Climate Change Methods - Spring 2022
Sarah Coffin
7/3/2022
#Dataset
CC <- read.csv("Climate Change Methods_CLEAN 3_June 29, 2022.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))Participants
#Number of responses (rows)
nrow(CC)## [1] 1007#Age range
range(CC$Dem_Age, na.rm = T)## [1] 18 93#Average age
mean(CC$Dem_Age, na.rm = T)## [1] 45.40321#Standard deviation of age
sd(CC$Dem_Age, na.rm = T)## [1] 16.20402#Gender frequencies
table(CC$Dem_Gen)##
## 1 2 3
## 507 488 12#Ethnicity
table(CC$Dem_Ethnicity)##
## 1 2 3 4 5 6 7
## 61 129 44 1 4 758 10CC$Ethnicity <- NA
CC$Ethnicity[CC$Dem_Ethnicity == 1] <- 'Asian'
CC$Ethnicity[CC$Dem_Ethnicity == 2] <- 'Black'
CC$Ethnicity[CC$Dem_Ethnicity == 3] <- 'Hispanic'
CC$Ethnicity[CC$Dem_Ethnicity == 4] <- 'Nat Amer'
CC$Ethnicity[CC$Dem_Ethnicity == 5] <- 'Nat Pac'
CC$Ethnicity[CC$Dem_Ethnicity == 6] <- 'White'
CC$Ethnicity[CC$Dem_Ethnicity == 7] <- 'Other'
describe(CC$Dem_Ethnicity)## CC$Dem_Ethnicity
## n missing distinct Info Mean Gmd
## 1007 0 7 0.571 5.058 1.518
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 61 129 44 1 4 758 10
## Proportion 0.061 0.128 0.044 0.001 0.004 0.753 0.010#Gender
CC$Dem_Gender <- as.numeric(as.character(CC$Dem_Gen))
describe(CC$Dem_Gen)## CC$Dem_Gen
## n missing distinct Info Mean Gmd
## 1007 0 3 0.759 1.508 0.524
##
## Value 1 2 3
## Frequency 507 488 12
## Proportion 0.503 0.485 0.012#Age
CC$Demograph_Age <- as.numeric(as.character(CC$Dem_Age))
describe(CC$Demograph_Age)## CC$Demograph_Age
## n missing distinct Info Mean Gmd .05 .10
## 997 10 67 1 45.4 18.66 21 24
## .25 .50 .75 .90 .95
## 31 44 59 67 71
##
## lowest : 18 19 20 21 22, highest: 80 81 82 91 93range(CC$Demograph_Age ,na.rm = T)## [1] 18 93#Political Orientation
##"Which of the following describes your political orientation?"
CC$polOR <- factor(CC$PI_Orientation, levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Strongly Conservative", "Moderately Conservative", "Slightly Conservative", "Neither Conservative Nor Liberal", "Slightly Liberal", "Moderately Liberal", "Strongly Liberal"))
table(CC$polOR)##
## Strongly Conservative Moderately Conservative
## 62 102
## Slightly Conservative Neither Conservative Nor Liberal
## 72 183
## Slightly Liberal Moderately Liberal
## 125 239
## Strongly Liberal
## 224Scales
Aversion to Tampering with Nature
#Aversion to Tampering with Nature
#Aversion to Tampering with Nature Item Definitions
CC$ATNS_1 <- as.numeric(as.character(CC$ATNS_1_36))
CC$ATNS_2 <- as.numeric(as.character(CC$ATNS_1_37))
CC$ATNS_3 <- as.numeric(as.character(CC$ATNS_1_38))
CC$ATNS_4 <- as.numeric(as.character(CC$ATNS_1_39))
CC$ATNS_5 <- as.numeric(as.character(CC$ATNS_1_40))
#Recode item 2
CC$ATNS_2R <- (100- CC$ATNS_2)
#Aversion to Tampering with Nature Scale Descriptives (No reversed codes)
describe(CC$ATNS_1)## CC$ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.999 49.63 30.88 5.00 15.00
## .25 .50 .75 .90 .95
## 27.25 50.00 70.00 88.00 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100sd(CC$ATNS_1)## [1] NArange(CC$ATNS_1, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_2)## CC$ATNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.999 42.53 32.5 0.0 2.0
## .25 .50 .75 .90 .95
## 19.5 41.0 63.0 82.0 91.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100sd(CC$ATNS_2)## [1] 28.28759range(CC$ATNS_2, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_3)## CC$ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1005 2 101 0.999 49.43 32.8 0.0 10.4
## .25 .50 .75 .90 .95
## 27.0 50.0 70.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(CC$ATNS_3)## [1] NArange(CC$ATNS_3, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_4)## CC$ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.998 61.51 30.6 12 21
## .25 .50 .75 .90 .95
## 45 64 82 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(CC$ATNS_4)## [1] 26.89782range(CC$ATNS_4, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_5)## CC$ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 101 0.999 54.71 33.05 3.3 13.0
## .25 .50 .75 .90 .95
## 32.0 57.0 76.0 96.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(CC$ATNS_5)## [1] 28.8092range(CC$ATNS_5, na.rm=TRUE)## [1] 0 100#Aversion to Tampering with Nature Scale Histograms by Item (No reversed codes)
hist(CC$ATNS_1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks.')
hist(CC$ATNS_2, main = 'ATNS #2: People who say we shouldn’t tamper with nature are just being naïve.')
hist(CC$ATNS_3, main = 'ATNS #3: Human beings have no right to meddle with the natural environment.')
hist(CC$ATNS_4, main = 'ATNS #4: I would prefer to live in a world where humans leave nature alone.')
hist(CC$ATNS_5, main = 'ATNS #5: Altering nature will be our downfall as a species.')
#Cronbach's Alpha (4 and 5 reverse coded)
CC$ATNS_Scale <- data.frame(CC$ATNS_1, CC$ATNS_2R, CC$ATNS_3, CC$ATNS_4, CC$ATNS_5)
CC$ATNS_Score <- rowMeans(CC [, c("ATNS_1", "ATNS_2R", "ATNS_3", "ATNS_4", "ATNS_5")], na.rm=TRUE)
psych::alpha(CC$ATNS_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.81 0.49 4.8 0.0085 55 21 0.52
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.ATNS_1 0.85 0.85 0.81 0.58 5.6 0.0079 0.0053 0.58
## CC.ATNS_2R 0.81 0.81 0.78 0.51 4.2 0.0098 0.0211 0.51
## CC.ATNS_3 0.76 0.76 0.72 0.44 3.2 0.0122 0.0164 0.45
## CC.ATNS_4 0.77 0.77 0.73 0.45 3.3 0.0118 0.0156 0.45
## CC.ATNS_5 0.78 0.78 0.75 0.46 3.5 0.0115 0.0252 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ATNS_1 1006 0.62 0.63 0.47 0.43 50 27
## CC.ATNS_2R 1007 0.74 0.74 0.64 0.58 57 28
## CC.ATNS_3 1005 0.84 0.84 0.81 0.73 49 29
## CC.ATNS_4 1007 0.83 0.83 0.80 0.72 62 27
## CC.ATNS_5 1007 0.81 0.81 0.75 0.69 55 29describe(CC$ATNS_Scale)## CC$ATNS_Scale
##
## 5 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.999 49.63 30.88 5.00 15.00
## .25 .50 .75 .90 .95
## 27.25 50.00 70.00 88.00 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_2R
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.999 57.47 32.5 9.0 18.0
## .25 .50 .75 .90 .95
## 37.0 59.0 80.5 98.0 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1005 2 101 0.999 49.43 32.8 0.0 10.4
## .25 .50 .75 .90 .95
## 27.0 50.0 70.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.998 61.51 30.6 12 21
## .25 .50 .75 .90 .95
## 45 64 82 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 101 0.999 54.71 33.05 3.3 13.0
## .25 .50 .75 .90 .95
## 32.0 57.0 76.0 96.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Pearsons r Correlation
correlation <- cor(CC$ATNS_Scale, method = 'pearson')
correlation## CC.ATNS_1 CC.ATNS_2R CC.ATNS_3 CC.ATNS_4 CC.ATNS_5
## CC.ATNS_1 1 NA NA NA NA
## CC.ATNS_2R NA 1.0000000 NA 0.5384829 0.4918514
## CC.ATNS_3 NA NA 1 NA NA
## CC.ATNS_4 NA 0.5384829 NA 1.0000000 0.6127687
## CC.ATNS_5 NA 0.4918514 NA 0.6127687 1.0000000Benefit
# Benefit was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented naturalness perception of the technology rated.
## 1. This is likely to lead to achieving carbon neutral climate goals.Descriptives
# Define Variables
CC$Ben_AFSCS <- CC$Ben_AFSCS_18
CC$Ben_BIO <- CC$Ben_BIO_18
CC$Ben_BECCS <- CC$Ben_BECCS_18
CC$Ben_DACCS <- CC$Ben_DACCS_18
CC$Ben_EW <- CC$Ben_EW_18
CC$Ben_OF <- CC$Ben_OF_18
CC$Ben_BF <- CC$Ben_BF_18
CC$Ben_NE <- CC$Ben_NE_18
CC$Ben_SE <- CC$Ben_SE_18
CC$Ben_WE <- CC$Ben_WE_18
#Descriptives
describe(CC$Ben_AFSCS)## CC$Ben_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 80 0.999 68.42 26.15 22.1 37.0
## .25 .50 .75 .90 .95
## 55.5 72.0 85.0 97.0 100.0
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100sd(CC$Ben_AFSCS, na.rm = TRUE)## [1] 23.72132hist(CC$Ben_AFSCS)
describe(CC$Ben_BIO)## CC$Ben_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 85 0.999 53.47 29.28 6.10 20.00
## .25 .50 .75 .90 .95
## 33.00 56.50 72.25 86.00 92.45
##
## lowest : 0 1 3 5 7, highest: 95 97 98 99 100sd(CC$Ben_BIO, na.rm = TRUE)## [1] 25.6215hist(CC$Ben_BIO)
describe(CC$Ben_BECCS) ## CC$Ben_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.999 55 29.13 10.00 18.00
## .25 .50 .75 .90 .95
## 36.00 57.00 74.75 88.00 95.00
##
## lowest : 0 1 3 6 7, highest: 94 95 96 97 100sd(CC$Ben_BECCS, na.rm = TRUE)## [1] 25.51696hist(CC$Ben_BECCS)
describe(CC$Ben_DACCS)## CC$Ben_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 89 0.999 55.35 30.26 3.0 15.0
## .25 .50 .75 .90 .95
## 37.0 59.0 75.0 90.0 99.4
##
## lowest : 0 1 2 3 5, highest: 93 95 96 98 100sd(CC$Ben_DACCS, na.rm = TRUE)## [1] 26.63817hist(CC$Ben_DACCS)
describe(CC$Ben_EW)## CC$Ben_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.999 52.15 27.98 0.0 13.8
## .25 .50 .75 .90 .95
## 37.0 55.0 70.0 81.2 90.0
##
## lowest : 0 3 4 5 6, highest: 95 96 97 99 100sd(CC$Ben_EW, na.rm = TRUE)## [1] 24.84342hist(CC$Ben_EW)
describe(CC$Ben_OF)## CC$Ben_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 54.54 28.93 7.6 17.0
## .25 .50 .75 .90 .95
## 36.0 58.0 74.5 86.0 91.7
##
## lowest : 0 2 4 5 7, highest: 92 93 95 96 100sd(CC$Ben_OF, na.rm = TRUE)## [1] 25.43145hist(CC$Ben_OF)
describe(CC$Ben_BF)## CC$Ben_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 51.92 30.47 3.05 10.00
## .25 .50 .75 .90 .95
## 34.00 57.00 70.00 85.00 94.30
##
## lowest : 0 1 2 5 6, highest: 93 95 96 97 100sd(CC$Ben_BF, na.rm = TRUE)## [1] 26.71672hist(CC$Ben_BF)
describe(CC$Ben_NE)## CC$Ben_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.999 60.18 30.92 0.0 19.2
## .25 .50 .75 .90 .95
## 44.0 66.0 80.0 92.4 98.4
##
## lowest : 0 6 9 10 11, highest: 94 95 97 98 100sd(CC$Ben_NE, na.rm = TRUE)## [1] 27.56813hist(CC$Ben_NE)
describe(CC$Ben_SE)## CC$Ben_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.998 66.31 29.55 10.4 25.0
## .25 .50 .75 .90 .95
## 50.0 71.0 86.0 100.0 100.0
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100sd(CC$Ben_SE, na.rm = TRUE)## [1] 26.49281hist(CC$Ben_SE)
describe(CC$Ben_WE) ## CC$Ben_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.998 64.88 28.99 9.6 25.0
## .25 .50 .75 .90 .95
## 51.0 68.0 85.0 100.0 100.0
##
## lowest : 0 5 6 8 10, highest: 96 97 98 99 100sd(CC$Ben_WE, na.rm = TRUE)## [1] 26.12863hist(CC$Ben_WE)
Score(s) & Scale(s)
# Note: Benefit Scores & scales not present because measure is one item.)Climate Change Belief
Descriptives
#Climate Change Belief Item Definitions
CC$CCB1 <- as.numeric(as.character(CC$CCB_1_48))
CC$CCB2 <- as.numeric(as.character(CC$CCB_1_49))
CC$CCB3 <- as.numeric(as.character(CC$CCB_1_50))
CC$CCB4 <- as.numeric(as.character(CC$CCB_1_51))
#Climate Change Belief Descriptives
describe(CC$CCB1)## CC$CCB1
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 73 0.859 86.91 19.52 33.25 58.00
## .25 .50 .75 .90 .95
## 83.25 100.00 100.00 100.00 100.00
##
## lowest : 0 8 11 13 15, highest: 96 97 98 99 100range(CC$CCB1, na.rm=TRUE)## [1] 0 100sd(CC$CCB1, na.rm=TRUE)## [1] 21.93689describe(CC$CCB2)## CC$CCB2
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 87 0.89 83.6 23.83 19 50
## .25 .50 .75 .90 .95
## 79 98 100 100 100
##
## lowest : 0 3 5 7 8, highest: 96 97 98 99 100range(CC$CCB2, na.rm=TRUE)## [1] 0 100sd(CC$CCB2, na.rm=TRUE)## [1] 25.67106describe(CC$CCB3)## CC$CCB3
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 89 0.936 79.65 27.61 4 35
## .25 .50 .75 .90 .95
## 70 91 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(CC$CCB3, na.rm=TRUE)## [1] 0 100sd(CC$CCB3, na.rm=TRUE)## [1] 28.29363describe(CC$CCB4)## CC$CCB4
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 86 0.98 76.35 27.33 15 40
## .25 .50 .75 .90 .95
## 65 85 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(CC$CCB4, na.rm=TRUE)## [1] 0 100sd(CC$CCB4, na.rm=TRUE)## [1] 26.23131#Climate Change Belief Histograms
hist(CC$CCB1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(CC$CCB2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(CC$CCB3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(CC$CCB4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
Score(s) & Scale(s)
#Score & Scale
CC$CCB_Score <- rowMeans(CC[, c('CCB1', 'CCB2', 'CCB3','CCB4')], na.rm=T)
describe(CC$CCB_Score)## CC$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 250 0.987 81.61 23.23 25.00 47.05
## .25 .50 .75 .90 .95
## 75.00 91.25 98.88 100.00 100.00
##
## lowest : 0.00 2.00 3.75 4.00 4.75, highest: 99.00 99.25 99.50 99.75 100.00CC$CCB_Scale <- data.frame(CC$CCB_1_48, CC$CCB_1_49, CC$CCB_1_50, CC$CCB_1_51)
describe(CC$CCB_Scale)## CC$CCB_Scale
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.CCB_1_48
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 73 0.859 86.91 19.52 33.25 58.00
## .25 .50 .75 .90 .95
## 83.25 100.00 100.00 100.00 100.00
##
## lowest : 0 8 11 13 15, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_49
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 87 0.89 83.6 23.83 19 50
## .25 .50 .75 .90 .95
## 79 98 100 100 100
##
## lowest : 0 3 5 7 8, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_50
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 89 0.936 79.65 27.61 4 35
## .25 .50 .75 .90 .95
## 70 91 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_51
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 86 0.98 76.35 27.33 15 40
## .25 .50 .75 .90 .95
## 65 85 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Cronbach's Alpha
psych::alpha(CC$CCB_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.93 0.8 16 0.0031 82 24 0.8
##
## lower alpha upper 95% confidence boundaries
## 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CCB_1_48 0.93 0.93 0.90 0.82 13.4 0.0038 0.0013 0.82
## CC.CCB_1_49 0.90 0.91 0.87 0.77 9.8 0.0049 0.0033 0.78
## CC.CCB_1_50 0.91 0.92 0.89 0.78 11.0 0.0048 0.0069 0.78
## CC.CCB_1_51 0.93 0.94 0.92 0.83 15.1 0.0036 0.0024 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CCB_1_48 1006 0.90 0.91 0.87 0.84 87 22
## CC.CCB_1_49 1006 0.95 0.95 0.94 0.91 84 26
## CC.CCB_1_50 1007 0.94 0.93 0.91 0.88 80 28
## CC.CCB_1_51 1007 0.90 0.89 0.84 0.82 76 26#Correlation CCB
cor(CC$CCB_Scale, use= "complete.obs")## CC.CCB_1_48 CC.CCB_1_49 CC.CCB_1_50 CC.CCB_1_51
## CC.CCB_1_48 1.0000000 0.8696179 0.7786829 0.7037607
## CC.CCB_1_49 0.8696179 1.0000000 0.8542424 0.7818553
## CC.CCB_1_50 0.7786829 0.8542424 1.0000000 0.8154086
## CC.CCB_1_51 0.7037607 0.7818553 0.8154086 1.0000000Connectedness to Nature
Descriptives
#Connectedness to Nature Item Definitions
CC$CNS_1 <- as.numeric(as.character(CC$CNS_1_47))
CC$CNS_2 <- as.numeric(as.character(CC$CNS_1_48))
CC$CNS_3 <- as.numeric(as.character(CC$CNS_1_49))
CC$CNS_4 <- as.numeric(as.character(CC$CNS_1_50))
CC$CNS_5 <- as.numeric(as.character(CC$CNS_1_51))
#Descriptives
describe(CC$CNS_1)## CC$CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 97 0.998 66.82 27.67 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100range(CC$CNS_1, na.rm=TRUE)## [1] 0 100describe(CC$CNS_2)## CC$CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 94 0.995 73.34 24.81 25.0 43.6
## .25 .50 .75 .90 .95
## 62.0 78.0 90.5 100.0 100.0
##
## lowest : 0 5 7 8 10, highest: 96 97 98 99 100range(CC$CNS_2, na.rm=TRUE)## [1] 0 100describe(CC$CNS_3)## CC$CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 98 0.996 65.79 32.06 0 17
## .25 .50 .75 .90 .95
## 51 70 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(CC$CNS_3, na.rm=TRUE)## [1] 0 100describe(CC$CNS_4)## CC$CNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.996 39.73 36.96 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 33.00 67.75 89.50 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(CC$CNS_4, na.rm=TRUE)## [1] 0 100describe(CC$CNS_5)## CC$CNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 98 0.999 49.45 34.86 0.0 4.6
## .25 .50 .75 .90 .95
## 23.0 51.0 72.5 90.0 100.0
##
## lowest : 0 1 3 4 5, highest: 95 97 98 99 100range(CC$CNS_5, na.rm=TRUE)## [1] 0 100#Histograms
hist(CC$CNS_1, main = 'I often feel a sense of oneness with the natural world around me.')
hist(CC$CNS_2, main = 'I think of the natural world as a community to which I belong.')
hist(CC$CNS_3, main = 'I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’.')
hist(CC$CNS_4, main = 'My personal welfare is independent of the welfare of the natural world.')
hist(CC$CNS_5, main = 'When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.')
#Recode items 4 and 5
CC$CNS_4R <- (100 - CC$CNS_4)
CC$CNS_5R <- (100 - CC$CNS_5)Score(s) & Scale(s)
#Score & Scale
CC$CNS_Score <- rowMeans(CC [, c("CNS_1", "CNS_2", "CNS_3", "CNS_4R", "CNS_5R")], na.rm=TRUE)
describe(CC$CNS_Score)## CC$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 322 1 63.36 18.72 35.00 43.12
## .25 .50 .75 .90 .95
## 52.90 63.00 74.60 84.88 91.80
##
## lowest : 0.0 8.6 10.0 12.8 16.0, highest: 97.8 98.2 98.6 99.6 100.0CC$CNS_Scale2 <- data.frame(CC$CNS_1, CC$CNS_2, CC$CNS_3, CC$CNS_4R, CC$CNS_5R)
psych::alpha(CC$CNS_Scale2)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CNS_Scale2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.54 0.59 0.63 0.22 1.4 0.024 63 17 0.081
##
## lower alpha upper 95% confidence boundaries
## 0.5 0.54 0.59
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CNS_1 0.38 0.41 0.44 0.15 0.70 0.032 0.045 0.081
## CC.CNS_2 0.39 0.41 0.45 0.15 0.70 0.032 0.053 0.071
## CC.CNS_3 0.42 0.46 0.51 0.17 0.85 0.031 0.066 0.068
## CC.CNS_4R 0.63 0.66 0.67 0.33 1.93 0.020 0.092 0.314
## CC.CNS_5R 0.58 0.64 0.66 0.30 1.75 0.023 0.108 0.311
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CNS_1 1007 0.70 0.75 0.75 0.494 67 25
## CC.CNS_2 1007 0.70 0.75 0.74 0.511 73 23
## CC.CNS_3 1006 0.68 0.70 0.63 0.411 66 29
## CC.CNS_4R 1006 0.47 0.41 0.13 0.096 60 33
## CC.CNS_5R 1007 0.50 0.45 0.18 0.157 51 30describe(CC$CNS_Scale2)## CC$CNS_Scale2
##
## 5 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 97 0.998 66.82 27.67 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 94 0.995 73.34 24.81 25.0 43.6
## .25 .50 .75 .90 .95
## 62.0 78.0 90.5 100.0 100.0
##
## lowest : 0 5 7 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 98 0.996 65.79 32.06 0 17
## .25 .50 .75 .90 .95
## 51 70 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_4R
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.996 60.27 36.96 0.00 10.50
## .25 .50 .75 .90 .95
## 32.25 67.00 86.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_5R
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 98 0.999 50.55 34.86 0.0 10.0
## .25 .50 .75 .90 .95
## 27.5 49.0 77.0 95.4 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------## Drop reverse coded items
CC$CNS_Scale <- data.frame(CC$CNS_1, CC$CNS_2, CC$CNS_3)
psych::alpha(CC$CNS_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.82 0.76 0.6 4.5 0.01 69 22 0.58
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.81 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CNS_1 0.69 0.70 0.54 0.54 2.4 0.019 NA 0.54
## CC.CNS_2 0.73 0.73 0.58 0.58 2.7 0.017 NA 0.58
## CC.CNS_3 0.80 0.80 0.67 0.67 4.1 0.012 NA 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CNS_1 1007 0.87 0.88 0.79 0.70 67 25
## CC.CNS_2 1007 0.84 0.86 0.77 0.68 73 23
## CC.CNS_3 1006 0.85 0.83 0.67 0.61 66 29describe(CC$CNS_Scale)## CC$CNS_Scale
##
## 3 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 97 0.998 66.82 27.67 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 94 0.995 73.34 24.81 25.0 43.6
## .25 .50 .75 .90 .95
## 62.0 78.0 90.5 100.0 100.0
##
## lowest : 0 5 7 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 98 0.996 65.79 32.06 0 17
## .25 .50 .75 .90 .95
## 51 70 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Correlation CCB
cor(CC$CNS_Scale, use= "complete.obs")## CC.CNS_1 CC.CNS_2 CC.CNS_3
## CC.CNS_1 1.0000000 0.6736904 0.5782495
## CC.CNS_2 0.6736904 1.0000000 0.5437057
## CC.CNS_3 0.5782495 0.5437057 1.0000000Control
# Control was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented perception of control over the technology rated.
## 1. We have control over the processes in this method.Descriptives
# Define Variables
CC$Control_AFSCS <- CC$Risk_AFSCS_34
CC$Control_BIO <- CC$Risk_BIO_34
CC$Control_BECCS <- CC$Risk_BECCS_34
CC$Control_DACCS <- CC$Risk_DACCS_34
CC$Control_EW <- CC$Risk_EW_34
CC$Control_OF <- CC$Risk_OF_34
CC$Control_BF <- CC$Risk_BF_34
CC$Control_NE <- CC$Risk_NE_34
CC$Control_SE <- CC$Risk_SE_34
CC$Control_WE <- CC$Risk_WE_34
# Descriptives
describe(CC$Control_AFSCS)## CC$Control_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 71 0.997 74.48 22.1 36.0 50.2
## .25 .50 .75 .90 .95
## 65.0 77.0 88.0 100.0 100.0
##
## lowest : 0 5 7 8 20, highest: 96 97 98 99 100sd(CC$Control_AFSCS, na.rm = TRUE)## [1] 20.53265hist(CC$Control_AFSCS)
describe(CC$Control_BIO)## CC$Control_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 77 0.999 68.99 24.04 29.55 41.10
## .25 .50 .75 .90 .95
## 54.00 71.50 85.00 96.00 100.00
##
## lowest : 0 5 9 14 16, highest: 95 96 98 99 100sd(CC$Control_BIO, na.rm = TRUE)## [1] 21.35812hist(CC$Control_BIO)
describe(CC$Control_BECCS)## CC$Control_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 79 0.999 62.23 26.17 20 30
## .25 .50 .75 .90 .95
## 47 65 78 90 100
##
## lowest : 0 10 12 13 15, highest: 95 96 98 99 100sd(CC$Control_BECCS, na.rm = TRUE)## [1] 23.21631hist(CC$Control_BECCS)
describe(CC$Control_DACCS)## CC$Control_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 87 0.999 56.9 28.7 14.0 21.6
## .25 .50 .75 .90 .95
## 40.0 57.0 75.0 90.4 100.0
##
## lowest : 0 1 8 9 10, highest: 94 95 97 99 100sd(CC$Control_DACCS, na.rm = TRUE)## [1] 25.17765hist(CC$Control_DACCS)
describe(CC$Control_EW) ## CC$Control_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 82 0.999 54.69 26.2 14.0 25.0
## .25 .50 .75 .90 .95
## 39.0 55.0 71.0 85.6 92.0
##
## lowest : 0 9 10 12 13, highest: 92 94 95 99 100sd(CC$Control_EW, na.rm = TRUE)## [1] 23.03235hist(CC$Control_EW) 
describe(CC$Control_OF)## CC$Control_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 1 47.47 30.01 5.0 12.0
## .25 .50 .75 .90 .95
## 27.5 47.0 67.5 82.0 91.0
##
## lowest : 0 1 2 3 4, highest: 94 95 98 99 100sd(CC$Control_OF, na.rm = TRUE)## [1] 26.10864hist(CC$Control_OF)
describe(CC$Control_BF)## CC$Control_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 65 0.996 77.06 21.54 36.70 50.70
## .25 .50 .75 .90 .95
## 68.75 80.00 94.00 100.00 100.00
##
## lowest : 0 5 15 25 29, highest: 96 97 98 99 100sd(CC$Control_BF, na.rm = TRUE)## [1] 19.74781hist(CC$Control_BF)
describe(CC$Control_NE)## CC$Control_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 73 0.999 66.94 27.69 20 33
## .25 .50 .75 .90 .95
## 51 71 86 98 100
##
## lowest : 0 5 7 9 10, highest: 95 96 98 99 100sd(CC$Control_NE, na.rm = TRUE)## [1] 24.68996hist(CC$Control_NE)
describe(CC$Control_SE)## CC$Control_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 67 0.992 76.01 25.42 28.4 39.4
## .25 .50 .75 .90 .95
## 63.0 82.0 95.0 100.0 100.0
##
## lowest : 0 7 9 10 13, highest: 96 97 98 99 100sd(CC$Control_SE, na.rm = TRUE)## [1] 23.73107hist(CC$Control_SE)
describe(CC$Control_WE)## CC$Control_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 68 0.997 71.25 27.61 20.8 34.6
## .25 .50 .75 .90 .95
## 58.0 79.0 90.0 100.0 100.0
##
## lowest : 0 4 10 12 15, highest: 96 97 98 99 100sd(CC$Control_WE, na.rm = TRUE)## [1] 25.13561hist(CC$Control_WE)
Score(s) & Scale(s)
# Note: Control scores & scales not present because measure is one item.)Familiarity
# Familiarity was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented participant familiarity with the technology rated.
## 1. This is familiar.Descriptives
#Define Variables
CC$Familiar_AFSCS <- CC$Risk_AFSCS_31
CC$Familiar_BIO <- CC$Risk_BIO_31
CC$Familiar_BECCS <- CC$Risk_BECCS_31
CC$Familiar_DACCS <- CC$Risk_DACCS_31
CC$Familiar_EW <- CC$Risk_EW_31
CC$Familiar_OF <- CC$Risk_OF_31
CC$Familiar_BF <- CC$Risk_BF_31
CC$Familiar_NE <- CC$Risk_NE_31
CC$Familiar_SE <- CC$Risk_SE_31
CC$Familiar_WE <- CC$Risk_WE_31
# Descriptives
describe(CC$Familiar_AFSCS)## CC$Familiar_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 91 0.997 62.7 34.53 3 12
## .25 .50 .75 .90 .95
## 42 67 89 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(CC$Familiar_AFSCS, na.rm = TRUE)## [1] 30.60777hist(CC$Familiar_AFSCS)
describe(CC$Familiar_BIO)## CC$Familiar_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 81 0.993 27.79 29.57 0.00 0.00
## .25 .50 .75 .90 .95
## 4.75 20.00 44.00 68.90 82.00
##
## lowest : 0 1 2 3 4, highest: 92 93 94 95 100sd(CC$Familiar_BIO, na.rm = TRUE)## [1] 27.00687hist(CC$Familiar_BIO)
describe(CC$Familiar_BECCS)## CC$Familiar_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.994 29.64 30.74 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 21.00 50.00 73.00 83.55
##
## lowest : 0 1 2 3 4, highest: 91 92 94 98 100sd(CC$Familiar_BECCS, na.rm = TRUE)## [1] 27.82hist(CC$Familiar_BECCS)
describe(CC$Familiar_DACCS)## CC$Familiar_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.992 26.05 27.55 0.0 0.0
## .25 .50 .75 .90 .95
## 4.5 20.0 42.0 65.0 75.0
##
## lowest : 0 1 2 3 4, highest: 89 90 93 99 100sd(CC$Familiar_DACCS, na.rm = TRUE)## [1] 25.08586hist(CC$Familiar_DACCS)
describe(CC$Familiar_EW)## CC$Familiar_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 76 0.98 22.5 25.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 17.0 35.5 60.0 70.0
##
## lowest : 0 1 2 3 4, highest: 79 80 87 90 91sd(CC$Familiar_EW, na.rm = TRUE)## [1] 23.20217hist(CC$Familiar_EW)
describe(CC$Familiar_OF)## CC$Familiar_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 76 0.992 25.62 27.66 0.0 0.0
## .25 .50 .75 .90 .95
## 4.0 18.0 40.5 62.8 76.0
##
## lowest : 0 1 2 3 4, highest: 85 86 87 89 100sd(CC$Familiar_OF, na.rm = TRUE)## [1] 25.34433hist(CC$Familiar_OF)
describe(CC$Familiar_BF)## CC$Familiar_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 87 0.999 57.92 32.61 0.0 18.0
## .25 .50 .75 .90 .95
## 36.0 61.0 81.0 93.3 100.0
##
## lowest : 0 1 5 6 8, highest: 95 96 98 99 100sd(CC$Familiar_BF, na.rm = TRUE)## [1] 28.59492hist(CC$Familiar_BF)
describe(CC$Familiar_NE)## CC$Familiar_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 82 0.998 69.17 29.4 14.8 30.6
## .25 .50 .75 .90 .95
## 53.0 75.0 90.0 100.0 100.0
##
## lowest : 0 2 3 4 6, highest: 95 97 98 99 100sd(CC$Familiar_NE, na.rm = TRUE)## [1] 26.59004hist(CC$Familiar_NE)
describe(CC$Familiar_SE)## CC$Familiar_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 49 0.941 87.95 15.68 52.2 65.2
## .25 .50 .75 .90 .95
## 82.0 94.0 100.0 100.0 100.0
##
## lowest : 0 18 35 41 45, highest: 96 97 98 99 100sd(CC$Familiar_SE, na.rm = TRUE)## [1] 16.02333hist(CC$Familiar_SE)
describe(CC$Familiar_WE)## CC$Familiar_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 61 0.982 81.79 20.9 41.6 55.0
## .25 .50 .75 .90 .95
## 75.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 3 13 19, highest: 96 97 98 99 100sd(CC$Familiar_WE, na.rm = TRUE)## [1] 20.79082hist(CC$Familiar_WE)
Score(s) & Scale(s)
# Note: Familiarity scores & scales not present because measure is one item.)Ideology
Descriptives
Score(s) & Scale(s)
#Political Orientation
##Which of the following best describes your political orientation? ( 1 = Strongly Conservative to 7 = Strongly Liberal)
describe(CC$PI_Orientation)## CC$PI_Orientation
## n missing distinct Info Mean Gmd
## 1007 0 7 0.966 4.807 2.078
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 62 102 72 183 125 239 224
## Proportion 0.062 0.101 0.071 0.182 0.124 0.237 0.222CC$Orientation = as.numeric(recode_factor(CC$PI_Orientation,'1'= "3",'2'= "2",'3'= "1",
'4'= "0",'5'= "-1", '6'= "-2", '7'= "-3"))
describe(CC$Orientation)## CC$Orientation
## n missing distinct Info Mean Gmd
## 1007 0 7 0.966 4.807 2.078
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 62 102 72 183 125 239 224
## Proportion 0.062 0.101 0.071 0.182 0.124 0.237 0.222hist(CC$Orientation , main = 'Political Orientation (Liberal to Conservative)')
#Political Party Identification
##Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what? (1 = Republican, 2 = Democrat, 3 = Independent, 4 = Other (write-in), 5 = No Preference)
describe(CC$PP_Party)## CC$PP_Party
## n missing distinct Info Mean Gmd
## 1006 1 5 0.854 2.252 0.9154
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 176 497 272 25 36
## Proportion 0.175 0.494 0.270 0.025 0.036CC$Party <- as.numeric(as.character(CC$PP_Party))
CC$DemStrength <- as.numeric(as.character(CC$PP_DemStrength))
CC$RepStrength <- as.numeric(as.character(CC$PP_RepStrength))
CC$PartyClose <- as.numeric(as.character(CC$PP_CloserTo))
# Recode Party
CC$PartyFull <- NA
CC$PartyFull[CC$DemStrength == 1] <- -3
CC$PartyFull[CC$DemStrength == 2] <- -2
CC$PartyFull[CC$PartyClose == 1] <- -1
CC$PartyFull[CC$PartyClose == 3] <- 0
CC$PartyFull[CC$PartyClose == 2] <- 1
CC$PartyFull[CC$RepStrength == 2] <- 2
CC$PartyFull[CC$RepStrength == 1] <- 3
describe(CC$PartyFull)## CC$PartyFull
## n missing distinct Info Mean Gmd
## 1006 1 7 0.956 -0.9284 2.211
##
## lowest : -3 -2 -1 0 1, highest: -1 0 1 2 3
##
## Value -3 -2 -1 0 1 2 3
## Frequency 317 180 122 148 63 92 84
## Proportion 0.315 0.179 0.121 0.147 0.063 0.091 0.083hist(CC$PartyFull , main = 'Party Identification')
CC$PartyID <- NA
CC$PartyID[CC$PartyFull < 0] <- -0.5
CC$PartyID[CC$PartyFull == 0] <- 0
CC$PartyID[CC$PartyFull > 0] <- 0.5
#New Variable: Ideology
CC$Ideology <- rowMeans(CC[, c('PartyFull', 'Orientation')], na.rm=T)
describe(CC$Ideology)## CC$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 12 0.868 1.943 0.5671 1.0 1.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.0 2.5 3.0
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 2.5 3.0 3.5 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 5.0
## Frequency 1 4 4 11 53 236 492 131 62 11 1
## Proportion 0.001 0.004 0.004 0.011 0.053 0.234 0.489 0.130 0.062 0.011 0.001
##
## Value 6.0
## Frequency 1
## Proportion 0.001hist(CC$Ideology)
Individualism/Collectivism
#Individualism and Collectivism Scale (Code adapted from J.Cole Collectivism Study)
#Individualism and collectivism were each measured with 4 items (for a total of 8 items) on a 1-7 scale of agreement (0 = 'Strongly disagree' to 100 = 'Strongly agree').
##Collectivism Items
###Individualism/Collectivism Item #3 (C): It is important to me to think of myself as a member of my religious, national, or ethnic group.
###Individualism/Collectivism Item #4 (C): Learning about the traditions, values, and beliefs of my family is important to me.
###Individualism/Collectivism Item #7 (C): In the end, a person feels closest to members of their own religious, national, or ethnic group.
###Individualism/Collectivism Item #8 (C): It is important to me to respect decisions made by my family.
##Individualism Items
###Individualism/Collectivism Item #1 (I): It is important to me to develop my own personal style.
###Individualism/Collectivism Item #2 (I): It is better for me to follow my own ideas than to follow those of anyone else.
###Individualism/Collectivism Item #5 (I): I enjoy being unique and different from others in many respects.
###Individualism/Collectivism Item #6 (I): My personal achievements and accomplishments are very important to who I am.
#Individualism (Items 1,2,5,6)
CC$Ind_1 <- as.numeric(as.character(CC$Individualism_54))
CC$Ind_2 <- as.numeric(as.character(CC$Individualism_55))
CC$Ind_5 <- as.numeric(as.character(CC$Individualism_58))
CC$Ind_6 <- as.numeric(as.character(CC$Individualism_59))
CC$Individualism_Score <- rowMeans(CC[, c('Ind_1', 'Ind_2', 'Ind_5','Ind_6')], na.rm=T)
#Collectivism (Items 3,4,7,8)
CC$Ind_3 <- as.numeric(as.character(CC$Individualism_56))
CC$Ind_4 <- as.numeric(as.character(CC$Individualism_57))
CC$Ind_7 <- as.numeric(as.character(CC$Individualism_60))
CC$Ind_8 <- as.numeric(as.character(CC$Individualism_69))
CC$Collectivism_Score <- rowMeans(CC[, c('Ind_3', 'Ind_4', 'Ind_7','Ind_8')], na.rm=T)
#Individualism Alpha and Histogram (4 items)
psych::alpha(data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5,CC$Ind_6))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5, CC$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.69 0.4 2.6 0.015 71 17 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.69 0.72 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_1 0.56 0.56 0.47 0.30 1.3 0.024 0.010 0.31
## CC.Ind_2 0.75 0.75 0.69 0.50 3.0 0.014 0.015 0.48
## CC.Ind_5 0.61 0.61 0.54 0.34 1.6 0.022 0.021 0.36
## CC.Ind_6 0.69 0.70 0.64 0.44 2.3 0.017 0.031 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 1007 0.83 0.84 0.80 0.67 74 22
## CC.Ind_2 1007 0.64 0.63 0.41 0.35 67 23
## CC.Ind_5 1007 0.78 0.79 0.72 0.59 72 22
## CC.Ind_6 1007 0.70 0.70 0.53 0.45 70 23hist(CC$Individualism_Score , main = 'Individualism Score')
#Collectivism Alpha and Histogram (4 items)
psych::alpha(data.frame(CC$Ind_3, CC$Ind_4, CC$Ind_7, CC$Ind_8))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Ind_3, CC$Ind_4, CC$Ind_7, CC$Ind_8))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.8 0.54 4.8 0.0089 54 24 0.56
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_3 0.75 0.75 0.69 0.51 3.1 0.014 0.0156 0.44
## CC.Ind_4 0.76 0.76 0.69 0.52 3.2 0.013 0.0070 0.53
## CC.Ind_7 0.82 0.82 0.76 0.60 4.5 0.010 0.0037 0.62
## CC.Ind_8 0.79 0.79 0.73 0.55 3.7 0.011 0.0095 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 1007 0.86 0.85 0.78 0.71 44 32
## CC.Ind_4 1007 0.83 0.84 0.77 0.69 62 29
## CC.Ind_7 1006 0.76 0.76 0.63 0.57 53 28
## CC.Ind_8 1007 0.80 0.80 0.71 0.64 58 28hist(CC$Collectivism_Score , main = 'Collectivism Score')
#Cronbachs Alpha for Individualism and Collectivism scales
CC$IndScale <- data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5, CC$Ind_6)
psych::alpha(CC$IndScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$IndScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.69 0.4 2.6 0.015 71 17 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.69 0.72 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_1 0.56 0.56 0.47 0.30 1.3 0.024 0.010 0.31
## CC.Ind_2 0.75 0.75 0.69 0.50 3.0 0.014 0.015 0.48
## CC.Ind_5 0.61 0.61 0.54 0.34 1.6 0.022 0.021 0.36
## CC.Ind_6 0.69 0.70 0.64 0.44 2.3 0.017 0.031 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 1007 0.83 0.84 0.80 0.67 74 22
## CC.Ind_2 1007 0.64 0.63 0.41 0.35 67 23
## CC.Ind_5 1007 0.78 0.79 0.72 0.59 72 22
## CC.Ind_6 1007 0.70 0.70 0.53 0.45 70 23CC$CollScale <- data.frame(CC$Ind_3, CC$Ind_4, CC$Ind_7, CC$Ind_8)
psych::alpha(CC$CollScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CollScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.8 0.54 4.8 0.0089 54 24 0.56
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_3 0.75 0.75 0.69 0.51 3.1 0.014 0.0156 0.44
## CC.Ind_4 0.76 0.76 0.69 0.52 3.2 0.013 0.0070 0.53
## CC.Ind_7 0.82 0.82 0.76 0.60 4.5 0.010 0.0037 0.62
## CC.Ind_8 0.79 0.79 0.73 0.55 3.7 0.011 0.0095 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 1007 0.86 0.85 0.78 0.71 44 32
## CC.Ind_4 1007 0.83 0.84 0.77 0.69 62 29
## CC.Ind_7 1006 0.76 0.76 0.63 0.57 53 28
## CC.Ind_8 1007 0.80 0.80 0.71 0.64 58 28Naturalness
# Naturalness was rated on a four item scale (0 = Strongly disagree to 100 = Strongly agree) and a mean score was calculated to represent naturalness perception of the technology rated.
## 1. This is natural
## 2. This involves humans altering naturally occurring processes (Reverse code)
## 3. This relies on science-based technology (Reverse code)
## 4. This is artificial (Reverse code)Descriptives
#Define Variables
CC$Nat_1_AFSCS <- CC$Naturalness_AFSCS_30
CC$Nat_2R_AFSCS <- (100-CC$Naturalness_AFSCS_31)
CC$Nat_3R_AFSCS <- (100-CC$Naturalness_AFSCS_35)
CC$Nat_4R_AFSCS <- (100-CC$Naturalness_AFSCS_36)
CC$Nat_1_BIO <- CC$Naturalness_BIO_30
CC$Nat_2R_BIO <- (100-CC$Naturalness_BIO_31)
CC$Nat_3R_BIO <- (100-CC$Naturalness_BIO_35)
CC$Nat_4R_BIO <- (100-CC$Naturalness_BIO_36)
CC$Nat_1_BECCS <- CC$Naturalness_BECCS_30
CC$Nat_2R_BECCS <- (100-CC$Naturalness_BECCS_31)
CC$Nat_3R_BECCS <- (100-CC$Naturalness_BECCS_35)
CC$Nat_4R_BECCS <- (100-CC$Naturalness_BECCS_36)
CC$Nat_1_DACCS <- CC$Naturalness_DACCS_30
CC$Nat_2R_DACCS <- (100-CC$Naturalness_DACCS_31)
CC$Nat_3R_DACCS <- (100-CC$Naturalness_DACCS_35)
CC$Nat_4R_DACCS <- (100-CC$Naturalness_DACCS_36)
CC$Nat_1_EW <- CC$Naturalness_EW_30
CC$Nat_2R_EW <- (100-CC$Naturalness_EW_31)
CC$Nat_3R_EW <- (100-CC$Naturalness_EW_35)
CC$Nat_4R_EW <- (100-CC$Naturalness_EW_36)
CC$Nat_1_OF <- CC$Naturalness_OF_30
CC$Nat_2R_OF <- (100-CC$Naturalness_OF_31)
CC$Nat_3R_OF <- (100-CC$Naturalness_OF_35)
CC$Nat_4R_OF <- (100-CC$Naturalness_OF_36)
CC$Nat_1_BF <- CC$Naturalness_BF_30
CC$Nat_2R_BF <- (100-CC$Naturalness_BF_31)
CC$Nat_3R_BF <- (100-CC$Naturalness_BF_35)
CC$Nat_4R_BF <- (100-CC$Naturalness_BF_36)
CC$Nat_1_NE <- CC$Naturalness_NE_30
CC$Nat_2R_NE <- (100-CC$Naturalness_NE_31)
CC$Nat_3R_NE <- (100-CC$Naturalness_NE_35)
CC$Nat_4R_NE <- (100-CC$Naturalness_NE_36)
CC$Nat_1_SE <- CC$Naturalness_SE_30
CC$Nat_2R_SE <- (100-CC$Naturalness_SE_31)
CC$Nat_3R_SE <- (100-CC$Naturalness_SE_35)
CC$Nat_4R_SE <- (100-CC$Naturalness_SE_36)
CC$Nat_1_WE <- CC$Naturalness_WE_30
CC$Nat_2R_WE <- (100-CC$Naturalness_WE_31)
CC$Nat_3R_WE <- (100-CC$Naturalness_WE_35)
CC$Nat_4R_WE <- (100-CC$Naturalness_WE_36)
# Descriptives
describe(CC$Nat_1_AFSCS)## CC$Nat_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.991 74.92 27.28 19.1 37.0
## .25 .50 .75 .90 .95
## 60.5 83.0 95.0 100.0 100.0
##
## lowest : 0 3 6 7 10, highest: 96 97 98 99 100describe(CC$Nat_2R_AFSCS)## CC$Nat_2R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 95 0.999 53.22 35.19 0.0 14.0
## .25 .50 .75 .90 .95
## 30.0 50.0 82.5 97.8 100.0
##
## lowest : 0 2 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_3R_AFSCS)## CC$Nat_3R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 89 0.999 39.48 33.58 0.0 2.2
## .25 .50 .75 .90 .95
## 15.5 35.0 60.5 86.0 95.8
##
## lowest : 0 1 2 3 4, highest: 93 94 96 97 100describe(CC$Nat_4R_AFSCS)## CC$Nat_4R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.99 79.59 25.6 23.1 41.2
## .25 .50 .75 .90 .95
## 67.0 91.0 99.0 100.0 100.0
##
## lowest : 0 4 6 7 12, highest: 96 97 98 99 100describe(CC$Nat_1_BIO)## CC$Nat_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 0.999 45.61 31.02 0.0 6.1
## .25 .50 .75 .90 .95
## 25.0 46.0 64.0 83.9 96.0
##
## lowest : 0 2 3 4 5, highest: 90 95 96 97 100describe(CC$Nat_2R_BIO) ## CC$Nat_2R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 77 0.999 37.07 27.62 0.0 5.0
## .25 .50 .75 .90 .95
## 20.0 35.0 49.0 72.7 85.0
##
## lowest : 0 2 3 5 6, highest: 93 95 96 98 100describe(CC$Nat_3R_BIO)## CC$Nat_3R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 69 0.993 23.95 23.47 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 20.5 35.0 49.9 70.0
##
## lowest : 0 1 2 3 5, highest: 87 88 95 97 100describe(CC$Nat_4R_BIO) ## CC$Nat_4R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 96 0.999 49.87 35.67 0.0 8.0
## .25 .50 .75 .90 .95
## 25.0 49.0 78.0 95.9 100.0
##
## lowest : 0 1 4 5 6, highest: 95 96 97 99 100describe(CC$Nat_1_BECCS) ## CC$Nat_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 43.48 29.31 0.00 6.90
## .25 .50 .75 .90 .95
## 25.00 44.00 61.00 76.20 88.55
##
## lowest : 0 1 2 3 4, highest: 90 93 96 99 100describe(CC$Nat_2R_BECCS) ## CC$Nat_2R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 72 0.997 30.43 24.88 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 30.00 44.00 60.00 73.65
##
## lowest : 0 1 2 3 4, highest: 85 89 90 93 100describe(CC$Nat_3R_BECCS) ## CC$Nat_3R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 69 0.991 22.77 22.39 0.00 0.00
## .25 .50 .75 .90 .95
## 4.25 20.00 35.00 48.00 61.65
##
## lowest : 0 1 2 3 4, highest: 80 90 92 98 100describe(CC$Nat_4R_BECCS)## CC$Nat_4R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 41.83 31.45 0.0 6.0
## .25 .50 .75 .90 .95
## 20.0 39.0 60.0 82.1 93.0
##
## lowest : 0 2 3 4 5, highest: 93 94 95 98 100describe(CC$Nat_1_DACCS)## CC$Nat_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.996 29.22 27.38 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 25.0 41.0 63.4 81.7
##
## lowest : 0 1 3 4 5, highest: 94 95 97 98 100describe(CC$Nat_2R_DACCS) ## CC$Nat_2R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 78 0.995 27.79 26.85 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 23.0 39.0 66.8 78.7
##
## lowest : 0 1 2 3 4, highest: 87 90 91 99 100describe(CC$Nat_3R_DACCS)## CC$Nat_3R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 60 0.976 16.62 18.66 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 12.0 26.0 40.4 48.7
##
## lowest : 0 1 3 4 5, highest: 81 83 85 93 100describe(CC$Nat_4R_DACCS)## CC$Nat_4R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 79 0.995 28.49 27.02 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 41.5 63.0 82.0
##
## lowest : 0 1 3 4 5, highest: 88 89 95 98 100describe(CC$Nat_1_EW)## CC$Nat_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 91 0.999 46.07 31.02 0.0 7.0
## .25 .50 .75 .90 .95
## 25.5 50.0 67.0 81.0 89.0
##
## lowest : 0 2 3 4 5, highest: 91 92 95 98 100describe(CC$Nat_2R_EW)## CC$Nat_2R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 75 0.995 27.06 24.88 0.0 0.0
## .25 .50 .75 .90 .95
## 9.5 23.0 40.0 58.2 75.0
##
## lowest : 0 1 2 3 4, highest: 85 90 92 93 100describe(CC$Nat_3R_EW)## CC$Nat_3R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 70 0.994 25.62 23.99 0 0
## .25 .50 .75 .90 .95
## 7 24 39 50 70
##
## lowest : 0 1 2 3 4, highest: 86 88 90 95 100describe(CC$Nat_4R_EW)## CC$Nat_4R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 84 0.999 44.61 31.96 0 7
## .25 .50 .75 .90 .95
## 22 44 67 80 93
##
## lowest : 0 4 5 6 7, highest: 91 93 94 98 100describe(CC$Nat_1_OF)## CC$Nat_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 40.43 31.15 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 39.0 59.5 78.4 86.7
##
## lowest : 0 2 4 5 6, highest: 88 90 92 93 100describe(CC$Nat_2R_OF)## CC$Nat_2R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 65 0.996 22.48 21.68 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 19.0 32.0 46.0 60.7
##
## lowest : 0 1 3 4 5, highest: 80 81 82 89 100describe(CC$Nat_3R_OF)## CC$Nat_3R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 66 0.996 25.66 23.54 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 36.0 56.0 66.7
##
## lowest : 0 1 2 3 4, highest: 79 80 90 91 100describe(CC$Nat_4R_OF)## CC$Nat_4R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 87 0.999 38.51 30.22 0.0 5.0
## .25 .50 .75 .90 .95
## 16.5 38.0 55.0 77.4 91.7
##
## lowest : 0 2 3 4 5, highest: 92 93 95 99 100describe(CC$Nat_1_BF)## CC$Nat_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 88 0.999 52.38 31.87 2.00 12.40
## .25 .50 .75 .90 .95
## 34.00 51.00 75.00 90.60 99.65
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100describe(CC$Nat_2R_BF)## CC$Nat_2R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 79 0.998 38.07 30.26 0.00 0.00
## .25 .50 .75 .90 .95
## 20.00 35.00 54.00 79.30 90.65
##
## lowest : 0 2 3 4 5, highest: 90 91 95 99 100describe(CC$Nat_3R_BF)## CC$Nat_3R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 59 0.988 17.91 18.34 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 15.00 28.00 39.00 46.65
##
## lowest : 0 1 2 3 4, highest: 68 75 77 81 85describe(CC$Nat_4R_BF)## CC$Nat_4R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 84 0.999 48.67 33.41 1.35 10.00
## .25 .50 .75 .90 .95
## 26.00 49.00 73.25 90.30 98.65
##
## lowest : 0 1 2 4 5, highest: 95 96 98 99 100describe(CC$Nat_1_NE)## CC$Nat_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 80 0.995 31 28.97 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 27.0 49.0 68.2 80.4
##
## lowest : 0 1 2 3 4, highest: 89 90 93 95 100describe(CC$Nat_2R_NE)## CC$Nat_2R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 72 0.994 29.96 30.59 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 23.0 43.0 77.6 95.0
##
## lowest : 0 2 3 4 5, highest: 94 95 98 99 100describe(CC$Nat_3R_NE)## CC$Nat_3R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 48 0.931 11.18 14.75 0 0
## .25 .50 .75 .90 .95
## 0 6 17 33 43
##
## lowest : 0 1 2 3 4, highest: 49 50 64 92 100describe(CC$Nat_4R_NE)## CC$Nat_4R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.996 32.51 30.64 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 27.0 48.0 77.4 90.0
##
## lowest : 0 4 5 6 7, highest: 92 95 96 99 100describe(CC$Nat_1_SE)## CC$Nat_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.991 72.97 29.25 10.2 30.4
## .25 .50 .75 .90 .95
## 60.0 80.0 95.0 100.0 100.0
##
## lowest : 0 1 4 6 10, highest: 95 97 98 99 100describe(CC$Nat_2R_SE)## CC$Nat_2R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 77 0.989 66.22 35.98 6.2 18.4
## .25 .50 .75 .90 .95
## 38.0 78.0 96.0 100.0 100.0
##
## lowest : 0 1 5 6 7, highest: 96 97 98 99 100describe(CC$Nat_3R_SE)## CC$Nat_3R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 52 0.964 14.8 18.23 0 0
## .25 .50 .75 .90 .95
## 0 10 22 39 50
##
## lowest : 0 1 2 3 4, highest: 70 71 76 80 93describe(CC$Nat_4R_SE)## CC$Nat_4R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 76 0.997 66.2 33.02 11.0 22.2
## .25 .50 .75 .90 .95
## 47.0 74.0 92.0 100.0 100.0
##
## lowest : 0 5 7 8 11, highest: 96 97 98 99 100describe(CC$Nat_1_WE)## CC$Nat_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.993 70 30.85 9.8 21.6
## .25 .50 .75 .90 .95
## 57.0 78.0 92.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_2R_WE)## CC$Nat_2R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.991 63.74 35.55 10 20
## .25 .50 .75 .90 .95
## 38 72 93 100 100
##
## lowest : 0 1 5 8 10, highest: 96 97 98 99 100describe(CC$Nat_3R_WE)## CC$Nat_3R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.987 20.75 22.27 0.0 0.0
## .25 .50 .75 .90 .95
## 2.0 17.0 30.0 47.0 65.8
##
## lowest : 0 1 2 3 4, highest: 85 88 90 94 100describe(CC$Nat_4R_WE)## CC$Nat_4R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.997 62.96 35.06 4.8 14.2
## .25 .50 .75 .90 .95
## 42.0 68.0 90.0 100.0 100.0
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100sd(CC$Nat_1_AFSCS, na.rm = TRUE)## [1] 25.49721sd(CC$Nat_2R_AFSCS, na.rm = TRUE)## [1] 30.55137sd(CC$Nat_3R_AFSCS, na.rm = TRUE)## [1] 29.60784sd(CC$Nat_4R_AFSCS, na.rm = TRUE)## [1] 25.03207sd(CC$Nat_1_BIO, na.rm = TRUE)## [1] 27.09083sd(CC$Nat_2R_BIO, na.rm = TRUE)## [1] 24.56568sd(CC$Nat_3R_BIO, na.rm = TRUE)## [1] 21.92187sd(CC$Nat_4R_BIO, na.rm = TRUE) ## [1] 30.9548sd(CC$Nat_1_BECCS, na.rm = TRUE) ## [1] 25.65509sd(CC$Nat_2R_BECCS, na.rm = TRUE) ## [1] 22.39619sd(CC$Nat_3R_BECCS, na.rm = TRUE) ## [1] 20.56689sd(CC$Nat_4R_BECCS, na.rm = TRUE)## [1] 27.55096sd(CC$Nat_1_DACCS, na.rm = TRUE)## [1] 24.84149sd(CC$Nat_2R_DACCS, na.rm = TRUE) ## [1] 24.88956sd(CC$Nat_3R_DACCS, na.rm = TRUE)## [1] 17.79345sd(CC$Nat_4R_DACCS)## [1] NAsd(CC$Nat_1_EW, na.rm = TRUE)## [1] 26.98155sd(CC$Nat_2R_EW, na.rm = TRUE)## [1] 22.69374sd(CC$Nat_3R_EW, na.rm = TRUE)## [1] 21.89062sd(CC$Nat_4R_EW, na.rm = TRUE)## [1] 27.74477sd(CC$Nat_1_OF, na.rm = TRUE)## [1] 27.14107sd(CC$Nat_2R_OF, na.rm = TRUE)## [1] 20.49896sd(CC$Nat_3R_OF, na.rm = TRUE)## [1] 21.76612sd(CC$Nat_4R_OF, na.rm = TRUE)## [1] 26.69147sd(CC$Nat_1_BF, na.rm = TRUE)## [1] 27.74057sd(CC$Nat_2R_BF, na.rm = TRUE)## [1] 26.80887sd(CC$Nat_3R_BF, na.rm = TRUE)## [1] 17.00108sd(CC$Nat_4R_BF, na.rm = TRUE)## [1] 28.97902sd(CC$Nat_1_NE, na.rm = TRUE)## [1] 25.86564sd(CC$Nat_2R_NE, na.rm = TRUE)## [1] 28.30164sd(CC$Nat_3R_NE, na.rm = TRUE)## [1] 15.3576sd(CC$Nat_4R_NE, na.rm = TRUE)## [1] 27.60394sd(CC$Nat_1_SE, na.rm = TRUE)## [1] 27.34702sd(CC$Nat_2R_SE, na.rm = TRUE)## [1] 32.18289sd(CC$Nat_3R_SE, na.rm = TRUE)## [1] 18.00439sd(CC$Nat_4R_SE, na.rm = TRUE)## [1] 29.35406sd(CC$Nat_1_WE, na.rm = TRUE)## [1] 28.31885sd(CC$Nat_2R_WE, na.rm = TRUE)## [1] 31.29357sd(CC$Nat_3R_WE, na.rm = TRUE)## [1] 21.1787sd(CC$Nat_4R_WE, na.rm = TRUE)## [1] 30.8923hist(CC$Nat_1_AFSCS)
hist(CC$Nat_2R_AFSCS)
hist(CC$Nat_3R_AFSCS)
hist(CC$Nat_4R_AFSCS)
hist(CC$Nat_1_BIO)
hist(CC$Nat_2R_BIO) 
hist(CC$Nat_3R_BIO)
hist(CC$Nat_4R_BIO) 
hist(CC$Nat_1_BECCS) 
hist(CC$Nat_2R_BECCS) 
hist(CC$Nat_3R_BECCS) 
hist(CC$Nat_4R_BECCS)
hist(CC$Nat_1_DACCS)
hist(CC$Nat_2R_DACCS) 
hist(CC$Nat_3R_DACCS)
hist(CC$Nat_4R_DACCS)
hist(CC$Nat_1_EW)
hist(CC$Nat_2R_EW)
hist(CC$Nat_3R_EW)
hist(CC$Nat_4R_EW)
hist(CC$Nat_1_OF)
hist(CC$Nat_2R_OF)
hist(CC$Nat_3R_OF)
hist(CC$Nat_4R_OF)
hist(CC$Nat_1_BF)
hist(CC$Nat_2R_BF)
hist(CC$Nat_3R_BF)
hist(CC$Nat_4R_BF)
hist(CC$Nat_1_NE)
hist(CC$Nat_2R_NE)
hist(CC$Nat_3R_NE)
hist(CC$Nat_4R_NE)
hist(CC$Nat_1_SE)
hist(CC$Nat_2R_SE)
hist(CC$Nat_3R_SE)
hist(CC$Nat_4R_SE)
hist(CC$Nat_1_WE)
hist(CC$Nat_2R_WE)
hist(CC$Nat_3R_WE)
hist(CC$Nat_4R_WE)
Score(s) & Scale(s)
# Scores & Scales
CC$Nat_Score_AFSCS <- rowMeans(CC [, c("Nat_1_AFSCS", "Nat_2R_AFSCS", "Nat_3R_AFSCS", "Nat_4R_AFSCS")], na.rm=TRUE)
CC$Nat_Scale_AFSCS <- data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS, CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS)
CC$Nat_Score_BIO <- rowMeans(CC [, c("Nat_1_BIO", "Nat_2R_BIO", "Nat_3R_BIO", "Nat_4R_BIO")], na.rm=TRUE)
CC$Nat_Scale_BIO <- data.frame(CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO, CC$Nat_4R_BIO)
CC$Nat_Score_BECCS <- rowMeans(CC [, c("Nat_1_BECCS", "Nat_2R_BECCS", "Nat_3R_BECCS", "Nat_4R_BECCS")], na.rm=TRUE)
CC$Nat_Scale_BECCS <- data.frame(CC$Nat_1_BECCS, CC$Nat_2R_BECCS, CC$Nat_3R_BECCS, CC$Nat_4R_BECCS)
CC$Nat_Score_DACCS <- rowMeans(CC [, c("Nat_1_DACCS", "Nat_2R_DACCS", "Nat_3R_DACCS", "Nat_4R_DACCS")], na.rm=TRUE)
CC$Nat_Scale_DACCS <- data.frame(CC$Nat_1_DACCS, CC$Nat_2R_DACCS, CC$Nat_3R_DACCS, CC$Nat_4R_DACCS)
CC$Nat_Score_EW <- rowMeans(CC [, c("Nat_1_EW", "Nat_2R_EW", "Nat_3R_EW", "Nat_4R_EW")], na.rm=TRUE)
CC$Nat_Scale_EW <- data.frame(CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW, CC$Nat_4R_EW)
CC$Nat_Score_OF <- rowMeans(CC [, c("Nat_1_OF", "Nat_2R_OF", "Nat_3R_OF", "Nat_4R_OF")], na.rm=TRUE)
CC$Nat_Scale_OF <- data.frame(CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF, CC$Nat_4R_OF)
CC$Nat_Score_BF <- rowMeans(CC [, c("Nat_1_BF", "Nat_2R_BF", "Nat_3R_BF", "Nat_4R_BF")], na.rm=TRUE)
CC$Nat_Scale_BF <- data.frame(CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF, CC$Nat_4R_BF)
CC$Nat_Score_NE <- rowMeans(CC [, c("Nat_1_NE", "Nat_2R_NE", "Nat_3R_NE", "Nat_4R_NE")], na.rm=TRUE)
CC$Nat_Scale_NE <- data.frame(CC$Nat_1_NE, CC$Nat_2R_NE, CC$Nat_3R_NE, CC$Nat_4R_NE)
CC$Nat_Score_SE <- rowMeans(CC [, c("Nat_1_SE", "Nat_2R_SE", "Nat_3R_SE", "Nat_4R_SE")], na.rm=TRUE)
CC$Nat_Scale_SE <- data.frame(CC$Nat_1_SE, CC$Nat_2R_SE, CC$Nat_3R_SE, CC$Nat_4R_SE)
CC$Nat_Score_WE <- rowMeans(CC [, c("Nat_1_WE", "Nat_2R_WE", "Nat_3R_WE", "Nat_4R_WE")], na.rm=TRUE)
CC$Nat_Scale_WE <- data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE, CC$Nat_4R_WE)
# Describe Scores/Scales
describe(CC$Nat_Score_AFSCS)## CC$Nat_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 195 1 61.8 22.29 26.65 36.55
## .25 .50 .75 .90 .95
## 48.88 63.25 74.88 87.20 94.90
##
## lowest : 0.00 7.00 8.00 11.00 11.75, highest: 98.00 98.75 99.50 99.75 100.00describe(CC$Nat_Scale_AFSCS)## CC$Nat_Scale_AFSCS
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.991 74.92 27.28 19.1 37.0
## .25 .50 .75 .90 .95
## 60.5 83.0 95.0 100.0 100.0
##
## lowest : 0 3 6 7 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 95 0.999 53.22 35.19 0.0 14.0
## .25 .50 .75 .90 .95
## 30.0 50.0 82.5 97.8 100.0
##
## lowest : 0 2 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 89 0.999 39.48 33.58 0.0 2.2
## .25 .50 .75 .90 .95
## 15.5 35.0 60.5 86.0 95.8
##
## lowest : 0 1 2 3 4, highest: 93 94 96 97 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.99 79.59 25.6 23.1 41.2
## .25 .50 .75 .90 .95
## 67.0 91.0 99.0 100.0 100.0
##
## lowest : 0 4 6 7 12, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_AFSCS, na.rm = TRUE)## [1] 19.74064describe(CC$Nat_Score_BIO)## CC$Nat_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 182 1 39.12 20.95 5.75 13.50
## .25 .50 .75 .90 .95
## 26.88 39.25 51.06 63.25 68.75
##
## lowest : 0.00 0.75 1.75 2.50 2.75, highest: 76.75 78.00 87.25 96.50 97.50describe(CC$Nat_Scale_BIO)## CC$Nat_Scale_BIO
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 0.999 45.61 31.02 0.0 6.1
## .25 .50 .75 .90 .95
## 25.0 46.0 64.0 83.9 96.0
##
## lowest : 0 2 3 4 5, highest: 90 95 96 97 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 77 0.999 37.07 27.62 0.0 5.0
## .25 .50 .75 .90 .95
## 20.0 35.0 49.0 72.7 85.0
##
## lowest : 0 2 3 5 6, highest: 93 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 69 0.993 23.95 23.47 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 20.5 35.0 49.9 70.0
##
## lowest : 0 1 2 3 5, highest: 87 88 95 97 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 96 0.999 49.87 35.67 0.0 8.0
## .25 .50 .75 .90 .95
## 25.0 49.0 78.0 95.9 100.0
##
## lowest : 0 1 4 5 6, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BIO, na.rm = TRUE)## [1] 18.56122describe(CC$Nat_Score_BECCS)## CC$Nat_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 178 1 34.63 18.88 6.25 12.22
## .25 .50 .75 .90 .95
## 24.50 33.75 45.94 54.33 61.39
##
## lowest : 0.00 2.25 2.50 3.00 4.50, highest: 75.00 76.25 77.50 78.75 79.00describe(CC$Nat_Scale_BECCS)## CC$Nat_Scale_BECCS
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 43.48 29.31 0.00 6.90
## .25 .50 .75 .90 .95
## 25.00 44.00 61.00 76.20 88.55
##
## lowest : 0 1 2 3 4, highest: 90 93 96 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 72 0.997 30.43 24.88 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 30.00 44.00 60.00 73.65
##
## lowest : 0 1 2 3 4, highest: 85 89 90 93 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 69 0.991 22.77 22.39 0.00 0.00
## .25 .50 .75 .90 .95
## 4.25 20.00 35.00 48.00 61.65
##
## lowest : 0 1 2 3 4, highest: 80 90 92 98 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 41.83 31.45 0.0 6.0
## .25 .50 .75 .90 .95
## 20.0 39.0 60.0 82.1 93.0
##
## lowest : 0 2 3 4 5, highest: 93 94 95 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BECCS, na.rm = TRUE)## [1] 16.65608describe(CC$Nat_Score_DACCS)## CC$Nat_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 156 0.999 25.53 18.95 0.00 2.50
## .25 .50 .75 .90 .95
## 13.12 24.75 35.75 45.85 56.60
##
## lowest : 0.00 0.25 0.50 2.50 3.50, highest: 70.50 70.75 75.00 75.25 79.25describe(CC$Nat_Scale_DACCS)## CC$Nat_Scale_DACCS
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.996 29.22 27.38 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 25.0 41.0 63.4 81.7
##
## lowest : 0 1 3 4 5, highest: 94 95 97 98 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 78 0.995 27.79 26.85 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 23.0 39.0 66.8 78.7
##
## lowest : 0 1 2 3 4, highest: 87 90 91 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 60 0.976 16.62 18.66 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 12.0 26.0 40.4 48.7
##
## lowest : 0 1 3 4 5, highest: 81 83 85 93 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 79 0.995 28.49 27.02 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 41.5 63.0 82.0
##
## lowest : 0 1 3 4 5, highest: 88 89 95 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_DACCS, na.rm = TRUE)## [1] 16.89449describe(CC$Nat_Score_EW)## CC$Nat_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 187 1 35.84 20.57 5.425 13.000
## .25 .50 .75 .90 .95
## 22.500 36.000 49.125 57.750 65.550
##
## lowest : 0.00 0.50 0.75 2.25 2.50, highest: 75.00 76.75 78.50 78.75 87.50describe(CC$Nat_Scale_EW)## CC$Nat_Scale_EW
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 91 0.999 46.07 31.02 0.0 7.0
## .25 .50 .75 .90 .95
## 25.5 50.0 67.0 81.0 89.0
##
## lowest : 0 2 3 4 5, highest: 91 92 95 98 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 75 0.995 27.06 24.88 0.0 0.0
## .25 .50 .75 .90 .95
## 9.5 23.0 40.0 58.2 75.0
##
## lowest : 0 1 2 3 4, highest: 85 90 92 93 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 70 0.994 25.62 23.99 0 0
## .25 .50 .75 .90 .95
## 7 24 39 50 70
##
## lowest : 0 1 2 3 4, highest: 86 88 90 95 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 84 0.999 44.61 31.96 0 7
## .25 .50 .75 .90 .95
## 22 44 67 80 93
##
## lowest : 0 4 5 6 7, highest: 91 93 94 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_EW, na.rm = TRUE)## [1] 18.08834describe(CC$Nat_Score_OF)## CC$Nat_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 166 1 31.77 19.83 4.05 8.50
## .25 .50 .75 .90 .95
## 20.00 31.25 42.50 54.35 61.00
##
## lowest : 0.00 0.25 1.25 2.50 3.00, highest: 73.50 75.00 80.25 80.50 84.50describe(CC$Nat_Scale_OF)## CC$Nat_Scale_OF
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 40.43 31.15 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 39.0 59.5 78.4 86.7
##
## lowest : 0 2 4 5 6, highest: 88 90 92 93 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 65 0.996 22.48 21.68 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 19.0 32.0 46.0 60.7
##
## lowest : 0 1 3 4 5, highest: 80 81 82 89 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 66 0.996 25.66 23.54 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 36.0 56.0 66.7
##
## lowest : 0 1 2 3 4, highest: 79 80 90 91 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 87 0.999 38.51 30.22 0.0 5.0
## .25 .50 .75 .90 .95
## 16.5 38.0 55.0 77.4 91.7
##
## lowest : 0 2 3 4 5, highest: 92 93 95 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_OF, na.rm = TRUE)## [1] 17.48905describe(CC$Nat_Score_BF)## CC$Nat_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 147 1 39.26 20.21 8.088 14.450
## .25 .50 .75 .90 .95
## 26.688 39.250 50.062 60.725 70.075
##
## lowest : 0.00 0.25 1.00 1.50 2.00, highest: 72.50 73.00 74.25 75.00 86.75describe(CC$Nat_Scale_BF)## CC$Nat_Scale_BF
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 88 0.999 52.38 31.87 2.00 12.40
## .25 .50 .75 .90 .95
## 34.00 51.00 75.00 90.60 99.65
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 79 0.998 38.07 30.26 0.00 0.00
