025   0.9798  
## ModsSC.d        -0.842342   0.332082  -2.537   0.0116 *
## ModsC.d         -0.564295   0.285455  -1.977   0.0488 *
## ModsL.d          0.786217   0.343384   2.290   0.0226 *
## ModsSL.d         0.392568   0.461048   0.851   0.3951  
## cond.c          -0.228177   0.342117  -0.667   0.5052  
## ModsSC.d:cond.c  0.749113   0.664163   1.128   0.2601  
## ModsC.d:cond.c  -0.242411   0.570910  -0.425   0.6714  
## ModsL.d:cond.c   0.925279   0.686767   1.347   0.1787  
## ModsSL.d:cond.c  0.651707   0.922097   0.707   0.4802  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.149 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0624, Adjusted R-squared:  0.0399 
## F-statistic: 2.773 on 9 and 375 DF,  p-value: 0.003732

# Action 13
mod.c.b13 <- lm(act13 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b13) # yes, condition difference

## 
## Call:
## lm(formula = act13 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.07976    0.15156  -0.526   0.5990  
## ModsSC.d        -0.46782    0.30469  -1.535   0.1254  
## ModsC.d         -0.09002    0.26538  -0.339   0.7346  
## ModsL.d          0.41897    0.30107   1.392   0.1648  
## ModsSL.d         0.31785    0.38906   0.817   0.4144  
## cond.c           0.33533    0.30312   1.106   0.2692  
## ModsSC.d:cond.c -0.66125    0.60938  -1.085   0.2785  
## ModsC.d:cond.c  -1.21147    0.53075  -2.283   0.0229 *
## ModsL.d:cond.c  -0.48043    0.60214  -0.798   0.4254  
## ModsSL.d:cond.c -0.52581    0.77812  -0.676   0.4996  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

# Action 14
mod.c.b14 <- lm(act14 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b14) # no

## 
## Call:
## lm(formula = act14 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.3731  0.1429  1.6269  3.1429 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.42427    0.18054   2.350   0.0195 *
## ModsSC.d         0.02586    0.37292   0.069   0.9448  
## ModsC.d         -0.08978    0.32197  -0.279   0.7806  
## ModsL.d          0.57029    0.37801   1.509   0.1325  
## ModsSL.d        -0.24570    0.48686  -0.505   0.6142  
## cond.c           0.10228    0.36108   0.283   0.7772  
## ModsSC.d:cond.c  0.56269    0.74583   0.754   0.4512  
## ModsC.d:cond.c  -0.39626    0.64394  -0.615   0.5388  
## ModsL.d:cond.c   0.40859    0.75603   0.540   0.5893  
## ModsSL.d:cond.c  0.54058    0.97372   0.555   0.5792  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.02046,    Adjusted R-squared:  -0.01125 
## F-statistic: 0.6452 on 9 and 278 DF,  p-value: 0.7579

# Action 15
mod.c.b15 <- lm(act15 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b15) # no

## 
## Call:
## lm(formula = act15 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.09901    0.15896   0.623   0.5338  
## ModsSC.d        -0.47430    0.31144  -1.523   0.1286  
## ModsC.d          0.02274    0.27647   0.082   0.9345  
## ModsL.d          0.70599    0.34518   2.045   0.0415 *
## ModsSL.d         0.22648    0.39518   0.573   0.5669  
## cond.c          -0.09412    0.31791  -0.296   0.7674  
## ModsSC.d:cond.c  0.42046    0.62289   0.675   0.5001  
## ModsC.d:cond.c  -0.10683    0.55294  -0.193   0.8469  
## ModsL.d:cond.c  -1.41588    0.69036  -2.051   0.0410 *
## ModsSL.d:cond.c  0.50981    0.79036   0.645   0.5193  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

# Action 16
mod.c.b16 <- lm(act16 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b16) # no

## 
## Call:
## lm(formula = act16 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2105 -1.1000  0.2034  1.4500  2.4500 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.964286   0.155756   6.191 1.82e-09 ***
## ModsSC.d        -0.403571   0.311512  -1.296    0.196    
## ModsC.d          0.010172   0.267541   0.038    0.970    
## ModsL.d          0.119048   0.324647   0.367    0.714    
## ModsSL.d        -0.009023   0.392248  -0.023    0.982    
## cond.c           0.271429   0.311512   0.871    0.384    
## ModsSC.d:cond.c -0.250000   0.623024  -0.401    0.688    
## ModsC.d:cond.c   0.148076   0.535081   0.277    0.782    
## ModsL.d:cond.c  -0.104762   0.649294  -0.161    0.872    
## ModsSL.d:cond.c -0.781955   0.784497  -0.997    0.320    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.843 on 322 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.01422,    Adjusted R-squared:  -0.01334 
## F-statistic: 0.5159 on 9 and 322 DF,  p-value: 0.8629

# Action 17
mod.c.b17 <- lm(act17 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b17) # marginal

## 
## Call:
## lm(formula = act17 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.47028    0.15163   3.101  0.00206 **
## ModsSC.d        -0.12720    0.29728  -0.428  0.66895   
## ModsC.d         -0.22185    0.26304  -0.843  0.39949   
## ModsL.d          0.09033    0.29947   0.302  0.76309   
## ModsSL.d         0.81187    0.37848   2.145  0.03254 * 
## cond.c          -0.29858    0.30326  -0.985  0.32542   
## ModsSC.d:cond.c  1.12758    0.59455   1.897  0.05860 . 
## ModsC.d:cond.c  -0.53790    0.52607  -1.022  0.30716   
## ModsL.d:cond.c   0.51070    0.59894   0.853  0.39435   
## ModsSL.d:cond.c  1.16286    0.75697   1.536  0.12526   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

# Action 18
mod.c.b18 <- lm(act18 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.1538  0.2031  1.4211  3.0400 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.81402    0.15648   5.202 3.39e-07 ***
## ModsSC.d        -0.63759    0.29881  -2.134   0.0336 *  
## ModsC.d         -0.33010    0.26605  -1.241   0.2155    
## ModsL.d          0.11774    0.29157   0.404   0.6866    
## ModsSL.d        -0.04382    0.38709  -0.113   0.9099    
## cond.c          -0.03429    0.31297  -0.110   0.9128    
## ModsSC.d:cond.c  0.46715    0.59762   0.782   0.4349    
## ModsC.d:cond.c  -0.15576    0.53210  -0.293   0.7699    
## ModsL.d:cond.c   0.47846    0.58314   0.820   0.4125    
## ModsSL.d:cond.c  0.13025    0.77418   0.168   0.8665    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.85 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.0227, Adjusted R-squared:  -0.002867 
## F-statistic: 0.8879 on 9 and 344 DF,  p-value: 0.5362

# Action 19
mod.c.b19 <- lm(act19 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b19) # marginal

## 
## Call:
## lm(formula = act19 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.24858    0.16964   7.360 2.31e-12 ***
## ModsSC.d        -0.34812    0.33043  -1.054    0.293    
## ModsC.d         -0.10969    0.29133  -0.377    0.707    
## ModsL.d          0.15618    0.36243   0.431    0.667    
## ModsSL.d         0.04537    0.38551   0.118    0.906    
## cond.c          -0.46438    0.33928  -1.369    0.172    
## ModsSC.d:cond.c  0.51530    0.66086   0.780    0.436    
## ModsC.d:cond.c  -0.42451    0.58267  -0.729    0.467    
## ModsL.d:cond.c  -0.05943    0.72486  -0.082    0.935    
## ModsSL.d:cond.c  0.33800    0.77102   0.438    0.661    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

# Action 20
mod.c.b20 <- lm(act20 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b20) # no

## 
## Call:
## lm(formula = act20 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.589 -1.054  0.411  1.411  2.308 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.32131    0.15860   8.331 2.52e-15 ***
## ModsSC.d        -0.13218    0.29391  -0.450   0.6532    
## ModsC.d         -0.17646    0.26759  -0.659   0.5101    
## ModsL.d         -0.15654    0.30821  -0.508   0.6119    
## ModsSL.d        -0.33230    0.37869  -0.877   0.3809    
## cond.c          -0.53547    0.31720  -1.688   0.0924 .  
## ModsSC.d:cond.c -0.04279    0.58781  -0.073   0.9420    
## ModsC.d:cond.c   0.13767    0.53519   0.257   0.7972    
## ModsL.d:cond.c  -0.17501    0.61642  -0.284   0.7767    
## ModsSL.d:cond.c -0.05794    0.75738  -0.076   0.9391    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.786 on 314 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.028,  Adjusted R-squared:  0.0001354 
## F-statistic: 1.005 on 9 and 314 DF,  p-value: 0.4359

# Action 21
mod.c.b21 <- lm(act21 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b21) # no

## 
## Call:
## lm(formula = act21 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5556 -1.8723  0.1609  1.9286  3.9286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.055460   0.160051  -0.347   0.7291  
## ModsSC.d        -0.555885   0.308457  -1.802   0.0723 .
## ModsC.d         -0.236941   0.280775  -0.844   0.3992  
## ModsL.d          0.505112   0.313879   1.609   0.1084  
## ModsSL.d         0.483237   0.395097   1.223   0.2220  
## cond.c           0.210920   0.320103   0.659   0.5103  
## ModsSC.d:cond.c  0.423534   0.616913   0.687   0.4928  
## ModsC.d:cond.c   0.118564   0.561550   0.211   0.8329  
## ModsL.d:cond.c   0.000886   0.627759   0.001   0.9989  
## ModsSL.d:cond.c -0.133142   0.790195  -0.168   0.8663  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.067 on 393 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.03105,    Adjusted R-squared:  0.008857 
## F-statistic: 1.399 on 9 and 393 DF,  p-value: 0.1863

# Action 23
mod.c.b23 <- lm(act23 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b23) # no

## 
## Call:
## lm(formula = act23 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.222 -2.250  0.125  1.870  3.297 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.1054     0.2043   0.516    0.606
## ModsSC.d          0.1625     0.4084   0.398    0.691
## ModsC.d          -0.2719     0.3494  -0.778    0.437
## ModsL.d           0.3973     0.4213   0.943    0.346
## ModsSL.d          0.5995     0.5140   1.166    0.244
## cond.c           -0.4142     0.4086  -1.014    0.312
## ModsSC.d:cond.c   0.4499     0.8168   0.551    0.582
## ModsC.d:cond.c    0.1526     0.6989   0.218    0.827
## ModsL.d:cond.c   -0.8412     0.8426  -0.998    0.319
## ModsSL.d:cond.c   1.4489     1.0279   1.410    0.160
## 
## Residual standard error: 2.264 on 283 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.03197,    Adjusted R-squared:  0.001189 
## F-statistic: 1.039 on 9 and 283 DF,  p-value: 0.4092

# Action 24
mod.c.b24 <- lm(act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b24) # no

## 
## Call:
## lm(formula = act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.24854    0.15988  -1.555    0.121  
## ModsSC.d         0.11682    0.32785   0.356    0.722  
## ModsC.d         -0.22027    0.27431  -0.803    0.422  
## ModsL.d          0.09413    0.32301   0.291    0.771  
## ModsSL.d         0.15915    0.38706   0.411    0.681  
## cond.c          -0.64926    0.31976  -2.030    0.043 *
## ModsSC.d:cond.c  0.71915    0.65570   1.097    0.273  
## ModsC.d:cond.c   0.29058    0.54863   0.530    0.597  
## ModsL.d:cond.c   0.45808    0.64601   0.709    0.479  
## ModsSL.d:cond.c  0.56138    0.77413   0.725    0.469  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

# Action 25
mod.c.b25 <- lm(act25 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b25) # no

## 
## Call:
## lm(formula = act25 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -0.9307  0.1529  1.3778  3.3333 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.88888    0.13482   6.593 1.25e-10 ***
## ModsSC.d        -1.02524    0.27326  -3.752 0.000199 ***
## ModsC.d         -0.37601    0.22702  -1.656 0.098387 .  
## ModsL.d          0.24469    0.26732   0.915 0.360505    
## ModsSL.d         0.52779    0.34359   1.536 0.125237    
## cond.c          -0.08363    0.26964  -0.310 0.756582    
## ModsSC.d:cond.c  0.47757    0.54653   0.874 0.382690    
## ModsC.d:cond.c  -0.13508    0.45405  -0.298 0.766225    
## ModsL.d:cond.c   0.91327    0.53463   1.708 0.088307 .  
## ModsSL.d:cond.c  0.82173    0.68718   1.196 0.232424    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 436 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.06134,    Adjusted R-squared:  0.04197 
## F-statistic: 3.166 on 9 and 436 DF,  p-value: 0.001022

# Action 28
mod.c.b28 <- lm(act28 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b28) # no

## 
## Call:
## lm(formula = act28 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.97854    0.15089   6.485  2.9e-10 ***
## ModsSC.d        -0.15038    0.28587  -0.526   0.5992    
## ModsC.d          0.34289    0.24993   1.372   0.1709    
## ModsL.d          0.03980    0.29166   0.136   0.8915    
## ModsSL.d         0.24076    0.36893   0.653   0.5144    
## cond.c           0.18785    0.30179   0.622   0.5340    
## ModsSC.d:cond.c -1.06998    0.57174  -1.871   0.0621 .  
## ModsC.d:cond.c  -0.44975    0.49986  -0.900   0.3688    
## ModsL.d:cond.c   0.03634    0.58331   0.062   0.9504    
## ModsSL.d:cond.c  0.04022    0.73786   0.055   0.9566    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

# Action 30
mod.c.b30 <- lm(act30 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * cond.c, data = d)

summary(mod.c.b30) # no

## 
## Call:
## lm(formula = act30 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.04412    0.15965   6.540 2.22e-10 ***
## ModsSC.d         0.02838    0.29686   0.096   0.9239    
## ModsC.d         -0.02013    0.26436  -0.076   0.9394    
## ModsL.d          0.13848    0.30945   0.448   0.6548    
## ModsSL.d         0.11213    0.39203   0.286   0.7750    
## cond.c          -0.08824    0.31929  -0.276   0.7824    
## ModsSC.d:cond.c  1.19324    0.59371   2.010   0.0452 *  
## ModsC.d:cond.c  -0.14156    0.52872  -0.268   0.7891    
## ModsL.d:cond.c   0.17758    0.61890   0.287   0.7743    
## ModsSL.d:cond.c  0.27574    0.78407   0.352   0.7253    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

Climate > Control: Control > Climate: 24

a. Means for condition diffs

# Action 24
aggregate(d$act24[d$ideology == "Moderate"], list(d$cond[d$ideology == "Moderate"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1 climate -0.57
## 2    ctrl  0.08

summary(lm(d$act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * ctrl.d, data = d)) # Control not different from 0

## 
## Call:
## lm(formula = d$act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.07609    0.21951   0.347    0.729  
## ModsSC.d        -0.24275    0.48259  -0.503    0.615  
## ModsC.d         -0.36556    0.40601  -0.900    0.368  
## ModsL.d         -0.13491    0.42257  -0.319    0.750  
## ModsSL.d        -0.12154    0.49968  -0.243    0.808  
## ctrl.d          -0.64926    0.31976  -2.030    0.043 *
## ModsSC.d:ctrl.d  0.71915    0.65570   1.097    0.273  
## ModsC.d:ctrl.d   0.29058    0.54863   0.530    0.597  
## ModsL.d:ctrl.d   0.45808    0.64601   0.709    0.479  
## ModsSL.d:ctrl.d  0.56138    0.77413   0.725    0.469  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

summary(lm(d$act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * clim.d, data = d)) # Climate significantly lower than 0

## 
## Call:
## lm(formula = d$act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.57317    0.23251  -2.465   0.0141 *
## ModsSC.d         0.47640    0.44391   1.073   0.2838  
## ModsC.d         -0.07498    0.36899  -0.203   0.8391  
## ModsL.d          0.32317    0.48864   0.661   0.5087  
## ModsSL.d         0.43984    0.59126   0.744   0.4574  
## clim.d           0.64926    0.31976   2.030   0.0430 *
## ModsSC.d:clim.d -0.71915    0.65570  -1.097   0.2734  
## ModsC.d:clim.d  -0.29058    0.54863  -0.530   0.5966  
## ModsL.d:clim.d  -0.45808    0.64601  -0.709   0.4787  
## ModsSL.d:clim.d -0.56138    0.77413  -0.725   0.4688  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

2. Gender effects?

# Action 1
mod.g.b1 <- lm(act1 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b1)

## 
## Call:
## lm(formula = act1 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8261  0.2263  2.1538  4.1538 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.1369     0.1819   0.752  0.45225   
## ModsSC.d          -0.5333     0.3313  -1.610  0.10818   
## ModsC.d           -0.8422     0.3038  -2.772  0.00582 **
## ModsL.d            0.4698     0.3631   1.294  0.19642   
## ModsSL.d          -0.3108     0.4316  -0.720  0.47183   
## gend.mf            0.7263     0.3638   1.996  0.04653 * 
## ModsSC.d:gend.mf  -0.2811     0.6626  -0.424  0.67157   
## ModsC.d:gend.mf   -1.6234     0.6077  -2.671  0.00784 **
## ModsL.d:gend.mf   -0.8729     0.7262  -1.202  0.23002   
## ModsSL.d:gend.mf  -2.3785     0.8631  -2.756  0.00610 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.135 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.05646,    Adjusted R-squared:  0.03703 
## F-statistic: 2.906 on 9 and 437 DF,  p-value: 0.002374

# Action 2
mod.g.b2 <- lm(act2 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b2) # yes, higher than 0

## 
## Call:
## lm(formula = act2 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4375 -1.2184  0.5263  1.5625  2.6087 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.18920    0.20459   5.813 1.68e-08 ***
## ModsSC.d         -0.15606    0.33874  -0.461    0.645    
## ModsC.d          -0.41021    0.31073  -1.320    0.188    
## ModsL.d           0.52955    0.38541   1.374    0.171    
## ModsSL.d         -0.24753    0.44458  -0.557    0.578    
## gend.mf          -0.05839    0.40918  -0.143    0.887    
## ModsSC.d:gend.mf  0.93948    0.67748   1.387    0.167    
## ModsC.d:gend.mf  -0.71697    0.62146  -1.154    0.250    
## ModsL.d:gend.mf   0.62089    0.77083   0.805    0.421    
## ModsSL.d:gend.mf -0.32494    0.88916  -0.365    0.715    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.03738,    Adjusted R-squared:  0.006217 
## F-statistic: 1.199 on 9 and 278 DF,  p-value: 0.2951

# Action 3
mod.g.b3 <- lm(act3 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b3) # nothing

## 
## Call:
## lm(formula = act3 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.8390 -0.0368  1.4167  4.1579 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.06672    0.17073  -0.391   0.6961  
## ModsSC.d         -0.72961    0.31655  -2.305   0.0216 *
## ModsC.d          -0.34712    0.28567  -1.215   0.2250  
## ModsL.d          -0.04550    0.32568  -0.140   0.8890  
## ModsSL.d          0.30839    0.41638   0.741   0.4593  
## gend.mf          -0.20698    0.34145  -0.606   0.5447  
## ModsSC.d:gend.mf  0.93009    0.63310   1.469   0.1425  
## ModsC.d:gend.mf   0.21986    0.57135   0.385   0.7006  
## ModsL.d:gend.mf   0.54253    0.65136   0.833   0.4054  
## ModsSL.d:gend.mf -0.47636    0.83277  -0.572   0.5676  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.03535,    Adjusted R-squared:  0.01525 
## F-statistic: 1.759 on 9 and 432 DF,  p-value: 0.07405

# Action 4
mod.g.b4 <- lm(act4 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b4) # nothing

## 
## Call:
## lm(formula = act4 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.944 -1.904  0.200  2.056  3.762 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.21765    0.20985  -1.037   0.3004  
## ModsSC.d          0.43116    0.37047   1.164   0.2453  
## ModsC.d          -0.21138    0.35032  -0.603   0.5466  
## ModsL.d           0.67300    0.38161   1.764   0.0787 .
## ModsSL.d          0.53987    0.47659   1.133   0.2581  
## gend.mf          -0.03529    0.41969  -0.084   0.9330  
## ModsSC.d:gend.mf  0.39088    0.74095   0.528   0.5981  
## ModsC.d:gend.mf  -0.63046    0.70063  -0.900   0.3688  
## ModsL.d:gend.mf   0.37458    0.76322   0.491   0.6239  
## ModsSL.d:gend.mf -1.20915    0.95317  -1.269   0.2054  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.17 on 359 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02849,    Adjusted R-squared:  0.004137 
## F-statistic:  1.17 on 9 and 359 DF,  p-value: 0.3132

# Action 5
mod.g.b5 <- lm(act5 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b5) # yes, higher than 0

## 
## Call:
## lm(formula = act5 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3000 -1.2128  0.4615  1.7000  2.7000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.08642    0.20883   5.203 3.76e-07 ***
## ModsSC.d         -0.36652    0.34054  -1.076    0.283    
## ModsC.d          -0.21081    0.31042  -0.679    0.498    
## ModsL.d           0.39691    0.38293   1.037    0.301    
## ModsSL.d         -0.70565    0.44704  -1.579    0.116    
## gend.mf           0.04938    0.41765   0.118    0.906    
## ModsSC.d:gend.mf -0.53266    0.68108  -0.782    0.435    
## ModsC.d:gend.mf  -0.72369    0.62084  -1.166    0.245    
## ModsL.d:gend.mf   0.31728    0.76585   0.414    0.679    
## ModsSL.d:gend.mf -0.21092    0.89407  -0.236    0.814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 285 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  -0.0005149 
## F-statistic: 0.9832 on 9 and 285 DF,  p-value: 0.454

# Action 6
mod.g.b6 <- lm(act6 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b6) # yes, higher than 0

## 
## Call:
## lm(formula = act6 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5455 -1.2424  0.5667  1.5667  2.6667 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.3582     0.1823   7.450 7.48e-13 ***
## ModsSC.d          -0.5336     0.3119  -1.710   0.0881 .  
## ModsC.d           -0.1016     0.2825  -0.359   0.7195    
## ModsL.d            0.2245     0.3484   0.644   0.5197    
## ModsSL.d          -0.4188     0.4024  -1.041   0.2987    
## gend.mf            0.2210     0.3646   0.606   0.5448    
## ModsSC.d:gend.mf  -0.6323     0.6239  -1.014   0.3115    
## ModsC.d:gend.mf   -0.5744     0.5651  -1.016   0.3101    
## ModsL.d:gend.mf    0.4596     0.6968   0.660   0.5100    
## ModsSL.d:gend.mf  -1.4332     0.8049  -1.781   0.0759 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 347 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -1.033e-06 
## F-statistic:     1 on 9 and 347 DF,  p-value: 0.4397

# Action 7
mod.g.b7 <- lm(act7 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b7) # nothing

## 
## Call:
## lm(formula = act7 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8433  0.1567  1.6364  3.6364 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      -0.10161    0.18381  -0.553    0.581
## ModsSC.d         -0.01007    0.33941  -0.030    0.976
## ModsC.d          -0.31813    0.31775  -1.001    0.317
## ModsL.d           0.14480    0.36346   0.398    0.691
## ModsSL.d          0.21939    0.44719   0.491    0.624
## gend.mf           0.11020    0.36761   0.300    0.764
## ModsSC.d:gend.mf  0.29499    0.67882   0.435    0.664
## ModsC.d:gend.mf  -0.54344    0.63549  -0.855    0.393
## ModsL.d:gend.mf   0.20343    0.72692   0.280    0.780
## ModsSL.d:gend.mf -1.23465    0.89439  -1.380    0.168
## 
## Residual standard error: 2.097 on 403 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  -0.006717 
## F-statistic: 0.6946 on 9 and 403 DF,  p-value: 0.714

# Action 8
mod.g.b8 <- lm(act8 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b8) # marginally higher than 0

## 
## Call:
## lm(formula = act8 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9286 -1.9091 -0.2143  1.6875  4.5319 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.38340    0.17676  -2.169   0.0306 *
## ModsSC.d         -0.54165    0.32590  -1.662   0.0972 .
## ModsC.d          -0.35321    0.29225  -1.209   0.2274  
## ModsL.d           0.94462    0.36662   2.577   0.0103 *
## ModsSL.d          0.80223    0.41652   1.926   0.0547 .
## gend.mf           0.21360    0.35352   0.604   0.5460  
## ModsSC.d:gend.mf  1.00013    0.65180   1.534   0.1256  
## ModsC.d:gend.mf  -0.31181    0.58449  -0.533   0.5940  
## ModsL.d:gend.mf   0.09252    0.73325   0.126   0.8996  
## ModsSL.d:gend.mf -1.23308    0.83305  -1.480   0.1395  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 469 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.07584,    Adjusted R-squared:  0.05811 
## F-statistic: 4.276 on 9 and 469 DF,  p-value: 2.361e-05

# Action 9
mod.g.b9 <- lm(act9 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b9) # yes, higher than 0

## 
## Call:
## lm(formula = act9 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5882 -1.5882  0.1481  1.5417  3.8261 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.16736    0.16826   0.995  0.32044   
## ModsSC.d         -0.90473    0.31104  -2.909  0.00381 **
## ModsC.d          -0.38727    0.27882  -1.389  0.16555   
## ModsL.d           0.41399    0.32567   1.271  0.20433   
## ModsSL.d         -0.21092    0.39622  -0.532  0.59477   
## gend.mf          -0.06806    0.33651  -0.202  0.83983   
## ModsSC.d:gend.mf -0.10938    0.62208  -0.176  0.86051   
## ModsC.d:gend.mf   0.21157    0.55764   0.379  0.70457   
## ModsL.d:gend.mf   0.08182    0.65133   0.126  0.90009   
## ModsSL.d:gend.mf -0.93573    0.79245  -1.181  0.23832   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.97 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.04187,    Adjusted R-squared:  0.02214 
## F-statistic: 2.122 on 9 and 437 DF,  p-value: 0.02658

# Action 10
mod.g.b10 <- lm(act10 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b10) # yes, higher than 0

## 
## Call:
## lm(formula = act10 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5862 -2.0719  0.1948  1.5714  3.7778 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.0903     0.1739   0.519   0.6039  
## ModsSC.d          -0.6314     0.3171  -1.991   0.0470 *
## ModsC.d           -0.4020     0.2870  -1.401   0.1619  
## ModsL.d            0.8427     0.3449   2.444   0.0149 *
## ModsSL.d           0.6528     0.4173   1.564   0.1184  
## gend.mf            0.0368     0.3478   0.106   0.9158  
## ModsSC.d:gend.mf   0.4366     0.6341   0.689   0.4915  
## ModsC.d:gend.mf   -0.2706     0.5739  -0.471   0.6376  
## ModsL.d:gend.mf    0.9721     0.6898   1.409   0.1594  
## ModsSL.d:gend.mf  -1.7230     0.8346  -2.065   0.0395 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 466 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07046,    Adjusted R-squared:  0.05251 
## F-statistic: 3.925 on 9 and 466 DF,  p-value: 7.905e-05

# Action 11
mod.g.b11 <- lm(act11 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b11) # yes, higher than 0

## 
## Call:
## lm(formula = act11 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5625 -1.0000  0.2834  1.4375  2.7778 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.71659    0.18378   3.899 0.000119 ***
## ModsSC.d          0.07633    0.32529   0.235 0.814628    
## ModsC.d           0.01068    0.30624   0.035 0.972207    
## ModsL.d           1.01280    0.36352   2.786 0.005672 ** 
## ModsSL.d          0.77894    0.46165   1.687 0.092576 .  
## gend.mf          -0.06176    0.36755  -0.168 0.866671    
## ModsSC.d:gend.mf  1.20317    0.65059   1.849 0.065382 .  
## ModsC.d:gend.mf  -0.48369    0.61247  -0.790 0.430299    
## ModsL.d:gend.mf   0.44912    0.72703   0.618 0.537205    
## ModsSL.d:gend.mf -0.07217    0.92329  -0.078 0.937750    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 302 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.05336,    Adjusted R-squared:  0.02515 
## F-statistic: 1.891 on 9 and 302 DF,  p-value: 0.05276

# Action 12
mod.g.b12 <- lm(act12 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b12) # yes, higher than 0

## 
## Call:
## lm(formula = act12 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5625 -1.8571 -0.1345  1.8655  4.3043 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -0.1565     0.1983  -0.789  0.43065   
## ModsSC.d          -0.5968     0.3510  -1.700  0.08989 . 
## ModsC.d           -0.6748     0.3248  -2.078  0.03841 * 
## ModsL.d            1.2804     0.4017   3.188  0.00156 **
## ModsSL.d           0.3377     0.4726   0.714  0.47537   
## gend.mf           -0.5818     0.3966  -1.467  0.14324   
## ModsSC.d:gend.mf   1.3610     0.7019   1.939  0.05326 . 
## ModsC.d:gend.mf   -0.3643     0.6496  -0.561  0.57523   
## ModsL.d:gend.mf    2.0005     0.8034   2.490  0.01320 * 
## ModsSL.d:gend.mf  -0.1807     0.9453  -0.191  0.84853   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.129 on 373 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.08175,    Adjusted R-squared:  0.0596 
## F-statistic:  3.69 on 9 and 373 DF,  p-value: 0.0001907

# Action 13
mod.g.b13 <- lm(act13 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b13) # nothing

## 
## Call:
## lm(formula = act13 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.8095  0.1884  1.9558  3.3636 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.15187    0.17475  -0.869  0.38530   
## ModsSC.d         -0.14792    0.32254  -0.459  0.64675   
## ModsC.d          -0.09451    0.30339  -0.312  0.75557   
## ModsL.d           0.62901    0.35714   1.761  0.07890 . 
## ModsSL.d          0.27440    0.41609   0.659  0.50994   
## gend.mf          -0.26148    0.34950  -0.748  0.45478   
## ModsSC.d:gend.mf  2.04287    0.64507   3.167  0.00165 **
## ModsC.d:gend.mf   0.14554    0.60679   0.240  0.81056   
## ModsL.d:gend.mf   0.73577    0.71428   1.030  0.30354   
## ModsSL.d:gend.mf -0.71085    0.83217  -0.854  0.39346   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.04764,    Adjusted R-squared:  0.0278 
## F-statistic: 2.401 on 9 and 432 DF,  p-value: 0.01156

# Action 14
mod.g.b14 <- lm(act14 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b14) # yes, higher than 0

## 
## Call:
## lm(formula = act14 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8235 -1.2778  0.2195  1.5455  3.7222 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.3878     0.2061   1.882   0.0609 .
## ModsSC.d           0.1544     0.3827   0.403   0.6870  
## ModsC.d           -0.3587     0.3518  -1.020   0.3088  
## ModsL.d            0.7144     0.4138   1.727   0.0853 .
## ModsSL.d          -0.5307     0.5102  -1.040   0.2992  
## gend.mf           -0.1507     0.4122  -0.366   0.7150  
## ModsSC.d:gend.mf   0.7133     0.7654   0.932   0.3522  
## ModsC.d:gend.mf   -1.3521     0.7035  -1.922   0.0557 .
## ModsL.d:gend.mf    0.8552     0.8275   1.033   0.3023  
## ModsSL.d:gend.mf  -1.2779     1.0204  -1.252   0.2115  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.017 on 276 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.04977,    Adjusted R-squared:  0.01878 
## F-statistic: 1.606 on 9 and 276 DF,  p-value: 0.1132

# Action 15
mod.g.b15 <- lm(act15 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b15) # yes, higher than 0

## 
## Call:
## lm(formula = act15 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6562 -1.7674  0.4444  1.6917  3.5714 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.06806    0.17827  -0.382   0.7029   
## ModsSC.d         -0.23849    0.32379  -0.737   0.4618   
## ModsC.d           0.07169    0.29812   0.240   0.8101   
## ModsL.d           1.12695    0.37986   2.967   0.0032 **
## ModsSL.d          0.30139    0.41287   0.730   0.4659   
## gend.mf          -0.75278    0.35655  -2.111   0.0354 * 
## ModsSC.d:gend.mf  1.28254    0.64757   1.981   0.0484 * 
## ModsC.d:gend.mf   0.15291    0.59625   0.256   0.7977   
## ModsL.d:gend.mf   1.55807    0.75972   2.051   0.0410 * 
## ModsSL.d:gend.mf  0.11944    0.82575   0.145   0.8851   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 374 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02548 
## F-statistic: 2.113 on 9 and 374 DF,  p-value: 0.0277

# Action 16
mod.g.b16 <- lm(act16 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b16) # yes, higher than 0

## 
## Call:
## lm(formula = act16 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3636 -1.1400  0.1589  1.6364  2.7586 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.1024     0.1824   6.044 4.17e-09 ***
## ModsSC.d          -0.4554     0.3261  -1.396    0.164    
## ModsC.d           -0.2142     0.2970  -0.721    0.471    
## ModsL.d           -0.1306     0.3609  -0.362    0.718    
## ModsSL.d          -0.1579     0.4131  -0.382    0.702    
## gend.mf            0.5225     0.3648   1.432    0.153    
## ModsSC.d:gend.mf   0.2887     0.6522   0.443    0.658    
## ModsC.d:gend.mf   -1.0262     0.5939  -1.728    0.085 .  
## ModsL.d:gend.mf   -0.9661     0.7218  -1.338    0.182    
## ModsSL.d:gend.mf  -0.6336     0.8262  -0.767    0.444    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 321 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.02518,    Adjusted R-squared:  -0.002148 
## F-statistic: 0.9214 on 9 and 321 DF,  p-value: 0.5066

# Action 17
mod.g.b17 <- lm(act17 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b17) # yes, higher than 0

## 
## Call:
## lm(formula = act17 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.391 -1.477 -0.033  1.630  3.077 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.48265    0.17663   2.733  0.00656 **
## ModsSC.d         -0.09420    0.31868  -0.296  0.76768   
## ModsC.d          -0.38619    0.29321  -1.317  0.18855   
## ModsL.d           0.27356    0.35372   0.773  0.43975   
## ModsSL.d          0.61300    0.41933   1.462  0.14455   
## gend.mf           0.01145    0.35326   0.032  0.97416   
## ModsSC.d:gend.mf  0.08123    0.63736   0.127  0.89865   
## ModsC.d:gend.mf  -0.35821    0.58642  -0.611  0.54164   
## ModsL.d:gend.mf   0.76184    0.70745   1.077  0.28217   
## ModsSL.d:gend.mf -0.60275    0.83866  -0.719  0.47273   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.008 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.02291,    Adjusted R-squared:  0.001247 
## F-statistic: 1.058 on 9 and 406 DF,  p-value: 0.3934

# Action 18
mod.g.b18 <- lm(act18 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b18) # yes, higher than 0

## 
## Call:
## lm(formula = act18 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.1369  0.1776  1.4528  3.1000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.77485    0.18407   4.210 3.28e-05 ***
## ModsSC.d         -0.54224    0.31547  -1.719   0.0866 .  
## ModsC.d          -0.33460    0.30116  -1.111   0.2673    
## ModsL.d           0.19110    0.33882   0.564   0.5731    
## ModsSL.d          0.01462    0.41691   0.035   0.9720    
## gend.mf          -0.09516    0.36814  -0.258   0.7962    
## ModsSC.d:gend.mf  0.76037    0.63095   1.205   0.2290    
## ModsC.d:gend.mf  -0.11868    0.60233  -0.197   0.8439    
## ModsL.d:gend.mf   0.30612    0.67763   0.452   0.6517    
## ModsSL.d:gend.mf  0.51621    0.83381   0.619   0.5363    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.02394,    Adjusted R-squared:  -0.001748 
## F-statistic: 0.9319 on 9 and 342 DF,  p-value: 0.4973

# Action 19
mod.g.b19 <- lm(act19 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b19) # yes, higher than 0

## 
## Call:
## lm(formula = act19 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1622  0.3462  1.6250  2.3462 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.130952   0.197078   5.739 2.61e-08 ***
## ModsSC.d         -0.156970   0.343786  -0.457    0.648    
## ModsC.d          -0.115089   0.310397  -0.371    0.711    
## ModsL.d           0.369048   0.418066   0.883    0.378    
## ModsSL.d          0.007206   0.428628   0.017    0.987    
## gend.mf          -0.333333   0.394156  -0.846    0.398    
## ModsSC.d:gend.mf  0.973605   0.687572   1.416    0.158    
## ModsC.d:gend.mf   0.040736   0.620794   0.066    0.948    
## ModsL.d:gend.mf   0.583333   0.836131   0.698    0.486    
## ModsSL.d:gend.mf -0.442982   0.857257  -0.517    0.606    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.806 on 264 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.0208, Adjusted R-squared:  -0.01258 
## F-statistic: 0.6232 on 9 and 264 DF,  p-value: 0.7769

# Action 20
mod.g.b20 <- lm(act20 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b20) # yes, higher than 0

## 
## Call:
## lm(formula = act20 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.398 -1.200  0.602  1.602  2.391 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.29898    0.18779   6.917 2.61e-11 ***
## ModsSC.d         -0.13590    0.31136  -0.436    0.663    
## ModsC.d          -0.29898    0.29680  -1.007    0.315    
## ModsL.d          -0.03182    0.35579  -0.089    0.929    
## ModsSL.d         -0.40682    0.41582  -0.978    0.329    
## gend.mf          -0.19796    0.37558  -0.527    0.599    
## ModsSC.d:gend.mf  0.45514    0.62271   0.731    0.465    
## ModsC.d:gend.mf  -0.58465    0.59359  -0.985    0.325    
## ModsL.d:gend.mf   0.49698    0.71158   0.698    0.485    
## ModsSL.d:gend.mf -0.25302    0.83164  -0.304    0.761    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.8 on 312 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.01743,    Adjusted R-squared:  -0.01091 
## F-statistic: 0.615 on 9 and 312 DF,  p-value: 0.7842

# Action 21
mod.g.b21 <- lm(act21 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b21) # nothing

## 
## Call:
## lm(formula = act21 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.556 -1.840  0.160  1.696  3.818 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.01756    0.18612   0.094   0.9249  
## ModsSC.d         -0.65165    0.33164  -1.965   0.0501 .
## ModsC.d          -0.37819    0.31247  -1.210   0.2269  
## ModsL.d           0.45365    0.36089   1.257   0.2095  
## ModsSL.d          0.41239    0.44717   0.922   0.3570  
## gend.mf           0.35512    0.37223   0.954   0.3407  
## ModsSC.d:gend.mf -0.72330    0.66328  -1.090   0.2762  
## ModsC.d:gend.mf  -0.80052    0.62494  -1.281   0.2010  
## ModsL.d:gend.mf  -0.23088    0.72178  -0.320   0.7492  
## ModsSL.d:gend.mf -0.10391    0.89435  -0.116   0.9076  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.068 on 391 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.0295, Adjusted R-squared:  0.007162 
## F-statistic: 1.321 on 9 and 391 DF,  p-value: 0.2241

# Action 22
mod.g.b22 <- lm(act22 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b22) # yes, higher than 0

## 
## Call:
## lm(formula = act22 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.4912 -0.1538  1.6410  3.6410 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.3972     0.1821   2.181   0.0298 *
## ModsSC.d          -0.6041     0.3199  -1.888   0.0598 .
## ModsC.d           -0.1516     0.2986  -0.508   0.6120  
## ModsL.d            0.2280     0.3615   0.631   0.5285  
## ModsSL.d           0.6797     0.4098   1.659   0.0980 .
## gend.mf            0.4688     0.3642   1.287   0.1988  
## ModsSC.d:gend.mf   0.3995     0.6399   0.624   0.5328  
## ModsC.d:gend.mf   -0.9600     0.5972  -1.607   0.1088  
## ModsL.d:gend.mf   -0.3346     0.7230  -0.463   0.6437  
## ModsSL.d:gend.mf  -0.6226     0.8196  -0.760   0.4479  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.973 on 392 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.04639,    Adjusted R-squared:  0.02449 
## F-statistic: 2.119 on 9 and 392 DF,  p-value: 0.02709

# Action 23
mod.g.b23 <- lm(act23 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b23) # marginally higher than 0

## 
## Call:
## lm(formula = act23 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9474 -2.0103  0.1875  2.0227  3.5238 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.25515    0.24953   1.023    0.307
## ModsSC.d          0.05944    0.43299   0.137    0.891
## ModsC.d          -0.52842    0.38996  -1.355    0.176
## ModsL.d           0.33235    0.44785   0.742    0.459
## ModsSL.d          0.02610    0.54921   0.048    0.962
## gend.mf           0.48969    0.49905   0.981    0.327
## ModsSC.d:gend.mf  0.77586    0.86599   0.896    0.371
## ModsC.d:gend.mf  -0.99077    0.77992  -1.270    0.205
## ModsL.d:gend.mf  -0.73136    0.89569  -0.817    0.415
## ModsSL.d:gend.mf -1.55219    1.09842  -1.413    0.159
## 
## Residual standard error: 2.26 on 282 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03359,    Adjusted R-squared:  0.002749 
## F-statistic: 1.089 on 9 and 282 DF,  p-value: 0.3707

# Action 24
mod.g.b24 <- lm(act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b24) # nothing

## 
## Call:
## lm(formula = act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5455 -1.8425  0.1575  1.8214  3.8214 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.3287     0.1805  -1.821   0.0693 .
## ModsSC.d           0.3136     0.3405   0.921   0.3576  
## ModsC.d           -0.2617     0.2985  -0.877   0.3812  
## ModsL.d            0.2865     0.3690   0.776   0.4379  
## ModsSL.d           0.1158     0.4282   0.270   0.7870  
## gend.mf           -0.3425     0.3610  -0.949   0.3433  
## ModsSC.d:gend.mf   1.4637     0.6810   2.149   0.0322 *
## ModsC.d:gend.mf   -0.1195     0.5969  -0.200   0.8414  
## ModsL.d:gend.mf    0.7126     0.7381   0.966   0.3348  
## ModsSL.d:gend.mf  -0.2316     0.8564  -0.270   0.7870  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.098 on 405 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.02029,    Adjusted R-squared:  -0.001484 
## F-statistic: 0.9318 on 9 and 405 DF,  p-value: 0.4971

# Action 25
mod.g.b25 <- lm(act25 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b25) # yes, higher than 0

## 
## Call:
## lm(formula = act25 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -0.8936  0.1642  1.4389  3.2051 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.88521    0.15553   5.692 2.32e-08 ***
## ModsSC.d         -0.96397    0.29380  -3.281  0.00112 ** 
## ModsC.d          -0.47410    0.25867  -1.833  0.06751 .  
## ModsL.d           0.32356    0.30861   1.048  0.29501    
## ModsSL.d          0.47701    0.39031   1.222  0.22233    
## gend.mf           0.01681    0.31106   0.054  0.95694    
## ModsSC.d:gend.mf  0.23594    0.58761   0.402  0.68823    
## ModsC.d:gend.mf  -0.39458    0.51733  -0.763  0.44604    
## ModsL.d:gend.mf   0.31564    0.61722   0.511  0.60934    
## ModsSL.d:gend.mf  0.14764    0.78063   0.189  0.85008    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.05138,    Adjusted R-squared:  0.03171 
## F-statistic: 2.612 on 9 and 434 DF,  p-value: 0.006027

# Action 26
mod.g.b26 <- lm(act26 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b26) # yes, higher than 0

## 
## Call:
## lm(formula = act26 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0000  0.5278  1.3433  2.5000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.542644   0.229336   6.727 1.53e-10 ***
## ModsSC.d          0.001474   0.379614   0.004    0.997    
## ModsC.d          -0.014866   0.353264  -0.042    0.966    
## ModsL.d           0.028785   0.455812   0.063    0.950    
## ModsSL.d         -0.570422   0.479711  -1.189    0.236    
## gend.mf          -0.228145   0.458672  -0.497    0.619    
## ModsSC.d:gend.mf  0.316380   0.759228   0.417    0.677    
## ModsC.d:gend.mf  -0.049633   0.706527  -0.070    0.944    
## ModsL.d:gend.mf   1.371002   0.911624   1.504    0.134    
## ModsSL.d:gend.mf  1.172589   0.959422   1.222    0.223    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 216 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.02923,    Adjusted R-squared:  -0.01122 
## F-statistic: 0.7225 on 9 and 216 DF,  p-value: 0.688

# Action 27
mod.g.b27 <- lm(act27 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b27) # yes, higher than 0

## 
## Call:
## lm(formula = act27 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4545 -1.1667  0.2174  1.5521  3.0000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.6240     0.1965   3.176  0.00164 **
## ModsSC.d          -0.1448     0.3269  -0.443  0.65812   
## ModsC.d            0.1088     0.3138   0.347  0.72901   
## ModsL.d            0.5503     0.3726   1.477  0.14074   
## ModsSL.d           0.7349     0.4065   1.808  0.07159 . 
## gend.mf            0.3521     0.3929   0.896  0.37090   
## ModsSC.d:gend.mf   0.6062     0.6538   0.927  0.35450   
## ModsC.d:gend.mf   -0.2524     0.6276  -0.402  0.68783   
## ModsL.d:gend.mf   -0.3672     0.7452  -0.493  0.62251   
## ModsSL.d:gend.mf  -0.1607     0.8129  -0.198  0.84343   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 307 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.03928,    Adjusted R-squared:  0.01112 
## F-statistic: 1.395 on 9 and 307 DF,  p-value: 0.1895

# Action 28
mod.g.b28 <- lm(act28 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b28) # yes, higher than 0

## 
## Call:
## lm(formula = act28 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4262 -0.9511  0.1944  1.5738  2.4286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.00563    0.18137   5.545 5.69e-08 ***
## ModsSC.d         -0.22190    0.31023  -0.715    0.475    
## ModsC.d           0.22922    0.28844   0.795    0.427    
## ModsL.d          -0.09575    0.34993  -0.274    0.785    
## ModsSL.d         -0.02427    0.43526  -0.056    0.956    
## gend.mf           0.17056    0.36274   0.470    0.639    
## ModsSC.d:gend.mf -0.21421    0.62045  -0.345    0.730    
## ModsC.d:gend.mf  -0.55331    0.57688  -0.959    0.338    
## ModsL.d:gend.mf  -0.49032    0.69985  -0.701    0.484    
## ModsSL.d:gend.mf -0.99043    0.87051  -1.138    0.256    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 362 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.01604,    Adjusted R-squared:  -0.008426 
## F-statistic: 0.6556 on 9 and 362 DF,  p-value: 0.749

# Action 29
mod.g.b29 <- lm(act29 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b29) # yes, higher than 0

## 
## Call:
## lm(formula = act29 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1003  0.1951  1.5000  2.1951 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.114929   0.171938   6.484 2.96e-10 ***
## ModsSC.d          0.004118   0.299162   0.014    0.989    
## ModsC.d          -0.122698   0.275087  -0.446    0.656    
## ModsL.d           0.037510   0.323942   0.116    0.908    
## ModsSL.d          0.314895   0.397959   0.791    0.429    
## gend.mf          -0.058430   0.343876  -0.170    0.865    
## ModsSC.d:gend.mf  0.296525   0.598324   0.496    0.620    
## ModsC.d:gend.mf  -0.092700   0.550174  -0.168    0.866    
## ModsL.d:gend.mf   0.753552   0.647885   1.163    0.246    
## ModsSL.d:gend.mf -0.134553   0.795917  -0.169    0.866    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.774 on 358 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01019,    Adjusted R-squared:  -0.0147 
## F-statistic: 0.4093 on 9 and 358 DF,  p-value: 0.93

# Action 30
mod.g.b30 <- lm(act30 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)

summary(mod.g.b30) # yes, higher than 0

## 
## Call:
## lm(formula = act30 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3529 -1.0654  0.5928  1.6471  2.3913 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.98271    0.19441   5.055 7.04e-07 ***
## ModsSC.d          0.10680    0.31992   0.334    0.739    
## ModsC.d          -0.09064    0.30305  -0.299    0.765    
## ModsL.d           0.28753    0.35948   0.800    0.424    
## ModsSL.d          0.09376    0.42244   0.222    0.824    
## gend.mf          -0.16542    0.38883  -0.425    0.671    
## ModsSC.d:gend.mf -0.36143    0.63984  -0.565    0.573    
## ModsC.d:gend.mf  -0.40132    0.60610  -0.662    0.508    
## ModsL.d:gend.mf   0.54801    0.71896   0.762    0.446    
## ModsSL.d:gend.mf -0.38752    0.84487  -0.459    0.647    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.882 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.01188,    Adjusted R-squared:  -0.01412 
## F-statistic: 0.4569 on 9 and 342 DF,  p-value: 0.9027

a. Gender means

# Action 1
aggregate(d$act1[d$ideology == "Moderate"], list(d$gend[d$ideology == "Moderate"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1  Female -0.23
## 2    Male  0.50
## 3   Other  1.00

# Action 15
aggregate(d$act15[d$ideology == "Moderate"], list(d$gend[d$ideology == "Moderate"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1  Female  0.31
## 2    Male -0.44
## 3   Other  0.00

3. Gender x condition

# Action 1
summary(lm(act1 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.960  0.080  2.000  5.000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.10891    0.18161   0.600   0.5490   
## ModsSC.d                -0.51105    0.33098  -1.544   0.1233   
## ModsC.d                 -0.78305    0.31017  -2.525   0.0119 * 
## ModsL.d                  0.48556    0.36207   1.341   0.1806   
## ModsSL.d                -0.38182    0.43641  -0.875   0.3821   
## gend.mf                  0.70219    0.36322   1.933   0.0539 . 
## cond.c                  -0.70219    0.36322  -1.933   0.0539 . 
## ModsSC.d:gend.mf        -0.20092    0.66196  -0.304   0.7616   
## ModsC.d:gend.mf         -1.53691    0.62033  -2.478   0.0136 * 
## ModsL.d:gend.mf         -0.87326    0.72414  -1.206   0.2285   
## ModsSL.d:gend.mf        -1.95635    0.87283  -2.241   0.0255 * 
## ModsSC.d:cond.c          1.45345    0.66196   2.196   0.0287 * 
## ModsC.d:cond.c           0.49994    0.62033   0.806   0.4207   
## ModsL.d:cond.c           0.57612    0.72414   0.796   0.4267   
## ModsSL.d:cond.c          0.82302    0.87283   0.943   0.3462   
## gend.mf:cond.c          -0.43563    0.72645  -0.600   0.5490   
## ModsSC.d:gend.mf:cond.c  0.99371    1.32392   0.751   0.4533   
## ModsC.d:gend.mf:cond.c   0.02966    1.24066   0.024   0.9809   
## ModsL.d:gend.mf:cond.c  -0.77652    1.44828  -0.536   0.5921   
## ModsSL.d:gend.mf:cond.c  4.59396    1.74566   2.632   0.0088 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.124 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.0873, Adjusted R-squared:  0.04669 
## F-statistic:  2.15 on 19 and 427 DF,  p-value: 0.003504

# Action 2
summary(lm(act2 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6000 -1.0000  0.3485  1.5192  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.160668   0.205334   5.653 4.04e-08 ***
## ModsSC.d                -0.110953   0.342057  -0.324   0.7459    
## ModsC.d                 -0.364456   0.311151  -1.171   0.2425    
## ModsL.d                  0.510165   0.391941   1.302   0.1942    
## ModsSL.d                -0.352335   0.454456  -0.775   0.4389    
## gend.mf                  0.011996   0.410669   0.029   0.9767    
## cond.c                  -0.159432   0.410669  -0.388   0.6982    
## ModsSC.d:gend.mf         0.774935   0.684114   1.133   0.2583    
## ModsC.d:gend.mf         -0.755936   0.622303  -1.215   0.2255    
## ModsL.d:gend.mf          0.563004   0.783881   0.718   0.4732    
## ModsSL.d:gend.mf        -0.561996   0.908913  -0.618   0.5369    
## ModsSC.d:cond.c          0.008864   0.684114   0.013   0.9897    
## ModsC.d:cond.c          -0.566325   0.622303  -0.910   0.3636    
## ModsL.d:cond.c           0.151099   0.783881   0.193   0.8473    
## ModsSL.d:cond.c          0.776099   0.908913   0.854   0.3939    
## gend.mf:cond.c           0.985531   0.821338   1.200   0.2312    
## ModsSC.d:gend.mf:cond.c  0.088333   1.368228   0.065   0.9486    
## ModsC.d:gend.mf:cond.c  -2.564319   1.244606  -2.060   0.0403 *  
## ModsL.d:gend.mf:cond.c  -0.135531   1.567763  -0.086   0.9312    
## ModsSL.d:gend.mf:cond.c  1.247802   1.817826   0.686   0.4930    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 268 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.07459,    Adjusted R-squared:  0.008984 
## F-statistic: 1.137 on 19 and 268 DF,  p-value: 0.3138

# Action 3
summary(lm(act3 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7059 -1.8261  0.0278  1.7143  3.9412 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.064746   0.170869  -0.379   0.7049  
## ModsSC.d                -0.725473   0.317283  -2.287   0.0227 *
## ModsC.d                 -0.261643   0.293981  -0.890   0.3740  
## ModsL.d                  0.002246   0.330741   0.007   0.9946  
## ModsSL.d                 0.262645   0.427089   0.615   0.5389  
## gend.mf                 -0.211088   0.341738  -0.618   0.5371  
## cond.c                   0.064953   0.341738   0.190   0.8493  
## ModsSC.d:gend.mf         0.905162   0.634566   1.426   0.1545  
## ModsC.d:gend.mf          0.419422   0.587962   0.713   0.4760  
## ModsL.d:gend.mf          0.478946   0.661482   0.724   0.4694  
## ModsSL.d:gend.mf        -0.384710   0.854179  -0.450   0.6527  
## ModsSC.d:cond.c         -0.067850   0.634566  -0.107   0.9149  
## ModsC.d:cond.c          -0.384398   0.587962  -0.654   0.5136  
## ModsL.d:cond.c           0.702904   0.661482   1.063   0.2886  
## ModsSL.d:cond.c          0.024963   0.854179   0.029   0.9767  
## gend.mf:cond.c          -0.144399   0.683475  -0.211   0.8328  
## ModsSC.d:gend.mf:cond.c  0.922920   1.269132   0.727   0.4675  
## ModsC.d:gend.mf:cond.c  -1.438934   1.175924  -1.224   0.2218  
## ModsL.d:gend.mf:cond.c  -1.105601   1.322965  -0.836   0.4038  
## ModsSL.d:gend.mf:cond.c  1.164567   1.708357   0.682   0.4958  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.05725,    Adjusted R-squared:  0.01481 
## F-statistic: 1.349 on 19 and 422 DF,  p-value: 0.1485

# Action 4
summary(lm(act4 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2000 -1.8551  0.1449  2.0909  3.5000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.212571   0.211693  -1.004   0.3160  
## ModsSC.d                 0.412437   0.375956   1.097   0.2734  
## ModsC.d                 -0.145981   0.358402  -0.407   0.6840  
## ModsL.d                  0.727331   0.391029   1.860   0.0637 .
## ModsSL.d                 0.574110   0.496493   1.156   0.2483  
## gend.mf                 -0.012358   0.423385  -0.029   0.9767  
## cond.c                  -0.342715   0.423385  -0.809   0.4188  
## ModsSC.d:gend.mf         0.385352   0.751912   0.512   0.6086  
## ModsC.d:gend.mf         -0.453231   0.716805  -0.632   0.5276  
## ModsL.d:gend.mf          0.382838   0.782059   0.490   0.6248  
## ModsSL.d:gend.mf        -1.310719   0.992985  -1.320   0.1877  
## ModsSC.d:cond.c          0.333357   0.751912   0.443   0.6578  
## ModsC.d:cond.c          -0.181207   0.716805  -0.253   0.8006  
## ModsL.d:cond.c           1.102669   0.782059   1.410   0.1594  
## ModsSL.d:cond.c          0.419638   0.992985   0.423   0.6728  
## gend.mf:cond.c          -0.439570   0.846770  -0.519   0.6040  
## ModsSC.d:gend.mf:cond.c  0.003742   1.503824   0.002   0.9980  
## ModsC.d:gend.mf:cond.c  -1.377970   1.433610  -0.961   0.3371  
## ModsL.d:gend.mf:cond.c   0.119662   1.564118   0.077   0.9391  
## ModsSL.d:gend.mf:cond.c -0.114276   1.985970  -0.058   0.9541  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.183 on 349 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.04372,    Adjusted R-squared:  -0.008345 
## F-statistic: 0.8397 on 19 and 349 DF,  p-value: 0.6588

# Action 5
summary(lm(act5 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4800 -1.2500  0.1622  1.5200  3.2500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.07416    0.20961   5.124 5.63e-07 ***
## ModsSC.d                -0.34454    0.34680  -0.993   0.3213    
## ModsC.d                 -0.11414    0.31715  -0.360   0.7192    
## ModsL.d                  0.39724    0.38741   1.025   0.3061    
## ModsSL.d                -0.80332    0.46523  -1.727   0.0853 .  
## gend.mf                  0.06048    0.41923   0.144   0.8854    
## cond.c                  -0.38740    0.41923  -0.924   0.3563    
## ModsSC.d:gend.mf        -0.54243    0.69361  -0.782   0.4349    
## ModsC.d:gend.mf         -0.52953    0.63429  -0.835   0.4045    
## ModsL.d:gend.mf          0.42530    0.77482   0.549   0.5835    
## ModsSL.d:gend.mf        -0.18548    0.93047  -0.199   0.8421    
## ModsSC.d:cond.c          0.11935    0.69361   0.172   0.8635    
## ModsC.d:cond.c           0.06501    0.63429   0.102   0.9184    
## ModsL.d:cond.c          -0.43451    0.77482  -0.561   0.5754    
## ModsSL.d:cond.c          0.51240    0.93047   0.551   0.5823    
## gend.mf:cond.c           0.04952    0.83846   0.059   0.9529    
## ModsSC.d:gend.mf:cond.c -0.46797    1.38722  -0.337   0.7361    
## ModsC.d:gend.mf:cond.c  -1.83612    1.26858  -1.447   0.1489    
## ModsL.d:gend.mf:cond.c   0.45144    1.54965   0.291   0.7710    
## ModsSL.d:gend.mf:cond.c  1.53381    1.86093   0.824   0.4105    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.884 on 275 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.0598, Adjusted R-squared:  -0.005162 
## F-statistic: 0.9205 on 19 and 275 DF,  p-value: 0.5577

# Action 6
summary(lm(act6 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.0816  0.5333  1.5294  2.7500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.35056    0.18353   7.359 1.43e-12 ***
## ModsSC.d                -0.50693    0.31455  -1.612   0.1080    
## ModsC.d                 -0.03754    0.28643  -0.131   0.8958    
## ModsL.d                  0.21868    0.35145   0.622   0.5342    
## ModsSL.d                -0.42020    0.40941  -1.026   0.3055    
## gend.mf                  0.23614    0.36705   0.643   0.5204    
## cond.c                  -0.15281    0.36705  -0.416   0.6774    
## ModsSC.d:gend.mf        -0.71431    0.62910  -1.135   0.2570    
## ModsC.d:gend.mf         -0.46217    0.57286  -0.807   0.4204    
## ModsL.d:gend.mf          0.38729    0.70290   0.551   0.5820    
## ModsSL.d:gend.mf        -1.44686    0.81882  -1.767   0.0781 .  
## ModsSC.d:cond.c          0.11149    0.62910   0.177   0.8594    
## ModsC.d:cond.c          -0.22806    0.57286  -0.398   0.6908    
## ModsL.d:cond.c          -0.29518    0.70290  -0.420   0.6748    
## ModsSL.d:cond.c          0.19210    0.81882   0.235   0.8147    
## gend.mf:cond.c           0.29778    0.73410   0.406   0.6853    
## ModsSC.d:gend.mf:cond.c  1.00304    1.25819   0.797   0.4259    
## ModsC.d:gend.mf:cond.c  -1.93604    1.14572  -1.690   0.0920 .  
## ModsL.d:gend.mf:cond.c  -1.59227    1.40579  -1.133   0.2582    
## ModsSL.d:gend.mf:cond.c -0.07635    1.63764  -0.047   0.9628    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 337 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.04582,    Adjusted R-squared:  -0.00798 
## F-statistic: 0.8517 on 19 and 337 DF,  p-value: 0.6439

# Action 7
summary(lm(act7 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0625 -1.8571  0.0625  1.7143  3.6000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.09582    0.18244  -0.525   0.5997  
## ModsSC.d                -0.00593    0.33712  -0.018   0.9860  
## ModsC.d                 -0.24063    0.32160  -0.748   0.4548  
## ModsL.d                  0.17638    0.36506   0.483   0.6293  
## ModsSL.d                 0.06978    0.46432   0.150   0.8806  
## gend.mf                  0.11372    0.36488   0.312   0.7555  
## cond.c                   0.41485    0.36488   1.137   0.2563  
## ModsSC.d:gend.mf         0.27160    0.67425   0.403   0.6873  
## ModsC.d:gend.mf         -0.33368    0.64321  -0.519   0.6042  
## ModsL.d:gend.mf          0.11406    0.73013   0.156   0.8759  
## ModsSL.d:gend.mf        -1.22830    0.92864  -1.323   0.1867  
## ModsSC.d:cond.c         -0.28313    0.67425  -0.420   0.6748  
## ModsC.d:cond.c          -0.73775    0.64321  -1.147   0.2521  
## ModsL.d:cond.c           0.44070    0.73013   0.604   0.5465  
## ModsSL.d:cond.c         -0.52944    0.92864  -0.570   0.5689  
## gend.mf:cond.c           0.46899    0.72976   0.643   0.5208  
## ModsSC.d:gend.mf:cond.c  0.72210    1.34849   0.535   0.5926  
## ModsC.d:gend.mf:cond.c  -2.60891    1.28641  -2.028   0.0432 *
## ModsL.d:gend.mf:cond.c  -2.29122    1.46026  -1.569   0.1174  
## ModsSL.d:gend.mf:cond.c  1.42684    1.85728   0.768   0.4428  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.081 on 393 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.05477,    Adjusted R-squared:  0.009077 
## F-statistic: 1.199 on 19 and 393 DF,  p-value: 0.2548

# Action 8
summary(lm(act8 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4000 -1.6786 -0.1429  1.5466  4.8421 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.38604    0.17541  -2.201  0.02825 * 
## ModsSC.d                -0.56393    0.32461  -1.737  0.08301 . 
## ModsC.d                 -0.07658    0.29989  -0.255  0.79856   
## ModsL.d                  0.96938    0.36425   2.661  0.00806 **
## ModsSL.d                 0.81938    0.41830   1.959  0.05074 . 
## gend.mf                  0.20868    0.35082   0.595  0.55225   
## cond.c                   0.23746    0.35082   0.677  0.49883   
## ModsSC.d:gend.mf         1.05491    0.64923   1.625  0.10488   
## ModsC.d:gend.mf          0.20194    0.59978   0.337  0.73650   
## ModsL.d:gend.mf          0.05323    0.72851   0.073  0.94179   
## ModsSL.d:gend.mf        -1.40868    0.83661  -1.684  0.09290 . 
## ModsSC.d:cond.c         -0.11349    0.64923  -0.175  0.86131   
## ModsC.d:cond.c          -1.91270    0.59978  -3.189  0.00153 **
## ModsL.d:cond.c           0.35778    0.72851   0.491  0.62358   
## ModsSL.d:cond.c          0.96254    0.83661   1.151  0.25053   
## gend.mf:cond.c           0.48523    0.70165   0.692  0.48957   
## ModsSC.d:gend.mf:cond.c -1.27863    1.29845  -0.985  0.32527   
## ModsC.d:gend.mf:cond.c  -3.05288    1.19955  -2.545  0.01125 * 
## ModsL.d:gend.mf:cond.c  -0.53284    1.45701  -0.366  0.71475   
## ModsSL.d:gend.mf:cond.c  0.44811    1.67322   0.268  0.78896   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.102 on 459 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.07388 
## F-statistic: 3.007 on 19 and 459 DF,  p-value: 2.47e-05

# Action 9
summary(lm(act9 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8000 -1.5455  0.0455  1.5000  4.4545 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.16707    0.16834   0.992  0.32155   
## ModsSC.d                -0.96669    0.31252  -3.093  0.00211 **
## ModsC.d                 -0.23118    0.28667  -0.806  0.42044   
## ModsL.d                  0.39603    0.32859   1.205  0.22878   
## ModsSL.d                -0.23373    0.41341  -0.565  0.57211   
## gend.mf                 -0.07524    0.33668  -0.223  0.82327   
## cond.c                   0.20459    0.33668   0.608  0.54374   
## ModsSC.d:gend.mf        -0.03006    0.62503  -0.048  0.96166   
## ModsC.d:gend.mf          0.53680    0.57334   0.936  0.34967   
## ModsL.d:gend.mf          0.03476    0.65719   0.053  0.95784   
## ModsSL.d:gend.mf        -0.89143    0.82682  -1.078  0.28158   
## ModsSC.d:cond.c          0.91738    0.62503   1.468  0.14291   
## ModsC.d:cond.c          -1.09098    0.57334  -1.903  0.05773 . 
## ModsL.d:cond.c          -0.37840    0.65719  -0.576  0.56506   
## ModsSL.d:cond.c         -0.23792    0.82682  -0.288  0.77367   
## gend.mf:cond.c           0.29043    0.67337   0.431  0.66646   
## ModsSC.d:gend.mf:cond.c -0.12528    1.25006  -0.100  0.92022   
## ModsC.d:gend.mf:cond.c  -2.29542    1.14668  -2.002  0.04594 * 
## ModsL.d:gend.mf:cond.c  -0.97138    1.31437  -0.739  0.46029   
## ModsSL.d:gend.mf:cond.c -0.02376    1.65363  -0.014  0.98854   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.06597,    Adjusted R-squared:  0.02441 
## F-statistic: 1.587 on 19 and 427 DF,  p-value: 0.05552

# Action 10
summary(lm(act10 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.8630  0.1739  1.7370  4.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.08573    0.17364   0.494   0.6218  
## ModsSC.d                -0.62338    0.31757  -1.963   0.0503 .
## ModsC.d                 -0.28312    0.29240  -0.968   0.3334  
## ModsL.d                  0.88001    0.34536   2.548   0.0112 *
## ModsSL.d                 0.64458    0.41924   1.538   0.1249  
## gend.mf                  0.04594    0.34728   0.132   0.8948  
## cond.c                   0.08287    0.34728   0.239   0.8115  
## ModsSC.d:gend.mf         0.42330    0.63514   0.666   0.5055  
## ModsC.d:gend.mf         -0.05116    0.58481  -0.087   0.9303  
## ModsL.d:gend.mf          0.89760    0.69072   1.299   0.1944  
## ModsSL.d:gend.mf        -1.70654    0.83847  -2.035   0.0424 *
## ModsSC.d:cond.c         -0.14859    0.63514  -0.234   0.8151  
## ModsC.d:cond.c          -0.88320    0.58481  -1.510   0.1317  
## ModsL.d:cond.c           0.08567    0.69072   0.124   0.9013  
## ModsSL.d:cond.c          1.11107    0.83847   1.325   0.1858  
## gend.mf:cond.c           0.96470    0.69456   1.389   0.1655  
## ModsSC.d:gend.mf:cond.c -0.95446    1.27028  -0.751   0.4528  
## ModsC.d:gend.mf:cond.c  -2.56403    1.16961  -2.192   0.0289 *
## ModsL.d:gend.mf:cond.c  -2.05177    1.38145  -1.485   0.1382  
## ModsSL.d:gend.mf:cond.c  1.84742    1.67694   1.102   0.2712  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 456 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.0938, Adjusted R-squared:  0.05604 
## F-statistic: 2.484 on 19 and 456 DF,  p-value: 0.0005392

# Action 11
summary(lm(act11 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9000 -1.0292  0.3902  1.5200  3.4167 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.71252    0.18277   3.899  0.00012 ***
## ModsSC.d                 0.07781    0.32350   0.241  0.81008    
## ModsC.d                  0.06157    0.30519   0.202  0.84025    
## ModsL.d                  1.05950    0.37144   2.852  0.00465 ** 
## ModsSL.d                 0.72081    0.46566   1.548  0.12272    
## gend.mf                 -0.07537    0.36553  -0.206  0.83679    
## cond.c                  -0.65092    0.36553  -1.781  0.07599 .  
## ModsSC.d:gend.mf         1.22197    0.64701   1.889  0.05993 .  
## ModsC.d:gend.mf         -0.38949    0.61038  -0.638  0.52390    
## ModsL.d:gend.mf          0.26942    0.74287   0.363  0.71712    
## ModsSL.d:gend.mf         0.04203    0.93131   0.045  0.96403    
## ModsSC.d:cond.c          1.06376    0.64701   1.644  0.10123    
## ModsC.d:cond.c          -0.40089    0.61038  -0.657  0.51184    
## ModsL.d:cond.c           0.27354    0.74287   0.368  0.71297    
## ModsSL.d:cond.c          0.28426    0.93131   0.305  0.76042    
## gend.mf:cond.c          -0.22103    0.73107  -0.302  0.76261    
## ModsSC.d:gend.mf:cond.c  0.48626    1.29402   0.376  0.70735    
## ModsC.d:gend.mf:cond.c  -1.50868    1.22076  -1.236  0.21751    
## ModsL.d:gend.mf:cond.c  -1.83373    1.48575  -1.234  0.21812    
## ModsSL.d:gend.mf:cond.c  1.28770    1.86263   0.691  0.48991    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.858 on 292 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.09531,    Adjusted R-squared:  0.03644 
## F-statistic: 1.619 on 19 and 292 DF,  p-value: 0.05071

# Action 12
summary(lm(act12 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.000 -1.875  0.125  1.635  4.235 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.16366    0.19735  -0.829  0.40748   
## ModsSC.d                -0.59087    0.34921  -1.692  0.09150 . 
## ModsC.d                 -0.40754    0.33653  -1.211  0.22669   
## ModsL.d                  1.31746    0.40064   3.288  0.00111 **
## ModsSL.d                 0.40457    0.48224   0.839  0.40205   
## gend.mf                 -0.56741    0.39471  -1.438  0.15142   
## cond.c                   0.04443    0.39471   0.113  0.91043   
## ModsSC.d:gend.mf         1.34920    0.69842   1.932  0.05416 . 
## ModsC.d:gend.mf          0.15625    0.67307   0.232  0.81656   
## ModsL.d:gend.mf          1.92648    0.80128   2.404  0.01671 * 
## ModsSL.d:gend.mf        -0.31441    0.96447  -0.326  0.74462   
## ModsSC.d:cond.c          0.59089    0.69842   0.846  0.39809   
## ModsC.d:cond.c          -1.22702    0.67307  -1.823  0.06912 . 
## ModsL.d:cond.c           0.47150    0.80128   0.588  0.55661   
## ModsSL.d:cond.c          0.67375    0.96447   0.699  0.48527   
## gend.mf:cond.c           1.06903    0.78941   1.354  0.17651   
## ModsSC.d:gend.mf:cond.c -0.15786    1.39684  -0.113  0.91009   
## ModsC.d:gend.mf:cond.c  -4.09671    1.34614  -3.043  0.00251 **
## ModsL.d:gend.mf:cond.c  -1.43422    1.60256  -0.895  0.37140   
## ModsSL.d:gend.mf:cond.c -0.90539    1.92895  -0.469  0.63909   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.117 on 363 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.06946 
## F-statistic: 2.501 on 19 and 363 DF,  p-value: 0.0005499

# Action 13
summary(lm(act13 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1333 -1.8000  0.1852  1.8235  4.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.14786    0.17361  -0.852  0.39486   
## ModsSC.d                -0.15298    0.32381  -0.472  0.63686   
## ModsC.d                  0.05948    0.30902   0.192  0.84746   
## ModsL.d                  0.64946    0.35698   1.819  0.06957 . 
## ModsSL.d                 0.09578    0.41982   0.228  0.81964   
## gend.mf                 -0.26949    0.34721  -0.776  0.43809   
## cond.c                   0.29828    0.34721   0.859  0.39079   
## ModsSC.d:gend.mf         2.05299    0.64763   3.170  0.00164 **
## ModsC.d:gend.mf          0.44626    0.61804   0.722  0.47066   
## ModsL.d:gend.mf          0.80795    0.71395   1.132  0.25842   
## ModsSL.d:gend.mf        -0.59301    0.83963  -0.706  0.48041   
## ModsSC.d:cond.c         -0.42724    0.64763  -0.660  0.50981   
## ModsC.d:cond.c          -1.56596    0.61804  -2.534  0.01165 * 
## ModsL.d:cond.c          -0.09315    0.71395  -0.130  0.89625   
## ModsSL.d:cond.c          0.26422    0.83963   0.315  0.75316   
## gend.mf:cond.c          -0.16178    0.69443  -0.233  0.81590   
## ModsSC.d:gend.mf:cond.c -0.12576    1.29526  -0.097  0.92270   
## ModsC.d:gend.mf:cond.c  -1.30286    1.23608  -1.054  0.29247   
## ModsL.d:gend.mf:cond.c   1.33486    1.42790   0.935  0.35041   
## ModsSL.d:gend.mf:cond.c  4.30345    1.67926   2.563  0.01073 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.08224,    Adjusted R-squared:  0.04091 
## F-statistic:  1.99 on 19 and 422 DF,  p-value: 0.008085

# Action 14
summary(lm(act14 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1818 -1.2411  0.3129  1.5849  3.9000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.38552    0.20939   1.841   0.0667 .
## ModsSC.d                 0.11613    0.39029   0.298   0.7663  
## ModsC.d                 -0.34138    0.35685  -0.957   0.3396  
## ModsL.d                  0.75258    0.42107   1.787   0.0750 .
## ModsSL.d                -0.59385    0.53390  -1.112   0.2670  
## gend.mf                 -0.16786    0.41879  -0.401   0.6889  
## cond.c                   0.14166    0.41879   0.338   0.7354  
## ModsSC.d:gend.mf         0.77885    0.78059   0.998   0.3193  
## ModsC.d:gend.mf         -1.32041    0.71370  -1.850   0.0654 .
## ModsL.d:gend.mf          0.79166    0.84214   0.940   0.3480  
## ModsSL.d:gend.mf        -1.49880    1.06780  -1.404   0.1616  
## ModsSC.d:cond.c          0.44735    0.78059   0.573   0.5671  
## ModsC.d:cond.c          -0.44812    0.71370  -0.628   0.5306  
## ModsL.d:cond.c           0.16396    0.84214   0.195   0.8458  
## ModsSL.d:cond.c          1.27501    1.06780   1.194   0.2335  
## gend.mf:cond.c           0.06589    0.83757   0.079   0.9374  
## ModsSC.d:gend.mf:cond.c -0.87248    1.56118  -0.559   0.5767  
## ModsC.d:gend.mf:cond.c  -0.25297    1.42739  -0.177   0.8595  
## ModsL.d:gend.mf:cond.c  -0.87712    1.68427  -0.521   0.6030  
## ModsSL.d:gend.mf:cond.c  2.26745    2.13559   1.062   0.2893  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.033 on 266 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.06875,    Adjusted R-squared:  0.002233 
## F-statistic: 1.034 on 19 and 266 DF,  p-value: 0.4224

# Action 15
summary(lm(act15 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5294 -1.7357  0.3167  1.4947  4.2000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.07935    0.17704  -0.448  0.65427   
## ModsSC.d                -0.26291    0.32298  -0.814  0.41616   
## ModsC.d                  0.17671    0.30040   0.588  0.55673   
## ModsL.d                  1.08462    0.38262   2.835  0.00484 **
## ModsSL.d                 0.21834    0.41487   0.526  0.59901   
## gend.mf                 -0.74248    0.35408  -2.097  0.03669 * 
## cond.c                   0.13573    0.35408   0.383  0.70169   
## ModsSC.d:gend.mf         1.34367    0.64595   2.080  0.03821 * 
## ModsC.d:gend.mf          0.36541    0.60081   0.608  0.54344   
## ModsL.d:gend.mf          1.55694    0.76524   2.035  0.04262 * 
## ModsSL.d:gend.mf        -0.02121    0.82973  -0.026  0.97962   
## ModsSC.d:cond.c          0.17974    0.64595   0.278  0.78097   
## ModsC.d:cond.c          -0.67947    0.60081  -1.131  0.25883   
## ModsL.d:cond.c          -1.27961    0.76524  -1.672  0.09535 . 
## ModsSL.d:cond.c          0.67558    0.82973   0.814  0.41606   
## gend.mf:cond.c           0.83525    0.70816   1.179  0.23899   
## ModsSC.d:gend.mf:cond.c -1.63287    1.29190  -1.264  0.20707   
## ModsC.d:gend.mf:cond.c  -2.91248    1.20161  -2.424  0.01585 * 
## ModsL.d:gend.mf:cond.c   0.60249    1.53047   0.394  0.69406   
## ModsSL.d:gend.mf:cond.c  1.37070    1.65947   0.826  0.40935   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.021 on 364 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.09033,    Adjusted R-squared:  0.04285 
## F-statistic: 1.902 on 19 and 364 DF,  p-value: 0.013

# Action 16
summary(lm(act16 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -0.9811  0.2963  1.4800  3.3333 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.10136    0.18177   6.059 3.95e-09 ***
## ModsSC.d                -0.46752    0.32784  -1.426   0.1549    
## ModsC.d                 -0.20230    0.29806  -0.679   0.4978    
## ModsL.d                 -0.07833    0.36158  -0.217   0.8286    
## ModsSL.d                -0.58469    0.45061  -1.298   0.1954    
## gend.mf                  0.51788    0.36354   1.425   0.1553    
## cond.c                   0.24901    0.36354   0.685   0.4939    
## ModsSC.d:gend.mf         0.32556    0.65568   0.497   0.6199    
## ModsC.d:gend.mf         -0.99976    0.59612  -1.677   0.0945 .  
## ModsL.d:gend.mf         -1.06393    0.72316  -1.471   0.1422    
## ModsSL.d:gend.mf        -0.38454    0.90123  -0.427   0.6699    
## ModsSC.d:cond.c         -0.18335    0.65568  -0.280   0.7799    
## ModsC.d:cond.c           0.01133    0.59612   0.019   0.9848    
## ModsL.d:cond.c          -0.54506    0.72316  -0.754   0.4516    
## ModsSL.d:cond.c         -0.28234    0.90123  -0.313   0.7543    
## gend.mf:cond.c          -0.05684    0.72708  -0.078   0.9377    
## ModsSC.d:gend.mf:cond.c -0.29670    1.31137  -0.226   0.8212    
## ModsC.d:gend.mf:cond.c  -0.94248    1.19224  -0.791   0.4298    
## ModsL.d:gend.mf:cond.c  -2.35105    1.44631  -1.626   0.1051    
## ModsSL.d:gend.mf:cond.c  3.79017    1.80246   2.103   0.0363 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 311 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.06263,    Adjusted R-squared:  0.005359 
## F-statistic: 1.094 on 19 and 311 DF,  p-value: 0.3561

# Action 17
summary(lm(act17 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4286 -1.3889 -0.0571  1.6000  4.2500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.48316    0.17525   2.757   0.0061 **
## ModsSC.d                -0.12899    0.31709  -0.407   0.6844   
## ModsC.d                 -0.20647    0.30216  -0.683   0.4948   
## ModsL.d                  0.28635    0.35115   0.815   0.4153   
## ModsSL.d                 0.43976    0.42574   1.033   0.3023   
## gend.mf                  0.02719    0.35050   0.078   0.9382   
## cond.c                  -0.44012    0.35050  -1.256   0.2100   
## ModsSC.d:gend.mf         0.15842    0.63418   0.250   0.8029   
## ModsC.d:gend.mf         -0.06667    0.60433  -0.110   0.9122   
## ModsL.d:gend.mf          0.71951    0.70231   1.024   0.3062   
## ModsSL.d:gend.mf        -0.95636    0.85148  -1.123   0.2620   
## ModsSC.d:cond.c          1.23179    0.63418   1.942   0.0528 . 
## ModsC.d:cond.c          -0.66722    0.60433  -1.104   0.2702   
## ModsL.d:cond.c           0.35825    0.70231   0.510   0.6103   
## ModsSL.d:cond.c          2.13596    0.85148   2.509   0.0125 * 
## gend.mf:cond.c          -0.56131    0.70100  -0.801   0.4238   
## ModsSC.d:gend.mf:cond.c  0.09919    1.26837   0.078   0.9377   
## ModsC.d:gend.mf:cond.c  -0.69623    1.20865  -0.576   0.5649   
## ModsL.d:gend.mf:cond.c  -0.41781    1.40462  -0.297   0.7663   
## ModsSL.d:gend.mf:cond.c  4.00298    1.70296   2.351   0.0192 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 396 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.0642, Adjusted R-squared:  0.0193 
## F-statistic:  1.43 on 19 and 396 DF,  p-value: 0.1086

# Action 18
summary(lm(act18 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.2632  0.2372  1.3333  3.3846 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.77048    0.18463   4.173 3.84e-05 ***
## ModsSC.d                -0.54935    0.31679  -1.734   0.0838 .  
## ModsC.d                 -0.28341    0.30321  -0.935   0.3506    
## ModsL.d                  0.21644    0.33959   0.637   0.5243    
## ModsSL.d                -0.14388    0.42704  -0.337   0.7364    
## gend.mf                 -0.08876    0.36926  -0.240   0.8102    
## cond.c                  -0.01539    0.36926  -0.042   0.9668    
## ModsSC.d:gend.mf         0.79800    0.63358   1.260   0.2087    
## ModsC.d:gend.mf         -0.05205    0.60641  -0.086   0.9317    
## ModsL.d:gend.mf          0.25777    0.67919   0.380   0.7045    
## ModsSL.d:gend.mf         0.58555    0.85407   0.686   0.4934    
## ModsSC.d:cond.c          0.50894    0.63358   0.803   0.4224    
## ModsC.d:cond.c          -0.45875    0.60641  -0.756   0.4499    
## ModsL.d:cond.c           0.16185    0.67919   0.238   0.8118    
## ModsSL.d:cond.c          0.59552    0.85407   0.697   0.4861    
## gend.mf:cond.c           0.18519    0.73851   0.251   0.8022    
## ModsSC.d:gend.mf:cond.c -0.20260    1.26716  -0.160   0.8731    
## ModsC.d:gend.mf:cond.c  -1.57025    1.21282  -1.295   0.1963    
## ModsL.d:gend.mf:cond.c  -1.33525    1.35838  -0.983   0.3263    
## ModsSL.d:gend.mf:cond.c  3.15455    1.70814   1.847   0.0657 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.05189,    Adjusted R-squared:  -0.002371 
## F-statistic: 0.9563 on 19 and 332 DF,  p-value: 0.5132

# Action 19
summary(lm(act19 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6364 -1.0465  0.3693  1.4390  2.8750 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.15321    0.20089   5.741 2.68e-08 ***
## ModsSC.d                -0.24349    0.35433  -0.687    0.493    
## ModsC.d                 -0.03203    0.31718  -0.101    0.920    
## ModsL.d                  0.39124    0.43098   0.908    0.365    
## ModsSL.d                -0.06703    0.43994  -0.152    0.879    
## gend.mf                 -0.30107    0.40177  -0.749    0.454    
## cond.c                  -0.43638    0.40177  -1.086    0.278    
## ModsSC.d:gend.mf         1.10663    0.70865   1.562    0.120    
## ModsC.d:gend.mf          0.12221    0.63435   0.193    0.847    
## ModsL.d:gend.mf          0.74551    0.86197   0.865    0.388    
## ModsSL.d:gend.mf        -0.53795    0.87987  -0.611    0.541    
## ModsSC.d:cond.c          0.50582    0.70865   0.714    0.476    
## ModsC.d:cond.c          -0.56572    0.63435  -0.892    0.373    
## ModsL.d:cond.c          -0.34140    0.86197  -0.396    0.692    
## ModsSL.d:cond.c          0.63903    0.87987   0.726    0.468    
## gend.mf:cond.c           0.15618    0.80355   0.194    0.846    
## ModsSC.d:gend.mf:cond.c -1.54506    1.41730  -1.090    0.277    
## ModsC.d:gend.mf:cond.c  -1.13612    1.26871  -0.895    0.371    
## ModsL.d:gend.mf:cond.c  -0.86729    1.72394  -0.503    0.615    
## ModsSL.d:gend.mf:cond.c  0.77186    1.75975   0.439    0.661    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.809 on 254 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.05536,    Adjusted R-squared:  -0.0153 
## F-statistic: 0.7834 on 19 and 254 DF,  p-value: 0.7262

# Action 20
summary(lm(act20 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7000 -1.1616  0.4211  1.4211  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.266144   0.188211   6.727 8.67e-11 ***
## ModsSC.d                -0.062229   0.313841  -0.198   0.8430    
## ModsC.d                 -0.241304   0.297672  -0.811   0.4182    
## ModsL.d                 -0.015705   0.355802  -0.044   0.9648    
## ModsSL.d                -0.561382   0.430179  -1.305   0.1929    
## gend.mf                 -0.193002   0.376421  -0.513   0.6085    
## cond.c                  -0.671660   0.376421  -1.784   0.0754 .  
## ModsSC.d:gend.mf         0.368507   0.627681   0.587   0.5576    
## ModsC.d:gend.mf         -0.548986   0.595344  -0.922   0.3572    
## ModsL.d:gend.mf          0.525458   0.711604   0.738   0.4608    
## ModsSL.d:gend.mf        -0.383189   0.860358  -0.445   0.6564    
## ModsSC.d:cond.c          0.124943   0.627681   0.199   0.8424    
## ModsC.d:cond.c           0.221340   0.595344   0.372   0.7103    
## ModsL.d:cond.c          -0.195884   0.711604  -0.275   0.7833    
## ModsSL.d:cond.c          0.714517   0.860358   0.830   0.4069    
## gend.mf:cond.c          -0.478108   0.752843  -0.635   0.5259    
## ModsSC.d:gend.mf:cond.c  0.738209   1.255363   0.588   0.5569    
## ModsC.d:gend.mf:cond.c  -0.005866   1.190688  -0.005   0.9961    
## ModsL.d:gend.mf:cond.c  -0.120137   1.423207  -0.084   0.9328    
## ModsSL.d:gend.mf:cond.c  3.392394   1.720715   1.972   0.0496 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.795 on 302 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.05394,    Adjusted R-squared:  -0.00558 
## F-statistic: 0.9063 on 19 and 302 DF,  p-value: 0.5755

# Action 21
summary(lm(act21 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1250 -1.7424  0.0508  1.7059  4.2727 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.02111    0.18603   0.113   0.9097  
## ModsSC.d                -0.67189    0.33227  -2.022   0.0439 *
## ModsC.d                 -0.28277    0.32685  -0.865   0.3875  
## ModsL.d                  0.44526    0.36127   1.232   0.2185  
## ModsSL.d                 0.33851    0.44928   0.753   0.4517  
## gend.mf                  0.35064    0.37206   0.942   0.3466  
## cond.c                   0.15694    0.37206   0.422   0.6734  
## ModsSC.d:gend.mf        -0.68545    0.66454  -1.031   0.3030  
## ModsC.d:gend.mf         -0.56682    0.65370  -0.867   0.3864  
## ModsL.d:gend.mf         -0.30124    0.72255  -0.417   0.6770  
## ModsSL.d:gend.mf        -0.16987    0.89857  -0.189   0.8502  
## ModsSC.d:cond.c          0.52012    0.66454   0.783   0.4343  
## ModsC.d:cond.c          -0.21597    0.65370  -0.330   0.7413  
## ModsL.d:cond.c          -0.43253    0.72255  -0.599   0.5498  
## ModsSL.d:cond.c          0.52383    0.89857   0.583   0.5603  
## gend.mf:cond.c          -0.09959    0.74411  -0.134   0.8936  
## ModsSC.d:gend.mf:cond.c  0.56366    1.32908   0.424   0.6717  
## ModsC.d:gend.mf:cond.c  -1.83277    1.30740  -1.402   0.1618  
## ModsL.d:gend.mf:cond.c  -1.88494    1.44510  -1.304   0.1929  
## ModsSL.d:gend.mf:cond.c  2.53805    1.79713   1.412   0.1587  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 381 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05638,    Adjusted R-squared:  0.009323 
## F-statistic: 1.198 on 19 and 381 DF,  p-value: 0.2555

# Action 22
summary(lm(act22 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2222 -1.4571 -0.0508  1.7429  3.7778 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.392787   0.182509   2.152   0.0320 *
## ModsSC.d                -0.604548   0.320713  -1.885   0.0602 .
## ModsC.d                 -0.105287   0.302563  -0.348   0.7280  
## ModsL.d                  0.240467   0.363540   0.661   0.5087  
## ModsSL.d                 0.692180   0.415200   1.667   0.0963 .
## gend.mf                  0.477584   0.365019   1.308   0.1915  
## cond.c                   0.002115   0.365019   0.006   0.9954  
## ModsSC.d:gend.mf         0.400483   0.641426   0.624   0.5328  
## ModsC.d:gend.mf         -0.819250   0.605126  -1.354   0.1766  
## ModsL.d:gend.mf         -0.482187   0.727081  -0.663   0.5076  
## ModsSL.d:gend.mf        -0.647518   0.830401  -0.780   0.4360  
## ModsSC.d:cond.c          0.261232   0.641426   0.407   0.6840  
## ModsC.d:cond.c          -0.377115   0.605126  -0.623   0.5335  
## ModsL.d:cond.c          -0.324179   0.727081  -0.446   0.6559  
## ModsSL.d:cond.c         -0.549828   0.830401  -0.662   0.5083  
## gend.mf:cond.c           0.416822   0.730038   0.571   0.5684  
## ModsSC.d:gend.mf:cond.c -0.398063   1.282851  -0.310   0.7565  
## ModsC.d:gend.mf:cond.c  -2.000155   1.210252  -1.653   0.0992 .
## ModsL.d:gend.mf:cond.c  -2.963171   1.454161  -2.038   0.0423 *
## ModsSL.d:gend.mf:cond.c -1.721397   1.660801  -1.036   0.3006  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.976 on 382 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.0681, Adjusted R-squared:  0.02175 
## F-statistic: 1.469 on 19 and 382 DF,  p-value: 0.0928

# Action 23
summary(lm(act23 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -2.000  0.125  1.920  4.500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.22113    0.25111   0.881   0.3793  
## ModsSC.d                 0.08367    0.43322   0.193   0.8470  
## ModsC.d                 -0.45588    0.39257  -1.161   0.2465  
## ModsL.d                  0.29861    0.45210   0.660   0.5095  
## ModsSL.d                 0.14023    0.56027   0.250   0.8025  
## gend.mf                  0.42440    0.50223   0.845   0.3988  
## cond.c                  -0.56726    0.50223  -1.129   0.2597  
## ModsSC.d:gend.mf         0.82155    0.86644   0.948   0.3439  
## ModsC.d:gend.mf         -0.81601    0.78513  -1.039   0.2996  
## ModsL.d:gend.mf         -0.69603    0.90421  -0.770   0.4421  
## ModsSL.d:gend.mf        -1.64713    1.12055  -1.470   0.1427  
## ModsSC.d:cond.c          0.62130    0.86644   0.717   0.4739  
## ModsC.d:cond.c           0.03343    0.78513   0.043   0.9661  
## ModsL.d:cond.c          -0.88492    0.90421  -0.979   0.3286  
## ModsSL.d:cond.c          2.24453    1.12055   2.003   0.0462 *
## gend.mf:cond.c          -0.59881    1.00445  -0.596   0.5516  
## ModsSC.d:gend.mf:cond.c  1.97962    1.73289   1.142   0.2543  
## ModsC.d:gend.mf:cond.c  -0.94464    1.57027  -0.602   0.5480  
## ModsL.d:gend.mf:cond.c  -1.46111    1.80842  -0.808   0.4198  
## ModsSL.d:gend.mf:cond.c  2.24426    2.24110   1.001   0.3175  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.253 on 272 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.07369,    Adjusted R-squared:  0.008984 
## F-statistic: 1.139 on 19 and 272 DF,  p-value: 0.3118

# Action 24
summary(lm(act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8750 -0.0429  1.6429  4.6667 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.32596    0.17959  -1.815   0.0703 .
## ModsSC.d                 0.29388    0.34053   0.863   0.3887  
## ModsC.d                 -0.11114    0.30273  -0.367   0.7137  
## ModsL.d                  0.30070    0.37646   0.799   0.4249  
## ModsSL.d                 0.08290    0.43636   0.190   0.8494  
## gend.mf                 -0.29127    0.35917  -0.811   0.4179  
## cond.c                  -0.87659    0.35917  -2.441   0.0151 *
## ModsSC.d:gend.mf         1.44634    0.68106   2.124   0.0343 *
## ModsC.d:gend.mf          0.19880    0.60545   0.328   0.7428  
## ModsL.d:gend.mf          0.74179    0.75291   0.985   0.3251  
## ModsSL.d:gend.mf        -0.30596    0.87273  -0.351   0.7261  
## ModsSC.d:cond.c          1.03621    0.68106   1.521   0.1289  
## ModsC.d:cond.c          -0.05476    0.60545  -0.090   0.9280  
## ModsL.d:cond.c           0.90607    0.75291   1.203   0.2295  
## ModsSL.d:cond.c          1.02937    0.87273   1.179   0.2389  
## gend.mf:cond.c          -0.86045    0.71835  -1.198   0.2317  
## ModsSC.d:gend.mf:cond.c  0.54122    1.36213   0.397   0.6913  
## ModsC.d:gend.mf:cond.c  -2.01018    1.21090  -1.660   0.0977 .
## ModsL.d:gend.mf:cond.c   1.60150    1.50583   1.064   0.2882  
## ModsSL.d:gend.mf:cond.c  1.38823    1.74545   0.795   0.4269  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.083 on 395 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.05818,    Adjusted R-squared:  0.01287 
## F-statistic: 1.284 on 19 and 395 DF,  p-value: 0.1894

# Action 25
summary(lm(act25 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6087 -0.8846  0.2222  1.3913  3.4000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.8866     0.1555   5.702 2.22e-08 ***
## ModsSC.d                 -0.9806     0.2940  -3.336 0.000926 ***
## ModsC.d                  -0.3663     0.2657  -1.379 0.168677    
## ModsL.d                   0.3280     0.3080   1.065 0.287454    
## ModsSL.d                  0.3241     0.4104   0.790 0.430033    
## gend.mf                   0.0220     0.3110   0.071 0.943624    
## cond.c                   -0.1983     0.3110  -0.638 0.524060    
## ModsSC.d:gend.mf          0.2205     0.5880   0.375 0.707830    
## ModsC.d:gend.mf          -0.1737     0.5314  -0.327 0.743901    
## ModsL.d:gend.mf           0.2987     0.6160   0.485 0.628047    
## ModsSL.d:gend.mf         -0.2769     0.8207  -0.337 0.735988    
## ModsSC.d:cond.c           0.7044     0.5880   1.198 0.231568    
## ModsC.d:cond.c           -0.3722     0.5314  -0.700 0.484011    
## ModsL.d:cond.c            0.7526     0.6160   1.222 0.222448    
## ModsSL.d:cond.c           1.4434     0.8207   1.759 0.079354 .  
## gend.mf:cond.c           -0.3607     0.6220  -0.580 0.562283    
## ModsSC.d:gend.mf:cond.c   1.0575     1.1760   0.899 0.369043    
## ModsC.d:gend.mf:cond.c   -1.1650     1.0628  -1.096 0.273632    
## ModsL.d:gend.mf:cond.c   -0.7480     1.2320  -0.607 0.544060    
## ModsSL.d:gend.mf:cond.c   2.2038     1.6415   1.343 0.180119    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.837 on 424 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.07813,    Adjusted R-squared:  0.03682 
## F-statistic: 1.891 on 19 and 424 DF,  p-value: 0.0133

# Action 26
summary(lm(act26 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2857 -1.1563  0.5577  1.3947  3.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.553504   0.236263   6.575 3.92e-10 ***
## ModsSC.d                 0.083401   0.395694   0.211   0.8333    
## ModsC.d                  0.082508   0.362558   0.228   0.8202    
## ModsL.d                  0.146496   0.490109   0.299   0.7653    
## ModsSL.d                -0.749933   0.513059  -1.462   0.1454    
## gend.mf                 -0.222393   0.472525  -0.471   0.6384    
## cond.c                   0.001745   0.472525   0.004   0.9971    
## ModsSC.d:gend.mf         0.115250   0.791388   0.146   0.8844    
## ModsC.d:gend.mf         -0.124631   0.725117  -0.172   0.8637    
## ModsL.d:gend.mf          1.622393   0.980218   1.655   0.0994 .  
## ModsSL.d:gend.mf         1.115250   1.026118   1.087   0.2784    
## ModsSC.d:cond.c         -0.394602   0.791388  -0.499   0.6186    
## ModsC.d:cond.c          -0.140436   0.725117  -0.194   0.8466    
## ModsL.d:cond.c          -0.601745   0.980218  -0.614   0.5400    
## ModsSL.d:cond.c          0.224445   1.026118   0.219   0.8271    
## gend.mf:cond.c          -0.234259   0.945050  -0.248   0.8045    
## ModsSC.d:gend.mf:cond.c  1.353307   1.582776   0.855   0.3935    
## ModsC.d:gend.mf:cond.c  -2.138360   1.450234  -1.474   0.1419    
## ModsL.d:gend.mf:cond.c  -0.965741   1.960436  -0.493   0.6228    
## ModsSL.d:gend.mf:cond.c  2.115212   2.052236   1.031   0.3039    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.844 on 206 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.06431,    Adjusted R-squared:  -0.02199 
## F-statistic: 0.7452 on 19 and 206 DF,  p-value: 0.7689

# Action 27
summary(lm(act27 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2143  0.1667  1.4167  3.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.62126    0.19568   3.175  0.00166 **
## ModsSC.d                -0.14209    0.32920  -0.432  0.66632   
## ModsC.d                  0.20691    0.31500   0.657  0.51177   
## ModsL.d                  0.49616    0.37476   1.324  0.18654   
## ModsSL.d                 0.73480    0.40620   1.809  0.07147 . 
## gend.mf                  0.40926    0.39136   1.046  0.29653   
## cond.c                  -0.68874    0.39136  -1.760  0.07946 . 
## ModsSC.d:gend.mf         0.54907    0.65840   0.834  0.40498   
## ModsC.d:gend.mf         -0.10529    0.62999  -0.167  0.86738   
## ModsL.d:gend.mf         -0.31078    0.74952  -0.415  0.67871   
## ModsSL.d:gend.mf        -0.28805    0.81240  -0.355  0.72316   
## ModsSC.d:cond.c          0.06374    0.65840   0.097  0.92294   
## ModsC.d:cond.c           0.05620    0.62999   0.089  0.92897   
## ModsL.d:cond.c           0.34783    0.74952   0.464  0.64293   
## ModsSL.d:cond.c          1.30996    0.81240   1.612  0.10792   
## gend.mf:cond.c          -0.17608    0.78272  -0.225  0.82216   
## ModsSC.d:gend.mf:cond.c -1.07392    1.31679  -0.816  0.41541   
## ModsC.d:gend.mf:cond.c  -2.19376    1.25999  -1.741  0.08270 . 
## ModsL.d:gend.mf:cond.c   0.85790    1.49905   0.572  0.56755   
## ModsSL.d:gend.mf:cond.c  0.60033    1.62479   0.369  0.71203   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 297 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.0834, Adjusted R-squared:  0.02476 
## F-statistic: 1.422 on 19 and 297 DF,  p-value: 0.1144

# Action 28
summary(lm(act28 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6250 -1.1321  0.2667  1.5522  3.2000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.996908   0.181831   5.483 8.01e-08 ***
## ModsSC.d                -0.178726   0.310887  -0.575    0.566    
## ModsC.d                  0.290238   0.295226   0.983    0.326    
## ModsL.d                 -0.124765   0.353475  -0.353    0.724    
## ModsSL.d                -0.003158   0.442139  -0.007    0.994    
## gend.mf                  0.128407   0.363662   0.353    0.724    
## cond.c                  -0.128407   0.363662  -0.353    0.724    
## ModsSC.d:gend.mf        -0.192043   0.621775  -0.309    0.758    
## ModsC.d:gend.mf         -0.402699   0.590452  -0.682    0.496    
## ModsL.d:gend.mf         -0.644121   0.706951  -0.911    0.363    
## ModsSL.d:gend.mf        -1.032573   0.884278  -1.168    0.244    
## ModsSC.d:cond.c         -0.785230   0.621775  -1.263    0.207    
## ModsC.d:cond.c          -0.238478   0.590452  -0.404    0.687    
## ModsL.d:cond.c          -0.315879   0.706951  -0.447    0.655    
## ModsSL.d:cond.c          0.515907   0.884278   0.583    0.560    
## gend.mf:cond.c          -1.054298   0.727325  -1.450    0.148    
## ModsSC.d:gend.mf:cond.c  0.827025   1.243549   0.665    0.506    
## ModsC.d:gend.mf:cond.c   0.388067   1.180903   0.329    0.743    
## ModsL.d:gend.mf:cond.c  -1.314274   1.413902  -0.930    0.353    
## ModsSL.d:gend.mf:cond.c  1.112631   1.768555   0.629    0.530    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 352 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  -0.006203 
## F-statistic: 0.8796 on 19 and 352 DF,  p-value: 0.6089

# Action 29
summary(lm(act29 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6923 -1.0447  0.3077  1.4286  2.4286 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.10986    0.17387   6.383 5.54e-10 ***
## ModsSC.d                -0.02144    0.30394  -0.071    0.944    
## ModsC.d                 -0.16381    0.29045  -0.564    0.573    
## ModsL.d                  0.05252    0.32813   0.160    0.873    
## ModsSL.d                 0.24238    0.41146   0.589    0.556    
## gend.mf                 -0.04979    0.34774  -0.143    0.886    
## cond.c                  -0.06861    0.34774  -0.197    0.844    
## ModsSC.d:gend.mf         0.34567    0.60788   0.569    0.570    
## ModsC.d:gend.mf         -0.21205    0.58090  -0.365    0.715    
## ModsL.d:gend.mf          0.72503    0.65625   1.105    0.270    
## ModsSL.d:gend.mf        -0.10470    0.82292  -0.127    0.899    
## ModsSC.d:cond.c          0.32987    0.60788   0.543    0.588    
## ModsC.d:cond.c           0.20409    0.58090   0.351    0.726    
## ModsL.d:cond.c           0.25646    0.65625   0.391    0.696    
## ModsSL.d:cond.c          0.33079    0.82292   0.402    0.688    
## gend.mf:cond.c           0.24180    0.69548   0.348    0.728    
## ModsSC.d:gend.mf:cond.c -0.90976    1.21575  -0.748    0.455    
## ModsC.d:gend.mf:cond.c   0.46203    1.16179   0.398    0.691    
## ModsL.d:gend.mf:cond.c  -0.33178    1.31250  -0.253    0.801    
## ModsSL.d:gend.mf:cond.c  1.33385    1.64584   0.810    0.418    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 348 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01904,    Adjusted R-squared:  -0.03452 
## F-statistic: 0.3554 on 19 and 348 DF,  p-value: 0.995

# Action 30
summary(lm(act30 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (ModsSC.d + ModsC.d + ModsL.d + ModsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.545 -1.059  0.400  1.466  3.250 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.96881    0.19543   4.957 1.14e-06 ***
## ModsSC.d                 0.07513    0.32157   0.234   0.8154    
## ModsC.d                 -0.08817    0.30976  -0.285   0.7761    
## ModsL.d                  0.29918    0.36094   0.829   0.4078    
## ModsSL.d                -0.01995    0.43233  -0.046   0.9632    
## gend.mf                 -0.18650    0.39086  -0.477   0.6336    
## cond.c                   0.06350    0.39086   0.162   0.8710    
## ModsSC.d:gend.mf        -0.32562    0.64314  -0.506   0.6130    
## ModsC.d:gend.mf         -0.40813    0.61952  -0.659   0.5105    
## ModsL.d:gend.mf          0.48385    0.72187   0.670   0.5032    
## ModsSL.d:gend.mf        -0.46123    0.86466  -0.533   0.5941    
## ModsSC.d:cond.c          1.11529    0.64314   1.734   0.0838 .  
## ModsC.d:cond.c          -0.12364    0.61952  -0.200   0.8419    
## ModsL.d:cond.c          -0.39115    0.72187  -0.542   0.5883    
## ModsSL.d:cond.c          0.53877    0.86466   0.623   0.5336    
## gend.mf:cond.c           0.60603    0.78172   0.775   0.4387    
## ModsSC.d:gend.mf:cond.c  0.55155    1.28629   0.429   0.6684    
## ModsC.d:gend.mf:cond.c  -0.15241    1.23904  -0.123   0.9022    
## ModsL.d:gend.mf:cond.c  -2.28406    1.44375  -1.582   0.1146    
## ModsSL.d:gend.mf:cond.c  1.68943    1.72932   0.977   0.3293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.04439,    Adjusted R-squared:  -0.0103 
## F-statistic: 0.8117 on 19 and 332 DF,  p-value: 0.6931

iv. Conservatives

# Action 1
con.b1 <- lm(act1 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b1) 

## 
## Call:
## lm(formula = act1 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.646 -1.962  0.038  2.038  3.490 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.49000    0.21507  -2.278 0.023181 *  
## ConsSC.d     0.02846    0.34266   0.083 0.933841    
## ConsM.d      0.45196    0.26720   1.691 0.091446 .  
## ConsL.d      1.13615    0.34266   3.316 0.000989 ***
## ConsSL.d     0.66143    0.42239   1.566 0.118078    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.151 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.02106 
## F-statistic: 3.409 on 4 and 444 DF,  p-value: 0.00923

# Action 2
con.b2 <- lm(act2 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b2) 

## 
## Call:
## lm(formula = act2 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.2212  0.1077  1.7788  2.1077 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.89231    0.22392   3.985 8.58e-05 ***
## ConsSC.d     0.06421    0.34784   0.185   0.8537    
## ConsM.d      0.32893    0.28104   1.170   0.2428    
## ConsL.d      0.67912    0.35741   1.900   0.0584 .  
## ConsSL.d     0.14936    0.43121   0.346   0.7293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 285 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01519,    Adjusted R-squared:  0.001372 
## F-statistic: 1.099 on 4 and 285 DF,  p-value: 0.3572

# Action 3
con.b3 <- lm(act3 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b3)

## 
## Call:
## lm(formula = act3 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.9837  0.0163  1.4167  3.8852 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.4167     0.2051  -2.031   0.0428 *
## ConsSC.d     -0.4686     0.3291  -1.424   0.1552  
## ConsM.d       0.4004     0.2530   1.582   0.1143  
## ConsL.d       0.2255     0.3185   0.708   0.4794  
## ConsSL.d      0.7881     0.3968   1.986   0.0477 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 439 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02777,    Adjusted R-squared:  0.01891 
## F-statistic: 3.134 on 4 and 439 DF,  p-value: 0.01467

# Action 4
con.b4 <- lm(act4 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b4) 

## 
## Call:
## lm(formula = act4 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4828 -1.7938  0.2062  2.0051  3.2877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.28767    0.25299  -1.137   0.2563  
## ConsSC.d     0.48375    0.39449   1.226   0.2209  
## ConsM.d      0.08142    0.30530   0.267   0.7899  
## ConsL.d      0.66698    0.38021   1.754   0.0802 .
## ConsSL.d     0.77043    0.47447   1.624   0.1053  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.162 on 366 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.006351 
## F-statistic: 1.591 on 4 and 366 DF,  p-value: 0.176

# Action 5
con.b5 <- lm(act5 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b5) 

## 
## Call:
## lm(formula = act5 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4048 -1.0917  0.2653  1.5952  2.5833 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.9726     0.2190   4.441 1.27e-05 ***
## ConsSC.d     -0.2379     0.3456  -0.688    0.492    
## ConsM.d       0.1191     0.2830   0.421    0.674    
## ConsL.d       0.4322     0.3624   1.192    0.234    
## ConsSL.d     -0.5559     0.4403  -1.263    0.208    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 292 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.01867,    Adjusted R-squared:  0.005229 
## F-statistic: 1.389 on 4 and 292 DF,  p-value: 0.2378

# Action 6
con.b6 <- lm(act6 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b6) 

## 
## Call:
## lm(formula = act6 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4348 -1.3099  0.6706  1.6706  2.1296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.32941    0.19663   6.761 5.67e-11 ***
## ConsSC.d    -0.45904    0.31548  -1.455    0.147    
## ConsM.d     -0.01955    0.24861  -0.079    0.937    
## ConsL.d      0.10537    0.33183   0.318    0.751    
## ConsSL.d    -0.14191    0.37599  -0.377    0.706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 354 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.00896,    Adjusted R-squared:  -0.002238 
## F-statistic: 0.8001 on 4 and 354 DF,  p-value: 0.5257

# Action 7
con.b7 <- lm(act7 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b7) 

## 
## Call:
## lm(formula = act7 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.8652  0.1348  1.6286  3.3140 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.3140     0.2252  -1.394    0.164
## ConsSC.d      0.1561     0.3568   0.437    0.662
## ConsM.d       0.1791     0.2743   0.653    0.514
## ConsL.d       0.2801     0.3531   0.793    0.428
## ConsSL.d      0.6854     0.4188   1.636    0.103
## 
## Residual standard error: 2.089 on 410 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.006798,   Adjusted R-squared:  -0.002892 
## F-statistic: 0.7016 on 4 and 410 DF,  p-value: 0.5912

# Action 8
con.b8 <- lm(act8 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b8) 

## 
## Call:
## lm(formula = act8 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.625 -1.855 -0.287  1.713  4.145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.7130     0.2041  -3.493 0.000522 ***
## ConsSC.d     -0.4320     0.3269  -1.321 0.186978    
## ConsM.d       0.2702     0.2531   1.068 0.286207    
## ConsL.d       1.1892     0.3362   3.537 0.000445 ***
## ConsSL.d      1.3380     0.3926   3.408 0.000710 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 476 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.06121,    Adjusted R-squared:  0.05332 
## F-statistic: 7.759 on 4 and 476 DF,  p-value: 4.609e-06

# Action 9
con.b9 <- lm(act9 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b9) 

## 
## Call:
## lm(formula = act9 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5781 -1.5781  0.2525  1.7167  3.7167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.2525     0.1976  -1.278  0.20196   
## ConsSC.d     -0.4641     0.3217  -1.443  0.14978   
## ConsM.d       0.4525     0.2437   1.857  0.06401 . 
## ConsL.d       0.8307     0.3154   2.634  0.00874 **
## ConsSL.d      0.4470     0.3827   1.168  0.24343   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.966 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03759,    Adjusted R-squared:  0.02892 
## F-statistic: 4.336 on 4 and 444 DF,  p-value: 0.00189

# Action 10
con.b10 <- lm(act10 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b10) 

## 
## Call:
## lm(formula = act10 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2571     0.2025  -1.270 0.204753    
## ConsSC.d     -0.3605     0.3230  -1.116 0.264918    
## ConsM.d       0.3375     0.2503   1.349 0.178086    
## ConsL.d       0.9341     0.3275   2.852 0.004531 ** 
## ConsSL.d      1.4071     0.3855   3.650 0.000292 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

# Action 11
con.b11 <- lm(act11 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b11) 

## 
## Call:
## lm(formula = act11 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6585 -0.8154  0.2741  1.3415  2.2741 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.81538    0.23160   3.521 0.000495 ***
## ConsSC.d    -0.08069    0.35327  -0.228 0.819476    
## ConsM.d     -0.08946    0.28190  -0.317 0.751199    
## ConsL.d      0.84315    0.37240   2.264 0.024260 *  
## ConsSL.d     0.72628    0.44600   1.628 0.104453    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 309 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03501,    Adjusted R-squared:  0.02252 
## F-statistic: 2.803 on 4 and 309 DF,  p-value: 0.02604

# Action 12
con.b12 <- lm(act12 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b12) 

## 
## Call:
## lm(formula = act12 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.736 -2.158  0.000  2.000  3.842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.6000     0.2262  -2.653 0.008317 ** 
## ConsSC.d     -0.2421     0.3632  -0.667 0.505456    
## ConsM.d       0.6000     0.2834   2.117 0.034871 *  
## ConsL.d       1.3358     0.3715   3.596 0.000366 ***
## ConsSL.d      0.9333     0.4708   1.982 0.048158 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.146 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0525, Adjusted R-squared:  0.04253 
## F-statistic: 5.264 on 4 and 380 DF,  p-value: 0.0003892

# Action 13
con.b13 <- lm(act13 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b13) 

## 
## Call:
## lm(formula = act13 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3438 -1.9149  0.0851  1.6562  3.5781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.2174     0.2167  -1.003    0.316  
## ConsSC.d     -0.3607     0.3384  -1.066    0.287  
## ConsM.d       0.1323     0.2645   0.500    0.617  
## ConsL.d       0.5611     0.3384   1.658    0.098 .
## ConsSL.d      0.4745     0.4129   1.149    0.251  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.079 on 438 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01713,    Adjusted R-squared:  0.008155 
## F-statistic: 1.909 on 4 and 438 DF,  p-value: 0.108

# Action 14
con.b14 <- lm(act14 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b14) 

## 
## Call:
## lm(formula = act14 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9487 -1.4219 -0.0198  1.6780  2.9091 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.32203    0.26446   1.218    0.224
## ConsSC.d     0.17797    0.41606   0.428    0.669
## ConsM.d      0.09984    0.31966   0.312    0.755
## ConsL.d      0.62668    0.41923   1.495    0.136
## ConsSL.d    -0.23112    0.50746  -0.455    0.649
## 
## Residual standard error: 2.031 on 283 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.01158,    Adjusted R-squared:  -0.002388 
## F-statistic: 0.829 on 4 and 283 DF,  p-value: 0.5076

# Action 15
con.b15 <- lm(act15 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b15) 

## 
## Call:
## lm(formula = act15 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8889 -2.1024  0.1111  1.8916  3.3559 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.108434   0.224818   0.482   0.6299  
## ConsSC.d    -0.464366   0.348778  -1.331   0.1839  
## ConsM.d     -0.006024   0.275344  -0.022   0.9826  
## ConsL.d      0.780455   0.379166   2.058   0.0402 *
## ConsSL.d     0.204066   0.426191   0.479   0.6323  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.048 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.02508,    Adjusted R-squared:  0.01482 
## F-statistic: 2.444 on 4 and 380 DF,  p-value: 0.0462

# Action 16
con.b16 <- lm(act16 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b16) 

## 
## Call:
## lm(formula = act16 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0698 -0.9861  0.0357  1.4375  2.4375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.98611    0.21629   4.559 7.26e-06 ***
## ConsSC.d    -0.42361    0.34198  -1.239    0.216    
## ConsM.d     -0.02183    0.26615  -0.082    0.935    
## ConsL.d      0.08366    0.35371   0.237    0.813    
## ConsSL.d     0.04837    0.40364   0.120    0.905    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 327 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.007238,   Adjusted R-squared:  -0.004905 
## F-statistic: 0.5961 on 4 and 327 DF,  p-value: 0.6657

# Action 17
con.b17 <- lm(act17 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b17) 

## 
## Call:
## lm(formula = act17 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2059 -1.4798 -0.1685  1.6230  2.8315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.1685     0.2120   0.795   0.4271  
## ConsSC.d      0.2085     0.3325   0.627   0.5309  
## ConsM.d       0.3112     0.2609   1.193   0.2336  
## ConsL.d       0.3815     0.3341   1.142   0.2542  
## ConsSL.d      1.0373     0.4033   2.572   0.0105 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 412 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.006815 
## F-statistic: 1.714 on 4 and 412 DF,  p-value: 0.146

