Study 1 Analyses

DATA PREP

Data & libraries

library(psych)
library(tidyverse)
library(knitr)
library(lme4)
library(lmerTest)

d <- read.csv("CarnegieProject_study1.csv")
nrow(d) #657

## [1] 654

knit2 <- function(filename = rstudioapi::getSourceEditorContext()$path,
                  output_dir = tempdir()) {
  result <- rmarkdown::render(filename, output_dir = output_dir)
  getOption("viewer")(result)
}

1. Cleaning Data

d <- d[d$Consent == 1,]
nrow(d) #636 - 19 did not consent
d <- d[d$DistributionChannel == "anonymous",]
nrow(d) #632 - 4 preview
d <- d[d$age > 0,]
nrow(d) #631
d <- d[-grep("Black Tea", d$persRes1_comments),] # cleaning - omit nonsense response
nrow(d) #n = 630

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"

## behaviors measures

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

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

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

d <- d %>%
   mutate(across(beh1:beh30, ~na_if(., -98)))

d <- d %>%
   mutate(across(beh1:beh30, ~na_if(., -99)))

## Checks
#addmargins(table(d$behavs_1, d$beh1, exclude = F))
#addmargins(table(d$behavs_30, d$beh30, exclude = F))

b. Checks for missing data

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

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

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

Looks good.

c. Does time look reasonable?

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

## [1] 8.44

median(mins, na.rm = T)

## [1] 6.75

BEHAVIORS ANALYSES

2. Descriptive Stats

a. Demographics

## Ns by group
# gender
gend <- d %>% 
  mutate(gender = recode(gender, `1` = "female",
                `2` = "male",
                `3` = "other"
     )
) %>%
  group_by(gender) %>%
  summarise(n = n()) %>%
  mutate(proportion = round(n / sum(n), 2)) 
kable(gend)

gender	n	proportion
female	403	0.74
male	140	0.26
other	2	0.00

# race/eth
race <- d %>% 
  mutate(race = recode(race, `1` = "Asian, Asian-American",
                `2` = "Black, African-American",
                `3` = "Hispanic, Latino-American",
                `4` = "Native American",
                `5` = "Native Pacific Islander",
                `6` = "White, Caucasian-American",
                `7` = "Other"
     )
) %>%
  group_by(race) %>%
  summarise(n = n()) %>%
  mutate(proportion = round(n / sum(n), 2)) 

kable(race)

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
party <- d %>% 
  group_by(party_factor) %>%
  summarise(n = n()) %>%
  mutate(proportion = round(n / sum(n), 2)) 

kable(party)

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

# education
edu <- d %>% 
#  mutate(education = recode_factor(education, `1` = "",
#                `2` = "",
#                `3` = "",
#                `4` = "",
#                `5` = "",
#                `6` = "",
#                `7` = "",
#                `8` = "",
#                `9` = "",
#                `10` = ""
#     )
#) %>%
  group_by(education) %>%
  summarise(n = n()) %>%
  mutate(proportion = round(n / sum(n), 2)) 

kable(edu)

education	n	proportion
1	1	0.00
2	4	0.01
3	133	0.24
4	56	0.10
5	138	0.25
6	148	0.27
7	45	0.08
8	6	0.01
9	9	0.02
10	3	0.01
NA	2	0.00

# income
inc <- d %>% 
  mutate(income = recode_factor(income, `1` = "Prefer not to say",
                `2` = "≤ $10,000",
                `3` = "$10,000 - $19,999",
                `4` = "$20,000 - $29,999",
                `5` = "$30,000 - $39,999",
                `6` = "$40,000 - $49,999",
                `7` = "$50,000 - $74,999",
                `8` = "$75,000 - $99,999",
                `9` = "$100,000 - $149,999",
                `10` = "≥ $150,000"
     )
) %>%
  group_by(income) %>%
  summarise(n = n()) %>%
  mutate(proportion = round(n / sum(n), 2)) 

kable(inc)

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

## Numeric
# age
age <-round(psych::describe(d$age)[c('n','min','max','mean','sd','median')],2)
kable(age)

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

b. M, SD of all behavior questions

i. By Condition

1. Control

ctrl <- d[d$cond == "ctrl",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrl <- ctrl[order(ctrl$name),]

kable(ctrl)

name	value_mean	value_SD	value_n
beh1	0.04	2.21	227
beh2	1.31	1.73	154
beh3	-0.28	2.07	228
beh4	0.01	2.25	190
beh5	1.16	1.84	145
beh6	1.34	1.85	185
beh7	-0.24	2.13	210
beh8	-0.34	2.15	239
beh9	0.02	2.04	231
beh10	0.17	2.11	240
beh11	1.17	1.90	162
beh12	-0.10	2.21	196
beh13	-0.01	2.07	219
beh14	0.37	2.09	148
beh15	0.25	2.09	193
beh16	0.85	1.83	168
beh17	0.53	1.98	209
beh18	0.63	1.90	189
beh19	1.44	1.72	124
beh20	1.49	1.59	167
beh21	-0.19	2.05	200
beh22	0.34	2.02	207
beh23	0.35	2.35	151
beh24	-0.05	2.11	210
beh25	0.70	1.86	226
beh26	1.47	1.87	114
beh27	0.96	1.80	163
beh28	1.08	1.77	197
beh29	1.11	1.83	186
beh30	1.02	1.94	174

df <- d %>% pivot_longer(beh1:beh30)
# Democratic vs. Republican
df$pDem_Rep <- NA
df$pDem_Rep[df$party_factor == 'Democrat'] <- -.5
df$pDem_Rep[df$party_factor == 'Independent'] <- 0
df$pDem_Rep[df$party_factor == 'Republican'] <- .5

# Partisan vs. Independent
df$pParty_Ind <- NA
df$pParty_Ind[df$party_factor == 'Democrat'] <- -.33
df$pParty_Ind[df$party_factor == 'Independent'] <- .67
df$pParty_Ind[df$party_factor == 'Republican'] <- -.33

# Code 1a: Democrats vs. Republicans
df$pDemR <- NA
df$pDemR[df$party_factor == 'Democrat'] <- 0
df$pDemR[df$party_factor == 'Independent'] <- 0
df$pDemR[df$party_factor == 'Republican'] <- 1

# Code 2a: Democrats vs. Independents
df$pDemI <- NA
df$pDemI[df$party_factor == 'Democrat'] <- 0
df$pDemI[df$party_factor == 'Independent'] <- 1
df$pDemI[df$party_factor == 'Republican'] <- 0

# Code 2a: Republicans vs. Democrats
df$pRepD <- NA
df$pRepD[df$party_factor == 'Democrat'] <- 1
df$pRepD[df$party_factor == 'Independent'] <- 0
df$pRepD[df$party_factor == 'Republican'] <- 0