## .25 .50 .75 .90 .95
## 20.00 35.00 54.00 79.30 90.65
##
## lowest : 0 2 3 4 5, highest: 90 91 95 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 59 0.988 17.91 18.34 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 15.00 28.00 39.00 46.65
##
## lowest : 0 1 2 3 4, highest: 68 75 77 81 85
## --------------------------------------------------------------------------------
## CC.Nat_4R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 84 0.999 48.67 33.41 1.35 10.00
## .25 .50 .75 .90 .95
## 26.00 49.00 73.25 90.30 98.65
##
## lowest : 0 1 2 4 5, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BF, na.rm = TRUE)## [1] 17.80321describe(CC$Nat_Score_NE)## CC$Nat_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 139 0.999 26.16 19.39 0.00 2.30
## .25 .50 .75 .90 .95
## 13.50 25.00 38.25 48.25 55.60
##
## lowest : 0.00 1.25 1.50 2.00 2.50, highest: 60.50 63.75 65.00 69.75 75.00describe(CC$Nat_Scale_NE)## CC$Nat_Scale_NE
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 80 0.995 31 28.97 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 27.0 49.0 68.2 80.4
##
## lowest : 0 1 2 3 4, highest: 89 90 93 95 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 72 0.994 29.96 30.59 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 23.0 43.0 77.6 95.0
##
## lowest : 0 2 3 4 5, highest: 94 95 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 48 0.931 11.18 14.75 0 0
## .25 .50 .75 .90 .95
## 0 6 17 33 43
##
## lowest : 0 1 2 3 4, highest: 49 50 64 92 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.996 32.51 30.64 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 27.0 48.0 77.4 90.0
##
## lowest : 0 4 5 6 7, highest: 92 95 96 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_NE, na.rm = TRUE)## [1] 17.14904describe(CC$Nat_Score_SE)## CC$Nat_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 143 1 55.05 20.62 24.40 31.25
## .25 .50 .75 .90 .95
## 41.75 54.75 69.75 75.00 83.20
##
## lowest : 0.00 2.00 5.50 14.50 16.00, highest: 87.25 87.50 90.00 92.00 94.00describe(CC$Nat_Scale_SE)## CC$Nat_Scale_SE
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.991 72.97 29.25 10.2 30.4
## .25 .50 .75 .90 .95
## 60.0 80.0 95.0 100.0 100.0
##
## lowest : 0 1 4 6 10, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 77 0.989 66.22 35.98 6.2 18.4
## .25 .50 .75 .90 .95
## 38.0 78.0 96.0 100.0 100.0
##
## lowest : 0 1 5 6 7, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 52 0.964 14.8 18.23 0 0
## .25 .50 .75 .90 .95
## 0 10 22 39 50
##
## lowest : 0 1 2 3 4, highest: 70 71 76 80 93
## --------------------------------------------------------------------------------
## CC.Nat_4R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 76 0.997 66.2 33.02 11.0 22.2
## .25 .50 .75 .90 .95
## 47.0 74.0 92.0 100.0 100.0
##
## lowest : 0 5 7 8 11, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_SE, na.rm = TRUE)## [1] 18.17609describe(CC$Nat_Score_WE)## CC$Nat_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 146 1 54.36 21.33 21.80 25.75
## .25 .50 .75 .90 .95
## 42.50 55.00 69.50 75.00 80.30
##
## lowest : 0.00 6.00 7.75 15.00 15.50, highest: 86.75 90.50 91.50 92.00 100.00describe(CC$Nat_Scale_WE)## CC$Nat_Scale_WE
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.993 70 30.85 9.8 21.6
## .25 .50 .75 .90 .95
## 57.0 78.0 92.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.991 63.74 35.55 10 20
## .25 .50 .75 .90 .95
## 38 72 93 100 100
##
## lowest : 0 1 5 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.987 20.75 22.27 0.0 0.0
## .25 .50 .75 .90 .95
## 2.0 17.0 30.0 47.0 65.8
##
## lowest : 0 1 2 3 4, highest: 85 88 90 94 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.997 62.96 35.06 4.8 14.2
## .25 .50 .75 .90 .95
## 42.0 68.0 90.0 100.0 100.0
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_WE, na.rm = TRUE)## [1] 18.78237#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS, CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS,
## CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.67 0.69 0.66 0.35 2.2 0.017 62 20 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.64 0.67 0.71
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_AFSCS 0.58 0.59 0.53 0.33 1.5 0.023 0.0318 0.22
## CC.Nat_2R_AFSCS 0.54 0.57 0.54 0.30 1.3 0.026 0.0697 0.22
## CC.Nat_3R_AFSCS 0.76 0.77 0.70 0.53 3.3 0.013 0.0053 0.53
## CC.Nat_4R_AFSCS 0.50 0.51 0.45 0.25 1.0 0.027 0.0337 0.22
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_AFSCS 343 0.72 0.75 0.65 0.50 75 25
## CC.Nat_2R_AFSCS 343 0.79 0.77 0.66 0.54 53 31
## CC.Nat_3R_AFSCS 343 0.56 0.53 0.26 0.22 39 30
## CC.Nat_4R_AFSCS 343 0.80 0.82 0.77 0.63 80 25hist(CC$Nat_Score_AFSCS, main = 'AFSCS Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO, CC$Nat_4R_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO,
## CC$Nat_4R_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.64 0.64 0.31 1.8 0.016 39 19 0.31
##
## lower alpha upper 95% confidence boundaries
## 0.63 0.66 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BIO 0.51 0.51 0.43 0.25 1.03 0.025 0.017 0.24
## CC.Nat_2R_BIO 0.61 0.58 0.58 0.31 1.37 0.020 0.090 0.24
## CC.Nat_3R_BIO 0.73 0.73 0.67 0.48 2.72 0.014 0.021 0.39
## CC.Nat_4R_BIO 0.43 0.42 0.36 0.20 0.73 0.030 0.030 0.14
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BIO 332 0.78 0.75 0.70 0.55 46 27
## CC.Nat_2R_BIO 332 0.68 0.69 0.50 0.43 37 25
## CC.Nat_3R_BIO 332 0.46 0.52 0.23 0.18 24 22
## CC.Nat_4R_BIO 332 0.85 0.82 0.79 0.64 50 31hist(CC$Nat_Score_BIO, main = 'BIO Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_BECCS, CC$Nat_2R_BECCS, CC$Nat_3R_BECCS, CC$Nat_4R_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_BECCS, CC$Nat_2R_BECCS,
## CC$Nat_3R_BECCS, CC$Nat_4R_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.61 0.62 0.28 1.6 0.018 35 17 0.25
##
## lower alpha upper 95% confidence boundaries
## 0.59 0.63 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BECCS 0.44 0.44 0.38 0.21 0.79 0.030 0.027 0.195
## CC.Nat_2R_BECCS 0.56 0.52 0.56 0.26 1.07 0.023 0.138 0.052
## CC.Nat_3R_BECCS 0.73 0.72 0.68 0.46 2.58 0.014 0.041 0.380
## CC.Nat_4R_BECCS 0.41 0.41 0.34 0.19 0.68 0.032 0.018 0.195
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BECCS 330 0.79 0.76 0.72 0.55 43 26
## CC.Nat_2R_BECCS 330 0.67 0.70 0.50 0.42 30 22
## CC.Nat_3R_BECCS 330 0.41 0.48 0.16 0.11 23 21
## CC.Nat_4R_BECCS 330 0.82 0.78 0.76 0.59 42 28hist(CC$Nat_Score_BECCS, main = 'BECCS Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_DACCS, CC$Nat_2R_DACCS, CC$Nat_3R_DACCS, CC$Nat_4R_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_DACCS, CC$Nat_2R_DACCS,
## CC$Nat_3R_DACCS, CC$Nat_4R_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.69 0.67 0.35 2.2 0.015 26 17 0.33
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_DACCS 0.54 0.53 0.45 0.27 1.1 0.024 0.014 0.25
## CC.Nat_2R_DACCS 0.67 0.65 0.63 0.38 1.8 0.017 0.076 0.25
## CC.Nat_3R_DACCS 0.75 0.75 0.70 0.50 3.0 0.014 0.028 0.41
## CC.Nat_4R_DACCS 0.52 0.51 0.43 0.25 1.0 0.025 0.019 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_DACCS 347 0.82 0.80 0.77 0.63 29 25
## CC.Nat_2R_DACCS 347 0.71 0.69 0.50 0.44 28 25
## CC.Nat_3R_DACCS 347 0.49 0.56 0.29 0.25 17 18
## CC.Nat_4R_DACCS 347 0.84 0.82 0.79 0.65 28 25hist(CC$Nat_Score_DACCS, main = 'DACCS Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW, CC$Nat_4R_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW,
## CC$Nat_4R_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.68 0.7 0.35 2.2 0.015 36 18 0.31
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_EW 0.57 0.57 0.50 0.30 1.31 0.023 0.033 0.21
## CC.Nat_2R_EW 0.62 0.59 0.64 0.32 1.43 0.020 0.140 0.19
## CC.Nat_3R_EW 0.79 0.79 0.75 0.56 3.75 0.011 0.030 0.51
## CC.Nat_4R_EW 0.46 0.46 0.40 0.22 0.84 0.029 0.034 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_EW 335 0.80 0.76 0.73 0.58 46 27
## CC.Nat_2R_EW 335 0.73 0.74 0.59 0.51 27 23
## CC.Nat_3R_EW 335 0.45 0.50 0.21 0.17 26 22
## CC.Nat_4R_EW 335 0.88 0.85 0.86 0.72 45 28hist(CC$Nat_Score_EW, main = 'EW Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF, CC$Nat_4R_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF,
## CC$Nat_4R_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.69 0.69 0.35 2.2 0.015 32 17 0.32
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.69 0.72
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_OF 0.56 0.57 0.49 0.30 1.31 0.023 0.019 0.24
## CC.Nat_2R_OF 0.63 0.60 0.63 0.34 1.53 0.020 0.115 0.21
## CC.Nat_3R_OF 0.77 0.77 0.73 0.53 3.35 0.012 0.030 0.46
## CC.Nat_4R_OF 0.48 0.49 0.42 0.24 0.95 0.028 0.024 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_OF 327 0.81 0.77 0.73 0.58 40 27
## CC.Nat_2R_OF 327 0.70 0.73 0.57 0.49 22 20
## CC.Nat_3R_OF 327 0.49 0.53 0.26 0.21 26 22
## CC.Nat_4R_OF 327 0.86 0.83 0.83 0.69 39 27hist(CC$Nat_Score_OF, main = 'OF Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF, CC$Nat_4R_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF,
## CC$Nat_4R_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.65 0.6 0.63 0.27 1.5 0.016 39 18 0.25
##
## lower alpha upper 95% confidence boundaries
## 0.61 0.65 0.68
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BF 0.47 0.44 0.39 0.20 0.77 0.026 0.043 0.139
## CC.Nat_2R_BF 0.55 0.46 0.55 0.22 0.86 0.020 0.176 0.039
## CC.Nat_3R_BF 0.75 0.75 0.71 0.50 3.03 0.013 0.031 0.436
## CC.Nat_4R_BF 0.38 0.34 0.32 0.15 0.51 0.030 0.049 0.139
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BF 248 0.80 0.74 0.71 0.560 52 28
## CC.Nat_2R_BF 248 0.73 0.72 0.55 0.461 38 27
## CC.Nat_3R_BF 248 0.28 0.41 0.07 0.042 18 17
## CC.Nat_4R_BF 248 0.85 0.81 0.80 0.652 49 29hist(CC$Nat_Score_BF, main = 'BF Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_NE, CC$Nat_2R_NE, CC$Nat_3R_NE, CC$Nat_4R_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_NE, CC$Nat_2R_NE, CC$Nat_3R_NE,
## CC$Nat_4R_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.6 0.62 0.28 1.5 0.017 26 17 0.21
##
## lower alpha upper 95% confidence boundaries
## 0.6 0.63 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_NE 0.42 0.40 0.33 0.18 0.66 0.028 0.022 0.15
## CC.Nat_2R_NE 0.65 0.60 0.62 0.33 1.48 0.016 0.123 0.15
## CC.Nat_3R_NE 0.70 0.71 0.69 0.45 2.44 0.017 0.062 0.34
## CC.Nat_4R_NE 0.36 0.34 0.27 0.15 0.51 0.032 0.013 0.11
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_NE 257 0.81 0.78 0.78 0.59 31 26
## CC.Nat_2R_NE 257 0.66 0.62 0.37 0.32 30 28
## CC.Nat_3R_NE 257 0.35 0.48 0.16 0.13 11 15
## CC.Nat_4R_NE 257 0.85 0.82 0.84 0.65 33 28hist(CC$Nat_Score_NE, main = 'NE Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_SE, CC$Nat_2R_SE, CC$Nat_3R_SE, CC$Nat_4R_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_SE, CC$Nat_2R_SE, CC$Nat_3R_SE,
## CC$Nat_4R_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.58 0.58 0.57 0.26 1.4 0.02 55 18 0.22
##
## lower alpha upper 95% confidence boundaries
## 0.54 0.58 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_SE 0.43 0.46 0.36 0.22 0.84 0.029 0.0034 0.24
## CC.Nat_2R_SE 0.61 0.58 0.56 0.32 1.39 0.019 0.0689 0.27
## CC.Nat_3R_SE 0.60 0.62 0.57 0.35 1.60 0.022 0.0484 0.24
## CC.Nat_4R_SE 0.34 0.34 0.26 0.15 0.52 0.033 0.0038 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_SE 245 0.73 0.71 0.63 0.46 73 27
## CC.Nat_2R_SE 245 0.65 0.60 0.33 0.27 66 32
## CC.Nat_3R_SE 245 0.45 0.56 0.30 0.23 15 18
## CC.Nat_4R_SE 245 0.80 0.79 0.75 0.55 66 29hist(CC$Nat_Score_SE, main = 'SE Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE, CC$Nat_4R_WE))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Nat_3R_WE ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE,
## CC$Nat_4R_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.58 0.53 0.58 0.22 1.1 0.02 54 19 0.18
##
## lower alpha upper 95% confidence boundaries
## 0.54 0.58 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_WE 0.33 0.28 0.27 0.114 0.39 0.033 0.053 0.017
## CC.Nat_2R_WE 0.53 0.47 0.55 0.225 0.87 0.023 0.182 0.017
## CC.Nat_3R_WE 0.73 0.73 0.69 0.476 2.72 0.015 0.044 0.377
## CC.Nat_4R_WE 0.26 0.20 0.20 0.076 0.25 0.038 0.050 -0.051
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_WE 257 0.79 0.77 0.767 0.566 70 28
## CC.Nat_2R_WE 257 0.68 0.64 0.412 0.343 64 31
## CC.Nat_3R_WE 257 0.25 0.35 -0.039 -0.037 21 21
## CC.Nat_4R_WE 257 0.84 0.82 0.834 0.626 63 31hist(CC$Nat_Score_WE, main = 'WE Naturalness Scale Score')
#Correlations
cor.plot(CC$Nat_Scale_AFSCS, labels = c('1','2', '3', '4'), main = "Correlation Between AFSCS Support Items")
cor.plot(CC$Nat_Scale_BIO, labels = c('1','2', '3', '4'), main = "Correlation Between BIO Support Items")
cor.plot(CC$Nat_Scale_BECCS, labels = c('1','2', '3', '4'), main = "Correlation Between BECCS Support Items")
cor.plot(CC$Nat_Scale_DACCS, labels = c('1','2', '3', '4'), main = "Correlation Between DACCS Support Items")
cor.plot(CC$Nat_Scale_EW, labels = c('1','2', '3', '4'), main = "Correlation Between EW Support Items")
cor.plot(CC$Nat_Scale_OF, labels = c('1','2', '3', '4'), main = "Correlation Between OF Support Items")
cor.plot(CC$Nat_Scale_BF, labels = c('1','2', '3', '4'), main = "Correlation Between BF Support Items")
cor.plot(CC$Nat_Scale_NE, labels = c('1','2', '3', '4'), main = "Correlation Between NE Support Items")
cor.plot(CC$Nat_Scale_SE, labels = c('1','2', '3', '4'), main = "Correlation Between SE Support Items")
cor.plot(CC$Nat_Scale_WE, labels = c('1','2', '3', '4'), main = "Correlation Between WE Support Items")
Support
# Support was rated on a two item scale (0 = Strongly disagree to 100 = Strongly agree) and a mean score was calculated to represent intent to support of the technology rated, used in this study as a proxy for support.
## 1. I would personally support non-government entities deploying these on a large scale.
## 2. I would personally support spending government tax dollars to deploy these on a large scale. Descriptives
# Define Variables
CC$Support1_AFSCS <- as.numeric(as.character(CC$BI_AFSCS_18))
CC$Support2_AFSCS <- as.numeric(as.character(CC$BI_AFSCS_19))
CC$Support1_BIO <- CC$BI_BIO_18
CC$Support2_BIO <- CC$BI_BIO_19
CC$Support1_BECCS <- CC$BI_BECCS_18
CC$Support2_BECCS <- CC$BI_BECCS_19
CC$Support1_DACCS <- CC$BI_DACCS_18
CC$Support2_DACCS <- CC$BI_DACCS_19
CC$Support1_EW <- CC$BI_EW_18
CC$Support2_EW <- CC$BI_EW_19
CC$Support1_OF <- CC$BI_OF_18
CC$Support2_OF <- CC$BI_OF_19
CC$Support1_BF <- CC$BI_BF_18
CC$Support2_BF <- CC$BI_BF_19
CC$Support1_NE <- CC$BI_NE_18
CC$Support2_NE <- CC$BI_NE_19
CC$Support1_SE <- CC$BI_SE_18
CC$Support2_SE <- CC$BI_SE_19
CC$Support1_WE <- CC$BI_WE_18
CC$Support2_WE <- CC$BI_WE_19
# Descriptives
describe(CC$Support1_AFSCS)## CC$Support1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 67 0.98 78.22 25.24 25 42
## .25 .50 .75 .90 .95
## 68 85 100 100 100
##
## lowest : 0 1 4 9 10, highest: 96 97 98 99 100describe(CC$Support2_AFSCS)## CC$Support2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 69 0.987 74.06 29.37 4.0 25.6
## .25 .50 .75 .90 .95
## 63.5 82.0 96.0 100.0 100.0
##
## lowest : 0 2 4 5 7, highest: 95 96 98 99 100describe(CC$Support1_BIO)## CC$Support1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 86 0.999 55.82 31.12 0.00 12.00
## .25 .50 .75 .90 .95
## 39.75 59.00 76.00 90.90 100.00
##
## lowest : 0 4 5 6 7, highest: 95 96 97 98 100describe(CC$Support2_BIO)## CC$Support2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 90 0.999 51.39 33.78 0.0 3.2
## .25 .50 .75 .90 .95
## 30.0 54.0 75.0 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 98 100describe(CC$Support1_BECCS)## CC$Support1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 55.58 32.42 0.00 10.90
## .25 .50 .75 .90 .95
## 36.25 60.00 75.00 93.00 100.00
##
## lowest : 0 1 2 4 5, highest: 94 95 96 98 100describe(CC$Support2_BECCS)## CC$Support2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.998 51.04 34.04 0.00 0.90
## .25 .50 .75 .90 .95
## 28.25 54.00 73.00 89.00 100.00
##
## lowest : 0 1 3 4 5, highest: 93 94 96 98 100describe(CC$Support1_DACCS)## CC$Support1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 86 0.998 54.59 33.75 0.0 5.6
## .25 .50 .75 .90 .95
## 35.0 60.0 75.0 97.4 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100describe(CC$Support2_DACCS)## CC$Support2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 91 0.999 51.18 33.83 0.0 1.6
## .25 .50 .75 .90 .95
## 29.5 55.0 73.5 89.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 97 98 99 100describe(CC$Support1_EW)## CC$Support1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 93 0.998 50.29 33.57 0.0 4.4
## .25 .50 .75 .90 .95
## 27.5 51.0 72.0 90.0 100.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 98 100describe(CC$Support2_EW)## CC$Support2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 87 0.998 48.3 34.89 0.0 0.0
## .25 .50 .75 .90 .95
## 25.0 50.0 72.5 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100describe(CC$Support1_OF)## CC$Support1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 90 0.999 53.27 34.92 0.0 6.0
## .25 .50 .75 .90 .95
## 29.5 59.0 75.0 94.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100describe(CC$Support2_OF)## CC$Support2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.998 49.17 35.2 0.0 0.0
## .25 .50 .75 .90 .95
## 20.0 53.0 74.5 89.0 97.1
##
## lowest : 0 1 3 4 5, highest: 93 94 95 98 100describe(CC$Support1_BF)## CC$Support1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 78 0.999 63.28 29.39 9.35 20.70
## .25 .50 .75 .90 .95
## 50.00 68.50 82.00 95.00 100.00
##
## lowest : 0 4 5 7 8, highest: 94 95 96 98 100describe(CC$Support2_BF)## CC$Support2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 58.35 31.34 0.00 13.00
## .25 .50 .75 .90 .95
## 45.00 61.00 77.25 93.00 100.00
##
## lowest : 0 3 4 5 6, highest: 94 95 96 98 100describe(CC$Support1_NE)## CC$Support1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 49.19 39.16 0 0
## .25 .50 .75 .90 .95
## 15 52 79 95 100
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100describe(CC$Support2_NE)## CC$Support2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 51.91 37.72 0 0
## .25 .50 .75 .90 .95
## 25 55 80 95 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 97 100describe(CC$Support1_SE)## CC$Support1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.956 82.66 22.78 35.0 51.4
## .25 .50 .75 .90 .95
## 75.0 91.0 100.0 100.0 100.0
##
## lowest : 0 1 5 10 14, highest: 96 97 98 99 100describe(CC$Support2_SE)## CC$Support2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 66 0.965 76.36 29.66 2.4 29.4
## .25 .50 .75 .90 .95
## 65.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 2 4 10, highest: 96 97 98 99 100describe(CC$Support1_WE)## CC$Support1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.988 76.82 25.62 22.6 43.0
## .25 .50 .75 .90 .95
## 69.0 81.0 98.0 100.0 100.0
##
## lowest : 0 4 10 17 20, highest: 95 96 98 99 100describe(CC$Support2_WE)## CC$Support2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 65 0.989 73.32 29.82 1.6 24.6
## .25 .50 .75 .90 .95
## 62.0 80.0 97.0 100.0 100.0
##
## lowest : 0 2 7 10 11, highest: 96 97 98 99 100sd(CC$Support1_AFSCS, na.rm = TRUE)## [1] 24.36924sd(CC$Support2_AFSCS, na.rm = TRUE)## [1] 28.19222sd(CC$Support1_BIO, na.rm = TRUE)## [1] 27.4581sd(CC$Support2_BIO, na.rm = TRUE)## [1] 29.52825sd(CC$Support1_BECCS, na.rm = TRUE)## [1] 28.53122sd(CC$Support2_BECCS, na.rm = TRUE)## [1] 29.75296sd(CC$Support1_DACCS, na.rm = TRUE)## [1] 29.6834sd(CC$Support2_DACCS, na.rm = TRUE)## [1] 29.5563sd(CC$Support1_EW, na.rm = TRUE)## [1] 29.25292sd(CC$Support2_EW, na.rm = TRUE)## [1] 30.30295sd(CC$Support1_OF, na.rm = TRUE)## [1] 30.54466sd(CC$Support2_OF, na.rm = TRUE)## [1] 30.72261sd(CC$Support1_BF, na.rm = TRUE)## [1] 26.33298sd(CC$Support2_BF, na.rm = TRUE)## [1] 27.8222sd(CC$Support1_NE, na.rm = TRUE)## [1] 34.00086sd(CC$Support2_NE, na.rm = TRUE)## [1] 32.86786sd(CC$Support1_SE, na.rm = TRUE)## [1] 23.17443sd(CC$Support2_SE, na.rm = TRUE)## [1] 28.89797sd(CC$Support1_WE, na.rm = TRUE)## [1] 24.49376sd(CC$Support2_WE, na.rm = TRUE)## [1] 28.52271hist(CC$Support1_AFSCS)
hist(CC$Support2_AFSCS)
hist(CC$Support1_BIO)
hist(CC$Support2_BIO)
hist(CC$Support1_BECCS)
hist(CC$Support2_BECCS)
hist(CC$Support1_DACCS)
hist(CC$Support2_DACCS)
hist(CC$Support1_EW)
hist(CC$Support2_EW)
hist(CC$Support1_OF)
hist(CC$Support2_OF)
hist(CC$Support1_BF)
hist(CC$Support2_BF)
hist(CC$Support1_NE)
hist(CC$Support2_NE)
hist(CC$Support1_SE)
hist(CC$Support2_SE)
hist(CC$Support1_WE)
hist(CC$Support2_WE)
Score(s) & Scale(s)
# Scores & Scales
CC$Support_Score_AFSCS <- rowMeans(CC [, c("Support1_AFSCS", "Support2_AFSCS")], na.rm=TRUE)
CC$Support_Scale_AFSCS <- data.frame(CC$Support1_AFSCS, CC$Support2_AFSCS)
CC$Support_Score_BIO <- rowMeans(CC [, c("Support1_BIO", "Support2_BIO")], na.rm=TRUE)
CC$Support_Scale_BIO <- data.frame(CC$Support1_BIO, CC$Support2_BIO)
CC$Support_Score_BECCS <- rowMeans(CC [, c("Support1_BECCS", "Support2_BECCS")], na.rm=TRUE)
CC$Support_Scale_BECCS <- data.frame(CC$Support1_BECCS, CC$Support2_BECCS)
CC$Support_Score_DACCS <- rowMeans(CC [, c("Support1_DACCS", "Support2_DACCS")], na.rm=TRUE)
CC$Support_Scale_DACCS <- data.frame(CC$Support1_DACCS, CC$Support2_DACCS)
CC$Support_Score_EW <- rowMeans(CC [, c("Support1_EW", "Support2_EW")], na.rm=TRUE)
CC$Support_Scale_EW <- data.frame(CC$Support1_EW, CC$Support2_EW)
CC$Support_Score_OF <- rowMeans(CC [, c("Support1_OF", "Support2_OF")], na.rm=TRUE)
CC$Support_Scale_OF <- data.frame(CC$Support1_OF, CC$Support2_OF)
CC$Support_Score_BF <- rowMeans(CC [, c("Support1_BF", "Support2_BF")], na.rm=TRUE)
CC$Support_Scale_BF <- data.frame(CC$Support1_BF, CC$Support2_BF)
CC$Support_Score_NE <- rowMeans(CC [, c("Support1_NE", "Support2_NE")], na.rm=TRUE)
CC$Support_Scale_NE <- data.frame(CC$Support1_NE, CC$Support2_NE)
CC$Support_Score_SE <- rowMeans(CC [, c("Support1_SE", "Support2_SE")], na.rm=TRUE)
CC$Support_Scale_SE <- data.frame(CC$Support1_SE, CC$Support2_SE)
CC$Support_Score_WE <- rowMeans(CC [, c("Support1_WE", "Support2_WE")], na.rm=TRUE)
CC$Support_Scale_WE <- data.frame(CC$Support1_WE, CC$Support2_WE)
# Describe Scores/Scales
describe(CC$Support_Score_AFSCS)## CC$Support_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 114 0.991 76.14 25.37 30.25 46.40
## .25 .50 .75 .90 .95
## 62.50 82.00 95.25 100.00 100.00
##
## lowest : 0.0 4.0 5.0 10.0 12.5, highest: 97.0 97.5 98.0 99.5 100.0describe(CC$Support_Scale_AFSCS)## CC$Support_Scale_AFSCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 67 0.98 78.22 25.24 25 42
## .25 .50 .75 .90 .95
## 68 85 100 100 100
##
## lowest : 0 1 4 9 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 69 0.987 74.06 29.37 4.0 25.6
## .25 .50 .75 .90 .95
## 63.5 82.0 96.0 100.0 100.0
##
## lowest : 0 2 4 5 7, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_AFSCS, na.rm = TRUE)## [1] 23.61434describe(CC$Support_Score_BIO)## CC$Support_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 142 0.999 53.6 29.85 0.55 13.55
## .25 .50 .75 .90 .95
## 36.50 54.25 74.00 87.00 95.22
##
## lowest : 0.0 1.0 2.5 3.5 5.0, highest: 94.0 95.0 95.5 97.5 100.0describe(CC$Support_Scale_BIO)## CC$Support_Scale_BIO
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 86 0.999 55.82 31.12 0.00 12.00
## .25 .50 .75 .90 .95
## 39.75 59.00 76.00 90.90 100.00
##
## lowest : 0 4 5 6 7, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 90 0.999 51.39 33.78 0.0 3.2
## .25 .50 .75 .90 .95
## 30.0 54.0 75.0 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BIO, na.rm = TRUE)## [1] 26.28137describe(CC$Support_Score_BECCS)## CC$Support_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 136 0.999 53.31 31 0.00 9.90
## .25 .50 .75 .90 .95
## 35.25 55.00 74.25 85.00 98.20
##
## lowest : 0.0 1.0 1.5 2.0 5.0, highest: 93.0 93.5 95.0 96.0 100.0describe(CC$Support_Scale_BECCS)## CC$Support_Scale_BECCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 55.58 32.42 0.00 10.90
## .25 .50 .75 .90 .95
## 36.25 60.00 75.00 93.00 100.00
##
## lowest : 0 1 2 4 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.998 51.04 34.04 0.00 0.90
## .25 .50 .75 .90 .95
## 28.25 54.00 73.00 89.00 100.00
##
## lowest : 0 1 3 4 5, highest: 93 94 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BECCS, na.rm = TRUE)## [1] 27.28703describe(CC$Support_Score_DACCS)## CC$Support_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 148 0.999 52.88 32.02 0.00 5.80
## .25 .50 .75 .90 .95
## 35.50 55.50 73.25 89.40 99.85
##
## lowest : 0.0 0.5 1.0 2.0 2.5, highest: 96.5 97.0 98.5 99.5 100.0describe(CC$Support_Scale_DACCS)## CC$Support_Scale_DACCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 86 0.998 54.59 33.75 0.0 5.6
## .25 .50 .75 .90 .95
## 35.0 60.0 75.0 97.4 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 91 0.999 51.18 33.83 0.0 1.6
## .25 .50 .75 .90 .95
## 29.5 55.0 73.5 89.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_DACCS, na.rm = TRUE)## [1] 28.11655describe(CC$Support_Score_EW)## CC$Support_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 137 0.999 49.29 31.61 0.0 8.0
## .25 .50 .75 .90 .95
## 29.5 50.5 68.5 85.8 98.6
##
## lowest : 0.0 0.5 1.0 2.0 2.5, highest: 94.5 95.0 95.5 98.0 100.0describe(CC$Support_Scale_EW)## CC$Support_Scale_EW
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 93 0.998 50.29 33.57 0.0 4.4
## .25 .50 .75 .90 .95
## 27.5 51.0 72.0 90.0 100.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 87 0.998 48.3 34.89 0.0 0.0
## .25 .50 .75 .90 .95
## 25.0 50.0 72.5 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_EW, na.rm = TRUE)## [1] 27.63767describe(CC$Support_Score_OF)## CC$Support_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 145 0.999 51.22 33 0.00 5.80
## .25 .50 .75 .90 .95
## 27.50 54.50 73.75 89.00 95.00
##
## lowest : 0.0 0.5 2.0 3.0 3.5, highest: 95.0 95.5 97.0 97.5 100.0describe(CC$Support_Scale_OF)## CC$Support_Scale_OF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 90 0.999 53.27 34.92 0.0 6.0
## .25 .50 .75 .90 .95
## 29.5 59.0 75.0 94.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------
## CC.Support2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.998 49.17 35.2 0.0 0.0
## .25 .50 .75 .90 .95
## 20.0 53.0 74.5 89.0 97.1
##
## lowest : 0 1 3 4 5, highest: 93 94 95 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_OF, na.rm = TRUE)## [1] 28.83405describe(CC$Support_Score_BF)## CC$Support_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 120 1 60.82 27.72 10.00 22.05
## .25 .50 .75 .90 .95
## 50.00 64.00 78.12 92.00 99.00
##
## lowest : 0.0 2.5 4.0 5.0 7.0, highest: 95.0 96.5 98.0 99.0 100.0describe(CC$Support_Scale_BF)## CC$Support_Scale_BF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 78 0.999 63.28 29.39 9.35 20.70
## .25 .50 .75 .90 .95
## 50.00 68.50 82.00 95.00 100.00
##
## lowest : 0 4 5 7 8, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 58.35 31.34 0.00 13.00
## .25 .50 .75 .90 .95
## 45.00 61.00 77.25 93.00 100.00
##
## lowest : 0 3 4 5 6, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BF, na.rm = TRUE)## [1] 24.89028describe(CC$Support_Score_NE)## CC$Support_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 122 0.999 50.55 35.85 0.0 0.6
## .25 .50 .75 .90 .95
## 27.5 52.0 76.5 91.1 99.2
##
## lowest : 0.0 1.0 2.0 3.0 3.5, highest: 95.0 95.5 96.0 99.0 100.0describe(CC$Support_Scale_NE)## CC$Support_Scale_NE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 49.19 39.16 0 0
## .25 .50 .75 .90 .95
## 15 52 79 95 100
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 51.91 37.72 0 0
## .25 .50 .75 .90 .95
## 25 55 80 95 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 97 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_NE, na.rm = TRUE)## [1] 31.10287describe(CC$Support_Score_SE)## CC$Support_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 93 0.979 79.51 24.49 30.4 49.2
## .25 .50 .75 .90 .95
## 68.0 87.5 100.0 100.0 100.0
##
## lowest : 0.0 0.5 2.5 10.0 12.0, highest: 97.5 98.5 99.0 99.5 100.0describe(CC$Support_Scale_SE)## CC$Support_Scale_SE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.956 82.66 22.78 35.0 51.4
## .25 .50 .75 .90 .95
## 75.0 91.0 100.0 100.0 100.0
##
## lowest : 0 1 5 10 14, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 66 0.965 76.36 29.66 2.4 29.4
## .25 .50 .75 .90 .95
## 65.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 2 4 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_SE, na.rm = TRUE)## [1] 23.53217describe(CC$Support_Score_WE)## CC$Support_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 99 0.993 75.07 26.24 19.9 42.5
## .25 .50 .75 .90 .95
## 64.0 80.0 95.5 100.0 100.0
##
## lowest : 0.0 3.0 10.5 11.0 15.0, highest: 98.0 98.5 99.0 99.5 100.0describe(CC$Support_Scale_WE)## CC$Support_Scale_WE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.988 76.82 25.62 22.6 43.0
## .25 .50 .75 .90 .95
## 69.0 81.0 98.0 100.0 100.0
##
## lowest : 0 4 10 17 20, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 65 0.989 73.32 29.82 1.6 24.6
## .25 .50 .75 .90 .95
## 62.0 80.0 97.0 100.0 100.0
##
## lowest : 0 2 7 10 11, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_WE, na.rm = TRUE)## [1] 24.65437#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$Support1_AFSCS, CC$Support2_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_AFSCS, CC$Support2_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.76 0.61 0.61 3.2 0.015 76 24 0.61
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.75 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_AFSCS 0.53 0.61 0.38 0.61 1.6 NA 0
## CC.Support2_AFSCS 0.71 0.61 0.38 0.61 1.6 NA 0
## med.r
## CC.Support1_AFSCS 0.61
## CC.Support2_AFSCS 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_AFSCS 343 0.88 0.9 0.7 0.61 78 24
## CC.Support2_AFSCS 343 0.91 0.9 0.7 0.61 74 28hist(CC$Support_Score_AFSCS, main = 'AFSCS Support Scale Score')
psych::alpha(data.frame(CC$Support1_BIO, CC$Support2_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_BIO, CC$Support2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.7 0.7 4.7 0.011 54 26 0.7
##
## lower alpha upper 95% confidence boundaries
## 0.8 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_BIO 0.65 0.7 0.49 0.7 2.3 NA 0 0.7
## CC.Support2_BIO 0.75 0.7 0.49 0.7 2.3 NA 0 0.7
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BIO 332 0.92 0.92 0.77 0.7 56 27
## CC.Support2_BIO 332 0.93 0.92 0.77 0.7 51 30hist(CC$Support_Score_BIO, main = 'BIO Support Scale Score')
psych::alpha(data.frame(CC$Support1_BECCS, CC$Support2_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_BECCS, CC$Support2_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.75 0.75 6.1 0.0089 53 27 0.75
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_BECCS 0.72 0.75 0.57 0.75 3.1 NA 0
## CC.Support2_BECCS 0.79 0.75 0.57 0.75 3.1 NA 0
## med.r
## CC.Support1_BECCS 0.75
## CC.Support2_BECCS 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BECCS 330 0.93 0.94 0.81 0.75 56 29
## CC.Support2_BECCS 330 0.94 0.94 0.81 0.75 51 30hist(CC$Support_Score_BECCS, main = 'BECCS Support Scale Score')
psych::alpha(data.frame(CC$Support1_DACCS, CC$Support2_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_DACCS, CC$Support2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.8 0.8 8.1 0.0069 53 28 0.8
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_DACCS 0.81 0.8 0.64 0.8 4.1 NA 0
## CC.Support2_DACCS 0.80 0.8 0.64 0.8 4.1 NA 0
## med.r
## CC.Support1_DACCS 0.8
## CC.Support2_DACCS 0.8
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_DACCS 347 0.95 0.95 0.85 0.8 55 30
## CC.Support2_DACCS 347 0.95 0.95 0.85 0.8 51 30hist(CC$Support_Score_DACCS, main = 'DACCS Support Scale Score')
psych::alpha(data.frame(CC$Support1_EW, CC$Support2_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_EW, CC$Support2_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.72 0.72 5.2 0.01 49 28 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_EW 0.70 0.72 0.52 0.72 2.6 NA 0 0.72
## CC.Support2_EW 0.75 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_EW 335 0.93 0.93 0.79 0.72 50 29
## CC.Support2_EW 335 0.93 0.93 0.79 0.72 48 30hist(CC$Support_Score_EW, main = 'EW Support Scale Score')
psych::alpha(data.frame(CC$Support1_OF, CC$Support2_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_OF, CC$Support2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.77 0.77 6.8 0.0081 51 29 0.77
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_OF 0.77 0.77 0.6 0.77 3.4 NA 0 0.77
## CC.Support2_OF 0.78 0.77 0.6 0.77 3.4 NA 0 0.77
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_OF 327 0.94 0.94 0.83 0.77 53 31
## CC.Support2_OF 327 0.94 0.94 0.83 0.77 49 31hist(CC$Support_Score_OF, main = 'OF Support Scale Score')
psych::alpha(data.frame(CC$Support1_BF, CC$Support2_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_BF, CC$Support2_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.69 0.69 4.4 0.012 61 25 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_BF 0.65 0.69 0.48 0.69 2.2 NA 0 0.69
## CC.Support2_BF 0.73 0.69 0.48 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BF 248 0.91 0.92 0.76 0.69 63 26
## CC.Support2_BF 248 0.92 0.92 0.76 0.69 58 28hist(CC$Support_Score_BF, main = 'BF Support Scale Score')
psych::alpha(data.frame(CC$Support1_NE, CC$Support2_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_NE, CC$Support2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.73 0.73 5.4 0.0098 51 31 0.73
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_NE 0.76 0.73 0.53 0.73 2.7 NA 0 0.73
## CC.Support2_NE 0.71 0.73 0.53 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_NE 257 0.93 0.93 0.8 0.73 49 34
## CC.Support2_NE 257 0.93 0.93 0.8 0.73 52 33hist(CC$Support_Score_NE, main = 'NE Support Scale Score')
psych::alpha(data.frame(CC$Support1_SE, CC$Support2_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_SE, CC$Support2_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.63 0.63 3.4 0.014 80 24 0.63
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_SE 0.50 0.63 0.4 0.63 1.7 NA 0 0.63
## CC.Support2_SE 0.78 0.63 0.4 0.63 1.7 NA 0 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_SE 245 0.88 0.9 0.72 0.63 83 23
## CC.Support2_SE 245 0.92 0.9 0.72 0.63 76 29hist(CC$Support_Score_SE, main = 'SE Support Scale Score')
psych::alpha(data.frame(CC$Support1_WE, CC$Support2_WE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_WE, CC$Support2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.73 0.73 5.4 0.01 75 25 0.73
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_WE 0.63 0.73 0.53 0.73 2.7 NA 0 0.73
## CC.Support2_WE 0.85 0.73 0.53 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_WE 257 0.92 0.93 0.79 0.73 77 24
## CC.Support2_WE 257 0.94 0.93 0.79 0.73 73 29hist(CC$Support_Score_WE, main = 'WE Support Scale Score')
#Correlations
cor.plot(CC$Support_Scale_AFSCS, labels = c('1','2'), main = "Correlation Between AFSCS Support Items")
cor.plot(CC$Support_Scale_BIO, labels = c('1','2'), main = "Correlation Between BIO Support Items")
cor.plot(CC$Support_Scale_BECCS, labels = c('1','2'), main = "Correlation Between BECCS Support Items")
cor.plot(CC$Support_Scale_DACCS, labels = c('1','2'), main = "Correlation Between DACCS Support Items")
cor.plot(CC$Support_Scale_EW, labels = c('1','2'), main = "Correlation Between EW Support Items")
cor.plot(CC$Support_Scale_OF, labels = c('1','2'), main = "Correlation Between OF Support Items")
cor.plot(CC$Support_Scale_BF, labels = c('1','2'), main = "Correlation Between BF Support Items")
cor.plot(CC$Support_Scale_NE, labels = c('1','2'), main = "Correlation Between NE Support Items")
cor.plot(CC$Support_Scale_SE, labels = c('1','2'), main = "Correlation Between SE Support Items")
cor.plot(CC$Support_Scale_WE, labels = c('1','2'), main = "Correlation Between WE Support Items")
Risk
# Risk was rated on a two item scale (0 = Strongly disagree to 100 = Strongly agree) and a mean score was calculated to represent risk perception of the technology rated.
## 1. This is risky to deploy.
## 2. This is frightening.Descriptives
# Define Variables
CC$Risk_1_AFSCS <- CC$Risk_AFSCS_32
CC$Risk_2_AFSCS <- CC$Risk_AFSCS_33
CC$Risk_1_BIO <- CC$Risk_BIO_32
CC$Risk_2_BIO <- CC$Risk_BIO_33
CC$Risk_1_BECCS <- CC$Risk_BECCS_32
CC$Risk_2_BECCS <- CC$Risk_BECCS_33
CC$Risk_1_DACCS <- CC$Risk_DACCS_32
CC$Risk_2_DACCS <- CC$Risk_DACCS_33
CC$Risk_1_EW <- CC$Risk_EW_32
CC$Risk_2_EW <- CC$Risk_EW_33
CC$Risk_1_OF <- CC$Risk_OF_32
CC$Risk_2_OF <- CC$Risk_OF_33
CC$Risk_1_BF <- CC$Risk_BF_32
CC$Risk_2_BF <- CC$Risk_BF_33
CC$Risk_1_NE <- CC$Risk_NE_32
CC$Risk_2_NE <- CC$Risk_NE_33
CC$Risk_1_SE <- CC$Risk_SE_32
CC$Risk_2_SE <- CC$Risk_SE_33
CC$Risk_1_WE <- CC$Risk_WE_32
CC$Risk_2_WE <- CC$Risk_WE_33
# Descriptives
describe(CC$Risk_1_AFSCS)## CC$Risk_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 65 0.983 19.29 23.37 0 0
## .25 .50 .75 .90 .95
## 0 11 30 51 70
##
## lowest : 0 1 2 3 4, highest: 79 80 81 85 100describe(CC$Risk_2_AFSCS)## CC$Risk_2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 56 0.933 13.06 18.81 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 16.0 44.0 59.8
##
## lowest : 0 1 2 3 4, highest: 74 75 80 85 100describe(CC$Risk_1_BIO)## CC$Risk_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 83 0.999 39.39 28.83 0.0 4.0
## .25 .50 .75 .90 .95
## 19.0 40.0 56.0 74.9 80.0
##
## lowest : 0 1 2 3 4, highest: 86 90 95 96 100describe(CC$Risk_2_BIO)## CC$Risk_2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 82 0.992 27.98 28.3 0.00 0.00
## .25 .50 .75 .90 .95
## 4.00 25.00 47.25 63.00 75.00
##
## lowest : 0 1 2 3 4, highest: 90 92 95 96 100describe(CC$Risk_1_BECCS)## CC$Risk_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 45.35 30.93 0.00 6.90
## .25 .50 .75 .90 .95
## 24.25 49.50 64.00 80.00 92.55
##
## lowest : 0 1 4 5 6, highest: 93 94 95 96 100describe(CC$Risk_2_BECCS)## CC$Risk_2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 80 0.992 31.86 31.91 0.0 0.0
## .25 .50 .75 .90 .95
## 5.0 25.5 51.0 72.3 90.0
##
## lowest : 0 1 2 3 4, highest: 90 91 92 96 100describe(CC$Risk_1_DACCS)## CC$Risk_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 90 0.999 50.12 31.12 0.0 9.0
## .25 .50 .75 .90 .95
## 30.0 52.0 70.0 84.4 95.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100describe(CC$Risk_2_DACCS)## CC$Risk_2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 92 0.994 35.84 33.73 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 33.0 59.0 79.4 89.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100describe(CC$Risk_1_EW)## CC$Risk_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 85 0.999 45.98 30.54 3.7 10.0
## .25 .50 .75 .90 .95
## 25.0 50.0 64.0 82.6 93.3
##
## lowest : 0 2 3 4 5, highest: 93 94 95 99 100describe(CC$Risk_2_EW)## CC$Risk_2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.994 30.92 31.26 0.0 0.0
## .25 .50 .75 .90 .95
## 5.5 24.0 51.0 74.6 85.0
##
## lowest : 0 1 2 3 4, highest: 92 95 98 99 100describe(CC$Risk_1_OF)## CC$Risk_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 94 0.999 54.49 31.47 1.3 14.6
## .25 .50 .75 .90 .95
## 33.0 57.0 75.0 89.4 97.7
##
## lowest : 0 1 2 4 7, highest: 95 96 97 98 100describe(CC$Risk_2_OF)## CC$Risk_2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.996 38.24 33.86 0.0 0.0
## .25 .50 .75 .90 .95
## 10.5 37.0 62.5 80.0 90.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100describe(CC$Risk_1_BF)## CC$Risk_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 74 0.998 32.13 27.09 0.00 0.00
## .25 .50 .75 .90 .95
## 12.75 30.00 50.00 69.00 74.00
##
## lowest : 0 1 2 4 5, highest: 83 84 87 93 100describe(CC$Risk_2_BF)## CC$Risk_2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 65 0.984 19.96 22.59 0.00 0.00
## .25 .50 .75 .90 .95
## 0.75 14.00 32.00 51.00 63.65
##
## lowest : 0 1 2 3 4, highest: 80 81 87 88 100describe(CC$Risk_1_NE)## CC$Risk_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 76 0.998 56.87 34.87 3.8 10.0
## .25 .50 .75 .90 .95
## 31.0 62.0 80.0 100.0 100.0
##
## lowest : 0 1 3 4 5, highest: 93 94 95 99 100describe(CC$Risk_2_NE)## CC$Risk_2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 84 0.999 48.97 37.15 0.0 2.6
## .25 .50 .75 .90 .95
## 20.0 54.0 76.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 93 95 97 99 100describe(CC$Risk_1_SE)## CC$Risk_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 54 0.945 13.63 18.58 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 5.0 21.0 40.6 52.0
##
## lowest : 0 1 2 3 4, highest: 67 79 80 82 88describe(CC$Risk_2_SE)## CC$Risk_2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 42 0.821 6.735 10.85 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 7.0 22.6 33.8
##
## lowest : 0 1 2 3 4, highest: 51 64 75 79 88describe(CC$Risk_1_WE)## CC$Risk_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.984 23.56 28.04 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 13.0 38.0 68.0 80.2
##
## lowest : 0 1 2 3 4, highest: 90 91 92 97 100describe(CC$Risk_2_WE)## CC$Risk_2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 55 0.915 14.02 20.08 0 0
## .25 .50 .75 .90 .95
## 0 4 19 43 68
##
## lowest : 0 1 2 3 4, highest: 84 89 90 99 100sd(CC$Risk_1_AFSCS, na.rm = TRUE)## [1] 22.70809sd(CC$Risk_2_AFSCS, na.rm = TRUE)## [1] 20.35588sd(CC$Risk_1_BIO, na.rm = TRUE)## [1] 25.12406sd(CC$Risk_2_BIO, na.rm = TRUE)## [1] 25.33067sd(CC$Risk_1_BECCS, na.rm = TRUE)## [1] 26.96612sd(CC$Risk_2_BECCS, na.rm = TRUE)## [1] 28.51304sd(CC$Risk_1_DACCS, na.rm = TRUE)## [1] 27.21191sd(CC$Risk_2_DACCS, na.rm = TRUE)## [1] 29.61833sd(CC$Risk_1_EW, na.rm = TRUE)## [1] 26.62165sd(CC$Risk_2_EW, na.rm = TRUE)## [1] 27.95912sd(CC$Risk_1_OF, na.rm = TRUE)## [1] 27.45413sd(CC$Risk_2_OF, na.rm = TRUE)## [1] 29.57223sd(CC$Risk_1_BF, na.rm = TRUE)## [1] 23.86879sd(CC$Risk_2_BF, na.rm = TRUE)## [1] 21.27785sd(CC$Risk_1_NE, na.rm = TRUE)## [1] 30.46242sd(CC$Risk_2_NE, na.rm = TRUE)## [1] 32.24126sd(CC$Risk_1_SE, na.rm = TRUE)## [1] 19.15884sd(CC$Risk_2_SE, na.rm = TRUE)## [1] 13.82528sd(CC$Risk_1_WE, na.rm = TRUE)## [1] 26.48551sd(CC$Risk_2_WE, na.rm = TRUE)## [1] 21.76884hist(CC$Risk_1_AFSCS)
hist(CC$Risk_2_AFSCS)
hist(CC$Risk_1_BIO)
hist(CC$Risk_2_BIO)
hist(CC$Risk_1_BECCS)
hist(CC$Risk_2_BECCS)
hist(CC$Risk_1_DACCS)
hist(CC$Risk_2_DACCS)
hist(CC$Risk_1_EW)
hist(CC$Risk_2_EW)
hist(CC$Risk_1_OF)
hist(CC$Risk_2_OF)
hist(CC$Risk_1_BF)
hist(CC$Risk_2_BF)
hist(CC$Risk_1_NE)
hist(CC$Risk_2_NE)
hist(CC$Risk_1_SE)
hist(CC$Risk_2_SE)
hist(CC$Risk_1_WE)
hist(CC$Risk_2_WE)
Score(s) & Scale(s)
# Scores & Scales
CC$Risk_Score_AFSCS <- rowMeans(CC [, c("Risk_1_AFSCS", "Risk_2_AFSCS")], na.rm=TRUE)
CC$Risk_Scale_AFSCS <- data.frame(CC$Risk_1_AFSCS, CC$Risk_2_AFSCS)
CC$Risk_Score_BIO <- rowMeans(CC [, c("Risk_1_BIO", "Risk_2_BIO")], na.rm=TRUE)
CC$Risk_Scale_BIO <- data.frame(CC$Risk_1_BIO, CC$Risk_2_BIO)
CC$Risk_Score_BECCS <- rowMeans(CC [, c("Risk_1_BECCS", "Risk_2_BECCS")], na.rm=TRUE)
CC$Risk_Scale_BECCS <- data.frame(CC$Risk_1_BECCS, CC$Risk_2_BECCS)
CC$Risk_Score_DACCS <- rowMeans(CC [, c("Risk_1_DACCS", "Risk_2_DACCS")], na.rm=TRUE)
CC$Risk_Scale_DACCS <- data.frame(CC$Risk_1_DACCS, CC$Risk_2_DACCS)
CC$Risk_Score_EW <- rowMeans(CC [, c("Risk_1_EW", "Risk_2_EW")], na.rm=TRUE)
CC$Risk_Scale_EW <- data.frame(CC$Risk_1_EW, CC$Risk_2_EW)
CC$Risk_Score_OF <- rowMeans(CC [, c("Risk_1_OF", "Risk_2_OF")], na.rm=TRUE)
CC$Risk_Scale_OF <- data.frame(CC$Risk_1_OF, CC$Risk_2_OF)
CC$Risk_Score_BF <- rowMeans(CC [, c("Risk_1_BF", "Risk_2_BF")], na.rm=TRUE)
CC$Risk_Scale_BF <- data.frame(CC$Risk_1_BF, CC$Risk_2_BF)
CC$Risk_Score_NE <- rowMeans(CC [, c("Risk_1_NE", "Risk_2_NE")], na.rm=TRUE)
CC$Risk_Scale_NE <- data.frame(CC$Risk_1_NE, CC$Risk_2_NE)
CC$Risk_Score_SE <- rowMeans(CC [, c("Risk_1_SE", "Risk_2_SE")], na.rm=TRUE)
CC$Risk_Scale_SE <- data.frame(CC$Risk_1_SE, CC$Risk_2_SE)
CC$Risk_Score_WE <- rowMeans(CC [, c("Risk_1_WE", "Risk_2_WE")], na.rm=TRUE)
CC$Risk_Scale_WE <- data.frame(CC$Risk_1_WE, CC$Risk_2_WE)
# Describe Scores/Scales
describe(CC$Risk_Score_AFSCS)## CC$Risk_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 103 0.987 16.18 20.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.5 8.0 24.5 47.3 62.5
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 78.0 79.0 80.0 85.0 100.0describe(CC$Risk_Scale_AFSCS)## CC$Risk_Scale_AFSCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 65 0.983 19.29 23.37 0 0
## .25 .50 .75 .90 .95
## 0 11 30 51 70
##
## lowest : 0 1 2 3 4, highest: 79 80 81 85 100
## --------------------------------------------------------------------------------
## CC.Risk_2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 56 0.933 13.06 18.81 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 16.0 44.0 59.8
##
## lowest : 0 1 2 3 4, highest: 74 75 80 85 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_AFSCS, na.rm = TRUE)## [1] 20.1135describe(CC$Risk_Score_BIO)## CC$Risk_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 133 1 33.69 26.45 0.00 3.00
## .25 .50 .75 .90 .95
## 12.50 32.50 50.00 63.00 75.67
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 84.0 88.0 90.0 93.0 95.0describe(CC$Risk_Scale_BIO)## CC$Risk_Scale_BIO
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 83 0.999 39.39 28.83 0.0 4.0
## .25 .50 .75 .90 .95
## 19.0 40.0 56.0 74.9 80.0
##
## lowest : 0 1 2 3 4, highest: 86 90 95 96 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 82 0.992 27.98 28.3 0.00 0.00
## .25 .50 .75 .90 .95
## 4.00 25.00 47.25 63.00 75.00
##
## lowest : 0 1 2 3 4, highest: 90 92 95 96 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BIO, na.rm = TRUE)## [1] 23.16999describe(CC$Risk_Score_BECCS)## CC$Risk_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 140 0.999 38.61 28.99 0.00 3.95
## .25 .50 .75 .90 .95
## 19.50 37.75 55.00 72.05 85.55
##
## lowest : 0.0 0.5 1.0 2.5 3.0, highest: 92.5 93.0 94.0 98.0 100.0describe(CC$Risk_Scale_BECCS)## CC$Risk_Scale_BECCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 45.35 30.93 0.00 6.90
## .25 .50 .75 .90 .95
## 24.25 49.50 64.00 80.00 92.55
##
## lowest : 0 1 4 5 6, highest: 93 94 95 96 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 80 0.992 31.86 31.91 0.0 0.0
## .25 .50 .75 .90 .95
## 5.0 25.5 51.0 72.3 90.0
##
## lowest : 0 1 2 3 4, highest: 90 91 92 96 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BECCS, na.rm = TRUE)## [1] 25.48881describe(CC$Risk_Score_DACCS)## CC$Risk_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 151 1 42.98 30.22 0.00 5.50
## .25 .50 .75 .90 .95
## 22.25 45.00 62.50 78.10 89.35
##
## lowest : 0.0 0.5 1.0 2.5 3.0, highest: 95.5 98.0 98.5 99.5 100.0describe(CC$Risk_Scale_DACCS)## CC$Risk_Scale_DACCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 90 0.999 50.12 31.12 0.0 9.0
## .25 .50 .75 .90 .95
## 30.0 52.0 70.0 84.4 95.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 92 0.994 35.84 33.73 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 33.0 59.0 79.4 89.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_DACCS, na.rm = TRUE)## [1] 26.347describe(CC$Risk_Score_EW)## CC$Risk_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 146 1 38.45 28.7 2.35 7.50
## .25 .50 .75 .90 .95
## 17.75 37.00 55.00 75.00 85.00
##
## lowest : 0.0 1.0 2.0 2.5 3.0, highest: 94.0 96.0 97.5 99.5 100.0describe(CC$Risk_Scale_EW)## CC$Risk_Scale_EW
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 85 0.999 45.98 30.54 3.7 10.0
## .25 .50 .75 .90 .95
## 25.0 50.0 64.0 82.6 93.3
##
## lowest : 0 2 3 4 5, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.994 30.92 31.26 0.0 0.0
## .25 .50 .75 .90 .95
## 5.5 24.0 51.0 74.6 85.0
##
## lowest : 0 1 2 3 4, highest: 92 95 98 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_EW, na.rm = TRUE)## [1] 25.2467describe(CC$Risk_Score_OF)## CC$Risk_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 153 1 46.37 30.58 0.80 10.00
## .25 .50 .75 .90 .95
## 25.25 45.50 66.25 81.50 89.85
##
## lowest : 0.0 0.5 1.5 2.0 5.0, highest: 96.0 97.0 98.5 99.0 100.0describe(CC$Risk_Scale_OF)## CC$Risk_Scale_OF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 94 0.999 54.49 31.47 1.3 14.6
## .25 .50 .75 .90 .95
## 33.0 57.0 75.0 89.4 97.7
##
## lowest : 0 1 2 4 7, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------
## CC.Risk_2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.996 38.24 33.86 0.0 0.0
## .25 .50 .75 .90 .95
## 10.5 37.0 62.5 80.0 90.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_OF, na.rm = TRUE)## [1] 26.54645describe(CC$Risk_Score_BF)## CC$Risk_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 105 0.999 26.04 22.96 0.000 0.500
## .25 .50 .75 .90 .95
## 8.375 22.250 40.250 52.300 60.650
##
## lowest : 0.0 0.5 1.0 1.5 2.5, highest: 81.5 83.5 85.5 86.0 100.0describe(CC$Risk_Scale_BF)## CC$Risk_Scale_BF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 74 0.998 32.13 27.09 0.00 0.00
## .25 .50 .75 .90 .95
## 12.75 30.00 50.00 69.00 74.00
##
## lowest : 0 1 2 4 5, highest: 83 84 87 93 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 65 0.984 19.96 22.59 0.00 0.00
## .25 .50 .75 .90 .95
## 0.75 14.00 32.00 51.00 63.65
##
## lowest : 0 1 2 3 4, highest: 80 81 87 88 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BF, na.rm = TRUE)## [1] 20.52519describe(CC$Risk_Score_NE)## CC$Risk_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 126 1 52.92 34.7 2.4 6.9
## .25 .50 .75 .90 .95
## 25.0 58.0 75.5 91.5 100.0
##
## lowest : 0.0 0.5 1.5 2.0 2.5, highest: 95.5 96.5 98.5 99.0 100.0describe(CC$Risk_Scale_NE)## CC$Risk_Scale_NE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 76 0.998 56.87 34.87 3.8 10.0
## .25 .50 .75 .90 .95
## 31.0 62.0 80.0 100.0 100.0
##
## lowest : 0 1 3 4 5, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 84 0.999 48.97 37.15 0.0 2.6
## .25 .50 .75 .90 .95
## 20.0 54.0 76.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 93 95 97 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_NE, na.rm = TRUE)## [1] 30.17134describe(CC$Risk_Score_SE)## CC$Risk_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 69 0.955 10.18 13.87 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 15.0 34.0 42.9
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 45.0 48.5 51.0 63.0 78.5describe(CC$Risk_Scale_SE)## CC$Risk_Scale_SE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 54 0.945 13.63 18.58 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 5.0 21.0 40.6 52.0
##
## lowest : 0 1 2 3 4, highest: 67 79 80 82 88
## --------------------------------------------------------------------------------
## CC.Risk_2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 42 0.821 6.735 10.85 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 7.0 22.6 33.8
##
## lowest : 0 1 2 3 4, highest: 51 64 75 79 88
## --------------------------------------------------------------------------------sd(CC$Risk_Score_SE, na.rm = TRUE)## [1] 14.22085describe(CC$Risk_Score_WE)## CC$Risk_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 94 0.988 18.79 23.08 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 11.0 25.5 52.2 67.3
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 89.0 89.5 92.0 98.0 98.5describe(CC$Risk_Scale_WE)## CC$Risk_Scale_WE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.984 23.56 28.04 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 13.0 38.0 68.0 80.2
##
## lowest : 0 1 2 3 4, highest: 90 91 92 97 100
## --------------------------------------------------------------------------------
## CC.Risk_2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 55 0.915 14.02 20.08 0 0
## .25 .50 .75 .90 .95
## 0 4 19 43 68
##
## lowest : 0 1 2 3 4, highest: 84 89 90 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_WE, na.rm = TRUE)## [1] 22.76701#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$Risk_1_AFSCS, CC$Risk_2_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_AFSCS, CC$Risk_2_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.74 0.74 5.8 0.0093 16 20 0.74
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_AFSCS 0.83 0.74 0.55 0.74 2.9 NA 0 0.74
## CC.Risk_2_AFSCS 0.67 0.74 0.55 0.74 2.9 NA 0 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_AFSCS 343 0.94 0.93 0.81 0.74 19 23
## CC.Risk_2_AFSCS 343 0.93 0.93 0.81 0.74 13 20hist(CC$Risk_Score_AFSCS, main = 'AFSCS Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_BIO, CC$Risk_2_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_BIO, CC$Risk_2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.69 0.69 4.4 0.012 34 23 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BIO 0.68 0.69 0.47 0.69 2.2 NA 0 0.69
## CC.Risk_2_BIO 0.69 0.69 0.47 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BIO 332 0.92 0.92 0.76 0.69 39 25
## CC.Risk_2_BIO 332 0.92 0.92 0.76 0.69 28 25hist(CC$Risk_Score_BIO, main = 'BIO Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_BECCS, CC$Risk_2_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_BECCS, CC$Risk_2_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.82 0.69 0.69 4.4 0.012 39 25 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BECCS 0.65 0.69 0.47 0.69 2.2 NA 0 0.69
## CC.Risk_2_BECCS 0.73 0.69 0.47 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BECCS 330 0.91 0.92 0.76 0.69 45 27
## CC.Risk_2_BECCS 330 0.92 0.92 0.76 0.69 32 29hist(CC$Risk_Score_BECCS, main = 'BECCS Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_DACCS, CC$Risk_2_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_DACCS, CC$Risk_2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.84 0.72 0.72 5.1 0.01 43 26 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_DACCS 0.66 0.72 0.52 0.72 2.6 NA 0 0.72
## CC.Risk_2_DACCS 0.78 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_DACCS 347 0.92 0.93 0.79 0.72 50 27
## CC.Risk_2_DACCS 347 0.93 0.93 0.79 0.72 36 30hist(CC$Risk_Score_DACCS, main = 'DACCS Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_EW, CC$Risk_2_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_EW, CC$Risk_2_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.71 0.71 4.9 0.011 38 25 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_EW 0.68 0.71 0.51 0.71 2.5 NA 0 0.71
## CC.Risk_2_EW 0.75 0.71 0.51 0.71 2.5 NA 0 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_EW 335 0.92 0.93 0.78 0.71 46 27
## CC.Risk_2_EW 335 0.93 0.93 0.78 0.71 31 28hist(CC$Risk_Score_EW, main = 'EW Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_OF, CC$Risk_2_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_OF, CC$Risk_2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.73 0.73 5.5 0.0097 46 27 0.73
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_OF 0.68 0.73 0.54 0.73 2.7 NA 0 0.73
## CC.Risk_2_OF 0.79 0.73 0.54 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_OF 327 0.93 0.93 0.8 0.73 54 27
## CC.Risk_2_OF 327 0.94 0.93 0.8 0.73 38 30hist(CC$Risk_Score_OF, main = 'OF Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_BF, CC$Risk_2_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_BF, CC$Risk_2_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.65 0.65 3.8 0.013 26 21 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.76 0.79 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BF 0.73 0.65 0.43 0.65 1.9 NA 0 0.65
## CC.Risk_2_BF 0.58 0.65 0.43 0.65 1.9 NA 0 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BF 248 0.92 0.91 0.73 0.65 32 24
## CC.Risk_2_BF 248 0.90 0.91 0.73 0.65 20 21hist(CC$Risk_Score_BF, main = 'BF Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_NE, CC$Risk_2_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_NE, CC$Risk_2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.85 0.85 12 0.0051 53 30 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_NE 0.81 0.85 0.73 0.85 5.8 NA 0 0.85
## CC.Risk_2_NE 0.90 0.85 0.73 0.85 5.8 NA 0 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_NE 257 0.96 0.96 0.89 0.85 57 30
## CC.Risk_2_NE 257 0.96 0.96 0.89 0.85 49 32hist(CC$Risk_Score_NE, main = 'NE Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_SE, CC$Risk_2_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_SE, CC$Risk_2_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.62 0.64 0.47 0.47 1.8 0.022 10 14 0.47
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.62 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_SE 0.66 0.47 0.22 0.47 0.9 NA 0 0.47
## CC.Risk_2_SE 0.34 0.47 0.22 0.47 0.9 NA 0 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_SE 245 0.9 0.86 0.59 0.47 13.6 19
## CC.Risk_2_SE 245 0.8 0.86 0.59 0.47 6.7 14hist(CC$Risk_Score_SE, main = 'SE Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_WE, CC$Risk_2_WE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_WE, CC$Risk_2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.88 0.78 0.78 7 0.008 19 23 0.78
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_WE 0.95 0.78 0.61 0.78 3.5 NA 0 0.78
## CC.Risk_2_WE 0.64 0.78 0.61 0.78 3.5 NA 0 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_WE 257 0.95 0.94 0.83 0.78 24 26
## CC.Risk_2_WE 257 0.93 0.94 0.83 0.78 14 22hist(CC$Risk_Score_WE, main = 'WE Risk Scale Score')
#Correlations
cor.plot(CC$Risk_Scale_AFSCS, labels = c('1','2'), main = "Correlation Between AFSCS Risk Items")
cor.plot(CC$Risk_Scale_BIO, labels = c('1','2'), main = "Correlation Between BIO Risk Items")
cor.plot(CC$Risk_Scale_BECCS, labels = c('1','2'), main = "Correlation Between BECCS Risk Items")
cor.plot(CC$Risk_Scale_DACCS, labels = c('1','2'), main = "Correlation Between DACCS Risk Items")
cor.plot(CC$Risk_Scale_EW, labels = c('1','2'), main = "Correlation Between EW Risk Items")
cor.plot(CC$Risk_Scale_OF, labels = c('1','2'), main = "Correlation Between OF Risk Items")
cor.plot(CC$Risk_Scale_BF, labels = c('1','2'), main = "Correlation Between BF Risk Items")
cor.plot(CC$Risk_Scale_NE, labels = c('1','2'), main = "Correlation Between NE Risk Items")
cor.plot(CC$Risk_Scale_SE, labels = c('1','2'), main = "Correlation Between SE Risk Items")
cor.plot(CC$Risk_Scale_WE, labels = c('1','2'), main = "Correlation Between WE Risk Items")
Understanding
# Understanding was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented participant understanding of the technology rated.