# Action 18
con.b18 <- lm(act18 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b18) 

## 
## Call:
## lm(formula = act18 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1196  0.1844  1.5135  2.8113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4865     0.2141   2.272   0.0237 *
## ConsSC.d     -0.2978     0.3314  -0.899   0.3695  
## ConsM.d       0.3291     0.2644   1.245   0.2140  
## ConsL.d       0.4258     0.3246   1.312   0.1904  
## ConsSL.d      0.2721     0.4035   0.674   0.5004  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01769,    Adjusted R-squared:  0.006435 
## F-statistic: 1.572 on 4 and 349 DF,  p-value: 0.1814

# Action 19
con.b19 <- lm(act19 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b19) 

## 
## Call:
## lm(formula = act19 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.438 -1.050  0.093  1.704  2.093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0500     0.2325   4.515 9.45e-06 ***
## ConsSC.d     -0.1430     0.3599  -0.397    0.691    
## ConsM.d       0.1801     0.2877   0.626    0.532    
## ConsL.d       0.3875     0.3943   0.983    0.327    
## ConsSL.d      0.2463     0.4174   0.590    0.556    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 270 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.00789,    Adjusted R-squared:  -0.006807 
## F-statistic: 0.5368 on 4 and 270 DF,  p-value: 0.7088

# Action 20
con.b20 <- lm(act20 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b20) 

## 
## Call:
## lm(formula = act20 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3566 -1.1957  0.6434  1.6434  2.0000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.13043    0.21584   5.237 2.96e-07 ***
## ConsSC.d     0.02051    0.32748   0.063    0.950    
## ConsM.d      0.22615    0.26741   0.846    0.398    
## ConsL.d      0.06522    0.34128   0.191    0.849    
## ConsSL.d    -0.13043    0.40700  -0.320    0.749    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.793 on 319 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.004413,   Adjusted R-squared:  -0.008071 
## F-statistic: 0.3535 on 4 and 319 DF,  p-value: 0.8415

# Action 21
con.b21 <- lm(act21 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b21) 

## 
## Call:
## lm(formula = act21 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -1.9401  0.0599  1.5806  3.5806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.2683     0.2275  -1.179   0.2390  
## ConsSC.d     -0.3124     0.3467  -0.901   0.3682  
## ConsM.d       0.2084     0.2778   0.750   0.4536  
## ConsL.d       0.7090     0.3517   2.016   0.0445 *
## ConsSL.d      0.6925     0.4247   1.631   0.1038  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 398 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02474,    Adjusted R-squared:  0.01494 
## F-statistic: 2.524 on 4 and 398 DF,  p-value: 0.04049

# Action 22
con.b22 <- lm(act22 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b22) 

## 
## Call:
## lm(formula = act22 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1351 -1.3415 -0.1351  1.7143  3.3279 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.34146    0.21808   1.566   0.1182  
## ConsSC.d    -0.66933    0.33390  -2.005   0.0457 *
## ConsM.d     -0.05575    0.26603  -0.210   0.8341  
## ConsL.d      0.24782    0.34234   0.724   0.4696  
## ConsSL.d     0.79367    0.39110   2.029   0.0431 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.975 on 399 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03383,    Adjusted R-squared:  0.02414 
## F-statistic: 3.492 on 4 and 399 DF,  p-value: 0.008087

# Action 23
con.b23 <- lm(act23 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b23) 

## 
## Call:
## lm(formula = act23 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6154 -2.1138  0.1846  2.1846  3.1846 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.1846     0.2810  -0.657   0.5116  
## ConsSC.d      0.4529     0.4517   1.003   0.3169  
## ConsM.d       0.2984     0.3473   0.859   0.3909  
## ConsL.d       0.8000     0.4588   1.744   0.0823 .
## ConsSL.d      0.7446     0.5331   1.397   0.1635  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.265 on 288 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.01368,    Adjusted R-squared:  -2.033e-05 
## F-statistic: 0.9985 on 4 and 288 DF,  p-value: 0.4087

# Action 24
con.b24 <- lm(act24 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b24) 

## 
## Call:
## lm(formula = act24 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9189 -1.8727  0.1273  1.6453  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.5000     0.2195  -2.278   0.0232 *
## ConsSC.d      0.3727     0.3588   1.039   0.2995  
## ConsM.d       0.2701     0.2714   0.995   0.3202  
## ConsL.d       0.3621     0.3530   1.026   0.3056  
## ConsSL.d      0.4189     0.4098   1.022   0.3073  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.105 on 411 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.004608,   Adjusted R-squared:  -0.005079 
## F-statistic: 0.4757 on 4 and 411 DF,  p-value: 0.7536

# Action 25
con.b25 <- lm(act25 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b25)

## 
## Call:
## lm(formula = act25 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3429 -0.8925  0.1167  1.5000  3.1167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.5000     0.1815   2.754  0.00613 **
## ConsSC.d     -0.6167     0.2983  -2.067  0.03929 * 
## ConsM.d       0.3925     0.2259   1.737  0.08301 . 
## ConsL.d       0.6270     0.2938   2.134  0.03338 * 
## ConsSL.d      0.8429     0.3592   2.347  0.01938 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 441 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.04037 
## F-statistic:  5.68 on 4 and 441 DF,  p-value: 0.0001824

# Action 26
con.b26 <- lm(act26 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b26) 

## 
## Call:
## lm(formula = act26 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5686 -1.2581  0.4595  1.4314  2.0526 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.56863    0.25593   6.129 3.99e-09 ***
## ConsSC.d    -0.02809    0.39470  -0.071    0.943    
## ConsM.d      0.04935    0.32099   0.154    0.878    
## ConsL.d     -0.31056    0.41624  -0.746    0.456    
## ConsSL.d    -0.62126    0.49124  -1.265    0.207    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.828 on 222 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01216,    Adjusted R-squared:  -0.005638 
## F-statistic: 0.6832 on 4 and 222 DF,  p-value: 0.6042

# Action 27
con.b27 <- lm(act27 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b27)

## 
## Call:
## lm(formula = act27 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3871 -0.9483  0.2813  1.5577  2.5577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.7187     0.2347   3.062  0.00239 **
## ConsSC.d     -0.2764     0.3506  -0.789  0.43096   
## ConsM.d      -0.1676     0.2878  -0.582  0.56088   
## ConsL.d       0.4590     0.3653   1.257  0.20984   
## ConsSL.d      0.6683     0.4109   1.627  0.10483   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 314 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02704,    Adjusted R-squared:  0.01464 
## F-statistic: 2.182 on 4 and 314 DF,  p-value: 0.07092

# Action 28
con.b28 <- lm(act28 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b28) 

## 
## Call:
## lm(formula = act28 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3214 -0.9728  0.2105  1.6786  2.2105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3214     0.1991   6.637 1.14e-10 ***
## ConsSC.d     -0.5320     0.3131  -1.699   0.0902 .  
## ConsM.d      -0.3486     0.2496  -1.397   0.1633    
## ConsL.d      -0.3214     0.3165  -1.016   0.3105    
## ConsSL.d     -0.1279     0.3834  -0.334   0.7389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 369 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.009436,   Adjusted R-squared:  -0.001302 
## F-statistic: 0.8787 on 4 and 369 DF,  p-value: 0.4767

# Action 29
con.b29 <- lm(act29 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b29) 

## 
## Call:
## lm(formula = act29 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4828 -1.0893  0.0182  1.5172  2.0182 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.02410    0.19371   5.287 2.15e-07 ***
## ConsSC.d     0.06519    0.30518   0.214    0.831    
## ConsM.d      0.11876    0.24230   0.490    0.624    
## ConsL.d     -0.04228    0.30683  -0.138    0.890    
## ConsSL.d     0.45866    0.38067   1.205    0.229    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.765 on 365 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.005014,   Adjusted R-squared:  -0.00589 
## F-statistic: 0.4598 on 4 and 365 DF,  p-value: 0.7652

# Action 30
con.b30 <- lm(act30 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, data = d)

summary(con.b30) 

## 
## Call:
## lm(formula = act30 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1765 -1.0435  0.8235  1.8235  1.9875 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.01250    0.20976   4.827 2.08e-06 ***
## ConsSC.d     0.12785    0.32519   0.393    0.694    
## ConsM.d      0.03098    0.26364   0.118    0.907    
## ConsL.d      0.16397    0.33618   0.488    0.626    
## ConsSL.d     0.13036    0.41196   0.316    0.752    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.001091,   Adjusted R-squared:  -0.01036 
## F-statistic: 0.09525 on 4 and 349 DF,  p-value: 0.9839

Significantly higher than 0: 2; 5; 6; 11; 16; 18; 19; 20; 25; 26; 27; 28; 29; 30 Not different from 0: 4, 7, 9, 10, 13, 14, 15, 17, 21, 22, 23 Significantly lower than 0: 1, 3, 8, 12, 24

1. Condition Differences?

# Action 1
con.c.b1 <- lm(act1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b1) # no

## 
## Call:
## lm(formula = act1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.750 -2.200  0.250  1.765  3.800 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -0.478896   0.216361  -2.213  0.02738 * 
## ConsSC.d        -0.006818   0.343826  -0.020  0.98419   
## ConsM.d          0.421824   0.268317   1.572  0.11665   
## ConsL.d          1.126623   0.343235   3.282  0.00111 **
## ConsSL.d         0.552323   0.433554   1.274  0.20336   
## cond.c          -0.185065   0.432722  -0.428  0.66910   
## ConsSC.d:cond.c  0.813636   0.687652   1.183  0.23737   
## ConsM.d:cond.c  -0.398466   0.536635  -0.743  0.45816   
## ConsL.d:cond.c   0.389610   0.686471   0.568  0.57063   
## ConsSL.d:cond.c -0.577173   0.867107  -0.666  0.50600   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04314,    Adjusted R-squared:  0.02353 
## F-statistic: 2.199 on 9 and 439 DF,  p-value: 0.02116

# Action 2
con.c.b2 <- lm(act2 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b2) #no

## 
## Call:
## lm(formula = act2 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -1.068  0.375  1.615  2.375 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.8883     0.2248   3.952 9.83e-05 ***
## ConsSC.d          0.0903     0.3523   0.256   0.7979    
## ConsM.d           0.3040     0.2833   1.073   0.2841    
## ConsL.d           0.6742     0.3610   1.868   0.0629 .  
## ConsSL.d          0.1572     0.4339   0.362   0.7174    
## cond.c           -0.5265     0.4496  -1.171   0.2425    
## ConsSC.d:cond.c   0.2731     0.7046   0.388   0.6986    
## ConsM.d:cond.c    0.1419     0.5666   0.250   0.8024    
## ConsL.d:cond.c    0.4015     0.7220   0.556   0.5786    
## ConsSL.d:cond.c   0.6174     0.8679   0.711   0.4774    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.812 on 280 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -0.006049 
## F-statistic: 0.8069 on 9 and 280 DF,  p-value: 0.6102

# Action 3
con.c.b3 <- lm(act3 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b3) #no

## 
## Call:
## lm(formula = act3 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9375  0.0625  1.6410  3.9394 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.42063    0.20678  -2.034   0.0425 *
## ConsSC.d        -0.45978    0.33083  -1.390   0.1653  
## ConsM.d          0.40643    0.25448   1.597   0.1110  
## ConsL.d          0.30702    0.32170   0.954   0.3404  
## ConsSL.d         0.76303    0.41335   1.846   0.0656 .
## cond.c           0.06349    0.41356   0.154   0.8781  
## ConsSC.d:cond.c -0.18146    0.66166  -0.274   0.7840  
## ConsM.d:cond.c   0.03310    0.50896   0.065   0.9482  
## ConsL.d:cond.c   0.99133    0.64341   1.541   0.1241  
## ConsSL.d:cond.c -0.24827    0.82670  -0.300   0.7641  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03846,    Adjusted R-squared:  0.01852 
## F-statistic: 1.929 on 9 and 434 DF,  p-value: 0.04628

# Action 4
con.c.b4 <- lm(act4 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b4) # no

## 
## Call:
## lm(formula = act4 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8400 -1.8941  0.1059  2.0378  3.3488 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.27442    0.25799  -1.064   0.2882  
## ConsSC.d         0.47240    0.39963   1.182   0.2379  
## ConsM.d          0.06148    0.30997   0.198   0.8429  
## ConsL.d          0.70957    0.38633   1.837   0.0671 .
## ConsSL.d         0.73758    0.49607   1.487   0.1379  
## cond.c          -0.14884    0.51598  -0.288   0.7732  
## ConsSC.d:cond.c  0.11002    0.79926   0.138   0.8906  
## ConsM.d:cond.c  -0.06528    0.61993  -0.105   0.9162  
## ConsL.d:cond.c   0.95853    0.77266   1.241   0.2156  
## ConsSL.d:cond.c  0.02252    0.99214   0.023   0.9819  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.169 on 361 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0238, Adjusted R-squared:  -0.0005384 
## F-statistic: 0.9779 on 9 and 361 DF,  p-value: 0.4579

# Action 5
con.c.b5 <- lm(act5 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b5) # no

## 
## Call:
## lm(formula = act5 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2586 -1.0323  0.1818  1.7414  2.6000 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.98041    0.22216   4.413 1.44e-05 ***
## ConsSC.d        -0.22783    0.35174  -0.648    0.518    
## ConsM.d          0.09988    0.28599   0.349    0.727    
## ConsL.d          0.40368    0.36521   1.105    0.270    
## ConsSL.d        -0.56613    0.44750  -1.265    0.207    
## cond.c          -0.10369    0.44432  -0.233    0.816    
## ConsSC.d:cond.c -0.09114    0.70348  -0.130    0.897    
## ConsM.d:cond.c  -0.25297    0.57198  -0.442    0.659    
## ConsL.d:cond.c  -0.76450    0.73042  -1.047    0.296    
## ConsSL.d:cond.c  0.07512    0.89500   0.084    0.933    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 287 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.03018,    Adjusted R-squared:  -0.0002301 
## F-statistic: 0.9924 on 9 and 287 DF,  p-value: 0.4463

# Action 6
con.c.b6 <- lm(act6 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b6) # no

## 
## Call:
## lm(formula = act6 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2000  0.5974  1.5974  2.2000 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.33166    0.19851   6.708 7.96e-11 ***
## ConsSC.d        -0.45249    0.31903  -1.418    0.157    
## ConsM.d         -0.03036    0.25100  -0.121    0.904    
## ConsL.d          0.09334    0.33612   0.278    0.781    
## ConsSL.d        -0.16162    0.38361  -0.421    0.674    
## cond.c          -0.05463    0.39702  -0.138    0.891    
## ConsSC.d:cond.c -0.10371    0.63807  -0.163    0.871    
## ConsM.d:cond.c  -0.14797    0.50201  -0.295    0.768    
## ConsL.d:cond.c  -0.09537    0.67225  -0.142    0.887    
## ConsSL.d:cond.c -0.13161    0.76722  -0.172    0.864    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.824 on 349 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.01098,    Adjusted R-squared:  -0.01453 
## F-statistic: 0.4303 on 9 and 349 DF,  p-value: 0.9185

# Action 7
con.c.b7 <- lm(act7 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b7) # no

## 
## Call:
## lm(formula = act7 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8333  0.1667  1.4211  3.6061 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)     -0.32511    0.22601  -1.438    0.151
## ConsSC.d         0.16770    0.35682   0.470    0.639
## ConsM.d          0.19505    0.27469   0.710    0.478
## ConsL.d          0.36823    0.35437   1.039    0.299
## ConsSL.d         0.64178    0.42128   1.523    0.128
## cond.c           0.19189    0.45202   0.425    0.671
## ConsSC.d:cond.c -0.21040    0.71363  -0.295    0.768
## ConsM.d:cond.c   0.09148    0.54937   0.167    0.868
## ConsL.d:cond.c   1.10648    0.70874   1.561    0.119
## ConsSL.d:cond.c -0.95855    0.84255  -1.138    0.256
## 
## Residual standard error: 2.082 on 405 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.02562,    Adjusted R-squared:  0.00397 
## F-statistic: 1.183 on 9 and 405 DF,  p-value: 0.3039

# Action 8
con.c.b8 <- lm(act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b8) # yes, cond difference (negative)

## 
## Call:
## lm(formula = act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.6302     0.2061  -3.057 0.002364 ** 
## ConsSC.d         -0.5250     0.3291  -1.595 0.111314    
## ConsM.d           0.1898     0.2544   0.746 0.456004    
## ConsL.d           1.1409     0.3380   3.376 0.000797 ***
## ConsSL.d          1.3385     0.3984   3.360 0.000843 ***
## cond.c           -0.9937     0.4123  -2.410 0.016334 *  
## ConsSC.d:cond.c   1.1501     0.6581   1.747 0.081213 .  
## ConsM.d:cond.c    1.1035     0.5089   2.168 0.030629 *  
## ConsL.d:cond.c    1.6151     0.6759   2.389 0.017264 *  
## ConsSL.d:cond.c   1.8270     0.7967   2.293 0.022285 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

# Action 9
con.c.b9 <- lm(act9 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b9) # no

## 
## Call:
## lm(formula = act9 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.5893  0.0465  1.4286  4.3462 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.22861    0.19907  -1.148   0.2514  
## ConsSC.d        -0.56211    0.32411  -1.734   0.0836 .
## ConsM.d          0.43438    0.24501   1.773   0.0769 .
## ConsL.d          0.80599    0.31753   2.538   0.0115 *
## ConsSL.d         0.35361    0.40014   0.884   0.3773  
## cond.c          -0.36420    0.39813  -0.915   0.3608  
## ConsSC.d:cond.c  1.47506    0.64822   2.276   0.0234 *
## ConsM.d:cond.c   0.52084    0.49002   1.063   0.2884  
## ConsL.d:cond.c   0.35230    0.63506   0.555   0.5794  
## ConsSL.d:cond.c -0.05246    0.80028  -0.066   0.9478  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.964 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.05102,    Adjusted R-squared:  0.03157 
## F-statistic: 2.623 on 9 and 439 DF,  p-value: 0.00582

# Action 10
con.c.b10 <- lm(act10 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b10) # no

## 
## Call:
## lm(formula = act10 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3750 -1.9896  0.0217  1.8350  3.6579 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.2312     0.2046  -1.130  0.25896    
## ConsSC.d         -0.3811     0.3261  -1.168  0.24323    
## ConsM.d           0.3085     0.2522   1.223  0.22186    
## ConsL.d           0.9431     0.3312   2.847  0.00460 ** 
## ConsSL.d          1.4187     0.3931   3.609  0.00034 ***
## cond.c           -0.4189     0.4091  -1.024  0.30638    
## ConsSC.d:cond.c   0.3277     0.6523   0.502  0.61562    
## ConsM.d:cond.c    0.2435     0.5044   0.483  0.62957    
## ConsL.d:cond.c    0.9238     0.6625   1.394  0.16384    
## ConsSL.d:cond.c   0.7939     0.7862   1.010  0.31308    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.08 on 467 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05853,    Adjusted R-squared:  0.04038 
## F-statistic: 3.226 on 9 and 467 DF,  p-value: 0.000826

# Action 11
con.c.b11 <- lm(act11 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b11) # no

## 
## Call:
## lm(formula = act11 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -1.0299  0.3452  1.5429  2.5735 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.8452     0.2314   3.653 0.000305 ***
## ConsSC.d         -0.1144     0.3524  -0.325 0.745648    
## ConsM.d          -0.1171     0.2814  -0.416 0.677615    
## ConsL.d           0.8095     0.3838   2.109 0.035763 *  
## ConsSL.d          0.6476     0.4492   1.442 0.150417    
## cond.c           -0.7762     0.4628  -1.677 0.094506 .  
## ConsSC.d:cond.c   1.1545     0.7047   1.638 0.102403    
## ConsM.d:cond.c    0.1728     0.5627   0.307 0.758974    
## ConsL.d:cond.c    0.7524     0.7677   0.980 0.327835    
## ConsSL.d:cond.c   0.1905     0.8984   0.212 0.832238    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 304 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.0581, Adjusted R-squared:  0.03021 
## F-statistic: 2.083 on 9 and 304 DF,  p-value: 0.03077

# Action 12
con.c.b12 <- lm(act12 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b12) # no

## 
## Call:
## lm(formula = act12 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1304 -1.8929 -0.1098  1.8696  4.1071 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.5686     0.2285  -2.488 0.013271 *  
## ConsSC.d         -0.2780     0.3650  -0.762 0.446701    
## ConsM.d           0.5643     0.2855   1.977 0.048793 *  
## ConsL.d           1.3505     0.3753   3.598 0.000363 ***
## ConsSL.d          0.9569     0.4853   1.972 0.049385 *  
## cond.c           -0.4706     0.4570  -1.030 0.303851    
## ConsSC.d:cond.c   0.9915     0.7300   1.358 0.175226    
## ConsM.d:cond.c    0.2424     0.5709   0.425 0.671368    
## ConsL.d:cond.c    1.1677     0.7507   1.556 0.120662    
## ConsSL.d:cond.c   0.8941     0.9706   0.921 0.357550    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.149 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0624, Adjusted R-squared:  0.0399 
## F-statistic: 2.773 on 9 and 375 DF,  p-value: 0.003732

# Action 13
con.c.b13 <- lm(act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b13) # yes, condition difference

## 
## Call:
## lm(formula = act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.16978    0.21784  -0.779   0.4362  
## ConsSC.d        -0.37780    0.34252  -1.103   0.2706  
## ConsM.d          0.09002    0.26538   0.339   0.7346  
## ConsL.d          0.50899    0.33930   1.500   0.1343  
## ConsSL.d         0.40787    0.41934   0.973   0.3313  
## cond.c          -0.87614    0.43568  -2.011   0.0449 *
## ConsSC.d:cond.c  0.55022    0.68503   0.803   0.4223  
## ConsM.d:cond.c   1.21147    0.53075   2.283   0.0229 *
## ConsL.d:cond.c   0.73104    0.67860   1.077   0.2820  
## ConsSL.d:cond.c  0.68566    0.83869   0.818   0.4141  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

# Action 14
con.c.b14 <- lm(act14 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b14) # no

## 
## Call:
## lm(formula = act14 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.3731  0.1429  1.6269  3.1429 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)      0.33449    0.26659   1.255    0.211
## ConsSC.d         0.11564    0.42136   0.274    0.784
## ConsM.d          0.08978    0.32197   0.279    0.781
## ConsL.d          0.66007    0.42587   1.550    0.122
## ConsSL.d        -0.15592    0.52489  -0.297    0.767
## cond.c          -0.29398    0.53318  -0.551    0.582
## ConsSC.d:cond.c  0.95894    0.84272   1.138    0.256
## ConsM.d:cond.c   0.39626    0.64394   0.615    0.539
## ConsL.d:cond.c   0.80485    0.85175   0.945    0.346
## ConsSL.d:cond.c  0.93684    1.04977   0.892    0.373
## 
## Residual standard error: 2.04 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.02046,    Adjusted R-squared:  -0.01125 
## F-statistic: 0.6452 on 9 and 278 DF,  p-value: 0.7579

# Action 15
con.c.b15 <- lm(act15 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b15) # no

## 
## Call:
## lm(formula = act15 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.12175    0.22621   0.538   0.5907  
## ConsSC.d        -0.49704    0.35057  -1.418   0.1571  
## ConsM.d         -0.02274    0.27647  -0.082   0.9345  
## ConsL.d          0.68325    0.38085   1.794   0.0736 .
## ConsSL.d         0.20374    0.42670   0.477   0.6333  
## cond.c          -0.20095    0.45241  -0.444   0.6572  
## ConsSC.d:cond.c  0.52729    0.70114   0.752   0.4525  
## ConsM.d:cond.c   0.10683    0.55294   0.193   0.8469  
## ConsL.d:cond.c  -1.30905    0.76171  -1.719   0.0865 .
## ConsSL.d:cond.c  0.61663    0.85340   0.723   0.4704  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

# Action 16
con.c.b16 <- lm(act16 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b16) # no

## 
## Call:
## lm(formula = act16 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2105 -1.1000  0.2034  1.4500  2.4500 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.97446    0.21753   4.480 1.04e-05 ***
## ConsSC.d        -0.41374    0.34655  -1.194    0.233    
## ConsM.d         -0.01017    0.26754  -0.038    0.970    
## ConsL.d          0.10888    0.35840   0.304    0.761    
## ConsSL.d        -0.01920    0.42061  -0.046    0.964    
## cond.c           0.41950    0.43505   0.964    0.336    
## ConsSC.d:cond.c -0.39808    0.69310  -0.574    0.566    
## ConsM.d:cond.c  -0.14808    0.53508  -0.277    0.782    
## ConsL.d:cond.c  -0.25284    0.71681  -0.353    0.725    
## ConsSL.d:cond.c -0.93003    0.84123  -1.106    0.270    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.843 on 322 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.01422,    Adjusted R-squared:  -0.01334 
## F-statistic: 0.5159 on 9 and 322 DF,  p-value: 0.8629

# Action 17
con.c.b17 <- lm(act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b17) # marginal

## 
## Call:
## lm(formula = act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.24843    0.21493   1.156   0.2484  
## ConsSC.d         0.09465    0.33403   0.283   0.7771  
## ConsM.d          0.22185    0.26304   0.843   0.3995  
## ConsL.d          0.31218    0.33599   0.929   0.3534  
## ConsSL.d         1.03372    0.40799   2.534   0.0117 *
## cond.c          -0.83648    0.42987  -1.946   0.0524 .
## ConsSC.d:cond.c  1.66548    0.66807   2.493   0.0131 *
## ConsM.d:cond.c   0.53790    0.52607   1.022   0.3072  
## ConsL.d:cond.c   1.04860    0.67198   1.560   0.1194  
## ConsSL.d:cond.c  1.70076    0.81598   2.084   0.0378 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

# Action 18
con.c.b18 <- lm(act18 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.1538  0.2031  1.4211  3.0400 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.4839     0.2152   2.249   0.0251 *
## ConsSC.d         -0.3075     0.3333  -0.923   0.3569  
## ConsM.d           0.3301     0.2661   1.241   0.2155  
## ConsL.d           0.4478     0.3268   1.370   0.1715  
## ConsSL.d          0.2863     0.4143   0.691   0.4900  
## cond.c           -0.1901     0.4303  -0.442   0.6590  
## ConsSC.d:cond.c   0.6229     0.6666   0.934   0.3507  
## ConsM.d:cond.c    0.1558     0.5321   0.293   0.7699  
## ConsL.d:cond.c    0.6342     0.6537   0.970   0.3326  
## ConsSL.d:cond.c   0.2860     0.8286   0.345   0.7302  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.85 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.0227, Adjusted R-squared:  -0.002867 
## F-statistic: 0.8879 on 9 and 344 DF,  p-value: 0.5362

# Action 19
con.c.b19 <- lm(act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b19) # marginal

## 
## Call:
## lm(formula = act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.1389     0.2368   4.808 2.55e-06 ***
## ConsSC.d         -0.2384     0.3695  -0.645   0.5193    
## ConsM.d           0.1097     0.2913   0.377   0.7068    
## ConsL.d           0.2659     0.3983   0.667   0.5051    
## ConsSL.d          0.1551     0.4194   0.370   0.7119    
## cond.c           -0.8889     0.4737  -1.876   0.0617 .  
## ConsSC.d:cond.c   0.9398     0.7389   1.272   0.2045    
## ConsM.d:cond.c    0.4245     0.5827   0.729   0.4669    
## ConsL.d:cond.c    0.3651     0.7967   0.458   0.6471    
## ConsSL.d:cond.c   0.7625     0.8389   0.909   0.3642    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

# Action 20
con.c.b20 <- lm(act20 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b20) # no

## 
## Call:
## lm(formula = act20 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.589 -1.054  0.411  1.411  2.308 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.14485    0.21553   5.312 2.06e-07 ***
## ConsSC.d         0.04428    0.32815   0.135    0.893    
## ConsM.d          0.17646    0.26759   0.659    0.510    
## ConsL.d          0.01991    0.34102   0.058    0.953    
## ConsSL.d        -0.15584    0.40584  -0.384    0.701    
## cond.c          -0.39780    0.43106  -0.923    0.357    
## ConsSC.d:cond.c -0.18046    0.65629  -0.275    0.784    
## ConsM.d:cond.c  -0.13767    0.53519  -0.257    0.797    
## ConsL.d:cond.c  -0.31267    0.68204  -0.458    0.647    
## ConsSL.d:cond.c -0.19560    0.81168  -0.241    0.810    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.786 on 314 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.028,  Adjusted R-squared:  0.0001354 
## F-statistic: 1.005 on 9 and 314 DF,  p-value: 0.4359

# Action 21
con.c.b21 <- lm(act21 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b21) # no

## 
## Call:
## lm(formula = act21 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5556 -1.8723  0.1609  1.9286  3.9286 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.2924     0.2307  -1.268   0.2057  
## ConsSC.d         -0.3189     0.3504  -0.910   0.3632  
## ConsM.d           0.2369     0.2808   0.844   0.3992  
## ConsL.d           0.7421     0.3551   2.089   0.0373 *
## ConsSL.d          0.7202     0.4286   1.680   0.0937 .
## cond.c            0.3295     0.4614   0.714   0.4756  
## ConsSC.d:cond.c   0.3050     0.7007   0.435   0.6636  
## ConsM.d:cond.c   -0.1186     0.5615  -0.211   0.8329  
## ConsL.d:cond.c   -0.1177     0.7103  -0.166   0.8685  
## ConsSL.d:cond.c  -0.2517     0.8572  -0.294   0.7692  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.067 on 393 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.03105,    Adjusted R-squared:  0.008857 
## F-statistic: 1.399 on 9 and 393 DF,  p-value: 0.1863

# Action 22
con.c.b22 <- lm(act22 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b22) # no

## 
## Call:
## lm(formula = act22 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2609 -1.3146 -0.2532  1.6854  3.4483 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.347426   0.222463   1.562   0.1192  
## ConsSC.d        -0.680939   0.337986  -2.015   0.0446 *
## ConsM.d         -0.063541   0.270231  -0.235   0.8142  
## ConsL.d          0.267157   0.348296   0.767   0.4435  
## ConsSL.d         0.747294   0.403323   1.853   0.0647 .
## cond.c          -0.069853   0.444926  -0.157   0.8753  
## ConsSC.d:cond.c  0.299379   0.675971   0.443   0.6581  
## ConsM.d:cond.c   0.008411   0.540462   0.016   0.9876  
## ConsL.d:cond.c   0.424020   0.696592   0.609   0.5431  
## ConsSL.d:cond.c -0.262445   0.806645  -0.325   0.7451  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.985 on 394 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03615,    Adjusted R-squared:  0.01413 
## F-statistic: 1.642 on 9 and 394 DF,  p-value: 0.1015

# Action 23
con.c.b23 <- lm(act23 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b23) # no

## 
## Call:
## lm(formula = act23 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.222 -2.250  0.125  1.870  3.297 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)      -0.1665     0.2835  -0.587    0.557
## ConsSC.d          0.4344     0.4533   0.958    0.339
## ConsM.d           0.2719     0.3494   0.778    0.437
## ConsL.d           0.6692     0.4649   1.439    0.151
## ConsSL.d          0.8714     0.5503   1.584    0.114
## cond.c           -0.2616     0.5670  -0.461    0.645
## ConsSC.d:cond.c   0.2973     0.9065   0.328    0.743
## ConsM.d:cond.c   -0.1526     0.6989  -0.218    0.827
## ConsL.d:cond.c   -0.9939     0.9298  -1.069    0.286
## ConsSL.d:cond.c   1.2963     1.1005   1.178    0.240
## 
## Residual standard error: 2.264 on 283 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.03197,    Adjusted R-squared:  0.001189 
## F-statistic: 1.039 on 9 and 283 DF,  p-value: 0.4092

# Action 24
con.c.b24 <- lm(act24 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b24) # no

## 
## Call:
## lm(formula = act24 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.4688     0.2229  -2.103   0.0361 *
## ConsSC.d          0.3371     0.3628   0.929   0.3534  
## ConsM.d           0.2203     0.2743   0.803   0.4225  
## ConsL.d           0.3144     0.3584   0.877   0.3809  
## ConsSL.d          0.3794     0.4171   0.910   0.3635  
## cond.c           -0.3587     0.4458  -0.805   0.4216  
## ConsSC.d:cond.c   0.4286     0.7256   0.591   0.5551  
## ConsM.d:cond.c   -0.2906     0.5486  -0.530   0.5966  
## ConsL.d:cond.c    0.1675     0.7168   0.234   0.8154  
## ConsSL.d:cond.c   0.2708     0.8341   0.325   0.7456  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

# Action 25
con.c.b25 <- lm(act25 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b25) # no

## 
## Call:
## lm(formula = act25 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -0.9307  0.1529  1.3778  3.3333 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.5129     0.1827   2.808  0.00521 **
## ConsSC.d         -0.6492     0.2998  -2.166  0.03087 * 
## ConsM.d           0.3760     0.2270   1.656  0.09839 . 
## ConsL.d           0.6207     0.2944   2.109  0.03554 * 
## ConsSL.d          0.9038     0.3650   2.476  0.01366 * 
## cond.c           -0.2187     0.3653  -0.599  0.54968   
## ConsSC.d:cond.c   0.6127     0.5995   1.022  0.30740   
## ConsM.d:cond.c    0.1351     0.4540   0.298  0.76623   
## ConsL.d:cond.c    1.0484     0.5887   1.781  0.07565 . 
## ConsSL.d:cond.c   0.9568     0.7300   1.311  0.19067   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 436 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.06134,    Adjusted R-squared:  0.04197 
## F-statistic: 3.166 on 9 and 436 DF,  p-value: 0.001022

# Action 26
con.c.b26 <- lm(act26 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b26) # no

## 
## Call:
## lm(formula = act26 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6364 -1.2583  0.6087  1.4130  2.3000 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.57680    0.26082   6.046 6.43e-09 ***
## ConsSC.d         0.01171    0.40720   0.029    0.977    
## ConsM.d          0.04226    0.32607   0.130    0.897    
## ConsL.d         -0.31847    0.42184  -0.755    0.451    
## ConsSL.d        -0.61569    0.49768  -1.237    0.217    
## cond.c           0.11912    0.52164   0.228    0.820    
## ConsSC.d:cond.c -0.51353    0.81441  -0.631    0.529    
## ConsM.d:cond.c  -0.05492    0.65213  -0.084    0.933    
## ConsL.d:cond.c  -0.13579    0.84368  -0.161    0.872    
## ConsSL.d:cond.c  0.40310    0.99536   0.405    0.686    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.845 on 217 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01605,    Adjusted R-squared:  -0.02476 
## F-statistic: 0.3932 on 9 and 217 DF,  p-value: 0.9376

# Action 27
con.c.b27 <- lm(act27 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b27) # no

## 
## Call:
## lm(formula = act27 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6429 -0.9985  0.1379  1.4000  2.8667 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.7310     0.2353   3.107  0.00206 **
## ConsSC.d         -0.2325     0.3528  -0.659  0.51034   
## ConsM.d          -0.1956     0.2882  -0.679  0.49791   
## ConsL.d           0.3887     0.3719   1.045  0.29677   
## ConsSL.d          0.6786     0.4119   1.648  0.10047   
## cond.c           -0.2621     0.4705  -0.557  0.57794   
## ConsSC.d:cond.c  -0.4682     0.7057  -0.664  0.50750   
## ConsM.d:cond.c   -0.3088     0.5765  -0.536  0.59253   
## ConsL.d:cond.c   -0.2127     0.7438  -0.286  0.77509   
## ConsSL.d:cond.c   0.7285     0.8238   0.884  0.37726   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.874 on 309 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.04657,    Adjusted R-squared:  0.0188 
## F-statistic: 1.677 on 9 and 309 DF,  p-value: 0.09363

# Action 28
con.c.b28 <- lm(act28 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b28) # no

## 
## Call:
## lm(formula = act28 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.3214     0.1992   6.632  1.2e-10 ***
## ConsSC.d         -0.4933     0.3141  -1.570    0.117    
## ConsM.d          -0.3429     0.2499  -1.372    0.171    
## ConsL.d          -0.3031     0.3194  -0.949    0.343    
## ConsSL.d         -0.1021     0.3912  -0.261    0.794    
## cond.c           -0.2619     0.3985  -0.657    0.511    
## ConsSC.d:cond.c  -0.6202     0.6282  -0.987    0.324    
## ConsM.d:cond.c    0.4498     0.4999   0.900    0.369    
## ConsL.d:cond.c    0.4861     0.6387   0.761    0.447    
## ConsSL.d:cond.c   0.4900     0.7824   0.626    0.532    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

# Action 29
con.c.b29 <- lm(act29 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b29) # no

## 
## Call:
## lm(formula = act29 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.500 -1.087  0.129  1.546  2.129 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.02941    0.19808   5.197  3.4e-07 ***
## ConsSC.d         0.04349    0.31007   0.140    0.889    
## ConsM.d          0.11022    0.24647   0.447    0.655    
## ConsL.d         -0.03143    0.31218  -0.101    0.920    
## ConsSL.d         0.44786    0.39317   1.139    0.255    
## cond.c          -0.05882    0.39616  -0.148    0.882    
## ConsSC.d:cond.c  0.36463    0.62014   0.588    0.557    
## ConsM.d:cond.c  -0.04653    0.49293  -0.094    0.925    
## ConsL.d:cond.c   0.31286    0.62436   0.501    0.617    
## ConsSL.d:cond.c  0.01337    0.78634   0.017    0.986    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.775 on 360 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.00734,    Adjusted R-squared:  -0.01748 
## F-statistic: 0.2958 on 9 and 360 DF,  p-value: 0.9757

# Action 30
con.c.b30 <- lm(act30 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * cond.c, data = d)

summary(con.c.b30) # no

## 
## Call:
## lm(formula = act30 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.02399    0.21071   4.860 1.79e-06 ***
## ConsSC.d         0.04851    0.32716   0.148   0.8822    
## ConsM.d          0.02013    0.26436   0.076   0.9394    
## ConsL.d          0.15861    0.33863   0.468   0.6398    
## ConsSL.d         0.13226    0.41545   0.318   0.7504    
## cond.c          -0.22980    0.42142  -0.545   0.5859    
## ConsSC.d:cond.c  1.33480    0.65433   2.040   0.0421 *  
## ConsM.d:cond.c   0.14156    0.52872   0.268   0.7891    
## ConsL.d:cond.c   0.31914    0.67727   0.471   0.6378    
## ConsSL.d:cond.c  0.41730    0.83091   0.502   0.6158    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

Climate > Control: 17 Control > Climate: 8, 13, 19

a. Means for condition diffs

# Action 8

aggregate(d$act8[d$ideology == "Conservative"], list(d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1 climate -1.13
## 2    ctrl -0.13

 # both opposed (not different from 0 in ctrl; different in climate)
summary(lm(d$act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = d$act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.1333     0.3149  -0.423   0.6722  
## ConsSC.d         -1.1000     0.4979  -2.209   0.0276 *
## ConsM.d          -0.3619     0.3764  -0.962   0.3368  
## ConsL.d           0.3333     0.4761   0.700   0.4842  
## ConsSL.d          0.4250     0.5339   0.796   0.4264  
## ctrl.d           -0.9937     0.4123  -2.410   0.0163 *
## ConsSC.d:ctrl.d   1.1501     0.6581   1.747   0.0812 .
## ConsM.d:ctrl.d    1.1035     0.5089   2.168   0.0306 *
## ConsL.d:ctrl.d    1.6151     0.6759   2.389   0.0173 *
## ConsSL.d:ctrl.d   1.8270     0.7967   2.293   0.0223 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

summary(lm(d$act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * clim.d, data = d))

## 
## Call:
## lm(formula = d$act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -1.12698    0.26614  -4.235 2.75e-05 ***
## ConsSC.d         0.05006    0.43040   0.116 0.907455    
## ConsM.d          0.74157    0.34251   2.165 0.030880 *  
## ConsL.d          1.94841    0.47979   4.061 5.72e-05 ***
## ConsSL.d         2.25198    0.59137   3.808 0.000159 ***
## clim.d           0.99365    0.41230   2.410 0.016334 *  
## ConsSC.d:clim.d -1.15006    0.65814  -1.747 0.081213 .  
## ConsM.d:clim.d  -1.10347    0.50889  -2.168 0.030629 *  
## ConsL.d:clim.d  -1.61508    0.67591  -2.389 0.017264 *  
## ConsSL.d:clim.d -1.82698    0.79675  -2.293 0.022285 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

# Action 13

aggregate(d$act13[d$ideology == "Conservative"], list(d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1 climate -0.61
## 2    ctrl  0.27

summary(lm(d$act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = d$act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.26829    0.32438   0.827   0.4086  
## ConsSC.d        -0.65291    0.52072  -1.254   0.2106  
## ConsM.d         -0.51572    0.38691  -1.333   0.1833  
## ConsL.d          0.14347    0.48178   0.298   0.7660  
## ConsSL.d         0.06504    0.55737   0.117   0.9072  
## ctrl.d          -0.87614    0.43568  -2.011   0.0449 *
## ConsSC.d:ctrl.d  0.55022    0.68503   0.803   0.4223  
## ConsM.d:ctrl.d   1.21147    0.53075   2.283   0.0229 *
## ConsL.d:ctrl.d   0.73104    0.67860   1.077   0.2820  
## ConsSL.d:ctrl.d  0.68566    0.83869   0.818   0.4141  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

summary(lm(d$act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * clim.d, data = d))

## 
## Call:
## lm(formula = d$act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.6078     0.2908  -2.090   0.0372 *
## ConsSC.d         -0.1027     0.4451  -0.231   0.8177  
## ConsM.d           0.6958     0.3633   1.915   0.0561 .
## ConsL.d           0.8745     0.4779   1.830   0.0680 .
## ConsSL.d          0.7507     0.6267   1.198   0.2316  
## clim.d            0.8761     0.4357   2.011   0.0449 *
## ConsSC.d:clim.d  -0.5502     0.6850  -0.803   0.4223  
## ConsM.d:clim.d   -1.2115     0.5308  -2.283   0.0229 *
## ConsL.d:clim.d   -0.7310     0.6786  -1.077   0.2820  
## ConsSL.d:clim.d  -0.6857     0.8387  -0.818   0.4141  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

## ctrl not different from 0, so not significantly supported; climate different from 0, so significantly opposed


# Action 17

aggregate(d$act17[d$ideology == "Conservative"], list(d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1 climate -0.17
## 2    ctrl  0.67

# opposed in climate, supported in ctrl


summary(lm(d$act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = d$act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.6667     0.3317   2.010   0.0451 *
## ConsSC.d         -0.7381     0.5015  -1.472   0.1419  
## ConsM.d          -0.0471     0.3913  -0.120   0.9042  
## ConsL.d          -0.2121     0.4797  -0.442   0.6586  
## ConsSL.d          0.1833     0.5551   0.330   0.7414  
## ctrl.d           -0.8365     0.4299  -1.946   0.0524 .
## ConsSC.d:ctrl.d   1.6655     0.6681   2.493   0.0131 *
## ConsM.d:ctrl.d    0.5379     0.5261   1.022   0.3072  
## ConsL.d:ctrl.d    1.0486     0.6720   1.560   0.1194  
## ConsSL.d:ctrl.d   1.7008     0.8160   2.084   0.0378 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

summary(lm(d$act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * clim.d, data = d))

## 
## Call:
## lm(formula = d$act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.1698     0.2734  -0.621  0.53487   
## ConsSC.d          0.9274     0.4413   2.101  0.03623 * 
## ConsM.d           0.4908     0.3516   1.396  0.16355   
## ConsL.d           0.8365     0.4706   1.777  0.07624 . 
## ConsSL.d          1.8841     0.5981   3.150  0.00175 **
## clim.d            0.8365     0.4299   1.946  0.05235 . 
## ConsSC.d:clim.d  -1.6655     0.6681  -2.493  0.01306 * 
## ConsM.d:clim.d   -0.5379     0.5261  -1.022  0.30716   
## ConsL.d:clim.d   -1.0486     0.6720  -1.560  0.11943   
## ConsSL.d:clim.d  -1.7008     0.8160  -2.084  0.03775 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

## ctrl different from 0, so significantly supported; climate not different from 0, so not significantly opposed


# Action 19

aggregate(d$act19[d$ideology == "Conservative"], list(d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1    x
## 1 climate 0.69
## 2    ctrl 1.58

summary(lm(d$act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = d$act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.58333    0.36693   4.315 2.25e-05 ***
## ConsSC.d        -0.70833    0.58016  -1.221   0.2232    
## ConsM.d         -0.10256    0.44359  -0.231   0.8173    
## ConsL.d          0.08333    0.56049   0.149   0.8819    
## ConsSL.d        -0.22619    0.60451  -0.374   0.7086    
## ctrl.d          -0.88889    0.47370  -1.876   0.0617 .  
## ConsSC.d:ctrl.d  0.93981    0.73893   1.272   0.2045    
## ConsM.d:ctrl.d   0.42451    0.58267   0.729   0.4669    
## ConsL.d:ctrl.d   0.36508    0.79668   0.458   0.6471    
## ConsSL.d:ctrl.d  0.76252    0.83890   0.909   0.3642    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

summary(lm(d$act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * clim.d, data = d))

## 
## Call:
## lm(formula = d$act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.6944     0.2996   2.318   0.0212 *
## ConsSC.d          0.2315     0.4576   0.506   0.6134  
## ConsM.d           0.3219     0.3778   0.852   0.3949  
## ConsL.d           0.4484     0.5662   0.792   0.4291  
## ConsSL.d          0.5363     0.5816   0.922   0.3573  
## clim.d            0.8889     0.4737   1.876   0.0617 .
## ConsSC.d:clim.d  -0.9398     0.7389  -1.272   0.2045  
## ConsM.d:clim.d   -0.4245     0.5827  -0.729   0.4669  
## ConsL.d:clim.d   -0.3651     0.7967  -0.458   0.6471  
## ConsSL.d:clim.d  -0.7625     0.8389  -0.909   0.3642  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

## both higher than 0

2. Gender effects?

# Action 1
con.g.b1 <- lm(act1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b1) # yes, higher than 0

## 
## Call:
## lm(formula = act1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8261  0.2263  2.1538  4.1538 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -0.7053     0.2434  -2.898  0.00394 **
## ConsSC.d           0.3088     0.3687   0.838  0.40267   
## ConsM.d            0.8422     0.3038   2.772  0.00582 **
## ConsL.d            1.3120     0.3975   3.301  0.00104 **
## ConsSL.d           0.5314     0.4608   1.153  0.24951   
## gend.mf           -0.8971     0.4867  -1.843  0.06600 . 
## ConsSC.d:gend.mf   1.3422     0.7373   1.820  0.06938 . 
## ConsM.d:gend.mf    1.6234     0.6077   2.671  0.00784 **
## ConsL.d:gend.mf    0.7504     0.7950   0.944  0.34571   
## ConsSL.d:gend.mf  -0.7551     0.9217  -0.819  0.41310   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.135 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.05646,    Adjusted R-squared:  0.03703 
## F-statistic: 2.906 on 9 and 437 DF,  p-value: 0.002374

# Action 2
con.g.b2 <- lm(act2 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b2) # yes, higher than 0

## 
## Call:
## lm(formula = act2 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4375 -1.2184  0.5263  1.5625  2.6087 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.7790     0.2339   3.331 0.000983 ***
## ConsSC.d           0.2542     0.3572   0.712 0.477344    
## ConsM.d            0.4102     0.3107   1.320 0.187868    
## ConsL.d            0.9398     0.4017   2.339 0.020026 *  
## ConsSL.d           0.1627     0.4588   0.355 0.723169    
## gend.mf           -0.7754     0.4677  -1.658 0.098509 .  
## ConsSC.d:gend.mf   1.6565     0.7144   2.319 0.021133 *  
## ConsM.d:gend.mf    0.7170     0.6215   1.154 0.249615    
## ConsL.d:gend.mf    1.3379     0.8034   1.665 0.097008 .  
## ConsSL.d:gend.mf   0.3920     0.9176   0.427 0.669535    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.03738,    Adjusted R-squared:  0.006217 
## F-statistic: 1.199 on 9 and 278 DF,  p-value: 0.2951

# Action 3
con.g.b3 <- lm(act3 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b3) # nothing

## 
## Call:
## lm(formula = act3 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.8390 -0.0368  1.4167  4.1579 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.41385    0.22904  -1.807   0.0715 .
## ConsSC.d         -0.38249    0.35145  -1.088   0.2771  
## ConsM.d           0.34712    0.28567   1.215   0.2250  
## ConsL.d           0.30163    0.35970   0.839   0.4022  
## ConsSL.d          0.65552    0.44350   1.478   0.1401  
## gend.mf           0.01288    0.45809   0.028   0.9776  
## ConsSC.d:gend.mf  0.71023    0.70290   1.010   0.3129  
## ConsM.d:gend.mf  -0.21986    0.57135  -0.385   0.7006  
## ConsL.d:gend.mf   0.32267    0.71940   0.449   0.6540  
## ConsSL.d:gend.mf -0.69622    0.88699  -0.785   0.4329  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.03535,    Adjusted R-squared:  0.01525 
## F-statistic: 1.759 on 9 and 432 DF,  p-value: 0.07405

# Action 4
con.g.b4 <- lm(act4 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b4) # nothing

## 
## Call:
## lm(formula = act4 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.944 -1.904  0.200  2.056  3.762 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.4290     0.2805  -1.529    0.127  
## ConsSC.d           0.6425     0.4146   1.550    0.122  
## ConsM.d            0.2114     0.3503   0.603    0.547  
## ConsL.d            0.8844     0.4246   2.083    0.038 *
## ConsSL.d           0.7513     0.5116   1.468    0.143  
## gend.mf           -0.6658     0.5610  -1.187    0.236  
## ConsSC.d:gend.mf   1.0213     0.8292   1.232    0.219  
## ConsM.d:gend.mf    0.6305     0.7006   0.900    0.369  
## ConsL.d:gend.mf    1.0050     0.8492   1.184    0.237  
## ConsSL.d:gend.mf  -0.5787     1.0233  -0.566    0.572  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.17 on 359 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02849,    Adjusted R-squared:  0.004137 
## F-statistic:  1.17 on 9 and 359 DF,  p-value: 0.3132

# Action 5
con.g.b5 <- lm(act5 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b5) # yes, higher than 0

## 
## Call:
## lm(formula = act5 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3000 -1.2128  0.4615  1.7000  2.7000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.8756     0.2297   3.812 0.000169 ***
## ConsSC.d          -0.1557     0.3537  -0.440 0.660103    
## ConsM.d            0.2108     0.3104   0.679 0.497626    
## ConsL.d            0.6077     0.3947   1.540 0.124729    
## ConsSL.d          -0.4948     0.4572  -1.082 0.279965    
## gend.mf           -0.6743     0.4594  -1.468 0.143227    
## ConsSC.d:gend.mf   0.1910     0.7074   0.270 0.787330    
## ConsM.d:gend.mf    0.7237     0.6208   1.166 0.244727    
## ConsL.d:gend.mf    1.0410     0.7894   1.319 0.188316    
## ConsSL.d:gend.mf   0.5128     0.9143   0.561 0.575355    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 285 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  -0.0005149 
## F-statistic: 0.9832 on 9 and 285 DF,  p-value: 0.454

# Action 6
con.g.b6 <- lm(act6 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b6) # yes, higher than 0

## 
## Call:
## lm(formula = act6 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5455 -1.2424  0.5667  1.5667  2.6667 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.25667    0.21585   5.822 1.33e-08 ***
## ConsSC.d         -0.43199    0.33265  -1.299    0.195    
## ConsM.d           0.10156    0.28254   0.359    0.719    
## ConsL.d           0.32608    0.36709   0.888    0.375    
## ConsSL.d         -0.31727    0.41871  -0.758    0.449    
## gend.mf          -0.35333    0.43170  -0.818    0.414    
## ConsSC.d:gend.mf -0.05792    0.66531  -0.087    0.931    
## ConsM.d:gend.mf   0.57438    0.56508   1.016    0.310    
## ConsL.d:gend.mf   1.03399    0.73417   1.408    0.160    
## ConsSL.d:gend.mf -0.85879    0.83742  -1.026    0.306    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 347 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -1.033e-06 
## F-statistic:     1 on 9 and 347 DF,  p-value: 0.4397

# Action 7
con.g.b7 <- lm(act7 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b7) # nothing

## 
## Call:
## lm(formula = act7 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8433  0.1567  1.6364  3.6364 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -0.4197     0.2592  -1.619    0.106
## ConsSC.d           0.3081     0.3855   0.799    0.425
## ConsM.d            0.3181     0.3177   1.001    0.317
## ConsL.d            0.4629     0.4068   1.138    0.256
## ConsSL.d           0.5375     0.4831   1.113    0.267
## gend.mf           -0.4332     0.5184  -0.836    0.404
## ConsSC.d:gend.mf   0.8384     0.7710   1.088    0.277
## ConsM.d:gend.mf    0.5434     0.6355   0.855    0.393
## ConsL.d:gend.mf    0.7469     0.8136   0.918    0.359
## ConsSL.d:gend.mf  -0.6912     0.9662  -0.715    0.475
## 
## Residual standard error: 2.097 on 403 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  -0.006717 
## F-statistic: 0.6946 on 9 and 403 DF,  p-value: 0.714

# Action 8
con.g.b8 <- lm(act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b8) # marginally higher than 0

## 
## Call:
## lm(formula = act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9286 -1.9091 -0.2143  1.6875  4.5319 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.73661    0.23273  -3.165  0.00165 **
## ConsSC.d         -0.18844    0.35934  -0.524  0.60025   
## ConsM.d           0.35321    0.29225   1.209  0.22742   
## ConsL.d           1.29783    0.39665   3.272  0.00115 **
## ConsSL.d          1.15544    0.44318   2.607  0.00942 **
## gend.mf          -0.09821    0.46546  -0.211  0.83298   
## ConsSC.d:gend.mf  1.31195    0.71869   1.825  0.06857 . 
## ConsM.d:gend.mf   0.31181    0.58449   0.533  0.59395   
## ConsL.d:gend.mf   0.40434    0.79330   0.510  0.61051   
## ConsSL.d:gend.mf -0.92127    0.88636  -1.039  0.29917   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 469 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.07584,    Adjusted R-squared:  0.05811 
## F-statistic: 4.276 on 9 and 469 DF,  p-value: 2.361e-05

# Action 9
con.g.b9 <- lm(act9 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b9) # yes, higher than 0

## 
## Call:
## lm(formula = act9 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5882 -1.5882  0.1481  1.5417  3.8261 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.2199     0.2223  -0.989   0.3232  
## ConsSC.d          -0.5175     0.3433  -1.507   0.1325  
## ConsM.d            0.3873     0.2788   1.389   0.1656  
## ConsL.d            0.8013     0.3566   2.247   0.0252 *
## ConsSL.d           0.1763     0.4220   0.418   0.6763  
## gend.mf            0.1435     0.4447   0.323   0.7470  
## ConsSC.d:gend.mf  -0.3210     0.6866  -0.467   0.6404  
## ConsM.d:gend.mf   -0.2116     0.5576  -0.379   0.7046  
## ConsL.d:gend.mf   -0.1298     0.7132  -0.182   0.8557  
## ConsSL.d:gend.mf  -1.1473     0.8441  -1.359   0.1748  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.97 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.04187,    Adjusted R-squared:  0.02214 
## F-statistic: 2.122 on 9 and 437 DF,  p-value: 0.02658

# Action 10
con.g.b10 <- lm(act10 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b10) # yes, higher than 0

## 
## Call:
## lm(formula = act10 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5862 -2.0719  0.1948  1.5714  3.7778 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.3117     0.2283  -1.366  0.17275    
## ConsSC.d          -0.2294     0.3498  -0.656  0.51237    
## ConsM.d            0.4020     0.2870   1.401  0.16194    
## ConsL.d            1.2447     0.3752   3.317  0.00098 ***
## ConsSL.d           1.0548     0.4427   2.383  0.01759 *  
## gend.mf           -0.2338     0.4565  -0.512  0.60885    
## ConsSC.d:gend.mf   0.7072     0.6997   1.011  0.31266    
## ConsM.d:gend.mf    0.2706     0.5739   0.471  0.63756    
## ConsL.d:gend.mf    1.2427     0.7505   1.656  0.09841 .  
## ConsSL.d:gend.mf  -1.4524     0.8854  -1.640  0.10158    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 466 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07046,    Adjusted R-squared:  0.05251 
## F-statistic: 3.925 on 9 and 466 DF,  p-value: 7.905e-05

# Action 11
con.g.b11 <- lm(act11 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b11) # yes, higher than 0

## 
## Call:
## lm(formula = act11 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5625 -1.0000  0.2834  1.4375  2.7778 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.72727    0.24496   2.969  0.00323 **
## ConsSC.d          0.06566    0.36339   0.181  0.85674   
## ConsM.d          -0.01068    0.30624  -0.035  0.97221   
## ConsL.d           1.00212    0.39797   2.518  0.01232 * 
## ConsSL.d          0.76826    0.48924   1.570  0.11738   
## gend.mf          -0.54545    0.48993  -1.113  0.26645   
## ConsSC.d:gend.mf  1.68687    0.72677   2.321  0.02095 * 
## ConsM.d:gend.mf   0.48369    0.61247   0.790  0.43030   
## ConsL.d:gend.mf   0.93282    0.79593   1.172  0.24213   
## ConsSL.d:gend.mf  0.41153    0.97847   0.421  0.67436   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 302 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.05336,    Adjusted R-squared:  0.02515 
## F-statistic: 1.891 on 9 and 302 DF,  p-value: 0.05276

# Action 12
con.g.b12 <- lm(act12 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b12) # yes, higher than 0

## 
## Call:
## lm(formula = act12 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5625 -1.8571 -0.1345  1.8655  4.3043 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.83128    0.25721  -3.232  0.00134 ** 
## ConsSC.d          0.07803    0.38731   0.201  0.84044    
## ConsM.d           0.67482    0.32478   2.078  0.03841 *  
## ConsL.d           1.95526    0.43380   4.507  8.8e-06 ***
## ConsSL.d          1.01253    0.50023   2.024  0.04367 *  
## gend.mf          -0.94614    0.51442  -1.839  0.06667 .  
## ConsSC.d:gend.mf  1.72536    0.77462   2.227  0.02652 *  
## ConsM.d:gend.mf   0.36432    0.64957   0.561  0.57523    
## ConsL.d:gend.mf   2.36484    0.86759   2.726  0.00672 ** 
## ConsSL.d:gend.mf  0.18364    1.00045   0.184  0.85446    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.129 on 373 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.08175,    Adjusted R-squared:  0.0596 
## F-statistic:  3.69 on 9 and 373 DF,  p-value: 0.0001907

# Action 13
con.g.b13 <- lm(act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b13) # nothing

## 
## Call:
## lm(formula = act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.8095  0.1884  1.9558  3.3636 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.24638    0.24801  -0.993   0.3211  
## ConsSC.d         -0.05341    0.36743  -0.145   0.8845  
## ConsM.d           0.09451    0.30339   0.312   0.7556  
## ConsL.d           0.72352    0.39814   1.817   0.0699 .
## ConsSL.d          0.36891    0.45177   0.817   0.4146  
## gend.mf          -0.11594    0.49602  -0.234   0.8153  
## ConsSC.d:gend.mf  1.89733    0.73485   2.582   0.0102 *
## ConsM.d:gend.mf  -0.14554    0.60679  -0.240   0.8106  
## ConsL.d:gend.mf   0.59023    0.79629   0.741   0.4590  
## ConsSL.d:gend.mf -0.85639    0.90355  -0.948   0.3438  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.04764,    Adjusted R-squared:  0.0278 
## F-statistic: 2.401 on 9 and 432 DF,  p-value: 0.01156

# Action 14
con.g.b14 <- lm(act14 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b14) # yes, higher than 0

## 
## Call:
## lm(formula = act14 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8235 -1.2778  0.2195  1.5455  3.7222 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.02913    0.28508   0.102  0.91868   
## ConsSC.d          0.51307    0.43043   1.192  0.23429   
## ConsM.d           0.35870    0.35177   1.020  0.30876   
## ConsL.d           1.07314    0.45825   2.342  0.01990 * 
## ConsSL.d         -0.17199    0.54691  -0.314  0.75340   
## gend.mf          -1.50271    0.57016  -2.636  0.00887 **
## ConsSC.d:gend.mf  2.06537    0.86086   2.399  0.01709 * 
## ConsM.d:gend.mf   1.35205    0.70353   1.922  0.05566 . 
## ConsL.d:gend.mf   2.20726    0.91650   2.408  0.01668 * 
## ConsSL.d:gend.mf  0.07414    1.09382   0.068  0.94601   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.017 on 276 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.04977,    Adjusted R-squared:  0.01878 
## F-statistic: 1.606 on 9 and 276 DF,  p-value: 0.1132

# Action 15
con.g.b15 <- lm(act15 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b15) # yes, higher than 0

## 
## Call:
## lm(formula = act15 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6562 -1.7674  0.4444  1.6917  3.5714 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.003638   0.238949   0.015   0.9879  
## ConsSC.d         -0.310185   0.360766  -0.860   0.3905  
## ConsM.d          -0.071693   0.298124  -0.240   0.8101  
## ConsL.d           1.055257   0.411837   2.562   0.0108 *
## ConsSL.d          0.229696   0.442469   0.519   0.6040  
## gend.mf          -0.599868   0.477897  -1.255   0.2102  
## ConsSC.d:gend.mf  1.129630   0.721533   1.566   0.1183  
## ConsM.d:gend.mf  -0.152910   0.596248  -0.256   0.7977  
## ConsL.d:gend.mf   1.405156   0.823673   1.706   0.0888 .
## ConsSL.d:gend.mf -0.033466   0.884938  -0.038   0.9699  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 374 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02548 
## F-statistic: 2.113 on 9 and 374 DF,  p-value: 0.0277

# Action 16
con.g.b16 <- lm(act16 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b16) # yes, higher than 0

## 
## Call:
## lm(formula = act16 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3636 -1.1400  0.1589  1.6364  2.7586 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.88818    0.23434   3.790  0.00018 ***
## ConsSC.d         -0.24118    0.35778  -0.674  0.50074    
## ConsM.d           0.21420    0.29695   0.721  0.47124    
## ConsL.d           0.08359    0.38974   0.214  0.83031    
## ConsSL.d          0.05626    0.43852   0.128  0.89799    
## gend.mf          -0.50364    0.46869  -1.075  0.28337    
## ConsSC.d:gend.mf  1.31489    0.71556   1.838  0.06705 .  
## ConsM.d:gend.mf   1.02615    0.59391   1.728  0.08499 .  
## ConsL.d:gend.mf   0.06009    0.77948   0.077  0.93860    
## ConsSL.d:gend.mf  0.39253    0.87703   0.448  0.65477    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 321 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.02518,    Adjusted R-squared:  -0.002148 
## F-statistic: 0.9214 on 9 and 321 DF,  p-value: 0.5066

# Action 17
con.g.b17 <- lm(act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b17) # yes, higher than 0

## 
## Call:
## lm(formula = act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.391 -1.477 -0.033  1.630  3.077 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.09646    0.23404   0.412   0.6804  
## ConsSC.d          0.29198    0.35374   0.825   0.4096  
## ConsM.d           0.38619    0.29321   1.317   0.1885  
## ConsL.d           0.65975    0.38561   1.711   0.0879 .
## ConsSL.d          0.99919    0.44656   2.238   0.0258 *
## gend.mf          -0.34676    0.46808  -0.741   0.4592  
## ConsSC.d:gend.mf  0.43944    0.70748   0.621   0.5349  
## ConsM.d:gend.mf   0.35821    0.58642   0.611   0.5416  
## ConsL.d:gend.mf   1.12006    0.77122   1.452   0.1472  
## ConsSL.d:gend.mf -0.24454    0.89312  -0.274   0.7844  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.008 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.02291,    Adjusted R-squared:  0.001247 
## F-statistic: 1.058 on 9 and 406 DF,  p-value: 0.3934

# Action 18
con.g.b18 <- lm(act18 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b18) # yes, higher than 0

## 
## Call:
## lm(formula = act18 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.1369  0.1776  1.4528  3.1000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.4403     0.2384   1.847   0.0656 .
## ConsSC.d          -0.2076     0.3499  -0.593   0.5533  
## ConsM.d            0.3346     0.3012   1.111   0.2673  
## ConsL.d            0.5257     0.3711   1.417   0.1575  
## ConsSL.d           0.3492     0.4436   0.787   0.4316  
## gend.mf           -0.2138     0.4767  -0.449   0.6540  
## ConsSC.d:gend.mf   0.8791     0.6999   1.256   0.2100  
## ConsM.d:gend.mf    0.1187     0.6023   0.197   0.8439  
## ConsL.d:gend.mf    0.4248     0.7422   0.572   0.5675  
## ConsSL.d:gend.mf   0.6349     0.8871   0.716   0.4747  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.02394,    Adjusted R-squared:  -0.001748 
## F-statistic: 0.9319 on 9 and 342 DF,  p-value: 0.4973

# Action 19
con.g.b19 <- lm(act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b19) # yes, higher than 0

## 
## Call:
## lm(formula = act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1622  0.3462  1.6250  2.3462 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.01586    0.23981   4.236 3.14e-05 ***
## ConsSC.d         -0.04188    0.36994  -0.113    0.910    
## ConsM.d           0.11509    0.31040   0.371    0.711    
## ConsL.d           0.48414    0.43982   1.101    0.272    
## ConsSL.d          0.12229    0.44988   0.272    0.786    
## gend.mf          -0.29260    0.47961  -0.610    0.542    
## ConsSC.d:gend.mf  0.93287    0.73988   1.261    0.208    
## ConsM.d:gend.mf  -0.04074    0.62079  -0.066    0.948    
## ConsL.d:gend.mf   0.54260    0.87965   0.617    0.538    
## ConsSL.d:gend.mf -0.48372    0.89975  -0.538    0.591    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.806 on 264 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.0208, Adjusted R-squared:  -0.01258 
## F-statistic: 0.6232 on 9 and 264 DF,  p-value: 0.7769

# Action 20
con.g.b20 <- lm(act20 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b20) # yes, higher than 0

## 
## Call:
## lm(formula = act20 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.398 -1.200  0.602  1.602  2.391 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.0000     0.2298   4.351 1.84e-05 ***
## ConsSC.d           0.1631     0.3384   0.482   0.6302    
## ConsM.d            0.2990     0.2968   1.007   0.3145    
## ConsL.d            0.2672     0.3797   0.704   0.4822    
## ConsSL.d          -0.1078     0.4364  -0.247   0.8050    
## gend.mf           -0.7826     0.4597  -1.703   0.0897 .  
## ConsSC.d:gend.mf   1.0398     0.6768   1.536   0.1255    
## ConsM.d:gend.mf    0.5846     0.5936   0.985   0.3254    
## ConsL.d:gend.mf    1.0816     0.7593   1.424   0.1553    
## ConsSL.d:gend.mf   0.3316     0.8729   0.380   0.7043    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.8 on 312 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.01743,    Adjusted R-squared:  -0.01091 
## F-statistic: 0.615 on 9 and 312 DF,  p-value: 0.7842

# Action 21
con.g.b21 <- lm(act21 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b21) # nothing

## 
## Call:
## lm(formula = act21 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.556 -1.840  0.160  1.696  3.818 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.36063    0.25100  -1.437   0.1516  
## ConsSC.d         -0.27346    0.37195  -0.735   0.4627  
## ConsM.d           0.37819    0.31247   1.210   0.2269  
## ConsL.d           0.83184    0.39825   2.089   0.0374 *
## ConsSL.d          0.79058    0.47783   1.655   0.0988 .
## gend.mf          -0.44540    0.50199  -0.887   0.3755  
## ConsSC.d:gend.mf  0.07722    0.74390   0.104   0.9174  
## ConsM.d:gend.mf   0.80052    0.62494   1.281   0.2010  
## ConsL.d:gend.mf   0.56964    0.79649   0.715   0.4749  
## ConsSL.d:gend.mf  0.69661    0.95566   0.729   0.4665  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.068 on 391 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.0295, Adjusted R-squared:  0.007162 
## F-statistic: 1.321 on 9 and 391 DF,  p-value: 0.2241

# Action 22
con.g.b22 <- lm(act22 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b22) # yes, higher than 0

## 
## Call:
## lm(formula = act22 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.4912 -0.1538  1.6410  3.6410 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.2456     0.2367   1.038   0.3000  
## ConsSC.d          -0.4525     0.3539  -1.279   0.2017  
## ConsM.d            0.1516     0.2986   0.508   0.6120  
## ConsL.d            0.3796     0.3918   0.969   0.3332  
## ConsSL.d           0.8313     0.4368   1.903   0.0577 .
## gend.mf           -0.4912     0.4733  -1.038   0.3000  
## ConsSC.d:gend.mf   1.3595     0.7077   1.921   0.0555 .
## ConsM.d:gend.mf    0.9600     0.5972   1.607   0.1088  
## ConsL.d:gend.mf    0.6254     0.7836   0.798   0.4253  
## ConsSL.d:gend.mf   0.3374     0.8736   0.386   0.6996  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.973 on 392 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.04639,    Adjusted R-squared:  0.02449 
## F-statistic: 2.119 on 9 and 392 DF,  p-value: 0.02709

# Action 23
con.g.b23 <- lm(act23 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b23) # marginally higher than 0

## 
## Call:
## lm(formula = act23 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9474 -2.0103  0.1875  2.0227  3.5238 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.2733     0.2997  -0.912   0.3626  
## ConsSC.d           0.5879     0.4637   1.268   0.2059  
## ConsM.d            0.5284     0.3900   1.355   0.1765  
## ConsL.d            0.8608     0.4776   1.802   0.0726 .
## ConsSL.d           0.5545     0.5737   0.966   0.3346  
## gend.mf           -0.5011     0.5994  -0.836   0.4038  
## ConsSC.d:gend.mf   1.7666     0.9274   1.905   0.0578 .
## ConsM.d:gend.mf    0.9908     0.7799   1.270   0.2050  
## ConsL.d:gend.mf    0.2594     0.9552   0.272   0.7861  
## ConsSL.d:gend.mf  -0.5614     1.1475  -0.489   0.6250  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.26 on 282 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03359,    Adjusted R-squared:  0.002749 
## F-statistic: 1.089 on 9 and 282 DF,  p-value: 0.3707

# Action 24
con.g.b24 <- lm(act24 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b24) # nothing

## 
## Call:
## lm(formula = act24 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5455 -1.8425  0.1575  1.8214  3.8214 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.5904     0.2377  -2.484   0.0134 *
## ConsSC.d           0.5753     0.3740   1.538   0.1248  
## ConsM.d            0.2617     0.2985   0.877   0.3812  
## ConsL.d            0.5482     0.4001   1.370   0.1714  
## ConsSL.d           0.3774     0.4553   0.829   0.4076  
## gend.mf           -0.4621     0.4753  -0.972   0.3316  
## ConsSC.d:gend.mf   1.5833     0.7479   2.117   0.0349 *
## ConsM.d:gend.mf    0.1195     0.5969   0.200   0.8414  
## ConsL.d:gend.mf    0.8322     0.8002   1.040   0.2990  
## ConsSL.d:gend.mf  -0.1120     0.9105  -0.123   0.9021  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.098 on 405 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.02029,    Adjusted R-squared:  -0.001484 
## F-statistic: 0.9318 on 9 and 405 DF,  p-value: 0.4971

# Action 25
con.g.b25 <- lm(act25 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b25) # yes, higher than 0

## 
## Call:
## lm(formula = act25 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -0.8936  0.1642  1.4389  3.2051 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.4111     0.2067   1.989   0.0473 *
## ConsSC.d          -0.4899     0.3238  -1.513   0.1310  
## ConsM.d            0.4741     0.2587   1.833   0.0675 .
## ConsL.d            0.7977     0.3373   2.365   0.0185 *
## ConsSL.d           0.9511     0.4134   2.301   0.0219 *
## gend.mf           -0.3778     0.4134  -0.914   0.3613  
## ConsSC.d:gend.mf   0.6305     0.6476   0.974   0.3308  
## ConsM.d:gend.mf    0.3946     0.5173   0.763   0.4460  
## ConsL.d:gend.mf    0.7102     0.6746   1.053   0.2930  
## ConsSL.d:gend.mf   0.5422     0.8267   0.656   0.5123  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.05138,    Adjusted R-squared:  0.03171 
## F-statistic: 2.612 on 9 and 434 DF,  p-value: 0.006027

# Action 26
con.g.b26 <- lm(act26 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b26) # yes, higher than 0

## 
## Call:
## lm(formula = act26 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0000  0.5278  1.3433  2.5000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.52778    0.26870   5.686 4.19e-08 ***
## ConsSC.d          0.01634    0.40461   0.040    0.968    
## ConsM.d           0.01487    0.35326   0.042    0.966    
## ConsL.d           0.04365    0.47683   0.092    0.927    
## ConsSL.d         -0.55556    0.49973  -1.112    0.267    
## gend.mf          -0.27778    0.53740  -0.517    0.606    
## ConsSC.d:gend.mf  0.36601    0.80923   0.452    0.652    
## ConsM.d:gend.mf   0.04963    0.70653   0.070    0.944    
## ConsL.d:gend.mf   1.42063    0.95367   1.490    0.138    
## ConsSL.d:gend.mf  1.22222    0.99946   1.223    0.223    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 216 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.02923,    Adjusted R-squared:  -0.01122 
## F-statistic: 0.7225 on 9 and 216 DF,  p-value: 0.688

# Action 27
con.g.b27 <- lm(act27 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b27) # yes, higher than 0

## 
## Call:
## lm(formula = act27 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4545 -1.1667  0.2174  1.5521  3.0000 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.73277    0.24468   2.995  0.00297 **
## ConsSC.d         -0.25360    0.35795  -0.708  0.47919   
## ConsM.d          -0.10881    0.31379  -0.347  0.72901   
## ConsL.d           0.44147    0.40014   1.103  0.27076   
## ConsSL.d          0.62608    0.43185   1.450  0.14814   
## gend.mf           0.09968    0.48937   0.204  0.83873   
## ConsSC.d:gend.mf  0.85865    0.71591   1.199  0.23130   
## ConsM.d:gend.mf   0.25240    0.62758   0.402  0.68783   
## ConsL.d:gend.mf  -0.11483    0.80028  -0.143  0.88600   
## ConsSL.d:gend.mf  0.09171    0.86370   0.106  0.91551   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 307 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.03928,    Adjusted R-squared:  0.01112 
## F-statistic: 1.395 on 9 and 307 DF,  p-value: 0.1895

# Action 28
con.g.b28 <- lm(act28 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b28) # yes, higher than 0

## 
## Call:
## lm(formula = act28 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4262 -0.9511  0.1944  1.5738  2.4286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.23485    0.22428   5.506 6.98e-08 ***
## ConsSC.d         -0.45112    0.33712  -1.338    0.182    
## ConsM.d          -0.22922    0.28844  -0.795    0.427    
## ConsL.d          -0.32497    0.37397  -0.869    0.385    
## ConsSL.d         -0.25349    0.45481  -0.557    0.578    
## gend.mf          -0.38275    0.44856  -0.853    0.394    
## ConsSC.d:gend.mf  0.33910    0.67424   0.503    0.615    
## ConsM.d:gend.mf   0.55331    0.57688   0.959    0.338    
## ConsL.d:gend.mf   0.06298    0.74795   0.084    0.933    
## ConsSL.d:gend.mf -0.43712    0.90963  -0.481    0.631    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 362 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.01604,    Adjusted R-squared:  -0.008426 
## F-statistic: 0.6556 on 9 and 362 DF,  p-value: 0.749

# Action 29
con.g.b29 <- lm(act29 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b29) # yes, higher than 0

## 
## Call:
## lm(formula = act29 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1003  0.1951  1.5000  2.1951 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.99223    0.21473   4.621 5.34e-06 ***
## ConsSC.d          0.12682    0.32565   0.389    0.697    
## ConsM.d           0.12270    0.27509   0.446    0.656    
## ConsL.d           0.16021    0.34855   0.460    0.646    
## ConsSL.d          0.43759    0.41823   1.046    0.296    
## gend.mf          -0.15113    0.42947  -0.352    0.725    
## ConsSC.d:gend.mf  0.38923    0.65129   0.598    0.550    
## ConsM.d:gend.mf   0.09270    0.55017   0.168    0.866    
## ConsL.d:gend.mf   0.84625    0.69710   1.214    0.226    
## ConsSL.d:gend.mf -0.04185    0.83647  -0.050    0.960    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.774 on 358 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01019,    Adjusted R-squared:  -0.0147 
## F-statistic: 0.4093 on 9 and 358 DF,  p-value: 0.93

# Action 30
con.g.b30 <- lm(act30 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)

summary(con.g.b30) # yes, higher than 0

## 
## Call:
## lm(formula = act30 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3529 -1.0654  0.5928  1.6471  2.3913 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.89207    0.23247   3.837 0.000148 ***
## ConsSC.d          0.19745    0.34437   0.573 0.566785    
## ConsM.d           0.09064    0.30305   0.299 0.765042    
## ConsL.d           0.37818    0.38141   0.992 0.322128    
## ConsSL.d          0.18440    0.44124   0.418 0.676270    
## gend.mf          -0.56674    0.46494  -1.219 0.223697    
## ConsSC.d:gend.mf  0.03989    0.68875   0.058 0.953850    
## ConsM.d:gend.mf   0.40132    0.60610   0.662 0.508327    
## ConsL.d:gend.mf   0.94933    0.76281   1.245 0.214161    
## ConsSL.d:gend.mf  0.01380    0.88249   0.016 0.987531    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.882 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.01188,    Adjusted R-squared:  -0.01412 
## F-statistic: 0.4569 on 9 and 342 DF,  p-value: 0.9027

a. Means for gender

# Action 14

aggregate(d$act14[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1  Female  0.78
## 2    Male -0.72

3. Gender x condition

Actions 7, 8, 9, 12, 15, 24, 26, 27

# Action 1
summary(lm(act1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.960  0.080  2.000  5.000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.67414    0.25143  -2.681  0.00762 **
## ConsSC.d                 0.27199    0.37388   0.727  0.46732   
## ConsM.d                  0.78305    0.31017   2.525  0.01194 * 
## ConsL.d                  1.26860    0.40166   3.158  0.00170 **
## ConsSL.d                 0.40122    0.46978   0.854  0.39355   
## gend.mf                 -0.83473    0.50287  -1.660  0.09766 . 
## cond.c                  -0.20225    0.50287  -0.402  0.68775   
## ConsSC.d:gend.mf         1.33599    0.74776   1.787  0.07470 . 
## ConsM.d:gend.mf          1.53691    0.62033   2.478  0.01361 * 
## ConsL.d:gend.mf          0.66366    0.80332   0.826  0.40919   
## ConsSL.d:gend.mf        -0.41944    0.93956  -0.446  0.65552   
## ConsSC.d:cond.c          0.95351    0.74776   1.275  0.20294   
## ConsM.d:cond.c          -0.49994    0.62033  -0.806  0.42074   
## ConsL.d:cond.c           0.07618    0.80332   0.095  0.92450   
## ConsSL.d:cond.c          0.32308    0.93956   0.344  0.73112   
## gend.mf:cond.c          -0.40596    1.00574  -0.404  0.68668   
## ConsSC.d:gend.mf:cond.c  0.96404    1.49551   0.645  0.51952   
## ConsM.d:gend.mf:cond.c  -0.02966    1.24066  -0.024  0.98094   
## ConsL.d:gend.mf:cond.c  -0.80618    1.60664  -0.502  0.61608   
## ConsSL.d:gend.mf:cond.c  4.56429    1.87912   2.429  0.01555 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.124 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.0873, Adjusted R-squared:  0.04669 
## F-statistic:  2.15 on 19 and 427 DF,  p-value: 0.003504

# Action 2
summary(lm(act2 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6000 -1.0000  0.3485  1.5192  3.3333 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.79621    0.23378   3.406 0.000761 ***
## ConsSC.d                 0.25350    0.35985   0.704 0.481754    
## ConsM.d                  0.36446    0.31115   1.171 0.242512    
## ConsL.d                  0.87462    0.40756   2.146 0.032773 *  
## ConsSL.d                 0.01212    0.46800   0.026 0.979356    
## gend.mf                 -0.74394    0.46756  -1.591 0.112763    
## cond.c                  -0.72576    0.46756  -1.552 0.121789    
## ConsSC.d:gend.mf         1.53087    0.71970   2.127 0.034327 *  
## ConsM.d:gend.mf          0.75594    0.62230   1.215 0.225535    
## ConsL.d:gend.mf          1.31894    0.81513   1.618 0.106822    
## ConsSL.d:gend.mf         0.19394    0.93599   0.207 0.836010    
## ConsSC.d:cond.c          0.57519    0.71970   0.799 0.424881    
## ConsM.d:cond.c           0.56633    0.62230   0.910 0.363615    
## ConsL.d:cond.c           0.71742    0.81513   0.880 0.379573    
## ConsSL.d:cond.c          1.34242    0.93599   1.434 0.152675    
## gend.mf:cond.c          -1.57879    0.93512  -1.688 0.092512 .  
## ConsSC.d:gend.mf:cond.c  2.65265    1.43941   1.843 0.066452 .  
## ConsM.d:gend.mf:cond.c   2.56432    1.24461   2.060 0.040331 *  
## ConsL.d:gend.mf:cond.c   2.42879    1.63026   1.490 0.137447    
## ConsSL.d:gend.mf:cond.c  3.81212    1.87199   2.036 0.042693 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 268 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.07459,    Adjusted R-squared:  0.008984 
## F-statistic: 1.137 on 19 and 268 DF,  p-value: 0.3138

# Action 3
summary(lm(act3 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7059 -1.8261  0.0278  1.7143  3.9412 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.32639    0.23923  -1.364   0.1732  
## ConsSC.d                -0.46383    0.35875  -1.293   0.1968  
## ConsM.d                  0.26164    0.29398   0.890   0.3740  
## ConsL.d                  0.26389    0.37070   0.712   0.4769  
## ConsSL.d                 0.52429    0.45873   1.143   0.2537  
## gend.mf                  0.20833    0.47845   0.435   0.6635  
## cond.c                  -0.31944    0.47845  -0.668   0.5047  
## ConsSC.d:gend.mf         0.48574    0.71750   0.677   0.4988  
## ConsM.d:gend.mf         -0.41942    0.58796  -0.713   0.4760  
## ConsL.d:gend.mf          0.05952    0.74141   0.080   0.9360  
## ConsSL.d:gend.mf        -0.80413    0.91747  -0.876   0.3813  
## ConsSC.d:cond.c          0.31655    0.71750   0.441   0.6593  
## ConsM.d:cond.c           0.38440    0.58796   0.654   0.5136  
## ConsL.d:cond.c           1.08730    0.74141   1.467   0.1432  
## ConsSL.d:cond.c          0.40936    0.91747   0.446   0.6557  
## gend.mf:cond.c          -1.58333    0.95690  -1.655   0.0987 .
## ConsSC.d:gend.mf:cond.c  2.36185    1.43500   1.646   0.1005  
## ConsM.d:gend.mf:cond.c   1.43893    1.17592   1.224   0.2218  
## ConsL.d:gend.mf:cond.c   0.33333    1.48282   0.225   0.8222  
## ConsSL.d:gend.mf:cond.c  2.60350    1.83494   1.419   0.1567  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.05725,    Adjusted R-squared:  0.01481 
## F-statistic: 1.349 on 19 and 422 DF,  p-value: 0.1485

# Action 4
summary(lm(act4 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2000 -1.8551  0.1449  2.0909  3.5000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              -0.3586     0.2892  -1.240   0.2159  
## ConsSC.d                  0.5584     0.4245   1.316   0.1892  
## ConsM.d                   0.1460     0.3584   0.407   0.6840  
## ConsL.d                   0.8733     0.4379   1.994   0.0469 *
## ConsSL.d                  0.7201     0.5342   1.348   0.1785  
## gend.mf                  -0.4656     0.5784  -0.805   0.4214  
## cond.c                   -0.5239     0.5784  -0.906   0.3657  
## ConsSC.d:gend.mf          0.8386     0.8489   0.988   0.3239  
## ConsM.d:gend.mf           0.4532     0.7168   0.632   0.5276  
## ConsL.d:gend.mf           0.8361     0.8757   0.955   0.3404  
## ConsSL.d:gend.mf         -0.8575     1.0683  -0.803   0.4227  
## ConsSC.d:cond.c           0.5146     0.8489   0.606   0.5448  
## ConsM.d:cond.c            0.1812     0.7168   0.253   0.8006  
## ConsL.d:cond.c            1.2839     0.8757   1.466   0.1435  
## ConsSL.d:cond.c           0.6008     1.0683   0.562   0.5742  
## gend.mf:cond.c           -1.8175     1.1568  -1.571   0.1171  
## ConsSC.d:gend.mf:cond.c   1.3817     1.6978   0.814   0.4163  
## ConsM.d:gend.mf:cond.c    1.3780     1.4336   0.961   0.3371  
## ConsL.d:gend.mf:cond.c    1.4976     1.7515   0.855   0.3931  
## ConsSL.d:gend.mf:cond.c   1.2637     2.1367   0.591   0.5546  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.183 on 349 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.04372,    Adjusted R-squared:  -0.008345 
## F-statistic: 0.8397 on 19 and 349 DF,  p-value: 0.6588

# Action 5
summary(lm(act5 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4800 -1.2500  0.1622  1.5200  3.2500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.96002    0.23800   4.034 7.12e-05 ***
## ConsSC.d                -0.23041    0.36466  -0.632   0.5280    
## ConsM.d                  0.11414    0.31715   0.360   0.7192    
## ConsL.d                  0.51138    0.40348   1.267   0.2061    
## ConsSL.d                -0.68918    0.47869  -1.440   0.1511    
## gend.mf                 -0.46906    0.47600  -0.985   0.3253    
## cond.c                  -0.32239    0.47600  -0.677   0.4988    
## ConsSC.d:gend.mf        -0.01290    0.72932  -0.018   0.9859    
## ConsM.d:gend.mf          0.52953    0.63429   0.835   0.4045    
## ConsL.d:gend.mf          0.95483    0.80695   1.183   0.2377    
## ConsSL.d:gend.mf         0.34406    0.95739   0.359   0.7196    
## ConsSC.d:cond.c          0.05434    0.72932   0.075   0.9407    
## ConsM.d:cond.c          -0.06501    0.63429  -0.102   0.9184    
## ConsL.d:cond.c          -0.49952    0.80695  -0.619   0.5364    
## ConsSL.d:cond.c          0.44739    0.95739   0.467   0.6407    
## gend.mf:cond.c          -1.78660    0.95199  -1.877   0.0616 .  
## ConsSC.d:gend.mf:cond.c  1.36815    1.45865   0.938   0.3491    
## ConsM.d:gend.mf:cond.c   1.83612    1.26858   1.447   0.1489    
## ConsL.d:gend.mf:cond.c   2.28756    1.61390   1.417   0.1575    
## ConsSL.d:gend.mf:cond.c  3.36993    1.91477   1.760   0.0795 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.884 on 275 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.0598, Adjusted R-squared:  -0.005162 
## F-statistic: 0.9205 on 19 and 275 DF,  p-value: 0.5577

# Action 6
summary(lm(act6 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.0816  0.5333  1.5294  2.7500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.31301    0.21991   5.971 5.99e-09 ***
## ConsSC.d                -0.46939    0.33707  -1.393   0.1647    
## ConsM.d                  0.03754    0.28643   0.131   0.8958    
## ConsL.d                  0.25622    0.37174   0.689   0.4911    
## ConsSL.d                -0.38266    0.42696  -0.896   0.3708    
## gend.mf                 -0.22603    0.43982  -0.514   0.6076    
## cond.c                  -0.38087    0.43982  -0.866   0.3871    
## ConsSC.d:gend.mf        -0.25213    0.67415  -0.374   0.7086    
## ConsM.d:gend.mf          0.46217    0.57286   0.807   0.4204    
## ConsL.d:gend.mf          0.84946    0.74349   1.143   0.2540    
## ConsSL.d:gend.mf        -0.98469    0.85392  -1.153   0.2497    
## ConsSC.d:cond.c          0.33955    0.67415   0.504   0.6148    
## ConsM.d:cond.c           0.22806    0.57286   0.398   0.6908    
## ConsL.d:cond.c          -0.06713    0.74349  -0.090   0.9281    
## ConsSL.d:cond.c          0.42015    0.85392   0.492   0.6230    
## gend.mf:cond.c          -1.63826    0.87963  -1.862   0.0634 .  
## ConsSC.d:gend.mf:cond.c  2.93908    1.34829   2.180   0.0300 *  
## ConsM.d:gend.mf:cond.c   1.93604    1.14572   1.690   0.0920 .  
## ConsL.d:gend.mf:cond.c   0.34378    1.48698   0.231   0.8173    
## ConsSL.d:gend.mf:cond.c  1.85969    1.70783   1.089   0.2770    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 337 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.04582,    Adjusted R-squared:  -0.00798 
## F-statistic: 0.8517 on 19 and 337 DF,  p-value: 0.6439

# Action 7
summary(lm(act7 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0625 -1.8571  0.0625  1.7143  3.6000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              -0.3364     0.2648  -1.270   0.2047  
## ConsSC.d                  0.2347     0.3880   0.605   0.5456  
## ConsM.d                   0.2406     0.3216   0.748   0.4548  
## ConsL.d                   0.4170     0.4125   1.011   0.3126  
## ConsSL.d                  0.3104     0.5024   0.618   0.5371  
## gend.mf                  -0.2200     0.5297  -0.415   0.6782  
## cond.c                   -0.3229     0.5297  -0.610   0.5425  
## ConsSC.d:gend.mf          0.6053     0.7759   0.780   0.4358  
## ConsM.d:gend.mf           0.3337     0.6432   0.519   0.6042  
## ConsL.d:gend.mf           0.4477     0.8249   0.543   0.5876  
## ConsSL.d:gend.mf         -0.8946     1.0049  -0.890   0.3739  
## ConsSC.d:cond.c           0.4546     0.7759   0.586   0.5583  
## ConsM.d:cond.c            0.7378     0.6432   1.147   0.2521  
## ConsL.d:cond.c            1.1785     0.8249   1.429   0.1539  
## ConsSL.d:cond.c           0.2083     1.0049   0.207   0.8359  
## gend.mf:cond.c           -2.1399     1.0594  -2.020   0.0441 *
## ConsSC.d:gend.mf:cond.c   3.3310     1.5518   2.147   0.0324 *
## ConsM.d:gend.mf:cond.c    2.6089     1.2864   2.028   0.0432 *
## ConsL.d:gend.mf:cond.c    0.3177     1.6499   0.193   0.8474  
## ConsSL.d:gend.mf:cond.c   4.0357     2.0098   2.008   0.0453 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.081 on 393 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.05477,    Adjusted R-squared:  0.009077 
## F-statistic: 1.199 on 19 and 393 DF,  p-value: 0.2548

# Action 8
summary(lm(act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4000 -1.6786 -0.1429  1.5466  4.8421 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -0.46262    0.24324  -1.902 0.057804 .  
## ConsSC.d                -0.48735    0.36574  -1.333 0.183355    
## ConsM.d                  0.07658    0.29989   0.255 0.798564    
## ConsL.d                  1.04595    0.40134   2.606 0.009454 ** 
## ConsSL.d                 0.89595    0.45097   1.987 0.047546 *  
## gend.mf                  0.41062    0.48647   0.844 0.399065    
## cond.c                  -1.67524    0.48647  -3.444 0.000627 ***
## ConsSC.d:gend.mf         0.85297    0.73149   1.166 0.244191    
## ConsM.d:gend.mf         -0.20194    0.59978  -0.337 0.736501    
## ConsL.d:gend.mf         -0.14871    0.80268  -0.185 0.853097    
## ConsSL.d:gend.mf        -1.61062    0.90194  -1.786 0.074802 .  
## ConsSC.d:cond.c          1.79921    0.73149   2.460 0.014274 *  
## ConsM.d:cond.c           1.91270    0.59978   3.189 0.001525 ** 
## ConsL.d:cond.c           2.27048    0.80268   2.829 0.004880 ** 
## ConsSL.d:cond.c          2.87524    0.90194   3.188 0.001531 ** 
## gend.mf:cond.c          -2.56765    0.97294  -2.639 0.008596 ** 
## ConsSC.d:gend.mf:cond.c  1.77425    1.46297   1.213 0.225842    
## ConsM.d:gend.mf:cond.c   3.05288    1.19955   2.545 0.011254 *  
## ConsL.d:gend.mf:cond.c   2.52003    1.60536   1.570 0.117160    
## ConsSL.d:gend.mf:cond.c  3.50098    1.80387   1.941 0.052893 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.102 on 459 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.07388 
## F-statistic: 3.007 on 19 and 459 DF,  p-value: 2.47e-05

# Action 9
summary(lm(act9 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8000 -1.5455  0.0455  1.5000  4.4545 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.064112   0.232036  -0.276  0.78245   
## ConsSC.d                -0.735509   0.350953  -2.096  0.03669 * 
## ConsM.d                  0.231180   0.286670   0.806  0.42044   
## ConsL.d                  0.627208   0.365342   1.717  0.08675 . 
## ConsSL.d                -0.002554   0.443179  -0.006  0.99540   
## gend.mf                  0.461558   0.464071   0.995  0.32050   
## cond.c                  -0.886395   0.464071  -1.910  0.05680 . 
## ConsSC.d:gend.mf        -0.566861   0.701906  -0.808  0.41977   
## ConsM.d:gend.mf         -0.536799   0.573340  -0.936  0.34967   
## ConsL.d:gend.mf         -0.502034   0.730684  -0.687  0.49241   
## ConsSL.d:gend.mf        -1.428225   0.886358  -1.611  0.10784   
## ConsSC.d:cond.c          2.008365   0.701906   2.861  0.00443 **
## ConsM.d:cond.c           1.090984   0.573340   1.903  0.05773 . 
## ConsL.d:cond.c           0.712585   0.730684   0.975  0.33000   
## ConsSL.d:cond.c          0.853062   0.886358   0.962  0.33638   
## gend.mf:cond.c          -2.004988   0.928143  -2.160  0.03131 * 
## ConsSC.d:gend.mf:cond.c  2.170139   1.403811   1.546  0.12287   
## ConsM.d:gend.mf:cond.c   2.295415   1.146680   2.002  0.04594 * 
## ConsL.d:gend.mf:cond.c   1.324036   1.461368   0.906  0.36543   
## ConsSL.d:gend.mf:cond.c  2.271655   1.772717   1.281  0.20073   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.06597,    Adjusted R-squared:  0.02441 
## F-statistic: 1.587 on 19 and 427 DF,  p-value: 0.05552

# Action 10
summary(lm(act10 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.8630  0.1739  1.7370  4.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.197392   0.235263  -0.839  0.40190   
## ConsSC.d                -0.340257   0.355033  -0.958  0.33838   
## ConsM.d                  0.283118   0.292403   0.968  0.33344   
## ConsL.d                  1.163124   0.380096   3.060  0.00234 **
## ConsSL.d                 0.927695   0.448282   2.069  0.03907 * 
## gend.mf                 -0.005217   0.470527  -0.011  0.99116   
## cond.c                  -0.800339   0.470527  -1.701  0.08964 . 
## ConsSC.d:gend.mf         0.474454   0.710066   0.668  0.50436   
## ConsM.d:gend.mf          0.051156   0.584806   0.087  0.93033   
## ConsL.d:gend.mf          0.948751   0.760192   1.248  0.21266   
## ConsSL.d:gend.mf        -1.655389   0.896564  -1.846  0.06549 . 
## ConsSC.d:cond.c          0.734611   0.710066   1.035  0.30142   
## ConsM.d:cond.c           0.883204   0.584806   1.510  0.13167   
## ConsL.d:cond.c           0.968873   0.760192   1.275  0.20313   
## ConsSL.d:cond.c          1.994278   0.896564   2.224  0.02661 * 
## gend.mf:cond.c          -1.599322   0.941053  -1.700  0.08991 . 
## ConsSC.d:gend.mf:cond.c  1.609567   1.420133   1.133  0.25765   
## ConsM.d:gend.mf:cond.c   2.564026   1.169612   2.192  0.02887 * 
## ConsL.d:gend.mf:cond.c   0.512254   1.520385   0.337  0.73633   
## ConsSL.d:gend.mf:cond.c  4.411444   1.793127   2.460  0.01426 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 456 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.0938, Adjusted R-squared:  0.05604 
## F-statistic: 2.484 on 19 and 456 DF,  p-value: 0.0005392

# Action 11
summary(lm(act11 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9000 -1.0292  0.3902  1.5200  3.4167 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.77409    0.24441   3.167   0.0017 **
## ConsSC.d                 0.01624    0.36192   0.045   0.9642   
## ConsM.d                 -0.06157    0.30519  -0.202   0.8402   
## ConsL.d                  0.99793    0.40534   2.462   0.0144 * 
## ConsSL.d                 0.65924    0.49312   1.337   0.1823   
## gend.mf                 -0.46486    0.48882  -0.951   0.3424   
## cond.c                  -1.05181    0.48882  -2.152   0.0322 * 
## ConsSC.d:gend.mf         1.61146    0.72385   2.226   0.0268 * 
## ConsM.d:gend.mf          0.38949    0.61038   0.638   0.5239   
## ConsL.d:gend.mf          0.65890    0.81068   0.813   0.4170   
## ConsSL.d:gend.mf         0.43152    0.98624   0.438   0.6620   
## ConsSC.d:cond.c          1.46465    0.72385   2.023   0.0439 * 
## ConsM.d:cond.c           0.40089    0.61038   0.657   0.5118   
## ConsL.d:cond.c           0.67443    0.81068   0.832   0.4061   
## ConsSL.d:cond.c          0.68514    0.98624   0.695   0.4878   
## gend.mf:cond.c          -1.72971    0.97764  -1.769   0.0779 . 
## ConsSC.d:gend.mf:cond.c  1.99494    1.44769   1.378   0.1693   
## ConsM.d:gend.mf:cond.c   1.50868    1.22076   1.236   0.2175   
## ConsL.d:gend.mf:cond.c  -0.32505    1.62135  -0.200   0.8412   
## ConsSL.d:gend.mf:cond.c  2.79638    1.97249   1.418   0.1573   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.858 on 292 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.09531,    Adjusted R-squared:  0.03644 
## F-statistic: 1.619 on 19 and 292 DF,  p-value: 0.05071

# Action 12
summary(lm(act12 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.000 -1.875  0.125  1.635  4.235 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -0.5712     0.2726  -2.095 0.036824 *  
## ConsSC.d                 -0.1833     0.3966  -0.462 0.644200    
## ConsM.d                   0.4075     0.3365   1.211 0.226686    
## ConsL.d                   1.7250     0.4426   3.898 0.000116 ***
## ConsSL.d                  0.8121     0.5176   1.569 0.117520    
## gend.mf                  -0.4112     0.5452  -0.754 0.451240    
## cond.c                   -1.1826     0.5452  -2.169 0.030720 *  
## ConsSC.d:gend.mf          1.1929     0.7932   1.504 0.133477    
## ConsM.d:gend.mf          -0.1562     0.6731  -0.232 0.816559    
## ConsL.d:gend.mf           1.7702     0.8851   2.000 0.046253 *  
## ConsSL.d:gend.mf         -0.4707     1.0352  -0.455 0.649631    
## ConsSC.d:cond.c           1.8179     0.7932   2.292 0.022491 *  
## ConsM.d:cond.c            1.2270     0.6731   1.823 0.069122 .  
## ConsL.d:cond.c            1.6985     0.8851   1.919 0.055779 .  
## ConsSL.d:cond.c           1.9008     1.0352   1.836 0.067155 .  
## gend.mf:cond.c           -3.0277     1.0904  -2.777 0.005776 ** 
## ConsSC.d:gend.mf:cond.c   3.9388     1.5865   2.483 0.013488 *  
## ConsM.d:gend.mf:cond.c    4.0967     1.3461   3.043 0.002510 ** 
## ConsL.d:gend.mf:cond.c    2.6625     1.7703   1.504 0.133457    
## ConsSL.d:gend.mf:cond.c   3.1913     2.0704   1.541 0.124092    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.117 on 363 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.06946 
## F-statistic: 2.501 on 19 and 363 DF,  p-value: 0.0005499

# Action 13
summary(lm(act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1333 -1.8000  0.1852  1.8235  4.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.08838    0.25565  -0.346  0.72972   
## ConsSC.d                -0.21246    0.37426  -0.568  0.57056   
## ConsM.d                 -0.05948    0.30902  -0.192  0.84746   
## ConsL.d                  0.58999    0.40329   1.463  0.14423   
## ConsSL.d                 0.03630    0.45985   0.079  0.93712   
## gend.mf                  0.17677    0.51129   0.346  0.72972   
## cond.c                  -1.26768    0.51129  -2.479  0.01355 * 
## ConsSC.d:gend.mf         1.60673    0.74852   2.147  0.03240 * 
## ConsM.d:gend.mf         -0.44626    0.61804  -0.722  0.47066   
## ConsL.d:gend.mf          0.36169    0.80659   0.448  0.65408   
## ConsSL.d:gend.mf        -1.03927    0.91970  -1.130  0.25911   
## ConsSC.d:cond.c          1.13872    0.74852   1.521  0.12893   
## ConsM.d:cond.c           1.56596    0.61804   2.534  0.01165 * 
## ConsL.d:cond.c           1.47280    0.80659   1.826  0.06856 . 
## ConsSL.d:cond.c          1.83018    0.91970   1.990  0.04724 * 
## gend.mf:cond.c          -1.46465    1.02258  -1.432  0.15280   
## ConsSC.d:gend.mf:cond.c  1.17710    1.49704   0.786  0.43214   
## ConsM.d:gend.mf:cond.c   1.30286    1.23608   1.054  0.29247   
## ConsL.d:gend.mf:cond.c   2.63772    1.61318   1.635  0.10277   
## ConsSL.d:gend.mf:cond.c  5.60631    1.83939   3.048  0.00245 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.08224,    Adjusted R-squared:  0.04091 
## F-statistic:  1.99 on 19 and 422 DF,  p-value: 0.008085

# Action 14
summary(lm(act14 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1818 -1.2411  0.3129  1.5849  3.9000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.04414    0.28896   0.153   0.8787  
## ConsSC.d                 0.45751    0.43815   1.044   0.2974  
## ConsM.d                  0.34138    0.35685   0.957   0.3396  
## ConsL.d                  1.09396    0.46578   2.349   0.0196 *
## ConsSL.d                -0.25247    0.56982  -0.443   0.6581  
## gend.mf                 -1.48828    0.57791  -2.575   0.0106 *
## cond.c                  -0.30646    0.57791  -0.530   0.5964  
## ConsSC.d:gend.mf         2.09927    0.87631   2.396   0.0173 *
## ConsM.d:gend.mf          1.32041    0.71370   1.850   0.0654 .
## ConsL.d:gend.mf          2.11207    0.93155   2.267   0.0242 *
## ConsSL.d:gend.mf        -0.17839    1.13964  -0.157   0.8757  
## ConsSC.d:cond.c          0.89547    0.87631   1.022   0.3078  
## ConsM.d:cond.c           0.44812    0.71370   0.628   0.5306  
## ConsL.d:cond.c           0.61207    0.93155   0.657   0.5117  
## ConsSL.d:cond.c          1.72313    1.13964   1.512   0.1317  
## gend.mf:cond.c          -0.18708    1.15582  -0.162   0.8715  
## ConsSC.d:gend.mf:cond.c -0.61951    1.75262  -0.353   0.7240  
## ConsM.d:gend.mf:cond.c   0.25297    1.42739   0.177   0.8595  
## ConsL.d:gend.mf:cond.c  -0.62415    1.86311  -0.335   0.7379  
## ConsSL.d:gend.mf:cond.c  2.52041    2.27929   1.106   0.2698  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.033 on 266 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.06875,    Adjusted R-squared:  0.002233 
## F-statistic: 1.034 on 19 and 266 DF,  p-value: 0.4224

# Action 15
summary(lm(act15 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5294 -1.7357  0.3167  1.4947  4.2000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.09736    0.24269   0.401   0.6885  
## ConsSC.d                -0.43962    0.36314  -1.211   0.2268  
## ConsM.d                 -0.17671    0.30040  -0.588   0.5567  
## ConsL.d                  0.90791    0.41707   2.177   0.0301 *
## ConsSL.d                 0.04163    0.44684   0.093   0.9258  
## gend.mf                 -0.37707    0.48538  -0.777   0.4377  
## cond.c                  -0.54374    0.48538  -1.120   0.2634  
## ConsSC.d:gend.mf         0.97826    0.72627   1.347   0.1788  
## ConsM.d:gend.mf         -0.36541    0.60081  -0.608   0.5434  
## ConsL.d:gend.mf          1.19153    0.83415   1.428   0.1540  
## ConsSL.d:gend.mf        -0.38662    0.89369  -0.433   0.6656  
## ConsSC.d:cond.c          0.85922    0.72627   1.183   0.2376  
## ConsM.d:cond.c           0.67947    0.60081   1.131   0.2588  
## ConsL.d:cond.c          -0.60013    0.83415  -0.719   0.4723  
## ConsSL.d:cond.c          1.35505    0.89369   1.516   0.1303  
## gend.mf:cond.c          -2.07722    0.97076  -2.140   0.0330 *
## ConsSC.d:gend.mf:cond.c  1.27961    1.45255   0.881   0.3789  
## ConsM.d:gend.mf:cond.c   2.91248    1.20161   2.424   0.0158 *
## ConsL.d:gend.mf:cond.c   3.51497    1.66830   2.107   0.0358 *
## ConsSL.d:gend.mf:cond.c  4.28318    1.78738   2.396   0.0171 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.021 on 364 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.09033,    Adjusted R-squared:  0.04285 
## F-statistic: 1.902 on 19 and 364 DF,  p-value: 0.013

# Action 16
summary(lm(act16 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -0.9811  0.2963  1.4800  3.3333 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.89906    0.23622   3.806  0.00017 ***
## ConsSC.d                -0.26522    0.36089  -0.735  0.46294    
## ConsM.d                  0.20230    0.29806   0.679  0.49783    
## ConsL.d                  0.12397    0.39179   0.316  0.75190    
## ConsSL.d                -0.38239    0.47520  -0.805  0.42161    
## gend.mf                 -0.48188    0.47244  -1.020  0.30853    
## cond.c                   0.26034    0.47244   0.551  0.58199    
## ConsSC.d:gend.mf         1.32531    0.72177   1.836  0.06728 .  
## ConsM.d:gend.mf          0.99976    0.59612   1.677  0.09453 .  
## ConsL.d:gend.mf         -0.06417    0.78358  -0.082  0.93478    
## ConsSL.d:gend.mf         0.61521    0.95040   0.647  0.51790    
## ConsSC.d:cond.c         -0.19469    0.72177  -0.270  0.78755    
## ConsM.d:cond.c          -0.01133    0.59612  -0.019  0.98484    
## ConsL.d:cond.c          -0.55639    0.78358  -0.710  0.47819    
## ConsSL.d:cond.c         -0.29368    0.95040  -0.309  0.75753    
## gend.mf:cond.c          -0.99932    0.94488  -1.058  0.29105    
## ConsSC.d:gend.mf:cond.c  0.64578    1.44355   0.447  0.65493    
## ConsM.d:gend.mf:cond.c   0.94248    1.19224   0.791  0.42983    
## ConsL.d:gend.mf:cond.c  -1.40858    1.56715  -0.899  0.36945    
## ConsSL.d:gend.mf:cond.c  4.73265    1.90079   2.490  0.01330 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 311 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.06263,    Adjusted R-squared:  0.005359 
## F-statistic: 1.094 on 19 and 311 DF,  p-value: 0.3561

# Action 17
summary(lm(act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4286 -1.3889 -0.0571  1.6000  4.2500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.27669    0.24615   1.124  0.26167   
## ConsSC.d                 0.07748    0.36114   0.215  0.83024   
## ConsM.d                  0.20647    0.30216   0.683  0.49481   
## ConsL.d                  0.49282    0.39139   1.259  0.20872   
## ConsSL.d                 0.64623    0.45949   1.406  0.16039   
## gend.mf                 -0.03948    0.49230  -0.080  0.93612   
## cond.c                  -1.10734    0.49230  -2.249  0.02504 * 
## ConsSC.d:gend.mf         0.22509    0.72229   0.312  0.75548   
## ConsM.d:gend.mf          0.06667    0.60433   0.110  0.91221   
## ConsL.d:gend.mf          0.78619    0.78278   1.004  0.31582   
## ConsSL.d:gend.mf        -0.88968    0.91898  -0.968  0.33358   
## ConsSC.d:cond.c          1.89901    0.72229   2.629  0.00889 **
## ConsM.d:cond.c           0.66722    0.60433   1.104  0.27023   
## ConsL.d:cond.c           1.02547    0.78278   1.310  0.19094   
## ConsSL.d:cond.c          2.80317    0.91898   3.050  0.00244 **
## gend.mf:cond.c          -1.25754    0.98460  -1.277  0.20228   
## ConsSC.d:gend.mf:cond.c  0.79542    1.44458   0.551  0.58220   
## ConsM.d:gend.mf:cond.c   0.69623    1.20865   0.576  0.56492   
## ConsL.d:gend.mf:cond.c   0.27842    1.56556   0.178  0.85894   
## ConsSL.d:gend.mf:cond.c  4.69921    1.83797   2.557  0.01094 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 396 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.0642, Adjusted R-squared:  0.0193 
## F-statistic:  1.43 on 19 and 396 DF,  p-value: 0.1086

# Action 18
summary(lm(act18 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.2632  0.2372  1.3333  3.3846 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.48707    0.24051   2.025  0.04365 * 
## ConsSC.d                -0.26593    0.35230  -0.755  0.45087   
## ConsM.d                  0.28341    0.30321   0.935  0.35061   
## ConsL.d                  0.49985    0.37294   1.340  0.18106   
## ConsSL.d                 0.13953    0.45400   0.307  0.75878   
## gend.mf                 -0.14080    0.48102  -0.293  0.76992   
## cond.c                  -0.47414    0.48102  -0.986  0.32501   
## ConsSC.d:gend.mf         0.85005    0.70460   1.206  0.22851   
## ConsM.d:gend.mf          0.05205    0.60641   0.086  0.93166   
## ConsL.d:gend.mf          0.30982    0.74588   0.415  0.67813   
## ConsSL.d:gend.mf         0.63760    0.90800   0.702  0.48305   
## ConsSC.d:cond.c          0.96769    0.70460   1.373  0.17055   
## ConsM.d:cond.c           0.45875    0.60641   0.756  0.44989   
## ConsL.d:cond.c           0.62060    0.74588   0.832  0.40599   
## ConsSL.d:cond.c          1.05427    0.90800   1.161  0.24644   
## gend.mf:cond.c          -1.38506    0.96205  -1.440  0.15090   
## ConsSC.d:gend.mf:cond.c  1.36764    1.40919   0.971  0.33250   
## ConsM.d:gend.mf:cond.c   1.57025    1.21282   1.295  0.19632   
## ConsL.d:gend.mf:cond.c   0.23499    1.49175   0.158  0.87492   
## ConsSL.d:gend.mf:cond.c  4.72480    1.81601   2.602  0.00969 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.05189,    Adjusted R-squared:  -0.002371 
## F-statistic: 0.9563 on 19 and 332 DF,  p-value: 0.5132

# Action 19
summary(lm(act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6364 -1.0465  0.3693  1.4390  2.8750 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.12118    0.24545   4.568 7.69e-06 ***
## ConsSC.d                -0.21145    0.38136  -0.554   0.5797    
## ConsM.d                  0.03203    0.31718   0.101   0.9196    
## ConsL.d                  0.42327    0.45347   0.933   0.3515    
## ConsSL.d                -0.03500    0.46199  -0.076   0.9397    
## gend.mf                 -0.17886    0.49090  -0.364   0.7159    
## cond.c                  -1.00209    0.49090  -2.041   0.0423 *  
## ConsSC.d:gend.mf         0.98442    0.76272   1.291   0.1980    
## ConsM.d:gend.mf         -0.12221    0.63435  -0.193   0.8474    
## ConsL.d:gend.mf          0.62330    0.90695   0.687   0.4925    
## ConsSL.d:gend.mf        -0.66016    0.92398  -0.714   0.4756    
## ConsSC.d:cond.c          1.07154    0.76272   1.405   0.1613    
## ConsM.d:cond.c           0.56572    0.63435   0.892   0.3733    
## ConsL.d:cond.c           0.22431    0.90695   0.247   0.8049    
## ConsSL.d:cond.c          1.20474    0.92398   1.304   0.1935    
## gend.mf:cond.c          -0.97994    0.98180  -0.998   0.3192    
## ConsSC.d:gend.mf:cond.c -0.40895    1.52544  -0.268   0.7889    
## ConsM.d:gend.mf:cond.c   1.13612    1.26871   0.895   0.3714    
## ConsL.d:gend.mf:cond.c   0.26883    1.81389   0.148   0.8823    
## ConsSL.d:gend.mf:cond.c  1.90797    1.84796   1.032   0.3028    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.809 on 254 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.05536,    Adjusted R-squared:  -0.0153 
## F-statistic: 0.7834 on 19 and 254 DF,  p-value: 0.7262

# Action 20
summary(lm(act20 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7000 -1.1616  0.4211  1.4211  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.024840   0.230619   4.444 1.24e-05 ***
## ConsSC.d                 0.179074   0.340966   0.525   0.5998    
## ConsM.d                  0.241304   0.297672   0.811   0.4182    
## ConsL.d                  0.225599   0.379943   0.594   0.5531    
## ConsSL.d                -0.320078   0.450351  -0.711   0.4778    
## gend.mf                 -0.741987   0.461239  -1.609   0.1087    
## cond.c                  -0.450321   0.461239  -0.976   0.3297    
## ConsSC.d:gend.mf         0.917492   0.681933   1.345   0.1795    
## ConsM.d:gend.mf          0.548986   0.595344   0.922   0.3572    
## ConsL.d:gend.mf          1.074443   0.759887   1.414   0.1584    
## ConsSL.d:gend.mf         0.165797   0.900702   0.184   0.8541    
## ConsSC.d:cond.c         -0.096397   0.681933  -0.141   0.8877    
## ConsM.d:cond.c          -0.221340   0.595344  -0.372   0.7103    
## ConsL.d:cond.c          -0.417223   0.759887  -0.549   0.5834    
## ConsSL.d:cond.c          0.493178   0.900702   0.548   0.5844    
## gend.mf:cond.c          -0.483974   0.922478  -0.525   0.6002    
## ConsSC.d:gend.mf:cond.c  0.744075   1.363865   0.546   0.5858    
## ConsM.d:gend.mf:cond.c   0.005866   1.190688   0.005   0.9961    
## ConsL.d:gend.mf:cond.c  -0.114271   1.519774  -0.075   0.9401    
## ConsSL.d:gend.mf:cond.c  3.398260   1.801404   1.886   0.0602 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.795 on 302 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.05394,    Adjusted R-squared:  -0.00558 
## F-statistic: 0.9063 on 19 and 302 DF,  p-value: 0.5755