# Code 2b: Republicans vs. Independents
df$pRepI <- NA
df$pRepI[df$party_factor == 'Democrat'] <- 0
df$pRepI[df$party_factor == 'Independent'] <- 1
df$pRepI[df$party_factor == 'Republican'] <- 0

rep <- lmer(value ~ pRepD + pRepI + (1 | name), data = df)
summary(rep)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ pRepD + pRepI + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46562.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36226 -0.78509  0.09782  0.86256  1.87779 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3125   0.5591  
##  Residual             3.8968   1.9740  
## Number of obs: 11065, groups:  name, 30
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept) 1.534e-01  1.074e-01 3.312e+01   1.429    0.162    
## pRepD       5.634e-01  4.631e-02 1.103e+04  12.165   <2e-16 ***
## pRepI       4.038e-01  4.617e-02 1.103e+04   8.747   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##       (Intr) pRepD 
## pRepD -0.222       
## pRepI -0.222  0.515

#cond
df$ctrl_clim <- ifelse(df$cond == "ctrl", 0, 1)

repcond <- lmer(value ~ (pRepD + pRepI) + ctrl_clim + (1 | name), data = df)
summary(repcond)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (pRepD + pRepI) + ctrl_clim + (1 | name)
##    Data: df
## 
## REML criterion at convergence: 46562.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3816 -0.7790  0.0980  0.8591  1.8952 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  name     (Intercept) 0.3125   0.5591  
##  Residual             3.8958   1.9738  
## Number of obs: 11065, groups:  name, 30
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)  1.944e-01  1.093e-01  3.557e+01   1.778   0.0839 .  
## pRepD        5.596e-01  4.634e-02  1.103e+04  12.076   <2e-16 ***
## pRepI        3.963e-01  4.631e-02  1.103e+04   8.556   <2e-16 ***
## ctrl_clim   -7.552e-02  3.767e-02  1.103e+04  -2.005   0.0450 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) pRepD  pRepI 
## pRepD     -0.225              
## pRepI     -0.233  0.517       
## ctrl_clim -0.187  0.041  0.081

2. Climate

climate <- d[d$cond=="climate",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climate <- climate[order(climate$name),]

kable(climate)

name	value_mean	value_SD	value_n
beh1	-0.22	2.13	222
beh2	0.96	1.88	136
beh3	-0.16	1.99	216
beh4	-0.05	2.09	181
beh5	0.84	1.90	152
beh6	1.17	1.77	174
beh7	0.01	2.04	205
beh8	-0.45	2.21	242
beh9	0.04	1.95	218
beh10	-0.01	2.14	237
beh11	0.68	1.85	152
beh12	-0.19	2.18	189
beh13	-0.18	2.11	224
beh14	0.55	1.97	140
beh15	0.04	2.03	192
beh16	1.01	1.83	164
beh17	0.40	2.04	208
beh18	0.70	1.79	165
beh19	0.95	1.83	151
beh20	0.94	1.93	157
beh21	0.05	2.10	203
beh22	0.30	1.98	197
beh23	-0.01	2.16	142
beh24	-0.45	2.08	206
beh25	0.77	1.88	220
beh26	1.50	1.78	113
beh27	0.50	1.96	156
beh28	1.01	1.89	177
beh29	1.11	1.69	184
beh30	1.13	1.80	180

ii. By Party ID

1. Democrats

dem <- d[d$party_factor == "Democrat",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
dem <- dem[order(dem$name),]

kable(dem)

name	value_mean	value_SD	value_n
beh1	0.28	2.15	148
beh2	1.28	1.88	100
beh3	0.13	2.05	151
beh4	0.30	2.11	132
beh5	1.12	1.93	103
beh6	1.20	1.89	114
beh7	0.10	2.02	141
beh8	0.23	2.14	159
beh9	0.26	1.97	149
beh10	0.59	1.98	155
beh11	1.08	1.81	106
beh12	0.32	2.15	120
beh13	0.19	2.12	143
beh14	0.55	2.04	104
beh15	0.53	2.07	122
beh16	1.06	1.82	109
beh17	0.65	2.01	140
beh18	0.84	1.83	121
beh19	1.13	1.92	87
beh20	1.10	1.92	110
beh21	0.01	2.08	141
beh22	0.80	1.88	133
beh23	0.16	2.36	100
beh24	-0.21	2.12	141
beh25	1.11	1.86	148
beh26	1.40	1.92	78
beh27	1.14	1.86	114
beh28	1.23	1.74	128
beh29	1.32	1.78	125
beh30	1.06	2.02	117

2. Republicans

rep <- d[d$party_factor == "Republican",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
rep <- rep[order(rep$name),]

kable(rep)

name	value_mean	value_SD	value_n
beh1	-0.58	2.16	154
beh2	0.98	1.71	91
beh3	-0.69	1.99	138
beh4	-0.21	2.20	116
beh5	0.96	1.81	96
beh6	1.11	1.78	117
beh7	-0.50	2.08	131
beh8	-1.13	2.05	158
beh9	-0.44	2.02	143
beh10	-0.78	2.08	154
beh11	0.69	1.97	97
beh12	-0.90	2.13	131
beh13	-0.56	2.01	136
beh14	0.32	2.07	82
beh15	-0.20	2.07	123
beh16	0.92	1.82	107
beh17	0.30	2.03	131
beh18	0.42	1.88	106
beh19	1.00	1.82	90
beh20	1.26	1.62	103
beh21	-0.50	2.07	128
beh22	-0.29	2.02	126
beh23	-0.08	2.28	85
beh24	-0.50	2.11	133
beh25	0.24	1.84	146
beh26	1.57	1.73	67
beh27	0.43	1.89	97
beh28	0.95	1.81	122
beh29	0.93	1.74	117
beh30	0.97	1.86	114

3. Independents

ind <- d[d$party_factor == "Independent",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ind <- ind[order(ind$name),]

kable(ind)

name	value_mean	value_SD	value_n
beh1	0.05	2.12	144
beh2	1.10	1.83	96
beh3	-0.17	1.96	152
beh4	-0.23	2.16	120
beh5	0.89	1.86	95
beh6	1.40	1.77	125
beh7	-0.01	2.09	140
beh8	-0.33	2.12	161
beh9	0.20	1.92	154
beh10	0.35	2.06	165
beh11	0.98	1.88	108
beh12	0.16	2.10	131
beh13	0.02	2.06	161
beh14	0.44	1.99	99
beh15	0.07	2.00	137
beh16	0.82	1.83	113
beh17	0.42	1.98	143
beh18	0.69	1.80	124
beh19	1.34	1.67	95
beh20	1.31	1.77	108
beh21	0.22	2.02	131
beh22	0.39	1.96	142
beh23	0.38	2.15	105
beh24	-0.07	2.04	139
beh25	0.84	1.80	149
beh26	1.52	1.80	79
beh27	0.56	1.85	105
beh28	0.94	1.90	121
beh29	1.07	1.72	125
beh30	1.19	1.70	120