## 1. I understand how this works.Descriptives
# Define Variables
CC$Und_AFSCS <- CC$Risk_AFSCS_30
CC$Und_BIO <- CC$Risk_BIO_30
CC$Und_BECCS <- CC$Risk_BECCS_30
CC$Und_DACCS <- CC$Risk_DACCS_30
CC$Und_EW <- CC$Risk_EW_30
CC$Und_OF <- CC$Risk_OF_30
CC$Und_BF <- CC$Risk_BF_30
CC$Und_NE <- CC$Risk_NE_30
CC$Und_SE <- CC$Risk_SE_30
CC$Und_WE <- CC$Risk_WE_30
# Descriptives
describe(CC$Und_AFSCS)## CC$Und_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 85 0.995 70.96 28.64 17.3 32.0
## .25 .50 .75 .90 .95
## 57.0 77.0 92.0 100.0 100.0
##
## lowest : 0 1 3 5 7, highest: 96 97 98 99 100describe(CC$Und_BIO)## CC$Und_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 1 48.06 32.06 1 10
## .25 .50 .75 .90 .95
## 25 50 70 86 95
##
## lowest : 0 1 2 5 6, highest: 94 95 97 98 100describe(CC$Und_BECCS)## CC$Und_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 93 0.999 45.42 32.95 0.0 5.0
## .25 .50 .75 .90 .95
## 22.0 44.5 67.0 85.0 92.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100describe(CC$Und_DACCS)## CC$Und_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 93 1 45.61 34.44 0.0 5.0
## .25 .50 .75 .90 .95
## 19.0 46.0 70.0 85.0 92.7
##
## lowest : 0 1 2 3 4, highest: 93 95 98 99 100describe(CC$Und_EW)## CC$Und_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 90 0.999 43.44 31.42 0.0 5.0
## .25 .50 .75 .90 .95
## 21.5 41.0 63.0 80.6 88.0
##
## lowest : 0 1 2 3 4, highest: 88 91 93 94 100describe(CC$Und_OF)## CC$Und_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 1 51.18 32.34 5.0 9.0
## .25 .50 .75 .90 .95
## 28.0 53.0 73.5 87.0 94.0
##
## lowest : 0 2 4 5 6, highest: 94 95 97 98 100describe(CC$Und_BF)## CC$Und_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 80 0.999 61.88 30.79 6.75 23.40
## .25 .50 .75 .90 .95
## 42.75 66.00 81.25 99.00 100.00
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100describe(CC$Und_NE)## CC$Und_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.999 65.29 30.66 10.8 23.6
## .25 .50 .75 .90 .95
## 50.0 71.0 88.0 99.4 100.0
##
## lowest : 0 2 3 4 6, highest: 96 97 98 99 100describe(CC$Und_SE)## CC$Und_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.978 83.81 19.14 50.0 56.4
## .25 .50 .75 .90 .95
## 75.0 90.0 100.0 100.0 100.0
##
## lowest : 2 5 21 22 30, highest: 96 97 98 99 100describe(CC$Und_WE)## CC$Und_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 53 0.982 82.82 19.09 51.0 61.2
## .25 .50 .75 .90 .95
## 74.0 87.0 100.0 100.0 100.0
##
## lowest : 0 15 16 19 26, highest: 96 97 98 99 100sd(CC$Und_AFSCS, na.rm = TRUE)## [1] 26.01993sd(CC$Und_BIO, na.rm = TRUE)## [1] 27.80563sd(CC$Und_BECCS, na.rm = TRUE)## [1] 28.56679sd(CC$Und_DACCS, na.rm = TRUE)## [1] 29.85122sd(CC$Und_EW, na.rm = TRUE)## [1] 27.30677sd(CC$Und_OF, na.rm = TRUE)## [1] 28.08305sd(CC$Und_BF, na.rm = TRUE)## [1] 27.24634sd(CC$Und_NE, na.rm = TRUE)## [1] 27.26283sd(CC$Und_SE, na.rm = TRUE)## [1] 18.50476sd(CC$Und_WE, na.rm = TRUE)## [1] 18.43885hist(CC$Und_AFSCS)
hist(CC$Und_BIO)
hist(CC$Und_BECCS)
hist(CC$Und_DACCS)
hist(CC$Und_EW)
hist(CC$Und_OF)
hist(CC$Und_BF)
hist(CC$Und_NE)
hist(CC$Und_SE)
hist(CC$Und_WE)
Score(s) & Scale(s)
# Note: Understanding scores & scales not present because measure is one item.)Benefit - Risk Difference
Descriptives / Score(s) & Scale(s)
#Difference Score
CC$BRDiff.AFSCS <- (CC$Ben_AFSCS - CC$Risk_Score_AFSCS)
CC$BRDiff.BIO <- (CC$Ben_BIO - CC$Risk_Score_BIO)
CC$BRDiff.BECCS <- (CC$Ben_BECCS - CC$Risk_Score_BECCS)
CC$BRDiff.DACCS <- (CC$Ben_DACCS - CC$Risk_Score_DACCS)
CC$BRDiff.EW <- (CC$Ben_EW - CC$Risk_Score_EW)
CC$BRDiff.OF <- (CC$Ben_OF - CC$Risk_Score_OF)
CC$BRDiff.BF <- (CC$Ben_BF - CC$Risk_Score_BF)
CC$BRDiff.NE <- (CC$Ben_NE - CC$Risk_Score_NE)
CC$BRDiff.SE <- (CC$Ben_SE - CC$Risk_Score_SE)
CC$BRDiff.WE <- (CC$Ben_WE - CC$Risk_Score_WE)
#Descriptives
describe(CC$BRDiff.AFSCS)## CC$BRDiff.AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 167 1 52.25 38.7 -10.9 1.8
## .25 .50 .75 .90 .95
## 32.5 57.0 80.0 92.0 100.0
##
## lowest : -100.0 -56.5 -45.0 -42.0 -37.5, highest: 97.0 98.0 98.5 99.5 100.0describe(CC$BRDiff.BIO)## CC$BRDiff.BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 182 1 19.78 44.51 -47.500 -27.000
## .25 .50 .75 .90 .95
## -6.125 18.250 45.625 74.450 87.000
##
## lowest : -90.0 -80.0 -77.5 -76.5 -75.0, highest: 91.5 93.0 95.5 99.5 100.0describe(CC$BRDiff.BECCS)## CC$BRDiff.BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 187 1 16.39 47.73 -59.65 -36.05
## .25 .50 .75 .90 .95
## -10.75 19.00 46.00 72.55 80.55
##
## lowest : -100.0 -93.0 -90.0 -87.0 -83.0, highest: 87.5 88.0 90.0 94.0 100.0describe(CC$BRDiff.DACCS)## CC$BRDiff.DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 200 1 12.37 48.68 -60.35 -40.00
## .25 .50 .75 .90 .95
## -15.00 10.00 43.25 68.20 85.40
##
## lowest : -100.0 -93.0 -92.0 -83.5 -80.0, highest: 95.0 96.0 98.0 99.0 100.0describe(CC$BRDiff.EW)## CC$BRDiff.EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 184 1 13.7 44.02 -51.95 -36.60
## .25 .50 .75 .90 .95
## -9.75 12.00 44.25 61.30 71.05
##
## lowest : -100.0 -88.5 -87.5 -81.0 -80.0, highest: 89.0 91.0 95.0 97.5 100.0describe(CC$BRDiff.OF)## CC$BRDiff.OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 188 1 8.17 47.47 -69.85 -53.90
## .25 .50 .75 .90 .95
## -16.50 10.00 36.00 62.70 73.90
##
## lowest : -100.0 -99.0 -90.5 -87.0 -85.0, highest: 87.5 88.0 89.0 98.0 100.0describe(CC$BRDiff.BF)## CC$BRDiff.BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 152 1 25.88 42.11 -33.77 -22.15
## .25 .50 .75 .90 .95
## 0.00 24.25 51.75 75.30 84.82
##
## lowest : -100.0 -81.0 -60.5 -58.5 -57.5, highest: 92.0 92.5 95.5 99.5 100.0describe(CC$BRDiff.NE)## CC$BRDiff.NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 169 1 7.265 55.56 -82.6 -63.7
## .25 .50 .75 .90 .95
## -20.0 4.0 44.5 78.7 86.8
##
## lowest : -100.0 -90.0 -89.0 -87.5 -86.5, highest: 92.5 94.5 95.5 97.0 100.0describe(CC$BRDiff.SE)## CC$BRDiff.SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 132 0.999 56.12 35.25 -1.4 15.5
## .25 .50 .75 .90 .95
## 34.0 60.5 80.0 96.3 100.0
##
## lowest : -30.0 -27.5 -25.5 -22.5 -20.0, highest: 96.5 97.5 98.0 99.5 100.0describe(CC$BRDiff.WE)## CC$BRDiff.WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 145 1 46.1 44.66 -30.5 -10.2
## .25 .50 .75 .90 .95
## 23.5 55.5 74.0 90.7 100.0
##
## lowest : -92.5 -89.5 -84.0 -79.0 -74.5, highest: 95.0 96.5 98.5 99.5 100.0#Histograms
hist(CC$BRDiff.AFSCS)
hist(CC$BRDiff.BIO)
hist(CC$BRDiff.BECCS)
hist(CC$BRDiff.DACCS)
hist(CC$BRDiff.EW)
hist(CC$BRDiff.OF)
hist(CC$BRDiff.BF)
hist(CC$BRDiff.NE)
hist(CC$BRDiff.SE)
hist(CC$BRDiff.WE)
#SD
sd(CC$BRDiff.AFSCS, na.rm = TRUE)## [1] 35.00885sd(CC$BRDiff.BIO, na.rm = TRUE)## [1] 39.30418sd(CC$BRDiff.BECCS, na.rm = TRUE)## [1] 42.27369sd(CC$BRDiff.DACCS, na.rm = TRUE)## [1] 42.78619sd(CC$BRDiff.EW, na.rm = TRUE)## [1] 39.15126sd(CC$BRDiff.OF, na.rm = TRUE)## [1] 42.17408sd(CC$BRDiff.BF, na.rm = TRUE)## [1] 37.18901sd(CC$BRDiff.NE, na.rm = TRUE)## [1] 48.96707sd(CC$BRDiff.SE, na.rm = TRUE)## [1] 31.2928sd(CC$BRDiff.WE, na.rm = TRUE)## [1] 40.94974Familiarity/Understanding
Descriptives / Score(s) & Scale(s)
#Mean understanding/familiarity scores by technology
CC$FR.AFSCS <- rowMeans(CC [, c("Familiar_AFSCS", "Und_AFSCS")], na.rm=TRUE)
CC$FR.BIO <- rowMeans(CC [, c("Familiar_BIO", "Und_BIO")], na.rm=TRUE)
CC$FR.BECCS <- rowMeans(CC [, c("Familiar_BECCS", "Und_BECCS")], na.rm=TRUE)
CC$FR.DACCS <- rowMeans(CC [, c("Familiar_DACCS", "Und_DACCS")], na.rm=TRUE)
CC$FR.EW <- rowMeans(CC [, c("Familiar_EW", "Und_EW")], na.rm=TRUE)
CC$FR.OF <- rowMeans(CC [, c("Familiar_OF", "Und_OF")], na.rm=TRUE)
CC$FR.BF <- rowMeans(CC [, c("Familiar_BF", "Und_BF")], na.rm=TRUE)
CC$FR.NE <- rowMeans(CC [, c("Familiar_NE", "Und_NE")], na.rm=TRUE)
CC$FR.SE <- rowMeans(CC [, c("Familiar_SE", "Und_SE")], na.rm=TRUE)
CC$FR.WE <- rowMeans(CC [, c("Familiar_WE", "Und_WE")], na.rm=TRUE)
#Descriptives
describe(CC$FR.AFSCS)## CC$FR.AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 149 0.999 66.83 29.53 16.6 26.6
## .25 .50 .75 .90 .95
## 50.0 71.0 88.5 100.0 100.0
##
## lowest : 0.0 0.5 2.5 3.5 5.0, highest: 98.0 98.5 99.0 99.5 100.0describe(CC$FR.BIO)## CC$FR.BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 138 1 37.92 27.57 1.00 9.10
## .25 .50 .75 .90 .95
## 18.88 34.75 54.50 73.95 80.00
##
## lowest : 0.0 0.5 1.0 3.0 3.5, highest: 92.5 93.0 93.5 98.5 100.0describe(CC$FR.BECCS)## CC$FR.BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 142 1 37.53 28.26 0.225 4.500
## .25 .50 .75 .90 .95
## 18.000 35.000 52.500 71.550 82.325
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 92.5 93.5 95.0 99.5 100.0describe(CC$FR.DACCS)## CC$FR.DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 149 1 35.83 27.9 0.50 5.00
## .25 .50 .75 .90 .95
## 15.00 34.00 52.00 69.70 78.55
##
## lowest : 0.0 0.5 1.0 2.0 2.5, highest: 93.0 93.5 95.0 99.5 100.0describe(CC$FR.EW)## CC$FR.EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 128 1 32.97 25.23 0.0 3.0
## .25 .50 .75 .90 .95
## 16.0 30.5 47.5 64.6 75.5
##
## lowest : 0.0 1.0 1.5 2.0 2.5, highest: 86.0 88.5 89.5 95.0 95.5describe(CC$FR.OF)## CC$FR.OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 145 1 38.4 25.63 4.50 7.50
## .25 .50 .75 .90 .95
## 20.00 38.50 52.50 67.50 80.35
##
## lowest : 0.0 0.5 1.0 2.0 3.5, highest: 86.5 87.0 92.5 93.0 100.0describe(CC$FR.BF)## CC$FR.BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 117 1 59.9 28.49 12.70 26.70
## .25 .50 .75 .90 .95
## 44.38 61.00 77.50 93.00 99.32
##
## lowest : 0.0 0.5 2.5 10.0 11.5, highest: 97.5 98.5 99.0 99.5 100.0describe(CC$FR.NE)## CC$FR.NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 120 1 67.23 26.85 19.2 33.6
## .25 .50 .75 .90 .95
## 51.0 72.5 87.0 95.0 99.2
##
## lowest : 0.0 2.5 3.0 6.0 7.5, highest: 96.5 97.0 97.5 99.0 100.0describe(CC$FR.SE)## CC$FR.SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 78 0.987 85.88 16.23 52.2 63.7
## .25 .50 .75 .90 .95
## 80.0 90.0 98.5 100.0 100.0
##
## lowest : 23.0 25.0 33.0 40.5 42.5, highest: 98.0 98.5 99.0 99.5 100.0describe(CC$FR.WE)## CC$FR.WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 88 0.994 82.31 17.72 50.4 63.0
## .25 .50 .75 .90 .95
## 73.5 86.0 95.0 100.0 100.0
##
## lowest : 1.5 8.0 16.0 23.5 34.0, highest: 97.0 97.5 98.0 99.5 100.0#SD
sd(CC$FR.AFSCS, na.rm = TRUE)## [1] 26.26246sd(CC$FR.BIO, na.rm = TRUE)## [1] 24.31053sd(CC$FR.BECCS, na.rm = TRUE)## [1] 24.87139sd(CC$FR.DACCS, na.rm = TRUE)## [1] 24.4548sd(CC$FR.EW, na.rm = TRUE)## [1] 22.23358sd(CC$FR.OF, na.rm = TRUE)## [1] 22.49241sd(CC$FR.BF, na.rm = TRUE)## [1] 25.10918sd(CC$FR.NE, na.rm = TRUE)## [1] 24.0517sd(CC$FR.SE, na.rm = TRUE)## [1] 15.59678sd(CC$FR.WE, na.rm = TRUE)## [1] 16.95988#Histograms
hist(CC$FR.AFSCS)
hist(CC$FR.BIO)
hist(CC$FR.BECCS)
hist(CC$FR.DACCS)
hist(CC$FR.EW)
hist(CC$FR.OF)
hist(CC$FR.BF)
hist(CC$FR.NE)
hist(CC$FR.SE)
hist(CC$FR.WE)
#Scales
CC$FR2.AFSCS <- data.frame(CC$Familiar_AFSCS, CC$Und_AFSCS)
CC$FR2.BIO <- data.frame(CC$Familiar_BIO, CC$Und_BIO)
CC$FR2.BECCS <- data.frame(CC$Familiar_BECCS, CC$Und_BECCS)
CC$FR2.DACCS <- data.frame(CC$Familiar_DACCS, CC$Und_DACCS)
CC$FR2.EW <- data.frame(CC$Familiar_EW, CC$Und_EW)
CC$FR2.OF <- data.frame(CC$Familiar_OF, CC$Und_OF)
CC$FR2.BF <- data.frame(CC$Familiar_BF, CC$Und_BF)
CC$FR2.NE <- data.frame(CC$Familiar_NE, CC$Und_NE)
CC$FR2.SE <- data.frame(CC$Familiar_SE, CC$Und_SE)
CC$FR2.WE <- data.frame(CC$Familiar_WE, CC$Und_WE)
#Alphas
psych::alpha(CC$FR2.AFSCS)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.AFSCS)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.84 0.72 0.72 5.1 0.01 67 26 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_AFSCS 0.85 0.72 0.52 0.72 2.6 NA 0
## CC.Und_AFSCS 0.61 0.72 0.52 0.72 2.6 NA 0
## med.r
## CC.Familiar_AFSCS 0.72
## CC.Und_AFSCS 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_AFSCS 343 0.94 0.93 0.79 0.72 63 31
## CC.Und_AFSCS 343 0.91 0.93 0.79 0.72 71 26psych::alpha(CC$FR2.BIO)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.BIO)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.73 0.57 0.57 2.7 0.017 38 24 0.57
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.73 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_BIO 0.56 0.57 0.33 0.57 1.3 NA 0 0.57
## CC.Und_BIO 0.59 0.57 0.33 0.57 1.3 NA 0 0.57
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_BIO 332 0.88 0.89 0.67 0.57 28 27
## CC.Und_BIO 332 0.89 0.89 0.67 0.57 48 28psych::alpha(CC$FR2.BECCS)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.BECCS)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.71 0.56 0.56 2.5 0.018 38 25 0.56
##
## lower alpha upper 95% confidence boundaries
## 0.68 0.71 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_BECCS 0.54 0.56 0.31 0.56 1.3 NA 0
## CC.Und_BECCS 0.57 0.56 0.31 0.56 1.3 NA 0
## med.r
## CC.Familiar_BECCS 0.56
## CC.Und_BECCS 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_BECCS 330 0.88 0.88 0.66 0.56 30 28
## CC.Und_BECCS 330 0.89 0.88 0.66 0.56 45 29psych::alpha(CC$FR2.DACCS)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.DACCS)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.74 0.58 0.58 2.8 0.017 36 24 0.58
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.73 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_DACCS 0.49 0.58 0.34 0.58 1.4 NA 0
## CC.Und_DACCS 0.69 0.58 0.34 0.58 1.4 NA 0
## med.r
## CC.Familiar_DACCS 0.58
## CC.Und_DACCS 0.58
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_DACCS 347 0.87 0.89 0.68 0.58 26 25
## CC.Und_DACCS 347 0.91 0.89 0.68 0.58 46 30psych::alpha(CC$FR2.EW)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.EW)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.71 0.55 0.55 2.4 0.018 33 22 0.55
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.7 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_EW 0.46 0.55 0.3 0.55 1.2 NA 0 0.55
## CC.Und_EW 0.64 0.55 0.3 0.55 1.2 NA 0 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_EW 335 0.86 0.88 0.65 0.55 22 23
## CC.Und_EW 335 0.90 0.88 0.65 0.55 43 27psych::alpha(CC$FR2.OF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.OF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.59 0.59 0.42 0.42 1.4 0.026 38 22 0.42
##
## lower alpha upper 95% confidence boundaries
## 0.53 0.59 0.64
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_OF 0.38 0.42 0.17 0.42 0.71 NA 0 0.42
## CC.Und_OF 0.46 0.42 0.17 0.42 0.71 NA 0 0.42
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_OF 327 0.82 0.84 0.54 0.42 26 25
## CC.Und_OF 327 0.86 0.84 0.54 0.42 51 28psych::alpha(CC$FR2.BF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.BF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.62 0.62 3.2 0.015 60 25 0.62
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_BF 0.65 0.62 0.38 0.62 1.6 NA 0 0.62
## CC.Und_BF 0.59 0.62 0.38 0.62 1.6 NA 0 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_BF 248 0.90 0.9 0.71 0.62 58 29
## CC.Und_BF 248 0.89 0.9 0.71 0.62 62 27psych::alpha(CC$FR2.NE)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.NE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.6 0.6 2.9 0.016 67 24 0.6
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.75 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_NE 0.58 0.6 0.35 0.6 1.5 NA 0 0.6
## CC.Und_NE 0.61 0.6 0.35 0.6 1.5 NA 0 0.6
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_NE 257 0.89 0.89 0.69 0.6 69 27
## CC.Und_NE 257 0.90 0.89 0.69 0.6 65 27psych::alpha(CC$FR2.SE)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.SE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.63 0.63 3.4 0.014 86 16 0.63
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.77 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_SE 0.55 0.63 0.4 0.63 1.7 NA 0 0.63
## CC.Und_SE 0.73 0.63 0.4 0.63 1.7 NA 0 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_SE 245 0.89 0.9 0.72 0.63 88 16
## CC.Und_SE 245 0.92 0.9 0.72 0.63 84 19psych::alpha(CC$FR2.WE)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.WE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.66 0.49 0.49 1.9 0.021 82 17 0.49
##
## lower alpha upper 95% confidence boundaries
## 0.62 0.66 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_WE 0.56 0.49 0.24 0.49 0.97 NA 0 0.49
## CC.Und_WE 0.44 0.49 0.24 0.49 0.97 NA 0 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_WE 257 0.88 0.86 0.61 0.49 82 21
## CC.Und_WE 257 0.85 0.86 0.61 0.49 83 18#Overall
CC$FR2.AFSCS <- data.frame(CC$Familiar_AFSCS, CC$Und_AFSCS,CC$Familiar_BIO, CC$Und_BIO)Scale Correlations
length(CC$Nat_1_NE)## [1] 1007length(CC$Nat_2R_NE)## [1] 1007length(CC$Nat_3R_NE)## [1] 1007length(CC$Nat_4R_NE)## [1] 1007length(CC$Nat_1_SE)## [1] 1007length(CC$Nat_2R_SE)## [1] 1007length(CC$Nat_3R_SE)## [1] 1007length(CC$Nat_4R_SE)## [1] 1007length(CC$Nat_1_WE)## [1] 1007length(CC$Nat_2R_WE)## [1] 1007length(CC$Nat_3R_WE)## [1] 1007length(CC$Nat_4R_WE)## [1] 1007Naturalness by Technology
psych::alpha(data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS, CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS, CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO, CC$Nat_4R_BIO, CC$Nat_1_BECCS, CC$Nat_2R_BECCS, CC$Nat_3R_BECCS, CC$Nat_4R_BECCS, CC$Nat_1_DACCS, CC$Nat_2R_DACCS, CC$Nat_3R_DACCS, CC$Nat_4R_DACCS, CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW, CC$Nat_4R_EW, CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF, CC$Nat_4R_OF, CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF, CC$Nat_4R_BF))## Number of categories should be increased in order to count frequencies.## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS,
## CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS, CC$Nat_1_BIO, CC$Nat_2R_BIO,
## CC$Nat_3R_BIO, CC$Nat_4R_BIO, CC$Nat_1_BECCS, CC$Nat_2R_BECCS,
## CC$Nat_3R_BECCS, CC$Nat_4R_BECCS, CC$Nat_1_DACCS, CC$Nat_2R_DACCS,
## CC$Nat_3R_DACCS, CC$Nat_4R_DACCS, CC$Nat_1_EW, CC$Nat_2R_EW,
## CC$Nat_3R_EW, CC$Nat_4R_EW, CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF,
## CC$Nat_4R_OF, CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF, CC$Nat_4R_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.93 0.17 5.6 0.0069 38 16 0.15
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.85 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_AFSCS 0.85 0.85 0.92 0.18 5.8 0.0068 0.042 0.16
## CC.Nat_2R_AFSCS 0.85 0.85 0.92 0.17 5.6 0.0068 0.043 0.16
## CC.Nat_3R_AFSCS 0.85 0.85 0.97 0.18 5.7 0.0067 0.042 0.15
## CC.Nat_4R_AFSCS 0.85 0.85 0.92 0.17 5.7 0.0069 0.043 0.17
## CC.Nat_1_BIO 0.84 0.84 0.92 0.17 5.4 0.0072 0.042 0.15
## CC.Nat_2R_BIO 0.84 0.84 0.92 0.17 5.4 0.0072 0.044 0.15
## CC.Nat_3R_BIO 0.85 0.85 0.92 0.17 5.5 0.0070 0.042 0.15
## CC.Nat_4R_BIO 0.84 0.84 0.92 0.17 5.3 0.0073 0.043 0.15
## CC.Nat_1_BECCS 0.84 0.84 0.92 0.17 5.4 0.0071 0.043 0.15
## CC.Nat_2R_BECCS 0.84 0.84 0.92 0.17 5.4 0.0071 0.044 0.15
## CC.Nat_3R_BECCS 0.85 0.85 0.92 0.17 5.7 0.0068 0.042 0.16
## CC.Nat_4R_BECCS 0.84 0.84 0.91 0.17 5.4 0.0072 0.042 0.15
## CC.Nat_1_DACCS 0.84 0.84 0.90 0.16 5.3 0.0073 0.043 0.14
## CC.Nat_2R_DACCS 0.84 0.84 0.92 0.17 5.4 0.0071 0.044 0.15
## CC.Nat_3R_DACCS 0.85 0.85 0.92 0.17 5.6 0.0069 0.042 0.16
## CC.Nat_4R_DACCS 0.84 0.84 0.91 0.16 5.2 0.0074 0.044 0.14
## CC.Nat_1_EW 0.84 0.84 0.91 0.17 5.4 0.0072 0.042 0.15
## CC.Nat_2R_EW 0.84 0.84 0.91 0.16 5.1 0.0075 0.043 0.14
## CC.Nat_3R_EW 0.84 0.85 0.92 0.17 5.5 0.0070 0.042 0.15
## CC.Nat_4R_EW 0.83 0.84 0.92 0.16 5.1 0.0076 0.043 0.14
## CC.Nat_1_OF 0.84 0.84 0.92 0.16 5.3 0.0074 0.042 0.15
## CC.Nat_2R_OF 0.84 0.84 0.91 0.16 5.3 0.0072 0.044 0.14
## CC.Nat_3R_OF 0.84 0.85 0.91 0.17 5.5 0.0070 0.043 0.15
## CC.Nat_4R_OF 0.84 0.84 0.91 0.16 5.3 0.0073 0.042 0.15
## CC.Nat_1_BF 0.85 0.85 0.92 0.17 5.6 0.0069 0.042 0.15
## CC.Nat_2R_BF 0.85 0.85 0.92 0.17 5.6 0.0069 0.044 0.16
## CC.Nat_3R_BF 0.85 0.85 0.91 0.17 5.5 0.0070 0.041 0.15
## CC.Nat_4R_BF 0.84 0.85 0.92 0.17 5.5 0.0070 0.044 0.16
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_AFSCS 343 0.49 0.21 0.19 0.16 75 25
## CC.Nat_2R_AFSCS 343 0.59 0.32 0.30 0.25 53 31
## CC.Nat_3R_AFSCS 343 0.41 0.23 0.18 0.13 39 30
## CC.Nat_4R_AFSCS 343 0.55 0.26 0.24 0.21 80 25
## CC.Nat_1_BIO 332 0.57 0.47 0.45 0.44 46 27
## CC.Nat_2R_BIO 332 0.51 0.50 0.47 0.45 37 25
## CC.Nat_3R_BIO 332 0.37 0.38 0.35 0.28 24 22
## CC.Nat_4R_BIO 332 0.62 0.52 0.51 0.47 50 31
## CC.Nat_1_BECCS 330 0.57 0.44 0.43 0.39 43 26
## CC.Nat_2R_BECCS 330 0.51 0.45 0.40 0.38 30 22
## CC.Nat_3R_BECCS 330 0.32 0.25 0.25 0.13 23 21
## CC.Nat_4R_BECCS 330 0.63 0.49 0.48 0.43 42 28
## CC.Nat_1_DACCS 347 0.61 0.56 0.55 0.50 29 25
## CC.Nat_2R_DACCS 347 0.51 0.50 0.47 0.42 28 25
## CC.Nat_3R_DACCS 347 0.28 0.31 0.27 0.18 17 18
## CC.Nat_4R_DACCS 347 0.64 0.64 0.64 0.60 28 25
## CC.Nat_1_EW 335 0.55 0.50 0.51 0.46 46 27
## CC.Nat_2R_EW 335 0.62 0.69 0.69 0.65 27 23
## CC.Nat_3R_EW 335 0.35 0.42 0.39 0.31 26 22
## CC.Nat_4R_EW 335 0.65 0.68 0.69 0.64 45 28
## CC.Nat_1_OF 327 0.62 0.58 0.57 0.54 40 27
## CC.Nat_2R_OF 327 0.57 0.56 0.57 0.49 22 20
## CC.Nat_3R_OF 327 0.39 0.44 0.43 0.33 26 22
## CC.Nat_4R_OF 327 0.65 0.54 0.55 0.50 39 27
## CC.Nat_1_BF 248 0.49 0.32 0.31 0.28 52 28
## CC.Nat_2R_BF 248 0.47 0.34 0.31 0.26 38 27
## CC.Nat_3R_BF 248 0.29 0.43 0.41 0.30 18 17
## CC.Nat_4R_BF 248 0.54 0.40 0.38 0.35 49 29Long Form
Rename Variables
#Renaming variables to fit pivot_longer command
## Benefit
CC$Ben.AFSCS <- CC$Ben_AFSCS
length(CC$Ben.AFSCS)## [1] 1007CC$Ben.BIO <- CC$Ben_BIO
length(CC$Ben.BIO)## [1] 1007CC$Ben.BECCS <- CC$Ben_BECCS
length(CC$Ben.BECCS) ## [1] 1007CC$Ben.DACCS <- CC$Ben_DACCS
length(CC$Ben.DACCS)## [1] 1007CC$Ben.EW <- CC$Ben_EW
length(CC$Ben.EW) ## [1] 1007CC$Ben.OF <- CC$Ben_OF
length(CC$Ben.OF) ## [1] 1007CC$Ben.BF <- CC$Ben_BF
length(CC$Ben.BF) ## [1] 1007CC$Ben.NE <- CC$Ben_NE
length(CC$Ben.NE) ## [1] 1007CC$Ben.SE <- CC$Ben_SE
length(CC$Ben.SE) ## [1] 1007CC$Ben.WE <- CC$Ben_WE
length(CC$Ben.WE) ## [1] 1007## Control
CC$Control.AFSCS <- CC$Control_AFSCS
length(CC$Control.AFSCS)## [1] 1007CC$Control.BIO <- CC$Control_BIO
length(CC$Control.BIO)## [1] 1007CC$Control.BECCS <- CC$Control_BECCS
length(CC$Control.BECCS)## [1] 1007CC$Control.DACCS <- CC$Control_DACCS
length(CC$Control.DACCS)## [1] 1007CC$Control.EW <- CC$Control_EW
length(CC$Control.EW)## [1] 1007CC$Control.OF <- CC$Control_OF
length(CC$Control.OF)## [1] 1007CC$Control.BF <- CC$Control_BF
length(CC$Control.BF)## [1] 1007CC$Control.NE <- CC$Control_NE
length(CC$Control.NE)## [1] 1007CC$Control.SE <- CC$Control_SE
length(CC$Control.SE)## [1] 1007CC$Control.WE <- CC$Control_WE
length(CC$Control.WE)## [1] 1007## Familiarity
CC$Familiar.AFSCS <- CC$Familiar_AFSCS
length(CC$Familiar.AFSCS)## [1] 1007CC$Familiar.BIO <- CC$Familiar_BIO
length(CC$Familiar.BIO)## [1] 1007CC$Familiar.BECCS <- CC$Familiar_BECCS
length(CC$Familiar.BECCS)## [1] 1007CC$Familiar.DACCS <- CC$Familiar_DACCS
length(CC$Familiar.DACCS)## [1] 1007CC$Familiar.EW <- CC$Familiar_EW
length(CC$Familiar.EW)## [1] 1007CC$Familiar.OF <- CC$Familiar_OF
length(CC$Familiar.OF)## [1] 1007CC$Familiar.BF <- CC$Familiar_BF
length(CC$Familiar.BF)## [1] 1007CC$Familiar.NE <- CC$Familiar_NE
length(CC$Familiar.NE)## [1] 1007CC$Familiar.SE <- CC$Familiar_SE
length(CC$Familiar.SE)## [1] 1007CC$Familiar.WE <- CC$Familiar_WE
length(CC$Familiar.WE)## [1] 1007## Naturalness
CC$Naturalness.AFSCS <- CC$Nat_Score_AFSCS
length(CC$Naturalness.AFSCS)## [1] 1007CC$Naturalness.BIO <- CC$Nat_Score_BIO
length(CC$Naturalness.BIO)## [1] 1007CC$Naturalness.BECCS <- CC$Nat_Score_BECCS
length(CC$Naturalness.BECCS)## [1] 1007CC$Naturalness.DACCS <- CC$Nat_Score_DACCS
length(CC$Naturalness.DACCS)## [1] 1007CC$Naturalness.EW <- CC$Nat_Score_EW
length(CC$Naturalness.EW)## [1] 1007CC$Naturalness.OF <- CC$Nat_Score_OF
length(CC$Naturalness.OF)## [1] 1007CC$Naturalness.BF <- CC$Nat_Score_BF
length(CC$Naturalness.BF)## [1] 1007CC$Naturalness.NE <- CC$Nat_Score_NE
length(CC$Naturalness.NE)## [1] 1007CC$Naturalness.SE <- CC$Nat_Score_SE
length(CC$Naturalness.SE)## [1] 1007CC$Naturalness.WE <- CC$Nat_Score_WE
length(CC$Naturalness.WE)## [1] 1007## Risk
CC$Risk.AFSCS <- CC$Risk_Score_AFSCS
length(CC$Risk.AFSCS)## [1] 1007CC$Risk.BIO <- CC$Risk_Score_BIO
length(CC$Risk.BIO)## [1] 1007CC$Risk.BECCS <- CC$Risk_Score_BECCS
length(CC$Risk.BECCS)## [1] 1007CC$Risk.DACCS <- CC$Risk_Score_DACCS
length(CC$Risk.DACCS)## [1] 1007CC$Risk.EW <- CC$Risk_Score_EW
length(CC$Risk.EW)## [1] 1007CC$Risk.OF <- CC$Risk_Score_OF
length(CC$Risk.OF)## [1] 1007CC$Risk.BF <- CC$Risk_Score_BF
length(CC$Risk.BF)## [1] 1007CC$Risk.NE <- CC$Risk_Score_NE
length(CC$Risk.NE)## [1] 1007CC$Risk.SE <- CC$Risk_Score_SE
length(CC$Risk.SE)## [1] 1007CC$Risk.WE <- CC$Risk_Score_WE
length(CC$Risk.WE)## [1] 1007## Support
CC$Support.AFSCS <- CC$Support_Score_AFSCS
length(CC$Support.AFSCS)## [1] 1007CC$Support.BIO <- CC$Support_Score_BIO
length(CC$Support.BIO)## [1] 1007CC$Support.BECCS <- CC$Support_Score_BECCS
length(CC$Support.BECCS)## [1] 1007CC$Support.DACCS <- CC$Support_Score_DACCS
length(CC$Support.DACCS)## [1] 1007CC$Support.EW <- CC$Support_Score_EW
length(CC$Support.EW)## [1] 1007CC$Support.OF <- CC$Support_Score_OF
length(CC$Support.OF)## [1] 1007CC$Support.BF <- CC$Support_Score_BF
length(CC$Support.BF)## [1] 1007CC$Support.NE <- CC$Support_Score_NE
length(CC$Support.NE)## [1] 1007CC$Support.SE <- CC$Support_Score_SE
length(CC$Support.SE)## [1] 1007CC$Support.WE <- CC$Support_Score_WE
length(CC$Support.WE)## [1] 1007## Understanding
CC$Understanding.AFSCS <- CC$Und_AFSCS
length(CC$Understanding.AFSCS)## [1] 1007CC$Understanding.BIO <- CC$Und_BIO
length(CC$Understanding.BIO)## [1] 1007CC$Understanding.BECCS <- CC$Und_BECCS
length(CC$Understanding.BECCS)## [1] 1007CC$Understanding.DACCS <- CC$Und_DACCS
length(CC$Understanding.DACCS)## [1] 1007CC$Understanding.EW <- CC$Und_EW
length(CC$Understanding.EW)## [1] 1007CC$Understanding.OF <- CC$Und_OF
length(CC$Understanding.OF)## [1] 1007CC$Understanding.BF <- CC$Und_BF
length(CC$Understanding.BF)## [1] 1007CC$Understanding.NE <- CC$Und_NE
length(CC$Understanding.NE)## [1] 1007CC$Understanding.SE <- CC$Und_SE
length(CC$Understanding.SE)## [1] 1007CC$Understanding.WE <- CC$Und_AFSCS
length(CC$Understanding.WE)## [1] 1007## Familiarity/Understanding (Mean)
length(CC$FR.AFSCS)## [1] 1007length(CC$FR.BIO)## [1] 1007length(CC$FR.BECCS)## [1] 1007length(CC$FR.DACCS)## [1] 1007length(CC$FR.EW)## [1] 1007length(CC$FR.OF)## [1] 1007length(CC$FR.BF)## [1] 1007length(CC$FR.NE)## [1] 1007length(CC$FR.SE)## [1] 1007length(CC$FR.WE)## [1] 1007#Benefit - Risk Difference Score
length(CC$BRDiff.AFSCS)## [1] 1007length(CC$BRDiff.BIO)## [1] 1007length(CC$BRDiff.BECCS)## [1] 1007length(CC$BRDiff.DACCS)## [1] 1007length(CC$BRDiff.EW)## [1] 1007length(CC$BRDiff.OF)## [1] 1007length(CC$BRDiff.BF)## [1] 1007length(CC$BRDiff.NE)## [1] 1007length(CC$BRDiff.SE)## [1] 1007length(CC$BRDiff.WE)## [1] 1007Transform: Wide to Long
library(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(lme4)
#Reshape to long form
CCvector <- c("Ben.AFSCS", "Ben.BIO", "Ben.BECCS", "Ben.DACCS", "Ben.EW", "Ben.OF" , "Ben.BF", "Ben.NE", "Ben.SE", "Ben.WE", "Control.AFSCS" , "Control.BIO" , "Control.BECCS" , "Control.DACCS", "Control.EW", "Control.OF", "Control.BF", "Control.NE", "Control.SE", "Control.WE", "Familiar.AFSCS" , "Familiar.BIO", "Familiar.BECCS" , "Familiar.DACCS", "Familiar.EW", "Familiar.OF", "Familiar.BF", "Familiar.NE", "Familiar.SE", "Familiar.WE", "Naturalness.AFSCS", "Naturalness.BIO" , "Naturalness.BECCS", "Naturalness.DACCS", "Naturalness.EW", "Naturalness.OF", "Naturalness.BF", "Naturalness.NE", "Naturalness.SE", "Naturalness.WE", "Risk.AFSCS", "Risk.BIO", "Risk.BECCS", "Risk.DACCS", "Risk.EW", "Risk.OF", "Risk.BF", "Risk.NE" , "Risk.SE", "Risk.WE", "Support.AFSCS", "Support.BIO", "Support.BECCS" , "Support.DACCS", "Support.EW" , "Support.OF", "Support.BF", "Support.NE", "Support.SE", "Support.WE", "Understanding.AFSCS", "Understanding.BIO", "Understanding.BECCS", "Understanding.DACCS", "Understanding.EW", "Understanding.OF", "Understanding.BF", "Understanding.NE","Understanding.SE","Understanding.WE", "FR.AFSCS", "FR.BIO", "FR.BECCS", "FR.DACCS", "FR.EW", "FR.OF", "FR.BF", "FR.NE", "FR.SE", "FR.WE", "BRDiff.AFSCS", "BRDiff.BIO", "BRDiff.BECCS", "BRDiff.DACCS", "BRDiff.EW", "BRDiff.OF", "BRDiff.BF", "BRDiff.NE", "BRDiff.SE", "BRDiff.WE")
L <- reshape(data = CC,
varying = CCvector,
timevar = "Type",
direction = "long")
view(L)Correlations
Tech by Variables: Benefit, Familiarity/Understanding, Naturalness, Risk, and Support
L$corR <- data.frame(L$Ben, L$FR, L$Naturalness, L$Risk, L$Support)
mydata.cor11 = cor(L$corR, use = "pairwise.complete.obs")
head(round(mydata.cor11,2))## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 1.00 0.24 0.26 -0.33 0.63
## L.FR 0.24 1.00 0.38 -0.34 0.38
## L.Naturalness 0.26 0.38 1.00 -0.49 0.43
## L.Risk -0.33 -0.34 -0.49 1.00 -0.61
## L.Support 0.63 0.38 0.43 -0.61 1.00library("Hmisc")
mydata.rcorr11 = rcorr(as.matrix(mydata.cor11))
mydata.rcorr11## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 1.00 0.35 0.45 -0.75 0.85
## L.FR 0.35 1.00 0.61 -0.74 0.59
## L.Naturalness 0.45 0.61 1.00 -0.87 0.69
## L.Risk -0.75 -0.74 -0.87 1.00 -0.95
## L.Support 0.85 0.59 0.69 -0.95 1.00
##
## n= 5
##
##
## P
## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 0.5605 0.4480 0.1424 0.0682
## L.FR 0.5605 0.2730 0.1540 0.2901
## L.Naturalness 0.4480 0.2730 0.0574 0.1931
## L.Risk 0.1424 0.1540 0.0574 0.0140
## L.Support 0.0682 0.2901 0.1931 0.0140library(corrplot)## corrplot 0.92 loadedcorrplot(mydata.cor11, method="color")
corrplot(mydata.cor11, addCoef.col = 1, number.cex = 0.3, method = 'number')
### Individual Differences
#Individual Differences
L$corID <- data.frame(L$ATNS_Score, L$CCB_Score, L$CNS_Score, L$Individualism_Score, L$Collectivism_Score, L$Ideology)
mydata.cor2 = cor(L$corID, use = "pairwise.complete.obs")
head(round(mydata.cor2,2))## L.ATNS_Score L.CCB_Score L.CNS_Score
## L.ATNS_Score 1.00 -0.05 0.29
## L.CCB_Score -0.05 1.00 0.30
## L.CNS_Score 0.29 0.30 1.00
## L.Individualism_Score 0.14 0.03 0.12
## L.Collectivism_Score 0.09 -0.19 -0.02
## L.Ideology 0.00 0.03 0.08
## L.Individualism_Score L.Collectivism_Score L.Ideology
## L.ATNS_Score 0.14 0.09 0.00
## L.CCB_Score 0.03 -0.19 0.03
## L.CNS_Score 0.12 -0.02 0.08
## L.Individualism_Score 1.00 0.21 0.03
## L.Collectivism_Score 0.21 1.00 -0.17
## L.Ideology 0.03 -0.17 1.00library("Hmisc")
mydata.rcorr2 = rcorr(as.matrix(mydata.cor2))
mydata.rcorr2## L.ATNS_Score L.CCB_Score L.CNS_Score
## L.ATNS_Score 1.00 -0.36 0.21
## L.CCB_Score -0.36 1.00 0.34
## L.CNS_Score 0.21 0.34 1.00
## L.Individualism_Score -0.07 -0.29 -0.26
## L.Collectivism_Score -0.01 -0.59 -0.41
## L.Ideology -0.28 -0.07 -0.13
## L.Individualism_Score L.Collectivism_Score L.Ideology
## L.ATNS_Score -0.07 -0.01 -0.28
## L.CCB_Score -0.29 -0.59 -0.07
## L.CNS_Score -0.26 -0.41 -0.13
## L.Individualism_Score 1.00 0.23 -0.27
## L.Collectivism_Score 0.23 1.00 -0.53
## L.Ideology -0.27 -0.53 1.00
##
## n= 6
##
##
## P
## L.ATNS_Score L.CCB_Score L.CNS_Score
## L.ATNS_Score 0.4890 0.6937
## L.CCB_Score 0.4890 0.5051
## L.CNS_Score 0.6937 0.5051
## L.Individualism_Score 0.8978 0.5766 0.6249
## L.Collectivism_Score 0.9808 0.2156 0.4164
## L.Ideology 0.5897 0.8880 0.8013
## L.Individualism_Score L.Collectivism_Score L.Ideology
## L.ATNS_Score 0.8978 0.9808 0.5897
## L.CCB_Score 0.5766 0.2156 0.8880
## L.CNS_Score 0.6249 0.4164 0.8013
## L.Individualism_Score 0.6543 0.6091
## L.Collectivism_Score 0.6543 0.2758
## L.Ideology 0.6091 0.2758library(corrplot)
corrplot(mydata.cor2, method="color")
corrplot(mydata.cor2, addCoef.col = 1, number.cex = 0.3, method = 'number')
Mixed Effects Models
Center Variables
# Describe & Mean Center Long Variables
## By Technology Measures
table(L$Type) ##
## AFSCS BECCS BF BIO DACCS EW NE OF SE WE
## 1007 1007 1007 1007 1007 1007 1007 1007 1007 1007describe(L$Ben) ## L$Ben
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 101 0.999 57.98 29.92 5 20
## .25 .50 .75 .90 .95
## 40 61 77 90 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Control) ## L$Control
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 100 0.999 64.82 28.44 17 29
## .25 .50 .75 .90 .95
## 50 69 85 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Familiar) ## L$Familiar
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 101 0.997 46.4 39.99 0 0
## .25 .50 .75 .90 .95
## 13 45 79 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Naturalness) ## L$Naturalness
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 366 1 39.98 24.53 5.00 12.00
## .25 .50 .75 .90 .95
## 24.75 39.00 54.25 70.25 75.00
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 98.00 98.75 99.50 99.75 100.00describe(L$Risk) ## L$Risk
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 201 0.998 33.04 30.73 0.0 0.0
## .25 .50 .75 .90 .95
## 8.0 28.5 52.0 72.5 84.5
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$Support) ## L$Support
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 201 0.999 59.57 32.84 0.0 13.0
## .25 .50 .75 .90 .95
## 41.5 62.5 82.5 99.0 100.0
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$Understanding) ## L$Understanding
## n missing distinct Info Mean Gmd .05 .10
## 3107 6963 101 0.999 57.74 34.27 4 12
## .25 .50 .75 .90 .95
## 34 61 83 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$FR) ## L$FR
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 201 1 52.39 34.4 4.0 11.0
## .25 .50 .75 .90 .95
## 27.5 51.0 78.0 94.0 100.0
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$BRDiff) ## L$BRDiff
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 364 1 24.94 49.58 -54.5 -31.0
## .25 .50 .75 .90 .95
## -3.5 26.0 58.5 82.0 91.5
##
## lowest : -100.0 -99.0 -93.0 -92.5 -92.0, highest: 98.0 98.5 99.0 99.5 100.0L$Benefit.c <- L$Ben - 57.98
L$Control.c <- L$Control - 64.83
L$Familiarity <- L$Familiar
L$Familiarity.c <- L$Familiarity - 46.43
L$Naturalness.c <- L$Naturalness - 39.99
L$Risk.c <- L$Risk - 33.1
L$Support.c <- L$Support - 59.7
L$Understanding.c <- L$Understanding - 57.62
L$FR.c <- L$FR - 52.35
L$BFDiff.c <- L$BRDiff - 24.88
## Individual Difference Measures
describe(L$ATNS_Score)## L$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 366 1 54.56 24.38 18.8 26.0
## .25 .50 .75 .90 .95
## 40.2 54.4 69.0 82.0 92.4
##
## lowest : 0.0 2.0 3.0 4.0 6.4, highest: 97.6 98.8 99.2 99.8 100.0L$ATNS_Score.c <- L$ATNS_Score - 54.69
describe(L$CCB_Score)## L$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 250 0.987 81.61 23.21 25.00 46.75
## .25 .50 .75 .90 .95
## 75.00 91.25 99.00 100.00 100.00
##
## lowest : 0.00 2.00 3.75 4.00 4.75, highest: 99.00 99.25 99.50 99.75 100.00L$CCBelief_Score.c <- L$CCB_Score - 81.61
describe(L$CNS_Score)## L$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 322 1 63.36 18.7 35.0 43.0
## .25 .50 .75 .90 .95
## 52.8 63.0 74.6 85.0 91.8
##
## lowest : 0.0 8.6 10.0 12.8 16.0, highest: 97.8 98.2 98.6 99.6 100.0L$CNS_Score.c <- L$CNS_Score -63.42
describe(L$Individualism_Score)## L$Individualism_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 266 1 70.77 18.9 40.25 50.00
## .25 .50 .75 .90 .95
## 60.00 71.50 83.75 91.75 95.75
##
## lowest : 0.75 6.00 6.25 6.50 15.50, highest: 99.00 99.25 99.50 99.75 100.00L$Individualism_Score.c <- L$Individualism_Score - 70.81
describe(L$Collectivism_Score)## L$Collectivism_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 341 1 54.19 27.22 12.75 21.50
## .25 .50 .75 .90 .95
## 38.50 54.50 72.00 86.00 93.25
##
## lowest : 0.00 0.25 0.50 1.00 1.75, highest: 98.25 98.50 99.50 99.75 100.00L$Collectivism_Score.c <- L$Collectivism_Score - 54.17
describe(L$Ideology)## L$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 12 0.868 1.943 0.5666 1.0 1.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.0 2.5 3.0
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 2.5 3.0 3.5 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 5.0
## Frequency 10 40 40 110 530 2360 4920 1310 620 110 10
## Proportion 0.001 0.004 0.004 0.011 0.053 0.234 0.489 0.130 0.062 0.011 0.001
##
## Value 6.0
## Frequency 10
## Proportion 0.001L$Ideology.c <- L$Ideology - 1.947Contrast Codes (Deviation Coding)
#C1. Direct air capture and carbon sequestration vs. Grand mean
L$C1 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') +(1)*(L$Type == 'DACCS') +(0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C2. Nuclear Energy vs. Grand Mean
L$C2 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (1)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C3. Ocean Fertilization vs. Grand Mean
L$C3 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (1)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C4. BECCS vs. Grand Mean
L$C4 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (1)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C5. Enhanced Weathering vs. Grand Mean
L$C5 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (1)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C6. Biofuel vs. Grand Mean
L$C6 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (1)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C7. Wind Energy vs. Grand Mean
L$C7 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (1)*(L$Type == 'WE')
#C8. Solar Energy vs. Grand Mean
L$C8 <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (1)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C9. Afforestation/reforestation and Soil Carbon Sequestration vs. Grand Mean
L$C9 <- (1)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')ANOVAs
Benefit
modA.4 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.4 <- lmer(Ben ~ 1 + (1|id), data = L)
summary(modA.4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27683.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4246 -0.5150 0.0654 0.5678 3.1565
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.4 16.84
## Residual 381.8 19.54
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.2205 0.6407 1017.4032 90.874 < 2e-16 ***
## C1 -3.1843 1.1019 2386.9763 -2.890 0.003888 **
## C2 1.4717 1.2849 2490.3918 1.145 0.252168
## C3 -4.3571 1.1337 2399.1210 -3.843 0.000125 ***
## C4 -2.9518 1.1284 2394.8266 -2.616 0.008955 **
## C5 -5.5747 1.1205 2392.8913 -4.975 6.98e-07 ***
## C6 -7.3526 1.3064 2491.1763 -5.628 2.02e-08 ***
## C7 7.5302 1.2847 2488.4305 5.862 5.19e-09 ***
## C8 8.7549 1.3138 2491.7176 6.664 3.28e-11 ***
## C9 10.4528 1.1080 2389.8849 9.434 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.028
## C2 0.023 -0.092
## C3 -0.016 -0.115 -0.073
## C4 -0.018 -0.111 -0.098 -0.118
## C5 -0.021 -0.107 -0.085 -0.116 -0.109
## C6 0.031 -0.094 -0.171 -0.110 -0.096 -0.097
## C7 0.023 -0.080 -0.169 -0.094 -0.092 -0.097 -0.171
## C8 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.172 -0.171
## C9 -0.026 -0.110 -0.109 -0.110 -0.118 -0.111 -0.093 -0.088 -0.081tab_model(modA.4,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.22 | 0.64 | 56.96 – 59.48 | 90.87 | <0.001 |
| C1 | -3.18 | 1.10 | -5.34 – -1.02 | -2.89 | 0.004 |
| C2 | 1.47 | 1.28 | -1.05 – 3.99 | 1.15 | 0.252 |
| C3 | -4.36 | 1.13 | -6.58 – -2.13 | -3.84 | <0.001 |
| C4 | -2.95 | 1.13 | -5.16 – -0.74 | -2.62 | 0.009 |
| C5 | -5.57 | 1.12 | -7.77 – -3.38 | -4.98 | <0.001 |
| C6 | -7.35 | 1.31 | -9.91 – -4.79 | -5.63 | <0.001 |
| C7 | 7.53 | 1.28 | 5.01 – 10.05 | 5.86 | <0.001 |
| C8 | 8.75 | 1.31 | 6.18 – 11.33 | 6.66 | <0.001 |
| C9 | 10.45 | 1.11 | 8.28 – 12.63 | 9.43 | <0.001 |
| Random Effects | |||||
| σ2 | 381.81 | ||||
| τ00 id | 283.44 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.457 | ||||
summary(modC.4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27935.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0907 -0.5332 0.0524 0.6099 2.8266
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 273.3 16.53
## Residual 424.8 20.61
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.9752 0.6419 1006.0000 90.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.4,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.98 | 0.64 | 56.72 – 59.23 | 90.32 | <0.001 |
| Random Effects | |||||
| σ2 | 424.79 | ||||
| τ00 id | 273.30 | ||||
| ICC | 0.39 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.392 | ||||
anova(modC.4, modA.4)## refitting model(s) with ML (instead of REML)Difference Benefit/Risk Scores
modA.5 <- lmer(BRDiff ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.5 <- lmer(BRDiff ~ 1 + (1|id), data = L)
summary(modA.5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30498.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8901 -0.5430 0.0446 0.5733 3.1036
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 584.4 24.17
## Residual 1032.4 32.13
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.7958 0.9639 1018.4237 26.762 < 2e-16 ***
## C1 -14.0197 1.7957 2450.9270 -7.808 8.57e-15 ***
## C2 -19.0289 2.0889 2564.5205 -9.110 < 2e-16 ***
## C3 -17.7845 1.8471 2464.5022 -9.628 < 2e-16 ***
## C4 -8.6096 1.8386 2459.9287 -4.683 2.98e-06 ***
## C5 -11.3284 1.8257 2457.6798 -6.205 6.40e-10 ***
## C6 -1.5867 2.1238 2565.7134 -0.747 0.455
## C7 22.5093 2.0886 2562.6624 10.777 < 2e-16 ***
## C8 30.2190 2.1358 2566.3661 14.149 < 2e-16 ***
## C9 26.9722 1.8055 2454.0429 14.939 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.031
## C2 0.025 -0.094
## C3 -0.018 -0.113 -0.078
## C4 -0.020 -0.110 -0.099 -0.117
## C5 -0.023 -0.105 -0.088 -0.115 -0.108
## C6 0.034 -0.096 -0.167 -0.110 -0.098 -0.099
## C7 0.025 -0.083 -0.166 -0.096 -0.094 -0.099 -0.167
## C8 0.037 -0.104 -0.168 -0.105 -0.095 -0.099 -0.169 -0.167
## C9 -0.028 -0.109 -0.109 -0.108 -0.115 -0.110 -0.095 -0.091 -0.085tab_model(modA.5,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.80 | 0.96 | 23.91 – 27.69 | 26.76 | <0.001 |
| C1 | -14.02 | 1.80 | -17.54 – -10.50 | -7.81 | <0.001 |
| C2 | -19.03 | 2.09 | -23.12 – -14.93 | -9.11 | <0.001 |
| C3 | -17.78 | 1.85 | -21.41 – -14.16 | -9.63 | <0.001 |
| C4 | -8.61 | 1.84 | -12.21 – -5.00 | -4.68 | <0.001 |
| C5 | -11.33 | 1.83 | -14.91 – -7.75 | -6.20 | <0.001 |
| C6 | -1.59 | 2.12 | -5.75 – 2.58 | -0.75 | 0.455 |
| C7 | 22.51 | 2.09 | 18.41 – 26.60 | 10.78 | <0.001 |
| C8 | 30.22 | 2.14 | 26.03 – 34.41 | 14.15 | <0.001 |
| C9 | 26.97 | 1.81 | 23.43 – 30.51 | 14.94 | <0.001 |
| Random Effects | |||||
| σ2 | 1032.43 | ||||
| τ00 id | 584.41 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.466 | ||||
summary(modC.5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 31187.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4840 -0.5778 0.0270 0.6663 2.7403
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 533.5 23.10
## Residual 1380.4 37.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 24.9358 0.9933 1005.9999 25.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.5,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 24.94 | 0.99 | 22.99 – 26.88 | 25.10 | <0.001 |
| Random Effects | |||||
| σ2 | 1380.43 | ||||
| τ00 id | 533.46 | ||||
| ICC | 0.28 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.279 | ||||
anova(modC.5, modA.5)## refitting model(s) with ML (instead of REML)Familiarity
modA.7 <- lmer(Familiarity ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.7 <- lmer(Familiarity ~ 1 + (1|id), data = L)## boundary (singular) fit: see ?isSingularsummary(modA.7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Familiarity ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 27934.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5100 -0.6395 -0.0514 0.6158 3.4713
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 182.4 13.51
## Residual 475.1 21.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.1121 0.5845 1022.2933 84.025 < 2e-16 ***
## C1 -23.1499 1.2017 2544.1702 -19.264 < 2e-16 ***
## C2 20.2968 1.3931 2666.9288 14.570 < 2e-16 ***
## C3 -23.7279 1.2356 2559.3204 -19.203 < 2e-16 ***
## C4 -18.7593 1.2301 2554.5949 -15.250 < 2e-16 ***
## C5 -27.3208 1.2216 2551.9418 -22.365 < 2e-16 ***
## C6 7.9907 1.4163 2668.7543 5.642 1.86e-08 ***
## C7 32.6024 1.3930 2665.3983 23.404 < 2e-16 ***
## C8 39.4911 1.4243 2669.5537 27.726 < 2e-16 ***
## C9 13.9820 1.2082 2547.4228 11.573 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.035
## C2 0.029 -0.097
## C3 -0.020 -0.110 -0.084
## C4 -0.022 -0.107 -0.102 -0.114
## C5 -0.026 -0.104 -0.092 -0.112 -0.106
## C6 0.038 -0.099 -0.161 -0.111 -0.101 -0.102
## C7 0.029 -0.088 -0.160 -0.099 -0.097 -0.101 -0.161
## C8 0.041 -0.106 -0.162 -0.107 -0.099 -0.102 -0.163 -0.162
## C9 -0.032 -0.106 -0.109 -0.106 -0.112 -0.107 -0.098 -0.094 -0.090tab_model(modA.7,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 49.11 | 0.58 | 47.97 – 50.26 | 84.03 | <0.001 |
| C1 | -23.15 | 1.20 | -25.51 – -20.79 | -19.26 | <0.001 |
| C2 | 20.30 | 1.39 | 17.57 – 23.03 | 14.57 | <0.001 |
| C3 | -23.73 | 1.24 | -26.15 – -21.31 | -19.20 | <0.001 |
| C4 | -18.76 | 1.23 | -21.17 – -16.35 | -15.25 | <0.001 |
| C5 | -27.32 | 1.22 | -29.72 – -24.93 | -22.37 | <0.001 |
| C6 | 7.99 | 1.42 | 5.21 – 10.77 | 5.64 | <0.001 |
| C7 | 32.60 | 1.39 | 29.87 – 35.33 | 23.40 | <0.001 |
| C8 | 39.49 | 1.42 | 36.70 – 42.28 | 27.73 | <0.001 |
| C9 | 13.98 | 1.21 | 11.61 – 16.35 | 11.57 | <0.001 |
| Random Effects | |||||
| σ2 | 475.12 | ||||
| τ00 id | 182.39 | ||||
| ICC | 0.28 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.463 / 0.612 | ||||
summary(modC.7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Familiarity ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30025.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.33178 -0.95865 -0.04018 0.93570 1.53844
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0 0.00
## Residual 1214 34.84
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.3999 0.6339 3020.0000 73.2 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingulartab_model(modC.7,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.40 | 0.63 | 45.16 – 47.64 | 73.20 | <0.001 |
| Random Effects | |||||
| σ2 | 1213.86 | ||||
| τ00 id | 0.00 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / NA | ||||
anova(modC.7, modA.7)## refitting model(s) with ML (instead of REML)Familiarity/Understanding Mean Scores (COMBINED)
modA.6 <- lmer(FR ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.6 <- lmer(FR ~ 1 + (1|id), data = L)
summary(modA.6)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27119.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0412 -0.5869 -0.0111 0.5966 3.1013
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.7 14.31
## Residual 329.5 18.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.4788 0.5608 1019.6835 97.137 < 2e-16 ***
## C1 -18.6588 1.0176 2429.9000 -18.337 < 2e-16 ***
## C2 12.8814 1.1847 2540.1072 10.873 < 2e-16 ***
## C3 -16.1090 1.0468 2442.9867 -15.389 < 2e-16 ***
## C4 -16.5934 1.0420 2438.5014 -15.925 < 2e-16 ***
## C5 -22.1040 1.0346 2436.3623 -21.364 < 2e-16 ***
## C6 5.1720 1.2045 2541.1519 4.294 1.82e-05 ***
## C7 27.7437 1.1846 2538.2041 23.421 < 2e-16 ***
## C8 31.6044 1.2114 2541.7653 26.090 < 2e-16 ***
## C9 12.8248 1.0232 2432.9494 12.534 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.030
## C2 0.024 -0.093
## C3 -0.017 -0.113 -0.076
## C4 -0.019 -0.110 -0.099 -0.117
## C5 -0.022 -0.106 -0.087 -0.115 -0.108
## C6 0.033 -0.095 -0.169 -0.110 -0.098 -0.098
## C7 0.024 -0.082 -0.167 -0.095 -0.093 -0.098 -0.168
## C8 0.036 -0.104 -0.169 -0.105 -0.095 -0.098 -0.170 -0.169
## C9 -0.027 -0.109 -0.109 -0.109 -0.116 -0.110 -0.094 -0.090 -0.083tab_model(modA.6,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.48 | 0.56 | 53.38 – 55.58 | 97.14 | <0.001 |
| C1 | -18.66 | 1.02 | -20.65 – -16.66 | -18.34 | <0.001 |