# Action 21
summary(lm(act21 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1250 -1.7424  0.0508  1.7059  4.2727 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.26166    0.26875  -0.974   0.3309  
## ConsSC.d                -0.38912    0.38474  -1.011   0.3125  
## ConsM.d                  0.28277    0.32685   0.865   0.3875  
## ConsL.d                  0.72803    0.41005   1.775   0.0766 .
## ConsSL.d                 0.62128    0.48936   1.270   0.2050  
## gend.mf                 -0.21618    0.53749  -0.402   0.6878  
## cond.c                  -0.05903    0.53749  -0.110   0.9126  
## ConsSC.d:gend.mf        -0.11863    0.76947  -0.154   0.8776  
## ConsM.d:gend.mf          0.56682    0.65370   0.867   0.3864  
## ConsL.d:gend.mf          0.26558    0.82009   0.324   0.7462  
## ConsSL.d:gend.mf         0.39695    0.97872   0.406   0.6853  
## ConsSC.d:cond.c          0.73609    0.76947   0.957   0.3394  
## ConsM.d:cond.c           0.21597    0.65370   0.330   0.7413  
## ConsL.d:cond.c          -0.21656    0.82009  -0.264   0.7919  
## ConsSL.d:cond.c          0.73980    0.97872   0.756   0.4502  
## gend.mf:cond.c          -1.93235    1.07498  -1.798   0.0730 .
## ConsSC.d:gend.mf:cond.c  2.39643    1.53894   1.557   0.1203  
## ConsM.d:gend.mf:cond.c   1.83277    1.30740   1.402   0.1618  
## ConsL.d:gend.mf:cond.c  -0.05217    1.64018  -0.032   0.9746  
## ConsSL.d:gend.mf:cond.c  4.37081    1.95744   2.233   0.0261 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 381 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05638,    Adjusted R-squared:  0.009323 
## F-statistic: 1.198 on 19 and 381 DF,  p-value: 0.2555

# Action 22
summary(lm(act22 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2222 -1.4571 -0.0508  1.7429  3.7778 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.28750    0.24132   1.191   0.2342  
## ConsSC.d                -0.49926    0.35747  -1.397   0.1633  
## ConsM.d                  0.10529    0.30256   0.348   0.7280  
## ConsL.d                  0.34575    0.39634   0.872   0.3836  
## ConsSL.d                 0.79747    0.44420   1.795   0.0734 .
## gend.mf                 -0.34167    0.48264  -0.708   0.4794  
## cond.c                  -0.37500    0.48264  -0.777   0.4377  
## ConsSC.d:gend.mf         1.21973    0.71493   1.706   0.0888 .
## ConsM.d:gend.mf          0.81925    0.60513   1.354   0.1766  
## ConsL.d:gend.mf          0.33706    0.79268   0.425   0.6709  
## ConsSL.d:gend.mf         0.17173    0.88841   0.193   0.8468  
## ConsSC.d:cond.c          0.63835    0.71493   0.893   0.3725  
## ConsM.d:cond.c           0.37712    0.60513   0.623   0.5335  
## ConsL.d:cond.c           0.05294    0.79268   0.067   0.9468  
## ConsSL.d:cond.c         -0.17271    0.88841  -0.194   0.8460  
## gend.mf:cond.c          -1.58333    0.96527  -1.640   0.1018  
## ConsSC.d:gend.mf:cond.c  1.60209    1.42986   1.120   0.2632  
## ConsM.d:gend.mf:cond.c   2.00016    1.21025   1.653   0.0992 .
## ConsL.d:gend.mf:cond.c  -0.96302    1.58537  -0.607   0.5439  
## ConsSL.d:gend.mf:cond.c  0.27876    1.77681   0.157   0.8754  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.976 on 382 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.0681, Adjusted R-squared:  0.02175 
## F-statistic: 1.469 on 19 and 382 DF,  p-value: 0.0928

# Action 23
summary(lm(act23 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -2.000  0.125  1.920  4.500 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             -0.23475    0.30175  -0.778   0.4373  
## ConsSC.d                 0.53955    0.46441   1.162   0.2463  
## ConsM.d                  0.45588    0.39257   1.161   0.2465  
## ConsL.d                  0.75449    0.48207   1.565   0.1187  
## ConsSL.d                 0.59612    0.58472   1.019   0.3089  
## gend.mf                 -0.39161    0.60349  -0.649   0.5169  
## cond.c                  -0.53383    0.60349  -0.885   0.3772  
## ConsSC.d:gend.mf         1.63757    0.92881   1.763   0.0790 .
## ConsM.d:gend.mf          0.81601    0.78513   1.039   0.2996  
## ConsL.d:gend.mf          0.11998    0.96414   0.124   0.9011  
## ConsSL.d:gend.mf        -0.83112    1.16944  -0.711   0.4779  
## ConsSC.d:cond.c          0.58787    0.92881   0.633   0.5273  
## ConsM.d:cond.c          -0.03343    0.78513  -0.043   0.9661  
## ConsL.d:cond.c          -0.91835    0.96414  -0.953   0.3417  
## ConsSL.d:cond.c          2.21110    1.16944   1.891   0.0597 .
## gend.mf:cond.c          -1.54345    1.20698  -1.279   0.2021  
## ConsSC.d:gend.mf:cond.c  2.92426    1.85763   1.574   0.1166  
## ConsM.d:gend.mf:cond.c   0.94464    1.57027   0.602   0.5480  
## ConsL.d:gend.mf:cond.c  -0.51647    1.92828  -0.268   0.7890  
## ConsSL.d:gend.mf:cond.c  3.18890    2.33889   1.363   0.1739  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.253 on 272 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.07369,    Adjusted R-squared:  0.008984 
## F-statistic: 1.139 on 19 and 272 DF,  p-value: 0.3118

# Action 24
summary(lm(act24 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8750 -0.0429  1.6429  4.6667 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -0.43710    0.24370  -1.794  0.07364 . 
## ConsSC.d                 0.40502    0.37829   1.071  0.28497   
## ConsM.d                  0.11114    0.30273   0.367  0.71371   
## ConsL.d                  0.41184    0.41093   1.002  0.31685   
## ConsSL.d                 0.19405    0.46643   0.416  0.67761   
## gend.mf                 -0.09246    0.48741  -0.190  0.84964   
## cond.c                  -0.93135    0.48741  -1.911  0.05675 . 
## ConsSC.d:gend.mf         1.24753    0.75658   1.649  0.09996 . 
## ConsM.d:gend.mf         -0.19880    0.60545  -0.328  0.74282   
## ConsL.d:gend.mf          0.54299    0.82185   0.661  0.50920   
## ConsSL.d:gend.mf        -0.50476    0.93285  -0.541  0.58875   
## ConsSC.d:cond.c          1.09096    0.75658   1.442  0.15010   
## ConsM.d:cond.c           0.05476    0.60545   0.090  0.92798   
## ConsL.d:cond.c           0.96082    0.82185   1.169  0.24307   
## ConsSL.d:cond.c          1.08413    0.93285   1.162  0.24587   
## gend.mf:cond.c          -2.87063    0.97481  -2.945  0.00342 **
## ConsSC.d:gend.mf:cond.c  2.55140    1.51315   1.686  0.09256 . 
## ConsM.d:gend.mf:cond.c   2.01018    1.21090   1.660  0.09769 . 
## ConsL.d:gend.mf:cond.c   3.61169    1.64370   2.197  0.02858 * 
## ConsSL.d:gend.mf:cond.c  3.39841    1.86570   1.822  0.06928 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.083 on 395 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.05818,    Adjusted R-squared:  0.01287 
## F-statistic: 1.284 on 19 and 395 DF,  p-value: 0.1894

# Action 25
summary(lm(act25 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6087 -0.8846  0.2222  1.3913  3.4000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.5203     0.2154   2.415   0.0162 *
## ConsSC.d                 -0.6143     0.3297  -1.863   0.0631 .
## ConsM.d                   0.3663     0.2657   1.379   0.1687  
## ConsL.d                   0.6944     0.3422   2.029   0.0431 *
## ConsSL.d                  0.6905     0.4366   1.581   0.1145  
## gend.mf                  -0.1517     0.4309  -0.352   0.7250  
## cond.c                   -0.5705     0.4309  -1.324   0.1862  
## ConsSC.d:gend.mf          0.3942     0.6593   0.598   0.5502  
## ConsM.d:gend.mf           0.1737     0.5314   0.327   0.7439  
## ConsL.d:gend.mf           0.4724     0.6844   0.690   0.4905  
## ConsSL.d:gend.mf         -0.1032     0.8732  -0.118   0.9060  
## ConsSC.d:cond.c           1.0767     0.6593   1.633   0.1032  
## ConsM.d:cond.c            0.3722     0.5314   0.700   0.4840  
## ConsL.d:cond.c            1.1249     0.6844   1.644   0.1010  
## ConsSL.d:cond.c           1.8156     0.8732   2.079   0.0382 *
## gend.mf:cond.c           -1.5256     0.8618  -1.770   0.0774 .
## ConsSC.d:gend.mf:cond.c   2.2224     1.3186   1.685   0.0926 .
## ConsM.d:gend.mf:cond.c    1.1650     1.0628   1.096   0.2736  
## ConsL.d:gend.mf:cond.c    0.4169     1.3688   0.305   0.7608  
## ConsSL.d:gend.mf:cond.c   3.3688     1.7465   1.929   0.0544 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.837 on 424 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.07813,    Adjusted R-squared:  0.03682 
## F-statistic: 1.891 on 19 and 424 DF,  p-value: 0.0133

# Action 26
summary(lm(act26 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2857 -1.1562  0.5577  1.3947  3.0000 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.6360119  0.2750065   5.949 1.14e-08 ***
## ConsSC.d                 0.0008929  0.4199790   0.002   0.9983    
## ConsM.d                 -0.0825078  0.3625584  -0.228   0.8202    
## ConsL.d                  0.0639881  0.5099170   0.125   0.9003    
## ConsSL.d                -0.8324405  0.5320132  -1.565   0.1192    
## gend.mf                 -0.3470238  0.5500130  -0.631   0.5288    
## cond.c                  -0.1386905  0.5500130  -0.252   0.8012    
## ConsSC.d:gend.mf         0.2398810  0.8399581   0.286   0.7755    
## ConsM.d:gend.mf          0.1246310  0.7251168   0.172   0.8637    
## ConsL.d:gend.mf          1.7470238  1.0198341   1.713   0.0882 .  
## ConsSL.d:gend.mf         1.2398810  1.0640265   1.165   0.2453    
## ConsSC.d:cond.c         -0.2541667  0.8399581  -0.303   0.7625    
## ConsM.d:cond.c           0.1404356  0.7251168   0.194   0.8466    
## ConsL.d:cond.c          -0.4613095  1.0198341  -0.452   0.6515    
## ConsSL.d:cond.c          0.3648810  1.0640265   0.343   0.7320    
## gend.mf:cond.c          -2.3726190  1.1000261  -2.157   0.0322 *  
## ConsSC.d:gend.mf:cond.c  3.4916667  1.6799161   2.078   0.0389 *  
## ConsM.d:gend.mf:cond.c   2.1383597  1.4502336   1.474   0.1419    
## ConsL.d:gend.mf:cond.c   1.1726190  2.0396681   0.575   0.5660    
## ConsSL.d:gend.mf:cond.c  4.2535714  2.1280529   1.999   0.0469 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.844 on 206 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.06431,    Adjusted R-squared:  -0.02199 
## F-statistic: 0.7452 on 19 and 206 DF,  p-value: 0.7689

# Action 27
summary(lm(act27 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2143  0.1667  1.4167  3.0000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.82817    0.24685   3.355 0.000897 ***
## ConsSC.d                -0.34901    0.36196  -0.964 0.335720    
## ConsM.d                 -0.20691    0.31500  -0.657 0.511771    
## ConsL.d                  0.28925    0.40384   0.716 0.474404    
## ConsSL.d                 0.52789    0.43317   1.219 0.223945    
## gend.mf                  0.30397    0.49369   0.616 0.538561    
## cond.c                  -0.63254    0.49369  -1.281 0.201107    
## ConsSC.d:gend.mf         0.65436    0.72392   0.904 0.366769    
## ConsM.d:gend.mf          0.10529    0.62999   0.167 0.867377    
## ConsL.d:gend.mf         -0.20548    0.80768  -0.254 0.799355    
## ConsSL.d:gend.mf        -0.18276    0.86635  -0.211 0.833071    
## ConsSC.d:cond.c          0.00754    0.72392   0.010 0.991697    
## ConsM.d:cond.c          -0.05620    0.62999  -0.089 0.928972    
## ConsL.d:cond.c           0.29163    0.80768   0.361 0.718304    
## ConsSL.d:cond.c          1.25375    0.86635   1.447 0.148904    
## gend.mf:cond.c          -2.36984    0.98738  -2.400 0.017005 *  
## ConsSC.d:gend.mf:cond.c  1.11984    1.44783   0.773 0.439866    
## ConsM.d:gend.mf:cond.c   2.19376    1.25999   1.741 0.082702 .  
## ConsL.d:gend.mf:cond.c   3.05166    1.61537   1.889 0.059847 .  
## ConsSL.d:gend.mf:cond.c  2.79408    1.73269   1.613 0.107901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 297 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.0834, Adjusted R-squared:  0.02476 
## F-statistic: 1.422 on 19 and 297 DF,  p-value: 0.1144

# Action 28
summary(lm(act28 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6250 -1.1321  0.2667  1.5522  3.2000 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.2872     0.2326   5.534 6.12e-08 ***
## ConsSC.d                 -0.4690     0.3431  -1.367    0.172    
## ConsM.d                  -0.2902     0.2952  -0.983    0.326    
## ConsL.d                  -0.4150     0.3821  -1.086    0.278    
## ConsSL.d                 -0.2934     0.4653  -0.631    0.529    
## gend.mf                  -0.2743     0.4652  -0.590    0.556    
## cond.c                   -0.3669     0.4652  -0.789    0.431    
## ConsSC.d:gend.mf          0.2107     0.6861   0.307    0.759    
## ConsM.d:gend.mf           0.4027     0.5905   0.682    0.496    
## ConsL.d:gend.mf          -0.2414     0.7641  -0.316    0.752    
## ConsSL.d:gend.mf         -0.6299     0.9306  -0.677    0.499    
## ConsSC.d:cond.c          -0.5467     0.6861  -0.797    0.426    
## ConsM.d:cond.c            0.2385     0.5905   0.404    0.687    
## ConsL.d:cond.c           -0.0774     0.7641  -0.101    0.919    
## ConsSL.d:cond.c           0.7544     0.9306   0.811    0.418    
## gend.mf:cond.c           -0.6662     0.9303  -0.716    0.474    
## ConsSC.d:gend.mf:cond.c   0.4390     1.3722   0.320    0.749    
## ConsM.d:gend.mf:cond.c   -0.3881     1.1809  -0.329    0.743    
## ConsL.d:gend.mf:cond.c   -1.7023     1.5283  -1.114    0.266    
## ConsSL.d:gend.mf:cond.c   0.7246     1.8613   0.389    0.697    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 352 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  -0.006203 
## F-statistic: 0.8796 on 19 and 352 DF,  p-value: 0.6089

# Action 29
summary(lm(act29 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6923 -1.0447  0.3077  1.4286  2.4286 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.94605    0.23266   4.066 5.91e-05 ***
## ConsSC.d                 0.14237    0.34100   0.418    0.677    
## ConsM.d                  0.16381    0.29045   0.564    0.573    
## ConsL.d                  0.21633    0.36272   0.596    0.551    
## ConsSL.d                 0.40620    0.43954   0.924    0.356    
## gend.mf                 -0.26184    0.46532  -0.563    0.574    
## cond.c                   0.13548    0.46532   0.291    0.771    
## ConsSC.d:gend.mf         0.55773    0.68199   0.818    0.414    
## ConsM.d:gend.mf          0.21205    0.58090   0.365    0.715    
## ConsL.d:gend.mf          0.93709    0.72544   1.292    0.197    
## ConsSL.d:gend.mf         0.10735    0.87909   0.122    0.903    
## ConsSC.d:cond.c          0.12578    0.68199   0.184    0.854    
## ConsM.d:cond.c          -0.20409    0.58090  -0.351    0.726    
## ConsL.d:cond.c           0.05237    0.72544   0.072    0.942    
## ConsSL.d:cond.c          0.12670    0.87909   0.144    0.885    
## gend.mf:cond.c           0.70383    0.93063   0.756    0.450    
## ConsSC.d:gend.mf:cond.c -1.37180    1.36398  -1.006    0.315    
## ConsM.d:gend.mf:cond.c  -0.46203    1.16179  -0.398    0.691    
## ConsL.d:gend.mf:cond.c  -0.79382    1.45088  -0.547    0.585    
## ConsSL.d:gend.mf:cond.c  0.87181    1.75817   0.496    0.620    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 348 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01904,    Adjusted R-squared:  -0.03452 
## F-statistic: 0.3554 on 19 and 348 DF,  p-value: 0.995

# Action 30
summary(lm(act30 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.545 -1.059  0.400  1.466  3.250 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.88064    0.24033   3.664 0.000289 ***
## ConsSC.d                 0.16329    0.35068   0.466 0.641766    
## ConsM.d                  0.08817    0.30976   0.285 0.776099    
## ConsL.d                  0.38735    0.38709   1.001 0.317720    
## ConsSL.d                 0.06822    0.45439   0.150 0.880752    
## gend.mf                 -0.59462    0.48066  -1.237 0.216926    
## cond.c                  -0.06014    0.48066  -0.125 0.900505    
## ConsSC.d:gend.mf         0.08250    0.70135   0.118 0.906431    
## ConsM.d:gend.mf          0.40813    0.61952   0.659 0.510497    
## ConsL.d:gend.mf          0.89197    0.77418   1.152 0.250092    
## ConsSL.d:gend.mf        -0.05310    0.90879  -0.058 0.953437    
## ConsSC.d:cond.c          1.23893    0.70135   1.766 0.078234 .  
## ConsM.d:cond.c           0.12364    0.61952   0.200 0.841934    
## ConsL.d:cond.c          -0.26751    0.77418  -0.346 0.729907    
## ConsSL.d:cond.c          0.66241    0.90879   0.729 0.466579    
## gend.mf:cond.c           0.45361    0.96132   0.472 0.637334    
## ConsSC.d:gend.mf:cond.c  0.70396    1.40270   0.502 0.616098    
## ConsM.d:gend.mf:cond.c   0.15241    1.23904   0.123 0.902174    
## ConsL.d:gend.mf:cond.c  -2.13164    1.54837  -1.377 0.169532    
## ConsSL.d:gend.mf:cond.c  1.84184    1.81758   1.013 0.311631    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.04439,    Adjusted R-squared:  -0.0103 
## F-statistic: 0.8117 on 19 and 332 DF,  p-value: 0.6931

a. Means for Gender x Condition

# Action 7

aggregate(d$act7[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate  0.15
## 2    Male climate -1.14
## 3  Female    ctrl -0.60
## 4    Male    ctrl  0.25

# Action 8

aggregate(d$act8[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.86
## 2    Male climate -1.74
## 3  Female    ctrl -0.47
## 4    Male    ctrl  1.22

# Action 9

aggregate(d$act9[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.24
## 2    Male climate -0.78
## 3  Female    ctrl -0.35
## 4    Male    ctrl  1.11

# Action 12

aggregate(d$act12[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.20
## 2    Male climate -2.12
## 3  Female    ctrl -0.53
## 4    Male    ctrl  0.57

# Action 15

aggregate(d$act15[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate  0.53
## 2    Male climate -0.88
## 3  Female    ctrl  0.04
## 4    Male    ctrl  0.70

# Action 24

aggregate(d$act24[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2     x
## 1  Female climate -0.14
## 2    Male climate -1.67
## 3  Female    ctrl -0.64
## 4    Male    ctrl  0.70

# Action 26

aggregate(d$act26[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2    x
## 1  Female climate 2.33
## 2    Male climate 0.80
## 3  Female    ctrl 1.29
## 4    Male    ctrl 2.12

# Action 27

aggregate(d$act27[d$ideology == "Conservative"], list(d$gend[d$ideology == "Conservative"], d$cond[d$ideology == "Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1 Group.2    x
## 1  Female climate 0.95
## 2    Male climate 0.07
## 3  Female    ctrl 0.40
## 4    Male    ctrl 1.89

v. Strong Conservatives

# Action 1
scon.b1 <- lm(act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b1)

## 
## Call:
## lm(formula = act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.646 -1.962  0.038  2.038  3.490 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.46154    0.26676  -1.730   0.0843 . 
## SconsC.d    -0.02846    0.34266  -0.083   0.9338   
## SconsM.d     0.42349    0.31032   1.365   0.1730   
## SconsL.d     1.10769    0.37726   2.936   0.0035 **
## SconsSL.d    0.63297    0.45091   1.404   0.1611   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.151 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.02106 
## F-statistic: 3.409 on 4 and 444 DF,  p-value: 0.00923

# Action 2
scon.b2 <- lm(act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b2) # yes, above 0

## 
## Call:
## lm(formula = act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.2212  0.1077  1.7788  2.1077 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.95652    0.26618   3.594 0.000384 ***
## SconsC.d    -0.06421    0.34784  -0.185 0.853668    
## SconsM.d     0.26472    0.31574   0.838 0.402514    
## SconsL.d     0.61491    0.38529   1.596 0.111611    
## SconsSL.d    0.08514    0.45459   0.187 0.851558    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 285 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01519,    Adjusted R-squared:  0.001372 
## F-statistic: 1.099 on 4 and 285 DF,  p-value: 0.3572

# Action 3
scon.b3 <- lm(act3 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b3) # yes, above 0

## 
## Call:
## lm(formula = act3 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.9837  0.0163  1.4167  3.8852 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8852     0.2573  -3.440 0.000637 ***
## SconsC.d      0.4686     0.3291   1.424 0.155175    
## SconsM.d      0.8689     0.2969   2.926 0.003607 ** 
## SconsL.d      0.6941     0.3544   1.958 0.050825 .  
## SconsSL.d     1.2567     0.4262   2.949 0.003361 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 439 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02777,    Adjusted R-squared:  0.01891 
## F-statistic: 3.134 on 4 and 439 DF,  p-value: 0.01467

# Action 4
scon.b4 <- lm(act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b4) # no

## 
## Call:
## lm(formula = act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4828 -1.7938  0.2062  2.0051  3.2877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.1961     0.3027   0.648    0.518
## SconsC.d     -0.4837     0.3945  -1.226    0.221
## SconsM.d     -0.4023     0.3476  -1.157    0.248
## SconsL.d      0.1832     0.4149   0.442    0.659
## SconsSL.d     0.2867     0.5027   0.570    0.569
## 
## Residual standard error: 2.162 on 366 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.006351 
## F-statistic: 1.591 on 4 and 366 DF,  p-value: 0.176

# Action 5
scon.b5 <- lm(act5 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b5) # yes, above 0

## 
## Call:
## lm(formula = act5 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4048 -1.0917  0.2653  1.5952  2.5833 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.7347     0.2673   2.748  0.00636 **
## SconsC.d      0.2379     0.3456   0.688  0.49175   
## SconsM.d      0.3570     0.3219   1.109  0.26820   
## SconsL.d      0.6701     0.3935   1.703  0.08966 . 
## SconsSL.d    -0.3180     0.4662  -0.682  0.49570   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 292 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.01867,    Adjusted R-squared:  0.005229 
## F-statistic: 1.389 on 4 and 292 DF,  p-value: 0.2378

# Action 6
scon.b6 <- lm(act6 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b6) # yes, above 0

## 
## Call:
## lm(formula = act6 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4348 -1.3099  0.6706  1.6706  2.1296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8704     0.2467   3.528 0.000474 ***
## SconsC.d      0.4590     0.3155   1.455 0.146535    
## SconsM.d      0.4395     0.2898   1.516 0.130327    
## SconsL.d      0.5644     0.3637   1.552 0.121629    
## SconsSL.d     0.3171     0.4044   0.784 0.433482    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 354 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.00896,    Adjusted R-squared:  -0.002238 
## F-statistic: 0.8001 on 4 and 354 DF,  p-value: 0.5257

# Action 7
scon.b7 <- lm(act7 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b7) # yes, above 0

## 
## Call:
## lm(formula = act7 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3714 -1.8652  0.1348  1.6286  3.3140 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15789    0.27668  -0.571    0.569
## SconsC.d    -0.15606    0.35677  -0.437    0.662
## SconsM.d     0.02306    0.31790   0.073    0.942
## SconsL.d     0.12400    0.38795   0.320    0.749
## SconsSL.d    0.52932    0.44857   1.180    0.239
## 
## Residual standard error: 2.089 on 410 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.006798,   Adjusted R-squared:  -0.002892 
## F-statistic: 0.7016 on 4 and 410 DF,  p-value: 0.5912

# Action 8
scon.b8 <- lm(act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b8) # yes, above 0

## 
## Call:
## lm(formula = act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.625 -1.855 -0.287  1.713  4.145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.1449     0.2553  -4.484 9.19e-06 ***
## SconsC.d      0.4320     0.3269   1.321   0.1870    
## SconsM.d      0.7021     0.2959   2.373   0.0181 *  
## SconsL.d      1.6211     0.3696   4.386 1.42e-05 ***
## SconsSL.d     1.7699     0.4215   4.199 3.20e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 476 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.06121,    Adjusted R-squared:  0.05332 
## F-statistic: 7.759 on 4 and 476 DF,  p-value: 4.609e-06

# Action 9
scon.b9 <- lm(act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b9) # yes, above 0

## 
## Call:
## lm(formula = act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5781 -1.5781  0.2525  1.7167  3.7167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.7167     0.2538  -2.823 0.004967 ** 
## SconsC.d      0.4641     0.3217   1.443 0.149776    
## SconsM.d      0.9167     0.2912   3.148 0.001754 ** 
## SconsL.d      1.2948     0.3533   3.665 0.000278 ***
## SconsSL.d     0.9111     0.4145   2.198 0.028464 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.966 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03759,    Adjusted R-squared:  0.02892 
## F-statistic: 4.336 on 4 and 444 DF,  p-value: 0.00189

# Action 10
scon.b10 <- lm(act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b10) # yes, above 0

## 
## Call:
## lm(formula = act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.6176     0.2516  -2.455 0.014462 *  
## SconsC.d      0.3605     0.3230   1.116 0.264918    
## SconsM.d      0.6980     0.2915   2.395 0.017009 *  
## SconsL.d      1.2946     0.3599   3.597 0.000356 ***
## SconsSL.d     1.7676     0.4135   4.275 2.31e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

# Action 11
scon.b11 <- lm(act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b11) # yes, above 0

## 
## Call:
## lm(formula = act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6585 -0.8154  0.2741  1.3415  2.2741 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.734694   0.266751   2.754  0.00623 **
## SconsC.d     0.080691   0.353266   0.228  0.81948   
## SconsM.d    -0.008768   0.311421  -0.028  0.97756   
## SconsL.d     0.923843   0.395216   2.338  0.02005 * 
## SconsSL.d    0.806973   0.465223   1.735  0.08381 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 309 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03501,    Adjusted R-squared:  0.02252 
## F-statistic: 2.803 on 4 and 309 DF,  p-value: 0.02604

# Action 12
scon.b12 <- lm(act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b12) # yes, above 0

## 
## Call:
## lm(formula = act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.736 -2.158  0.000  2.000  3.842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8421     0.2842  -2.963 0.003238 ** 
## SconsC.d      0.2421     0.3632   0.667 0.505456    
## SconsM.d      0.8421     0.3315   2.540 0.011480 *  
## SconsL.d      1.5780     0.4094   3.854 0.000136 ***
## SconsSL.d     1.1754     0.5013   2.345 0.019548 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.146 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0525, Adjusted R-squared:  0.04253 
## F-statistic: 5.264 on 4 and 380 DF,  p-value: 0.0003892

# Action 13
scon.b13 <- lm(act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b13) # yes, above 0

## 
## Call:
## lm(formula = act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3438 -1.9149  0.0851  1.6562  3.5781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.5781     0.2599  -2.225   0.0266 *
## SconsC.d      0.3607     0.3384   1.066   0.2870  
## SconsM.d      0.4930     0.3009   1.639   0.1020  
## SconsL.d      0.9219     0.3675   2.508   0.0125 *
## SconsSL.d     0.8353     0.4371   1.911   0.0566 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.079 on 438 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01713,    Adjusted R-squared:  0.008155 
## F-statistic: 1.909 on 4 and 438 DF,  p-value: 0.108

# Action 14
scon.b14 <- lm(act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b14) # no

## 
## Call:
## lm(formula = act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9487 -1.4219 -0.0198  1.6780  2.9091 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.50000    0.32119   1.557    0.121
## SconsC.d    -0.17797    0.41606  -0.428    0.669
## SconsM.d    -0.07812    0.36797  -0.212    0.832
## SconsL.d     0.44872    0.45714   0.982    0.327
## SconsSL.d   -0.40909    0.53920  -0.759    0.449
## 
## Residual standard error: 2.031 on 283 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.01158,    Adjusted R-squared:  -0.002388 
## F-statistic: 0.829 on 4 and 283 DF,  p-value: 0.5076

# Action 15
scon.b15 <- lm(act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b15) # yes, above 0

## 
## Call:
## lm(formula = act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8889 -2.1024  0.1111  1.8916  3.3559 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.3559     0.2667  -1.335  0.18273   
## SconsC.d      0.4644     0.3488   1.331  0.18385   
## SconsM.d      0.4583     0.3104   1.476  0.14066   
## SconsL.d      1.2448     0.4054   3.071  0.00229 **
## SconsSL.d     0.6684     0.4497   1.487  0.13797   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.048 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.02508,    Adjusted R-squared:  0.01482 
## F-statistic: 2.444 on 4 and 380 DF,  p-value: 0.0462

# Action 16
scon.b16 <- lm(act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b16) # yes, above 0

## 
## Call:
## lm(formula = act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0698 -0.9861  0.0357  1.4375  2.4375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.5625     0.2649   2.123   0.0345 *
## SconsC.d      0.4236     0.3420   1.239   0.2163  
## SconsM.d      0.4018     0.3070   1.309   0.1915  
## SconsL.d      0.5073     0.3854   1.316   0.1890  
## SconsSL.d     0.4720     0.4316   1.093   0.2750  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 327 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.007238,   Adjusted R-squared:  -0.004905 
## F-statistic: 0.5961 on 4 and 327 DF,  p-value: 0.6657

# Action 17
scon.b17 <- lm(act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b17) # no

## 
## Call:
## lm(formula = act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2059 -1.4798 -0.1685  1.6230  2.8315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3770     0.2561   1.472   0.1417  
## SconsC.d     -0.2085     0.3325  -0.627   0.5309  
## SconsM.d      0.1027     0.2979   0.345   0.7304  
## SconsL.d      0.1730     0.3637   0.476   0.6347  
## SconsSL.d     0.8288     0.4281   1.936   0.0535 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 412 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.006815 
## F-statistic: 1.714 on 4 and 412 DF,  p-value: 0.146

# Action 18
scon.b18 <- lm(act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1196  0.1844  1.5135  2.8113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.1887     0.2530   0.746   0.4563  
## SconsC.d      0.2978     0.3314   0.899   0.3695  
## SconsM.d      0.6269     0.2967   2.113   0.0353 *
## SconsL.d      0.7236     0.3514   2.059   0.0402 *
## SconsSL.d     0.5699     0.4254   1.340   0.1812  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01769,    Adjusted R-squared:  0.006435 
## F-statistic: 1.572 on 4 and 349 DF,  p-value: 0.1814

# Action 19
scon.b19 <- lm(act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b19) # yes, above 0

## 
## Call:
## lm(formula = act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.438 -1.050  0.093  1.704  2.093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.9070     0.2747   3.302  0.00109 **
## SconsC.d      0.1430     0.3599   0.397  0.69139   
## SconsM.d      0.3231     0.3227   1.001  0.31766   
## SconsL.d      0.5305     0.4205   1.262  0.20819   
## SconsSL.d     0.3893     0.4423   0.880  0.37951   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 270 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.00789,    Adjusted R-squared:  -0.006807 
## F-statistic: 0.5368 on 4 and 270 DF,  p-value: 0.7088

# Action 20
scon.b20 <- lm(act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b20) # yes, above 0

## 
## Call:
## lm(formula = act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3566 -1.1957  0.6434  1.6434  2.0000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.15094    0.24628   4.673 4.38e-06 ***
## SconsC.d    -0.02051    0.32748  -0.063    0.950    
## SconsM.d     0.20565    0.29253   0.703    0.483    
## SconsL.d     0.04471    0.36130   0.124    0.902    
## SconsSL.d   -0.15094    0.42392  -0.356    0.722    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.793 on 319 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.004413,   Adjusted R-squared:  -0.008071 
## F-statistic: 0.3535 on 4 and 319 DF,  p-value: 0.8415

# Action 21
scon.b21 <- lm(act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b21) # yes, above 0

## 
## Call:
## lm(formula = act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -1.9401  0.0599  1.5806  3.5806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.5806     0.2616  -2.219   0.0270 * 
## SconsC.d      0.3124     0.3467   0.901   0.3682   
## SconsM.d      0.5208     0.3064   1.700   0.0900 . 
## SconsL.d      1.0213     0.3747   2.726   0.0067 **
## SconsSL.d     1.0049     0.4439   2.264   0.0241 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 398 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02474,    Adjusted R-squared:  0.01494 
## F-statistic: 2.524 on 4 and 398 DF,  p-value: 0.04049

# Action 22
scon.b22 <- lm(act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b22) # yes, above 0

## 
## Call:
## lm(formula = act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1351 -1.3415 -0.1351  1.7143  3.3279 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.3279     0.2528  -1.297 0.195483    
## SconsC.d      0.6693     0.3339   2.005 0.045684 *  
## SconsM.d      0.6136     0.2952   2.079 0.038302 *  
## SconsL.d      0.9172     0.3655   2.509 0.012486 *  
## SconsSL.d     1.4630     0.4115   3.555 0.000423 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.975 on 399 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03383,    Adjusted R-squared:  0.02414 
## F-statistic: 3.492 on 4 and 399 DF,  p-value: 0.008087

# Action 23
scon.b23 <- lm(act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b23) # no

## 
## Call:
## lm(formula = act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6154 -2.1138  0.1846  2.1846  3.1846 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.2683     0.3537   0.758    0.449
## SconsC.d     -0.4529     0.4517  -1.003    0.317
## SconsM.d     -0.1545     0.4085  -0.378    0.706
## SconsL.d      0.3471     0.5066   0.685    0.494
## SconsSL.d     0.2917     0.5748   0.508    0.612
## 
## Residual standard error: 2.265 on 288 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.01368,    Adjusted R-squared:  -2.033e-05 
## F-statistic: 0.9985 on 4 and 288 DF,  p-value: 0.4087

# Action 24
scon.b24 <- lm(act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b24) # yes, above 0

## 
## Call:
## lm(formula = act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9189 -1.8727  0.1273  1.6453  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.12727    0.28387  -0.448    0.654
## SconsC.d    -0.37273    0.35883  -1.039    0.300
## SconsM.d    -0.10261    0.32566  -0.315    0.753
## SconsL.d    -0.01066    0.39623  -0.027    0.979
## SconsSL.d    0.04619    0.44762   0.103    0.918
## 
## Residual standard error: 2.105 on 411 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.004608,   Adjusted R-squared:  -0.005079 
## F-statistic: 0.4757 on 4 and 411 DF,  p-value: 0.7536

# Action 25
scon.b25 <- lm(act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b25) # yes, above 0

## 
## Call:
## lm(formula = act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3429 -0.8925  0.1167  1.5000  3.1167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.1167     0.2367  -0.493 0.622329    
## SconsC.d      0.6167     0.2983   2.067 0.039288 *  
## SconsM.d      1.0091     0.2722   3.707 0.000236 ***
## SconsL.d      1.2437     0.3307   3.760 0.000193 ***
## SconsSL.d     1.4595     0.3900   3.743 0.000206 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 441 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.04037 
## F-statistic:  5.68 on 4 and 441 DF,  p-value: 0.0001824

# Action 26
scon.b26 <- lm(act26 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b26) # yes, above 0

## 
## Call:
## lm(formula = act26 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5686 -1.2581  0.4595  1.4314  2.0526 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.54054    0.30047   5.127  6.4e-07 ***
## SconsC.d     0.02809    0.39470   0.071    0.943    
## SconsM.d     0.07744    0.35752   0.217    0.829    
## SconsL.d    -0.28248    0.44502  -0.635    0.526    
## SconsSL.d   -0.59317    0.51585  -1.150    0.251    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.828 on 222 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01216,    Adjusted R-squared:  -0.005638 
## F-statistic: 0.6832 on 4 and 222 DF,  p-value: 0.6042

# Action 27
scon.b27 <- lm(act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b27) # no

## 
## Call:
## lm(formula = act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3871 -0.9483  0.2812  1.5577  2.5577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4423     0.2604   1.699   0.0904 .
## SconsC.d      0.2764     0.3506   0.789   0.4310  
## SconsM.d      0.1089     0.3091   0.352   0.7249  
## SconsL.d      0.7355     0.3823   1.924   0.0553 .
## SconsSL.d     0.9448     0.4261   2.217   0.0273 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 314 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02704,    Adjusted R-squared:  0.01464 
## F-statistic: 2.182 on 4 and 314 DF,  p-value: 0.07092

# Action 28
scon.b28 <- lm(act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b28) # yes, above 0

## 
## Call:
## lm(formula = act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3214 -0.9728  0.2105  1.6786  2.2105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.7895     0.2417   3.267  0.00119 **
## SconsC.d      0.5320     0.3131   1.699  0.09018 . 
## SconsM.d      0.1833     0.2847   0.644  0.52006   
## SconsL.d      0.2105     0.3449   0.610  0.54195   
## SconsSL.d     0.4041     0.4072   0.992  0.32168   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 369 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.009436,   Adjusted R-squared:  -0.001302 
## F-statistic: 0.8787 on 4 and 369 DF,  p-value: 0.4767

# Action 29
scon.b29 <- lm(act29 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b29) # yes, above 0

## 
## Call:
## lm(formula = act29 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4828 -1.0893  0.0182  1.5172  2.0182 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.08929    0.23582   4.619 5.35e-06 ***
## SconsC.d    -0.06519    0.30518  -0.214    0.831    
## SconsM.d     0.05357    0.27713   0.193    0.847    
## SconsL.d    -0.10747    0.33502  -0.321    0.749    
## SconsSL.d    0.39347    0.40374   0.975    0.330    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.765 on 365 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.005014,   Adjusted R-squared:  -0.00589 
## F-statistic: 0.4598 on 4 and 365 DF,  p-value: 0.7652

# Action 30
scon.b30 <- lm(act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)

summary(scon.b30) # yes, above 0

## 
## Call:
## lm(formula = act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1765 -1.0435  0.8235  1.8235  1.9875 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.140351   0.248501   4.589 6.22e-06 ***
## SconsC.d    -0.127851   0.325194  -0.393    0.694    
## SconsM.d    -0.096873   0.295396  -0.328    0.743    
## SconsL.d     0.036120   0.361622   0.100    0.920    
## SconsSL.d    0.002506   0.432970   0.006    0.995    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.001091,   Adjusted R-squared:  -0.01036 
## F-statistic: 0.09525 on 4 and 349 DF,  p-value: 0.9839

Significantly above 0: 2, 5, 6, 11, 16, 19, 20, 26, 28, 29, 30 Not different from 0: 1, 4, 7, 14, 15, 17, 18, 22, 23, 24, 25, 27 Significantly below 0: 3, 8, 9, 10, 12, 13, 21

1. Condition differences?

# Action 1
scon.c.b1 <- lm(act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b1) # no

## 
## Call:
## lm(formula = act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.750 -2.200  0.250  1.765  3.800 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.485714   0.267215  -1.818  0.06979 . 
## SconsC.d          0.006818   0.343826   0.020  0.98419   
## SconsM.d          0.428643   0.310783   1.379  0.16853   
## SconsL.d          1.133442   0.377363   3.004  0.00282 **
## SconsSL.d         0.559141   0.461043   1.213  0.22587   
## cond.c            0.628571   0.534431   1.176  0.24017   
## SconsC.d:cond.c  -0.813636   0.687652  -1.183  0.23737   
## SconsM.d:cond.c  -1.212103   0.621567  -1.950  0.05180 . 
## SconsL.d:cond.c  -0.424026   0.754725  -0.562  0.57452   
## SconsSL.d:cond.c -1.390809   0.922086  -1.508  0.13219   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04314,    Adjusted R-squared:  0.02353 
## F-statistic: 2.199 on 9 and 439 DF,  p-value: 0.02116

# Action 2
scon.c.b2 <- lm(act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b2) #no

## 
## Call:
## lm(formula = act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -1.068  0.375  1.615  2.375 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.9786     0.2713   3.607 0.000367 ***
## SconsC.d          -0.0903     0.3523  -0.256 0.797908    
## SconsM.d           0.2137     0.3215   0.665 0.506635    
## SconsL.d           0.5839     0.3917   1.491 0.137119    
## SconsSL.d          0.0669     0.4597   0.146 0.884415    
## cond.c            -0.2534     0.5426  -0.467 0.640843    
## SconsC.d:cond.c   -0.2731     0.7046  -0.388 0.698624    
## SconsM.d:cond.c   -0.1312     0.6429  -0.204 0.838441    
## SconsL.d:cond.c    0.1284     0.7833   0.164 0.869908    
## SconsSL.d:cond.c   0.3443     0.9195   0.374 0.708343    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.812 on 280 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -0.006049 
## F-statistic: 0.8069 on 9 and 280 DF,  p-value: 0.6102

# Action 4
scon.c.b4 <- lm(act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b4) # no

## 
## Call:
## lm(formula = act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8400 -1.8941  0.1059  2.0378  3.3488 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.19798    0.30520   0.649    0.517
## SconsC.d         -0.47240    0.39963  -1.182    0.238
## SconsM.d         -0.41092    0.35024  -1.173    0.241
## SconsL.d          0.23717    0.41933   0.566    0.572
## SconsSL.d         0.26518    0.52218   0.508    0.612
## cond.c           -0.03882    0.61040  -0.064    0.949
## SconsC.d:cond.c  -0.11002    0.79926  -0.138    0.891
## SconsM.d:cond.c  -0.17530    0.70047  -0.250    0.803
## SconsL.d:cond.c   0.84852    0.83865   1.012    0.312
## SconsSL.d:cond.c -0.08750    1.04436  -0.084    0.933
## 
## Residual standard error: 2.169 on 361 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0238, Adjusted R-squared:  -0.0005384 
## F-statistic: 0.9779 on 9 and 361 DF,  p-value: 0.4579

# Action 8
scon.c.b8 <- lm(act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b8) # no

## 
## Call:
## lm(formula = act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.125 -1.873 -0.125  1.800  4.233 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -1.15513    0.25649  -4.504 8.44e-06 ***
## SconsC.d          0.52497    0.32907   1.595   0.1113    
## SconsM.d          0.71480    0.29671   2.409   0.0164 *  
## SconsL.d          1.66584    0.37082   4.492 8.87e-06 ***
## SconsSL.d         1.86346    0.42661   4.368 1.54e-05 ***
## cond.c            0.15641    0.51299   0.305   0.7606    
## SconsC.d:cond.c  -1.15006    0.65814  -1.747   0.0812 .  
## SconsM.d:cond.c  -0.04659    0.59341  -0.079   0.9375    
## SconsL.d:cond.c   0.46502    0.74163   0.627   0.5309    
## SconsSL.d:cond.c  0.67692    0.85322   0.793   0.4280    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.112 on 471 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.07857,    Adjusted R-squared:  0.06097 
## F-statistic: 4.463 on 9 and 471 DF,  p-value: 1.237e-05

# Action 9
scon.c.b9 <- lm(act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b9) # yes, condition difference

## 
## Call:
## lm(formula = act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.5893  0.0465  1.4286  4.3462 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.7907     0.2558  -3.091 0.002119 ** 
## SconsC.d           0.5621     0.3241   1.734 0.083565 .  
## SconsM.d           0.9965     0.2930   3.402 0.000732 ***
## SconsL.d           1.3681     0.3558   3.845 0.000139 ***
## SconsSL.d          0.9157     0.4312   2.124 0.034243 *  
## cond.c             1.1109     0.5116   2.172 0.030424 *  
## SconsC.d:cond.c   -1.4751     0.6482  -2.276 0.023355 *  
## SconsM.d:cond.c   -0.9542     0.5859  -1.629 0.104116    
## SconsL.d:cond.c   -1.1228     0.7117  -1.578 0.115368    
## SconsSL.d:cond.c  -1.5275     0.8623  -1.771 0.077191 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.964 on 439 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.05102,    Adjusted R-squared:  0.03157 
## F-statistic: 2.623 on 9 and 439 DF,  p-value: 0.00582

# Action 10
scon.c.b10 <- lm(act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b10) # no

## 
## Call:
## lm(formula = act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3750 -1.9896  0.0217  1.8350  3.6579 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.61228    0.25401  -2.410 0.016317 *  
## SconsC.d          0.38107    0.32614   1.168 0.243228    
## SconsM.d          0.68960    0.29375   2.348 0.019313 *  
## SconsL.d          1.32415    0.36385   3.639 0.000304 ***
## SconsSL.d         1.79978    0.42094   4.276 2.31e-05 ***
## cond.c           -0.09123    0.50801  -0.180 0.857562    
## SconsC.d:cond.c  -0.32771    0.65228  -0.502 0.615617    
## SconsM.d:cond.c  -0.08424    0.58750  -0.143 0.886050    
## SconsL.d:cond.c   0.59605    0.72769   0.819 0.413146    
## SconsSL.d:cond.c  0.46623    0.84188   0.554 0.579986    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.08 on 467 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05853,    Adjusted R-squared:  0.04038 
## F-statistic: 3.226 on 9 and 467 DF,  p-value: 0.000826

# Action 11
scon.c.b11 <- lm(act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b11) # no

## 
## Call:
## lm(formula = act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -1.0299  0.3452  1.5429  2.5735 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.730833   0.265754   2.750  0.00632 **
## SconsC.d          0.114405   0.352364   0.325  0.74565   
## SconsM.d         -0.002673   0.310242  -0.009  0.99313   
## SconsL.d          0.923929   0.405495   2.279  0.02339 * 
## SconsSL.d         0.762024   0.467842   1.629  0.10439   
## cond.c            0.378333   0.531508   0.712  0.47713   
## SconsC.d:cond.c  -1.154524   0.704728  -1.638  0.10240   
## SconsM.d:cond.c  -0.981713   0.620485  -1.582  0.11465   
## SconsL.d:cond.c  -0.402143   0.810990  -0.496  0.62035   
## SconsSL.d:cond.c -0.964048   0.935684  -1.030  0.30368   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 304 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.0581, Adjusted R-squared:  0.03021 
## F-statistic: 2.083 on 9 and 304 DF,  p-value: 0.03077

# Action 12
scon.c.b12 <- lm(act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b12) # no

## 
## Call:
## lm(formula = act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1304 -1.8929 -0.1098  1.8696  4.1071 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.84667    0.28464  -2.975  0.00312 ** 
## SconsC.d          0.27805    0.36502   0.762  0.44670    
## SconsM.d          0.84234    0.33208   2.537  0.01160 *  
## SconsL.d          1.62856    0.41191   3.954  9.2e-05 ***
## SconsSL.d         1.23491    0.51412   2.402  0.01679 *  
## cond.c            0.52094    0.56927   0.915  0.36073    
## SconsC.d:cond.c  -0.99152    0.73004  -1.358  0.17523    
## SconsM.d:cond.c  -0.74911    0.66416  -1.128  0.26008    
## SconsL.d:cond.c   0.17617    0.82382   0.214  0.83079    
## SconsSL.d:cond.c -0.09741    1.02825  -0.095  0.92458    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.149 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0624, Adjusted R-squared:  0.0399 
## F-statistic: 2.773 on 9 and 375 DF,  p-value: 0.003732

# Action 13
scon.c.b13 <- lm(act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b13) # no

## 
## Call:
## lm(formula = act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4118 -1.9202  0.2474  1.7317  3.7105 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.5476     0.2643  -2.072   0.0389 *
## SconsC.d           0.3778     0.3425   1.103   0.2706  
## SconsM.d           0.4678     0.3047   1.535   0.1254  
## SconsL.d           0.8868     0.3709   2.391   0.0172 *
## SconsSL.d          0.7857     0.4453   1.764   0.0784 .
## cond.c            -0.3259     0.5286  -0.617   0.5379  
## SconsC.d:cond.c   -0.5502     0.6850  -0.803   0.4223  
## SconsM.d:cond.c    0.6612     0.6094   1.085   0.2785  
## SconsL.d:cond.c    0.1808     0.7417   0.244   0.8075  
## SconsSL.d:cond.c   0.1354     0.8905   0.152   0.8792  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 433 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  0.009955 
## F-statistic: 1.494 on 9 and 433 DF,  p-value: 0.1475

# Action 14
scon.c.b14 <- lm(act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b14) # no

## 
## Call:
## lm(formula = act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.3731  0.1429  1.6269  3.1429 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.45013    0.32630   1.379    0.169
## SconsC.d         -0.11564    0.42136  -0.274    0.784
## SconsM.d         -0.02586    0.37292  -0.069    0.945
## SconsL.d          0.54444    0.46559   1.169    0.243
## SconsSL.d        -0.27156    0.55759  -0.487    0.627
## cond.c            0.66496    0.65260   1.019    0.309
## SconsC.d:cond.c  -0.95894    0.84272  -1.138    0.256
## SconsM.d:cond.c  -0.56269    0.74583  -0.754    0.451
## SconsL.d:cond.c  -0.15409    0.93117  -0.165    0.869
## SconsSL.d:cond.c -0.02210    1.11518  -0.020    0.984
## 
## Residual standard error: 2.04 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.02046,    Adjusted R-squared:  -0.01125 
## F-statistic: 0.6452 on 9 and 278 DF,  p-value: 0.7579

# Action 15
scon.c.b15 <- lm(act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b15) # no

## 
## Call:
## lm(formula = act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5600 -2.0213 -0.0213  1.8539  3.5385 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.37529    0.26782  -1.401  0.16196   
## SconsC.d          0.49704    0.35057   1.418  0.15708   
## SconsM.d          0.47430    0.31144   1.523  0.12863   
## SconsL.d          1.18029    0.40695   2.900  0.00395 **
## SconsSL.d         0.70078    0.45015   1.557  0.12036   
## cond.c            0.32634    0.53565   0.609  0.54273   
## SconsC.d:cond.c  -0.52729    0.70114  -0.752  0.45250   
## SconsM.d:cond.c  -0.42046    0.62289  -0.675  0.50008   
## SconsL.d:cond.c  -1.83634    0.81391  -2.256  0.02463 * 
## SconsSL.d:cond.c  0.08935    0.90029   0.099  0.92100   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 375 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04309,    Adjusted R-squared:  0.02013 
## F-statistic: 1.876 on 9 and 375 DF,  p-value: 0.05411

# Action 16
scon.c.b16 <- lm(act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b16) # no

## 
## Call:
## lm(formula = act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2105 -1.1000  0.2034  1.4500  2.4500 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.56071    0.26978   2.078   0.0385 *
## SconsC.d          0.41374    0.34655   1.194   0.2334  
## SconsM.d          0.40357    0.31151   1.296   0.1961  
## SconsL.d          0.52262    0.39232   1.332   0.1838  
## SconsSL.d         0.39455    0.44987   0.877   0.3811  
## cond.c            0.02143    0.53955   0.040   0.9683  
## SconsC.d:cond.c   0.39808    0.69310   0.574   0.5661  
## SconsM.d:cond.c   0.25000    0.62302   0.401   0.6885  
## SconsL.d:cond.c   0.14524    0.78464   0.185   0.8533  
## SconsSL.d:cond.c -0.53195    0.89973  -0.591   0.5548  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.843 on 322 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.01422,    Adjusted R-squared:  -0.01334 
## F-statistic: 0.5159 on 9 and 322 DF,  p-value: 0.8629

# Action 17
scon.c.b17 <- lm(act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b17) # no

## 
## Call:
## lm(formula = act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7143 -1.3210  0.1698  1.5455  3.1698 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.34307    0.25570   1.342   0.1804  
## SconsC.d         -0.09465    0.33403  -0.283   0.7771  
## SconsM.d          0.12720    0.29728   0.428   0.6690  
## SconsL.d          0.21753    0.36342   0.599   0.5498  
## SconsSL.d         0.93907    0.43086   2.180   0.0299 *
## cond.c            0.82900    0.51140   1.621   0.1058  
## SconsC.d:cond.c  -1.66548    0.66807  -2.493   0.0131 *
## SconsM.d:cond.c  -1.12758    0.59455  -1.897   0.0586 .
## SconsL.d:cond.c  -0.61688    0.72684  -0.849   0.3965  
## SconsSL.d:cond.c  0.03528    0.86172   0.041   0.9674  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 407 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.03789,    Adjusted R-squared:  0.01661 
## F-statistic: 1.781 on 9 and 407 DF,  p-value: 0.06997

# Action 18
scon.c.b18 <- lm(act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.1538  0.2031  1.4211  3.0400 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.17643    0.25456   0.693   0.4887  
## SconsC.d          0.30749    0.33331   0.923   0.3569  
## SconsM.d          0.63759    0.29881   2.134   0.0336 *
## SconsL.d          0.75533    0.35401   2.134   0.0336 *
## SconsSL.d         0.59377    0.43606   1.362   0.1742  
## cond.c            0.43286    0.50912   0.850   0.3958  
## SconsC.d:cond.c  -0.62292    0.66662  -0.934   0.3507  
## SconsM.d:cond.c  -0.46715    0.59762  -0.782   0.4349  
## SconsL.d:cond.c   0.01131    0.70802   0.016   0.9873  
## SconsSL.d:cond.c -0.33690    0.87212  -0.386   0.6995  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.85 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.0227, Adjusted R-squared:  -0.002867 
## F-statistic: 0.8879 on 9 and 344 DF,  p-value: 0.5362

# Action 19
scon.c.b19 <- lm(act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b19) # no

## 
## Call:
## lm(formula = act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0164  0.3056  1.4679  2.3056 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       0.90046    0.28356   3.176  0.00167 **
## SconsC.d          0.23843    0.36946   0.645  0.51927   
## SconsM.d          0.34812    0.33043   1.054  0.29306   
## SconsL.d          0.50430    0.42777   1.179  0.23949   
## SconsSL.d         0.39349    0.44749   0.879  0.38002   
## cond.c            0.05093    0.56712   0.090  0.92852   
## SconsC.d:cond.c  -0.93981    0.73893  -1.272  0.20454   
## SconsM.d:cond.c  -0.51530    0.66086  -0.780  0.43624   
## SconsL.d:cond.c  -0.57474    0.85554  -0.672  0.50231   
## SconsSL.d:cond.c -0.17730    0.89498  -0.198  0.84311   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 265 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.03023,    Adjusted R-squared:  -0.002705 
## F-statistic: 0.9179 on 9 and 265 DF,  p-value: 0.51

# Action 20
scon.c.b20 <- lm(act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b20) # no

## 
## Call:
## lm(formula = act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.589 -1.054  0.411  1.411  2.308 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.18913    0.24744   4.806 2.39e-06 ***
## SconsC.d         -0.04428    0.32815  -0.135    0.893    
## SconsM.d          0.13218    0.29391   0.450    0.653    
## SconsL.d         -0.02437    0.36203  -0.067    0.946    
## SconsSL.d        -0.20012    0.42365  -0.472    0.637    
## cond.c           -0.57826    0.49488  -1.168    0.243    
## SconsC.d:cond.c   0.18046    0.65629   0.275    0.784    
## SconsM.d:cond.c   0.04279    0.58781   0.073    0.942    
## SconsL.d:cond.c  -0.13222    0.72407  -0.183    0.855    
## SconsSL.d:cond.c -0.01515    0.84730  -0.018    0.986    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.786 on 314 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.028,  Adjusted R-squared:  0.0001354 
## F-statistic: 1.005 on 9 and 314 DF,  p-value: 0.4359

# Action 21
scon.c.b21 <- lm(act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b21) # no

## 
## Call:
## lm(formula = act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5556 -1.8723  0.1609  1.9286  3.9286 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -0.6113     0.2637  -2.318  0.02094 * 
## SconsC.d           0.3189     0.3504   0.910  0.36320   
## SconsM.d           0.5559     0.3085   1.802  0.07229 . 
## SconsL.d           1.0610     0.3774   2.811  0.00518 **
## SconsSL.d          1.0391     0.4472   2.323  0.02066 * 
## cond.c             0.6345     0.5274   1.203  0.22968   
## SconsC.d:cond.c   -0.3050     0.7007  -0.435  0.66363   
## SconsM.d:cond.c   -0.4235     0.6169  -0.687  0.49278   
## SconsL.d:cond.c   -0.4226     0.7548  -0.560  0.57584   
## SconsSL.d:cond.c  -0.5567     0.8945  -0.622  0.53407   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.067 on 393 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.03105,    Adjusted R-squared:  0.008857 
## F-statistic: 1.399 on 9 and 393 DF,  p-value: 0.1863

# Action 23
scon.c.b23 <- lm(act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b23) # no

## 
## Call:
## lm(formula = act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.222 -2.250  0.125  1.870  3.297 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.26786    0.35364   0.757    0.449
## SconsC.d         -0.43436    0.45325  -0.958    0.339
## SconsM.d         -0.16245    0.40840  -0.398    0.691
## SconsL.d          0.23486    0.51072   0.460    0.646
## SconsSL.d         0.43700    0.58947   0.741    0.459
## cond.c            0.03571    0.70728   0.050    0.960
## SconsC.d:cond.c  -0.29730    0.90651  -0.328    0.743
## SconsM.d:cond.c  -0.44991    0.81680  -0.551    0.582
## SconsL.d:cond.c  -1.29115    1.02143  -1.264    0.207
## SconsSL.d:cond.c  0.99901    1.17894   0.847    0.398
## 
## Residual standard error: 2.264 on 283 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.03197,    Adjusted R-squared:  0.001189 
## F-statistic: 1.039 on 9 and 283 DF,  p-value: 0.4092

# Action 24
scon.c.b24 <- lm(act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b24) # no

## 
## Call:
## lm(formula = act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0761 -1.9412 -0.0761  1.7171  3.6481 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      -0.13172    0.28623  -0.460    0.646
## SconsC.d         -0.33709    0.36278  -0.929    0.353
## SconsM.d         -0.11682    0.32785  -0.356    0.722
## SconsL.d         -0.02269    0.40087  -0.057    0.955
## SconsSL.d         0.04233    0.45407   0.093    0.926
## cond.c            0.06989    0.57245   0.122    0.903
## SconsC.d:cond.c  -0.42857    0.72557  -0.591    0.555
## SconsM.d:cond.c  -0.71915    0.65570  -1.097    0.273
## SconsL.d:cond.c  -0.26107    0.80174  -0.326    0.745
## SconsSL.d:cond.c -0.15777    0.90815  -0.174    0.862
## 
## Residual standard error: 2.105 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01652,    Adjusted R-squared:  -0.005283 
## F-statistic: 0.7577 on 9 and 406 DF,  p-value: 0.6558

# Action 25
scon.c.b25 <- lm(act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b25) # no

## 
## Call:
## lm(formula = act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7857 -0.9307  0.1529  1.3778  3.3333 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.1364     0.2377  -0.574 0.566463    
## SconsC.d           0.6492     0.2998   2.166 0.030868 *  
## SconsM.d           1.0252     0.2733   3.752 0.000199 ***
## SconsL.d           1.2699     0.3313   3.833 0.000145 ***
## SconsSL.d          1.5530     0.3954   3.927 9.98e-05 ***
## cond.c             0.3939     0.4754   0.829 0.407735    
## SconsC.d:cond.c   -0.6127     0.5995  -1.022 0.307399    
## SconsM.d:cond.c   -0.4776     0.5465  -0.874 0.382690    
## SconsL.d:cond.c    0.4357     0.6627   0.658 0.511203    
## SconsSL.d:cond.c   0.3442     0.7909   0.435 0.663665    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 436 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.06134,    Adjusted R-squared:  0.04197 
## F-statistic: 3.166 on 9 and 436 DF,  p-value: 0.001022

# Action 28
scon.c.b28 <- lm(act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b28) # marginal

## 
## Call:
## lm(formula = act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.8282     0.2428   3.411  0.00072 ***
## SconsC.d           0.4933     0.3141   1.570  0.11717    
## SconsM.d           0.1504     0.2859   0.526  0.59919    
## SconsL.d           0.1902     0.3482   0.546  0.58528    
## SconsSL.d          0.3911     0.4151   0.942  0.34666    
## cond.c            -0.8821     0.4856  -1.817  0.07010 .  
## SconsC.d:cond.c    0.6202     0.6282   0.987  0.32412    
## SconsM.d:cond.c    1.0700     0.5717   1.871  0.06208 .  
## SconsL.d:cond.c    1.1063     0.6964   1.589  0.11302    
## SconsSL.d:cond.c   1.1102     0.8302   1.337  0.18195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

# Action 30
scon.c.b30 <- lm(act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * cond.c, data = d)

summary(scon.c.b30) # yes, condition difference

## 
## Call:
## lm(formula = act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.07250    0.25027   4.285 2.37e-05 ***
## SconsC.d         -0.04851    0.32716  -0.148   0.8822    
## SconsM.d         -0.02838    0.29686  -0.096   0.9239    
## SconsL.d          0.11010    0.36457   0.302   0.7628    
## SconsSL.d         0.08375    0.43685   0.192   0.8481    
## cond.c            1.10500    0.50055   2.208   0.0279 *  
## SconsC.d:cond.c  -1.33480    0.65433  -2.040   0.0421 *  
## SconsM.d:cond.c  -1.19324    0.59371  -2.010   0.0452 *  
## SconsL.d:cond.c  -1.01566    0.72914  -1.393   0.1645    
## SconsSL.d:cond.c -0.91750    0.87370  -1.050   0.2944    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

Significant condition difference: 9; 28; 30

a. Means for condition diffs

describeBy(d$act9[d$ideology=="Strong Conservative"], d$cond[d$ideology=="Strong Conservative"]) # marginal; both opposed, climate cond less opposed

## 
##  Descriptive statistics by group 
## group: climate
##    vars  n  mean  sd median trimmed  mad min max range skew kurtosis   se
## X1    1 34 -0.24 2.3      0   -0.29 2.97  -3   3     6 0.05    -1.56 0.39
## ------------------------------------------------------------ 
## group: ctrl
##    vars  n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 26 -1.35 2.12     -3   -1.59   0  -3   3     6 0.85    -0.65 0.41

describeBy(d$act28[d$ideology=="Strong Conservative"], d$cond[d$ideology=="Strong Conservative"]) # both supported, ctrl supported more

## 
##  Descriptive statistics by group 
## group: climate
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 31 0.39 2.11      0    0.48 2.97  -3   3     6 -0.21    -1.29 0.38
## ------------------------------------------------------------ 
## group: ctrl
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## X1    1 26 1.27 1.54      1    1.41 1.48  -3   3     6 -0.75     0.16 0.3

### is climate different from 0?
summary(lm(act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * clim.d, data = d))

## 
## Call:
## lm(formula = act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.3871     0.3280   1.180   0.2387  
## SconsC.d           0.8034     0.4324   1.858   0.0640 .
## SconsM.d           0.6854     0.3948   1.736   0.0834 .
## SconsL.d           0.7433     0.5025   1.479   0.1400  
## SconsSL.d          0.9462     0.6208   1.524   0.1283  
## clim.d             0.8821     0.4856   1.817   0.0701 .
## SconsC.d:clim.d   -0.6202     0.6282  -0.987   0.3241  
## SconsM.d:clim.d   -1.0700     0.5717  -1.871   0.0621 .
## SconsL.d:clim.d   -1.1063     0.6964  -1.589   0.1130  
## SconsSL.d:clim.d  -1.1102     0.8302  -1.337   0.1820  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

### Not different from 0 in the climate condition

summary(lm(act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4524 -1.0725  0.1154  1.6129  2.6129 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.2692     0.3581   3.544 0.000445 ***
## SconsC.d           0.1832     0.4557   0.402 0.687970    
## SconsM.d          -0.3846     0.4135  -0.930 0.352932    
## SconsL.d          -0.3630     0.4821  -0.753 0.452014    
## SconsSL.d         -0.1640     0.5511  -0.298 0.766246    
## ctrl.d            -0.8821     0.4856  -1.817 0.070104 .  
## SconsC.d:ctrl.d    0.6202     0.6282   0.987 0.324119    
## SconsM.d:ctrl.d    1.0700     0.5717   1.871 0.062084 .  
## SconsL.d:ctrl.d    1.1063     0.6964   1.589 0.113018    
## SconsSL.d:ctrl.d   1.1102     0.8302   1.337 0.181951    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 364 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.02136,    Adjusted R-squared:  -0.002835 
## F-statistic: 0.8828 on 9 and 364 DF,  p-value: 0.5407

### Different in the ctrl condition

describeBy(d$act30[d$ideology=="Strong Conservative"], d$cond[d$ideology=="Strong Conservative"])

## 
##  Descriptive statistics by group 
## group: climate
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 32 1.62 1.43      2    1.81 1.48  -2   3     5 -0.76    -0.47 0.25
## ------------------------------------------------------------ 
## group: ctrl
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## X1    1 25 0.52 1.98      1    0.62 2.97  -3   3     6 -0.31    -1.04 0.4

### is ctrl different from 0?
summary(lm(act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.5200     0.3750   1.387   0.1665  
## SconsC.d           0.6189     0.4882   1.268   0.2058  
## SconsM.d           0.5682     0.4386   1.296   0.1960  
## SconsL.d           0.6179     0.5118   1.207   0.2281  
## SconsSL.d          0.5425     0.6004   0.904   0.3668  
## ctrl.d             1.1050     0.5005   2.208   0.0279 *
## SconsC.d:ctrl.d   -1.3348     0.6543  -2.040   0.0421 *
## SconsM.d:ctrl.d   -1.1932     0.5937  -2.010   0.0452 *
## SconsL.d:ctrl.d   -1.0157     0.7291  -1.393   0.1645  
## SconsSL.d:ctrl.d  -0.9175     0.8737  -1.050   0.2944  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

### Not different from 0 in the control condition

summary(lm(act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * clim.d, data = d))

## 
## Call:
## lm(formula = act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2500 -1.0882  0.4275  1.7670  2.4800 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.6250     0.3315   4.902 1.46e-06 ***
## SconsC.d          -0.7159     0.4357  -1.643   0.1012    
## SconsM.d          -0.6250     0.4002  -1.562   0.1192    
## SconsL.d          -0.3977     0.5194  -0.766   0.4443    
## SconsSL.d         -0.3750     0.6348  -0.591   0.5551    
## clim.d            -1.1050     0.5005  -2.208   0.0279 *  
## SconsC.d:clim.d    1.3348     0.6543   2.040   0.0421 *  
## SconsM.d:clim.d    1.1932     0.5937   2.010   0.0452 *  
## SconsL.d:clim.d    1.0157     0.7291   1.393   0.1645    
## SconsSL.d:clim.d   0.9175     0.8737   1.050   0.2944    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.875 on 344 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  -0.009363 
## F-statistic: 0.6362 on 9 and 344 DF,  p-value: 0.766

### Different from 0 in the climate condition

2. Gender effects?

# Action 1
scon.g.b1 <- lm(act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b1)

## 
## Call:
## lm(formula = act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8261  0.2263  2.1538  4.1538 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        -0.3965     0.2769  -1.432   0.1529  
## SconsC.d           -0.3088     0.3687  -0.838   0.4027  
## SconsM.d            0.5333     0.3313   1.610   0.1082  
## SconsL.d            1.0031     0.4189   2.395   0.0170 *
## SconsSL.d           0.2226     0.4794   0.464   0.6427  
## gend.mf             0.4451     0.5538   0.804   0.4220  
## SconsC.d:gend.mf   -1.3422     0.7373  -1.820   0.0694 .
## SconsM.d:gend.mf    0.2811     0.6626   0.424   0.6716  
## SconsL.d:gend.mf   -0.5918     0.8377  -0.706   0.4803  
## SconsSL.d:gend.mf  -2.0973     0.9588  -2.187   0.0292 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.135 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.05646,    Adjusted R-squared:  0.03703 
## F-statistic: 2.906 on 9 and 437 DF,  p-value: 0.002374

# Action 2
scon.g.b2 <- lm(act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b2) # yes, above 0

## 
## Call:
## lm(formula = act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4375 -1.2184  0.5263  1.5625  2.6087 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.03314    0.26998   3.827  0.00016 ***
## SconsC.d          -0.25415    0.35719  -0.712  0.47734    
## SconsM.d           0.15606    0.33874   0.461  0.64537    
## SconsL.d           0.68561    0.42376   1.618  0.10681    
## SconsSL.d         -0.09147    0.47821  -0.191  0.84845    
## gend.mf            0.88109    0.53995   1.632  0.10385    
## SconsC.d:gend.mf  -1.65645    0.71437  -2.319  0.02113 *  
## SconsM.d:gend.mf  -0.93948    0.67748  -1.387  0.16663    
## SconsL.d:gend.mf  -0.31859    0.84752  -0.376  0.70727    
## SconsSL.d:gend.mf -1.26442    0.95642  -1.322  0.18724    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 278 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.03738,    Adjusted R-squared:  0.006217 
## F-statistic: 1.199 on 9 and 278 DF,  p-value: 0.2951

# Action 3
scon.g.b3 <- lm(act3 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b3) # yes, above 0

## 
## Call:
## lm(formula = act3 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5833 -1.8390 -0.0368  1.4167  4.1579 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        -0.7963     0.2666  -2.987  0.00297 **
## SconsC.d            0.3825     0.3515   1.088  0.27706   
## SconsM.d            0.7296     0.3165   2.305  0.02165 * 
## SconsL.d            0.6841     0.3847   1.778  0.07604 . 
## SconsSL.d           1.0380     0.4640   2.237  0.02579 * 
## gend.mf             0.7231     0.5331   1.356  0.17569   
## SconsC.d:gend.mf   -0.7102     0.7029  -1.010  0.31286   
## SconsM.d:gend.mf   -0.9301     0.6331  -1.469  0.14253   
## SconsL.d:gend.mf   -0.3876     0.7694  -0.504  0.61470   
## SconsSL.d:gend.mf  -1.4064     0.9280  -1.516  0.13035   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.03535,    Adjusted R-squared:  0.01525 
## F-statistic: 1.759 on 9 and 432 DF,  p-value: 0.07405

# Action 4
scon.g.b4 <- lm(act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b4) # no

## 
## Call:
## lm(formula = act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.944 -1.904  0.200  2.056  3.762 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.2135     0.3053   0.699    0.485
## SconsC.d           -0.6425     0.4146  -1.550    0.122
## SconsM.d           -0.4312     0.3705  -1.164    0.245
## SconsL.d            0.2419     0.4414   0.548    0.584
## SconsSL.d           0.1087     0.5256   0.207    0.836
## gend.mf             0.3556     0.6106   0.582    0.561
## SconsC.d:gend.mf   -1.0213     0.8292  -1.232    0.219
## SconsM.d:gend.mf   -0.3909     0.7409  -0.528    0.598
## SconsL.d:gend.mf   -0.0163     0.8827  -0.018    0.985
## SconsSL.d:gend.mf  -1.6000     1.0513  -1.522    0.129
## 
## Residual standard error: 2.17 on 359 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02849,    Adjusted R-squared:  0.004137 
## F-statistic:  1.17 on 9 and 359 DF,  p-value: 0.3132

# Action 5
scon.g.b5 <- lm(act5 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b5) # yes, above 0

## 
## Call:
## lm(formula = act5 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3000 -1.2128  0.4615  1.7000  2.7000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         0.7199     0.2690   2.676  0.00788 **
## SconsC.d            0.1557     0.3537   0.440  0.66010   
## SconsM.d            0.3665     0.3405   1.076  0.28270   
## SconsL.d            0.7634     0.4188   1.823  0.06936 . 
## SconsSL.d          -0.3391     0.4781  -0.709  0.47871   
## gend.mf            -0.4833     0.5380  -0.898  0.36978   
## SconsC.d:gend.mf   -0.1910     0.7074  -0.270  0.78733   
## SconsM.d:gend.mf    0.5327     0.6811   0.782  0.43481   
## SconsL.d:gend.mf    0.8499     0.8376   1.015  0.31108   
## SconsSL.d:gend.mf   0.3217     0.9562   0.336  0.73677   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 285 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.03011,    Adjusted R-squared:  -0.0005149 
## F-statistic: 0.9832 on 9 and 285 DF,  p-value: 0.454

# Action 6
scon.g.b6 <- lm(act6 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b6) # yes, above 0

## 
## Call:
## lm(formula = act6 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5455 -1.2424  0.5667  1.5667  2.6667 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.82468    0.25312   3.258  0.00123 **
## SconsC.d           0.43199    0.33265   1.299  0.19494   
## SconsM.d           0.53355    0.31194   1.710  0.08808 . 
## SconsL.d           0.75808    0.39017   1.943  0.05283 . 
## SconsSL.d          0.11472    0.43909   0.261  0.79404   
## gend.mf           -0.41126    0.50623  -0.812  0.41713   
## SconsC.d:gend.mf   0.05792    0.66531   0.087  0.93067   
## SconsM.d:gend.mf   0.63230    0.62387   1.014  0.31152   
## SconsL.d:gend.mf   1.09191    0.78033   1.399  0.16262   
## SconsSL.d:gend.mf -0.80087    0.87817  -0.912  0.36242   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 347 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.02528,    Adjusted R-squared:  -1.033e-06 
## F-statistic:     1 on 9 and 347 DF,  p-value: 0.4397

# Action 7
scon.g.b7 <- lm(act7 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b7) # yes, above 0

## 
## Call:
## lm(formula = act7 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6800 -1.8433  0.1567  1.6364  3.6364 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -0.11169    0.28533  -0.391    0.696
## SconsC.d          -0.30806    0.38548  -0.799    0.425
## SconsM.d           0.01007    0.33941   0.030    0.976
## SconsL.d           0.15487    0.42395   0.365    0.715
## SconsSL.d          0.22947    0.49761   0.461    0.645
## gend.mf            0.40519    0.57067   0.710    0.478
## SconsC.d:gend.mf  -0.83843    0.77095  -1.088    0.277
## SconsM.d:gend.mf  -0.29499    0.67882  -0.435    0.664
## SconsL.d:gend.mf  -0.09156    0.84790  -0.108    0.914
## SconsSL.d:gend.mf -1.52964    0.99521  -1.537    0.125
## 
## Residual standard error: 2.097 on 403 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  -0.006717 
## F-statistic: 0.6946 on 9 and 403 DF,  p-value: 0.714

# Action 8
scon.g.b8 <- lm(act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b8) # yes, above 0

## 
## Call:
## lm(formula = act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9286 -1.9091 -0.2143  1.6875  4.5319 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -0.9250     0.2738  -3.379 0.000789 ***
## SconsC.d            0.1884     0.3593   0.524 0.600246    
## SconsM.d            0.5417     0.3259   1.662 0.097174 .  
## SconsL.d            1.4863     0.4221   3.521 0.000471 ***
## SconsSL.d           1.3439     0.4661   2.883 0.004114 ** 
## gend.mf             1.2137     0.5476   2.216 0.027138 *  
## SconsC.d:gend.mf   -1.3119     0.7187  -1.825 0.068565 .  
## SconsM.d:gend.mf   -1.0001     0.6518  -1.534 0.125599    
## SconsL.d:gend.mf   -0.9076     0.8441  -1.075 0.282829    
## SconsSL.d:gend.mf  -2.2332     0.9321  -2.396 0.016973 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 469 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.07584,    Adjusted R-squared:  0.05811 
## F-statistic: 4.276 on 9 and 469 DF,  p-value: 2.361e-05

# Action 9
scon.g.b9 <- lm(act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b9) # yes, above 0

## 
## Call:
## lm(formula = act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5882 -1.5882  0.1481  1.5417  3.8261 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -0.7374     0.2616  -2.819 0.005041 ** 
## SconsC.d            0.5175     0.3433   1.507 0.132470    
## SconsM.d            0.9047     0.3110   2.909 0.003814 ** 
## SconsL.d            1.3187     0.3823   3.449 0.000617 ***
## SconsSL.d           0.6938     0.4440   1.563 0.118847    
## gend.mf            -0.1774     0.5232  -0.339 0.734668    
## SconsC.d:gend.mf    0.3210     0.6866   0.467 0.640420    
## SconsM.d:gend.mf    0.1094     0.6221   0.176 0.860505    
## SconsL.d:gend.mf    0.1912     0.7647   0.250 0.802668    
## SconsSL.d:gend.mf  -0.8263     0.8880  -0.931 0.352566    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.97 on 437 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.04187,    Adjusted R-squared:  0.02214 
## F-statistic: 2.122 on 9 and 437 DF,  p-value: 0.02658

# Action 10
scon.g.b10 <- lm(act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b10) # yes, above 0

## 
## Call:
## lm(formula = act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5862 -2.0719  0.1948  1.5714  3.7778 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -0.5411     0.2651  -2.041 0.041829 *  
## SconsC.d            0.2294     0.3498   0.656 0.512369    
## SconsM.d            0.6314     0.3171   1.991 0.047042 *  
## SconsL.d            1.4741     0.3987   3.697 0.000244 ***
## SconsSL.d           1.2842     0.4628   2.775 0.005743 ** 
## gend.mf             0.4734     0.5302   0.893 0.372387    
## SconsC.d:gend.mf   -0.7072     0.6997  -1.011 0.312664    
## SconsM.d:gend.mf   -0.4366     0.6341  -0.689 0.491461    
## SconsL.d:gend.mf    0.5355     0.7975   0.672 0.502227    
## SconsSL.d:gend.mf  -2.1596     0.9255  -2.333 0.020053 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 466 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07046,    Adjusted R-squared:  0.05251 
## F-statistic: 3.925 on 9 and 466 DF,  p-value: 7.905e-05

# Action 11
scon.g.b11 <- lm(act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b11) # yes, above 0

## 
## Call:
## lm(formula = act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5625 -1.0000  0.2834  1.4375  2.7778 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.79293    0.26841   2.954  0.00338 **
## SconsC.d          -0.06566    0.36339  -0.181  0.85674   
## SconsM.d          -0.07633    0.32529  -0.235  0.81463   
## SconsL.d           0.93647    0.41281   2.269  0.02400 * 
## SconsSL.d          0.70261    0.50138   1.401  0.16214   
## gend.mf            1.14141    0.53681   2.126  0.03429 * 
## SconsC.d:gend.mf  -1.68687    0.72677  -2.321  0.02095 * 
## SconsM.d:gend.mf  -1.20317    0.65059  -1.849  0.06538 . 
## SconsL.d:gend.mf  -0.75405    0.82562  -0.913  0.36180   
## SconsSL.d:gend.mf -1.27534    1.00277  -1.272  0.20442   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 302 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.05336,    Adjusted R-squared:  0.02515 
## F-statistic: 1.891 on 9 and 302 DF,  p-value: 0.05276

# Action 12
scon.g.b12 <- lm(act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b12) # yes, above 0

## 
## Call:
## lm(formula = act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5625 -1.8571 -0.1345  1.8655  4.3043 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.75325    0.28957  -2.601  0.00966 ** 
## SconsC.d          -0.07803    0.38731  -0.201  0.84044    
## SconsM.d           0.59679    0.35097   1.700  0.08989 .  
## SconsL.d           1.87723    0.45373   4.137 4.35e-05 ***
## SconsSL.d          0.93450    0.51761   1.805  0.07182 .  
## gend.mf            0.77922    0.57914   1.345  0.17929    
## SconsC.d:gend.mf  -1.72536    0.77462  -2.227  0.02652 *  
## SconsM.d:gend.mf  -1.36104    0.70194  -1.939  0.05326 .  
## SconsL.d:gend.mf   0.63948    0.90747   0.705  0.48145    
## SconsSL.d:gend.mf -1.54172    1.03522  -1.489  0.13726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.129 on 373 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.08175,    Adjusted R-squared:  0.0596 
## F-statistic:  3.69 on 9 and 373 DF,  p-value: 0.0001907

# Action 13
scon.g.b13 <- lm(act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b13) # yes, above 0

## 
## Call:
## lm(formula = act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.8095  0.1884  1.9558  3.3636 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -0.29978    0.27109  -1.106  0.26942   
## SconsC.d           0.05341    0.36743   0.145  0.88450   
## SconsM.d           0.14792    0.32254   0.459  0.64675   
## SconsL.d           0.77693    0.41292   1.882  0.06057 . 
## SconsSL.d          0.42231    0.46485   0.908  0.36412   
## gend.mf            1.78139    0.54219   3.286  0.00110 **
## SconsC.d:gend.mf  -1.89733    0.73485  -2.582  0.01015 * 
## SconsM.d:gend.mf  -2.04287    0.64507  -3.167  0.00165 **
## SconsL.d:gend.mf  -1.30710    0.82584  -1.583  0.11421   
## SconsSL.d:gend.mf -2.75372    0.92969  -2.962  0.00323 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 432 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.04764,    Adjusted R-squared:  0.0278 
## F-statistic: 2.401 on 9 and 432 DF,  p-value: 0.01156

# Action 14
scon.g.b14 <- lm(act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b14) # no

## 
## Call:
## lm(formula = act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8235 -1.2778  0.2195  1.5455  3.7222 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.5422     0.3225   1.681   0.0938 .
## SconsC.d           -0.5131     0.4304  -1.192   0.2343  
## SconsM.d           -0.1544     0.3827  -0.403   0.6870  
## SconsL.d            0.5601     0.4824   1.161   0.2466  
## SconsSL.d          -0.6851     0.5673  -1.208   0.2282  
## gend.mf             0.5627     0.6450   0.872   0.3838  
## SconsC.d:gend.mf   -2.0654     0.8609  -2.399   0.0171 *
## SconsM.d:gend.mf   -0.7133     0.7654  -0.932   0.3522  
## SconsL.d:gend.mf    0.1419     0.9648   0.147   0.8832  
## SconsSL.d:gend.mf  -1.9912     1.1346  -1.755   0.0804 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.017 on 276 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.04977,    Adjusted R-squared:  0.01878 
## F-statistic: 1.606 on 9 and 276 DF,  p-value: 0.1132

# Action 15
scon.g.b15 <- lm(act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b15) # yes, above 0

## 
## Call:
## lm(formula = act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6562 -1.7674  0.4444  1.6917  3.5714 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        -0.3065     0.2703  -1.134  0.25746   
## SconsC.d            0.3102     0.3608   0.860  0.39045   
## SconsM.d            0.2385     0.3238   0.737  0.46184   
## SconsL.d            1.3654     0.4308   3.170  0.00165 **
## SconsSL.d           0.5399     0.4602   1.173  0.24143   
## gend.mf             0.5298     0.5406   0.980  0.32772   
## SconsC.d:gend.mf   -1.1296     0.7215  -1.566  0.11829   
## SconsM.d:gend.mf   -1.2825     0.6476  -1.981  0.04838 * 
## SconsL.d:gend.mf    0.2755     0.8616   0.320  0.74930   
## SconsSL.d:gend.mf  -1.1631     0.9203  -1.264  0.20708   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 374 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02548 
## F-statistic: 2.113 on 9 and 374 DF,  p-value: 0.0277

# Action 16
scon.g.b16 <- lm(act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b16) # yes, above 0

## 
## Call:
## lm(formula = act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3636 -1.1400  0.1589  1.6364  2.7586 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.6470     0.2704   2.393   0.0173 *
## SconsC.d            0.2412     0.3578   0.674   0.5007  
## SconsM.d            0.4554     0.3261   1.396   0.1636  
## SconsL.d            0.3248     0.4124   0.788   0.4316  
## SconsSL.d           0.2974     0.4588   0.648   0.5172  
## gend.mf             0.8113     0.5407   1.500   0.1345  
## SconsC.d:gend.mf   -1.3149     0.7156  -1.838   0.0670 .
## SconsM.d:gend.mf   -0.2887     0.6522  -0.443   0.6583  
## SconsL.d:gend.mf   -1.2548     0.8248  -1.521   0.1292  
## SconsSL.d:gend.mf  -0.9224     0.9175  -1.005   0.3155  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 321 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.02518,    Adjusted R-squared:  -0.002148 
## F-statistic: 0.9214 on 9 and 321 DF,  p-value: 0.5066

# Action 17
scon.g.b17 <- lm(act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b17) # no

## 
## Call:
## lm(formula = act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.391 -1.477 -0.033  1.630  3.077 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0.38844    0.26525   1.464    0.144
## SconsC.d          -0.29198    0.35374  -0.825    0.410
## SconsM.d           0.09420    0.31868   0.296    0.768
## SconsL.d           0.36777    0.40532   0.907    0.365
## SconsSL.d          0.70721    0.46368   1.525    0.128
## gend.mf            0.09268    0.53051   0.175    0.861
## SconsC.d:gend.mf  -0.43944    0.70748  -0.621    0.535
## SconsM.d:gend.mf  -0.08123    0.63736  -0.127    0.899
## SconsL.d:gend.mf   0.68061    0.81063   0.840    0.402
## SconsSL.d:gend.mf -0.68398    0.92736  -0.738    0.461
## 
## Residual standard error: 2.008 on 406 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.02291,    Adjusted R-squared:  0.001247 
## F-statistic: 1.058 on 9 and 406 DF,  p-value: 0.3934

# Action 18
scon.g.b18 <- lm(act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.1369  0.1776  1.4528  3.1000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.2326     0.2562   0.908   0.3646  
## SconsC.d            0.2076     0.3499   0.593   0.5533  
## SconsM.d            0.5422     0.3155   1.719   0.0866 .
## SconsL.d            0.7333     0.3828   1.916   0.0562 .
## SconsSL.d           0.5569     0.4534   1.228   0.2202  
## gend.mf             0.6652     0.5124   1.298   0.1951  
## SconsC.d:gend.mf   -0.8791     0.6999  -1.256   0.2100  
## SconsM.d:gend.mf   -0.7604     0.6309  -1.205   0.2290  
## SconsL.d:gend.mf   -0.4543     0.7656  -0.593   0.5534  
## SconsSL.d:gend.mf  -0.2442     0.9068  -0.269   0.7879  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.02394,    Adjusted R-squared:  -0.001748 
## F-statistic: 0.9319 on 9 and 342 DF,  p-value: 0.4973

# Action 19
scon.g.b19 <- lm(act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b19) # yes, above 0

## 
## Call:
## lm(formula = act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1622  0.3462  1.6250  2.3462 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.97398    0.28169   3.458 0.000635 ***
## SconsC.d           0.04188    0.36994   0.113 0.909948    
## SconsM.d           0.15697    0.34379   0.457 0.648339    
## SconsL.d           0.52602    0.46399   1.134 0.257957    
## SconsSL.d          0.16418    0.47353   0.347 0.729088    
## gend.mf            0.64027    0.56338   1.136 0.256785    
## SconsC.d:gend.mf  -0.93287    0.73988  -1.261 0.208481    
## SconsM.d:gend.mf  -0.97360    0.68757  -1.416 0.157953    
## SconsL.d:gend.mf  -0.39027    0.92798  -0.421 0.674419    
## SconsSL.d:gend.mf -1.41659    0.94706  -1.496 0.135908    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.806 on 264 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.0208, Adjusted R-squared:  -0.01258 
## F-statistic: 0.6232 on 9 and 264 DF,  p-value: 0.7769

# Action 20
scon.g.b20 <- lm(act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b20) # yes, above 0

## 
## Call:
## lm(formula = act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.398 -1.200  0.602  1.602  2.391 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.16307    0.24835   4.683 4.22e-06 ***
## SconsC.d          -0.16307    0.33838  -0.482    0.630    
## SconsM.d           0.13590    0.31136   0.436    0.663    
## SconsL.d           0.10408    0.39115   0.266    0.790    
## SconsSL.d         -0.27092    0.44645  -0.607    0.544    
## gend.mf            0.25718    0.49671   0.518    0.605    
## SconsC.d:gend.mf  -1.03979    0.67677  -1.536    0.125    
## SconsM.d:gend.mf  -0.45514    0.62271  -0.731    0.465    
## SconsL.d:gend.mf   0.04184    0.78230   0.053    0.957    
## SconsSL.d:gend.mf -0.70816    0.89291  -0.793    0.428    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.8 on 312 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.01743,    Adjusted R-squared:  -0.01091 
## F-statistic: 0.615 on 9 and 312 DF,  p-value: 0.7842

# Action 21
scon.g.b21 <- lm(act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b21) # yes, above 0

## 
## Call:
## lm(formula = act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.556 -1.840  0.160  1.696  3.818 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -0.63409    0.27449  -2.310  0.02141 * 
## SconsC.d           0.27346    0.37195   0.735  0.46265   
## SconsM.d           0.65165    0.33164   1.965  0.05013 . 
## SconsL.d           1.10530    0.41346   2.673  0.00783 **
## SconsSL.d          1.06404    0.49058   2.169  0.03069 * 
## gend.mf           -0.36818    0.54899  -0.671  0.50284   
## SconsC.d:gend.mf  -0.07722    0.74390  -0.104  0.91738   
## SconsM.d:gend.mf   0.72330    0.66328   1.090  0.27617   
## SconsL.d:gend.mf   0.49242    0.82692   0.595  0.55186   
## SconsSL.d:gend.mf  0.61939    0.98116   0.631  0.52823   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.068 on 391 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.0295, Adjusted R-squared:  0.007162 
## F-statistic: 1.321 on 9 and 391 DF,  p-value: 0.2241

# Action 22
scon.g.b22 <- lm(act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b22) # yes, above 0

## 
## Call:
## lm(formula = act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1538 -1.4912 -0.1538  1.6410  3.6410 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        -0.2069     0.2631  -0.786  0.43210   
## SconsC.d            0.4525     0.3539   1.279  0.20175   
## SconsM.d            0.6041     0.3199   1.888  0.05976 . 
## SconsL.d            0.8321     0.4083   2.038  0.04223 * 
## SconsSL.d           1.2838     0.4516   2.843  0.00471 **
## gend.mf             0.8683     0.5261   1.650  0.09967 . 
## SconsC.d:gend.mf   -1.3595     0.7077  -1.921  0.05546 . 
## SconsM.d:gend.mf   -0.3995     0.6399  -0.624  0.53277   
## SconsL.d:gend.mf   -0.7341     0.8166  -0.899  0.36921   
## SconsSL.d:gend.mf  -1.0221     0.9033  -1.132  0.25850   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.973 on 392 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.04639,    Adjusted R-squared:  0.02449 
## F-statistic: 2.119 on 9 and 392 DF,  p-value: 0.02709

# Action 23
scon.g.b23 <- lm(act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b23) # no

## 
## Call:
## lm(formula = act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9474 -2.0103  0.1875  2.0227  3.5238 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.31459    0.35387   0.889   0.3748  
## SconsC.d          -0.58786    0.46371  -1.268   0.2059  
## SconsM.d          -0.05944    0.43299  -0.137   0.8909  
## SconsL.d           0.27291    0.51335   0.532   0.5954  
## SconsSL.d         -0.03334    0.60381  -0.055   0.9560  
## gend.mf            1.26555    0.70773   1.788   0.0748 .
## SconsC.d:gend.mf  -1.76663    0.92742  -1.905   0.0578 .
## SconsM.d:gend.mf  -0.77586    0.86599  -0.896   0.3711  
## SconsL.d:gend.mf  -1.50722    1.02669  -1.468   0.1432  
## SconsSL.d:gend.mf -2.32805    1.20763  -1.928   0.0549 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.26 on 282 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03359,    Adjusted R-squared:  0.002749 
## F-statistic: 1.089 on 9 and 282 DF,  p-value: 0.3707

# Action 24
scon.g.b24 <- lm(act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b24) # yes, above 0

## 
## Call:
## lm(formula = act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5455 -1.8425  0.1575  1.8214  3.8214 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.01515    0.28872  -0.052   0.9582  
## SconsC.d          -0.57525    0.37396  -1.538   0.1248  
## SconsM.d          -0.31359    0.34050  -0.921   0.3576  
## SconsL.d          -0.02706    0.43239  -0.063   0.9501  
## SconsSL.d         -0.19781    0.48388  -0.409   0.6829  
## gend.mf            1.12121    0.57743   1.942   0.0529 .
## SconsC.d:gend.mf  -1.58327    0.74792  -2.117   0.0349 *
## SconsM.d:gend.mf  -1.46373    0.68100  -2.149   0.0322 *
## SconsL.d:gend.mf  -0.75108    0.86477  -0.869   0.3856  
## SconsSL.d:gend.mf -1.69529    0.96776  -1.752   0.0806 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.098 on 405 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.02029,    Adjusted R-squared:  -0.001484 
## F-statistic: 0.9318 on 9 and 405 DF,  p-value: 0.4971

# Action 25
scon.g.b25 <- lm(act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b25) # yes, above 0

## 
## Call:
## lm(formula = act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -0.8936  0.1642  1.4389  3.2051 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.07875    0.24926  -0.316 0.752190    
## SconsC.d           0.48987    0.32380   1.513 0.131047    
## SconsM.d           0.96397    0.29380   3.281 0.001118 ** 
## SconsL.d           1.28753    0.36494   3.528 0.000463 ***
## SconsSL.d          1.44098    0.43622   3.303 0.001035 ** 
## gend.mf            0.25275    0.49852   0.507 0.612417    
## SconsC.d:gend.mf  -0.63053    0.64761  -0.974 0.330788    
## SconsM.d:gend.mf  -0.23594    0.58761  -0.402 0.688228    
## SconsL.d:gend.mf   0.07970    0.72988   0.109 0.913098    
## SconsSL.d:gend.mf -0.08830    0.87244  -0.101 0.919427    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 434 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.05138,    Adjusted R-squared:  0.03171 
## F-statistic: 2.612 on 9 and 434 DF,  p-value: 0.006027

# Action 26
scon.g.b26 <- lm(act26 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b26) # yes, above 0

## 
## Call:
## lm(formula = act26 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.0000  0.5278  1.3433  2.5000 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.544118   0.302509   5.104 7.27e-07 ***
## SconsC.d          -0.016340   0.404614  -0.040    0.968    
## SconsM.d          -0.001474   0.379614  -0.004    0.997    
## SconsL.d           0.027311   0.496671   0.055    0.956    
## SconsSL.d         -0.571895   0.518690  -1.103    0.271    
## gend.mf            0.088235   0.605019   0.146    0.884    
## SconsC.d:gend.mf  -0.366013   0.809227  -0.452    0.652    
## SconsM.d:gend.mf  -0.316380   0.759228  -0.417    0.677    
## SconsL.d:gend.mf   1.054622   0.993342   1.062    0.290    
## SconsSL.d:gend.mf  0.856209   1.037381   0.825    0.410    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 216 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.02923,    Adjusted R-squared:  -0.01122 
## F-statistic: 0.7225 on 9 and 216 DF,  p-value: 0.688

# Action 27
scon.g.b27 <- lm(act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b27) # no

## 
## Call:
## lm(formula = act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4545 -1.1667  0.2174  1.5521  3.0000 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.4792     0.2613   1.834   0.0676 .
## SconsC.d            0.2536     0.3580   0.708   0.4792  
## SconsM.d            0.1448     0.3269   0.443   0.6581  
## SconsL.d            0.6951     0.4105   1.693   0.0914 .
## SconsSL.d           0.8797     0.4415   1.993   0.0472 *
## gend.mf             0.9583     0.5225   1.834   0.0676 .
## SconsC.d:gend.mf   -0.8587     0.7159  -1.199   0.2313  
## SconsM.d:gend.mf   -0.6063     0.6538  -0.927   0.3545  
## SconsL.d:gend.mf   -0.9735     0.8210  -1.186   0.2366  
## SconsSL.d:gend.mf  -0.7669     0.8829  -0.869   0.3857  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 307 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.03928,    Adjusted R-squared:  0.01112 
## F-statistic: 1.395 on 9 and 307 DF,  p-value: 0.1895

# Action 28
scon.g.b28 <- lm(act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b28) # yes, above 0

## 
## Call:
## lm(formula = act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4262 -0.9511  0.1944  1.5738  2.4286 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.78373    0.25169   3.114  0.00199 **
## SconsC.d           0.45112    0.33712   1.338  0.18168   
## SconsM.d           0.22190    0.31023   0.715  0.47489   
## SconsL.d           0.12615    0.39102   0.323  0.74717   
## SconsSL.d          0.19764    0.46893   0.421  0.67367   
## gend.mf           -0.04365    0.50337  -0.087  0.93094   
## SconsC.d:gend.mf  -0.33910    0.67424  -0.503  0.61531   
## SconsM.d:gend.mf   0.21421    0.62045   0.345  0.73011   
## SconsL.d:gend.mf  -0.27612    0.78205  -0.353  0.72424   
## SconsSL.d:gend.mf -0.77622    0.93787  -0.828  0.40842   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 362 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.01604,    Adjusted R-squared:  -0.008426 
## F-statistic: 0.6556 on 9 and 362 DF,  p-value: 0.749

# Action 29
scon.g.b29 <- lm(act29 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b29) # yes, above 0

## 
## Call:
## lm(formula = act29 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5263 -1.1003  0.1951  1.5000  2.1951 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.119048   0.244817   4.571 6.69e-06 ***
## SconsC.d          -0.126816   0.325646  -0.389    0.697    
## SconsM.d          -0.004118   0.299162  -0.014    0.989    
## SconsL.d           0.033391   0.367847   0.091    0.928    
## SconsSL.d          0.310777   0.434446   0.715    0.475    
## gend.mf            0.238095   0.489634   0.486    0.627    
## SconsC.d:gend.mf  -0.389225   0.651293  -0.598    0.550    
## SconsM.d:gend.mf  -0.296525   0.598324  -0.496    0.620    
## SconsL.d:gend.mf   0.457027   0.735694   0.621    0.535    
## SconsSL.d:gend.mf -0.431078   0.868893  -0.496    0.620    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.774 on 358 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01019,    Adjusted R-squared:  -0.0147 
## F-statistic: 0.4093 on 9 and 358 DF,  p-value: 0.93

# Action 30
scon.g.b30 <- lm(act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)

summary(scon.g.b30) # yes, above 0

## 
## Call:
## lm(formula = act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3529 -1.0654  0.5928  1.6471  2.3913 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.08951    0.25407   4.288 2.35e-05 ***
## SconsC.d          -0.19745    0.34437  -0.573    0.567    
## SconsM.d          -0.10680    0.31992  -0.334    0.739    
## SconsL.d           0.18073    0.39494   0.458    0.648    
## SconsSL.d         -0.01304    0.45300  -0.029    0.977    
## gend.mf           -0.52685    0.50814  -1.037    0.301    
## SconsC.d:gend.mf  -0.03989    0.68875  -0.058    0.954    
## SconsM.d:gend.mf   0.36143    0.63984   0.565    0.573    
## SconsL.d:gend.mf   0.90945    0.78989   1.151    0.250    
## SconsSL.d:gend.mf -0.02609    0.90600  -0.029    0.977    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.882 on 342 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.01188,    Adjusted R-squared:  -0.01412 
## F-statistic: 0.4569 on 9 and 342 DF,  p-value: 0.9027

a. Means by Gender

# Action 8

aggregate(d$act8[d$ideology == "Strong Conservative"], list(d$gend[d$ideology == "Strong Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1  Female -1.53
## 2    Male -0.32

# Action 11

aggregate(d$act11[d$ideology == "Strong Conservative"], list(d$gend[d$ideology == "Strong Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1    x
## 1  Female 0.22
## 2    Male 1.36

# Action 13

aggregate(d$act13[d$ideology == "Strong Conservative"], list(d$gend[d$ideology == "Strong Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1  Female -1.19
## 2    Male  0.59

# Action 24

aggregate(d$act24[d$ideology == "Strong Conservative"], list(d$gend[d$ideology == "Strong Conservative"]), FUN = function(x) round(mean(x, na.rm = T), 2))

##   Group.1     x
## 1  Female -0.58
## 2    Male  0.55

3. Gender x condition

None

# Action 1
summary(lm(act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.960  0.080  2.000  5.000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               -0.4021     0.2767  -1.453   0.1469  
## SconsC.d                  -0.2720     0.3739  -0.727   0.4673  
## SconsM.d                   0.5111     0.3310   1.544   0.1233  
## SconsL.d                   0.9966     0.4179   2.385   0.0175 *
## SconsSL.d                  0.1292     0.4838   0.267   0.7895  
## gend.mf                    0.5013     0.5534   0.906   0.3656  
## cond.c                     0.7513     0.5534   1.358   0.1753  
## SconsC.d:gend.mf          -1.3360     0.7478  -1.787   0.0747 .
## SconsM.d:gend.mf           0.2009     0.6620   0.304   0.7616  
## SconsL.d:gend.mf          -0.6723     0.8359  -0.804   0.4216  
## SconsSL.d:gend.mf         -1.7554     0.9676  -1.814   0.0703 .
## SconsC.d:cond.c           -0.9535     0.7478  -1.275   0.2029  
## SconsM.d:cond.c           -1.4535     0.6620  -2.196   0.0287 *
## SconsL.d:cond.c           -0.8773     0.8359  -1.050   0.2945  
## SconsSL.d:cond.c          -0.6304     0.9676  -0.652   0.5150  
## gend.mf:cond.c             0.5581     1.1068   0.504   0.6144  
## SconsC.d:gend.mf:cond.c   -0.9640     1.4955  -0.645   0.5195  
## SconsM.d:gend.mf:cond.c   -0.9937     1.3239  -0.751   0.4533  
## SconsL.d:gend.mf:cond.c   -1.7702     1.6718  -1.059   0.2902  
## SconsSL.d:gend.mf:cond.c   3.6003     1.9351   1.860   0.0635 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.124 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.0873, Adjusted R-squared:  0.04669 
## F-statistic:  2.15 on 19 and 427 DF,  p-value: 0.003504

# Action 2
summary(lm(act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6000 -1.0000  0.3485  1.5192  3.3333 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.049716   0.273571   3.837 0.000155 ***
## SconsC.d                 -0.253504   0.359852  -0.704 0.481754    
## SconsM.d                  0.110953   0.342057   0.324 0.745912    
## SconsL.d                  0.621117   0.431620   1.439 0.151307    
## SconsSL.d                -0.241383   0.489090  -0.494 0.622040    
## gend.mf                   0.786932   0.547141   1.438 0.151527    
## cond.c                   -0.150568   0.547141  -0.275 0.783382    
## SconsC.d:gend.mf         -1.530871   0.719705  -2.127 0.034327 *  
## SconsM.d:gend.mf         -0.774935   0.684114  -1.133 0.258329    
## SconsL.d:gend.mf         -0.211932   0.863241  -0.246 0.806252    
## SconsSL.d:gend.mf        -1.336932   0.978180  -1.367 0.172848    
## SconsC.d:cond.c          -0.575189   0.719705  -0.799 0.424881    
## SconsM.d:cond.c          -0.008864   0.684114  -0.013 0.989672    
## SconsL.d:cond.c           0.142235   0.863241   0.165 0.869250    
## SconsSL.d:cond.c          0.767235   0.978180   0.784 0.433528    
## gend.mf:cond.c            1.073864   1.094282   0.981 0.327310    
## SconsC.d:gend.mf:cond.c  -2.652652   1.439410  -1.843 0.066452 .  
## SconsM.d:gend.mf:cond.c  -0.088333   1.368228  -0.065 0.948573    
## SconsL.d:gend.mf:cond.c  -0.223864   1.726481  -0.130 0.896929    
## SconsSL.d:gend.mf:cond.c  1.159470   1.956361   0.593 0.553904    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.801 on 268 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.07459,    Adjusted R-squared:  0.008984 
## F-statistic: 1.137 on 19 and 268 DF,  p-value: 0.3138

# Action 3
summary(lm(act3 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7059 -1.8261  0.0278  1.7143  3.9412 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              -0.790218   0.267343  -2.956  0.00329 **
## SconsC.d                  0.463829   0.358749   1.293  0.19675   
## SconsM.d                  0.725473   0.317283   2.287  0.02272 * 
## SconsL.d                  0.727718   0.389443   1.869  0.06237 . 
## SconsSL.d                 0.988118   0.474006   2.085  0.03771 * 
## gend.mf                   0.694073   0.534686   1.298  0.19496   
## cond.c                   -0.002897   0.534686  -0.005  0.99568   
## SconsC.d:gend.mf         -0.485740   0.717498  -0.677  0.49878   
## SconsM.d:gend.mf         -0.905162   0.634566  -1.426  0.15448   
## SconsL.d:gend.mf         -0.426216   0.778886  -0.547  0.58452   
## SconsSL.d:gend.mf        -1.289871   0.948011  -1.361  0.17436   
## SconsC.d:cond.c          -0.316548   0.717498  -0.441  0.65931   
## SconsM.d:cond.c           0.067850   0.634566   0.107  0.91490   
## SconsL.d:cond.c           0.770754   0.778886   0.990  0.32296   
## SconsSL.d:cond.c          0.092813   0.948011   0.098  0.92206   
## gend.mf:cond.c            0.778520   1.069372   0.728  0.46701   
## SconsC.d:gend.mf:cond.c  -2.361854   1.434996  -1.646  0.10053   
## SconsM.d:gend.mf:cond.c  -0.922920   1.269132  -0.727  0.46750   
## SconsL.d:gend.mf:cond.c  -2.028520   1.557772  -1.302  0.19356   
## SconsSL.d:gend.mf:cond.c  0.241648   1.896023   0.127  0.89865   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.05725,    Adjusted R-squared:  0.01481 
## F-statistic: 1.349 on 19 and 422 DF,  p-value: 0.1485

# Action 4
summary(lm(act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2000 -1.8551  0.1449  2.0909  3.5000 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.199866   0.310692   0.643    0.520
## SconsC.d                 -0.558418   0.424462  -1.316    0.189
## SconsM.d                 -0.412437   0.375956  -1.097    0.273
## SconsL.d                  0.314893   0.452349   0.696    0.487
## SconsSL.d                 0.161672   0.546096   0.296    0.767
## gend.mf                   0.372995   0.621383   0.600    0.549
## cond.c                   -0.009358   0.621383  -0.015    0.988
## SconsC.d:gend.mf         -0.838583   0.848924  -0.988    0.324
## SconsM.d:gend.mf         -0.385352   0.751912  -0.512    0.609
## SconsL.d:gend.mf         -0.002514   0.904698  -0.003    0.998
## SconsSL.d:gend.mf        -1.696072   1.092191  -1.553    0.121
## SconsC.d:cond.c          -0.514564   0.848924  -0.606    0.545
## SconsM.d:cond.c          -0.333357   0.751912  -0.443    0.658
## SconsL.d:cond.c           0.769313   0.904698   0.850    0.396
## SconsSL.d:cond.c          0.086281   1.092191   0.079    0.937
## gend.mf:cond.c           -0.435829   1.242766  -0.351    0.726
## SconsC.d:gend.mf:cond.c  -1.381712   1.697848  -0.814    0.416
## SconsM.d:gend.mf:cond.c  -0.003742   1.503824  -0.002    0.998
## SconsL.d:gend.mf:cond.c   0.115920   1.809396   0.064    0.949
## SconsSL.d:gend.mf:cond.c -0.118017   2.184382  -0.054    0.957
## 
## Residual standard error: 2.183 on 349 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.04372,    Adjusted R-squared:  -0.008345 
## F-statistic: 0.8397 on 19 and 349 DF,  p-value: 0.6588

# Action 5
summary(lm(act5 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4800 -1.2500  0.1622  1.5200  3.2500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.72961    0.27629   2.641  0.00874 **
## SconsC.d                  0.23041    0.36466   0.632  0.52802   
## SconsM.d                  0.34454    0.34680   0.993  0.32135   
## SconsL.d                  0.74178    0.42718   1.736  0.08360 . 
## SconsSL.d                -0.45878    0.49884  -0.920  0.35854   
## gend.mf                  -0.48195    0.55258  -0.872  0.38387   
## cond.c                   -0.26805    0.55258  -0.485  0.62800   
## SconsC.d:gend.mf          0.01290    0.72932   0.018  0.98590   
## SconsM.d:gend.mf          0.54243    0.69361   0.782  0.43486   
## SconsL.d:gend.mf          0.96773    0.85437   1.133  0.25833   
## SconsSL.d:gend.mf         0.35695    0.99768   0.358  0.72078   
## SconsC.d:cond.c          -0.05434    0.72932  -0.075  0.94066   
## SconsM.d:cond.c          -0.11935    0.69361  -0.172  0.86351   
## SconsL.d:cond.c          -0.55387    0.85437  -0.648  0.51735   
## SconsSL.d:cond.c          0.39305    0.99768   0.394  0.69391   
## gend.mf:cond.c           -0.41845    1.10515  -0.379  0.70525   
## SconsC.d:gend.mf:cond.c  -1.36815    1.45865  -0.938  0.34909   
## SconsM.d:gend.mf:cond.c   0.46797    1.38722   0.337  0.73611   
## SconsL.d:gend.mf:cond.c   0.91942    1.70873   0.538  0.59096   
## SconsSL.d:gend.mf:cond.c  2.00178    1.99535   1.003  0.31664   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.884 on 275 degrees of freedom
##   (250 observations deleted due to missingness)
## Multiple R-squared:  0.0598, Adjusted R-squared:  -0.005162 
## F-statistic: 0.9205 on 19 and 275 DF,  p-value: 0.5577

# Action 6
summary(lm(act6 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5714 -1.0816  0.5333  1.5294  2.7500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.84363    0.25546   3.302  0.00106 **
## SconsC.d                  0.46939    0.33707   1.393  0.16468   
## SconsM.d                  0.50693    0.31455   1.612  0.10798   
## SconsL.d                  0.72561    0.39382   1.842  0.06628 . 
## SconsSL.d                 0.08673    0.44631   0.194  0.84604   
## gend.mf                  -0.47816    0.51092  -0.936  0.35000   
## cond.c                   -0.04132    0.51092  -0.081  0.93559   
## SconsC.d:gend.mf          0.25213    0.67415   0.374  0.70864   
## SconsM.d:gend.mf          0.71431    0.62910   1.135  0.25699   
## SconsL.d:gend.mf          1.10159    0.78764   1.399  0.16285   
## SconsSL.d:gend.mf        -0.73255    0.89262  -0.821  0.41241   
## SconsC.d:cond.c          -0.33955    0.67415  -0.504  0.61482   
## SconsM.d:cond.c          -0.11149    0.62910  -0.177  0.85944   
## SconsL.d:cond.c          -0.40668    0.78764  -0.516  0.60597   
## SconsSL.d:cond.c          0.08060    0.89262   0.090  0.92810   
## gend.mf:cond.c            1.30082    1.02183   1.273  0.20388   
## SconsC.d:gend.mf:cond.c  -2.93908    1.34829  -2.180  0.02996 * 
## SconsM.d:gend.mf:cond.c  -1.00304    1.25819  -0.797  0.42589   
## SconsL.d:gend.mf:cond.c  -2.59531    1.57527  -1.648  0.10038   
## SconsSL.d:gend.mf:cond.c -1.07939    1.78524  -0.605  0.54584   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 337 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.04582,    Adjusted R-squared:  -0.00798 
## F-statistic: 0.8517 on 19 and 337 DF,  p-value: 0.6439

# Action 7
summary(lm(act7 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0625 -1.8571  0.0625  1.7143  3.6000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              -0.10175    0.28349  -0.359   0.7199  
## SconsC.d                 -0.23470    0.38796  -0.605   0.5456  
## SconsM.d                  0.00593    0.33712   0.018   0.9860  
## SconsL.d                  0.18230    0.42468   0.429   0.6680  
## SconsSL.d                 0.07571    0.51252   0.148   0.8826  
## gend.mf                   0.38532    0.56698   0.680   0.4972  
## cond.c                    0.13173    0.56698   0.232   0.8164  
## SconsC.d:gend.mf         -0.60528    0.77592  -0.780   0.4358  
## SconsM.d:gend.mf         -0.27160    0.67425  -0.403   0.6873  
## SconsL.d:gend.mf         -0.15754    0.84936  -0.185   0.8529  
## SconsSL.d:gend.mf        -1.49990    1.02504  -1.463   0.1442  
## SconsC.d:cond.c          -0.45463    0.77592  -0.586   0.5583  
## SconsM.d:cond.c           0.28313    0.67425   0.420   0.6748  
## SconsL.d:cond.c           0.72383    0.84936   0.852   0.3946  
## SconsSL.d:cond.c         -0.24631    1.02504  -0.240   0.8102  
## gend.mf:cond.c            1.19109    1.13397   1.050   0.2942  
## SconsC.d:gend.mf:cond.c  -3.33100    1.55183  -2.147   0.0324 *
## SconsM.d:gend.mf:cond.c  -0.72210    1.34849  -0.535   0.5926  
## SconsL.d:gend.mf:cond.c  -3.01331    1.69873  -1.774   0.0769 .
## SconsSL.d:gend.mf:cond.c  0.70474    2.05008   0.344   0.7312  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.081 on 393 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.05477,    Adjusted R-squared:  0.009077 
## F-statistic: 1.199 on 19 and 393 DF,  p-value: 0.2548

# Action 8
summary(lm(act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4000 -1.6786 -0.1429  1.5466  4.8421 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               -0.9500     0.2731  -3.478 0.000553 ***
## SconsC.d                   0.4874     0.3657   1.333 0.183355    
## SconsM.d                   0.5639     0.3246   1.737 0.083015 .  
## SconsL.d                   1.5333     0.4201   3.650 0.000293 ***
## SconsSL.d                  1.3833     0.4678   2.957 0.003265 ** 
## gend.mf                    1.2636     0.5463   2.313 0.021159 *  
## cond.c                     0.1240     0.5463   0.227 0.820567    
## SconsC.d:gend.mf          -0.8530     0.7315  -1.166 0.244191    
## SconsM.d:gend.mf          -1.0549     0.6492  -1.625 0.104877    
## SconsL.d:gend.mf          -1.0017     0.8403  -1.192 0.233843    
## SconsSL.d:gend.mf         -2.4636     0.9355  -2.633 0.008741 ** 
## SconsC.d:cond.c           -1.7992     0.7315  -2.460 0.014274 *  
## SconsM.d:cond.c            0.1135     0.6492   0.175 0.861314    
## SconsL.d:cond.c            0.4713     0.8403   0.561 0.575177    
## SconsSL.d:cond.c           1.0760     0.9355   1.150 0.250680    
## gend.mf:cond.c            -0.7934     1.0926  -0.726 0.468091    
## SconsC.d:gend.mf:cond.c   -1.7742     1.4630  -1.213 0.225842    
## SconsM.d:gend.mf:cond.c    1.2786     1.2985   0.985 0.325274    
## SconsL.d:gend.mf:cond.c    0.7458     1.6805   0.444 0.657414    
## SconsSL.d:gend.mf:cond.c   1.7267     1.8711   0.923 0.356572    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.102 on 459 degrees of freedom
##   (66 observations deleted due to missingness)
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.07388 
## F-statistic: 3.007 on 19 and 459 DF,  p-value: 2.47e-05