iii. By ideology

1. Strong Liberals

Slibs <- d[d$ideology == -2,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
Slibs <- Slibs[order(Slibs$name),]

kable(Slibs)

name	value_mean	value_SD	value_n
beh1	0.17	2.50	35
beh2	1.04	2.18	24
beh3	0.37	2.02	35
beh4	0.48	2.29	29
beh5	0.42	2.19	24
beh6	1.19	2.05	32
beh7	0.37	2.17	35
beh8	0.62	2.14	40
beh9	0.19	2.25	36
beh10	1.15	2.03	40
beh11	1.54	1.91	24
beh12	0.33	2.29	27
beh13	0.26	2.50	35
beh14	0.09	2.27	22
beh15	0.31	2.38	32
beh16	1.03	1.94	29
beh17	1.21	2.03	34
beh18	0.76	2.12	29
beh19	1.30	2.33	27
beh20	1.00	2.20	27
beh21	0.42	2.21	33
beh22	1.14	1.80	37
beh23	0.56	2.75	25
beh24	-0.08	2.18	37
beh25	1.34	2.03	35
beh26	0.95	2.07	19
beh27	1.39	2.01	31
beh28	1.19	1.87	31
beh29	1.48	1.86	29
beh30	1.14	2.29	28

2. Moderate Liberals

libs <- d[d$ideology == -1,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
libs <- libs[order(libs$name),]

kable(libs)

name	value_mean	value_SD	value_n
beh1	0.65	2.05	65
beh2	1.57	1.71	42
beh3	-0.19	2.12	68
beh4	0.38	2.14	58
beh5	1.40	1.82	42
beh6	1.43	1.76	46
beh7	-0.03	2.03	59
beh8	0.48	2.13	63
beh9	0.58	1.72	64
beh10	0.68	2.04	65
beh11	1.66	1.54	41
beh12	0.74	2.12	53
beh13	0.34	1.98	64
beh14	0.95	1.93	39
beh15	0.89	2.13	45
beh16	1.07	1.65	43
beh17	0.55	2.17	60
beh18	0.91	1.71	57
beh19	1.44	1.54	32
beh20	1.20	1.88	46
beh21	0.44	2.09	59
beh22	0.59	2.05	56
beh23	0.62	2.22	39
beh24	-0.14	2.03	58
beh25	1.13	1.86	63
beh26	1.26	2.16	31
beh27	1.18	1.77	45
beh28	1.00	1.93	55
beh29	0.98	1.86	55
beh30	1.18	2.05	51

3. Moderates

mod <- d[d$ideology == 0,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
mod <- mod[order(mod$name),]

kable(mod)

name	value_mean	value_SD	value_n
beh1	-0.04	2.08	184
beh2	1.22	1.64	113
beh3	-0.02	1.96	184
beh4	-0.21	2.09	160
beh5	1.09	1.73	109
beh6	1.31	1.80	142
beh7	-0.13	2.02	178
beh8	-0.44	2.04	201
beh9	0.20	1.88	190
beh10	0.08	2.07	199
beh11	0.73	1.85	135
beh12	0.00	2.07	158
beh13	-0.09	2.02	188
beh14	0.42	1.94	128
beh15	0.10	1.91	166
beh16	0.96	1.75	140
beh17	0.48	1.91	173
beh18	0.82	1.72	141
beh19	1.23	1.71	113
beh20	1.36	1.61	129
beh21	-0.06	1.98	167
beh22	0.29	1.92	168
beh23	0.11	2.18	123
beh24	-0.23	2.11	174
beh25	0.89	1.81	186
beh26	1.62	1.65	89
beh27	0.55	1.89	127
beh28	0.97	1.77	147
beh29	1.14	1.70	147
beh30	1.04	1.72	138

4. Moderate Conservatives

cons <- d[d$ideology == 1,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
cons <- cons[order(cons$name),]

kable(cons)

name	value_mean	value_SD	value_n
beh1	-0.49	2.10	100
beh2	0.89	1.86	65
beh3	-0.42	2.00	96
beh4	-0.29	2.24	73
beh5	0.97	1.83	73
beh6	1.33	1.69	85
beh7	-0.31	2.10	86
beh8	-0.71	2.15	108
beh9	-0.25	1.97	99
beh10	-0.26	2.03	105
beh11	0.82	1.86	65
beh12	-0.60	2.17	90
beh13	-0.22	2.13	92
beh14	0.32	2.11	59
beh15	0.11	2.01	83
beh16	0.99	1.84	72
beh17	0.17	2.04	89
beh18	0.49	1.92	74
beh19	1.05	1.84	60
beh20	1.13	1.85	69
beh21	-0.27	2.06	82
beh22	0.34	1.99	82
beh23	-0.18	2.19	65
beh24	-0.50	2.14	92
beh25	0.50	1.70	102
beh26	1.57	1.98	51
beh27	0.72	1.82	64
beh28	1.32	1.76	84
beh29	1.02	1.82	83
beh30	1.01	1.95	80

5. Strong Conservatives

Scons <- d[d$ideology == 2,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
Scons <- Scons[order(Scons$name),]

kable(Scons)

name	value_mean	value_SD	value_n
beh1	-0.46	2.33	65
beh2	0.96	1.99	46
beh3	-0.89	2.04	61
beh4	0.20	2.23	51
beh5	0.73	2.10	49
beh6	0.87	1.94	54
beh7	-0.16	2.27	57
beh8	-1.14	2.28	69
beh9	-0.72	2.27	60
beh10	-0.62	2.21	68
beh11	0.73	2.14	49
beh12	-0.84	2.27	57
beh13	-0.58	2.04	64
beh14	0.50	2.15	40
beh15	-0.36	2.22	59
beh16	0.56	2.14	48
beh17	0.38	2.01	61
beh18	0.19	2.02	53
beh19	0.91	1.78	43
beh20	1.15	1.82	53
beh21	-0.58	2.17	62
beh22	-0.33	2.13	61
beh23	0.27	2.33	41
beh24	-0.13	2.07	55
beh25	-0.12	1.98	60
beh26	1.54	1.57	37
beh27	0.44	1.92	52
beh28	0.79	1.91	57
beh29	1.09	1.69	56
beh30	1.14	1.77	57

iv. By Condition x Party ID

1. Control: Republicans

ctrlR <- d[d$cond == "ctrl" & d$party_factor == "Republican",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlR <- ctrlR[order(ctrlR$name),]

kable(ctrlR)

name	value_mean	value_SD	value_n
beh1	-0.59	2.16	71
beh2	1.21	1.63	43
beh3	-0.75	2.05	64
beh4	-0.19	2.38	54
beh5	1.15	1.74	41
beh6	1.17	1.82	52
beh7	-0.66	2.14	62
beh8	-0.84	2.12	70
beh9	-0.48	2.13	65
beh10	-0.59	2.08	70
beh11	0.74	2.08	46
beh12	-0.78	2.17	64
beh13	-0.25	2.09	61
beh14	0.27	2.14	37
beh15	-0.21	2.13	58
beh16	0.98	1.76	50
beh17	0.44	2.02	57
beh18	0.29	1.99	52
beh19	1.54	1.40	35
beh20	1.51	1.52	49
beh21	-0.71	2.15	56
beh22	-0.31	2.05	58
beh23	-0.22	2.30	40
beh24	-0.36	2.18	58
beh25	0.34	1.84	64
beh26	1.69	1.69	32
beh27	0.73	1.73	44
beh28	1.14	1.80	58
beh29	0.96	1.77	55
beh30	0.86	1.84	51