| C2 | 12.88 | 1.18 | 10.56 – 15.20 | 10.87 | <0.001 |
| C3 | -16.11 | 1.05 | -18.16 – -14.06 | -15.39 | <0.001 |
| C4 | -16.59 | 1.04 | -18.64 – -14.55 | -15.93 | <0.001 |
| C5 | -22.10 | 1.03 | -24.13 – -20.08 | -21.36 | <0.001 |
| C6 | 5.17 | 1.20 | 2.81 – 7.53 | 4.29 | <0.001 |
| C7 | 27.74 | 1.18 | 25.42 – 30.07 | 23.42 | <0.001 |
| C8 | 31.60 | 1.21 | 29.23 – 33.98 | 26.09 | <0.001 |
| C9 | 12.82 | 1.02 | 10.82 – 14.83 | 12.53 | <0.001 |
| Random Effects | |||||
| σ2 | 329.45 | ||||
| τ00 id | 204.67 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.403 / 0.632 | ||||
summary(modC.6)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 29062.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94488 -0.79508 -0.04019 0.83609 1.81382
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 74.97 8.659
## Residual 813.70 28.525
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.3860 0.5863 1006.0000 89.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.6,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 52.39 | 0.59 | 51.24 – 53.54 | 89.34 | <0.001 |
| Random Effects | |||||
| σ2 | 813.70 | ||||
| τ00 id | 74.97 | ||||
| ICC | 0.08 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.084 | ||||
anova(modC.6, modA.6)## refitting model(s) with ML (instead of REML)Naturalness
modA.2 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.2 <- lmer(Naturalness ~ 1 + (1|id), data = L)
summary(modA.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 25882.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5623 -0.6131 -0.0219 0.6137 3.4124
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.98 8.123
## Residual 256.42 16.013
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.3544 0.3901 1027.4504 103.457 < 2e-16 ***
## C1 -14.9730 0.8706 2633.0772 -17.198 < 2e-16 ***
## C2 -14.4713 1.0060 2757.7982 -14.386 < 2e-16 ***
## C3 -8.4667 0.8948 2649.1062 -9.462 < 2e-16 ***
## C4 -5.4749 0.8910 2644.5116 -6.145 9.20e-10 ***
## C5 -4.6006 0.8848 2641.5512 -5.199 2.15e-07 ***
## C6 -1.0643 1.0226 2760.2000 -1.041 0.298
## C7 13.9641 1.0059 2756.7292 13.882 < 2e-16 ***
## C8 15.0008 1.0284 2761.1074 14.586 < 2e-16 ***
## C9 21.5355 0.8753 2636.2795 24.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.038
## C2 0.033 -0.100
## C3 -0.023 -0.107 -0.090
## C4 -0.025 -0.105 -0.104 -0.111
## C5 -0.029 -0.102 -0.096 -0.109 -0.104
## C6 0.043 -0.102 -0.155 -0.112 -0.104 -0.104
## C7 0.033 -0.093 -0.154 -0.102 -0.101 -0.103 -0.155
## C8 0.046 -0.108 -0.156 -0.109 -0.103 -0.105 -0.157 -0.156
## C9 -0.035 -0.103 -0.110 -0.104 -0.109 -0.105 -0.101 -0.098 -0.095tab_model(modA.2,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 0.39 | 39.59 – 41.12 | 103.46 | <0.001 |
| C1 | -14.97 | 0.87 | -16.68 – -13.27 | -17.20 | <0.001 |
| C2 | -14.47 | 1.01 | -16.44 – -12.50 | -14.39 | <0.001 |
| C3 | -8.47 | 0.89 | -10.22 – -6.71 | -9.46 | <0.001 |
| C4 | -5.47 | 0.89 | -7.22 – -3.73 | -6.15 | <0.001 |
| C5 | -4.60 | 0.88 | -6.34 – -2.87 | -5.20 | <0.001 |
| C6 | -1.06 | 1.02 | -3.07 – 0.94 | -1.04 | 0.298 |
| C7 | 13.96 | 1.01 | 11.99 – 15.94 | 13.88 | <0.001 |
| C8 | 15.00 | 1.03 | 12.98 – 17.02 | 14.59 | <0.001 |
| C9 | 21.54 | 0.88 | 19.82 – 23.25 | 24.60 | <0.001 |
| Random Effects | |||||
| σ2 | 256.42 | ||||
| τ00 id | 65.98 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.310 / 0.451 | ||||
summary(modC.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27086.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16770 -0.68297 -0.04925 0.61757 3.02331
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 52.22 7.227
## Residual 412.08 20.300
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.9817 0.4339 1006.0000 92.15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.2,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.98 | 0.43 | 39.13 – 40.83 | 92.15 | <0.001 |
| Random Effects | |||||
| σ2 | 412.08 | ||||
| τ00 id | 52.22 | ||||
| ICC | 0.11 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.112 | ||||
anova(modC.2, modA.2)## refitting model(s) with ML (instead of REML)Risk
modA.3 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.3 <- lmer(Risk ~ 1 + (1|id), data = L)
summary(modA.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27466.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5812 -0.6117 -0.0694 0.5565 3.6749
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Residual 391.9 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.4243 0.5609 1018.7068 57.808 < 2e-16 ***
## C1 10.8426 1.0990 2495.8856 9.866 < 2e-16 ***
## C2 20.5519 1.2763 2615.0096 16.103 < 2e-16 ***
## C3 13.5376 1.1303 2510.3315 11.977 < 2e-16 ***
## C4 5.6756 1.1252 2505.6400 5.044 4.88e-07 ***
## C5 5.7300 1.1173 2503.1806 5.128 3.15e-07 ***
## C6 -5.7162 1.2976 2616.5136 -4.405 1.10e-05 ***
## C7 -14.9462 1.2762 2613.2863 -11.711 < 2e-16 ***
## C8 -21.6013 1.3050 2617.2425 -16.553 < 2e-16 ***
## C9 -16.5218 1.1050 2499.0970 -14.952 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.033
## C2 0.027 -0.095
## C3 -0.019 -0.111 -0.081
## C4 -0.021 -0.108 -0.100 -0.115
## C5 -0.025 -0.105 -0.090 -0.113 -0.107
## C6 0.036 -0.097 -0.164 -0.111 -0.100 -0.100
## C7 0.027 -0.085 -0.163 -0.097 -0.096 -0.100 -0.164
## C8 0.039 -0.105 -0.165 -0.106 -0.097 -0.100 -0.166 -0.165
## C9 -0.030 -0.107 -0.109 -0.107 -0.114 -0.109 -0.096 -0.092 -0.087tab_model(modA.3,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.42 | 0.56 | 31.32 – 33.52 | 57.81 | <0.001 |
| C1 | 10.84 | 1.10 | 8.69 – 13.00 | 9.87 | <0.001 |
| C2 | 20.55 | 1.28 | 18.05 – 23.05 | 16.10 | <0.001 |
| C3 | 13.54 | 1.13 | 11.32 – 15.75 | 11.98 | <0.001 |
| C4 | 5.68 | 1.13 | 3.47 – 7.88 | 5.04 | <0.001 |
| C5 | 5.73 | 1.12 | 3.54 – 7.92 | 5.13 | <0.001 |
| C6 | -5.72 | 1.30 | -8.26 – -3.17 | -4.41 | <0.001 |
| C7 | -14.95 | 1.28 | -17.45 – -12.44 | -11.71 | <0.001 |
| C8 | -21.60 | 1.30 | -24.16 – -19.04 | -16.55 | <0.001 |
| C9 | -16.52 | 1.11 | -18.69 – -14.36 | -14.95 | <0.001 |
| Random Effects | |||||
| σ2 | 391.88 | ||||
| τ00 id | 183.49 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.472 | ||||
summary(modC.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28382.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0398 -0.7447 -0.1323 0.6469 2.8048
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 172.9 13.15
## Residual 567.6 23.82
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 33.0394 0.5996 1006.0000 55.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.3,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 33.04 | 0.60 | 31.86 – 34.22 | 55.10 | <0.001 |
| Random Effects | |||||
| σ2 | 567.55 | ||||
| τ00 id | 172.87 | ||||
| ICC | 0.23 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.233 | ||||
anova(modC.3, modA.3)## refitting model(s) with ML (instead of REML)Support (Behavioral Intent)
modA.1 <- lmer(Support ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.1 <- lmer(Support ~ 1 + (1|id), data = L)
summary(modA.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27888.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2299 -0.5111 0.0607 0.5552 3.1022
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.7 17.60
## Residual 406.2 20.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.2345 0.6670 1016.3399 90.313 < 2e-16 ***
## C1 -7.7095 1.1375 2380.1041 -6.777 1.54e-11 ***
## C2 -9.3404 1.3268 2482.4889 -7.040 2.48e-12 ***
## C3 -9.9708 1.1705 2392.1093 -8.519 < 2e-16 ***
## C4 -5.8768 1.1650 2387.8451 -5.045 4.89e-07 ***
## C5 -10.0224 1.1568 2385.9393 -8.664 < 2e-16 ***
## C6 -0.7135 1.3490 2483.2374 -0.529 0.597
## C7 15.6042 1.3266 2480.5218 11.763 < 2e-16 ***
## C8 19.4142 1.3567 2483.7684 14.310 < 2e-16 ***
## C9 15.9948 1.1439 2382.9907 13.983 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.028
## C2 0.023 -0.092
## C3 -0.016 -0.115 -0.073
## C4 -0.018 -0.111 -0.098 -0.119
## C5 -0.021 -0.107 -0.085 -0.116 -0.109
## C6 0.030 -0.094 -0.171 -0.110 -0.096 -0.097
## C7 0.022 -0.080 -0.170 -0.094 -0.092 -0.097 -0.171
## C8 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.173 -0.172
## C9 -0.025 -0.111 -0.109 -0.110 -0.118 -0.112 -0.092 -0.088 -0.081tab_model(modA.1,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.23 | 0.67 | 58.93 – 61.54 | 90.31 | <0.001 |
| C1 | -7.71 | 1.14 | -9.94 – -5.48 | -6.78 | <0.001 |
| C2 | -9.34 | 1.33 | -11.94 – -6.74 | -7.04 | <0.001 |
| C3 | -9.97 | 1.17 | -12.27 – -7.68 | -8.52 | <0.001 |
| C4 | -5.88 | 1.16 | -8.16 – -3.59 | -5.04 | <0.001 |
| C5 | -10.02 | 1.16 | -12.29 – -7.75 | -8.66 | <0.001 |
| C6 | -0.71 | 1.35 | -3.36 – 1.93 | -0.53 | 0.597 |
| C7 | 15.60 | 1.33 | 13.00 – 18.21 | 11.76 | <0.001 |
| C8 | 19.41 | 1.36 | 16.75 – 22.07 | 14.31 | <0.001 |
| C9 | 15.99 | 1.14 | 13.75 – 18.24 | 13.98 | <0.001 |
| Random Effects | |||||
| σ2 | 406.25 | ||||
| τ00 id | 309.74 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.150 / 0.517 | ||||
summary(modC.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28574.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73591 -0.57170 0.06852 0.65552 2.42090
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 291.3 17.07
## Residual 546.1 23.37
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.5702 0.6856 1006.0000 86.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.1,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.57 | 0.69 | 58.23 – 60.91 | 86.89 | <0.001 |
| Random Effects | |||||
| σ2 | 546.14 | ||||
| τ00 id | 291.27 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.348 | ||||
anova(modC.1, modA.1)## refitting model(s) with ML (instead of REML)Understanding
modA.8 <- lmer(Understanding ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
modC.8 <- lmer(Understanding ~ 1 + (1|id), data = L)
summary(modA.8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Understanding ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 28489.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9390 -0.5471 0.0421 0.5826 3.2060
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 377.4 19.43
## Residual 355.7 18.86
## Number of obs: 3107, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.7078 0.7051 1006.2021 83.256 < 2e-16 ***
## C1 -13.2272 1.0864 2461.8225 -12.175 < 2e-16 ***
## C2 6.6323 1.2405 2434.0036 5.347 9.80e-08 ***
## C3 -7.1693 1.1147 2458.1470 -6.431 1.52e-10 ***
## C4 -13.4085 1.1121 2466.5262 -12.057 < 2e-16 ***
## C5 -15.5329 1.1016 2453.8665 -14.100 < 2e-16 ***
## C6 3.7536 1.2572 2419.6458 2.986 0.00286 **
## C7 12.5241 1.0707 2415.9724 11.697 < 2e-16 ***
## C8 24.6683 1.2620 2411.6040 19.547 < 2e-16 ***
## C9 12.5241 1.0707 2415.9724 11.697 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.036
## C2 0.027 -0.089
## C3 -0.022 -0.105 -0.071
## C4 -0.026 -0.099 -0.097 -0.109
## C5 -0.026 -0.094 -0.085 -0.108 -0.100
## C6 0.037 -0.095 -0.186 -0.114 -0.097 -0.100
## C7 0.007 -0.131 -0.134 -0.129 -0.139 -0.132 -0.116
## C8 0.042 -0.106 -0.188 -0.108 -0.096 -0.100 -0.189 -0.104
## C9 0.007 -0.131 -0.134 -0.129 -0.139 -0.132 -0.116 0.095 -0.104tab_model(modA.8,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.71 | 0.71 | 57.33 – 60.09 | 83.26 | <0.001 |
| C1 | -13.23 | 1.09 | -15.36 – -11.10 | -12.18 | <0.001 |
| C2 | 6.63 | 1.24 | 4.20 – 9.06 | 5.35 | <0.001 |
| C3 | -7.17 | 1.11 | -9.35 – -4.98 | -6.43 | <0.001 |
| C4 | -13.41 | 1.11 | -15.59 – -11.23 | -12.06 | <0.001 |
| C5 | -15.53 | 1.10 | -17.69 – -13.37 | -14.10 | <0.001 |
| C6 | 3.75 | 1.26 | 1.29 – 6.22 | 2.99 | 0.003 |
| C7 | 12.52 | 1.07 | 10.42 – 14.62 | 11.70 | <0.001 |
| C8 | 24.67 | 1.26 | 22.19 – 27.14 | 19.55 | <0.001 |
| C9 | 12.52 | 1.07 | 10.42 – 14.62 | 11.70 | <0.001 |
| Random Effects | |||||
| σ2 | 355.73 | ||||
| τ00 id | 377.43 | ||||
| ICC | 0.51 | ||||
| N id | 1007 | ||||
| Observations | 3107 | ||||
| Marginal R2 / Conditional R2 | 0.187 / 0.606 | ||||
summary(modC.8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Understanding ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 29465.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.02514 -0.64577 0.08639 0.61700 2.86170
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 360.5 18.99
## Residual 537.1 23.18
## Number of obs: 3107, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.9784 0.7323 992.7264 77.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.8,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.98 | 0.73 | 55.54 – 58.41 | 77.81 | <0.001 |
| Random Effects | |||||
| σ2 | 537.10 | ||||
| τ00 id | 360.49 | ||||
| ICC | 0.40 | ||||
| N id | 1007 | ||||
| Observations | 3107 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.402 | ||||
anova(modC.8, modA.8)## refitting model(s) with ML (instead of REML)Mixed Models
Support
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict support?
modA.71 <- lmer(Support ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.71)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27888.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2299 -0.5111 0.0607 0.5552 3.1022
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.7 17.60
## Residual 406.2 20.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.2345 0.6670 1016.3399 90.313 < 2e-16 ***
## C1 -7.7095 1.1375 2380.1041 -6.777 1.54e-11 ***
## C2 -9.3404 1.3268 2482.4889 -7.040 2.48e-12 ***
## C3 -9.9708 1.1705 2392.1093 -8.519 < 2e-16 ***
## C4 -5.8768 1.1650 2387.8451 -5.045 4.89e-07 ***
## C5 -10.0224 1.1568 2385.9393 -8.664 < 2e-16 ***
## C6 -0.7135 1.3490 2483.2374 -0.529 0.597
## C7 15.6042 1.3266 2480.5218 11.763 < 2e-16 ***
## C8 19.4142 1.3567 2483.7684 14.310 < 2e-16 ***
## C9 15.9948 1.1439 2382.9907 13.983 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.028
## C2 0.023 -0.092
## C3 -0.016 -0.115 -0.073
## C4 -0.018 -0.111 -0.098 -0.119
## C5 -0.021 -0.107 -0.085 -0.116 -0.109
## C6 0.030 -0.094 -0.171 -0.110 -0.096 -0.097
## C7 0.022 -0.080 -0.170 -0.094 -0.092 -0.097 -0.171
## C8 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.173 -0.172
## C9 -0.025 -0.111 -0.109 -0.110 -0.118 -0.112 -0.092 -0.088 -0.081tab_model(modA.71,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.23 | 0.67 | 58.93 – 61.54 | 90.31 | <0.001 |
| C1 | -7.71 | 1.14 | -9.94 – -5.48 | -6.78 | <0.001 |
| C2 | -9.34 | 1.33 | -11.94 – -6.74 | -7.04 | <0.001 |
| C3 | -9.97 | 1.17 | -12.27 – -7.68 | -8.52 | <0.001 |
| C4 | -5.88 | 1.16 | -8.16 – -3.59 | -5.04 | <0.001 |
| C5 | -10.02 | 1.16 | -12.29 – -7.75 | -8.66 | <0.001 |
| C6 | -0.71 | 1.35 | -3.36 – 1.93 | -0.53 | 0.597 |
| C7 | 15.60 | 1.33 | 13.00 – 18.21 | 11.76 | <0.001 |
| C8 | 19.41 | 1.36 | 16.75 – 22.07 | 14.31 | <0.001 |
| C9 | 15.99 | 1.14 | 13.75 – 18.24 | 13.98 | <0.001 |
| Random Effects | |||||
| σ2 | 406.25 | ||||
| τ00 id | 309.74 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.150 / 0.517 | ||||
Q.2: Does naturalness predict support, over and above climate change method contrasts?
modA.7 <- lmer(Support ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id), data = L)
summary(modA.7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27555.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5008 -0.5399 0.0313 0.5419 3.2984
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.4 17.16
## Residual 356.4 18.88
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.07125 0.64251 1015.84563 93.495 < 2e-16 ***
## Naturalness.c 0.44546 0.02349 2795.60249 18.961 < 2e-16 ***
## C1 -0.99636 1.12542 2415.54374 -0.885 0.37607
## C2 -2.77864 1.29367 2496.67042 -2.148 0.03182 *
## C3 -6.26532 1.11661 2383.86521 -5.611 2.24e-08 ***
## C4 -3.49410 1.10092 2369.62157 -3.174 0.00152 **
## C5 -7.91475 1.09171 2372.39824 -7.250 5.62e-13 ***
## C6 -0.28951 1.26777 2460.26450 -0.228 0.81939
## C7 9.42505 1.28868 2480.04873 7.314 3.49e-13 ***
## C8 12.62230 1.32421 2499.36106 9.532 < 2e-16 ***
## C9 6.37199 1.18798 2455.76384 5.364 8.92e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4 C5 C6 C7
## Naturlnss.c -0.013
## C1 -0.030 0.315
## C2 0.017 0.267 0.001
## C3 -0.017 0.176 -0.052 -0.021
## C4 -0.018 0.113 -0.070 -0.063 -0.097
## C5 -0.021 0.100 -0.069 -0.054 -0.097 -0.097
## C6 0.029 0.019 -0.083 -0.161 -0.105 -0.093 -0.094
## C7 0.024 -0.254 -0.152 -0.227 -0.133 -0.116 -0.118 -0.171
## C8 0.034 -0.271 -0.179 -0.233 -0.146 -0.119 -0.120 -0.172 -0.092
## C9 -0.016 -0.427 -0.230 -0.209 -0.173 -0.154 -0.144 -0.091 0.032
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.046tab_model(modA.7,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.07 | 0.64 | 58.81 – 61.33 | 93.50 | <0.001 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.49 | 18.96 | <0.001 |
| C1 | -1.00 | 1.13 | -3.20 – 1.21 | -0.89 | 0.376 |
| C2 | -2.78 | 1.29 | -5.32 – -0.24 | -2.15 | 0.032 |
| C3 | -6.27 | 1.12 | -8.45 – -4.08 | -5.61 | <0.001 |
| C4 | -3.49 | 1.10 | -5.65 – -1.34 | -3.17 | 0.002 |
| C5 | -7.91 | 1.09 | -10.06 – -5.77 | -7.25 | <0.001 |
| C6 | -0.29 | 1.27 | -2.78 – 2.20 | -0.23 | 0.819 |
| C7 | 9.43 | 1.29 | 6.90 – 11.95 | 7.31 | <0.001 |
| C8 | 12.62 | 1.32 | 10.03 – 15.22 | 9.53 | <0.001 |
| C9 | 6.37 | 1.19 | 4.04 – 8.70 | 5.36 | <0.001 |
| Random Effects | |||||
| σ2 | 356.37 | ||||
| τ00 id | 294.39 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.224 / 0.575 | ||||
Q.3: Does perceived naturalness predict support, over and above risk perception and climate change method contrasts?
modA.9 <- lmer(Support ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.9)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26638.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6736 -0.4985 0.0353 0.5226 3.9571
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 244.0 15.62
## Residual 252.4 15.89
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.79153 0.57248 1014.82481 104.443 < 2e-16 ***
## Naturalness.c 0.18833 0.02146 2788.91181 8.778 < 2e-16 ***
## Risk.c -0.55425 0.01681 2921.67549 -32.972 < 2e-16 ***
## C1 1.16611 0.95462 2378.05637 1.222 0.222003
## C2 4.86585 1.11999 2468.91997 4.345 1.45e-05 ***
## C3 -1.05074 0.95793 2358.02983 -1.097 0.272801
## C4 -1.74710 0.93245 2332.70023 -1.874 0.061101 .
## C5 -5.94283 0.92510 2335.63836 -6.424 1.60e-10 ***
## C6 -3.66618 1.07809 2417.08535 -3.401 0.000683 ***
## C7 4.50373 1.10182 2457.86421 4.088 4.50e-05 ***
## C8 4.70222 1.14726 2461.24057 4.099 4.29e-05 ***
## C9 2.72283 1.01192 2418.67540 2.691 0.007178 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3 C4 C5 C6
## Naturlnss.c -0.007
## Risk.c 0.015 0.363
## C1 -0.029 0.270 -0.069
## C2 0.013 0.170 -0.206 0.017
## C3 -0.019 0.102 -0.167 -0.040 0.017
## C4 -0.018 0.085 -0.055 -0.066 -0.049 -0.087
## C5 -0.021 0.071 -0.063 -0.065 -0.038 -0.085 -0.093
## C6 0.029 0.052 0.093 -0.087 -0.178 -0.118 -0.097 -0.098
## C7 0.025 -0.186 0.137 -0.159 -0.251 -0.153 -0.122 -0.125 -0.158
## C8 0.035 -0.172 0.209 -0.190 -0.268 -0.176 -0.126 -0.129 -0.150
## C9 -0.014 -0.358 0.110 -0.238 -0.228 -0.189 -0.161 -0.151 -0.079
## C7 C8
## Naturlnss.c
## Risk.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 -0.061
## C9 0.049 0.071tab_model(modA.9,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.79 | 0.57 | 58.67 – 60.91 | 104.44 | <0.001 |
| Naturalness c | 0.19 | 0.02 | 0.15 – 0.23 | 8.78 | <0.001 |
| Risk c | -0.55 | 0.02 | -0.59 – -0.52 | -32.97 | <0.001 |
| C1 | 1.17 | 0.95 | -0.71 – 3.04 | 1.22 | 0.222 |
| C2 | 4.87 | 1.12 | 2.67 – 7.06 | 4.34 | <0.001 |
| C3 | -1.05 | 0.96 | -2.93 – 0.83 | -1.10 | 0.273 |
| C4 | -1.75 | 0.93 | -3.58 – 0.08 | -1.87 | 0.061 |
| C5 | -5.94 | 0.93 | -7.76 – -4.13 | -6.42 | <0.001 |
| C6 | -3.67 | 1.08 | -5.78 – -1.55 | -3.40 | 0.001 |
| C7 | 4.50 | 1.10 | 2.34 – 6.66 | 4.09 | <0.001 |
| C8 | 4.70 | 1.15 | 2.45 – 6.95 | 4.10 | <0.001 |
| C9 | 2.72 | 1.01 | 0.74 – 4.71 | 2.69 | 0.007 |
| Random Effects | |||||
| σ2 | 252.41 | ||||
| τ00 id | 244.03 | ||||
| ICC | 0.49 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.407 / 0.698 | ||||
Q.4: Does perceived benefit predict behavioral intent, over and above naturalness and climate change method contrasts?
modA.10 <- lmer(Support ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.10)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26360.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8012 -0.5020 0.0076 0.5082 4.1558
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 166.9 12.92
## Residual 251.5 15.86
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.98546 0.50078 1010.09428 119.784 < 2e-16 ***
## Naturalness.c 0.30458 0.01976 2851.71099 15.415 < 2e-16 ***
## Benefit.c 0.57313 0.01486 3007.05817 38.576 < 2e-16 ***
## C1 -1.28362 0.93785 2465.07388 -1.369 0.171225
## C2 -5.74966 1.07898 2553.19087 -5.329 1.08e-07 ***
## C3 -4.88346 0.93165 2436.10395 -5.242 1.73e-07 ***
## C4 -2.61507 0.91854 2417.13603 -2.847 0.004451 **
## C5 -5.44603 0.91288 2422.79449 -5.966 2.79e-09 ***
## C6 3.86400 1.06069 2531.65727 3.643 0.000275 ***
## C7 7.02406 1.07420 2545.73917 6.539 7.46e-11 ***
## C8 9.77263 1.10413 2567.17871 8.851 < 2e-16 ***
## C9 3.42868 0.99210 2522.10203 3.456 0.000557 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c C1 C2 C3 C4 C5 C6
## Naturlnss.c -0.013
## Benefit.c -0.004 -0.185
## C1 -0.032 0.308 -0.009
## C2 0.020 0.272 -0.070 -0.002
## C3 -0.019 0.165 0.036 -0.052 -0.027
## C4 -0.020 0.105 0.027 -0.069 -0.066 -0.094
## C5 -0.023 0.084 0.072 -0.070 -0.061 -0.093 -0.094
## C6 0.031 0.001 0.099 -0.085 -0.164 -0.101 -0.091 -0.088
## C7 0.027 -0.237 -0.056 -0.153 -0.219 -0.136 -0.119 -0.123 -0.173
## C8 0.037 -0.250 -0.067 -0.177 -0.223 -0.148 -0.122 -0.125 -0.175
## C9 -0.018 -0.401 -0.077 -0.225 -0.201 -0.174 -0.154 -0.147 -0.100
## C7 C8
## Naturlnss.c
## Benefit.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 -0.086
## C9 0.033 0.046tab_model(modA.10,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.99 | 0.50 | 59.00 – 60.97 | 119.78 | <0.001 |
| Naturalness c | 0.30 | 0.02 | 0.27 – 0.34 | 15.41 | <0.001 |
| Benefit c | 0.57 | 0.01 | 0.54 – 0.60 | 38.58 | <0.001 |
| C1 | -1.28 | 0.94 | -3.12 – 0.56 | -1.37 | 0.171 |
| C2 | -5.75 | 1.08 | -7.87 – -3.63 | -5.33 | <0.001 |
| C3 | -4.88 | 0.93 | -6.71 – -3.06 | -5.24 | <0.001 |
| C4 | -2.62 | 0.92 | -4.42 – -0.81 | -2.85 | 0.004 |
| C5 | -5.45 | 0.91 | -7.24 – -3.66 | -5.97 | <0.001 |
| C6 | 3.86 | 1.06 | 1.78 – 5.94 | 3.64 | <0.001 |
| C7 | 7.02 | 1.07 | 4.92 – 9.13 | 6.54 | <0.001 |
| C8 | 9.77 | 1.10 | 7.61 – 11.94 | 8.85 | <0.001 |
| C9 | 3.43 | 0.99 | 1.48 – 5.37 | 3.46 | 0.001 |
| Random Effects | |||||
| σ2 | 251.48 | ||||
| τ00 id | 166.93 | ||||
| ICC | 0.40 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.490 / 0.693 | ||||
Q.5: Does perceived benefit predict support, over and above perceived risk, naturalness, and climate change method contrasts?
modA.101 <- lmer(Support ~ Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.101)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25689.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4550 -0.5095 0.0335 0.5025 3.7798
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.6 12.07
## Residual 195.7 13.99
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.79137 0.45919 1003.65962 130.211 < 2e-16 ***
## Naturalness.c 0.14095 0.01857 2863.29821 7.590 4.31e-14 ***
## Risk.c -0.41425 0.01501 2990.28692 -27.599 < 2e-16 ***
## Benefit.c 0.46634 0.01381 3007.84868 33.764 < 2e-16 ***
## C1 0.42091 0.83319 2432.47909 0.505 0.6135
## C2 0.55870 0.98316 2540.76780 0.568 0.5699
## C3 -1.17842 0.83619 2410.74493 -1.409 0.1589
## C4 -1.45967 0.81447 2383.74279 -1.792 0.0732 .
## C5 -4.42861 0.80929 2389.20448 -5.472 4.91e-08 ***
## C6 0.57440 0.94768 2502.85410 0.606 0.5445
## C7 3.80039 0.95962 2525.91992 3.960 7.69e-05 ***
## C8 4.29501 0.99893 2529.50674 4.300 1.78e-05 ***
## C9 1.20536 0.88309 2486.72827 1.365 0.1724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.101,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.79 | 0.46 | 58.89 – 60.69 | 130.21 | <0.001 |
| Naturalness c | 0.14 | 0.02 | 0.10 – 0.18 | 7.59 | <0.001 |
| Risk c | -0.41 | 0.02 | -0.44 – -0.38 | -27.60 | <0.001 |
| Benefit c | 0.47 | 0.01 | 0.44 – 0.49 | 33.76 | <0.001 |
| C1 | 0.42 | 0.83 | -1.21 – 2.05 | 0.51 | 0.613 |
| C2 | 0.56 | 0.98 | -1.37 – 2.49 | 0.57 | 0.570 |
| C3 | -1.18 | 0.84 | -2.82 – 0.46 | -1.41 | 0.159 |
| C4 | -1.46 | 0.81 | -3.06 – 0.14 | -1.79 | 0.073 |
| C5 | -4.43 | 0.81 | -6.02 – -2.84 | -5.47 | <0.001 |
| C6 | 0.57 | 0.95 | -1.28 – 2.43 | 0.61 | 0.544 |
| C7 | 3.80 | 0.96 | 1.92 – 5.68 | 3.96 | <0.001 |
| C8 | 4.30 | 1.00 | 2.34 – 6.25 | 4.30 | <0.001 |
| C9 | 1.21 | 0.88 | -0.53 – 2.94 | 1.36 | 0.172 |
| Random Effects | |||||
| σ2 | 195.74 | ||||
| τ00 id | 145.65 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.580 / 0.759 | ||||
Q.6: Does perceived familiarity/understanding predict support, over and above perceived benefit, risk, naturalness, and climate change method contrasts?
modA.115 <- lmer(Support ~ FR.c + Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | FR.c) + (1 | Naturalness.c) + (1 | Benefit.c) + (1 | id), data = L)## boundary (singular) fit: see ?isSingularsummary(modA.115)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ FR.c + Naturalness.c + Risk.c + Benefit.c + C1 + C2 +
## C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | FR.c) + (1 | Naturalness.c) +
## (1 | Benefit.c) + (1 | id)
## Data: L
##
## REML criterion at convergence: 25653.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4258 -0.5004 0.0275 0.5004 3.6767
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.424e+02 1.193e+01
## Naturalness.c (Intercept) 4.515e-01 6.719e-01
## FR.c (Intercept) 1.157e-07 3.402e-04
## Benefit.c (Intercept) 2.033e+00 1.426e+00
## Residual 1.915e+02 1.384e+01
## Number of obs: 3021, groups:
## id, 1007; Naturalness.c, 366; FR.c, 201; Benefit.c, 101
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.978e+01 4.924e-01 2.047e+02 121.416 < 2e-16 ***
## FR.c 9.418e-02 1.503e-02 2.980e+03 6.267 4.21e-10 ***
## Naturalness.c 1.166e-01 1.894e-02 4.039e+02 6.156 1.80e-09 ***
## Risk.c -4.034e-01 1.501e-02 2.986e+03 -26.870 < 2e-16 ***
## Benefit.c 4.619e-01 1.546e-02 1.264e+02 29.877 < 2e-16 ***
## C1 1.652e+00 8.524e-01 2.446e+03 1.938 0.05270 .
## C2 -1.245e+00 1.017e+00 2.621e+03 -1.225 0.22076
## C3 1.149e-02 8.506e-01 2.442e+03 0.014 0.98923
## C4 -1.468e-01 8.371e-01 2.422e+03 -0.175 0.86083
## C5 -2.522e+00 8.580e-01 2.516e+03 -2.940 0.00332 **
## C6 -7.311e-03 9.455e-01 2.507e+03 -0.008 0.99383
## C7 1.781e+00 1.009e+00 2.577e+03 1.766 0.07758 .
## C8 2.040e+00 1.058e+00 2.604e+03 1.927 0.05404 .
## C9 7.410e-01 8.806e-01 2.386e+03 0.842 0.40015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingulartab_model(modA.115,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.78 | 0.49 | 58.82 – 60.75 | 121.42 | <0.001 |
| FR c | 0.09 | 0.02 | 0.06 – 0.12 | 6.27 | <0.001 |
| Naturalness c | 0.12 | 0.02 | 0.08 – 0.15 | 6.16 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.87 | <0.001 |
| Benefit c | 0.46 | 0.02 | 0.43 – 0.49 | 29.88 | <0.001 |
| C1 | 1.65 | 0.85 | -0.02 – 3.32 | 1.94 | 0.053 |
| C2 | -1.25 | 1.02 | -3.24 – 0.75 | -1.22 | 0.221 |
| C3 | 0.01 | 0.85 | -1.66 – 1.68 | 0.01 | 0.989 |
| C4 | -0.15 | 0.84 | -1.79 – 1.49 | -0.18 | 0.861 |
| C5 | -2.52 | 0.86 | -4.20 – -0.84 | -2.94 | 0.003 |
| C6 | -0.01 | 0.95 | -1.86 – 1.85 | -0.01 | 0.994 |
| C7 | 1.78 | 1.01 | -0.20 – 3.76 | 1.77 | 0.078 |
| C8 | 2.04 | 1.06 | -0.04 – 4.11 | 1.93 | 0.054 |
| C9 | 0.74 | 0.88 | -0.99 – 2.47 | 0.84 | 0.400 |
| Random Effects | |||||
| σ2 | 191.48 | ||||
| τ00 id | 142.40 | ||||
| τ00 Naturalness.c | 0.45 | ||||
| τ00 FR.c | 0.00 | ||||
| τ00 Benefit.c | 2.03 | ||||
| ICC | 0.43 | ||||
| N FR.c | 201 | ||||
| N Naturalness.c | 366 | ||||
| N Benefit.c | 101 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.587 / 0.765 | ||||
Naturalness
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict naturalness perception?
modA.89 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.89)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 25882.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5623 -0.6131 -0.0219 0.6137 3.4124
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.98 8.123
## Residual 256.42 16.013
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.3544 0.3901 1027.4504 103.457 < 2e-16 ***
## C1 -14.9730 0.8706 2633.0772 -17.198 < 2e-16 ***
## C2 -14.4713 1.0060 2757.7982 -14.386 < 2e-16 ***
## C3 -8.4667 0.8948 2649.1062 -9.462 < 2e-16 ***
## C4 -5.4749 0.8910 2644.5116 -6.145 9.20e-10 ***
## C5 -4.6006 0.8848 2641.5512 -5.199 2.15e-07 ***
## C6 -1.0643 1.0226 2760.2000 -1.041 0.298
## C7 13.9641 1.0059 2756.7292 13.882 < 2e-16 ***
## C8 15.0008 1.0284 2761.1074 14.586 < 2e-16 ***
## C9 21.5355 0.8753 2636.2795 24.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.038
## C2 0.033 -0.100
## C3 -0.023 -0.107 -0.090
## C4 -0.025 -0.105 -0.104 -0.111
## C5 -0.029 -0.102 -0.096 -0.109 -0.104
## C6 0.043 -0.102 -0.155 -0.112 -0.104 -0.104
## C7 0.033 -0.093 -0.154 -0.102 -0.101 -0.103 -0.155
## C8 0.046 -0.108 -0.156 -0.109 -0.103 -0.105 -0.157 -0.156
## C9 -0.035 -0.103 -0.110 -0.104 -0.109 -0.105 -0.101 -0.098 -0.095tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 0.39 | 39.59 – 41.12 | 103.46 | <0.001 |
| C1 | -14.97 | 0.87 | -16.68 – -13.27 | -17.20 | <0.001 |
| C2 | -14.47 | 1.01 | -16.44 – -12.50 | -14.39 | <0.001 |
| C3 | -8.47 | 0.89 | -10.22 – -6.71 | -9.46 | <0.001 |
| C4 | -5.47 | 0.89 | -7.22 – -3.73 | -6.15 | <0.001 |
| C5 | -4.60 | 0.88 | -6.34 – -2.87 | -5.20 | <0.001 |
| C6 | -1.06 | 1.02 | -3.07 – 0.94 | -1.04 | 0.298 |
| C7 | 13.96 | 1.01 | 11.99 – 15.94 | 13.88 | <0.001 |
| C8 | 15.00 | 1.03 | 12.98 – 17.02 | 14.59 | <0.001 |
| C9 | 21.54 | 0.88 | 19.82 – 23.25 | 24.60 | <0.001 |
| Random Effects | |||||
| σ2 | 256.42 | ||||
| τ00 id | 65.98 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.310 / 0.451 | ||||
Q.2: Does understanding/familiarity (mean score) predict naturalness perception, over and above climate change method contrasts?
#Note: Understanding/familiarity mean score taken from two item measure.
modA.94 <- lmer(Naturalness ~ FR.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.94)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ FR.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25711.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6726 -0.6020 -0.0055 0.5870 3.4727
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 75.23 8.674
## Residual 233.53 15.282
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.93852 0.39309 1010.66033 101.600 < 2e-16 ***
## FR.c 0.19551 0.01429 2905.25270 13.678 < 2e-16 ***
## C1 -11.34817 0.87875 2670.25450 -12.914 < 2e-16 ***
## C2 -17.02598 0.98645 2731.53037 -17.260 < 2e-16 ***
## C3 -5.30225 0.89102 2658.68685 -5.951 3.02e-09 ***
## C4 -2.17434 0.88959 2649.34015 -2.444 0.0146 *
## C5 -0.32442 0.90701 2723.22672 -0.358 0.7206
## C6 -2.08562 0.98818 2705.34944 -2.111 0.0349 *
## C7 8.52841 1.04729 2812.13953 8.143 5.72e-16 ***
## C8 8.88119 1.08865 2843.00505 8.158 5.06e-16 ***
## C9 19.06727 0.86114 2634.03019 22.142 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c C1 C2 C3 C4 C5 C6 C7 C8
## FR.c -0.077
## C1 -0.058 0.303
## C2 0.044 -0.186 -0.148
## C3 -0.041 0.258 -0.022 -0.130
## C4 -0.043 0.268 -0.016 -0.147 -0.035
## C5 -0.052 0.346 0.013 -0.151 -0.011 -0.003
## C6 0.046 -0.076 -0.118 -0.141 -0.127 -0.119 -0.122
## C7 0.057 -0.379 -0.194 -0.072 -0.187 -0.190 -0.219 -0.118
## C8 0.072 -0.414 -0.218 -0.065 -0.202 -0.199 -0.231 -0.114 0.023
## C9 -0.016 -0.211 -0.162 -0.066 -0.154 -0.161 -0.170 -0.081 -0.007 0.005tab_model(modA.94,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.94 | 0.39 | 39.17 – 40.71 | 101.60 | <0.001 |
| FR c | 0.20 | 0.01 | 0.17 – 0.22 | 13.68 | <0.001 |
| C1 | -11.35 | 0.88 | -13.07 – -9.63 | -12.91 | <0.001 |
| C2 | -17.03 | 0.99 | -18.96 – -15.09 | -17.26 | <0.001 |
| C3 | -5.30 | 0.89 | -7.05 – -3.56 | -5.95 | <0.001 |
| C4 | -2.17 | 0.89 | -3.92 – -0.43 | -2.44 | 0.015 |
| C5 | -0.32 | 0.91 | -2.10 – 1.45 | -0.36 | 0.721 |
| C6 | -2.09 | 0.99 | -4.02 – -0.15 | -2.11 | 0.035 |
| C7 | 8.53 | 1.05 | 6.47 – 10.58 | 8.14 | <0.001 |
| C8 | 8.88 | 1.09 | 6.75 – 11.02 | 8.16 | <0.001 |
| C9 | 19.07 | 0.86 | 17.38 – 20.76 | 22.14 | <0.001 |
| Random Effects | |||||
| σ2 | 233.53 | ||||
| τ00 id | 75.23 | ||||
| ICC | 0.24 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.348 / 0.507 | ||||
Q.3: Does familiarity predict naturalness perception, over and above familiarity and climate change method contrasts?
modA.9433 <- lmer(Naturalness ~ Familiarity.c + Understanding.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.9433)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Familiarity.c + Understanding.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 24256
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7129 -0.6039 -0.0013 0.5788 3.4411
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 78.47 8.858
## Residual 227.11 15.070
## Number of obs: 2854, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.87336 0.41889 1152.16172 95.188 < 2e-16 ***
## Familiarity.c 0.13698 0.01531 2840.25728 8.950 < 2e-16 ***
## Understanding.c 0.05301 0.01530 2679.18271 3.465 0.000538 ***
## C1 -11.04399 0.89415 2547.18305 -12.351 < 2e-16 ***
## C2 -17.59892 1.00678 2554.27233 -17.480 < 2e-16 ***
## C3 -4.80210 0.92091 2547.26223 -5.215 1.99e-07 ***
## C4 -2.10442 0.89746 2518.90027 -2.345 0.019112 *
## C5 -0.06607 0.92410 2587.37355 -0.071 0.943009
## C6 -2.29141 0.99062 2502.12545 -2.313 0.020797 *
## C7 8.30957 1.63819 2624.31878 5.072 4.20e-07 ***
## C8 8.40101 1.11290 2661.49025 7.549 6.00e-14 ***
## C9 19.01212 0.85692 2385.63368 22.186 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Fmlrt. Undrs. C1 C2 C3 C4 C5 C6
## Familirty.c -0.064
## Undrstndng. 0.006 -0.553
## C1 -0.115 0.268 0.008
## C2 -0.001 -0.258 0.075 -0.145
## C3 -0.098 0.322 -0.092 0.040 -0.143
## C4 -0.100 0.187 0.056 0.023 -0.131 0.010
## C5 -0.107 0.305 0.009 0.064 -0.153 0.053 0.036
## C6 -0.003 -0.099 0.017 -0.100 -0.101 -0.114 -0.097 -0.107
## C7 0.295 -0.213 0.030 -0.262 -0.134 -0.261 -0.253 -0.272 -0.177
## C8 0.026 -0.358 -0.037 -0.213 -0.001 -0.211 -0.182 -0.231 -0.074
## C9 -0.042 -0.130 -0.081 -0.150 -0.042 -0.142 -0.147 -0.160 -0.062
## C7 C8
## Familirty.c
## Undrstndng.
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 -0.074
## C9 -0.069 0.030tab_model(modA.9433,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.87 | 0.42 | 39.05 – 40.69 | 95.19 | <0.001 |
| Familiarity c | 0.14 | 0.02 | 0.11 – 0.17 | 8.95 | <0.001 |
| Understanding c | 0.05 | 0.02 | 0.02 – 0.08 | 3.47 | 0.001 |
| C1 | -11.04 | 0.89 | -12.80 – -9.29 | -12.35 | <0.001 |
| C2 | -17.60 | 1.01 | -19.57 – -15.62 | -17.48 | <0.001 |
| C3 | -4.80 | 0.92 | -6.61 – -3.00 | -5.21 | <0.001 |
| C4 | -2.10 | 0.90 | -3.86 – -0.34 | -2.34 | 0.019 |
| C5 | -0.07 | 0.92 | -1.88 – 1.75 | -0.07 | 0.943 |
| C6 | -2.29 | 0.99 | -4.23 – -0.35 | -2.31 | 0.021 |
| C7 | 8.31 | 1.64 | 5.10 – 11.52 | 5.07 | <0.001 |
| C8 | 8.40 | 1.11 | 6.22 – 10.58 | 7.55 | <0.001 |
| C9 | 19.01 | 0.86 | 17.33 – 20.69 | 22.19 | <0.001 |
| Random Effects | |||||
| σ2 | 227.11 | ||||
| τ00 id | 78.47 | ||||
| ICC | 0.26 | ||||
| N id | 1007 | ||||
| Observations | 2854 | ||||
| Marginal R2 / Conditional R2 | 0.343 / 0.512 | ||||
Risk
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict risk perception?
modA.82 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.82)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27466.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5812 -0.6117 -0.0694 0.5565 3.6749
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Residual 391.9 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.4243 0.5609 1018.7068 57.808 < 2e-16 ***
## C1 10.8426 1.0990 2495.8856 9.866 < 2e-16 ***
## C2 20.5519 1.2763 2615.0096 16.103 < 2e-16 ***
## C3 13.5376 1.1303 2510.3315 11.977 < 2e-16 ***
## C4 5.6756 1.1252 2505.6400 5.044 4.88e-07 ***
## C5 5.7300 1.1173 2503.1806 5.128 3.15e-07 ***
## C6 -5.7162 1.2976 2616.5136 -4.405 1.10e-05 ***
## C7 -14.9462 1.2762 2613.2863 -11.711 < 2e-16 ***
## C8 -21.6013 1.3050 2617.2425 -16.553 < 2e-16 ***
## C9 -16.5218 1.1050 2499.0970 -14.952 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.033
## C2 0.027 -0.095
## C3 -0.019 -0.111 -0.081
## C4 -0.021 -0.108 -0.100 -0.115
## C5 -0.025 -0.105 -0.090 -0.113 -0.107
## C6 0.036 -0.097 -0.164 -0.111 -0.100 -0.100
## C7 0.027 -0.085 -0.163 -0.097 -0.096 -0.100 -0.164
## C8 0.039 -0.105 -0.165 -0.106 -0.097 -0.100 -0.166 -0.165
## C9 -0.030 -0.107 -0.109 -0.107 -0.114 -0.109 -0.096 -0.092 -0.087tab_model(modA.82,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.42 | 0.56 | 31.32 – 33.52 | 57.81 | <0.001 |
| C1 | 10.84 | 1.10 | 8.69 – 13.00 | 9.87 | <0.001 |
| C2 | 20.55 | 1.28 | 18.05 – 23.05 | 16.10 | <0.001 |
| C3 | 13.54 | 1.13 | 11.32 – 15.75 | 11.98 | <0.001 |
| C4 | 5.68 | 1.13 | 3.47 – 7.88 | 5.04 | <0.001 |
| C5 | 5.73 | 1.12 | 3.54 – 7.92 | 5.13 | <0.001 |
| C6 | -5.72 | 1.30 | -8.26 – -3.17 | -4.41 | <0.001 |
| C7 | -14.95 | 1.28 | -17.45 – -12.44 | -11.71 | <0.001 |
| C8 | -21.60 | 1.30 | -24.16 – -19.04 | -16.55 | <0.001 |
| C9 | -16.52 | 1.11 | -18.69 – -14.36 | -14.95 | <0.001 |
| Random Effects | |||||
| σ2 | 391.88 | ||||
| τ00 id | 183.49 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.472 | ||||
Q.2: Does naturalness predict risk perception, over and above climate change method contrasts?
modA.8 <- lmer(Risk ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27070.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3272 -0.6040 -0.0230 0.5668 3.7072
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.2 13.43
## Residual 333.2 18.25
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.5919 0.5400 1016.6237 60.353 < 2e-16 ***
## Naturalness.c -0.4576 0.0220 2923.4729 -20.801 < 2e-16 ***
## C1 3.9747 1.0710 2522.2842 3.711 0.000211 ***
## C2 13.8649 1.2273 2616.1275 11.297 < 2e-16 ***
## C3 9.6591 1.0639 2486.6509 9.079 < 2e-16 ***
## C4 3.1917 1.0495 2470.5141 3.041 0.002381 **
## C5 3.5831 1.0406 2473.4781 3.443 0.000584 ***
## C6 -6.1464 1.2044 2576.1166 -5.103 3.58e-07 ***
## C7 -8.6654 1.2233 2598.2314 -7.083 1.80e-12 ***
## C8 -14.6155 1.2561 2619.6555 -11.635 < 2e-16 ***
## C9 -6.6652 1.1288 2567.9794 -5.904 4.01e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4 C5 C6 C7
## Naturlnss.c -0.015
## C1 -0.034 0.309
## C2 0.021 0.262 -0.005
## C3 -0.020 0.174 -0.052 -0.029
## C4 -0.021 0.112 -0.069 -0.066 -0.094
## C5 -0.025 0.098 -0.069 -0.059 -0.095 -0.096
## C6 0.034 0.019 -0.086 -0.156 -0.105 -0.096 -0.097
## C7 0.028 -0.251 -0.154 -0.220 -0.135 -0.119 -0.120 -0.166
## C8 0.040 -0.266 -0.178 -0.225 -0.146 -0.122 -0.121 -0.167 -0.089
## C9 -0.020 -0.421 -0.224 -0.206 -0.170 -0.151 -0.140 -0.094 0.026
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.037tab_model(modA.8,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.59 | 0.54 | 31.53 – 33.65 | 60.35 | <0.001 |
| Naturalness c | -0.46 | 0.02 | -0.50 – -0.41 | -20.80 | <0.001 |
| C1 | 3.97 | 1.07 | 1.87 – 6.07 | 3.71 | <0.001 |
| C2 | 13.86 | 1.23 | 11.46 – 16.27 | 11.30 | <0.001 |
| C3 | 9.66 | 1.06 | 7.57 – 11.75 | 9.08 | <0.001 |
| C4 | 3.19 | 1.05 | 1.13 – 5.25 | 3.04 | 0.002 |
| C5 | 3.58 | 1.04 | 1.54 – 5.62 | 3.44 | 0.001 |
| C6 | -6.15 | 1.20 | -8.51 – -3.78 | -5.10 | <0.001 |
| C7 | -8.67 | 1.22 | -11.06 – -6.27 | -7.08 | <0.001 |
| C8 | -14.62 | 1.26 | -17.08 – -12.15 | -11.64 | <0.001 |
| C9 | -6.67 | 1.13 | -8.88 – -4.45 | -5.90 | <0.001 |
| Random Effects | |||||
| σ2 | 333.24 | ||||
| τ00 id | 180.23 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.312 / 0.554 | ||||
Q.3: Does benefit predict risk perception, over and above climate change method contrasts?
modA.88 <- lmer(Risk ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.88)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 27157.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4711 -0.6153 -0.0514 0.5895 3.5733
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.1 13.19
## Residual 348.6 18.67
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.49743 0.53913 1014.83213 60.278 < 2e-16 ***
## Benefit.c -0.30315 0.01662 2963.05117 -18.237 < 2e-16 ***
## C1 9.90027 1.04021 2481.76069 9.518 < 2e-16 ***
## C2 21.02454 1.20745 2595.07549 17.412 < 2e-16 ***
## C3 12.23551 1.07085 2500.31944 11.426 < 2e-16 ***
## C4 4.75139 1.06481 2488.48710 4.462 8.47e-06 ***
## C5 4.00716 1.06039 2489.75535 3.779 0.000161 ***
## C6 -7.87076 1.23312 2612.78201 -6.383 2.05e-10 ***
## C7 -12.75032 1.21330 2609.62119 -10.509 < 2e-16 ***
## C8 -18.96017 1.24263 2615.52667 -15.258 < 2e-16 ***
## C9 -13.37406 1.05882 2513.88731 -12.631 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c C1 C2 C3 C4 C5 C6 C7 C8
## Benefit.c -0.007
## C1 -0.032 0.050
## C2 0.027 -0.021 -0.096
## C3 -0.019 0.066 -0.108 -0.081
## C4 -0.021 0.047 -0.106 -0.101 -0.112
## C5 -0.025 0.089 -0.100 -0.091 -0.107 -0.103
## C6 0.034 0.097 -0.092 -0.167 -0.103 -0.094 -0.090
## C7 0.027 -0.101 -0.089 -0.161 -0.103 -0.099 -0.107 -0.173
## C8 0.039 -0.116 -0.110 -0.162 -0.113 -0.101 -0.109 -0.176 -0.152
## C9 -0.028 -0.163 -0.114 -0.104 -0.117 -0.120 -0.122 -0.110 -0.073 -0.066tab_model(modA.88,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.50 | 0.54 | 31.44 – 33.55 | 60.28 | <0.001 |
| Benefit c | -0.30 | 0.02 | -0.34 – -0.27 | -18.24 | <0.001 |
| C1 | 9.90 | 1.04 | 7.86 – 11.94 | 9.52 | <0.001 |
| C2 | 21.02 | 1.21 | 18.66 – 23.39 | 17.41 | <0.001 |
| C3 | 12.24 | 1.07 | 10.14 – 14.34 | 11.43 | <0.001 |
| C4 | 4.75 | 1.06 | 2.66 – 6.84 | 4.46 | <0.001 |
| C5 | 4.01 | 1.06 | 1.93 – 6.09 | 3.78 | <0.001 |
| C6 | -7.87 | 1.23 | -10.29 – -5.45 | -6.38 | <0.001 |
| C7 | -12.75 | 1.21 | -15.13 – -10.37 | -10.51 | <0.001 |
| C8 | -18.96 | 1.24 | -21.40 – -16.52 | -15.26 | <0.001 |
| C9 | -13.37 | 1.06 | -15.45 – -11.30 | -12.63 | <0.001 |
| Random Effects | |||||
| σ2 | 348.59 | ||||
| τ00 id | 174.09 | ||||
| ICC | 0.33 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.302 / 0.535 | ||||
Q.4: Does benefit predict risk perception, over and above naturalness and climate change method contrasts?
modA.99 <- lmer(Risk ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.99)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1133 -0.5867 -0.0161 0.5774 3.7285
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 175.8 13.26
## Residual 304.5 17.45
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.62968 0.52677 1011.71369 61.943 < 2e-16 ***
## Naturalness.c -0.39588 0.02151 2892.93622 -18.400 < 2e-16 ***
## Benefit.c -0.24981 0.01609 2994.53357 -15.529 < 2e-16 ***
## C1 4.12154 1.02646 2501.49389 4.015 6.11e-05 ***
## C2 15.14426 1.17969 2593.51166 12.837 < 2e-16 ***
## C3 9.09503 1.02001 2471.38800 8.917 < 2e-16 ***
## C4 2.76589 1.00587 2451.91888 2.750 0.00601 **
## C5 2.45397 0.99960 2457.63302 2.455 0.01416 *
## C6 -7.86802 1.16000 2571.01255 -6.783 1.46e-11 ***
## C7 -7.71224 1.17457 2585.57224 -6.566 6.22e-11 ***
## C8 -13.35513 1.20699 2607.60143 -11.065 < 2e-16 ***
## C9 -5.39958 1.08511 2560.07989 -4.976 6.92e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c C1 C2 C3 C4 C5 C6
## Naturlnss.c -0.014
## Benefit.c -0.005 -0.187
## C1 -0.034 0.306 -0.009
## C2 0.021 0.271 -0.071 -0.004
## C3 -0.020 0.165 0.035 -0.052 -0.030
## C4 -0.021 0.105 0.026 -0.069 -0.067 -0.094
## C5 -0.024 0.083 0.072 -0.070 -0.063 -0.092 -0.094
## C6 0.033 0.001 0.097 -0.086 -0.162 -0.101 -0.092 -0.089
## C7 0.028 -0.236 -0.054 -0.153 -0.216 -0.137 -0.120 -0.123 -0.171
## C8 0.039 -0.249 -0.065 -0.177 -0.221 -0.148 -0.123 -0.125 -0.173
## C9 -0.019 -0.399 -0.075 -0.223 -0.200 -0.172 -0.153 -0.145 -0.100
## C7 C8
## Naturlnss.c
## Benefit.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 -0.086
## C9 0.031 0.043tab_model(modA.99,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.63 | 0.53 | 31.60 – 33.66 | 61.94 | <0.001 |
| Naturalness c | -0.40 | 0.02 | -0.44 – -0.35 | -18.40 | <0.001 |
| Benefit c | -0.25 | 0.02 | -0.28 – -0.22 | -15.53 | <0.001 |
| C1 | 4.12 | 1.03 | 2.11 – 6.13 | 4.02 | <0.001 |
| C2 | 15.14 | 1.18 | 12.83 – 17.46 | 12.84 | <0.001 |
| C3 | 9.10 | 1.02 | 7.10 – 11.10 | 8.92 | <0.001 |
| C4 | 2.77 | 1.01 | 0.79 – 4.74 | 2.75 | 0.006 |
| C5 | 2.45 | 1.00 | 0.49 – 4.41 | 2.45 | 0.014 |
| C6 | -7.87 | 1.16 | -10.14 – -5.59 | -6.78 | <0.001 |
| C7 | -7.71 | 1.17 | -10.02 – -5.41 | -6.57 | <0.001 |
| C8 | -13.36 | 1.21 | -15.72 – -10.99 | -11.06 | <0.001 |
| C9 | -5.40 | 1.09 | -7.53 – -3.27 | -4.98 | <0.001 |
| Random Effects | |||||
| σ2 | 304.49 | ||||
| τ00 id | 175.78 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.364 / 0.597 | ||||
Q.5: Does understanding/familiarity predict risk perception, over and above naturalness, benefit, and climate change method contrasts?
modA.100 <- lmer(Risk ~ Naturalness.c + Benefit.c + FR.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.100)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + Benefit.c + FR.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26808.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5539 -0.6058 -0.0052 0.5672 3.9418
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.2 13.20
## Residual 299.9 17.32
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.86610 0.52502 1018.55041 62.600 < 2e-16 ***
## Naturalness.c -0.36268 0.02195 2934.01779 -16.520 < 2e-16 ***
## Benefit.c -0.23569 0.01612 2992.23158 -14.620 < 2e-16 ***
## FR.c -0.11835 0.01802 2992.51333 -6.569 5.96e-11 ***
## C1 2.45813 1.04989 2536.25081 2.341 0.0193 *
## C2 17.13221 1.20962 2663.12734 14.163 < 2e-16 ***
## C3 7.52683 1.04019 2512.50253 7.236 6.11e-13 ***
## C4 1.01914 1.03321 2506.20951 0.986 0.3240
## C5 0.07287 1.05631 2584.33815 0.069 0.9450
## C6 -7.11631 1.15719 2577.56329 -6.150 8.97e-10 ***
## C7 -4.99995 1.23706 2653.33980 -4.042 5.45e-05 ***
## C8 -10.23919 1.28841 2690.85251 -7.947 2.78e-15 ***
## C9 -4.74780 1.08171 2559.01865 -4.389 1.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.100,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.87 | 0.53 | 31.84 – 33.90 | 62.60 | <0.001 |
| Naturalness c | -0.36 | 0.02 | -0.41 – -0.32 | -16.52 | <0.001 |
| Benefit c | -0.24 | 0.02 | -0.27 – -0.20 | -14.62 | <0.001 |
| FR c | -0.12 | 0.02 | -0.15 – -0.08 | -6.57 | <0.001 |
| C1 | 2.46 | 1.05 | 0.40 – 4.52 | 2.34 | 0.019 |
| C2 | 17.13 | 1.21 | 14.76 – 19.50 | 14.16 | <0.001 |
| C3 | 7.53 | 1.04 | 5.49 – 9.57 | 7.24 | <0.001 |
| C4 | 1.02 | 1.03 | -1.01 – 3.05 | 0.99 | 0.324 |
| C5 | 0.07 | 1.06 | -2.00 – 2.14 | 0.07 | 0.945 |
| C6 | -7.12 | 1.16 | -9.39 – -4.85 | -6.15 | <0.001 |
| C7 | -5.00 | 1.24 | -7.43 – -2.57 | -4.04 | <0.001 |
| C8 | -10.24 | 1.29 | -12.77 – -7.71 | -7.95 | <0.001 |
| C9 | -4.75 | 1.08 | -6.87 – -2.63 | -4.39 | <0.001 |
| Random Effects | |||||
| σ2 | 299.90 | ||||
| τ00 id | 174.19 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.371 / 0.602 | ||||
Benefit
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict perceived benefit?