# Action 9
summary(lm(act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8000 -1.5455  0.0455  1.5000  4.4545 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -0.79962    0.26330  -3.037 0.002537 ** 
## SconsC.d                  0.73551    0.35095   2.096 0.036693 *  
## SconsM.d                  0.96669    0.31252   3.093 0.002110 ** 
## SconsL.d                  1.36272    0.38596   3.531 0.000459 ***
## SconsSL.d                 0.73295    0.46032   1.592 0.112064    
## gend.mf                  -0.10530    0.52660  -0.200 0.841601    
## cond.c                    1.12197    0.52660   2.131 0.033694 *  
## SconsC.d:gend.mf          0.56686    0.70191   0.808 0.419769    
## SconsM.d:gend.mf          0.03006    0.62503   0.048 0.961661    
## SconsL.d:gend.mf          0.06483    0.77191   0.084 0.933110    
## SconsSL.d:gend.mf        -0.86136    0.92064  -0.936 0.350001    
## SconsC.d:cond.c          -2.00836    0.70191  -2.861 0.004426 ** 
## SconsM.d:cond.c          -0.91738    0.62503  -1.468 0.142913    
## SconsL.d:cond.c          -1.29578    0.77191  -1.679 0.093949 .  
## SconsSL.d:cond.c         -1.15530    0.92064  -1.255 0.210205    
## gend.mf:cond.c            0.16515    1.05320   0.157 0.875470    
## SconsC.d:gend.mf:cond.c  -2.17014    1.40381  -1.546 0.122872    
## SconsM.d:gend.mf:cond.c   0.12528    1.25006   0.100 0.920220    
## SconsL.d:gend.mf:cond.c  -0.84610    1.54382  -0.548 0.583938    
## SconsSL.d:gend.mf:cond.c  0.10152    1.84128   0.055 0.956058    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 427 degrees of freedom
##   (98 observations deleted due to missingness)
## Multiple R-squared:  0.06597,    Adjusted R-squared:  0.02441 
## F-statistic: 1.587 on 19 and 427 DF,  p-value: 0.05552

# Action 10
summary(lm(act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6667 -1.8630  0.1739  1.7370  4.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -0.53765    0.26589  -2.022 0.043756 *  
## SconsC.d                  0.34026    0.35503   0.958 0.338378    
## SconsM.d                  0.62338    0.31757   1.963 0.050259 .  
## SconsL.d                  1.50338    0.39978   3.761 0.000192 ***
## SconsSL.d                 1.26795    0.46509   2.726 0.006652 ** 
## gend.mf                   0.46924    0.53179   0.882 0.378039    
## cond.c                   -0.06573    0.53179  -0.124 0.901688    
## SconsC.d:gend.mf         -0.47445    0.71007  -0.668 0.504355    
## SconsM.d:gend.mf         -0.42330    0.63514  -0.666 0.505451    
## SconsL.d:gend.mf          0.47430    0.79956   0.593 0.553343    
## SconsSL.d:gend.mf        -2.12984    0.93018  -2.290 0.022494 *  
## SconsC.d:cond.c          -0.73461    0.71007  -1.035 0.301420    
## SconsM.d:cond.c           0.14859    0.63514   0.234 0.815125    
## SconsL.d:cond.c           0.23426    0.79956   0.293 0.769664    
## SconsSL.d:cond.c          1.25967    0.93018   1.354 0.176336    
## gend.mf:cond.c            0.01024    1.06358   0.010 0.992319    
## SconsC.d:gend.mf:cond.c  -1.60957    1.42013  -1.133 0.257645    
## SconsM.d:gend.mf:cond.c   0.95446    1.27028   0.751 0.452813    
## SconsL.d:gend.mf:cond.c  -1.09731    1.59912  -0.686 0.492937    
## SconsSL.d:gend.mf:cond.c  2.80188    1.86036   1.506 0.132734    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 456 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.0938, Adjusted R-squared:  0.05604 
## F-statistic: 2.484 on 19 and 456 DF,  p-value: 0.0005392

# Action 11
summary(lm(act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9000 -1.0292  0.3902  1.5200  3.4167 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.79033    0.26693   2.961  0.00332 **
## SconsC.d                 -0.01624    0.36192  -0.045  0.96424   
## SconsM.d                 -0.07781    0.32350  -0.241  0.81008   
## SconsL.d                  0.98169    0.41930   2.341  0.01989 * 
## SconsSL.d                 0.64300    0.50466   1.274  0.20364   
## gend.mf                   1.14660    0.53386   2.148  0.03256 * 
## cond.c                    0.41284    0.53386   0.773  0.43997   
## SconsC.d:gend.mf         -1.61146    0.72385  -2.226  0.02676 * 
## SconsM.d:gend.mf         -1.22197    0.64701  -1.889  0.05993 . 
## SconsL.d:gend.mf         -0.95256    0.83860  -1.136  0.25694   
## SconsSL.d:gend.mf        -1.17994    1.00933  -1.169  0.24334   
## SconsC.d:cond.c          -1.46465    0.72385  -2.023  0.04394 * 
## SconsM.d:cond.c          -1.06376    0.64701  -1.644  0.10123   
## SconsL.d:cond.c          -0.79022    0.83860  -0.942  0.34682   
## SconsSL.d:cond.c         -0.77950    1.00933  -0.772  0.44056   
## gend.mf:cond.c            0.26523    1.06772   0.248  0.80399   
## SconsC.d:gend.mf:cond.c  -1.99494    1.44769  -1.378  0.16925   
## SconsM.d:gend.mf:cond.c  -0.48626    1.29402  -0.376  0.70735   
## SconsL.d:gend.mf:cond.c  -2.32000    1.67720  -1.383  0.16764   
## SconsSL.d:gend.mf:cond.c  0.80143    2.01865   0.397  0.69165   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.858 on 292 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.09531,    Adjusted R-squared:  0.03644 
## F-statistic: 1.619 on 19 and 292 DF,  p-value: 0.05071

# Action 12
summary(lm(act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.000 -1.875  0.125  1.635  4.235 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -0.75453    0.28810  -2.619  0.00919 ** 
## SconsC.d                  0.18333    0.39662   0.462  0.64420    
## SconsM.d                  0.59087    0.34921   1.692  0.09150 .  
## SconsL.d                  1.90833    0.45229   4.219  3.1e-05 ***
## SconsSL.d                 0.99544    0.52593   1.893  0.05919 .  
## gend.mf                   0.78179    0.57619   1.357  0.17568    
## cond.c                    0.63532    0.57619   1.103  0.27092    
## SconsC.d:gend.mf         -1.19295    0.79324  -1.504  0.13348    
## SconsM.d:gend.mf         -1.34920    0.69842  -1.932  0.05416 .  
## SconsL.d:gend.mf          0.57728    0.90457   0.638  0.52376    
## SconsSL.d:gend.mf        -1.66361    1.05186  -1.582  0.11462    
## SconsC.d:cond.c          -1.81791    0.79324  -2.292  0.02249 *  
## SconsM.d:cond.c          -0.59089    0.69842  -0.846  0.39809    
## SconsL.d:cond.c          -0.11939    0.90457  -0.132  0.89507    
## SconsSL.d:cond.c          0.08286    1.05186   0.079  0.93726    
## gend.mf:cond.c            0.91117    1.15238   0.791  0.42965    
## SconsC.d:gend.mf:cond.c  -3.93885    1.58648  -2.483  0.01349 *  
## SconsM.d:gend.mf:cond.c   0.15786    1.39684   0.113  0.91009    
## SconsL.d:gend.mf:cond.c  -1.27637    1.80915  -0.706  0.48095    
## SconsSL.d:gend.mf:cond.c -0.74753    2.10372  -0.355  0.72254    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.117 on 363 degrees of freedom
##   (162 observations deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.06946 
## F-statistic: 2.501 on 19 and 363 DF,  p-value: 0.0005499

# Action 13
summary(lm(act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1333 -1.8000  0.1852  1.8235  4.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               -0.3008     0.2733  -1.101  0.27170   
## SconsC.d                   0.2125     0.3743   0.568  0.57056   
## SconsM.d                   0.1530     0.3238   0.472  0.63686   
## SconsL.d                   0.8024     0.4147   1.935  0.05368 . 
## SconsSL.d                  0.2488     0.4699   0.529  0.59683   
## gend.mf                    1.7835     0.5467   3.262  0.00119 **
## cond.c                    -0.1290     0.5467  -0.236  0.81363   
## SconsC.d:gend.mf          -1.6067     0.7485  -2.147  0.03240 * 
## SconsM.d:gend.mf          -2.0530     0.6476  -3.170  0.00164 **
## SconsL.d:gend.mf          -1.2450     0.8295  -1.501  0.13410   
## SconsSL.d:gend.mf         -2.6460     0.9398  -2.815  0.00510 **
## SconsC.d:cond.c           -1.1387     0.7485  -1.521  0.12893   
## SconsM.d:cond.c            0.4272     0.6476   0.660  0.50981   
## SconsL.d:cond.c            0.3341     0.8295   0.403  0.68733   
## SconsSL.d:cond.c           0.6915     0.9398   0.736  0.46231   
## gend.mf:cond.c            -0.2875     1.0934  -0.263  0.79269   
## SconsC.d:gend.mf:cond.c   -1.1771     1.4970  -0.786  0.43214   
## SconsM.d:gend.mf:cond.c    0.1258     1.2953   0.097  0.92270   
## SconsL.d:gend.mf:cond.c    1.4606     1.6590   0.880  0.37912   
## SconsSL.d:gend.mf:cond.c   4.4292     1.8797   2.356  0.01891 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 422 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.08224,    Adjusted R-squared:  0.04091 
## F-statistic:  1.99 on 19 and 422 DF,  p-value: 0.008085

# Action 14
summary(lm(act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1818 -1.2411  0.3129  1.5849  3.9000 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.501648   0.329369   1.523   0.1289  
## SconsC.d                 -0.457510   0.438154  -1.044   0.2974  
## SconsM.d                 -0.116129   0.390294  -0.298   0.7663  
## SconsL.d                  0.636453   0.491871   1.294   0.1968  
## SconsSL.d                -0.709982   0.591342  -1.201   0.2310  
## gend.mf                   0.610989   0.658737   0.928   0.3545  
## cond.c                    0.589011   0.658737   0.894   0.3720  
## SconsC.d:gend.mf         -2.099267   0.876308  -2.396   0.0173 *
## SconsM.d:gend.mf         -0.778854   0.780588  -0.998   0.3193  
## SconsL.d:gend.mf          0.012808   0.983741   0.013   0.9896  
## SconsSL.d:gend.mf        -2.277656   1.182683  -1.926   0.0552 .
## SconsC.d:cond.c          -0.895470   0.876308  -1.022   0.3078  
## SconsM.d:cond.c          -0.447352   0.780588  -0.573   0.5671  
## SconsL.d:cond.c          -0.283396   0.983741  -0.288   0.7735  
## SconsSL.d:cond.c          0.827656   1.182683   0.700   0.4847  
## gend.mf:cond.c           -0.806593   1.317475  -0.612   0.5409  
## SconsC.d:gend.mf:cond.c   0.619512   1.752616   0.353   0.7240  
## SconsM.d:gend.mf:cond.c   0.872481   1.561176   0.559   0.5767  
## SconsL.d:gend.mf:cond.c  -0.004637   1.967483  -0.002   0.9981  
## SconsSL.d:gend.mf:cond.c  3.139927   2.365366   1.327   0.1855  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.033 on 266 degrees of freedom
##   (259 observations deleted due to missingness)
## Multiple R-squared:  0.06875,    Adjusted R-squared:  0.002233 
## F-statistic: 1.034 on 19 and 266 DF,  p-value: 0.4224

# Action 15
summary(lm(act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5294 -1.7357  0.3167  1.4947  4.2000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               -0.3423     0.2701  -1.267  0.20595   
## SconsC.d                   0.4396     0.3631   1.211  0.22683   
## SconsM.d                   0.2629     0.3230   0.814  0.41616   
## SconsL.d                   1.3475     0.4336   3.108  0.00203 **
## SconsSL.d                  0.4812     0.4623   1.041  0.29859   
## gend.mf                    0.6012     0.5403   1.113  0.26654   
## cond.c                     0.3155     0.5403   0.584  0.55963   
## SconsC.d:gend.mf          -0.9783     0.7263  -1.347  0.17883   
## SconsM.d:gend.mf          -1.3437     0.6460  -2.080  0.03821 * 
## SconsL.d:gend.mf           0.2133     0.8672   0.246  0.80588   
## SconsSL.d:gend.mf         -1.3649     0.9246  -1.476  0.14078   
## SconsC.d:cond.c           -0.8592     0.7263  -1.183  0.23756   
## SconsM.d:cond.c           -0.1797     0.6460  -0.278  0.78097   
## SconsL.d:cond.c           -1.4593     0.8672  -1.683  0.09328 . 
## SconsSL.d:cond.c           0.4958     0.9246   0.536  0.59212   
## gend.mf:cond.c            -0.7976     1.0805  -0.738  0.46088   
## SconsC.d:gend.mf:cond.c   -1.2796     1.4525  -0.881  0.37893   
## SconsM.d:gend.mf:cond.c    1.6329     1.2919   1.264  0.20707   
## SconsL.d:gend.mf:cond.c    2.2354     1.7345   1.289  0.19829   
## SconsSL.d:gend.mf:cond.c   3.0036     1.8493   1.624  0.10520   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.021 on 364 degrees of freedom
##   (161 observations deleted due to missingness)
## Multiple R-squared:  0.09033,    Adjusted R-squared:  0.04285 
## F-statistic: 1.902 on 19 and 364 DF,  p-value: 0.013

# Action 16
summary(lm(act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -0.9811  0.2963  1.4800  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.63384    0.27284   2.323   0.0208 *
## SconsC.d                  0.26522    0.36089   0.735   0.4629  
## SconsM.d                  0.46752    0.32784   1.426   0.1549  
## SconsL.d                  0.38919    0.41490   0.938   0.3490  
## SconsSL.d                -0.11717    0.49442  -0.237   0.8128  
## gend.mf                   0.84343    0.54567   1.546   0.1232  
## cond.c                    0.06566    0.54567   0.120   0.9043  
## SconsC.d:gend.mf         -1.32531    0.72177  -1.836   0.0673 .
## SconsM.d:gend.mf         -0.32556    0.65568  -0.497   0.6199  
## SconsL.d:gend.mf         -1.38949    0.82979  -1.675   0.0950 .
## SconsSL.d:gend.mf        -0.71010    0.98885  -0.718   0.4732  
## SconsC.d:cond.c           0.19469    0.72177   0.270   0.7875  
## SconsM.d:cond.c           0.18335    0.65568   0.280   0.7799  
## SconsL.d:cond.c          -0.36171    0.82979  -0.436   0.6632  
## SconsSL.d:cond.c         -0.09899    0.98885  -0.100   0.9203  
## gend.mf:cond.c           -0.35354    1.09135  -0.324   0.7462  
## SconsC.d:gend.mf:cond.c  -0.64578    1.44355  -0.447   0.6549  
## SconsM.d:gend.mf:cond.c   0.29670    1.31137   0.226   0.8212  
## SconsL.d:gend.mf:cond.c  -2.05436    1.65958  -1.238   0.2167  
## SconsSL.d:gend.mf:cond.c  4.08687    1.97769   2.066   0.0396 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 311 degrees of freedom
##   (214 observations deleted due to missingness)
## Multiple R-squared:  0.06263,    Adjusted R-squared:  0.005359 
## F-statistic: 1.094 on 19 and 311 DF,  p-value: 0.3561

# Action 17
summary(lm(act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4286 -1.3889 -0.0571  1.6000  4.2500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.35417    0.26426   1.340  0.18095   
## SconsC.d                 -0.07748    0.36114  -0.215  0.83024   
## SconsM.d                  0.12899    0.31709   0.407  0.68438   
## SconsL.d                  0.41534    0.40303   1.031  0.30338   
## SconsSL.d                 0.56875    0.46944   1.212  0.22641   
## gend.mf                   0.18561    0.52853   0.351  0.72564   
## cond.c                    0.79167    0.52853   1.498  0.13496   
## SconsC.d:gend.mf         -0.22509    0.72229  -0.312  0.75548   
## SconsM.d:gend.mf         -0.15842    0.63418  -0.250  0.80288   
## SconsL.d:gend.mf          0.56110    0.80606   0.696  0.48677   
## SconsSL.d:gend.mf        -1.11477    0.93889  -1.187  0.23581   
## SconsC.d:cond.c          -1.89901    0.72229  -2.629  0.00889 **
## SconsM.d:cond.c          -1.23179    0.63418  -1.942  0.05281 . 
## SconsL.d:cond.c          -0.87353    0.80606  -1.084  0.27915   
## SconsSL.d:cond.c          0.90417    0.93889   0.963  0.33613   
## gend.mf:cond.c           -0.46212    1.05705  -0.437  0.66222   
## SconsC.d:gend.mf:cond.c  -0.79542    1.44458  -0.551  0.58220   
## SconsM.d:gend.mf:cond.c  -0.09919    1.26837  -0.078  0.93770   
## SconsL.d:gend.mf:cond.c  -0.51700    1.61211  -0.321  0.74861   
## SconsSL.d:gend.mf:cond.c  3.90379    1.87778   2.079  0.03827 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.99 on 396 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.0642, Adjusted R-squared:  0.0193 
## F-statistic:  1.43 on 19 and 396 DF,  p-value: 0.1086

# Action 18
summary(lm(act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.2632  0.2372  1.3333  3.3846 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.22114    0.25743   0.859   0.3909  
## SconsC.d                  0.26593    0.35230   0.755   0.4509  
## SconsM.d                  0.54935    0.31679   1.734   0.0838 .
## SconsL.d                  0.76578    0.38406   1.994   0.0470 *
## SconsSL.d                 0.40547    0.46318   0.875   0.3820  
## gend.mf                   0.70924    0.51485   1.378   0.1693  
## cond.c                    0.49356    0.51485   0.959   0.3384  
## SconsC.d:gend.mf         -0.85005    0.70460  -1.206   0.2285  
## SconsM.d:gend.mf         -0.79800    0.63358  -1.260   0.2087  
## SconsL.d:gend.mf         -0.54023    0.76813  -0.703   0.4824  
## SconsSL.d:gend.mf        -0.21245    0.92637  -0.229   0.8188  
## SconsC.d:cond.c          -0.96769    0.70460  -1.373   0.1706  
## SconsM.d:cond.c          -0.50894    0.63358  -0.803   0.4224  
## SconsL.d:cond.c          -0.34710    0.76813  -0.452   0.6517  
## SconsSL.d:cond.c          0.08657    0.92637   0.093   0.9256  
## gend.mf:cond.c           -0.01741    1.02970  -0.017   0.9865  
## SconsC.d:gend.mf:cond.c  -1.36764    1.40919  -0.971   0.3325  
## SconsM.d:gend.mf:cond.c   0.20260    1.26716   0.160   0.8731  
## SconsL.d:gend.mf:cond.c  -1.13265    1.53625  -0.737   0.4615  
## SconsSL.d:gend.mf:cond.c  3.35716    1.85274   1.812   0.0709 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.05189,    Adjusted R-squared:  -0.002371 
## F-statistic: 0.9563 on 19 and 332 DF,  p-value: 0.5132

# Action 19
summary(lm(act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6364 -1.0465  0.3693  1.4390  2.8750 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.90972    0.29187   3.117  0.00204 **
## SconsC.d                  0.21145    0.38136   0.554  0.57974   
## SconsM.d                  0.24349    0.35433   0.687  0.49259   
## SconsL.d                  0.63472    0.48019   1.322  0.18742   
## SconsSL.d                 0.17645    0.48824   0.361  0.71810   
## gend.mf                   0.80556    0.58375   1.380  0.16881   
## cond.c                    0.06944    0.58375   0.119  0.90540   
## SconsC.d:gend.mf         -0.98442    0.76272  -1.291  0.19800   
## SconsM.d:gend.mf         -1.10663    0.70865  -1.562  0.11963   
## SconsL.d:gend.mf         -0.36111    0.96038  -0.376  0.70722   
## SconsSL.d:gend.mf        -1.64457    0.97648  -1.684  0.09338 . 
## SconsC.d:cond.c          -1.07154    0.76272  -1.405  0.16128   
## SconsM.d:cond.c          -0.50582    0.70865  -0.714  0.47602   
## SconsL.d:cond.c          -0.84722    0.96038  -0.882  0.37852   
## SconsSL.d:cond.c          0.13321    0.97648   0.136  0.89160   
## gend.mf:cond.c           -1.38889    1.16750  -1.190  0.23530   
## SconsC.d:gend.mf:cond.c   0.40895    1.52544   0.268  0.78885   
## SconsM.d:gend.mf:cond.c   1.54506    1.41730   1.090  0.27668   
## SconsL.d:gend.mf:cond.c   0.67778    1.92076   0.353  0.72448   
## SconsSL.d:gend.mf:cond.c  2.31692    1.95296   1.186  0.23659   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.809 on 254 degrees of freedom
##   (271 observations deleted due to missingness)
## Multiple R-squared:  0.05536,    Adjusted R-squared:  -0.0153 
## F-statistic: 0.7834 on 19 and 254 DF,  p-value: 0.7262

# Action 20
summary(lm(act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7000 -1.1616  0.4211  1.4211  3.3333 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.20391    0.25114   4.794 2.57e-06 ***
## SconsC.d                 -0.17907    0.34097  -0.525    0.600    
## SconsM.d                  0.06223    0.31384   0.198    0.843    
## SconsL.d                  0.04652    0.39274   0.118    0.906    
## SconsSL.d                -0.49915    0.46120  -1.082    0.280    
## gend.mf                   0.17551    0.50229   0.349    0.727    
## cond.c                   -0.54672    0.50229  -1.088    0.277    
## SconsC.d:gend.mf         -0.91749    0.68193  -1.345    0.179    
## SconsM.d:gend.mf         -0.36851    0.62768  -0.587    0.558    
## SconsL.d:gend.mf          0.15695    0.78548   0.200    0.842    
## SconsSL.d:gend.mf        -0.75170    0.92240  -0.815    0.416    
## SconsC.d:cond.c           0.09640    0.68193   0.141    0.888    
## SconsM.d:cond.c          -0.12494    0.62768  -0.199    0.842    
## SconsL.d:cond.c          -0.32083    0.78548  -0.408    0.683    
## SconsSL.d:cond.c          0.58957    0.92240   0.639    0.523    
## gend.mf:cond.c            0.26010    1.00457   0.259    0.796    
## SconsC.d:gend.mf:cond.c  -0.74408    1.36387  -0.546    0.586    
## SconsM.d:gend.mf:cond.c  -0.73821    1.25536  -0.588    0.557    
## SconsL.d:gend.mf:cond.c  -0.85835    1.57096  -0.546    0.585    
## SconsSL.d:gend.mf:cond.c  2.65418    1.84479   1.439    0.151    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.795 on 302 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.05394,    Adjusted R-squared:  -0.00558 
## F-statistic: 0.9063 on 19 and 302 DF,  p-value: 0.5755

# Action 21
summary(lm(act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1250 -1.7424  0.0508  1.7059  4.2727 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              -0.650779   0.275312  -2.364  0.01859 * 
## SconsC.d                  0.389119   0.384735   1.011  0.31247   
## SconsM.d                  0.671887   0.332270   2.022  0.04386 * 
## SconsL.d                  1.117148   0.414379   2.696  0.00733 **
## SconsSL.d                 1.010394   0.492997   2.049  0.04110 * 
## gend.mf                  -0.334806   0.550625  -0.608  0.54352   
## cond.c                    0.677052   0.550625   1.230  0.21960   
## SconsC.d:gend.mf          0.118629   0.769471   0.154  0.87756   
## SconsM.d:gend.mf          0.685446   0.664540   1.031  0.30298   
## SconsL.d:gend.mf          0.384211   0.828758   0.464  0.64320   
## SconsSL.d:gend.mf         0.515575   0.985994   0.523  0.60135   
## SconsC.d:cond.c          -0.736085   0.769471  -0.957  0.33937   
## SconsM.d:cond.c          -0.520116   0.664540  -0.783  0.43431   
## SconsL.d:cond.c          -0.952647   0.828758  -1.149  0.25108   
## SconsSL.d:cond.c          0.003717   0.985994   0.004  0.99699   
## gend.mf:cond.c            0.464078   1.101250   0.421  0.67369   
## SconsC.d:gend.mf:cond.c  -2.396431   1.538941  -1.557  0.12025   
## SconsM.d:gend.mf:cond.c  -0.563664   1.329080  -0.424  0.67173   
## SconsL.d:gend.mf:cond.c  -2.448602   1.657516  -1.477  0.14043   
## SconsSL.d:gend.mf:cond.c  1.974383   1.971989   1.001  0.31736   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 381 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05638,    Adjusted R-squared:  0.009323 
## F-statistic: 1.198 on 19 and 381 DF,  p-value: 0.2555

# Action 22
summary(lm(act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2222 -1.4571 -0.0508  1.7429  3.7778 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              -0.21176    0.26372  -0.803  0.42248   
## SconsC.d                  0.49926    0.35747   1.397  0.16332   
## SconsM.d                  0.60455    0.32071   1.885  0.06019 . 
## SconsL.d                  0.84501    0.41036   2.059  0.04015 * 
## SconsSL.d                 1.29673    0.45676   2.839  0.00477 **
## gend.mf                   0.87807    0.52744   1.665  0.09678 . 
## cond.c                    0.26335    0.52744   0.499  0.61786   
## SconsC.d:gend.mf         -1.21973    0.71493  -1.706  0.08881 . 
## SconsM.d:gend.mf         -0.40048    0.64143  -0.624  0.53276   
## SconsL.d:gend.mf         -0.88267    0.82073  -1.075  0.28284   
## SconsSL.d:gend.mf        -1.04800    0.91352  -1.147  0.25201   
## SconsC.d:cond.c          -0.63835    0.71493  -0.893  0.37248   
## SconsM.d:cond.c          -0.26123    0.64143  -0.407  0.68404   
## SconsL.d:cond.c          -0.58541    0.82073  -0.713  0.47611   
## SconsSL.d:cond.c         -0.81106    0.91352  -0.888  0.37518   
## gend.mf:cond.c            0.01876    1.05487   0.018  0.98582   
## SconsC.d:gend.mf:cond.c  -1.60209    1.42986  -1.120  0.26323   
## SconsM.d:gend.mf:cond.c   0.39806    1.28285   0.310  0.75651   
## SconsL.d:gend.mf:cond.c  -2.56511    1.64146  -1.563  0.11895   
## SconsSL.d:gend.mf:cond.c -1.32333    1.82704  -0.724  0.46932   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.976 on 382 degrees of freedom
##   (143 observations deleted due to missingness)
## Multiple R-squared:  0.0681, Adjusted R-squared:  0.02175 
## F-statistic: 1.469 on 19 and 382 DF,  p-value: 0.0928

# Action 23
summary(lm(act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.625 -2.000  0.125  1.920  4.500 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.30480    0.35302   0.863   0.3887  
## SconsC.d                 -0.53955    0.46441  -1.162   0.2463  
## SconsM.d                 -0.08367    0.43322  -0.193   0.8470  
## SconsL.d                  0.21494    0.51572   0.417   0.6772  
## SconsSL.d                 0.05657    0.61276   0.092   0.9265  
## gend.mf                   1.24596    0.70604   1.765   0.0787 .
## cond.c                    0.05404    0.70604   0.077   0.9390  
## SconsC.d:gend.mf         -1.63757    0.92881  -1.763   0.0790 .
## SconsM.d:gend.mf         -0.82155    0.86644  -0.948   0.3439  
## SconsL.d:gend.mf         -1.51759    1.03143  -1.471   0.1424  
## SconsSL.d:gend.mf        -2.46869    1.22552  -2.014   0.0450 *
## SconsC.d:cond.c          -0.58787    0.92881  -0.633   0.5273  
## SconsM.d:cond.c          -0.62130    0.86644  -0.717   0.4739  
## SconsL.d:cond.c          -1.50622    1.03143  -1.460   0.1454  
## SconsSL.d:cond.c          1.62323    1.22552   1.325   0.1864  
## gend.mf:cond.c            1.38081    1.41208   0.978   0.3290  
## SconsC.d:gend.mf:cond.c  -2.92426    1.85763  -1.574   0.1166  
## SconsM.d:gend.mf:cond.c  -1.97962    1.73289  -1.142   0.2543  
## SconsL.d:gend.mf:cond.c  -3.44073    2.06287  -1.668   0.0965 .
## SconsSL.d:gend.mf:cond.c  0.26465    2.45103   0.108   0.9141  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.253 on 272 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.07369,    Adjusted R-squared:  0.008984 
## F-statistic: 1.139 on 19 and 272 DF,  p-value: 0.3118

# Action 24
summary(lm(act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8750 -0.0429  1.6429  4.6667 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              -0.032080   0.289327  -0.111   0.9118  
## SconsC.d                 -0.405023   0.378288  -1.071   0.2850  
## SconsM.d                 -0.293878   0.340532  -0.863   0.3887  
## SconsL.d                  0.006817   0.439521   0.016   0.9876  
## SconsSL.d                -0.210975   0.491805  -0.429   0.6682  
## gend.mf                   1.155070   0.578655   1.996   0.0466 *
## cond.c                    0.159615   0.578655   0.276   0.7828  
## SconsC.d:gend.mf         -1.247530   0.756575  -1.649   0.1000 .
## SconsM.d:gend.mf         -1.446335   0.681064  -2.124   0.0343 *
## SconsL.d:gend.mf         -0.704544   0.879042  -0.801   0.4233  
## SconsSL.d:gend.mf        -1.752292   0.983609  -1.781   0.0756 .
## SconsC.d:cond.c          -1.090965   0.756575  -1.442   0.1501  
## SconsM.d:cond.c          -1.036207   0.681064  -1.521   0.1289  
## SconsL.d:cond.c          -0.130142   0.879042  -0.148   0.8824  
## SconsSL.d:cond.c         -0.006838   0.983609  -0.007   0.9945  
## gend.mf:cond.c           -0.319231   1.157309  -0.276   0.7828  
## SconsC.d:gend.mf:cond.c  -2.551404   1.513151  -1.686   0.0926 .
## SconsM.d:gend.mf:cond.c  -0.541221   1.362127  -0.397   0.6913  
## SconsL.d:gend.mf:cond.c   1.060283   1.758084   0.603   0.5468  
## SconsSL.d:gend.mf:cond.c  0.847009   1.967219   0.431   0.6670  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.083 on 395 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.05818,    Adjusted R-squared:  0.01287 
## F-statistic: 1.284 on 19 and 395 DF,  p-value: 0.1894

# Action 25
summary(lm(act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6087 -0.8846  0.2222  1.3913  3.4000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -0.09398    0.24951  -0.377 0.706606    
## SconsC.d                  0.61428    0.32966   1.863 0.063096 .  
## SconsM.d                  0.98063    0.29399   3.336 0.000926 ***
## SconsL.d                  1.30866    0.36461   3.589 0.000370 ***
## SconsSL.d                 1.30477    0.45440   2.871 0.004291 ** 
## gend.mf                   0.24251    0.49902   0.486 0.627235    
## cond.c                    0.50615    0.49902   1.014 0.311027    
## SconsC.d:gend.mf         -0.39422    0.65931  -0.598 0.550205    
## SconsM.d:gend.mf         -0.22051    0.58799  -0.375 0.707830    
## SconsL.d:gend.mf          0.07814    0.72922   0.107 0.914717    
## SconsSL.d:gend.mf        -0.49742    0.90879  -0.547 0.584436    
## SconsC.d:cond.c          -1.07666    0.65931  -1.633 0.103209    
## SconsM.d:cond.c          -0.70444    0.58799  -1.198 0.231568    
## SconsL.d:cond.c           0.04820    0.72922   0.066 0.947333    
## SconsSL.d:cond.c          0.73895    0.90879   0.813 0.416612    
## gend.mf:cond.c            0.69679    0.99805   0.698 0.485462    
## SconsC.d:gend.mf:cond.c  -2.22243    1.31862  -1.685 0.092643 .  
## SconsM.d:gend.mf:cond.c  -1.05747    1.17597  -0.899 0.369043    
## SconsL.d:gend.mf:cond.c  -1.80549    1.45843  -1.238 0.216414    
## SconsSL.d:gend.mf:cond.c  1.14635    1.81759   0.631 0.528579    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.837 on 424 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.07813,    Adjusted R-squared:  0.03682 
## F-statistic: 1.891 on 19 and 424 DF,  p-value: 0.0133

# Action 26
summary(lm(act26 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2857 -1.1562  0.5577  1.3947  3.0000 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.6369048  0.3174174   5.157 5.88e-07 ***
## SconsC.d                 -0.0008929  0.4199790  -0.002   0.9983    
## SconsM.d                 -0.0834006  0.3956941  -0.211   0.8333    
## SconsL.d                  0.0630952  0.5339856   0.118   0.9061    
## SconsSL.d                -0.8333333  0.5551246  -1.501   0.1348    
## gend.mf                  -0.1071429  0.6348348  -0.169   0.8661    
## cond.c                   -0.3928571  0.6348348  -0.619   0.5367    
## SconsC.d:gend.mf         -0.2398810  0.8399581  -0.286   0.7755    
## SconsM.d:gend.mf         -0.1152500  0.7913882  -0.146   0.8844    
## SconsL.d:gend.mf          1.5071429  1.0679712   1.411   0.1597    
## SconsSL.d:gend.mf         1.0000000  1.1102492   0.901   0.3688    
## SconsC.d:cond.c           0.2541667  0.8399581   0.303   0.7625    
## SconsM.d:cond.c           0.3946022  0.7913882   0.499   0.6186    
## SconsL.d:cond.c          -0.2071429  1.0679712  -0.194   0.8464    
## SconsSL.d:cond.c          0.6190476  1.1102492   0.558   0.5777    
## gend.mf:cond.c            1.1190476  1.2696696   0.881   0.3791    
## SconsC.d:gend.mf:cond.c  -3.4916667  1.6799161  -2.078   0.0389 *  
## SconsM.d:gend.mf:cond.c  -1.3533070  1.5827763  -0.855   0.3935    
## SconsL.d:gend.mf:cond.c  -2.3190476  2.1359423  -1.086   0.2789    
## SconsSL.d:gend.mf:cond.c  0.7619048  2.2204983   0.343   0.7319    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.844 on 206 degrees of freedom
##   (319 observations deleted due to missingness)
## Multiple R-squared:  0.06431,    Adjusted R-squared:  -0.02199 
## F-statistic: 0.7452 on 19 and 206 DF,  p-value: 0.7689

# Action 27
summary(lm(act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2143  0.1667  1.4167  3.0000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.47917    0.26473   1.810   0.0713 .
## SconsC.d                  0.34901    0.36196   0.964   0.3357  
## SconsM.d                  0.14209    0.32920   0.432   0.6663  
## SconsL.d                  0.63826    0.41502   1.538   0.1251  
## SconsSL.d                 0.87689    0.44361   1.977   0.0490 *
## gend.mf                   0.95833    0.52946   1.810   0.0713 .
## cond.c                   -0.62500    0.52946  -1.180   0.2388  
## SconsC.d:gend.mf         -0.65436    0.72392  -0.904   0.3668  
## SconsM.d:gend.mf         -0.54907    0.65840  -0.834   0.4050  
## SconsL.d:gend.mf         -0.85985    0.83003  -1.036   0.3011  
## SconsSL.d:gend.mf        -0.83712    0.88722  -0.944   0.3462  
## SconsC.d:cond.c          -0.00754    0.72392  -0.010   0.9917  
## SconsM.d:cond.c          -0.06374    0.65840  -0.097   0.9229  
## SconsL.d:cond.c           0.28409    0.83003   0.342   0.7324  
## SconsSL.d:cond.c          1.24621    0.88722   1.405   0.1612  
## gend.mf:cond.c           -1.25000    1.05892  -1.180   0.2388  
## SconsC.d:gend.mf:cond.c  -1.11984    1.44783  -0.773   0.4399  
## SconsM.d:gend.mf:cond.c   1.07392    1.31679   0.816   0.4154  
## SconsL.d:gend.mf:cond.c   1.93182    1.66006   1.164   0.2455  
## SconsSL.d:gend.mf:cond.c  1.67424    1.77443   0.944   0.3462  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 297 degrees of freedom
##   (228 observations deleted due to missingness)
## Multiple R-squared:  0.0834, Adjusted R-squared:  0.02476 
## F-statistic: 1.422 on 19 and 297 DF,  p-value: 0.1144

# Action 28
summary(lm(act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6250 -1.1321  0.2667  1.5522  3.2000 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               0.81818    0.25217   3.245  0.00129 **
## SconsC.d                  0.46896    0.34305   1.367  0.17249   
## SconsM.d                  0.17873    0.31089   0.575  0.56573   
## SconsL.d                  0.05396    0.39430   0.137  0.89122   
## SconsSL.d                 0.17557    0.47541   0.369  0.71213   
## gend.mf                  -0.06364    0.50433  -0.126  0.89966   
## cond.c                   -0.91364    0.50433  -1.812  0.07091 . 
## SconsC.d:gend.mf         -0.21066    0.68610  -0.307  0.75900   
## SconsM.d:gend.mf          0.19204    0.62177   0.309  0.75761   
## SconsL.d:gend.mf         -0.45208    0.78860  -0.573  0.56683   
## SconsSL.d:gend.mf        -0.84053    0.95082  -0.884  0.37729   
## SconsC.d:cond.c           0.54675    0.68610   0.797  0.42605   
## SconsM.d:cond.c           0.78523    0.62177   1.263  0.20747   
## SconsL.d:cond.c           0.46935    0.78860   0.595  0.55211   
## SconsSL.d:cond.c          1.30114    0.95082   1.368  0.17205   
## gend.mf:cond.c           -0.22727    1.00867  -0.225  0.82186   
## SconsC.d:gend.mf:cond.c  -0.43896    1.37220  -0.320  0.74924   
## SconsM.d:gend.mf:cond.c  -0.82702    1.24355  -0.665  0.50645   
## SconsL.d:gend.mf:cond.c  -2.14130    1.57719  -1.358  0.17544   
## SconsSL.d:gend.mf:cond.c  0.28561    1.90163   0.150  0.88070   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 352 degrees of freedom
##   (173 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  -0.006203 
## F-statistic: 0.8796 on 19 and 352 DF,  p-value: 0.6089

# Action 29
summary(lm(act29 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6923 -1.0447  0.3077  1.4286  2.4286 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.0884199  0.2492946   4.366 1.67e-05 ***
## SconsC.d                 -0.1423731  0.3409950  -0.418    0.677    
## SconsM.d                  0.0214408  0.3039381   0.071    0.944    
## SconsL.d                  0.0739575  0.3736089   0.198    0.843    
## SconsSL.d                 0.2638237  0.4485709   0.588    0.557    
## gend.mf                   0.2958874  0.4985892   0.593    0.553    
## cond.c                    0.2612554  0.4985892   0.524    0.601    
## SconsC.d:gend.mf         -0.5577290  0.6819900  -0.818    0.414    
## SconsM.d:gend.mf         -0.3456742  0.6078763  -0.569    0.570    
## SconsL.d:gend.mf          0.3793577  0.7472178   0.508    0.612    
## SconsSL.d:gend.mf        -0.4503746  0.8971417  -0.502    0.616    
## SconsC.d:cond.c          -0.1257755  0.6819900  -0.184    0.854    
## SconsM.d:cond.c          -0.3298657  0.6078763  -0.543    0.588    
## SconsL.d:cond.c          -0.0734053  0.7472178  -0.098    0.922    
## SconsSL.d:cond.c          0.0009241  0.8971417   0.001    0.999    
## gend.mf:cond.c           -0.6679654  0.9971783  -0.670    0.503    
## SconsC.d:gend.mf:cond.c   1.3717955  1.3639800   1.006    0.315    
## SconsM.d:gend.mf:cond.c   0.9097611  1.2157526   0.748    0.455    
## SconsL.d:gend.mf:cond.c   0.5779794  1.4944357   0.387    0.699    
## SconsSL.d:gend.mf:cond.c  2.2436064  1.7942835   1.250    0.212    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 348 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01904,    Adjusted R-squared:  -0.03452 
## F-statistic: 0.3554 on 19 and 348 DF,  p-value: 0.995

# Action 30
summary(lm(act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.545 -1.059  0.400  1.466  3.250 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.04394    0.25537   4.088 5.47e-05 ***
## SconsC.d                 -0.16329    0.35068  -0.466   0.6418    
## SconsM.d                 -0.07513    0.32157  -0.234   0.8154    
## SconsL.d                  0.22405    0.39661   0.565   0.5725    
## SconsSL.d                -0.09508    0.46253  -0.206   0.8373    
## gend.mf                  -0.51212    0.51075  -1.003   0.3167    
## cond.c                    1.17879    0.51075   2.308   0.0216 *  
## SconsC.d:gend.mf         -0.08250    0.70135  -0.118   0.9064    
## SconsM.d:gend.mf          0.32562    0.64314   0.506   0.6130    
## SconsL.d:gend.mf          0.80947    0.79321   1.020   0.3082    
## SconsSL.d:gend.mf        -0.13561    0.92505  -0.147   0.8835    
## SconsC.d:cond.c          -1.23893    0.70135  -1.766   0.0782 .  
## SconsM.d:cond.c          -1.11529    0.64314  -1.734   0.0838 .  
## SconsL.d:cond.c          -1.50644    0.79321  -1.899   0.0584 .  
## SconsSL.d:cond.c         -0.57652    0.92505  -0.623   0.5336    
## gend.mf:cond.c            1.15758    1.02149   1.133   0.2579    
## SconsC.d:gend.mf:cond.c  -0.70396    1.40270  -0.502   0.6161    
## SconsM.d:gend.mf:cond.c  -0.55155    1.28629  -0.429   0.6684    
## SconsL.d:gend.mf:cond.c  -2.83561    1.58643  -1.787   0.0748 .  
## SconsSL.d:gend.mf:cond.c  1.13788    1.85011   0.615   0.5390    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 332 degrees of freedom
##   (193 observations deleted due to missingness)
## Multiple R-squared:  0.04439,    Adjusted R-squared:  -0.0103 
## F-statistic: 0.8117 on 19 and 332 DF,  p-value: 0.6931

vi. Most polarizing actions

1. Moderate Liberals vs. Moderate Conservatives

cbind(libs$name, libs$value_mean, cons$value_mean)

##       [,1]  [,2]  [,3]
##  [1,]    1  0.65 -0.49
##  [2,]    2  1.57  0.89
##  [3,]    3 -0.19 -0.42
##  [4,]    4  0.38 -0.29
##  [5,]    5  1.40  0.97
##  [6,]    6  1.43  1.33
##  [7,]    7 -0.03 -0.31
##  [8,]    8  0.48 -0.71
##  [9,]    9  0.58 -0.25
## [10,]   10  0.68 -0.26
## [11,]   11  1.66  0.82
## [12,]   12  0.74 -0.60
## [13,]   13  0.34 -0.22
## [14,]   14  0.95  0.32
## [15,]   15  0.89  0.11
## [16,]   16  1.07  0.99
## [17,]   17  0.55  0.17
## [18,]   18  0.91  0.49
## [19,]   19  1.44  1.05
## [20,]   20  1.20  1.13
## [21,]   21  0.44 -0.27
## [22,]   22  0.59  0.34
## [23,]   23  0.62 -0.18
## [24,]   24 -0.14 -0.50
## [25,]   25  1.13  0.50
## [26,]   26  1.26  1.57
## [27,]   27  1.18  0.72
## [28,]   28  1.00  1.32
## [29,]   29  0.98  1.02
## [30,]   30  1.18  1.01

# Action 1 -- YES
summary(con.b1)

## 
## Call:
## lm(formula = act1 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.646 -1.962  0.038  2.038  3.490 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.49000    0.21507  -2.278 0.023181 *  
## ConsSC.d     0.02846    0.34266   0.083 0.933841    
## ConsM.d      0.45196    0.26720   1.691 0.091446 .  
## ConsL.d      1.13615    0.34266   3.316 0.000989 ***
## ConsSL.d     0.66143    0.42239   1.566 0.118078    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.151 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.02106 
## F-statistic: 3.409 on 4 and 444 DF,  p-value: 0.00923

round(mean(d$act1[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.65

round(mean(d$act1[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.49

round((con.b1$coefficients['ConsL.d']),2)

## ConsL.d 
##    1.14

# Action 4 -- marginal
summary(con.b4)

## 
## Call:
## lm(formula = act4 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4828 -1.7938  0.2062  2.0051  3.2877 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.28767    0.25299  -1.137   0.2563  
## ConsSC.d     0.48375    0.39449   1.226   0.2209  
## ConsM.d      0.08142    0.30530   0.267   0.7899  
## ConsL.d      0.66698    0.38021   1.754   0.0802 .
## ConsSL.d     0.77043    0.47447   1.624   0.1053  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.162 on 366 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.006351 
## F-statistic: 1.591 on 4 and 366 DF,  p-value: 0.176

round(mean(d$act4[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.38

round(mean(d$act4[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.29

round((con.b4$coefficients['ConsL.d']),2)

## ConsL.d 
##    0.67

# Action 8 -- YES
summary(con.b8)

## 
## Call:
## lm(formula = act8 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.625 -1.855 -0.287  1.713  4.145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.7130     0.2041  -3.493 0.000522 ***
## ConsSC.d     -0.4320     0.3269  -1.321 0.186978    
## ConsM.d       0.2702     0.2531   1.068 0.286207    
## ConsL.d       1.1892     0.3362   3.537 0.000445 ***
## ConsSL.d      1.3380     0.3926   3.408 0.000710 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 476 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.06121,    Adjusted R-squared:  0.05332 
## F-statistic: 7.759 on 4 and 476 DF,  p-value: 4.609e-06

round(mean(d$act8[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.48

round(mean(d$act8[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.71

round((con.b8$coefficients['ConsL.d']),2)

## ConsL.d 
##    1.19

# Action 9
summary(con.b9)

## 
## Call:
## lm(formula = act9 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5781 -1.5781  0.2525  1.7167  3.7167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.2525     0.1976  -1.278  0.20196   
## ConsSC.d     -0.4641     0.3217  -1.443  0.14978   
## ConsM.d       0.4525     0.2437   1.857  0.06401 . 
## ConsL.d       0.8307     0.3154   2.634  0.00874 **
## ConsSL.d      0.4470     0.3827   1.168  0.24343   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.966 on 444 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03759,    Adjusted R-squared:  0.02892 
## F-statistic: 4.336 on 4 and 444 DF,  p-value: 0.00189

round(mean(d$act9[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.58

round(mean(d$act9[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.25

round((con.b9$coefficients['ConsL.d']),2)

## ConsL.d 
##    0.83

# Action 10
summary(con.b10)

## 
## Call:
## lm(formula = act10 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2571     0.2025  -1.270 0.204753    
## ConsSC.d     -0.3605     0.3230  -1.116 0.264918    
## ConsM.d       0.3375     0.2503   1.349 0.178086    
## ConsL.d       0.9341     0.3275   2.852 0.004531 ** 
## ConsSL.d      1.4071     0.3855   3.650 0.000292 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

round(mean(d$act10[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.68

round(mean(d$act10[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.26

round((con.b10$coefficients['ConsL.d']),2)

## ConsL.d 
##    0.93

# Action 12
summary(con.b12)

## 
## Call:
## lm(formula = act12 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.736 -2.158  0.000  2.000  3.842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.6000     0.2262  -2.653 0.008317 ** 
## ConsSC.d     -0.2421     0.3632  -0.667 0.505456    
## ConsM.d       0.6000     0.2834   2.117 0.034871 *  
## ConsL.d       1.3358     0.3715   3.596 0.000366 ***
## ConsSL.d      0.9333     0.4708   1.982 0.048158 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.146 on 380 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.0525, Adjusted R-squared:  0.04253 
## F-statistic: 5.264 on 4 and 380 DF,  p-value: 0.0003892

round(mean(d$act12[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.74

round(mean(d$act12[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.6

round((con.b12$coefficients['ConsL.d']),2)

## ConsL.d 
##    1.34

# Action 13 -- marginal
summary(con.b13)

## 
## Call:
## lm(formula = act13 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3438 -1.9149  0.0851  1.6562  3.5781 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.2174     0.2167  -1.003    0.316  
## ConsSC.d     -0.3607     0.3384  -1.066    0.287  
## ConsM.d       0.1323     0.2645   0.500    0.617  
## ConsL.d       0.5611     0.3384   1.658    0.098 .
## ConsSL.d      0.4745     0.4129   1.149    0.251  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.079 on 438 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01713,    Adjusted R-squared:  0.008155 
## F-statistic: 1.909 on 4 and 438 DF,  p-value: 0.108

round(mean(d$act13[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.34

round(mean(d$act13[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.22

round((con.b13$coefficients['ConsL.d']),2)

## ConsL.d 
##    0.56

# Action 21 -- YES
summary(con.b21)

## 
## Call:
## lm(formula = act21 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4407 -1.9401  0.0599  1.5806  3.5806 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.2683     0.2275  -1.179   0.2390  
## ConsSC.d     -0.3124     0.3467  -0.901   0.3682  
## ConsM.d       0.2084     0.2778   0.750   0.4536  
## ConsL.d       0.7090     0.3517   2.016   0.0445 *
## ConsSL.d      0.6925     0.4247   1.631   0.1038  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.06 on 398 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02474,    Adjusted R-squared:  0.01494 
## F-statistic: 2.524 on 4 and 398 DF,  p-value: 0.04049

round(mean(d$act21[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.44

round(mean(d$act21[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.27

round((con.b21$coefficients['ConsL.d']),2)

## ConsL.d 
##    0.71

# Action 23 -- marginal
summary(con.b23)

## 
## Call:
## lm(formula = act23 ~ ConsSC.d + ConsM.d + ConsL.d + ConsSL.d, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6154 -2.1138  0.1846  2.1846  3.1846 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.1846     0.2810  -0.657   0.5116  
## ConsSC.d      0.4529     0.4517   1.003   0.3169  
## ConsM.d       0.2984     0.3473   0.859   0.3909  
## ConsL.d       0.8000     0.4588   1.744   0.0823 .
## ConsSL.d      0.7446     0.5331   1.397   0.1635  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.265 on 288 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.01368,    Adjusted R-squared:  -2.033e-05 
## F-statistic: 0.9985 on 4 and 288 DF,  p-value: 0.4087

round(mean(d$act23[d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.62

round(mean(d$act23[d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.18

round((con.b23$coefficients['ConsL.d']),2)

## ConsL.d 
##     0.8

Polarizing actions: 1, 8, 9, 10, 12, 21

Marginally polarizing actions:4, 13, 23

2. Strong Liberals vs. Strong Conservatives

cbind(Slibs$name, Slibs$value_mean, Scons$value_mean)

##       [,1]  [,2]  [,3]
##  [1,]    1  0.17 -0.46
##  [2,]    2  1.04  0.96
##  [3,]    3  0.37 -0.89
##  [4,]    4  0.48  0.20
##  [5,]    5  0.42  0.73
##  [6,]    6  1.19  0.87
##  [7,]    7  0.37 -0.16
##  [8,]    8  0.62 -1.14
##  [9,]    9  0.19 -0.72
## [10,]   10  1.15 -0.62
## [11,]   11  1.54  0.73
## [12,]   12  0.33 -0.84
## [13,]   13  0.26 -0.58
## [14,]   14  0.09  0.50
## [15,]   15  0.31 -0.36
## [16,]   16  1.03  0.56
## [17,]   17  1.21  0.38
## [18,]   18  0.76  0.19
## [19,]   19  1.30  0.91
## [20,]   20  1.00  1.15
## [21,]   21  0.42 -0.58
## [22,]   22  1.14 -0.33
## [23,]   23  0.56  0.27
## [24,]   24 -0.08 -0.13
## [25,]   25  1.34 -0.12
## [26,]   26  0.95  1.54
## [27,]   27  1.39  0.44
## [28,]   28  1.19  0.79
## [29,]   29  1.48  1.09
## [30,]   30  1.14  1.14

# Action 10
summary(scon.b10)

## 
## Call:
## lm(formula = act10 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1500 -2.0804  0.2571  1.8500  3.6176 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.6176     0.2516  -2.455 0.014462 *  
## SconsC.d      0.3605     0.3230   1.116 0.264918    
## SconsM.d      0.6980     0.2915   2.395 0.017009 *  
## SconsL.d      1.2946     0.3599   3.597 0.000356 ***
## SconsSL.d     1.7676     0.4135   4.275 2.31e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.075 on 472 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.05311,    Adjusted R-squared:  0.04509 
## F-statistic: 6.619 on 4 and 472 DF,  p-value: 3.451e-05

round(mean(d$act10[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 1.15

round(mean(d$act10[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] -0.62

round((scon.b10$coefficients['SconsSL.d']),2)

## SconsSL.d 
##      1.77

# Action 17
summary(scon.b17)

## 
## Call:
## lm(formula = act17 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2059 -1.4798 -0.1685  1.6230  2.8315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3770     0.2561   1.472   0.1417  
## SconsC.d     -0.2085     0.3325  -0.627   0.5309  
## SconsM.d      0.1027     0.2979   0.345   0.7304  
## SconsL.d      0.1730     0.3637   0.476   0.6347  
## SconsSL.d     0.8288     0.4281   1.936   0.0535 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2 on 412 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.006815 
## F-statistic: 1.714 on 4 and 412 DF,  p-value: 0.146

round(mean(d$act17[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 1.21

round(mean(d$act17[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] 0.38

round((scon.b17$coefficients['SconsSL.d']),2)

## SconsSL.d 
##      0.83

# Action 18
summary(scon.b18)

## 
## Call:
## lm(formula = act18 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1196  0.1844  1.5135  2.8113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.1887     0.2530   0.746   0.4563  
## SconsC.d      0.2978     0.3314   0.899   0.3695  
## SconsM.d      0.6269     0.2967   2.113   0.0353 *
## SconsL.d      0.7236     0.3514   2.059   0.0402 *
## SconsSL.d     0.5699     0.4254   1.340   0.1812  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.842 on 349 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01769,    Adjusted R-squared:  0.006435 
## F-statistic: 1.572 on 4 and 349 DF,  p-value: 0.1814

round(mean(d$act18[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 0.76

round(mean(d$act18[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] 0.19

round((scon.b18$coefficients['SconsSL.d']),2)

## SconsSL.d 
##      0.57

# 18 not negative

# Action 22
summary(scon.b22)

## 
## Call:
## lm(formula = act22 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1351 -1.3415 -0.1351  1.7143  3.3279 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.3279     0.2528  -1.297 0.195483    
## SconsC.d      0.6693     0.3339   2.005 0.045684 *  
## SconsM.d      0.6136     0.2952   2.079 0.038302 *  
## SconsL.d      0.9172     0.3655   2.509 0.012486 *  
## SconsSL.d     1.4630     0.4115   3.555 0.000423 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.975 on 399 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.03383,    Adjusted R-squared:  0.02414 
## F-statistic: 3.492 on 4 and 399 DF,  p-value: 0.008087

round(mean(d$act22[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 1.14

round(mean(d$act22[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] -0.33

round((scon.b22$coefficients['SconsSL.d']),2)

## SconsSL.d 
##      1.46

# Action 25
summary(scon.b25)

## 
## Call:
## lm(formula = act25 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3429 -0.8925  0.1167  1.5000  3.1167 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.1167     0.2367  -0.493 0.622329    
## SconsC.d      0.6167     0.2983   2.067 0.039288 *  
## SconsM.d      1.0091     0.2722   3.707 0.000236 ***
## SconsL.d      1.2437     0.3307   3.760 0.000193 ***
## SconsSL.d     1.4595     0.3900   3.743 0.000206 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.833 on 441 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.049,  Adjusted R-squared:  0.04037 
## F-statistic:  5.68 on 4 and 441 DF,  p-value: 0.0001824

round(mean(d$act25[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 1.34

round(mean(d$act25[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] -0.12

round((scon.b25$coefficients['SconsSL.d']),2)

## SconsSL.d 
##      1.46

# Action 27
summary(scon.b27)

## 
## Call:
## lm(formula = act27 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3871 -0.9483  0.2812  1.5577  2.5577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4423     0.2604   1.699   0.0904 .
## SconsC.d      0.2764     0.3506   0.789   0.4310  
## SconsM.d      0.1089     0.3091   0.352   0.7249  
## SconsL.d      0.7355     0.3823   1.924   0.0553 .
## SconsSL.d     0.9448     0.4261   2.217   0.0273 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.878 on 314 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02704,    Adjusted R-squared:  0.01464 
## F-statistic: 2.182 on 4 and 314 DF,  p-value: 0.07092

round(mean(d$act27[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 1.39

round(mean(d$act27[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] 0.44

round((scon.b27$coefficients['SconsSL.d']),2)

## SconsSL.d 
##      0.94

# 27 not negative

Polarized Actions: 10, 22, 25

Actions not polarized: 17, 18, 27

3. All Liberals vs. All Conservatives

# Action 1
ideo.b1 <- lm(act1 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b1) # yes, higher than 0

## 
## Call:
## lm(formula = act1 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.480 -1.962  0.038  2.038  3.479 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.01228    0.10500  -0.117 0.906974    
## Libs_Cons.c -0.95879    0.27229  -3.521 0.000474 ***
## Mods_Ideo.c  0.03865    0.20886   0.185 0.853276    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.149 on 446 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.02736,    Adjusted R-squared:  0.023 
## F-statistic: 6.273 on 2 and 446 DF,  p-value: 0.002058

# Action 2
ideo.b2 <- lm(act2 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b2) # yes, higher than 0

## 
## Call:
## lm(formula = act2 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3788 -1.2212  0.0811  1.6212  2.0811 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.17298    0.10921  10.741   <2e-16 ***
## Libs_Cons.c -0.45987    0.28030  -1.641    0.102    
## Mods_Ideo.c -0.07239    0.22004  -0.329    0.742    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 287 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01053,    Adjusted R-squared:  0.003637 
## F-statistic: 1.527 on 2 and 287 DF,  p-value: 0.2189

# Action 3
ideo.b3 <- lm(act3 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b3) # nothing

## 
## Call:
## lm(formula = act3 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0000 -1.9837  0.0163  1.5987  3.5987 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.20501    0.09846  -2.082   0.0379 *
## Libs_Cons.c -0.59873    0.25537  -2.345   0.0195 *
## Mods_Ideo.c -0.28306    0.19582  -1.445   0.1490  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.014 on 441 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.01927,    Adjusted R-squared:  0.01482 
## F-statistic: 4.332 on 2 and 441 DF,  p-value: 0.01371

# Action 4
ideo.b4 <- lm(act4 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b4) # nothing

## 
## Call:
## lm(formula = act4 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4138 -1.9113  0.2062  2.0887  3.2062 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.03961    0.11568   0.342   0.7322  
## Libs_Cons.c -0.50250    0.30212  -1.663   0.0971 .
## Mods_Ideo.c  0.36879    0.22800   1.617   0.1066  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.16 on 368 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01294,    Adjusted R-squared:  0.007571 
## F-statistic: 2.411 on 2 and 368 DF,  p-value: 0.09111

# Action 5
ideo.b5 <- lm(act5 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b5) # yes, higher than 0

## 
## Call:
## lm(formula = act5 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.092 -1.092  0.123  1.908  2.123 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0047     0.1130   8.891   <2e-16 ***
## Libs_Cons.c  -0.1684     0.2873  -0.586    0.558    
## Mods_Ideo.c  -0.1305     0.2303  -0.567    0.571    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.88 on 294 degrees of freedom
##   (248 observations deleted due to missingness)
## Multiple R-squared:  0.002768,   Adjusted R-squared:  -0.004016 
## F-statistic: 0.4081 on 2 and 294 DF,  p-value: 0.6653

# Action 6
ideo.b6 <- lm(act6 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b6) # yes, higher than 0

## 
## Call:
## lm(formula = act6 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3333 -1.1511  0.6667  1.6901  1.8489 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.26476    0.09946   12.72   <2e-16 ***
## Libs_Cons.c -0.18225    0.25664   -0.71    0.478    
## Mods_Ideo.c -0.06765    0.19910   -0.34    0.734    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 356 degrees of freedom
##   (186 observations deleted due to missingness)
## Multiple R-squared:  0.00205,    Adjusted R-squared:  -0.003557 
## F-statistic: 0.3656 on 2 and 356 DF,  p-value: 0.694

# Action 7
ideo.b7 <- lm(act7 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b7) # nothing

## 
## Call:
## lm(formula = act7 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1170 -1.8652  0.1348  1.8830  3.2517 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08985    0.10604  -0.847    0.397
## Libs_Cons.c -0.36877    0.27703  -1.331    0.184
## Mods_Ideo.c  0.06747    0.20891   0.323    0.747
## 
## Residual standard error: 2.086 on 412 degrees of freedom
##   (130 observations deleted due to missingness)
## Multiple R-squared:  0.004331,   Adjusted R-squared:  -0.0005026 
## F-statistic: 0.896 on 2 and 412 DF,  p-value: 0.409

# Action 8
ideo.b8 <- lm(act8 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b8) # marginally higher than 0

## 
## Call:
## lm(formula = act8 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5340 -2.1186 -0.1186  1.8814  3.8814 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2634     0.1008  -2.613  0.00926 ** 
## Libs_Cons.c  -1.4153     0.2628  -5.385 1.14e-07 ***
## Mods_Ideo.c   0.2691     0.1991   1.352  0.17716    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 478 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.05753,    Adjusted R-squared:  0.05358 
## F-statistic: 14.59 on 2 and 478 DF,  p-value: 7.084e-07

# Action 9
ideo.b9 <- lm(act9 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b9) # yes, higher than 0

## 
## Call:
## lm(formula = act9 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4400 -1.5723  0.4277  1.5600  3.4277 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07078    0.09632   0.735 0.462859    
## Libs_Cons.c -0.86767    0.25122  -3.454 0.000605 ***
## Mods_Ideo.c -0.19384    0.19018  -1.019 0.308651    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 446 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03118,    Adjusted R-squared:  0.02683 
## F-statistic: 7.176 on 2 and 446 DF,  p-value: 0.0008562

# Action 10
ideo.b10 <- lm(act10 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b10) # yes, higher than 0

## 
## Call:
## lm(formula = act10 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8571 -2.0804  0.1429  1.9196  3.3988 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17957    0.09867   1.820   0.0694 .  
## Libs_Cons.c -1.25599    0.25684  -4.890 1.38e-06 ***
## Mods_Ideo.c  0.14875    0.19532   0.762   0.4467    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.076 on 474 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.04803,    Adjusted R-squared:  0.04401 
## F-statistic: 11.96 on 2 and 474 DF,  p-value: 8.584e-06

# Action 11
ideo.b11 <- lm(act11 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b11) # yes, higher than 0

## 
## Call:
## lm(formula = act11 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6154 -0.7807  0.2741  1.3846  2.2741 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0407     0.1102   9.440  < 2e-16 ***
## Libs_Cons.c  -0.8347     0.2893  -2.885  0.00419 ** 
## Mods_Ideo.c   0.4721     0.2159   2.187  0.02948 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 311 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03466,    Adjusted R-squared:  0.02845 
## F-statistic: 5.583 on 2 and 311 DF,  p-value: 0.004147

# Action 12
ideo.b12 <- lm(act12 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b12) # yes, higher than 0

## 
## Call:
## lm(formula = act12 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.600 -2.306  0.000  2.000  3.694 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03129    0.11437  -0.274    0.785    
## Libs_Cons.c -1.29388    0.29775  -4.346 1.78e-05 ***
## Mods_Ideo.c -0.04694    0.22634  -0.207    0.836    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.143 on 382 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04983,    Adjusted R-squared:  0.04485 
## F-statistic: 10.02 on 2 and 382 DF,  p-value: 5.759e-05

# Action 13
ideo.b13 <- lm(act13 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b13) # nothing

## 
## Call:
## lm(formula = act13 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3131 -1.9149  0.0851  1.6869  3.3654 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.04579    0.10229  -0.448   0.6547  
## Libs_Cons.c -0.67852    0.26688  -2.542   0.0114 *
## Mods_Ideo.c  0.05898    0.20187   0.292   0.7703  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 440 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01449,    Adjusted R-squared:  0.01001 
## F-statistic: 3.235 on 2 and 440 DF,  p-value: 0.04028

# Action 14
ideo.b14 <- lm(act14 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b14) # yes, higher than 0

## 
## Call:
## lm(formula = act14 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6393 -1.4219 -0.0166  1.6061  2.6061 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.48505    0.12557   3.863 0.000139 ***
## Libs_Cons.c -0.24540    0.33105  -0.741 0.459130    
## Mods_Ideo.c  0.09477    0.24437   0.388 0.698451    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.034 on 285 degrees of freedom
##   (257 observations deleted due to missingness)
## Multiple R-squared:  0.002183,   Adjusted R-squared:  -0.004819 
## F-statistic: 0.3118 on 2 and 285 DF,  p-value: 0.7324

# Action 15
ideo.b15 <- lm(act15 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b15) # yes, higher than 0

## 
## Call:
## lm(formula = act15 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6494 -1.9155  0.0845  1.8976  3.0845 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.2224     0.1104   2.015   0.0446 *
## Libs_Cons.c  -0.7339     0.2903  -2.528   0.0119 *
## Mods_Ideo.c   0.1800     0.2155   0.835   0.4040  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.052 on 382 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.01674,    Adjusted R-squared:  0.01159 
## F-statistic: 3.251 on 2 and 382 DF,  p-value: 0.03981

# Action 16
ideo.b16 <- lm(act16 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b16) # yes, higher than 0

## 
## Call:
## lm(formula = act16 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0556 -0.9643  0.1833  1.1833  2.1833 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.94550    0.10476   9.026   <2e-16 ***
## Libs_Cons.c -0.23889    0.27339  -0.874    0.383    
## Mods_Ideo.c -0.02817    0.20666  -0.136    0.892    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 329 degrees of freedom
##   (213 observations deleted due to missingness)
## Multiple R-squared:  0.002561,   Adjusted R-squared:  -0.003503 
## F-statistic: 0.4223 on 2 and 329 DF,  p-value: 0.6559

# Action 17
ideo.b17 <- lm(act17 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b17) # yes, higher than 0

## 
## Call:
## lm(formula = act17 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7872 -1.4798  0.2128  1.7467  2.7467 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.50678    0.10139   4.998 8.56e-07 ***
## Libs_Cons.c -0.53390    0.26336  -2.027   0.0433 *  
## Mods_Ideo.c  0.04051    0.20126   0.201   0.8406    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.002 on 414 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.009855,   Adjusted R-squared:  0.005072 
## F-statistic:  2.06 on 2 and 414 DF,  p-value: 0.1287

# Action 18
ideo.b18 <- lm(act18 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b18) # yes, higher than 0

## 
## Call:
## lm(formula = act18 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8605 -1.2368  0.1844  1.6378  2.6378 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.67942    0.09996   6.797  4.6e-11 ***
## Libs_Cons.c -0.49826    0.25680  -1.940   0.0531 .  
## Mods_Ideo.c -0.20427    0.20117  -1.015   0.3106    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 351 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01504,    Adjusted R-squared:  0.009431 
## F-statistic:  2.68 on 2 and 351 DF,  p-value: 0.06994

# Action 19
ideo.b19 <- lm(act19 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b19) # yes, higher than 0

## 
## Call:
## lm(formula = act19 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3729 -0.9903  0.0097  1.6271  2.0097 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1978     0.1128  10.621   <2e-16 ***
## Libs_Cons.c  -0.3826     0.2931  -1.305    0.193    
## Mods_Ideo.c  -0.0485     0.2236  -0.217    0.828    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.795 on 272 degrees of freedom
##   (270 observations deleted due to missingness)
## Multiple R-squared:  0.00698,    Adjusted R-squared:  -0.0003221 
## F-statistic: 0.9559 on 2 and 272 DF,  p-value: 0.3858

# Action 20
ideo.b20 <- lm(act20 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b20) # yes, higher than 0

## 
## Call:
## lm(formula = act20 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3566 -1.1393  0.6434  1.6434  1.8767 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.20641    0.10262  11.756   <2e-16 ***
## Libs_Cons.c  0.01606    0.26456   0.061    0.952    
## Mods_Ideo.c -0.22527    0.20562  -1.096    0.274    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.788 on 321 degrees of freedom
##   (221 observations deleted due to missingness)
## Multiple R-squared:  0.003768,   Adjusted R-squared:  -0.002439 
## F-statistic: 0.6071 on 2 and 321 DF,  p-value: 0.5455

# Action 21
ideo.b21 <- lm(act21 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b21) # nothing

## 
## Call:
## lm(formula = act21 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9401  0.0599  1.5652  3.4028 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.009292   0.105789  -0.088  0.93005   
## Libs_Cons.c -0.837560   0.274558  -3.051  0.00244 **
## Mods_Ideo.c  0.075883   0.210201   0.361  0.71829   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 400 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02275,    Adjusted R-squared:  0.01786 
## F-statistic: 4.656 on 2 and 400 DF,  p-value: 0.01002

# Action 22
ideo.b22 <- lm(act22 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b22) # yes, higher than 0

## 
## Call:
## lm(formula = act22 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8065 -1.2857 -0.0559  1.7143  2.9441 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3827     0.1018   3.759 0.000196 ***
## Libs_Cons.c  -0.7505     0.2643  -2.840 0.004745 ** 
## Mods_Ideo.c   0.1455     0.2022   0.719 0.472282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.984 on 401 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.01997,    Adjusted R-squared:  0.01509 
## F-statistic: 4.086 on 2 and 401 DF,  p-value: 0.01751

# Action 23
ideo.b23 <- lm(act23 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b23) # marginally higher than 0

## 
## Call:
## lm(formula = act23 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5938 -2.1138  0.0094  2.0094  3.0094 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.2327     0.1373   1.695   0.0912 .
## Libs_Cons.c  -0.6032     0.3580  -1.685   0.0930 .
## Mods_Ideo.c   0.1783     0.2713   0.657   0.5115  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.261 on 290 degrees of freedom
##   (252 observations deleted due to missingness)
## Multiple R-squared:  0.0102, Adjusted R-squared:  0.003379 
## F-statistic: 1.495 on 2 and 290 DF,  p-value: 0.226

# Action 24
ideo.b24 <- lm(act24 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b24) # nothing

## 
## Call:
## lm(formula = act24 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8842 -1.8842  0.2299  1.5494  3.3605 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.235406   0.106484  -2.211   0.0276 *
## Libs_Cons.c -0.244755   0.276830  -0.884   0.3771  
## Mods_Ideo.c -0.008282   0.211126  -0.039   0.9687  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.103 on 413 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.001955,   Adjusted R-squared:  -0.002878 
## F-statistic: 0.4045 on 2 and 413 DF,  p-value: 0.6676

# Action 25
ideo.b25 <- lm(act25 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b25) # yes, higher than 0

## 
## Call:
## lm(formula = act25 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2041 -1.2041  0.1075  1.7284  2.7284 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.7894     0.0904   8.732  < 2e-16 ***
## Libs_Cons.c  -0.9325     0.2353  -3.963 8.63e-05 ***
## Mods_Ideo.c  -0.1546     0.1789  -0.864    0.388    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.03911,    Adjusted R-squared:  0.03477 
## F-statistic: 9.015 on 2 and 443 DF,  p-value: 0.0001453

# Action 26
ideo.b26 <- lm(act26 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b26) # yes, higher than 0

## 
## Call:
## lm(formula = act26 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5568 -1.1400  0.4432  1.4432  1.8600 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.4383     0.1253  11.481   <2e-16 ***
## Libs_Cons.c   0.4168     0.3225   1.293    0.198    
## Mods_Ideo.c  -0.2696     0.2515  -1.072    0.285    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 224 degrees of freedom
##   (318 observations deleted due to missingness)
## Multiple R-squared:  0.01062,    Adjusted R-squared:  0.00179 
## F-statistic: 1.203 on 2 and 224 DF,  p-value: 0.3023

# Action 27
ideo.b27 <- lm(act27 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b27) # yes, higher than 0

## 
## Call:
## lm(formula = act27 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -0.9290  0.4052  1.4488  2.4488 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8031     0.1076   7.464 8.19e-13 ***
## Libs_Cons.c  -0.6683     0.2766  -2.416   0.0162 *  
## Mods_Ideo.c   0.3778     0.2163   1.747   0.0817 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.874 on 316 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.0244, Adjusted R-squared:  0.01823 
## F-statistic: 3.952 on 2 and 316 DF,  p-value: 0.02017

# Action 28
ideo.b28 <- lm(act28 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b28) # yes, higher than 0

## 
## Call:
## lm(formula = act28 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1064 -1.0698  0.0272  1.8936  2.0272 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.04965    0.09731  10.786   <2e-16 ***
## Libs_Cons.c  0.03662    0.25002   0.146    0.884    
## Mods_Ideo.c  0.11529    0.19582   0.589    0.556    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.827 on 371 degrees of freedom
##   (171 observations deleted due to missingness)
## Multiple R-squared:  0.001089,   Adjusted R-squared:  -0.004296 
## F-statistic: 0.2022 on 2 and 371 DF,  p-value: 0.817

# Action 29
ideo.b29 <- lm(act29 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b29) # yes, higher than 0

## 
## Call:
## lm(formula = act29 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1548 -1.1429 -0.0504  1.8452  1.9496 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.11599    0.09462  11.795   <2e-16 ***
## Libs_Cons.c -0.10440    0.24375  -0.428    0.669    
## Mods_Ideo.c -0.04030    0.18977  -0.212    0.832    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.764 on 367 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0007188,  Adjusted R-squared:  -0.004727 
## F-statistic: 0.132 on 2 and 367 DF,  p-value: 0.8764

# Action 30
ideo.b30 <- lm(act30 ~ Libs_Cons.c + Mods_Ideo.c, data = d)

summary(ideo.b30) # yes, higher than 0

## 
## Call:
## lm(formula = act30 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1646 -1.0657  0.8354  1.8354  1.9565 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.09124    0.10288  10.607   <2e-16 ***
## Libs_Cons.c -0.09886    0.26435  -0.374    0.709    
## Mods_Ideo.c  0.07165    0.20699   0.346    0.729    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 351 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.0006315,  Adjusted R-squared:  -0.005063 
## F-statistic: 0.1109 on 2 and 351 DF,  p-value: 0.8951

Significant difference between Libs & Cons: 1, 3, 8, 9, 10, 11, 12, 13, 15, 17, 18, 21, 22, 25, 27

# Action 1
summary(ideo.b10)

## 
## Call:
## lm(formula = act10 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8571 -2.0804  0.1429  1.9196  3.3988 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17957    0.09867   1.820   0.0694 .  
## Libs_Cons.c -1.25599    0.25684  -4.890 1.38e-06 ***
## Mods_Ideo.c  0.14875    0.19532   0.762   0.4467    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.076 on 474 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.04803,    Adjusted R-squared:  0.04401 
## F-statistic: 11.96 on 2 and 474 DF,  p-value: 8.584e-06

round(mean(d$act1[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.48

round(mean(d$act1[d$ideology == "Strong Conservative" | d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.48

round((ideo.b1$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.96

# Action 3
summary(ideo.b3)

## 
## Call:
## lm(formula = act3 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0000 -1.9837  0.0163  1.5987  3.5987 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.20501    0.09846  -2.082   0.0379 *
## Libs_Cons.c -0.59873    0.25537  -2.345   0.0195 *
## Mods_Ideo.c -0.28306    0.19582  -1.445   0.1490  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.014 on 441 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.01927,    Adjusted R-squared:  0.01482 
## F-statistic: 4.332 on 2 and 441 DF,  p-value: 0.01371

round(mean(d$act3[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0

round(mean(d$act3[d$ideology == "Strong Conservative" | d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.6

round((ideo.b3$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##        -0.6

# Action 8
summary(ideo.b8)

## 
## Call:
## lm(formula = act8 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5340 -2.1186 -0.1186  1.8814  3.8814 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2634     0.1008  -2.613  0.00926 ** 
## Libs_Cons.c  -1.4153     0.2628  -5.385 1.14e-07 ***
## Mods_Ideo.c   0.2691     0.1991   1.352  0.17716    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 478 degrees of freedom
##   (64 observations deleted due to missingness)
## Multiple R-squared:  0.05753,    Adjusted R-squared:  0.05358 
## F-statistic: 14.59 on 2 and 478 DF,  p-value: 7.084e-07

round(mean(d$act8[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.53

round(mean(d$act8[d$ideology == "Strong Conservative" | d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.88

round((ideo.b8$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -1.42

# Action 9
summary(ideo.b9)

## 
## Call:
## lm(formula = act9 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4400 -1.5723  0.4277  1.5600  3.4277 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07078    0.09632   0.735 0.462859    
## Libs_Cons.c -0.86767    0.25122  -3.454 0.000605 ***
## Mods_Ideo.c -0.19384    0.19018  -1.019 0.308651    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 446 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.03118,    Adjusted R-squared:  0.02683 
## F-statistic: 7.176 on 2 and 446 DF,  p-value: 0.0008562

round(mean(d$act9[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.44

round(mean(d$act9[d$ideology == "Strong Conservative" | d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.43

round((ideo.b9$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.87

# Action 10
summary(ideo.b10)

## 
## Call:
## lm(formula = act10 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8571 -2.0804  0.1429  1.9196  3.3988 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17957    0.09867   1.820   0.0694 .  
## Libs_Cons.c -1.25599    0.25684  -4.890 1.38e-06 ***
## Mods_Ideo.c  0.14875    0.19532   0.762   0.4467    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.076 on 474 degrees of freedom
##   (68 observations deleted due to missingness)
## Multiple R-squared:  0.04803,    Adjusted R-squared:  0.04401 
## F-statistic: 11.96 on 2 and 474 DF,  p-value: 8.584e-06

round(mean(d$act10[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.86

round(mean(d$act10[d$ideology == "Strong Conservative" | d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.4

round((ideo.b10$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -1.26

# Action 11
summary(ideo.b11)

## 
## Call:
## lm(formula = act11 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6154 -0.7807  0.2741  1.3846  2.2741 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0407     0.1102   9.440  < 2e-16 ***
## Libs_Cons.c  -0.8347     0.2893  -2.885  0.00419 ** 
## Mods_Ideo.c   0.4721     0.2159   2.187  0.02948 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 311 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03466,    Adjusted R-squared:  0.02845 
## F-statistic: 5.583 on 2 and 311 DF,  p-value: 0.004147

round(mean(d$act11[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 1.62

round(mean(d$act11[d$ideology == "Strong Conservative" | d$ideology == "Conservative"], na.rm = T),2)

## [1] 0.78

round((ideo.b11$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.83

## Action 11 not polarized

# Action 12
summary(ideo.b12)

## 
## Call:
## lm(formula = act12 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.600 -2.306  0.000  2.000  3.694 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03129    0.11437  -0.274    0.785    
## Libs_Cons.c -1.29388    0.29775  -4.346 1.78e-05 ***
## Mods_Ideo.c -0.04694    0.22634  -0.207    0.836    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.143 on 382 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.04983,    Adjusted R-squared:  0.04485 
## F-statistic: 10.02 on 2 and 382 DF,  p-value: 5.759e-05

round(mean(d$act12[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.6

round(mean(d$act12[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.69

round((ideo.b12$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -1.29

# Action 13
summary(ideo.b13)

## 
## Call:
## lm(formula = act13 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3131 -1.9149  0.0851  1.6869  3.3654 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.04579    0.10229  -0.448   0.6547  
## Libs_Cons.c -0.67852    0.26688  -2.542   0.0114 *
## Mods_Ideo.c  0.05898    0.20187   0.292   0.7703  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 440 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.01449,    Adjusted R-squared:  0.01001 
## F-statistic: 3.235 on 2 and 440 DF,  p-value: 0.04028

round(mean(d$act13[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.31

round(mean(d$act13[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.37

round((ideo.b13$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.68

# Action 15
summary(ideo.b15)

## 
## Call:
## lm(formula = act15 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6494 -1.9155  0.0845  1.8976  3.0845 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.2224     0.1104   2.015   0.0446 *
## Libs_Cons.c  -0.7339     0.2903  -2.528   0.0119 *
## Mods_Ideo.c   0.1800     0.2155   0.835   0.4040  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.052 on 382 degrees of freedom
##   (160 observations deleted due to missingness)
## Multiple R-squared:  0.01674,    Adjusted R-squared:  0.01159 
## F-statistic: 3.251 on 2 and 382 DF,  p-value: 0.03981

round(mean(d$act15[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.65

round(mean(d$act15[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.08

round((ideo.b15$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.73

# Action 17
summary(ideo.b17)

## 
## Call:
## lm(formula = act17 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7872 -1.4798  0.2128  1.7467  2.7467 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.50678    0.10139   4.998 8.56e-07 ***
## Libs_Cons.c -0.53390    0.26336  -2.027   0.0433 *  
## Mods_Ideo.c  0.04051    0.20126   0.201   0.8406    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.002 on 414 degrees of freedom
##   (128 observations deleted due to missingness)
## Multiple R-squared:  0.009855,   Adjusted R-squared:  0.005072 
## F-statistic:  2.06 on 2 and 414 DF,  p-value: 0.1287

round(mean(d$act17[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.79

round(mean(d$act17[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] 0.25

round((ideo.b17$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.53

## Action 17 not polarized

# Action 18
summary(ideo.b18)

## 
## Call:
## lm(formula = act18 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8605 -1.2368  0.1844  1.6378  2.6378 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.67942    0.09996   6.797  4.6e-11 ***
## Libs_Cons.c -0.49826    0.25680  -1.940   0.0531 .  
## Mods_Ideo.c -0.20427    0.20117  -1.015   0.3106    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 351 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01504,    Adjusted R-squared:  0.009431 
## F-statistic:  2.68 on 2 and 351 DF,  p-value: 0.06994

round(mean(d$act18[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.86

round(mean(d$act18[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] 0.36

round((ideo.b18$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##        -0.5

## Action 18 not polarized

# Action 21
summary(ideo.b21)

## 
## Call:
## lm(formula = act21 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4348 -1.9401  0.0599  1.5652  3.4028 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.009292   0.105789  -0.088  0.93005   
## Libs_Cons.c -0.837560   0.274558  -3.051  0.00244 **
## Mods_Ideo.c  0.075883   0.210201   0.361  0.71829   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 400 degrees of freedom
##   (142 observations deleted due to missingness)
## Multiple R-squared:  0.02275,    Adjusted R-squared:  0.01786 
## F-statistic: 4.656 on 2 and 400 DF,  p-value: 0.01002

round(mean(d$act21[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.43

round(mean(d$act21[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] -0.4

round((ideo.b21$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.84

# Action 22
summary(ideo.b22)

## 
## Call:
## lm(formula = act22 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8065 -1.2857 -0.0559  1.7143  2.9441 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3827     0.1018   3.759 0.000196 ***
## Libs_Cons.c  -0.7505     0.2643  -2.840 0.004745 ** 
## Mods_Ideo.c   0.1455     0.2022   0.719 0.472282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.984 on 401 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.01997,    Adjusted R-squared:  0.01509 
## F-statistic: 4.086 on 2 and 401 DF,  p-value: 0.01751

round(mean(d$act22[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 0.81

round(mean(d$act22[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] 0.06

round((ideo.b22$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.75

## Action 22 not polarized

# Action 25
summary(ideo.b25)

## 
## Call:
## lm(formula = act25 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2041 -1.2041  0.1075  1.7284  2.7284 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.7894     0.0904   8.732  < 2e-16 ***
## Libs_Cons.c  -0.9325     0.2353  -3.963 8.63e-05 ***
## Mods_Ideo.c  -0.1546     0.1789  -0.864    0.388    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.03911,    Adjusted R-squared:  0.03477 
## F-statistic: 9.015 on 2 and 443 DF,  p-value: 0.0001453

round(mean(d$act25[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 1.2

round(mean(d$act25[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] 0.27

round((ideo.b25$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.93

## Action 25 not polarized

# Action 27
summary(ideo.b27)

## 
## Call:
## lm(formula = act27 ~ Libs_Cons.c + Mods_Ideo.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -0.9290  0.4052  1.4488  2.4488 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8031     0.1076   7.464 8.19e-13 ***
## Libs_Cons.c  -0.6683     0.2766  -2.416   0.0162 *  
## Mods_Ideo.c   0.3778     0.2163   1.747   0.0817 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.874 on 316 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.0244, Adjusted R-squared:  0.01823 
## F-statistic: 3.952 on 2 and 316 DF,  p-value: 0.02017

round(mean(d$act27[d$ideology == "Strong Liberal" | d$ideology == "Liberal"], na.rm = T),2)

## [1] 1.26

round(mean(d$act27[d$ideology == "Strong Conservative"| d$ideology == "Conservative"], na.rm = T),2)

## [1] 0.59

round((ideo.b27$coefficients['Libs_Cons.c']),2)

## Libs_Cons.c 
##       -0.67

# 27 not polarized

Actions that are polarizing: 1, 3, 8, 9, 10, 12, 13, 15, 21 Nonpolarized actions: 11, 17, 18, 22, 25, 27

c. Actions by Party ID

i. Republicans

Actions opposed: 1, 3, 7, 11, 16, 18, 19, 20, 25, 26, 27, 28, 29, 30

Action neutral: 4, 14, 15, 17, 22, 23

Actions supported: 2, 5, 6, 8, 9, 10, 12, 13, 21, 24

# Action 1
rep.b1 <- lm(act1 ~ (RepD.d + RepI.d), data = d)

summary(rep.b1) 

## 
## Call:
## lm(formula = act1 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2770 -2.2199 -0.0486  1.7230  3.5844 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.5844     0.1727  -3.385 0.000776 ***
## RepD.d        0.8614     0.2466   3.493 0.000526 ***
## RepI.d        0.6330     0.2484   2.549 0.011150 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.143 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02882,    Adjusted R-squared:  0.02443 
## F-statistic: 6.572 on 2 and 443 DF,  p-value: 0.001539

# Action 2
rep.b2 <- lm(act2 ~ (RepD.d + RepI.d), data = d)

summary(rep.b2) 

## 
## Call:
## lm(formula = act2 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.280 -1.104  0.022  1.720  2.022 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.9780     0.1898   5.153 4.81e-07 ***
## RepD.d        0.3020     0.2623   1.151    0.251    
## RepI.d        0.1261     0.2649   0.476    0.634    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.811 on 284 degrees of freedom
##   (258 observations deleted due to missingness)
## Multiple R-squared:  0.004714,   Adjusted R-squared:  -0.002295 
## F-statistic: 0.6726 on 2 and 284 DF,  p-value: 0.5112

# Action 3
rep.b3 <- lm(act3 ~ (RepD.d + RepI.d), data = d)

summary(rep.b3) 

## 
## Call:
## lm(formula = act3 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1325 -1.8289  0.1711  1.6884  3.6884 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.6884     0.1702  -4.045 6.18e-05 ***
## RepD.d        0.8209     0.2354   3.487 0.000539 ***
## RepI.d        0.5174     0.2351   2.201 0.028263 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.999 on 438 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.02743,    Adjusted R-squared:  0.02299 
## F-statistic: 6.176 on 2 and 438 DF,  p-value: 0.002265

# Action 5
rep.b5 <- lm(act5 ~ (RepD.d + RepI.d), data = d)

summary(rep.b5)

## 
## Call:
## lm(formula = act5 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1165 -1.1165  0.1053  1.8835  2.1053 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.9583     0.1906   5.027 8.73e-07 ***
## RepD.d        0.1582     0.2650   0.597    0.551    
## RepI.d       -0.0636     0.2703  -0.235    0.814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.868 on 291 degrees of freedom
##   (251 observations deleted due to missingness)
## Multiple R-squared:  0.002558,   Adjusted R-squared:  -0.004298 
## F-statistic: 0.3731 on 2 and 291 DF,  p-value: 0.6889

# Action 6
rep.b6 <- lm(act6 ~ (RepD.d + RepI.d), data = d)

summary(rep.b6) 

## 
## Call:
## lm(formula = act6 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.400 -1.111  0.600  1.600  1.889 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.11111    0.16773   6.624  1.3e-10 ***
## RepD.d       0.09064    0.23876   0.380    0.704    
## RepI.d       0.28889    0.23338   1.238    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 353 degrees of freedom
##   (189 observations deleted due to missingness)
## Multiple R-squared:  0.004549,   Adjusted R-squared:  -0.001091 
## F-statistic: 0.8065 on 2 and 353 DF,  p-value: 0.4472

# Action 7
rep.b7 <- lm(act7 ~ (RepD.d + RepI.d), data = d)

summary(rep.b7) 

## 
## Call:
## lm(formula = act7 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0993 -1.9929  0.0071  1.9007  3.4962 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.4962     0.1805  -2.748  0.00625 **
## RepD.d        0.5955     0.2508   2.375  0.01802 * 
## RepI.d        0.4890     0.2512   1.947  0.05222 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 409 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  0.01046 
## F-statistic: 3.172 on 2 and 409 DF,  p-value: 0.04296

# Action 8
rep.b8 <- lm(act8 ~ (RepD.d + RepI.d), data = d)

summary(rep.b8) 

## 
## Call:
## lm(formula = act8 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2327 -1.8734 -0.2327  1.7673  4.1266 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.1266     0.1676  -6.723 5.12e-11 ***
## RepD.d        1.3593     0.2366   5.745 1.64e-08 ***
## RepI.d        0.7974     0.2359   3.381 0.000783 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.106 on 475 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.06556,    Adjusted R-squared:  0.06162 
## F-statistic: 16.66 on 2 and 475 DF,  p-value: 1.014e-07

# Action 9
rep.b9 <- lm(act9 ~ (RepD.d + RepI.d), data = d)

summary(rep.b9) 

## 
## Call:
## lm(formula = act9 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2617 -1.5594  0.1196  1.7383  3.4406 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.4406     0.1648  -2.673  0.00779 **
## RepD.d        0.7023     0.2307   3.044  0.00247 **
## RepI.d        0.6419     0.2289   2.804  0.00526 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.971 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02498,    Adjusted R-squared:  0.02058 
## F-statistic: 5.676 on 2 and 443 DF,  p-value: 0.003682

# Action 10
rep.b10 <- lm(act10 ~ (RepD.d + RepI.d), data = d)

summary(rep.b10) 

## 
## Call:
## lm(formula = act10 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5935 -2.2208  0.4065  1.6545  3.7792 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.7792     0.1643  -4.741 2.82e-06 ***
## RepD.d        1.3728     0.2320   5.916 6.36e-09 ***
## RepI.d        1.1247     0.2285   4.922 1.19e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 471 degrees of freedom
##   (71 observations deleted due to missingness)
## Multiple R-squared:  0.07812,    Adjusted R-squared:  0.07421 
## F-statistic: 19.96 on 2 and 471 DF,  p-value: 4.791e-09

# Action 11
rep.b11 <- lm(act11 ~ (RepD.d + RepI.d), data = d)

summary(rep.b11) 

## 
## Call:
## lm(formula = act11 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0755 -1.0755  0.3093  1.9245  2.3093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6907     0.1915   3.606 0.000362 ***
## RepD.d        0.3848     0.2651   1.452 0.147652    
## RepI.d        0.2908     0.2639   1.102 0.271402    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.886 on 308 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.007306,   Adjusted R-squared:  0.0008601 
## F-statistic: 1.133 on 2 and 308 DF,  p-value: 0.3233

# Action 12
rep.b12 <- lm(act12 ~ (RepD.d + RepI.d), data = d)

summary(rep.b12) 

## 
## Call:
## lm(formula = act12 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3167 -2.0992 -0.1603  1.8397  3.9008 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.9008     0.1858  -4.849 1.81e-06 ***
## RepD.d        1.2174     0.2687   4.531 7.86e-06 ***
## RepI.d        1.0611     0.2627   4.039 6.50e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.126 on 379 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.06166,    Adjusted R-squared:  0.0567 
## F-statistic: 12.45 on 2 and 379 DF,  p-value: 5.789e-06

# Action 13
rep.b13 <- lm(act13 ~ (RepD.d + RepI.d), data = d)

summary(rep.b13) 

## 
## Call:
## lm(formula = act13 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1888 -2.0186 -0.0186  1.8112  3.5588 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.5588     0.1769  -3.159  0.00170 **
## RepD.d        0.7476     0.2471   3.025  0.00263 **
## RepI.d        0.5775     0.2403   2.403  0.01667 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.063 on 437 degrees of freedom
##   (105 observations deleted due to missingness)
## Multiple R-squared:  0.0225, Adjusted R-squared:  0.01802 
## F-statistic: 5.029 on 2 and 437 DF,  p-value: 0.006932

# Action 14
rep.b14 <- lm(act14 ~ (RepD.d + RepI.d), data = d)

summary(rep.b14) 

## 
## Call:
## lm(formula = act14 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5481 -1.4444 -0.3171  1.5556  2.6829 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.3171     0.2244   1.413    0.159
## RepD.d        0.2310     0.3000   0.770    0.442
## RepI.d        0.1274     0.3034   0.420    0.675
## 
## Residual standard error: 2.032 on 282 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.002098,   Adjusted R-squared:  -0.00498 
## F-statistic: 0.2964 on 2 and 282 DF,  p-value: 0.7437

# Action 15
rep.b15 <- lm(act15 ~ (RepD.d + RepI.d), data = d)

summary(rep.b15) 

## 
## Call:
## lm(formula = act15 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5328 -1.8049  0.1951  1.4672  3.1951 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.1951     0.1842  -1.060  0.29003   
## RepD.d        0.7279     0.2610   2.789  0.00555 **
## RepI.d        0.2681     0.2537   1.057  0.29126   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.042 on 379 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.02059,    Adjusted R-squared:  0.01542 
## F-statistic: 3.984 on 2 and 379 DF,  p-value: 0.0194

# Action 16
rep.b16 <- lm(act16 ~ (RepD.d + RepI.d), data = d)

summary(rep.b16) 

## 
## Call:
## lm(formula = act16 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0550 -1.0550  0.0841  1.1770  2.1770 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.91589    0.17615   5.199 3.54e-07 ***
## RepD.d       0.13916    0.24797   0.561    0.575    
## RepI.d      -0.09288    0.24579  -0.378    0.706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 326 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.002782,   Adjusted R-squared:  -0.003336 
## F-statistic: 0.4547 on 2 and 326 DF,  p-value: 0.6351

# Action 17
rep.b17 <- lm(act17 ~ (RepD.d + RepI.d), data = d)

summary(rep.b17) 

## 
## Call:
## lm(formula = act17 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.650 -1.420  0.350  1.702  2.702 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.2977     0.1752   1.700    0.090 .
## RepD.d        0.3523     0.2437   1.446    0.149  
## RepI.d        0.1219     0.2425   0.503    0.616  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.005 on 411 degrees of freedom
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.005261,   Adjusted R-squared:  0.0004204 
## F-statistic: 1.087 on 2 and 411 DF,  p-value: 0.3382

# Action 18
rep.b18 <- lm(act18 ~ (RepD.d + RepI.d), data = d)

summary(rep.b18) 

## 
## Call:
## lm(formula = act18 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8430 -1.1338  0.3145  1.3145  2.5755 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4245     0.1781   2.383   0.0177 *
## RepD.d        0.4184     0.2440   1.715   0.0872 .
## RepI.d        0.2610     0.2426   1.076   0.2829  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 348 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.008477,   Adjusted R-squared:  0.002779 
## F-statistic: 1.488 on 2 and 348 DF,  p-value: 0.2273

# Action 19
rep.b19 <- lm(act19 ~ (RepD.d + RepI.d), data = d)

summary(rep.b19) 

## 
## Call:
## lm(formula = act19 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.337 -1.032  0.000  1.663  2.000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0000     0.1897   5.273 2.76e-07 ***
## RepD.d        0.1264     0.2705   0.467    0.641    
## RepI.d        0.3368     0.2647   1.273    0.204    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.799 on 269 degrees of freedom
##   (273 observations deleted due to missingness)
## Multiple R-squared:  0.006131,   Adjusted R-squared:  -0.001258 
## F-statistic: 0.8297 on 2 and 269 DF,  p-value: 0.4373

# Action 20
rep.b20 <- lm(act20 ~ (RepD.d + RepI.d), data = d)

summary(rep.b20) 

## 
## Call:
## lm(formula = act20 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3148 -1.2621  0.6852  1.6852  1.9000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.26214    0.17500   7.212 4.05e-12 ***
## RepD.d      -0.16214    0.24352  -0.666    0.506    
## RepI.d       0.05268    0.24461   0.215    0.830    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 318 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.002716,   Adjusted R-squared:  -0.003556 
## F-statistic: 0.433 on 2 and 318 DF,  p-value: 0.6489

# Action 21
rep.b21 <- lm(act21 ~ (RepD.d + RepI.d), data = d)

summary(rep.b21) 

## 
## Call:
## lm(formula = act21 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2214 -2.0071 -0.0071  1.7786  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.5000     0.1817  -2.752  0.00620 **
## RepD.d        0.5071     0.2510   2.021  0.04399 * 
## RepI.d        0.7214     0.2555   2.824  0.00499 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.056 on 397 degrees of freedom
##   (145 observations deleted due to missingness)
## Multiple R-squared:  0.02074,    Adjusted R-squared:  0.01581 
## F-statistic: 4.205 on 2 and 397 DF,  p-value: 0.01559

# Action 22
rep.b22 <- lm(act22 ~ (RepD.d + RepI.d), data = d)

summary(rep.b22) 

## 
## Call:
## lm(formula = act22 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8045 -1.3873  0.1955  1.6127  3.2857 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2857     0.1739  -1.643  0.10115    
## RepD.d        1.0902     0.2427   4.493 9.22e-06 ***
## RepI.d        0.6730     0.2389   2.817  0.00508 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.952 on 398 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.04897,    Adjusted R-squared:  0.04419 
## F-statistic: 10.25 on 2 and 398 DF,  p-value: 4.577e-05

# Action 23
rep.b23 <- lm(act23 ~ (RepD.d + RepI.d), data = d)

summary(rep.b23) 

## 
## Call:
## lm(formula = act23 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3810 -2.3810  0.0824  2.0824  3.0824 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08235    0.24519  -0.336    0.737
## RepD.d       0.24235    0.33350   0.727    0.468
## RepI.d       0.46331    0.32983   1.405    0.161
## 
## Residual standard error: 2.261 on 287 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.006836,   Adjusted R-squared:  -8.474e-05 
## F-statistic: 0.9878 on 2 and 287 DF,  p-value: 0.3737

# Action 24
rep.b24 <- lm(act24 ~ (RepD.d + RepI.d), data = d)

summary(rep.b24)

## 
## Call:
## lm(formula = act24 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9281 -1.9281  0.0719  1.5038  3.5038 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.5038     0.1811  -2.782  0.00565 **
## RepD.d        0.2910     0.2524   1.153  0.24969   
## RepI.d        0.4318     0.2533   1.705  0.08901 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.088 on 410 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.007291,   Adjusted R-squared:  0.002448 
## F-statistic: 1.506 on 2 and 410 DF,  p-value: 0.2231

# Action 25
rep.b25 <- lm(act25 ~ (RepD.d + RepI.d), data = d)

summary(rep.b25) 

## 
## Call:
## lm(formula = act25 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1149 -1.1149  0.1611  1.7603  2.7603 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2397     0.1516   1.582  0.11445    
## RepD.d        0.8751     0.2136   4.097 4.99e-05 ***
## RepI.d        0.5992     0.2133   2.810  0.00518 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 440 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.0383, Adjusted R-squared:  0.03393 
## F-statistic: 8.762 on 2 and 440 DF,  p-value: 0.0001856

# Action 26
rep.b26 <- lm(act26 ~ (RepD.d + RepI.d), data = d)

summary(rep.b26) 

## 
## Call:
## lm(formula = act26 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5672 -1.3974  0.6026  1.4810  1.6026 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.56716    0.22247   7.044 2.34e-11 ***
## RepD.d      -0.16973    0.30333  -0.560    0.576    
## RepI.d      -0.04818    0.30244  -0.159    0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 221 degrees of freedom
##   (321 observations deleted due to missingness)
## Multiple R-squared:  0.001544,   Adjusted R-squared:  -0.007492 
## F-statistic: 0.1709 on 2 and 221 DF,  p-value: 0.843

# Action 27
rep.b27 <- lm(act27 ~ (RepD.d + RepI.d), data = d)

summary(rep.b27) 

## 
## Call:
## lm(formula = act27 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1404 -1.1404  0.1489  1.5670  2.5670 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.4330     0.1894   2.286   0.0229 * 
## RepD.d        0.7074     0.2577   2.745   0.0064 **
## RepI.d        0.1289     0.2627   0.491   0.6240   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 313 degrees of freedom
##   (229 observations deleted due to missingness)
## Multiple R-squared:  0.02742,    Adjusted R-squared:  0.02121 
## F-statistic: 4.413 on 2 and 313 DF,  p-value: 0.01288

# Action 28
rep.b28 <- lm(act28 ~ (RepD.d + RepI.d), data = d)

summary(rep.b28) 

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2266 -0.9508  0.0579  1.7734  2.0579 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.950820   0.164624   5.776 1.63e-08 ***
## RepD.d       0.275743   0.230069   1.199    0.231    
## RepI.d      -0.008671   0.233294  -0.037    0.970    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.818 on 368 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.005379,   Adjusted R-squared:  -2.643e-05 
## F-statistic: 0.9951 on 2 and 368 DF,  p-value: 0.3707

# Action 29
rep.b29 <- lm(act29 ~ (RepD.d + RepI.d), data = d)

summary(rep.b29) 

## 
## Call:
## lm(formula = act29 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3200 -1.0720  0.0684  1.6800  2.0684 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.9316     0.1614   5.773 1.67e-08 ***
## RepD.d        0.3884     0.2245   1.730   0.0845 .  
## RepI.d        0.1404     0.2245   0.625   0.5322    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.745 on 364 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.008418,   Adjusted R-squared:  0.00297 
## F-statistic: 1.545 on 2 and 364 DF,  p-value: 0.2147

# Action 30
rep.b30 <- lm(act30 ~ (RepD.d + RepI.d), data = d)

summary(rep.b30) 

## 
## Call:
## lm(formula = act30 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1917 -1.0598  0.8083  1.8083  2.0263 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.97368    0.17465   5.575 4.97e-08 ***
## RepD.d       0.08614    0.24540   0.351    0.726    
## RepI.d       0.21798    0.24388   0.894    0.372    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 348 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.002333,   Adjusted R-squared:  -0.003401 
## F-statistic: 0.4068 on 2 and 348 DF,  p-value: 0.6661

Significantly above 0: 2; 5; 6; 11; 16; 18; 19; 20; 26; 28; 29; 30

1. condition differences?