2. Control: Democrats

ctrlD <- d[d$cond == "ctrl" & d$party_factor == "Democrat",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlD <- ctrlD[order(ctrlD$name),]

kable(ctrlD)

name	value_mean	value_SD	value_n
beh1	0.42	2.19	72
beh2	1.38	1.75	50
beh3	0.01	2.11	77
beh4	0.12	2.04	67
beh5	1.36	1.89	53
beh6	1.22	1.88	60
beh7	-0.03	1.98	68
beh8	0.10	2.13	80
beh9	0.23	1.93	75
beh10	0.58	1.94	80
beh11	1.29	1.75	58
beh12	0.33	2.16	63
beh13	0.38	2.01	71
beh14	0.54	2.03	54
beh15	0.70	2.03	60
beh16	1.07	1.79	57
beh17	0.68	1.98	71
beh18	0.78	1.69	64
beh19	1.28	1.95	39
beh20	1.62	1.50	56
beh21	0.06	1.93	72
beh22	0.93	1.72	67
beh23	0.39	2.52	51
beh24	-0.04	2.12	72
beh25	0.92	1.87	76
beh26	1.28	2.05	36
beh27	1.30	1.77	61
beh28	1.31	1.60	68
beh29	1.30	1.82	61
beh30	1.00	2.12	57

3. Control: Independents

ctrlI <- d[d$cond == "ctrl" & d$party_factor == "Independent",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlI <- ctrlI[order(ctrlI$name),]

kable(ctrlI)

name	value_mean	value_SD	value_n
beh1	0.23	2.18	83
beh2	1.28	1.80	60
beh3	-0.22	2.00	86
beh4	0.00	2.34	68
beh5	0.92	1.87	50
beh6	1.53	1.85	72
beh7	-0.14	2.20	79
beh8	-0.38	2.11	88
beh9	0.18	2.03	90
beh10	0.36	2.13	89
beh11	1.35	1.89	57
beh12	0.10	2.17	68
beh13	-0.20	2.07	86
beh14	0.23	2.11	56
beh15	0.20	2.05	74
beh16	0.50	1.91	60
beh17	0.44	1.96	80
beh18	0.72	1.99	72
beh19	1.45	1.77	49
beh20	1.33	1.75	61
beh21	-0.07	2.04	71
beh22	0.30	2.09	81
beh23	0.66	2.19	59
beh24	0.13	2.03	79
beh25	0.74	1.86	85
beh26	1.44	1.88	45
beh27	0.75	1.84	57
beh28	0.79	1.87	70
beh29	1.03	1.89	69
beh30	1.14	1.85	65

4. Climate: Republicans

climateR <- d[d$cond == "climate" & d$party_factor=="Republican",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateR <- climateR[order(climateR$name),]

kable(climateR)

name	value_mean	value_SD	value_n
beh1	-0.58	2.18	83
beh2	0.77	1.78	48
beh3	-0.64	1.95	74
beh4	-0.23	2.04	62
beh5	0.82	1.86	55
beh6	1.06	1.76	65
beh7	-0.35	2.04	69
beh8	-1.35	1.98	88
beh9	-0.41	1.94	78
beh10	-0.94	2.07	84
beh11	0.65	1.88	51
beh12	-1.01	2.09	67
beh13	-0.81	1.92	75
beh14	0.36	2.04	45
beh15	-0.18	2.02	65
beh16	0.86	1.88	57
beh17	0.19	2.04	74
beh18	0.56	1.77	54
beh19	0.65	1.97	55
beh20	1.04	1.69	54
beh21	-0.33	2.01	72
beh22	-0.26	2.01	68
beh23	0.04	2.29	45
beh24	-0.61	2.05	75
beh25	0.16	1.84	82
beh26	1.46	1.77	35
beh27	0.19	1.99	53
beh28	0.78	1.82	64
beh29	0.90	1.72	62
beh30	1.06	1.88	63

5. Climate: Democrats

climateD <- d[d$cond == "climate" & d$party_factor=="Democrat",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateD <- climateD[order(climateD$name),]

kable(climateD)

name	value_mean	value_SD	value_n
beh1	0.14	2.11	76
beh2	1.18	2.01	50
beh3	0.26	1.99	74
beh4	0.49	2.19	65
beh5	0.86	1.95	50
beh6	1.19	1.92	54
beh7	0.22	2.06	73
beh8	0.37	2.16	79
beh9	0.30	2.03	74
beh10	0.61	2.04	75
beh11	0.81	1.88	48
beh12	0.30	2.16	57
beh13	0.00	2.23	72
beh14	0.56	2.06	50
beh15	0.37	2.11	62
beh16	1.04	1.87	52
beh17	0.62	2.05	69
beh18	0.91	1.98	57
beh19	1.00	1.90	48
beh20	0.56	2.15	54
beh21	-0.04	2.24	69
beh22	0.68	2.03	66
beh23	-0.08	2.17	49
beh24	-0.39	2.12	69
beh25	1.32	1.84	72
beh26	1.50	1.82	42
beh27	0.96	1.96	53
beh28	1.13	1.91	60
beh29	1.34	1.75	64
beh30	1.12	1.94	60