modA.109 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.109)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27683.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4246 -0.5150 0.0654 0.5678 3.1565
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.4 16.84
## Residual 381.8 19.54
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.2205 0.6407 1017.4032 90.874 < 2e-16 ***
## C1 -3.1843 1.1019 2386.9763 -2.890 0.003888 **
## C2 1.4717 1.2849 2490.3918 1.145 0.252168
## C3 -4.3571 1.1337 2399.1210 -3.843 0.000125 ***
## C4 -2.9518 1.1284 2394.8266 -2.616 0.008955 **
## C5 -5.5747 1.1205 2392.8913 -4.975 6.98e-07 ***
## C6 -7.3526 1.3064 2491.1763 -5.628 2.02e-08 ***
## C7 7.5302 1.2847 2488.4305 5.862 5.19e-09 ***
## C8 8.7549 1.3138 2491.7176 6.664 3.28e-11 ***
## C9 10.4528 1.1080 2389.8849 9.434 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.028
## C2 0.023 -0.092
## C3 -0.016 -0.115 -0.073
## C4 -0.018 -0.111 -0.098 -0.118
## C5 -0.021 -0.107 -0.085 -0.116 -0.109
## C6 0.031 -0.094 -0.171 -0.110 -0.096 -0.097
## C7 0.023 -0.080 -0.169 -0.094 -0.092 -0.097 -0.171
## C8 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.172 -0.171
## C9 -0.026 -0.110 -0.109 -0.110 -0.118 -0.111 -0.093 -0.088 -0.081tab_model(modA.109,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.22 | 0.64 | 56.96 – 59.48 | 90.87 | <0.001 |
| C1 | -3.18 | 1.10 | -5.34 – -1.02 | -2.89 | 0.004 |
| C2 | 1.47 | 1.28 | -1.05 – 3.99 | 1.15 | 0.252 |
| C3 | -4.36 | 1.13 | -6.58 – -2.13 | -3.84 | <0.001 |
| C4 | -2.95 | 1.13 | -5.16 – -0.74 | -2.62 | 0.009 |
| C5 | -5.57 | 1.12 | -7.77 – -3.38 | -4.98 | <0.001 |
| C6 | -7.35 | 1.31 | -9.91 – -4.79 | -5.63 | <0.001 |
| C7 | 7.53 | 1.28 | 5.01 – 10.05 | 5.86 | <0.001 |
| C8 | 8.75 | 1.31 | 6.18 – 11.33 | 6.66 | <0.001 |
| C9 | 10.45 | 1.11 | 8.28 – 12.63 | 9.43 | <0.001 |
| Random Effects | |||||
| σ2 | 381.81 | ||||
| τ00 id | 283.44 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.457 | ||||
Q.2: How does naturalness predict benefit, over and above climate change method contrasts?
modA.110 <- lmer(Ben ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.110)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27584.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4756 -0.5126 0.0537 0.5631 3.2650
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.1 16.34
## Residual 371.7 19.28
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.13083 0.62517 1014.30504 92.984 < 2e-16 ***
## Naturalness.c 0.24492 0.02375 2842.27358 10.313 < 2e-16 ***
## C1 0.51260 1.14354 2449.15335 0.448 0.654009
## C2 5.07346 1.31317 2535.18635 3.864 0.000115 ***
## C3 -2.29365 1.13504 2415.90554 -2.021 0.043413 *
## C4 -1.65703 1.11928 2400.92320 -1.480 0.138884
## C5 -4.43919 1.10987 2403.79126 -4.000 6.53e-05 ***
## C6 -7.08282 1.28747 2497.20915 -5.501 4.15e-08 ***
## C7 4.10696 1.30838 2517.95324 3.139 0.001715 **
## C8 5.01639 1.34413 2538.16006 3.732 0.000194 ***
## C9 5.15954 1.20652 2491.52085 4.276 1.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4 C5 C6 C7
## Naturlnss.c -0.014
## C1 -0.031 0.313
## C2 0.019 0.265 -0.001
## C3 -0.018 0.176 -0.052 -0.024
## C4 -0.019 0.112 -0.069 -0.064 -0.096
## C5 -0.022 0.100 -0.069 -0.055 -0.096 -0.096
## C6 0.031 0.019 -0.084 -0.159 -0.105 -0.094 -0.095
## C7 0.026 -0.253 -0.153 -0.225 -0.134 -0.117 -0.119 -0.170
## C8 0.036 -0.269 -0.179 -0.230 -0.146 -0.120 -0.120 -0.171 -0.091
## C9 -0.017 -0.425 -0.228 -0.208 -0.172 -0.153 -0.143 -0.092 0.030
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.043tab_model(modA.110,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.13 | 0.63 | 56.91 – 59.36 | 92.98 | <0.001 |
| Naturalness c | 0.24 | 0.02 | 0.20 – 0.29 | 10.31 | <0.001 |
| C1 | 0.51 | 1.14 | -1.73 – 2.75 | 0.45 | 0.654 |
| C2 | 5.07 | 1.31 | 2.50 – 7.65 | 3.86 | <0.001 |
| C3 | -2.29 | 1.14 | -4.52 – -0.07 | -2.02 | 0.043 |
| C4 | -1.66 | 1.12 | -3.85 – 0.54 | -1.48 | 0.139 |
| C5 | -4.44 | 1.11 | -6.62 – -2.26 | -4.00 | <0.001 |
| C6 | -7.08 | 1.29 | -9.61 – -4.56 | -5.50 | <0.001 |
| C7 | 4.11 | 1.31 | 1.54 – 6.67 | 3.14 | 0.002 |
| C8 | 5.02 | 1.34 | 2.38 – 7.65 | 3.73 | <0.001 |
| C9 | 5.16 | 1.21 | 2.79 – 7.53 | 4.28 | <0.001 |
| Random Effects | |||||
| σ2 | 371.70 | ||||
| τ00 id | 267.05 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.082 / 0.466 | ||||
Q.3: How does risk perception predict benefit, over and above climate change method contrasts?
modA.113 <- lmer(Ben ~ Risk.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 27367.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5767 -0.5166 0.0713 0.5419 3.2054
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.8 16.27
## Residual 339.2 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.99879 0.61453 1017.59674 94.379 < 2e-16 ***
## Risk.c -0.32860 0.01781 2947.72272 -18.451 < 2e-16 ***
## C1 0.39429 1.05821 2403.22100 0.373 0.709480
## C2 8.21888 1.26760 2526.86263 6.484 1.07e-10 ***
## C3 0.04484 1.09679 2412.11753 0.041 0.967392
## C4 -1.12268 1.06990 2384.12634 -1.049 0.294131
## C5 -3.70706 1.06259 2384.19349 -3.489 0.000494 ***
## C6 -9.19352 1.23778 2475.99282 -7.427 1.52e-13 ***
## C7 2.52342 1.24352 2525.84295 2.029 0.042538 *
## C8 1.72404 1.29829 2514.77457 1.328 0.184322
## C9 5.00702 1.08694 2436.59516 4.607 4.30e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c C1 C2 C3 C4 C5 C6 C7 C8
## Risk.c 0.020
## C1 -0.031 -0.184
## C2 0.016 -0.289 -0.033
## C3 -0.020 -0.218 -0.070 -0.005
## C4 -0.019 -0.092 -0.092 -0.066 -0.095
## C5 -0.022 -0.095 -0.087 -0.053 -0.093 -0.100
## C6 0.032 0.080 -0.107 -0.187 -0.124 -0.103 -0.104
## C7 0.026 0.219 -0.116 -0.222 -0.137 -0.109 -0.115 -0.149
## C8 0.037 0.294 -0.151 -0.243 -0.161 -0.115 -0.120 -0.141 -0.096
## C9 -0.019 0.272 -0.155 -0.179 -0.162 -0.138 -0.133 -0.067 -0.023 0.006tab_model(modA.113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.00 | 0.61 | 56.79 – 59.20 | 94.38 | <0.001 |
| Risk c | -0.33 | 0.02 | -0.36 – -0.29 | -18.45 | <0.001 |
| C1 | 0.39 | 1.06 | -1.68 – 2.47 | 0.37 | 0.709 |
| C2 | 8.22 | 1.27 | 5.73 – 10.70 | 6.48 | <0.001 |
| C3 | 0.04 | 1.10 | -2.11 – 2.20 | 0.04 | 0.967 |
| C4 | -1.12 | 1.07 | -3.22 – 0.98 | -1.05 | 0.294 |
| C5 | -3.71 | 1.06 | -5.79 – -1.62 | -3.49 | <0.001 |
| C6 | -9.19 | 1.24 | -11.62 – -6.77 | -7.43 | <0.001 |
| C7 | 2.52 | 1.24 | 0.09 – 4.96 | 2.03 | 0.043 |
| C8 | 1.72 | 1.30 | -0.82 – 4.27 | 1.33 | 0.184 |
| C9 | 5.01 | 1.09 | 2.88 – 7.14 | 4.61 | <0.001 |
| Random Effects | |||||
| σ2 | 339.21 | ||||
| τ00 id | 264.75 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.141 / 0.518 | ||||
Q.4: How does risk perception predict benefit, over and above naturalness and climate change method contrasts?
modA.114 <- lmer(Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27354.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5827 -0.5132 0.0670 0.5443 3.3001
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 258.0 16.06
## Residual 339.3 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.98005 0.60907 1012.45913 95.195 < 2e-16 ***
## Naturalness.c 0.10591 0.02442 2869.77305 4.337 1.49e-05 ***
## Risk.c -0.29888 0.01904 2978.41274 -15.697 < 2e-16 ***
## C1 1.66980 1.09741 2435.23479 1.522 0.12825
## C2 9.16876 1.28488 2536.11022 7.136 1.25e-12 ***
## C3 0.54201 1.10170 2413.06293 0.492 0.62278
## C4 -0.72811 1.07300 2385.32406 -0.679 0.49747
## C5 -3.38484 1.06447 2388.48994 -3.180 0.00149 **
## C6 -8.91009 1.23825 2479.90081 -7.196 8.19e-13 ***
## C7 1.49863 1.26435 2523.79863 1.185 0.23601
## C8 0.73745 1.31638 2528.12689 0.560 0.57539
## C9 3.21040 1.16223 2480.08350 2.762 0.00578 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3 C4 C5 C6
## Naturlnss.c -0.007
## Risk.c 0.016 0.359
## C1 -0.032 0.268 -0.069
## C2 0.015 0.170 -0.204 0.013
## C3 -0.020 0.102 -0.165 -0.040 0.012
## C4 -0.020 0.085 -0.055 -0.066 -0.051 -0.086
## C5 -0.023 0.071 -0.063 -0.065 -0.041 -0.085 -0.093
## C6 0.031 0.051 0.093 -0.089 -0.175 -0.118 -0.098 -0.100
## C7 0.027 -0.186 0.133 -0.160 -0.246 -0.153 -0.123 -0.126 -0.156
## C8 0.038 -0.171 0.209 -0.189 -0.264 -0.175 -0.128 -0.130 -0.148
## C9 -0.015 -0.356 0.109 -0.234 -0.225 -0.187 -0.159 -0.149 -0.081
## C7 C8
## Naturlnss.c
## Risk.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 -0.061
## C9 0.045 0.066tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.98 | 0.61 | 56.79 – 59.17 | 95.19 | <0.001 |
| Naturalness c | 0.11 | 0.02 | 0.06 – 0.15 | 4.34 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.34 – -0.26 | -15.70 | <0.001 |
| C1 | 1.67 | 1.10 | -0.48 – 3.82 | 1.52 | 0.128 |
| C2 | 9.17 | 1.28 | 6.65 – 11.69 | 7.14 | <0.001 |
| C3 | 0.54 | 1.10 | -1.62 – 2.70 | 0.49 | 0.623 |
| C4 | -0.73 | 1.07 | -2.83 – 1.38 | -0.68 | 0.497 |
| C5 | -3.38 | 1.06 | -5.47 – -1.30 | -3.18 | 0.001 |
| C6 | -8.91 | 1.24 | -11.34 – -6.48 | -7.20 | <0.001 |
| C7 | 1.50 | 1.26 | -0.98 – 3.98 | 1.19 | 0.236 |
| C8 | 0.74 | 1.32 | -1.84 – 3.32 | 0.56 | 0.575 |
| C9 | 3.21 | 1.16 | 0.93 – 5.49 | 2.76 | 0.006 |
| Random Effects | |||||
| σ2 | 339.29 | ||||
| τ00 id | 257.97 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.514 | ||||
Q.5: How does risk perception predict benefit, over and above naturalness and climate change method contrasts?
modA.114 <- lmer(Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27354.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5827 -0.5132 0.0670 0.5443 3.3001
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 258.0 16.06
## Residual 339.3 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.98005 0.60907 1012.45913 95.195 < 2e-16 ***
## Naturalness.c 0.10591 0.02442 2869.77305 4.337 1.49e-05 ***
## Risk.c -0.29888 0.01904 2978.41274 -15.697 < 2e-16 ***
## C1 1.66980 1.09741 2435.23479 1.522 0.12825
## C2 9.16876 1.28488 2536.11022 7.136 1.25e-12 ***
## C3 0.54201 1.10170 2413.06293 0.492 0.62278
## C4 -0.72811 1.07300 2385.32406 -0.679 0.49747
## C5 -3.38484 1.06447 2388.48994 -3.180 0.00149 **
## C6 -8.91009 1.23825 2479.90081 -7.196 8.19e-13 ***
## C7 1.49863 1.26435 2523.79863 1.185 0.23601
## C8 0.73745 1.31638 2528.12689 0.560 0.57539
## C9 3.21040 1.16223 2480.08350 2.762 0.00578 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3 C4 C5 C6
## Naturlnss.c -0.007
## Risk.c 0.016 0.359
## C1 -0.032 0.268 -0.069
## C2 0.015 0.170 -0.204 0.013
## C3 -0.020 0.102 -0.165 -0.040 0.012
## C4 -0.020 0.085 -0.055 -0.066 -0.051 -0.086
## C5 -0.023 0.071 -0.063 -0.065 -0.041 -0.085 -0.093
## C6 0.031 0.051 0.093 -0.089 -0.175 -0.118 -0.098 -0.100
## C7 0.027 -0.186 0.133 -0.160 -0.246 -0.153 -0.123 -0.126 -0.156
## C8 0.038 -0.171 0.209 -0.189 -0.264 -0.175 -0.128 -0.130 -0.148
## C9 -0.015 -0.356 0.109 -0.234 -0.225 -0.187 -0.159 -0.149 -0.081
## C7 C8
## Naturlnss.c
## Risk.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 -0.061
## C9 0.045 0.066tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.98 | 0.61 | 56.79 – 59.17 | 95.19 | <0.001 |
| Naturalness c | 0.11 | 0.02 | 0.06 – 0.15 | 4.34 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.34 – -0.26 | -15.70 | <0.001 |
| C1 | 1.67 | 1.10 | -0.48 – 3.82 | 1.52 | 0.128 |
| C2 | 9.17 | 1.28 | 6.65 – 11.69 | 7.14 | <0.001 |
| C3 | 0.54 | 1.10 | -1.62 – 2.70 | 0.49 | 0.623 |
| C4 | -0.73 | 1.07 | -2.83 – 1.38 | -0.68 | 0.497 |
| C5 | -3.38 | 1.06 | -5.47 – -1.30 | -3.18 | 0.001 |
| C6 | -8.91 | 1.24 | -11.34 – -6.48 | -7.20 | <0.001 |
| C7 | 1.50 | 1.26 | -0.98 – 3.98 | 1.19 | 0.236 |
| C8 | 0.74 | 1.32 | -1.84 – 3.32 | 0.56 | 0.575 |
| C9 | 3.21 | 1.16 | 0.93 – 5.49 | 2.76 | 0.006 |
| Random Effects | |||||
| σ2 | 339.29 | ||||
| τ00 id | 257.97 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.514 | ||||
Q.6: How does understanding/familiarity predict benefit, over and above risk perception, naturalness, and climate change method contrasts?
modA.117 <- lmer(Ben ~ FR.c + Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.117)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ FR.c + Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27330.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6121 -0.4980 0.0692 0.5470 3.3281
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 256.9 16.03
## Residual 335.6 18.32
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.77166 0.60832 1016.77851 94.969 < 2e-16 ***
## FR.c 0.10753 0.01972 3007.14629 5.452 5.37e-08 ***
## Naturalness.c 0.07893 0.02479 2896.03309 3.184 0.00147 **
## Risk.c -0.28313 0.01917 2978.72943 -14.770 < 2e-16 ***
## C1 3.09707 1.12269 2464.89329 2.759 0.00585 **
## C2 7.06107 1.33519 2615.46207 5.288 1.34e-07 ***
## C3 1.83432 1.12134 2445.74170 1.636 0.10200
## C4 0.82275 1.10459 2435.97833 0.745 0.45643
## C5 -1.21656 1.13102 2512.02166 -1.076 0.28219
## C6 -9.40672 1.23508 2480.43867 -7.616 3.69e-14 ***
## C7 -0.86640 1.33062 2580.74909 -0.651 0.51502
## C8 -1.91176 1.39701 2604.22658 -1.368 0.17129
## C9 2.67142 1.16040 2478.46802 2.302 0.02141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.117,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.77 | 0.61 | 56.58 – 58.96 | 94.97 | <0.001 |
| FR c | 0.11 | 0.02 | 0.07 – 0.15 | 5.45 | <0.001 |
| Naturalness c | 0.08 | 0.02 | 0.03 – 0.13 | 3.18 | 0.001 |
| Risk c | -0.28 | 0.02 | -0.32 – -0.25 | -14.77 | <0.001 |
| C1 | 3.10 | 1.12 | 0.90 – 5.30 | 2.76 | 0.006 |
| C2 | 7.06 | 1.34 | 4.44 – 9.68 | 5.29 | <0.001 |
| C3 | 1.83 | 1.12 | -0.36 – 4.03 | 1.64 | 0.102 |
| C4 | 0.82 | 1.10 | -1.34 – 2.99 | 0.74 | 0.456 |
| C5 | -1.22 | 1.13 | -3.43 – 1.00 | -1.08 | 0.282 |
| C6 | -9.41 | 1.24 | -11.83 – -6.99 | -7.62 | <0.001 |
| C7 | -0.87 | 1.33 | -3.48 – 1.74 | -0.65 | 0.515 |
| C8 | -1.91 | 1.40 | -4.65 – 0.83 | -1.37 | 0.171 |
| C9 | 2.67 | 1.16 | 0.40 – 4.95 | 2.30 | 0.021 |
| Random Effects | |||||
| σ2 | 335.56 | ||||
| τ00 id | 256.86 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.151 / 0.519 | ||||
Difference Benefit - Risk
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict the difference between perceived benefit and risk?
modA.118 <- lmer(BRDiff ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.118)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30498.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8901 -0.5430 0.0446 0.5733 3.1036
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 584.4 24.17
## Residual 1032.4 32.13
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.7958 0.9639 1018.4237 26.762 < 2e-16 ***
## C1 -14.0197 1.7957 2450.9270 -7.808 8.57e-15 ***
## C2 -19.0289 2.0889 2564.5205 -9.110 < 2e-16 ***
## C3 -17.7845 1.8471 2464.5022 -9.628 < 2e-16 ***
## C4 -8.6096 1.8386 2459.9287 -4.683 2.98e-06 ***
## C5 -11.3284 1.8257 2457.6798 -6.205 6.40e-10 ***
## C6 -1.5867 2.1238 2565.7134 -0.747 0.455
## C7 22.5093 2.0886 2562.6624 10.777 < 2e-16 ***
## C8 30.2190 2.1358 2566.3661 14.149 < 2e-16 ***
## C9 26.9722 1.8055 2454.0429 14.939 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.031
## C2 0.025 -0.094
## C3 -0.018 -0.113 -0.078
## C4 -0.020 -0.110 -0.099 -0.117
## C5 -0.023 -0.105 -0.088 -0.115 -0.108
## C6 0.034 -0.096 -0.167 -0.110 -0.098 -0.099
## C7 0.025 -0.083 -0.166 -0.096 -0.094 -0.099 -0.167
## C8 0.037 -0.104 -0.168 -0.105 -0.095 -0.099 -0.169 -0.167
## C9 -0.028 -0.109 -0.109 -0.108 -0.115 -0.110 -0.095 -0.091 -0.085tab_model(modA.118,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.80 | 0.96 | 23.91 – 27.69 | 26.76 | <0.001 |
| C1 | -14.02 | 1.80 | -17.54 – -10.50 | -7.81 | <0.001 |
| C2 | -19.03 | 2.09 | -23.12 – -14.93 | -9.11 | <0.001 |
| C3 | -17.78 | 1.85 | -21.41 – -14.16 | -9.63 | <0.001 |
| C4 | -8.61 | 1.84 | -12.21 – -5.00 | -4.68 | <0.001 |
| C5 | -11.33 | 1.83 | -14.91 – -7.75 | -6.20 | <0.001 |
| C6 | -1.59 | 2.12 | -5.75 – 2.58 | -0.75 | 0.455 |
| C7 | 22.51 | 2.09 | 18.41 – 26.60 | 10.78 | <0.001 |
| C8 | 30.22 | 2.14 | 26.03 – 34.41 | 14.15 | <0.001 |
| C9 | 26.97 | 1.81 | 23.43 – 30.51 | 14.94 | <0.001 |
| Random Effects | |||||
| σ2 | 1032.43 | ||||
| τ00 id | 584.41 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.466 | ||||
Q.2: How does naturalness predict the difference between perceived benefit and risk, over and above climate change method contrasts?
modA.11 <- lmer(BRDiff ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30151.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3940 -0.5391 0.0308 0.5727 2.9142
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 532.1 23.07
## Residual 913.3 30.22
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.53690 0.91491 1017.84864 27.912 < 2e-16 ***
## Naturalness.c 0.70784 0.03663 2904.80064 19.323 < 2e-16 ***
## C1 -3.36870 1.77822 2504.30510 -1.894 0.05828 .
## C2 -8.67566 2.03880 2596.19694 -4.255 2.16e-05 ***
## C3 -11.83652 1.76602 2469.26459 -6.702 2.53e-11 ***
## C4 -4.82208 1.74196 2453.41655 -2.768 0.00568 **
## C5 -8.02781 1.72724 2456.36087 -4.648 3.53e-06 ***
## C6 -0.86676 2.00028 2556.65812 -0.433 0.66482
## C7 12.67374 2.03201 2578.44118 6.237 5.19e-10 ***
## C8 19.47237 2.08675 2599.57655 9.331 < 2e-16 ***
## C9 11.69956 1.87472 2549.16909 6.241 5.09e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4 C5 C6 C7
## Naturlnss.c -0.015
## C1 -0.034 0.310
## C2 0.020 0.263 -0.004
## C3 -0.020 0.174 -0.052 -0.027
## C4 -0.021 0.112 -0.069 -0.066 -0.095
## C5 -0.024 0.099 -0.069 -0.058 -0.095 -0.096
## C6 0.033 0.019 -0.085 -0.157 -0.105 -0.095 -0.096
## C7 0.028 -0.251 -0.154 -0.221 -0.135 -0.119 -0.120 -0.167
## C8 0.039 -0.266 -0.178 -0.227 -0.146 -0.121 -0.121 -0.168 -0.090
## C9 -0.019 -0.422 -0.225 -0.207 -0.170 -0.152 -0.141 -0.094 0.027
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.039tab_model(modA.11,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.54 | 0.91 | 23.74 – 27.33 | 27.91 | <0.001 |
| Naturalness c | 0.71 | 0.04 | 0.64 – 0.78 | 19.32 | <0.001 |
| C1 | -3.37 | 1.78 | -6.86 – 0.12 | -1.89 | 0.058 |
| C2 | -8.68 | 2.04 | -12.67 – -4.68 | -4.26 | <0.001 |
| C3 | -11.84 | 1.77 | -15.30 – -8.37 | -6.70 | <0.001 |
| C4 | -4.82 | 1.74 | -8.24 – -1.41 | -2.77 | 0.006 |
| C5 | -8.03 | 1.73 | -11.41 – -4.64 | -4.65 | <0.001 |
| C6 | -0.87 | 2.00 | -4.79 – 3.06 | -0.43 | 0.665 |
| C7 | 12.67 | 2.03 | 8.69 – 16.66 | 6.24 | <0.001 |
| C8 | 19.47 | 2.09 | 15.38 – 23.56 | 9.33 | <0.001 |
| C9 | 11.70 | 1.87 | 8.02 – 15.38 | 6.24 | <0.001 |
| Random Effects | |||||
| σ2 | 913.26 | ||||
| τ00 id | 532.06 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.247 / 0.524 | ||||
Familiarity/Understanding (Mean score)
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict understanding and familiarity (mean score)?
modA.12 <- lmer(FR ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27119.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0412 -0.5869 -0.0111 0.5966 3.1013
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.7 14.31
## Residual 329.5 18.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.4788 0.5608 1019.6835 97.137 < 2e-16 ***
## C1 -18.6588 1.0176 2429.9000 -18.337 < 2e-16 ***
## C2 12.8814 1.1847 2540.1072 10.873 < 2e-16 ***
## C3 -16.1090 1.0468 2442.9867 -15.389 < 2e-16 ***
## C4 -16.5934 1.0420 2438.5014 -15.925 < 2e-16 ***
## C5 -22.1040 1.0346 2436.3623 -21.364 < 2e-16 ***
## C6 5.1720 1.2045 2541.1519 4.294 1.82e-05 ***
## C7 27.7437 1.1846 2538.2041 23.421 < 2e-16 ***
## C8 31.6044 1.2114 2541.7653 26.090 < 2e-16 ***
## C9 12.8248 1.0232 2432.9494 12.534 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.030
## C2 0.024 -0.093
## C3 -0.017 -0.113 -0.076
## C4 -0.019 -0.110 -0.099 -0.117
## C5 -0.022 -0.106 -0.087 -0.115 -0.108
## C6 0.033 -0.095 -0.169 -0.110 -0.098 -0.098
## C7 0.024 -0.082 -0.167 -0.095 -0.093 -0.098 -0.168
## C8 0.036 -0.104 -0.169 -0.105 -0.095 -0.098 -0.170 -0.169
## C9 -0.027 -0.109 -0.109 -0.109 -0.116 -0.110 -0.094 -0.090 -0.083tab_model(modA.12,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.48 | 0.56 | 53.38 – 55.58 | 97.14 | <0.001 |
| C1 | -18.66 | 1.02 | -20.65 – -16.66 | -18.34 | <0.001 |
| C2 | 12.88 | 1.18 | 10.56 – 15.20 | 10.87 | <0.001 |
| C3 | -16.11 | 1.05 | -18.16 – -14.06 | -15.39 | <0.001 |
| C4 | -16.59 | 1.04 | -18.64 – -14.55 | -15.93 | <0.001 |
| C5 | -22.10 | 1.03 | -24.13 – -20.08 | -21.36 | <0.001 |
| C6 | 5.17 | 1.20 | 2.81 – 7.53 | 4.29 | <0.001 |
| C7 | 27.74 | 1.18 | 25.42 – 30.07 | 23.42 | <0.001 |
| C8 | 31.60 | 1.21 | 29.23 – 33.98 | 26.09 | <0.001 |
| C9 | 12.82 | 1.02 | 10.82 – 14.83 | 12.53 | <0.001 |
| Random Effects | |||||
| σ2 | 329.45 | ||||
| τ00 id | 204.67 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.403 / 0.632 | ||||
Q.2: How does naturalness predict understanding and familiarity (mean score), over and above climate change method contrasts?
modA.130 <- lmer(FR ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.130)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26915.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9409 -0.5696 0.0042 0.5983 3.1937
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213.5 14.61
## Residual 297.7 17.25
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.36322 0.55913 1014.65701 97.229 < 2e-16 ***
## Naturalness.c 0.31552 0.02125 2843.02165 14.848 < 2e-16 ***
## C1 -13.90200 1.02337 2449.90959 -13.585 < 2e-16 ***
## C2 17.52007 1.17515 2535.98030 14.909 < 2e-16 ***
## C3 -13.46299 1.01576 2416.65135 -13.254 < 2e-16 ***
## C4 -14.89955 1.00166 2401.66368 -14.875 < 2e-16 ***
## C5 -20.66833 0.99325 2404.53197 -20.809 < 2e-16 ***
## C6 5.48603 1.15217 2497.99616 4.761 2.03e-06 ***
## C7 23.34344 1.17088 2518.74575 19.937 < 2e-16 ***
## C8 26.81364 1.20286 2538.95709 22.292 < 2e-16 ***
## C9 6.03803 1.07972 2492.29200 5.592 2.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4 C5 C6 C7
## Naturlnss.c -0.014
## C1 -0.031 0.313
## C2 0.019 0.265 -0.001
## C3 -0.018 0.176 -0.052 -0.024
## C4 -0.019 0.112 -0.069 -0.064 -0.096
## C5 -0.022 0.100 -0.069 -0.055 -0.096 -0.096
## C6 0.031 0.019 -0.084 -0.159 -0.105 -0.094 -0.095
## C7 0.026 -0.253 -0.153 -0.225 -0.134 -0.117 -0.119 -0.170
## C8 0.036 -0.269 -0.179 -0.230 -0.146 -0.120 -0.120 -0.171 -0.091
## C9 -0.017 -0.425 -0.228 -0.208 -0.172 -0.153 -0.143 -0.092 0.030
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.043tab_model(modA.130,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.36 | 0.56 | 53.27 – 55.46 | 97.23 | <0.001 |
| Naturalness c | 0.32 | 0.02 | 0.27 – 0.36 | 14.85 | <0.001 |
| C1 | -13.90 | 1.02 | -15.91 – -11.90 | -13.58 | <0.001 |
| C2 | 17.52 | 1.18 | 15.22 – 19.82 | 14.91 | <0.001 |
| C3 | -13.46 | 1.02 | -15.45 – -11.47 | -13.25 | <0.001 |
| C4 | -14.90 | 1.00 | -16.86 – -12.94 | -14.87 | <0.001 |
| C5 | -20.67 | 0.99 | -22.62 – -18.72 | -20.81 | <0.001 |
| C6 | 5.49 | 1.15 | 3.23 – 7.75 | 4.76 | <0.001 |
| C7 | 23.34 | 1.17 | 21.05 – 25.64 | 19.94 | <0.001 |
| C8 | 26.81 | 1.20 | 24.46 – 29.17 | 22.29 | <0.001 |
| C9 | 6.04 | 1.08 | 3.92 – 8.16 | 5.59 | <0.001 |
| Random Effects | |||||
| σ2 | 297.73 | ||||
| τ00 id | 213.46 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.434 / 0.670 | ||||
Moderators
Support
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict support, over and above climate change method contrasts?
modA.8901 <- lmer(Support ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8+ C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)
summary(modA.8901)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c *
## C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 + ATNS_Score.c *
## C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 + ATNS_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27815.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4472 -0.5236 0.0573 0.5493 3.3694
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 282.6 16.81
## Residual 399.0 19.98
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.023e+01 6.447e-01 1.015e+03 93.421 < 2e-16 ***
## ATNS_Score.c -2.437e-01 3.001e-02 1.016e+03 -8.120 1.34e-15 ***
## C1 -7.703e+00 1.125e+00 2.388e+03 -6.848 9.50e-12 ***
## C2 -9.206e+00 1.311e+00 2.493e+03 -7.021 2.84e-12 ***
## C3 -9.713e+00 1.159e+00 2.401e+03 -8.381 < 2e-16 ***
## C4 -6.210e+00 1.154e+00 2.399e+03 -5.383 8.02e-08 ***
## C5 -1.007e+01 1.144e+00 2.396e+03 -8.804 < 2e-16 ***
## C6 -6.625e-01 1.334e+00 2.494e+03 -0.497 0.61950
## C7 1.547e+01 1.312e+00 2.492e+03 11.794 < 2e-16 ***
## C8 1.951e+01 1.342e+00 2.496e+03 14.540 < 2e-16 ***
## C9 1.592e+01 1.131e+00 2.392e+03 14.069 < 2e-16 ***
## ATNS_Score.c:C1 -2.518e-03 5.305e-02 2.399e+03 -0.047 0.96214
## ATNS_Score.c:C2 -2.706e-01 6.133e-02 2.494e+03 -4.411 1.07e-05 ***
## ATNS_Score.c:C3 -7.580e-02 5.607e-02 2.417e+03 -1.352 0.17652
## ATNS_Score.c:C4 -1.272e-01 5.157e-02 2.386e+03 -2.467 0.01369 *
## ATNS_Score.c:C5 -3.550e-02 5.212e-02 2.390e+03 -0.681 0.49587
## ATNS_Score.c:C6 1.594e-01 6.153e-02 2.497e+03 2.592 0.00961 **
## ATNS_Score.c:C7 1.762e-01 6.173e-02 2.501e+03 2.854 0.00435 **
## ATNS_Score.c:C8 1.666e-01 6.207e-02 2.496e+03 2.684 0.00732 **
## ATNS_Score.c:C9 8.163e-02 5.262e-02 2.391e+03 1.551 0.12094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8901,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.23 | 0.64 | 58.96 – 61.49 | 93.42 | <0.001 |
| ATNS Score c | -0.24 | 0.03 | -0.30 – -0.18 | -8.12 | <0.001 |
| C1 | -7.70 | 1.12 | -9.91 – -5.50 | -6.85 | <0.001 |
| C2 | -9.21 | 1.31 | -11.78 – -6.63 | -7.02 | <0.001 |
| C3 | -9.71 | 1.16 | -11.99 – -7.44 | -8.38 | <0.001 |
| C4 | -6.21 | 1.15 | -8.47 – -3.95 | -5.38 | <0.001 |
| C5 | -10.07 | 1.14 | -12.32 – -7.83 | -8.80 | <0.001 |
| C6 | -0.66 | 1.33 | -3.28 – 1.95 | -0.50 | 0.619 |
| C7 | 15.47 | 1.31 | 12.90 – 18.04 | 11.79 | <0.001 |
| C8 | 19.51 | 1.34 | 16.88 – 22.14 | 14.54 | <0.001 |
| C9 | 15.92 | 1.13 | 13.70 – 18.14 | 14.07 | <0.001 |
| ATNS Score c * C1 | -0.00 | 0.05 | -0.11 – 0.10 | -0.05 | 0.962 |
| ATNS Score c * C2 | -0.27 | 0.06 | -0.39 – -0.15 | -4.41 | <0.001 |
| ATNS Score c * C3 | -0.08 | 0.06 | -0.19 – 0.03 | -1.35 | 0.176 |
| ATNS Score c * C4 | -0.13 | 0.05 | -0.23 – -0.03 | -2.47 | 0.014 |
| ATNS Score c * C5 | -0.04 | 0.05 | -0.14 – 0.07 | -0.68 | 0.496 |
| ATNS Score c * C6 | 0.16 | 0.06 | 0.04 – 0.28 | 2.59 | 0.010 |
| ATNS Score c * C7 | 0.18 | 0.06 | 0.06 – 0.30 | 2.85 | 0.004 |
| ATNS Score c * C8 | 0.17 | 0.06 | 0.04 – 0.29 | 2.68 | 0.007 |
| ATNS Score c * C9 | 0.08 | 0.05 | -0.02 – 0.18 | 1.55 | 0.121 |
| Random Effects | |||||
| σ2 | 399.03 | ||||
| τ00 id | 282.58 | ||||
| ICC | 0.41 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.193 / 0.527 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on naturalness in predicting support, over and above climate change method contrasts?
modA.89012 <- lmer(Support ~ ATNS_Score.c*Naturalness.c +C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8+ C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89012)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + ATNS_Score.c * C1 + ATNS_Score.c *
## C2 + ATNS_Score.c * C3 + ATNS_Score.c * C4 + ATNS_Score.c *
## C5 + ATNS_Score.c * C6 + ATNS_Score.c * C7 + ATNS_Score.c *
## C8 + ATNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27483.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5553 -0.5246 0.0338 0.5422 3.4309
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.3 16.35
## Residual 349.0 18.68
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.020e+01 6.197e-01 1.015e+03 97.141 < 2e-16
## ATNS_Score.c -2.173e-01 2.886e-02 1.018e+03 -7.527 1.14e-13
## Naturalness.c 4.176e-01 2.334e-02 2.805e+03 17.893 < 2e-16
## C1 -1.567e+00 1.113e+00 2.424e+03 -1.408 0.15913
## C2 -2.826e+00 1.277e+00 2.507e+03 -2.213 0.02696
## C3 -6.115e+00 1.104e+00 2.391e+03 -5.540 3.35e-08
## C4 -3.560e+00 1.090e+00 2.381e+03 -3.265 0.00111
## C5 -7.879e+00 1.079e+00 2.380e+03 -7.303 3.81e-13
## C6 -3.945e-01 1.252e+00 2.469e+03 -0.315 0.75270
## C7 9.596e+00 1.272e+00 2.488e+03 7.542 6.42e-14
## C8 1.268e+01 1.310e+00 2.515e+03 9.683 < 2e-16
## C9 6.794e+00 1.174e+00 2.465e+03 5.788 8.05e-09
## ATNS_Score.c:Naturalness.c 5.436e-03 9.222e-04 2.810e+03 5.895 4.20e-09
## ATNS_Score.c:C1 5.822e-02 5.195e-02 2.416e+03 1.121 0.26255
## ATNS_Score.c:C2 -1.459e-01 5.923e-02 2.510e+03 -2.463 0.01383
## ATNS_Score.c:C3 -9.782e-03 5.293e-02 2.393e+03 -0.185 0.85338
## ATNS_Score.c:C4 -4.361e-02 4.877e-02 2.367e+03 -0.894 0.37129
## ATNS_Score.c:C5 3.058e-02 4.922e-02 2.374e+03 0.621 0.53448
## ATNS_Score.c:C6 1.474e-01 5.775e-02 2.472e+03 2.553 0.01075
## ATNS_Score.c:C7 7.106e-02 5.965e-02 2.511e+03 1.191 0.23362
## ATNS_Score.c:C8 1.502e-02 6.019e-02 2.508e+03 0.249 0.80301
## ATNS_Score.c:C9 -5.357e-02 5.327e-02 2.426e+03 -1.006 0.31472
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## C1
## C2 *
## C3 ***
## C4 **
## C5 ***
## C6
## C7 ***
## C8 ***
## C9 ***
## ATNS_Score.c:Naturalness.c ***
## ATNS_Score.c:C1
## ATNS_Score.c:C2 *
## ATNS_Score.c:C3
## ATNS_Score.c:C4
## ATNS_Score.c:C5
## ATNS_Score.c:C6 *
## ATNS_Score.c:C7
## ATNS_Score.c:C8
## ATNS_Score.c:C9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89012,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.20 | 0.62 | 58.99 – 61.42 | 97.14 | <0.001 |
| ATNS Score c | -0.22 | 0.03 | -0.27 – -0.16 | -7.53 | <0.001 |
| Naturalness c | 0.42 | 0.02 | 0.37 – 0.46 | 17.89 | <0.001 |
| C1 | -1.57 | 1.11 | -3.75 – 0.61 | -1.41 | 0.159 |
| C2 | -2.83 | 1.28 | -5.33 – -0.32 | -2.21 | 0.027 |
| C3 | -6.11 | 1.10 | -8.28 – -3.95 | -5.54 | <0.001 |
| C4 | -3.56 | 1.09 | -5.70 – -1.42 | -3.27 | 0.001 |
| C5 | -7.88 | 1.08 | -9.99 – -5.76 | -7.30 | <0.001 |
| C6 | -0.39 | 1.25 | -2.85 – 2.06 | -0.32 | 0.753 |
| C7 | 9.60 | 1.27 | 7.10 – 12.09 | 7.54 | <0.001 |
| C8 | 12.68 | 1.31 | 10.12 – 15.25 | 9.68 | <0.001 |
| C9 | 6.79 | 1.17 | 4.49 – 9.10 | 5.79 | <0.001 |
|
ATNS Score c * Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 5.89 | <0.001 |
| ATNS Score c * C1 | 0.06 | 0.05 | -0.04 – 0.16 | 1.12 | 0.263 |
| ATNS Score c * C2 | -0.15 | 0.06 | -0.26 – -0.03 | -2.46 | 0.014 |
| ATNS Score c * C3 | -0.01 | 0.05 | -0.11 – 0.09 | -0.18 | 0.853 |
| ATNS Score c * C4 | -0.04 | 0.05 | -0.14 – 0.05 | -0.89 | 0.371 |
| ATNS Score c * C5 | 0.03 | 0.05 | -0.07 – 0.13 | 0.62 | 0.534 |
| ATNS Score c * C6 | 0.15 | 0.06 | 0.03 – 0.26 | 2.55 | 0.011 |
| ATNS Score c * C7 | 0.07 | 0.06 | -0.05 – 0.19 | 1.19 | 0.234 |
| ATNS Score c * C8 | 0.02 | 0.06 | -0.10 – 0.13 | 0.25 | 0.803 |
| ATNS Score c * C9 | -0.05 | 0.05 | -0.16 – 0.05 | -1.01 | 0.315 |
| Random Effects | |||||
| σ2 | 348.97 | ||||
| τ00 id | 267.27 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.268 / 0.585 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict support, over and above climate change method contrasts?
modA.8971 <- lmer(Support ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.8971)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c *
## C3 + CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c *
## C6 + CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27860.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3617 -0.5269 0.0514 0.5740 3.2352
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 303.5 17.42
## Residual 399.2 19.98
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.20810 0.66079 1014.12192 91.115 < 2e-16 ***
## CNS_Score.c 0.14383 0.03961 1016.19192 3.631 0.000296 ***
## C1 -7.47991 1.12818 2373.67490 -6.630 4.14e-11 ***
## C2 -9.22484 1.31597 2476.28284 -7.010 3.06e-12 ***
## C3 -10.01655 1.16234 2384.77043 -8.618 < 2e-16 ***
## C4 -5.81206 1.15694 2380.98009 -5.024 5.45e-07 ***
## C5 -10.15718 1.14736 2378.73946 -8.853 < 2e-16 ***
## C6 -0.56963 1.34167 2473.59180 -0.425 0.671189
## C7 15.37456 1.31665 2471.29633 11.677 < 2e-16 ***
## C8 19.46287 1.34534 2476.13091 14.467 < 2e-16 ***
## C9 15.78009 1.13576 2374.42167 13.894 < 2e-16 ***
## CNS_Score.c:C1 0.01070 0.06556 2364.97486 0.163 0.870432
## CNS_Score.c:C2 -0.47104 0.08141 2483.37194 -5.786 8.13e-09 ***
## CNS_Score.c:C3 -0.09782 0.06928 2381.22834 -1.412 0.158061
## CNS_Score.c:C4 -0.06699 0.06686 2377.90567 -1.002 0.316466
## CNS_Score.c:C5 -0.05658 0.07100 2393.30451 -0.797 0.425583
## CNS_Score.c:C6 0.03732 0.07687 2475.99032 0.486 0.627363
## CNS_Score.c:C7 0.23145 0.07966 2476.96009 2.905 0.003699 **
## CNS_Score.c:C8 0.24431 0.08118 2476.63443 3.010 0.002642 **
## CNS_Score.c:C9 0.18088 0.07161 2395.82469 2.526 0.011606 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8971,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.21 | 0.66 | 58.91 – 61.50 | 91.11 | <0.001 |
| CNS Score c | 0.14 | 0.04 | 0.07 – 0.22 | 3.63 | <0.001 |
| C1 | -7.48 | 1.13 | -9.69 – -5.27 | -6.63 | <0.001 |
| C2 | -9.22 | 1.32 | -11.81 – -6.64 | -7.01 | <0.001 |
| C3 | -10.02 | 1.16 | -12.30 – -7.74 | -8.62 | <0.001 |
| C4 | -5.81 | 1.16 | -8.08 – -3.54 | -5.02 | <0.001 |
| C5 | -10.16 | 1.15 | -12.41 – -7.91 | -8.85 | <0.001 |
| C6 | -0.57 | 1.34 | -3.20 – 2.06 | -0.42 | 0.671 |
| C7 | 15.37 | 1.32 | 12.79 – 17.96 | 11.68 | <0.001 |
| C8 | 19.46 | 1.35 | 16.82 – 22.10 | 14.47 | <0.001 |
| C9 | 15.78 | 1.14 | 13.55 – 18.01 | 13.89 | <0.001 |
| CNS Score c * C1 | 0.01 | 0.07 | -0.12 – 0.14 | 0.16 | 0.870 |
| CNS Score c * C2 | -0.47 | 0.08 | -0.63 – -0.31 | -5.79 | <0.001 |
| CNS Score c * C3 | -0.10 | 0.07 | -0.23 – 0.04 | -1.41 | 0.158 |
| CNS Score c * C4 | -0.07 | 0.07 | -0.20 – 0.06 | -1.00 | 0.316 |
| CNS Score c * C5 | -0.06 | 0.07 | -0.20 – 0.08 | -0.80 | 0.426 |
| CNS Score c * C6 | 0.04 | 0.08 | -0.11 – 0.19 | 0.49 | 0.627 |
| CNS Score c * C7 | 0.23 | 0.08 | 0.08 – 0.39 | 2.91 | 0.004 |
| CNS Score c * C8 | 0.24 | 0.08 | 0.09 – 0.40 | 3.01 | 0.003 |
| CNS Score c * C9 | 0.18 | 0.07 | 0.04 – 0.32 | 2.53 | 0.012 |
| Random Effects | |||||
| σ2 | 399.20 | ||||
| τ00 id | 303.54 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.167 / 0.527 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting support, over and above climate change method contrasts?
modA.897133 <- lmer(Support ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.897133)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + CNS_Score.c * C1 + CNS_Score.c * C2 +
## CNS_Score.c * C3 + CNS_Score.c * C4 + CNS_Score.c * C5 +
## CNS_Score.c * C6 + CNS_Score.c * C7 + CNS_Score.c * C8 +
## CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27540.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6307 -0.5299 0.0393 0.5437 3.3351
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 291.1 17.06
## Residual 349.4 18.69
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.007e+01 6.384e-01 1.010e+03 94.096 < 2e-16 ***
## CNS_Score.c 1.488e-01 3.827e-02 1.012e+03 3.888 0.000108 ***
## Naturalness.c 4.313e-01 2.340e-02 2.782e+03 18.430 < 2e-16 ***
## C1 -9.699e-01 1.115e+00 2.400e+03 -0.870 0.384432
## C2 -2.642e+00 1.284e+00 2.485e+03 -2.058 0.039687 *
## C3 -6.291e+00 1.109e+00 2.370e+03 -5.674 1.56e-08 ***
## C4 -3.477e+00 1.093e+00 2.356e+03 -3.182 0.001480 **
## C5 -8.027e+00 1.082e+00 2.360e+03 -7.417 1.67e-13 ***
## C6 -2.638e-01 1.260e+00 2.445e+03 -0.209 0.834208
## C7 9.253e+00 1.278e+00 2.465e+03 7.238 6.08e-13 ***
## C8 1.269e+01 1.313e+00 2.486e+03 9.665 < 2e-16 ***
## C9 6.452e+00 1.179e+00 2.440e+03 5.472 4.91e-08 ***
## CNS_Score.c:Naturalness.c 4.710e-03 1.313e-03 2.831e+03 3.588 0.000339 ***
## CNS_Score.c:C1 1.113e-01 6.567e-02 2.445e+03 1.695 0.090186 .
## CNS_Score.c:C2 -3.220e-01 7.833e-02 2.488e+03 -4.111 4.07e-05 ***
## CNS_Score.c:C3 -2.474e-02 6.585e-02 2.359e+03 -0.376 0.707210
## CNS_Score.c:C4 -2.512e-02 6.349e-02 2.360e+03 -0.396 0.692429
## CNS_Score.c:C5 -8.112e-03 6.704e-02 2.370e+03 -0.121 0.903696
## CNS_Score.c:C6 2.481e-02 7.221e-02 2.446e+03 0.344 0.731228
## CNS_Score.c:C7 8.138e-02 7.759e-02 2.479e+03 1.049 0.294333
## CNS_Score.c:C8 1.159e-01 7.875e-02 2.468e+03 1.472 0.141235
## CNS_Score.c:C9 5.511e-02 7.425e-02 2.495e+03 0.742 0.458073
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.897133,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.07 | 0.64 | 58.82 – 61.32 | 94.10 | <0.001 |
| CNS Score c | 0.15 | 0.04 | 0.07 – 0.22 | 3.89 | <0.001 |
| Naturalness c | 0.43 | 0.02 | 0.39 – 0.48 | 18.43 | <0.001 |
| C1 | -0.97 | 1.11 | -3.16 – 1.22 | -0.87 | 0.384 |
| C2 | -2.64 | 1.28 | -5.16 – -0.12 | -2.06 | 0.040 |
| C3 | -6.29 | 1.11 | -8.47 – -4.12 | -5.67 | <0.001 |
| C4 | -3.48 | 1.09 | -5.62 – -1.33 | -3.18 | 0.001 |
| C5 | -8.03 | 1.08 | -10.15 – -5.91 | -7.42 | <0.001 |
| C6 | -0.26 | 1.26 | -2.73 – 2.21 | -0.21 | 0.834 |
| C7 | 9.25 | 1.28 | 6.75 – 11.76 | 7.24 | <0.001 |
| C8 | 12.69 | 1.31 | 10.12 – 15.27 | 9.66 | <0.001 |
| C9 | 6.45 | 1.18 | 4.14 – 8.76 | 5.47 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 3.59 | <0.001 |
| CNS Score c * C1 | 0.11 | 0.07 | -0.02 – 0.24 | 1.70 | 0.090 |
| CNS Score c * C2 | -0.32 | 0.08 | -0.48 – -0.17 | -4.11 | <0.001 |
| CNS Score c * C3 | -0.02 | 0.07 | -0.15 – 0.10 | -0.38 | 0.707 |
| CNS Score c * C4 | -0.03 | 0.06 | -0.15 – 0.10 | -0.40 | 0.692 |
| CNS Score c * C5 | -0.01 | 0.07 | -0.14 – 0.12 | -0.12 | 0.904 |
| CNS Score c * C6 | 0.02 | 0.07 | -0.12 – 0.17 | 0.34 | 0.731 |
| CNS Score c * C7 | 0.08 | 0.08 | -0.07 – 0.23 | 1.05 | 0.294 |
| CNS Score c * C8 | 0.12 | 0.08 | -0.04 – 0.27 | 1.47 | 0.141 |
| CNS Score c * C9 | 0.06 | 0.07 | -0.09 – 0.20 | 0.74 | 0.458 |
| Random Effects | |||||
| σ2 | 349.42 | ||||
| τ00 id | 291.12 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.240 / 0.585 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict support, over and above climate change method contrasts?
modA.8961 <- lmer(Support ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)
summary(modA.8961)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 +
## CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27521
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5156 -0.5188 0.0451 0.5806 3.3798
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 182.5 13.51
## Residual 395.7 19.89
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.019e+01 5.613e-01 1.015e+03 107.230 < 2e-16 ***
## CCBelief_Score.c 4.785e-01 2.386e-02 1.021e+03 20.057 < 2e-16 ***
## C1 -7.573e+00 1.104e+00 2.489e+03 -6.860 8.65e-12 ***
## C2 -9.522e+00 1.282e+00 2.610e+03 -7.425 1.52e-13 ***
## C3 -9.686e+00 1.135e+00 2.504e+03 -8.530 < 2e-16 ***
## C4 -5.593e+00 1.133e+00 2.500e+03 -4.936 8.52e-07 ***
## C5 -1.051e+01 1.123e+00 2.498e+03 -9.358 < 2e-16 ***
## C6 -5.156e-01 1.304e+00 2.610e+03 -0.396 0.69249
## C7 1.550e+01 1.282e+00 2.606e+03 12.091 < 2e-16 ***
## C8 1.935e+01 1.311e+00 2.612e+03 14.762 < 2e-16 ***
## C9 1.580e+01 1.111e+00 2.496e+03 14.221 < 2e-16 ***
## CCBelief_Score.c:C1 -1.988e-02 4.665e-02 2.490e+03 -0.426 0.67013
## CCBelief_Score.c:C2 -4.187e-01 5.103e-02 2.602e+03 -8.205 3.58e-16 ***
## CCBelief_Score.c:C3 -2.946e-02 4.795e-02 2.503e+03 -0.614 0.53902
## CCBelief_Score.c:C4 -8.328e-03 4.559e-02 2.477e+03 -0.183 0.85507
## CCBelief_Score.c:C5 -7.694e-03 4.940e-02 2.517e+03 -0.156 0.87623
## CCBelief_Score.c:C6 -7.573e-03 5.757e-02 2.617e+03 -0.132 0.89534
## CCBelief_Score.c:C7 2.482e-01 5.542e-02 2.615e+03 4.479 7.82e-06 ***
## CCBelief_Score.c:C8 1.610e-01 5.660e-02 2.616e+03 2.844 0.00449 **
## CCBelief_Score.c:C9 8.782e-02 4.883e-02 2.515e+03 1.798 0.07224 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8961,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.19 | 0.56 | 59.09 – 61.29 | 107.23 | <0.001 |
| CCBelief Score c | 0.48 | 0.02 | 0.43 – 0.53 | 20.06 | <0.001 |
| C1 | -7.57 | 1.10 | -9.74 – -5.41 | -6.86 | <0.001 |
| C2 | -9.52 | 1.28 | -12.04 – -7.01 | -7.43 | <0.001 |
| C3 | -9.69 | 1.14 | -11.91 – -7.46 | -8.53 | <0.001 |
| C4 | -5.59 | 1.13 | -7.82 – -3.37 | -4.94 | <0.001 |
| C5 | -10.51 | 1.12 | -12.72 – -8.31 | -9.36 | <0.001 |
| C6 | -0.52 | 1.30 | -3.07 – 2.04 | -0.40 | 0.692 |
| C7 | 15.50 | 1.28 | 12.98 – 18.01 | 12.09 | <0.001 |
| C8 | 19.35 | 1.31 | 16.78 – 21.92 | 14.76 | <0.001 |
| C9 | 15.80 | 1.11 | 13.63 – 17.98 | 14.22 | <0.001 |
| CCBelief Score c * C1 | -0.02 | 0.05 | -0.11 – 0.07 | -0.43 | 0.670 |
| CCBelief Score c * C2 | -0.42 | 0.05 | -0.52 – -0.32 | -8.21 | <0.001 |
| CCBelief Score c * C3 | -0.03 | 0.05 | -0.12 – 0.06 | -0.61 | 0.539 |
| CCBelief Score c * C4 | -0.01 | 0.05 | -0.10 – 0.08 | -0.18 | 0.855 |
| CCBelief Score c * C5 | -0.01 | 0.05 | -0.10 – 0.09 | -0.16 | 0.876 |
| CCBelief Score c * C6 | -0.01 | 0.06 | -0.12 – 0.11 | -0.13 | 0.895 |
| CCBelief Score c * C7 | 0.25 | 0.06 | 0.14 – 0.36 | 4.48 | <0.001 |
| CCBelief Score c * C8 | 0.16 | 0.06 | 0.05 – 0.27 | 2.84 | 0.004 |
| CCBelief Score c * C9 | 0.09 | 0.05 | -0.01 – 0.18 | 1.80 | 0.072 |
| Random Effects | |||||
| σ2 | 395.72 | ||||
| τ00 id | 182.52 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.314 / 0.531 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perception sof naturalness in predicting support, over and above climate change method contrasts?
modA.89614 <- lmer(Support ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89614)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c *
## C2 + CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27209.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8106 -0.5349 0.0525 0.5418 3.1129
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 176.2 13.27
## Residual 348.4 18.67
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.007e+01 5.416e-01 1.017e+03 110.929
## CCBelief_Score.c 4.572e-01 2.311e-02 1.034e+03 19.782
## Naturalness.c 4.165e-01 2.250e-02 2.927e+03 18.515
## C1 -1.349e+00 1.093e+00 2.529e+03 -1.234
## C2 -3.493e+00 1.255e+00 2.630e+03 -2.784
## C3 -6.235e+00 1.086e+00 2.493e+03 -5.743
## C4 -3.366e+00 1.074e+00 2.478e+03 -3.134
## C5 -8.517e+00 1.063e+00 2.482e+03 -8.014
## C6 -8.487e-02 1.229e+00 2.586e+03 -0.069
## C7 9.748e+00 1.249e+00 2.604e+03 7.807
## C8 1.309e+01 1.282e+00 2.628e+03 10.210
## C9 6.806e+00 1.154e+00 2.582e+03 5.899
## CCBelief_Score.c:Naturalness.c -1.095e-03 8.496e-04 2.939e+03 -1.289
## CCBelief_Score.c:C1 -2.310e-02 4.580e-02 2.522e+03 -0.504
## CCBelief_Score.c:C2 -3.852e-01 4.931e-02 2.598e+03 -7.812
## CCBelief_Score.c:C3 -2.657e-02 4.543e-02 2.484e+03 -0.585
## CCBelief_Score.c:C4 -4.439e-05 4.313e-02 2.451e+03 -0.001
## CCBelief_Score.c:C5 -1.467e-02 4.669e-02 2.489e+03 -0.314
## CCBelief_Score.c:C6 -3.071e-02 5.438e-02 2.613e+03 -0.565
## CCBelief_Score.c:C7 2.357e-01 5.358e-02 2.625e+03 4.398
## CCBelief_Score.c:C8 1.495e-01 5.433e-02 2.636e+03 2.752
## CCBelief_Score.c:C9 1.196e-01 5.086e-02 2.639e+03 2.351
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 0.21731
## C2 0.00541 **
## C3 1.04e-08 ***
## C4 0.00174 **
## C5 1.69e-15 ***
## C6 0.94493
## C7 8.39e-15 ***
## C8 < 2e-16 ***
## C9 4.14e-09 ***
## CCBelief_Score.c:Naturalness.c 0.19764
## CCBelief_Score.c:C1 0.61396
## CCBelief_Score.c:C2 8.12e-15 ***
## CCBelief_Score.c:C3 0.55872
## CCBelief_Score.c:C4 0.99918
## CCBelief_Score.c:C5 0.75339
## CCBelief_Score.c:C6 0.57226
## CCBelief_Score.c:C7 1.13e-05 ***
## CCBelief_Score.c:C8 0.00597 **
## CCBelief_Score.c:C9 0.01878 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89614,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.07 | 0.54 | 59.01 – 61.14 | 110.93 | <0.001 |
| CCBelief Score c | 0.46 | 0.02 | 0.41 – 0.50 | 19.78 | <0.001 |
| Naturalness c | 0.42 | 0.02 | 0.37 – 0.46 | 18.51 | <0.001 |
| C1 | -1.35 | 1.09 | -3.49 – 0.79 | -1.23 | 0.217 |
| C2 | -3.49 | 1.25 | -5.95 – -1.03 | -2.78 | 0.005 |
| C3 | -6.24 | 1.09 | -8.36 – -4.11 | -5.74 | <0.001 |
| C4 | -3.37 | 1.07 | -5.47 – -1.26 | -3.13 | 0.002 |
| C5 | -8.52 | 1.06 | -10.60 – -6.43 | -8.01 | <0.001 |
| C6 | -0.08 | 1.23 | -2.49 – 2.32 | -0.07 | 0.945 |
| C7 | 9.75 | 1.25 | 7.30 – 12.20 | 7.81 | <0.001 |
| C8 | 13.09 | 1.28 | 10.57 – 15.60 | 10.21 | <0.001 |
| C9 | 6.81 | 1.15 | 4.54 – 9.07 | 5.90 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.29 | 0.198 |
| CCBelief Score c * C1 | -0.02 | 0.05 | -0.11 – 0.07 | -0.50 | 0.614 |
| CCBelief Score c * C2 | -0.39 | 0.05 | -0.48 – -0.29 | -7.81 | <0.001 |
| CCBelief Score c * C3 | -0.03 | 0.05 | -0.12 – 0.06 | -0.58 | 0.559 |
| CCBelief Score c * C4 | -0.00 | 0.04 | -0.08 – 0.08 | -0.00 | 0.999 |
| CCBelief Score c * C5 | -0.01 | 0.05 | -0.11 – 0.08 | -0.31 | 0.753 |
| CCBelief Score c * C6 | -0.03 | 0.05 | -0.14 – 0.08 | -0.56 | 0.572 |
| CCBelief Score c * C7 | 0.24 | 0.05 | 0.13 – 0.34 | 4.40 | <0.001 |
| CCBelief Score c * C8 | 0.15 | 0.05 | 0.04 – 0.26 | 2.75 | 0.006 |
| CCBelief Score c * C9 | 0.12 | 0.05 | 0.02 – 0.22 | 2.35 | 0.019 |
| Random Effects | |||||
| σ2 | 348.42 | ||||
| τ00 id | 176.17 | ||||
| ICC | 0.34 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.379 / 0.587 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict support, over and above climate change method contrasts?
modA.8951 <- lmer(Support ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 +Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)
summary(modA.8951)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27910.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1181 -0.5127 0.0561 0.5639 3.2895
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 308.8 17.57
## Residual 404.5 20.11
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.027e+01 6.661e-01 1.016e+03 90.477 < 2e-16 ***
## Collectivism_Score.c -6.800e-02 2.797e-02 1.019e+03 -2.432 0.0152 *
## C1 -7.713e+00 1.135e+00 2.371e+03 -6.793 1.38e-11 ***
## C2 -9.260e+00 1.327e+00 2.473e+03 -6.978 3.82e-12 ***
## C3 -1.002e+01 1.169e+00 2.383e+03 -8.573 < 2e-16 ***
## C4 -5.878e+00 1.164e+00 2.380e+03 -5.050 4.75e-07 ***
## C5 -1.007e+01 1.159e+00 2.378e+03 -8.685 < 2e-16 ***
## C6 -8.403e-01 1.347e+00 2.474e+03 -0.624 0.5328
## C7 1.578e+01 1.327e+00 2.471e+03 11.895 < 2e-16 ***
## C8 1.936e+01 1.354e+00 2.474e+03 14.293 < 2e-16 ***
## C9 1.605e+01 1.142e+00 2.373e+03 14.050 < 2e-16 ***
## Collectivism_Score.c:C1 -7.187e-02 5.177e-02 2.405e+03 -1.388 0.1652
## Collectivism_Score.c:C2 1.059e-01 5.366e-02 2.470e+03 1.974 0.0485 *
## Collectivism_Score.c:C3 1.280e-02 4.955e-02 2.387e+03 0.258 0.7961
## Collectivism_Score.c:C4 -4.879e-03 4.917e-02 2.383e+03 -0.099 0.9210
## Collectivism_Score.c:C5 3.218e-02 4.719e-02 2.368e+03 0.682 0.4953
## Collectivism_Score.c:C6 1.377e-01 5.676e-02 2.477e+03 2.426 0.0154 *
## Collectivism_Score.c:C7 -1.039e-01 5.753e-02 2.477e+03 -1.806 0.0710 .
## Collectivism_Score.c:C8 -4.367e-02 5.704e-02 2.482e+03 -0.766 0.4439
## Collectivism_Score.c:C9 -7.764e-02 4.875e-02 2.383e+03 -1.593 0.1114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8951,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.27 | 0.67 | 58.96 – 61.58 | 90.48 | <0.001 |
| Collectivism Score c | -0.07 | 0.03 | -0.12 – -0.01 | -2.43 | 0.015 |
| C1 | -7.71 | 1.14 | -9.94 – -5.49 | -6.79 | <0.001 |
| C2 | -9.26 | 1.33 | -11.86 – -6.66 | -6.98 | <0.001 |
| C3 | -10.02 | 1.17 | -12.31 – -7.73 | -8.57 | <0.001 |
| C4 | -5.88 | 1.16 | -8.16 – -3.60 | -5.05 | <0.001 |
| C5 | -10.07 | 1.16 | -12.34 – -7.80 | -8.68 | <0.001 |
| C6 | -0.84 | 1.35 | -3.48 – 1.80 | -0.62 | 0.533 |
| C7 | 15.78 | 1.33 | 13.18 – 18.39 | 11.90 | <0.001 |
| C8 | 19.36 | 1.35 | 16.70 – 22.01 | 14.29 | <0.001 |
| C9 | 16.05 | 1.14 | 13.81 – 18.29 | 14.05 | <0.001 |
| Collectivism Score c * C1 | -0.07 | 0.05 | -0.17 – 0.03 | -1.39 | 0.165 |
| Collectivism Score c * C2 | 0.11 | 0.05 | 0.00 – 0.21 | 1.97 | 0.049 |
| Collectivism Score c * C3 | 0.01 | 0.05 | -0.08 – 0.11 | 0.26 | 0.796 |
| Collectivism Score c * C4 | -0.00 | 0.05 | -0.10 – 0.09 | -0.10 | 0.921 |
| Collectivism Score c * C5 | 0.03 | 0.05 | -0.06 – 0.12 | 0.68 | 0.495 |
| Collectivism Score c * C6 | 0.14 | 0.06 | 0.03 – 0.25 | 2.43 | 0.015 |
| Collectivism Score c * C7 | -0.10 | 0.06 | -0.22 – 0.01 | -1.81 | 0.071 |
| Collectivism Score c * C8 | -0.04 | 0.06 | -0.16 – 0.07 | -0.77 | 0.444 |
| Collectivism Score c * C9 | -0.08 | 0.05 | -0.17 – 0.02 | -1.59 | 0.111 |
| Random Effects | |||||
| σ2 | 404.48 | ||||
| τ00 id | 308.83 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.155 / 0.521 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting support, over and above climate change method contrasts?