# Action 1
rep.c.b1 <- lm(act1 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b1) # no

## 
## Call:
## lm(formula = act1 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.417 -2.229  0.026  1.771  3.591 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -0.58493    0.17338  -3.374 0.000807 ***
## RepD.d         0.86563    0.24733   3.500 0.000513 ***
## RepI.d         0.60103    0.25056   2.399 0.016866 *  
## cond.c         0.01324    0.34676   0.038 0.969569    
## RepD.d:cond.c -0.28517    0.49467  -0.576 0.564587    
## RepI.d:cond.c -0.43887    0.50111  -0.876 0.381620    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.145 on 440 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.03317,    Adjusted R-squared:  0.02218 
## F-statistic: 3.019 on 5 and 440 DF,  p-value: 0.01084

# Action 2
rep.c.b2 <- lm(act2 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b2) #no

## 
## Call:
## lm(formula = act2 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3800 -1.1947  0.2292  1.6200  2.2292 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.99007    0.19003   5.210 3.66e-07 ***
## RepD.d         0.28993    0.26243   1.105    0.270    
## RepI.d         0.05438    0.26928   0.202    0.840    
## cond.c        -0.43847    0.38005  -1.154    0.250    
## RepD.d:cond.c  0.23847    0.52486   0.454    0.650    
## RepI.d:cond.c -0.03931    0.53856  -0.073    0.942    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 281 degrees of freedom
##   (258 observations deleted due to missingness)
## Multiple R-squared:  0.01593,    Adjusted R-squared:  -0.001575 
## F-statistic:  0.91 on 5 and 281 DF,  p-value: 0.4749

# Action 3
rep.c.b3 <- lm(act3 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b3) # no

## 
## Call:
## lm(formula = act3 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2568 -1.8939  0.1061  1.6351  3.7500 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -6.926e-01  1.711e-01  -4.049  6.1e-05 ***
## RepD.d         8.274e-01  2.364e-01   3.501 0.000512 ***
## RepI.d         5.291e-01  2.370e-01   2.233 0.026078 *  
## cond.c         1.149e-01  3.421e-01   0.336 0.737223    
## RepD.d:cond.c  1.289e-01  4.727e-01   0.273 0.785236    
## RepI.d:cond.c  4.762e-06  4.739e-01   0.000 0.999992    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.004 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.0292, Adjusted R-squared:  0.01804 
## F-statistic: 2.617 on 5 and 435 DF,  p-value: 0.024

# Action 4
rep.c.b4 <- lm(act4 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b4) # no

## 
## Call:
## lm(formula = act4 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4923 -2.0000  0.2258  2.0000  3.5385 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.20550    0.20057  -1.025   0.3063  
## RepD.d         0.51135    0.27463   1.862   0.0634 .
## RepI.d        -0.06373    0.28219  -0.226   0.8214  
## cond.c        -0.04062    0.40114  -0.101   0.9194  
## RepD.d:cond.c  0.41353    0.54925   0.753   0.4520  
## RepI.d:cond.c -0.49784    0.56437  -0.882   0.3783  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.155 on 362 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.0212, Adjusted R-squared:  0.007677 
## F-statistic: 1.568 on 5 and 362 DF,  p-value: 0.1683

# Action 5
rep.c.b5 <- lm(act5 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b5) # no

## 
## Call:
## lm(formula = act5 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3585 -0.9200  0.1818  1.6415  2.1818 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.98226    0.19285   5.093 6.37e-07 ***
## RepD.d         0.12698    0.26673   0.476    0.634    
## RepI.d        -0.08893    0.27217  -0.327    0.744    
## cond.c        -0.32816    0.38570  -0.851    0.396    
## RepD.d:cond.c -0.17033    0.53347  -0.319    0.750    
## RepI.d:cond.c  0.27483    0.54434   0.505    0.614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 288 degrees of freedom
##   (251 observations deleted due to missingness)
## Multiple R-squared:  0.01139,    Adjusted R-squared:  -0.005774 
## F-statistic: 0.6636 on 5 and 288 DF,  p-value: 0.6514

# Action 6
rep.c.b6 <- lm(act6 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b6) # no

## 
## Call:
## lm(formula = act6 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5278 -1.1731  0.4722  1.7736  1.9385 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.11731    0.16926   6.601 1.52e-10 ***
## RepD.d         0.08362    0.24036   0.348    0.728    
## RepI.d         0.25979    0.23614   1.100    0.272    
## cond.c        -0.11154    0.33853  -0.329    0.742    
## RepD.d:cond.c  0.08006    0.48072   0.167    0.868    
## RepI.d:cond.c -0.18982    0.47228  -0.402    0.688    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.82 on 350 degrees of freedom
##   (189 observations deleted due to missingness)
## Multiple R-squared:  0.007256,   Adjusted R-squared:  -0.006926 
## F-statistic: 0.5116 on 5 and 350 DF,  p-value: 0.7675

# Action 7
rep.c.b7 <- lm(act7 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b7) # no

## 
## Call:
## lm(formula = act7 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2192 -1.9706  0.0294  1.7808  3.6613 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -0.50456    0.18102  -2.787  0.00556 **
## RepD.d         0.59944    0.25132   2.385  0.01753 * 
## RepI.d         0.51691    0.25270   2.046  0.04144 * 
## cond.c         0.31346    0.36204   0.866  0.38709   
## RepD.d:cond.c -0.06487    0.50264  -0.129  0.89737   
## RepI.d:cond.c -0.01029    0.50539  -0.020  0.98377   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 406 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.02009,    Adjusted R-squared:  0.008026 
## F-statistic: 1.665 on 5 and 406 DF,  p-value: 0.1419

# Action 8
rep.c.b8 <- lm(act8 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b8) # no

## 
## Call:
## lm(formula = act8 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.367 -1.648 -0.100  1.843  4.352 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -1.0976     0.1687  -6.507 1.96e-10 ***
## RepD.d          1.3311     0.2374   5.607 3.50e-08 ***
## RepI.d          0.7731     0.2372   3.260   0.0012 ** 
## cond.c         -0.5094     0.3373  -1.510   0.1317    
## RepD.d:cond.c   0.7765     0.4748   1.636   0.1026    
## RepI.d:cond.c   0.6104     0.4743   1.287   0.1987    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.106 on 472 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.07148,    Adjusted R-squared:  0.06165 
## F-statistic: 7.267 on 5 and 472 DF,  p-value: 1.426e-06

# Action 9
rep.c.b9 <- lm(act9 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b9) # no

## 
## Call:
## lm(formula = act9 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2973 -1.5231  0.1162  1.7027  3.4769 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -0.443590   0.166040  -2.672  0.00783 **
## RepD.d         0.705572   0.231972   3.042  0.00249 **
## RepI.d         0.649666   0.231739   2.803  0.00528 **
## cond.c         0.066667   0.332081   0.201  0.84098   
## RepD.d:cond.c  0.003964   0.463945   0.009  0.99319   
## RepI.d:cond.c -0.010069   0.463478  -0.022  0.98268   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.977 on 440 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02525,    Adjusted R-squared:  0.01417 
## F-statistic: 2.279 on 5 and 440 DF,  p-value: 0.04594

# Action 10
rep.c.b10 <- lm(act10 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b10) # no

## 
## Call:
## lm(formula = act10 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6133 -2.0595  0.3867  1.6404  3.9405 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.7631     0.1654  -4.615 5.08e-06 ***
## RepD.d          1.3573     0.2330   5.824 1.07e-08 ***
## RepI.d          1.1073     0.2298   4.819 1.96e-06 ***
## cond.c         -0.3548     0.3307  -1.073    0.284    
## RepD.d:cond.c   0.3931     0.4661   0.843    0.399    
## RepI.d:cond.c   0.3242     0.4596   0.705    0.481    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 468 degrees of freedom
##   (71 observations deleted due to missingness)
## Multiple R-squared:  0.08043,    Adjusted R-squared:  0.0706 
## F-statistic: 8.187 on 5 and 468 DF,  p-value: 2.004e-07

# Action 11
rep.c.b11 <- lm(act11 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b11) # no

## 
## Call:
## lm(formula = act11 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3509 -1.0528  0.4314  1.6491  2.4314 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.69309    0.19073   3.634 0.000327 ***
## RepD.d         0.35971    0.26434   1.361 0.174585    
## RepI.d         0.26666    0.26280   1.015 0.311057    
## cond.c        -0.09207    0.38145  -0.241 0.809430    
## RepD.d:cond.c -0.38853    0.52867  -0.735 0.462954    
## RepI.d:cond.c -0.69018    0.52559  -1.313 0.190122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 305 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.0279, Adjusted R-squared:  0.01197 
## F-statistic: 1.751 on 5 and 305 DF,  p-value: 0.1228

# Action 12
rep.c.b12 <- lm(act12 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b12) # no

## 
## Call:
## lm(formula = act12 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3333 -1.9851 -0.1029  1.8971  4.0149 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.8981     0.1864  -4.817 2.11e-06 ***
## RepD.d          1.2139     0.2698   4.500 9.08e-06 ***
## RepI.d          1.0607     0.2637   4.022 6.97e-05 ***
## cond.c         -0.2337     0.3729  -0.627    0.531    
## RepD.d:cond.c   0.1986     0.5395   0.368    0.713    
## RepI.d:cond.c   0.3530     0.5274   0.669    0.504    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.133 on 376 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.06291,    Adjusted R-squared:  0.05045 
## F-statistic: 5.048 on 5 and 376 DF,  p-value: 0.0001718

# Action 13
rep.c.b13 <- lm(act13 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b13) # no

## 
## Call:
## lm(formula = act13 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3803 -2.0000  0.1977  1.7533  3.8133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.5296     0.1773  -2.987  0.00298 **
## RepD.d          0.7198     0.2470   2.914  0.00375 **
## RepI.d          0.5641     0.2405   2.346  0.01944 * 
## cond.c         -0.5674     0.3546  -1.600  0.11028   
## RepD.d:cond.c   0.1872     0.4940   0.379  0.70500   
## RepI.d:cond.c   1.0318     0.4810   2.145  0.03249 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 434 degrees of freedom
##   (105 observations deleted due to missingness)
## Multiple R-squared:  0.03544,    Adjusted R-squared:  0.02433 
## F-statistic: 3.189 on 5 and 434 DF,  p-value: 0.007706

# Action 14
rep.c.b14 <- lm(act14 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b14) # no

## 
## Call:
## lm(formula = act14 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7209 -1.5370 -0.2321  1.7297  2.7679 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)    0.31291    0.22606   1.384    0.167
## RepD.d         0.23561    0.30178   0.781    0.436
## RepI.d         0.16362    0.30621   0.534    0.594
## cond.c         0.08529    0.45212   0.189    0.851
## RepD.d:cond.c -0.06232    0.60356  -0.103    0.918
## RepI.d:cond.c  0.40350    0.61241   0.659    0.511
## 
## Residual standard error: 2.037 on 279 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.007218,   Adjusted R-squared:  -0.01057 
## F-statistic: 0.4057 on 5 and 279 DF,  p-value: 0.8447

# Action 15
rep.c.b15 <- lm(act15 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b15) # no

## 
## Call:
## lm(formula = act15 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8098  0.2069  1.6290  3.2069 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -0.19576    0.18484  -1.059  0.29025   
## RepD.d         0.73124    0.26174   2.794  0.00548 **
## RepI.d         0.25742    0.25483   1.010  0.31305   
## cond.c         0.02228    0.36967   0.060  0.95197   
## RepD.d:cond.c -0.35131    0.52348  -0.671  0.50256   
## RepI.d:cond.c -0.30435    0.50966  -0.597  0.55076   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.047 on 376 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  0.01135 
## F-statistic: 1.875 on 5 and 376 DF,  p-value: 0.09788

# Action 16
rep.c.b16 <- lm(act16 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b16) # no

## 
## Call:
## lm(formula = act16 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1887 -1.0385  0.1404  1.5000  2.5000 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.91982    0.17622   5.220 3.21e-07 ***
## RepD.d         0.13449    0.24793   0.542    0.588    
## RepI.d        -0.07548    0.24585  -0.307    0.759    
## cond.c        -0.12035    0.35243  -0.341    0.733    
## RepD.d:cond.c  0.08864    0.49586   0.179    0.858    
## RepI.d:cond.c  0.80903    0.49170   1.645    0.101    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 323 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.01546,    Adjusted R-squared:  0.0002187 
## F-statistic: 1.014 on 5 and 323 DF,  p-value: 0.4091

# Action 17
rep.c.b17 <- lm(act17 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b17) # no

## 
## Call:
## lm(formula = act17 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6761 -1.4375  0.3239  1.6032  2.8108 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.3139     0.1772   1.771   0.0772 .
## RepD.d          0.3357     0.2455   1.367   0.1723  
## RepI.d          0.1033     0.2451   0.421   0.6738  
## cond.c         -0.2494     0.3544  -0.704   0.4820  
## RepD.d:cond.c   0.1965     0.4911   0.400   0.6892  
## RepI.d:cond.c   0.2087     0.4902   0.426   0.6705  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.011 on 408 degrees of freedom
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.006561,   Adjusted R-squared:  -0.005614 
## F-statistic: 0.5389 on 5 and 408 DF,  p-value: 0.7468

# Action 18
rep.c.b18 <- lm(act18 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b18) #  no

## 
## Call:
## lm(formula = act18 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1004  0.2778  1.3654  2.7115 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.4220     0.1787   2.361   0.0188 *
## RepD.d          0.4248     0.2450   1.734   0.0839 .
## RepI.d          0.2564     0.2449   1.047   0.2959  
## cond.c          0.2671     0.3575   0.747   0.4555  
## RepD.d:cond.c  -0.1361     0.4900  -0.278   0.7814  
## RepI.d:cond.c  -0.3547     0.4898  -0.724   0.4695  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.84 on 345 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.01071,    Adjusted R-squared:  -0.003625 
## F-statistic: 0.7472 on 5 and 345 DF,  p-value: 0.5886

# Action 19
rep.c.b19 <- lm(act19 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b19) # yes--climate less supported

## 
## Call:
## lm(formula = act19 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5429 -1.0543  0.3455  1.5510  2.3455 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.09870    0.19337   5.682 3.49e-08 ***
## RepD.d         0.04232    0.27306   0.155   0.8769    
## RepI.d         0.23448    0.26665   0.879   0.3800    
## cond.c        -0.88831    0.38674  -2.297   0.0224 *  
## RepD.d:cond.c  0.60626    0.54612   1.110   0.2679    
## RepI.d:cond.c  0.65672    0.53330   1.231   0.2192    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.789 on 266 degrees of freedom
##   (273 observations deleted due to missingness)
## Multiple R-squared:  0.0288, Adjusted R-squared:  0.01054 
## F-statistic: 1.578 on 5 and 266 DF,  p-value: 0.1666

# Action 20
rep.c.b20 <- lm(act20 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b20) # no

## 
## Call:
## lm(formula = act20 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5102 -1.2979  0.4444  1.4444  2.4444 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.27362    0.17274   7.373 1.48e-12 ***
## RepD.d        -0.18334    0.24026  -0.763    0.446    
## RepI.d         0.03925    0.24232   0.162    0.871    
## cond.c        -0.47317    0.34549  -1.370    0.172    
## RepD.d:cond.c -0.59628    0.48052  -1.241    0.216    
## RepI.d:cond.c  0.44317    0.48464   0.914    0.361    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.751 on 315 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.03972,    Adjusted R-squared:  0.02447 
## F-statistic: 2.606 on 5 and 315 DF,  p-value: 0.02506

# Action 21
rep.c.b21 <- lm(act21 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b21) # no

## 
## Call:
## lm(formula = act21 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5667 -1.9565  0.0704  1.7143  3.7143 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.5238     0.1828  -2.865  0.00439 **
## RepD.d          0.5298     0.2516   2.106  0.03586 * 
## RepI.d          0.7719     0.2565   3.009  0.00279 **
## cond.c          0.3810     0.3657   1.042  0.29814   
## RepD.d:cond.c  -0.4800     0.5032  -0.954  0.34077   
## RepI.d:cond.c   0.2561     0.5131   0.499  0.61789   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.052 on 394 degrees of freedom
##   (145 observations deleted due to missingness)
## Multiple R-squared:  0.03132,    Adjusted R-squared:  0.01903 
## F-statistic: 2.548 on 5 and 394 DF,  p-value: 0.0276

# Action 22
rep.c.b22 <- lm(act22 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b22) # no

## 
## Call:
## lm(formula = act22 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9254 -1.5082  0.2647  1.7037  3.3103 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -0.28753    0.17489  -1.644  0.10097    
## RepD.d         1.09112    0.24369   4.478 9.89e-06 ***
## RepI.d         0.68977    0.24104   2.862  0.00444 ** 
## cond.c         0.04564    0.34978   0.130  0.89625    
## RepD.d:cond.c -0.28919    0.48737  -0.593  0.55327    
## RepI.d:cond.c  0.16626    0.48209   0.345  0.73037    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.957 on 395 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05123,    Adjusted R-squared:  0.03922 
## F-statistic: 4.266 on 5 and 395 DF,  p-value: 0.0008644

# Action 23
rep.c.b23 <- lm(act23 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b23) # no

## 
## Call:
## lm(formula = act23 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.661 -2.044  0.225  2.189  3.225 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -0.09028    0.24542  -0.368    0.713
## RepD.d         0.24554    0.33357   0.736    0.462
## RepI.d         0.43166    0.33102   1.304    0.193
## cond.c         0.26944    0.49083   0.549    0.583
## RepD.d:cond.c -0.74323    0.66714  -1.114    0.266
## RepI.d:cond.c -0.90872    0.66204  -1.373    0.171
## 
## Residual standard error: 2.259 on 284 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01883,    Adjusted R-squared:  0.001555 
## F-statistic:  1.09 on 5 and 284 DF,  p-value: 0.366

# Action 24
rep.c.b24 <- lm(act24 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b24) # no

## 
## Call:
## lm(formula = act24 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1266 -1.9583 -0.1266  1.6133  3.6133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -0.48770    0.18256  -2.671  0.00785 **
## RepD.d         0.27122    0.25350   1.070  0.28531   
## RepI.d         0.38433    0.25552   1.504  0.13334   
## cond.c        -0.25126    0.36511  -0.688  0.49173   
## RepD.d:cond.c -0.09837    0.50701  -0.194  0.84625   
## RepI.d:cond.c -0.20865    0.51105  -0.408  0.68328   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.088 on 407 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01483,    Adjusted R-squared:  0.00273 
## F-statistic: 1.226 on 5 and 407 DF,  p-value: 0.2962

# Action 25
rep.c.b25 <- lm(act25 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b25) # no

## 
## Call:
## lm(formula = act25 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3194 -0.9688  0.0789  1.6562  2.8415 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.2511     0.1528   1.644  0.10095    
## RepD.d          0.8691     0.2146   4.050 6.05e-05 ***
## RepI.d          0.6038     0.2152   2.805  0.00525 ** 
## cond.c         -0.1852     0.3056  -0.606  0.54476    
## RepD.d:cond.c   0.5836     0.4291   1.360  0.17455    
## RepI.d:cond.c   0.4128     0.4305   0.959  0.33814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 437 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.04416,    Adjusted R-squared:  0.03323 
## F-statistic: 4.038 on 5 and 437 DF,  p-value: 0.001359

# Action 26
rep.c.b26 <- lm(act26 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b26) # no

## 
## Call:
## lm(formula = act26 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6875 -1.4444  0.5556  1.5000  1.7222 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.57232    0.22385   7.024 2.71e-11 ***
## RepD.d        -0.18343    0.30548  -0.600    0.549    
## RepI.d        -0.04128    0.30555  -0.135    0.893    
## cond.c        -0.23036    0.44770  -0.515    0.607    
## RepD.d:cond.c  0.45258    0.61097   0.741    0.460    
## RepI.d:cond.c  0.40356    0.61110   0.660    0.510    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.83 on 218 degrees of freedom
##   (321 observations deleted due to missingness)
## Multiple R-squared:  0.004848,   Adjusted R-squared:  -0.01798 
## F-statistic: 0.2124 on 5 and 218 DF,  p-value: 0.957

# Action 27
rep.c.b27 <- lm(act25 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b27) # no

## 
## Call:
## lm(formula = act25 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3194 -0.9688  0.0789  1.6562  2.8415 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.2511     0.1528   1.644  0.10095    
## RepD.d          0.8691     0.2146   4.050 6.05e-05 ***
## RepI.d          0.6038     0.2152   2.805  0.00525 ** 
## cond.c         -0.1852     0.3056  -0.606  0.54476    
## RepD.d:cond.c   0.5836     0.4291   1.360  0.17455    
## RepI.d:cond.c   0.4128     0.4305   0.959  0.33814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 437 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.04416,    Adjusted R-squared:  0.03323 
## F-statistic: 4.038 on 5 and 437 DF,  p-value: 0.001359

# Action 28
rep.c.b28 <- lm(act28 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b28) # no

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2187  1.6912  2.2187 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.9596     0.1649   5.820 1.29e-08 ***
## RepD.d          0.2615     0.2305   1.134    0.257    
## RepI.d          0.0117     0.2350   0.050    0.960    
## cond.c         -0.3567     0.3298  -1.082    0.280    
## RepD.d:cond.c   0.1812     0.4611   0.393    0.695    
## RepI.d:cond.c   0.7278     0.4700   1.549    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

# Action 29
rep.c.b29 <- lm(act29 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b29) # no

## 
## Call:
## lm(formula = act29 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3438 -1.1250  0.0968  1.6563  2.0968 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.93343    0.16230   5.751 1.89e-08 ***
## RepD.d         0.38598    0.22565   1.711    0.088 .  
## RepI.d         0.14356    0.22622   0.635    0.526    
## cond.c        -0.06041    0.32459  -0.186    0.852    
## RepD.d:cond.c  0.10908    0.45131   0.242    0.809    
## RepI.d:cond.c  0.15643    0.45244   0.346    0.730    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.752 on 361 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.008834,   Adjusted R-squared:  -0.004894 
## F-statistic: 0.6435 on 5 and 361 DF,  p-value: 0.6667

# Action 30
rep.c.b30 <- lm(act30 ~ (RepD.d + RepI.d) * cond.c, data = d)

summary(rep.c.b30) # no

## 
## Call:
## lm(formula = act30 ~ (RepD.d + RepI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2545 -1.1167  0.7455  1.8615  2.1373 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.96312    0.17625   5.465  8.9e-08 ***
## RepD.d         0.09521    0.24701   0.385    0.700    
## RepI.d         0.23338    0.24586   0.949    0.343    
## cond.c         0.20075    0.35249   0.570    0.569    
## RepD.d:cond.c -0.08408    0.49402  -0.170    0.865    
## RepI.d:cond.c -0.08466    0.49173  -0.172    0.863    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 345 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.003928,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.2721 on 5 and 345 DF,  p-value: 0.9282

Significant condition difference: 19

a. Means for condition diffs

describeBy(d$act19[d$party_factor=="Republican"], d$cond[d$party_factor=="Republican"]) # both supported, ctrl supported more

## 
##  Descriptive statistics by group 
## group: climate
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 55 0.65 1.97      1     0.8 1.48  -3   3     6 -0.53    -0.81 0.27
## ------------------------------------------------------------ 
## group: ctrl
##    vars  n mean  sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 35 1.54 1.4      2    1.66 1.48  -3   3     6 -0.93     0.88 0.24

### is climate different from 0?
summary(lm(act28 ~ (RepD.d + RepI.d) * clim.d, data = d))

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2188  1.6912  2.2188 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.7812     0.2274   3.436 0.000659 ***
## RepD.d          0.3521     0.3269   1.077 0.282157    
## RepI.d          0.3756     0.3415   1.100 0.272035    
## clim.d          0.3567     0.3298   1.082 0.280162    
## RepD.d:clim.d  -0.1812     0.4611  -0.393 0.694555    
## RepI.d:clim.d  -0.7278     0.4700  -1.549 0.122362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

### Higher than 0 in the climate condition

summary(lm(act28 ~ (RepD.d + RepI.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2187  1.6912  2.2188 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.1379     0.2389   4.764 2.74e-06 ***
## RepD.d          0.1709     0.3251   0.526    0.599    
## RepI.d         -0.3522     0.3230  -1.090    0.276    
## ctrl.d         -0.3567     0.3298  -1.082    0.280    
## RepD.d:ctrl.d   0.1812     0.4611   0.393    0.695    
## RepI.d:ctrl.d   0.7278     0.4700   1.549    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

### Higher than 0 in the ctrl condition

2. Gender differences

Action: 5, 14, 20

summary(lm(act1 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act1 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3182 -2.0095 -0.0095  1.8462  4.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.7265     0.2026  -3.586 0.000373 ***
## RepD.d           1.0032     0.2803   3.579 0.000383 ***
## RepI.d           0.8082     0.2857   2.829 0.004880 ** 
## gend.mf         -0.5470     0.4051  -1.350 0.177661    
## RepD.d:gend.mf   0.6299     0.5606   1.124 0.261770    
## RepI.d:gend.mf   0.6913     0.5713   1.210 0.226899    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 438 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03231,    Adjusted R-squared:  0.02127 
## F-statistic: 2.925 on 5 and 438 DF,  p-value: 0.01307

summary(lm(act2 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act2 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.292 -1.076  0.280  1.708  2.280 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8979     0.2137   4.201 3.58e-05 ***
## RepD.d           0.3392     0.2890   1.174    0.242    
## RepI.d           0.2461     0.2990   0.823    0.411    
## gend.mf         -0.3558     0.4275  -0.832    0.406    
## RepD.d:gend.mf   0.2453     0.5780   0.424    0.672    
## RepI.d:gend.mf   0.5294     0.5979   0.885    0.377    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.82 on 279 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.007276,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.409 on 5 and 279 DF,  p-value: 0.8424

summary(lm(act3 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act3 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1845 -1.8571  0.1429  1.6667  3.7500 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.70833    0.19483  -3.636 0.000311 ***
## RepD.d          0.81144    0.26405   3.073 0.002253 ** 
## RepI.d          0.54600    0.26681   2.046 0.041326 *  
## gend.mf        -0.08333    0.38966  -0.214 0.830757    
## RepD.d:gend.mf -0.07939    0.52810  -0.150 0.880568    
## RepI.d:gend.mf  0.12229    0.53363   0.229 0.818841    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 433 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.02799,    Adjusted R-squared:  0.01676 
## F-statistic: 2.493 on 5 and 433 DF,  p-value: 0.0305

summary(lm(act4 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act4 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3721 -1.9412  0.2198  2.0588  3.6129 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.33586    0.22709  -1.479   0.1400  
## RepD.d          0.65984    0.30377   2.172   0.0305 *
## RepI.d          0.08804    0.32379   0.272   0.7858  
## gend.mf        -0.55408    0.45418  -1.220   0.2233  
## RepD.d:gend.mf  0.65031    0.60755   1.070   0.2852  
## RepI.d:gend.mf  0.49800    0.64757   0.769   0.4424  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.165 on 360 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01789,    Adjusted R-squared:  0.004246 
## F-statistic: 1.311 on 5 and 360 DF,  p-value: 0.2585

summary(lm(act5 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act5 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2540 -1.1852  0.4133  1.7460  2.6061 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8240     0.2001   4.118    5e-05 ***
## RepD.d           0.2485     0.2775   0.895   0.3713    
## RepI.d           0.1583     0.2913   0.544   0.5872    
## gend.mf         -0.8600     0.4001  -2.149   0.0324 *  
## RepD.d:gend.mf   0.6611     0.5550   1.191   0.2345    
## RepI.d:gend.mf   1.2658     0.5827   2.172   0.0306 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 286 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.02198,    Adjusted R-squared:  0.004878 
## F-statistic: 1.285 on 5 and 286 DF,  p-value: 0.2702

summary(lm(act6 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act6 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7273 -1.2184  0.6579  1.6579  2.2000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.00920    0.19196   5.257 2.56e-07 ***
## RepD.d          0.09241    0.26552   0.348   0.7280    
## RepI.d          0.49575    0.26588   1.865   0.0631 .  
## gend.mf        -0.41839    0.38392  -1.090   0.2766    
## RepD.d:gend.mf -0.06260    0.53104  -0.118   0.9062    
## RepI.d:gend.mf  0.86305    0.53177   1.623   0.1055    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 348 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01702,    Adjusted R-squared:  0.002894 
## F-statistic: 1.205 on 5 and 348 DF,  p-value: 0.3064

summary(lm(act7 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act7 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1684 -1.9773  0.0472  1.8316  3.6563 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.55035    0.21114  -2.607  0.00949 **
## RepD.d          0.62319    0.28361   2.197  0.02856 * 
## RepI.d          0.58559    0.29404   1.991  0.04710 * 
## gend.mf        -0.21181    0.42229  -0.502  0.61624   
## RepD.d:gend.mf  0.02066    0.56721   0.036  0.97097   
## RepI.d:gend.mf  0.37662    0.58809   0.640  0.52226   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 404 degrees of freedom
##   (135 observations deleted due to missingness)
## Multiple R-squared:  0.01716,    Adjusted R-squared:  0.004995 
## F-statistic: 1.411 on 5 and 404 DF,  p-value: 0.2193

summary(lm(act8 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act8 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4545 -1.8926 -0.1593  1.8407  4.1892 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -1.14831    0.19868  -5.780 1.36e-08 ***
## RepD.d          1.45523    0.27348   5.321 1.60e-07 ***
## RepI.d          0.82889    0.27691   2.993  0.00291 ** 
## gend.mf        -0.08175    0.39736  -0.206  0.83709    
## RepD.d:gend.mf  0.37700    0.54697   0.689  0.49100    
## RepI.d:gend.mf  0.12059    0.55383   0.218  0.82772    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.115 on 470 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.06738,    Adjusted R-squared:  0.05746 
## F-statistic: 6.791 on 5 and 470 DF,  p-value: 3.961e-06

summary(lm(act9 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act9 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3564 -1.6055  0.0435  1.6436  3.5882 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.4914     0.1937  -2.536   0.0115 *
## RepD.d           0.6478     0.2613   2.479   0.0136 *
## RepI.d           0.6744     0.2634   2.561   0.0108 *
## gend.mf         -0.1937     0.3874  -0.500   0.6173  
## RepD.d:gend.mf  -0.2062     0.5227  -0.394   0.6934  
## RepI.d:gend.mf   0.1134     0.5268   0.215   0.8297  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.972 on 438 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02744,    Adjusted R-squared:  0.01634 
## F-statistic: 2.472 on 5 and 438 DF,  p-value: 0.03179

summary(lm(act10 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act10 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.9722  0.2857  1.7034  4.0278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.8656     0.1948  -4.444 1.10e-05 ***
## RepD.d           1.4950     0.2687   5.564 4.45e-08 ***
## RepI.d           1.1879     0.2653   4.478 9.49e-06 ***
## gend.mf         -0.3244     0.3896  -0.833    0.405    
## RepD.d:gend.mf   0.4940     0.5374   0.919    0.358    
## RepI.d:gend.mf   0.2252     0.5306   0.424    0.671    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 467 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.07975,    Adjusted R-squared:  0.0699 
## F-statistic: 8.094 on 5 and 467 DF,  p-value: 2.448e-07

summary(lm(act11 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act11 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0519 -1.0519  0.3382  1.7105  2.3382 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.71019    0.21031   3.377 0.000829 ***
## RepD.d          0.41182    0.28551   1.442 0.150232    
## RepI.d          0.21901    0.29140   0.752 0.452898    
## gend.mf         0.09686    0.42062   0.230 0.818039    
## RepD.d:gend.mf  0.23807    0.57103   0.417 0.677034    
## RepI.d:gend.mf -0.34235    0.58281  -0.587 0.557358    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.897 on 303 degrees of freedom
##   (236 observations deleted due to missingness)
## Multiple R-squared:  0.01116,    Adjusted R-squared:  -0.005162 
## F-statistic: 0.6836 on 5 and 303 DF,  p-value: 0.6362

summary(lm(act12 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act12 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4217 -2.1579 -0.1579  1.8355  4.3333 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -1.0442     0.2144  -4.871 1.64e-06 ***
## RepD.d           1.2551     0.3034   4.137 4.35e-05 ***
## RepI.d           1.2065     0.2990   4.035 6.63e-05 ***
## gend.mf         -0.5782     0.4288  -1.349    0.178    
## RepD.d:gend.mf   0.1565     0.6068   0.258    0.797    
## RepI.d:gend.mf   0.5870     0.5981   0.982    0.327    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.13 on 374 degrees of freedom
##   (165 observations deleted due to missingness)
## Multiple R-squared:  0.06734,    Adjusted R-squared:  0.05487 
## F-statistic: 5.401 on 5 and 374 DF,  p-value: 8.241e-05

summary(lm(act13 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act13 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5000 -2.0784 -0.0784  1.8067  3.6990 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.4101     0.2057  -1.994   0.0468 *
## RepD.d           0.6993     0.2813   2.486   0.0133 *
## RepI.d           0.2687     0.2764   0.972   0.3315  
## gend.mf          0.5778     0.4114   1.405   0.1609  
## RepD.d:gend.mf  -0.1562     0.5626  -0.278   0.7813  
## RepI.d:gend.mf  -1.2473     0.5527  -2.257   0.0245 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 433 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.03722,    Adjusted R-squared:  0.0261 
## F-statistic: 3.347 on 5 and 433 DF,  p-value: 0.005603

summary(lm(act14 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act14 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.730 -1.520  0.322  1.538  3.609 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.03464    0.24852   0.139   0.8893  
## RepD.d          0.56100    0.32421   1.730   0.0847 .
## RepI.d          0.32953    0.34348   0.959   0.3382  
## gend.mf        -1.28666    0.49704  -2.589   0.0101 *
## RepD.d:gend.mf  1.55485    0.64842   2.398   0.0172 *
## RepI.d:gend.mf  0.97500    0.68696   1.419   0.1569  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.022 on 277 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.02875,    Adjusted R-squared:  0.01121 
## F-statistic:  1.64 on 5 and 277 DF,  p-value: 0.1496

summary(lm(act15 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act15 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5581 -1.5581  0.4634  1.5278  3.5278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -0.2926     0.2020  -1.449  0.14823   
## RepD.d           0.8345     0.2798   2.983  0.00304 **
## RepI.d           0.2120     0.2774   0.764  0.44530   
## gend.mf         -0.4703     0.4040  -1.164  0.24506   
## RepD.d:gend.mf   0.5028     0.5595   0.899  0.36944   
## RepI.d:gend.mf  -0.2952     0.5548  -0.532  0.59500   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.038 on 375 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.03469,    Adjusted R-squared:  0.02182 
## F-statistic: 2.695 on 5 and 375 DF,  p-value: 0.02078

summary(lm(act16 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act16 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2692 -0.9860  0.1667  1.3103  2.3103 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.89069    0.19623   4.539    8e-06 ***
## RepD.d          0.11983    0.26881   0.446    0.656    
## RepI.d          0.08875    0.28291   0.314    0.754    
## gend.mf        -0.11472    0.39247  -0.292    0.770    
## RepD.d:gend.mf -0.06423    0.53763  -0.119    0.905    
## RepI.d:gend.mf  0.69429    0.56582   1.227    0.221    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.824 on 322 degrees of freedom
##   (217 observations deleted due to missingness)
## Multiple R-squared:  0.009561,   Adjusted R-squared:  -0.005818 
## F-statistic: 0.6217 on 5 and 322 DF,  p-value: 0.6834

summary(lm(act17 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act17 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8837 -1.4362  0.4583  1.5741  3.0541 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.1911     0.1949   0.980   0.3276  
## RepD.d           0.5216     0.2682   1.945   0.0525 .
## RepI.d           0.2219     0.2760   0.804   0.4218  
## gend.mf         -0.4902     0.3898  -1.258   0.2093  
## RepD.d:gend.mf   0.8323     0.5365   1.551   0.1216  
## RepI.d:gend.mf   0.4643     0.5519   0.841   0.4007  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.009 on 407 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01111,    Adjusted R-squared:  -0.001043 
## F-statistic: 0.9141 on 5 and 407 DF,  p-value: 0.4717

summary(lm(act18 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act18 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9730 -1.3947  0.2561  1.5000  2.6053 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.4474     0.1983   2.256   0.0247 *
## RepD.d           0.4111     0.2692   1.527   0.1277  
## RepI.d           0.1573     0.2738   0.574   0.5661  
## gend.mf          0.1053     0.3966   0.265   0.7908  
## RepD.d:gend.mf   0.1238     0.5385   0.230   0.8183  
## RepI.d:gend.mf  -0.4395     0.5475  -0.803   0.4227  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 343 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01109,    Adjusted R-squared:  -0.003321 
## F-statistic: 0.7696 on 5 and 343 DF,  p-value: 0.5722

summary(lm(act19 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act19 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3538 -1.1719  0.4231  1.6462  2.4231 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8744     0.2096   4.173 4.08e-05 ***
## RepD.d           0.2420     0.2962   0.817    0.415    
## RepI.d           0.4525     0.2889   1.566    0.118    
## gend.mf         -0.5950     0.4191  -1.420    0.157    
## RepD.d:gend.mf   0.6584     0.5924   1.111    0.267    
## RepI.d:gend.mf   0.5411     0.5778   0.936    0.350    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.802 on 265 degrees of freedom
##   (274 observations deleted due to missingness)
## Multiple R-squared:  0.01397,    Adjusted R-squared:  -0.00463 
## F-statistic: 0.7511 on 5 and 265 DF,  p-value: 0.5859

summary(lm(act20 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act20 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5139 -1.1429  0.4861  1.5556  2.3226 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.09565    0.19038   5.755 2.06e-08 ***
## RepD.d          0.02878    0.26102   0.110   0.9123    
## RepI.d          0.16327    0.27223   0.600   0.5491    
## gend.mf        -0.83647    0.38075  -2.197   0.0288 *  
## RepD.d:gend.mf  1.11391    0.52204   2.134   0.0336 *  
## RepI.d:gend.mf  0.60433    0.54446   1.110   0.2679    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.772 on 313 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02125,    Adjusted R-squared:  0.005613 
## F-statistic: 1.359 on 5 and 313 DF,  p-value: 0.2396

summary(lm(act21 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act21 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.308 -1.850  0.150  1.760  3.889 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -0.6184     0.2020  -3.061  0.00236 **
## RepD.d           0.6972     0.2801   2.489  0.01322 * 
## RepI.d           0.8239     0.2863   2.877  0.00423 **
## gend.mf         -0.5411     0.4041  -1.339  0.18132   
## RepD.d:gend.mf   0.9988     0.5602   1.783  0.07538 . 
## RepI.d:gend.mf   0.4729     0.5727   0.826  0.40943   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.055 on 392 degrees of freedom
##   (147 observations deleted due to missingness)
## Multiple R-squared:  0.02841,    Adjusted R-squared:  0.01602 
## F-statistic: 2.293 on 5 and 392 DF,  p-value: 0.04499

summary(lm(act22 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act22 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.5263  0.2421  1.5686  3.4194 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.3307     0.2024  -1.634  0.10310    
## RepD.d           1.1748     0.2766   4.248 2.69e-05 ***
## RepI.d           0.7622     0.2746   2.776  0.00577 ** 
## gend.mf         -0.1772     0.4049  -0.438  0.66177    
## RepD.d:gend.mf   0.4891     0.5531   0.884  0.37711    
## RepI.d:gend.mf   0.3670     0.5492   0.668  0.50430    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.957 on 393 degrees of freedom
##   (146 observations deleted due to missingness)
## Multiple R-squared:  0.04966,    Adjusted R-squared:  0.03757 
## F-statistic: 4.107 on 5 and 393 DF,  p-value: 0.001201

summary(lm(act23 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act23 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6667 -2.2821  0.0678  2.0678  3.1154 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)    -0.09159    0.26687  -0.343    0.732
## RepD.d          0.24494    0.35781   0.685    0.494
## RepI.d          0.56595    0.36783   1.539    0.125
## gend.mf        -0.04759    0.53374  -0.089    0.929
## RepD.d:gend.mf  0.19803    0.71561   0.277    0.782
## RepI.d:gend.mf  0.43220    0.73567   0.587    0.557
## 
## Residual standard error: 2.267 on 283 degrees of freedom
##   (256 observations deleted due to missingness)
## Multiple R-squared:  0.009342,   Adjusted R-squared:  -0.008161 
## F-statistic: 0.5337 on 5 and 283 DF,  p-value: 0.7507

summary(lm(act24 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act24 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -3.13  -2.00  -0.13   1.59   3.59 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.48684    0.19980  -2.437   0.0153 *
## RepD.d          0.31847    0.27709   1.149   0.2511  
## RepI.d          0.25697    0.28025   0.917   0.3597  
## gend.mf         0.07895    0.39960   0.198   0.8435  
## RepD.d:gend.mf  0.25779    0.55417   0.465   0.6421  
## RepI.d:gend.mf -0.79869    0.56050  -1.425   0.1549  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.082 on 406 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.01727,    Adjusted R-squared:  0.005164 
## F-statistic: 1.427 on 5 and 406 DF,  p-value: 0.2135

summary(lm(act25 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act25 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2791 -1.0194  0.0833  1.6847  3.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.15766    0.17784   0.887    0.376    
## RepD.d          0.99159    0.24366   4.070 5.59e-05 ***
## RepI.d          0.61775    0.24484   2.523    0.012 *  
## gend.mf        -0.31532    0.35568  -0.887    0.376    
## RepD.d:gend.mf  0.57497    0.48731   1.180    0.239    
## RepI.d:gend.mf  0.03279    0.48967   0.067    0.947    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.04154,    Adjusted R-squared:  0.03052 
## F-statistic:  3.77 on 5 and 435 DF,  p-value: 0.002362

summary(lm(act26 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act26 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7609 -1.1429  0.5614  1.3929  1.8571 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.45186    0.24004   6.048 6.33e-09 ***
## RepD.d         -0.02584    0.32286  -0.080    0.936    
## RepI.d          0.13107    0.33159   0.395    0.693    
## gend.mf        -0.61801    0.48008  -1.287    0.199    
## RepD.d:gend.mf  0.98026    0.64573   1.518    0.130    
## RepI.d:gend.mf  0.90669    0.66318   1.367    0.173    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.823 on 217 degrees of freedom
##   (322 observations deleted due to missingness)
## Multiple R-squared:  0.01451,    Adjusted R-squared:  -0.008199 
## F-statistic: 0.6389 on 5 and 217 DF,  p-value: 0.6702

summary(lm(act27 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act27 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4324 -0.9467  0.0533  1.5676  2.6000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.4500     0.2017   2.231  0.02638 * 
## RepD.d           0.7396     0.2755   2.685  0.00765 **
## RepI.d           0.1733     0.2853   0.608  0.54389   
## gend.mf          0.1000     0.4034   0.248  0.80436   
## RepD.d:gend.mf   0.3858     0.5509   0.700  0.48432   
## RepI.d:gend.mf   0.1867     0.5705   0.327  0.74376   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.868 on 308 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03174,    Adjusted R-squared:  0.01602 
## F-statistic: 2.019 on 5 and 308 DF,  p-value: 0.07577

summary(lm(act28 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2151 -1.0449  0.2069  1.7849  2.3030 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.87096    0.18603   4.682 4.02e-06 ***
## RepD.d          0.34263    0.26233   1.306    0.192    
## RepI.d          0.02016    0.26906   0.075    0.940    
## gend.mf        -0.34797    0.37207  -0.935    0.350    
## RepD.d:gend.mf  0.34504    0.52465   0.658    0.511    
## RepI.d:gend.mf  0.15195    0.53812   0.282    0.778    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 363 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.007968,   Adjusted R-squared:  -0.005696 
## F-statistic: 0.5831 on 5 and 363 DF,  p-value: 0.7129

summary(lm(act29 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act29 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.056  0.069  1.667  2.069 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.932184   0.185773   5.018 8.24e-07 ***
## RepD.d         0.378161   0.254453   1.486    0.138    
## RepI.d         0.151462   0.254062   0.596    0.551    
## gend.mf        0.002299   0.371547   0.006    0.995    
## RepD.d:gend.mf 0.043678   0.508906   0.086    0.932    
## RepI.d:gend.mf 0.052632   0.508124   0.104    0.918    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.755 on 359 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.00763,    Adjusted R-squared:  -0.006191 
## F-statistic: 0.552 on 5 and 359 DF,  p-value: 0.7368

summary(lm(act30 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act30 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2581 -1.1685  0.7419  1.8228  2.4194 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8506     0.1964   4.330 1.96e-05 ***
## RepD.d           0.1130     0.2717   0.416    0.678    
## RepI.d           0.3627     0.2765   1.312    0.190    
## gend.mf         -0.5398     0.3929  -1.374    0.170    
## RepD.d:gend.mf   0.1126     0.5434   0.207    0.836    
## RepI.d:gend.mf   0.6294     0.5531   1.138    0.256    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 343 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01173,    Adjusted R-squared:  -0.002672 
## F-statistic: 0.8145 on 5 and 343 DF,  p-value: 0.5399

a. Means for gender differences

summary(lm(act5 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act5 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2540 -1.1852  0.4133  1.7460  2.6061 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8240     0.2001   4.118    5e-05 ***
## RepD.d           0.2485     0.2775   0.895   0.3713    
## RepI.d           0.1583     0.2913   0.544   0.5872    
## gend.mf         -0.8600     0.4001  -2.149   0.0324 *  
## RepD.d:gend.mf   0.6611     0.5550   1.191   0.2345    
## RepI.d:gend.mf   1.2658     0.5827   2.172   0.0306 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 286 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.02198,    Adjusted R-squared:  0.004878 
## F-statistic: 1.285 on 5 and 286 DF,  p-value: 0.2702

round(mean(d$act5[d$party_factor == "Republican" & d$gend == "Female"], na.rm = T),2)

## [1] 1.25

round(mean(d$act5[d$party_factor == "Republican" & d$gend == "Male"], na.rm = T),2)

## [1] 0.39

summary(lm(act14 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act14 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.730 -1.520  0.322  1.538  3.609 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.03464    0.24852   0.139   0.8893  
## RepD.d          0.56100    0.32421   1.730   0.0847 .
## RepI.d          0.32953    0.34348   0.959   0.3382  
## gend.mf        -1.28666    0.49704  -2.589   0.0101 *
## RepD.d:gend.mf  1.55485    0.64842   2.398   0.0172 *
## RepI.d:gend.mf  0.97500    0.68696   1.419   0.1569  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.022 on 277 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.02875,    Adjusted R-squared:  0.01121 
## F-statistic:  1.64 on 5 and 277 DF,  p-value: 0.1496

round(mean(d$act14[d$party_factor == "Republican" & d$gend == "Female"], na.rm = T),2)

## [1] 0.68

round(mean(d$act14[d$party_factor == "Republican" & d$gend == "Male"], na.rm = T),2)

## [1] -0.61

summary(lm(act20 ~ (RepD.d + RepI.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act20 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5139 -1.1429  0.4861  1.5556  2.3226 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.09565    0.19038   5.755 2.06e-08 ***
## RepD.d          0.02878    0.26102   0.110   0.9123    
## RepI.d          0.16327    0.27223   0.600   0.5491    
## gend.mf        -0.83647    0.38075  -2.197   0.0288 *  
## RepD.d:gend.mf  1.11391    0.52204   2.134   0.0336 *  
## RepI.d:gend.mf  0.60433    0.54446   1.110   0.2679    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.772 on 313 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02125,    Adjusted R-squared:  0.005613 
## F-statistic: 1.359 on 5 and 313 DF,  p-value: 0.2396

round(mean(d$act20[d$party_factor == "Republican" & d$gend == "Female"], na.rm = T),2)

## [1] 1.51

round(mean(d$act20[d$party_factor == "Republican" & d$gend == "Male"], na.rm = T),2)

## [1] 0.68

3. Gender x Condition

Significant effects: Actions 26

summary(lm(act1 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7273 -2.0492 -0.0019  1.9273  3.6522 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            -0.7811     0.2073  -3.768 0.000188 ***
## RepD.d                  1.0640     0.2842   3.744 0.000206 ***
## RepI.d                  0.8168     0.2904   2.813 0.005134 ** 
## gend.mf                -0.6614     0.4147  -1.595 0.111450    
## cond.c                  0.3601     0.4147   0.868 0.385684    
## RepD.d:gend.mf          0.7289     0.5684   1.282 0.200436    
## RepI.d:gend.mf          0.7290     0.5808   1.255 0.210055    
## RepD.d:cond.c          -0.5531     0.5684  -0.973 0.331065    
## RepI.d:cond.c          -1.0651     0.5808  -1.834 0.067336 .  
## gend.mf:cond.c          1.1184     0.8293   1.349 0.178183    
## RepD.d:gend.mf:cond.c  -0.7989     1.1369  -0.703 0.482614    
## RepI.d:gend.mf:cond.c  -2.3393     1.1615  -2.014 0.044634 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 432 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.04596,    Adjusted R-squared:  0.02167 
## F-statistic: 1.892 on 11 and 432 DF,  p-value: 0.03851

summary(lm(act2 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6765 -0.9062  0.3235  1.3889  2.5000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.93816    0.21937   4.277 2.63e-05 ***
## RepD.d                 0.26814    0.29308   0.915   0.3611    
## RepI.d                 0.15133    0.30540   0.496   0.6206    
## gend.mf               -0.26522    0.43874  -0.604   0.5460    
## cond.c                -0.47008    0.43874  -1.071   0.2849    
## RepD.d:gend.mf         0.13039    0.58616   0.222   0.8241    
## RepI.d:gend.mf         0.46198    0.61081   0.756   0.4501    
## RepD.d:cond.c          0.53955    0.58616   0.920   0.3581    
## RepI.d:cond.c         -0.03982    0.61081  -0.065   0.9481    
## gend.mf:cond.c        -0.28207    0.87747  -0.321   0.7481    
## RepD.d:gend.mf:cond.c  2.03201    1.17232   1.733   0.0842 .  
## RepI.d:gend.mf:cond.c  0.18671    1.22162   0.153   0.8786    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 273 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.03645,    Adjusted R-squared:  -0.002379 
## F-statistic: 0.9387 on 11 and 273 DF,  p-value: 0.5035

summary(lm(act3 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4808 -1.7697  0.1176  1.5192  3.7000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -0.77115    0.20396  -3.781 0.000179 ***
## RepD.d                 0.86051    0.27136   3.171 0.001628 ** 
## RepI.d                 0.61337    0.27451   2.234 0.025975 *  
## gend.mf               -0.20769    0.40793  -0.509 0.610917    
## cond.c                 0.34231    0.40793   0.839 0.401862    
## RepD.d:gend.mf         0.02327    0.54271   0.043 0.965813    
## RepI.d:gend.mf         0.22579    0.54903   0.411 0.681096    
## RepD.d:cond.c         -0.32596    0.54271  -0.601 0.548421    
## RepI.d:cond.c         -0.30109    0.54903  -0.548 0.583702    
## gend.mf:cond.c         0.81538    0.81586   0.999 0.318156    
## RepD.d:gend.mf:cond.c -1.97952    1.08543  -1.824 0.068893 .  
## RepI.d:gend.mf:cond.c -1.14497    1.09805  -1.043 0.297665    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.013 on 427 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.03842,    Adjusted R-squared:  0.01365 
## F-statistic: 1.551 on 11 and 427 DF,  p-value: 0.1107

summary(lm(act4 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6923 -1.9844  0.1636  1.9750  4.0625 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -0.3181     0.2315  -1.374   0.1702  
## RepD.d                  0.6464     0.3092   2.091   0.0373 *
## RepI.d                  0.1083     0.3284   0.330   0.7418  
## gend.mf                -0.5172     0.4629  -1.117   0.2646  
## cond.c                 -0.1006     0.4629  -0.217   0.8282  
## RepD.d:gend.mf          0.6594     0.6184   1.066   0.2870  
## RepI.d:gend.mf          0.5667     0.6568   0.863   0.3888  
## RepD.d:cond.c           0.4628     0.6184   0.748   0.4547  
## RepI.d:cond.c          -0.8478     0.6568  -1.291   0.1976  
## gend.mf:cond.c         -0.4392     0.9259  -0.474   0.6355  
## RepD.d:gend.mf:cond.c   0.2350     1.2368   0.190   0.8494  
## RepI.d:gend.mf:cond.c  -1.1737     1.3136  -0.893   0.3722  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.164 on 354 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.03473,    Adjusted R-squared:  0.004739 
## F-statistic: 1.158 on 11 and 354 DF,  p-value: 0.3153

summary(lm(act5 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2647 -1.2414  0.2432  1.4847  2.9048 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.87950    0.20588   4.272 2.66e-05 ***
## RepD.d                 0.18038    0.28208   0.639   0.5230    
## RepI.d                 0.10746    0.29590   0.363   0.7168    
## gend.mf               -0.74709    0.41176  -1.814   0.0707 .  
## cond.c                -0.39905    0.41176  -0.969   0.3333    
## RepD.d:gend.mf         0.57470    0.56417   1.019   0.3092    
## RepI.d:gend.mf         1.15779    0.59180   1.956   0.0514 .  
## RepD.d:cond.c         -0.04001    0.56417  -0.071   0.9435    
## RepI.d:cond.c          0.23159    0.59180   0.391   0.6958    
## gend.mf:cond.c        -0.84476    0.82352  -1.026   0.3059    
## RepD.d:gend.mf:cond.c  1.61761    1.12834   1.434   0.1528    
## RepI.d:gend.mf:cond.c  0.41044    1.18360   0.347   0.7290    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 280 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03883,    Adjusted R-squared:  0.001066 
## F-statistic: 1.028 on 11 and 280 DF,  p-value: 0.4213

summary(lm(act6 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.500 -1.108  0.600  1.600  2.400 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.02202    0.20198   5.060 6.86e-07 ***
## RepD.d                 0.06093    0.27483   0.222   0.8247    
## RepI.d                 0.46530    0.27469   1.694   0.0912 .  
## gend.mf               -0.39405    0.40396  -0.975   0.3300    
## cond.c                -0.09405    0.40396  -0.233   0.8160    
## RepD.d:gend.mf        -0.12425    0.54967  -0.226   0.8213    
## RepI.d:gend.mf         0.86058    0.54938   1.566   0.1182    
## RepD.d:cond.c         -0.10345    0.54967  -0.188   0.8508    
## RepI.d:cond.c         -0.27249    0.54938  -0.496   0.6202    
## gend.mf:cond.c        -0.11190    0.80792  -0.139   0.8899    
## RepD.d:gend.mf:cond.c -0.38835    1.09934  -0.353   0.7241    
## RepI.d:gend.mf:cond.c -0.03738    1.09875  -0.034   0.9729    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 342 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.02172,    Adjusted R-squared:  -0.009744 
## F-statistic: 0.6903 on 11 and 342 DF,  p-value: 0.7479

summary(lm(act7 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3725 -1.9167  0.0893  1.6275  3.5600 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            -0.5925     0.2172  -2.728  0.00665 **
## RepD.d                  0.6556     0.2888   2.270  0.02372 * 
## RepI.d                  0.6427     0.2995   2.146  0.03249 * 
## gend.mf                -0.2984     0.4343  -0.687  0.49247   
## cond.c                  0.4584     0.4343   1.055  0.29189   
## RepD.d:gend.mf          0.1203     0.5775   0.208  0.83505   
## RepI.d:gend.mf          0.4341     0.5990   0.725  0.46901   
## RepD.d:cond.c          -0.3074     0.5775  -0.532  0.59484   
## RepI.d:cond.c          -0.2560     0.5990  -0.427  0.66931   
## gend.mf:cond.c          0.4499     0.8687   0.518  0.60484   
## RepD.d:gend.mf:cond.c  -1.0293     1.1551  -0.891  0.37339   
## RepI.d:gend.mf:cond.c  -0.8269     1.1979  -0.690  0.49045   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.084 on 398 degrees of freedom
##   (135 observations deleted due to missingness)
## Multiple R-squared:  0.02484,    Adjusted R-squared:  -0.002116 
## F-statistic: 0.9215 on 11 and 398 DF,  p-value: 0.5194

summary(lm(act8 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5909 -1.6719 -0.1404  1.8596  4.4167 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -1.0934179  0.2064887  -5.295 1.84e-07 ***
## RepD.d                 1.4009694  0.2795089   5.012 7.67e-07 ***
## RepI.d                 0.7822775  0.2831360   2.763  0.00596 ** 
## gend.mf                0.0009383  0.4129773   0.002  0.99819    
## cond.c                -0.5579559  0.4129773  -1.351  0.17734    
## RepD.d:gend.mf         0.2930495  0.5590177   0.524  0.60037    
## RepI.d:gend.mf         0.0213425  0.5662720   0.038  0.96995    
## RepD.d:cond.c          0.8373333  0.5590177   1.498  0.13485    
## RepI.d:cond.c          0.4915574  0.5662720   0.868  0.38581    
## gend.mf:cond.c        -0.1789600  0.8259546  -0.217  0.82856    
## RepD.d:gend.mf:cond.c  0.1656597  1.1180355   0.148  0.88227    
## RepI.d:gend.mf:cond.c -0.4882431  1.1325440  -0.431  0.66659    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 464 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07492,    Adjusted R-squared:  0.05299 
## F-statistic: 3.416 on 11 and 464 DF,  p-value: 0.0001407

summary(lm(act9 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4615 -1.6087  0.2381  1.6512  3.4182 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            -0.5450     0.2049  -2.659  0.00812 **
## RepD.d                  0.6920     0.2706   2.557  0.01089 * 
## RepI.d                  0.7398     0.2732   2.708  0.00703 **
## gend.mf                -0.3014     0.4098  -0.735  0.46252   
## cond.c                  0.2804     0.4098   0.684  0.49417   
## RepD.d:gend.mf         -0.1109     0.5412  -0.205  0.83775   
## RepI.d:gend.mf          0.1974     0.5464   0.361  0.71813   
## RepD.d:cond.c          -0.3512     0.5412  -0.649  0.51677   
## RepI.d:cond.c          -0.3213     0.5464  -0.588  0.55675   
## gend.mf:cond.c          0.6565     0.8197   0.801  0.42361   
## RepD.d:gend.mf:cond.c  -1.2312     1.0824  -1.137  0.25596   
## RepI.d:gend.mf:cond.c  -1.1461     1.0928  -1.049  0.29484   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.982 on 432 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03168,    Adjusted R-squared:  0.007022 
## F-statistic: 1.285 on 11 and 432 DF,  p-value: 0.2302

summary(lm(act10 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9000 -1.7391  0.4211  1.5789  4.2609 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            -0.8187     0.2017  -4.059 5.80e-05 ***
## RepD.d                  1.4521     0.2744   5.292 1.87e-07 ***
## RepI.d                  1.1356     0.2711   4.189 3.36e-05 ***
## gend.mf                -0.2388     0.4035  -0.592    0.554    
## cond.c                 -0.4431     0.4035  -1.098    0.273    
## RepD.d:gend.mf          0.4175     0.5487   0.761    0.447    
## RepI.d:gend.mf          0.1506     0.5421   0.278    0.781    
## RepD.d:cond.c           0.5854     0.5487   1.067    0.287    
## RepI.d:cond.c           0.5687     0.5421   1.049    0.295    
## gend.mf:cond.c         -0.4048     0.8069  -0.502    0.616    
## RepD.d:gend.mf:cond.c   0.8292     1.0975   0.756    0.450    
## RepI.d:gend.mf:cond.c   1.0627     1.0843   0.980    0.328    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.054 on 461 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.08452,    Adjusted R-squared:  0.06267 
## F-statistic: 3.869 on 11 and 461 DF,  p-value: 2.315e-05

summary(lm(act11 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -1.1111  0.3636  1.4286  3.0000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.72446    0.21422   3.382 0.000816 ***
## RepD.d                 0.37314    0.28868   1.293 0.197167    
## RepI.d                 0.16413    0.29432   0.558 0.577494    
## gend.mf                0.12684    0.42844   0.296 0.767398    
## cond.c                -0.14589    0.42844  -0.341 0.733714    
## RepD.d:gend.mf         0.19057    0.57736   0.330 0.741573    
## RepI.d:gend.mf        -0.43344    0.58865  -0.736 0.462113    
## RepD.d:cond.c         -0.34146    0.57736  -0.591 0.554690    
## RepI.d:cond.c         -0.84751    0.58865  -1.440 0.150988    
## gend.mf:cond.c        -0.19307    0.85688  -0.225 0.821884    
## RepD.d:gend.mf:cond.c -0.09273    1.15472  -0.080 0.936046    
## RepI.d:gend.mf:cond.c -0.76130    1.17730  -0.647 0.518358    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.891 on 297 degrees of freedom
##   (236 observations deleted due to missingness)
## Multiple R-squared:  0.03642,    Adjusted R-squared:  0.0007281 
## F-statistic:  1.02 on 11 and 297 DF,  p-value: 0.428

summary(lm(act12 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4359 -2.0600 -0.1111  1.9130  4.2381 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -1.06663    0.22236  -4.797 2.35e-06 ***
## RepD.d                 1.27706    0.31024   4.116 4.76e-05 ***
## RepI.d                 1.23163    0.30585   4.027 6.87e-05 ***
## gend.mf               -0.60483    0.44472  -1.360    0.175    
## cond.c                -0.01788    0.44472  -0.040    0.968    
## RepD.d:gend.mf         0.18071    0.62049   0.291    0.771    
## RepI.d:gend.mf         0.60817    0.61170   0.994    0.321    
## RepD.d:cond.c         -0.02591    0.62049  -0.042    0.967    
## RepI.d:cond.c          0.06565    0.61170   0.107    0.915    
## gend.mf:cond.c         0.55956    0.88945   0.629    0.530    
## RepD.d:gend.mf:cond.c -0.70075    1.24097  -0.565    0.573    
## RepI.d:gend.mf:cond.c -0.87734    1.22340  -0.717    0.474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.145 on 368 degrees of freedom
##   (165 observations deleted due to missingness)
## Multiple R-squared:  0.06951,    Adjusted R-squared:  0.04169 
## F-statistic: 2.499 on 11 and 368 DF,  p-value: 0.004871

summary(lm(act13 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7778 -1.8242  0.0923  1.5417  3.9444 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.33730    0.21098  -1.599   0.1106  
## RepD.d                 0.64970    0.28511   2.279   0.0232 *
## RepI.d                 0.21039    0.27991   0.752   0.4527  
## gend.mf                0.69841    0.42195   1.655   0.0986 .
## cond.c                -0.74603    0.42195  -1.768   0.0778 .
## RepD.d:gend.mf        -0.27270    0.57022  -0.478   0.6327  
## RepI.d:gend.mf        -1.39697    0.55981  -2.495   0.0130 *
## RepD.d:cond.c          0.63976    0.57022   1.122   0.2625  
## RepI.d:cond.c          1.10832    0.55981   1.980   0.0484 *
## gend.mf:cond.c        -0.46032    0.84390  -0.545   0.5857  
## RepD.d:gend.mf:cond.c  1.68296    1.14044   1.476   0.1408  
## RepI.d:gend.mf:cond.c -0.07379    1.11963  -0.066   0.9475  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.047 on 427 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.05903,    Adjusted R-squared:  0.03479 
## F-statistic: 2.435 on 11 and 427 DF,  p-value: 0.00595

summary(lm(act14 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8947 -1.5556  0.2667  1.6471  3.2667 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           -0.04009    0.25875  -0.155  0.87700   
## RepD.d                 0.63606    0.33284   1.911  0.05706 . 
## RepI.d                 0.41885    0.35298   1.187  0.23641   
## gend.mf               -1.43649    0.51751  -2.776  0.00589 **
## cond.c                 0.48017    0.51751   0.928  0.35431   
## RepD.d:gend.mf         1.69485    0.66568   2.546  0.01145 * 
## RepI.d:gend.mf         1.04959    0.70596   1.487  0.13824   
## RepD.d:cond.c         -0.53591    0.66568  -0.805  0.42149   
## RepI.d:cond.c          0.05716    0.70596   0.081  0.93552   
## gend.mf:cond.c         1.00632    1.03502   0.972  0.33178   
## RepD.d:gend.mf:cond.c -1.57321    1.33135  -1.182  0.23838   
## RepI.d:gend.mf:cond.c -0.97610    1.41191  -0.691  0.48995   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.032 on 271 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.04048,    Adjusted R-squared:  0.001535 
## F-statistic: 1.039 on 11 and 271 DF,  p-value: 0.4117

summary(lm(act15 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8421 -1.7727  0.1579  1.7727  3.7727 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           -0.25664    0.20617  -1.245  0.21399   
## RepD.d                 0.80372    0.28318   2.838  0.00479 **
## RepI.d                 0.16628    0.28103   0.592  0.55443   
## gend.mf               -0.40230    0.41234  -0.976  0.32989   
## cond.c                -0.14316    0.41234  -0.347  0.72865   
## RepD.d:gend.mf         0.42935    0.56636   0.758  0.44889   
## RepI.d:gend.mf        -0.34316    0.56206  -0.611  0.54188   
## RepD.d:cond.c         -0.05933    0.56636  -0.105  0.91662   
## RepI.d:cond.c         -0.03308    0.56206  -0.059  0.95310   
## gend.mf:cond.c        -0.97342    0.82469  -1.180  0.23862   
## RepD.d:gend.mf:cond.c  1.80265    1.13272   1.591  0.11237   
## RepI.d:gend.mf:cond.c  1.37829    1.12412   1.226  0.22094   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 369 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.0454, Adjusted R-squared:  0.01694 
## F-statistic: 1.595 on 11 and 369 DF,  p-value: 0.09792

summary(lm(act16 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2121 -1.0500  0.0526  1.3846  2.6170 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.91780    0.20195   4.545 7.85e-06 ***
## RepD.d                 0.09576    0.27332   0.350    0.726    
## RepI.d                 0.07506    0.28729   0.261    0.794    
## gend.mf               -0.06048    0.40391  -0.150    0.881    
## cond.c                -0.20402    0.40391  -0.505    0.614    
## RepD.d:gend.mf        -0.12452    0.54665  -0.228    0.820    
## RepI.d:gend.mf         0.61322    0.57458   1.067    0.287    
## RepD.d:cond.c          0.12587    0.54665   0.230    0.818    
## RepI.d:cond.c          0.88369    0.57458   1.538    0.125    
## gend.mf:cond.c        -0.40535    0.80782  -0.502    0.616    
## RepD.d:gend.mf:cond.c -0.17519    1.09330  -0.160    0.873    
## RepI.d:gend.mf:cond.c  0.43064    1.14916   0.375    0.708    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.827 on 316 degrees of freedom
##   (217 observations deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  -0.009643 
## F-statistic: 0.7161 on 11 and 316 DF,  p-value: 0.7231

summary(lm(act17 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.5111  0.2941  1.6667  3.1304 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.20812    0.20059   1.038   0.3001  
## RepD.d                 0.50602    0.27314   1.853   0.0647 .
## RepI.d                 0.21518    0.28129   0.765   0.4447  
## gend.mf               -0.47524    0.40118  -1.185   0.2369  
## cond.c                -0.21333    0.40118  -0.532   0.5952  
## RepD.d:gend.mf         0.81970    0.54628   1.501   0.1343  
## RepI.d:gend.mf         0.44564    0.56258   0.792   0.4287  
## RepD.d:cond.c          0.11099    0.54628   0.203   0.8391  
## RepI.d:cond.c         -0.01103    0.56258  -0.020   0.9844  
## gend.mf:cond.c         0.02294    0.80237   0.029   0.9772  
## RepD.d:gend.mf:cond.c -0.27279    1.09256  -0.250   0.8030  
## RepI.d:gend.mf:cond.c -0.76375    1.12516  -0.679   0.4977  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.02 on 401 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01457,    Adjusted R-squared:  -0.01246 
## F-statistic: 0.539 on 11 and 401 DF,  p-value: 0.8766

summary(lm(act18 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.167 -1.150  0.250  1.333  2.850 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             0.4750     0.2024   2.347   0.0195 *
## RepD.d                  0.3857     0.2731   1.413   0.1587  
## RepI.d                  0.1251     0.2783   0.449   0.6534  
## gend.mf                 0.1333     0.4048   0.329   0.7421  
## cond.c                  0.0500     0.4048   0.124   0.9018  
## RepD.d:gend.mf          0.1013     0.5462   0.185   0.8529  
## RepI.d:gend.mf         -0.4629     0.5566  -0.832   0.4062  
## RepD.d:cond.c           0.1320     0.5462   0.242   0.8092  
## RepI.d:cond.c          -0.1204     0.5566  -0.216   0.8288  
## gend.mf:cond.c         -0.9333     0.8096  -1.153   0.2498  
## RepD.d:gend.mf:cond.c   1.3237     1.0924   1.212   0.2265  
## RepI.d:gend.mf:cond.c   0.9330     1.1132   0.838   0.4026  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 337 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01772,    Adjusted R-squared:  -0.01434 
## F-statistic: 0.5527 on 11 and 337 DF,  p-value: 0.8662

summary(lm(act19 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4722 -1.1034  0.2222  1.5331  3.0588 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.03854    0.21728   4.780 2.94e-06 ***
## RepD.d                 0.09398    0.30185   0.311  0.75579    
## RepI.d                 0.28621    0.29502   0.970  0.33288    
## gend.mf               -0.35813    0.43456  -0.824  0.41062    
## cond.c                -1.16223    0.43456  -2.675  0.00796 ** 
## RepD.d:gend.mf         0.42642    0.60371   0.706  0.48061    
## RepI.d:gend.mf         0.32853    0.59004   0.557  0.57815    
## RepD.d:cond.c          0.83468    0.60371   1.383  0.16798    
## RepI.d:cond.c          0.95490    0.59004   1.618  0.10680    
## gend.mf:cond.c        -1.34875    0.86911  -1.552  0.12192    
## RepD.d:gend.mf:cond.c  1.33717    1.20742   1.107  0.26912    
## RepI.d:gend.mf:cond.c  1.46475    1.18008   1.241  0.21564    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.794 on 259 degrees of freedom
##   (274 observations deleted due to missingness)
## Multiple R-squared:  0.045,  Adjusted R-squared:  0.004435 
## F-statistic: 1.109 on 11 and 259 DF,  p-value: 0.354

summary(lm(act20 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6111 -1.2857  0.3889  1.5000  2.7222 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.13408    0.18944   5.987 5.98e-09 ***
## RepD.d                -0.01622    0.25872  -0.063   0.9500    
## RepI.d                 0.13077    0.27017   0.484   0.6287    
## gend.mf               -0.75962    0.37887  -2.005   0.0458 *  
## cond.c                -0.57372    0.37887  -1.514   0.1310    
## RepD.d:gend.mf         1.02390    0.51744   1.979   0.0487 *  
## RepI.d:gend.mf         0.51563    0.54035   0.954   0.3407    
## RepD.d:cond.c         -0.37628    0.51744  -0.727   0.4677    
## RepI.d:cond.c          0.42713    0.54035   0.790   0.4299    
## gend.mf:cond.c        -0.75855    0.75774  -1.001   0.3176    
## RepD.d:gend.mf:cond.c  1.65855    1.03489   1.603   0.1100    
## RepI.d:gend.mf:cond.c  0.19458    1.08070   0.180   0.8572    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.747 on 307 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.06688,    Adjusted R-squared:  0.03345 
## F-statistic:     2 on 11 and 307 DF,  p-value: 0.02801

summary(lm(act21 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7442 -1.8400  0.1698  1.6250  3.6250 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            -0.6880     0.2107  -3.265  0.00119 **
## RepD.d                  0.7598     0.2866   2.651  0.00835 **
## RepI.d                  0.9165     0.2926   3.132  0.00187 **
## gend.mf                -0.6657     0.4215  -1.580  0.11503   
## cond.c                  0.5634     0.4215   1.337  0.18205   
## RepD.d:gend.mf          1.1094     0.5732   1.936  0.05366 . 
## RepI.d:gend.mf          0.5485     0.5853   0.937  0.34930   
## RepD.d:cond.c          -0.7360     0.5732  -1.284  0.19989   
## RepI.d:cond.c          -0.1587     0.5853  -0.271  0.78637   
## gend.mf:cond.c          0.4564     0.8429   0.541  0.58848   
## RepD.d:gend.mf:cond.c  -0.8415     1.1463  -0.734  0.46333   
## RepI.d:gend.mf:cond.c  -1.4750     1.1706  -1.260  0.20840   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.053 on 386 degrees of freedom
##   (147 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  0.01812 
## F-statistic: 1.666 on 11 and 386 DF,  p-value: 0.07902

summary(lm(act22 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7021 -1.4886  0.1667  1.5238  3.5000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            -0.3146     0.2102  -1.497  0.13521    
## RepD.d                  1.1462     0.2829   4.051 6.15e-05 ***
## RepI.d                  0.7584     0.2817   2.692  0.00741 ** 
## gend.mf                -0.1435     0.4204  -0.341  0.73308    
## cond.c                 -0.0374     0.4204  -0.089  0.92916    
## RepD.d:gend.mf          0.4304     0.5658   0.761  0.44727    
## RepI.d:gend.mf          0.3204     0.5635   0.569  0.56998    
## RepD.d:cond.c          -0.4235     0.5658  -0.748  0.45462    
## RepI.d:cond.c           0.2154     0.5635   0.382  0.70248    
## gend.mf:cond.c         -0.3797     0.8407  -0.452  0.65173    
## RepD.d:gend.mf:cond.c  -0.5985     1.1316  -0.529  0.59721    
## RepI.d:gend.mf:cond.c   0.2479     1.1269   0.220  0.82603    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.965 on 387 degrees of freedom
##   (146 observations deleted due to missingness)
## Multiple R-squared:  0.05667,    Adjusted R-squared:  0.02986 
## F-statistic: 2.114 on 11 and 387 DF,  p-value: 0.01862

summary(lm(act23 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5294 -2.2500  0.1563  1.9412  3.8000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -0.1367     0.2709  -0.505   0.6141  
## RepD.d                  0.2877     0.3602   0.799   0.4251  
## RepI.d                  0.4590     0.3748   1.225   0.2217  
## gend.mf                -0.1390     0.5418  -0.257   0.7977  
## cond.c                  0.4265     0.5418   0.787   0.4319  
## RepD.d:gend.mf          0.2847     0.7205   0.395   0.6930  
## RepI.d:gend.mf          0.2240     0.7495   0.299   0.7653  
## RepD.d:cond.c          -0.8259     0.7205  -1.146   0.2526  
## RepI.d:cond.c          -1.6210     0.7495  -2.163   0.0314 *
## gend.mf:cond.c          0.7220     1.0837   0.666   0.5058  
## RepD.d:gend.mf:cond.c  -0.5833     1.4409  -0.405   0.6859  
## RepI.d:gend.mf:cond.c  -2.9919     1.4991  -1.996   0.0469 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.258 on 277 degrees of freedom
##   (256 observations deleted due to missingness)
## Multiple R-squared:  0.03833,    Adjusted R-squared:  0.0001456 
## F-statistic: 1.004 on 11 and 277 DF,  p-value: 0.4431

summary(lm(act24 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3182 -1.6875 -0.1455  1.5822  4.5263 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.43190    0.20403  -2.117   0.0349 *
## RepD.d                 0.25415    0.27951   0.909   0.3638  
## RepI.d                 0.19715    0.28317   0.696   0.4867  
## gend.mf                0.18522    0.40806   0.454   0.6501  
## cond.c                -0.43522    0.40806  -1.067   0.2868  
## RepD.d:gend.mf         0.13845    0.55903   0.248   0.8045  
## RepI.d:gend.mf        -0.94204    0.56634  -1.663   0.0970 .
## RepD.d:cond.c         -0.03845    0.55903  -0.069   0.9452  
## RepI.d:cond.c         -0.40208    0.56634  -0.710   0.4781  
## gend.mf:cond.c        -0.77241    0.81611  -0.946   0.3445  
## RepD.d:gend.mf:cond.c  0.38339    1.11806   0.343   0.7318  
## RepI.d:gend.mf:cond.c -1.20562    1.13268  -1.064   0.2878  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.07 on 400 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.04277,    Adjusted R-squared:  0.01644 
## F-statistic: 1.625 on 11 and 400 DF,  p-value: 0.08932

summary(lm(act25 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3125 -1.1591  0.2364  1.6875  3.0435 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.17025    0.18563   0.917   0.3596    
## RepD.d                 0.98900    0.24993   3.957 8.88e-05 ***
## RepI.d                 0.62310    0.25140   2.479   0.0136 *  
## gend.mf               -0.30064    0.37125  -0.810   0.4185    
## cond.c                -0.14668    0.37125  -0.395   0.6930    
## RepD.d:gend.mf         0.54301    0.49987   1.086   0.2780    
## RepI.d:gend.mf        -0.02176    0.50280  -0.043   0.9655    
## RepD.d:cond.c          0.40155    0.49987   0.803   0.4222    
## RepI.d:cond.c          0.26909    0.50280   0.535   0.5928    
## gend.mf:cond.c         0.03975    0.74250   0.054   0.9573    
## RepD.d:gend.mf:cond.c -0.62774    0.99973  -0.628   0.5304    
## RepI.d:gend.mf:cond.c -0.61312    1.00560  -0.610   0.5424    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 429 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.05022,    Adjusted R-squared:  0.02587 
## F-statistic: 2.062 on 11 and 429 DF,  p-value: 0.02189

summary(lm(act26 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2400  0.6111  1.4762  2.5385 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.5642     0.2448   6.390 1.05e-09 ***
## RepD.d                 -0.1553     0.3273  -0.475   0.6356    
## RepI.d                  0.0229     0.3384   0.068   0.9461    
## gend.mf                -0.4170     0.4896  -0.852   0.3954    
## cond.c                 -0.6215     0.4896  -1.269   0.2057    
## RepD.d:gend.mf          0.7449     0.6545   1.138   0.2564    
## RepI.d:gend.mf          0.6786     0.6769   1.002   0.3173    
## RepD.d:cond.c           0.8561     0.6545   1.308   0.1923    
## RepI.d:cond.c           0.7402     0.6769   1.094   0.2754    
## gend.mf:cond.c         -2.3339     0.9792  -2.383   0.0180 *  
## RepD.d:gend.mf:cond.c   2.8231     1.3090   2.157   0.0322 *  
## RepI.d:gend.mf:cond.c   2.3016     1.3538   1.700   0.0906 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 211 degrees of freedom
##   (322 observations deleted due to missingness)
## Multiple R-squared:  0.04338,    Adjusted R-squared:  -0.006486 
## F-statistic: 0.8699 on 11 and 211 DF,  p-value: 0.5707

summary(lm(act27 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7895 -1.0714  0.1471  1.6544  3.0000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             0.5103     0.2040   2.502   0.0129 *
## RepD.d                  0.6703     0.2772   2.418   0.0162 *
## RepI.d                  0.1079     0.2874   0.375   0.7076  
## gend.mf                 0.2103     0.4079   0.515   0.6066  
## cond.c                 -0.7264     0.4079  -1.781   0.0760 .
## RepD.d:gend.mf          0.2736     0.5544   0.493   0.6220  
## RepI.d:gend.mf          0.1249     0.5748   0.217   0.8282  
## RepD.d:cond.c           0.2737     0.5544   0.494   0.6219  
## RepI.d:cond.c           0.2401     0.5748   0.418   0.6764  
## gend.mf:cond.c         -1.0088     0.8158  -1.236   0.2172  
## RepD.d:gend.mf:cond.c   0.4463     1.1088   0.403   0.6876  
## RepI.d:gend.mf:cond.c   0.8385     1.1495   0.729   0.4663  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 302 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.05283,    Adjusted R-squared:  0.01833 
## F-statistic: 1.531 on 11 and 302 DF,  p-value: 0.1192

summary(lm(act28 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3421 -1.0652  0.2593  1.6579  2.7143 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.94771    0.19104   4.961 1.09e-06 ***
## RepD.d                 0.24624    0.26607   0.925    0.355    
## RepI.d                -0.03878    0.27334  -0.142    0.887    
## gend.mf               -0.19305    0.38208  -0.505    0.614    
## cond.c                -0.58646    0.38208  -1.535    0.126    
## RepD.d:gend.mf         0.14959    0.53215   0.281    0.779    
## RepI.d:gend.mf        -0.07193    0.54668  -0.132    0.895    
## RepD.d:cond.c          0.15916    0.53215   0.299    0.765    
## RepI.d:cond.c          0.72608    0.54668   1.328    0.185    
## gend.mf:cond.c        -1.08899    0.76416  -1.425    0.155    
## RepD.d:gend.mf:cond.c  0.18802    1.06430   0.177    0.860    
## RepI.d:gend.mf:cond.c  0.16551    1.09337   0.151    0.880    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 357 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02841,    Adjusted R-squared:  -0.001523 
## F-statistic: 0.9491 on 11 and 357 DF,  p-value: 0.4931

summary(lm(act29 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -1.0541  0.1053  1.6905  2.1053 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.93906    0.19247   4.879 1.62e-06 ***
## RepD.d                 0.37165    0.26031   1.428    0.154    
## RepI.d                 0.13996    0.26053   0.537    0.591    
## gend.mf                0.01661    0.38495   0.043    0.966    
## cond.c                -0.07642    0.38495  -0.199    0.843    
## RepD.d:gend.mf         0.02863    0.52062   0.055    0.956    
## RepI.d:gend.mf         0.02969    0.52105   0.057    0.955    
## RepD.d:cond.c          0.16610    0.52062   0.319    0.750    
## RepI.d:cond.c          0.23559    0.52105   0.452    0.651    
## gend.mf:cond.c        -0.05769    0.76989  -0.075    0.940    
## RepD.d:gend.mf:cond.c  0.32277    1.04125   0.310    0.757    
## RepI.d:gend.mf:cond.c  0.38331    1.04210   0.368    0.713    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.768 on 353 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.009013,   Adjusted R-squared:  -0.02187 
## F-statistic: 0.2919 on 11 and 353 DF,  p-value: 0.9872

summary(lm(act30 ~ (RepD.d + RepI.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (RepD.d + RepI.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.1579  0.5556  1.7368  2.7273 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.8143     0.2041   3.990 8.11e-05 ***
## RepD.d                  0.1509     0.2780   0.543    0.588    
## RepI.d                  0.3870     0.2836   1.365    0.173    
## gend.mf                -0.6058     0.4081  -1.484    0.139    
## cond.c                  0.3308     0.4081   0.810    0.418    
## RepD.d:gend.mf          0.1754     0.5560   0.316    0.753    
## RepI.d:gend.mf          0.6739     0.5672   1.188    0.236    
## RepD.d:cond.c          -0.1080     0.5560  -0.194    0.846    
## RepI.d:cond.c          -0.1048     0.5672  -0.185    0.854    
## gend.mf:cond.c          0.2930     0.8163   0.359    0.720    
## RepD.d:gend.mf:cond.c   0.4837     1.1119   0.435    0.664    
## RepI.d:gend.mf:cond.c   0.1962     1.1344   0.173    0.863    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.877 on 337 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01829,    Adjusted R-squared:  -0.01375 
## F-statistic: 0.5709 on 11 and 337 DF,  p-value: 0.8523

a. Means, etc.

aggregate(d$act26[d$party_factor == "Republican" & d$gend == "Female"], list(d$cond[d$party_factor == "Republican" & d$gend == "Female"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1    x
## 1 climate 2.05
## 2    ctrl 1.50

aggregate(d$act26[d$party_factor == "Republican" & d$gend == "Male"], list(d$cond[d$party_factor == "Republican" & d$gend == "Male"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1    x
## 1 climate 0.46
## 2    ctrl 2.25

ii. Independents

Above 0: 2;5;6;11;14;16;17;18;19;20;25;26;27;28;29;30 below: 8,

# Action 1
ind.b1 <- lm(act1 ~ (IndD.d + IndR.d), data = d)

summary(ind.b1) 

## 
## Call:
## lm(formula = act1 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2770 -2.2199 -0.0486  1.7230  3.5844 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.04861    0.17855   0.272   0.7856  
## IndD.d       0.22842    0.25080   0.911   0.3629  
## IndR.d      -0.63303    0.24838  -2.549   0.0111 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.143 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02882,    Adjusted R-squared:  0.02443 
## F-statistic: 6.572 on 2 and 443 DF,  p-value: 0.001539

# Action 2
ind.b2 <- lm(act2 ~ (IndD.d + IndR.d), data = d)

summary(ind.b2) # yes, above 0

## 
## Call:
## lm(formula = act2 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.280 -1.104  0.022  1.720  2.022 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1042     0.1848   5.975 6.87e-09 ***
## IndD.d        0.1758     0.2587   0.680    0.497    
## IndR.d       -0.1261     0.2649  -0.476    0.634    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.811 on 284 degrees of freedom
##   (258 observations deleted due to missingness)
## Multiple R-squared:  0.004714,   Adjusted R-squared:  -0.002295 
## F-statistic: 0.6726 on 2 and 284 DF,  p-value: 0.5112

# Action 3
ind.b3 <- lm(act3 ~ (IndD.d + IndR.d), data = d)

summary(ind.b3) 

## 
## Call:
## lm(formula = act3 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1325 -1.8289  0.1711  1.6884  3.6884 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.1711     0.1622  -1.055   0.2921  
## IndD.d        0.3035     0.2297   1.321   0.1871  
## IndR.d       -0.5174     0.2351  -2.201   0.0283 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.999 on 438 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.02743,    Adjusted R-squared:  0.02299 
## F-statistic: 6.176 on 2 and 438 DF,  p-value: 0.002265

# Action 4
ind.b4 <- lm(act4 ~ (IndD.d + IndR.d), data = d)

summary(ind.b4)

## 
## Call:
## lm(formula = act4 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3030 -1.7931  0.2069  2.2069  3.2333 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.23333    0.19668  -1.186   0.2363  
## IndD.d       0.53636    0.27176   1.974   0.0492 *
## IndR.d       0.02644    0.28054   0.094   0.9250  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.155 on 365 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01352,    Adjusted R-squared:  0.008119 
## F-statistic: 2.502 on 2 and 365 DF,  p-value: 0.08333

# Action 5
ind.b5 <- lm(act5 ~ (IndD.d + IndR.d), data = d)

summary(ind.b5) # yes, above 0

## 
## Call:
## lm(formula = act5 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1165 -1.1165  0.1053  1.8835  2.1053 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8947     0.1916   4.669 4.63e-06 ***
## IndD.d        0.2218     0.2657   0.835    0.405    
## IndR.d        0.0636     0.2703   0.235    0.814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.868 on 291 degrees of freedom
##   (251 observations deleted due to missingness)
## Multiple R-squared:  0.002558,   Adjusted R-squared:  -0.004298 
## F-statistic: 0.3731 on 2 and 291 DF,  p-value: 0.6889

# Action 6
ind.b6 <- lm(act6 ~ (IndD.d + IndR.d), data = d)

summary(ind.b6) # yes, above 0

## 
## Call:
## lm(formula = act6 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.400 -1.111  0.600  1.600  1.889 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.4000     0.1623   8.627   <2e-16 ***
## IndD.d       -0.1982     0.2350  -0.844    0.399    
## IndR.d       -0.2889     0.2334  -1.238    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 353 degrees of freedom
##   (189 observations deleted due to missingness)
## Multiple R-squared:  0.004549,   Adjusted R-squared:  -0.001091 
## F-statistic: 0.8065 on 2 and 353 DF,  p-value: 0.4472

# Action 7
ind.b7 <- lm(act7 ~ (IndD.d + IndR.d), data = d)

summary(ind.b7)

## 
## Call:
## lm(formula = act7 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0993 -1.9929  0.0071  1.9007  3.4962 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.007143   0.174639  -0.041   0.9674  
## IndD.d       0.106434   0.246538   0.432   0.6662  
## IndR.d      -0.489040   0.251182  -1.947   0.0522 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 409 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  0.01046 
## F-statistic: 3.172 on 2 and 409 DF,  p-value: 0.04296

# Action 8
ind.b8 <- lm(act8 ~ (IndD.d + IndR.d), data = d)

summary(ind.b8) # yes, below 0

## 
## Call:
## lm(formula = act8 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2327 -1.8734 -0.2327  1.7673  4.1266 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.3292     0.1660  -1.983 0.047937 *  
## IndD.d        0.5619     0.2355   2.386 0.017425 *  
## IndR.d       -0.7974     0.2359  -3.381 0.000783 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.106 on 475 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.06556,    Adjusted R-squared:  0.06162 
## F-statistic: 16.66 on 2 and 475 DF,  p-value: 1.014e-07

# Action 9
ind.b9 <- lm(act9 ~ (IndD.d + IndR.d), data = d)

summary(ind.b9) 

## 
## Call:
## lm(formula = act9 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2617 -1.5594  0.1196  1.7383  3.4406 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.20130    0.15882   1.267  0.20565   
## IndD.d       0.06045    0.22648   0.267  0.78967   
## IndR.d      -0.64186    0.22888  -2.804  0.00526 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.971 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02498,    Adjusted R-squared:  0.02058 
## F-statistic: 5.676 on 2 and 443 DF,  p-value: 0.003682

# Action 10
ind.b10 <- lm(act10 ~ (IndD.d + IndR.d), data = d)

summary(ind.b10) # yes, above 0

## 
## Call:
## lm(formula = act10 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5935 -2.2208  0.4065  1.6545  3.7792 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3455     0.1588   2.176   0.0301 *  
## IndD.d        0.2481     0.2281   1.087   0.2774    
## IndR.d       -1.1247     0.2285  -4.922 1.19e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 471 degrees of freedom
##   (71 observations deleted due to missingness)
## Multiple R-squared:  0.07812,    Adjusted R-squared:  0.07421 
## F-statistic: 19.96 on 2 and 471 DF,  p-value: 4.791e-09

# Action 11
ind.b11 <- lm(act11 ~ (IndD.d + IndR.d), data = d)

summary(ind.b11) # yes, above 0

## 
## Call:
## lm(formula = act11 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0755 -1.0755  0.3093  1.9245  2.3093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.98148    0.18152   5.407 1.29e-07 ***
## IndD.d       0.09399    0.25792   0.364    0.716    
## IndR.d      -0.29076    0.26389  -1.102    0.271    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.886 on 308 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.007306,   Adjusted R-squared:  0.0008601 
## F-statistic: 1.133 on 2 and 308 DF,  p-value: 0.3233

# Action 12
ind.b12 <- lm(act12 ~ (IndD.d + IndR.d), data = d)

summary(ind.b12) 

## 
## Call:
## lm(formula = act12 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3167 -2.0992 -0.1603  1.8397  3.9008 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.1603     0.1858   0.863    0.389    
## IndD.d        0.1564     0.2687   0.582    0.561    
## IndR.d       -1.0611     0.2627  -4.039  6.5e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.126 on 379 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.06166,    Adjusted R-squared:  0.0567 
## F-statistic: 12.45 on 2 and 379 DF,  p-value: 5.789e-06

# Action 13
ind.b13 <- lm(act13 ~ (IndD.d + IndR.d), data = d)

summary(ind.b13) 

## 
## Call:
## lm(formula = act13 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1888 -2.0186 -0.0186  1.8112  3.5588 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.01863    0.16261   0.115   0.9088  
## IndD.d       0.17018    0.23709   0.718   0.4733  
## IndR.d      -0.57746    0.24030  -2.403   0.0167 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.063 on 437 degrees of freedom
##   (105 observations deleted due to missingness)
## Multiple R-squared:  0.0225, Adjusted R-squared:  0.01802 
## F-statistic: 5.029 on 2 and 437 DF,  p-value: 0.006932

# Action 14
ind.b14 <- lm(act14 ~ (IndD.d + IndR.d), data = d)

summary(ind.b14) # yes, above 0

## 
## Call:
## lm(formula = act14 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5481 -1.4444 -0.3171  1.5556  2.6829 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4444     0.2042   2.177   0.0303 *
## IndD.d        0.1036     0.2853   0.363   0.7167  
## IndR.d       -0.1274     0.3034  -0.420   0.6749  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.032 on 282 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.002098,   Adjusted R-squared:  -0.00498 
## F-statistic: 0.2964 on 2 and 282 DF,  p-value: 0.7437

# Action 15
ind.b15 <- lm(act15 ~ (IndD.d + IndR.d), data = d)

summary(ind.b15) 

## 
## Call:
## lm(formula = act15 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5328 -1.8049  0.1951  1.4672  3.1951 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.07299    0.17449   0.418   0.6760  
## IndD.d       0.45979    0.25424   1.808   0.0713 .
## IndR.d      -0.26811    0.25370  -1.057   0.2913  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.042 on 379 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.02059,    Adjusted R-squared:  0.01542 
## F-statistic: 3.984 on 2 and 379 DF,  p-value: 0.0194

# Action 16
ind.b16 <- lm(act16 ~ (IndD.d + IndR.d), data = d)

summary(ind.b16) # yes, above 0

## 
## Call:
## lm(formula = act16 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0550 -1.0550  0.0841  1.1770  2.1770 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.82301    0.17141   4.801 2.41e-06 ***
## IndD.d       0.23204    0.24463   0.949    0.344    
## IndR.d       0.09288    0.24579   0.378    0.706    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 326 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.002782,   Adjusted R-squared:  -0.003336 
## F-statistic: 0.4547 on 2 and 326 DF,  p-value: 0.6351

# Action 17
ind.b17 <- lm(act17 ~ (IndD.d + IndR.d), data = d)

summary(ind.b17) # yes, above 0

## 
## Call:
## lm(formula = act17 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.650 -1.420  0.350  1.702  2.702 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.4196     0.1677   2.503   0.0127 *
## IndD.d        0.2304     0.2384   0.967   0.3343  
## IndR.d       -0.1219     0.2425  -0.503   0.6155  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.005 on 411 degrees of freedom
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.005261,   Adjusted R-squared:  0.0004204 
## F-statistic: 1.087 on 2 and 411 DF,  p-value: 0.3382

# Action 18
ind.b18 <- lm(act18 ~ (IndD.d + IndR.d), data = d)

summary(ind.b18) #  yes, above 0

## 
## Call:
## lm(formula = act18 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8430 -1.1338  0.3145  1.3145  2.5755 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6855     0.1647   4.162 3.98e-05 ***
## IndD.d        0.1575     0.2344   0.672    0.502    
## IndR.d       -0.2610     0.2426  -1.076    0.283    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 348 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.008477,   Adjusted R-squared:  0.002779 
## F-statistic: 1.488 on 2 and 348 DF,  p-value: 0.2273

# Action 19
ind.b19 <- lm(act19 ~ (IndD.d + IndR.d), data = d)

summary(ind.b19) # yes, above 0

## 
## Call:
## lm(formula = act19 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.337 -1.032  0.000  1.663  2.000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3368     0.1846   7.242 4.66e-12 ***
## IndD.d       -0.2104     0.2670  -0.788    0.431    
## IndR.d       -0.3368     0.2647  -1.273    0.204    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.799 on 269 degrees of freedom
##   (273 observations deleted due to missingness)
## Multiple R-squared:  0.006131,   Adjusted R-squared:  -0.001258 
## F-statistic: 0.8297 on 2 and 269 DF,  p-value: 0.4373

# Action 20
ind.b20 <- lm(act20 ~ (IndD.d + IndR.d), data = d)

summary(ind.b20) # yes, above 0

## 
## Call:
## lm(formula = act20 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3148 -1.2621  0.6852  1.6852  1.9000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.31481    0.17090   7.693 1.82e-13 ***
## IndD.d      -0.21481    0.24059  -0.893    0.373    
## IndR.d      -0.05268    0.24461  -0.215    0.830    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 318 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.002716,   Adjusted R-squared:  -0.003556 
## F-statistic: 0.433 on 2 and 318 DF,  p-value: 0.6489

# Action 21
ind.b21 <- lm(act21 ~ (IndD.d + IndR.d), data = d)

summary(ind.b21) 

## 
## Call:
## lm(formula = act21 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2214 -2.0071 -0.0071  1.7786  3.5000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.2214     0.1796   1.233  0.21846   
## IndD.d       -0.2143     0.2495  -0.859  0.39085   
## IndR.d       -0.7214     0.2555  -2.824  0.00499 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.056 on 397 degrees of freedom
##   (145 observations deleted due to missingness)
## Multiple R-squared:  0.02074,    Adjusted R-squared:  0.01581 
## F-statistic: 4.205 on 2 and 397 DF,  p-value: 0.01559

# Action 22
ind.b22 <- lm(act22 ~ (IndD.d + IndR.d), data = d)

summary(ind.b22) # yes, above 0

## 
## Call:
## lm(formula = act22 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8045 -1.3873  0.1955  1.6127  3.2857 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.3873     0.1638   2.365  0.01853 * 
## IndD.d        0.4172     0.2355   1.771  0.07728 . 
## IndR.d       -0.6730     0.2389  -2.817  0.00508 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.952 on 398 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.04897,    Adjusted R-squared:  0.04419 
## F-statistic: 10.25 on 2 and 398 DF,  p-value: 4.577e-05

# Action 23
ind.b23 <- lm(act23 ~ (IndD.d + IndR.d), data = d)

summary(ind.b23) 

## 
## Call:
## lm(formula = act23 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3810 -2.3810  0.0824  2.0824  3.0824 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3810     0.2206   1.727   0.0853 .
## IndD.d       -0.2210     0.3159  -0.700   0.4848  
## IndR.d       -0.4633     0.3298  -1.405   0.1612  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.261 on 287 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.006836,   Adjusted R-squared:  -8.474e-05 
## F-statistic: 0.9878 on 2 and 287 DF,  p-value: 0.3737

# Action 24
ind.b24 <- lm(act24 ~ (IndD.d + IndR.d), data = d)

summary(ind.b24) 

## 
## Call:
## lm(formula = act24 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9281 -1.9281  0.0719  1.5038  3.5038 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.07194    0.17713  -0.406    0.685  
## IndD.d      -0.14082    0.24962  -0.564    0.573  
## IndR.d      -0.43182    0.25331  -1.705    0.089 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.088 on 410 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.007291,   Adjusted R-squared:  0.002448 
## F-statistic: 1.506 on 2 and 410 DF,  p-value: 0.2231

# Action 25
ind.b25 <- lm(act25 ~ (IndD.d + IndR.d), data = d)

summary(ind.b25) # yes, above 0

## 
## Call:
## lm(formula = act25 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1149 -1.1149  0.1611  1.7603  2.7603 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8389     0.1500   5.592 3.96e-08 ***
## IndD.d        0.2759     0.2125   1.298  0.19487    
## IndR.d       -0.5992     0.2133  -2.810  0.00518 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 440 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.0383, Adjusted R-squared:  0.03393 
## F-statistic: 8.762 on 2 and 440 DF,  p-value: 0.0001856

# Action 26
ind.b26 <- lm(act26 ~ (IndD.d + IndR.d), data = d)

summary(ind.b26) # yes, above 0

## 
## Call:
## lm(formula = act26 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5672 -1.3974  0.6026  1.4810  1.6026 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.51899    0.20488   7.414 2.59e-12 ***
## IndD.d      -0.12155    0.29067  -0.418    0.676    
## IndR.d       0.04818    0.30244   0.159    0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 221 degrees of freedom
##   (321 observations deleted due to missingness)
## Multiple R-squared:  0.001544,   Adjusted R-squared:  -0.007492 
## F-statistic: 0.1709 on 2 and 221 DF,  p-value: 0.843

# Action 27
ind.b27 <- lm(act27 ~ (IndD.d + IndR.d), data = d)

summary(ind.b27) # yes, above 0

## 
## Call:
## lm(formula = act27 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1404 -1.1404  0.1489  1.5670  2.5670 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.5619     0.1821   3.086  0.00221 **
## IndD.d        0.5784     0.2523   2.292  0.02255 * 
## IndR.d       -0.1289     0.2627  -0.491  0.62401   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 313 degrees of freedom
##   (229 observations deleted due to missingness)
## Multiple R-squared:  0.02742,    Adjusted R-squared:  0.02121 
## F-statistic: 4.413 on 2 and 313 DF,  p-value: 0.01288

# Action 28
ind.b28 <- lm(act28 ~ (IndD.d + IndR.d), data = d)

summary(ind.b28) # yes, above 0

## 
## Call:
## lm(formula = act28 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2266 -0.9508  0.0579  1.7734  2.0579 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.942149   0.165303   5.700 2.47e-08 ***
## IndD.d      0.284414   0.230555   1.234    0.218    
## IndR.d      0.008671   0.233294   0.037    0.970    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.818 on 368 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.005379,   Adjusted R-squared:  -2.643e-05 
## F-statistic: 0.9951 on 2 and 368 DF,  p-value: 0.3707

# Action 29
ind.b29 <- lm(act29 ~ (IndD.d + IndR.d), data = d)

summary(ind.b29) # yes, above 0

## 
## Call:
## lm(formula = act29 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3200 -1.0720  0.0684  1.6800  2.0684 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0720     0.1561   6.866 2.85e-11 ***
## IndD.d        0.2480     0.2208   1.123    0.262    
## IndR.d       -0.1404     0.2245  -0.625    0.532    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.745 on 364 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.008418,   Adjusted R-squared:  0.00297 
## F-statistic: 1.545 on 2 and 364 DF,  p-value: 0.2147

# Action 30
ind.b30 <- lm(act30 ~ (IndD.d + IndR.d), data = d)

summary(ind.b30) # yes, above 0

## 
## Call:
## lm(formula = act30 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1917 -1.0598  0.8083  1.8083  2.0263 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1917     0.1702   7.000 1.32e-11 ***
## IndD.d       -0.1318     0.2423  -0.544    0.587    
## IndR.d       -0.2180     0.2439  -0.894    0.372    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 348 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.002333,   Adjusted R-squared:  -0.003401 
## F-statistic: 0.4068 on 2 and 348 DF,  p-value: 0.6661

Significantly above 0:

1. condition differences?

actions: 11, 16

summary(lm(act1 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.417 -2.229  0.026  1.771  3.591 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.0161     0.1809   0.089   0.9291  
## IndD.d          0.2646     0.2526   1.047   0.2955  
## IndR.d         -0.6010     0.2506  -2.399   0.0169 *
## cond.c         -0.4256     0.3618  -1.177   0.2400  
## IndD.d:cond.c   0.1537     0.5053   0.304   0.7611  
## IndR.d:cond.c   0.4389     0.5011   0.876   0.3816  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.145 on 440 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.03317,    Adjusted R-squared:  0.02218 
## F-statistic: 3.019 on 5 and 440 DF,  p-value: 0.01084

summary(lm(act2 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3800 -1.1947  0.2292  1.6200  2.2292 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.04444    0.19079   5.474 9.74e-08 ***
## IndD.d         0.23556    0.26299   0.896    0.371    
## IndR.d        -0.05438    0.26928  -0.202    0.840    
## cond.c        -0.47778    0.38158  -1.252    0.212    
## IndD.d:cond.c  0.27778    0.52597   0.528    0.598    
## IndR.d:cond.c  0.03931    0.53856   0.073    0.942    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 281 degrees of freedom
##   (258 observations deleted due to missingness)
## Multiple R-squared:  0.01593,    Adjusted R-squared:  -0.001575 
## F-statistic:  0.91 on 5 and 281 DF,  p-value: 0.4749

summary(lm(act3 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2568 -1.8939  0.1061  1.6351  3.7500 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -1.635e-01  1.640e-01  -0.997   0.3193  
## IndD.d         2.984e-01  2.313e-01   1.290   0.1978  
## IndR.d        -5.291e-01  2.370e-01  -2.233   0.0261 *
## cond.c         1.149e-01  3.280e-01   0.350   0.7263  
## IndD.d:cond.c  1.289e-01  4.626e-01   0.279   0.7807  
## IndR.d:cond.c -4.762e-06  4.739e-01   0.000   1.0000  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.004 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.0292, Adjusted R-squared:  0.01804 
## F-statistic: 2.617 on 5 and 435 DF,  p-value: 0.024

summary(lm(act4 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4923 -2.0000  0.2258  2.0000  3.5385 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.26923    0.19850  -1.356   0.1758  
## IndD.d         0.57509    0.27312   2.106   0.0359 *
## IndR.d         0.06373    0.28219   0.226   0.8214  
## cond.c        -0.53846    0.39700  -1.356   0.1758  
## IndD.d:cond.c  0.91137    0.54624   1.668   0.0961 .
## IndR.d:cond.c  0.49784    0.56437   0.882   0.3783  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.155 on 362 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.0212, Adjusted R-squared:  0.007677 
## F-statistic: 1.568 on 5 and 362 DF,  p-value: 0.1683

summary(lm(act5 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3585 -0.9200  0.1818  1.6415  2.1818 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.89333    0.19206   4.651 5.03e-06 ***
## IndD.d         0.21591    0.26616   0.811    0.418    
## IndR.d         0.08893    0.27217   0.327    0.744    
## cond.c        -0.05333    0.38411  -0.139    0.890    
## IndD.d:cond.c -0.44516    0.53232  -0.836    0.404    
## IndR.d:cond.c -0.27483    0.54434  -0.505    0.614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 288 degrees of freedom
##   (251 observations deleted due to missingness)
## Multiple R-squared:  0.01139,    Adjusted R-squared:  -0.005774 
## F-statistic: 0.6636 on 5 and 288 DF,  p-value: 0.6514

summary(lm(act6 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5278 -1.1731  0.4722  1.7736  1.9385 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.3771     0.1647   8.363 1.47e-15 ***
## IndD.d         -0.1762     0.2371  -0.743    0.458    
## IndR.d         -0.2598     0.2361  -1.100    0.272    
## cond.c         -0.3014     0.3293  -0.915    0.361    
## IndD.d:cond.c   0.2699     0.4743   0.569    0.570    
## IndR.d:cond.c   0.1898     0.4723   0.402    0.688    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.82 on 350 degrees of freedom
##   (189 observations deleted due to missingness)
## Multiple R-squared:  0.007256,   Adjusted R-squared:  -0.006926 
## F-statistic: 0.5116 on 5 and 350 DF,  p-value: 0.7675

summary(lm(act7 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2192 -1.9706  0.0294  1.7808  3.6613 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.01235    0.17632   0.070   0.9442  
## IndD.d         0.08254    0.24796   0.333   0.7394  
## IndR.d        -0.51691    0.25270  -2.046   0.0414 *
## cond.c         0.30317    0.35263   0.860   0.3904  
## IndD.d:cond.c -0.05459    0.49591  -0.110   0.9124  
## IndR.d:cond.c  0.01029    0.50539   0.020   0.9838  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 406 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.02009,    Adjusted R-squared:  0.008026 
## F-statistic: 1.665 on 5 and 406 DF,  p-value: 0.1419

summary(lm(act8 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.367 -1.648 -0.100  1.843  4.352 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.3245     0.1667  -1.946   0.0522 . 
## IndD.d          0.5580     0.2360   2.364   0.0185 * 
## IndR.d         -0.7731     0.2372  -3.260   0.0012 **
## cond.c          0.1010     0.3335   0.303   0.7620   
## IndD.d:cond.c   0.1661     0.4720   0.352   0.7251   
## IndR.d:cond.c  -0.6104     0.4743  -1.287   0.1987   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.106 on 472 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.07148,    Adjusted R-squared:  0.06165 
## F-statistic: 7.267 on 5 and 472 DF,  p-value: 1.426e-06

summary(lm(act9 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2973 -1.5231  0.1162  1.7027  3.4769 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.20608    0.16166   1.275  0.20307   
## IndD.d         0.05591    0.22886   0.244  0.80713   
## IndR.d        -0.64967    0.23174  -2.803  0.00528 **
## cond.c         0.05660    0.32332   0.175  0.86112   
## IndD.d:cond.c  0.01403    0.45771   0.031  0.97555   
## IndR.d:cond.c  0.01007    0.46348   0.022  0.98268   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.977 on 440 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02525,    Adjusted R-squared:  0.01417 
## F-statistic: 2.279 on 5 and 440 DF,  p-value: 0.04594

summary(lm(act10 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6133 -2.0595  0.3867  1.6404  3.9405 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.34425    0.15958   2.157   0.0315 *  
## IndD.d         0.24992    0.22899   1.091   0.2757    
## IndR.d        -1.10734    0.22980  -4.819 1.96e-06 ***
## cond.c        -0.03060    0.31916  -0.096   0.9237    
## IndD.d:cond.c  0.06894    0.45797   0.151   0.8804    
## IndR.d:cond.c -0.32416    0.45960  -0.705   0.4810    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 468 degrees of freedom
##   (71 observations deleted due to missingness)
## Multiple R-squared:  0.08043,    Adjusted R-squared:  0.0706 
## F-statistic: 8.187 on 5 and 468 DF,  p-value: 2.004e-07

summary(lm(act11 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3509 -1.0528  0.4314  1.6491  2.4314 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.95975    0.18079   5.309 2.13e-07 ***
## IndD.d         0.09305    0.25726   0.362   0.7178    
## IndR.d        -0.26666    0.26280  -1.015   0.3111    
## cond.c        -0.78225    0.36158  -2.163   0.0313 *  
## IndD.d:cond.c  0.30165    0.51452   0.586   0.5581    
## IndR.d:cond.c  0.69018    0.52559   1.313   0.1901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 305 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.0279, Adjusted R-squared:  0.01197 
## F-statistic: 1.751 on 5 and 305 DF,  p-value: 0.1228

summary(lm(act11 ~ (IndD.d + IndR.d)*clim.d, data = d))

## 
## Call:
## lm(formula = act11 ~ (IndD.d + IndR.d) * clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3509 -1.0528  0.4314  1.6491  2.4314 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.56863    0.26268   2.165   0.0312 *
## IndD.d         0.24387    0.37725   0.646   0.5185  
## IndR.d         0.07843    0.37149   0.211   0.8329  
## clim.d         0.78225    0.36158   2.163   0.0313 *
## IndD.d:clim.d -0.30165    0.51452  -0.586   0.5581  
## IndR.d:clim.d -0.69018    0.52559  -1.313   0.1901  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 305 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.0279, Adjusted R-squared:  0.01197 
## F-statistic: 1.751 on 5 and 305 DF,  p-value: 0.1228

summary(lm(act11 ~ (IndD.d + IndR.d)*ctrl.d, data = d))

## 
## Call:
## lm(formula = act11 ~ (IndD.d + IndR.d) * ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3509 -1.0528  0.4314  1.6491  2.4314 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.35088    0.24847   5.437 1.11e-07 ***
## IndD.d        -0.05777    0.34988  -0.165   0.8690    
## IndR.d        -0.61175    0.37181  -1.645   0.1009    
## ctrl.d        -0.78225    0.36158  -2.163   0.0313 *  
## IndD.d:ctrl.d  0.30165    0.51452   0.586   0.5581    
## IndR.d:ctrl.d  0.69018    0.52559   1.313   0.1901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 305 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.0279, Adjusted R-squared:  0.01197 
## F-statistic: 1.751 on 5 and 305 DF,  p-value: 0.1228

summary(lm(act12 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3333 -1.9851 -0.1029  1.8971  4.0149 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.1626     0.1865   0.872    0.384    
## IndD.d          0.1532     0.2698   0.568    0.571    
## IndR.d         -1.0607     0.2637  -4.022 6.97e-05 ***
## cond.c          0.1193     0.3730   0.320    0.749    
## IndD.d:cond.c  -0.1544     0.5396  -0.286    0.775    
## IndR.d:cond.c  -0.3530     0.5274  -0.669    0.504    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.133 on 376 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.06291,    Adjusted R-squared:  0.05045 
## F-statistic: 5.048 on 5 and 376 DF,  p-value: 0.0001718

summary(lm(act13 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3803 -2.0000  0.1977  1.7533  3.8133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.0345     0.1625   0.212   0.8320  
## IndD.d          0.1556     0.2366   0.658   0.5110  
## IndR.d         -0.5641     0.2405  -2.346   0.0194 *
## cond.c          0.4643     0.3249   1.429   0.1537  
## IndD.d:cond.c  -0.8446     0.4732  -1.785   0.0750 .
## IndR.d:cond.c  -1.0318     0.4810  -2.145   0.0325 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 434 degrees of freedom
##   (105 observations deleted due to missingness)
## Multiple R-squared:  0.03544,    Adjusted R-squared:  0.02433 
## F-statistic: 3.189 on 5 and 434 DF,  p-value: 0.007706

summary(lm(act14 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7209 -1.5370 -0.2321  1.7297  2.7679 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.47654    0.20654   2.307   0.0218 *
## IndD.d         0.07198    0.28745   0.250   0.8025  
## IndR.d        -0.16362    0.30621  -0.534   0.5935  
## cond.c         0.48879    0.41308   1.183   0.2377  
## IndD.d:cond.c -0.46582    0.57490  -0.810   0.4185  
## IndR.d:cond.c -0.40350    0.61241  -0.659   0.5105  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.037 on 279 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.007218,   Adjusted R-squared:  -0.01057 
## F-statistic: 0.4057 on 5 and 279 DF,  p-value: 0.8447

summary(lm(act15 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8098  0.2069  1.6290  3.2069 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.06167    0.17542   0.352   0.7254  
## IndD.d         0.47382    0.25518   1.857   0.0641 .
## IndR.d        -0.25742    0.25483  -1.010   0.3131  
## cond.c        -0.28207    0.35084  -0.804   0.4219  
## IndD.d:cond.c -0.04696    0.51035  -0.092   0.9267  
## IndR.d:cond.c  0.30435    0.50966   0.597   0.5508  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.047 on 376 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  0.01135 
## F-statistic: 1.875 on 5 and 376 DF,  p-value: 0.09788

summary(lm(act16 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1887 -1.0385  0.1404  1.5000  2.5000 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.84434    0.17144   4.925 1.35e-06 ***
## IndD.d         0.20998    0.24455   0.859   0.3912    
## IndR.d         0.07548    0.24585   0.307   0.7590    
## cond.c         0.68868    0.34287   2.009   0.0454 *  
## IndD.d:cond.c -0.72039    0.48911  -1.473   0.1418    
## IndR.d:cond.c -0.80903    0.49170  -1.645   0.1009    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 323 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.01546,    Adjusted R-squared:  0.0002187 
## F-statistic: 1.014 on 5 and 323 DF,  p-value: 0.4091

summary(lm(act16 ~ (IndD.d + IndR.d)*clim.d, data = d))

## 
## Call:
## lm(formula = act16 ~ (IndD.d + IndR.d) * clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1887 -1.0385  0.1404  1.5000  2.5000 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.1887     0.2498   4.758 2.96e-06 ***
## IndD.d         -0.1502     0.3550  -0.423   0.6725    
## IndR.d         -0.3290     0.3471  -0.948   0.3438    
## clim.d         -0.6887     0.3429  -2.009   0.0454 *  
## IndD.d:clim.d   0.7204     0.4891   1.473   0.1418    
## IndR.d:clim.d   0.8090     0.4917   1.645   0.1009    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 323 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.01546,    Adjusted R-squared:  0.0002187 
## F-statistic: 1.014 on 5 and 323 DF,  p-value: 0.4091

summary(lm(act16 ~ (IndD.d + IndR.d)*ctrl.d, data = d))

## 
## Call:
## lm(formula = act16 ~ (IndD.d + IndR.d) * ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1887 -1.0385  0.1404  1.5000  2.5000 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.5000     0.2348   2.129   0.0340 *
## IndD.d          0.5702     0.3364   1.695   0.0911 .
## IndR.d          0.4800     0.3483   1.378   0.1691  
## ctrl.d          0.6887     0.3429   2.009   0.0454 *
## IndD.d:ctrl.d  -0.7204     0.4891  -1.473   0.1418  
## IndR.d:ctrl.d  -0.8090     0.4917  -1.645   0.1009  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 323 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.01546,    Adjusted R-squared:  0.0002187 
## F-statistic: 1.014 on 5 and 323 DF,  p-value: 0.4091

summary(lm(act17 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6761 -1.4375  0.3239  1.6032  2.8108 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.41716    0.16936   2.463   0.0142 *
## IndD.d         0.23246    0.23995   0.969   0.3332  
## IndR.d        -0.10327    0.24512  -0.421   0.6738  
## cond.c        -0.04067    0.33873  -0.120   0.9045  
## IndD.d:cond.c -0.01219    0.47990  -0.025   0.9797  
## IndR.d:cond.c -0.20873    0.49023  -0.426   0.6705  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.011 on 408 degrees of freedom
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.006561,   Adjusted R-squared:  -0.005614 
## F-statistic: 0.5389 on 5 and 408 DF,  p-value: 0.7468

summary(lm(act18 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1004  0.2778  1.3654  2.7115 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.67842    0.16743   4.052 6.28e-05 ***
## IndD.d         0.16835    0.23687   0.711    0.478    
## IndR.d        -0.25641    0.24491  -1.047    0.296    
## cond.c        -0.08761    0.33485  -0.262    0.794    
## IndD.d:cond.c  0.21864    0.47373   0.462    0.645    
## IndR.d:cond.c  0.35470    0.48983   0.724    0.469    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.84 on 345 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.01071,    Adjusted R-squared:  -0.003625 
## F-statistic: 0.7472 on 5 and 345 DF,  p-value: 0.5886

summary(lm(act19 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5429 -1.0543  0.3455  1.5510  2.3455 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.33319    0.18360   7.261 4.23e-12 ***
## IndD.d        -0.19216    0.26623  -0.722    0.471    
## IndR.d        -0.23448    0.26665  -0.879    0.380    
## cond.c        -0.23159    0.36720  -0.631    0.529    
## IndD.d:cond.c -0.05046    0.53246  -0.095    0.925    
## IndR.d:cond.c -0.65672    0.53330  -1.231    0.219    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.789 on 266 degrees of freedom
##   (273 observations deleted due to missingness)
## Multiple R-squared:  0.0288, Adjusted R-squared:  0.01054 
## F-statistic: 1.578 on 5 and 266 DF,  p-value: 0.1666

summary(lm(act20 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5102 -1.2979  0.4444  1.4444  2.4444 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.31287    0.16993   7.726  1.5e-13 ***
## IndD.d        -0.22259    0.23825  -0.934   0.3509    
## IndR.d        -0.03925    0.24232  -0.162   0.8714    
## cond.c        -0.03000    0.33987  -0.088   0.9297    
## IndD.d:cond.c -1.03945    0.47650  -2.181   0.0299 *  
## IndR.d:cond.c -0.44317    0.48464  -0.914   0.3612    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.751 on 315 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.03972,    Adjusted R-squared:  0.02447 
## F-statistic: 2.606 on 5 and 315 DF,  p-value: 0.02506

summary(lm(act21 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5667 -1.9565  0.0704  1.7143  3.7143 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.2481     0.1799   1.379  0.16871   
## IndD.d         -0.2421     0.2495  -0.970  0.33256   
## IndR.d         -0.7719     0.2565  -3.009  0.00279 **
## cond.c          0.6371     0.3599   1.770  0.07746 . 
## IndD.d:cond.c  -0.7361     0.4991  -1.475  0.14100   
## IndR.d:cond.c  -0.2561     0.5131  -0.499  0.61789   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.052 on 394 degrees of freedom
##   (145 observations deleted due to missingness)
## Multiple R-squared:  0.03132,    Adjusted R-squared:  0.01903 
## F-statistic: 2.548 on 5 and 394 DF,  p-value: 0.0276

summary(lm(act22 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9254 -1.5082  0.2647  1.7037  3.3103 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.4022     0.1659   2.425  0.01576 * 
## IndD.d          0.4013     0.2373   1.691  0.09157 . 
## IndR.d         -0.6898     0.2410  -2.862  0.00444 **
## cond.c          0.2119     0.3318   0.639  0.52337   
## IndD.d:cond.c  -0.4555     0.4746  -0.960  0.33781   
## IndR.d:cond.c  -0.1663     0.4821  -0.345  0.73037   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.957 on 395 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05123,    Adjusted R-squared:  0.03922 
## F-statistic: 4.266 on 5 and 395 DF,  p-value: 0.0008644

summary(lm(act23 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.661 -2.044  0.225  2.189  3.225 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.3414     0.2221   1.537    0.125
## IndD.d         -0.1861     0.3168  -0.587    0.557
## IndR.d         -0.4317     0.3310  -1.304    0.193
## cond.c         -0.6393     0.4443  -1.439    0.151
## IndD.d:cond.c   0.1655     0.6337   0.261    0.794
## IndR.d:cond.c   0.9087     0.6620   1.373    0.171
## 
## Residual standard error: 2.259 on 284 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01883,    Adjusted R-squared:  0.001555 
## F-statistic:  1.09 on 5 and 284 DF,  p-value: 0.366

summary(lm(act24 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1266 -1.9583 -0.1266  1.6133  3.6133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)    -0.1034     0.1788  -0.578    0.563
## IndD.d         -0.1131     0.2508  -0.451    0.652
## IndR.d         -0.3843     0.2555  -1.504    0.133
## cond.c         -0.4599     0.3576  -1.286    0.199
## IndD.d:cond.c   0.1103     0.5016   0.220    0.826
## IndR.d:cond.c   0.2087     0.5110   0.408    0.683
## 
## Residual standard error: 2.088 on 407 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01483,    Adjusted R-squared:  0.00273 
## F-statistic: 1.226 on 5 and 407 DF,  p-value: 0.2962

summary(lm(act25 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3194 -0.9687  0.0789  1.6562  2.8415 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.8550     0.1516   5.639 3.07e-08 ***
## IndD.d          0.2653     0.2137   1.241  0.21519    
## IndR.d         -0.6038     0.2152  -2.805  0.00525 ** 
## cond.c          0.2276     0.3032   0.751  0.45332    
## IndD.d:cond.c   0.1708     0.4275   0.400  0.68963    
## IndR.d:cond.c  -0.4128     0.4305  -0.959  0.33814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 437 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.04416,    Adjusted R-squared:  0.03323 
## F-statistic: 4.038 on 5 and 437 DF,  p-value: 0.001359

summary(lm(act26 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6875 -1.4444  0.5556  1.5000  1.7222 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.53105    0.20797   7.362 3.67e-12 ***
## IndD.d        -0.14216    0.29404  -0.483    0.629    
## IndR.d         0.04128    0.30555   0.135    0.893    
## cond.c         0.17320    0.41594   0.416    0.678    
## IndD.d:cond.c  0.04902    0.58809   0.083    0.934    
## IndR.d:cond.c -0.40356    0.61110  -0.660    0.510    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.83 on 218 degrees of freedom
##   (321 observations deleted due to missingness)
## Multiple R-squared:  0.004848,   Adjusted R-squared:  -0.01798 
## F-statistic: 0.2124 on 5 and 218 DF,  p-value: 0.957

summary(lm(act27 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2951 -0.9623  0.1417  1.7049  2.8113 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.54386    0.18238   2.982  0.00309 **
## IndD.d         0.58481    0.25263   2.315  0.02127 * 
## IndR.d        -0.08588    0.26327  -0.326  0.74448   
## cond.c        -0.42105    0.36475  -1.154  0.24924   
## IndD.d:cond.c  0.08823    0.50526   0.175  0.86148   
## IndR.d:cond.c -0.11754    0.52654  -0.223  0.82350   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 310 degrees of freedom
##   (229 observations deleted due to missingness)
## Multiple R-squared:  0.04058,    Adjusted R-squared:  0.0251 
## F-statistic: 2.622 on 5 and 310 DF,  p-value: 0.0243

summary(lm(act28 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2188  1.6912  2.2188 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.9713     0.1674   5.801 1.43e-08 ***
## IndD.d          0.2498     0.2324   1.075    0.283    
## IndR.d         -0.0117     0.2350  -0.050    0.960    
## cond.c          0.3711     0.3349   1.108    0.268    
## IndD.d:cond.c  -0.5466     0.4647  -1.176    0.240    
## IndR.d:cond.c  -0.7278     0.4700  -1.549    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

summary(lm(act29 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3438 -1.1250  0.0968  1.6563  2.0968 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.07699    0.15759   6.834 3.52e-11 ***
## IndD.d         0.24242    0.22229   1.091    0.276    
## IndR.d        -0.14356    0.22622  -0.635    0.526    
## cond.c         0.09601    0.31518   0.305    0.761    
## IndD.d:cond.c -0.04735    0.44459  -0.106    0.915    
## IndR.d:cond.c -0.15643    0.45244  -0.346    0.730    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.752 on 361 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.008834,   Adjusted R-squared:  -0.004894 
## F-statistic: 0.6435 on 5 and 361 DF,  p-value: 0.6667

summary(lm(act30 ~ (IndD.d + IndR.d)*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (IndD.d + IndR.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2545 -1.1167  0.7455  1.8615  2.1373 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.1965035  0.1714254   6.980 1.52e-11 ***
## IndD.d        -0.1381702  0.2435923  -0.567    0.571    
## IndR.d        -0.2333849  0.2458645  -0.949    0.343    
## cond.c         0.1160839  0.3428507   0.339    0.735    
## IndD.d:cond.c  0.0005828  0.4871845   0.001    0.999    
## IndR.d:cond.c  0.0846630  0.4917290   0.172    0.863    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 345 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.003928,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.2721 on 5 and 345 DF,  p-value: 0.9282

Significant condition difference: 19

a. Means for condition diffs

describeBy(d$act19[d$party_factor=="Republican"], d$cond[d$party_factor=="Republican"]) # both supported, ctrl supported more

## 
##  Descriptive statistics by group 
## group: climate
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 55 0.65 1.97      1     0.8 1.48  -3   3     6 -0.53    -0.81 0.27
## ------------------------------------------------------------ 
## group: ctrl
##    vars  n mean  sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 35 1.54 1.4      2    1.66 1.48  -3   3     6 -0.93     0.88 0.24

### is climate different from 0?
summary(lm(act28 ~ (RepD.d + RepI.d) * clim.d, data = d))

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2188  1.6912  2.2188 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.7812     0.2274   3.436 0.000659 ***
## RepD.d          0.3521     0.3269   1.077 0.282157    
## RepI.d          0.3756     0.3415   1.100 0.272035    
## clim.d          0.3567     0.3298   1.082 0.280162    
## RepD.d:clim.d  -0.1812     0.4611  -0.393 0.694555    
## RepI.d:clim.d  -0.7278     0.4700  -1.549 0.122362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

### Higher than 0 in the climate condition

summary(lm(act28 ~ (RepD.d + RepI.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = act28 ~ (RepD.d + RepI.d) * ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2187  1.6912  2.2188 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.1379     0.2389   4.764 2.74e-06 ***
## RepD.d          0.1709     0.3251   0.526    0.599    
## RepI.d         -0.3522     0.3230  -1.090    0.276    
## ctrl.d         -0.3567     0.3298  -1.082    0.280    
## RepD.d:ctrl.d   0.1812     0.4611   0.393    0.695    
## RepI.d:ctrl.d   0.7278     0.4700   1.549    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