6. Climate: Independents

climateI <- d[d$cond == "climate" & d$party_factor=="Independent",] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateI <- climateI[order(climateI$name),]

kable(climateI)

name	value_mean	value_SD	value_n
beh1	-0.20	2.02	61
beh2	0.81	1.86	36
beh3	-0.11	1.93	66
beh4	-0.54	1.86	52
beh5	0.87	1.88	45
beh6	1.23	1.66	53
beh7	0.16	1.95	61
beh8	-0.27	2.14	73
beh9	0.23	1.79	64
beh10	0.33	1.98	76
beh11	0.57	1.80	51
beh12	0.22	2.05	63
beh13	0.27	2.03	75
beh14	0.72	1.80	43
beh15	-0.08	1.95	63
beh16	1.19	1.68	53
beh17	0.40	2.03	63
beh18	0.63	1.51	52
beh19	1.22	1.56	46
beh20	1.30	1.82	47
beh21	0.57	1.95	60
beh22	0.51	1.77	61
beh23	0.02	2.05	46
beh24	-0.33	2.04	60
beh25	0.97	1.72	64
beh26	1.62	1.71	34
beh27	0.33	1.85	48
beh28	1.16	1.93	51
beh29	1.12	1.51	56
beh30	1.25	1.52	55

v. By Condition x ideology

1. Control: Strong liberal

ctrlSL <- d[d$cond == "ctrl" & d$ideology == -2,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlSL <- ctrlSL[order(ctrlSL$name),]

kable(ctrlSL)

name	value_mean	value_SD	value_n
beh1	0.45	2.52	22
beh2	1.00	2.20	13
beh3	0.43	2.11	23
beh4	0.53	2.17	19
beh5	0.43	2.24	14
beh6	1.26	2.08	19
beh7	0.70	1.98	20
beh8	0.29	2.20	24
beh9	0.33	2.18	24
beh10	1.00	2.11	24
beh11	1.79	1.76	14
beh12	0.18	2.46	17
beh13	0.33	2.35	21
beh14	-0.14	2.44	14
beh15	0.12	2.47	17
beh16	1.21	1.93	19
beh17	0.85	2.11	20
beh18	0.72	2.05	18
beh19	1.36	2.47	14
beh20	1.29	2.16	14
beh21	0.39	2.28	18
beh22	1.26	1.74	23
beh23	0.19	2.95	16
beh24	-0.05	2.32	22
beh25	1.05	2.18	21
beh26	0.70	2.06	10
beh27	1.18	2.16	17
beh28	1.11	1.79	19
beh29	1.50	1.79	18
beh30	1.06	2.41	16

2. Control: Liberal

ctrlL <- d[d$cond == "ctrl" & d$ideology == -1,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlL <- ctrlL[order(ctrlL$name),]

kable(ctrlL)

name	value_mean	value_SD	value_n
beh1	0.55	2.20	33
beh2	1.62	1.61	24
beh3	-0.64	1.97	39
beh4	0.03	1.98	33
beh5	1.82	1.62	22
beh6	1.50	1.79	26
beh7	-0.61	1.87	33
beh8	0.20	2.17	35
beh9	0.58	1.57	36
beh10	0.46	2.18	37
beh11	1.67	1.62	27
beh12	0.43	2.05	30
beh13	0.41	1.94	34
beh14	0.74	1.94	23
beh15	1.56	1.69	25
beh16	1.00	1.68	25
beh17	0.45	2.17	33
beh18	0.71	1.81	31
beh19	1.67	1.53	18
beh20	1.52	1.50	25
beh21	0.34	1.91	32
beh22	0.44	2.05	32
beh23	1.13	2.16	23
beh24	-0.06	2.00	34
beh25	0.72	1.90	32
beh26	1.27	2.37	15
beh27	1.36	1.64	28
beh28	0.91	1.71	32
beh29	0.87	1.93	31
beh30	1.14	2.01	29

3. Control: Moderates

ctrlM <- d[d$cond == "ctrl" & d$ideology == 0,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlM <- ctrlM[order(ctrlM$name),]

kable(ctrlM)

name	value_mean	value_SD	value_n
beh1	0.23	2.09	98
beh2	1.38	1.64	65
beh3	-0.06	2.03	96
beh4	-0.11	2.29	85
beh5	1.26	1.58	58
beh6	1.40	1.83	77
beh7	-0.27	2.10	92
beh8	-0.50	2.01	105
beh9	0.13	2.05	102
beh10	0.17	2.02	103
beh11	1.03	1.79	67
beh12	0.11	2.08	82
beh13	-0.25	1.97	97
beh14	0.37	1.94	67
beh15	0.15	1.94	89
beh16	0.83	1.73	70
beh17	0.62	1.83	92
beh18	0.83	1.73	77
beh19	1.48	1.53	52
beh20	1.59	1.43	73
beh21	-0.16	1.94	87
beh22	0.31	1.95	89
beh23	0.31	2.25	64
beh24	0.08	2.12	92
beh25	0.93	1.78	101
beh26	1.59	1.67	46
beh27	0.82	1.85	67
beh28	0.88	1.81	78
beh29	1.19	1.78	78
beh30	1.09	1.83	68

4. Control: Conservatives

ctrlC <- d[d$cond == "ctrl" & d$ideology == 1,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlC <- ctrlC[order(ctrlC$name),]

kable(ctrlC)

name	value_mean	value_SD	value_n
beh1	-0.39	2.07	44
beh2	1.15	1.72	33
beh3	-0.45	2.01	42
beh4	-0.20	2.46	30
beh5	1.03	1.78	31
beh6	1.36	1.72	39
beh7	-0.42	2.30	38
beh8	-0.13	2.18	45
beh9	-0.05	1.98	43
beh10	-0.02	1.91	46
beh11	1.23	1.77	30
beh12	-0.33	2.24	39
beh13	0.27	2.10	41
beh14	0.48	2.23	27
beh15	0.22	1.99	36
beh16	0.76	2.00	34
beh17	0.67	1.96	36
beh18	0.58	2.10	38
beh19	1.58	1.64	24
beh20	1.34	1.77	32
beh21	-0.46	2.16	35
beh22	0.38	2.00	34
beh23	-0.04	2.33	28
beh24	-0.29	2.13	38
beh25	0.62	1.64	45
beh26	1.52	2.06	29
beh27	0.86	1.64	29
beh28	1.45	1.85	42
beh29	1.06	1.86	34
beh30	1.14	1.87	36

5. Control: Strong Conservatives

ctrlSC <- d[d$cond == "ctrl" & d$ideology == 2,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
ctrlSC <- ctrlSC[order(ctrlSC$name),]

kable(ctrlSC)

name	value_mean	value_SD	value_n
beh1	-0.80	2.38	30
beh2	1.11	1.94	19
beh3	-0.82	2.26	28
beh4	0.22	2.35	23
beh5	0.85	2.39	20
beh6	0.96	2.07	24
beh7	-0.15	2.33	27
beh8	-1.23	2.28	30
beh9	-1.35	2.12	26
beh10	-0.57	2.40	30
beh11	0.54	2.54	24
beh12	-1.11	2.35	28
beh13	-0.38	2.26	26
beh14	0.12	2.45	17
beh15	-0.54	2.40	26
beh16	0.55	2.09	20
beh17	-0.07	2.18	28
beh18	-0.04	2.09	25
beh19	0.88	1.96	16
beh20	1.48	1.65	23
beh21	-0.93	2.12	28
beh22	-0.45	2.23	29
beh23	0.25	2.43	20
beh24	-0.17	2.12	24
beh25	-0.33	1.98	27
beh26	1.79	1.42	14
beh27	0.86	1.81	22
beh28	1.27	1.54	26
beh29	0.92	1.91	25
beh30	0.52	1.98	25