modA.89516 <- lmer(Support ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 +(1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89516)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c * C1 +
## Collectivism_Score.c * C2 + Collectivism_Score.c * C3 + Collectivism_Score.c *
## C4 + Collectivism_Score.c * C5 + Collectivism_Score.c * C6 +
## Collectivism_Score.c * C7 + Collectivism_Score.c * C8 + Collectivism_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27587
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4611 -0.5395 0.0232 0.5424 3.3678
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.3 17.15
## Residual 354.1 18.82
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.012e+01 6.421e-01 1.015e+03 93.622
## Collectivism_Score.c -6.337e-02 2.696e-02 1.019e+03 -2.351
## Naturalness.c 4.437e-01 2.352e-02 2.788e+03 18.869
## C1 -1.056e+00 1.123e+00 2.406e+03 -0.940
## C2 -2.819e+00 1.293e+00 2.484e+03 -2.181
## C3 -6.270e+00 1.114e+00 2.374e+03 -5.627
## C4 -3.505e+00 1.099e+00 2.359e+03 -3.189
## C5 -7.840e+00 1.094e+00 2.363e+03 -7.166
## C6 -4.399e-01 1.265e+00 2.448e+03 -0.348
## C7 9.643e+00 1.288e+00 2.467e+03 7.487
## C8 1.254e+01 1.321e+00 2.487e+03 9.489
## C9 6.489e+00 1.188e+00 2.449e+03 5.461
## Collectivism_Score.c:Naturalness.c 1.441e-03 9.229e-04 2.781e+03 1.561
## Collectivism_Score.c:C1 -6.418e-02 5.128e-02 2.443e+03 -1.252
## Collectivism_Score.c:C2 1.030e-01 5.225e-02 2.496e+03 1.971
## Collectivism_Score.c:C3 4.508e-02 4.717e-02 2.368e+03 0.956
## Collectivism_Score.c:C4 9.408e-03 4.630e-02 2.364e+03 0.203
## Collectivism_Score.c:C5 6.814e-02 4.472e-02 2.351e+03 1.524
## Collectivism_Score.c:C6 1.314e-01 5.330e-02 2.450e+03 2.465
## Collectivism_Score.c:C7 -1.358e-01 5.607e-02 2.489e+03 -2.423
## Collectivism_Score.c:C8 -9.141e-02 5.600e-02 2.491e+03 -1.632
## Collectivism_Score.c:C9 -7.321e-02 4.997e-02 2.444e+03 -1.465
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.01893 *
## Naturalness.c < 2e-16 ***
## C1 0.34737
## C2 0.02931 *
## C3 2.05e-08 ***
## C4 0.00144 **
## C5 1.03e-12 ***
## C6 0.72804
## C7 9.72e-14 ***
## C8 < 2e-16 ***
## C9 5.22e-08 ***
## Collectivism_Score.c:Naturalness.c 0.11857
## Collectivism_Score.c:C1 0.21085
## Collectivism_Score.c:C2 0.04887 *
## Collectivism_Score.c:C3 0.33935
## Collectivism_Score.c:C4 0.83902
## Collectivism_Score.c:C5 0.12775
## Collectivism_Score.c:C6 0.01378 *
## Collectivism_Score.c:C7 0.01547 *
## Collectivism_Score.c:C8 0.10275
## Collectivism_Score.c:C9 0.14297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89516,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.12 | 0.64 | 58.86 – 61.38 | 93.62 | <0.001 |
| Collectivism Score c | -0.06 | 0.03 | -0.12 – -0.01 | -2.35 | 0.019 |
| Naturalness c | 0.44 | 0.02 | 0.40 – 0.49 | 18.87 | <0.001 |
| C1 | -1.06 | 1.12 | -3.26 – 1.15 | -0.94 | 0.347 |
| C2 | -2.82 | 1.29 | -5.35 – -0.28 | -2.18 | 0.029 |
| C3 | -6.27 | 1.11 | -8.46 – -4.09 | -5.63 | <0.001 |
| C4 | -3.51 | 1.10 | -5.66 – -1.35 | -3.19 | 0.001 |
| C5 | -7.84 | 1.09 | -9.99 – -5.69 | -7.17 | <0.001 |
| C6 | -0.44 | 1.27 | -2.92 – 2.04 | -0.35 | 0.728 |
| C7 | 9.64 | 1.29 | 7.12 – 12.17 | 7.49 | <0.001 |
| C8 | 12.54 | 1.32 | 9.95 – 15.13 | 9.49 | <0.001 |
| C9 | 6.49 | 1.19 | 4.16 – 8.82 | 5.46 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.56 | 0.119 |
| Collectivism Score c * C1 | -0.06 | 0.05 | -0.16 – 0.04 | -1.25 | 0.211 |
| Collectivism Score c * C2 | 0.10 | 0.05 | 0.00 – 0.21 | 1.97 | 0.049 |
| Collectivism Score c * C3 | 0.05 | 0.05 | -0.05 – 0.14 | 0.96 | 0.339 |
| Collectivism Score c * C4 | 0.01 | 0.05 | -0.08 – 0.10 | 0.20 | 0.839 |
| Collectivism Score c * C5 | 0.07 | 0.04 | -0.02 – 0.16 | 1.52 | 0.128 |
| Collectivism Score c * C6 | 0.13 | 0.05 | 0.03 – 0.24 | 2.46 | 0.014 |
| Collectivism Score c * C7 | -0.14 | 0.06 | -0.25 – -0.03 | -2.42 | 0.015 |
| Collectivism Score c * C8 | -0.09 | 0.06 | -0.20 – 0.02 | -1.63 | 0.103 |
| Collectivism Score c * C9 | -0.07 | 0.05 | -0.17 – 0.02 | -1.47 | 0.143 |
| Random Effects | |||||
| σ2 | 354.14 | ||||
| τ00 id | 294.26 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.230 / 0.580 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict support, over and above climate change method contrasts?
modA.8941 <- lmer(Support ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9+ (1|id), data = L)
summary(modA.8941)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C6 + Individualism_Score.c * C7 + Individualism_Score.c *
## C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27913.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2170 -0.5222 0.0620 0.5538 3.2027
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.0 17.58
## Residual 406.3 20.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.026e+01 6.668e-01 1.016e+03 90.372 < 2e-16 ***
## Individualism_Score.c 5.466e-02 3.960e-02 1.021e+03 1.380 0.1678
## C1 -7.758e+00 1.138e+00 2.372e+03 -6.816 1.18e-11 ***
## C2 -9.171e+00 1.332e+00 2.474e+03 -6.883 7.42e-12 ***
## C3 -1.000e+01 1.172e+00 2.384e+03 -8.537 < 2e-16 ***
## C4 -5.934e+00 1.166e+00 2.380e+03 -5.091 3.84e-07 ***
## C5 -1.001e+01 1.157e+00 2.379e+03 -8.650 < 2e-16 ***
## C6 -6.913e-01 1.353e+00 2.476e+03 -0.511 0.6094
## C7 1.556e+01 1.327e+00 2.472e+03 11.726 < 2e-16 ***
## C8 1.950e+01 1.359e+00 2.475e+03 14.350 < 2e-16 ***
## C9 1.595e+01 1.145e+00 2.375e+03 13.936 < 2e-16 ***
## Individualism_Score.c:C1 -7.227e-02 6.745e-02 2.372e+03 -1.071 0.2841
## Individualism_Score.c:C2 -1.440e-01 7.520e-02 2.473e+03 -1.915 0.0556 .
## Individualism_Score.c:C3 -2.612e-02 7.230e-02 2.402e+03 -0.361 0.7179
## Individualism_Score.c:C4 -7.614e-02 6.959e-02 2.383e+03 -1.094 0.2740
## Individualism_Score.c:C5 1.186e-01 6.932e-02 2.383e+03 1.711 0.0873 .
## Individualism_Score.c:C6 2.672e-02 8.358e-02 2.481e+03 0.320 0.7492
## Individualism_Score.c:C7 6.072e-02 8.377e-02 2.481e+03 0.725 0.4687
## Individualism_Score.c:C8 6.832e-02 7.742e-02 2.475e+03 0.882 0.3777
## Individualism_Score.c:C9 -8.782e-04 6.680e-02 2.373e+03 -0.013 0.9895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8941,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.26 | 0.67 | 58.95 – 61.57 | 90.37 | <0.001 |
| Individualism Score c | 0.05 | 0.04 | -0.02 – 0.13 | 1.38 | 0.168 |
| C1 | -7.76 | 1.14 | -9.99 – -5.53 | -6.82 | <0.001 |
| C2 | -9.17 | 1.33 | -11.78 – -6.56 | -6.88 | <0.001 |
| C3 | -10.00 | 1.17 | -12.30 – -7.70 | -8.54 | <0.001 |
| C4 | -5.93 | 1.17 | -8.22 – -3.65 | -5.09 | <0.001 |
| C5 | -10.01 | 1.16 | -12.28 – -7.74 | -8.65 | <0.001 |
| C6 | -0.69 | 1.35 | -3.34 – 1.96 | -0.51 | 0.609 |
| C7 | 15.56 | 1.33 | 12.96 – 18.16 | 11.73 | <0.001 |
| C8 | 19.50 | 1.36 | 16.84 – 22.17 | 14.35 | <0.001 |
| C9 | 15.95 | 1.14 | 13.71 – 18.19 | 13.94 | <0.001 |
|
Individualism Score c * C1 |
-0.07 | 0.07 | -0.20 – 0.06 | -1.07 | 0.284 |
|
Individualism Score c * C2 |
-0.14 | 0.08 | -0.29 – 0.00 | -1.91 | 0.056 |
|
Individualism Score c * C3 |
-0.03 | 0.07 | -0.17 – 0.12 | -0.36 | 0.718 |
|
Individualism Score c * C4 |
-0.08 | 0.07 | -0.21 – 0.06 | -1.09 | 0.274 |
|
Individualism Score c * C5 |
0.12 | 0.07 | -0.02 – 0.25 | 1.71 | 0.087 |
|
Individualism Score c * C6 |
0.03 | 0.08 | -0.14 – 0.19 | 0.32 | 0.749 |
|
Individualism Score c * C7 |
0.06 | 0.08 | -0.10 – 0.22 | 0.72 | 0.469 |
|
Individualism Score c * C8 |
0.07 | 0.08 | -0.08 – 0.22 | 0.88 | 0.378 |
|
Individualism Score c * C9 |
-0.00 | 0.07 | -0.13 – 0.13 | -0.01 | 0.990 |
| Random Effects | |||||
| σ2 | 406.27 | ||||
| τ00 id | 309.03 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.152 / 0.518 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting support, over and above climate change method contrasts?
modA.89417 <- lmer(Support ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.89417)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c * C1 +
## Individualism_Score.c * C2 + Individualism_Score.c * C3 +
## Individualism_Score.c * C4 + Individualism_Score.c * C5 +
## Individualism_Score.c * C6 + Individualism_Score.c * C7 +
## Individualism_Score.c * C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27587.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4987 -0.5329 0.0365 0.5396 3.2403
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 293.2 17.12
## Residual 355.8 18.86
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.011e+01 6.418e-01 1.016e+03 93.668
## Individualism_Score.c 6.421e-02 3.811e-02 1.020e+03 1.685
## Naturalness.c 4.436e-01 2.364e-02 2.787e+03 18.765
## C1 -1.098e+00 1.126e+00 2.406e+03 -0.975
## C2 -2.645e+00 1.298e+00 2.484e+03 -2.037
## C3 -6.266e+00 1.117e+00 2.375e+03 -5.610
## C4 -3.544e+00 1.101e+00 2.360e+03 -3.220
## C5 -7.861e+00 1.092e+00 2.365e+03 -7.202
## C6 -2.683e-01 1.270e+00 2.452e+03 -0.211
## C7 9.388e+00 1.288e+00 2.469e+03 7.290
## C8 1.271e+01 1.326e+00 2.490e+03 9.583
## C9 6.358e+00 1.189e+00 2.444e+03 5.349
## Individualism_Score.c:Naturalness.c 2.142e-03 1.338e-03 2.833e+03 1.601
## Individualism_Score.c:C1 -4.784e-02 6.663e-02 2.407e+03 -0.718
## Individualism_Score.c:C2 -1.085e-01 7.348e-02 2.487e+03 -1.476
## Individualism_Score.c:C3 1.666e-02 6.889e-02 2.391e+03 0.242
## Individualism_Score.c:C4 -5.017e-02 6.583e-02 2.370e+03 -0.762
## Individualism_Score.c:C5 1.766e-01 6.554e-02 2.363e+03 2.695
## Individualism_Score.c:C6 3.519e-02 7.847e-02 2.456e+03 0.449
## Individualism_Score.c:C7 -4.913e-03 8.083e-02 2.476e+03 -0.061
## Individualism_Score.c:C8 2.133e-02 7.658e-02 2.498e+03 0.278
## Individualism_Score.c:C9 -6.002e-02 6.950e-02 2.449e+03 -0.864
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.09234 .
## Naturalness.c < 2e-16 ***
## C1 0.32956
## C2 0.04172 *
## C3 2.25e-08 ***
## C4 0.00130 **
## C5 7.92e-13 ***
## C6 0.83279
## C7 4.16e-13 ***
## C8 < 2e-16 ***
## C9 9.68e-08 ***
## Individualism_Score.c:Naturalness.c 0.10941
## Individualism_Score.c:C1 0.47290
## Individualism_Score.c:C2 0.13994
## Individualism_Score.c:C3 0.80890
## Individualism_Score.c:C4 0.44602
## Individualism_Score.c:C5 0.00709 **
## Individualism_Score.c:C6 0.65382
## Individualism_Score.c:C7 0.95153
## Individualism_Score.c:C8 0.78067
## Individualism_Score.c:C9 0.38790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.89417,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.11 | 0.64 | 58.86 – 61.37 | 93.67 | <0.001 |
| Individualism Score c | 0.06 | 0.04 | -0.01 – 0.14 | 1.68 | 0.092 |
| Naturalness c | 0.44 | 0.02 | 0.40 – 0.49 | 18.76 | <0.001 |
| C1 | -1.10 | 1.13 | -3.31 – 1.11 | -0.98 | 0.330 |
| C2 | -2.64 | 1.30 | -5.19 – -0.10 | -2.04 | 0.042 |
| C3 | -6.27 | 1.12 | -8.46 – -4.08 | -5.61 | <0.001 |
| C4 | -3.54 | 1.10 | -5.70 – -1.39 | -3.22 | 0.001 |
| C5 | -7.86 | 1.09 | -10.00 – -5.72 | -7.20 | <0.001 |
| C6 | -0.27 | 1.27 | -2.76 – 2.22 | -0.21 | 0.833 |
| C7 | 9.39 | 1.29 | 6.86 – 11.91 | 7.29 | <0.001 |
| C8 | 12.71 | 1.33 | 10.11 – 15.31 | 9.58 | <0.001 |
| C9 | 6.36 | 1.19 | 4.03 – 8.69 | 5.35 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.60 | 0.109 |
|
Individualism Score c * C1 |
-0.05 | 0.07 | -0.18 – 0.08 | -0.72 | 0.473 |
|
Individualism Score c * C2 |
-0.11 | 0.07 | -0.25 – 0.04 | -1.48 | 0.140 |
|
Individualism Score c * C3 |
0.02 | 0.07 | -0.12 – 0.15 | 0.24 | 0.809 |
|
Individualism Score c * C4 |
-0.05 | 0.07 | -0.18 – 0.08 | -0.76 | 0.446 |
|
Individualism Score c * C5 |
0.18 | 0.07 | 0.05 – 0.31 | 2.69 | 0.007 |
|
Individualism Score c * C6 |
0.04 | 0.08 | -0.12 – 0.19 | 0.45 | 0.654 |
|
Individualism Score c * C7 |
-0.00 | 0.08 | -0.16 – 0.15 | -0.06 | 0.952 |
|
Individualism Score c * C8 |
0.02 | 0.08 | -0.13 – 0.17 | 0.28 | 0.781 |
|
Individualism Score c * C9 |
-0.06 | 0.07 | -0.20 – 0.08 | -0.86 | 0.388 |
| Random Effects | |||||
| σ2 | 355.77 | ||||
| τ00 id | 293.18 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.227 / 0.576 | ||||
Political Ideology
Q.1 (Ideology) How does ideology predict support, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.8931 <- lmer(Support ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.8931)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 +
## Ideology.c * C4 + Ideology.c * C5 + Ideology.c * C6 + Ideology.c *
## C7 + Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27849
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2203 -0.5200 0.0609 0.5608 3.2137
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 310.0 17.61
## Residual 406.4 20.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.26058 0.66747 1016.23821 90.282 < 2e-16 ***
## Ideology.c 0.01963 1.17017 1018.12738 0.017 0.9866
## C1 -7.73681 1.13957 2373.68452 -6.789 1.42e-11 ***
## C2 -9.32336 1.32729 2474.09924 -7.024 2.77e-12 ***
## C3 -9.90026 1.17193 2385.77892 -8.448 < 2e-16 ***
## C4 -5.83842 1.16553 2379.65315 -5.009 5.87e-07 ***
## C5 -10.09765 1.16080 2378.65854 -8.699 < 2e-16 ***
## C6 -0.70419 1.34937 2474.08789 -0.522 0.6018
## C7 15.53447 1.32769 2473.69004 11.700 < 2e-16 ***
## C8 19.39169 1.35763 2476.56660 14.283 < 2e-16 ***
## C9 16.08420 1.15065 2378.15075 13.978 < 2e-16 ***
## Ideology.c:C1 1.25841 2.02762 2387.93291 0.621 0.5349
## Ideology.c:C2 -2.29079 2.36267 2491.12419 -0.970 0.3324
## Ideology.c:C3 -3.32891 2.09026 2409.55749 -1.593 0.1114
## Ideology.c:C4 -1.51629 2.09392 2410.51681 -0.724 0.4691
## Ideology.c:C5 0.85815 2.00598 2389.15417 0.428 0.6688
## Ideology.c:C6 -1.86889 2.26974 2478.38281 -0.823 0.4104
## Ideology.c:C7 -0.25652 2.45509 2484.38961 -0.104 0.9168
## Ideology.c:C8 4.40536 2.32990 2482.85434 1.891 0.0588 .
## Ideology.c:C9 2.26236 2.04520 2387.82732 1.106 0.2688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8931,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.26 | 0.67 | 58.95 – 61.57 | 90.28 | <0.001 |
| Ideology c | 0.02 | 1.17 | -2.27 – 2.31 | 0.02 | 0.987 |
| C1 | -7.74 | 1.14 | -9.97 – -5.50 | -6.79 | <0.001 |
| C2 | -9.32 | 1.33 | -11.93 – -6.72 | -7.02 | <0.001 |
| C3 | -9.90 | 1.17 | -12.20 – -7.60 | -8.45 | <0.001 |
| C4 | -5.84 | 1.17 | -8.12 – -3.55 | -5.01 | <0.001 |
| C5 | -10.10 | 1.16 | -12.37 – -7.82 | -8.70 | <0.001 |
| C6 | -0.70 | 1.35 | -3.35 – 1.94 | -0.52 | 0.602 |
| C7 | 15.53 | 1.33 | 12.93 – 18.14 | 11.70 | <0.001 |
| C8 | 19.39 | 1.36 | 16.73 – 22.05 | 14.28 | <0.001 |
| C9 | 16.08 | 1.15 | 13.83 – 18.34 | 13.98 | <0.001 |
| Ideology c * C1 | 1.26 | 2.03 | -2.72 – 5.23 | 0.62 | 0.535 |
| Ideology c * C2 | -2.29 | 2.36 | -6.92 – 2.34 | -0.97 | 0.332 |
| Ideology c * C3 | -3.33 | 2.09 | -7.43 – 0.77 | -1.59 | 0.111 |
| Ideology c * C4 | -1.52 | 2.09 | -5.62 – 2.59 | -0.72 | 0.469 |
| Ideology c * C5 | 0.86 | 2.01 | -3.08 – 4.79 | 0.43 | 0.669 |
| Ideology c * C6 | -1.87 | 2.27 | -6.32 – 2.58 | -0.82 | 0.410 |
| Ideology c * C7 | -0.26 | 2.46 | -5.07 – 4.56 | -0.10 | 0.917 |
| Ideology c * C8 | 4.41 | 2.33 | -0.16 – 8.97 | 1.89 | 0.059 |
| Ideology c * C9 | 2.26 | 2.05 | -1.75 – 6.27 | 1.11 | 0.269 |
| Random Effects | |||||
| σ2 | 406.36 | ||||
| τ00 id | 310.02 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.151 / 0.518 | ||||
Q.2 (Ideology) Does ideology depend on perceptions of naturalness in predicting support, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.89317 <- lmer(Support ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.89317)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c *
## C3 + Ideology.c * C4 + Ideology.c * C5 + Ideology.c * C6 +
## Ideology.c * C7 + Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5136 -0.5415 0.0304 0.5433 3.2958
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.5 17.16
## Residual 356.7 18.89
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.009e+01 6.430e-01 1.016e+03 93.457 < 2e-16 ***
## Ideology.c 3.151e-01 1.128e+00 1.021e+03 0.279 0.78008
## Naturalness.c 4.457e-01 2.363e-02 2.791e+03 18.861 < 2e-16 ***
## C1 -1.014e+00 1.129e+00 2.410e+03 -0.898 0.36909
## C2 -2.764e+00 1.295e+00 2.488e+03 -2.135 0.03287 *
## C3 -6.191e+00 1.119e+00 2.377e+03 -5.535 3.46e-08 ***
## C4 -3.464e+00 1.102e+00 2.361e+03 -3.144 0.00169 **
## C5 -7.938e+00 1.097e+00 2.366e+03 -7.239 6.09e-13 ***
## C6 -2.859e-01 1.269e+00 2.451e+03 -0.225 0.82173
## C7 9.370e+00 1.291e+00 2.472e+03 7.258 5.23e-13 ***
## C8 1.261e+01 1.326e+00 2.491e+03 9.513 < 2e-16 ***
## C9 6.372e+00 1.197e+00 2.452e+03 5.325 1.10e-07 ***
## Ideology.c:Naturalness.c -6.074e-03 4.172e-02 2.745e+03 -0.146 0.88426
## Ideology.c:C1 8.261e-01 1.979e+00 2.394e+03 0.417 0.67647
## Ideology.c:C2 -1.167e+00 2.350e+00 2.576e+03 -0.496 0.61962
## Ideology.c:C3 -3.332e+00 1.986e+00 2.400e+03 -1.678 0.09355 .
## Ideology.c:C4 -1.010e+00 1.980e+00 2.409e+03 -0.510 0.60986
## Ideology.c:C5 -1.217e-01 1.887e+00 2.369e+03 -0.064 0.94858
## Ideology.c:C6 -2.442e+00 2.135e+00 2.451e+03 -1.144 0.25284
## Ideology.c:C7 8.138e-01 2.404e+00 2.512e+03 0.338 0.73504
## Ideology.c:C8 4.498e+00 2.266e+00 2.483e+03 1.985 0.04721 *
## Ideology.c:C9 9.589e-01 2.100e+00 2.478e+03 0.457 0.64801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.89317,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.09 | 0.64 | 58.83 – 61.35 | 93.46 | <0.001 |
| Ideology c | 0.32 | 1.13 | -1.90 – 2.53 | 0.28 | 0.780 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.49 | 18.86 | <0.001 |
| C1 | -1.01 | 1.13 | -3.23 – 1.20 | -0.90 | 0.369 |
| C2 | -2.76 | 1.29 | -5.30 – -0.23 | -2.13 | 0.033 |
| C3 | -6.19 | 1.12 | -8.38 – -4.00 | -5.53 | <0.001 |
| C4 | -3.46 | 1.10 | -5.62 – -1.30 | -3.14 | 0.002 |
| C5 | -7.94 | 1.10 | -10.09 – -5.79 | -7.24 | <0.001 |
| C6 | -0.29 | 1.27 | -2.77 – 2.20 | -0.23 | 0.822 |
| C7 | 9.37 | 1.29 | 6.84 – 11.90 | 7.26 | <0.001 |
| C8 | 12.61 | 1.33 | 10.01 – 15.21 | 9.51 | <0.001 |
| C9 | 6.37 | 1.20 | 4.03 – 8.72 | 5.32 | <0.001 |
|
Ideology c * Naturalness c |
-0.01 | 0.04 | -0.09 – 0.08 | -0.15 | 0.884 |
| Ideology c * C1 | 0.83 | 1.98 | -3.06 – 4.71 | 0.42 | 0.676 |
| Ideology c * C2 | -1.17 | 2.35 | -5.77 – 3.44 | -0.50 | 0.620 |
| Ideology c * C3 | -3.33 | 1.99 | -7.23 – 0.56 | -1.68 | 0.094 |
| Ideology c * C4 | -1.01 | 1.98 | -4.89 – 2.87 | -0.51 | 0.610 |
| Ideology c * C5 | -0.12 | 1.89 | -3.82 – 3.58 | -0.06 | 0.949 |
| Ideology c * C6 | -2.44 | 2.14 | -6.63 – 1.74 | -1.14 | 0.253 |
| Ideology c * C7 | 0.81 | 2.40 | -3.90 – 5.53 | 0.34 | 0.735 |
| Ideology c * C8 | 4.50 | 2.27 | 0.06 – 8.94 | 1.99 | 0.047 |
| Ideology c * C9 | 0.96 | 2.10 | -3.16 – 5.08 | 0.46 | 0.648 |
| Random Effects | |||||
| σ2 | 356.73 | ||||
| τ00 id | 294.49 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.575 | ||||
Naturalness
Q.1 How do climate change method contrasts predict naturalness perception?
modA.89 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.89)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 25882.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5623 -0.6131 -0.0219 0.6137 3.4124
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.98 8.123
## Residual 256.42 16.013
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.3544 0.3901 1027.4504 103.457 < 2e-16 ***
## C1 -14.9730 0.8706 2633.0772 -17.198 < 2e-16 ***
## C2 -14.4713 1.0060 2757.7982 -14.386 < 2e-16 ***
## C3 -8.4667 0.8948 2649.1062 -9.462 < 2e-16 ***
## C4 -5.4749 0.8910 2644.5116 -6.145 9.20e-10 ***
## C5 -4.6006 0.8848 2641.5512 -5.199 2.15e-07 ***
## C6 -1.0643 1.0226 2760.2000 -1.041 0.298
## C7 13.9641 1.0059 2756.7292 13.882 < 2e-16 ***
## C8 15.0008 1.0284 2761.1074 14.586 < 2e-16 ***
## C9 21.5355 0.8753 2636.2795 24.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.038
## C2 0.033 -0.100
## C3 -0.023 -0.107 -0.090
## C4 -0.025 -0.105 -0.104 -0.111
## C5 -0.029 -0.102 -0.096 -0.109 -0.104
## C6 0.043 -0.102 -0.155 -0.112 -0.104 -0.104
## C7 0.033 -0.093 -0.154 -0.102 -0.101 -0.103 -0.155
## C8 0.046 -0.108 -0.156 -0.109 -0.103 -0.105 -0.157 -0.156
## C9 -0.035 -0.103 -0.110 -0.104 -0.109 -0.105 -0.101 -0.098 -0.095tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 0.39 | 39.59 – 41.12 | 103.46 | <0.001 |
| C1 | -14.97 | 0.87 | -16.68 – -13.27 | -17.20 | <0.001 |
| C2 | -14.47 | 1.01 | -16.44 – -12.50 | -14.39 | <0.001 |
| C3 | -8.47 | 0.89 | -10.22 – -6.71 | -9.46 | <0.001 |
| C4 | -5.47 | 0.89 | -7.22 – -3.73 | -6.15 | <0.001 |
| C5 | -4.60 | 0.88 | -6.34 – -2.87 | -5.20 | <0.001 |
| C6 | -1.06 | 1.02 | -3.07 – 0.94 | -1.04 | 0.298 |
| C7 | 13.96 | 1.01 | 11.99 – 15.94 | 13.88 | <0.001 |
| C8 | 15.00 | 1.03 | 12.98 – 17.02 | 14.59 | <0.001 |
| C9 | 21.54 | 0.88 | 19.82 – 23.25 | 24.60 | <0.001 |
| Random Effects | |||||
| σ2 | 256.42 | ||||
| τ00 id | 65.98 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.310 / 0.451 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict naturalness perception, over and above climate change method contrasts?
modA.890 <- lmer(Naturalness ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)
summary(modA.890)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c *
## C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 + ATNS_Score.c *
## C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 + ATNS_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25886.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3554 -0.6121 -0.0251 0.6130 3.4304
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 64.08 8.005
## Residual 254.33 15.948
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.36517 0.38687 1023.53993 104.339 < 2e-16 ***
## ATNS_Score.c -0.05041 0.01801 1025.06480 -2.798 0.00523 **
## C1 -14.97989 0.86653 2626.40658 -17.287 < 2e-16 ***
## C2 -14.43113 1.00116 2751.32045 -14.414 < 2e-16 ***
## C3 -8.33854 0.89180 2643.36972 -9.350 < 2e-16 ***
## C4 -5.65731 0.88784 2641.25362 -6.372 2.19e-10 ***
## C5 -4.65034 0.88091 2637.40080 -5.279 1.40e-07 ***
## C6 -1.05901 1.01831 2754.13571 -1.040 0.29845
## C7 13.92452 1.00162 2750.86436 13.902 < 2e-16 ***
## C8 15.13404 1.02429 2756.09437 14.775 < 2e-16 ***
## C9 21.51708 0.87136 2630.62585 24.694 < 2e-16 ***
## ATNS_Score.c:C1 0.05900 0.04083 2637.83599 1.445 0.14861
## ATNS_Score.c:C2 -0.10189 0.04683 2751.54865 -2.176 0.02966 *
## ATNS_Score.c:C3 -0.08860 0.04309 2663.28329 -2.056 0.03984 *
## ATNS_Score.c:C4 -0.11514 0.03974 2621.67183 -2.897 0.00379 **
## ATNS_Score.c:C5 -0.08307 0.04015 2627.22976 -2.069 0.03862 *
## ATNS_Score.c:C6 0.06676 0.04696 2755.11180 1.422 0.15523
## ATNS_Score.c:C7 0.02993 0.04710 2758.35019 0.636 0.52515
## ATNS_Score.c:C8 0.15325 0.04738 2755.18407 3.235 0.00123 **
## ATNS_Score.c:C9 0.04485 0.04052 2630.42446 1.107 0.26847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.890,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.37 | 0.39 | 39.61 – 41.12 | 104.34 | <0.001 |
| ATNS Score c | -0.05 | 0.02 | -0.09 – -0.02 | -2.80 | 0.005 |
| C1 | -14.98 | 0.87 | -16.68 – -13.28 | -17.29 | <0.001 |
| C2 | -14.43 | 1.00 | -16.39 – -12.47 | -14.41 | <0.001 |
| C3 | -8.34 | 0.89 | -10.09 – -6.59 | -9.35 | <0.001 |
| C4 | -5.66 | 0.89 | -7.40 – -3.92 | -6.37 | <0.001 |
| C5 | -4.65 | 0.88 | -6.38 – -2.92 | -5.28 | <0.001 |
| C6 | -1.06 | 1.02 | -3.06 – 0.94 | -1.04 | 0.298 |
| C7 | 13.92 | 1.00 | 11.96 – 15.89 | 13.90 | <0.001 |
| C8 | 15.13 | 1.02 | 13.13 – 17.14 | 14.78 | <0.001 |
| C9 | 21.52 | 0.87 | 19.81 – 23.23 | 24.69 | <0.001 |
| ATNS Score c * C1 | 0.06 | 0.04 | -0.02 – 0.14 | 1.44 | 0.149 |
| ATNS Score c * C2 | -0.10 | 0.05 | -0.19 – -0.01 | -2.18 | 0.030 |
| ATNS Score c * C3 | -0.09 | 0.04 | -0.17 – -0.00 | -2.06 | 0.040 |
| ATNS Score c * C4 | -0.12 | 0.04 | -0.19 – -0.04 | -2.90 | 0.004 |
| ATNS Score c * C5 | -0.08 | 0.04 | -0.16 – -0.00 | -2.07 | 0.039 |
| ATNS Score c * C6 | 0.07 | 0.05 | -0.03 – 0.16 | 1.42 | 0.155 |
| ATNS Score c * C7 | 0.03 | 0.05 | -0.06 – 0.12 | 0.64 | 0.525 |
| ATNS Score c * C8 | 0.15 | 0.05 | 0.06 – 0.25 | 3.23 | 0.001 |
| ATNS Score c * C9 | 0.04 | 0.04 | -0.03 – 0.12 | 1.11 | 0.268 |
| Random Effects | |||||
| σ2 | 254.33 | ||||
| τ00 id | 64.08 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.319 / 0.456 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict naturalness perception, over and above climate change method contrasts?
modA.897 <- lmer(Naturalness ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.897)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c *
## C3 + CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c *
## C6 + CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25898.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5962 -0.6116 -0.0181 0.6040 3.4606
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.08 8.129
## Residual 254.78 15.962
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.034e+01 3.897e-01 1.027e+03 103.496 < 2e-16 ***
## CNS_Score.c -8.003e-03 2.337e-02 1.030e+03 -0.342 0.73209
## C1 -1.490e+01 8.683e-01 2.625e+03 -17.158 < 2e-16 ***
## C2 -1.442e+01 1.003e+00 2.749e+03 -14.367 < 2e-16 ***
## C3 -8.553e+00 8.937e-01 2.640e+03 -9.571 < 2e-16 ***
## C4 -5.467e+00 8.898e-01 2.636e+03 -6.144 9.28e-10 ***
## C5 -4.616e+00 8.826e-01 2.633e+03 -5.230 1.83e-07 ***
## C6 -9.951e-01 1.023e+00 2.749e+03 -0.973 0.33086
## C7 1.384e+01 1.004e+00 2.746e+03 13.785 < 2e-16 ***
## C8 1.502e+01 1.026e+00 2.752e+03 14.640 < 2e-16 ***
## C9 2.152e+01 8.741e-01 2.627e+03 24.625 < 2e-16 ***
## CNS_Score.c:C1 -2.947e-02 5.051e-02 2.609e+03 -0.583 0.55961
## CNS_Score.c:C2 -2.017e-01 6.203e-02 2.758e+03 -3.251 0.00116 **
## CNS_Score.c:C3 -9.618e-02 5.328e-02 2.635e+03 -1.805 0.07118 .
## CNS_Score.c:C4 -2.504e-02 5.145e-02 2.626e+03 -0.487 0.62656
## CNS_Score.c:C5 -5.227e-02 5.455e-02 2.651e+03 -0.958 0.33799
## CNS_Score.c:C6 3.692e-02 5.862e-02 2.746e+03 0.630 0.52894
## CNS_Score.c:C7 1.589e-01 6.073e-02 2.751e+03 2.617 0.00892 **
## CNS_Score.c:C8 1.518e-01 6.189e-02 2.752e+03 2.453 0.01424 *
## CNS_Score.c:C9 2.682e-02 5.500e-02 2.654e+03 0.488 0.62585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.897,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.34 | 0.39 | 39.57 – 41.10 | 103.50 | <0.001 |
| CNS Score c | -0.01 | 0.02 | -0.05 – 0.04 | -0.34 | 0.732 |
| C1 | -14.90 | 0.87 | -16.60 – -13.20 | -17.16 | <0.001 |
| C2 | -14.42 | 1.00 | -16.38 – -12.45 | -14.37 | <0.001 |
| C3 | -8.55 | 0.89 | -10.31 – -6.80 | -9.57 | <0.001 |
| C4 | -5.47 | 0.89 | -7.21 – -3.72 | -6.14 | <0.001 |
| C5 | -4.62 | 0.88 | -6.35 – -2.89 | -5.23 | <0.001 |
| C6 | -1.00 | 1.02 | -3.00 – 1.01 | -0.97 | 0.331 |
| C7 | 13.84 | 1.00 | 11.88 – 15.81 | 13.79 | <0.001 |
| C8 | 15.02 | 1.03 | 13.00 – 17.03 | 14.64 | <0.001 |
| C9 | 21.52 | 0.87 | 19.81 – 23.24 | 24.63 | <0.001 |
| CNS Score c * C1 | -0.03 | 0.05 | -0.13 – 0.07 | -0.58 | 0.560 |
| CNS Score c * C2 | -0.20 | 0.06 | -0.32 – -0.08 | -3.25 | 0.001 |
| CNS Score c * C3 | -0.10 | 0.05 | -0.20 – 0.01 | -1.81 | 0.071 |
| CNS Score c * C4 | -0.03 | 0.05 | -0.13 – 0.08 | -0.49 | 0.627 |
| CNS Score c * C5 | -0.05 | 0.05 | -0.16 – 0.05 | -0.96 | 0.338 |
| CNS Score c * C6 | 0.04 | 0.06 | -0.08 – 0.15 | 0.63 | 0.529 |
| CNS Score c * C7 | 0.16 | 0.06 | 0.04 – 0.28 | 2.62 | 0.009 |
| CNS Score c * C8 | 0.15 | 0.06 | 0.03 – 0.27 | 2.45 | 0.014 |
| CNS Score c * C9 | 0.03 | 0.06 | -0.08 – 0.13 | 0.49 | 0.626 |
| Random Effects | |||||
| σ2 | 254.78 | ||||
| τ00 id | 66.08 | ||||
| ICC | 0.21 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.314 / 0.455 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict naturalness perception, over and above climate change method contrasts?
modA.896 <- lmer(Naturalness ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 +(1|id), data = L)
summary(modA.896)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c *
## C2 + CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25912.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3221 -0.6173 -0.0148 0.6076 3.6334
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.4 8.087
## Residual 255.9 15.997
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.33495 0.38923 1022.61614 103.627 < 2e-16 ***
## CCBelief_Score.c 0.04448 0.01655 1029.01907 2.687 0.00732 **
## C1 -14.95444 0.86964 2623.35271 -17.196 < 2e-16 ***
## C2 -14.50160 1.00514 2749.48736 -14.427 < 2e-16 ***
## C3 -8.45456 0.89390 2639.96439 -9.458 < 2e-16 ***
## C4 -5.45790 0.89228 2636.14575 -6.117 1.10e-09 ***
## C5 -4.64756 0.88463 2633.59490 -5.254 1.61e-07 ***
## C6 -1.07428 1.02166 2750.98655 -1.052 0.29312
## C7 13.98781 1.00467 2746.96028 13.923 < 2e-16 ***
## C8 14.99079 1.02726 2752.64456 14.593 < 2e-16 ***
## C9 21.56269 0.87521 2630.16320 24.637 < 2e-16 ***
## CCBelief_Score.c:C1 -0.03175 0.03675 2623.32741 -0.864 0.38778
## CCBelief_Score.c:C2 -0.10646 0.04002 2739.00119 -2.661 0.00785 **
## CCBelief_Score.c:C3 -0.02674 0.03775 2638.91669 -0.708 0.47889
## CCBelief_Score.c:C4 -0.03222 0.03593 2608.96516 -0.897 0.36991
## CCBelief_Score.c:C5 0.01011 0.03887 2653.06539 0.260 0.79479
## CCBelief_Score.c:C6 0.02990 0.04510 2758.78778 0.663 0.50751
## CCBelief_Score.c:C7 0.07591 0.04343 2754.36939 1.748 0.08058 .
## CCBelief_Score.c:C8 0.05616 0.04435 2756.25944 1.266 0.20554
## CCBelief_Score.c:C9 -0.00613 0.03843 2651.70728 -0.160 0.87326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.896,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.33 | 0.39 | 39.57 – 41.10 | 103.63 | <0.001 |
| CCBelief Score c | 0.04 | 0.02 | 0.01 – 0.08 | 2.69 | 0.007 |
| C1 | -14.95 | 0.87 | -16.66 – -13.25 | -17.20 | <0.001 |
| C2 | -14.50 | 1.01 | -16.47 – -12.53 | -14.43 | <0.001 |
| C3 | -8.45 | 0.89 | -10.21 – -6.70 | -9.46 | <0.001 |
| C4 | -5.46 | 0.89 | -7.21 – -3.71 | -6.12 | <0.001 |
| C5 | -4.65 | 0.88 | -6.38 – -2.91 | -5.25 | <0.001 |
| C6 | -1.07 | 1.02 | -3.08 – 0.93 | -1.05 | 0.293 |
| C7 | 13.99 | 1.00 | 12.02 – 15.96 | 13.92 | <0.001 |
| C8 | 14.99 | 1.03 | 12.98 – 17.00 | 14.59 | <0.001 |
| C9 | 21.56 | 0.88 | 19.85 – 23.28 | 24.64 | <0.001 |
| CCBelief Score c * C1 | -0.03 | 0.04 | -0.10 – 0.04 | -0.86 | 0.388 |
| CCBelief Score c * C2 | -0.11 | 0.04 | -0.18 – -0.03 | -2.66 | 0.008 |
| CCBelief Score c * C3 | -0.03 | 0.04 | -0.10 – 0.05 | -0.71 | 0.479 |
| CCBelief Score c * C4 | -0.03 | 0.04 | -0.10 – 0.04 | -0.90 | 0.370 |
| CCBelief Score c * C5 | 0.01 | 0.04 | -0.07 – 0.09 | 0.26 | 0.795 |
| CCBelief Score c * C6 | 0.03 | 0.05 | -0.06 – 0.12 | 0.66 | 0.508 |
| CCBelief Score c * C7 | 0.08 | 0.04 | -0.01 – 0.16 | 1.75 | 0.081 |
| CCBelief Score c * C8 | 0.06 | 0.04 | -0.03 – 0.14 | 1.27 | 0.206 |
| CCBelief Score c * C9 | -0.01 | 0.04 | -0.08 – 0.07 | -0.16 | 0.873 |
| Random Effects | |||||
| σ2 | 255.90 | ||||
| τ00 id | 65.40 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.314 / 0.454 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict naturalness perception, over and above climate change method contrasts?
modA.895 <- lmer(Naturalness ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)
summary(modA.895)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25919.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5389 -0.6163 -0.0259 0.6125 3.3941
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.52 8.156
## Residual 255.82 15.994
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.035e+01 3.908e-01 1.026e+03 103.259 < 2e-16 ***
## Collectivism_Score.c -8.400e-03 1.642e-02 1.032e+03 -0.512 0.6090
## C1 -1.497e+01 8.701e-01 2.621e+03 -17.208 < 2e-16 ***
## C2 -1.438e+01 1.007e+00 2.746e+03 -14.275 < 2e-16 ***
## C3 -8.502e+00 8.945e-01 2.638e+03 -9.504 < 2e-16 ***
## C4 -5.431e+00 8.912e-01 2.634e+03 -6.094 1.26e-09 ***
## C5 -4.751e+00 8.878e-01 2.631e+03 -5.351 9.49e-08 ***
## C6 -1.077e+00 1.022e+00 2.749e+03 -1.053 0.2923
## C7 1.395e+01 1.008e+00 2.745e+03 13.841 < 2e-16 ***
## C8 1.502e+01 1.028e+00 2.749e+03 14.614 < 2e-16 ***
## C9 2.161e+01 8.751e-01 2.624e+03 24.692 < 2e-16 ***
## Collectivism_Score.c:C1 3.073e-02 3.955e-02 2.666e+03 0.777 0.4372
## Collectivism_Score.c:C2 5.367e-02 4.075e-02 2.741e+03 1.317 0.1879
## Collectivism_Score.c:C3 -2.664e-02 3.791e-02 2.644e+03 -0.703 0.4823
## Collectivism_Score.c:C4 -2.945e-02 3.764e-02 2.639e+03 -0.782 0.4340
## Collectivism_Score.c:C5 -5.772e-02 3.617e-02 2.616e+03 -1.596 0.1107
## Collectivism_Score.c:C6 2.912e-02 4.307e-02 2.752e+03 0.676 0.4990
## Collectivism_Score.c:C7 9.114e-03 4.366e-02 2.751e+03 0.209 0.8346
## Collectivism_Score.c:C8 5.004e-02 4.327e-02 2.755e+03 1.157 0.2475
## Collectivism_Score.c:C9 -7.927e-02 3.732e-02 2.635e+03 -2.124 0.0337 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.895,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 0.39 | 39.59 – 41.12 | 103.26 | <0.001 |
| Collectivism Score c | -0.01 | 0.02 | -0.04 – 0.02 | -0.51 | 0.609 |
| C1 | -14.97 | 0.87 | -16.68 – -13.27 | -17.21 | <0.001 |
| C2 | -14.38 | 1.01 | -16.35 – -12.40 | -14.27 | <0.001 |
| C3 | -8.50 | 0.89 | -10.26 – -6.75 | -9.50 | <0.001 |
| C4 | -5.43 | 0.89 | -7.18 – -3.68 | -6.09 | <0.001 |
| C5 | -4.75 | 0.89 | -6.49 – -3.01 | -5.35 | <0.001 |
| C6 | -1.08 | 1.02 | -3.08 – 0.93 | -1.05 | 0.292 |
| C7 | 13.95 | 1.01 | 11.97 – 15.92 | 13.84 | <0.001 |
| C8 | 15.02 | 1.03 | 13.01 – 17.04 | 14.61 | <0.001 |
| C9 | 21.61 | 0.88 | 19.89 – 23.32 | 24.69 | <0.001 |
| Collectivism Score c * C1 | 0.03 | 0.04 | -0.05 – 0.11 | 0.78 | 0.437 |
| Collectivism Score c * C2 | 0.05 | 0.04 | -0.03 – 0.13 | 1.32 | 0.188 |
| Collectivism Score c * C3 | -0.03 | 0.04 | -0.10 – 0.05 | -0.70 | 0.482 |
| Collectivism Score c * C4 | -0.03 | 0.04 | -0.10 – 0.04 | -0.78 | 0.434 |
| Collectivism Score c * C5 | -0.06 | 0.04 | -0.13 – 0.01 | -1.60 | 0.111 |
| Collectivism Score c * C6 | 0.03 | 0.04 | -0.06 – 0.11 | 0.68 | 0.499 |
| Collectivism Score c * C7 | 0.01 | 0.04 | -0.08 – 0.09 | 0.21 | 0.835 |
| Collectivism Score c * C8 | 0.05 | 0.04 | -0.03 – 0.13 | 1.16 | 0.248 |
| Collectivism Score c * C9 | -0.08 | 0.04 | -0.15 – -0.01 | -2.12 | 0.034 |
| Random Effects | |||||
| σ2 | 255.82 | ||||
| τ00 id | 66.52 | ||||
| ICC | 0.21 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.311 / 0.454 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict naturalness perception, over and above climate change method contrasts?
modA.894 <- lmer(Naturalness ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.894)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C5 + Individualism_Score.c * C6 + Individualism_Score.c *
## C7 + Individualism_Score.c * C8 + Individualism_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25915.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5577 -0.6241 -0.0221 0.6063 3.3612
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.66 8.103
## Residual 256.73 16.023
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.33556 0.39013 1025.01900 103.391 < 2e-16 ***
## Individualism_Score.c -0.02360 0.02319 1031.78679 -1.018 0.3090
## C1 -14.93801 0.87141 2624.61407 -17.142 < 2e-16 ***
## C2 -14.46476 1.01059 2749.14050 -14.313 < 2e-16 ***
## C3 -8.50842 0.89596 2640.38207 -9.496 < 2e-16 ***
## C4 -5.47312 0.89175 2635.51274 -6.137 9.65e-10 ***
## C5 -4.57789 0.88544 2633.86150 -5.170 2.51e-07 ***
## C6 -1.07380 1.02596 2752.04103 -1.047 0.2954
## C7 13.94925 1.00664 2747.59979 13.857 < 2e-16 ***
## C8 15.02073 1.03055 2752.23394 14.575 < 2e-16 ***
## C9 21.54978 0.87610 2626.98245 24.597 < 2e-16 ***
## Individualism_Score.c:C1 0.03411 0.05164 2623.08285 0.660 0.5090
## Individualism_Score.c:C2 0.02092 0.05706 2742.06424 0.367 0.7139
## Individualism_Score.c:C3 -0.06688 0.05521 2663.38516 -1.211 0.2258
## Individualism_Score.c:C4 -0.03002 0.05322 2639.90270 -0.564 0.5728
## Individualism_Score.c:C5 -0.09541 0.05302 2638.45194 -1.799 0.0721 .
## Individualism_Score.c:C6 -0.01937 0.06334 2759.33754 -0.306 0.7598
## Individualism_Score.c:C7 0.05956 0.06349 2759.34064 0.938 0.3483
## Individualism_Score.c:C8 0.01010 0.05872 2748.37389 0.172 0.8635
## Individualism_Score.c:C9 0.01058 0.05115 2620.09621 0.207 0.8361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.894,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.34 | 0.39 | 39.57 – 41.10 | 103.39 | <0.001 |
| Individualism Score c | -0.02 | 0.02 | -0.07 – 0.02 | -1.02 | 0.309 |
| C1 | -14.94 | 0.87 | -16.65 – -13.23 | -17.14 | <0.001 |
| C2 | -14.46 | 1.01 | -16.45 – -12.48 | -14.31 | <0.001 |
| C3 | -8.51 | 0.90 | -10.27 – -6.75 | -9.50 | <0.001 |
| C4 | -5.47 | 0.89 | -7.22 – -3.72 | -6.14 | <0.001 |
| C5 | -4.58 | 0.89 | -6.31 – -2.84 | -5.17 | <0.001 |
| C6 | -1.07 | 1.03 | -3.09 – 0.94 | -1.05 | 0.295 |
| C7 | 13.95 | 1.01 | 11.98 – 15.92 | 13.86 | <0.001 |
| C8 | 15.02 | 1.03 | 13.00 – 17.04 | 14.58 | <0.001 |
| C9 | 21.55 | 0.88 | 19.83 – 23.27 | 24.60 | <0.001 |
|
Individualism Score c * C1 |
0.03 | 0.05 | -0.07 – 0.14 | 0.66 | 0.509 |
|
Individualism Score c * C2 |
0.02 | 0.06 | -0.09 – 0.13 | 0.37 | 0.714 |
|
Individualism Score c * C3 |
-0.07 | 0.06 | -0.18 – 0.04 | -1.21 | 0.226 |
|
Individualism Score c * C4 |
-0.03 | 0.05 | -0.13 – 0.07 | -0.56 | 0.573 |
|
Individualism Score c * C5 |
-0.10 | 0.05 | -0.20 – 0.01 | -1.80 | 0.072 |
|
Individualism Score c * C6 |
-0.02 | 0.06 | -0.14 – 0.10 | -0.31 | 0.760 |
|
Individualism Score c * C7 |
0.06 | 0.06 | -0.06 – 0.18 | 0.94 | 0.348 |
|
Individualism Score c * C8 |
0.01 | 0.06 | -0.11 – 0.13 | 0.17 | 0.863 |
|
Individualism Score c * C9 |
0.01 | 0.05 | -0.09 – 0.11 | 0.21 | 0.836 |
| Random Effects | |||||
| σ2 | 256.73 | ||||
| τ00 id | 65.66 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.311 / 0.451 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does individualism predict naturalness perception, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.893 <- lmer(Naturalness ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.893)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c *
## C3 + Ideology.c * C4 + Ideology.c * C5 + Ideology.c * C6 +
## Ideology.c * C7 + Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25847.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5601 -0.6154 -0.0239 0.6158 3.4188
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.5 8.155
## Residual 256.1 16.002
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.36007 0.39083 1026.24080 103.268 < 2e-16 ***
## Ideology.c -0.64349 0.68542 1029.15599 -0.939 0.3480
## C1 -14.98484 0.87154 2623.56403 -17.193 < 2e-16 ***
## C2 -14.47462 1.00576 2746.50524 -14.392 < 2e-16 ***
## C3 -8.46632 0.89529 2639.72334 -9.457 < 2e-16 ***
## C4 -5.46242 0.89085 2633.37109 -6.132 1.00e-09 ***
## C5 -4.67983 0.88732 2631.85826 -5.274 1.44e-07 ***
## C6 -1.05619 1.02243 2748.21661 -1.033 0.3017
## C7 13.91382 1.00607 2746.95960 13.830 < 2e-16 ***
## C8 14.97721 1.02845 2751.13613 14.563 < 2e-16 ***
## C9 21.71419 0.87966 2629.40609 24.685 < 2e-16 ***
## Ideology.c:C1 0.69660 1.54893 2637.81470 0.450 0.6529
## Ideology.c:C2 -2.21791 1.78796 2759.34273 -1.240 0.2149
## Ideology.c:C3 -0.13495 1.59389 2660.52547 -0.085 0.9325
## Ideology.c:C4 -0.70555 1.59645 2663.17662 -0.442 0.6586
## Ideology.c:C5 1.89206 1.53244 2635.19627 1.235 0.2171
## Ideology.c:C6 0.97770 1.71946 2747.98441 0.569 0.5697
## Ideology.c:C7 -2.51397 1.85852 2759.19112 -1.353 0.1763
## Ideology.c:C8 0.02545 1.76434 2752.63655 0.014 0.9885
## Ideology.c:C9 3.20372 1.56218 2641.17392 2.051 0.0404 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.893,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.36 | 0.39 | 39.59 – 41.13 | 103.27 | <0.001 |
| Ideology c | -0.64 | 0.69 | -1.99 – 0.70 | -0.94 | 0.348 |
| C1 | -14.98 | 0.87 | -16.69 – -13.28 | -17.19 | <0.001 |
| C2 | -14.47 | 1.01 | -16.45 – -12.50 | -14.39 | <0.001 |
| C3 | -8.47 | 0.90 | -10.22 – -6.71 | -9.46 | <0.001 |
| C4 | -5.46 | 0.89 | -7.21 – -3.72 | -6.13 | <0.001 |
| C5 | -4.68 | 0.89 | -6.42 – -2.94 | -5.27 | <0.001 |
| C6 | -1.06 | 1.02 | -3.06 – 0.95 | -1.03 | 0.302 |
| C7 | 13.91 | 1.01 | 11.94 – 15.89 | 13.83 | <0.001 |
| C8 | 14.98 | 1.03 | 12.96 – 16.99 | 14.56 | <0.001 |
| C9 | 21.71 | 0.88 | 19.99 – 23.44 | 24.68 | <0.001 |
| Ideology c * C1 | 0.70 | 1.55 | -2.34 – 3.73 | 0.45 | 0.653 |
| Ideology c * C2 | -2.22 | 1.79 | -5.72 – 1.29 | -1.24 | 0.215 |
| Ideology c * C3 | -0.13 | 1.59 | -3.26 – 2.99 | -0.08 | 0.933 |
| Ideology c * C4 | -0.71 | 1.60 | -3.84 – 2.42 | -0.44 | 0.659 |
| Ideology c * C5 | 1.89 | 1.53 | -1.11 – 4.90 | 1.23 | 0.217 |
| Ideology c * C6 | 0.98 | 1.72 | -2.39 – 4.35 | 0.57 | 0.570 |
| Ideology c * C7 | -2.51 | 1.86 | -6.16 – 1.13 | -1.35 | 0.176 |
| Ideology c * C8 | 0.03 | 1.76 | -3.43 – 3.48 | 0.01 | 0.988 |
| Ideology c * C9 | 3.20 | 1.56 | 0.14 – 6.27 | 2.05 | 0.040 |
| Random Effects | |||||
| σ2 | 256.07 | ||||
| τ00 id | 66.50 | ||||
| ICC | 0.21 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.311 / 0.453 | ||||
Risk
Q.1 How do climate change method contrasts predict risk perception?
modA.860 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.860)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27466.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5812 -0.6117 -0.0694 0.5565 3.6749
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Residual 391.9 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.4243 0.5609 1018.7068 57.808 < 2e-16 ***
## C1 10.8426 1.0990 2495.8856 9.866 < 2e-16 ***
## C2 20.5519 1.2763 2615.0096 16.103 < 2e-16 ***
## C3 13.5376 1.1303 2510.3315 11.977 < 2e-16 ***
## C4 5.6756 1.1252 2505.6400 5.044 4.88e-07 ***
## C5 5.7300 1.1173 2503.1806 5.128 3.15e-07 ***
## C6 -5.7162 1.2976 2616.5136 -4.405 1.10e-05 ***
## C7 -14.9462 1.2762 2613.2863 -11.711 < 2e-16 ***
## C8 -21.6013 1.3050 2617.2425 -16.553 < 2e-16 ***
## C9 -16.5218 1.1050 2499.0970 -14.952 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.033
## C2 0.027 -0.095
## C3 -0.019 -0.111 -0.081
## C4 -0.021 -0.108 -0.100 -0.115
## C5 -0.025 -0.105 -0.090 -0.113 -0.107
## C6 0.036 -0.097 -0.164 -0.111 -0.100 -0.100
## C7 0.027 -0.085 -0.163 -0.097 -0.096 -0.100 -0.164
## C8 0.039 -0.105 -0.165 -0.106 -0.097 -0.100 -0.166 -0.165
## C9 -0.030 -0.107 -0.109 -0.107 -0.114 -0.109 -0.096 -0.092 -0.087tab_model(modA.860,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.42 | 0.56 | 31.32 – 33.52 | 57.81 | <0.001 |
| C1 | 10.84 | 1.10 | 8.69 – 13.00 | 9.87 | <0.001 |
| C2 | 20.55 | 1.28 | 18.05 – 23.05 | 16.10 | <0.001 |
| C3 | 13.54 | 1.13 | 11.32 – 15.75 | 11.98 | <0.001 |
| C4 | 5.68 | 1.13 | 3.47 – 7.88 | 5.04 | <0.001 |
| C5 | 5.73 | 1.12 | 3.54 – 7.92 | 5.13 | <0.001 |
| C6 | -5.72 | 1.30 | -8.26 – -3.17 | -4.41 | <0.001 |
| C7 | -14.95 | 1.28 | -17.45 – -12.44 | -11.71 | <0.001 |
| C8 | -21.60 | 1.30 | -24.16 – -19.04 | -16.55 | <0.001 |
| C9 | -16.52 | 1.11 | -18.69 – -14.36 | -14.95 | <0.001 |
| Random Effects | |||||
| σ2 | 391.88 | ||||
| τ00 id | 183.49 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.472 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict risk perception, over and above climate change method contrasts?
modA.861 <- lmer(Risk ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)
summary(modA.861)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c *
## C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 + ATNS_Score.c *
## C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 + ATNS_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27331.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1012 -0.5982 -0.0857 0.5966 3.6474
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 147.7 12.16
## Residual 384.6 19.61
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.46753 0.52613 1018.60753 61.710 < 2e-16 ***
## ATNS_Score.c 0.27267 0.02449 1019.94321 11.132 < 2e-16 ***
## C1 10.83119 1.08127 2533.08463 10.017 < 2e-16 ***
## C2 20.40756 1.25363 2656.04884 16.279 < 2e-16 ***
## C3 13.38151 1.11329 2549.07193 12.020 < 2e-16 ***
## C4 6.11637 1.10829 2547.17700 5.519 3.76e-08 ***
## C5 5.77784 1.09952 2543.42325 5.255 1.60e-07 ***
## C6 -5.73268 1.27521 2658.23283 -4.495 7.24e-06 ***
## C7 -14.76121 1.25418 2655.13110 -11.770 < 2e-16 ***
## C8 -21.81600 1.28277 2660.24627 -17.007 < 2e-16 ***
## C9 -16.46949 1.08741 2537.44978 -15.146 < 2e-16 ***
## ATNS_Score.c:C1 0.08984 0.05097 2544.99643 1.763 0.078056 .
## ATNS_Score.c:C2 0.13866 0.05864 2656.41771 2.365 0.018115 *
## ATNS_Score.c:C3 -0.01524 0.05382 2568.16817 -0.283 0.777002
## ATNS_Score.c:C4 0.19404 0.04958 2529.44133 3.914 9.33e-05 ***
## ATNS_Score.c:C5 0.10513 0.05010 2534.73476 2.099 0.035952 *
## ATNS_Score.c:C6 -0.06786 0.05881 2659.91735 -1.154 0.248657
## ATNS_Score.c:C7 -0.18671 0.05899 2663.87770 -3.165 0.001569 **
## ATNS_Score.c:C8 -0.21320 0.05933 2659.40768 -3.593 0.000332 ***
## ATNS_Score.c:C9 -0.07539 0.05057 2537.08330 -1.491 0.136168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.861,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.47 | 0.53 | 31.44 – 33.50 | 61.71 | <0.001 |
| ATNS Score c | 0.27 | 0.02 | 0.22 – 0.32 | 11.13 | <0.001 |
| C1 | 10.83 | 1.08 | 8.71 – 12.95 | 10.02 | <0.001 |
| C2 | 20.41 | 1.25 | 17.95 – 22.87 | 16.28 | <0.001 |
| C3 | 13.38 | 1.11 | 11.20 – 15.56 | 12.02 | <0.001 |
| C4 | 6.12 | 1.11 | 3.94 – 8.29 | 5.52 | <0.001 |
| C5 | 5.78 | 1.10 | 3.62 – 7.93 | 5.25 | <0.001 |
| C6 | -5.73 | 1.28 | -8.23 – -3.23 | -4.50 | <0.001 |
| C7 | -14.76 | 1.25 | -17.22 – -12.30 | -11.77 | <0.001 |
| C8 | -21.82 | 1.28 | -24.33 – -19.30 | -17.01 | <0.001 |
| C9 | -16.47 | 1.09 | -18.60 – -14.34 | -15.15 | <0.001 |
| ATNS Score c * C1 | 0.09 | 0.05 | -0.01 – 0.19 | 1.76 | 0.078 |
| ATNS Score c * C2 | 0.14 | 0.06 | 0.02 – 0.25 | 2.36 | 0.018 |
| ATNS Score c * C3 | -0.02 | 0.05 | -0.12 – 0.09 | -0.28 | 0.777 |
| ATNS Score c * C4 | 0.19 | 0.05 | 0.10 – 0.29 | 3.91 | <0.001 |
| ATNS Score c * C5 | 0.11 | 0.05 | 0.01 – 0.20 | 2.10 | 0.036 |
| ATNS Score c * C6 | -0.07 | 0.06 | -0.18 – 0.05 | -1.15 | 0.249 |
| ATNS Score c * C7 | -0.19 | 0.06 | -0.30 – -0.07 | -3.16 | 0.002 |
| ATNS Score c * C8 | -0.21 | 0.06 | -0.33 – -0.10 | -3.59 | <0.001 |
| ATNS Score c * C9 | -0.08 | 0.05 | -0.17 – 0.02 | -1.49 | 0.136 |
| Random Effects | |||||
| σ2 | 384.57 | ||||
| τ00 id | 147.75 | ||||
| ICC | 0.28 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.285 / 0.484 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on perceptions of naturalness in predicting risk perception, over and above climate change method contrasts?
modA.8617 <- lmer(Risk ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8617)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 +
## ATNS_Score.c * C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 +
## ATNS_Score.c * C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 +
## ATNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26941.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0924 -0.6033 -0.0406 0.5643 3.6474
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.7 12.07
## Residual 326.8 18.08
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.251e+01 5.057e-01 1.017e+03 64.276 < 2e-16
## ATNS_Score.c 2.463e-01 2.356e-02 1.020e+03 10.456 < 2e-16
## Naturalness.c -4.257e-01 2.167e-02 2.949e+03 -19.643 < 2e-16
## C1 4.589e+00 1.055e+00 2.560e+03 4.352 1.40e-05
## C2 1.400e+01 1.205e+00 2.658e+03 11.616 < 2e-16
## C3 9.657e+00 1.047e+00 2.524e+03 9.219 < 2e-16
## C4 3.403e+00 1.035e+00 2.511e+03 3.288 0.001024
## C5 3.564e+00 1.024e+00 2.511e+03 3.479 0.000511
## C6 -5.999e+00 1.184e+00 2.616e+03 -5.067 4.32e-07
## C7 -8.853e+00 1.202e+00 2.637e+03 -7.366 2.34e-13
## C8 -1.491e+01 1.236e+00 2.666e+03 -12.068 < 2e-16
## C9 -7.211e+00 1.110e+00 2.607e+03 -6.494 9.97e-11
## ATNS_Score.c:Naturalness.c -4.955e-03 8.559e-04 2.952e+03 -5.789 7.81e-09
## ATNS_Score.c:C1 3.505e-02 4.925e-02 2.551e+03 0.712 0.476657
## ATNS_Score.c:C2 2.326e-02 5.590e-02 2.660e+03 0.416 0.677371
## ATNS_Score.c:C3 -8.171e-02 5.023e-02 2.527e+03 -1.627 0.103907
## ATNS_Score.c:C4 1.113e-01 4.634e-02 2.494e+03 2.401 0.016419
## ATNS_Score.c:C5 3.916e-02 4.675e-02 2.502e+03 0.838 0.402348
## ATNS_Score.c:C6 -5.163e-02 5.461e-02 2.618e+03 -0.945 0.344505
## ATNS_Score.c:C7 -9.297e-02 5.629e-02 2.661e+03 -1.652 0.098702
## ATNS_Score.c:C8 -6.984e-02 5.681e-02 2.658e+03 -1.229 0.219001
## ATNS_Score.c:C9 5.162e-02 5.048e-02 2.563e+03 1.023 0.306578
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## C1 ***
## C2 ***
## C3 ***
## C4 **
## C5 ***
## C6 ***
## C7 ***
## C8 ***
## C9 ***
## ATNS_Score.c:Naturalness.c ***
## ATNS_Score.c:C1
## ATNS_Score.c:C2
## ATNS_Score.c:C3
## ATNS_Score.c:C4 *
## ATNS_Score.c:C5
## ATNS_Score.c:C6
## ATNS_Score.c:C7 .
## ATNS_Score.c:C8
## ATNS_Score.c:C9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8617,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.51 | 0.51 | 31.52 – 33.50 | 64.28 | <0.001 |
| ATNS Score c | 0.25 | 0.02 | 0.20 – 0.29 | 10.46 | <0.001 |
| Naturalness c | -0.43 | 0.02 | -0.47 – -0.38 | -19.64 | <0.001 |
| C1 | 4.59 | 1.05 | 2.52 – 6.66 | 4.35 | <0.001 |
| C2 | 14.00 | 1.21 | 11.64 – 16.36 | 11.62 | <0.001 |
| C3 | 9.66 | 1.05 | 7.60 – 11.71 | 9.22 | <0.001 |
| C4 | 3.40 | 1.04 | 1.37 – 5.43 | 3.29 | 0.001 |
| C5 | 3.56 | 1.02 | 1.56 – 5.57 | 3.48 | 0.001 |
| C6 | -6.00 | 1.18 | -8.32 – -3.68 | -5.07 | <0.001 |
| C7 | -8.85 | 1.20 | -11.21 – -6.50 | -7.37 | <0.001 |
| C8 | -14.91 | 1.24 | -17.34 – -12.49 | -12.07 | <0.001 |
| C9 | -7.21 | 1.11 | -9.39 – -5.03 | -6.49 | <0.001 |
|
ATNS Score c * Naturalness c |
-0.00 | 0.00 | -0.01 – -0.00 | -5.79 | <0.001 |
| ATNS Score c * C1 | 0.04 | 0.05 | -0.06 – 0.13 | 0.71 | 0.477 |
| ATNS Score c * C2 | 0.02 | 0.06 | -0.09 – 0.13 | 0.42 | 0.677 |
| ATNS Score c * C3 | -0.08 | 0.05 | -0.18 – 0.02 | -1.63 | 0.104 |
| ATNS Score c * C4 | 0.11 | 0.05 | 0.02 – 0.20 | 2.40 | 0.016 |
| ATNS Score c * C5 | 0.04 | 0.05 | -0.05 – 0.13 | 0.84 | 0.402 |
| ATNS Score c * C6 | -0.05 | 0.05 | -0.16 – 0.06 | -0.95 | 0.344 |
| ATNS Score c * C7 | -0.09 | 0.06 | -0.20 – 0.02 | -1.65 | 0.099 |
| ATNS Score c * C8 | -0.07 | 0.06 | -0.18 – 0.04 | -1.23 | 0.219 |
| ATNS Score c * C9 | 0.05 | 0.05 | -0.05 – 0.15 | 1.02 | 0.307 |
| Random Effects | |||||
| σ2 | 326.83 | ||||
| τ00 id | 145.70 | ||||
| ICC | 0.31 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.369 / 0.564 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict risk perception, over and above climate change method contrasts?