### Higher than 0 in the ctrl condition

2. Gender effects

Actions 15

summary(lm(act1 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act1 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3182 -2.0095 -0.0095  1.8462  4.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.08168    0.20140   0.406  0.68525   
## IndD.d          0.19505    0.27944   0.698  0.48554   
## IndR.d         -0.80818    0.28565  -2.829  0.00488 **
## gend.mf         0.14432    0.40281   0.358  0.72030   
## IndD.d:gend.mf -0.06143    0.55889  -0.110  0.91252   
## IndR.d:gend.mf -0.69133    0.57131  -1.210  0.22690   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 438 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03231,    Adjusted R-squared:  0.02127 
## F-statistic: 2.925 on 5 and 438 DF,  p-value: 0.01307

summary(lm(act2 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act2 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.292 -1.076  0.280  1.708  2.280 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.14396    0.20903   5.473 9.87e-08 ***
## IndD.d          0.09311    0.28554   0.326    0.745    
## IndR.d         -0.24608    0.29896  -0.823    0.411    
## gend.mf         0.17363    0.41805   0.415    0.678    
## IndD.d:gend.mf -0.28412    0.57109  -0.497    0.619    
## IndR.d:gend.mf -0.52938    0.59791  -0.885    0.377    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.82 on 279 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.007276,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.409 on 5 and 279 DF,  p-value: 0.8424

summary(lm(act3 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act3 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1845 -1.8571  0.1429  1.6667  3.7500 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.16234    0.18229  -0.891   0.3737  
## IndD.d          0.26544    0.25494   1.041   0.2984  
## IndR.d         -0.54600    0.26681  -2.046   0.0413 *
## gend.mf         0.03896    0.36459   0.107   0.9149  
## IndD.d:gend.mf -0.20169    0.50988  -0.396   0.6926  
## IndR.d:gend.mf -0.12229    0.53363  -0.229   0.8188  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 433 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.02799,    Adjusted R-squared:  0.01676 
## F-statistic: 2.493 on 5 and 433 DF,  p-value: 0.0305

summary(lm(act4 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act4 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3721 -1.9412  0.2198  2.0588  3.6129 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.24782    0.23080  -1.074    0.284  
## IndD.d          0.57180    0.30655   1.865    0.063 .
## IndR.d         -0.08804    0.32379  -0.272    0.786  
## gend.mf        -0.05608    0.46160  -0.121    0.903  
## IndD.d:gend.mf  0.15231    0.61311   0.248    0.804  
## IndR.d:gend.mf -0.49800    0.64757  -0.769    0.442  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.165 on 360 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01789,    Adjusted R-squared:  0.004246 
## F-statistic: 1.311 on 5 and 360 DF,  p-value: 0.2585

summary(lm(act5 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act5 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2540 -1.1852  0.4133  1.7460  2.6061 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.98230    0.21178   4.638 5.35e-06 ***
## IndD.d          0.09013    0.28605   0.315   0.7529    
## IndR.d         -0.15834    0.29134  -0.544   0.5872    
## gend.mf         0.40577    0.42357   0.958   0.3389    
## IndD.d:gend.mf -0.60468    0.57210  -1.057   0.2914    
## IndR.d:gend.mf -1.26580    0.58268  -2.172   0.0306 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 286 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.02198,    Adjusted R-squared:  0.004878 
## F-statistic: 1.285 on 5 and 286 DF,  p-value: 0.2702

summary(lm(act6 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act6 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7273 -1.2184  0.6579  1.6579  2.2000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.5049     0.1840   8.180 5.37e-15 ***
## IndD.d          -0.4033     0.2598  -1.552   0.1215    
## IndR.d          -0.4957     0.2659  -1.865   0.0631 .  
## gend.mf          0.4447     0.3679   1.209   0.2277    
## IndD.d:gend.mf  -0.9257     0.5196  -1.781   0.0757 .  
## IndR.d:gend.mf  -0.8631     0.5318  -1.623   0.1055    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 348 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01702,    Adjusted R-squared:  0.002894 
## F-statistic: 1.205 on 5 and 348 DF,  p-value: 0.3064

summary(lm(act7 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act7 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1684 -1.9773  0.0472  1.8316  3.6562 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.03524    0.20465   0.172   0.8634  
## IndD.d          0.03761    0.27880   0.135   0.8928  
## IndR.d         -0.58559    0.29404  -1.991   0.0471 *
## gend.mf         0.16482    0.40929   0.403   0.6874  
## IndD.d:gend.mf -0.35597    0.55761  -0.638   0.5236  
## IndR.d:gend.mf -0.37662    0.58809  -0.640   0.5223  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 404 degrees of freedom
##   (135 observations deleted due to missingness)
## Multiple R-squared:  0.01716,    Adjusted R-squared:  0.004995 
## F-statistic: 1.411 on 5 and 404 DF,  p-value: 0.2193

summary(lm(act8 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act8 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4545 -1.8926 -0.1593  1.8407  4.1892 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.31942    0.19289  -1.656  0.09840 . 
## IndD.d          0.62634    0.26931   2.326  0.02046 * 
## IndR.d         -0.82889    0.27691  -2.993  0.00291 **
## gend.mf         0.03884    0.38578   0.101  0.91984   
## IndD.d:gend.mf  0.25641    0.53862   0.476  0.63426   
## IndR.d:gend.mf -0.12059    0.55383  -0.218  0.82772   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.115 on 470 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.06738,    Adjusted R-squared:  0.05746 
## F-statistic: 6.791 on 5 and 470 DF,  p-value: 3.961e-06

summary(lm(act9 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act9 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3564 -1.6055  0.0435  1.6436  3.5882 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.18304    0.17844   1.026   0.3056  
## IndD.d         -0.02656    0.25022  -0.106   0.9155  
## IndR.d         -0.67440    0.26338  -2.561   0.0108 *
## gend.mf        -0.08036    0.35687  -0.225   0.8220  
## IndD.d:gend.mf -0.31956    0.50045  -0.639   0.5235  
## IndR.d:gend.mf -0.11338    0.52675  -0.215   0.8297  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.972 on 438 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02744,    Adjusted R-squared:  0.01634 
## F-statistic: 2.472 on 5 and 438 DF,  p-value: 0.03179

summary(lm(act10 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act10 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.9722  0.2857  1.7034  4.0278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.32231    0.18009   1.790   0.0742 .  
## IndD.d          0.30715    0.25825   1.189   0.2349    
## IndR.d         -1.18790    0.26528  -4.478 9.49e-06 ***
## gend.mf        -0.09917    0.36019  -0.275   0.7832    
## IndD.d:gend.mf  0.26882    0.51651   0.520   0.6030    
## IndR.d:gend.mf -0.22521    0.53056  -0.424   0.6714    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 467 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.07975,    Adjusted R-squared:  0.0699 
## F-statistic: 8.094 on 5 and 467 DF,  p-value: 2.448e-07

summary(lm(act11 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act11 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0519 -1.0519  0.3382  1.7105  2.3382 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.9292     0.2017   4.607 6.03e-06 ***
## IndD.d           0.1928     0.2792   0.690    0.490    
## IndR.d          -0.2190     0.2914  -0.752    0.453    
## gend.mf         -0.2455     0.4034  -0.609    0.543    
## IndD.d:gend.mf   0.5804     0.5585   1.039    0.299    
## IndR.d:gend.mf   0.3424     0.5828   0.587    0.557    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.897 on 303 degrees of freedom
##   (236 observations deleted due to missingness)
## Multiple R-squared:  0.01116,    Adjusted R-squared:  -0.005162 
## F-statistic: 0.6836 on 5 and 303 DF,  p-value: 0.6362

summary(lm(act12 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act12 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4217 -2.1579 -0.1579  1.8355  4.3333 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.162281   0.208468   0.778    0.437    
## IndD.d          0.048563   0.299241   0.162    0.871    
## IndR.d         -1.206498   0.299027  -4.035 6.63e-05 ***
## gend.mf         0.008772   0.416936   0.021    0.983    
## IndD.d:gend.mf -0.430459   0.598482  -0.719    0.472    
## IndR.d:gend.mf -0.587003   0.598055  -0.982    0.327    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.13 on 374 degrees of freedom
##   (165 observations deleted due to missingness)
## Multiple R-squared:  0.06734,    Adjusted R-squared:  0.05487 
## F-statistic: 5.401 on 5 and 374 DF,  p-value: 8.241e-05

summary(lm(act13 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act13 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5000 -2.0784 -0.0784  1.8067  3.6990 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.1415     0.1846  -0.766   0.4438  
## IndD.d           0.4307     0.2662   1.618   0.1064  
## IndR.d          -0.2687     0.2764  -0.972   0.3315  
## gend.mf         -0.6695     0.3691  -1.814   0.0704 .
## IndD.d:gend.mf   1.0910     0.5324   2.049   0.0410 *
## IndR.d:gend.mf   1.2473     0.5527   2.257   0.0245 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 433 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.03722,    Adjusted R-squared:  0.0261 
## F-statistic: 3.347 on 5 and 433 DF,  p-value: 0.005603

summary(lm(act14 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act14 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.730 -1.520  0.322  1.538  3.609 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      0.3642     0.2371   1.536    0.126
## IndD.d           0.2315     0.3155   0.734    0.464
## IndR.d          -0.3295     0.3435  -0.959    0.338
## gend.mf         -0.3117     0.4742  -0.657    0.512
## IndD.d:gend.mf   0.5799     0.6311   0.919    0.359
## IndR.d:gend.mf  -0.9750     0.6870  -1.419    0.157
## 
## Residual standard error: 2.022 on 277 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.02875,    Adjusted R-squared:  0.01121 
## F-statistic:  1.64 on 5 and 277 DF,  p-value: 0.1496

summary(lm(act15 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act15 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5581 -1.5581  0.4634  1.5278  3.5278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.08067    0.19015  -0.424   0.6716  
## IndD.d          0.62256    0.27135   2.294   0.0223 *
## IndR.d         -0.21196    0.27740  -0.764   0.4453  
## gend.mf        -0.76550    0.38030  -2.013   0.0448 *
## IndD.d:gend.mf  0.79800    0.54271   1.470   0.1423  
## IndR.d:gend.mf  0.29519    0.55480   0.532   0.5950  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.038 on 375 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.03469,    Adjusted R-squared:  0.02182 
## F-statistic: 2.695 on 5 and 375 DF,  p-value: 0.02078

summary(lm(act16 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act16 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2692 -0.9860  0.1667  1.3103  2.3103 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.97944    0.20379   4.806 2.36e-06 ***
## IndD.d          0.03108    0.27438   0.113    0.910    
## IndR.d         -0.08875    0.28291  -0.314    0.754    
## gend.mf         0.57958    0.40758   1.422    0.156    
## IndD.d:gend.mf -0.75852    0.54876  -1.382    0.168    
## IndR.d:gend.mf -0.69429    0.56582  -1.227    0.221    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.824 on 322 degrees of freedom
##   (217 observations deleted due to missingness)
## Multiple R-squared:  0.009561,   Adjusted R-squared:  -0.005818 
## F-statistic: 0.6217 on 5 and 322 DF,  p-value: 0.6834

summary(lm(act17 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act17 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8837 -1.4362  0.4583  1.5741  3.0541 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.41296    0.19534   2.114   0.0351 *
## IndD.d          0.29973    0.26855   1.116   0.2650  
## IndR.d         -0.22190    0.27595  -0.804   0.4218  
## gend.mf        -0.02593    0.39068  -0.066   0.9471  
## IndD.d:gend.mf  0.36798    0.53711   0.685   0.4937  
## IndR.d:gend.mf -0.46430    0.55190  -0.841   0.4007  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.009 on 407 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01111,    Adjusted R-squared:  -0.001043 
## F-statistic: 0.9141 on 5 and 407 DF,  p-value: 0.4717

summary(lm(act18 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act18 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9730 -1.3947  0.2561  1.5000  2.6053 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.6046     0.1887   3.204  0.00148 **
## IndD.d           0.2538     0.2623   0.968  0.33386   
## IndR.d          -0.1573     0.2738  -0.574  0.56605   
## gend.mf         -0.3342     0.3775  -0.885  0.37653   
## IndD.d:gend.mf   0.5633     0.5246   1.074  0.28364   
## IndR.d:gend.mf   0.4395     0.5475   0.803  0.42268   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 343 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01109,    Adjusted R-squared:  -0.003321 
## F-statistic: 0.7696 on 5 and 343 DF,  p-value: 0.5722

summary(lm(act19 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act19 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3538 -1.1719  0.4231  1.6462  2.4231 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.32692    0.19888   6.672 1.47e-10 ***
## IndD.d         -0.21048    0.28876  -0.729    0.467    
## IndR.d         -0.45252    0.28890  -1.566    0.118    
## gend.mf        -0.05385    0.39776  -0.135    0.892    
## IndD.d:gend.mf  0.11725    0.57753   0.203    0.839    
## IndR.d:gend.mf -0.54111    0.57781  -0.936    0.350    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.802 on 265 degrees of freedom
##   (274 observations deleted due to missingness)
## Multiple R-squared:  0.01397,    Adjusted R-squared:  -0.00463 
## F-statistic: 0.7511 on 5 and 265 DF,  p-value: 0.5859

summary(lm(act20 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act20 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5139 -1.1429  0.4861  1.5556  2.3226 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.2589     0.1946   6.470 3.78e-10 ***
## IndD.d          -0.1345     0.2641  -0.509    0.611    
## IndR.d          -0.1633     0.2722  -0.600    0.549    
## gend.mf         -0.2321     0.3892  -0.596    0.551    
## IndD.d:gend.mf   0.5096     0.5282   0.965    0.335    
## IndR.d:gend.mf  -0.6043     0.5445  -1.110    0.268    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.772 on 313 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02125,    Adjusted R-squared:  0.005613 
## F-statistic: 1.359 on 5 and 313 DF,  p-value: 0.2396

summary(lm(act21 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act21 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.308 -1.850  0.150  1.760  3.889 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.20551    0.20292   1.013  0.31180   
## IndD.d         -0.12666    0.28074  -0.451  0.65212   
## IndR.d         -0.82386    0.28634  -2.877  0.00423 **
## gend.mf        -0.06815    0.40583  -0.168  0.86672   
## IndD.d:gend.mf  0.52585    0.56148   0.937  0.34957   
## IndR.d:gend.mf -0.47291    0.57268  -0.826  0.40943   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.055 on 392 degrees of freedom
##   (147 observations deleted due to missingness)
## Multiple R-squared:  0.02841,    Adjusted R-squared:  0.01602 
## F-statistic: 2.293 on 5 and 392 DF,  p-value: 0.04499

summary(lm(act22 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act22 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.5263  0.2421  1.5686  3.4194 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.4314     0.1855   2.326  0.02055 * 
## IndD.d           0.4127     0.2644   1.561  0.11942   
## IndR.d          -0.7622     0.2746  -2.776  0.00577 **
## gend.mf          0.1898     0.3710   0.511  0.60929   
## IndD.d:gend.mf   0.1221     0.5288   0.231  0.81760   
## IndR.d:gend.mf  -0.3670     0.5492  -0.668  0.50430   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.957 on 393 degrees of freedom
##   (146 observations deleted due to missingness)
## Multiple R-squared:  0.04966,    Adjusted R-squared:  0.03757 
## F-statistic: 4.107 on 5 and 393 DF,  p-value: 0.001201

summary(lm(act23 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act23 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6667 -2.2821  0.0678  2.0678  3.1154 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.4744     0.2531   1.874    0.062 .
## IndD.d          -0.3210     0.3477  -0.923    0.357  
## IndR.d          -0.5659     0.3678  -1.539    0.125  
## gend.mf          0.3846     0.5063   0.760    0.448  
## IndD.d:gend.mf  -0.2342     0.6954  -0.337    0.737  
## IndR.d:gend.mf  -0.4322     0.7357  -0.587    0.557  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.267 on 283 degrees of freedom
##   (256 observations deleted due to missingness)
## Multiple R-squared:  0.009342,   Adjusted R-squared:  -0.008161 
## F-statistic: 0.5337 on 5 and 283 DF,  p-value: 0.7507

summary(lm(act24 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act24 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -3.13  -2.00  -0.13   1.59   3.59 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.2299     0.1965  -1.170   0.2428  
## IndD.d           0.0615     0.2747   0.224   0.8230  
## IndR.d          -0.2570     0.2802  -0.917   0.3597  
## gend.mf         -0.7197     0.3930  -1.831   0.0678 .
## IndD.d:gend.mf   1.0565     0.5495   1.923   0.0552 .
## IndR.d:gend.mf   0.7987     0.5605   1.425   0.1549  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.082 on 406 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.01727,    Adjusted R-squared:  0.005164 
## F-statistic: 1.427 on 5 and 406 DF,  p-value: 0.2135

summary(lm(act25 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act25 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2791 -1.0194  0.0833  1.6847  3.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.77541    0.16828   4.608 5.35e-06 ***
## IndD.d          0.37384    0.23677   1.579    0.115    
## IndR.d         -0.61775    0.24484  -2.523    0.012 *  
## gend.mf        -0.28252    0.33656  -0.839    0.402    
## IndD.d:gend.mf  0.54217    0.47354   1.145    0.253    
## IndR.d:gend.mf -0.03279    0.48967  -0.067    0.947    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.04154,    Adjusted R-squared:  0.03052 
## F-statistic:  3.77 on 5 and 435 DF,  p-value: 0.002362

summary(lm(act26 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act26 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7609 -1.1429  0.5614  1.3929  1.8571 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.58293    0.22877   6.919 5.03e-11 ***
## IndD.d         -0.15691    0.31458  -0.499    0.618    
## IndR.d         -0.13107    0.33159  -0.395    0.693    
## gend.mf         0.28868    0.45754   0.631    0.529    
## IndD.d:gend.mf  0.07357    0.62915   0.117    0.907    
## IndR.d:gend.mf -0.90669    0.66318  -1.367    0.173    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.823 on 217 degrees of freedom
##   (322 observations deleted due to missingness)
## Multiple R-squared:  0.01451,    Adjusted R-squared:  -0.008199 
## F-statistic: 0.6389 on 5 and 217 DF,  p-value: 0.6702

summary(lm(act27 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act27 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4324 -0.9467  0.0533  1.5676  2.6000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.6233     0.2017   3.090  0.00219 **
## IndD.d           0.5662     0.2755   2.055  0.04071 * 
## IndR.d          -0.1733     0.2853  -0.608  0.54389   
## gend.mf          0.2867     0.4035   0.710  0.47796   
## IndD.d:gend.mf   0.1991     0.5510   0.361  0.71810   
## IndR.d:gend.mf  -0.1867     0.5705  -0.327  0.74376   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.868 on 308 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.03174,    Adjusted R-squared:  0.01602 
## F-statistic: 2.019 on 5 and 308 DF,  p-value: 0.07577

summary(lm(act28 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act28 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2151 -1.0449  0.2069  1.7849  2.3030 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.89112    0.19439   4.584 6.28e-06 ***
## IndD.d          0.32247    0.26831   1.202    0.230    
## IndR.d         -0.02016    0.26906  -0.075    0.940    
## gend.mf        -0.19603    0.38877  -0.504    0.614    
## IndD.d:gend.mf  0.19309    0.53663   0.360    0.719    
## IndR.d:gend.mf -0.15195    0.53812  -0.282    0.778    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 363 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.007968,   Adjusted R-squared:  -0.005696 
## F-statistic: 0.5831 on 5 and 363 DF,  p-value: 0.7129

summary(lm(act29 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act29 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.056  0.069  1.667  2.069 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.083645   0.173308   6.253 1.15e-09 ***
## IndD.d          0.226699   0.245500   0.923    0.356    
## IndR.d         -0.151462   0.254062  -0.596    0.551    
## gend.mf         0.054931   0.346617   0.158    0.874    
## IndD.d:gend.mf -0.008954   0.491001  -0.018    0.985    
## IndR.d:gend.mf -0.052632   0.508124  -0.104    0.918    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.755 on 359 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.00763,    Adjusted R-squared:  -0.006191 
## F-statistic: 0.552 on 5 and 359 DF,  p-value: 0.7368

summary(lm(act30 ~ (IndD.d + IndR.d)*gend.mf, data = d))

## 
## Call:
## lm(formula = act30 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2581 -1.1685  0.7419  1.8228  2.4194 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.21330    0.19464   6.234 1.34e-09 ***
## IndD.d         -0.24969    0.27038  -0.924    0.356    
## IndR.d         -0.36274    0.27654  -1.312    0.190    
## gend.mf         0.08953    0.38927   0.230    0.818    
## IndD.d:gend.mf -0.51674    0.54075  -0.956    0.340    
## IndR.d:gend.mf -0.62936    0.55308  -1.138    0.256    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 343 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01173,    Adjusted R-squared:  -0.002672 
## F-statistic: 0.8145 on 5 and 343 DF,  p-value: 0.5399

3. Condition x Gender effects

Actions: 23, 24

summary(lm(act1 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7273 -2.0492 -0.0019  1.9273  3.6522 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.0356908  0.2033129   0.176  0.86073   
## IndD.d                 0.2472073  0.2813081   0.879  0.38001   
## IndR.d                -0.8168107  0.2903803  -2.813  0.00513 **
## gend.mf                0.0676558  0.4066258   0.166  0.86793   
## cond.c                -0.7050714  0.4066258  -1.734  0.08364 . 
## IndD.d:gend.mf        -0.0001188  0.5626162   0.000  0.99983   
## IndR.d:gend.mf        -0.7290185  0.5807605  -1.255  0.21005   
## IndD.d:cond.c          0.5120025  0.5626162   0.910  0.36331   
## IndR.d:cond.c          1.0651374  0.5807605   1.834  0.06734 . 
## gend.mf:cond.c        -1.2208731  0.8132516  -1.501  0.13403   
## IndD.d:gend.mf:cond.c  1.5403445  1.1252324   1.369  0.17174   
## IndR.d:gend.mf:cond.c  2.3392506  1.1615211   2.014  0.04463 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 432 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.04596,    Adjusted R-squared:  0.02167 
## F-statistic: 1.892 on 11 and 432 DF,  p-value: 0.03851

summary(lm(act2 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6765 -0.9063  0.3235  1.3889  2.5000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.08949    0.21248   5.127 5.57e-07 ***
## IndD.d                 0.11681    0.28796   0.406    0.685    
## IndR.d                -0.15133    0.30540  -0.496    0.621    
## gend.mf                0.19677    0.42497   0.463    0.644    
## cond.c                -0.50990    0.42497  -1.200    0.231    
## IndD.d:gend.mf        -0.33160    0.57593  -0.576    0.565    
## IndR.d:gend.mf        -0.46198    0.61081  -0.756    0.450    
## IndD.d:cond.c          0.57937    0.57593   1.006    0.315    
## IndR.d:cond.c          0.03982    0.61081   0.065    0.948    
## gend.mf:cond.c        -0.09535    0.84993  -0.112    0.911    
## IndD.d:gend.mf:cond.c  1.84530    1.15185   1.602    0.110    
## IndR.d:gend.mf:cond.c -0.18671    1.22162  -0.153    0.879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 273 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.03645,    Adjusted R-squared:  -0.002379 
## F-statistic: 0.9387 on 11 and 273 DF,  p-value: 0.5035

summary(lm(act3 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4808 -1.7697  0.1176  1.5192  3.7000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.15779    0.18373  -0.859    0.391  
## IndD.d                 0.24714    0.25649   0.964    0.336  
## IndR.d                -0.61337    0.27451  -2.234    0.026 *
## gend.mf                0.01809    0.36746   0.049    0.961  
## cond.c                 0.04122    0.36746   0.112    0.911  
## IndD.d:gend.mf        -0.20251    0.51299  -0.395    0.693  
## IndR.d:gend.mf        -0.22579    0.54903  -0.411    0.681  
## IndD.d:cond.c         -0.02487    0.51299  -0.048    0.961  
## IndR.d:cond.c          0.30109    0.54903   0.548    0.584  
## gend.mf:cond.c        -0.32958    0.73491  -0.448    0.654  
## IndD.d:gend.mf:cond.c -0.83455    1.02597  -0.813    0.416  
## IndR.d:gend.mf:cond.c  1.14497    1.09805   1.043    0.298  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.013 on 427 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.03842,    Adjusted R-squared:  0.01365 
## F-statistic: 1.551 on 11 and 427 DF,  p-value: 0.1107

summary(lm(act4 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6923 -1.9844  0.1636  1.9750  4.0625 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.20985    0.23295  -0.901   0.3683  
## IndD.d                 0.53810    0.31030   1.734   0.0838 .
## IndR.d                -0.10829    0.32839  -0.330   0.7418  
## gend.mf                0.04950    0.46590   0.106   0.9154  
## cond.c                -0.94836    0.46590  -2.036   0.0425 *
## IndD.d:gend.mf         0.09264    0.62060   0.149   0.8814  
## IndR.d:gend.mf        -0.56673    0.65678  -0.863   0.3888  
## IndD.d:cond.c          1.31063    0.62060   2.112   0.0354 *
## IndR.d:cond.c          0.84780    0.65678   1.291   0.1976  
## gend.mf:cond.c        -1.61289    0.93179  -1.731   0.0843 .
## IndD.d:gend.mf:cond.c  1.40871    1.24121   1.135   0.2572  
## IndR.d:gend.mf:cond.c  1.17367    1.31356   0.893   0.3722  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.164 on 354 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.03473,    Adjusted R-squared:  0.004739 
## F-statistic: 1.158 on 11 and 354 DF,  p-value: 0.3153

summary(lm(act5 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2647 -1.2414  0.2432  1.4847  2.9048 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.98696    0.21253   4.644 5.27e-06 ***
## IndD.d                 0.07293    0.28697   0.254   0.7996    
## IndR.d                -0.10746    0.29590  -0.363   0.7168    
## gend.mf                0.41070    0.42506   0.966   0.3348    
## cond.c                -0.16746    0.42506  -0.394   0.6939    
## IndD.d:gend.mf        -0.58310    0.57395  -1.016   0.3105    
## IndR.d:gend.mf        -1.15779    0.59180  -1.956   0.0514 .  
## IndD.d:cond.c         -0.27160    0.57395  -0.473   0.6364    
## IndR.d:cond.c         -0.23159    0.59180  -0.391   0.6958    
## gend.mf:cond.c        -0.43431    0.85012  -0.511   0.6098    
## IndD.d:gend.mf:cond.c  1.20717    1.14789   1.052   0.2939    
## IndR.d:gend.mf:cond.c -0.41044    1.18360  -0.347   0.7290    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 280 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03883,    Adjusted R-squared:  0.001066 
## F-statistic: 1.028 on 11 and 280 DF,  p-value: 0.4213

summary(lm(act6 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.500 -1.108  0.600  1.600  2.400 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.48732    0.18616   7.989  2.1e-14 ***
## IndD.d                -0.40436    0.26343  -1.535   0.1257    
## IndR.d                -0.46530    0.27469  -1.694   0.0912 .  
## gend.mf                0.46653    0.37233   1.253   0.2111    
## cond.c                -0.36653    0.37233  -0.984   0.3256    
## IndD.d:gend.mf        -0.98483    0.52686  -1.869   0.0624 .  
## IndR.d:gend.mf        -0.86058    0.54938  -1.566   0.1182    
## IndD.d:cond.c          0.16904    0.52686   0.321   0.7485    
## IndR.d:cond.c          0.27249    0.54938   0.496   0.6202    
## gend.mf:cond.c        -0.14928    0.74466  -0.200   0.8412    
## IndD.d:gend.mf:cond.c -0.35097    1.05371  -0.333   0.7393    
## IndR.d:gend.mf:cond.c  0.03738    1.09875   0.034   0.9729    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 342 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.02172,    Adjusted R-squared:  -0.009744 
## F-statistic: 0.6903 on 11 and 342 DF,  p-value: 0.7479

summary(lm(act7 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3725 -1.9167  0.0893  1.6275  3.5600 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.05019    0.20623   0.243   0.8078  
## IndD.d                 0.01296    0.28062   0.046   0.9632  
## IndR.d                -0.64266    0.29949  -2.146   0.0325 *
## gend.mf                0.13572    0.41245   0.329   0.7423  
## cond.c                 0.20239    0.41245   0.491   0.6239  
## IndD.d:gend.mf        -0.31379    0.56125  -0.559   0.5764  
## IndR.d:gend.mf        -0.43413    0.59897  -0.725   0.4690  
## IndD.d:cond.c         -0.05138    0.56125  -0.092   0.9271  
## IndR.d:cond.c          0.25601    0.59897   0.427   0.6693  
## gend.mf:cond.c        -0.37700    0.82491  -0.457   0.6479  
## IndD.d:gend.mf:cond.c -0.20244    1.12250  -0.180   0.8570  
## IndR.d:gend.mf:cond.c  0.82687    1.19795   0.690   0.4904  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.084 on 398 degrees of freedom
##   (135 observations deleted due to missingness)
## Multiple R-squared:  0.02484,    Adjusted R-squared:  -0.002116 
## F-statistic: 0.9215 on 11 and 398 DF,  p-value: 0.5194

summary(lm(act8 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5909 -1.6719 -0.1404  1.8596  4.4167 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           -0.31114    0.19372  -1.606  0.10893   
## IndD.d                 0.61869    0.27021   2.290  0.02249 * 
## IndR.d                -0.78228    0.28314  -2.763  0.00596 **
## gend.mf                0.02228    0.38745   0.058  0.95417   
## cond.c                -0.06640    0.38745  -0.171  0.86400   
## IndD.d:gend.mf         0.27171    0.54043   0.503  0.61537   
## IndR.d:gend.mf        -0.02134    0.56627  -0.038  0.96995   
## IndD.d:cond.c          0.34578    0.54043   0.640  0.52261   
## IndR.d:cond.c         -0.49156    0.56627  -0.868  0.38581   
## gend.mf:cond.c        -0.66720    0.77489  -0.861  0.38967   
## IndD.d:gend.mf:cond.c  0.65390    1.08086   0.605  0.54549   
## IndR.d:gend.mf:cond.c  0.48824    1.13254   0.431  0.66659   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 464 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07492,    Adjusted R-squared:  0.05299 
## F-statistic: 3.416 on 11 and 464 DF,  p-value: 0.0001407

summary(lm(act9 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4615 -1.6087  0.2381  1.6512  3.4182 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.19487    0.18067   1.079  0.28136   
## IndD.d                -0.04778    0.25273  -0.189  0.85013   
## IndR.d                -0.73983    0.27319  -2.708  0.00703 **
## gend.mf               -0.10403    0.36133  -0.288  0.77357   
## cond.c                -0.04090    0.36133  -0.113  0.90993   
## IndD.d:gend.mf        -0.30824    0.50547  -0.610  0.54231   
## IndR.d:gend.mf        -0.19735    0.54638  -0.361  0.71813   
## IndD.d:cond.c         -0.02983    0.50547  -0.059  0.95298   
## IndR.d:cond.c          0.32134    0.54638   0.588  0.55675   
## gend.mf:cond.c        -0.48962    0.72267  -0.678  0.49844   
## IndD.d:gend.mf:cond.c -0.08511    1.01094  -0.084  0.93294   
## IndR.d:gend.mf:cond.c  1.14613    1.09276   1.049  0.29484   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.982 on 432 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03168,    Adjusted R-squared:  0.007022 
## F-statistic: 1.285 on 11 and 432 DF,  p-value: 0.2302

summary(lm(act10 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9000 -1.7391  0.4211  1.5789  4.2609 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.31685    0.18107   1.750   0.0808 .  
## IndD.d                 0.31652    0.25957   1.219   0.2233    
## IndR.d                -1.13557    0.27107  -4.189 3.36e-05 ***
## gend.mf               -0.08825    0.36215  -0.244   0.8076    
## cond.c                 0.12556    0.36215   0.347   0.7290    
## IndD.d:gend.mf         0.26695    0.51914   0.514   0.6073    
## IndR.d:gend.mf        -0.15057    0.54215  -0.278   0.7813    
## IndD.d:cond.c          0.01679    0.51914   0.032   0.9742    
## IndR.d:cond.c         -0.56866    0.54215  -1.049   0.2948    
## gend.mf:cond.c         0.65797    0.72429   0.908   0.3641    
## IndD.d:gend.mf:cond.c -0.23357    1.03827  -0.225   0.8221    
## IndR.d:gend.mf:cond.c -1.06273    1.08430  -0.980   0.3275    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.054 on 461 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.08452,    Adjusted R-squared:  0.06267 
## F-statistic: 3.869 on 11 and 461 DF,  p-value: 2.315e-05

summary(lm(act11 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -1.1111  0.3636  1.4286  3.0000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.8886     0.2018   4.403 1.49e-05 ***
## IndD.d                  0.2090     0.2796   0.747   0.4554    
## IndR.d                 -0.1641     0.2943  -0.558   0.5775    
## gend.mf                -0.3066     0.4037  -0.760   0.4481    
## cond.c                 -0.9934     0.4037  -2.461   0.0144 *  
## IndD.d:gend.mf          0.6240     0.5592   1.116   0.2654    
## IndR.d:gend.mf          0.4334     0.5886   0.736   0.4621    
## IndD.d:cond.c           0.5061     0.5592   0.905   0.3662    
## IndR.d:cond.c           0.8475     0.5886   1.440   0.1510    
## gend.mf:cond.c         -0.9544     0.8073  -1.182   0.2381    
## IndD.d:gend.mf:cond.c   0.6686     1.1184   0.598   0.5505    
## IndR.d:gend.mf:cond.c   0.7613     1.1773   0.647   0.5184    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.891 on 297 degrees of freedom
##   (236 observations deleted due to missingness)
## Multiple R-squared:  0.03642,    Adjusted R-squared:  0.0007281 
## F-statistic:  1.02 on 11 and 297 DF,  p-value: 0.428

summary(lm(act12 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4359 -2.0600 -0.1111  1.9130  4.2381 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.165000   0.209997   0.786    0.433    
## IndD.d                 0.045430   0.301504   0.151    0.880    
## IndR.d                -1.231631   0.305849  -4.027 6.87e-05 ***
## gend.mf                0.003333   0.419994   0.008    0.994    
## cond.c                 0.047778   0.419994   0.114    0.909    
## IndD.d:gend.mf        -0.427461   0.603009  -0.709    0.479    
## IndR.d:gend.mf        -0.608167   0.611698  -0.994    0.321    
## IndD.d:cond.c         -0.091564   0.603009  -0.152    0.879    
## IndR.d:cond.c         -0.065655   0.611698  -0.107    0.915    
## gend.mf:cond.c        -0.317778   0.839988  -0.378    0.705    
## IndD.d:gend.mf:cond.c  0.176592   1.206017   0.146    0.884    
## IndR.d:gend.mf:cond.c  0.877341   1.223396   0.717    0.474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.145 on 368 degrees of freedom
##   (165 observations deleted due to missingness)
## Multiple R-squared:  0.06951,    Adjusted R-squared:  0.04169 
## F-statistic: 2.499 on 11 and 368 DF,  p-value: 0.004871

summary(lm(act13 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7778 -1.8242  0.0923  1.5417  3.9444 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.12691    0.18395  -0.690   0.4906  
## IndD.d                 0.43931    0.26573   1.653   0.0990 .
## IndR.d                -0.21039    0.27991  -0.752   0.4527  
## gend.mf               -0.69856    0.36790  -1.899   0.0583 .
## cond.c                 0.36229    0.36790   0.985   0.3253  
## IndD.d:gend.mf         1.12427    0.53147   2.115   0.0350 *
## IndR.d:gend.mf         1.39697    0.55981   2.495   0.0130 *
## IndD.d:cond.c         -0.46856    0.53147  -0.882   0.3785  
## IndR.d:cond.c         -1.10832    0.55981  -1.980   0.0484 *
## gend.mf:cond.c        -0.53411    0.73579  -0.726   0.4683  
## IndD.d:gend.mf:cond.c  1.75675    1.06293   1.653   0.0991 .
## IndR.d:gend.mf:cond.c  0.07379    1.11963   0.066   0.9475  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.047 on 427 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.05903,    Adjusted R-squared:  0.03479 
## F-statistic: 2.435 on 11 and 427 DF,  p-value: 0.00595

summary(lm(act14 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8947 -1.5556  0.2667  1.6471  3.2667 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.37877    0.24008   1.578    0.116
## IndD.d                 0.21720    0.31854   0.682    0.496
## IndR.d                -0.41885    0.35298  -1.187    0.236
## gend.mf               -0.38691    0.48017  -0.806    0.421
## cond.c                 0.53733    0.48017   1.119    0.264
## IndD.d:gend.mf         0.64526    0.63708   1.013    0.312
## IndR.d:gend.mf        -1.04959    0.70596  -1.487    0.138
## IndD.d:cond.c         -0.59307    0.63708  -0.931    0.353
## IndR.d:cond.c         -0.05716    0.70596  -0.081    0.936
## gend.mf:cond.c         0.03023    0.96033   0.031    0.975
## IndD.d:gend.mf:cond.c -0.59711    1.27416  -0.469    0.640
## IndR.d:gend.mf:cond.c  0.97610    1.41191   0.691    0.490
## 
## Residual standard error: 2.032 on 271 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.04048,    Adjusted R-squared:  0.001535 
## F-statistic: 1.039 on 11 and 271 DF,  p-value: 0.4117

summary(lm(act15 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8421 -1.7727  0.1579  1.7727  3.7727 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.09037    0.19098  -0.473   0.6364  
## IndD.d                 0.63745    0.27232   2.341   0.0198 *
## IndR.d                -0.16628    0.28103  -0.592   0.5544  
## gend.mf               -0.74546    0.38195  -1.952   0.0517 .
## cond.c                -0.17624    0.38195  -0.461   0.6448  
## IndD.d:gend.mf         0.77251    0.54463   1.418   0.1569  
## IndR.d:gend.mf         0.34316    0.56206   0.611   0.5419  
## IndD.d:cond.c         -0.02625    0.54463  -0.048   0.9616  
## IndR.d:cond.c          0.03308    0.56206   0.059   0.9531  
## gend.mf:cond.c         0.40487    0.76390   0.530   0.5964  
## IndD.d:gend.mf:cond.c  0.42436    1.08927   0.390   0.6971  
## IndR.d:gend.mf:cond.c -1.37829    1.12412  -1.226   0.2209  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 369 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.0454, Adjusted R-squared:  0.01694 
## F-statistic: 1.595 on 11 and 369 DF,  p-value: 0.09792

summary(lm(act16 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2121 -1.0500  0.0526  1.3846  2.6170 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.99286    0.20433   4.859 1.86e-06 ***
## IndD.d                 0.02070    0.27508   0.075   0.9401    
## IndR.d                -0.07506    0.28729  -0.261   0.7941    
## gend.mf                0.55274    0.40866   1.353   0.1772    
## cond.c                 0.67966    0.40866   1.663   0.0973 .  
## IndD.d:gend.mf        -0.73775    0.55017  -1.341   0.1809    
## IndR.d:gend.mf        -0.61322    0.57458  -1.067   0.2867    
## IndD.d:cond.c         -0.75781    0.55017  -1.377   0.1694    
## IndR.d:cond.c         -0.88369    0.57458  -1.538   0.1251    
## gend.mf:cond.c         0.02529    0.81732   0.031   0.9753    
## IndD.d:gend.mf:cond.c -0.60583    1.10034  -0.551   0.5823    
## IndR.d:gend.mf:cond.c -0.43064    1.14916  -0.375   0.7081    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.827 on 316 degrees of freedom
##   (217 observations deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  -0.009643 
## F-statistic: 0.7161 on 11 and 316 DF,  p-value: 0.7231

summary(lm(act17 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.5111  0.2941  1.6667  3.1304 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.42330    0.19720   2.147   0.0324 *
## IndD.d                 0.29084    0.27066   1.075   0.2832  
## IndR.d                -0.21518    0.28129  -0.765   0.4447  
## gend.mf               -0.02960    0.39439  -0.075   0.9402  
## cond.c                -0.22437    0.39439  -0.569   0.5697  
## IndD.d:gend.mf         0.37406    0.54131   0.691   0.4899  
## IndR.d:gend.mf        -0.44564    0.56258  -0.792   0.4287  
## IndD.d:cond.c          0.12202    0.54131   0.225   0.8218  
## IndR.d:cond.c          0.01103    0.56258   0.020   0.9844  
## gend.mf:cond.c        -0.74080    0.78879  -0.939   0.3482  
## IndD.d:gend.mf:cond.c  0.49095    1.08263   0.453   0.6504  
## IndR.d:gend.mf:cond.c  0.76375    1.12516   0.679   0.4977  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.02 on 401 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01457,    Adjusted R-squared:  -0.01246 
## F-statistic: 0.539 on 11 and 401 DF,  p-value: 0.8766

summary(lm(act18 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.167 -1.150  0.250  1.333  2.850 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.600079   0.191017   3.141  0.00183 **
## IndD.d                 0.260666   0.264757   0.985  0.32555   
## IndR.d                -0.125080   0.278311  -0.449  0.65341   
## gend.mf               -0.329571   0.382034  -0.863  0.38893   
## cond.c                -0.070429   0.382034  -0.184  0.85385   
## IndD.d:gend.mf         0.564220   0.529513   1.066  0.28739   
## IndR.d:gend.mf         0.462904   0.556622   0.832  0.40621   
## IndD.d:cond.c          0.252447   0.529513   0.477  0.63385   
## IndR.d:cond.c          0.120429   0.556622   0.216  0.82884   
## gend.mf:cond.c        -0.000318   0.764068   0.000  0.99967   
## IndD.d:gend.mf:cond.c  0.390669   1.059026   0.369  0.71244   
## IndR.d:gend.mf:cond.c -0.933015   1.113244  -0.838  0.40257   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 337 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01772,    Adjusted R-squared:  -0.01434 
## F-statistic: 0.5527 on 11 and 337 DF,  p-value: 0.8662

summary(lm(act19 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4722 -1.1034  0.2222  1.5331  3.0588 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.32476    0.19957   6.638 1.85e-10 ***
## IndD.d                -0.19223    0.28937  -0.664    0.507    
## IndR.d                -0.28621    0.29502  -0.970    0.333    
## gend.mf               -0.02960    0.39914  -0.074    0.941    
## cond.c                -0.20732    0.39914  -0.519    0.604    
## IndD.d:gend.mf         0.09789    0.57873   0.169    0.866    
## IndR.d:gend.mf        -0.32853    0.59004  -0.557    0.578    
## IndD.d:cond.c         -0.12022    0.57873  -0.208    0.836    
## IndR.d:cond.c         -0.95490    0.59004  -1.618    0.107    
## gend.mf:cond.c         0.11600    0.79827   0.145    0.885    
## IndD.d:gend.mf:cond.c -0.12758    1.15747  -0.110    0.912    
## IndR.d:gend.mf:cond.c -1.46475    1.18008  -1.241    0.216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.794 on 259 degrees of freedom
##   (274 observations deleted due to missingness)
## Multiple R-squared:  0.045,  Adjusted R-squared:  0.004435 
## F-statistic: 1.109 on 11 and 259 DF,  p-value: 0.354

summary(lm(act20 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6111 -1.2857  0.3889  1.5000  2.7222 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.2649     0.1926   6.566  2.2e-10 ***
## IndD.d                 -0.1470     0.2611  -0.563    0.574    
## IndR.d                 -0.1308     0.2702  -0.484    0.629    
## gend.mf                -0.2440     0.3853  -0.633    0.527    
## cond.c                 -0.1466     0.3853  -0.380    0.704    
## IndD.d:gend.mf          0.5083     0.5221   0.973    0.331    
## IndR.d:gend.mf         -0.5156     0.5403  -0.954    0.341    
## IndD.d:cond.c          -0.8034     0.5221  -1.539    0.125    
## IndR.d:cond.c          -0.4271     0.5403  -0.790    0.430    
## gend.mf:cond.c         -0.5640     0.7705  -0.732    0.465    
## IndD.d:gend.mf:cond.c   1.4640     1.0443   1.402    0.162    
## IndR.d:gend.mf:cond.c  -0.1946     1.0807  -0.180    0.857    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.747 on 307 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.06688,    Adjusted R-squared:  0.03345 
## F-statistic:     2 on 11 and 307 DF,  p-value: 0.02801

summary(lm(act21 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7442 -1.8400  0.1698  1.6250  3.6250 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             0.2286     0.2031   1.126  0.26103   
## IndD.d                 -0.1567     0.2810  -0.558  0.57730   
## IndR.d                 -0.9165     0.2926  -3.132  0.00187 **
## gend.mf                -0.1173     0.4061  -0.289  0.77295   
## cond.c                  0.4047     0.4061   0.997  0.31961   
## IndD.d:gend.mf          0.5609     0.5620   0.998  0.31886   
## IndR.d:gend.mf         -0.5485     0.5853  -0.937  0.34930   
## IndD.d:cond.c          -0.5773     0.5620  -1.027  0.30497   
## IndR.d:cond.c           0.1587     0.5853   0.271  0.78637   
## gend.mf:cond.c         -1.0186     0.8122  -1.254  0.21058   
## IndD.d:gend.mf:cond.c   0.6335     1.1239   0.564  0.57333   
## IndR.d:gend.mf:cond.c   1.4750     1.1706   1.260  0.20840   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.053 on 386 degrees of freedom
##   (147 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  0.01812 
## F-statistic: 1.666 on 11 and 386 DF,  p-value: 0.07902

summary(lm(act22 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7021 -1.4886  0.1667  1.5238  3.5000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             0.4438     0.1876   2.365  0.01851 * 
## IndD.d                  0.3878     0.2666   1.455  0.14659   
## IndR.d                 -0.7584     0.2817  -2.692  0.00741 **
## gend.mf                 0.1769     0.3752   0.471  0.63757   
## cond.c                  0.1780     0.3752   0.474  0.63551   
## IndD.d:gend.mf          0.1101     0.5331   0.206  0.83654   
## IndR.d:gend.mf         -0.3204     0.5635  -0.569  0.56998   
## IndD.d:cond.c          -0.6389     0.5331  -1.198  0.23151   
## IndR.d:cond.c          -0.2154     0.5635  -0.382  0.70248   
## gend.mf:cond.c         -0.1319     0.7505  -0.176  0.86058   
## IndD.d:gend.mf:cond.c  -0.8463     1.0663  -0.794  0.42785   
## IndR.d:gend.mf:cond.c  -0.2479     1.1269  -0.220  0.82603   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.965 on 387 degrees of freedom
##   (146 observations deleted due to missingness)
## Multiple R-squared:  0.05667,    Adjusted R-squared:  0.02986 
## F-statistic: 2.114 on 11 and 387 DF,  p-value: 0.01862

summary(lm(act23 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5294 -2.2500  0.1563  1.9412  3.8000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.32223    0.25895   1.244   0.2144  
## IndD.d                -0.17124    0.35131  -0.487   0.6263  
## IndR.d                -0.45898    0.37477  -1.225   0.2217  
## gend.mf                0.08494    0.51789   0.164   0.8698  
## cond.c                -1.19447    0.51789  -2.306   0.0218 *
## IndD.d:gend.mf         0.06079    0.70262   0.087   0.9311  
## IndR.d:gend.mf        -0.22395    0.74953  -0.299   0.7653  
## IndD.d:cond.c          0.79506    0.70262   1.132   0.2588  
## IndR.d:cond.c          1.62098    0.74953   2.163   0.0314 *
## gend.mf:cond.c        -2.26989    1.03578  -2.191   0.0293 *
## IndD.d:gend.mf:cond.c  2.40857    1.40525   1.714   0.0877 .
## IndR.d:gend.mf:cond.c  2.99187    1.49906   1.996   0.0469 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.258 on 277 degrees of freedom
##   (256 observations deleted due to missingness)
## Multiple R-squared:  0.03833,    Adjusted R-squared:  0.0001456 
## F-statistic: 1.004 on 11 and 277 DF,  p-value: 0.4431

summary(lm(act24 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3182 -1.6875 -0.1455  1.5822  4.5263 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.23475    0.19636  -1.196   0.2326  
## IndD.d                 0.05701    0.27397   0.208   0.8353  
## IndR.d                -0.19715    0.28317  -0.696   0.4867  
## gend.mf               -0.75681    0.39273  -1.927   0.0547 .
## cond.c                -0.83730    0.39273  -2.132   0.0336 *
## IndD.d:gend.mf         1.08049    0.54794   1.972   0.0493 *
## IndR.d:gend.mf         0.94204    0.56634   1.663   0.0970 .
## IndD.d:cond.c          0.36363    0.54794   0.664   0.5073  
## IndR.d:cond.c          0.40208    0.56634   0.710   0.4781  
## gend.mf:cond.c        -1.97803    0.78545  -2.518   0.0122 *
## IndD.d:gend.mf:cond.c  1.58902    1.09588   1.450   0.1478  
## IndR.d:gend.mf:cond.c  1.20562    1.13268   1.064   0.2878  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.07 on 400 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.04277,    Adjusted R-squared:  0.01644 
## F-statistic: 1.625 on 11 and 400 DF,  p-value: 0.08932

summary(lm(act25 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3125 -1.1591  0.2364  1.6875  3.0435 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.79334    0.16954   4.679 3.86e-06 ***
## IndD.d                 0.36591    0.23823   1.536   0.1253    
## IndR.d                -0.62310    0.25140  -2.479   0.0136 *  
## gend.mf               -0.32240    0.33909  -0.951   0.3422    
## cond.c                 0.12240    0.33909   0.361   0.7183    
## IndD.d:gend.mf         0.56477    0.47646   1.185   0.2365    
## IndR.d:gend.mf         0.02176    0.50280   0.043   0.9655    
## IndD.d:cond.c          0.13246    0.47646   0.278   0.7811    
## IndR.d:cond.c         -0.26909    0.50280  -0.535   0.5928    
## gend.mf:cond.c        -0.57338    0.67817  -0.845   0.3983    
## IndD.d:gend.mf:cond.c -0.01462    0.95292  -0.015   0.9878    
## IndR.d:gend.mf:cond.c  0.61312    1.00560   0.610   0.5424    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 429 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.05022,    Adjusted R-squared:  0.02587 
## F-statistic: 2.062 on 11 and 429 DF,  p-value: 0.02189

summary(lm(act26 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2400  0.6111  1.4762  2.5385 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.58715    0.23370   6.791 1.11e-10 ***
## IndD.d                -0.17819    0.31904  -0.559   0.5771    
## IndR.d                -0.02290    0.33845  -0.068   0.9461    
## gend.mf                0.26160    0.46741   0.560   0.5763    
## cond.c                 0.11874    0.46741   0.254   0.7997    
## IndD.d:gend.mf         0.06632    0.63808   0.104   0.9173    
## IndR.d:gend.mf        -0.67856    0.67689  -1.002   0.3173    
## IndD.d:cond.c          0.11584    0.63808   0.182   0.8561    
## IndR.d:cond.c         -0.74025    0.67689  -1.094   0.2754    
## gend.mf:cond.c        -0.03236    0.93482  -0.035   0.9724    
## IndD.d:gend.mf:cond.c  0.52152    1.27615   0.409   0.6832    
## IndR.d:gend.mf:cond.c -2.30156    1.35378  -1.700   0.0906 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 211 degrees of freedom
##   (322 observations deleted due to missingness)
## Multiple R-squared:  0.04338,    Adjusted R-squared:  -0.006486 
## F-statistic: 0.8699 on 11 and 211 DF,  p-value: 0.5707

summary(lm(act27 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act27 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7895 -1.0714  0.1471  1.6544  3.0000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.61815    0.20246   3.053  0.00247 **
## IndD.d                 0.56244    0.27610   2.037  0.04251 * 
## IndR.d                -0.10789    0.28739  -0.375  0.70760   
## gend.mf                0.33513    0.40493   0.828  0.40853   
## cond.c                -0.48630    0.40493  -1.201  0.23071   
## IndD.d:gend.mf         0.14872    0.55220   0.269  0.78787   
## IndR.d:gend.mf        -0.12487    0.57477  -0.217  0.82816   
## IndD.d:cond.c          0.03361    0.55220   0.061  0.95150   
## IndR.d:cond.c         -0.24009    0.57477  -0.418  0.67645   
## gend.mf:cond.c        -0.17027    0.80986  -0.210  0.83362   
## IndD.d:gend.mf:cond.c -0.39220    1.10440  -0.355  0.72274   
## IndR.d:gend.mf:cond.c -0.83849    1.14954  -0.729  0.46631   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 302 degrees of freedom
##   (231 observations deleted due to missingness)
## Multiple R-squared:  0.05283,    Adjusted R-squared:  0.01833 
## F-statistic: 1.531 on 11 and 302 DF,  p-value: 0.1192

summary(lm(act28 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3421 -1.0652  0.2593  1.6579  2.7143 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.90893    0.19550   4.649  4.7e-06 ***
## IndD.d                 0.28502    0.26929   1.058    0.291    
## IndR.d                 0.03878    0.27334   0.142    0.887    
## gend.mf               -0.26498    0.39100  -0.678    0.498    
## cond.c                 0.13962    0.39100   0.357    0.721    
## IndD.d:gend.mf         0.22153    0.53859   0.411    0.681    
## IndR.d:gend.mf         0.07193    0.54668   0.132    0.895    
## IndD.d:cond.c         -0.56692    0.53859  -1.053    0.293    
## IndR.d:cond.c         -0.72608    0.54668  -1.328    0.185    
## gend.mf:cond.c        -0.92348    0.78200  -1.181    0.238    
## IndD.d:gend.mf:cond.c  0.02251    1.07718   0.021    0.983    
## IndR.d:gend.mf:cond.c -0.16551    1.09337  -0.151    0.880    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 357 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02841,    Adjusted R-squared:  -0.001523 
## F-statistic: 0.9491 on 11 and 357 DF,  p-value: 0.4931

summary(lm(act29 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -1.0541  0.1053  1.6905  2.1053 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.079020   0.175578   6.146 2.15e-09 ***
## IndD.d                 0.231694   0.248081   0.934    0.351    
## IndR.d                -0.139955   0.260526  -0.537    0.591    
## gend.mf                0.046294   0.351157   0.132    0.895    
## cond.c                 0.159172   0.351157   0.453    0.651    
## IndD.d:gend.mf        -0.001056   0.496162  -0.002    0.998    
## IndR.d:gend.mf        -0.029687   0.521051  -0.057    0.955    
## IndD.d:cond.c         -0.069489   0.496162  -0.140    0.889    
## IndR.d:cond.c         -0.235588   0.521051  -0.452    0.651    
## gend.mf:cond.c         0.325620   0.702314   0.464    0.643    
## IndD.d:gend.mf:cond.c -0.060540   0.992324  -0.061    0.951    
## IndR.d:gend.mf:cond.c -0.383314   1.042103  -0.368    0.713    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.768 on 353 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.009013,   Adjusted R-squared:  -0.02187 
## F-statistic: 0.2919 on 11 and 353 DF,  p-value: 0.9872

summary(lm(act30 ~ (IndD.d + IndR.d)*gend.mf*cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (IndD.d + IndR.d) * gend.mf * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.1579  0.5556  1.7368  2.7273 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.201238   0.196943   6.099 2.92e-09 ***
## IndD.d                -0.236059   0.272785  -0.865    0.387    
## IndR.d                -0.386981   0.283606  -1.365    0.173    
## gend.mf                0.068111   0.393885   0.173    0.863    
## cond.c                 0.226006   0.393885   0.574    0.566    
## IndD.d:gend.mf        -0.498471   0.545570  -0.914    0.362    
## IndR.d:gend.mf        -0.673899   0.567213  -1.188    0.236    
## IndD.d:cond.c         -0.003249   0.545570  -0.006    0.995    
## IndR.d:cond.c          0.104781   0.567213   0.185    0.854    
## gend.mf:cond.c         0.489164   0.787771   0.621    0.535    
## IndD.d:gend.mf:cond.c  0.287544   1.091140   0.264    0.792    
## IndR.d:gend.mf:cond.c -0.196194   1.134425  -0.173    0.863    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.877 on 337 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01829,    Adjusted R-squared:  -0.01375 
## F-statistic: 0.5709 on 11 and 337 DF,  p-value: 0.8523

a. Means, etc.

aggregate(d$act23[d$party_factor == "Independent" & d$gend == "Female"], list(d$cond[d$party_factor == "Independent" & d$gend == "Female"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1    x
## 1 climate 0.25
## 2    ctrl 0.31

aggregate(d$act23[d$party_factor == "Independent" & d$gend == "Male"], list(d$cond[d$party_factor == "Independent" & d$gend == "Male"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1     x
## 1 climate -0.80
## 2    ctrl  1.53

aggregate(d$act24[d$party_factor == "Independent" & d$gend == "Female"], list(d$cond[d$party_factor == "Independent" & d$gend == "Female"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1    x
## 1 climate 0.22
## 2    ctrl 0.07

aggregate(d$act24[d$party_factor == "Independent" & d$gend == "Male"], list(d$cond[d$party_factor == "Independent" & d$gend == "Male"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1     x
## 1 climate -1.53
## 2    ctrl  0.30

iii. Democrats

Support: 2, 5, 6, 10, 11,14, 15, 16, 17, 18, 19, 20, 22, 25, 26, 27, 28, 29, 30 Oppose: None Neutral:

# Action 1
dem.b1 <- lm(act1 ~ (DemR.d + DemI.d), data = d)

summary(dem.b1) 

## 
## Call:
## lm(formula = act1 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2770 -2.2199 -0.0486  1.7230  3.5844 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2770     0.1761   1.573 0.116448    
## DemR.d       -0.8614     0.2466  -3.493 0.000526 ***
## DemI.d       -0.2284     0.2508  -0.911 0.362916    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.143 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02882,    Adjusted R-squared:  0.02443 
## F-statistic: 6.572 on 2 and 443 DF,  p-value: 0.001539

# Action 2
dem.b2 <- lm(act2 ~ (DemR.d + DemI.d), data = d)

summary(dem.b2) # yes, above 0

## 
## Call:
## lm(formula = act2 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.280 -1.104  0.022  1.720  2.022 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.2800     0.1811   7.069 1.21e-11 ***
## DemR.d       -0.3020     0.2623  -1.151    0.251    
## DemI.d       -0.1758     0.2587  -0.680    0.497    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.811 on 284 degrees of freedom
##   (258 observations deleted due to missingness)
## Multiple R-squared:  0.004714,   Adjusted R-squared:  -0.002295 
## F-statistic: 0.6726 on 2 and 284 DF,  p-value: 0.5112

# Action 3
dem.b3 <- lm(act3 ~ (DemR.d + DemI.d), data = d)

summary(dem.b3) 

## 
## Call:
## lm(formula = act3 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1325 -1.8289  0.1711  1.6884  3.6884 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.1325     0.1627   0.814 0.416010    
## DemR.d       -0.8209     0.2354  -3.487 0.000539 ***
## DemI.d       -0.3035     0.2297  -1.321 0.187085    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.999 on 438 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.02743,    Adjusted R-squared:  0.02299 
## F-statistic: 6.176 on 2 and 438 DF,  p-value: 0.002265

# Action 4
dem.b4 <- lm(act4 ~ (DemR.d + DemI.d), data = d)

summary(dem.b4)

## 
## Call:
## lm(formula = act4 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3030 -1.7931  0.2069  2.2069  3.2333 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3030     0.1875   1.616   0.1070  
## DemR.d       -0.5099     0.2742  -1.860   0.0637 .
## DemI.d       -0.5364     0.2718  -1.974   0.0492 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.155 on 365 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.01352,    Adjusted R-squared:  0.008119 
## F-statistic: 2.502 on 2 and 365 DF,  p-value: 0.08333

# Action 5
dem.b5 <- lm(act5 ~ (DemR.d + DemI.d), data = d)

summary(dem.b5) # yes, above 0

## 
## Call:
## lm(formula = act5 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1165 -1.1165  0.1053  1.8835  2.1053 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1165     0.1841   6.066 4.07e-09 ***
## DemR.d       -0.1582     0.2650  -0.597    0.551    
## DemI.d       -0.2218     0.2657  -0.835    0.405    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.868 on 291 degrees of freedom
##   (251 observations deleted due to missingness)
## Multiple R-squared:  0.002558,   Adjusted R-squared:  -0.004298 
## F-statistic: 0.3731 on 2 and 291 DF,  p-value: 0.6889

# Action 6
dem.b6 <- lm(act6 ~ (DemR.d + DemI.d), data = d)

summary(dem.b6) # yes, above 0

## 
## Call:
## lm(formula = act6 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.400 -1.111  0.600  1.600  1.889 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.20175    0.16992   7.072 8.22e-12 ***
## DemR.d      -0.09064    0.23876  -0.380    0.704    
## DemI.d       0.19825    0.23496   0.844    0.399    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.814 on 353 degrees of freedom
##   (189 observations deleted due to missingness)
## Multiple R-squared:  0.004549,   Adjusted R-squared:  -0.001091 
## F-statistic: 0.8065 on 2 and 353 DF,  p-value: 0.4472

# Action 7
dem.b7 <- lm(act7 ~ (DemR.d + DemI.d), data = d)

summary(dem.b7)

## 
## Call:
## lm(formula = act7 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0993 -1.9929  0.0071  1.9007  3.4962 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.09929    0.17402   0.571    0.569  
## DemR.d      -0.59547    0.25075  -2.375    0.018 *
## DemI.d      -0.10643    0.24654  -0.432    0.666  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 409 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.01527,    Adjusted R-squared:  0.01046 
## F-statistic: 3.172 on 2 and 409 DF,  p-value: 0.04296

# Action 8
dem.b8 <- lm(act8 ~ (DemR.d + DemI.d), data = d)

summary(dem.b8) # yes, below 0

## 
## Call:
## lm(formula = act8 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2327 -1.8734 -0.2327  1.7673  4.1266 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2327     0.1670   1.393   0.1642    
## DemR.d       -1.3593     0.2366  -5.745 1.64e-08 ***
## DemI.d       -0.5619     0.2355  -2.386   0.0174 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.106 on 475 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.06556,    Adjusted R-squared:  0.06162 
## F-statistic: 16.66 on 2 and 475 DF,  p-value: 1.014e-07

# Action 9
dem.b9 <- lm(act9 ~ (DemR.d + DemI.d), data = d)

summary(dem.b9) 

## 
## Call:
## lm(formula = act9 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2617 -1.5594  0.1196  1.7383  3.4406 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.26174    0.16146   1.621  0.10571   
## DemR.d      -0.70230    0.23072  -3.044  0.00247 **
## DemI.d      -0.06045    0.22648  -0.267  0.78967   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.971 on 443 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02498,    Adjusted R-squared:  0.02058 
## F-statistic: 5.676 on 2 and 443 DF,  p-value: 0.003682

# Action 10
dem.b10 <- lm(act10 ~ (DemR.d + DemI.d), data = d)

summary(dem.b10) # yes, above 0

## 
## Call:
## lm(formula = act10 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5935 -2.2208  0.4065  1.6545  3.7792 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.5935     0.1638   3.623 0.000323 ***
## DemR.d       -1.3728     0.2320  -5.916 6.36e-09 ***
## DemI.d       -0.2481     0.2281  -1.087 0.277382    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 471 degrees of freedom
##   (71 observations deleted due to missingness)
## Multiple R-squared:  0.07812,    Adjusted R-squared:  0.07421 
## F-statistic: 19.96 on 2 and 471 DF,  p-value: 4.791e-09

# Action 11
dem.b11 <- lm(act11 ~ (DemR.d + DemI.d), data = d)

summary(dem.b11) # yes, above 0

## 
## Call:
## lm(formula = act11 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0755 -1.0755  0.3093  1.9245  2.3093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.07547    0.18323   5.870 1.13e-08 ***
## DemR.d      -0.38475    0.26507  -1.452    0.148    
## DemI.d      -0.09399    0.25792  -0.364    0.716    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.886 on 308 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.007306,   Adjusted R-squared:  0.0008601 
## F-statistic: 1.133 on 2 and 308 DF,  p-value: 0.3233

# Action 12
dem.b12 <- lm(act12 ~ (DemR.d + DemI.d), data = d)

summary(dem.b12) 

## 
## Call:
## lm(formula = act12 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3167 -2.0992 -0.1603  1.8397  3.9008 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3167     0.1941   1.632    0.104    
## DemR.d       -1.2174     0.2687  -4.531 7.86e-06 ***
## DemI.d       -0.1564     0.2687  -0.582    0.561    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.126 on 379 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.06166,    Adjusted R-squared:  0.0567 
## F-statistic: 12.45 on 2 and 379 DF,  p-value: 5.789e-06

# Action 13
dem.b13 <- lm(act13 ~ (DemR.d + DemI.d), data = d)

summary(dem.b13) 

## 
## Call:
## lm(formula = act13 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1888 -2.0186 -0.0186  1.8112  3.5588 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.1888     0.1725   1.094  0.27443   
## DemR.d       -0.7476     0.2471  -3.025  0.00263 **
## DemI.d       -0.1702     0.2371  -0.718  0.47328   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.063 on 437 degrees of freedom
##   (105 observations deleted due to missingness)
## Multiple R-squared:  0.0225, Adjusted R-squared:  0.01802 
## F-statistic: 5.029 on 2 and 437 DF,  p-value: 0.006932

# Action 14
dem.b14 <- lm(act14 ~ (DemR.d + DemI.d), data = d)

summary(dem.b14) # yes, above 0

## 
## Call:
## lm(formula = act14 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5481 -1.4444 -0.3171  1.5556  2.6829 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.5481     0.1992   2.751  0.00632 **
## DemR.d       -0.2310     0.3000  -0.770  0.44200   
## DemI.d       -0.1036     0.2853  -0.363  0.71667   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.032 on 282 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.002098,   Adjusted R-squared:  -0.00498 
## F-statistic: 0.2964 on 2 and 282 DF,  p-value: 0.7437

# Action 15
dem.b15 <- lm(act15 ~ (DemR.d + DemI.d), data = d)

summary(dem.b15) 

## 
## Call:
## lm(formula = act15 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5328 -1.8049  0.1951  1.4672  3.1951 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.5328     0.1849   2.881  0.00419 **
## DemR.d       -0.7279     0.2610  -2.789  0.00555 **
## DemI.d       -0.4598     0.2542  -1.808  0.07132 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.042 on 379 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.02059,    Adjusted R-squared:  0.01542 
## F-statistic: 3.984 on 2 and 379 DF,  p-value: 0.0194

# Action 16
dem.b16 <- lm(act16 ~ (DemR.d + DemI.d), data = d)

summary(dem.b16) # yes, above 0

## 
## Call:
## lm(formula = act16 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0550 -1.0550  0.0841  1.1770  2.1770 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0550     0.1745   6.045 4.08e-09 ***
## DemR.d       -0.1392     0.2480  -0.561    0.575    
## DemI.d       -0.2320     0.2446  -0.949    0.344    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 326 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.002782,   Adjusted R-squared:  -0.003336 
## F-statistic: 0.4547 on 2 and 326 DF,  p-value: 0.6351

# Action 17
dem.b17 <- lm(act17 ~ (DemR.d + DemI.d), data = d)

summary(dem.b17) # yes, above 0

## 
## Call:
## lm(formula = act17 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.650 -1.420  0.350  1.702  2.702 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6500     0.1694   3.836 0.000145 ***
## DemR.d       -0.3523     0.2437  -1.446 0.149075    
## DemI.d       -0.2304     0.2384  -0.967 0.334292    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.005 on 411 degrees of freedom
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.005261,   Adjusted R-squared:  0.0004204 
## F-statistic: 1.087 on 2 and 411 DF,  p-value: 0.3382

# Action 18
dem.b18 <- lm(act18 ~ (DemR.d + DemI.d), data = d)

summary(dem.b18) #  yes, above 0

## 
## Call:
## lm(formula = act18 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8430 -1.1338  0.3145  1.3145  2.5755 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8430     0.1667   5.056 6.95e-07 ***
## DemR.d       -0.4184     0.2440  -1.715   0.0872 .  
## DemI.d       -0.1575     0.2344  -0.672   0.5020    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 348 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.008477,   Adjusted R-squared:  0.002779 
## F-statistic: 1.488 on 2 and 348 DF,  p-value: 0.2273

# Action 19
dem.b19 <- lm(act19 ~ (DemR.d + DemI.d), data = d)

summary(dem.b19) # yes, above 0

## 
## Call:
## lm(formula = act19 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.337 -1.032  0.000  1.663  2.000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1264     0.1929   5.840 1.51e-08 ***
## DemR.d       -0.1264     0.2705  -0.467    0.641    
## DemI.d        0.2104     0.2670   0.788    0.431    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.799 on 269 degrees of freedom
##   (273 observations deleted due to missingness)
## Multiple R-squared:  0.006131,   Adjusted R-squared:  -0.001258 
## F-statistic: 0.8297 on 2 and 269 DF,  p-value: 0.4373

# Action 20
dem.b20 <- lm(act20 ~ (DemR.d + DemI.d), data = d)

summary(dem.b20) # yes, above 0

## 
## Call:
## lm(formula = act20 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3148 -1.2621  0.6852  1.6852  1.9000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1000     0.1693   6.496 3.18e-10 ***
## DemR.d        0.1621     0.2435   0.666    0.506    
## DemI.d        0.2148     0.2406   0.893    0.373    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 318 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.002716,   Adjusted R-squared:  -0.003556 
## F-statistic: 0.433 on 2 and 318 DF,  p-value: 0.6489

# Action 21
dem.b21 <- lm(act21 ~ (DemR.d + DemI.d), data = d)

summary(dem.b21) 

## 
## Call:
## lm(formula = act21 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2214 -2.0071 -0.0071  1.7786  3.5000 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.007092   0.173115   0.041    0.967  
## DemR.d      -0.507092   0.250961  -2.021    0.044 *
## DemI.d       0.214282   0.249451   0.859    0.391  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.056 on 397 degrees of freedom
##   (145 observations deleted due to missingness)
## Multiple R-squared:  0.02074,    Adjusted R-squared:  0.01581 
## F-statistic: 4.205 on 2 and 397 DF,  p-value: 0.01559

# Action 22
dem.b22 <- lm(act22 ~ (DemR.d + DemI.d), data = d)

summary(dem.b22) # yes, above 0

## 
## Call:
## lm(formula = act22 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8045 -1.3873  0.1955  1.6127  3.2857 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.8045     0.1693   4.753 2.80e-06 ***
## DemR.d       -1.0902     0.2427  -4.493 9.22e-06 ***
## DemI.d       -0.4172     0.2355  -1.771   0.0773 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.952 on 398 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.04897,    Adjusted R-squared:  0.04419 
## F-statistic: 10.25 on 2 and 398 DF,  p-value: 4.577e-05

# Action 23
dem.b23 <- lm(act23 ~ (DemR.d + DemI.d), data = d)

summary(dem.b23) 

## 
## Call:
## lm(formula = act23 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3810 -2.3810  0.0824  2.0824  3.0824 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.1600     0.2261   0.708    0.480
## DemR.d       -0.2424     0.3335  -0.727    0.468
## DemI.d        0.2210     0.3159   0.700    0.485
## 
## Residual standard error: 2.261 on 287 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.006836,   Adjusted R-squared:  -8.474e-05 
## F-statistic: 0.9878 on 2 and 287 DF,  p-value: 0.3737

# Action 24
dem.b24 <- lm(act24 ~ (DemR.d + DemI.d), data = d)

summary(dem.b24) 

## 
## Call:
## lm(formula = act24 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9281 -1.9281  0.0719  1.5038  3.5038 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.2128     0.1759  -1.210    0.227
## DemR.d       -0.2910     0.2524  -1.153    0.250
## DemI.d        0.1408     0.2496   0.564    0.573
## 
## Residual standard error: 2.088 on 410 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.007291,   Adjusted R-squared:  0.002448 
## F-statistic: 1.506 on 2 and 410 DF,  p-value: 0.2231

# Action 25
dem.b25 <- lm(act25 ~ (DemR.d + DemI.d), data = d)

summary(dem.b25) # yes, above 0

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1149 -1.1149  0.1611  1.7603  2.7603 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1149     0.1505   7.406 6.71e-13 ***
## DemR.d       -0.8751     0.2136  -4.097 4.99e-05 ***
## DemI.d       -0.2759     0.2125  -1.298    0.195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.831 on 440 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.0383, Adjusted R-squared:  0.03393 
## F-statistic: 8.762 on 2 and 440 DF,  p-value: 0.0001856

# Action 26
dem.b26 <- lm(act26 ~ (DemR.d + DemI.d), data = d)

summary(dem.b26) # yes, above 0

## 
## Call:
## lm(formula = act26 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5672 -1.3974  0.6026  1.4810  1.6026 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3974     0.2062   6.777  1.1e-10 ***
## DemR.d        0.1697     0.3033   0.560    0.576    
## DemI.d        0.1216     0.2907   0.418    0.676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 221 degrees of freedom
##   (321 observations deleted due to missingness)
## Multiple R-squared:  0.001544,   Adjusted R-squared:  -0.007492 
## F-statistic: 0.1709 on 2 and 221 DF,  p-value: 0.843

# Action 27
dem.b27 <- lm(act27 ~ (DemR.d + DemI.d), data = d)

summary(dem.b27) # yes, above 0

## 
## Call:
## lm(formula = act27 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1404 -1.1404  0.1489  1.5670  2.5670 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.1404     0.1747   6.526 2.71e-10 ***
## DemR.d       -0.7074     0.2577  -2.745   0.0064 ** 
## DemI.d       -0.5784     0.2523  -2.292   0.0226 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 313 degrees of freedom
##   (229 observations deleted due to missingness)
## Multiple R-squared:  0.02742,    Adjusted R-squared:  0.02121 
## F-statistic: 4.413 on 2 and 313 DF,  p-value: 0.01288

# Action 28
dem.b28 <- lm(act28 ~ (DemR.d + DemI.d), data = d)

summary(dem.b28) # yes, above 0

## 
## Call:
## lm(formula = act28 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2266 -0.9508  0.0579  1.7734  2.0579 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.2266     0.1607   7.632 2.01e-13 ***
## DemR.d       -0.2757     0.2301  -1.199    0.231    
## DemI.d       -0.2844     0.2306  -1.234    0.218    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.818 on 368 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.005379,   Adjusted R-squared:  -2.643e-05 
## F-statistic: 0.9951 on 2 and 368 DF,  p-value: 0.3707

# Action 29
dem.b29 <- lm(act29 ~ (DemR.d + DemI.d), data = d)

summary(dem.b29) # yes, above 0

## 
## Call:
## lm(formula = act29 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3200 -1.0720  0.0684  1.6800  2.0684 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3200     0.1561   8.455 6.83e-16 ***
## DemR.d       -0.3884     0.2245  -1.730   0.0845 .  
## DemI.d       -0.2480     0.2208  -1.123   0.2621    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.745 on 364 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.008418,   Adjusted R-squared:  0.00297 
## F-statistic: 1.545 on 2 and 364 DF,  p-value: 0.2147

# Action 30
dem.b30 <- lm(act30 ~ (DemR.d + DemI.d), data = d)

summary(dem.b30) # yes, above 0

## 
## Call:
## lm(formula = act30 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1917 -1.0598  0.8083  1.8083  2.0263 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.05983    0.17240   6.148 2.16e-09 ***
## DemR.d      -0.08614    0.24540  -0.351    0.726    
## DemI.d       0.13184    0.24228   0.544    0.587    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.865 on 348 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.002333,   Adjusted R-squared:  -0.003401 
## F-statistic: 0.4068 on 2 and 348 DF,  p-value: 0.6661

Significantly above 0:

1. condition differences?

Actions: 20

# Action 1
summary(lm(act1 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act1 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.417 -2.229  0.026  1.771  3.591 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.2807     0.1764   1.591 0.112242    
## DemR.d         -0.8656     0.2473  -3.500 0.000513 ***
## DemI.d         -0.2646     0.2526  -1.047 0.295522    
## cond.c         -0.2719     0.3528  -0.771 0.441224    
## DemR.d:cond.c   0.2852     0.4947   0.576 0.564587    
## DemI.d:cond.c  -0.1537     0.5053  -0.304 0.761124    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.145 on 440 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.03317,    Adjusted R-squared:  0.02218 
## F-statistic: 3.019 on 5 and 440 DF,  p-value: 0.01084

# Action 2
summary(lm(act2 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act2 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3800 -1.1947  0.2292  1.6200  2.2292 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.2800     0.1810   7.072 1.22e-11 ***
## DemR.d         -0.2899     0.2624  -1.105    0.270    
## DemI.d         -0.2356     0.2630  -0.896    0.371    
## cond.c         -0.2000     0.3620  -0.552    0.581    
## DemR.d:cond.c  -0.2385     0.5249  -0.454    0.650    
## DemI.d:cond.c  -0.2778     0.5260  -0.528    0.598    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 281 degrees of freedom
##   (258 observations deleted due to missingness)
## Multiple R-squared:  0.01593,    Adjusted R-squared:  -0.001575 
## F-statistic:  0.91 on 5 and 281 DF,  p-value: 0.4749

# Action 3
summary(lm(act3 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act3 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2568 -1.8939  0.1061  1.6351  3.7500 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.1349     0.1631   0.827 0.408823    
## DemR.d         -0.8274     0.2364  -3.501 0.000512 ***
## DemI.d         -0.2984     0.2313  -1.290 0.197769    
## cond.c          0.2438     0.3263   0.747 0.455373    
## DemR.d:cond.c  -0.1289     0.4727  -0.273 0.785236    
## DemI.d:cond.c  -0.1289     0.4626  -0.279 0.780660    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.004 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.0292, Adjusted R-squared:  0.01804 
## F-statistic: 2.617 on 5 and 435 DF,  p-value: 0.024

# Action 4
summary(lm(act4 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act4 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4923 -2.0000  0.2258  2.0000  3.5385 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.3059     0.1876   1.630   0.1039  
## DemR.d         -0.5114     0.2746  -1.862   0.0634 .
## DemI.d         -0.5751     0.2731  -2.106   0.0359 *
## cond.c          0.3729     0.3752   0.994   0.3209  
## DemR.d:cond.c  -0.4135     0.5493  -0.753   0.4520  
## DemI.d:cond.c  -0.9114     0.5462  -1.668   0.0961 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.155 on 362 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.0212, Adjusted R-squared:  0.007677 
## F-statistic: 1.568 on 5 and 362 DF,  p-value: 0.1683

# Action 5
summary(lm(act5 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act5 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3585 -0.9200  0.1818  1.6415  2.1818 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.1092     0.1843   6.020 5.31e-09 ***
## DemR.d         -0.1270     0.2667  -0.476    0.634    
## DemI.d         -0.2159     0.2662  -0.811    0.418    
## cond.c         -0.4985     0.3685  -1.353    0.177    
## DemR.d:cond.c   0.1703     0.5335   0.319    0.750    
## DemI.d:cond.c   0.4452     0.5323   0.836    0.404    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.869 on 288 degrees of freedom
##   (251 observations deleted due to missingness)
## Multiple R-squared:  0.01139,    Adjusted R-squared:  -0.005774 
## F-statistic: 0.6636 on 5 and 288 DF,  p-value: 0.6514

# Action 6
summary(lm(act6 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act6 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5278 -1.1731  0.4722  1.7736  1.9385 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.20093    0.17065   7.037 1.04e-11 ***
## DemR.d        -0.08362    0.24036  -0.348    0.728    
## DemI.d         0.17617    0.23714   0.743    0.458    
## cond.c        -0.03148    0.34130  -0.092    0.927    
## DemR.d:cond.c -0.08006    0.48072  -0.167    0.868    
## DemI.d:cond.c -0.26988    0.47428  -0.569    0.570    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.82 on 350 degrees of freedom
##   (189 observations deleted due to missingness)
## Multiple R-squared:  0.007256,   Adjusted R-squared:  -0.006926 
## F-statistic: 0.5116 on 5 and 350 DF,  p-value: 0.7675

# Action 7
summary(lm(act7 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act7 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2192 -1.9706  0.0294  1.7808  3.6613 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.09488    0.17434   0.544   0.5866  
## DemR.d        -0.59944    0.25132  -2.385   0.0175 *
## DemI.d        -0.08254    0.24796  -0.333   0.7394  
## cond.c         0.24859    0.34868   0.713   0.4763  
## DemR.d:cond.c  0.06487    0.50264   0.129   0.8974  
## DemI.d:cond.c  0.05459    0.49591   0.110   0.9124  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.069 on 406 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.02009,    Adjusted R-squared:  0.008026 
## F-statistic: 1.665 on 5 and 406 DF,  p-value: 0.1419

# Action 8
summary(lm(act8 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act8 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.367 -1.648 -0.100  1.843  4.352 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.2335     0.1670   1.398   0.1627    
## DemR.d         -1.3311     0.2374  -5.607  3.5e-08 ***
## DemI.d         -0.5580     0.2360  -2.364   0.0185 *  
## cond.c          0.2671     0.3341   0.799   0.4244    
## DemR.d:cond.c  -0.7765     0.4748  -1.636   0.1026    
## DemI.d:cond.c  -0.1661     0.4720  -0.352   0.7251    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.106 on 472 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.07148,    Adjusted R-squared:  0.06165 
## F-statistic: 7.267 on 5 and 472 DF,  p-value: 1.426e-06

# Action 9
summary(lm(act9 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act9 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2973 -1.5231  0.1162  1.7027  3.4769 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.261982   0.161993   1.617  0.10654   
## DemR.d        -0.705572   0.231972  -3.042  0.00249 **
## DemI.d        -0.055906   0.228857  -0.244  0.80713   
## cond.c         0.070631   0.323986   0.218  0.82753   
## DemR.d:cond.c -0.003964   0.463945  -0.009  0.99319   
## DemI.d:cond.c -0.014033   0.457713  -0.031  0.97555   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.977 on 440 degrees of freedom
##   (99 observations deleted due to missingness)
## Multiple R-squared:  0.02525,    Adjusted R-squared:  0.01417 
## F-statistic: 2.279 on 5 and 440 DF,  p-value: 0.04594

# Action 10
summary(lm(act10 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act10 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6133 -2.0595  0.3867  1.6404  3.9405 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.59417    0.16422   3.618 0.000329 ***
## DemR.d        -1.35726    0.23305  -5.824 1.07e-08 ***
## DemI.d        -0.24992    0.22899  -1.091 0.275657    
## cond.c         0.03833    0.32844   0.117 0.907139    
## DemR.d:cond.c -0.39310    0.46609  -0.843 0.399445    
## DemI.d:cond.c -0.06894    0.45797  -0.151 0.880416    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 468 degrees of freedom
##   (71 observations deleted due to missingness)
## Multiple R-squared:  0.08043,    Adjusted R-squared:  0.0706 
## F-statistic: 8.187 on 5 and 468 DF,  p-value: 2.004e-07

# Action 11
summary(lm(act11 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act11 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3509 -1.0528  0.4314  1.6491  2.4314 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.05280    0.18302   5.752 2.14e-08 ***
## DemR.d        -0.35971    0.26434  -1.361    0.175    
## DemI.d        -0.09305    0.25726  -0.362    0.718    
## cond.c        -0.48060    0.36605  -1.313    0.190    
## DemR.d:cond.c  0.38853    0.52867   0.735    0.463    
## DemI.d:cond.c -0.30165    0.51452  -0.586    0.558    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 305 degrees of freedom
##   (234 observations deleted due to missingness)
## Multiple R-squared:  0.0279, Adjusted R-squared:  0.01197 
## F-statistic: 1.751 on 5 and 305 DF,  p-value: 0.1228

# Action 12
summary(lm(act12 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act12 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3333 -1.9851 -0.1029  1.8971  4.0149 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.31579    0.19498   1.620    0.106    
## DemR.d        -1.21388    0.26976  -4.500 9.08e-06 ***
## DemI.d        -0.15321    0.26982  -0.568    0.571    
## cond.c        -0.03509    0.38996  -0.090    0.928    
## DemR.d:cond.c -0.19859    0.53953  -0.368    0.713    
## DemI.d:cond.c  0.15437    0.53965   0.286    0.775    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.133 on 376 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.06291,    Adjusted R-squared:  0.05045 
## F-statistic: 5.048 on 5 and 376 DF,  p-value: 0.0001718

# Action 13
summary(lm(act13 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act13 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3803 -2.0000  0.1977  1.7533  3.8133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.1901     0.1720   1.106  0.26954   
## DemR.d         -0.7198     0.2470  -2.914  0.00375 **
## DemI.d         -0.1556     0.2366  -0.658  0.51098   
## cond.c         -0.3803     0.3440  -1.106  0.26954   
## DemR.d:cond.c  -0.1872     0.4940  -0.379  0.70500   
## DemI.d:cond.c   0.8446     0.4732   1.785  0.07496 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 434 degrees of freedom
##   (105 observations deleted due to missingness)
## Multiple R-squared:  0.03544,    Adjusted R-squared:  0.02433 
## F-statistic: 3.189 on 5 and 434 DF,  p-value: 0.007706

# Action 14
summary(lm(act14 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act14 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7209 -1.5370 -0.2321  1.7297  2.7679 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.54852    0.19992   2.744  0.00647 **
## DemR.d        -0.23561    0.30178  -0.781  0.43563   
## DemI.d        -0.07198    0.28745  -0.250  0.80245   
## cond.c         0.02296    0.39984   0.057  0.95424   
## DemR.d:cond.c  0.06232    0.60356   0.103  0.91783   
## DemI.d:cond.c  0.46582    0.57490   0.810  0.41848   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.037 on 279 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.007218,   Adjusted R-squared:  -0.01057 
## F-statistic: 0.4057 on 5 and 279 DF,  p-value: 0.8447

# Action 15
summary(lm(act15 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act15 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7000 -1.8098  0.2069  1.6290  3.2069 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.53548    0.18532   2.890  0.00408 **
## DemR.d        -0.73124    0.26174  -2.794  0.00548 **
## DemI.d        -0.47382    0.25518  -1.857  0.06412 . 
## cond.c        -0.32903    0.37063  -0.888  0.37524   
## DemR.d:cond.c  0.35131    0.52348   0.671  0.50256   
## DemI.d:cond.c  0.04696    0.51035   0.092  0.92673   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.047 on 376 degrees of freedom
##   (163 observations deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  0.01135 
## F-statistic: 1.875 on 5 and 376 DF,  p-value: 0.09788

# Action 16
summary(lm(act16 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act16 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1887 -1.0385  0.1404  1.5000  2.5000 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.05432    0.17440   6.045 4.11e-09 ***
## DemR.d        -0.13449    0.24793  -0.542    0.588    
## DemI.d        -0.20998    0.24455  -0.859    0.391    
## cond.c        -0.03171    0.34881  -0.091    0.928    
## DemR.d:cond.c -0.08864    0.49586  -0.179    0.858    
## DemI.d:cond.c  0.72039    0.48911   1.473    0.142    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 323 degrees of freedom
##   (216 observations deleted due to missingness)
## Multiple R-squared:  0.01546,    Adjusted R-squared:  0.0002187 
## F-statistic: 1.014 on 5 and 323 DF,  p-value: 0.4091

# Action 17
summary(lm(act17 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act17 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6761 -1.4375  0.3239  1.6032  2.8108 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.64962    0.16997   3.822 0.000153 ***
## DemR.d        -0.33573    0.24554  -1.367 0.172277    
## DemI.d        -0.23246    0.23995  -0.969 0.333223    
## cond.c        -0.05287    0.33995  -0.156 0.876489    
## DemR.d:cond.c -0.19654    0.49108  -0.400 0.689203    
## DemI.d:cond.c  0.01219    0.47990   0.025 0.979742    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.011 on 408 degrees of freedom
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.006561,   Adjusted R-squared:  -0.005614 
## F-statistic: 0.5389 on 5 and 408 DF,  p-value: 0.7468

# Action 18
summary(lm(act18 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act18 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9123 -1.1004  0.2778  1.3654  2.7115 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.8468     0.1676   5.054 7.04e-07 ***
## DemR.d         -0.4248     0.2450  -1.734   0.0839 .  
## DemI.d         -0.1683     0.2369  -0.711   0.4777    
## cond.c          0.1310     0.3351   0.391   0.6960    
## DemR.d:cond.c   0.1361     0.4900   0.278   0.7814    
## DemI.d:cond.c  -0.2186     0.4737  -0.462   0.6447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.84 on 345 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.01071,    Adjusted R-squared:  -0.003625 
## F-statistic: 0.7472 on 5 and 345 DF,  p-value: 0.5886

# Action 19
summary(lm(act19 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act19 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5429 -1.0543  0.3455  1.5510  2.3455 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.14103    0.19279   5.918 9.98e-09 ***
## DemR.d        -0.04232    0.27306  -0.155    0.877    
## DemI.d         0.19216    0.26623   0.722    0.471    
## cond.c        -0.28205    0.38559  -0.731    0.465    
## DemR.d:cond.c -0.60626    0.54612  -1.110    0.268    
## DemI.d:cond.c  0.05046    0.53246   0.095    0.925    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.789 on 266 degrees of freedom
##   (273 observations deleted due to missingness)
## Multiple R-squared:  0.0288, Adjusted R-squared:  0.01054 
## F-statistic: 1.578 on 5 and 266 DF,  p-value: 0.1666

# Action 20
summary(lm(act20 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act20 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5102 -1.2979  0.4444  1.4444  2.4444 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.0903     0.1670   6.529 2.65e-10 ***
## DemR.d          0.1833     0.2403   0.763   0.4460    
## DemI.d          0.2226     0.2382   0.934   0.3509    
## cond.c         -1.0694     0.3340  -3.202   0.0015 ** 
## DemR.d:cond.c   0.5963     0.4805   1.241   0.2156    
## DemI.d:cond.c   1.0394     0.4765   2.181   0.0299 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.751 on 315 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.03972,    Adjusted R-squared:  0.02447 
## F-statistic: 2.606 on 5 and 315 DF,  p-value: 0.02506

summary(lm(act20 ~ (DemR.d + DemI.d) * ctrl.d, data = d))

## 
## Call:
## lm(formula = act20 ~ (DemR.d + DemI.d) * ctrl.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5102 -1.2979  0.4444  1.4444  2.4444 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.6250     0.2340   6.944 2.18e-11 ***
## DemR.d         -0.1148     0.3425  -0.335   0.7378    
## DemI.d         -0.2971     0.3241  -0.917   0.3599    
## ctrl.d         -1.0694     0.3340  -3.202   0.0015 ** 
## DemR.d:ctrl.d   0.5963     0.4805   1.241   0.2156    
## DemI.d:ctrl.d   1.0394     0.4765   2.181   0.0299 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.751 on 315 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.03972,    Adjusted R-squared:  0.02447 
## F-statistic: 2.606 on 5 and 315 DF,  p-value: 0.02506

summary(lm(act20 ~ (DemR.d + DemI.d) * clim.d, data = d))

## 
## Call:
## lm(formula = act20 ~ (DemR.d + DemI.d) * clim.d, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5102 -1.2979  0.4444  1.4444  2.4444 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.5556     0.2383   2.331   0.0204 * 
## DemR.d          0.4815     0.3370   1.429   0.1541   
## DemI.d          0.7423     0.3493   2.125   0.0344 * 
## clim.d          1.0694     0.3340   3.202   0.0015 **
## DemR.d:clim.d  -0.5963     0.4805  -1.241   0.2156   
## DemI.d:clim.d  -1.0394     0.4765  -2.181   0.0299 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.751 on 315 degrees of freedom
##   (224 observations deleted due to missingness)
## Multiple R-squared:  0.03972,    Adjusted R-squared:  0.02447 
## F-statistic: 2.606 on 5 and 315 DF,  p-value: 0.02506

# Action 21
summary(lm(act21 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act21 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5667 -1.9565  0.0704  1.7143  3.7143 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.006039   0.172872   0.035   0.9722  
## DemR.d        -0.529848   0.251618  -2.106   0.0359 *
## DemI.d         0.242083   0.249528   0.970   0.3326  
## cond.c        -0.099034   0.345743  -0.286   0.7747  
## DemR.d:cond.c  0.479986   0.503236   0.954   0.3408  
## DemI.d:cond.c  0.736123   0.499056   1.475   0.1410  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.052 on 394 degrees of freedom
##   (145 observations deleted due to missingness)
## Multiple R-squared:  0.03132,    Adjusted R-squared:  0.01903 
## F-statistic: 2.548 on 5 and 394 DF,  p-value: 0.0276

# Action 22
summary(lm(act22 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act22 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9254 -1.5082  0.2647  1.7037  3.3103 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.8036     0.1697   4.736 3.05e-06 ***
## DemR.d         -1.0911     0.2437  -4.478 9.89e-06 ***
## DemI.d         -0.4013     0.2373  -1.691   0.0916 .  
## cond.c         -0.2436     0.3394  -0.718   0.4734    
## DemR.d:cond.c   0.2892     0.4874   0.593   0.5533    
## DemI.d:cond.c   0.4555     0.4746   0.960   0.3378    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.957 on 395 degrees of freedom
##   (144 observations deleted due to missingness)
## Multiple R-squared:  0.05123,    Adjusted R-squared:  0.03922 
## F-statistic: 4.266 on 5 and 395 DF,  p-value: 0.0008644

# Action 23
summary(lm(act23 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act23 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.661 -2.044  0.225  2.189  3.225 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.1553     0.2259   0.687    0.492
## DemR.d         -0.2455     0.3336  -0.736    0.462
## DemI.d          0.1861     0.3168   0.587    0.557
## cond.c         -0.4738     0.4518  -1.049    0.295
## DemR.d:cond.c   0.7432     0.6671   1.114    0.266
## DemI.d:cond.c  -0.1655     0.6337  -0.261    0.794
## 
## Residual standard error: 2.259 on 284 degrees of freedom
##   (255 observations deleted due to missingness)
## Multiple R-squared:  0.01883,    Adjusted R-squared:  0.001555 
## F-statistic:  1.09 on 5 and 284 DF,  p-value: 0.366

# Action 24
summary(lm(act24 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act24 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1266 -1.9583 -0.1266  1.6133  3.6133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -0.21649    0.17589  -1.231    0.219
## DemR.d        -0.27122    0.25350  -1.070    0.285
## DemI.d         0.11311    0.25080   0.451    0.652
## cond.c        -0.34964    0.35178  -0.994    0.321
## DemR.d:cond.c  0.09837    0.50701   0.194    0.846
## DemI.d:cond.c -0.11028    0.50160  -0.220    0.826
## 
## Residual standard error: 2.088 on 407 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01483,    Adjusted R-squared:  0.00273 
## F-statistic: 1.226 on 5 and 407 DF,  p-value: 0.2962

# Action 25
summary(lm(act25 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3194 -0.9688  0.0789  1.6563  2.8415 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.1202     0.1507   7.436 5.53e-13 ***
## DemR.d         -0.8691     0.2146  -4.050 6.05e-05 ***
## DemI.d         -0.2653     0.2137  -1.241    0.215    
## cond.c          0.3984     0.3013   1.322    0.187    
## DemR.d:cond.c  -0.5836     0.4291  -1.360    0.175    
## DemI.d:cond.c  -0.1708     0.4275  -0.400    0.690    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 437 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.04416,    Adjusted R-squared:  0.03323 
## F-statistic: 4.038 on 5 and 437 DF,  p-value: 0.001359

# Action 26
summary(lm(act26 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act26 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6875 -1.4444  0.5556  1.5000  1.7222 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.38889    0.20787   6.681 1.95e-10 ***
## DemR.d         0.18343    0.30548   0.600    0.549    
## DemI.d         0.14216    0.29404   0.483    0.629    
## cond.c         0.22222    0.41575   0.535    0.594    
## DemR.d:cond.c -0.45258    0.61097  -0.741    0.460    
## DemI.d:cond.c -0.04902    0.58809  -0.083    0.934    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.83 on 218 degrees of freedom
##   (321 observations deleted due to missingness)
## Multiple R-squared:  0.004848,   Adjusted R-squared:  -0.01798 
## F-statistic: 0.2124 on 5 and 218 DF,  p-value: 0.957

# Action 27
summary(lm(act25 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3194 -0.9688  0.0789  1.6563  2.8415 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.1202     0.1507   7.436 5.53e-13 ***
## DemR.d         -0.8691     0.2146  -4.050 6.05e-05 ***
## DemI.d         -0.2653     0.2137  -1.241    0.215    
## cond.c          0.3984     0.3013   1.322    0.187    
## DemR.d:cond.c  -0.5836     0.4291  -1.360    0.175    
## DemI.d:cond.c  -0.1708     0.4275  -0.400    0.690    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 437 degrees of freedom
##   (102 observations deleted due to missingness)
## Multiple R-squared:  0.04416,    Adjusted R-squared:  0.03323 
## F-statistic: 4.038 on 5 and 437 DF,  p-value: 0.001359

# Action 28
summary(lm(act28 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act28 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3088 -1.1333  0.2188  1.6912  2.2188 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.2211     0.1611   7.580 2.89e-13 ***
## DemR.d         -0.2615     0.2305  -1.134    0.257    
## DemI.d         -0.2498     0.2324  -1.075    0.283    
## cond.c         -0.1755     0.3222  -0.545    0.586    
## DemR.d:cond.c  -0.1812     0.4611  -0.393    0.695    
## DemI.d:cond.c   0.5466     0.4647   1.176    0.240    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.819 on 365 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01267,    Adjusted R-squared:  -0.0008569 
## F-statistic: 0.9366 on 5 and 365 DF,  p-value: 0.4571

# Action 29
summary(lm(act29 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act29 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3437 -1.1250  0.0968  1.6562  2.0968 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.31942    0.15678   8.416 9.26e-16 ***
## DemR.d        -0.38598    0.22565  -1.711    0.088 .  
## DemI.d        -0.24242    0.22229  -1.091    0.276    
## cond.c         0.04867    0.31356   0.155    0.877    
## DemR.d:cond.c -0.10908    0.45131  -0.242    0.809    
## DemI.d:cond.c  0.04735    0.44459   0.106    0.915    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.752 on 361 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.008834,   Adjusted R-squared:  -0.004894 
## F-statistic: 0.6435 on 5 and 361 DF,  p-value: 0.6667

# Action 30
summary(lm(act30 ~ (DemR.d + DemI.d) * cond.c, data = d))

## 
## Call:
## lm(formula = act30 ~ (DemR.d + DemI.d) * cond.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2545 -1.1167  0.7455  1.8615  2.1373 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.0583333  0.1730622   6.115 2.61e-09 ***
## DemR.d        -0.0952148  0.2470086  -0.385    0.700    
## DemI.d         0.1381702  0.2435923   0.567    0.571    
## cond.c         0.1166667  0.3461245   0.337    0.736    
## DemR.d:cond.c  0.0840803  0.4940171   0.170    0.865    
## DemI.d:cond.c -0.0005828  0.4871845  -0.001    0.999    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.871 on 345 degrees of freedom
##   (194 observations deleted due to missingness)
## Multiple R-squared:  0.003928,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.2721 on 5 and 345 DF,  p-value: 0.9282

2. gender effects

none

# Action 1
summary(lm(act1 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act1 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3182 -2.0095 -0.0095  1.8462  4.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.27674    0.19371   1.429 0.153835    
## DemR.d         -1.00323    0.28029  -3.579 0.000383 ***
## DemI.d         -0.19505    0.27944  -0.698 0.485543    
## gend.mf         0.08289    0.38743   0.214 0.830691    
## DemR.d:gend.mf -0.62990    0.56057  -1.124 0.261770    
## DemI.d:gend.mf  0.06143    0.55889   0.110 0.912520    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 438 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03231,    Adjusted R-squared:  0.02127 
## F-statistic: 2.925 on 5 and 438 DF,  p-value: 0.01307

# Action 2
summary(lm(act2 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act2 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.292 -1.076  0.280  1.708  2.280 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.23706    0.19454   6.359 8.27e-10 ***
## DemR.d         -0.33918    0.28901  -1.174    0.242    
## DemI.d         -0.09311    0.28554  -0.326    0.745    
## gend.mf        -0.11049    0.38907  -0.284    0.777    
## DemR.d:gend.mf -0.24527    0.57802  -0.424    0.672    
## DemI.d:gend.mf  0.28412    0.57109   0.497    0.619    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.82 on 279 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.007276,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.409 on 5 and 279 DF,  p-value: 0.8424

# Action 3
summary(lm(act3 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act3 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1845 -1.8571  0.1429  1.6667  3.7500 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.10310    0.17822   0.579  0.56323   
## DemR.d         -0.81144    0.26405  -3.073  0.00225 **
## DemI.d         -0.26544    0.25494  -1.041  0.29837   
## gend.mf        -0.16273    0.35645  -0.457  0.64824   
## DemR.d:gend.mf  0.07939    0.52810   0.150  0.88057   
## DemI.d:gend.mf  0.20169    0.50988   0.396  0.69263   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 433 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.02799,    Adjusted R-squared:  0.01676 
## F-statistic: 2.493 on 5 and 433 DF,  p-value: 0.0305

# Action 4
summary(lm(act4 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act4 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3721 -1.9412  0.2198  2.0588  3.6129 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.32398    0.20176   1.606   0.1092  
## DemR.d         -0.65984    0.30377  -2.172   0.0305 *
## DemI.d         -0.57180    0.30655  -1.865   0.0630 .
## gend.mf         0.09623    0.40352   0.238   0.8116  
## DemR.d:gend.mf -0.65031    0.60755  -1.070   0.2852  
## DemI.d:gend.mf -0.15231    0.61311  -0.248   0.8039  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.165 on 360 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01789,    Adjusted R-squared:  0.004246 
## F-statistic: 1.311 on 5 and 360 DF,  p-value: 0.2585

# Action 5
summary(lm(act5 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act5 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2540 -1.1852  0.4133  1.7460  2.6061 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.07242    0.19228   5.577 5.66e-08 ***
## DemR.d         -0.24847    0.27749  -0.895    0.371    
## DemI.d         -0.09013    0.28605  -0.315    0.753    
## gend.mf        -0.19890    0.38456  -0.517    0.605    
## DemR.d:gend.mf -0.66113    0.55498  -1.191    0.235    
## DemI.d:gend.mf  0.60468    0.57210   1.057    0.291    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 286 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.02198,    Adjusted R-squared:  0.004878 
## F-statistic: 1.285 on 5 and 286 DF,  p-value: 0.2702

# Action 6
summary(lm(act6 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act6 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7273 -1.2184  0.6579  1.6579  2.2000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.10161    0.18344   6.005 4.81e-09 ***
## DemR.d         -0.09241    0.26552  -0.348   0.7280    
## DemI.d          0.40333    0.25980   1.552   0.1215    
## gend.mf        -0.48099    0.36688  -1.311   0.1907    
## DemR.d:gend.mf  0.06260    0.53104   0.118   0.9062    
## DemI.d:gend.mf  0.92566    0.51960   1.781   0.0757 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 348 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.01702,    Adjusted R-squared:  0.002894 
## F-statistic: 1.205 on 5 and 348 DF,  p-value: 0.3064

# Action 7
summary(lm(act7 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act7 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1684 -1.9773  0.0472  1.8316  3.6562 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.07285    0.18934   0.385   0.7006  
## DemR.d         -0.62319    0.28361  -2.197   0.0286 *
## DemI.d         -0.03761    0.27880  -0.135   0.8928  
## gend.mf        -0.19115    0.37869  -0.505   0.6140  
## DemR.d:gend.mf -0.02066    0.56721  -0.036   0.9710  
## DemI.d:gend.mf  0.35597    0.55761   0.638   0.5236  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.077 on 404 degrees of freedom
##   (135 observations deleted due to missingness)
## Multiple R-squared:  0.01716,    Adjusted R-squared:  0.004995 
## F-statistic: 1.411 on 5 and 404 DF,  p-value: 0.2193

# Action 8
summary(lm(act8 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act8 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4545 -1.8926 -0.1593  1.8407  4.1892 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.3069     0.1879   1.633   0.1031    
## DemR.d          -1.4552     0.2735  -5.321  1.6e-07 ***
## DemI.d          -0.6263     0.2693  -2.326   0.0205 *  
## gend.mf          0.2953     0.3759   0.786   0.4325    
## DemR.d:gend.mf  -0.3770     0.5470  -0.689   0.4910    
## DemI.d:gend.mf  -0.2564     0.5386  -0.476   0.6343    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.115 on 470 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.06738,    Adjusted R-squared:  0.05746 
## F-statistic: 6.791 on 5 and 470 DF,  p-value: 3.961e-06

# Action 9
summary(lm(act9 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act9 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3564 -1.6055  0.0435  1.6436  3.5882 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.15648    0.17542   0.892   0.3729  
## DemR.d         -0.64784    0.26134  -2.479   0.0136 *
## DemI.d          0.02656    0.25022   0.106   0.9155  
## gend.mf        -0.39991    0.35084  -1.140   0.2550  
## DemR.d:gend.mf  0.20617    0.52268   0.394   0.6934  
## DemI.d:gend.mf  0.31956    0.50045   0.639   0.5235  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.972 on 438 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02744,    Adjusted R-squared:  0.01634 
## F-statistic: 2.472 on 5 and 438 DF,  p-value: 0.03179

# Action 10
summary(lm(act10 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act10 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7143 -1.9722  0.2857  1.7034  4.0278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.6295     0.1851   3.401  0.00073 ***
## DemR.d          -1.4950     0.2687  -5.564 4.45e-08 ***
## DemI.d          -0.3072     0.2583  -1.189  0.23491    
## gend.mf          0.1696     0.3702   0.458  0.64699    
## DemR.d:gend.mf  -0.4940     0.5374  -0.919  0.35842    
## DemI.d:gend.mf  -0.2688     0.5165  -0.520  0.60300    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 467 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.07975,    Adjusted R-squared:  0.0699 
## F-statistic: 8.094 on 5 and 467 DF,  p-value: 2.448e-07

# Action 11
summary(lm(act11 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act11 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0519 -1.0519  0.3382  1.7105  2.3382 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.1220     0.1931   5.810 1.58e-08 ***
## DemR.d          -0.4118     0.2855  -1.442    0.150    
## DemI.d          -0.1928     0.2792  -0.690    0.490    
## gend.mf          0.3349     0.3862   0.867    0.386    
## DemR.d:gend.mf  -0.2381     0.5710  -0.417    0.677    
## DemI.d:gend.mf  -0.5804     0.5585  -1.039    0.299    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.897 on 303 degrees of freedom
##   (236 observations deleted due to missingness)
## Multiple R-squared:  0.01116,    Adjusted R-squared:  -0.005162 
## F-statistic: 0.6836 on 5 and 303 DF,  p-value: 0.6362

# Action 12
summary(lm(act12 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act12 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4217 -2.1579 -0.1579  1.8355  4.3333 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.21084    0.21468   0.982    0.327    
## DemR.d         -1.25506    0.30339  -4.137 4.35e-05 ***
## DemI.d         -0.04856    0.29924  -0.162    0.871    
## gend.mf        -0.42169    0.42935  -0.982    0.327    
## DemR.d:gend.mf -0.15654    0.60678  -0.258    0.797    
## DemI.d:gend.mf  0.43046    0.59848   0.719    0.472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.13 on 374 degrees of freedom
##   (165 observations deleted due to missingness)
## Multiple R-squared:  0.06734,    Adjusted R-squared:  0.05487 
## F-statistic: 5.401 on 5 and 374 DF,  p-value: 8.241e-05

# Action 13
summary(lm(act13 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act13 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5000 -2.0784 -0.0784  1.8067  3.6990 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.2892     0.1918   1.508   0.1324  
## DemR.d          -0.6993     0.2813  -2.486   0.0133 *
## DemI.d          -0.4307     0.2662  -1.618   0.1064  
## gend.mf          0.4216     0.3837   1.099   0.2725  
## DemR.d:gend.mf   0.1562     0.5626   0.278   0.7813  
## DemI.d:gend.mf  -1.0910     0.5324  -2.049   0.0410 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.057 on 433 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.03722,    Adjusted R-squared:  0.0261 
## F-statistic: 3.347 on 5 and 433 DF,  p-value: 0.005603

# Action 14
summary(lm(act14 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act14 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.730 -1.520  0.322  1.538  3.609 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.5956     0.2082   2.861  0.00455 **
## DemR.d          -0.5610     0.3242  -1.730  0.08468 . 
## DemI.d          -0.2315     0.3155  -0.734  0.46384   
## gend.mf          0.2682     0.4164   0.644  0.52007   
## DemR.d:gend.mf  -1.5549     0.6484  -2.398  0.01715 * 
## DemI.d:gend.mf  -0.5799     0.6311  -0.919  0.35898   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.022 on 277 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.02875,    Adjusted R-squared:  0.01121 
## F-statistic:  1.64 on 5 and 277 DF,  p-value: 0.1496

# Action 15
summary(lm(act15 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act15 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5581 -1.5581  0.4634  1.5278  3.5278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.5419     0.1936   2.799  0.00539 **
## DemR.d          -0.8345     0.2798  -2.983  0.00304 **
## DemI.d          -0.6226     0.2713  -2.294  0.02233 * 
## gend.mf          0.0325     0.3872   0.084  0.93315   
## DemR.d:gend.mf  -0.5028     0.5595  -0.899  0.36944   
## DemI.d:gend.mf  -0.7980     0.5427  -1.470  0.14229   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.038 on 375 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.03469,    Adjusted R-squared:  0.02182 
## F-statistic: 2.695 on 5 and 375 DF,  p-value: 0.02078

# Action 16
summary(lm(act16 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act16 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2692 -0.9860  0.1667  1.3103  2.3103 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.01053    0.18372   5.500 7.74e-08 ***
## DemR.d         -0.11983    0.26881  -0.446    0.656    
## DemI.d         -0.03108    0.27438  -0.113    0.910    
## gend.mf        -0.17895    0.36744  -0.487    0.627    
## DemR.d:gend.mf  0.06423    0.53763   0.119    0.905    
## DemI.d:gend.mf  0.75852    0.54876   1.382    0.168    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.824 on 322 degrees of freedom
##   (217 observations deleted due to missingness)
## Multiple R-squared:  0.009561,   Adjusted R-squared:  -0.005818 
## F-statistic: 0.6217 on 5 and 322 DF,  p-value: 0.6834

# Action 17
summary(lm(act17 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act17 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8837 -1.4362  0.4583  1.5741  3.0541 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.7127     0.1843   3.867 0.000128 ***
## DemR.d          -0.5216     0.2682  -1.945 0.052507 .  
## DemI.d          -0.2997     0.2686  -1.116 0.265041    
## gend.mf          0.3421     0.3686   0.928 0.353945    
## DemR.d:gend.mf  -0.8323     0.5365  -1.551 0.121595    
## DemI.d:gend.mf  -0.3680     0.5371  -0.685 0.493661    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.009 on 407 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01111,    Adjusted R-squared:  -0.001043 
## F-statistic: 0.9141 on 5 and 407 DF,  p-value: 0.4717

# Action 18
summary(lm(act18 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act18 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9730 -1.3947  0.2561  1.5000  2.6053 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8584     0.1821   4.713 3.55e-06 ***
## DemR.d          -0.4111     0.2692  -1.527    0.128    
## DemI.d          -0.2538     0.2623  -0.968    0.334    
## gend.mf          0.2291     0.3643   0.629    0.530    
## DemR.d:gend.mf  -0.1238     0.5385  -0.230    0.818    
## DemI.d:gend.mf  -0.5633     0.5246  -1.074    0.284    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 343 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01109,    Adjusted R-squared:  -0.003321 
## F-statistic: 0.7696 on 5 and 343 DF,  p-value: 0.5722

# Action 19
summary(lm(act19 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act19 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3538 -1.1719  0.4231  1.6462  2.4231 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.1164     0.2094   5.333 2.07e-07 ***
## DemR.d          -0.2420     0.2962  -0.817    0.415    
## DemI.d           0.2105     0.2888   0.729    0.467    
## gend.mf          0.0634     0.4187   0.151    0.880    
## DemR.d:gend.mf  -0.6583     0.5924  -1.111    0.267    
## DemI.d:gend.mf  -0.1172     0.5775  -0.203    0.839    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.802 on 265 degrees of freedom
##   (274 observations deleted due to missingness)
## Multiple R-squared:  0.01397,    Adjusted R-squared:  -0.00463 
## F-statistic: 0.7511 on 5 and 265 DF,  p-value: 0.5859

# Action 20
summary(lm(act20 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act20 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5139 -1.1429  0.4861  1.5556  2.3226 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.12444    0.17857   6.297 1.03e-09 ***
## DemR.d         -0.02878    0.26102  -0.110   0.9123    
## DemI.d          0.13449    0.26411   0.509   0.6110    
## gend.mf         0.27744    0.35714   0.777   0.4378    
## DemR.d:gend.mf -1.11391    0.52204  -2.134   0.0336 *  
## DemI.d:gend.mf -0.50959    0.52822  -0.965   0.3354    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.772 on 313 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.02125,    Adjusted R-squared:  0.005613 
## F-statistic: 1.359 on 5 and 313 DF,  p-value: 0.2396

# Action 21
summary(lm(act21 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act21 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.308 -1.850  0.150  1.760  3.889 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.07885    0.19401   0.406   0.6847  
## DemR.d         -0.69720    0.28010  -2.489   0.0132 *
## DemI.d          0.12666    0.28074   0.451   0.6521  
## gend.mf         0.45769    0.38802   1.180   0.2389  
## DemR.d:gend.mf -0.99876    0.56020  -1.783   0.0754 .
## DemI.d:gend.mf -0.52585    0.56148  -0.937   0.3496  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.055 on 392 degrees of freedom
##   (147 observations deleted due to missingness)
## Multiple R-squared:  0.02841,    Adjusted R-squared:  0.01602 
## F-statistic: 2.293 on 5 and 392 DF,  p-value: 0.04499

# Action 22
summary(lm(act22 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act22 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.5263  0.2421  1.5686  3.4194 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.8441     0.1884   4.480 9.81e-06 ***
## DemR.d          -1.1748     0.2766  -4.248 2.69e-05 ***
## DemI.d          -0.4127     0.2644  -1.561    0.119    
## gend.mf          0.3118     0.3768   0.827    0.408    
## DemR.d:gend.mf  -0.4891     0.5531  -0.884    0.377    
## DemI.d:gend.mf  -0.1221     0.5288  -0.231    0.818    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.957 on 393 degrees of freedom
##   (146 observations deleted due to missingness)
## Multiple R-squared:  0.04966,    Adjusted R-squared:  0.03757 
## F-statistic: 4.107 on 5 and 393 DF,  p-value: 0.001201

# Action 23
summary(lm(act23 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act23 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6667 -2.2821  0.0678  2.0678  3.1154 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      0.1533     0.2383   0.643    0.520
## DemR.d          -0.2449     0.3578  -0.685    0.494
## DemI.d           0.3210     0.3477   0.923    0.357
## gend.mf          0.1504     0.4767   0.316    0.753
## DemR.d:gend.mf  -0.1980     0.7156  -0.277    0.782
## DemI.d:gend.mf   0.2342     0.6954   0.337    0.737
## 
## Residual standard error: 2.267 on 283 degrees of freedom
##   (256 observations deleted due to missingness)
## Multiple R-squared:  0.009342,   Adjusted R-squared:  -0.008161 
## F-statistic: 0.5337 on 5 and 283 DF,  p-value: 0.7507

# Action 24
summary(lm(act24 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act24 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -3.13  -2.00  -0.13   1.59   3.59 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.1684     0.1920  -0.877   0.3810  
## DemR.d          -0.3185     0.2771  -1.149   0.2511  
## DemI.d          -0.0615     0.2747  -0.224   0.8230  
## gend.mf          0.3367     0.3840   0.877   0.3810  
## DemR.d:gend.mf  -0.2578     0.5542  -0.465   0.6421  
## DemI.d:gend.mf  -1.0565     0.5495  -1.923   0.0552 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.082 on 406 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.01727,    Adjusted R-squared:  0.005164 
## F-statistic: 1.427 on 5 and 406 DF,  p-value: 0.2135

# Action 25
summary(lm(act25 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2791 -1.0194  0.0833  1.6847  3.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.1492     0.1666   6.900 1.85e-11 ***
## DemR.d          -0.9916     0.2437  -4.070 5.59e-05 ***
## DemI.d          -0.3738     0.2368  -1.579    0.115    
## gend.mf          0.2597     0.3331   0.779    0.436    
## DemR.d:gend.mf  -0.5750     0.4873  -1.180    0.239    
## DemI.d:gend.mf  -0.5422     0.4735  -1.145    0.253    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.04154,    Adjusted R-squared:  0.03052 
## F-statistic:  3.77 on 5 and 435 DF,  p-value: 0.002362

# Action 26
summary(lm(act26 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act26 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7609 -1.1429  0.5614  1.3929  1.8571 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.42602    0.21592   6.604 3.03e-10 ***
## DemR.d          0.02584    0.32286   0.080    0.936    
## DemI.d          0.15691    0.31458   0.499    0.618    
## gend.mf         0.36224    0.43185   0.839    0.402    
## DemR.d:gend.mf -0.98026    0.64573  -1.518    0.130    
## DemI.d:gend.mf -0.07357    0.62915  -0.117    0.907    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.823 on 217 degrees of freedom
##   (322 observations deleted due to missingness)
## Multiple R-squared:  0.01451,    Adjusted R-squared:  -0.008199 
## F-statistic: 0.6389 on 5 and 217 DF,  p-value: 0.6702