6. Climate: Strong liberal

climateSL <- d[d$cond == "climate" & d$ideology == -2,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateSL <- climateSL[order(climateSL$name),]

kable(climateSL)

name	value_mean	value_SD	value_n
beh1	-0.31	2.50	13
beh2	1.09	2.26	11
beh3	0.25	1.91	12
beh4	0.40	2.63	10
beh5	0.40	2.22	10
beh6	1.08	2.10	13
beh7	-0.07	2.40	15
beh8	1.12	2.03	16
beh9	-0.08	2.47	12
beh10	1.38	1.96	16
beh11	1.20	2.15	10
beh12	0.60	2.07	10
beh13	0.14	2.80	14
beh14	0.50	2.00	8
beh15	0.53	2.33	15
beh16	0.70	2.00	10
beh17	1.71	1.86	14
beh18	0.82	2.32	11
beh19	1.23	2.28	13
beh20	0.69	2.29	13
beh21	0.47	2.20	15
beh22	0.93	1.94	14
beh23	1.22	2.39	9
beh24	-0.13	2.03	15
beh25	1.79	1.76	14
beh26	1.22	2.17	9
beh27	1.64	1.86	14
beh28	1.33	2.06	12
beh29	1.45	2.07	11
beh30	1.25	2.22	12

7. Climate: Liberal

climateL <- d[d$cond == "climate" & d$ideology == -1,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateL <- climateL[order(climateL$name),]

kable(climateL)

name	value_mean	value_SD	value_n
beh1	0.75	1.92	32
beh2	1.50	1.89	18
beh3	0.41	2.20	29
beh4	0.84	2.30	25
beh5	0.95	1.96	20
beh6	1.35	1.76	20
beh7	0.69	2.02	26
beh8	0.82	2.07	28
beh9	0.57	1.91	28
beh10	0.96	1.84	28
beh11	1.64	1.45	14
beh12	1.13	2.20	23
beh13	0.27	2.05	30
beh14	1.25	1.95	16
beh15	0.05	2.37	20
beh16	1.17	1.65	18
beh17	0.67	2.22	27
beh18	1.15	1.59	26
beh19	1.14	1.56	14
beh20	0.81	2.23	21
beh21	0.56	2.33	27
beh22	0.79	2.08	24
beh23	-0.12	2.16	16
beh24	-0.25	2.11	24
beh25	1.55	1.75	31
beh26	1.25	2.02	16
beh27	0.88	2.00	17
beh28	1.13	2.24	23
beh29	1.12	1.80	24
beh30	1.23	2.14	22

8. Climate: Moderates

climateM <- d[d$cond == "climate" & d$ideology == 0,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateM <- climateM[order(climateM$name),]

kable(climateM)

name	value_mean	value_SD	value_n
beh1	-0.35	2.03	86
beh2	1.00	1.62	48
beh3	0.03	1.89	88
beh4	-0.32	1.84	75
beh5	0.90	1.88	51
beh6	1.20	1.76	65
beh7	0.01	1.94	86
beh8	-0.39	2.07	96
beh9	0.28	1.67	88
beh10	-0.01	2.13	96
beh11	0.43	1.86	68
beh12	-0.12	2.06	76
beh13	0.09	2.06	91
beh14	0.48	1.96	61
beh15	0.05	1.88	77
beh16	1.10	1.77	70
beh17	0.32	2.00	81
beh18	0.80	1.72	64
beh19	1.02	1.84	61
beh20	1.05	1.79	56
beh21	0.05	2.02	80
beh22	0.25	1.90	79
beh23	-0.10	2.11	59
beh24	-0.57	2.04	82
beh25	0.85	1.85	85
beh26	1.65	1.65	43
beh27	0.25	1.90	60
beh28	1.07	1.74	69
beh29	1.09	1.62	69
beh30	1.00	1.62	70

9. Climate: Conservatives

climateC <- d[d$cond == "climate" & d$ideology == 1,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateC <- climateC[order(climateC$name),]

kable(climateC)

name	value_mean	value_SD	value_n
beh1	-0.57	2.13	56
beh2	0.62	2.00	32
beh3	-0.39	2.01	54
beh4	-0.35	2.10	43
beh5	0.93	1.89	42
beh6	1.30	1.67	46
beh7	-0.23	1.96	48
beh8	-1.13	2.05	63
beh9	-0.41	1.96	56
beh10	-0.44	2.12	59
beh11	0.46	1.88	35
beh12	-0.80	2.12	51
beh13	-0.61	2.09	51
beh14	0.19	2.04	32
beh15	0.02	2.05	47
beh16	1.18	1.69	38
beh17	-0.17	2.04	53
beh18	0.39	1.73	36
beh19	0.69	1.89	36
beh20	0.95	1.93	37
beh21	-0.13	1.98	47
beh22	0.31	2.00	48
beh23	-0.30	2.11	37
beh24	-0.65	2.16	54
beh25	0.40	1.76	57
beh26	1.64	1.92	22
beh27	0.60	1.97	35
beh28	1.19	1.69	42
beh29	1.00	1.81	49
beh30	0.91	2.02	44

10. Climate: Strong Conservatives

climateSC <- d[d$cond == "climate" & d$ideology == 2,] %>%
  select(beh1:beh30) %>%
  pivot_longer(beh1:beh30) %>%
  group_by(name) %>% 
  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("beh", 1:30)))
climateSC <- climateSC[order(climateSC$name),]

kable(climateSC)

name	value_mean	value_SD	value_n
beh1	-0.17	2.28	35
beh2	0.85	2.05	27
beh3	-0.94	1.87	33
beh4	0.18	2.16	28
beh5	0.66	1.91	29
beh6	0.80	1.86	30
beh7	-0.17	2.26	30
beh8	-1.08	2.30	39
beh9	-0.24	2.30	34
beh10	-0.66	2.07	38
beh11	0.92	1.71	25
beh12	-0.59	2.20	29
beh13	-0.71	1.89	38
beh14	0.78	1.91	23
beh15	-0.21	2.09	33
beh16	0.57	2.22	28
beh17	0.76	1.80	33
beh18	0.39	1.97	28
beh19	0.93	1.71	27
beh20	0.90	1.94	30
beh21	-0.29	2.20	34
beh22	-0.22	2.06	32
beh23	0.29	2.31	21
beh24	-0.10	2.07	31
beh25	0.06	1.98	33
beh26	1.39	1.67	23
beh27	0.13	1.98	30
beh28	0.39	2.11	31
beh29	1.23	1.50	31
beh30	1.62	1.43	32

vi. Condition x Party ID: By behavior

Behavior 1

kable(aggregate(d$beh1, list(d$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

kable(aggregate(d$beh1, list(d$party_factor, d$cond), FUN = function(x) round(sd(x, na.rm = T), 2)))