modA.863 <- lmer(Risk ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.863)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c *
## C3 + CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c *
## C6 + CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27466.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9641 -0.6024 -0.0726 0.5780 3.9963
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 181.9 13.49
## Residual 388.4 19.71
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.248e+01 5.588e-01 1.017e+03 58.122 < 2e-16 ***
## CNS_Score.c -2.138e-03 3.350e-02 1.020e+03 -0.064 0.949132
## C1 1.069e+01 1.095e+00 2.489e+03 9.766 < 2e-16 ***
## C2 2.044e+01 1.271e+00 2.608e+03 16.082 < 2e-16 ***
## C3 1.357e+01 1.127e+00 2.503e+03 12.038 < 2e-16 ***
## C4 5.735e+00 1.122e+00 2.499e+03 5.111 3.45e-07 ***
## C5 5.745e+00 1.113e+00 2.496e+03 5.162 2.63e-07 ***
## C6 -5.742e+00 1.296e+00 2.607e+03 -4.430 9.82e-06 ***
## C7 -1.473e+01 1.272e+00 2.604e+03 -11.582 < 2e-16 ***
## C8 -2.168e+01 1.300e+00 2.609e+03 -16.679 < 2e-16 ***
## C9 -1.643e+01 1.102e+00 2.491e+03 -14.912 < 2e-16 ***
## CNS_Score.c:C1 -7.767e-03 6.364e-02 2.477e+03 -0.122 0.902864
## CNS_Score.c:C2 2.847e-01 7.862e-02 2.617e+03 3.621 0.000300 ***
## CNS_Score.c:C3 7.460e-02 6.719e-02 2.499e+03 1.110 0.267005
## CNS_Score.c:C4 1.510e-01 6.486e-02 2.493e+03 2.328 0.020006 *
## CNS_Score.c:C5 5.511e-02 6.883e-02 2.513e+03 0.801 0.423431
## CNS_Score.c:C6 1.619e-02 7.426e-02 2.607e+03 0.218 0.827437
## CNS_Score.c:C7 -2.724e-01 7.695e-02 2.609e+03 -3.540 0.000408 ***
## CNS_Score.c:C8 -1.877e-01 7.841e-02 2.610e+03 -2.393 0.016767 *
## CNS_Score.c:C9 -1.335e-01 6.942e-02 2.516e+03 -1.923 0.054649 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.863,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.48 | 0.56 | 31.38 – 33.57 | 58.12 | <0.001 |
| CNS Score c | -0.00 | 0.03 | -0.07 – 0.06 | -0.06 | 0.949 |
| C1 | 10.69 | 1.09 | 8.54 – 12.84 | 9.77 | <0.001 |
| C2 | 20.44 | 1.27 | 17.95 – 22.94 | 16.08 | <0.001 |
| C3 | 13.57 | 1.13 | 11.36 – 15.78 | 12.04 | <0.001 |
| C4 | 5.73 | 1.12 | 3.53 – 7.94 | 5.11 | <0.001 |
| C5 | 5.75 | 1.11 | 3.56 – 7.93 | 5.16 | <0.001 |
| C6 | -5.74 | 1.30 | -8.28 – -3.20 | -4.43 | <0.001 |
| C7 | -14.73 | 1.27 | -17.23 – -12.24 | -11.58 | <0.001 |
| C8 | -21.68 | 1.30 | -24.22 – -19.13 | -16.68 | <0.001 |
| C9 | -16.43 | 1.10 | -18.59 – -14.27 | -14.91 | <0.001 |
| CNS Score c * C1 | -0.01 | 0.06 | -0.13 – 0.12 | -0.12 | 0.903 |
| CNS Score c * C2 | 0.28 | 0.08 | 0.13 – 0.44 | 3.62 | <0.001 |
| CNS Score c * C3 | 0.07 | 0.07 | -0.06 – 0.21 | 1.11 | 0.267 |
| CNS Score c * C4 | 0.15 | 0.06 | 0.02 – 0.28 | 2.33 | 0.020 |
| CNS Score c * C5 | 0.06 | 0.07 | -0.08 – 0.19 | 0.80 | 0.423 |
| CNS Score c * C6 | 0.02 | 0.07 | -0.13 – 0.16 | 0.22 | 0.827 |
| CNS Score c * C7 | -0.27 | 0.08 | -0.42 – -0.12 | -3.54 | <0.001 |
| CNS Score c * C8 | -0.19 | 0.08 | -0.34 – -0.03 | -2.39 | 0.017 |
| CNS Score c * C9 | -0.13 | 0.07 | -0.27 – 0.00 | -1.92 | 0.055 |
| Random Effects | |||||
| σ2 | 388.38 | ||||
| τ00 id | 181.93 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.233 / 0.478 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting risk perception, over and above climate change method contrasts?
modA.8638 <- lmer(Risk ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.8638)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + CNS_Score.c * C1 + CNS_Score.c * C2 +
## CNS_Score.c * C3 + CNS_Score.c * C4 + CNS_Score.c * C5 +
## CNS_Score.c * C6 + CNS_Score.c * C7 + CNS_Score.c * C8 +
## CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27093.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7838 -0.6068 -0.0263 0.5625 3.9387
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 178.9 13.38
## Residual 331.6 18.21
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.263e+01 5.386e-01 1.014e+03 60.576 < 2e-16 ***
## CNS_Score.c -6.460e-03 3.229e-02 1.017e+03 -0.200 0.841448
## Naturalness.c -4.480e-01 2.206e-02 2.915e+03 -20.310 < 2e-16 ***
## C1 3.980e+00 1.069e+00 2.512e+03 3.724 0.000200 ***
## C2 1.379e+01 1.226e+00 2.610e+03 11.251 < 2e-16 ***
## C3 9.677e+00 1.064e+00 2.478e+03 9.096 < 2e-16 ***
## C4 3.289e+00 1.049e+00 2.462e+03 3.136 0.001731 **
## C5 3.600e+00 1.039e+00 2.467e+03 3.466 0.000537 ***
## C6 -6.103e+00 1.205e+00 2.566e+03 -5.062 4.43e-07 ***
## C7 -8.540e+00 1.222e+00 2.588e+03 -6.988 3.52e-12 ***
## C8 -1.473e+01 1.254e+00 2.611e+03 -11.743 < 2e-16 ***
## C9 -6.779e+00 1.128e+00 2.558e+03 -6.008 2.14e-09 ***
## CNS_Score.c:Naturalness.c -2.458e-03 1.234e-03 2.949e+03 -1.992 0.046472 *
## CNS_Score.c:C1 -6.699e-02 6.283e-02 2.559e+03 -1.066 0.286427
## CNS_Score.c:C2 1.623e-01 7.480e-02 2.612e+03 2.170 0.030074 *
## CNS_Score.c:C3 1.622e-02 6.321e-02 2.466e+03 0.257 0.797502
## CNS_Score.c:C4 1.225e-01 6.094e-02 2.464e+03 2.009 0.044601 *
## CNS_Score.c:C5 1.564e-02 6.432e-02 2.478e+03 0.243 0.807886
## CNS_Score.c:C6 2.857e-02 6.907e-02 2.565e+03 0.414 0.679159
## CNS_Score.c:C7 -1.543e-01 7.412e-02 2.603e+03 -2.081 0.037502 *
## CNS_Score.c:C8 -8.888e-02 7.526e-02 2.591e+03 -1.181 0.237752
## CNS_Score.c:C9 -6.074e-02 7.090e-02 2.618e+03 -0.857 0.391658
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8638,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.63 | 0.54 | 31.57 – 33.68 | 60.58 | <0.001 |
| CNS Score c | -0.01 | 0.03 | -0.07 – 0.06 | -0.20 | 0.841 |
| Naturalness c | -0.45 | 0.02 | -0.49 – -0.40 | -20.31 | <0.001 |
| C1 | 3.98 | 1.07 | 1.88 – 6.07 | 3.72 | <0.001 |
| C2 | 13.79 | 1.23 | 11.39 – 16.20 | 11.25 | <0.001 |
| C3 | 9.68 | 1.06 | 7.59 – 11.76 | 9.10 | <0.001 |
| C4 | 3.29 | 1.05 | 1.23 – 5.35 | 3.14 | 0.002 |
| C5 | 3.60 | 1.04 | 1.56 – 5.64 | 3.47 | 0.001 |
| C6 | -6.10 | 1.21 | -8.47 – -3.74 | -5.06 | <0.001 |
| C7 | -8.54 | 1.22 | -10.94 – -6.14 | -6.99 | <0.001 |
| C8 | -14.73 | 1.25 | -17.19 – -12.27 | -11.74 | <0.001 |
| C9 | -6.78 | 1.13 | -8.99 – -4.57 | -6.01 | <0.001 |
|
CNS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – -0.00 | -1.99 | 0.046 |
| CNS Score c * C1 | -0.07 | 0.06 | -0.19 – 0.06 | -1.07 | 0.286 |
| CNS Score c * C2 | 0.16 | 0.07 | 0.02 – 0.31 | 2.17 | 0.030 |
| CNS Score c * C3 | 0.02 | 0.06 | -0.11 – 0.14 | 0.26 | 0.797 |
| CNS Score c * C4 | 0.12 | 0.06 | 0.00 – 0.24 | 2.01 | 0.045 |
| CNS Score c * C5 | 0.02 | 0.06 | -0.11 – 0.14 | 0.24 | 0.808 |
| CNS Score c * C6 | 0.03 | 0.07 | -0.11 – 0.16 | 0.41 | 0.679 |
| CNS Score c * C7 | -0.15 | 0.07 | -0.30 – -0.01 | -2.08 | 0.037 |
| CNS Score c * C8 | -0.09 | 0.08 | -0.24 – 0.06 | -1.18 | 0.238 |
| CNS Score c * C9 | -0.06 | 0.07 | -0.20 – 0.08 | -0.86 | 0.392 |
| Random Effects | |||||
| σ2 | 331.61 | ||||
| τ00 id | 178.92 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.317 / 0.556 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict risk perception, over and above climate change method contrasts?
modA.864 <- lmer(Risk ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)
summary(modA.864)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 +
## CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27420.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7357 -0.6093 -0.0569 0.5761 3.6883
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 168.8 12.99
## Residual 386.7 19.66
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.245e+01 5.463e-01 1.016e+03 59.402 < 2e-16 ***
## CCBelief_Score.c -1.574e-01 2.322e-02 1.021e+03 -6.780 2.03e-11 ***
## C1 1.081e+01 1.089e+00 2.502e+03 9.921 < 2e-16 ***
## C2 2.068e+01 1.265e+00 2.624e+03 16.353 < 2e-16 ***
## C3 1.351e+01 1.120e+00 2.517e+03 12.057 < 2e-16 ***
## C4 5.535e+00 1.118e+00 2.513e+03 4.951 7.88e-07 ***
## C5 5.867e+00 1.108e+00 2.511e+03 5.294 1.30e-07 ***
## C6 -5.743e+00 1.285e+00 2.625e+03 -4.468 8.24e-06 ***
## C7 -1.493e+01 1.264e+00 2.621e+03 -11.812 < 2e-16 ***
## C8 -2.166e+01 1.293e+00 2.626e+03 -16.753 < 2e-16 ***
## C9 -1.645e+01 1.096e+00 2.509e+03 -15.007 < 2e-16 ***
## CCBelief_Score.c:C1 -1.917e-02 4.603e-02 2.503e+03 -0.417 0.677
## CCBelief_Score.c:C2 2.819e-01 5.033e-02 2.616e+03 5.601 2.36e-08 ***
## CCBelief_Score.c:C3 -8.351e-03 4.731e-02 2.517e+03 -0.177 0.860
## CCBelief_Score.c:C4 2.324e-02 4.498e-02 2.490e+03 0.517 0.605
## CCBelief_Score.c:C5 -1.042e-02 4.873e-02 2.531e+03 -0.214 0.831
## CCBelief_Score.c:C6 1.906e-02 5.677e-02 2.632e+03 0.336 0.737
## CCBelief_Score.c:C7 -2.452e-01 5.465e-02 2.630e+03 -4.487 7.55e-06 ***
## CCBelief_Score.c:C8 2.985e-02 5.582e-02 2.630e+03 0.535 0.593
## CCBelief_Score.c:C9 -5.454e-02 4.818e-02 2.529e+03 -1.132 0.258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.864,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.45 | 0.55 | 31.38 – 33.52 | 59.40 | <0.001 |
| CCBelief Score c | -0.16 | 0.02 | -0.20 – -0.11 | -6.78 | <0.001 |
| C1 | 10.81 | 1.09 | 8.67 – 12.94 | 9.92 | <0.001 |
| C2 | 20.68 | 1.26 | 18.20 – 23.16 | 16.35 | <0.001 |
| C3 | 13.51 | 1.12 | 11.31 – 15.70 | 12.06 | <0.001 |
| C4 | 5.54 | 1.12 | 3.34 – 7.73 | 4.95 | <0.001 |
| C5 | 5.87 | 1.11 | 3.69 – 8.04 | 5.29 | <0.001 |
| C6 | -5.74 | 1.29 | -8.26 – -3.22 | -4.47 | <0.001 |
| C7 | -14.93 | 1.26 | -17.41 – -12.45 | -11.81 | <0.001 |
| C8 | -21.66 | 1.29 | -24.19 – -19.12 | -16.75 | <0.001 |
| C9 | -16.45 | 1.10 | -18.60 – -14.30 | -15.01 | <0.001 |
| CCBelief Score c * C1 | -0.02 | 0.05 | -0.11 – 0.07 | -0.42 | 0.677 |
| CCBelief Score c * C2 | 0.28 | 0.05 | 0.18 – 0.38 | 5.60 | <0.001 |
| CCBelief Score c * C3 | -0.01 | 0.05 | -0.10 – 0.08 | -0.18 | 0.860 |
| CCBelief Score c * C4 | 0.02 | 0.04 | -0.06 – 0.11 | 0.52 | 0.605 |
| CCBelief Score c * C5 | -0.01 | 0.05 | -0.11 – 0.09 | -0.21 | 0.831 |
| CCBelief Score c * C6 | 0.02 | 0.06 | -0.09 – 0.13 | 0.34 | 0.737 |
| CCBelief Score c * C7 | -0.25 | 0.05 | -0.35 – -0.14 | -4.49 | <0.001 |
| CCBelief Score c * C8 | 0.03 | 0.06 | -0.08 – 0.14 | 0.53 | 0.593 |
| CCBelief Score c * C9 | -0.05 | 0.05 | -0.15 – 0.04 | -1.13 | 0.258 |
| Random Effects | |||||
| σ2 | 386.70 | ||||
| τ00 id | 168.84 | ||||
| ICC | 0.30 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.254 / 0.481 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perceptions of naturalness in predicting risk perception, over and above climate change method contrasts?
modA.8649 <- lmer(Risk ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8649)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c *
## C2 + CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27049.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3372 -0.6084 -0.0147 0.5659 3.7259
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 168.3 12.97
## Residual 329.6 18.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.258e+01 5.283e-01 1.016e+03 61.666
## CCBelief_Score.c -1.346e-01 2.254e-02 1.033e+03 -5.971
## Naturalness.c -4.436e-01 2.190e-02 2.924e+03 -20.258
## C1 4.183e+00 1.064e+00 2.527e+03 3.933
## C2 1.428e+01 1.221e+00 2.627e+03 11.693
## C3 9.761e+00 1.056e+00 2.491e+03 9.240
## C4 3.157e+00 1.045e+00 2.475e+03 3.022
## C5 3.758e+00 1.034e+00 2.479e+03 3.634
## C6 -6.198e+00 1.195e+00 2.583e+03 -5.184
## C7 -8.885e+00 1.215e+00 2.602e+03 -7.313
## C8 -1.493e+01 1.247e+00 2.625e+03 -11.969
## C9 -6.880e+00 1.123e+00 2.579e+03 -6.128
## CCBelief_Score.c:Naturalness.c 1.236e-03 8.270e-04 2.937e+03 1.495
## CCBelief_Score.c:C1 -1.600e-02 4.456e-02 2.519e+03 -0.359
## CCBelief_Score.c:C2 2.484e-01 4.798e-02 2.595e+03 5.177
## CCBelief_Score.c:C3 -9.809e-03 4.421e-02 2.482e+03 -0.222
## CCBelief_Score.c:C4 1.609e-02 4.196e-02 2.449e+03 0.383
## CCBelief_Score.c:C5 -2.080e-03 4.543e-02 2.486e+03 -0.046
## CCBelief_Score.c:C6 4.118e-02 5.291e-02 2.610e+03 0.778
## CCBelief_Score.c:C7 -2.286e-01 5.214e-02 2.622e+03 -4.384
## CCBelief_Score.c:C8 3.819e-02 5.287e-02 2.633e+03 0.722
## CCBelief_Score.c:C9 -9.172e-02 4.949e-02 2.636e+03 -1.853
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 3.25e-09 ***
## Naturalness.c < 2e-16 ***
## C1 8.63e-05 ***
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 0.002540 **
## C5 0.000284 ***
## C6 2.34e-07 ***
## C7 3.46e-13 ***
## C8 < 2e-16 ***
## C9 1.03e-09 ***
## CCBelief_Score.c:Naturalness.c 0.135053
## CCBelief_Score.c:C1 0.719572
## CCBelief_Score.c:C2 2.43e-07 ***
## CCBelief_Score.c:C3 0.824422
## CCBelief_Score.c:C4 0.701440
## CCBelief_Score.c:C5 0.963491
## CCBelief_Score.c:C6 0.436442
## CCBelief_Score.c:C7 1.21e-05 ***
## CCBelief_Score.c:C8 0.470089
## CCBelief_Score.c:C9 0.063948 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8649,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.58 | 0.53 | 31.54 – 33.61 | 61.67 | <0.001 |
| CCBelief Score c | -0.13 | 0.02 | -0.18 – -0.09 | -5.97 | <0.001 |
| Naturalness c | -0.44 | 0.02 | -0.49 – -0.40 | -20.26 | <0.001 |
| C1 | 4.18 | 1.06 | 2.10 – 6.27 | 3.93 | <0.001 |
| C2 | 14.28 | 1.22 | 11.88 – 16.67 | 11.69 | <0.001 |
| C3 | 9.76 | 1.06 | 7.69 – 11.83 | 9.24 | <0.001 |
| C4 | 3.16 | 1.04 | 1.11 – 5.21 | 3.02 | 0.003 |
| C5 | 3.76 | 1.03 | 1.73 – 5.79 | 3.63 | <0.001 |
| C6 | -6.20 | 1.20 | -8.54 – -3.85 | -5.18 | <0.001 |
| C7 | -8.89 | 1.22 | -11.27 – -6.50 | -7.31 | <0.001 |
| C8 | -14.93 | 1.25 | -17.37 – -12.48 | -11.97 | <0.001 |
| C9 | -6.88 | 1.12 | -9.08 – -4.68 | -6.13 | <0.001 |
|
CCBelief Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.49 | 0.135 |
| CCBelief Score c * C1 | -0.02 | 0.04 | -0.10 – 0.07 | -0.36 | 0.720 |
| CCBelief Score c * C2 | 0.25 | 0.05 | 0.15 – 0.34 | 5.18 | <0.001 |
| CCBelief Score c * C3 | -0.01 | 0.04 | -0.10 – 0.08 | -0.22 | 0.824 |
| CCBelief Score c * C4 | 0.02 | 0.04 | -0.07 – 0.10 | 0.38 | 0.701 |
| CCBelief Score c * C5 | -0.00 | 0.05 | -0.09 – 0.09 | -0.05 | 0.963 |
| CCBelief Score c * C6 | 0.04 | 0.05 | -0.06 – 0.14 | 0.78 | 0.436 |
| CCBelief Score c * C7 | -0.23 | 0.05 | -0.33 – -0.13 | -4.38 | <0.001 |
| CCBelief Score c * C8 | 0.04 | 0.05 | -0.07 – 0.14 | 0.72 | 0.470 |
| CCBelief Score c * C9 | -0.09 | 0.05 | -0.19 – 0.01 | -1.85 | 0.064 |
| Random Effects | |||||
| σ2 | 329.63 | ||||
| τ00 id | 168.31 | ||||
| ICC | 0.34 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.336 / 0.560 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict risk perception, over and above climate change method contrasts?
modA.866 <- lmer(Risk ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)
summary(modA.866)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27487.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6337 -0.6157 -0.0667 0.5684 3.6895
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 179.2 13.39
## Residual 392.1 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.38425 0.55746 1017.85949 58.092 < 2e-16 ***
## Collectivism_Score.c 0.08139 0.02341 1022.64884 3.477 0.000529 ***
## C1 10.84721 1.09856 2492.17043 9.874 < 2e-16 ***
## C2 20.45178 1.27799 2611.83582 16.003 < 2e-16 ***
## C3 13.64331 1.13014 2507.53418 12.072 < 2e-16 ***
## C4 5.70815 1.12577 2503.54281 5.070 4.26e-07 ***
## C5 5.79114 1.12138 2501.35160 5.164 2.60e-07 ***
## C6 -5.71116 1.29730 2613.47197 -4.402 1.11e-05 ***
## C7 -15.07214 1.27813 2610.12661 -11.792 < 2e-16 ***
## C8 -21.57848 1.30436 2613.70242 -16.543 < 2e-16 ***
## C9 -16.52935 1.10501 2494.98911 -14.959 < 2e-16 ***
## Collectivism_Score.c:C1 0.07503 0.05002 2533.55970 1.500 0.133740
## Collectivism_Score.c:C2 -0.12852 0.05169 2608.05965 -2.486 0.012966 *
## Collectivism_Score.c:C3 0.04732 0.04791 2512.85061 0.988 0.323419
## Collectivism_Score.c:C4 -0.01909 0.04755 2508.08859 -0.401 0.688120
## Collectivism_Score.c:C5 -0.03596 0.04567 2488.50826 -0.788 0.431040
## Collectivism_Score.c:C6 0.01206 0.05465 2616.98541 0.221 0.825397
## Collectivism_Score.c:C7 0.07502 0.05540 2617.01591 1.354 0.175768
## Collectivism_Score.c:C8 -0.02668 0.05491 2621.38925 -0.486 0.627158
## Collectivism_Score.c:C9 0.01753 0.04714 2506.27642 0.372 0.710103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.866,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.38 | 0.56 | 31.29 – 33.48 | 58.09 | <0.001 |
| Collectivism Score c | 0.08 | 0.02 | 0.04 – 0.13 | 3.48 | 0.001 |
| C1 | 10.85 | 1.10 | 8.69 – 13.00 | 9.87 | <0.001 |
| C2 | 20.45 | 1.28 | 17.95 – 22.96 | 16.00 | <0.001 |
| C3 | 13.64 | 1.13 | 11.43 – 15.86 | 12.07 | <0.001 |
| C4 | 5.71 | 1.13 | 3.50 – 7.92 | 5.07 | <0.001 |
| C5 | 5.79 | 1.12 | 3.59 – 7.99 | 5.16 | <0.001 |
| C6 | -5.71 | 1.30 | -8.25 – -3.17 | -4.40 | <0.001 |
| C7 | -15.07 | 1.28 | -17.58 – -12.57 | -11.79 | <0.001 |
| C8 | -21.58 | 1.30 | -24.14 – -19.02 | -16.54 | <0.001 |
| C9 | -16.53 | 1.11 | -18.70 – -14.36 | -14.96 | <0.001 |
| Collectivism Score c * C1 | 0.08 | 0.05 | -0.02 – 0.17 | 1.50 | 0.134 |
| Collectivism Score c * C2 | -0.13 | 0.05 | -0.23 – -0.03 | -2.49 | 0.013 |
| Collectivism Score c * C3 | 0.05 | 0.05 | -0.05 – 0.14 | 0.99 | 0.323 |
| Collectivism Score c * C4 | -0.02 | 0.05 | -0.11 – 0.07 | -0.40 | 0.688 |
| Collectivism Score c * C5 | -0.04 | 0.05 | -0.13 – 0.05 | -0.79 | 0.431 |
| Collectivism Score c * C6 | 0.01 | 0.05 | -0.10 – 0.12 | 0.22 | 0.825 |
| Collectivism Score c * C7 | 0.08 | 0.06 | -0.03 – 0.18 | 1.35 | 0.176 |
| Collectivism Score c * C8 | -0.03 | 0.05 | -0.13 – 0.08 | -0.49 | 0.627 |
| Collectivism Score c * C9 | 0.02 | 0.05 | -0.07 – 0.11 | 0.37 | 0.710 |
| Random Effects | |||||
| σ2 | 392.05 | ||||
| τ00 id | 179.17 | ||||
| ICC | 0.31 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.232 / 0.473 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting risk perception, over and above climate change method contrasts?
modA.8665 <- lmer(Risk ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8665)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c * C1 +
## Collectivism_Score.c * C2 + Collectivism_Score.c * C3 + Collectivism_Score.c *
## C4 + Collectivism_Score.c * C5 + Collectivism_Score.c * C6 +
## Collectivism_Score.c * C7 + Collectivism_Score.c * C8 + Collectivism_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27104.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3323 -0.6042 -0.0235 0.5693 3.7167
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 176.2 13.28
## Residual 333.4 18.26
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.255e+01 5.367e-01 1.015e+03 60.642
## Collectivism_Score.c 7.716e-02 2.254e-02 1.020e+03 3.423
## Naturalness.c -4.568e-01 2.204e-02 2.922e+03 -20.725
## C1 4.005e+00 1.071e+00 2.520e+03 3.739
## C2 1.385e+01 1.229e+00 2.611e+03 11.266
## C3 9.736e+00 1.064e+00 2.484e+03 9.149
## C4 3.239e+00 1.050e+00 2.467e+03 3.085
## C5 3.550e+00 1.045e+00 2.471e+03 3.396
## C6 -6.131e+00 1.204e+00 2.571e+03 -5.091
## C7 -8.813e+00 1.225e+00 2.593e+03 -7.194
## C8 -1.458e+01 1.256e+00 2.615e+03 -11.612
## C9 -6.682e+00 1.131e+00 2.569e+03 -5.907
## Collectivism_Score.c:Naturalness.c -5.887e-04 8.653e-04 2.916e+03 -0.680
## Collectivism_Score.c:C1 8.051e-02 4.883e-02 2.563e+03 1.649
## Collectivism_Score.c:C2 -1.106e-01 4.964e-02 2.623e+03 -2.228
## Collectivism_Score.c:C3 2.403e-02 4.506e-02 2.477e+03 0.533
## Collectivism_Score.c:C4 -3.158e-02 4.423e-02 2.473e+03 -0.714
## Collectivism_Score.c:C5 -6.633e-02 4.275e-02 2.456e+03 -1.552
## Collectivism_Score.c:C6 2.153e-02 5.074e-02 2.574e+03 0.424
## Collectivism_Score.c:C7 9.149e-02 5.329e-02 2.616e+03 1.717
## Collectivism_Score.c:C8 4.986e-03 5.322e-02 2.619e+03 0.094
## Collectivism_Score.c:C9 -4.762e-03 4.758e-02 2.562e+03 -0.100
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.000643 ***
## Naturalness.c < 2e-16 ***
## C1 0.000189 ***
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 0.002059 **
## C5 0.000694 ***
## C6 3.81e-07 ***
## C7 8.19e-13 ***
## C8 < 2e-16 ***
## C9 3.95e-09 ***
## Collectivism_Score.c:Naturalness.c 0.496336
## Collectivism_Score.c:C1 0.099312 .
## Collectivism_Score.c:C2 0.025947 *
## Collectivism_Score.c:C3 0.593814
## Collectivism_Score.c:C4 0.475337
## Collectivism_Score.c:C5 0.120885
## Collectivism_Score.c:C6 0.671366
## Collectivism_Score.c:C7 0.086131 .
## Collectivism_Score.c:C8 0.925370
## Collectivism_Score.c:C9 0.920278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8665,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.55 | 0.54 | 31.50 – 33.60 | 60.64 | <0.001 |
| Collectivism Score c | 0.08 | 0.02 | 0.03 – 0.12 | 3.42 | 0.001 |
| Naturalness c | -0.46 | 0.02 | -0.50 – -0.41 | -20.73 | <0.001 |
| C1 | 4.00 | 1.07 | 1.90 – 6.10 | 3.74 | <0.001 |
| C2 | 13.85 | 1.23 | 11.44 – 16.26 | 11.27 | <0.001 |
| C3 | 9.74 | 1.06 | 7.65 – 11.82 | 9.15 | <0.001 |
| C4 | 3.24 | 1.05 | 1.18 – 5.30 | 3.08 | 0.002 |
| C5 | 3.55 | 1.05 | 1.50 – 5.60 | 3.40 | 0.001 |
| C6 | -6.13 | 1.20 | -8.49 – -3.77 | -5.09 | <0.001 |
| C7 | -8.81 | 1.23 | -11.22 – -6.41 | -7.19 | <0.001 |
| C8 | -14.58 | 1.26 | -17.05 – -12.12 | -11.61 | <0.001 |
| C9 | -6.68 | 1.13 | -8.90 – -4.46 | -5.91 | <0.001 |
|
Collectivism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.68 | 0.496 |
| Collectivism Score c * C1 | 0.08 | 0.05 | -0.02 – 0.18 | 1.65 | 0.099 |
| Collectivism Score c * C2 | -0.11 | 0.05 | -0.21 – -0.01 | -2.23 | 0.026 |
| Collectivism Score c * C3 | 0.02 | 0.05 | -0.06 – 0.11 | 0.53 | 0.594 |
| Collectivism Score c * C4 | -0.03 | 0.04 | -0.12 – 0.06 | -0.71 | 0.475 |
| Collectivism Score c * C5 | -0.07 | 0.04 | -0.15 – 0.02 | -1.55 | 0.121 |
| Collectivism Score c * C6 | 0.02 | 0.05 | -0.08 – 0.12 | 0.42 | 0.671 |
| Collectivism Score c * C7 | 0.09 | 0.05 | -0.01 – 0.20 | 1.72 | 0.086 |
| Collectivism Score c * C8 | 0.00 | 0.05 | -0.10 – 0.11 | 0.09 | 0.925 |
| Collectivism Score c * C9 | -0.00 | 0.05 | -0.10 – 0.09 | -0.10 | 0.920 |
| Random Effects | |||||
| σ2 | 333.38 | ||||
| τ00 id | 176.23 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.318 / 0.554 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict risk perception, over and above climate change method contrasts?
modA.867 <- lmer(Risk ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.867)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C5 + Individualism_Score.c * C6 + Individualism_Score.c *
## C7 + Individualism_Score.c * C8 + Individualism_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27495.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7316 -0.6078 -0.0713 0.5768 3.6643
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Residual 392.2 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.240e+01 5.614e-01 1.019e+03 57.718 < 2e-16 ***
## Individualism_Score.c 1.386e-02 3.335e-02 1.025e+03 0.416 0.6778
## C1 1.087e+01 1.100e+00 2.488e+03 9.883 < 2e-16 ***
## C2 2.034e+01 1.282e+00 2.607e+03 15.861 < 2e-16 ***
## C3 1.358e+01 1.132e+00 2.502e+03 12.002 < 2e-16 ***
## C4 5.716e+00 1.126e+00 2.497e+03 5.076 4.15e-07 ***
## C5 5.730e+00 1.118e+00 2.496e+03 5.124 3.21e-07 ***
## C6 -5.722e+00 1.302e+00 2.609e+03 -4.395 1.15e-05 ***
## C7 -1.490e+01 1.277e+00 2.605e+03 -11.663 < 2e-16 ***
## C8 -2.166e+01 1.308e+00 2.609e+03 -16.561 < 2e-16 ***
## C9 -1.652e+01 1.106e+00 2.491e+03 -14.938 < 2e-16 ***
## Individualism_Score.c:C1 1.925e-02 6.519e-02 2.487e+03 0.295 0.7678
## Individualism_Score.c:C2 1.358e-01 7.237e-02 2.603e+03 1.876 0.0608 .
## Individualism_Score.c:C3 -2.914e-03 6.979e-02 2.524e+03 -0.042 0.9667
## Individualism_Score.c:C4 3.468e-02 6.722e-02 2.501e+03 0.516 0.6060
## Individualism_Score.c:C5 1.276e-02 6.697e-02 2.500e+03 0.191 0.8489
## Individualism_Score.c:C6 -2.576e-02 8.040e-02 2.615e+03 -0.320 0.7487
## Individualism_Score.c:C7 -3.650e-02 8.059e-02 2.615e+03 -0.453 0.6506
## Individualism_Score.c:C8 -7.106e-02 7.450e-02 2.607e+03 -0.954 0.3403
## Individualism_Score.c:C9 4.915e-02 6.456e-02 2.487e+03 0.761 0.4465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.867,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.40 | 0.56 | 31.30 – 33.50 | 57.72 | <0.001 |
| Individualism Score c | 0.01 | 0.03 | -0.05 – 0.08 | 0.42 | 0.678 |
| C1 | 10.87 | 1.10 | 8.71 – 13.03 | 9.88 | <0.001 |
| C2 | 20.34 | 1.28 | 17.82 – 22.85 | 15.86 | <0.001 |
| C3 | 13.58 | 1.13 | 11.36 – 15.80 | 12.00 | <0.001 |
| C4 | 5.72 | 1.13 | 3.51 – 7.92 | 5.08 | <0.001 |
| C5 | 5.73 | 1.12 | 3.54 – 7.92 | 5.12 | <0.001 |
| C6 | -5.72 | 1.30 | -8.27 – -3.17 | -4.40 | <0.001 |
| C7 | -14.90 | 1.28 | -17.40 – -12.39 | -11.66 | <0.001 |
| C8 | -21.66 | 1.31 | -24.22 – -19.09 | -16.56 | <0.001 |
| C9 | -16.52 | 1.11 | -18.69 – -14.35 | -14.94 | <0.001 |
|
Individualism Score c * C1 |
0.02 | 0.07 | -0.11 – 0.15 | 0.30 | 0.768 |
|
Individualism Score c * C2 |
0.14 | 0.07 | -0.01 – 0.28 | 1.88 | 0.061 |
|
Individualism Score c * C3 |
-0.00 | 0.07 | -0.14 – 0.13 | -0.04 | 0.967 |
|
Individualism Score c * C4 |
0.03 | 0.07 | -0.10 – 0.17 | 0.52 | 0.606 |
|
Individualism Score c * C5 |
0.01 | 0.07 | -0.12 – 0.14 | 0.19 | 0.849 |
|
Individualism Score c * C6 |
-0.03 | 0.08 | -0.18 – 0.13 | -0.32 | 0.749 |
|
Individualism Score c * C7 |
-0.04 | 0.08 | -0.19 – 0.12 | -0.45 | 0.651 |
|
Individualism Score c * C8 |
-0.07 | 0.07 | -0.22 – 0.08 | -0.95 | 0.340 |
|
Individualism Score c * C9 |
0.05 | 0.06 | -0.08 – 0.18 | 0.76 | 0.447 |
| Random Effects | |||||
| σ2 | 392.15 | ||||
| τ00 id | 183.50 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.227 / 0.473 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting risk perception, over and above climate change method contrasts?
modA.8672 <- lmer(Risk ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.8672)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c * C1 +
## Individualism_Score.c * C2 + Individualism_Score.c * C3 +
## Individualism_Score.c * C4 + Individualism_Score.c * C5 +
## Individualism_Score.c * C6 + Individualism_Score.c * C7 +
## Individualism_Score.c * C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27107.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3505 -0.6092 -0.0238 0.5613 3.6939
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 179.8 13.41
## Residual 333.2 18.25
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.254e+01 5.400e-01 1.017e+03 60.267
## Individualism_Score.c 3.994e-03 3.208e-02 1.022e+03 0.125
## Naturalness.c -4.527e-01 2.215e-02 2.914e+03 -20.434
## C1 4.108e+00 1.072e+00 2.512e+03 3.832
## C2 1.374e+01 1.233e+00 2.603e+03 11.145
## C3 9.688e+00 1.065e+00 2.478e+03 9.097
## C4 3.235e+00 1.050e+00 2.461e+03 3.081
## C5 3.542e+00 1.041e+00 2.467e+03 3.402
## C6 -6.157e+00 1.208e+00 2.568e+03 -5.098
## C7 -8.651e+00 1.224e+00 2.587e+03 -7.071
## C8 -1.471e+01 1.259e+00 2.611e+03 -11.691
## C9 -6.759e+00 1.130e+00 2.556e+03 -5.979
## Individualism_Score.c:Naturalness.c -2.637e-03 1.251e-03 2.948e+03 -2.108
## Individualism_Score.c:C1 -1.048e-02 6.346e-02 2.513e+03 -0.165
## Individualism_Score.c:C2 9.658e-02 6.977e-02 2.605e+03 1.384
## Individualism_Score.c:C3 -5.404e-02 6.565e-02 2.497e+03 -0.823
## Individualism_Score.c:C4 4.565e-03 6.278e-02 2.472e+03 0.073
## Individualism_Score.c:C5 -4.908e-02 6.252e-02 2.464e+03 -0.785
## Individualism_Score.c:C6 -3.577e-02 7.459e-02 2.573e+03 -0.480
## Individualism_Score.c:C7 3.394e-02 7.677e-02 2.595e+03 0.442
## Individualism_Score.c:C8 -1.523e-02 7.268e-02 2.618e+03 -0.210
## Individualism_Score.c:C9 1.188e-01 6.609e-02 2.559e+03 1.797
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.90093
## Naturalness.c < 2e-16 ***
## C1 0.00013 ***
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 0.00209 **
## C5 0.00068 ***
## C6 3.68e-07 ***
## C7 1.98e-12 ***
## C8 < 2e-16 ***
## C9 2.55e-09 ***
## Individualism_Score.c:Naturalness.c 0.03509 *
## Individualism_Score.c:C1 0.86880
## Individualism_Score.c:C2 0.16641
## Individualism_Score.c:C3 0.41047
## Individualism_Score.c:C4 0.94204
## Individualism_Score.c:C5 0.43249
## Individualism_Score.c:C6 0.63161
## Individualism_Score.c:C7 0.65849
## Individualism_Score.c:C8 0.83405
## Individualism_Score.c:C9 0.07245 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8672,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.54 | 0.54 | 31.49 – 33.60 | 60.27 | <0.001 |
| Individualism Score c | 0.00 | 0.03 | -0.06 – 0.07 | 0.12 | 0.901 |
| Naturalness c | -0.45 | 0.02 | -0.50 – -0.41 | -20.43 | <0.001 |
| C1 | 4.11 | 1.07 | 2.01 – 6.21 | 3.83 | <0.001 |
| C2 | 13.74 | 1.23 | 11.32 – 16.15 | 11.14 | <0.001 |
| C3 | 9.69 | 1.06 | 7.60 – 11.78 | 9.10 | <0.001 |
| C4 | 3.23 | 1.05 | 1.18 – 5.29 | 3.08 | 0.002 |
| C5 | 3.54 | 1.04 | 1.50 – 5.58 | 3.40 | 0.001 |
| C6 | -6.16 | 1.21 | -8.53 – -3.79 | -5.10 | <0.001 |
| C7 | -8.65 | 1.22 | -11.05 – -6.25 | -7.07 | <0.001 |
| C8 | -14.71 | 1.26 | -17.18 – -12.25 | -11.69 | <0.001 |
| C9 | -6.76 | 1.13 | -8.98 – -4.54 | -5.98 | <0.001 |
|
Individualism Score c * Naturalness c |
-0.00 | 0.00 | -0.01 – -0.00 | -2.11 | 0.035 |
|
Individualism Score c * C1 |
-0.01 | 0.06 | -0.13 – 0.11 | -0.17 | 0.869 |
|
Individualism Score c * C2 |
0.10 | 0.07 | -0.04 – 0.23 | 1.38 | 0.166 |
|
Individualism Score c * C3 |
-0.05 | 0.07 | -0.18 – 0.07 | -0.82 | 0.410 |
|
Individualism Score c * C4 |
0.00 | 0.06 | -0.12 – 0.13 | 0.07 | 0.942 |
|
Individualism Score c * C5 |
-0.05 | 0.06 | -0.17 – 0.07 | -0.79 | 0.432 |
|
Individualism Score c * C6 |
-0.04 | 0.07 | -0.18 – 0.11 | -0.48 | 0.632 |
|
Individualism Score c * C7 |
0.03 | 0.08 | -0.12 – 0.18 | 0.44 | 0.658 |
|
Individualism Score c * C8 |
-0.02 | 0.07 | -0.16 – 0.13 | -0.21 | 0.834 |
|
Individualism Score c * C9 |
0.12 | 0.07 | -0.01 – 0.25 | 1.80 | 0.072 |
| Random Effects | |||||
| σ2 | 333.21 | ||||
| τ00 id | 179.78 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.314 / 0.554 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does ideology predict risk perception, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.868 <- lmer(Risk ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.868)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c *
## C4 + Ideology.c * C5 + Ideology.c * C6 + Ideology.c * C7 +
## Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27422.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5889 -0.6099 -0.0694 0.5661 3.6248
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.8 13.44
## Residual 392.7 19.82
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.4136 0.5590 1016.6753 57.982 < 2e-16 ***
## Ideology.c -2.1994 0.9802 1019.0715 -2.244 0.0251 *
## C1 10.7681 1.1013 2492.2275 9.778 < 2e-16 ***
## C2 20.5582 1.2770 2609.9378 16.099 < 2e-16 ***
## C3 13.5547 1.1319 2506.8938 11.975 < 2e-16 ***
## C4 5.6881 1.1260 2500.2425 5.051 4.70e-07 ***
## C5 5.8560 1.1215 2498.9633 5.221 1.92e-07 ***
## C6 -5.7144 1.2982 2610.6909 -4.402 1.12e-05 ***
## C7 -14.9121 1.2774 2609.8912 -11.674 < 2e-16 ***
## C8 -21.5955 1.3060 2613.5498 -16.536 < 2e-16 ***
## C9 -16.6222 1.1117 2497.6027 -14.951 < 2e-16 ***
## Ideology.c:C1 -1.4443 1.9583 2507.4665 -0.738 0.4609
## Ideology.c:C2 4.3540 2.2715 2626.2957 1.917 0.0554 .
## Ideology.c:C3 -0.2690 2.0169 2531.0391 -0.133 0.8939
## Ideology.c:C4 1.7118 2.0204 2532.8141 0.847 0.3969
## Ideology.c:C5 -1.0470 1.9373 2507.1637 -0.540 0.5889
## Ideology.c:C6 -3.0419 2.1833 2613.2843 -1.393 0.1637
## Ideology.c:C7 2.2315 2.3609 2622.0412 0.945 0.3447
## Ideology.c:C8 -0.5100 2.2407 2618.1008 -0.228 0.8200
## Ideology.c:C9 -0.7848 1.9752 2508.8499 -0.397 0.6912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.868,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.41 | 0.56 | 31.32 – 33.51 | 57.98 | <0.001 |
| Ideology c | -2.20 | 0.98 | -4.12 – -0.28 | -2.24 | 0.025 |
| C1 | 10.77 | 1.10 | 8.61 – 12.93 | 9.78 | <0.001 |
| C2 | 20.56 | 1.28 | 18.05 – 23.06 | 16.10 | <0.001 |
| C3 | 13.55 | 1.13 | 11.34 – 15.77 | 11.97 | <0.001 |
| C4 | 5.69 | 1.13 | 3.48 – 7.90 | 5.05 | <0.001 |
| C5 | 5.86 | 1.12 | 3.66 – 8.05 | 5.22 | <0.001 |
| C6 | -5.71 | 1.30 | -8.26 – -3.17 | -4.40 | <0.001 |
| C7 | -14.91 | 1.28 | -17.42 – -12.41 | -11.67 | <0.001 |
| C8 | -21.60 | 1.31 | -24.16 – -19.03 | -16.54 | <0.001 |
| C9 | -16.62 | 1.11 | -18.80 – -14.44 | -14.95 | <0.001 |
| Ideology c * C1 | -1.44 | 1.96 | -5.28 – 2.40 | -0.74 | 0.461 |
| Ideology c * C2 | 4.35 | 2.27 | -0.10 – 8.81 | 1.92 | 0.055 |
| Ideology c * C3 | -0.27 | 2.02 | -4.22 – 3.69 | -0.13 | 0.894 |
| Ideology c * C4 | 1.71 | 2.02 | -2.25 – 5.67 | 0.85 | 0.397 |
| Ideology c * C5 | -1.05 | 1.94 | -4.85 – 2.75 | -0.54 | 0.589 |
| Ideology c * C6 | -3.04 | 2.18 | -7.32 – 1.24 | -1.39 | 0.164 |
| Ideology c * C7 | 2.23 | 2.36 | -2.40 – 6.86 | 0.95 | 0.345 |
| Ideology c * C8 | -0.51 | 2.24 | -4.90 – 3.88 | -0.23 | 0.820 |
| Ideology c * C9 | -0.78 | 1.98 | -4.66 – 3.09 | -0.40 | 0.691 |
| Random Effects | |||||
| σ2 | 392.75 | ||||
| τ00 id | 180.76 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.228 / 0.472 | ||||
Q.2 (POLITICAL IDEOLOGY) Does political ideology depend on perceptions of naturalness in predicting risk perception, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.8683 <- lmer(Risk ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.8683)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c *
## C3 + Ideology.c * C4 + Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27048.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2856 -0.6118 -0.0289 0.5704 3.6737
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 177.2 13.31
## Residual 334.2 18.28
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.258e+01 5.378e-01 1.014e+03 60.577 < 2e-16 ***
## Ideology.c -2.484e+00 9.409e-01 1.007e+03 -2.640 0.008430 **
## Naturalness.c -4.574e-01 2.208e-02 2.925e+03 -20.715 < 2e-16 ***
## C1 3.904e+00 1.074e+00 2.522e+03 3.636 0.000283 ***
## C2 1.388e+01 1.228e+00 2.615e+03 11.301 < 2e-16 ***
## C3 9.681e+00 1.065e+00 2.484e+03 9.087 < 2e-16 ***
## C4 3.208e+00 1.050e+00 2.468e+03 3.054 0.002280 **
## C5 3.676e+00 1.045e+00 2.474e+03 3.518 0.000443 ***
## C6 -6.149e+00 1.205e+00 2.574e+03 -5.102 3.60e-07 ***
## C7 -8.660e+00 1.225e+00 2.597e+03 -7.070 1.99e-12 ***
## C8 -1.462e+01 1.257e+00 2.619e+03 -11.628 < 2e-16 ***
## C9 -6.706e+00 1.131e+00 2.568e+03 -5.930 3.43e-09 ***
## Ideology.c:Naturalness.c 3.745e-05 3.260e-02 2.712e+03 0.001 0.999083
## Ideology.c:C1 -1.160e+00 1.776e+00 2.539e+03 -0.653 0.513637
## Ideology.c:C2 2.767e+00 1.942e+00 2.371e+03 1.425 0.154406
## Ideology.c:C3 -5.192e-01 1.788e+00 2.473e+03 -0.290 0.771570
## Ideology.c:C4 9.951e-01 1.809e+00 2.524e+03 0.550 0.582256
## Ideology.c:C5 -3.015e-01 1.744e+00 2.509e+03 -0.173 0.862765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8683,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.58 | 0.54 | 31.52 – 33.63 | 60.58 | <0.001 |
| Ideology c | -2.48 | 0.94 | -4.33 – -0.64 | -2.64 | 0.008 |
| Naturalness c | -0.46 | 0.02 | -0.50 – -0.41 | -20.72 | <0.001 |
| C1 | 3.90 | 1.07 | 1.80 – 6.01 | 3.64 | <0.001 |
| C2 | 13.88 | 1.23 | 11.47 – 16.29 | 11.30 | <0.001 |
| C3 | 9.68 | 1.07 | 7.59 – 11.77 | 9.09 | <0.001 |
| C4 | 3.21 | 1.05 | 1.15 – 5.27 | 3.05 | 0.002 |
| C5 | 3.68 | 1.05 | 1.63 – 5.73 | 3.52 | <0.001 |
| C6 | -6.15 | 1.21 | -8.51 – -3.79 | -5.10 | <0.001 |
| C7 | -8.66 | 1.22 | -11.06 – -6.26 | -7.07 | <0.001 |
| C8 | -14.62 | 1.26 | -17.08 – -12.15 | -11.63 | <0.001 |
| C9 | -6.71 | 1.13 | -8.92 – -4.49 | -5.93 | <0.001 |
|
Ideology c * Naturalness c |
0.00 | 0.03 | -0.06 – 0.06 | 0.00 | 0.999 |
| Ideology c * C1 | -1.16 | 1.78 | -4.64 – 2.32 | -0.65 | 0.514 |
| Ideology c * C2 | 2.77 | 1.94 | -1.04 – 6.58 | 1.42 | 0.154 |
| Ideology c * C3 | -0.52 | 1.79 | -4.03 – 2.99 | -0.29 | 0.772 |
| Ideology c * C4 | 1.00 | 1.81 | -2.55 – 4.54 | 0.55 | 0.582 |
| Ideology c * C5 | -0.30 | 1.74 | -3.72 – 3.12 | -0.17 | 0.863 |
| Random Effects | |||||
| σ2 | 334.17 | ||||
| τ00 id | 177.20 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.315 / 0.552 | ||||
Benefit
Q.1 How do climate change method contrasts predict benefit perception?
modA.870 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.870)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27683.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4246 -0.5150 0.0654 0.5678 3.1565
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.4 16.84
## Residual 381.8 19.54
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.2205 0.6407 1017.4032 90.874 < 2e-16 ***
## C1 -3.1843 1.1019 2386.9763 -2.890 0.003888 **
## C2 1.4717 1.2849 2490.3918 1.145 0.252168
## C3 -4.3571 1.1337 2399.1210 -3.843 0.000125 ***
## C4 -2.9518 1.1284 2394.8266 -2.616 0.008955 **
## C5 -5.5747 1.1205 2392.8913 -4.975 6.98e-07 ***
## C6 -7.3526 1.3064 2491.1763 -5.628 2.02e-08 ***
## C7 7.5302 1.2847 2488.4305 5.862 5.19e-09 ***
## C8 8.7549 1.3138 2491.7176 6.664 3.28e-11 ***
## C9 10.4528 1.1080 2389.8849 9.434 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.028
## C2 0.023 -0.092
## C3 -0.016 -0.115 -0.073
## C4 -0.018 -0.111 -0.098 -0.118
## C5 -0.021 -0.107 -0.085 -0.116 -0.109
## C6 0.031 -0.094 -0.171 -0.110 -0.096 -0.097
## C7 0.023 -0.080 -0.169 -0.094 -0.092 -0.097 -0.171
## C8 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.172 -0.171
## C9 -0.026 -0.110 -0.109 -0.110 -0.118 -0.111 -0.093 -0.088 -0.081tab_model(modA.870,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.22 | 0.64 | 56.96 – 59.48 | 90.87 | <0.001 |
| C1 | -3.18 | 1.10 | -5.34 – -1.02 | -2.89 | 0.004 |
| C2 | 1.47 | 1.28 | -1.05 – 3.99 | 1.15 | 0.252 |
| C3 | -4.36 | 1.13 | -6.58 – -2.13 | -3.84 | <0.001 |
| C4 | -2.95 | 1.13 | -5.16 – -0.74 | -2.62 | 0.009 |
| C5 | -5.57 | 1.12 | -7.77 – -3.38 | -4.98 | <0.001 |
| C6 | -7.35 | 1.31 | -9.91 – -4.79 | -5.63 | <0.001 |
| C7 | 7.53 | 1.28 | 5.01 – 10.05 | 5.86 | <0.001 |
| C8 | 8.75 | 1.31 | 6.18 – 11.33 | 6.66 | <0.001 |
| C9 | 10.45 | 1.11 | 8.28 – 12.63 | 9.43 | <0.001 |
| Random Effects | |||||
| σ2 | 381.81 | ||||
| τ00 id | 283.44 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.457 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict benefit perception, over and above climate change method contrasts?
modA.871 <- lmer(Ben ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)
summary(modA.871)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c *
## C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 + ATNS_Score.c *
## C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 + ATNS_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27682.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2276 -0.5182 0.0580 0.5764 3.1073
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 279.5 16.72
## Residual 378.0 19.44
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.22657 0.63677 1014.61943 91.441 < 2e-16 ***
## ATNS_Score.c -0.11967 0.02964 1015.61741 -4.037 5.82e-05 ***
## C1 -3.18805 1.09632 2377.36267 -2.908 0.003672 **
## C2 1.56948 1.27854 2481.04518 1.228 0.219731
## C3 -4.24605 1.12961 2390.17947 -3.759 0.000175 ***
## C4 -3.15379 1.12443 2388.78520 -2.805 0.005076 **
## C5 -5.59659 1.11535 2385.61166 -5.018 5.61e-07 ***
## C6 -7.33824 1.30066 2482.10844 -5.642 1.87e-08 ***
## C7 7.50680 1.27902 2479.64720 5.869 4.96e-09 ***
## C8 8.84023 1.30850 2483.91150 6.756 1.76e-11 ***
## C9 10.41389 1.10278 2381.35485 9.443 < 2e-16 ***
## ATNS_Score.c:C1 0.05089 0.05170 2388.35637 0.984 0.325064
## ATNS_Score.c:C2 -0.23306 0.05980 2481.56448 -3.897 9.99e-05 ***
## ATNS_Score.c:C3 -0.03676 0.05465 2405.85334 -0.673 0.501304
## ATNS_Score.c:C4 -0.10915 0.05026 2375.43957 -2.172 0.029982 *
## ATNS_Score.c:C5 -0.01335 0.05080 2379.75736 -0.263 0.792778
## ATNS_Score.c:C6 0.12491 0.05999 2484.47460 2.082 0.037426 *
## ATNS_Score.c:C7 0.01992 0.06019 2488.83935 0.331 0.740760
## ATNS_Score.c:C8 0.09537 0.06052 2483.28404 1.576 0.115187
## ATNS_Score.c:C9 0.07396 0.05128 2380.83293 1.442 0.149405
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.871,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.23 | 0.64 | 56.98 – 59.48 | 91.44 | <0.001 |
| ATNS Score c | -0.12 | 0.03 | -0.18 – -0.06 | -4.04 | <0.001 |
| C1 | -3.19 | 1.10 | -5.34 – -1.04 | -2.91 | 0.004 |
| C2 | 1.57 | 1.28 | -0.94 – 4.08 | 1.23 | 0.220 |
| C3 | -4.25 | 1.13 | -6.46 – -2.03 | -3.76 | <0.001 |
| C4 | -3.15 | 1.12 | -5.36 – -0.95 | -2.80 | 0.005 |
| C5 | -5.60 | 1.12 | -7.78 – -3.41 | -5.02 | <0.001 |
| C6 | -7.34 | 1.30 | -9.89 – -4.79 | -5.64 | <0.001 |
| C7 | 7.51 | 1.28 | 5.00 – 10.01 | 5.87 | <0.001 |
| C8 | 8.84 | 1.31 | 6.27 – 11.41 | 6.76 | <0.001 |
| C9 | 10.41 | 1.10 | 8.25 – 12.58 | 9.44 | <0.001 |
| ATNS Score c * C1 | 0.05 | 0.05 | -0.05 – 0.15 | 0.98 | 0.325 |
| ATNS Score c * C2 | -0.23 | 0.06 | -0.35 – -0.12 | -3.90 | <0.001 |
| ATNS Score c * C3 | -0.04 | 0.05 | -0.14 – 0.07 | -0.67 | 0.501 |
| ATNS Score c * C4 | -0.11 | 0.05 | -0.21 – -0.01 | -2.17 | 0.030 |
| ATNS Score c * C5 | -0.01 | 0.05 | -0.11 – 0.09 | -0.26 | 0.793 |
| ATNS Score c * C6 | 0.12 | 0.06 | 0.01 – 0.24 | 2.08 | 0.037 |
| ATNS Score c * C7 | 0.02 | 0.06 | -0.10 – 0.14 | 0.33 | 0.741 |
| ATNS Score c * C8 | 0.10 | 0.06 | -0.02 – 0.21 | 1.58 | 0.115 |
| ATNS Score c * C9 | 0.07 | 0.05 | -0.03 – 0.17 | 1.44 | 0.149 |
| Random Effects | |||||
| σ2 | 378.01 | ||||
| τ00 id | 279.52 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.069 / 0.465 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature depend on naturalness perception in predicting benefit perception, over and above climate change method contrasts?
modA.8715 <- lmer(Ben ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 +C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8715)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 +
## ATNS_Score.c * C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 +
## ATNS_Score.c * C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 +
## ATNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27604.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4715 -0.5155 0.0547 0.5583 3.3629
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 263.0 16.22
## Residual 369.6 19.22
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.817e+01 6.220e-01 1.010e+03 93.530 < 2e-16
## ATNS_Score.c -1.067e-01 2.897e-02 1.012e+03 -3.685 0.000241
## Naturalness.c 2.306e-01 2.389e-02 2.827e+03 9.652 < 2e-16
## C1 2.552e-01 1.142e+00 2.438e+03 0.223 0.823181
## C2 5.017e+00 1.310e+00 2.525e+03 3.831 0.000131
## C3 -2.281e+00 1.133e+00 2.405e+03 -2.014 0.044152
## C4 -1.812e+00 1.119e+00 2.394e+03 -1.619 0.105544
## C5 -4.471e+00 1.107e+00 2.393e+03 -4.037 5.58e-05
## C6 -7.115e+00 1.285e+00 2.485e+03 -5.539 3.36e-08
## C7 4.273e+00 1.305e+00 2.505e+03 3.274 0.001074
## C8 5.176e+00 1.344e+00 2.532e+03 3.852 0.000120
## C9 5.406e+00 1.204e+00 2.481e+03 4.488 7.52e-06
## ATNS_Score.c:Naturalness.c 1.357e-03 9.438e-04 2.833e+03 1.437 0.150747
## ATNS_Score.c:C1 5.857e-02 5.332e-02 2.430e+03 1.099 0.272087
## ATNS_Score.c:C2 -1.884e-01 6.075e-02 2.527e+03 -3.102 0.001944
## ATNS_Score.c:C3 -1.094e-02 5.433e-02 2.407e+03 -0.201 0.840491
## ATNS_Score.c:C4 -7.279e-02 5.007e-02 2.380e+03 -1.454 0.146125
## ATNS_Score.c:C5 1.359e-02 5.053e-02 2.387e+03 0.269 0.788071
## ATNS_Score.c:C6 1.146e-01 5.926e-02 2.488e+03 1.933 0.053315
## ATNS_Score.c:C7 -1.165e-02 6.118e-02 2.529e+03 -0.190 0.849033
## ATNS_Score.c:C8 3.811e-02 6.174e-02 2.525e+03 0.617 0.537101
## ATNS_Score.c:C9 3.464e-02 5.467e-02 2.441e+03 0.634 0.526377
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## C1
## C2 ***
## C3 *
## C4
## C5 ***
## C6 ***
## C7 **
## C8 ***
## C9 ***
## ATNS_Score.c:Naturalness.c
## ATNS_Score.c:C1
## ATNS_Score.c:C2 **
## ATNS_Score.c:C3
## ATNS_Score.c:C4
## ATNS_Score.c:C5
## ATNS_Score.c:C6 .
## ATNS_Score.c:C7
## ATNS_Score.c:C8
## ATNS_Score.c:C9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8715,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.17 | 0.62 | 56.95 – 59.39 | 93.53 | <0.001 |
| ATNS Score c | -0.11 | 0.03 | -0.16 – -0.05 | -3.68 | <0.001 |
| Naturalness c | 0.23 | 0.02 | 0.18 – 0.28 | 9.65 | <0.001 |
| C1 | 0.26 | 1.14 | -1.98 – 2.49 | 0.22 | 0.823 |
| C2 | 5.02 | 1.31 | 2.45 – 7.59 | 3.83 | <0.001 |
| C3 | -2.28 | 1.13 | -4.50 – -0.06 | -2.01 | 0.044 |
| C4 | -1.81 | 1.12 | -4.01 – 0.38 | -1.62 | 0.106 |
| C5 | -4.47 | 1.11 | -6.64 – -2.30 | -4.04 | <0.001 |
| C6 | -7.11 | 1.28 | -9.63 – -4.60 | -5.54 | <0.001 |
| C7 | 4.27 | 1.31 | 1.71 – 6.83 | 3.27 | 0.001 |
| C8 | 5.18 | 1.34 | 2.54 – 7.81 | 3.85 | <0.001 |
| C9 | 5.41 | 1.20 | 3.04 – 7.77 | 4.49 | <0.001 |
|
ATNS Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.44 | 0.151 |
| ATNS Score c * C1 | 0.06 | 0.05 | -0.05 – 0.16 | 1.10 | 0.272 |
| ATNS Score c * C2 | -0.19 | 0.06 | -0.31 – -0.07 | -3.10 | 0.002 |
| ATNS Score c * C3 | -0.01 | 0.05 | -0.12 – 0.10 | -0.20 | 0.840 |
| ATNS Score c * C4 | -0.07 | 0.05 | -0.17 – 0.03 | -1.45 | 0.146 |
| ATNS Score c * C5 | 0.01 | 0.05 | -0.09 – 0.11 | 0.27 | 0.788 |
| ATNS Score c * C6 | 0.11 | 0.06 | -0.00 – 0.23 | 1.93 | 0.053 |
| ATNS Score c * C7 | -0.01 | 0.06 | -0.13 – 0.11 | -0.19 | 0.849 |
| ATNS Score c * C8 | 0.04 | 0.06 | -0.08 – 0.16 | 0.62 | 0.537 |
| ATNS Score c * C9 | 0.03 | 0.05 | -0.07 – 0.14 | 0.63 | 0.526 |
| Random Effects | |||||
| σ2 | 369.57 | ||||
| τ00 id | 263.01 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.095 / 0.471 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict benefit perception, over and above climate change method contrasts?
modA.873 <- lmer(Ben ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.873)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c * C6 +
## CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27684.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2980 -0.5180 0.0676 0.5615 3.1459
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 279.3 16.71
## Residual 379.2 19.47
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.22859 0.63700 1015.71915 91.411 < 2e-16 ***
## CNS_Score.c 0.12333 0.03818 1017.82732 3.230 0.001278 **
## C1 -3.07705 1.09838 2382.13618 -2.801 0.005128 **
## C2 1.43657 1.28085 2485.93228 1.122 0.262150
## C3 -4.35542 1.13161 2393.39959 -3.849 0.000122 ***
## C4 -2.94847 1.12636 2389.57752 -2.618 0.008909 **
## C5 -5.62645 1.11704 2387.29077 -5.037 5.08e-07 ***
## C6 -7.23709 1.30587 2483.28460 -5.542 3.31e-08 ***
## C7 7.45991 1.28153 2480.94873 5.821 6.60e-09 ***
## C8 8.77853 1.30944 2485.83760 6.704 2.50e-11 ***
## C9 10.33950 1.10576 2382.90946 9.351 < 2e-16 ***
## CNS_Score.c:C1 0.13051 0.06383 2373.23498 2.045 0.041013 *
## CNS_Score.c:C2 -0.27201 0.07924 2493.09893 -3.433 0.000607 ***
## CNS_Score.c:C3 0.01858 0.06744 2389.80395 0.276 0.782919
## CNS_Score.c:C4 -0.05921 0.06509 2386.34009 -0.910 0.363141
## CNS_Score.c:C5 -0.14107 0.06912 2402.03817 -2.041 0.041375 *
## CNS_Score.c:C6 0.02912 0.07482 2485.57622 0.389 0.697164
## CNS_Score.c:C7 0.05504 0.07753 2486.62893 0.710 0.477858
## CNS_Score.c:C8 0.20108 0.07901 2486.34724 2.545 0.010988 *
## CNS_Score.c:C9 0.07309 0.06972 2404.58991 1.048 0.294577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.873,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.23 | 0.64 | 56.98 – 59.48 | 91.41 | <0.001 |
| CNS Score c | 0.12 | 0.04 | 0.05 – 0.20 | 3.23 | 0.001 |
| C1 | -3.08 | 1.10 | -5.23 – -0.92 | -2.80 | 0.005 |
| C2 | 1.44 | 1.28 | -1.07 – 3.95 | 1.12 | 0.262 |
| C3 | -4.36 | 1.13 | -6.57 – -2.14 | -3.85 | <0.001 |
| C4 | -2.95 | 1.13 | -5.16 – -0.74 | -2.62 | 0.009 |
| C5 | -5.63 | 1.12 | -7.82 – -3.44 | -5.04 | <0.001 |
| C6 | -7.24 | 1.31 | -9.80 – -4.68 | -5.54 | <0.001 |
| C7 | 7.46 | 1.28 | 4.95 – 9.97 | 5.82 | <0.001 |
| C8 | 8.78 | 1.31 | 6.21 – 11.35 | 6.70 | <0.001 |
| C9 | 10.34 | 1.11 | 8.17 – 12.51 | 9.35 | <0.001 |
| CNS Score c * C1 | 0.13 | 0.06 | 0.01 – 0.26 | 2.04 | 0.041 |
| CNS Score c * C2 | -0.27 | 0.08 | -0.43 – -0.12 | -3.43 | 0.001 |
| CNS Score c * C3 | 0.02 | 0.07 | -0.11 – 0.15 | 0.28 | 0.783 |
| CNS Score c * C4 | -0.06 | 0.07 | -0.19 – 0.07 | -0.91 | 0.363 |
| CNS Score c * C5 | -0.14 | 0.07 | -0.28 – -0.01 | -2.04 | 0.041 |
| CNS Score c * C6 | 0.03 | 0.07 | -0.12 – 0.18 | 0.39 | 0.697 |
| CNS Score c * C7 | 0.06 | 0.08 | -0.10 – 0.21 | 0.71 | 0.478 |
| CNS Score c * C8 | 0.20 | 0.08 | 0.05 – 0.36 | 2.54 | 0.011 |
| CNS Score c * C9 | 0.07 | 0.07 | -0.06 – 0.21 | 1.05 | 0.295 |
| Random Effects | |||||
| σ2 | 379.17 | ||||
| τ00 id | 279.27 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.067 / 0.463 | ||||
Q.2 (CONNECTEDNESS TO NATURE) How does connectedness to nature depend on naturalness perception in predicting benefit perception, over and above climate change method contrasts?