# Action 27
summary(lm(act25 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2791 -1.0194  0.0833  1.6847  3.0000 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.1492     0.1666   6.900 1.85e-11 ***
## DemR.d          -0.9916     0.2437  -4.070 5.59e-05 ***
## DemI.d          -0.3738     0.2368  -1.579    0.115    
## gend.mf          0.2597     0.3331   0.779    0.436    
## DemR.d:gend.mf  -0.5750     0.4873  -1.180    0.239    
## DemI.d:gend.mf  -0.5422     0.4735  -1.145    0.253    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.835 on 435 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.04154,    Adjusted R-squared:  0.03052 
## F-statistic:  3.77 on 5 and 435 DF,  p-value: 0.002362

# Action 28
summary(lm(act28 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act28 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2151 -1.0449  0.2069  1.7849  2.3030 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.213587   0.184949   6.562 1.84e-10 ***
## DemR.d         -0.342631   0.262326  -1.306    0.192    
## DemI.d         -0.322471   0.268313  -1.202    0.230    
## gend.mf        -0.002933   0.369898  -0.008    0.994    
## DemR.d:gend.mf -0.345042   0.524651  -0.658    0.511    
## DemI.d:gend.mf -0.193094   0.536626  -0.360    0.719    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.826 on 363 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.007968,   Adjusted R-squared:  -0.005696 
## F-statistic: 0.5831 on 5 and 363 DF,  p-value: 0.7129

# Action 29
summary(lm(act29 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act29 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.333 -1.056  0.069  1.667  2.069 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.310345   0.173881   7.536 3.99e-13 ***
## DemR.d         -0.378161   0.254453  -1.486    0.138    
## DemI.d         -0.226699   0.245500  -0.923    0.356    
## gend.mf         0.045977   0.347762   0.132    0.895    
## DemR.d:gend.mf -0.043678   0.508906  -0.086    0.932    
## DemI.d:gend.mf  0.008954   0.491001   0.018    0.985    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.755 on 359 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.00763,    Adjusted R-squared:  -0.006191 
## F-statistic: 0.552 on 5 and 359 DF,  p-value: 0.7368

# Action 30
summary(lm(act30 ~ (DemR.d + DemI.d) * gend.mf, data = d))

## 
## Call:
## lm(formula = act30 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2581 -1.1685  0.7419  1.8228  2.4194 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.9636     0.1877   5.135 4.75e-07 ***
## DemR.d          -0.1130     0.2717  -0.416    0.678    
## DemI.d           0.2497     0.2704   0.924    0.356    
## gend.mf         -0.4272     0.3753  -1.138    0.256    
## DemR.d:gend.mf  -0.1126     0.5434  -0.207    0.836    
## DemI.d:gend.mf   0.5167     0.5408   0.956    0.340    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 343 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01173,    Adjusted R-squared:  -0.002672 
## F-statistic: 0.8145 on 5 and 343 DF,  p-value: 0.5399

3. condition x gender effects

Action 2

# Action 1
summary(lm(act1 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act1 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7273 -2.0492 -0.0019  1.9273  3.6522 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.2828981  0.1944174   1.455 0.146366    
## DemR.d                -1.0640180  0.2842229  -3.744 0.000206 ***
## DemI.d                -0.2472073  0.2813081  -0.879 0.380010    
## cond.c                -0.1930690  0.3888347  -0.497 0.619771    
## gend.mf                0.0675371  0.3888347   0.174 0.862190    
## DemR.d:cond.c          0.5531349  0.5684459   0.973 0.331065    
## DemI.d:cond.c         -0.5120025  0.5626162  -0.910 0.363310    
## DemR.d:gend.mf        -0.7288997  0.5684459  -1.282 0.200436    
## DemI.d:gend.mf         0.0001188  0.5626162   0.000 0.999832    
## cond.c:gend.mf         0.3194713  0.7776695   0.411 0.681418    
## DemR.d:cond.c:gend.mf  0.7989062  1.1368918   0.703 0.482614    
## DemI.d:cond.c:gend.mf -1.5403445  1.1252324  -1.369 0.171738    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 432 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.04596,    Adjusted R-squared:  0.02167 
## F-statistic: 1.892 on 11 and 432 DF,  p-value: 0.03851

# Action 2
summary(lm(act2 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act2 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6765 -0.9062  0.3235  1.3889  2.5000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.20630    0.19435   6.207    2e-09 ***
## DemR.d                -0.26814    0.29308  -0.915   0.3611    
## DemI.d                -0.11681    0.28796  -0.406   0.6853    
## cond.c                 0.06947    0.38871   0.179   0.8583    
## gend.mf               -0.13483    0.38871  -0.347   0.7290    
## DemR.d:cond.c         -0.53955    0.58616  -0.920   0.3581    
## DemI.d:cond.c         -0.57937    0.57593  -1.006   0.3153    
## DemR.d:gend.mf        -0.13039    0.58616  -0.222   0.8241    
## DemI.d:gend.mf         0.33160    0.57593   0.576   0.5652    
## cond.c:gend.mf         1.74995    0.77742   2.251   0.0252 *  
## DemR.d:cond.c:gend.mf -2.03201    1.17232  -1.733   0.0842 .  
## DemI.d:cond.c:gend.mf -1.84530    1.15185  -1.602   0.1103    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 273 degrees of freedom
##   (260 observations deleted due to missingness)
## Multiple R-squared:  0.03645,    Adjusted R-squared:  -0.002379 
## F-statistic: 0.9387 on 11 and 273 DF,  p-value: 0.5035

# Action 3
summary(lm(act3 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act3 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4808 -1.7697  0.1176  1.5192  3.7000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.08935    0.17898   0.499  0.61787   
## DemR.d                -0.86051    0.27136  -3.171  0.00163 **
## DemI.d                -0.24714    0.25649  -0.964  0.33582   
## cond.c                 0.01635    0.35796   0.046  0.96359   
## gend.mf               -0.18442    0.35796  -0.515  0.60668   
## DemR.d:cond.c          0.32596    0.54271   0.601  0.54842   
## DemI.d:cond.c          0.02487    0.51299   0.048  0.96136   
## DemR.d:gend.mf        -0.02327    0.54271  -0.043  0.96581   
## DemI.d:gend.mf         0.20251    0.51299   0.395  0.69321   
## cond.c:gend.mf        -1.16413    0.71591  -1.626  0.10467   
## DemR.d:cond.c:gend.mf  1.97952    1.08543   1.824  0.06889 . 
## DemI.d:cond.c:gend.mf  0.83455    1.02597   0.813  0.41643   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.013 on 427 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.03842,    Adjusted R-squared:  0.01365 
## F-statistic: 1.551 on 11 and 427 DF,  p-value: 0.1107

# Action 4
summary(lm(act4 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act4 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6923 -1.9844  0.1636  1.9750  4.0625 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.32825    0.20499   1.601   0.1102  
## DemR.d                -0.64639    0.30919  -2.091   0.0373 *
## DemI.d                -0.53810    0.31030  -1.734   0.0838 .
## cond.c                 0.36227    0.40999   0.884   0.3775  
## gend.mf                0.14214    0.40999   0.347   0.7290  
## DemR.d:cond.c         -0.46284    0.61838  -0.748   0.4547  
## DemI.d:cond.c         -1.31063    0.62060  -2.112   0.0354 *
## DemR.d:gend.mf        -0.65937    0.61838  -1.066   0.2870  
## DemI.d:gend.mf        -0.09264    0.62060  -0.149   0.8814  
## cond.c:gend.mf        -0.20418    0.81998  -0.249   0.8035  
## DemR.d:cond.c:gend.mf -0.23504    1.23676  -0.190   0.8494  
## DemI.d:cond.c:gend.mf -1.40871    1.24121  -1.135   0.2572  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.164 on 354 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.03473,    Adjusted R-squared:  0.004739 
## F-statistic: 1.158 on 11 and 354 DF,  p-value: 0.3153

# Action 5
summary(lm(act5 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act5 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2647 -1.2414  0.2432  1.4847  2.9048 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.05988    0.19283   5.496 8.72e-08 ***
## DemR.d                -0.18038    0.28208  -0.639    0.523    
## DemI.d                -0.07293    0.28697  -0.254    0.800    
## cond.c                -0.43906    0.38567  -1.138    0.256    
## gend.mf               -0.17239    0.38567  -0.447    0.655    
## DemR.d:cond.c          0.04001    0.56417   0.071    0.944    
## DemI.d:cond.c          0.27160    0.57395   0.473    0.636    
## DemR.d:gend.mf        -0.57470    0.56417  -1.019    0.309    
## DemI.d:gend.mf         0.58310    0.57395   1.016    0.311    
## cond.c:gend.mf         0.77286    0.77133   1.002    0.317    
## DemR.d:cond.c:gend.mf -1.61761    1.12834  -1.434    0.153    
## DemI.d:cond.c:gend.mf -1.20717    1.14789  -1.052    0.294    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.866 on 280 degrees of freedom
##   (253 observations deleted due to missingness)
## Multiple R-squared:  0.03883,    Adjusted R-squared:  0.001066 
## F-statistic: 1.028 on 11 and 280 DF,  p-value: 0.4213

# Action 6
summary(lm(act6 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act6 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.500 -1.108  0.600  1.600  2.400 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.08296    0.18638   5.810 1.43e-08 ***
## DemR.d                -0.06093    0.27483  -0.222   0.8247    
## DemI.d                 0.40436    0.26343   1.535   0.1257    
## cond.c                -0.19749    0.37276  -0.530   0.5966    
## gend.mf               -0.51830    0.37276  -1.390   0.1653    
## DemR.d:cond.c          0.10345    0.54967   0.188   0.8508    
## DemI.d:cond.c         -0.16904    0.52686  -0.321   0.7485    
## DemR.d:gend.mf         0.12425    0.54967   0.226   0.8213    
## DemI.d:gend.mf         0.98483    0.52686   1.869   0.0624 .  
## cond.c:gend.mf        -0.50025    0.74552  -0.671   0.5027    
## DemR.d:cond.c:gend.mf  0.38835    1.09934   0.353   0.7241    
## DemI.d:cond.c:gend.mf  0.35097    1.05371   0.333   0.7393    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.825 on 342 degrees of freedom
##   (191 observations deleted due to missingness)
## Multiple R-squared:  0.02172,    Adjusted R-squared:  -0.009744 
## F-statistic: 0.6903 on 11 and 342 DF,  p-value: 0.7479

# Action 7
summary(lm(act7 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act7 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3725 -1.9167  0.0893  1.6275  3.5600 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.06315    0.19032   0.332   0.7402  
## DemR.d                -0.65562    0.28876  -2.270   0.0237 *
## DemI.d                -0.01296    0.28062  -0.046   0.9632  
## cond.c                 0.15101    0.38064   0.397   0.6918  
## gend.mf               -0.17806    0.38064  -0.468   0.6402  
## DemR.d:cond.c          0.30739    0.57753   0.532   0.5948  
## DemI.d:cond.c          0.05138    0.56125   0.092   0.9271  
## DemR.d:gend.mf        -0.12034    0.57753  -0.208   0.8350  
## DemI.d:gend.mf         0.31379    0.56125   0.559   0.5764  
## cond.c:gend.mf        -0.57945    0.76127  -0.761   0.4470  
## DemR.d:cond.c:gend.mf  1.02931    1.15505   0.891   0.3734  
## DemI.d:cond.c:gend.mf  0.20244    1.12250   0.180   0.8570  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.084 on 398 degrees of freedom
##   (135 observations deleted due to missingness)
## Multiple R-squared:  0.02484,    Adjusted R-squared:  -0.002116 
## F-statistic: 0.9215 on 11 and 398 DF,  p-value: 0.5194

# Action 8
summary(lm(act8 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act8 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5909 -1.6719 -0.1404  1.8596  4.4167 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.3075     0.1884   1.633   0.1032    
## DemR.d                 -1.4010     0.2795  -5.012 7.67e-07 ***
## DemI.d                 -0.6187     0.2702  -2.290   0.0225 *  
## cond.c                  0.2794     0.3768   0.742   0.4588    
## gend.mf                 0.2940     0.3768   0.780   0.4356    
## DemR.d:cond.c          -0.8373     0.5590  -1.498   0.1348    
## DemI.d:cond.c          -0.3458     0.5404  -0.640   0.5226    
## DemR.d:gend.mf         -0.2930     0.5590  -0.524   0.6004    
## DemI.d:gend.mf         -0.2717     0.5404  -0.503   0.6154    
## cond.c:gend.mf         -0.0133     0.7535  -0.018   0.9859    
## DemR.d:cond.c:gend.mf  -0.1657     1.1180  -0.148   0.8823    
## DemI.d:cond.c:gend.mf  -0.6539     1.0809  -0.605   0.5455    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.12 on 464 degrees of freedom
##   (69 observations deleted due to missingness)
## Multiple R-squared:  0.07492,    Adjusted R-squared:  0.05299 
## F-statistic: 3.416 on 11 and 464 DF,  p-value: 0.0001407

# Action 9
summary(lm(act9 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act9 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4615 -1.6087  0.2381  1.6512  3.4182 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.14709    0.17673   0.832   0.4057  
## DemR.d                -0.69205    0.27060  -2.557   0.0109 *
## DemI.d                 0.04778    0.25273   0.189   0.8501  
## cond.c                -0.07073    0.35346  -0.200   0.8415  
## gend.mf               -0.41227    0.35346  -1.166   0.2441  
## DemR.d:cond.c          0.35117    0.54121   0.649   0.5168  
## DemI.d:cond.c          0.02983    0.50547   0.059   0.9530  
## DemR.d:gend.mf         0.11089    0.54121   0.205   0.8378  
## DemI.d:gend.mf         0.30824    0.50547   0.610   0.5423  
## cond.c:gend.mf        -0.57474    0.70693  -0.813   0.4167  
## DemR.d:cond.c:gend.mf  1.23124    1.08241   1.137   0.2560  
## DemI.d:cond.c:gend.mf  0.08511    1.01094   0.084   0.9329  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.982 on 432 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03168,    Adjusted R-squared:  0.007022 
## F-statistic: 1.285 on 11 and 432 DF,  p-value: 0.2302

# Action 10
summary(lm(act10 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act10 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9000 -1.7391  0.4211  1.5789  4.2609 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.63337    0.18598   3.406 0.000718 ***
## DemR.d                -1.45209    0.27437  -5.292 1.87e-07 ***
## DemI.d                -0.31652    0.25957  -1.219 0.223307    
## cond.c                 0.14234    0.37196   0.383 0.702124    
## gend.mf                0.17871    0.37196   0.480 0.631131    
## DemR.d:cond.c         -0.58545    0.54875  -1.067 0.286585    
## DemI.d:cond.c         -0.01679    0.51914  -0.032 0.974220    
## DemR.d:gend.mf        -0.41753    0.54875  -0.761 0.447126    
## DemI.d:gend.mf        -0.26695    0.51914  -0.514 0.607339    
## cond.c:gend.mf         0.42440    0.74391   0.571 0.568616    
## DemR.d:cond.c:gend.mf -0.82916    1.09750  -0.756 0.450334    
## DemI.d:cond.c:gend.mf  0.23357    1.03827   0.225 0.822109    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.054 on 461 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.08452,    Adjusted R-squared:  0.06267 
## F-statistic: 3.869 on 11 and 461 DF,  p-value: 2.315e-05

# Action 11
summary(lm(act11 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act11 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4706 -1.1111  0.3636  1.4286  3.0000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.09760    0.19351   5.672 3.35e-08 ***
## DemR.d                -0.37314    0.28868  -1.293    0.197    
## DemI.d                -0.20900    0.27961  -0.747    0.455    
## cond.c                -0.48735    0.38702  -1.259    0.209    
## gend.mf                0.31741    0.38702   0.820    0.413    
## DemR.d:cond.c          0.34146    0.57736   0.591    0.555    
## DemI.d:cond.c         -0.50605    0.55922  -0.905    0.366    
## DemR.d:gend.mf        -0.19057    0.57736  -0.330    0.742    
## DemI.d:gend.mf        -0.62401    0.55922  -1.116    0.265    
## cond.c:gend.mf        -0.28581    0.77404  -0.369    0.712    
## DemR.d:cond.c:gend.mf  0.09273    1.15472   0.080    0.936    
## DemI.d:cond.c:gend.mf -0.66856    1.11844  -0.598    0.550    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.891 on 297 degrees of freedom
##   (236 observations deleted due to missingness)
## Multiple R-squared:  0.03642,    Adjusted R-squared:  0.0007281 
## F-statistic:  1.02 on 11 and 297 DF,  p-value: 0.428

# Action 12
summary(lm(act12 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act12 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4359 -2.0600 -0.1111  1.9130  4.2381 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.21043    0.21635   0.973    0.331    
## DemR.d                -1.27706    0.31024  -4.116 4.76e-05 ***
## DemI.d                -0.04543    0.30150  -0.151    0.880    
## cond.c                -0.04379    0.43269  -0.101    0.919    
## gend.mf               -0.42413    0.43269  -0.980    0.328    
## DemR.d:cond.c          0.02591    0.62049   0.042    0.967    
## DemI.d:cond.c          0.09156    0.60301   0.152    0.879    
## DemR.d:gend.mf        -0.18071    0.62049  -0.291    0.771    
## DemI.d:gend.mf         0.42746    0.60301   0.709    0.479    
## cond.c:gend.mf        -0.14119    0.86539  -0.163    0.870    
## DemR.d:cond.c:gend.mf  0.70075    1.24097   0.565    0.573    
## DemI.d:cond.c:gend.mf -0.17659    1.20602  -0.146    0.884    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.145 on 368 degrees of freedom
##   (165 observations deleted due to missingness)
## Multiple R-squared:  0.06951,    Adjusted R-squared:  0.04169 
## F-statistic: 2.499 on 11 and 368 DF,  p-value: 0.004871

# Action 13
summary(lm(act13 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act13 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7778 -1.8242  0.0923  1.5417  3.9444 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             0.3124     0.1918   1.629   0.1041  
## DemR.d                 -0.6497     0.2851  -2.279   0.0232 *
## DemI.d                 -0.4393     0.2657  -1.653   0.0990 .
## cond.c                 -0.1063     0.3835  -0.277   0.7819  
## gend.mf                 0.4257     0.3835   1.110   0.2676  
## DemR.d:cond.c          -0.6398     0.5702  -1.122   0.2625  
## DemI.d:cond.c           0.4686     0.5315   0.882   0.3785  
## DemR.d:gend.mf          0.2727     0.5702   0.478   0.6327  
## DemI.d:gend.mf         -1.1243     0.5315  -2.115   0.0350 *
## cond.c:gend.mf          1.2226     0.7671   1.594   0.1117  
## DemR.d:cond.c:gend.mf  -1.6830     1.1404  -1.476   0.1408  
## DemI.d:cond.c:gend.mf  -1.7567     1.0629  -1.653   0.0991 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.047 on 427 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.05903,    Adjusted R-squared:  0.03479 
## F-statistic: 2.435 on 11 and 427 DF,  p-value: 0.00595

# Action 14
summary(lm(act14 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act14 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8947 -1.5556  0.2667  1.6471  3.2667 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.59597    0.20935   2.847  0.00475 **
## DemR.d                -0.63606    0.33284  -1.911  0.05706 . 
## DemI.d                -0.21720    0.31854  -0.682  0.49591   
## cond.c                -0.05574    0.41870  -0.133  0.89420   
## gend.mf                0.25835    0.41870   0.617  0.53773   
## DemR.d:cond.c          0.53591    0.66568   0.805  0.42149   
## DemI.d:cond.c          0.59307    0.63708   0.931  0.35272   
## DemR.d:gend.mf        -1.69485    0.66568  -2.546  0.01145 * 
## DemI.d:gend.mf        -0.64526    0.63708  -1.013  0.31204   
## cond.c:gend.mf        -0.56689    0.83740  -0.677  0.49901   
## DemR.d:cond.c:gend.mf  1.57321    1.33135   1.182  0.23838   
## DemI.d:cond.c:gend.mf  0.59711    1.27416   0.469  0.63971   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.032 on 271 degrees of freedom
##   (262 observations deleted due to missingness)
## Multiple R-squared:  0.04048,    Adjusted R-squared:  0.001535 
## F-statistic: 1.039 on 11 and 271 DF,  p-value: 0.4117

# Action 15
summary(lm(act15 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act15 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8421 -1.7727  0.1579  1.7727  3.7727 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.54708    0.19412   2.818  0.00509 **
## DemR.d                -0.80372    0.28318  -2.838  0.00479 **
## DemI.d                -0.63745    0.27232  -2.341  0.01977 * 
## cond.c                -0.20249    0.38825  -0.522  0.60230   
## gend.mf                0.02705    0.38825   0.070  0.94449   
## DemR.d:cond.c          0.05933    0.56636   0.105  0.91662   
## DemI.d:cond.c          0.02625    0.54463   0.048  0.96159   
## DemR.d:gend.mf        -0.42935    0.56636  -0.758  0.44889   
## DemI.d:gend.mf        -0.77251    0.54463  -1.418  0.15692   
## cond.c:gend.mf         0.82923    0.77650   1.068  0.28626   
## DemR.d:cond.c:gend.mf -1.80265    1.13272  -1.591  0.11237   
## DemI.d:cond.c:gend.mf -0.42436    1.08927  -0.390  0.69707   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 369 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.0454, Adjusted R-squared:  0.01694 
## F-statistic: 1.595 on 11 and 369 DF,  p-value: 0.09792

# Action 16
summary(lm(act16 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act16 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2121 -1.0500  0.0526  1.3846  2.6170 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.01356    0.18418   5.503 7.72e-08 ***
## DemR.d                -0.09576    0.27332  -0.350    0.726    
## DemI.d                -0.02070    0.27508  -0.075    0.940    
## cond.c                -0.07815    0.36835  -0.212    0.832    
## gend.mf               -0.18501    0.36835  -0.502    0.616    
## DemR.d:cond.c         -0.12587    0.54665  -0.230    0.818    
## DemI.d:cond.c          0.75781    0.55017   1.377    0.169    
## DemR.d:gend.mf         0.12452    0.54665   0.228    0.820    
## DemI.d:gend.mf         0.73775    0.55017   1.341    0.181    
## cond.c:gend.mf        -0.58054    0.73670  -0.788    0.431    
## DemR.d:cond.c:gend.mf  0.17519    1.09330   0.160    0.873    
## DemI.d:cond.c:gend.mf  0.60583    1.10034   0.551    0.582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.827 on 316 degrees of freedom
##   (217 observations deleted due to missingness)
## Multiple R-squared:  0.02432,    Adjusted R-squared:  -0.009643 
## F-statistic: 0.7161 on 11 and 316 DF,  p-value: 0.7231

# Action 17
summary(lm(act17 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act17 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0000 -1.5111  0.2941  1.6667  3.1304 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.7141     0.1854   3.852 0.000136 ***
## DemR.d                 -0.5060     0.2731  -1.853 0.064676 .  
## DemI.d                 -0.2908     0.2707  -1.075 0.283217    
## cond.c                 -0.1023     0.3708  -0.276 0.782663    
## gend.mf                 0.3445     0.3708   0.929 0.353430    
## DemR.d:cond.c          -0.1110     0.5463  -0.203 0.839105    
## DemI.d:cond.c          -0.1220     0.5413  -0.225 0.821767    
## DemR.d:gend.mf         -0.8197     0.5463  -1.501 0.134268    
## DemI.d:gend.mf         -0.3741     0.5413  -0.691 0.489950    
## cond.c:gend.mf         -0.2499     0.7415  -0.337 0.736344    
## DemR.d:cond.c:gend.mf   0.2728     1.0926   0.250 0.802960    
## DemI.d:cond.c:gend.mf  -0.4910     1.0826  -0.453 0.650447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.02 on 401 degrees of freedom
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01457,    Adjusted R-squared:  -0.01246 
## F-statistic: 0.539 on 11 and 401 DF,  p-value: 0.8766

# Action 18
summary(lm(act18 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act18 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.167 -1.150  0.250  1.333  2.850 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.8607     0.1833   4.695 3.88e-06 ***
## DemR.d                 -0.3857     0.2731  -1.413    0.159    
## DemI.d                 -0.2607     0.2648  -0.985    0.326    
## cond.c                  0.1820     0.3667   0.496    0.620    
## gend.mf                 0.2346     0.3667   0.640    0.523    
## DemR.d:cond.c          -0.1320     0.5462  -0.242    0.809    
## DemI.d:cond.c          -0.2524     0.5295  -0.477    0.634    
## DemR.d:gend.mf         -0.1013     0.5462  -0.185    0.853    
## DemI.d:gend.mf         -0.5642     0.5295  -1.066    0.287    
## cond.c:gend.mf          0.3904     0.7333   0.532    0.595    
## DemR.d:cond.c:gend.mf  -1.3237     1.0924  -1.212    0.226    
## DemI.d:cond.c:gend.mf  -0.3907     1.0590  -0.369    0.712    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.849 on 337 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01772,    Adjusted R-squared:  -0.01434 
## F-statistic: 0.5527 on 11 and 337 DF,  p-value: 0.8662

# Action 19
summary(lm(act19 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act19 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4722 -1.1034  0.2222  1.5331  3.0588 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.13252    0.20954   5.405 1.47e-07 ***
## DemR.d                -0.09398    0.30185  -0.311    0.756    
## DemI.d                 0.19223    0.28937   0.664    0.507    
## cond.c                -0.32755    0.41908  -0.782    0.435    
## gend.mf                0.06829    0.41908   0.163    0.871    
## DemR.d:cond.c         -0.83468    0.60371  -1.383    0.168    
## DemI.d:cond.c          0.12022    0.57873   0.208    0.836    
## DemR.d:gend.mf        -0.42642    0.60371  -0.706    0.481    
## DemI.d:gend.mf        -0.09789    0.57873  -0.169    0.866    
## cond.c:gend.mf        -0.01157    0.83815  -0.014    0.989    
## DemR.d:cond.c:gend.mf -1.33717    1.20742  -1.107    0.269    
## DemI.d:cond.c:gend.mf  0.12758    1.15747   0.110    0.912    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.794 on 259 degrees of freedom
##   (274 observations deleted due to missingness)
## Multiple R-squared:  0.045,  Adjusted R-squared:  0.004435 
## F-statistic: 1.109 on 11 and 259 DF,  p-value: 0.354

# Action 20
summary(lm(act20 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act20 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6111 -1.2857  0.3889  1.5000  2.7222 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.11786    0.17621   6.344 8.02e-10 ***
## DemR.d                 0.01622    0.25872   0.063  0.95004    
## DemI.d                 0.14700    0.26107   0.563  0.57382    
## cond.c                -0.95000    0.35242  -2.696  0.00741 ** 
## gend.mf                0.26429    0.35242   0.750  0.45388    
## DemR.d:cond.c          0.37628    0.51744   0.727  0.46766    
## DemI.d:cond.c          0.80341    0.52214   1.539  0.12491    
## DemR.d:gend.mf        -1.02390    0.51744  -1.979  0.04874 *  
## DemI.d:gend.mf        -0.50828    0.52214  -0.973  0.33110    
## cond.c:gend.mf         0.90000    0.70485   1.277  0.20261    
## DemR.d:cond.c:gend.mf -1.65855    1.03489  -1.603  0.11004    
## DemI.d:cond.c:gend.mf -1.46397    1.04429  -1.402  0.16196    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.747 on 307 degrees of freedom
##   (226 observations deleted due to missingness)
## Multiple R-squared:  0.06688,    Adjusted R-squared:  0.03345 
## F-statistic:     2 on 11 and 307 DF,  p-value: 0.02801

# Action 21
summary(lm(act21 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act21 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7442 -1.8400  0.1698  1.6250  3.6250 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.07183    0.19422   0.370  0.71172   
## DemR.d                -0.75980    0.28658  -2.651  0.00835 **
## DemI.d                 0.15674    0.28099   0.558  0.57730   
## cond.c                -0.17254    0.38844  -0.444  0.65715   
## gend.mf                0.44365    0.38844   1.142  0.25410   
## DemR.d:cond.c          0.73599    0.57316   1.284  0.19989   
## DemI.d:cond.c          0.57725    0.56197   1.027  0.30497   
## DemR.d:gend.mf        -1.10937    0.57316  -1.936  0.05366 . 
## DemI.d:gend.mf        -0.56090    0.56197  -0.998  0.31886   
## cond.c:gend.mf        -0.38508    0.77687  -0.496  0.62040   
## DemR.d:cond.c:gend.mf  0.84152    1.14633   0.734  0.46333   
## DemI.d:cond.c:gend.mf -0.63349    1.12394  -0.564  0.57333   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.053 on 386 degrees of freedom
##   (147 observations deleted due to missingness)
## Multiple R-squared:  0.04533,    Adjusted R-squared:  0.01812 
## F-statistic: 1.666 on 11 and 386 DF,  p-value: 0.07902

# Action 22
summary(lm(act22 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act22 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7021 -1.4886  0.1667  1.5238  3.5000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.8315     0.1894   4.391 1.46e-05 ***
## DemR.d                 -1.1461     0.2829  -4.051 6.15e-05 ***
## DemI.d                 -0.3878     0.2666  -1.455    0.147    
## cond.c                 -0.4609     0.3787  -1.217    0.224    
## gend.mf                 0.2870     0.3787   0.758    0.449    
## DemR.d:cond.c           0.4235     0.5658   0.748    0.455    
## DemI.d:cond.c           0.6389     0.5331   1.198    0.232    
## DemR.d:gend.mf         -0.4304     0.5658  -0.761    0.447    
## DemI.d:gend.mf         -0.1101     0.5331  -0.206    0.837    
## cond.c:gend.mf         -0.9782     0.7575  -1.291    0.197    
## DemR.d:cond.c:gend.mf   0.5985     1.1316   0.529    0.597    
## DemI.d:cond.c:gend.mf   0.8463     1.0663   0.794    0.428    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.965 on 387 degrees of freedom
##   (146 observations deleted due to missingness)
## Multiple R-squared:  0.05667,    Adjusted R-squared:  0.02986 
## F-statistic: 2.114 on 11 and 387 DF,  p-value: 0.01862

# Action 23
summary(lm(act23 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act23 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5294 -2.2500  0.1563  1.9412  3.8000 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.15099    0.23742   0.636   0.5253  
## DemR.d                -0.28774    0.36023  -0.799   0.4251  
## DemI.d                 0.17124    0.35131   0.487   0.6263  
## cond.c                -0.39941    0.47484  -0.841   0.4010  
## gend.mf                0.14573    0.47484   0.307   0.7591  
## DemR.d:cond.c          0.82592    0.72045   1.146   0.2526  
## DemI.d:cond.c         -0.79506    0.70262  -1.132   0.2588  
## DemR.d:gend.mf        -0.28474    0.72045  -0.395   0.6930  
## DemI.d:gend.mf        -0.06079    0.70262  -0.087   0.9311  
## cond.c:gend.mf         0.13868    0.94967   0.146   0.8840  
## DemR.d:cond.c:gend.mf  0.58330    1.44091   0.405   0.6859  
## DemI.d:cond.c:gend.mf -2.40857    1.40525  -1.714   0.0877 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.258 on 277 degrees of freedom
##   (256 observations deleted due to missingness)
## Multiple R-squared:  0.03833,    Adjusted R-squared:  0.0001456 
## F-statistic: 1.004 on 11 and 277 DF,  p-value: 0.4431

# Action 24
summary(lm(act24 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act24 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3182 -1.6875 -0.1455  1.5822  4.5263 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.17775    0.19105  -0.930   0.3527  
## DemR.d                -0.25415    0.27951  -0.909   0.3638  
## DemI.d                -0.05701    0.27397  -0.208   0.8353  
## cond.c                -0.47367    0.38210  -1.240   0.2158  
## gend.mf                0.32367    0.38210   0.847   0.3975  
## DemR.d:cond.c          0.03845    0.55903   0.069   0.9452  
## DemI.d:cond.c         -0.36363    0.54794  -0.664   0.5073  
## DemR.d:gend.mf        -0.13845    0.55903  -0.248   0.8045  
## DemI.d:gend.mf        -1.08049    0.54794  -1.972   0.0493 *
## cond.c:gend.mf        -0.38902    0.76421  -0.509   0.6110  
## DemR.d:cond.c:gend.mf -0.38339    1.11806  -0.343   0.7318  
## DemI.d:cond.c:gend.mf -1.58902    1.09588  -1.450   0.1478  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.07 on 400 degrees of freedom
##   (133 observations deleted due to missingness)
## Multiple R-squared:  0.04277,    Adjusted R-squared:  0.01644 
## F-statistic: 1.625 on 11 and 400 DF,  p-value: 0.08932

# Action 25
summary(lm(act25 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3125 -1.1591  0.2364  1.6875  3.0435 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.15925    0.16736   6.927 1.58e-11 ***
## DemR.d                -0.98900    0.24993  -3.957 8.88e-05 ***
## DemI.d                -0.36591    0.23823  -1.536    0.125    
## cond.c                 0.25487    0.33472   0.761    0.447    
## gend.mf                0.24237    0.33472   0.724    0.469    
## DemR.d:cond.c         -0.40155    0.49987  -0.803    0.422    
## DemI.d:cond.c         -0.13246    0.47646  -0.278    0.781    
## DemR.d:gend.mf        -0.54301    0.49987  -1.086    0.278    
## DemI.d:gend.mf        -0.56477    0.47646  -1.185    0.237    
## cond.c:gend.mf        -0.58799    0.66944  -0.878    0.380    
## DemR.d:cond.c:gend.mf  0.62774    0.99973   0.628    0.530    
## DemI.d:cond.c:gend.mf  0.01462    0.95292   0.015    0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 429 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.05022,    Adjusted R-squared:  0.02587 
## F-statistic: 2.062 on 11 and 429 DF,  p-value: 0.02189

# Action 26
summary(lm(act26 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act26 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5000 -1.2400  0.6111  1.4762  2.5385 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.40896    0.21718   6.487 6.11e-10 ***
## DemR.d                 0.15529    0.32726   0.475   0.6356    
## DemI.d                 0.17819    0.31904   0.559   0.5771    
## cond.c                 0.23458    0.43436   0.540   0.5897    
## gend.mf                0.32792    0.43436   0.755   0.4511    
## DemR.d:cond.c         -0.85609    0.65451  -1.308   0.1923    
## DemI.d:cond.c         -0.11584    0.63808  -0.182   0.8561    
## DemR.d:gend.mf        -0.74487    0.65451  -1.138   0.2564    
## DemI.d:gend.mf        -0.06632    0.63808  -0.104   0.9173    
## cond.c:gend.mf         0.48917    0.86873   0.563   0.5740    
## DemR.d:cond.c:gend.mf -2.82308    1.30902  -2.157   0.0322 *  
## DemI.d:cond.c:gend.mf -0.52152    1.27615  -0.409   0.6832    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.821 on 211 degrees of freedom
##   (322 observations deleted due to missingness)
## Multiple R-squared:  0.04338,    Adjusted R-squared:  -0.006486 
## F-statistic: 0.8699 on 11 and 211 DF,  p-value: 0.5707

# Action 27
summary(lm(act25 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act25 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3125 -1.1591  0.2364  1.6875  3.0435 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.15925    0.16736   6.927 1.58e-11 ***
## DemR.d                -0.98900    0.24993  -3.957 8.88e-05 ***
## DemI.d                -0.36591    0.23823  -1.536    0.125    
## cond.c                 0.25487    0.33472   0.761    0.447    
## gend.mf                0.24237    0.33472   0.724    0.469    
## DemR.d:cond.c         -0.40155    0.49987  -0.803    0.422    
## DemI.d:cond.c         -0.13246    0.47646  -0.278    0.781    
## DemR.d:gend.mf        -0.54301    0.49987  -1.086    0.278    
## DemI.d:gend.mf        -0.56477    0.47646  -1.185    0.237    
## cond.c:gend.mf        -0.58799    0.66944  -0.878    0.380    
## DemR.d:cond.c:gend.mf  0.62774    0.99973   0.628    0.530    
## DemI.d:cond.c:gend.mf  0.01462    0.95292   0.015    0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 429 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.05022,    Adjusted R-squared:  0.02587 
## F-statistic: 2.062 on 11 and 429 DF,  p-value: 0.02189

# Action 28
summary(lm(act28 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act28 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3421 -1.0652  0.2593  1.6579  2.7143 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.19395    0.18520   6.447 3.71e-10 ***
## DemR.d                -0.24624    0.26607  -0.925    0.355    
## DemI.d                -0.28502    0.26929  -1.058    0.291    
## cond.c                -0.42729    0.37040  -1.154    0.249    
## gend.mf               -0.04345    0.37040  -0.117    0.907    
## DemR.d:cond.c         -0.15916    0.53215  -0.299    0.765    
## DemI.d:cond.c          0.56692    0.53859   1.053    0.293    
## DemR.d:gend.mf        -0.14959    0.53215  -0.281    0.779    
## DemI.d:gend.mf        -0.22153    0.53859  -0.411    0.681    
## cond.c:gend.mf        -0.90097    0.74081  -1.216    0.225    
## DemR.d:cond.c:gend.mf -0.18802    1.06430  -0.177    0.860    
## DemI.d:cond.c:gend.mf -0.02251    1.07718  -0.021    0.983    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 357 degrees of freedom
##   (176 observations deleted due to missingness)
## Multiple R-squared:  0.02841,    Adjusted R-squared:  -0.001523 
## F-statistic: 0.9491 on 11 and 357 DF,  p-value: 0.4931

# Action 29
summary(lm(act29 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act29 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4444 -1.0541  0.1053  1.6905  2.1053 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.310714   0.175261   7.479    6e-13 ***
## DemR.d                -0.371650   0.260312  -1.428    0.154    
## DemI.d                -0.231694   0.248081  -0.934    0.351    
## cond.c                 0.089683   0.350522   0.256    0.798    
## gend.mf                0.045238   0.350522   0.129    0.897    
## DemR.d:cond.c         -0.166098   0.520623  -0.319    0.750    
## DemI.d:cond.c          0.069489   0.496162   0.140    0.889    
## DemR.d:gend.mf        -0.028631   0.520623  -0.055    0.956    
## DemI.d:gend.mf         0.001056   0.496162   0.002    0.998    
## cond.c:gend.mf         0.265079   0.701044   0.378    0.706    
## DemR.d:cond.c:gend.mf -0.322774   1.041247  -0.310    0.757    
## DemI.d:cond.c:gend.mf  0.060540   0.992324   0.061    0.951    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.768 on 353 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.009013,   Adjusted R-squared:  -0.02187 
## F-statistic: 0.2919 on 11 and 353 DF,  p-value: 0.9872

# Action 30
summary(lm(act30 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d))

## 
## Call:
## lm(formula = act30 ~ (DemR.d + DemI.d) * cond.c * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2632 -1.1579  0.5556  1.7368  2.7273 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.965180   0.188747   5.114 5.31e-07 ***
## DemR.d                -0.150922   0.277977  -0.543    0.588    
## DemI.d                 0.236059   0.272785   0.865    0.387    
## cond.c                 0.222757   0.377493   0.590    0.556    
## gend.mf               -0.430359   0.377493  -1.140    0.255    
## DemR.d:cond.c          0.108030   0.555955   0.194    0.846    
## DemI.d:cond.c          0.003249   0.545570   0.006    0.995    
## DemR.d:gend.mf        -0.175428   0.555955  -0.316    0.753    
## DemI.d:gend.mf         0.498471   0.545570   0.914    0.362    
## cond.c:gend.mf         0.776708   0.754986   1.029    0.304    
## DemR.d:cond.c:gend.mf -0.483738   1.111909  -0.435    0.664    
## DemI.d:cond.c:gend.mf -0.287544   1.091140  -0.264    0.792    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.877 on 337 degrees of freedom
##   (196 observations deleted due to missingness)
## Multiple R-squared:  0.01829,    Adjusted R-squared:  -0.01375 
## F-statistic: 0.5709 on 11 and 337 DF,  p-value: 0.8523

a. Means

aggregate(d$act2[d$party_factor == "Democrat"], list(d$cond[d$party_factor == "Democrat"], d$gend.mf[d$party_factor == "Democrat"]), FUN = function(x) round(mean(x, na.rm = T),2))

##   Group.1 Group.2    x
## 1 climate    -0.5 0.87
## 2    ctrl    -0.5 1.68
## 3 climate     0.5 1.61
## 4    ctrl     0.5 0.67

d. long-format data analysis

df <- d %>% pivot_longer(act1:act30)

i. Across all groups

## Across Groups

# Are the actions, across all groups, supported above 0?

acts.mx <- lmer(value ~ 1 + (1 | name) + (1 | transaction_id), data = df)
summary(acts.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ 1 + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42365.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1033 -0.6277  0.0257  0.6249  3.4178 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.7405   1.3193  
##  name           (Intercept) 0.2772   0.5265  
##  Residual                   2.2639   1.5046  
## Number of obs: 11155, groups:  transaction_id, 540; name, 30
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   0.4762     0.1128 52.1148    4.22 9.78e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Do women support the actions differently from men?

gend.mx <- lmer(value ~ gend.mf + (1 | name) + (1 | transaction_id), data = df)
summary(gend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ gend.mf + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42196.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0986 -0.6273  0.0258  0.6248  3.4141 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.7456   1.3212  
##  name           (Intercept) 0.2766   0.5259  
##  Residual                   2.2686   1.5062  
## Number of obs: 11104, groups:  transaction_id, 538; name, 30
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   0.46208    0.11727  60.72176   3.940 0.000213 ***
## gend.mf      -0.04105    0.13449 527.56095  -0.305 0.760285    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr)
## gend.mf 0.272

# Condition effects?

cond.mx <- lmer(value ~ cond.c + (1 | name) + (1 | transaction_id), data = df)
summary(cond.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ cond.c + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42365.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1013 -0.6275  0.0267  0.6247  3.4151 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.7392   1.3188  
##  name           (Intercept) 0.2772   0.5265  
##  Residual                   2.2638   1.5046  
## Number of obs: 11155, groups:  transaction_id, 540; name, 30
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   0.4759     0.1128  52.0884   4.217 9.88e-05 ***
## cond.c       -0.1513     0.1180 534.9777  -1.282      0.2    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##        (Intr)
## cond.c 0.002

# Condition x gender?

cond.gend.mx <- lmer(value ~ cond.c*gend.mf + (1 | name) + (1 | transaction_id), data = df)
summary(cond.gend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ cond.c * gend.mf + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42197.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0946 -0.6266  0.0264  0.6245  3.4129 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.7448   1.321   
##  name           (Intercept) 0.2767   0.526   
##  Residual                   2.2685   1.506   
## Number of obs: 11104, groups:  transaction_id, 538; name, 30
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)      0.46707    0.11734  60.84175   3.980 0.000186 ***
## cond.c          -0.20851    0.13472 525.66931  -1.548 0.122284    
## gend.mf         -0.02826    0.13472 525.73292  -0.210 0.833917    
## cond.c:gend.mf  -0.23682    0.26943 525.64497  -0.879 0.379821    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cond.c gnd.mf
## cond.c      -0.025              
## gend.mf      0.273 -0.060       
## cnd.c:gnd.m -0.034  0.476 -0.043

ii. Ideology

### Strong Cons

# Are the actions supported above 0?

scon.acts.mx <- lmer(value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d) + (1 | name) + (1 | transaction_id), data = df)
summary(scon.acts.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d) + (1 | name) +  
##     (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42354.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0915 -0.6282  0.0266  0.6239  3.4226 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6950   1.3019  
##  name           (Intercept) 0.2775   0.5268  
##  Residual                   2.2639   1.5046  
## Number of obs: 11155, groups:  transaction_id, 540; name, 30
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)   0.09217    0.18462 272.19381   0.499  0.61803   
## SconsSL.d     0.77615    0.25442 529.33177   3.051  0.00240 **
## SconsL.d      0.71508    0.22042 526.46810   3.244  0.00125 **
## SconsM.d      0.42287    0.18150 524.92838   2.330  0.02020 * 
## SconsC.d      0.17955    0.20122 525.21867   0.892  0.37262   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) ScnSL. ScnsL. ScnsM.
## SconsSL.d -0.529                     
## SconsL.d  -0.610  0.443              
## SconsM.d  -0.741  0.538  0.621       
## SconsC.d  -0.668  0.485  0.560  0.680

# Do women support the actions differently from men?

scon.gend.mx <- lmer(value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d)*gend.mf + (1 | name) + (1 | transaction_id), data = df)
summary(scon.gend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d) * gend.mf +  
##     (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42179.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0958 -0.6287  0.0247  0.6215  3.4132 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.688    1.2991  
##  name           (Intercept) 0.277    0.5263  
##  Residual                   2.269    1.5062  
## Number of obs: 11104, groups:  transaction_id, 538; name, 30
## 
## Fixed effects:
##                    Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)         0.16610    0.19239 297.98036   0.863  0.38864   
## SconsSL.d           0.53566    0.27756 517.34943   1.930  0.05416 . 
## SconsL.d            0.77567    0.24393 514.24150   3.180  0.00156 **
## SconsM.d            0.31638    0.19755 511.58548   1.602  0.10987   
## SconsC.d            0.04308    0.21838 511.71108   0.197  0.84371   
## gend.mf             0.44703    0.33333 506.66590   1.341  0.18048   
## SconsSL.d:gend.mf  -1.18626    0.55512 517.36662  -2.137  0.03307 * 
## SconsL.d:gend.mf    0.08711    0.48786 514.21382   0.179  0.85835   
## SconsM.d:gend.mf   -0.54742    0.39510 511.60336  -1.386  0.16650   
## SconsC.d:gend.mf   -0.71564    0.43676 511.71637  -1.639  0.10193   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) ScnSL. ScnsL. ScnsM. ScnsC. gnd.mf SSL.:. ScL.:. ScM.:.
## SconsSL.d   -0.520                                                        
## SconsL.d    -0.592  0.410                                                 
## SconsM.d    -0.731  0.507  0.576                                          
## SconsC.d    -0.661  0.458  0.521  0.644                                   
## gend.mf      0.287 -0.199 -0.226 -0.279 -0.253                            
## ScnsSL.d:g. -0.172  0.387  0.136  0.168  0.152 -0.600                     
## ScnsL.d:gn. -0.196  0.136  0.423  0.191  0.173 -0.683  0.410              
## ScnsM.d:gn. -0.242  0.168  0.191  0.388  0.213 -0.844  0.507  0.576       
## ScnsC.d:gn. -0.219  0.152  0.173  0.213  0.387 -0.763  0.458  0.521  0.644

# Condition effects?

scon.cond.mx <- lmer(value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d) * cond.c + (1 | name) + (1 | transaction_id), data = df)
summary(scon.cond.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d) * cond.c +  
##     (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42355.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0933 -0.6283  0.0268  0.6241  3.4164 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.7039   1.3053  
##  name           (Intercept) 0.2774   0.5267  
##  Residual                   2.2639   1.5046  
## Number of obs: 11155, groups:  transaction_id, 540; name, 30
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)        0.08557    0.18592 276.18600   0.460  0.64569   
## SconsSL.d          0.77430    0.25834 525.25113   2.997  0.00285 **
## SconsL.d           0.72496    0.22206 521.74973   3.265  0.00117 **
## SconsM.d           0.42576    0.18301 519.51080   2.326  0.02038 * 
## SconsC.d           0.20796    0.20327 519.86248   1.023  0.30673   
## cond.c             0.11018    0.31817 516.30260   0.346  0.72928   
## SconsSL.d:cond.c  -0.20387    0.51669 525.26344  -0.395  0.69332   
## SconsL.d:cond.c   -0.01905    0.44411 521.71282  -0.043  0.96579   
## SconsM.d:cond.c   -0.24141    0.36601 519.48895  -0.660  0.50982   
## SconsC.d:cond.c   -0.44631    0.40654 519.87616  -1.098  0.27279   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) ScnSL. ScnsL. ScnsM. ScnsC. cond.c SSL.:. ScL.:. ScM.:.
## SconsSL.d   -0.527                                                        
## SconsL.d    -0.613  0.441                                                 
## SconsM.d    -0.744  0.535  0.623                                          
## SconsC.d    -0.670  0.482  0.561  0.680                                   
## cond.c      -0.102  0.073  0.085  0.104  0.093                            
## ScnsSL.d:c.  0.063  0.066 -0.053 -0.064 -0.058 -0.616                     
## ScnsL.d:cn.  0.073 -0.053 -0.026 -0.074 -0.067 -0.716  0.441              
## ScnsM.d:cn.  0.089 -0.064 -0.074 -0.076 -0.081 -0.869  0.535  0.623       
## ScnsC.d:cn.  0.080 -0.058 -0.067 -0.081 -0.123 -0.783  0.482  0.561  0.680

# Condition x Gender effects?

scon.cond.gend.mx <- lmer(value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d)*cond.c*gend.mf + (1 | name) + (1 | transaction_id), data = df)
summary(scon.cond.gend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (SconsSL.d + SconsL.d + SconsM.d + SconsC.d) * cond.c *  
##     gend.mf + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42165.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0977 -0.6299  0.0248  0.6215  3.4137 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6796   1.2960  
##  name           (Intercept) 0.2768   0.5261  
##  Residual                   2.2686   1.5062  
## Number of obs: 11104, groups:  transaction_id, 538; name, 30
## 
## Fixed effects:
##                            Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)                0.159555   0.192837 297.751774   0.827  0.40867   
## SconsSL.d                  0.485242   0.279793 508.958767   1.734  0.08347 . 
## SconsL.d                   0.772147   0.245175 503.583646   3.149  0.00173 **
## SconsM.d                   0.321804   0.197987 501.382128   1.625  0.10471   
## SconsC.d                   0.146663   0.221646 501.484234   0.662  0.50847   
## cond.c                     0.148347   0.334388 496.568395   0.444  0.65750   
## gend.mf                    0.460016   0.334401 496.642093   1.376  0.16955   
## SconsSL.d:cond.c           0.219678   0.559592 508.975085   0.393  0.69480   
## SconsL.d:cond.c           -0.190985   0.490340 503.547843  -0.389  0.69708   
## SconsM.d:cond.c           -0.297743   0.395961 501.314701  -0.752  0.45243   
## SconsC.d:cond.c           -0.808466   0.443297 501.507478  -1.824  0.06878 . 
## SconsSL.d:gend.mf         -1.187123   0.559591 508.975151  -2.121  0.03437 * 
## SconsL.d:gend.mf           0.041840   0.490345 503.566158   0.085  0.93203   
## SconsM.d:gend.mf          -0.550022   0.395979 501.401162  -1.389  0.16544   
## SconsC.d:gend.mf          -0.532102   0.443294 501.487007  -1.200  0.23057   
## cond.c:gend.mf             0.006866   0.668778 496.571660   0.010  0.99181   
## SconsSL.d:cond.c:gend.mf   1.694676   1.119183 508.980671   1.514  0.13059   
## SconsL.d:cond.c:gend.mf   -0.613112   0.980695 503.576412  -0.625  0.53214   
## SconsM.d:cond.c:gend.mf   -0.044013   0.791933 501.343285  -0.056  0.95570   
## SconsC.d:cond.c:gend.mf   -1.392962   0.886599 501.510492  -1.571  0.11678   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

### Conservatives

# Are the actions supported above 0?

con.acts.mx <- lmer(value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) + (1 | name) + (1 | transaction_id), data = df)
summary(con.acts.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) + (1 | name) +  
##     (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42354.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0915 -0.6282  0.0266  0.6239  3.4226 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6950   1.3019  
##  name           (Intercept) 0.2775   0.5268  
##  Residual                   2.2639   1.5046  
## Number of obs: 11155, groups:  transaction_id, 540; name, 30
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)   
## (Intercept)   0.2717     0.1579 174.8523   1.721  0.08701 . 
## ConsSL.d      0.5966     0.2357 533.1175   2.531  0.01166 * 
## ConsL.d       0.5355     0.1986 531.0904   2.697  0.00722 **
## ConsM.d       0.2433     0.1542 531.9664   1.578  0.11521   
## ConsSC.d     -0.1796     0.2012 525.2187  -0.892  0.37262   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr) CnsSL. CnsL.d CnsM.d
## ConsSL.d -0.421                     
## ConsL.d  -0.500  0.335              
## ConsM.d  -0.643  0.431  0.512       
## ConsSC.d -0.493  0.330  0.392  0.505

# Do women support the actions differently from men?

con.gend.mx <- lmer(value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d)*gend.mf + (1 | name) + (1 | transaction_id), data = df)
summary(con.gend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) * gend.mf +  
##     (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42179.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0958 -0.6287  0.0247  0.6215  3.4132 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.688    1.2991  
##  name           (Intercept) 0.277    0.5263  
##  Residual                   2.269    1.5062  
## Number of obs: 11104, groups:  transaction_id, 538; name, 30
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)        0.20918    0.17075 221.24054   1.225  0.22186   
## ConsSL.d           0.49258    0.26301 522.24181   1.873  0.06165 . 
## ConsL.d            0.73259    0.22725 520.27530   3.224  0.00134 **
## ConsM.d            0.27331    0.17654 520.85079   1.548  0.12219   
## ConsSC.d          -0.04308    0.21838 511.71108  -0.197  0.84371   
## gend.mf           -0.26861    0.28225 519.05734  -0.952  0.34170   
## ConsSL.d:gend.mf  -0.47062    0.52604 522.28348  -0.895  0.37139   
## ConsL.d:gend.mf    0.80275    0.45449 520.25447   1.766  0.07794 . 
## ConsM.d:gend.mf    0.16822    0.35307 520.83975   0.476  0.63395   
## ConsSC.d:gend.mf   0.71564    0.43676 511.71636   1.639  0.10193   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) CnsSL. CnsL.d CnsM.d CnsSC. gnd.mf CSL.:. CnL.:. CnM.:.
## ConsSL.d    -0.443                                                        
## ConsL.d     -0.513  0.333                                                 
## ConsM.d     -0.661  0.429  0.496                                          
## ConsSC.d    -0.534  0.347  0.401  0.517                                   
## gend.mf      0.384 -0.250 -0.289 -0.372 -0.301                            
## CnsSL.d:gn. -0.206  0.431  0.155  0.200  0.161 -0.537                     
## CnsL.d:gnd. -0.239  0.155  0.489  0.231  0.187 -0.621  0.333              
## CnsM.d:gnd. -0.307  0.200  0.231  0.488  0.240 -0.799  0.429  0.496       
## CnsSC.d:gn. -0.249  0.161  0.187  0.240  0.387 -0.646  0.347  0.401  0.517

# Condition effects?

con.cond.mx <- lmer(value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) * cond.c + (1 | name) + (1 | transaction_id), data = df)
summary(con.cond.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) * cond.c +  
##     (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42355.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0933 -0.6283  0.0268  0.6241  3.4164 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.7039   1.3053  
##  name           (Intercept) 0.2774   0.5267  
##  Residual                   2.2639   1.5046  
## Number of obs: 11155, groups:  transaction_id, 540; name, 30
## 
## Fixed effects:
##                 Estimate Std. Error       df t value Pr(>|t|)  
## (Intercept)       0.2935     0.1590 178.5350   1.847   0.0665 .
## ConsSL.d          0.5663     0.2397 529.3843   2.363   0.0185 *
## ConsL.d           0.5170     0.2000 526.7981   2.585   0.0100 *
## ConsM.d           0.2178     0.1555 526.9563   1.400   0.1620  
## ConsSC.d         -0.2080     0.2033 519.8625  -1.023   0.3067  
## cond.c           -0.3361     0.2531 525.6187  -1.328   0.1847  
## ConsSL.d:cond.c   0.2424     0.4794 529.4355   0.506   0.6132  
## ConsL.d:cond.c    0.4273     0.4001 526.7788   1.068   0.2860  
## ConsM.d:cond.c    0.2049     0.3111 526.9236   0.659   0.5104  
## ConsSC.d:cond.c   0.4463     0.4065 519.8762   1.098   0.2728  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) CnsSL. CnsL.d CnsM.d CnsSC. cond.c CSL.:. CnL.:. CnM.:.
## ConsSL.d    -0.420                                                        
## ConsL.d     -0.503  0.334                                                 
## ConsM.d     -0.647  0.429  0.515                                          
## ConsSC.d    -0.496  0.329  0.394  0.506                                   
## cond.c      -0.103  0.069  0.082  0.106  0.081                            
## CnsSL.d:cn.  0.054  0.093 -0.043 -0.056 -0.043 -0.528                     
## CnsL.d:cnd.  0.065 -0.043 -0.008 -0.067 -0.051 -0.633  0.334              
## CnsM.d:cnd.  0.084 -0.056 -0.067 -0.067 -0.066 -0.813  0.429  0.515       
## CnsSC.d:cn.  0.064 -0.043 -0.051 -0.066 -0.123 -0.622  0.329  0.394  0.506

# Condition x Gender effects?

con.cond.gend.mx <- lmer(value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) * cond.c * gend.mf + (1 | name) + (1 | transaction_id), data = df)

summary(con.cond.gend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (ConsSL.d + ConsL.d + ConsM.d + ConsSC.d) * cond.c *  
##     gend.mf + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42165.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0977 -0.6299  0.0248  0.6215  3.4137 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6796   1.2960  
##  name           (Intercept) 0.2768   0.5261  
##  Residual                   2.2686   1.5062  
## Number of obs: 11104, groups:  transaction_id, 538; name, 30
## 
## Fixed effects:
##                          Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)               0.30622    0.17437 233.74364   1.756  0.08038 . 
## ConsSL.d                  0.33858    0.26740 513.66886   1.266  0.20602   
## ConsL.d                   0.62548    0.23093 509.13392   2.708  0.00699 **
## ConsM.d                   0.17514    0.18005 510.00441   0.973  0.33115   
## ConsSC.d                 -0.14666    0.22165 501.48425  -0.662  0.50847   
## cond.c                   -0.66012    0.29103 508.13803  -2.268  0.02373 * 
## gend.mf                  -0.07209    0.29104 508.18395  -0.248  0.80448   
## ConsSL.d:cond.c           1.02814    0.53483 513.75598   1.922  0.05511 . 
## ConsL.d:cond.c            0.61748    0.46187 509.11991   1.337  0.18184   
## ConsM.d:cond.c            0.51072    0.36009 509.95647   1.418  0.15671   
## ConsSC.d:cond.c           0.80847    0.44330 501.50749   1.824  0.06878 . 
## ConsSL.d:gend.mf         -0.65502    0.53482 513.72456  -1.225  0.22123   
## ConsL.d:gend.mf           0.57394    0.46187 509.11317   1.243  0.21456   
## ConsM.d:gend.mf          -0.01792    0.36010 510.00591  -0.050  0.96033   
## ConsSC.d:gend.mf          0.53210    0.44329 501.48702   1.200  0.23057   
## cond.c:gend.mf           -1.38610    0.58205 508.11251  -2.381  0.01761 * 
## ConsSL.d:cond.c:gend.mf   3.08764    1.06962 513.68682   2.887  0.00406 **
## ConsL.d:cond.c:gend.mf    0.77985    0.92374 509.13979   0.844  0.39894   
## ConsM.d:cond.c:gend.mf    1.34895    0.72019 509.95456   1.873  0.06163 . 
## ConsSC.d:cond.c:gend.mf   1.39296    0.88660 501.51050   1.571  0.11678   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Significant condition x gender effect

iii. Partisan Groups

### Republicans
repmod.mx <- lmer(value ~ RepD.d + RepI.d + (1 | name) + (1 | transaction_id), data = df)
summary(repmod.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ RepD.d + RepI.d + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42029.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0422 -0.6312  0.0283  0.6284  3.4104 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6648   1.2903  
##  name           (Intercept) 0.2818   0.5308  
##  Residual                   2.2687   1.5062  
## Number of obs: 11065, groups:  transaction_id, 537; name, 30
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   0.1195     0.1414 117.6476   0.845  0.39972    
## RepD.d        0.5996     0.1438 526.7259   4.168 3.58e-05 ***
## RepI.d        0.4341     0.1421 530.8499   3.055  0.00237 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##        (Intr) RepD.d
## RepD.d -0.521       
## RepI.d -0.527  0.518

repcond.mx <- lmer(value ~ (RepD.d + RepI.d) * cond.c + (1 | name), data = df)
summary(repcond.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (RepD.d + RepI.d) * cond.c + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46566.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.38514 -0.76483  0.09672  0.86163  1.90925 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3126   0.5591  
##  Residual             3.8957   1.9738  
## Number of obs: 11065, groups:  name, 30
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    1.593e-01  1.074e-01  3.316e+01   1.483   0.1476    
## RepD.d         5.569e-01  4.640e-02  1.103e+04  12.003   <2e-16 ***
## RepI.d         3.977e-01  4.639e-02  1.103e+04   8.572   <2e-16 ***
## cond.c        -1.363e-01  6.662e-02  1.103e+04  -2.047   0.0407 *  
## RepD.d:cond.c  4.718e-02  9.278e-02  1.103e+04   0.508   0.6111    
## RepI.d:cond.c  1.316e-01  9.278e-02  1.103e+04   1.419   0.1560    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) RepD.d RepI.d cond.c RpD.:.
## RepD.d      -0.223                            
## RepI.d      -0.223  0.516                     
## cond.c      -0.027  0.062  0.062              
## RpD.d:cnd.c  0.019 -0.038 -0.044 -0.718       
## RpI.d:cnd.c  0.019 -0.044  0.011 -0.718  0.515

repgend.mx <- lmer(value ~ (RepD.d + RepI.d) * gend.mf + (1 | name), data = df)
summary(repgend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (RepD.d + RepI.d) * gend.mf + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46366.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.34385 -0.76836  0.08881  0.86464  1.97927 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3125   0.559   
##  Residual             3.9010   1.975   
## Number of obs: 11014, groups:  name, 30
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     8.872e-02  1.088e-01  3.484e+01   0.816 0.420225    
## RepD.d          6.292e-01  5.129e-02  1.098e+04  12.269  < 2e-16 ***
## RepI.d          4.571e-01  5.216e-02  1.098e+04   8.764  < 2e-16 ***
## gend.mf        -2.807e-01  7.492e-02  1.098e+04  -3.747 0.000180 ***
## RepD.d:gend.mf  3.522e-01  1.026e-01  1.098e+04   3.434 0.000597 ***
## RepI.d:gend.mf  2.314e-01  1.043e-01  1.098e+04   2.218 0.026541 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) RepD.d RepI.d gnd.mf RpD.:.
## RepD.d      -0.252                            
## RepI.d      -0.247  0.524                     
## gend.mf      0.158 -0.337 -0.332              
## RpD.d:gnd.m -0.116  0.420  0.242 -0.730       
## RpI.d:gnd.m -0.114  0.242  0.464 -0.718  0.524

repcondgend.mx <- lmer(value ~ (RepD.d + RepI.d) * cond.c * gend.mf + (1 | name), data = df)
summary(repcondgend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (RepD.d + RepI.d) * cond.c * gend.mf + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46366.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36777 -0.76414  0.09556  0.86063  2.02883 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3133   0.5598  
##  Residual             3.8971   1.9741  
## Number of obs: 11014, groups:  name, 30
## 
## Fixed effects:
##                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            1.068e-01  1.092e-01  3.528e+01   0.977  0.33499    
## RepD.d                 6.100e-01  5.205e-02  1.097e+04  11.720  < 2e-16 ***
## RepI.d                 4.423e-01  5.297e-02  1.097e+04   8.349  < 2e-16 ***
## cond.c                -1.605e-01  7.695e-02  1.097e+04  -2.086  0.03697 *  
## gend.mf               -2.458e-01  7.699e-02  1.097e+04  -3.193  0.00141 ** 
## RepD.d:cond.c          5.581e-02  1.041e-01  1.097e+04   0.536  0.59180    
## RepI.d:cond.c          5.151e-02  1.059e-01  1.097e+04   0.486  0.62679    
## RepD.d:gend.mf         3.151e-01  1.041e-01  1.097e+04   3.027  0.00248 ** 
## RepI.d:gend.mf         1.844e-01  1.060e-01  1.097e+04   1.740  0.08185 .  
## cond.c:gend.mf        -2.062e-01  1.539e-01  1.097e+04  -1.340  0.18026    
## RepD.d:cond.c:gend.mf  1.531e-01  2.082e-01  1.097e+04   0.736  0.46202    
## RepI.d:cond.c:gend.mf -2.598e-01  2.119e-01  1.097e+04  -1.226  0.22020    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) RepD.d RepI.d cond.c gnd.mf RpD.d:c. RpI.d:c. RpD.d:g.
## RepD.d      -0.261                                                       
## RepI.d      -0.256  0.537                                                
## cond.c      -0.072  0.152  0.149                                         
## gend.mf      0.172 -0.362 -0.356 -0.196                                  
## RpD.d:cnd.c  0.053 -0.100 -0.110 -0.739  0.145                           
## RpI.d:cnd.c  0.052 -0.110 -0.083 -0.726  0.142  0.537                    
## RpD.d:gnd.m -0.128  0.437  0.264  0.145 -0.739 -0.096   -0.105           
## RpI.d:gnd.m -0.125  0.263  0.475  0.142 -0.726 -0.105   -0.117    0.537  
## cnd.c:gnd.m -0.069  0.145  0.142  0.491 -0.205 -0.363   -0.357    0.152  
## RpD.d:cn.:.  0.051 -0.096 -0.105 -0.363  0.152  0.437    0.264   -0.100  
## RpI.d:cn.:.  0.050 -0.105 -0.117 -0.356  0.149  0.264    0.475   -0.110  
##             RpI.d:g. cnd.:. RD.:.:
## RepD.d                            
## RepI.d                            
## cond.c                            
## gend.mf                           
## RpD.d:cnd.c                       
## RpI.d:cnd.c                       
## RpD.d:gnd.m                       
## RpI.d:gnd.m                       
## cnd.c:gnd.m  0.149                
## RpD.d:cn.:. -0.110   -0.739       
## RpI.d:cn.:. -0.084   -0.726  0.537

### Independents
indmod.mx <- lmer(value ~ IndD.d + IndR.d + (1 | name) + (1 | transaction_id), data = df)
summary(indmod.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ IndD.d + IndR.d + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42029.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0422 -0.6312  0.0283  0.6284  3.4104 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6648   1.2903  
##  name           (Intercept) 0.2818   0.5308  
##  Residual                   2.2687   1.5062  
## Number of obs: 11065, groups:  transaction_id, 537; name, 30
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   0.5536     0.1379 107.4675   4.016  0.00011 ***
## IndD.d        0.1655     0.1403 530.3526   1.179  0.23884    
## IndR.d       -0.4341     0.1421 530.8499  -3.055  0.00237 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##        (Intr) IndD.d
## IndD.d -0.496       
## IndR.d -0.490  0.481

indcond.mx <- lmer(value ~ (IndD.d + IndR.d) * cond.c + (1 | name), data = df)
summary(indcond.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (IndD.d + IndR.d) * cond.c + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46566.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.38514 -0.76483  0.09672  0.86163  1.90925 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3126   0.5591  
##  Residual             3.8957   1.9738  
## Number of obs: 11065, groups:  name, 30
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    5.570e-01  1.071e-01  3.277e+01   5.200 1.04e-05 ***
## IndD.d         1.592e-01  4.567e-02  1.103e+04   3.486 0.000491 ***
## IndR.d        -3.977e-01  4.639e-02  1.103e+04  -8.572  < 2e-16 ***
## cond.c        -4.710e-03  6.459e-02  1.103e+04  -0.073 0.941869    
## IndD.d:cond.c -8.446e-02  9.133e-02  1.103e+04  -0.925 0.355132    
## IndR.d:cond.c -1.316e-01  9.278e-02  1.103e+04  -1.419 0.156002    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) IndD.d IndR.d cond.c InD.:.
## IndD.d      -0.213                            
## IndR.d      -0.210  0.492                     
## cond.c       0.034 -0.081 -0.079              
## IndD.d:cnd. -0.024  0.064  0.056 -0.707       
## IndR.d:cnd. -0.024  0.056  0.011 -0.696  0.492

indgend.mx <- lmer(value ~ (IndD.d + IndR.d) * gend.mf + (1 | name), data = df)
summary(indgend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (IndD.d + IndR.d) * gend.mf + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46366.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.34385 -0.76836  0.08881  0.86464  1.97927 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3125   0.559   
##  Residual             3.9010   1.975   
## Number of obs: 11014, groups:  name, 30
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     5.458e-01  1.084e-01  3.434e+01   5.036 1.51e-05 ***
## IndD.d          1.721e-01  5.047e-02  1.098e+04   3.411  0.00065 ***
## IndR.d         -4.571e-01  5.216e-02  1.098e+04  -8.764  < 2e-16 ***
## gend.mf        -4.928e-02  7.262e-02  1.098e+04  -0.679  0.49744    
## IndD.d:gend.mf  1.208e-01  1.009e-01  1.098e+04   1.196  0.23158    
## IndR.d:gend.mf -2.314e-01  1.043e-01  1.098e+04  -2.218  0.02654 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) IndD.d IndR.d gnd.mf InD.:.
## IndD.d      -0.241                            
## IndR.d      -0.233  0.501                     
## gend.mf      0.156 -0.336 -0.325              
## IndD.d:gnd. -0.113  0.421  0.234 -0.719       
## IndR.d:gnd. -0.109  0.234  0.464 -0.696  0.501

indcondgend.mx <- lmer(value ~ (IndD.d + IndR.d) * cond.c * gend.mf + (1 | name), data = df)
summary(indcondgend.mx) 

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (IndD.d + IndR.d) * cond.c * gend.mf + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46366.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36777 -0.76414  0.09556  0.86063  2.02883 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3133   0.5598  
##  Residual             3.8971   1.9741  
## Number of obs: 11014, groups:  name, 30
## 
## Fixed effects:
##                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            5.491e-01  1.085e-01  3.437e+01   5.059  1.4e-05 ***
## IndD.d                 1.677e-01  5.053e-02  1.097e+04   3.318 0.000909 ***
## IndR.d                -4.423e-01  5.297e-02  1.097e+04  -8.349  < 2e-16 ***
## cond.c                -1.090e-01  7.281e-02  1.097e+04  -1.498 0.134267    
## gend.mf               -6.142e-02  7.281e-02  1.097e+04  -0.844 0.398895    
## IndD.d:cond.c          4.302e-03  1.011e-01  1.097e+04   0.043 0.966048    
## IndR.d:cond.c         -5.151e-02  1.059e-01  1.097e+04  -0.486 0.626793    
## IndD.d:gend.mf         1.307e-01  1.011e-01  1.097e+04   1.293 0.196038    
## IndR.d:gend.mf        -1.844e-01  1.060e-01  1.097e+04  -1.740 0.081854 .  
## cond.c:gend.mf        -4.660e-01  1.456e-01  1.097e+04  -3.200 0.001379 ** 
## IndD.d:cond.c:gend.mf  4.129e-01  2.021e-01  1.097e+04   2.043 0.041104 *  
## IndR.d:cond.c:gend.mf  2.598e-01  2.119e-01  1.097e+04   1.226 0.220203    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) IndD.d IndR.d cond.c gnd.mf IndD.d:c. IndR.d:c. IndD.d:g.
## IndD.d      -0.242                                                          
## IndR.d      -0.231  0.495                                                   
## cond.c       0.018 -0.038 -0.036                                            
## gend.mf      0.154 -0.330 -0.315 -0.028                                     
## IndD.d:cnd. -0.013  0.040  0.026 -0.720  0.020                              
## IndR.d:cnd. -0.012  0.026 -0.083 -0.687  0.019  0.495                       
## IndD.d:gnd. -0.111  0.417  0.227  0.020 -0.720 -0.003    -0.014             
## IndR.d:gnd. -0.106  0.227  0.475  0.019 -0.687 -0.014    -0.117     0.495   
## cnd.c:gnd.m -0.010  0.020  0.019  0.458  0.053 -0.330    -0.315    -0.038   
## IndD.d:c.:.  0.007 -0.003 -0.014 -0.330 -0.038  0.417     0.227     0.040   
## IndR.d:c.:.  0.007 -0.014 -0.117 -0.315 -0.036  0.227     0.475     0.026   
##             IndR.d:g. cnd.:. ID.:.:
## IndD.d                             
## IndR.d                             
## cond.c                             
## gend.mf                            
## IndD.d:cnd.                        
## IndR.d:cnd.                        
## IndD.d:gnd.                        
## IndR.d:gnd.                        
## cnd.c:gnd.m -0.036                 
## IndD.d:c.:.  0.026    -0.720       
## IndR.d:c.:. -0.084    -0.687  0.495

# Yes, gender x condition is significant


### Democrats
demmod.mx <- lmer(value ~ DemR.d + DemI.d + (1 | name) + (1 | transaction_id), data = df)
summary(demmod.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ DemR.d + DemI.d + (1 | name) + (1 | transaction_id)
##    Data: df
## 
## REML criterion at convergence: 42029.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0422 -0.6312  0.0283  0.6284  3.4104 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.6648   1.2903  
##  name           (Intercept) 0.2818   0.5308  
##  Residual                   2.2687   1.5062  
## Number of obs: 11065, groups:  transaction_id, 537; name, 30
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   0.7191     0.1396 112.4106   5.150 1.12e-06 ***
## DemR.d       -0.5996     0.1438 526.7259  -4.168 3.58e-05 ***
## DemI.d       -0.1655     0.1403 530.3526  -1.179    0.239    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##        (Intr) DemR.d
## DemR.d -0.502       
## DemI.d -0.515  0.500

demcond.mx <- lmer(value ~ (DemR.d + DemI.d) * cond.c + (1 | name), data = df)
summary(demcond.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (DemR.d + DemI.d) * cond.c + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46566.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.38514 -0.76483  0.09672  0.86163  1.90925 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3126   0.5591  
##  Residual             3.8957   1.9738  
## Number of obs: 11065, groups:  name, 30
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    7.162e-01  1.071e-01  3.276e+01   6.686 1.35e-07 ***
## DemR.d        -5.569e-01  4.640e-02  1.103e+04 -12.003  < 2e-16 ***
## DemI.d        -1.592e-01  4.567e-02  1.103e+04  -3.486 0.000491 ***
## cond.c        -8.917e-02  6.458e-02  1.103e+04  -1.381 0.167378    
## DemR.d:cond.c -4.718e-02  9.278e-02  1.103e+04  -0.508 0.611118    
## DemI.d:cond.c  8.446e-02  9.133e-02  1.103e+04   0.925 0.355132    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) DemR.d DemI.d cond.c DmR.:.
## DemR.d      -0.209                            
## DemI.d      -0.213  0.492                     
## cond.c       0.004 -0.010 -0.010              
## DmR.d:cnd.c -0.003 -0.038  0.007 -0.696       
## DmI.d:cnd.c -0.003  0.007  0.064 -0.707  0.492

demgend.mx <- lmer(value ~ (DemR.d + DemI.d) * gend.mf + (1 | name), data = df)
summary(demgend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (DemR.d + DemI.d) * gend.mf + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46366.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.34385 -0.76836  0.08881  0.86464  1.97927 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3125   0.559   
##  Residual             3.9010   1.975   
## Number of obs: 11014, groups:  name, 30
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     7.179e-01  1.080e-01  3.381e+01   6.651 1.28e-07 ***
## DemR.d         -6.292e-01  5.129e-02  1.098e+04 -12.269  < 2e-16 ***
## DemI.d         -1.721e-01  5.047e-02  1.098e+04  -3.411 0.000650 ***
## gend.mf         7.149e-02  7.012e-02  1.098e+04   1.019 0.307990    
## DemR.d:gend.mf -3.522e-01  1.026e-01  1.098e+04  -3.434 0.000597 ***
## DemI.d:gend.mf -1.208e-01  1.009e-01  1.098e+04  -1.196 0.231582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) DemR.d DemI.d gnd.mf DmR.:.
## DemR.d      -0.222                            
## DemI.d      -0.225  0.475                     
## gend.mf      0.120 -0.254 -0.258              
## DmR.d:gnd.m -0.083  0.420  0.176 -0.683       
## DmI.d:gnd.m -0.084  0.177  0.421 -0.695  0.475

demcondgend.mx <- lmer(value ~ (DemR.d + DemI.d) * cond.c * gend.mf + (1 | name), data = df)
summary(demcondgend.mx)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (DemR.d + DemI.d) * cond.c * gend.mf + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46366.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36777 -0.76414  0.09556  0.86063  2.02883 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3133   0.5598  
##  Residual             3.8971   1.9741  
## Number of obs: 11014, groups:  name, 30
## 
## Fixed effects:
##                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            7.168e-01  1.081e-01  3.379e+01   6.632 1.36e-07 ***
## DemR.d                -6.100e-01  5.205e-02  1.097e+04 -11.720  < 2e-16 ***
## DemI.d                -1.677e-01  5.053e-02  1.097e+04  -3.318 0.000909 ***
## cond.c                -1.047e-01  7.009e-02  1.097e+04  -1.494 0.135105    
## gend.mf                6.927e-02  7.012e-02  1.097e+04   0.988 0.323226    
## DemR.d:cond.c         -5.581e-02  1.041e-01  1.097e+04  -0.536 0.591803    
## DemI.d:cond.c         -4.302e-03  1.011e-01  1.097e+04  -0.043 0.966048    
## DemR.d:gend.mf        -3.151e-01  1.041e-01  1.097e+04  -3.027 0.002476 ** 
## DemI.d:gend.mf        -1.307e-01  1.011e-01  1.097e+04  -1.293 0.196038    
## cond.c:gend.mf        -5.310e-02  1.402e-01  1.097e+04  -0.379 0.704891    
## DemR.d:cond.c:gend.mf -1.531e-01  2.082e-01  1.097e+04  -0.736 0.462022    
## DemI.d:cond.c:gend.mf -4.129e-01  2.021e-01  1.097e+04  -2.043 0.041104 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) DemR.d DemI.d cond.c gnd.mf DmR.d:c. DmI.d:c. DmR.d:g.
## DemR.d      -0.218                                                       
## DemI.d      -0.225  0.467                                                
## cond.c       0.008 -0.018 -0.018                                         
## gend.mf      0.120 -0.251 -0.259  0.024                                  
## DmR.d:cnd.c -0.006 -0.100  0.012 -0.673 -0.016                           
## DmI.d:cnd.c -0.006  0.012  0.040 -0.693 -0.016  0.467                    
## DmR.d:gnd.m -0.081  0.437  0.174 -0.016 -0.673 -0.096    0.011           
## DmI.d:gnd.m -0.083  0.174  0.417 -0.016 -0.694  0.011   -0.003    0.467  
## cnd.c:gnd.m  0.007 -0.016 -0.016  0.373  0.026 -0.251   -0.258   -0.017  
## DmR.d:cn.:. -0.005 -0.096  0.011 -0.251 -0.018  0.437    0.174   -0.100  
## DmI.d:cn.:. -0.005  0.011 -0.003 -0.258 -0.018  0.174    0.417    0.012  
##             DmI.d:g. cnd.:. DR.:.:
## DemR.d                            
## DemI.d                            
## cond.c                            
## gend.mf                           
## DmR.d:cnd.c                       
## DmI.d:cnd.c                       
## DmR.d:gnd.m                       
## DmI.d:gnd.m                       
## cnd.c:gnd.m -0.018                
## DmR.d:cn.:.  0.012   -0.673       
## DmI.d:cn.:.  0.040   -0.694  0.467

4. PCA

a. data & correlation matrix

acts <- d[,c('act1',
             'act2',
             'act3',
             'act4',
             'act5',
             'act6',
             'act7',
             'act8',
             'act9',
             'act10',
             'act11',
             'act12',
             'act13',
             'act14',
             'act15',
             'act16',
             'act17',
             'act18',
             'act19',
             'act20',
             'act21',
             'act22',
             'act23',
             'act24',
             'act25',
             'act26',
             'act27',
             'act28',
             'act29',
             'act30')]

acts <- acts[rowSums(is.na(acts)) < 30,] # get rid of rows of all NAs

acts.corr <- cor(acts, use = "pairwise.complete.obs")

cortest.bartlett(acts.corr, n=540)

## $chisq
## [1] 10155.01
## 
## $p.value
## [1] 0
## 
## $df
## [1] 435

KMO(acts.corr)

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = acts.corr)
## Overall MSA =  0.95
## MSA for each item = 
##  act1  act2  act3  act4  act5  act6  act7  act8  act9 act10 act11 act12 act13 
##  0.96  0.95  0.97  0.97  0.96  0.93  0.94  0.93  0.96  0.93  0.93  0.95  0.95 
## act14 act15 act16 act17 act18 act19 act20 act21 act22 act23 act24 act25 act26 
##  0.97  0.95  0.97  0.96  0.97  0.93  0.94  0.96  0.96  0.93  0.94  0.95  0.87 
## act27 act28 act29 act30 
##  0.95  0.95  0.94  0.91

b. Parallel analysis

## Parallel analysis

fa.parallel(acts,
            fm = "pa" , 
            fa = "pc" ,  
            main = "Parallel Analysis Scree Plot" , 
            n.iter = 500 )

## Parallel analysis suggests that the number of factors =  NA  and the number of components =  2

colSums(is.na(acts))

##  act1  act2  act3  act4  act5  act6  act7  act8  act9 act10 act11 act12 act13 
##    91   250    96   169   243   181   125    59    91    63   226   155    97 
## act14 act15 act16 act17 act18 act19 act20 act21 act22 act23 act24 act25 act26 
##   252   155   208   123   186   265   216   137   136   247   124    94   313 
## act27 act28 act29 act30 
##   221   166   170   186

c. PCA & scree

## PCA
prc <- princomp(acts.corr)
summary(prc)

## Importance of components:
##                           Comp.1     Comp.2     Comp.3     Comp.4    Comp.5
## Standard deviation     0.4196697 0.20486070 0.19547142 0.17542971 0.1704698
## Proportion of Variance 0.3518962 0.08385262 0.07634241 0.06149016 0.0580623
## Cumulative Proportion  0.3518962 0.43574885 0.51209126 0.57358142 0.6316437
##                            Comp.6     Comp.7     Comp.8     Comp.9    Comp.10
## Standard deviation     0.14521267 0.13855032 0.13091762 0.12508272 0.11779438
## Proportion of Variance 0.04213164 0.03835433 0.03424487 0.03126036 0.02772353
## Cumulative Proportion  0.67377535 0.71212968 0.74637455 0.77763491 0.80535843
##                           Comp.11    Comp.12    Comp.13    Comp.14    Comp.15
## Standard deviation     0.11198053 0.10535514 0.09845361 0.09027273 0.08455831
## Proportion of Variance 0.02505442 0.02217741 0.01936701 0.01628218 0.01428604
## Cumulative Proportion  0.83041285 0.85259026 0.87195727 0.88823945 0.90252549
##                           Comp.16    Comp.17    Comp.18    Comp.19     Comp.20
## Standard deviation     0.08352200 0.08191457 0.07514694 0.07173134 0.066018895
## Proportion of Variance 0.01393802 0.01340669 0.01128293 0.01028057 0.008708349
## Cumulative Proportion  0.91646351 0.92987020 0.94115313 0.95143370 0.960142051
##                            Comp.21     Comp.22     Comp.23     Comp.24
## Standard deviation     0.061761582 0.060338240 0.054605196 0.050553659
## Proportion of Variance 0.007621424 0.007274189 0.005957544 0.005106279
## Cumulative Proportion  0.967763475 0.975037664 0.980995208 0.986101487
##                            Comp.25     Comp.26     Comp.27     Comp.28
## Standard deviation     0.044599639 0.041390418 0.036377396 0.032843441
## Proportion of Variance 0.003974312 0.003422937 0.002644007 0.002155245
## Cumulative Proportion  0.990075800 0.993498737 0.996142743 0.998297988
##                            Comp.29      Comp.30
## Standard deviation     0.029186473 1.654185e-09
## Proportion of Variance 0.001702012 5.467229e-18
## Cumulative Proportion  1.000000000 1.000000e+00

round(prc$loadings[,1:3],3)

##       Comp.1 Comp.2 Comp.3
## act1   0.197  0.170  0.041
## act2  -0.107  0.161 -0.187
## act3   0.246  0.286  0.065
## act4   0.163  0.220 -0.044
## act5  -0.093 -0.221 -0.096
## act6  -0.157  0.256  0.337
## act7   0.241  0.313  0.056
## act8   0.237 -0.022 -0.219
## act9   0.227  0.301  0.004
## act10  0.183 -0.103 -0.225
## act11 -0.026 -0.121 -0.315
## act12  0.298  0.119 -0.038
## act13  0.254 -0.083 -0.121
## act14  0.106 -0.047 -0.018
## act15  0.120 -0.173 -0.382
## act16 -0.114  0.156 -0.075
## act17 -0.030  0.188 -0.245
## act18 -0.025  0.236 -0.257
## act19 -0.112  0.019 -0.032
## act20 -0.207  0.007 -0.238
## act21  0.232 -0.002  0.010
## act22  0.070  0.177 -0.263
## act23  0.156 -0.051  0.115
## act24  0.186  0.098  0.042
## act25  0.007  0.179 -0.321
## act26 -0.301  0.037 -0.120
## act27 -0.046 -0.068 -0.252
## act28 -0.164  0.254  0.052
## act29 -0.249  0.317 -0.088
## act30 -0.286  0.266 -0.081

fviz_eig(prc, addlabels = TRUE)

fviz_pca_var(prc)

# fviz_cos2(prc, choice = "var", axes = 1:2)
fviz_pca_var(prc, col.var = "cos2",
            gradient.cols = c("black", "orange", "green"),
            repel = TRUE)

pca <- principal(acts, nfactors = 3, rotate = "Promax", n.obs = 540)
pca$loadings

## 
## Loadings:
##       RC1    RC2    RC3   
## act1   0.695              
## act2          0.515  0.244
## act3   0.867        -0.144
## act4   0.636  0.138       
## act5  -0.139  0.257  0.551
## act6   0.330  0.779 -0.437
## act7   0.874        -0.166
## act8   0.483 -0.193  0.511
## act9   0.826              
## act10  0.324 -0.167  0.595
## act11         0.154  0.683
## act12  0.777 -0.158  0.171
## act13  0.512 -0.251  0.456
## act14  0.352         0.283
## act15        -0.151  0.816
## act16  0.115  0.539  0.111
## act17  0.184  0.375  0.270
## act18  0.229  0.406  0.238
## act19         0.486  0.248
## act20 -0.233  0.558  0.442
## act21  0.613 -0.110  0.241
## act22  0.336  0.209  0.325
## act23  0.508         0.188
## act24  0.626              
## act25  0.192  0.281  0.363
## act26 -0.304  0.758  0.266
## act27         0.238  0.548
## act28  0.174  0.723 -0.129
## act29         0.850       
## act30         0.886       
## 
##                  RC1   RC2   RC3
## SS loadings    6.023 5.123 3.807
## Proportion Var 0.201 0.171 0.127
## Cumulative Var 0.201 0.372 0.498

prc$loadings[,1:3]

##             Comp.1       Comp.2       Comp.3
## act1   0.196973762  0.169867657  0.040934173
## act2  -0.107374438  0.161367790 -0.187174506
## act3   0.245748118  0.285690830  0.064754022
## act4   0.162734978  0.220139999 -0.044278296
## act5  -0.093437365 -0.221052844 -0.095677805
## act6  -0.157049410  0.256267351  0.336507611
## act7   0.240808521  0.313036111  0.056318396
## act8   0.237143277 -0.021830170 -0.218989303
## act9   0.227284199  0.300858231  0.003535633
## act10  0.183109104 -0.102763541 -0.225062190
## act11 -0.026447979 -0.120934905 -0.314883990
## act12  0.297592188  0.118668167 -0.037985049
## act13  0.254418740 -0.083492186 -0.120564174
## act14  0.105516127 -0.046650772 -0.017650966
## act15  0.119510422 -0.173140387 -0.381822345
## act16 -0.114342834  0.155563105 -0.075250165
## act17 -0.030073827  0.188255397 -0.244554235
## act18 -0.025328400  0.235911671 -0.256916130
## act19 -0.111659146  0.018651001 -0.031591502
## act20 -0.207329998  0.006698430 -0.237555524
## act21  0.231657076 -0.001867437  0.009692679
## act22  0.069792201  0.177092479 -0.263178170
## act23  0.156286863 -0.050939965  0.114849651
## act24  0.185880692  0.098445735  0.042191300
## act25  0.007211199  0.178576703 -0.321480979
## act26 -0.300670039  0.037203874 -0.120409585
## act27 -0.046077581 -0.067562131 -0.252253958
## act28 -0.164165414  0.254095010  0.051759659
## act29 -0.248555318  0.317179810 -0.088478168
## act30 -0.285812913  0.266019583 -0.080620994

pml <- pca$loadings
pml

## 
## Loadings:
##       RC1    RC2    RC3   
## act1   0.695              
## act2          0.515  0.244
## act3   0.867        -0.144
## act4   0.636  0.138       
## act5  -0.139  0.257  0.551
## act6   0.330  0.779 -0.437
## act7   0.874        -0.166
## act8   0.483 -0.193  0.511
## act9   0.826              
## act10  0.324 -0.167  0.595
## act11         0.154  0.683
## act12  0.777 -0.158  0.171
## act13  0.512 -0.251  0.456
## act14  0.352         0.283
## act15        -0.151  0.816
## act16  0.115  0.539  0.111
## act17  0.184  0.375  0.270
## act18  0.229  0.406  0.238
## act19         0.486  0.248
## act20 -0.233  0.558  0.442
## act21  0.613 -0.110  0.241
## act22  0.336  0.209  0.325
## act23  0.508         0.188
## act24  0.626              
## act25  0.192  0.281  0.363
## act26 -0.304  0.758  0.266
## act27         0.238  0.548
## act28  0.174  0.723 -0.129
## act29         0.850       
## act30         0.886       
## 
##                  RC1   RC2   RC3
## SS loadings    6.023 5.123 3.807
## Proportion Var 0.201 0.171 0.127
## Cumulative Var 0.201 0.372 0.498

prc$scores[,1:3]

##            Comp.1      Comp.2      Comp.3
## act1   0.38530867  0.10235818  0.12844032
## act2  -0.29716932  0.14052012 -0.16364073
## act3   0.49134911  0.21480345  0.17155265
## act4   0.32629573  0.20933922 -0.01335489
## act5  -0.33753179 -0.45933978  0.10387743
## act6  -0.48729026  0.06598401  0.57694388
## act7   0.48239826  0.25042157  0.15768441
## act8   0.48620020 -0.09130627 -0.17125406
## act9   0.48441773  0.32011618  0.01798408
## act10  0.33745892 -0.24254140 -0.11752932
## act11 -0.14059138 -0.24881846 -0.22808257
## act12  0.61972058  0.04995863  0.03907603
## act13  0.48754116 -0.25376264  0.02694957
## act14  0.12752069 -0.25130717  0.17555509
## act15  0.19103089 -0.32063490 -0.28633305
## act16 -0.34672998  0.05276797  0.03702440
## act17 -0.12286326  0.15856176 -0.21274509
## act18 -0.09124032  0.26241044 -0.27610902
## act19 -0.32396346 -0.05427082  0.03692843
## act20 -0.56036253 -0.11278658 -0.13827385
## act21  0.44934045 -0.12473537  0.12892000
## act22  0.12761186  0.19486823 -0.28114828
## act23  0.26117194 -0.21357402  0.27496984
## act24  0.33516732 -0.03477881  0.18628656
## act25 -0.03559237  0.14730380 -0.29439273
## act26 -0.79862677 -0.13434577  0.04256550
## act27 -0.18724461 -0.18950578 -0.16069895
## act28 -0.45989906  0.16872951  0.16747832
## act29 -0.65731208  0.22846635  0.02854730
## act30 -0.74611632  0.16509835  0.04277873

pca$values

##  [1] 13.5696215  2.2549274  1.0975914  0.9617430  0.9344756  0.8123450
##  [7]  0.7764269  0.7271510  0.6986718  0.6589212  0.6443438  0.6071556
## [13]  0.5524679  0.4973637  0.4912664  0.4617406  0.4487009  0.4377110
## [19]  0.3954344  0.3927614  0.3467796  0.3356412  0.3190265  0.2982780
## [25]  0.2736508  0.2421340  0.2267009  0.1986571  0.1797263  0.1585851

PERSONAL RESPONSIBILTY!

1. Descriptive Stats

aggregate(d$persRes1_capture, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T),2))

##               Group.1    x
## 1      Strong Liberal 5.72
## 2             Liberal 5.29
## 3            Moderate 5.12
## 4        Conservative 5.07
## 5 Strong Conservative 5.12

aggregate(d$persRes1_meaningful, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T),2))

##               Group.1    x
## 1      Strong Liberal 5.49
## 2             Liberal 5.31
## 3            Moderate 5.05
## 4        Conservative 5.04
## 5 Strong Conservative 4.92

aggregate(d$persRes1_value, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T),2))

##               Group.1    x
## 1      Strong Liberal 5.77
## 2             Liberal 5.36
## 3            Moderate 5.34
## 4        Conservative 5.11
## 5 Strong Conservative 5.20

aggregate(d$persRes2_capture, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T),2))

##               Group.1    x
## 1      Strong Liberal 4.06
## 2             Liberal 4.44
## 3            Moderate 5.04
## 4        Conservative 5.37
## 5 Strong Conservative 5.22

aggregate(d$persRes2_meaningful, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T),2))

##               Group.1    x
## 1      Strong Liberal 4.19
## 2             Liberal 4.38
## 3            Moderate 4.95
## 4        Conservative 5.34
## 5 Strong Conservative 4.93

aggregate(d$persRes2_value, list(d$ideology), FUN = function(x) round(mean(x, na.rm = T),2))

##               Group.1    x
## 1      Strong Liberal 4.43
## 2             Liberal 4.52
## 3            Moderate 5.17
## 4        Conservative 5.58
## 5 Strong Conservative 5.27

2. Inferential Stats

a. long-form data

dr <- d %>% pivot_longer(c("persRes1_capture","persRes1_meaningful", "persRes1_value","persRes2_capture","persRes2_meaningful","persRes2_value"))
colnames(dr)[(ncol(dr)-1)] <- "persResType"

b. codes & variables

dr$def <- NA
dr$def[dr$persResType == "persRes1_capture"] <- "one"
dr$def[dr$persResType == "persRes1_meaningful"] <- "one"
dr$def[dr$persResType == "persRes1_value"] <- "one"
dr$def[dr$persResType == "persRes2_capture"] <- "two"
dr$def[dr$persResType == "persRes2_meaningful"] <- "two"
dr$def[dr$persResType == "persRes2_value"] <- "two"

def1 <- grep( "persRes1_" , colnames(d) )
def2 <- grep( "persRes2_" , colnames(d) )

def1 <- def1[1:3]
def2 <- def2[1:3]

d$def1ratings <- rowMeans(d[, def1 ], na.rm = T)
d$def2ratings <- rowMeans(d[, def2 ], na.rm = T)

d$def2minus1 <- d$def2ratings - d$def1ratings

d$capture_2minus1 <- d$persRes2_capture - d$persRes1_capture
d$meaningful_2minus1 <- d$persRes2_meaningful - d$persRes1_meaningful
d$value_2minus1 <- d$persRes2_value - d$persRes1_value

d$capture_2minus1 <- d$persRes2_capture - d$persRes1_capture
d$meaningful_2minus1 <- d$persRes2_meaningful - d$persRes1_meaningful
d$value_2minus1 <- d$persRes2_value - d$persRes1_value
d$meaningful_2minus1 <- d$persRes2_meaningful - d$persRes1_meaningful

# definition codes

## definition 1 vs 2
dr$def.c <- ifelse(dr$def == "one", -1/2, 1/2)
dr$def1.d <- ifelse(dr$def == "one", 0, 1)
dr$def2.d <- ifelse(dr$def == "two", 0, 1)


# ideology codes - dr

## Dummy
dr$ConsSC.d <- NA
dr$ConsSC.d[dr$ideology == "Strong Conservative"] <- 1
dr$ConsSC.d[dr$ideology == "Conservative"] <- 0
dr$ConsSC.d[dr$ideology == "Moderate"] <- 0
dr$ConsSC.d[dr$ideology == "Liberal"] <- 0
dr$ConsSC.d[dr$ideology == "Strong Liberal"] <- 0

dr$ConsM.d <- NA
dr$ConsM.d[dr$ideology == "Strong Conservative"] <- 0
dr$ConsM.d[dr$ideology == "Conservative"] <- 0
dr$ConsM.d[dr$ideology == "Moderate"] <- 1
dr$ConsM.d[dr$ideology == "Liberal"] <- 0
dr$ConsM.d[dr$ideology == "Strong Liberal"] <- 0

dr$ConsL.d <- NA
dr$ConsL.d[dr$ideology == "Strong Conservative"] <- 0
dr$ConsL.d[dr$ideology == "Conservative"] <- 0
dr$ConsL.d[dr$ideology == "Moderate"] <- 0
dr$ConsL.d[dr$ideology == "Liberal"] <- 1
dr$ConsL.d[dr$ideology == "Strong Liberal"] <- 0

dr$ConsSL.d <- NA
dr$ConsSL.d[dr$ideology == "Strong Conservative"] <- 0
dr$ConsSL.d[dr$ideology == "Conservative"] <- 0
dr$ConsSL.d[dr$ideology == "Moderate"] <- 0
dr$ConsSL.d[dr$ideology == "Liberal"] <- 0
dr$ConsSL.d[dr$ideology == "Strong Liberal"] <- 1

## Dummy
dr$SconsC.d <- NA
dr$SconsC.d[dr$ideology == "Strong Conservative"] <- 0
dr$SconsC.d[dr$ideology == "Conservative"] <- 1
dr$SconsC.d[dr$ideology == "Moderate"] <- 0
dr$SconsC.d[dr$ideology == "Liberal"] <- 0
dr$SconsC.d[dr$ideology == "Strong Liberal"] <- 0

dr$SconsM.d <- NA
dr$SconsM.d[dr$ideology == "Strong Conservative"] <- 0
dr$SconsM.d[dr$ideology == "Conservative"] <- 0
dr$SconsM.d[dr$ideology == "Moderate"] <- 1
dr$SconsM.d[dr$ideology == "Liberal"] <- 0
dr$SconsM.d[dr$ideology == "Strong Liberal"] <- 0

dr$SconsL.d <- NA
dr$SconsL.d[dr$ideology == "Strong Conservative"] <- 0
dr$SconsL.d[dr$ideology == "Conservative"] <- 0
dr$SconsL.d[dr$ideology == "Moderate"] <- 0
dr$SconsL.d[dr$ideology == "Liberal"] <- 1
dr$SconsL.d[dr$ideology == "Strong Liberal"] <- 0

dr$SconsSL.d <- NA
dr$SconsSL.d[dr$ideology == "Strong Conservative"] <- 0
dr$SconsSL.d[dr$ideology == "Conservative"] <- 0
dr$SconsSL.d[dr$ideology == "Moderate"] <- 0
dr$SconsSL.d[dr$ideology == "Liberal"] <- 0
dr$SconsSL.d[dr$ideology == "Strong Liberal"] <- 1


summary(lm(meaningful_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d))

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0135 -0.8996  0.1004  0.9865  6.2979 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.9351     0.2041  -4.580 5.77e-06 ***
## LibsSC.d      0.9486     0.2916   3.253  0.00121 ** 
## LibsC.d       1.2317     0.2624   4.693 3.41e-06 ***
## LibsM.d       0.8346     0.2360   3.537  0.00044 ***
## LibsSL.d     -0.3628     0.3316  -1.094  0.27437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 540 degrees of freedom
## Multiple R-squared:  0.07117,    Adjusted R-squared:  0.06429 
## F-statistic: 10.34 on 4 and 540 DF,  p-value: 4.448e-08

summary(lm(value_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d))

## 
## Call:
## lm(formula = value_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8253 -0.6596  0.1747  0.8442  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8442     0.1928  -4.378 1.44e-05 ***
## LibsSC.d      0.9117     0.2754   3.310 0.000994 ***
## LibsC.d       1.3103     0.2479   5.286 1.81e-07 ***
## LibsM.d       0.6695     0.2229   3.004 0.002790 ** 
## LibsSL.d     -0.4963     0.3132  -1.585 0.113632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 540 degrees of freedom
## Multiple R-squared:  0.08893,    Adjusted R-squared:  0.08218 
## F-statistic: 13.18 on 4 and 540 DF,  p-value: 3.001e-10

summary(lm(capture_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d))

## 
## Call:
## lm(formula = capture_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3051 -0.9170  0.0830  0.9054  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8442     0.2047  -4.125 4.29e-05 ***
## LibsSC.d      0.9388     0.2923   3.211  0.00140 ** 
## LibsC.d       1.1492     0.2631   4.368 1.50e-05 ***
## LibsM.d       0.7612     0.2366   3.218  0.00137 ** 
## LibsSL.d     -0.8154     0.3324  -2.453  0.01448 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.796 on 540 degrees of freedom
## Multiple R-squared:  0.08968,    Adjusted R-squared:  0.08293 
## F-statistic:  13.3 on 4 and 540 DF,  p-value: 2.423e-10

## party codes
dr$Dem_Rep <- NA
dr$Dem_Rep[dr$party_factor == "Democrat"] <- -1/2
dr$Dem_Rep[dr$party_factor == "Independent"] <- 0
dr$Dem_Rep[dr$party_factor == "Republican"] <- 1/2

dr$Ind_Party <- NA
dr$Ind_Party[dr$party_factor == "Democrat"] <- 1/3
dr$Ind_Party[dr$party_factor == "Independent"] <- -2/3
dr$Ind_Party[dr$party_factor == "Republican"] <- 1/3

#Dummy
dr$RepD.d <- NA
dr$RepD.d[dr$party_factor == "Democrat"] <- 1
dr$RepD.d[dr$party_factor == "Independent"] <- 0
dr$RepD.d[dr$party_factor == "Republican"] <- 0

dr$RepI.d <- NA
dr$RepI.d[dr$party_factor == "Democrat"] <- 0
dr$RepI.d[dr$party_factor == "Independent"] <- 1
dr$RepI.d[dr$party_factor == "Republican"] <- 0

c. Analyses

i. By Ideology

pr.con.mod1 <- lmer(value ~ dr$def.c * (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) + (1 | transaction_id)  + (1 | persResType), data = dr)
summary(pr.con.mod1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ dr$def.c * (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) +  
##     (1 | transaction_id) + (1 | persResType)
##    Data: dr
## 
## REML criterion at convergence: 11139.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4747 -0.4468  0.0704  0.5097  3.9758 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.92489  1.3874  
##  persResType    (Intercept) 0.01004  0.1002  
##  Residual                   1.18023  1.0864  
## Number of obs: 3270, groups:  transaction_id, 545; persResType, 6
## 
## Fixed effects:
##                    Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          5.2514     0.1402  236.3761  37.459  < 2e-16 ***
## dr$def.c             0.3559     0.1156   10.7742   3.079   0.0107 *  
## ConsSC.d            -0.1411     0.2160  540.0003  -0.653   0.5140    
## ConsM.d             -0.1408     0.1651  540.0003  -0.853   0.3941    
## ConsL.d             -0.3683     0.2134  540.0003  -1.726   0.0849 .  
## ConsSL.d            -0.3082     0.2512  540.0003  -1.227   0.2205    
## dr$def.c:ConsSC.d   -0.2974     0.1315 2716.0001  -2.261   0.0238 *  
## dr$def.c:ConsM.d    -0.4753     0.1005 2716.0001  -4.728 2.38e-06 ***
## dr$def.c:ConsL.d    -1.2304     0.1299 2716.0001  -9.468  < 2e-16 ***
## dr$def.c:ConsSL.d   -1.7886     0.1530 2716.0001 -11.690  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dr$df. CnsSC. CnsM.d CnsL.d CnsSL. d$.:CSC d$.:CM d$.:CL
## dr$def.c     0.000                                                         
## ConsSC.d    -0.594  0.000                                                  
## ConsM.d     -0.777  0.000  0.504                                           
## ConsL.d     -0.601  0.000  0.390  0.510                                    
## ConsSL.d    -0.510  0.000  0.331  0.434  0.335                             
## dr$df.:CSC.  0.000 -0.439  0.000  0.000  0.000  0.000                      
## dr$df.c:CM.  0.000 -0.574  0.000  0.000  0.000  0.000  0.504               
## dr$df.c:CL.  0.000 -0.444  0.000  0.000  0.000  0.000  0.390   0.510       
## dr$df.:CSL.  0.000 -0.377  0.000  0.000  0.000  0.000  0.331   0.434  0.335

summary(lm(def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), data = d))

## 
## Call:
## lm(formula = def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3919 -0.5674  0.1194  0.8745  5.4527 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3559     0.1422   2.503  0.01262 *  
## ConsSC.d     -0.2974     0.2291  -1.298  0.19483    
## ConsM.d      -0.4753     0.1751  -2.715  0.00684 ** 
## ConsL.d      -1.2304     0.2263  -5.436 8.26e-08 ***
## ConsSL.d     -1.7886     0.2665  -6.712 4.88e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 540 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.09714 
## F-statistic: 15.63 on 4 and 540 DF,  p-value: 4.117e-12

pr.scon.mod1 <- lmer(value ~ dr$def * (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) + (1 | transaction_id), data = dr)
summary(pr.scon.mod1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ dr$def * (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) +  
##     (1 | transaction_id)
##    Data: dr
## 
## REML criterion at convergence: 11150.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5375 -0.4562  0.0983  0.5141  3.8829 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.924    1.387   
##  Residual                   1.188    1.090   
## Number of obs: 3270, groups:  transaction_id, 545
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)          5.081e+00  1.771e-01  6.444e+02  28.699  < 2e-16 ***
## dr$deftwo            5.856e-02  1.035e-01  2.720e+03   0.566   0.5715    
## SconsC.d            -7.635e-03  2.258e-01  6.444e+02  -0.034   0.9730    
## SconsM.d             8.922e-02  2.037e-01  6.444e+02   0.438   0.6615    
## SconsL.d             2.393e-01  2.479e-01  6.444e+02   0.965   0.3349    
## SconsSL.d            5.785e-01  2.841e-01  6.444e+02   2.036   0.0421 *  
## dr$deftwo:SconsC.d   2.974e-01  1.320e-01  2.720e+03   2.253   0.0243 *  
## dr$deftwo:SconsM.d  -1.779e-01  1.190e-01  2.720e+03  -1.495   0.1351    
## dr$deftwo:SconsL.d  -9.330e-01  1.449e-01  2.720e+03  -6.439 1.41e-10 ***
## dr$deftwo:SconsSL.d -1.491e+00  1.660e-01  2.720e+03  -8.982  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dr$dft ScnsC. ScnsM. ScnsL. ScnSL. d$:SC. d$:SM. d$:SL.
## dr$deftwo   -0.292                                                        
## SconsC.d    -0.784  0.229                                                 
## SconsM.d    -0.869  0.254  0.682                                          
## SconsL.d    -0.714  0.209  0.560  0.621                                   
## SconsSL.d   -0.623  0.182  0.489  0.542  0.445                            
## dr$dftw:SC.  0.229 -0.784 -0.292 -0.199 -0.164 -0.143                     
## dr$dftw:SM.  0.254 -0.869 -0.199 -0.292 -0.181 -0.158  0.682              
## dr$dftw:SL.  0.209 -0.714 -0.164 -0.181 -0.292 -0.130  0.560  0.621       
## dr$dft:SSL.  0.182 -0.623 -0.143 -0.158 -0.130 -0.292  0.489  0.542  0.445

Collapsing across attitude type, conservatives prefer definition 2 to definition 1; strong conservatives have no preference.

ii. By ideology: Wide data

1. Strong Conservatives

## rating types
summary(lm(capture_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + 
##     SconsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3051 -0.9170  0.0830  0.9054  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.09459    0.20876   0.453   0.6506    
## SconsC.d     0.21049    0.26629   0.790   0.4296    
## SconsM.d    -0.17756    0.24013  -0.739   0.4600    
## SconsL.d    -0.93875    0.29234  -3.211   0.0014 ** 
## SconsSL.d   -1.75417    0.33496  -5.237 2.34e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.796 on 540 degrees of freedom
## Multiple R-squared:  0.08968,    Adjusted R-squared:  0.08293 
## F-statistic:  13.3 on 4 and 540 DF,  p-value: 2.423e-10

summary(lm(meaningful_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + 
##     SconsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0135 -0.8996  0.1004  0.9865  6.2979 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01351    0.20824   0.065  0.94828    
## SconsC.d     0.28310    0.26563   1.066  0.28700    
## SconsM.d    -0.11395    0.23953  -0.476  0.63447    
## SconsL.d    -0.94858    0.29161  -3.253  0.00121 ** 
## SconsSL.d   -1.31139    0.33412  -3.925  9.8e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 540 degrees of freedom
## Multiple R-squared:  0.07117,    Adjusted R-squared:  0.06429 
## F-statistic: 10.34 on 4 and 540 DF,  p-value: 4.448e-08

summary(lm(value_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + 
##     SconsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8253 -0.6596  0.1747  0.8442  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06757    0.19668   0.344 0.731321    
## SconsC.d     0.39853    0.25088   1.589 0.112746    
## SconsM.d    -0.24224    0.22623  -1.071 0.284759    
## SconsL.d    -0.91172    0.27542  -3.310 0.000994 ***
## SconsSL.d   -1.40799    0.31557  -4.462  9.9e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 540 degrees of freedom
## Multiple R-squared:  0.08893,    Adjusted R-squared:  0.08218 
## F-statistic: 13.18 on 4 and 540 DF,  p-value: 3.001e-10

summary(lm(def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3919 -0.5674  0.1194  0.8745  5.4527 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05856    0.17960   0.326 0.744514    
## SconsC.d     0.29737    0.22910   1.298 0.194831    
## SconsM.d    -0.17792    0.20659  -0.861 0.389506    
## SconsL.d    -0.93302    0.25151  -3.710 0.000229 ***
## SconsSL.d   -1.49118    0.28817  -5.175 3.23e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 540 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.09714 
## F-statistic: 15.63 on 4 and 540 DF,  p-value: 4.117e-12

round(mean(d$def1ratings[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] 5.08

round(mean(d$def2ratings[d$ideology == "Strong Conservative"], na.rm = T),2)

## [1] 5.14

round(mean(d$def2minus1[d$ideology == "Strong Conservative"],na.rm = T),2)

## [1] 0.06

a. Gender effects?