Group.1	Group.2	x
Democrat	climate	2.11
Independent	climate	2.02
Republican	climate	2.18
Democrat	ctrl	2.19
Independent	ctrl	2.18
Republican	ctrl	2.16

Behavior 2

kable(aggregate(d$beh2, list(d$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

Behavior 3

kable(aggregate(d$beh3, list(d$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

Behavior 4

kable(aggregate(d$beh4, list(d$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

Behavior 5

kable(aggregate(d$beh5, list(d$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

Behavior 6

kable(aggregate(d$beh6, list(d$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

Behavior 7

kable(aggregate(d$beh7, list(d$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

Behavior 8

kable(aggregate(d$beh8, list(d$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

Behavior 9

kable(aggregate(d$beh9, list(d$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

Behavior 10

kable(aggregate(d$beh10, list(d$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

Behavior 11

kable(aggregate(d$beh11, list(d$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

Behavior 12

kable(aggregate(d$beh12, list(d$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

Behavior 13

kable(aggregate(d$beh13, list(d$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

Behavior 14

kable(aggregate(d$beh14, list(d$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

Behavior 15

kable(aggregate(d$beh15, list(d$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

Behavior 16

kable(aggregate(d$beh16, list(d$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

Behavior 17

kable(aggregate(d$beh17, list(d$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

Behavior 18

kable(aggregate(d$beh18, list(d$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

Behavior 19

kable(aggregate(d$beh19, list(d$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

Behavior 20

kable(aggregate(d$beh20, list(d$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

Behavior 21

kable(aggregate(d$beh21, list(d$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

Behavior 22

kable(aggregate(d$beh22, list(d$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

Behavior 23

kable(aggregate(d$beh23, list(d$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

Behavior 24

kable(aggregate(d$beh24, list(d$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

Behavior 25

kable(aggregate(d$beh25, list(d$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

Behavior 26

kable(aggregate(d$beh26, list(d$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

Behavior 27

kable(aggregate(d$beh27, list(d$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

Behavior 28

kable(aggregate(d$beh28, list(d$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

Behavior 29

kable(aggregate(d$beh29, list(d$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

Behavior 30

kable(aggregate(d$beh30, list(d$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

vii. Condition x Ideology: By behavior

Behavior 1

kable(aggregate(d$beh1, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	-0.31
-1	climate	0.75
0	climate	-0.35
1	climate	-0.57
2	climate	-0.17
-2	ctrl	0.45
-1	ctrl	0.55
0	ctrl	0.23
1	ctrl	-0.39
2	ctrl	-0.80

kable(aggregate(d$beh1, list(d$ideology, d$cond), FUN = function(x) round(sd(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	2.50
-1	climate	1.92
0	climate	2.03
1	climate	2.13
2	climate	2.28
-2	ctrl	2.52
-1	ctrl	2.20
0	ctrl	2.09
1	ctrl	2.07
2	ctrl	2.38

Behavior 2

kable(aggregate(d$beh2, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.09
-1	climate	1.50
0	climate	1.00
1	climate	0.62
2	climate	0.85
-2	ctrl	1.00
-1	ctrl	1.62
0	ctrl	1.38
1	ctrl	1.15
2	ctrl	1.11

Behavior 3

kable(aggregate(d$beh3, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.25
-1	climate	0.41
0	climate	0.03
1	climate	-0.39
2	climate	-0.94
-2	ctrl	0.43
-1	ctrl	-0.64
0	ctrl	-0.06
1	ctrl	-0.45
2	ctrl	-0.82

Behavior 4

kable(aggregate(d$beh4, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.40
-1	climate	0.84
0	climate	-0.32
1	climate	-0.35
2	climate	0.18
-2	ctrl	0.53
-1	ctrl	0.03
0	ctrl	-0.11
1	ctrl	-0.20
2	ctrl	0.22

Behavior 5

kable(aggregate(d$beh5, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.40
-1	climate	0.95
0	climate	0.90
1	climate	0.93
2	climate	0.66
-2	ctrl	0.43
-1	ctrl	1.82
0	ctrl	1.26
1	ctrl	1.03
2	ctrl	0.85

Behavior 6

kable(aggregate(d$beh6, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.08
-1	climate	1.35
0	climate	1.20
1	climate	1.30
2	climate	0.80
-2	ctrl	1.26
-1	ctrl	1.50
0	ctrl	1.40
1	ctrl	1.36
2	ctrl	0.96

Behavior 7

kable(aggregate(d$beh7, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	-0.07
-1	climate	0.69
0	climate	0.01
1	climate	-0.23
2	climate	-0.17
-2	ctrl	0.70
-1	ctrl	-0.61
0	ctrl	-0.27
1	ctrl	-0.42
2	ctrl	-0.15

Behavior 8

kable(aggregate(d$beh8, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.12
-1	climate	0.82
0	climate	-0.39
1	climate	-1.13
2	climate	-1.08
-2	ctrl	0.29
-1	ctrl	0.20
0	ctrl	-0.50
1	ctrl	-0.13
2	ctrl	-1.23

Behavior 9

kable(aggregate(d$beh9, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	-0.08
-1	climate	0.57
0	climate	0.28
1	climate	-0.41
2	climate	-0.24
-2	ctrl	0.33
-1	ctrl	0.58
0	ctrl	0.13
1	ctrl	-0.05
2	ctrl	-1.35

Behavior 10

kable(aggregate(d$beh10, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.38
-1	climate	0.96
0	climate	-0.01
1	climate	-0.44
2	climate	-0.66
-2	ctrl	1.00
-1	ctrl	0.46
0	ctrl	0.17
1	ctrl	-0.02
2	ctrl	-0.57

Behavior 11

kable(aggregate(d$beh11, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.20
-1	climate	1.64
0	climate	0.43
1	climate	0.46
2	climate	0.92
-2	ctrl	1.79
-1	ctrl	1.67
0	ctrl	1.03
1	ctrl	1.23
2	ctrl	0.54

Behavior 12

kable(aggregate(d$beh12, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.60
-1	climate	1.13
0	climate	-0.12
1	climate	-0.80
2	climate	-0.59
-2	ctrl	0.18
-1	ctrl	0.43
0	ctrl	0.11
1	ctrl	-0.33
2	ctrl	-1.11

Behavior 13

kable(aggregate(d$beh13, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.14
-1	climate	0.27
0	climate	0.09
1	climate	-0.61
2	climate	-0.71
-2	ctrl	0.33
-1	ctrl	0.41
0	ctrl	-0.25
1	ctrl	0.27
2	ctrl	-0.38

Behavior 14

kable(aggregate(d$beh14, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.50
-1	climate	1.25
0	climate	0.48
1	climate	0.19
2	climate	0.78
-2	ctrl	-0.14
-1	ctrl	0.74
0	ctrl	0.37
1	ctrl	0.48
2	ctrl	0.12