## rating types
summary(lm(capture_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + 
##     SconsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         0.1675     0.2216   0.756    0.450    
## SconsC.d            0.3197     0.2898   1.103    0.270    
## SconsM.d           -0.3027     0.2621  -1.155    0.249    
## SconsL.d           -0.8163     0.3237  -2.522    0.012 *  
## SconsSL.d          -1.6617     0.3668  -4.531 7.27e-06 ***
## gend.mf             0.4150     0.4432   0.936    0.350    
## SconsC.d:gend.mf    0.3525     0.5795   0.608    0.543    
## SconsM.d:gend.mf   -0.5597     0.5242  -1.068    0.286    
## SconsL.d:gend.mf    0.3563     0.6474   0.550    0.582    
## SconsSL.d:gend.mf   0.1118     0.7335   0.152    0.879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + 
##     SconsSL.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -0.02250    0.22046  -0.102  0.91875   
## SconsC.d           0.40014    0.28824   1.388  0.16565   
## SconsM.d          -0.08467    0.26072  -0.325  0.74549   
## SconsL.d          -0.77514    0.32198  -2.407  0.01640 * 
## SconsSL.d         -0.84114    0.36484  -2.305  0.02152 * 
## gend.mf           -0.20500    0.44091  -0.465  0.64216   
## SconsC.d:gend.mf   0.54649    0.57648   0.948  0.34357   
## SconsM.d:gend.mf   0.23066    0.52144   0.442  0.65841   
## SconsL.d:gend.mf   0.74765    0.64395   1.161  0.24615   
## SconsSL.d:gend.mf  1.93227    0.72968   2.648  0.00833 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + 
##     SconsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        0.06083    0.20996   0.290  0.77213   
## SconsC.d           0.41099    0.27452   1.497  0.13495   
## SconsM.d          -0.24374    0.24831  -0.982  0.32673   
## SconsL.d          -0.76274    0.30665  -2.487  0.01317 * 
## SconsSL.d         -1.10978    0.34747  -3.194  0.00149 **
## gend.mf           -0.03833    0.41992  -0.091  0.92730   
## SconsC.d:gend.mf   0.06243    0.54904   0.114  0.90951   
## SconsM.d:gend.mf   0.02680    0.49661   0.054  0.95699   
## SconsL.d:gend.mf   0.60004    0.61330   0.978  0.32833   
## SconsSL.d:gend.mf  1.21316    0.69494   1.746  0.08144 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.mf, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.06861    0.19065   0.360 0.719082    
## SconsC.d           0.37695    0.24927   1.512 0.131080    
## SconsM.d          -0.21037    0.22547  -0.933 0.351226    
## SconsL.d          -0.78473    0.27845  -2.818 0.005008 ** 
## SconsSL.d         -1.20420    0.31552  -3.817 0.000151 ***
## gend.mf            0.05722    0.38130   0.150 0.880766    
## SconsC.d:gend.mf   0.32048    0.49855   0.643 0.520609    
## SconsM.d:gend.mf  -0.10074    0.45094  -0.223 0.823304    
## SconsL.d:gend.mf   0.56800    0.55690   1.020 0.308215    
## SconsSL.d:gend.mf  1.08575    0.63103   1.721 0.085905 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

round(mean(d$def1ratings[d$ideology == "Strong Conservative" & d$gend == "Female"],na.rm = T),2)

## [1] 5.05

round(mean(d$def1ratings[d$ideology == "Strong Conservative" & d$gend == "Male"],na.rm = T),2)

## [1] 5.14

round(mean(d$def2ratings[d$ideology == "Strong Conservative" & d$gend == "Female"],na.rm = T),2)

## [1] 5.09

round(mean(d$def2ratings[d$ideology == "Strong Conservative" & d$gend == "Male"],na.rm = T),2)

## [1] 5.24

summary(lm(def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.f, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.04000    0.21715   0.184 0.853923    
## SconsC.d          0.21670    0.27250   0.795 0.426817    
## SconsM.d         -0.16000    0.24623  -0.650 0.516094    
## SconsL.d         -1.06874    0.29632  -3.607 0.000339 ***
## SconsSL.d        -1.74707    0.34438  -5.073 5.41e-07 ***
## gend.f            0.05722    0.38130   0.150 0.880766    
## SconsC.d:gend.f   0.32048    0.49855   0.643 0.520609    
## SconsM.d:gend.f  -0.10074    0.45094  -0.223 0.823304    
## SconsL.d:gend.f   0.56800    0.55690   1.020 0.308215    
## SconsSL.d:gend.f  1.08575    0.63103   1.721 0.085905 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

summary(lm(def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d)*gend.m, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (SconsC.d + SconsM.d + SconsL.d + SconsSL.d) * 
##     gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.09722    0.31343   0.310   0.7565  
## SconsC.d          0.53719    0.41748   1.287   0.1987  
## SconsM.d         -0.26074    0.37779  -0.690   0.4904  
## SconsL.d         -0.50073    0.47152  -1.062   0.2887  
## SconsSL.d        -0.66132    0.52877  -1.251   0.2116  
## gend.m           -0.05722    0.38130  -0.150   0.8808  
## SconsC.d:gend.m  -0.32048    0.49855  -0.643   0.5206  
## SconsM.d:gend.m   0.10074    0.45094   0.223   0.8233  
## SconsL.d:gend.m  -0.56800    0.55690  -1.020   0.3082  
## SconsSL.d:gend.m -1.08575    0.63103  -1.721   0.0859 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

round(mean(d$def1ratings[d$ideology == "Strong Liberal" & d$gend == "Female"],na.rm = T),2)

## [1] 5.73

round(mean(d$def1ratings[d$ideology == "Strong Liberal" & d$gend == "Male"],na.rm = T),2)

## [1] 5.49

round(mean(d$def2ratings[d$ideology == "Strong Liberal" & d$gend == "Female"],na.rm = T),2)

## [1] 4.02

round(mean(d$def2ratings[d$ideology == "Strong Liberal" & d$gend == "Male"],na.rm = T),2)

## [1] 4.92

round(mean(d$def1ratings[d$ideology == "Liberal" & d$gend == "Female"],na.rm = T),2)

## [1] 5.46

round(mean(d$def1ratings[d$ideology == "Liberal" & d$gend == "Male"],na.rm = T),2)

## [1] 4.89

round(mean(d$def2ratings[d$ideology == "Liberal" & d$gend == "Female"],na.rm = T),2)

## [1] 4.43

round(mean(d$def2ratings[d$ideology == "Liberal" & d$gend == "Male"],na.rm = T),2)

## [1] 4.49

round(mean(d$def1ratings[d$ideology == "Moderate" & d$gend == "Female"],na.rm = T),2)

## [1] 5.2

round(mean(d$def1ratings[d$ideology == "Moderate" & d$gend == "Male"],na.rm = T),2)

## [1] 5.09

round(mean(d$def2ratings[d$ideology == "Moderate" & d$gend == "Female"],na.rm = T),2)

## [1] 5.08

round(mean(d$def2ratings[d$ideology == "Moderate" & d$gend == "Male"],na.rm = T),2)

## [1] 4.92

round(mean(d$def1ratings[d$ideology == "Conservative" & d$gend == "Female"],na.rm = T),2)

## [1] 5.31

round(mean(d$def1ratings[d$ideology == "Conservative" & d$gend == "Male"],na.rm = T),2)

## [1] 4.41

round(mean(d$def2ratings[d$ideology == "Conservative" & d$gend == "Female"],na.rm = T),2)

## [1] 5.57

round(mean(d$def2ratings[d$ideology == "Conservative" & d$gend == "Male"],na.rm = T),2)

## [1] 5.04

round(mean(d$def1ratings[d$ideology == "Strong Conservative" & d$gend == "Female"],na.rm = T),2)

## [1] 5.05

round(mean(d$def1ratings[d$ideology == "Strong Conservative" & d$gend == "Male"],na.rm = T),2)

## [1] 5.14

round(mean(d$def2ratings[d$ideology == "Strong Conservative" & d$gend == "Female"],na.rm = T),2)

## [1] 5.09

round(mean(d$def2ratings[d$ideology == "Strong Conservative" & d$gend == "Male"],na.rm = T),2)

## [1] 5.24

table(d$ideology[d$gend != "Other"], d$gend[d$gend != "Other"])

##                      
##                       Female Male
##   Strong Liberal          33   13
##   Liberal                 58   19
##   Moderate               175   53
##   Conservative            87   31
##   Strong Conservative     50   24

2. Conservatives

## rating types
summary(lm(capture_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), data = d)) # marginal effect: 2 is better captured than 1

## 
## Call:
## lm(formula = capture_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + 
##     ConsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3051 -0.9170  0.0830  0.9054  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3051     0.1653   1.845   0.0655 .  
## ConsSC.d     -0.2105     0.2663  -0.790   0.4296    
## ConsM.d      -0.3881     0.2035  -1.907   0.0571 .  
## ConsL.d      -1.1492     0.2631  -4.368 1.50e-05 ***
## ConsSL.d     -1.9647     0.3098  -6.343 4.78e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.796 on 540 degrees of freedom
## Multiple R-squared:  0.08968,    Adjusted R-squared:  0.08293 
## F-statistic:  13.3 on 4 and 540 DF,  p-value: 2.423e-10

summary(lm(meaningful_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), data = d)) # marginal effect: 2 is more meaningful than 1

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + 
##     ConsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0135 -0.8996  0.1004  0.9865  6.2979 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2966     0.1649   1.799   0.0726 .  
## ConsSC.d     -0.2831     0.2656  -1.066   0.2870    
## ConsM.d      -0.3970     0.2030  -1.956   0.0510 .  
## ConsL.d      -1.2317     0.2624  -4.693 3.41e-06 ***
## ConsSL.d     -1.5945     0.3090  -5.160 3.47e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 540 degrees of freedom
## Multiple R-squared:  0.07117,    Adjusted R-squared:  0.06429 
## F-statistic: 10.34 on 4 and 540 DF,  p-value: 4.448e-08

summary(lm(value_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), data = d)) # 2 is considered more valuable than 1

## 
## Call:
## lm(formula = value_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + 
##     ConsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8253 -0.6596  0.1747  0.8442  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.4661     0.1557   2.993 0.002892 ** 
## ConsSC.d     -0.3985     0.2509  -1.589 0.112746    
## ConsM.d      -0.6408     0.1917  -3.342 0.000889 ***
## ConsL.d      -1.3103     0.2479  -5.286 1.81e-07 ***
## ConsSL.d     -1.8065     0.2918  -6.190 1.19e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 540 degrees of freedom
## Multiple R-squared:  0.08893,    Adjusted R-squared:  0.08218 
## F-statistic: 13.18 on 4 and 540 DF,  p-value: 3.001e-10

summary(lm(def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), data = d)) # 2 preferred to 1--driven by effect of "value"

## 
## Call:
## lm(formula = def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3919 -0.5674  0.1194  0.8745  5.4527 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3559     0.1422   2.503  0.01262 *  
## ConsSC.d     -0.2974     0.2291  -1.298  0.19483    
## ConsM.d      -0.4753     0.1751  -2.715  0.00684 ** 
## ConsL.d      -1.2304     0.2263  -5.436 8.26e-08 ***
## ConsSL.d     -1.7886     0.2665  -6.712 4.88e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 540 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.09714 
## F-statistic: 15.63 on 4 and 540 DF,  p-value: 4.117e-12

round(mean(d$persRes1_value[d$ideology == "Conservative"], na.rm = T),2)

## [1] 5.11

round(mean(d$persRes2_value[d$ideology == "Conservative"], na.rm = T),2)

## [1] 5.58

round(mean(d$def1ratings[d$ideology == "Conservative"], na.rm = T),2)

## [1] 5.07

round(mean(d$def2ratings[d$ideology == "Conservative"], na.rm = T),2)

## [1] 5.43

a. Gender effects?

## rating types
summary(lm(capture_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)) # effect: women higher than men

## 
## Call:
## lm(formula = capture_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + 
##     ConsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.487208   0.186676   2.610 0.009311 ** 
## ConsSC.d         -0.319708   0.289767  -1.103 0.270382    
## ConsM.d          -0.622410   0.233297  -2.668 0.007865 ** 
## ConsL.d          -1.136028   0.300834  -3.776 0.000177 ***
## ConsSL.d         -1.981381   0.346774  -5.714 1.84e-08 ***
## gend.mf           0.767519   0.373353   2.056 0.040293 *  
## ConsSC.d:gend.mf -0.352519   0.579533  -0.608 0.543260    
## ConsM.d:gend.mf  -0.912209   0.466594  -1.955 0.051101 .  
## ConsL.d:gend.mf   0.003805   0.601667   0.006 0.994956    
## ConsSL.d:gend.mf -0.240713   0.693548  -0.347 0.728672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + 
##     ConsSL.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.3776     0.1857   2.034  0.04248 *  
## ConsSC.d          -0.4001     0.2882  -1.388  0.16565    
## ConsM.d           -0.4848     0.2321  -2.089  0.03717 *  
## ConsL.d           -1.1753     0.2992  -3.927 9.71e-05 ***
## ConsSL.d          -1.2413     0.3449  -3.598  0.00035 ***
## gend.mf            0.3415     0.3714   0.920  0.35825    
## ConsSC.d:gend.mf  -0.5465     0.5765  -0.948  0.34357    
## ConsM.d:gend.mf   -0.3158     0.4641  -0.680  0.49650    
## ConsL.d:gend.mf    0.2012     0.5985   0.336  0.73692    
## ConsSL.d:gend.mf   1.3858     0.6899   2.009  0.04507 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + 
##     ConsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.47182    0.17685   2.668  0.00787 ** 
## ConsSC.d         -0.41099    0.27452  -1.497  0.13495    
## ConsM.d          -0.65473    0.22102  -2.962  0.00319 ** 
## ConsL.d          -1.17373    0.28500  -4.118 4.42e-05 ***
## ConsSL.d         -1.52077    0.32853  -4.629 4.62e-06 ***
## gend.mf           0.02410    0.35371   0.068  0.94570    
## ConsSC.d:gend.mf -0.06243    0.54904  -0.114  0.90951    
## ConsM.d:gend.mf  -0.03564    0.44204  -0.081  0.93577    
## ConsL.d:gend.mf   0.53761    0.57001   0.943  0.34603    
## ConsSL.d:gend.mf  1.15072    0.65705   1.751  0.08046 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.4456     0.1606   2.775  0.00572 ** 
## ConsSC.d          -0.3769     0.2493  -1.512  0.13108    
## ConsM.d           -0.5873     0.2007  -2.926  0.00358 ** 
## ConsL.d           -1.1617     0.2588  -4.489 8.78e-06 ***
## ConsSL.d          -1.5811     0.2983  -5.300 1.70e-07 ***
## gend.mf            0.3777     0.3212   1.176  0.24012    
## ConsSC.d:gend.mf  -0.3205     0.4985  -0.643  0.52061    
## ConsM.d:gend.mf   -0.4212     0.4014  -1.049  0.29446    
## ConsL.d:gend.mf    0.2475     0.5176   0.478  0.63269    
## ConsSL.d:gend.mf   0.7653     0.5966   1.283  0.20017    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

d$def2minus1_capture <- d$persRes2_capture - d$persRes1_capture

round(mean(d$def2minus1_capture[d$ideology == "Conservative" & d$gend == "Female"], na.rm = T),2)

## [1] 0.1

round(mean(d$def2minus1_capture[d$ideology == "Conservative" & d$gend == "Male"], na.rm = T),2)

## [1] 0.87

round(mean(d$def2minus1[d$ideology == "Conservative" & d$gend == "Female"], na.rm = T),2)

## [1] 0.26

round(mean(d$def2minus1[d$ideology == "Conservative" & d$gend == "Male"], na.rm = T),2)

## [1] 0.63

summary(lm(def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.f, data = d)) # no

## 
## Call:
## lm(formula = def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.2567     0.1646   1.559    0.120    
## ConsSC.d         -0.2167     0.2725  -0.795    0.427    
## ConsM.d          -0.3767     0.2014  -1.870    0.062 .  
## ConsL.d          -1.2854     0.2603  -4.939 1.05e-06 ***
## ConsSL.d         -1.9638     0.3139  -6.256 8.13e-10 ***
## gend.f            0.3777     0.3212   1.176    0.240    
## ConsSC.d:gend.f  -0.3205     0.4985  -0.643    0.521    
## ConsM.d:gend.f   -0.4212     0.4014  -1.049    0.294    
## ConsL.d:gend.f    0.2475     0.5176   0.478    0.633    
## ConsSL.d:gend.f   0.7653     0.5966   1.283    0.200    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

summary(lm(def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d)*gend.m, data = d)) # no

## 
## Call:
## lm(formula = def2minus1 ~ (ConsSC.d + ConsM.d + ConsL.d + ConsSL.d) * 
##     gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       0.6344     0.2758   2.300   0.0218 *
## ConsSC.d         -0.5372     0.4175  -1.287   0.1987  
## ConsM.d          -0.7979     0.3472  -2.298   0.0219 *
## ConsL.d          -1.0379     0.4474  -2.320   0.0207 *
## ConsSL.d         -1.1985     0.5074  -2.362   0.0185 *
## gend.m           -0.3777     0.3212  -1.176   0.2401  
## ConsSC.d:gend.m   0.3205     0.4985   0.643   0.5206  
## ConsM.d:gend.m    0.4212     0.4014   1.049   0.2945  
## ConsL.d:gend.m   -0.2475     0.5176  -0.478   0.6327  
## ConsSL.d:gend.m  -0.7653     0.5966  -1.283   0.2002  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

3. Moderate

## rating types
summary(lm(capture_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + 
##     ModsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3051 -0.9170  0.0830  0.9054  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.08297    0.11867  -0.699  0.48475    
## ModsC.d      0.38805    0.20350   1.907  0.05706 .  
## ModsSC.d     0.17756    0.24013   0.739  0.45996    
## ModsL.d     -0.76119    0.23657  -3.218  0.00137 ** 
## ModsSL.d    -1.57661    0.28757  -5.482 6.45e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.796 on 540 degrees of freedom
## Multiple R-squared:  0.08968,    Adjusted R-squared:  0.08293 
## F-statistic:  13.3 on 4 and 540 DF,  p-value: 2.423e-10

summary(lm(meaningful_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + 
##     ModsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0135 -0.8996  0.1004  0.9865  6.2979 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.1004     0.1184  -0.848  0.39656    
## ModsC.d       0.3970     0.2030   1.956  0.05099 .  
## ModsSC.d      0.1140     0.2395   0.476  0.63447    
## ModsL.d      -0.8346     0.2360  -3.537  0.00044 ***
## ModsSL.d     -1.1974     0.2869  -4.174 3.48e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 540 degrees of freedom
## Multiple R-squared:  0.07117,    Adjusted R-squared:  0.06429 
## F-statistic: 10.34 on 4 and 540 DF,  p-value: 4.448e-08

summary(lm(value_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + 
##     ModsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8253 -0.6596  0.1747  0.8442  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.1747     0.1118  -1.562 0.118796    
## ModsC.d       0.6408     0.1917   3.342 0.000889 ***
## ModsSC.d      0.2422     0.2262   1.071 0.284759    
## ModsL.d      -0.6695     0.2229  -3.004 0.002790 ** 
## ModsSL.d     -1.1658     0.2709  -4.303    2e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 540 degrees of freedom
## Multiple R-squared:  0.08893,    Adjusted R-squared:  0.08218 
## F-statistic: 13.18 on 4 and 540 DF,  p-value: 3.001e-10

summary(lm(def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d), data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3919 -0.5674  0.1194  0.8745  5.4527 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.1194     0.1021  -1.169 0.242880    
## ModsC.d       0.4753     0.1751   2.715 0.006845 ** 
## ModsSC.d      0.1779     0.2066   0.861 0.389506    
## ModsL.d      -0.7551     0.2035  -3.710 0.000229 ***
## ModsSL.d     -1.3133     0.2474  -5.308 1.62e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 540 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.09714 
## F-statistic: 15.63 on 4 and 540 DF,  p-value: 4.117e-12

round(mean(d$def1ratings[d$ideology == "Moderate"], na.rm = T),2)

## [1] 5.17

round(mean(d$def2ratings[d$ideology == "Moderate"], na.rm = T),2)

## [1] 5.05

round(mean(d$def2minus1[d$ideology == "Moderate"],na.rm = T),2)

## [1] -0.12

a. Gender effects

## rating types
summary(lm(capture_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + 
##     ModsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.1352     0.1399  -0.966  0.33436    
## ModsC.d            0.6224     0.2333   2.668  0.00787 ** 
## ModsSC.d           0.3027     0.2621   1.155  0.24864    
## ModsL.d           -0.5136     0.2743  -1.873  0.06167 .  
## ModsSL.d          -1.3590     0.3240  -4.194 3.21e-05 ***
## gend.mf           -0.1447     0.2799  -0.517  0.60536    
## ModsC.d:gend.mf    0.9122     0.4666   1.955  0.05110 .  
## ModsSC.d:gend.mf   0.5597     0.5242   1.068  0.28614    
## ModsL.d:gend.mf    0.9160     0.5486   1.670  0.09554 .  
## ModsSL.d:gend.mf   0.6715     0.6480   1.036  0.30057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + 
##     ModsSL.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.10717    0.13919  -0.770  0.44167   
## ModsC.d           0.48481    0.23207   2.089  0.03717 * 
## ModsSC.d          0.08467    0.26072   0.325  0.74549   
## ModsL.d          -0.69047    0.27284  -2.531  0.01167 * 
## ModsSL.d         -0.75647    0.32231  -2.347  0.01929 * 
## gend.mf           0.02566    0.27838   0.092  0.92659   
## ModsC.d:gend.mf   0.31583    0.46414   0.680  0.49650   
## ModsSC.d:gend.mf -0.23066    0.52144  -0.442  0.65841   
## ModsL.d:gend.mf   0.51699    0.54568   0.947  0.34385   
## ModsSL.d:gend.mf  1.70161    0.64461   2.640  0.00854 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + 
##     ModsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -0.18291    0.13256  -1.380  0.16823   
## ModsC.d           0.65473    0.22102   2.962  0.00319 **
## ModsSC.d          0.24374    0.24831   0.982  0.32673   
## ModsL.d          -0.51899    0.25985  -1.997  0.04630 * 
## ModsSL.d         -0.86604    0.30696  -2.821  0.00496 **
## gend.mf          -0.01154    0.26513  -0.044  0.96531   
## ModsC.d:gend.mf   0.03564    0.44204   0.081  0.93577   
## ModsSC.d:gend.mf -0.02680    0.49661  -0.054  0.95699   
## ModsL.d:gend.mf   0.57324    0.51970   1.103  0.27052   
## ModsSL.d:gend.mf  1.18636    0.61392   1.932  0.05384 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.14176    0.12037  -1.178 0.239445    
## ModsC.d           0.58732    0.20069   2.926 0.003575 ** 
## ModsSC.d          0.21037    0.22547   0.933 0.351226    
## ModsL.d          -0.57436    0.23595  -2.434 0.015252 *  
## ModsSL.d         -0.99383    0.27873  -3.566 0.000396 ***
## gend.mf          -0.04352    0.24074  -0.181 0.856608    
## ModsC.d:gend.mf   0.42123    0.40139   1.049 0.294460    
## ModsSC.d:gend.mf  0.10074    0.45094   0.223 0.823304    
## ModsL.d:gend.mf   0.66875    0.47191   1.417 0.157033    
## ModsSL.d:gend.mf  1.18649    0.55746   2.128 0.033764 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

round(mean(d$def2minus1[d$ideology == "Moderate" & d$gend == "Female"], na.rm = T),2)

## [1] -0.12

round(mean(d$def2minus1[d$ideology == "Moderate" & d$gend == "Male"], na.rm = T),2)

## [1] -0.16

summary(lm(def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d)*gend.f, data = d)) 

## 
## Call:
## lm(formula = def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d) * 
##     gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.12000    0.11607  -1.034 0.301678    
## ModsC.d          0.37670    0.20143   1.870 0.062006 .  
## ModsSC.d         0.16000    0.24623   0.650 0.516094    
## ModsL.d         -0.90874    0.23264  -3.906 0.000106 ***
## ModsSL.d        -1.58707    0.29141  -5.446 7.87e-08 ***
## gend.f          -0.04352    0.24074  -0.181 0.856608    
## ModsC.d:gend.f   0.42123    0.40139   1.049 0.294460    
## ModsSC.d:gend.f  0.10074    0.45094   0.223 0.823304    
## ModsL.d:gend.f   0.66875    0.47191   1.417 0.157033    
## ModsSL.d:gend.f  1.18649    0.55746   2.128 0.033764 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

summary(lm(def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d)*gend.m, data = d)) 

## 
## Call:
## lm(formula = def2minus1 ~ (ModsC.d + ModsSC.d + ModsL.d + ModsSL.d) * 
##     gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.16352    0.21091  -0.775   0.4385  
## ModsC.d          0.79793    0.34719   2.298   0.0219 *
## ModsSC.d         0.26074    0.37779   0.690   0.4904  
## ModsL.d         -0.23999    0.41058  -0.585   0.5591  
## ModsSL.d        -0.40058    0.47523  -0.843   0.3997  
## gend.m           0.04352    0.24074   0.181   0.8566  
## ModsC.d:gend.m  -0.42123    0.40139  -1.049   0.2945  
## ModsSC.d:gend.m -0.10074    0.45094  -0.223   0.8233  
## ModsL.d:gend.m  -0.66875    0.47191  -1.417   0.1570  
## ModsSL.d:gend.m -1.18649    0.55746  -2.128   0.0338 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

4. Liberals

## rating types
summary(lm(capture_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d)) # 1 better captured than 2

## 
## Call:
## lm(formula = capture_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3051 -0.9170  0.0830  0.9054  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8442     0.2047  -4.125 4.29e-05 ***
## LibsSC.d      0.9388     0.2923   3.211  0.00140 ** 
## LibsC.d       1.1492     0.2631   4.368 1.50e-05 ***
## LibsM.d       0.7612     0.2366   3.218  0.00137 ** 
## LibsSL.d     -0.8154     0.3324  -2.453  0.01448 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.796 on 540 degrees of freedom
## Multiple R-squared:  0.08968,    Adjusted R-squared:  0.08293 
## F-statistic:  13.3 on 4 and 540 DF,  p-value: 2.423e-10

summary(lm(meaningful_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d)) # 1 more meaningful than 2

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0135 -0.8996  0.1004  0.9865  6.2979 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.9351     0.2041  -4.580 5.77e-06 ***
## LibsSC.d      0.9486     0.2916   3.253  0.00121 ** 
## LibsC.d       1.2317     0.2624   4.693 3.41e-06 ***
## LibsM.d       0.8346     0.2360   3.537  0.00044 ***
## LibsSL.d     -0.3628     0.3316  -1.094  0.27437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 540 degrees of freedom
## Multiple R-squared:  0.07117,    Adjusted R-squared:  0.06429 
## F-statistic: 10.34 on 4 and 540 DF,  p-value: 4.448e-08

summary(lm(value_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d)) # 1 more valuable than 2

## 
## Call:
## lm(formula = value_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8253 -0.6596  0.1747  0.8442  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8442     0.1928  -4.378 1.44e-05 ***
## LibsSC.d      0.9117     0.2754   3.310 0.000994 ***
## LibsC.d       1.3103     0.2479   5.286 1.81e-07 ***
## LibsM.d       0.6695     0.2229   3.004 0.002790 ** 
## LibsSL.d     -0.4963     0.3132  -1.585 0.113632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 540 degrees of freedom
## Multiple R-squared:  0.08893,    Adjusted R-squared:  0.08218 
## F-statistic: 13.18 on 4 and 540 DF,  p-value: 3.001e-10

summary(lm(def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), data = d)) # 1 preferred to 2

## 
## Call:
## lm(formula = def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d), 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3919 -0.5674  0.1194  0.8745  5.4527 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8745     0.1761  -4.967 9.15e-07 ***
## LibsSC.d      0.9330     0.2515   3.710 0.000229 ***
## LibsC.d       1.2304     0.2263   5.436 8.26e-08 ***
## LibsM.d       0.7551     0.2035   3.710 0.000229 ***
## LibsSL.d     -0.5582     0.2860  -1.952 0.051486 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 540 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.09714 
## F-statistic: 15.63 on 4 and 540 DF,  p-value: 4.117e-12

round(mean(d$def1ratings[d$ideology == "Liberal"], na.rm = T),2)

## [1] 5.32

round(mean(d$def2ratings[d$ideology == "Liberal"], na.rm = T),2)

## [1] 4.45

round(mean(d$def2minus1[d$ideology == "Liberal"],na.rm = T),2)

## [1] -0.87

a. Gender effects?

## rating types
summary(lm(capture_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.648820   0.235908  -2.750 0.006156 ** 
## LibsSC.d          0.816320   0.323681   2.522 0.011960 *  
## LibsC.d           1.136028   0.300834   3.776 0.000177 ***
## LibsM.d           0.513618   0.274285   1.873 0.061675 .  
## LibsSL.d         -0.845352   0.375575  -2.251 0.024804 *  
## gend.mf           0.771325   0.471817   1.635 0.102682    
## LibsSC.d:gend.mf -0.356325   0.647362  -0.550 0.582258    
## LibsC.d:gend.mf  -0.003805   0.601667  -0.006 0.994956    
## LibsM.d:gend.mf  -0.916015   0.548570  -1.670 0.095541 .  
## LibsSL.d:gend.mf -0.244518   0.751151  -0.326 0.744911    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.7976     0.2347  -3.399 0.000727 ***
## LibsSC.d           0.7751     0.3220   2.407 0.016404 *  
## LibsC.d            1.1753     0.2993   3.927 9.71e-05 ***
## LibsM.d            0.6905     0.2728   2.531 0.011671 *  
## LibsSL.d          -0.0660     0.3736  -0.177 0.859851    
## gend.mf            0.5426     0.4693   1.156 0.248111    
## LibsSC.d:gend.mf  -0.7477     0.6440  -1.161 0.246149    
## LibsC.d:gend.mf   -0.2012     0.5985  -0.336 0.736923    
## LibsM.d:gend.mf   -0.5170     0.5457  -0.947 0.343854    
## LibsSL.d:gend.mf   1.1846     0.7472   1.585 0.113463    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + 
##     LibsSL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.7019     0.2235  -3.141  0.00178 ** 
## LibsSC.d           0.7627     0.3066   2.487  0.01317 *  
## LibsC.d            1.1737     0.2850   4.118 4.42e-05 ***
## LibsM.d            0.5190     0.2599   1.997  0.04630 *  
## LibsSL.d          -0.3470     0.3558  -0.975  0.32982    
## gend.mf            0.5617     0.4470   1.257  0.20943    
## LibsSC.d:gend.mf  -0.6000     0.6133  -0.978  0.32833    
## LibsC.d:gend.mf   -0.5376     0.5700  -0.943  0.34603    
## LibsM.d:gend.mf   -0.5732     0.5197  -1.103  0.27052    
## LibsSL.d:gend.mf   0.6131     0.7116   0.862  0.38931    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.7161     0.2029  -3.529 0.000454 ***
## LibsSC.d           0.7847     0.2784   2.818 0.005008 ** 
## LibsC.d            1.1617     0.2588   4.489 8.78e-06 ***
## LibsM.d            0.5744     0.2360   2.434 0.015252 *  
## LibsSL.d          -0.4195     0.3231  -1.298 0.194749    
## gend.mf            0.6252     0.4059   1.540 0.124052    
## LibsSC.d:gend.mf  -0.5680     0.5569  -1.020 0.308215    
## LibsC.d:gend.mf   -0.2475     0.5176  -0.478 0.632685    
## LibsM.d:gend.mf   -0.6687     0.4719  -1.417 0.157033    
## LibsSL.d:gend.mf   0.5177     0.6462   0.801 0.423353    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

round(mean(d$def2minus1[d$ideology == "Liberal" & d$gend == "Female"], na.rm = T),2)

## [1] -1.03

round(mean(d$def2minus1[d$ideology == "Liberal" & d$gend == "Male"], na.rm = T),2)

## [1] -0.4

summary(lm(def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.f, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -1.0287     0.2016  -5.102 4.67e-07 ***
## LibsSC.d          1.0687     0.2963   3.607 0.000339 ***
## LibsC.d           1.2854     0.2603   4.939 1.05e-06 ***
## LibsM.d           0.9087     0.2326   3.906 0.000106 ***
## LibsSL.d         -0.6783     0.3348  -2.026 0.043258 *  
## gend.f            0.6252     0.4059   1.540 0.124052    
## LibsSC.d:gend.f  -0.5680     0.5569  -1.020 0.308215    
## LibsC.d:gend.f   -0.2475     0.5176  -0.478 0.632685    
## LibsM.d:gend.f   -0.6687     0.4719  -1.417 0.157033    
## LibsSL.d:gend.f   0.5177     0.6462   0.801 0.423353    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

summary(lm(def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d)*gend.m, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (LibsSC.d + LibsC.d + LibsM.d + LibsSL.d) * 
##     gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.4035     0.3523  -1.145   0.2525  
## LibsSC.d          0.5007     0.4715   1.062   0.2887  
## LibsC.d           1.0379     0.4474   2.320   0.0207 *
## LibsM.d           0.2400     0.4106   0.585   0.5591  
## LibsSL.d         -0.1606     0.5527  -0.291   0.7715  
## gend.m           -0.6252     0.4059  -1.540   0.1241  
## LibsSC.d:gend.m   0.5680     0.5569   1.020   0.3082  
## LibsC.d:gend.m    0.2475     0.5176   0.478   0.6327  
## LibsM.d:gend.m    0.6687     0.4719   1.417   0.1570  
## LibsSL.d:gend.m  -0.5177     0.6462  -0.801   0.4234  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

5. Strong Liberals

## rating types
summary(lm(capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d), data = d)) # 1 better captured than 2

## 
## Call:
## lm(formula = capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3051 -0.9170  0.0830  0.9054  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.6596     0.2619  -6.336 4.99e-10 ***
## SlibsSC.d     1.7542     0.3350   5.237 2.34e-07 ***
## SlibsC.d      1.9647     0.3098   6.343 4.78e-10 ***
## SlibsM.d      1.5766     0.2876   5.482 6.45e-08 ***
## SlibsL.d      0.8154     0.3324   2.453   0.0145 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.796 on 540 degrees of freedom
## Multiple R-squared:  0.08968,    Adjusted R-squared:  0.08293 
## F-statistic:  13.3 on 4 and 540 DF,  p-value: 2.423e-10

summary(lm(meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d), data = d)) # 1 more meaningful than 2

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0135 -0.8996  0.1004  0.9865  6.2979 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.2979     0.2613  -4.967 9.13e-07 ***
## SlibsSC.d     1.3114     0.3341   3.925 9.80e-05 ***
## SlibsC.d      1.5945     0.3090   5.160 3.47e-07 ***
## SlibsM.d      1.1974     0.2869   4.174 3.48e-05 ***
## SlibsL.d      0.3628     0.3316   1.094    0.274    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.791 on 540 degrees of freedom
## Multiple R-squared:  0.07117,    Adjusted R-squared:  0.06429 
## F-statistic: 10.34 on 4 and 540 DF,  p-value: 4.448e-08

summary(lm(value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d), data = d)) # 1 more valuable than 2

## 
## Call:
## lm(formula = value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8253 -0.6596  0.1747  0.8442  6.8442 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.3404     0.2468  -5.432 8.46e-08 ***
## SlibsSC.d     1.4080     0.3156   4.462 9.90e-06 ***
## SlibsC.d      1.8065     0.2918   6.190 1.19e-09 ***
## SlibsM.d      1.1658     0.2709   4.303 2.00e-05 ***
## SlibsL.d      0.4963     0.3132   1.585    0.114    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 540 degrees of freedom
## Multiple R-squared:  0.08893,    Adjusted R-squared:  0.08218 
## F-statistic: 13.18 on 4 and 540 DF,  p-value: 3.001e-10

summary(lm(def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d), data = d)) # 1 preferred to 2

## 
## Call:
## lm(formula = def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3919 -0.5674  0.1194  0.8745  5.4527 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.4326     0.2254  -6.357 4.38e-10 ***
## SlibsSC.d     1.4912     0.2882   5.175 3.23e-07 ***
## SlibsC.d      1.7886     0.2665   6.712 4.88e-11 ***
## SlibsM.d      1.3133     0.2474   5.308 1.62e-07 ***
## SlibsL.d      0.5582     0.2860   1.952   0.0515 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 540 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.09714 
## F-statistic: 15.63 on 4 and 540 DF,  p-value: 4.117e-12

round(mean(d$def1ratings[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 5.66

round(mean(d$def2ratings[d$ideology == "Strong Liberal"], na.rm = T),2)

## [1] 4.23

round(mean(d$def2minus1[d$ideology == "Strong Liberal"],na.rm = T),2)

## [1] -1.43

a. Gender effects?

## rating types
summary(lm(capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.4942     0.2922  -5.113 4.43e-07 ***
## SlibsSC.d           1.6617     0.3668   4.531 7.27e-06 ***
## SlibsC.d            1.9814     0.3468   5.714 1.84e-08 ***
## SlibsM.d            1.3590     0.3240   4.194 3.21e-05 ***
## SlibsL.d            0.8454     0.3756   2.251   0.0248 *  
## gend.mf             0.5268     0.5845   0.901   0.3678    
## SlibsSC.d:gend.mf  -0.1118     0.7335  -0.152   0.8789    
## SlibsC.d:gend.mf    0.2407     0.6935   0.347   0.7287    
## SlibsM.d:gend.mf   -0.6715     0.6480  -1.036   0.3006    
## SlibsL.d:gend.mf    0.2445     0.7512   0.326   0.7449    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)) # effect, larger in women

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.mf, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -0.8636     0.2907  -2.971  0.00310 ** 
## SlibsSC.d           0.8411     0.3648   2.305  0.02152 *  
## SlibsC.d            1.2413     0.3449   3.598  0.00035 ***
## SlibsM.d            0.7565     0.3223   2.347  0.01929 *  
## SlibsL.d            0.0660     0.3736   0.177  0.85985    
## gend.mf             1.7273     0.5814   2.971  0.00310 ** 
## SlibsSC.d:gend.mf  -1.9323     0.7297  -2.648  0.00833 ** 
## SlibsC.d:gend.mf   -1.3858     0.6899  -2.009  0.04507 *  
## SlibsM.d:gend.mf   -1.7016     0.6446  -2.640  0.00854 ** 
## SlibsL.d:gend.mf   -1.1846     0.7472  -1.585  0.11346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)) # effect, larger in women

## 
## Call:
## lm(formula = value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.0490     0.2769  -3.789 0.000169 ***
## SlibsSC.d           1.1098     0.3475   3.194 0.001487 ** 
## SlibsC.d            1.5208     0.3285   4.629 4.62e-06 ***
## SlibsM.d            0.8660     0.3070   2.821 0.004961 ** 
## SlibsL.d            0.3470     0.3558   0.975 0.329823    
## gend.mf             1.1748     0.5537   2.122 0.034325 *  
## SlibsSC.d:gend.mf  -1.2132     0.6949  -1.746 0.081439 .  
## SlibsC.d:gend.mf   -1.1507     0.6571  -1.751 0.080462 .  
## SlibsM.d:gend.mf   -1.1864     0.6139  -1.932 0.053836 .  
## SlibsL.d:gend.mf   -0.6131     0.7116  -0.862 0.389307    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.mf, data = d)) # effect, larger difference in women

## 
## Call:
## lm(formula = def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.1356     0.2514  -4.517 7.73e-06 ***
## SlibsSC.d           1.2042     0.3155   3.817 0.000151 ***
## SlibsC.d            1.5811     0.2983   5.300 1.70e-07 ***
## SlibsM.d            0.9938     0.2787   3.566 0.000396 ***
## SlibsL.d            0.4195     0.3231   1.298 0.194749    
## gend.mf             1.1430     0.5028   2.273 0.023411 *  
## SlibsSC.d:gend.mf  -1.0857     0.6310  -1.721 0.085905 .  
## SlibsC.d:gend.mf   -0.7653     0.5966  -1.283 0.200171    
## SlibsM.d:gend.mf   -1.1865     0.5575  -2.128 0.033764 *  
## SlibsL.d:gend.mf   -0.5177     0.6462  -0.801 0.423353    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

#####
# men
#####
summary(lm(capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.m, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1.2308     0.4950  -2.486 0.013218 *  
## SlibsSC.d          1.6058     0.6147   2.612 0.009244 ** 
## SlibsC.d           2.1017     0.5898   3.564 0.000399 ***
## SlibsM.d           1.0232     0.5524   1.852 0.064549 .  
## SlibsL.d           0.9676     0.6425   1.506 0.132632    
## gend.m            -0.5268     0.5845  -0.901 0.367822    
## SlibsSC.d:gend.m   0.1118     0.7335   0.152 0.878913    
## SlibsC.d:gend.m   -0.2407     0.6935  -0.347 0.728672    
## SlibsM.d:gend.m    0.6715     0.6480   1.036 0.300568    
## SlibsL.d:gend.m   -0.2445     0.7512  -0.326 0.744911    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.m, data = d)) # effect, larger in women

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.m, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       3.718e-14  4.924e-01   0.000  1.00000   
## SlibsSC.d        -1.250e-01  6.114e-01  -0.204  0.83809   
## SlibsC.d          5.484e-01  5.867e-01   0.935  0.35035   
## SlibsM.d         -9.434e-02  5.495e-01  -0.172  0.86376   
## SlibsL.d         -5.263e-01  6.391e-01  -0.824  0.41056   
## gend.m           -1.727e+00  5.814e-01  -2.971  0.00310 **
## SlibsSC.d:gend.m  1.932e+00  7.297e-01   2.648  0.00833 **
## SlibsC.d:gend.m   1.386e+00  6.899e-01   2.009  0.04507 * 
## SlibsM.d:gend.m   1.702e+00  6.446e-01   2.640  0.00854 **
## SlibsL.d:gend.m   1.185e+00  7.472e-01   1.585  0.11346   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.m, data = d)) # effect, larger in women

## 
## Call:
## lm(formula = value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      -0.46154    0.46900  -0.984   0.3255  
## SlibsSC.d         0.50321    0.58233   0.864   0.3879  
## SlibsC.d          0.94541    0.55875   1.692   0.0912 .
## SlibsM.d          0.27286    0.52336   0.521   0.6023  
## SlibsL.d          0.04049    0.60865   0.067   0.9470  
## gend.m           -1.17483    0.55372  -2.122   0.0343 *
## SlibsSC.d:gend.m  1.21316    0.69494   1.746   0.0814 .
## SlibsC.d:gend.m   1.15072    0.65705   1.751   0.0805 .
## SlibsM.d:gend.m   1.18636    0.61392   1.932   0.0538 .
## SlibsL.d:gend.m   0.61312    0.71162   0.862   0.3893  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.m, data = d))

## 
## Call:
## lm(formula = def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.5641     0.4259  -1.325   0.1859  
## SlibsSC.d          0.6613     0.5288   1.251   0.2116  
## SlibsC.d           1.1985     0.5074   2.362   0.0185 *
## SlibsM.d           0.4006     0.4752   0.843   0.3997  
## SlibsL.d           0.1606     0.5527   0.291   0.7715  
## gend.m            -1.1430     0.5028  -2.273   0.0234 *
## SlibsSC.d:gend.m   1.0857     0.6310   1.721   0.0859 .
## SlibsC.d:gend.m    0.7653     0.5966   1.283   0.2002  
## SlibsM.d:gend.m    1.1865     0.5575   2.128   0.0338 *
## SlibsL.d:gend.m    0.5177     0.6462   0.801   0.4234  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

#####
# women
#####
summary(lm(capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.f, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1034 -0.9371  0.0629  1.0400  7.0345 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1.7576     0.3107  -5.657 2.52e-08 ***
## SlibsSC.d          1.7176     0.4003   4.290 2.12e-05 ***
## SlibsC.d           1.8610     0.3649   5.100 4.73e-07 ***
## SlibsM.d           1.6947     0.3387   5.003 7.68e-07 ***
## SlibsL.d           0.7231     0.3892   1.858   0.0637 .  
## gend.f             0.5268     0.5845   0.901   0.3678    
## SlibsSC.d:gend.f  -0.1118     0.7335  -0.152   0.8789    
## SlibsC.d:gend.f    0.2407     0.6935   0.347   0.7287    
## SlibsM.d:gend.f   -0.6715     0.6480  -1.036   0.3006    
## SlibsL.d:gend.f    0.2445     0.7512   0.326   0.7449    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.785 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1007, Adjusted R-squared:  0.08556 
## F-statistic: 6.635 on 9 and 533 DF,  p-value: 5.124e-09

summary(lm(meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.f, data = d)) # effect, larger in women

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.f, data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.080 -0.880  0.120  1.069  6.120 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1.7273     0.3091  -5.588 3.66e-08 ***
## SlibsSC.d          1.8073     0.3982   4.538 7.01e-06 ***
## SlibsC.d           1.9342     0.3630   5.328 1.46e-07 ***
## SlibsM.d           1.6073     0.3370   4.770 2.38e-06 ***
## SlibsL.d           0.6583     0.3871   1.700  0.08964 .  
## gend.f             1.7273     0.5814   2.971  0.00310 ** 
## SlibsSC.d:gend.f  -1.9323     0.7297  -2.648  0.00833 ** 
## SlibsC.d:gend.f   -1.3858     0.6899  -2.009  0.04507 *  
## SlibsM.d:gend.f   -1.7016     0.6446  -2.640  0.00854 ** 
## SlibsL.d:gend.f   -1.1846     0.7472  -1.585  0.11346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.08726,    Adjusted R-squared:  0.07185 
## F-statistic: 5.662 on 9 and 533 DF,  p-value: 1.671e-07

summary(lm(value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.f, data = d)) # effect, larger in women

## 
## Call:
## lm(formula = value_2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8229 -0.6951  0.1771  0.9583  6.9828 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1.6364     0.2944  -5.559 4.29e-08 ***
## SlibsSC.d          1.7164     0.3793   4.526 7.43e-06 ***
## SlibsC.d           2.0961     0.3457   6.063 2.53e-09 ***
## SlibsM.d           1.4592     0.3209   4.547 6.74e-06 ***
## SlibsL.d           0.6536     0.3687   1.773   0.0769 .  
## gend.f             1.1748     0.5537   2.122   0.0343 *  
## SlibsSC.d:gend.f  -1.2132     0.6949  -1.746   0.0814 .  
## SlibsC.d:gend.f   -1.1507     0.6571  -1.751   0.0805 .  
## SlibsM.d:gend.f   -1.1864     0.6139  -1.932   0.0538 .  
## SlibsL.d:gend.f   -0.6131     0.7116  -0.862   0.3893    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09673,    Adjusted R-squared:  0.08148 
## F-statistic: 6.342 on 9 and 533 DF,  p-value: 1.466e-08

summary(lm(def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + SlibsL.d)*gend.f, data = d))

## 
## Call:
## lm(formula = def2minus1 ~ (SlibsSC.d + SlibsC.d + SlibsM.d + 
##     SlibsL.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3733 -0.6344  0.1200  0.7867  5.4969 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1.7071     0.2673  -6.387 3.70e-10 ***
## SlibsSC.d          1.7471     0.3444   5.073 5.41e-07 ***
## SlibsC.d           1.9638     0.3139   6.256 8.13e-10 ***
## SlibsM.d           1.5871     0.2914   5.446 7.87e-08 ***
## SlibsL.d           0.6783     0.3348   2.026   0.0433 *  
## gend.f             1.1430     0.5028   2.273   0.0234 *  
## SlibsSC.d:gend.f  -1.0857     0.6310  -1.721   0.0859 .  
## SlibsC.d:gend.f   -0.7653     0.5966  -1.283   0.2002    
## SlibsM.d:gend.f   -1.1865     0.5575  -2.128   0.0338 *  
## SlibsL.d:gend.f   -0.5177     0.6462  -0.801   0.4234    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.535 on 533 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1149, Adjusted R-squared:  0.09997 
## F-statistic: 7.689 on 9 and 533 DF,  p-value: 1.161e-10

describeBy(d$def1ratings[d$gend != "Other"], d$gend[d$gend != "Other"])

## 
##  Descriptive statistics by group 
## group: Female
##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 403 5.29 1.51   5.67    5.44 1.48   1   7     6 -0.72    -0.36 0.08
## ------------------------------------------------------------ 
## group: Male
##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 140 4.96 1.66      5    5.09 1.98   1   7     6 -0.47    -0.73 0.14

describeBy(d$def2ratings[d$gend != "Other"], d$gend[d$gend != "Other"])

## 
##  Descriptive statistics by group 
## group: Female
##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 403 5.01 1.78   5.33     5.2 1.98   1   7     6 -0.63    -0.67 0.09
## ------------------------------------------------------------ 
## group: Male
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 140 4.95 1.73      5    5.07 2.47   1   7     6 -0.4    -0.97 0.15

iii. Partisan ID

pr.rep.mod1 <- lmer(value ~ def.c * (RepD.d + RepI.d) + (1 | transaction_id) + (1 | persResType), data = dr)
summary(pr.rep.mod1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ def.c * (RepD.d + RepI.d) + (1 | transaction_id) + (1 |  
##     persResType)
##    Data: dr
## 
## REML criterion at convergence: 11194.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4499 -0.4309  0.0753  0.4846  3.4140 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  transaction_id (Intercept) 1.91152  1.3826  
##  persResType    (Intercept) 0.01002  0.1001  
##  Residual                   1.23376  1.1107  
## Number of obs: 3252, groups:  transaction_id, 542; persResType, 6
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     5.24419    0.11823  146.82263  44.355  < 2e-16 ***
## def.c           0.20155    0.10705    7.81836   1.883   0.0974 .  
## RepD.d         -0.21998    0.15536  539.00097  -1.416   0.1574    
## RepI.d         -0.23284    0.15295  539.00097  -1.522   0.1285    
## def.c:RepD.d   -0.77883    0.09683 2702.99979  -8.043 1.30e-15 ***
## def.c:RepI.d   -0.44064    0.09533 2702.99979  -4.622 3.98e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) def.c  RepD.d RepI.d d.:RD.
## def.c        0.000                            
## RepD.d      -0.670  0.000                     
## RepI.d      -0.681  0.000  0.518              
## def.c:RpD.d  0.000 -0.461  0.000  0.000       
## def.c:RpI.d  0.000 -0.469  0.000  0.000  0.518

summary(lm(def2minus1 ~ (RepD.d + RepI.d), data = d))

## 
## Call:
## lm(formula = def2minus1 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4276 -0.5349  0.2391  0.7984  5.5724 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2016     0.1220   1.653  0.09897 .  
## RepD.d       -0.7788     0.1708  -4.561 6.32e-06 ***
## RepI.d       -0.4406     0.1681  -2.621  0.00902 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.599 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03731,    Adjusted R-squared:  0.03374 
## F-statistic: 10.45 on 2 and 539 DF,  p-value: 3.542e-05

1. Republicans

Collapsing across attitude type, Republican marginally prefer definition 2 to definition 1.

summary(lm(capture_2minus1 ~ (RepD.d + RepI.d), data = d)) # no difference

## 
## Call:
## lm(formula = capture_2minus1 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.733 -0.733  0.267  0.843  6.531 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.1570     0.1420   1.106 0.269367    
## RepD.d       -0.6877     0.1988  -3.459 0.000585 ***
## RepI.d       -0.4240     0.1957  -2.166 0.030730 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.02206,    Adjusted R-squared:  0.01843 
## F-statistic: 6.078 on 2 and 539 DF,  p-value: 0.002452

summary(lm(meaningful_2minus1 ~ (RepD.d + RepI.d), data = d)) # marginal effect: 2 is more meaningful than 1

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2326 -0.7277  0.2723  0.7674  6.2723 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2326     0.1390   1.674  0.09478 .  
## RepD.d       -0.8192     0.1946  -4.210 2.99e-05 ***
## RepI.d       -0.5048     0.1916  -2.635  0.00865 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03233,    Adjusted R-squared:  0.02874 
## F-statistic: 9.003 on 2 and 539 DF,  p-value: 0.0001425

summary(lm(value_2minus1 ~ (RepD.d + RepI.d), data = d)) # no difference

## 
## Call:
## lm(formula = value_2minus1 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2151 -0.3855  0.1780  0.6145  6.6145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2151     0.1327   1.622    0.105    
## RepD.d       -0.8296     0.1858  -4.466  9.7e-06 ***
## RepI.d       -0.3931     0.1829  -2.150    0.032 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.74 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03575,    Adjusted R-squared:  0.03217 
## F-statistic: 9.992 on 2 and 539 DF,  p-value: 5.483e-05

summary(lm(def2minus1 ~ (RepD.d + RepI.d), data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (RepD.d + RepI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4276 -0.5349  0.2391  0.7984  5.5724 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2016     0.1220   1.653  0.09897 .  
## RepD.d       -0.7788     0.1708  -4.561 6.32e-06 ***
## RepI.d       -0.4406     0.1681  -2.621  0.00902 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.599 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03731,    Adjusted R-squared:  0.03374 
## F-statistic: 10.45 on 2 and 539 DF,  p-value: 3.542e-05

mean(d$persRes1_meaningful[d$party_factor == "Republican"], na.rm = T)

## [1] 5.040698

mean(d$persRes2_meaningful[d$party_factor == "Republican"], na.rm = T)

## [1] 5.273256

mean(d$def1ratings[d$party_factor == "Republican"], na.rm = T)

## [1] 5.143411

mean(d$def2ratings[d$party_factor == "Republican"], na.rm = T)

## [1] 5.344961

a. Gender effects?

summary(lm(capture_2minus1 ~ (RepD.d + RepI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5493 -0.5493  0.4118  0.7347  6.5794 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.21024    0.16825   1.250  0.21200   
## RepD.d         -0.70580    0.22764  -3.101  0.00203 **
## RepI.d         -0.30294    0.22746  -1.332  0.18349   
## gend.mf         0.19491    0.33649   0.579  0.56267   
## RepD.d:gend.mf -0.02731    0.45528  -0.060  0.95219   
## RepI.d:gend.mf  0.52110    0.45493   1.145  0.25253   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.848 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.03331,    Adjusted R-squared:  0.02426 
## F-statistic:  3.68 on 5 and 534 DF,  p-value: 0.002773

summary(lm(meaningful_2minus1 ~ (RepD.d + RepI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (RepD.d + RepI.d) * gend.mf, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2564 -0.5775  0.3725  0.7744  6.4225 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.24099    0.16481   1.462 0.144262    
## RepD.d         -0.76456    0.22299  -3.429 0.000653 ***
## RepI.d         -0.37062    0.22281  -1.663 0.096824 .  
## gend.mf         0.03085    0.32961   0.094 0.925475    
## RepD.d:gend.mf  0.27121    0.44597   0.608 0.543361    
## RepI.d:gend.mf  0.55495    0.44563   1.245 0.213555    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04136,    Adjusted R-squared:  0.03239 
## F-statistic: 4.608 on 5 and 534 DF,  p-value: 0.0003989

summary(lm(value_2minus1 ~ (RepD.d + RepI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2406 -0.5490  0.3099  0.6746  6.6746 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.1844     0.1582   1.166  0.24416    
## RepD.d          -0.7472     0.2140  -3.492  0.00052 ***
## RepI.d          -0.2373     0.2138  -1.110  0.26760    
## gend.mf         -0.1124     0.3163  -0.355  0.72248    
## RepD.d:gend.mf   0.3360     0.4280   0.785  0.43273    
## RepI.d:gend.mf   0.6263     0.4276   1.465  0.14362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.737 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04238,    Adjusted R-squared:  0.03342 
## F-statistic: 4.727 on 5 and 534 DF,  p-value: 0.0003104

summary(lm(def2minus1 ~ (RepD.d + RepI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (RepD.d + RepI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.21188    0.14452   1.466 0.143227    
## RepD.d         -0.73919    0.19554  -3.780 0.000174 ***
## RepI.d         -0.30362    0.19539  -1.554 0.120801    
## gend.mf         0.03779    0.28905   0.131 0.896039    
## RepD.d:gend.mf  0.19331    0.39108   0.494 0.621310    
## RepI.d:gend.mf  0.56746    0.39078   1.452 0.147053    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

mean(d$def2minus1[d$party_factor == "Republican" & d$gend == "Female"], na.rm = T)

## [1] 0.1929825

mean(d$def2minus1[d$party_factor == "Republican" & d$gend == "Male"], na.rm = T)

## [1] 0.2307692

round(mean(d$def1ratings[d$party_factor == "Republican" & d$gend == "Female"],na.rm = T),2)

## [1] 5.32

round(mean(d$def2ratings[d$party_factor == "Republican" & d$gend == "Female"],na.rm = T),2)

## [1] 5.52

round(mean(d$def1ratings[d$party_factor == "Republican" & d$gend == "Male"],na.rm = T),2)

## [1] 4.53

round(mean(d$def2ratings[d$party_factor == "Republican" & d$gend == "Male"],na.rm = T),2)

## [1] 4.76

summary(lm(def2minus1 ~ (RepD.d + RepI.d)*gend.f, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (RepD.d + RepI.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.19298    0.13764   1.402  0.16146    
## RepD.d        -0.83584    0.19733  -4.236 2.68e-05 ***
## RepI.d        -0.58735    0.19154  -3.066  0.00228 ** 
## gend.f         0.03779    0.28905   0.131  0.89604    
## RepD.d:gend.f  0.19331    0.39108   0.494  0.62131    
## RepI.d:gend.f  0.56746    0.39078   1.452  0.14705    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

summary(lm(def2minus1 ~ (RepD.d + RepI.d)*gend.m, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (RepD.d + RepI.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.23077    0.25417   0.908   0.3643  
## RepD.d        -0.64253    0.33765  -1.903   0.0576 .
## RepI.d        -0.01988    0.34062  -0.058   0.9535  
## gend.m        -0.03779    0.28905  -0.131   0.8960  
## RepD.d:gend.m -0.19331    0.39108  -0.494   0.6213  
## RepI.d:gend.m -0.56746    0.39078  -1.452   0.1471  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

round(mean(d$def1ratings[d$party_factor == "Independent" & d$gend == "Female"],na.rm = T),2)

## [1] 5.19

round(mean(d$def2ratings[d$party_factor == "Independent" & d$gend == "Female"],na.rm = T),2)

## [1] 4.8

round(mean(d$def1ratings[d$party_factor == "Independent" & d$gend == "Male"],na.rm = T),2)

## [1] 4.95

round(mean(d$def2ratings[d$party_factor == "Independent" & d$gend == "Male"],na.rm = T),2)

## [1] 5.16

summary(lm(def2minus1 ~ (IndD.d + IndR.d)*gend.f, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (IndD.d + IndR.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.3944     0.1332  -2.961  0.00321 **
## IndD.d         -0.2485     0.1943  -1.279  0.20141   
## IndR.d          0.5873     0.1915   3.066  0.00228 **
## gend.f          0.6053     0.2630   2.301  0.02175 * 
## IndD.d:gend.f  -0.3742     0.3722  -1.005  0.31528   
## IndR.d:gend.f  -0.5675     0.3908  -1.452  0.14705   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

summary(lm(def2minus1 ~ (IndD.d + IndR.d)*gend.m, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (IndD.d + IndR.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.21088    0.22676   0.930   0.3528  
## IndD.d        -0.62265    0.31752  -1.961   0.0504 .
## IndR.d         0.01988    0.34062   0.058   0.9535  
## gend.m        -0.60525    0.26299  -2.301   0.0218 *
## IndD.d:gend.m  0.37416    0.37224   1.005   0.3153  
## IndR.d:gend.m  0.56746    0.39078   1.452   0.1471  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

round(mean(d$def1ratings[d$party_factor == "Democrat" & d$gend == "Female"],na.rm = T),2)

## [1] 5.35

round(mean(d$def2ratings[d$party_factor == "Democrat" & d$gend == "Female"],na.rm = T),2)

## [1] 4.7

round(mean(d$def1ratings[d$party_factor == "Democrat" & d$gend == "Male"],na.rm = T),2)

## [1] 5.25

round(mean(d$def2ratings[d$party_factor == "Democrat" & d$gend == "Male"],na.rm = T),2)

## [1] 4.84

summary(lm(def2minus1 ~ (DemR.d + DemI.d)*gend.f, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (DemR.d + DemI.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.6429     0.1414  -4.546 6.77e-06 ***
## DemR.d          0.8358     0.1973   4.236 2.68e-05 ***
## DemI.d          0.2485     0.1943   1.279    0.201    
## gend.f          0.2311     0.2634   0.877    0.381    
## DemR.d:gend.f  -0.1933     0.3911  -0.494    0.621    
## DemI.d:gend.f   0.3742     0.3722   1.005    0.315    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

summary(lm(def2minus1 ~ (DemR.d + DemI.d)*gend.m, data = d)) # no difference

## 
## Call:
## lm(formula = def2minus1 ~ (DemR.d + DemI.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.4118     0.2223  -1.853   0.0645 .
## DemR.d          0.6425     0.3376   1.903   0.0576 .
## DemI.d          0.6226     0.3175   1.961   0.0504 .
## gend.m         -0.2311     0.2634  -0.877   0.3808  
## DemR.d:gend.m   0.1933     0.3911   0.494   0.6213  
## DemI.d:gend.m  -0.3742     0.3722  -1.005   0.3153  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

table(d$party_factor[d$gend != "Other"], d$gend[d$gend != "Other"])

##              
##               Female Male
##   Democrat       126   51
##   Independent    142   49
##   Republican     133   39

2. Independents

## rating types
summary(lm(capture_2minus1 ~ (IndD.d + IndR.d), data = d)) # 1 is better captured than 2

## 
## Call:
## lm(formula = capture_2minus1 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.733 -0.733  0.267  0.843  6.531 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.2670     0.1347  -1.982   0.0480 *
## IndD.d       -0.2637     0.1937  -1.361   0.1740  
## IndR.d        0.4240     0.1957   2.166   0.0307 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.02206,    Adjusted R-squared:  0.01843 
## F-statistic: 6.078 on 2 and 539 DF,  p-value: 0.002452

summary(lm(meaningful_2minus1 ~ (IndD.d + IndR.d), data = d)) # 1 is more meaningful than 2

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2326 -0.7277  0.2723  0.7674  6.2723 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.2723     0.1319  -2.065  0.03943 * 
## IndD.d       -0.3143     0.1896  -1.658  0.09787 . 
## IndR.d        0.5048     0.1916   2.635  0.00865 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03233,    Adjusted R-squared:  0.02874 
## F-statistic: 9.003 on 2 and 539 DF,  p-value: 0.0001425

summary(lm(value_2minus1 ~ (IndD.d + IndR.d), data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2151 -0.3855  0.1780  0.6145  6.6145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.1780     0.1259  -1.414   0.1579  
## IndD.d       -0.4365     0.1810  -2.412   0.0162 *
## IndR.d        0.3931     0.1829   2.150   0.0320 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.74 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03575,    Adjusted R-squared:  0.03217 
## F-statistic: 9.992 on 2 and 539 DF,  p-value: 5.483e-05

summary(lm(def2minus1 ~ (IndD.d + IndR.d), data = d)) # overall, 1 preferred to 2

## 
## Call:
## lm(formula = def2minus1 ~ (IndD.d + IndR.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4276 -0.5349  0.2391  0.7984  5.5724 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.2391     0.1157  -2.066  0.03931 * 
## IndD.d       -0.3382     0.1664  -2.033  0.04258 * 
## IndR.d        0.4406     0.1681   2.621  0.00902 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.599 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03731,    Adjusted R-squared:  0.03374 
## F-statistic: 10.45 on 2 and 539 DF,  p-value: 3.542e-05

round(mean(d$persRes1_capture[d$party_factor == "Independent"], na.rm = T),2)

## [1] 5.12

round(mean(d$persRes2_capture[d$party_factor == "Independent"], na.rm = T),2)

## [1] 4.85

round(mean(d$persRes1_meaningful[d$party_factor == "Independent"], na.rm = T),2)

## [1] 5.04

round(mean(d$persRes2_meaningful[d$party_factor == "Independent"], na.rm = T),2)

## [1] 4.77

round(mean(d$def1ratings[d$party_factor == "Independent"], na.rm = T),2)

## [1] 5.13

round(mean(d$def2ratings[d$party_factor == "Independent"], na.rm = T),2)

## [1] 4.89

a. Gender effects

## rating types
summary(lm(capture_2minus1 ~ (IndD.d + IndR.d)*gend.mf, data = d)) # difference bigger among women

## 
## Call:
## lm(formula = capture_2minus1 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5493 -0.5493  0.4118  0.7347  6.5794 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.0927     0.1531  -0.606   0.5451  
## IndD.d          -0.4029     0.2167  -1.859   0.0635 .
## IndR.d           0.3029     0.2275   1.332   0.1835  
## gend.mf          0.7160     0.3062   2.339   0.0197 *
## IndD.d:gend.mf  -0.5484     0.4333  -1.266   0.2062  
## IndR.d:gend.mf  -0.5211     0.4549  -1.145   0.2525  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.848 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.03331,    Adjusted R-squared:  0.02426 
## F-statistic:  3.68 on 5 and 534 DF,  p-value: 0.002773

summary(lm(meaningful_2minus1 ~ (IndD.d + IndR.d)*gend.mf, data = d)) # marginal effect: difference larger among women

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (IndD.d + IndR.d) * gend.mf, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2564 -0.5775  0.3725  0.7744  6.4225 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -0.1296     0.1499  -0.865   0.3877  
## IndD.d          -0.3939     0.2122  -1.856   0.0640 .
## IndR.d           0.3706     0.2228   1.663   0.0968 .
## gend.mf          0.5858     0.2999   1.953   0.0513 .
## IndD.d:gend.mf  -0.2837     0.4245  -0.668   0.5041  
## IndR.d:gend.mf  -0.5550     0.4456  -1.245   0.2136  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04136,    Adjusted R-squared:  0.03239 
## F-statistic: 4.608 on 5 and 534 DF,  p-value: 0.0003989

summary(lm(value_2minus1 ~ (IndD.d + IndR.d)*gend.mf, data = d)) # marginal effect: difference larger among women

## 
## Call:
## lm(formula = value_2minus1 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2406 -0.5490  0.3099  0.6746  6.6746 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.05289    0.14390  -0.368   0.7134  
## IndD.d         -0.50990    0.20368  -2.503   0.0126 *
## IndR.d          0.23729    0.21382   1.110   0.2676  
## gend.mf         0.51394    0.28780   1.786   0.0747 .
## IndD.d:gend.mf -0.29032    0.40736  -0.713   0.4764  
## IndR.d:gend.mf -0.62634    0.42765  -1.465   0.1436  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.737 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04238,    Adjusted R-squared:  0.03342 
## F-statistic: 4.727 on 5 and 534 DF,  p-value: 0.0003104

summary(lm(def2minus1 ~ (IndD.d + IndR.d)*gend.mf, data = d)) # difference bigger among women

## 
## Call:
## lm(formula = def2minus1 ~ (IndD.d + IndR.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.09174    0.13149  -0.698   0.4857  
## IndD.d         -0.43557    0.18612  -2.340   0.0196 *
## IndR.d          0.30362    0.19539   1.554   0.1208  
## gend.mf         0.60525    0.26299   2.301   0.0218 *
## IndD.d:gend.mf -0.37416    0.37224  -1.005   0.3153  
## IndR.d:gend.mf -0.56746    0.39078  -1.452   0.1471  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

#####
# men
#####

## rating types
summary(lm(capture_2minus1 ~ (IndD.d + IndR.d)*gend.m, data = d)) # difference bigger among women

## 
## Call:
## lm(formula = capture_2minus1 ~ (IndD.d + IndR.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5493 -0.5493  0.4118  0.7347  6.5794 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.26531    0.26398   1.005   0.3153  
## IndD.d        -0.67707    0.36965  -1.832   0.0676 .
## IndR.d         0.04239    0.39653   0.107   0.9149  
## gend.m        -0.71601    0.30616  -2.339   0.0197 *
## IndD.d:gend.m  0.54841    0.43334   1.266   0.2062  
## IndR.d:gend.m  0.52110    0.45493   1.145   0.2525  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.848 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.03331,    Adjusted R-squared:  0.02426 
## F-statistic:  3.68 on 5 and 534 DF,  p-value: 0.002773

summary(lm(meaningful_2minus1 ~ (IndD.d + IndR.d)*gend.m, data = d)) # marginal effect: difference larger among women

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (IndD.d + IndR.d) * gend.m, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2564 -0.5775  0.3725  0.7744  6.4225 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.16327    0.25858   0.631   0.5281  
## IndD.d        -0.53581    0.36209  -1.480   0.1395  
## IndR.d         0.09314    0.38843   0.240   0.8106  
## gend.m        -0.58580    0.29990  -1.953   0.0513 .
## IndD.d:gend.m  0.28375    0.42448   0.668   0.5041  
## IndR.d:gend.m  0.55495    0.44563   1.245   0.2136  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04136,    Adjusted R-squared:  0.03239 
## F-statistic: 4.608 on 5 and 534 DF,  p-value: 0.0003989

summary(lm(value_2minus1 ~ (IndD.d + IndR.d)*gend.m, data = d)) # marginal effect: difference larger among women

## 
## Call:
## lm(formula = value_2minus1 ~ (IndD.d + IndR.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2406 -0.5490  0.3099  0.6746  6.6746 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.20408    0.24815   0.822   0.4112  
## IndD.d        -0.65506    0.34748  -1.885   0.0599 .
## IndR.d        -0.07588    0.37276  -0.204   0.8388  
## gend.m        -0.51394    0.28780  -1.786   0.0747 .
## IndD.d:gend.m  0.29032    0.40736   0.713   0.4764  
## IndR.d:gend.m  0.62634    0.42765   1.465   0.1436  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.737 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04238,    Adjusted R-squared:  0.03342 
## F-statistic: 4.727 on 5 and 534 DF,  p-value: 0.0003104

summary(lm(def2minus1 ~ (IndD.d + IndR.d)*gend.m, data = d)) # difference bigger among women

## 
## Call:
## lm(formula = def2minus1 ~ (IndD.d + IndR.d) * gend.m, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.21088    0.22676   0.930   0.3528  
## IndD.d        -0.62265    0.31752  -1.961   0.0504 .
## IndR.d         0.01988    0.34062   0.058   0.9535  
## gend.m        -0.60525    0.26299  -2.301   0.0218 *
## IndD.d:gend.m  0.37416    0.37224   1.005   0.3153  
## IndR.d:gend.m  0.56746    0.39078   1.452   0.1471  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

#####
# wommen
#####

## rating types
summary(lm(capture_2minus1 ~ (IndD.d + IndR.d)*gend.f, data = d)) # difference bigger among women

## 
## Call:
## lm(formula = capture_2minus1 ~ (IndD.d + IndR.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5493 -0.5493  0.4118  0.7347  6.5794 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.4507     0.1551  -2.906  0.00381 **
## IndD.d         -0.1287     0.2262  -0.569  0.56966   
## IndR.d          0.5635     0.2230   2.527  0.01179 * 
## gend.f          0.7160     0.3062   2.339  0.01972 * 
## IndD.d:gend.f  -0.5484     0.4333  -1.266  0.20623   
## IndR.d:gend.f  -0.5211     0.4549  -1.145  0.25253   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.848 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.03331,    Adjusted R-squared:  0.02426 
## F-statistic:  3.68 on 5 and 534 DF,  p-value: 0.002773

summary(lm(meaningful_2minus1 ~ (IndD.d + IndR.d)*gend.f, data = d)) # marginal effect: difference larger among women

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (IndD.d + IndR.d) * gend.f, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2564 -0.5775  0.3725  0.7744  6.4225 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.4225     0.1519  -2.782  0.00560 **
## IndD.d         -0.2521     0.2215  -1.138  0.25570   
## IndR.d          0.6481     0.2184   2.967  0.00314 **
## gend.f          0.5858     0.2999   1.953  0.05130 . 
## IndD.d:gend.f  -0.2837     0.4245  -0.668  0.50413   
## IndR.d:gend.f  -0.5550     0.4456  -1.245  0.21355   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04136,    Adjusted R-squared:  0.03239 
## F-statistic: 4.608 on 5 and 534 DF,  p-value: 0.0003989

summary(lm(value_2minus1 ~ (IndD.d + IndR.d)*gend.f, data = d)) # marginal effect: difference larger among women

## 
## Call:
## lm(formula = value_2minus1 ~ (IndD.d + IndR.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2406 -0.5490  0.3099  0.6746  6.6746 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.3099     0.1458  -2.126  0.03399 * 
## IndD.d         -0.3647     0.2126  -1.716  0.08680 . 
## IndR.d          0.5505     0.2096   2.626  0.00888 **
## gend.f          0.5139     0.2878   1.786  0.07470 . 
## IndD.d:gend.f  -0.2903     0.4074  -0.713  0.47635   
## IndR.d:gend.f  -0.6263     0.4276  -1.465  0.14362   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.737 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04238,    Adjusted R-squared:  0.03342 
## F-statistic: 4.727 on 5 and 534 DF,  p-value: 0.0003104

summary(lm(def2minus1 ~ (IndD.d + IndR.d)*gend.f, data = d)) # difference bigger among women

## 
## Call:
## lm(formula = def2minus1 ~ (IndD.d + IndR.d) * gend.f, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -0.3944     0.1332  -2.961  0.00321 **
## IndD.d         -0.2485     0.1943  -1.279  0.20141   
## IndR.d          0.5873     0.1915   3.066  0.00228 **
## gend.f          0.6053     0.2630   2.301  0.02175 * 
## IndD.d:gend.f  -0.3742     0.3722  -1.005  0.31528   
## IndR.d:gend.f  -0.5675     0.3908  -1.452  0.14705   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

d$def2minus1_meaningful <- d$persRes2_meaningful - d$persRes1_meaningful
d$def2minus1_value <- d$persRes2_value - d$persRes1_value

round(mean(d$def2minus1_value[d$party_factor == "Independent" & d$gend == "Female"], na.rm = T),2)

## [1] -0.31

round(mean(d$def2minus1_value[d$party_factor == "Independent" & d$gend == "Male"], na.rm = T),2)

## [1] 0.2

round(mean(d$def2minus1_meaningful[d$party_factor == "Independent" & d$gend == "Female"], na.rm = T),2)

## [1] -0.42

round(mean(d$def2minus1_meaningful[d$party_factor == "Independent" & d$gend == "Male"], na.rm = T),2)

## [1] 0.16

round(mean(d$def2minus1_capture[d$party_factor == "Independent" & d$gend == "Female"], na.rm = T),2)

## [1] -0.45

round(mean(d$def2minus1_capture[d$party_factor == "Independent" & d$gend == "Male"], na.rm = T),2)

## [1] 0.27

round(mean(d$def2minus1[d$party_factor == "Independent" & d$gend == "Female"], na.rm = T),2)

## [1] -0.39

round(mean(d$def2minus1[d$party_factor == "Independent" & d$gend == "Male"], na.rm = T),2)

## [1] 0.21

3. Democrats

## rating types
summary(lm(capture_2minus1 ~ (DemR.d + DemI.d), data = d)) # 1 preferred to 2

## 
## Call:
## lm(formula = capture_2minus1 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.733 -0.733  0.267  0.843  6.531 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.5307     0.1392  -3.813 0.000153 ***
## DemR.d        0.6877     0.1988   3.459 0.000585 ***
## DemI.d        0.2637     0.1937   1.361 0.173950    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.02206,    Adjusted R-squared:  0.01843 
## F-statistic: 6.078 on 2 and 539 DF,  p-value: 0.002452

summary(lm(meaningful_2minus1 ~ (DemR.d + DemI.d), data = d)) # 1 preferred to 2

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2326 -0.7277  0.2723  0.7674  6.2723 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.5866     0.1362  -4.307 1.97e-05 ***
## DemR.d        0.8192     0.1946   4.210 2.99e-05 ***
## DemI.d        0.3143     0.1896   1.658   0.0979 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03233,    Adjusted R-squared:  0.02874 
## F-statistic: 9.003 on 2 and 539 DF,  p-value: 0.0001425

summary(lm(value_2minus1 ~ (DemR.d + DemI.d), data = d)) # 1 preferred to 2

## 
## Call:
## lm(formula = value_2minus1 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2151 -0.3855  0.1780  0.6145  6.6145 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.6145     0.1300  -4.726 2.93e-06 ***
## DemR.d        0.8296     0.1858   4.466 9.70e-06 ***
## DemI.d        0.4365     0.1810   2.412   0.0162 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.74 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03575,    Adjusted R-squared:  0.03217 
## F-statistic: 9.992 on 2 and 539 DF,  p-value: 5.483e-05

summary(lm(def2minus1 ~ (DemR.d + DemI.d), data = d)) # 1 preferred to 2

## 
## Call:
## lm(formula = def2minus1 ~ (DemR.d + DemI.d), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4276 -0.5349  0.2391  0.7984  5.5724 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.5773     0.1195  -4.829 1.79e-06 ***
## DemR.d        0.7788     0.1708   4.561 6.32e-06 ***
## DemI.d        0.3382     0.1664   2.033   0.0426 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.599 on 539 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03731,    Adjusted R-squared:  0.03374 
## F-statistic: 10.45 on 2 and 539 DF,  p-value: 3.542e-05

round(mean(d$def1ratings[d$party_factor == "Democrat"], na.rm = T),2)

## [1] 5.31

round(mean(d$def2ratings[d$party_factor == "Democrat"], na.rm = T),2)

## [1] 4.74

round(mean(d$def2minus1[d$party_factor == "Democrat"],na.rm = T),2)

## [1] -0.58

a. Gender effects?

## rating types
summary(lm(capture_2minus1 ~ (DemR.d + DemI.d*gend.mf), data = d)) # no effect

## 
## Call:
## lm(formula = capture_2minus1 ~ (DemR.d + DemI.d * gend.mf), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5493 -0.5493  0.4029  0.7347  6.5829 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.4929     0.1468  -3.357 0.000843 ***
## DemR.d           0.6991     0.1982   3.528 0.000455 ***
## DemI.d           0.4002     0.2120   1.888 0.059584 .  
## gend.mf          0.1800     0.2265   0.795 0.427062    
## DemI.d:gend.mf   0.5360     0.3806   1.408 0.159581    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.846 on 535 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.03331,    Adjusted R-squared:  0.02608 
## F-statistic: 4.608 on 4 and 535 DF,  p-value: 0.001151

summary(lm(meaningful_2minus1 ~ (DemR.d + DemI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = meaningful_2minus1 ~ (DemR.d + DemI.d) * gend.mf, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2564 -0.5775  0.3725  0.7744  6.4225 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.5236     0.1502  -3.486 0.000531 ***
## DemR.d           0.7646     0.2230   3.429 0.000653 ***
## DemI.d           0.3939     0.2122   1.856 0.063989 .  
## gend.mf          0.3021     0.3004   1.005 0.315124    
## DemR.d:gend.mf  -0.2712     0.4460  -0.608 0.543361    
## DemI.d:gend.mf   0.2837     0.4245   0.668 0.504132    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.81 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04136,    Adjusted R-squared:  0.03239 
## F-statistic: 4.608 on 5 and 534 DF,  p-value: 0.0003989

summary(lm(value_2minus1 ~ (DemR.d + DemI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = value_2minus1 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2406 -0.5490  0.3099  0.6746  6.6746 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.5628     0.1441  -3.904 0.000107 ***
## DemR.d           0.7472     0.2140   3.492 0.000520 ***
## DemI.d           0.5099     0.2037   2.503 0.012595 *  
## gend.mf          0.2236     0.2883   0.776 0.438278    
## DemR.d:gend.mf  -0.3360     0.4280  -0.785 0.432726    
## DemI.d:gend.mf   0.2903     0.4074   0.713 0.476350    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.737 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04238,    Adjusted R-squared:  0.03342 
## F-statistic: 4.727 on 5 and 534 DF,  p-value: 0.0003104

summary(lm(def2minus1 ~ (DemR.d + DemI.d)*gend.mf, data = d)) # no effect

## 
## Call:
## lm(formula = def2minus1 ~ (DemR.d + DemI.d) * gend.mf, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5641  0.1404  0.7277  5.3944 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.5273     0.1317  -4.003 7.13e-05 ***
## DemR.d           0.7392     0.1955   3.780 0.000174 ***
## DemI.d           0.4356     0.1861   2.340 0.019637 *  
## gend.mf          0.2311     0.2634   0.877 0.380761    
## DemR.d:gend.mf  -0.1933     0.3911  -0.494 0.621310    
## DemI.d:gend.mf   0.3742     0.3722   1.005 0.315276    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 534 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.04836,    Adjusted R-squared:  0.03945 
## F-statistic: 5.427 on 5 and 534 DF,  p-value: 7.005e-05

round(mean(d$def1ratings[d$party_factor == "Democrat"], na.rm = T),2)

## [1] 5.31

round(mean(d$def2ratings[d$party_factor == "Democrat"], na.rm = T),2)

## [1] 4.74

round(mean(d$def2minus1[d$party_factor == "Democrat"],na.rm = T),2)

## [1] -0.58

SUMMARIES

Actions Summary:

Ideology

Strong Liberals

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 11	Drive less by grouping multiple errands into one trip	1.54
Action 29	Purchase Energy Star electronic goods	1.48
Action 27	Lower/raise thermostat in winter/summer	1.39
Action 25	Choose renewable energy through your utility	1.34
Action 19	Recycle aluminum and glass containers	1.30
Action 17	Join your utility’s smart metering program	1.21
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.19
Action 28	Seal leaks in your home air duct system	1.19
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	1.15
Action 22	Implement low-flow showerheads and faucets in your home	1.14
Action 30	Purchase Energy Star appliances	1.14
Action 2	Replace incandescent lightbulbs with LEDs	1.04
Action 16	Perform annual HVAC maintenance	1.03
Action 20	Meal plan and store food property to limit food waste	1.00
Action 26	Turn off lights, fans when you leave home	0.95
Action 18	Set water heater to medium or standard setting	0.76

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	24	4
Action 2	Replace incandescent lightbulbs with LEDs	21	2
Action 5	Use cold water to wash clothes	20	3
Action 20	Meal plan and store food property to limit food waste	19	1
Action 19	Recycle aluminum and glass containers	18	2
Action 11	Drive less by grouping multiple errands into one trip	17	6
Action 29	Purchase Energy Star electronic goods	15	3
Action 30	Purchase Energy Star appliances	15	4
Action 27	Lower/raise thermostat in winter/summer	14	2
Action 15	Eat one less portion of red meat each week	13	2
Action 16	Perform annual HVAC maintenance	12	6
Action 18	Set water heater to medium or standard setting	12	6
Action 21	Compost your food scraps	12	2
Action 6	Caulk and weather-strip windows and doors; increase home insulation	11	4
Action 28	Seal leaks in your home air duct system	9	7
Action 4	Replace current stove with electric or convection stove	8	10
Action 12	Change from a gas lawn mower to electric mower	8	12
Action 17	Join your utility’s smart metering program	8	5
Action 25	Choose renewable energy through your utility	8	4
Action 13	Walk, bike, or take public transit instead of driving	7	5
Action 14	Take one less domestic flight each year	6	19
Action 22	Implement low-flow showerheads and faucets in your home	6	4
Action 24	Line dry your laundry instead of using dryer	6	4
Action 23	Work from home at least 2 days a week	5	17
Action 1	Install rooftop solar panels on your home	4	8
Action 3	Replace current clothes dryer with electric heat pump dryer	3	9
Action 7	Replace current HVAC system with electric heat pump	3	9
Action 9	Replace current water heater with heat pump water heater	3	8
Action 8	Choose an electric vehicle the next time you buy a vehicle	1	6
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	1	6

Condition Effects

None.

Gender Effects

Action 1

## [1] "Install rooftop solar panels on your home"

gender	act1_mean	act1_n
Female	0.65	23
Male	-1.00	11

Action 10

## [1] "Choose a hybrid vehicle the next time you buy a vehicle"

gender	act10_mean	act10_n
Female	1.59	29
Male	-0.10	10

Gender x Condition Effects

Action 1

## [1] "Install rooftop solar panels on your home"

cond	gender	act1_mean	act1_n
climate	Female	-0.62	8
climate	Male	0.20	5
ctrl	Female	1.33	15
ctrl	Male	-2.00	6

Action 13

## [1] "Walk, bike, or take public transit instead of driving"

cond	gender	act13_mean	act13_n
climate	Female	-0.38	8
climate	Male	0.83	6
ctrl	Female	1.13	15
ctrl	Male	-1.80	5

Action 16

## [1] "Perform annual HVAC maintenance"

cond	gender	act16_mean	act16_n
climate	Female	-0.50	4
climate	Male	1.50	6
ctrl	Female	1.40	15
ctrl	Male	-0.33	3

Action 17

## [1] "Join your utility's smart metering program"

cond	gender	act17_mean	act17_n
climate	Female	1.38	8
climate	Male	2.17	6
ctrl	Female	1.40	15
ctrl	Male	-1.25	4

Action 18

## [1] "Set water heater to medium or standard setting"

cond	gender	act18_mean	act18_n
climate	Female	-0.17	6
climate	Male	2.00	5
ctrl	Female	0.92	13
ctrl	Male	-0.25	4

Liberals

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 11	Drive less by grouping multiple errands into one trip	1.66
Action 2	Replace incandescent lightbulbs with LEDs	1.57
Action 19	Recycle aluminum and glass containers	1.44
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.43
Action 5	Use cold water to wash clothes	1.40
Action 26	Turn off lights, fans when you leave home	1.26
Action 20	Meal plan and store food property to limit food waste	1.20
Action 27	Lower/raise thermostat in winter/summer	1.18
Action 30	Purchase Energy Star appliances	1.18
Action 25	Choose renewable energy through your utility	1.13
Action 16	Perform annual HVAC maintenance	1.07
Action 28	Seal leaks in your home air duct system	1.00
Action 29	Purchase Energy Star electronic goods	0.98
Action 14	Take one less domestic flight each year	0.95
Action 18	Set water heater to medium or standard setting	0.91
Action 15	Eat one less portion of red meat each week	0.89
Action 12	Change from a gas lawn mower to electric mower	0.74
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	0.68
Action 1	Install rooftop solar panels on your home	0.65
Action 22	Implement low-flow showerheads and faucets in your home	0.59
Action 9	Replace current water heater with heat pump water heater	0.58
Action 17	Join your utility’s smart metering program	0.55

Already adopted actions or NA

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	44	2
Action 19	Recycle aluminum and glass containers	41	4
Action 2	Replace incandescent lightbulbs with LEDs	33	2
Action 5	Use cold water to wash clothes	33	2
Action 11	Drive less by grouping multiple errands into one trip	31	5
Action 20	Meal plan and store food property to limit food waste	31	0
Action 27	Lower/raise thermostat in winter/summer	30	2
Action 16	Perform annual HVAC maintenance	28	6
Action 6	Caulk and weather-strip windows and doors; increase home insulation	27	4
Action 15	Eat one less portion of red meat each week	27	5
Action 30	Purchase Energy Star appliances	23	3
Action 14	Take one less domestic flight each year	20	18
Action 29	Purchase Energy Star electronic goods	20	2
Action 18	Set water heater to medium or standard setting	18	2
Action 22	Implement low-flow showerheads and faucets in your home	17	4
Action 4	Replace current stove with electric or convection stove	15	4
Action 17	Join your utility’s smart metering program	15	2
Action 24	Line dry your laundry instead of using dryer	14	5
Action 21	Compost your food scraps	13	5
Action 28	Seal leaks in your home air duct system	13	9
Action 23	Work from home at least 2 days a week	11	27
Action 25	Choose renewable energy through your utility	10	4
Action 13	Walk, bike, or take public transit instead of driving	9	4
Action 7	Replace current HVAC system with electric heat pump	8	10
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	7	5
Action 1	Install rooftop solar panels on your home	6	6
Action 9	Replace current water heater with heat pump water heater	6	7
Action 8	Choose an electric vehicle the next time you buy a vehicle	5	9
Action 3	Replace current clothes dryer with electric heat pump dryer	3	6
Action 12	Change from a gas lawn mower to electric mower	3	21

Condition Effects

Action 3:

## [1] "Replace current clothes dryer with electric heat pump dryer"

cond	act3_mean	act3_n
climate	0.41	29
ctrl	-0.64	39

Supported in climate condition (M = 0.41), opposed in control (M = -0.64). Not significantly supported in climate condition (p = .269), but significantly opposed in control condition (p = .048).

Action 15:

## [1] "Eat one less portion of red meat each week"

cond	act15_mean	act15_n
climate	0.05	20
ctrl	1.56	25

Climate condition close to 0 (M = 0.05), strongly supported in control condition (M = 1.56). Different from 0 in control condition, not different in climate condition.

Gender Effects

Action 12

## [1] "Change from a gas lawn mower to electric mower"

gender	act12_mean	act12_n
Female	0.41	41
Male	1.83	12

Gender x Condition Effects

Action 22

## [1] "Implement low-flow showerheads and faucets in your home"

cond	gender	act22_mean	act22_n
climate	Female	1.11	18
climate	Male	-0.17	6
ctrl	Female	0.16	25
ctrl	Male	1.43	7

Moderates

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 26	Turn off lights, fans when you leave home	1.62
Action 20	Meal plan and store food property to limit food waste	1.36
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.31
Action 19	Recycle aluminum and glass containers	1.23
Action 2	Replace incandescent lightbulbs with LEDs	1.22
Action 29	Purchase Energy Star electronic goods	1.14
Action 5	Use cold water to wash clothes	1.09
Action 30	Purchase Energy Star appliances	1.04
Action 28	Seal leaks in your home air duct system	0.97
Action 16	Perform annual HVAC maintenance	0.96
Action 25	Choose renewable energy through your utility	0.89
Action 18	Set water heater to medium or standard setting	0.82
Action 11	Drive less by grouping multiple errands into one trip	0.73
Action 27	Lower/raise thermostat in winter/summer	0.55
Action 17	Join your utility’s smart metering program	0.48
Action 14	Take one less domestic flight each year	0.42

Most Opposed Actions

Action.Number	Action.Name	Support.Mean	Below.Neg0.5
Action 8	Choose an electric vehicle the next time you buy a vehicle	-0.44	No

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	136	4
Action 5	Use cold water to wash clothes	113	7
Action 2	Replace incandescent lightbulbs with LEDs	112	4
Action 19	Recycle aluminum and glass containers	106	10
Action 27	Lower/raise thermostat in winter/summer	93	9
Action 20	Meal plan and store food property to limit food waste	92	8
Action 11	Drive less by grouping multiple errands into one trip	80	14
Action 30	Purchase Energy Star appliances	73	18
Action 18	Set water heater to medium or standard setting	71	17
Action 29	Purchase Energy Star electronic goods	68	14
Action 6	Caulk and weather-strip windows and doors; increase home insulation	64	23
Action 16	Perform annual HVAC maintenance	61	28
Action 15	Eat one less portion of red meat each week	54	9
Action 4	Replace current stove with electric or convection stove	47	22
Action 21	Compost your food scraps	47	15
Action 22	Implement low-flow showerheads and faucets in your home	45	16
Action 28	Seal leaks in your home air duct system	45	37
Action 24	Line dry your laundry instead of using dryer	39	16
Action 14	Take one less domestic flight each year	32	69
Action 23	Work from home at least 2 days a week	32	74
Action 17	Join your utility’s smart metering program	28	28
Action 12	Change from a gas lawn mower to electric mower	22	49
Action 13	Walk, bike, or take public transit instead of driving	21	20
Action 3	Replace current clothes dryer with electric heat pump dryer	18	27
Action 7	Replace current HVAC system with electric heat pump	17	34
Action 25	Choose renewable energy through your utility	17	26
Action 1	Install rooftop solar panels on your home	11	34
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	10	20
Action 9	Replace current water heater with heat pump water heater	9	30
Action 8	Choose an electric vehicle the next time you buy a vehicle	8	20

Condition Effects

Action 24:

## [1] "Line dry your laundry instead of using dryer"

cond	act24_mean	act24_n
climate	-0.57	82
ctrl	0.08	92

Opposed in climate condition (M = -0.57), close to 0 in control condition (M = 0.08). Different from 0 in climate condition, not different in control condition.

Gender Effects

Action 1

## [1] "Install rooftop solar panels on your home"

gender	act1_mean	act1_n
Female	-0.23	137
Male	0.50	46

Action 15

## [1] "Eat one less portion of red meat each week"

gender	act15_mean	act15_n
Female	0.31	120
Male	-0.44	45

Gender x Condition Effects

None

Conservatives

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 26	Turn off lights, fans when you leave home	1.57
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.33
Action 28	Seal leaks in your home air duct system	1.32
Action 20	Meal plan and store food property to limit food waste	1.13
Action 19	Recycle aluminum and glass containers	1.05
Action 29	Purchase Energy Star electronic goods	1.02
Action 30	Purchase Energy Star appliances	1.01
Action 16	Perform annual HVAC maintenance	0.99
Action 5	Use cold water to wash clothes	0.97
Action 2	Replace incandescent lightbulbs with LEDs	0.89
Action 11	Drive less by grouping multiple errands into one trip	0.82
Action 27	Lower/raise thermostat in winter/summer	0.72
Action 25	Choose renewable energy through your utility	0.50
Action 18	Set water heater to medium or standard setting	0.49

Most Opposed Actions

Action.Number	Action.Name	Support.Mean	Below.Neg0.5
Action 8	Choose an electric vehicle the next time you buy a vehicle	-0.71	No
Action 12	Change from a gas lawn mower to electric mower	-0.60	No
Action 24	Line dry your laundry instead of using dryer	-0.50	No
Action 1	Install rooftop solar panels on your home	-0.49	No
Action 3	Replace current clothes dryer with electric heat pump dryer	-0.42	No

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	65	2
Action 19	Recycle aluminum and glass containers	54	4
Action 2	Replace incandescent lightbulbs with LEDs	50	3
Action 27	Lower/raise thermostat in winter/summer	49	5
Action 20	Meal plan and store food property to limit food waste	48	1
Action 11	Drive less by grouping multiple errands into one trip	46	7
Action 5	Use cold water to wash clothes	44	1
Action 18	Set water heater to medium or standard setting	38	6
Action 4	Replace current stove with electric or convection stove	32	13
Action 29	Purchase Energy Star electronic goods	32	3
Action 15	Eat one less portion of red meat each week	30	5
Action 30	Purchase Energy Star appliances	29	9
Action 16	Perform annual HVAC maintenance	28	18
Action 21	Compost your food scraps	28	8
Action 22	Implement low-flow showerheads and faucets in your home	28	8
Action 6	Caulk and weather-strip windows and doors; increase home insulation	25	8
Action 24	Line dry your laundry instead of using dryer	23	3
Action 17	Join your utility’s smart metering program	21	8
Action 13	Walk, bike, or take public transit instead of driving	17	9
Action 28	Seal leaks in your home air duct system	17	17
Action 14	Take one less domestic flight each year	16	43
Action 7	Replace current HVAC system with electric heat pump	13	19
Action 12	Change from a gas lawn mower to electric mower	13	15
Action 23	Work from home at least 2 days a week	9	44
Action 3	Replace current clothes dryer with electric heat pump dryer	8	14
Action 9	Replace current water heater with heat pump water heater	7	12
Action 25	Choose renewable energy through your utility	6	10
Action 1	Install rooftop solar panels on your home	5	13
Action 8	Choose an electric vehicle the next time you buy a vehicle	4	6
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	3	10

Condition Effects

Action 8:

## [1] "Choose an electric vehicle the next time you buy a vehicle"

cond	act8_mean	act8_n
climate	-1.13	63
ctrl	-0.13	45

Opposed in both conditions, more strongly in climate condition than in control condition. Not different from 0 in ctrl; different in climate.

Action 13:

## [1] "Walk, bike, or take public transit instead of driving"

cond	act13_mean	act13_n
climate	-0.61	51
ctrl	0.27	41

Supported in control condition, opposed in climate condition. Ctrl not different from 0, so not significantly supported; climate different from 0, so significantly opposed.

Action 17:

## [1] "Join your utility's smart metering program"

cond	act17_mean	act17_n
climate	-0.17	53
ctrl	0.67	36

Supported in control condition, opposed in climate condition. Ctrl different from 0, so significantly supported; climate not different from 0, so not significantly opposed.

Action 19:

## [1] "Recycle aluminum and glass containers"

cond	act19_mean	act19_n
climate	0.69	36
ctrl	1.58	24

Both higher than 0, ctrl > climate.

Gender effects

Action 14

## [1] "Take one less domestic flight each year"

gender	act14_mean	act14_n
Female	0.78	41
Male	-0.72	18

Gender x Condition Effects

Action 7

## [1] "Replace current HVAC system with electric heat pump"

cond	gender	act7_mean	act7_n
climate	Female	0.15	34
climate	Male	-1.14	14
ctrl	Female	-0.60	30
ctrl	Male	0.25	8

Action 8

## [1] "Choose an electric vehicle the next time you buy a vehicle"

cond	gender	act8_mean	act8_n
climate	Female	-0.86	44
climate	Male	-1.74	19
ctrl	Female	-0.47	36
ctrl	Male	1.22	9

Action 9

## [1] "Replace current water heater with heat pump water heater"

cond	gender	act9_mean	act9_n
climate	Female	-0.24	38
climate	Male	-0.78	18
ctrl	Female	-0.35	34
ctrl	Male	1.11	9

Action 12

## [1] "Change from a gas lawn mower to electric mower"

cond	gender	act12_mean	act12_n
climate	Female	-0.20	35
climate	Male	-2.12	16
ctrl	Female	-0.53	32
ctrl	Male	0.57	7

Action 15

## [1] "Eat one less portion of red meat each week"

cond	gender	act15_mean	act15_n
climate	Female	0.53	30
climate	Male	-0.88	17
ctrl	Female	0.04	26
ctrl	Male	0.70	10

Action 24

## [1] "Line dry your laundry instead of using dryer"

cond	gender	act24_mean	act24_n
climate	Female	-0.14	36
climate	Male	-1.67	18
ctrl	Female	-0.64	28
ctrl	Male	0.70	10

Action 26

## [1] "Turn off lights, fans when you leave home"

cond	gender	act26_mean	act26_n
climate	Female	2.33	12
climate	Male	0.80	10
ctrl	Female	1.29	21
ctrl	Male	2.12	8

Action 27

## [1] "Lower/raise thermostat in winter/summer"

cond	gender	act27_mean	act27_n
climate	Female	0.95	21
climate	Male	0.07	14
ctrl	Female	0.40	20
ctrl	Male	1.89	9

Strong Conservatives

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 26	Turn off lights, fans when you leave home	1.54
Action 20	Meal plan and store food property to limit food waste	1.15
Action 30	Purchase Energy Star appliances	1.14
Action 29	Purchase Energy Star electronic goods	1.09
Action 2	Replace incandescent lightbulbs with LEDs	0.96
Action 19	Recycle aluminum and glass containers	0.91
Action 6	Caulk and weather-strip windows and doors; increase home insulation	0.87
Action 28	Seal leaks in your home air duct system	0.79
Action 5	Use cold water to wash clothes	0.73
Action 11	Drive less by grouping multiple errands into one trip	0.73
Action 16	Perform annual HVAC maintenance	0.56
Action 27	Lower/raise thermostat in winter/summer	0.44

Most Opposed Actions

Action.Number	Action.Name	Support.Mean	Below.Neg0.5
Action 8	Choose an electric vehicle the next time you buy a vehicle	-1.14	Yes
Action 3	Replace current clothes dryer with electric heat pump dryer	-0.89	No
Action 12	Change from a gas lawn mower to electric mower	-0.84	No
Action 9	Replace current water heater with heat pump water heater	-0.72	No
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	-0.62	No
Action 13	Walk, bike, or take public transit instead of driving	-0.58	No
Action 21	Compost your food scraps	-0.58	No

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	36	1
Action 19	Recycle aluminum and glass containers	30	1
Action 2	Replace incandescent lightbulbs with LEDs	27	1
Action 5	Use cold water to wash clothes	24	1
Action 11	Drive less by grouping multiple errands into one trip	24	1
Action 20	Meal plan and store food property to limit food waste	20	1
Action 27	Lower/raise thermostat in winter/summer	20	2
Action 16	Perform annual HVAC maintenance	18	8
Action 4	Replace current stove with electric or convection stove	16	7
Action 29	Purchase Energy Star electronic goods	16	2
Action 18	Set water heater to medium or standard setting	15	6
Action 30	Purchase Energy Star appliances	14	3
Action 6	Caulk and weather-strip windows and doors; increase home insulation	13	7
Action 15	Eat one less portion of red meat each week	13	2
Action 24	Line dry your laundry instead of using dryer	13	6
Action 22	Implement low-flow showerheads and faucets in your home	11	2
Action 14	Take one less domestic flight each year	10	24
Action 21	Compost your food scraps	9	3
Action 28	Seal leaks in your home air duct system	9	8
Action 7	Replace current HVAC system with electric heat pump	7	10
Action 23	Work from home at least 2 days a week	7	26
Action 3	Replace current clothes dryer with electric heat pump dryer	6	7
Action 9	Replace current water heater with heat pump water heater	6	8
Action 12	Change from a gas lawn mower to electric mower	6	11
Action 13	Walk, bike, or take public transit instead of driving	6	4
Action 17	Join your utility’s smart metering program	6	7
Action 25	Choose renewable energy through your utility	6	8
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	3	3
Action 1	Install rooftop solar panels on your home	2	7
Action 8	Choose an electric vehicle the next time you buy a vehicle	1	4

Condition Effects

Action 9:

## [1] "Replace current water heater with heat pump water heater"

cond	act9_mean	act9_n
climate	-0.24	34
ctrl	-1.35	26

Marginal; both opposed, climate condition less opposed

Action 28:

## [1] "Seal leaks in your home air duct system"

cond	act28_mean	act28_n
climate	0.39	31
ctrl	1.27	26

Both supported, ctrl supported more; Not different from 0 in the climate condition

Action 30:

## [1] "Purchase Energy Star appliances"

cond	act30_mean	act30_n
climate	1.62	32
ctrl	0.52	25

Both supported, climate supported more; Not different from 0 in the ctrl condition

Gender Effects

Action 8

## [1] "Choose an electric vehicle the next time you buy a vehicle"

gender	act8_mean	act8_n
Female	-1.53	47
Male	-0.32	22

Action 11

## [1] "Drive less by grouping multiple errands into one trip"

gender	act11_mean	act11_n
Female	0.22	27
Male	1.36	22

Action 13

## [1] "Walk, bike, or take public transit instead of driving"

gender	act13_mean	act13_n
Female	-1.19	42
Male	0.59	22

Action 24

## [1] "Line dry your laundry instead of using dryer"

gender	act24_mean	act24_n
Female	-0.58	33
Male	0.55	22

Gender x Condition Effects

None.

Shared Actions

# Which actions that liberals support do conservatives also support?
conINlib <- act.sup.lib$Action.Number %in% act.sup.con$Action.Number

act.sup.lib$Action.Number[conINlib]

##  [1] "Action 2"  "Action 5"  "Action 6"  "Action 11" "Action 16" "Action 18"
##  [7] "Action 19" "Action 20" "Action 25" "Action 26" "Action 27" "Action 28"
## [13] "Action 29" "Action 30"

# Which actions that liberals support do conservatives oppose?
conoppINlib <- act.sup.lib$Action.Number %in% act.opp.con$Action.Number

act.sup.lib$Action.Number[conoppINlib]

## [1] "Action 1"  "Action 12"

# Which actions that liberals support do moderates also support?

modINlib <- act.sup.lib$Action.Number %in% act.sup.mod$Action.Number

act.sup.lib$Action.Number[modINlib]

##  [1] "Action 2"  "Action 5"  "Action 6"  "Action 11" "Action 14" "Action 16"
##  [7] "Action 17" "Action 18" "Action 19" "Action 20" "Action 25" "Action 26"
## [13] "Action 27" "Action 28" "Action 29" "Action 30"

# Which actions that moderates support do conservatives also support?

conINmod <- act.sup.mod$Action.Number %in% act.sup.con$Action.Number

act.sup.mod$Action.Number[conINmod]

##  [1] "Action 2"  "Action 5"  "Action 6"  "Action 11" "Action 16" "Action 18"
##  [7] "Action 19" "Action 20" "Action 25" "Action 26" "Action 27" "Action 28"
## [13] "Action 29" "Action 30"

# Which actions that strong liberals support do strong conservatives also support?
sconINslib <- act.sup.slib$Action.Number %in% act.sup.scon$Action.Number

act.sup.slib$Action.Number[sconINslib]

##  [1] "Action 2"  "Action 6"  "Action 11" "Action 16" "Action 19" "Action 20"
##  [7] "Action 26" "Action 27" "Action 28" "Action 29" "Action 30"

Partisan ID

Republicans

Most Supported Actions

Actions supported: 2, 5, 6, 11, 16, 18, 19, 20, 25, 26, 27, 28, 29, 30 Action neutral: 4, 14, 15, 17, 22, 23 Actions opposed: 1, 3, 7, 8, 9, 10, 12, 13, 21, 24

Action.Number	Action.Name	Support.Mean
Action 26	Turn off lights, fans when you leave home	1.57
Action 20	Meal plan and store food property to limit food waste	1.26
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.11
Action 19	Recycle aluminum and glass containers	1.00
Action 2	Replace incandescent lightbulbs with LEDs	0.98
Action 30	Purchase Energy Star appliances	0.97
Action 5	Use cold water to wash clothes	0.96
Action 28	Seal leaks in your home air duct system	0.95
Action 29	Purchase Energy Star electronic goods	0.93
Action 16	Perform annual HVAC maintenance	0.92
Action 11	Drive less by grouping multiple errands into one trip	0.69
Action 27	Lower/raise thermostat in winter/summer	0.43
Action 18	Set water heater to medium or standard setting	0.42
Action 25	Choose renewable energy through your utility	0.24

Most Opposed Actions

Action.Number	Action.Name	Support.Mean
Action 8	Choose an electric vehicle the next time you buy a vehicle	-1.13
Action 12	Change from a gas lawn mower to electric mower	-0.90
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	-0.78
Action 3	Replace current clothes dryer with electric heat pump dryer	-0.69
Action 1	Install rooftop solar panels on your home	-0.58
Action 13	Walk, bike, or take public transit instead of driving	-0.56
Action 7	Replace current HVAC system with electric heat pump	-0.50
Action 21	Compost your food scraps	-0.50
Action 24	Line dry your laundry instead of using dryer	-0.50
Action 9	Replace current water heater with heat pump water heater	-0.44

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	102	3
Action 19	Recycle aluminum and glass containers	78	4
Action 2	Replace incandescent lightbulbs with LEDs	76	5
Action 5	Use cold water to wash clothes	74	2
Action 11	Drive less by grouping multiple errands into one trip	69	6
Action 27	Lower/raise thermostat in winter/summer	69	6
Action 20	Meal plan and store food property to limit food waste	66	3
Action 18	Set water heater to medium or standard setting	58	8
Action 29	Purchase Energy Star electronic goods	49	6
Action 30	Purchase Energy Star appliances	48	10
Action 16	Perform annual HVAC maintenance	47	18
Action 15	Eat one less portion of red meat each week	43	6
Action 4	Replace current stove with electric or convection stove	41	15
Action 6	Caulk and weather-strip windows and doors; increase home insulation	41	14
Action 22	Implement low-flow showerheads and faucets in your home	39	7
Action 21	Compost your food scraps	34	10
Action 24	Line dry your laundry instead of using dryer	32	7
Action 28	Seal leaks in your home air duct system	28	22
Action 14	Take one less domestic flight each year	26	64
Action 17	Join your utility’s smart metering program	26	15
Action 7	Replace current HVAC system with electric heat pump	20	21
Action 13	Walk, bike, or take public transit instead of driving	18	18
Action 23	Work from home at least 2 days a week	17	70
Action 12	Change from a gas lawn mower to electric mower	16	25
Action 3	Replace current clothes dryer with electric heat pump dryer	15	19
Action 9	Replace current water heater with heat pump water heater	13	16
Action 25	Choose renewable energy through your utility	10	16
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	6	12
Action 1	Install rooftop solar panels on your home	2	16
Action 8	Choose an electric vehicle the next time you buy a vehicle	1	13

####Actions with Condition Differences

Action 19: Action supported in both conditions, supported more in control condition.

Independents

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 26	Turn off lights, fans when you leave home	1.52
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.40
Action 19	Recycle aluminum and glass containers	1.34
Action 20	Meal plan and store food property to limit food waste	1.31
Action 30	Purchase Energy Star appliances	1.19
Action 2	Replace incandescent lightbulbs with LEDs	1.10
Action 29	Purchase Energy Star electronic goods	1.07
Action 11	Drive less by grouping multiple errands into one trip	0.98
Action 28	Seal leaks in your home air duct system	0.94
Action 5	Use cold water to wash clothes	0.89
Action 25	Choose renewable energy through your utility	0.84
Action 16	Perform annual HVAC maintenance	0.82
Action 18	Set water heater to medium or standard setting	0.69
Action 27	Lower/raise thermostat in winter/summer	0.56
Action 14	Take one less domestic flight each year	0.44
Action 17	Join your utility’s smart metering program	0.42
Action 22	Implement low-flow showerheads and faucets in your home	0.39
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	0.35

Most Opposed Actions

Action.Number	Action.Name	Support.Mean
Action 8	Choose an electric vehicle the next time you buy a vehicle	-0.33

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	108	4
Action 2	Replace incandescent lightbulbs with LEDs	92	3
Action 5	Use cold water to wash clothes	89	7
Action 19	Recycle aluminum and glass containers	87	9
Action 27	Lower/raise thermostat in winter/summer	77	9
Action 20	Meal plan and store food property to limit food waste	76	7
Action 11	Drive less by grouping multiple errands into one trip	70	13
Action 30	Purchase Energy Star appliances	57	14
Action 29	Purchase Energy Star electronic goods	56	10
Action 18	Set water heater to medium or standard setting	51	16
Action 16	Perform annual HVAC maintenance	50	28
Action 6	Caulk and weather-strip windows and doors; increase home insulation	48	18
Action 4	Replace current stove with electric or convection stove	47	24
Action 15	Eat one less portion of red meat each week	45	9
Action 21	Compost your food scraps	44	16
Action 22	Implement low-flow showerheads and faucets in your home	35	14
Action 28	Seal leaks in your home air duct system	35	35
Action 24	Line dry your laundry instead of using dryer	33	19
Action 14	Take one less domestic flight each year	26	66
Action 17	Join your utility’s smart metering program	26	22
Action 23	Work from home at least 2 days a week	23	63
Action 12	Change from a gas lawn mower to electric mower	21	39
Action 13	Walk, bike, or take public transit instead of driving	21	9
Action 25	Choose renewable energy through your utility	19	23
Action 1	Install rooftop solar panels on your home	15	32
Action 7	Replace current HVAC system with electric heat pump	15	36
Action 3	Replace current clothes dryer with electric heat pump dryer	14	25
Action 8	Choose an electric vehicle the next time you buy a vehicle	11	19
Action 9	Replace current water heater with heat pump water heater	10	27
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	8	18

Democrats

Most Supported Actions

Action.Number	Action.Name	Support.Mean
Action 26	Turn off lights, fans when you leave home	1.40
Action 29	Purchase Energy Star electronic goods	1.32
Action 2	Replace incandescent lightbulbs with LEDs	1.28
Action 28	Seal leaks in your home air duct system	1.23
Action 6	Caulk and weather-strip windows and doors; increase home insulation	1.20
Action 27	Lower/raise thermostat in winter/summer	1.14
Action 19	Recycle aluminum and glass containers	1.13
Action 5	Use cold water to wash clothes	1.12
Action 25	Choose renewable energy through your utility	1.11
Action 20	Meal plan and store food property to limit food waste	1.10
Action 11	Drive less by grouping multiple errands into one trip	1.08
Action 16	Perform annual HVAC maintenance	1.06
Action 30	Purchase Energy Star appliances	1.06
Action 18	Set water heater to medium or standard setting	0.84
Action 22	Implement low-flow showerheads and faucets in your home	0.80
Action 17	Join your utility’s smart metering program	0.65
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	0.59
Action 14	Take one less domestic flight each year	0.55
Action 15	Eat one less portion of red meat each week	0.53

Already adopted actions

Action #	Action	Already Doing	N/A
Action 26	Turn off lights, fans when you leave home	95	6
Action 19	Recycle aluminum and glass containers	84	8
Action 2	Replace incandescent lightbulbs with LEDs	75	4
Action 5	Use cold water to wash clothes	71	5
Action 20	Meal plan and store food property to limit food waste	68	1
Action 27	Lower/raise thermostat in winter/summer	60	5
Action 11	Drive less by grouping multiple errands into one trip	59	14
Action 6	Caulk and weather-strip windows and doors; increase home insulation	51	14
Action 16	Perform annual HVAC maintenance	50	20
Action 15	Eat one less portion of red meat each week	49	8
Action 30	Purchase Energy Star appliances	49	13
Action 29	Purchase Energy Star electronic goods	46	8
Action 18	Set water heater to medium or standard setting	45	13
Action 22	Implement low-flow showerheads and faucets in your home	33	13
Action 14	Take one less domestic flight each year	32	43
Action 21	Compost your food scraps	31	7
Action 4	Replace current stove with electric or convection stove	30	17
Action 24	Line dry your laundry instead of using dryer	30	8
Action 28	Seal leaks in your home air duct system	30	21
Action 17	Join your utility’s smart metering program	26	13
Action 23	Work from home at least 2 days a week	24	55
Action 13	Walk, bike, or take public transit instead of driving	21	15
Action 25	Choose renewable energy through your utility	18	13
Action 12	Change from a gas lawn mower to electric mower	15	44
Action 7	Replace current HVAC system with electric heat pump	13	25
Action 1	Install rooftop solar panels on your home	11	20
Action 10	Choose a hybrid vehicle the next time you buy a vehicle	10	14
Action 3	Replace current clothes dryer with electric heat pump dryer	9	19
Action 9	Replace current water heater with heat pump water heater	8	22
Action 8	Choose an electric vehicle the next time you buy a vehicle	7	13

Personal Responsibility Definitions Summary:

Definition 1:

Personal responsibility means that each of us has free will and is responsible for our actions. Because of this, people should be rewarded or held accountable for the consequences their actions. Likewise, we all have a moral duty to act in ways that better ourselves, our families and communities, and society.

Definition 2:

We’ve got to get back to old-fashioned principles of personal responsibility, of common sense, and get away from this new culture where everybody plays the victim and blames other people for their problems.

Tables

Ideology

Means by specific ratings

ideology	d1_capture	d1_meaningful	d1_value	d2_capture	d2_meaningful	d2_value	n
Strong Liberal	5.72	5.49	5.77	4.06	4.19	4.43	47
Liberal	5.29	5.31	5.36	4.44	4.38	4.52	77
Moderate	5.12	5.05	5.34	5.04	4.95	5.17	229
Conservative	5.07	5.04	5.11	5.37	5.34	5.58	118
Strong Conservative	5.12	4.92	5.20	5.22	4.93	5.27	74

Means collapsing across ratings

ideology	def1_mean	def2_mean	SigDiff	n
Strong Liberal	5.66	4.23	***	47
Liberal	5.32	4.45	***	77
Moderate	5.17	5.05	NS	229
Conservative	5.07	5.43	*	118
Strong Conservative	5.08	5.14	NS	74

Partisan ID

Means by specific ratings

party_factor	d1_capture	d1_meaningful	d1_value	d2_capture	d2_meaningful	d2_value	n
Democrat	5.30	5.21	5.44	4.77	4.62	4.82	179
Independent	5.12	5.04	5.24	4.85	4.77	5.06	191
Republican	5.14	5.04	5.25	5.30	5.27	5.47	172

Means collapsing across ratings

party_factor	def1_mean	def2_mean	SigDiff	n
Democrat	5.31	4.74	***	179
Independent	5.13	4.89	*	191
Republican	5.14	5.34	NS	172

Write Ups

Ideology

Strong Liberals

Collapsing across all types of measured attitudes (valuable, meaningful, well-captured), strong liberals strongly prefer definition 1 (\(M_{definition 1}\) = 5.69) to definition 2 (\(M_{definition2}\) = 4.28, b = 1.40, t(540) = 9.58, p < .001). All three attitudes–valuable, meaningful, and well-captured–are significantly higher for definition 1 than definition 2 (p < .001).

Collapsing across rating types, strong liberal women expressed a preference for the second definition of personal responsibility over the first definition (p < .001), while strong liberal men did not prefer one to the other (p = .186). This was driven by women perceiving the second definition to be more meaningful, well-captured, and valuable than the first definition (all ps < .001), while men rated the second definition as more well-captured than the first (p = .013), but not more valuable (p = .326) or meaningful (p > .999).

Liberals

Collapsing across all types of measured attitudes (valuable, meaningful, well-captured), moderate liberals prefer definition 1 (\(M_{definition 1}\) = 5.32) to definition 2 (\(M_{definition2}\) = 4.45, b = 0.87, t(540) = 24.67, p < .001). All three attitudes–valuable, meaningful, and well-captured–are significantly higher for definition 1 than definition 2 (p < .001).

There were no differences in ratings by gender for liberals.

Moderates

Moderates endorse both definitions of personal responsibility (\(M_{definition 1}\) = 5.17, \(M_{definition2}\) = 5.05), and do not prefer one definition to the other. This holds true whether collapsing across measured attitudes toward the definitions (p = .572), or considering each attitude separately (p = .651, p = .948, and p = .731 for well-captured, meaningful, and valuable, respectively).

There were no differences in ratings by gender for moderates.

Conservatives

Moderate conservatives strongly endorse both definitions of personal responsibility (\(M_{definition 1}\) = 5.07, \(M_{definition2}\) = 5.43). Collapsing across all types of measured attitudes (valuable, meaningful, well-captured), moderate conservatives prefer definition 2 to definition 1 (b = 0.36, t(540.0) = 10.77, p = .011). This is driven by a significant difference in perceived value of the two definitions, such that definition 2 is considered more valuable than definition 1 (\(M_{definition1}\) = 5.11, \(M_{definition2}\) = 5.58, b = 0.47, t(540) = 2.99, p = .003). The other two ratings, well-captured and meaningful, are marginally different between definitions (p = .066 and p = .073, respectively).

Collapsing across rating types, there were no gender differences. However, there was a difference in “well-captured”: Men thought definition 2 better captured personal responsibility than definition 1, whereas women had no preference.

Strong Conservatives

Strong conservatives strongly endorse both definitions of personal responsibility (\(M_{definition 1}\) = 5.08, \(M_{definition2}\) = 5.14) and do not prefer one definition to the other. This holds true whether collapsing across measured attitudes toward the definitions (p = .572), or considering each attitude separately (p = .651, p = .948, and p = .731 for well-captured, meaningful, and valuable, respectively).

There were no differences in ratings by gender for strong conservatives.

Partisan ID

Republicans

Republicans strongly endorse both definitions of personal responsibility (\(M_{definition 1}\) = 5.14, \(M_{definition2}\) = 5.34) and do not prefer one definition to the other. This holds true whether collapsing across measured attitudes toward the definitions (p = .097), or considering each attitude separately (p = .269, p = .095, and p = .105 for well-captured, meaningful, and valuable, respectively).

There were no differences in ratings by gender for Republicans.

Independents

Independents endorse both definitions of personal responsibility (\(M_{definition 1}\) = 5.13, \(M_{definition2}\) = 4.89), but prefer definition 1 to definition 2 (b = 0.24, t(540) = 2.07, p = .039). This is driven by significant differences in the meaningful and well-captured attitudes, such that definition 1 is considered more meaningful (\(M_{definition1}\) = 5.04, \(M_{definition2}\) = 4.77, b = 0.27, t(540) = 2.07, p = .039), and better-captured (\(M_{definition1}\) = 5.12, \(M_{definition2}\) = 4.85, b = 0.27, t(540) = 1.98, p = .048), than definition 2. The other two ratings, well-captured and meaningful, are marginally different between definitions (p = .066 and p = .073, respectively).

Collapsing across rating types, Independent women expressed a preference for the second definition of personal responsibility over the first definition (p < .001), while Independent men did not prefer one to the other (p = .353). This was driven by women perceiving the second definition to be more meaningful (p = .006), well-captured (p = .004), and valuable (p = .034) than the first definition, while Independent men did not rate the definitions differently by any metric (all ps > .100).

Democrats

Democrats endorse both definitions of personal responsibility (\(M_{definition 1}\) = 5.32, \(M_{definition2}\) = 4.75), but prefer definition 1 to definition 2 (b = 0.57, t(540) = 4.82, p < .001). All three attitudes (well-captured, valuable, and meaningful) are significantly higher for definition 1 than definition 2, at the p < .001 level.

There were no differences in ratings by gender for Democrats.