Behavior 15

kable(aggregate(d$beh15, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.53
-1	climate	0.05
0	climate	0.05
1	climate	0.02
2	climate	-0.21
-2	ctrl	0.12
-1	ctrl	1.56
0	ctrl	0.15
1	ctrl	0.22
2	ctrl	-0.54

Behavior 16

kable(aggregate(d$beh16, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.70
-1	climate	1.17
0	climate	1.10
1	climate	1.18
2	climate	0.57
-2	ctrl	1.21
-1	ctrl	1.00
0	ctrl	0.83
1	ctrl	0.76
2	ctrl	0.55

Behavior 17

kable(aggregate(d$beh17, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.71
-1	climate	0.67
0	climate	0.32
1	climate	-0.17
2	climate	0.76
-2	ctrl	0.85
-1	ctrl	0.45
0	ctrl	0.62
1	ctrl	0.67
2	ctrl	-0.07

Behavior 18

kable(aggregate(d$beh18, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.82
-1	climate	1.15
0	climate	0.80
1	climate	0.39
2	climate	0.39
-2	ctrl	0.72
-1	ctrl	0.71
0	ctrl	0.83
1	ctrl	0.58
2	ctrl	-0.04

Behavior 19

kable(aggregate(d$beh19, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.23
-1	climate	1.14
0	climate	1.02
1	climate	0.69
2	climate	0.93
-2	ctrl	1.36
-1	ctrl	1.67
0	ctrl	1.48
1	ctrl	1.58
2	ctrl	0.88

Behavior 20

kable(aggregate(d$beh20, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.69
-1	climate	0.81
0	climate	1.05
1	climate	0.95
2	climate	0.90
-2	ctrl	1.29
-1	ctrl	1.52
0	ctrl	1.59
1	ctrl	1.34
2	ctrl	1.48

Behavior 21

kable(aggregate(d$beh21, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.47
-1	climate	0.56
0	climate	0.05
1	climate	-0.13
2	climate	-0.29
-2	ctrl	0.39
-1	ctrl	0.34
0	ctrl	-0.16
1	ctrl	-0.46
2	ctrl	-0.93

Behavior 22

kable(aggregate(d$beh22, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	0.93
-1	climate	0.79
0	climate	0.25
1	climate	0.31
2	climate	-0.22
-2	ctrl	1.26
-1	ctrl	0.44
0	ctrl	0.31
1	ctrl	0.38
2	ctrl	-0.45

Behavior 23

kable(aggregate(d$beh23, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.22
-1	climate	-0.12
0	climate	-0.10
1	climate	-0.30
2	climate	0.29
-2	ctrl	0.19
-1	ctrl	1.13
0	ctrl	0.31
1	ctrl	-0.04
2	ctrl	0.25

Behavior 24

kable(aggregate(d$beh24, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	-0.13
-1	climate	-0.25
0	climate	-0.57
1	climate	-0.65
2	climate	-0.10
-2	ctrl	-0.05
-1	ctrl	-0.06
0	ctrl	0.08
1	ctrl	-0.29
2	ctrl	-0.17

Behavior 25

kable(aggregate(d$beh25, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.79
-1	climate	1.55
0	climate	0.85
1	climate	0.40
2	climate	0.06
-2	ctrl	1.05
-1	ctrl	0.72
0	ctrl	0.93
1	ctrl	0.62
2	ctrl	-0.33

Behavior 26

kable(aggregate(d$beh26, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.22
-1	climate	1.25
0	climate	1.65
1	climate	1.64
2	climate	1.39
-2	ctrl	0.70
-1	ctrl	1.27
0	ctrl	1.59
1	ctrl	1.52
2	ctrl	1.79

Behavior 27

kable(aggregate(d$beh27, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.64
-1	climate	0.88
0	climate	0.25
1	climate	0.60
2	climate	0.13
-2	ctrl	1.18
-1	ctrl	1.36
0	ctrl	0.82
1	ctrl	0.86
2	ctrl	0.86

Behavior 28

kable(aggregate(d$beh28, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.33
-1	climate	1.13
0	climate	1.07
1	climate	1.19
2	climate	0.39
-2	ctrl	1.11
-1	ctrl	0.91
0	ctrl	0.88
1	ctrl	1.45
2	ctrl	1.27

Behavior 29

kable(aggregate(d$beh29, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.45
-1	climate	1.12
0	climate	1.09
1	climate	1.00
2	climate	1.23
-2	ctrl	1.50
-1	ctrl	0.87
0	ctrl	1.19
1	ctrl	1.06
2	ctrl	0.92

Behavior 30

kable(aggregate(d$beh30, list(d$ideology, d$cond), FUN = function(x) round(mean(x, na.rm = T), 2)))

Group.1	Group.2	x
-2	climate	1.25
-1	climate	1.23
0	climate	1.00
1	climate	0.91
2	climate	1.62
-2	ctrl	1.06
-1	ctrl	1.14
0	ctrl	1.09
1	ctrl	1.14
2	ctrl	0.52

PERSONAL RESPONSIBILTY

describeBy(d$persRes1_capture,d$ideology)

## 
##  Descriptive statistics by group 
## group: -2
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 47 5.72 1.56      6    5.92 1.48   1   7     6 -1.07     0.17 0.23
## ------------------------------------------------------------ 
## group: -1
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 77 5.29 1.54      6    5.44 1.48   1   7     6 -0.78    -0.08 0.18
## ------------------------------------------------------------ 
## group: 0
##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 229 5.12 1.76      6     5.3 1.48   1   7     6 -0.58     -0.8 0.12
## ------------------------------------------------------------ 
## group: 1
##    vars   n mean  sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 118 5.07 1.7      6    5.24 1.48   1   7     6 -0.71    -0.39 0.16
## ------------------------------------------------------------ 
## group: 2
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 74 5.12 1.89      6     5.3 1.48   1   7     6 -0.51    -1.16 0.22

describeBy(d$persRes2_capture, d$ideology)

## 
##  Descriptive statistics by group 
## group: -2
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 47 4.06 2.42      4    4.08 4.45   1   7     6 0.02    -1.66 0.35
## ------------------------------------------------------------ 
## group: -1
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 77 4.44 2.14      4    4.54 2.97   1   7     6 -0.3    -1.27 0.24
## ------------------------------------------------------------ 
## group: 0
##    vars   n mean  sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 229 5.04 1.8      5    5.22 2.97   1   7     6 -0.56    -0.77 0.12
## ------------------------------------------------------------ 
## group: 1
##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 118 5.37 1.73      6    5.59 1.48   1   7     6 -0.84    -0.48 0.16
## ------------------------------------------------------------ 
## group: 2
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 74 5.22 1.88      6    5.38 1.48   1   7     6 -0.46    -1.35 0.22