modA.8736 <- lmer(Ben ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.8736)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + CNS_Score.c * C1 + CNS_Score.c * C2 +
## CNS_Score.c * C3 + CNS_Score.c * C4 + CNS_Score.c * C5 +
## CNS_Score.c * C6 + CNS_Score.c * C7 + CNS_Score.c * C8 +
## CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27599.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6164 -0.5165 0.0561 0.5642 3.2568
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 263.8 16.24
## Residual 369.3 19.22
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.815e+01 6.222e-01 1.011e+03 93.452 < 2e-16 ***
## CNS_Score.c 1.258e-01 3.730e-02 1.014e+03 3.374 0.000770 ***
## Naturalness.c 2.389e-01 2.379e-02 2.835e+03 10.043 < 2e-16 ***
## C1 5.215e-01 1.140e+00 2.439e+03 0.458 0.647322
## C2 5.033e+00 1.311e+00 2.530e+03 3.840 0.000126 ***
## C3 -2.278e+00 1.134e+00 2.408e+03 -2.009 0.044619 *
## C4 -1.675e+00 1.118e+00 2.393e+03 -1.499 0.133969
## C5 -4.482e+00 1.107e+00 2.397e+03 -4.049 5.31e-05 ***
## C6 -7.014e+00 1.287e+00 2.487e+03 -5.448 5.61e-08 ***
## C7 4.074e+00 1.306e+00 2.509e+03 3.120 0.001832 **
## C8 5.055e+00 1.341e+00 2.530e+03 3.769 0.000168 ***
## C9 5.173e+00 1.205e+00 2.482e+03 4.293 1.83e-05 ***
## CNS_Score.c:Naturalness.c 1.789e-03 1.333e-03 2.880e+03 1.342 0.179703
## CNS_Score.c:C1 1.709e-01 6.710e-02 2.485e+03 2.547 0.010933 *
## CNS_Score.c:C2 -2.010e-01 7.998e-02 2.532e+03 -2.513 0.012016 *
## CNS_Score.c:C3 5.325e-02 6.736e-02 2.396e+03 0.790 0.429335
## CNS_Score.c:C4 -4.124e-02 6.494e-02 2.396e+03 -0.635 0.525465
## CNS_Score.c:C5 -1.173e-01 6.856e-02 2.407e+03 -1.711 0.087260 .
## CNS_Score.c:C6 2.138e-02 7.377e-02 2.488e+03 0.290 0.771984
## CNS_Score.c:C7 -1.202e-02 7.923e-02 2.523e+03 -0.152 0.879406
## CNS_Score.c:C8 1.401e-01 8.043e-02 2.512e+03 1.742 0.081657 .
## CNS_Score.c:C9 2.316e-02 7.581e-02 2.539e+03 0.306 0.759979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8736,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.15 | 0.62 | 56.93 – 59.37 | 93.45 | <0.001 |
| CNS Score c | 0.13 | 0.04 | 0.05 – 0.20 | 3.37 | 0.001 |
| Naturalness c | 0.24 | 0.02 | 0.19 – 0.29 | 10.04 | <0.001 |
| C1 | 0.52 | 1.14 | -1.71 – 2.76 | 0.46 | 0.647 |
| C2 | 5.03 | 1.31 | 2.46 – 7.60 | 3.84 | <0.001 |
| C3 | -2.28 | 1.13 | -4.50 – -0.06 | -2.01 | 0.045 |
| C4 | -1.68 | 1.12 | -3.87 – 0.52 | -1.50 | 0.134 |
| C5 | -4.48 | 1.11 | -6.65 – -2.31 | -4.05 | <0.001 |
| C6 | -7.01 | 1.29 | -9.54 – -4.49 | -5.45 | <0.001 |
| C7 | 4.07 | 1.31 | 1.51 – 6.63 | 3.12 | 0.002 |
| C8 | 5.06 | 1.34 | 2.43 – 7.68 | 3.77 | <0.001 |
| C9 | 5.17 | 1.20 | 2.81 – 7.54 | 4.29 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.34 | 0.180 |
| CNS Score c * C1 | 0.17 | 0.07 | 0.04 – 0.30 | 2.55 | 0.011 |
| CNS Score c * C2 | -0.20 | 0.08 | -0.36 – -0.04 | -2.51 | 0.012 |
| CNS Score c * C3 | 0.05 | 0.07 | -0.08 – 0.19 | 0.79 | 0.429 |
| CNS Score c * C4 | -0.04 | 0.06 | -0.17 – 0.09 | -0.64 | 0.525 |
| CNS Score c * C5 | -0.12 | 0.07 | -0.25 – 0.02 | -1.71 | 0.087 |
| CNS Score c * C6 | 0.02 | 0.07 | -0.12 – 0.17 | 0.29 | 0.772 |
| CNS Score c * C7 | -0.01 | 0.08 | -0.17 – 0.14 | -0.15 | 0.879 |
| CNS Score c * C8 | 0.14 | 0.08 | -0.02 – 0.30 | 1.74 | 0.082 |
| CNS Score c * C9 | 0.02 | 0.08 | -0.13 – 0.17 | 0.31 | 0.760 |
| Random Effects | |||||
| σ2 | 369.31 | ||||
| τ00 id | 263.84 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.094 / 0.471 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict benefit perception, over and above climate change method contrasts?
modA.874 <- lmer(Ben ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)
summary(modA.874)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + CCBelief_Score.c *
## C6 + CCBelief_Score.c * C7 + CCBelief_Score.c * C8 + CCBelief_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27510.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5461 -0.5042 0.0615 0.5686 3.0515
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 216.6 14.72
## Residual 377.2 19.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.819e+01 5.854e-01 1.018e+03 99.391 < 2e-16 ***
## CCBelief_Score.c 3.570e-01 2.488e-02 1.023e+03 14.352 < 2e-16 ***
## C1 -3.112e+00 1.086e+00 2.439e+03 -2.866 0.004199 **
## C2 1.546e+00 1.264e+00 2.553e+03 1.223 0.221359
## C3 -4.183e+00 1.117e+00 2.453e+03 -3.744 0.000186 ***
## C4 -2.779e+00 1.115e+00 2.449e+03 -2.493 0.012739 *
## C5 -5.859e+00 1.105e+00 2.447e+03 -5.301 1.26e-07 ***
## C6 -7.286e+00 1.285e+00 2.553e+03 -5.670 1.59e-08 ***
## C7 7.464e+00 1.263e+00 2.549e+03 5.908 3.92e-09 ***
## C8 8.630e+00 1.292e+00 2.555e+03 6.679 2.94e-11 ***
## C9 1.037e+01 1.093e+00 2.446e+03 9.488 < 2e-16 ***
## CCBelief_Score.c:C1 -8.385e-02 4.590e-02 2.440e+03 -1.827 0.067833 .
## CCBelief_Score.c:C2 -1.995e-01 5.030e-02 2.546e+03 -3.966 7.51e-05 ***
## CCBelief_Score.c:C3 1.313e-02 4.718e-02 2.452e+03 0.278 0.780863
## CCBelief_Score.c:C4 -6.098e-02 4.484e-02 2.428e+03 -1.360 0.173970
## CCBelief_Score.c:C5 3.777e-02 4.861e-02 2.466e+03 0.777 0.437228
## CCBelief_Score.c:C6 8.569e-04 5.675e-02 2.559e+03 0.015 0.987955
## CCBelief_Score.c:C7 1.469e-01 5.463e-02 2.558e+03 2.689 0.007221 **
## CCBelief_Score.c:C8 1.953e-01 5.580e-02 2.558e+03 3.500 0.000473 ***
## CCBelief_Score.c:C9 2.929e-02 4.806e-02 2.463e+03 0.610 0.542239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.874,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.19 | 0.59 | 57.04 – 59.33 | 99.39 | <0.001 |
| CCBelief Score c | 0.36 | 0.02 | 0.31 – 0.41 | 14.35 | <0.001 |
| C1 | -3.11 | 1.09 | -5.24 – -0.98 | -2.87 | 0.004 |
| C2 | 1.55 | 1.26 | -0.93 – 4.02 | 1.22 | 0.221 |
| C3 | -4.18 | 1.12 | -6.37 – -1.99 | -3.74 | <0.001 |
| C4 | -2.78 | 1.11 | -4.97 – -0.59 | -2.49 | 0.013 |
| C5 | -5.86 | 1.11 | -8.03 – -3.69 | -5.30 | <0.001 |
| C6 | -7.29 | 1.28 | -9.81 – -4.77 | -5.67 | <0.001 |
| C7 | 7.46 | 1.26 | 4.99 – 9.94 | 5.91 | <0.001 |
| C8 | 8.63 | 1.29 | 6.10 – 11.16 | 6.68 | <0.001 |
| C9 | 10.37 | 1.09 | 8.23 – 12.52 | 9.49 | <0.001 |
| CCBelief Score c * C1 | -0.08 | 0.05 | -0.17 – 0.01 | -1.83 | 0.068 |
| CCBelief Score c * C2 | -0.20 | 0.05 | -0.30 – -0.10 | -3.97 | <0.001 |
| CCBelief Score c * C3 | 0.01 | 0.05 | -0.08 – 0.11 | 0.28 | 0.781 |
| CCBelief Score c * C4 | -0.06 | 0.04 | -0.15 – 0.03 | -1.36 | 0.174 |
| CCBelief Score c * C5 | 0.04 | 0.05 | -0.06 – 0.13 | 0.78 | 0.437 |
| CCBelief Score c * C6 | 0.00 | 0.06 | -0.11 – 0.11 | 0.02 | 0.988 |
| CCBelief Score c * C7 | 0.15 | 0.05 | 0.04 – 0.25 | 2.69 | 0.007 |
| CCBelief Score c * C8 | 0.20 | 0.06 | 0.09 – 0.30 | 3.50 | <0.001 |
| CCBelief Score c * C9 | 0.03 | 0.05 | -0.06 – 0.12 | 0.61 | 0.542 |
| Random Effects | |||||
| σ2 | 377.16 | ||||
| τ00 id | 216.64 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.158 / 0.465 | ||||
Q.2 (CLIMATE CHANGE BELIEF) How does climate change belief depend on naturalness perception in predicting benefit perception, over and above climate change method contrasts?
modA.8746 <- lmer(Ben ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8746)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c *
## C2 + CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27424.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5747 -0.5208 0.0586 0.5706 3.4714
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 202.2 14.22
## Residual 368.0 19.18
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.816e+01 5.707e-01 1.015e+03 101.905
## CCBelief_Score.c 3.413e-01 2.435e-02 1.031e+03 14.016
## Naturalness.c 2.279e-01 2.327e-02 2.907e+03 9.795
## C1 2.831e-01 1.127e+00 2.507e+03 0.251
## C2 4.748e+00 1.294e+00 2.606e+03 3.668
## C3 -2.280e+00 1.119e+00 2.472e+03 -2.037
## C4 -1.602e+00 1.107e+00 2.457e+03 -1.448
## C5 -4.793e+00 1.095e+00 2.460e+03 -4.376
## C6 -6.962e+00 1.267e+00 2.563e+03 -5.494
## C7 4.347e+00 1.288e+00 2.581e+03 3.375
## C8 5.228e+00 1.322e+00 2.604e+03 3.953
## C9 5.413e+00 1.190e+00 2.559e+03 4.549
## CCBelief_Score.c:Naturalness.c -2.236e-03 8.789e-04 2.921e+03 -2.544
## CCBelief_Score.c:C1 -1.102e-01 4.722e-02 2.500e+03 -2.334
## CCBelief_Score.c:C2 -2.019e-01 5.086e-02 2.575e+03 -3.970
## CCBelief_Score.c:C3 5.389e-03 4.683e-02 2.463e+03 0.115
## CCBelief_Score.c:C4 -6.481e-02 4.444e-02 2.431e+03 -1.458
## CCBelief_Score.c:C5 2.584e-02 4.812e-02 2.468e+03 0.537
## CCBelief_Score.c:C6 -2.041e-02 5.609e-02 2.590e+03 -0.364
## CCBelief_Score.c:C7 1.621e-01 5.528e-02 2.601e+03 2.932
## CCBelief_Score.c:C8 2.113e-01 5.605e-02 2.612e+03 3.770
## CCBelief_Score.c:C9 8.961e-02 5.247e-02 2.616e+03 1.708
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 0.801735
## C2 0.000249 ***
## C3 0.041764 *
## C4 0.147813
## C5 1.26e-05 ***
## C6 4.31e-08 ***
## C7 0.000749 ***
## C8 7.91e-05 ***
## C9 5.64e-06 ***
## CCBelief_Score.c:Naturalness.c 0.010998 *
## CCBelief_Score.c:C1 0.019655 *
## CCBelief_Score.c:C2 7.38e-05 ***
## CCBelief_Score.c:C3 0.908395
## CCBelief_Score.c:C4 0.144898
## CCBelief_Score.c:C5 0.591380
## CCBelief_Score.c:C6 0.716029
## CCBelief_Score.c:C7 0.003401 **
## CCBelief_Score.c:C8 0.000167 ***
## CCBelief_Score.c:C9 0.087803 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8746,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.16 | 0.57 | 57.04 – 59.28 | 101.90 | <0.001 |
| CCBelief Score c | 0.34 | 0.02 | 0.29 – 0.39 | 14.02 | <0.001 |
| Naturalness c | 0.23 | 0.02 | 0.18 – 0.27 | 9.80 | <0.001 |
| C1 | 0.28 | 1.13 | -1.93 – 2.49 | 0.25 | 0.802 |
| C2 | 4.75 | 1.29 | 2.21 – 7.29 | 3.67 | <0.001 |
| C3 | -2.28 | 1.12 | -4.47 – -0.09 | -2.04 | 0.042 |
| C4 | -1.60 | 1.11 | -3.77 – 0.57 | -1.45 | 0.148 |
| C5 | -4.79 | 1.10 | -6.94 – -2.65 | -4.38 | <0.001 |
| C6 | -6.96 | 1.27 | -9.45 – -4.48 | -5.49 | <0.001 |
| C7 | 4.35 | 1.29 | 1.82 – 6.87 | 3.37 | 0.001 |
| C8 | 5.23 | 1.32 | 2.64 – 7.82 | 3.95 | <0.001 |
| C9 | 5.41 | 1.19 | 3.08 – 7.75 | 4.55 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – -0.00 | -2.54 | 0.011 |
| CCBelief Score c * C1 | -0.11 | 0.05 | -0.20 – -0.02 | -2.33 | 0.020 |
| CCBelief Score c * C2 | -0.20 | 0.05 | -0.30 – -0.10 | -3.97 | <0.001 |
| CCBelief Score c * C3 | 0.01 | 0.05 | -0.09 – 0.10 | 0.12 | 0.908 |
| CCBelief Score c * C4 | -0.06 | 0.04 | -0.15 – 0.02 | -1.46 | 0.145 |
| CCBelief Score c * C5 | 0.03 | 0.05 | -0.07 – 0.12 | 0.54 | 0.591 |
| CCBelief Score c * C6 | -0.02 | 0.06 | -0.13 – 0.09 | -0.36 | 0.716 |
| CCBelief Score c * C7 | 0.16 | 0.06 | 0.05 – 0.27 | 2.93 | 0.003 |
| CCBelief Score c * C8 | 0.21 | 0.06 | 0.10 – 0.32 | 3.77 | <0.001 |
| CCBelief Score c * C9 | 0.09 | 0.05 | -0.01 – 0.19 | 1.71 | 0.088 |
| Random Effects | |||||
| σ2 | 367.96 | ||||
| τ00 id | 202.15 | ||||
| ICC | 0.35 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.185 / 0.474 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict benefit perception, over and above climate change method contrasts?
modA.876 <- lmer(Ben ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)
summary(modA.876)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27705.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5302 -0.5155 0.0680 0.5592 3.2589
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 286.0 16.91
## Residual 378.8 19.46
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.23435 0.64212 1014.91283 90.690 < 2e-16 ***
## Collectivism_Score.c 0.04481 0.02696 1018.70930 1.662 0.096764 .
## C1 -3.20218 1.09829 2372.64391 -2.916 0.003583 **
## C2 1.32539 1.28344 2475.30125 1.033 0.301851
## C3 -4.34428 1.13049 2385.47974 -3.843 0.000125 ***
## C4 -3.02113 1.12595 2381.69301 -2.683 0.007343 **
## C5 -5.38511 1.12147 2380.14925 -4.802 1.67e-06 ***
## C6 -7.41561 1.30289 2476.17457 -5.692 1.41e-08 ***
## C7 7.75613 1.28347 2473.38045 6.043 1.74e-09 ***
## C8 8.71169 1.30999 2476.36381 6.650 3.59e-11 ***
## C9 10.49457 1.10485 2375.09184 9.499 < 2e-16 ***
## Collectivism_Score.c:C1 0.05018 0.05008 2407.44692 1.002 0.316495
## Collectivism_Score.c:C2 -0.07161 0.05190 2472.96934 -1.380 0.167808
## Collectivism_Score.c:C3 0.04399 0.04793 2389.69183 0.918 0.358841
## Collectivism_Score.c:C4 0.03382 0.04756 2385.44737 0.711 0.477103
## Collectivism_Score.c:C5 0.07288 0.04565 2370.16441 1.597 0.110503
## Collectivism_Score.c:C6 0.05159 0.05490 2479.32204 0.940 0.347467
## Collectivism_Score.c:C7 -0.13556 0.05565 2480.08628 -2.436 0.014916 *
## Collectivism_Score.c:C8 -0.05213 0.05517 2484.25225 -0.945 0.344754
## Collectivism_Score.c:C9 -0.06991 0.04715 2385.33196 -1.483 0.138322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.876,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.23 | 0.64 | 56.98 – 59.49 | 90.69 | <0.001 |
| Collectivism Score c | 0.04 | 0.03 | -0.01 – 0.10 | 1.66 | 0.097 |
| C1 | -3.20 | 1.10 | -5.36 – -1.05 | -2.92 | 0.004 |
| C2 | 1.33 | 1.28 | -1.19 – 3.84 | 1.03 | 0.302 |
| C3 | -4.34 | 1.13 | -6.56 – -2.13 | -3.84 | <0.001 |
| C4 | -3.02 | 1.13 | -5.23 – -0.81 | -2.68 | 0.007 |
| C5 | -5.39 | 1.12 | -7.58 – -3.19 | -4.80 | <0.001 |
| C6 | -7.42 | 1.30 | -9.97 – -4.86 | -5.69 | <0.001 |
| C7 | 7.76 | 1.28 | 5.24 – 10.27 | 6.04 | <0.001 |
| C8 | 8.71 | 1.31 | 6.14 – 11.28 | 6.65 | <0.001 |
| C9 | 10.49 | 1.10 | 8.33 – 12.66 | 9.50 | <0.001 |
| Collectivism Score c * C1 | 0.05 | 0.05 | -0.05 – 0.15 | 1.00 | 0.316 |
| Collectivism Score c * C2 | -0.07 | 0.05 | -0.17 – 0.03 | -1.38 | 0.168 |
| Collectivism Score c * C3 | 0.04 | 0.05 | -0.05 – 0.14 | 0.92 | 0.359 |
| Collectivism Score c * C4 | 0.03 | 0.05 | -0.06 – 0.13 | 0.71 | 0.477 |
| Collectivism Score c * C5 | 0.07 | 0.05 | -0.02 – 0.16 | 1.60 | 0.110 |
| Collectivism Score c * C6 | 0.05 | 0.05 | -0.06 – 0.16 | 0.94 | 0.347 |
| Collectivism Score c * C7 | -0.14 | 0.06 | -0.24 – -0.03 | -2.44 | 0.015 |
| Collectivism Score c * C8 | -0.05 | 0.06 | -0.16 – 0.06 | -0.94 | 0.345 |
| Collectivism Score c * C9 | -0.07 | 0.05 | -0.16 – 0.02 | -1.48 | 0.138 |
| Random Effects | |||||
| σ2 | 378.76 | ||||
| τ00 id | 285.95 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.060 / 0.464 | ||||
Q.2 (COLLECTIVISM) How does collectivism depend on naturalness perception in predicting benefit perception, over and above climate change method contrasts?
modA.8766 <- lmer(Ben ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8766)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27615.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5098 -0.5114 0.0620 0.5595 3.2749
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 268.7 16.39
## Residual 368.6 19.20
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.815e+01 6.260e-01 1.011e+03 92.891
## Collectivism_Score.c 4.747e-02 2.628e-02 1.015e+03 1.806
## Naturalness.c 2.466e-01 2.376e-02 2.831e+03 10.377
## C1 5.013e-01 1.140e+00 2.436e+03 0.440
## C2 4.897e+00 1.312e+00 2.519e+03 3.734
## C3 -2.233e+00 1.132e+00 2.403e+03 -1.973
## C4 -1.718e+00 1.116e+00 2.387e+03 -1.539
## C5 -4.166e+00 1.111e+00 2.390e+03 -3.749
## C6 -7.154e+00 1.284e+00 2.481e+03 -5.573
## C7 4.312e+00 1.307e+00 2.501e+03 3.300
## C8 4.912e+00 1.340e+00 2.522e+03 3.665
## C9 5.185e+00 1.206e+00 2.481e+03 4.299
## Collectivism_Score.c:Naturalness.c 9.201e-04 9.325e-04 2.824e+03 0.987
## Collectivism_Score.c:C1 5.686e-02 5.204e-02 2.475e+03 1.092
## Collectivism_Score.c:C2 -7.084e-02 5.299e-02 2.530e+03 -1.337
## Collectivism_Score.c:C3 6.227e-02 4.792e-02 2.396e+03 1.300
## Collectivism_Score.c:C4 4.267e-02 4.704e-02 2.392e+03 0.907
## Collectivism_Score.c:C5 9.381e-02 4.544e-02 2.378e+03 2.065
## Collectivism_Score.c:C6 4.634e-02 5.409e-02 2.484e+03 0.857
## Collectivism_Score.c:C7 -1.533e-01 5.688e-02 2.523e+03 -2.696
## Collectivism_Score.c:C8 -8.150e-02 5.680e-02 2.526e+03 -1.435
## Collectivism_Score.c:C9 -6.911e-02 5.071e-02 2.475e+03 -1.363
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.071227 .
## Naturalness.c < 2e-16 ***
## C1 0.660277
## C2 0.000193 ***
## C3 0.048638 *
## C4 0.124036
## C5 0.000182 ***
## C6 2.77e-08 ***
## C7 0.000980 ***
## C8 0.000252 ***
## C9 1.78e-05 ***
## Collectivism_Score.c:Naturalness.c 0.323899
## Collectivism_Score.c:C1 0.274733
## Collectivism_Score.c:C2 0.181427
## Collectivism_Score.c:C3 0.193885
## Collectivism_Score.c:C4 0.364460
## Collectivism_Score.c:C5 0.039069 *
## Collectivism_Score.c:C6 0.391743
## Collectivism_Score.c:C7 0.007074 **
## Collectivism_Score.c:C8 0.151451
## Collectivism_Score.c:C9 0.173067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8766,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.15 | 0.63 | 56.92 – 59.38 | 92.89 | <0.001 |
| Collectivism Score c | 0.05 | 0.03 | -0.00 – 0.10 | 1.81 | 0.071 |
| Naturalness c | 0.25 | 0.02 | 0.20 – 0.29 | 10.38 | <0.001 |
| C1 | 0.50 | 1.14 | -1.73 – 2.74 | 0.44 | 0.660 |
| C2 | 4.90 | 1.31 | 2.33 – 7.47 | 3.73 | <0.001 |
| C3 | -2.23 | 1.13 | -4.45 – -0.01 | -1.97 | 0.049 |
| C4 | -1.72 | 1.12 | -3.91 – 0.47 | -1.54 | 0.124 |
| C5 | -4.17 | 1.11 | -6.35 – -1.99 | -3.75 | <0.001 |
| C6 | -7.15 | 1.28 | -9.67 – -4.64 | -5.57 | <0.001 |
| C7 | 4.31 | 1.31 | 1.75 – 6.87 | 3.30 | 0.001 |
| C8 | 4.91 | 1.34 | 2.28 – 7.54 | 3.67 | <0.001 |
| C9 | 5.18 | 1.21 | 2.82 – 7.55 | 4.30 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.99 | 0.324 |
| Collectivism Score c * C1 | 0.06 | 0.05 | -0.05 – 0.16 | 1.09 | 0.275 |
| Collectivism Score c * C2 | -0.07 | 0.05 | -0.17 – 0.03 | -1.34 | 0.181 |
| Collectivism Score c * C3 | 0.06 | 0.05 | -0.03 – 0.16 | 1.30 | 0.194 |
| Collectivism Score c * C4 | 0.04 | 0.05 | -0.05 – 0.13 | 0.91 | 0.364 |
| Collectivism Score c * C5 | 0.09 | 0.05 | 0.00 – 0.18 | 2.06 | 0.039 |
| Collectivism Score c * C6 | 0.05 | 0.05 | -0.06 – 0.15 | 0.86 | 0.392 |
| Collectivism Score c * C7 | -0.15 | 0.06 | -0.26 – -0.04 | -2.70 | 0.007 |
| Collectivism Score c * C8 | -0.08 | 0.06 | -0.19 – 0.03 | -1.43 | 0.151 |
| Collectivism Score c * C9 | -0.07 | 0.05 | -0.17 – 0.03 | -1.36 | 0.173 |
| Random Effects | |||||
| σ2 | 368.60 | ||||
| τ00 id | 268.73 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.089 / 0.473 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict benefit perception, over and above climate change method contrasts?
modA.877 <- lmer(Ben ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.877)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C6 + Individualism_Score.c * C7 + Individualism_Score.c *
## C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27696.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2715 -0.5177 0.0684 0.5663 3.2139
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.3 16.83
## Residual 379.6 19.48
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.25895 0.64032 1016.47937 90.984 < 2e-16 ***
## Individualism_Score.c 0.08989 0.03803 1020.93895 2.364 0.018277 *
## C1 -3.23273 1.09946 2376.74282 -2.940 0.003311 **
## C2 1.80924 1.28695 2479.38118 1.406 0.159899
## C3 -4.44010 1.13167 2388.75121 -3.923 8.97e-05 ***
## C4 -2.93807 1.12593 2384.04703 -2.609 0.009125 **
## C5 -5.58612 1.11788 2383.66522 -4.997 6.24e-07 ***
## C6 -7.32490 1.30678 2480.93736 -5.605 2.31e-08 ***
## C7 7.47997 1.28174 2477.52281 5.836 6.05e-09 ***
## C8 8.78487 1.31261 2480.58303 6.693 2.70e-11 ***
## C9 10.35604 1.10559 2379.09919 9.367 < 2e-16 ***
## Individualism_Score.c:C1 -0.03613 0.06515 2376.19075 -0.555 0.579279
## Individualism_Score.c:C2 -0.24138 0.07263 2477.73226 -3.323 0.000903 ***
## Individualism_Score.c:C3 -0.08783 0.06984 2406.87010 -1.258 0.208677
## Individualism_Score.c:C4 0.07486 0.06722 2387.62905 1.114 0.265525
## Individualism_Score.c:C5 0.08777 0.06696 2387.08398 1.311 0.190089
## Individualism_Score.c:C6 0.03242 0.08072 2485.85263 0.402 0.687977
## Individualism_Score.c:C7 -0.01473 0.08091 2486.20597 -0.182 0.855543
## Individualism_Score.c:C8 0.03285 0.07478 2480.17386 0.439 0.660468
## Individualism_Score.c:C9 0.04123 0.06453 2377.07030 0.639 0.522952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.877,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.26 | 0.64 | 57.00 – 59.51 | 90.98 | <0.001 |
| Individualism Score c | 0.09 | 0.04 | 0.02 – 0.16 | 2.36 | 0.018 |
| C1 | -3.23 | 1.10 | -5.39 – -1.08 | -2.94 | 0.003 |
| C2 | 1.81 | 1.29 | -0.71 – 4.33 | 1.41 | 0.160 |
| C3 | -4.44 | 1.13 | -6.66 – -2.22 | -3.92 | <0.001 |
| C4 | -2.94 | 1.13 | -5.15 – -0.73 | -2.61 | 0.009 |
| C5 | -5.59 | 1.12 | -7.78 – -3.39 | -5.00 | <0.001 |
| C6 | -7.32 | 1.31 | -9.89 – -4.76 | -5.61 | <0.001 |
| C7 | 7.48 | 1.28 | 4.97 – 9.99 | 5.84 | <0.001 |
| C8 | 8.78 | 1.31 | 6.21 – 11.36 | 6.69 | <0.001 |
| C9 | 10.36 | 1.11 | 8.19 – 12.52 | 9.37 | <0.001 |
|
Individualism Score c * C1 |
-0.04 | 0.07 | -0.16 – 0.09 | -0.55 | 0.579 |
|
Individualism Score c * C2 |
-0.24 | 0.07 | -0.38 – -0.10 | -3.32 | 0.001 |
|
Individualism Score c * C3 |
-0.09 | 0.07 | -0.22 – 0.05 | -1.26 | 0.209 |
|
Individualism Score c * C4 |
0.07 | 0.07 | -0.06 – 0.21 | 1.11 | 0.266 |
|
Individualism Score c * C5 |
0.09 | 0.07 | -0.04 – 0.22 | 1.31 | 0.190 |
|
Individualism Score c * C6 |
0.03 | 0.08 | -0.13 – 0.19 | 0.40 | 0.688 |
|
Individualism Score c * C7 |
-0.01 | 0.08 | -0.17 – 0.14 | -0.18 | 0.856 |
|
Individualism Score c * C8 |
0.03 | 0.07 | -0.11 – 0.18 | 0.44 | 0.660 |
|
Individualism Score c * C9 |
0.04 | 0.06 | -0.09 – 0.17 | 0.64 | 0.523 |
| Random Effects | |||||
| σ2 | 379.57 | ||||
| τ00 id | 283.31 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.062 / 0.463 | ||||
Q.2 (INDIVIDUALISM) How does individualism depend on naturalness perception in predicting benefit perception, over and above climate change method contrasts?
modA.8775 <- lmer(Ben ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.8775)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c * C1 +
## Individualism_Score.c * C2 + Individualism_Score.c * C3 +
## Individualism_Score.c * C4 + Individualism_Score.c * C5 +
## Individualism_Score.c * C6 + Individualism_Score.c * C7 +
## Individualism_Score.c * C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27607
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4907 -0.5177 0.0489 0.5658 3.2770
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 266.2 16.32
## Residual 369.5 19.22
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.818e+01 6.243e-01 1.013e+03 93.188
## Individualism_Score.c 9.520e-02 3.708e-02 1.018e+03 2.568
## Naturalness.c 2.443e-01 2.385e-02 2.832e+03 10.241
## C1 4.418e-01 1.142e+00 2.437e+03 0.387
## C2 5.395e+00 1.315e+00 2.521e+03 4.102
## C3 -2.354e+00 1.133e+00 2.405e+03 -2.078
## C4 -1.637e+00 1.117e+00 2.390e+03 -1.465
## C5 -4.427e+00 1.108e+00 2.395e+03 -3.997
## C6 -7.050e+00 1.288e+00 2.487e+03 -5.475
## C7 4.053e+00 1.305e+00 2.505e+03 3.105
## C8 5.037e+00 1.343e+00 2.527e+03 3.750
## C9 5.071e+00 1.205e+00 2.478e+03 4.208
## Individualism_Score.c:Naturalness.c 1.205e-03 1.349e-03 2.875e+03 0.893
## Individualism_Score.c:C1 -2.127e-02 6.758e-02 2.439e+03 -0.315
## Individualism_Score.c:C2 -2.221e-01 7.445e-02 2.523e+03 -2.982
## Individualism_Score.c:C3 -6.395e-02 6.988e-02 2.422e+03 -0.915
## Individualism_Score.c:C4 8.918e-02 6.679e-02 2.400e+03 1.335
## Individualism_Score.c:C5 1.196e-01 6.650e-02 2.392e+03 1.799
## Individualism_Score.c:C6 3.813e-02 7.953e-02 2.492e+03 0.479
## Individualism_Score.c:C7 -5.041e-02 8.191e-02 2.513e+03 -0.615
## Individualism_Score.c:C8 5.595e-03 7.758e-02 2.535e+03 0.072
## Individualism_Score.c:C9 8.327e-03 7.045e-02 2.482e+03 0.118
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.010382 *
## Naturalness.c < 2e-16 ***
## C1 0.698850
## C2 4.23e-05 ***
## C3 0.037853 *
## C4 0.142931
## C5 6.61e-05 ***
## C6 4.82e-08 ***
## C7 0.001922 **
## C8 0.000181 ***
## C9 2.67e-05 ***
## Individualism_Score.c:Naturalness.c 0.371693
## Individualism_Score.c:C1 0.752997
## Individualism_Score.c:C2 0.002886 **
## Individualism_Score.c:C3 0.360217
## Individualism_Score.c:C4 0.181915
## Individualism_Score.c:C5 0.072126 .
## Individualism_Score.c:C6 0.631671
## Individualism_Score.c:C7 0.538350
## Individualism_Score.c:C8 0.942519
## Individualism_Score.c:C9 0.905922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8775,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.18 | 0.62 | 56.96 – 59.40 | 93.19 | <0.001 |
| Individualism Score c | 0.10 | 0.04 | 0.02 – 0.17 | 2.57 | 0.010 |
| Naturalness c | 0.24 | 0.02 | 0.20 – 0.29 | 10.24 | <0.001 |
| C1 | 0.44 | 1.14 | -1.80 – 2.68 | 0.39 | 0.699 |
| C2 | 5.39 | 1.32 | 2.82 – 7.97 | 4.10 | <0.001 |
| C3 | -2.35 | 1.13 | -4.58 – -0.13 | -2.08 | 0.038 |
| C4 | -1.64 | 1.12 | -3.83 – 0.55 | -1.47 | 0.143 |
| C5 | -4.43 | 1.11 | -6.60 – -2.26 | -4.00 | <0.001 |
| C6 | -7.05 | 1.29 | -9.58 – -4.53 | -5.47 | <0.001 |
| C7 | 4.05 | 1.31 | 1.49 – 6.61 | 3.11 | 0.002 |
| C8 | 5.04 | 1.34 | 2.40 – 7.67 | 3.75 | <0.001 |
| C9 | 5.07 | 1.20 | 2.71 – 7.43 | 4.21 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.89 | 0.372 |
|
Individualism Score c * C1 |
-0.02 | 0.07 | -0.15 – 0.11 | -0.31 | 0.753 |
|
Individualism Score c * C2 |
-0.22 | 0.07 | -0.37 – -0.08 | -2.98 | 0.003 |
|
Individualism Score c * C3 |
-0.06 | 0.07 | -0.20 – 0.07 | -0.92 | 0.360 |
|
Individualism Score c * C4 |
0.09 | 0.07 | -0.04 – 0.22 | 1.34 | 0.182 |
|
Individualism Score c * C5 |
0.12 | 0.07 | -0.01 – 0.25 | 1.80 | 0.072 |
|
Individualism Score c * C6 |
0.04 | 0.08 | -0.12 – 0.19 | 0.48 | 0.632 |
|
Individualism Score c * C7 |
-0.05 | 0.08 | -0.21 – 0.11 | -0.62 | 0.538 |
|
Individualism Score c * C8 |
0.01 | 0.08 | -0.15 – 0.16 | 0.07 | 0.943 |
|
Individualism Score c * C9 |
0.01 | 0.07 | -0.13 – 0.15 | 0.12 | 0.906 |
| Random Effects | |||||
| σ2 | 369.54 | ||||
| τ00 id | 266.23 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.090 / 0.471 | ||||
Political Ideology
Q.1 (POLITICAL ORIENTATION) How does political ideology predict benefit perception, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.878 <- lmer(Ben ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.878)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c *
## C4 + Ideology.c * C5 + Ideology.c * C6 + Ideology.c * C7 +
## Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27640.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4401 -0.5148 0.0605 0.5713 3.1658
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 284.9 16.88
## Residual 380.9 19.52
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.2358 0.6418 1016.8580 90.737 < 2e-16 ***
## Ideology.c -0.2122 1.1252 1018.7678 -0.189 0.850419
## C1 -3.2371 1.1026 2378.6627 -2.936 0.003358 **
## C2 1.4511 1.2840 2479.8537 1.130 0.258527
## C3 -4.3842 1.1339 2390.8641 -3.867 0.000113 ***
## C4 -2.9225 1.1277 2384.7084 -2.592 0.009611 **
## C5 -5.5027 1.1231 2383.7029 -4.900 1.03e-06 ***
## C6 -7.3459 1.3053 2479.8667 -5.628 2.03e-08 ***
## C7 7.5589 1.2844 2479.4554 5.885 4.51e-09 ***
## C8 8.7350 1.3133 2482.3630 6.651 3.57e-11 ***
## C9 10.4951 1.1133 2383.1675 9.427 < 2e-16 ***
## Ideology.c:C1 -0.3164 1.9617 2392.9733 -0.161 0.871869
## Ideology.c:C2 4.5854 2.2855 2496.8936 2.006 0.044933 *
## Ideology.c:C3 -0.6971 2.0223 2414.7056 -0.345 0.730324
## Ideology.c:C4 -1.4201 2.0258 2415.6933 -0.701 0.483376
## Ideology.c:C5 -1.4741 1.9408 2394.1501 -0.760 0.447617
## Ideology.c:C6 -2.0848 2.1957 2484.1167 -0.950 0.342444
## Ideology.c:C7 2.3573 2.3749 2490.2244 0.993 0.321021
## Ideology.c:C8 2.1116 2.2538 2488.6074 0.937 0.348902
## Ideology.c:C9 0.9301 1.9788 2392.9146 0.470 0.638381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.878,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.24 | 0.64 | 56.98 – 59.49 | 90.74 | <0.001 |
| Ideology c | -0.21 | 1.13 | -2.42 – 1.99 | -0.19 | 0.850 |
| C1 | -3.24 | 1.10 | -5.40 – -1.08 | -2.94 | 0.003 |
| C2 | 1.45 | 1.28 | -1.07 – 3.97 | 1.13 | 0.259 |
| C3 | -4.38 | 1.13 | -6.61 – -2.16 | -3.87 | <0.001 |
| C4 | -2.92 | 1.13 | -5.13 – -0.71 | -2.59 | 0.010 |
| C5 | -5.50 | 1.12 | -7.70 – -3.30 | -4.90 | <0.001 |
| C6 | -7.35 | 1.31 | -9.91 – -4.79 | -5.63 | <0.001 |
| C7 | 7.56 | 1.28 | 5.04 – 10.08 | 5.89 | <0.001 |
| C8 | 8.74 | 1.31 | 6.16 – 11.31 | 6.65 | <0.001 |
| C9 | 10.50 | 1.11 | 8.31 – 12.68 | 9.43 | <0.001 |
| Ideology c * C1 | -0.32 | 1.96 | -4.16 – 3.53 | -0.16 | 0.872 |
| Ideology c * C2 | 4.59 | 2.29 | 0.10 – 9.07 | 2.01 | 0.045 |
| Ideology c * C3 | -0.70 | 2.02 | -4.66 – 3.27 | -0.34 | 0.730 |
| Ideology c * C4 | -1.42 | 2.03 | -5.39 – 2.55 | -0.70 | 0.483 |
| Ideology c * C5 | -1.47 | 1.94 | -5.28 – 2.33 | -0.76 | 0.448 |
| Ideology c * C6 | -2.08 | 2.20 | -6.39 – 2.22 | -0.95 | 0.342 |
| Ideology c * C7 | 2.36 | 2.37 | -2.30 – 7.01 | 0.99 | 0.321 |
| Ideology c * C8 | 2.11 | 2.25 | -2.31 – 6.53 | 0.94 | 0.349 |
| Ideology c * C9 | 0.93 | 1.98 | -2.95 – 4.81 | 0.47 | 0.638 |
| Random Effects | |||||
| σ2 | 380.88 | ||||
| τ00 id | 284.88 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.057 / 0.460 | ||||
Q.2 (POLITICAL ORIENTATION) How does political ideology depend on naturalness perception in predicting benefit perception, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.8784 <- lmer(Ben ~ Ideology.c*Naturalness + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.8784)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Ideology.c * Naturalness + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c *
## C3 + Ideology.c * C4 + Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27561.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5121 -0.5128 0.0548 0.5644 3.2763
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 268.5 16.38
## Residual 370.4 19.25
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.37230 1.14783 2829.74957 42.142 < 2e-16 ***
## Ideology.c -1.46090 1.79447 2780.11184 -0.814 0.415651
## Naturalness 0.24448 0.02382 2837.45617 10.265 < 2e-16 ***
## C1 0.45587 1.14434 2443.55449 0.398 0.690393
## C2 5.07804 1.31172 2527.70001 3.871 0.000111 ***
## C3 -2.34476 1.13428 2408.30780 -2.067 0.038824 *
## C4 -1.64136 1.11789 2393.02744 -1.468 0.142166
## C5 -4.36031 1.11233 2399.05122 -3.920 9.10e-05 ***
## C6 -7.10153 1.28596 2489.07555 -5.522 3.69e-08 ***
## C7 4.14545 1.30765 2510.13595 3.170 0.001542 **
## C8 5.00220 1.34297 2531.00696 3.725 0.000200 ***
## C9 5.19194 1.20631 2485.34909 4.304 1.74e-05 ***
## Ideology.c:Naturalness 0.03249 0.03491 2613.68436 0.931 0.352065
## Ideology.c:C1 0.04757 1.89361 2455.27874 0.025 0.979961
## Ideology.c:C2 6.20186 2.06253 2309.97225 3.007 0.002668 **
## Ideology.c:C3 -0.27227 1.90309 2398.11820 -0.143 0.886250
## Ideology.c:C4 -0.83639 1.92749 2443.74340 -0.434 0.664379
## Ideology.c:C5 -1.79453 1.85800 2430.29449 -0.966 0.334219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8784,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 48.37 | 1.15 | 46.12 – 50.62 | 42.14 | <0.001 |
| Ideology c | -1.46 | 1.79 | -4.98 – 2.06 | -0.81 | 0.416 |
| Naturalness | 0.24 | 0.02 | 0.20 – 0.29 | 10.26 | <0.001 |
| C1 | 0.46 | 1.14 | -1.79 – 2.70 | 0.40 | 0.690 |
| C2 | 5.08 | 1.31 | 2.51 – 7.65 | 3.87 | <0.001 |
| C3 | -2.34 | 1.13 | -4.57 – -0.12 | -2.07 | 0.039 |
| C4 | -1.64 | 1.12 | -3.83 – 0.55 | -1.47 | 0.142 |
| C5 | -4.36 | 1.11 | -6.54 – -2.18 | -3.92 | <0.001 |
| C6 | -7.10 | 1.29 | -9.62 – -4.58 | -5.52 | <0.001 |
| C7 | 4.15 | 1.31 | 1.58 – 6.71 | 3.17 | 0.002 |
| C8 | 5.00 | 1.34 | 2.37 – 7.64 | 3.72 | <0.001 |
| C9 | 5.19 | 1.21 | 2.83 – 7.56 | 4.30 | <0.001 |
| Ideology c * Naturalness | 0.03 | 0.03 | -0.04 – 0.10 | 0.93 | 0.352 |
| Ideology c * C1 | 0.05 | 1.89 | -3.67 – 3.76 | 0.03 | 0.980 |
| Ideology c * C2 | 6.20 | 2.06 | 2.16 – 10.25 | 3.01 | 0.003 |
| Ideology c * C3 | -0.27 | 1.90 | -4.00 – 3.46 | -0.14 | 0.886 |
| Ideology c * C4 | -0.84 | 1.93 | -4.62 – 2.94 | -0.43 | 0.664 |
| Ideology c * C5 | -1.79 | 1.86 | -5.44 – 1.85 | -0.97 | 0.334 |
| Random Effects | |||||
| σ2 | 370.43 | ||||
| τ00 id | 268.47 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.085 / 0.469 | ||||
Difference Benefit - Risk
Q.1 How do climate change method contrasts predict benefit perception?
modA.910 <- lmer(BRDiff ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.910)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30498.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8901 -0.5430 0.0446 0.5733 3.1036
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 584.4 24.17
## Residual 1032.4 32.13
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.7958 0.9639 1018.4237 26.762 < 2e-16 ***
## C1 -14.0197 1.7957 2450.9270 -7.808 8.57e-15 ***
## C2 -19.0289 2.0889 2564.5205 -9.110 < 2e-16 ***
## C3 -17.7845 1.8471 2464.5022 -9.628 < 2e-16 ***
## C4 -8.6096 1.8386 2459.9287 -4.683 2.98e-06 ***
## C5 -11.3284 1.8257 2457.6798 -6.205 6.40e-10 ***
## C6 -1.5867 2.1238 2565.7134 -0.747 0.455
## C7 22.5093 2.0886 2562.6624 10.777 < 2e-16 ***
## C8 30.2190 2.1358 2566.3661 14.149 < 2e-16 ***
## C9 26.9722 1.8055 2454.0429 14.939 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.031
## C2 0.025 -0.094
## C3 -0.018 -0.113 -0.078
## C4 -0.020 -0.110 -0.099 -0.117
## C5 -0.023 -0.105 -0.088 -0.115 -0.108
## C6 0.034 -0.096 -0.167 -0.110 -0.098 -0.099
## C7 0.025 -0.083 -0.166 -0.096 -0.094 -0.099 -0.167
## C8 0.037 -0.104 -0.168 -0.105 -0.095 -0.099 -0.169 -0.167
## C9 -0.028 -0.109 -0.109 -0.108 -0.115 -0.110 -0.095 -0.091 -0.085tab_model(modA.910,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.80 | 0.96 | 23.91 – 27.69 | 26.76 | <0.001 |
| C1 | -14.02 | 1.80 | -17.54 – -10.50 | -7.81 | <0.001 |
| C2 | -19.03 | 2.09 | -23.12 – -14.93 | -9.11 | <0.001 |
| C3 | -17.78 | 1.85 | -21.41 – -14.16 | -9.63 | <0.001 |
| C4 | -8.61 | 1.84 | -12.21 – -5.00 | -4.68 | <0.001 |
| C5 | -11.33 | 1.83 | -14.91 – -7.75 | -6.20 | <0.001 |
| C6 | -1.59 | 2.12 | -5.75 – 2.58 | -0.75 | 0.455 |
| C7 | 22.51 | 2.09 | 18.41 – 26.60 | 10.78 | <0.001 |
| C8 | 30.22 | 2.14 | 26.03 – 34.41 | 14.15 | <0.001 |
| C9 | 26.97 | 1.81 | 23.43 – 30.51 | 14.94 | <0.001 |
| Random Effects | |||||
| σ2 | 1032.43 | ||||
| τ00 id | 584.41 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.466 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict the benefit-risk difference, over and above climate change method contrasts?
modA.911 <- lmer(BRDiff ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)
summary(modA.911)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c *
## C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 + ATNS_Score.c *
## C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 + ATNS_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30400
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0868 -0.5518 0.0381 0.5850 3.4207
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 518.2 22.76
## Residual 1012.3 31.82
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.76087 0.92580 1017.52131 27.826 < 2e-16 ***
## ATNS_Score.c -0.39259 0.04310 1018.71343 -9.109 < 2e-16 ***
## C1 -14.01419 1.77209 2465.46596 -7.908 3.90e-15 ***
## C2 -18.77727 2.05983 2582.07926 -9.116 < 2e-16 ***
## C3 -17.47606 1.82516 2480.25355 -9.575 < 2e-16 ***
## C4 -9.23394 1.81688 2478.56527 -5.082 4.01e-07 ***
## C5 -11.39653 1.80237 2475.01039 -6.323 3.03e-10 ***
## C6 -1.54323 2.09537 2583.75840 -0.736 0.461497
## C7 22.32160 2.06068 2580.89551 10.832 < 2e-16 ***
## C8 30.46775 2.10789 2585.72173 14.454 < 2e-16 ***
## C9 26.88716 1.78232 2469.75666 15.086 < 2e-16 ***
## ATNS_Score.c:C1 -0.03151 0.08355 2477.22602 -0.377 0.706075
## ATNS_Score.c:C2 -0.36972 0.09635 2582.53095 -3.837 0.000127 ***
## ATNS_Score.c:C3 -0.03004 0.08826 2498.11201 -0.340 0.733618
## ATNS_Score.c:C4 -0.29759 0.08125 2462.61500 -3.663 0.000255 ***
## ATNS_Score.c:C5 -0.11099 0.08211 2467.54893 -1.352 0.176558
## ATNS_Score.c:C6 0.18864 0.09664 2585.83370 1.952 0.051037 .
## ATNS_Score.c:C7 0.20430 0.09695 2590.10449 2.107 0.035195 *
## ATNS_Score.c:C8 0.31372 0.09750 2584.96609 3.218 0.001308 **
## ATNS_Score.c:C9 0.14631 0.08289 2469.30122 1.765 0.077670 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.911,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.76 | 0.93 | 23.95 – 27.58 | 27.83 | <0.001 |
| ATNS Score c | -0.39 | 0.04 | -0.48 – -0.31 | -9.11 | <0.001 |
| C1 | -14.01 | 1.77 | -17.49 – -10.54 | -7.91 | <0.001 |
| C2 | -18.78 | 2.06 | -22.82 – -14.74 | -9.12 | <0.001 |
| C3 | -17.48 | 1.83 | -21.05 – -13.90 | -9.58 | <0.001 |
| C4 | -9.23 | 1.82 | -12.80 – -5.67 | -5.08 | <0.001 |
| C5 | -11.40 | 1.80 | -14.93 – -7.86 | -6.32 | <0.001 |
| C6 | -1.54 | 2.10 | -5.65 – 2.57 | -0.74 | 0.461 |
| C7 | 22.32 | 2.06 | 18.28 – 26.36 | 10.83 | <0.001 |
| C8 | 30.47 | 2.11 | 26.33 – 34.60 | 14.45 | <0.001 |
| C9 | 26.89 | 1.78 | 23.39 – 30.38 | 15.09 | <0.001 |
| ATNS Score c * C1 | -0.03 | 0.08 | -0.20 – 0.13 | -0.38 | 0.706 |
| ATNS Score c * C2 | -0.37 | 0.10 | -0.56 – -0.18 | -3.84 | <0.001 |
| ATNS Score c * C3 | -0.03 | 0.09 | -0.20 – 0.14 | -0.34 | 0.734 |
| ATNS Score c * C4 | -0.30 | 0.08 | -0.46 – -0.14 | -3.66 | <0.001 |
| ATNS Score c * C5 | -0.11 | 0.08 | -0.27 – 0.05 | -1.35 | 0.177 |
| ATNS Score c * C6 | 0.19 | 0.10 | -0.00 – 0.38 | 1.95 | 0.051 |
| ATNS Score c * C7 | 0.20 | 0.10 | 0.01 – 0.39 | 2.11 | 0.035 |
| ATNS Score c * C8 | 0.31 | 0.10 | 0.12 – 0.50 | 3.22 | 0.001 |
| ATNS Score c * C9 | 0.15 | 0.08 | -0.02 – 0.31 | 1.77 | 0.078 |
| Random Effects | |||||
| σ2 | 1012.32 | ||||
| τ00 id | 518.23 | ||||
| ICC | 0.34 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.211 / 0.478 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature depend on naturalness perception in predicting the benefit-risk difference, over and above climate change method contrasts?
modA.9114 <- lmer(BRDiff ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 +
## ATNS_Score.c * C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 +
## ATNS_Score.c * C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 +
## ATNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30068
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9857 -0.5450 0.0357 0.5830 3.1327
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 464.3 21.55
## Residual 900.8 30.01
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.567e+01 8.760e-01 1.015e+03 29.306 < 2e-16
## ATNS_Score.c -3.526e-01 4.080e-02 1.018e+03 -8.642 < 2e-16
## Naturalness.c 6.645e-01 3.639e-02 2.919e+03 18.260 < 2e-16
## C1 -4.201e+00 1.761e+00 2.523e+03 -2.385 0.01713
## C2 -8.793e+00 2.015e+00 2.618e+03 -4.365 1.32e-05
## C3 -1.174e+01 1.748e+00 2.487e+03 -6.717 2.29e-11
## C4 -5.152e+00 1.728e+00 2.476e+03 -2.982 0.00289
## C5 -8.028e+00 1.710e+00 2.475e+03 -4.696 2.80e-06
## C6 -1.025e+00 1.978e+00 2.576e+03 -0.518 0.60434
## C7 1.299e+01 2.009e+00 2.597e+03 6.467 1.19e-10
## C8 1.981e+01 2.066e+00 2.627e+03 9.587 < 2e-16
## C9 1.242e+01 1.855e+00 2.569e+03 6.697 2.60e-11
## ATNS_Score.c:Naturalness.c 6.563e-03 1.437e-03 2.923e+03 4.566 5.17e-06
## ATNS_Score.c:C1 3.277e-02 8.223e-02 2.514e+03 0.399 0.69026
## ATNS_Score.c:C2 -2.038e-01 9.344e-02 2.620e+03 -2.181 0.02927
## ATNS_Score.c:C3 6.651e-02 8.384e-02 2.490e+03 0.793 0.42765
## ATNS_Score.c:C4 -1.769e-01 7.732e-02 2.459e+03 -2.288 0.02220
## ATNS_Score.c:C5 -1.731e-02 7.802e-02 2.467e+03 -0.222 0.82444
## ATNS_Score.c:C6 1.614e-01 9.124e-02 2.579e+03 1.769 0.07705
## ATNS_Score.c:C7 7.619e-02 9.410e-02 2.621e+03 0.810 0.41820
## ATNS_Score.c:C8 1.057e-01 9.496e-02 2.619e+03 1.113 0.26591
## ATNS_Score.c:C9 -2.545e-02 8.429e-02 2.526e+03 -0.302 0.76270
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## C1 *
## C2 ***
## C3 ***
## C4 **
## C5 ***
## C6
## C7 ***
## C8 ***
## C9 ***
## ATNS_Score.c:Naturalness.c ***
## ATNS_Score.c:C1
## ATNS_Score.c:C2 *
## ATNS_Score.c:C3
## ATNS_Score.c:C4 *
## ATNS_Score.c:C5
## ATNS_Score.c:C6 .
## ATNS_Score.c:C7
## ATNS_Score.c:C8
## ATNS_Score.c:C9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9114,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.67 | 0.88 | 23.95 – 27.39 | 29.31 | <0.001 |
| ATNS Score c | -0.35 | 0.04 | -0.43 – -0.27 | -8.64 | <0.001 |
| Naturalness c | 0.66 | 0.04 | 0.59 – 0.74 | 18.26 | <0.001 |
| C1 | -4.20 | 1.76 | -7.65 – -0.75 | -2.39 | 0.017 |
| C2 | -8.79 | 2.01 | -12.74 – -4.84 | -4.36 | <0.001 |
| C3 | -11.74 | 1.75 | -15.17 – -8.32 | -6.72 | <0.001 |
| C4 | -5.15 | 1.73 | -8.54 – -1.76 | -2.98 | 0.003 |
| C5 | -8.03 | 1.71 | -11.38 – -4.68 | -4.70 | <0.001 |
| C6 | -1.03 | 1.98 | -4.90 – 2.85 | -0.52 | 0.604 |
| C7 | 12.99 | 2.01 | 9.05 – 16.93 | 6.47 | <0.001 |
| C8 | 19.81 | 2.07 | 15.76 – 23.86 | 9.59 | <0.001 |
| C9 | 12.42 | 1.86 | 8.79 – 16.06 | 6.70 | <0.001 |
|
ATNS Score c * Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 4.57 | <0.001 |
| ATNS Score c * C1 | 0.03 | 0.08 | -0.13 – 0.19 | 0.40 | 0.690 |
| ATNS Score c * C2 | -0.20 | 0.09 | -0.39 – -0.02 | -2.18 | 0.029 |
| ATNS Score c * C3 | 0.07 | 0.08 | -0.10 – 0.23 | 0.79 | 0.428 |
| ATNS Score c * C4 | -0.18 | 0.08 | -0.33 – -0.03 | -2.29 | 0.022 |
| ATNS Score c * C5 | -0.02 | 0.08 | -0.17 – 0.14 | -0.22 | 0.824 |
| ATNS Score c * C6 | 0.16 | 0.09 | -0.02 – 0.34 | 1.77 | 0.077 |
| ATNS Score c * C7 | 0.08 | 0.09 | -0.11 – 0.26 | 0.81 | 0.418 |
| ATNS Score c * C8 | 0.11 | 0.09 | -0.08 – 0.29 | 1.11 | 0.266 |
| ATNS Score c * C9 | -0.03 | 0.08 | -0.19 – 0.14 | -0.30 | 0.763 |
| Random Effects | |||||
| σ2 | 900.83 | ||||
| τ00 id | 464.31 | ||||
| ICC | 0.34 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.291 / 0.532 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict the benefit-risk difference, over and above climate change method contrasts?
modA.913 <- lmer(BRDiff ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 +(1|id), data = L)
summary(modA.913)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c *
## C3 + CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c *
## C6 + CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30481
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3454 -0.5378 0.0342 0.5745 3.2309
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 576.9 24.02
## Residual 1020.7 31.95
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.75179 0.95843 1016.41635 26.869 < 2e-16 ***
## CNS_Score.c 0.12554 0.05746 1018.83094 2.185 0.02913 *
## C1 -13.75806 1.78623 2444.47732 -7.702 1.93e-14 ***
## C2 -18.95037 2.07804 2558.14981 -9.119 < 2e-16 ***
## C3 -17.80864 1.83980 2457.20129 -9.680 < 2e-16 ***
## C4 -8.65442 1.83143 2453.12941 -4.726 2.43e-06 ***
## C5 -11.40057 1.81636 2450.42756 -6.277 4.08e-10 ***
## C6 -1.43943 2.11875 2555.95359 -0.679 0.49696
## C7 22.21281 2.07938 2553.26199 10.682 < 2e-16 ***
## C8 30.31422 2.12441 2558.60447 14.269 < 2e-16 ***
## C9 26.76316 1.79820 2445.49599 14.883 < 2e-16 ***
## CNS_Score.c:C1 0.14127 0.10383 2433.75164 1.361 0.17378
## CNS_Score.c:C2 -0.55897 0.12854 2565.96003 -4.349 1.42e-05 ***
## CNS_Score.c:C3 -0.05848 0.10966 2453.13784 -0.533 0.59389
## CNS_Score.c:C4 -0.20800 0.10585 2448.35332 -1.965 0.04951 *
## CNS_Score.c:C5 -0.19088 0.11236 2466.72449 -1.699 0.08947 .
## CNS_Score.c:C6 0.01044 0.12139 2557.18965 0.086 0.93146
## CNS_Score.c:C7 0.33119 0.12579 2559.02354 2.633 0.00852 **
## CNS_Score.c:C8 0.38991 0.12818 2559.16525 3.042 0.00238 **
## CNS_Score.c:C9 0.20349 0.11332 2469.54501 1.796 0.07266 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.913,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.75 | 0.96 | 23.87 – 27.63 | 26.87 | <0.001 |
| CNS Score c | 0.13 | 0.06 | 0.01 – 0.24 | 2.18 | 0.029 |
| C1 | -13.76 | 1.79 | -17.26 – -10.26 | -7.70 | <0.001 |
| C2 | -18.95 | 2.08 | -23.02 – -14.88 | -9.12 | <0.001 |
| C3 | -17.81 | 1.84 | -21.42 – -14.20 | -9.68 | <0.001 |
| C4 | -8.65 | 1.83 | -12.25 – -5.06 | -4.73 | <0.001 |
| C5 | -11.40 | 1.82 | -14.96 – -7.84 | -6.28 | <0.001 |
| C6 | -1.44 | 2.12 | -5.59 – 2.71 | -0.68 | 0.497 |
| C7 | 22.21 | 2.08 | 18.14 – 26.29 | 10.68 | <0.001 |
| C8 | 30.31 | 2.12 | 26.15 – 34.48 | 14.27 | <0.001 |
| C9 | 26.76 | 1.80 | 23.24 – 30.29 | 14.88 | <0.001 |
| CNS Score c * C1 | 0.14 | 0.10 | -0.06 – 0.34 | 1.36 | 0.174 |
| CNS Score c * C2 | -0.56 | 0.13 | -0.81 – -0.31 | -4.35 | <0.001 |
| CNS Score c * C3 | -0.06 | 0.11 | -0.27 – 0.16 | -0.53 | 0.594 |
| CNS Score c * C4 | -0.21 | 0.11 | -0.42 – -0.00 | -1.97 | 0.049 |
| CNS Score c * C5 | -0.19 | 0.11 | -0.41 – 0.03 | -1.70 | 0.089 |
| CNS Score c * C6 | 0.01 | 0.12 | -0.23 – 0.25 | 0.09 | 0.931 |
| CNS Score c * C7 | 0.33 | 0.13 | 0.08 – 0.58 | 2.63 | 0.009 |
| CNS Score c * C8 | 0.39 | 0.13 | 0.14 – 0.64 | 3.04 | 0.002 |
| CNS Score c * C9 | 0.20 | 0.11 | -0.02 – 0.43 | 1.80 | 0.073 |
| Random Effects | |||||
| σ2 | 1020.75 | ||||
| τ00 id | 576.86 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.175 / 0.473 | ||||
Q.2 (CONNECTEDNESS TO NATURE) How does connectedness to nature depend on naturalness perception in predicting the benefit-risk difference, over and above climate change method contrasts?
modA.9135 <- lmer(BRDiff ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.9135)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + CNS_Score.c * C1 + CNS_Score.c * C2 +
## CNS_Score.c * C3 + CNS_Score.c * C4 + CNS_Score.c * C5 +
## CNS_Score.c * C6 + CNS_Score.c * C7 + CNS_Score.c * C8 +
## CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30152.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3773 -0.5327 0.0180 0.5775 3.0285
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 527.2 22.96
## Residual 905.1 30.09
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.552e+01 9.112e-01 1.014e+03 28.009 < 2e-16 ***
## CNS_Score.c 1.324e-01 5.462e-02 1.017e+03 2.423 0.01555 *
## Naturalness.c 6.922e-01 3.666e-02 2.895e+03 18.882 < 2e-16 ***
## C1 -3.368e+00 1.771e+00 2.492e+03 -1.902 0.05726 .
## C2 -8.645e+00 2.033e+00 2.589e+03 -4.253 2.19e-05 ***
## C3 -1.183e+01 1.762e+00 2.459e+03 -6.714 2.34e-11 ***
## C4 -4.930e+00 1.737e+00 2.444e+03 -2.838 0.00458 **
## C5 -8.089e+00 1.721e+00 2.448e+03 -4.701 2.74e-06 ***
## C6 -8.380e-01 1.998e+00 2.545e+03 -0.419 0.67498
## C7 1.251e+01 2.026e+00 2.567e+03 6.175 7.65e-10 ***
## C8 1.962e+01 2.080e+00 2.590e+03 9.435 < 2e-16 ***
## C9 1.182e+01 1.870e+00 2.538e+03 6.322 3.05e-10 ***
## CNS_Score.c:Naturalness.c 4.150e-03 2.052e-03 2.933e+03 2.023 0.04321 *
## CNS_Score.c:C1 2.381e-01 1.041e-01 2.539e+03 2.286 0.02233 *
## CNS_Score.c:C2 -3.656e-01 1.240e-01 2.591e+03 -2.948 0.00323 **
## CNS_Score.c:C3 3.554e-02 1.047e-01 2.447e+03 0.339 0.73434
## CNS_Score.c:C4 -1.629e-01 1.010e-01 2.446e+03 -1.614 0.10666
## CNS_Score.c:C5 -1.296e-01 1.066e-01 2.459e+03 -1.217 0.22390
## CNS_Score.c:C6 -1.013e-02 1.145e-01 2.544e+03 -0.088 0.92953
## CNS_Score.c:C7 1.485e-01 1.229e-01 2.582e+03 1.208 0.22704
## CNS_Score.c:C8 2.290e-01 1.248e-01 2.570e+03 1.835 0.06660 .
## CNS_Score.c:C9 8.448e-02 1.176e-01 2.597e+03 0.719 0.47244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9135,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.52 | 0.91 | 23.74 – 27.31 | 28.01 | <0.001 |
| CNS Score c | 0.13 | 0.05 | 0.03 – 0.24 | 2.42 | 0.015 |
| Naturalness c | 0.69 | 0.04 | 0.62 – 0.76 | 18.88 | <0.001 |
| C1 | -3.37 | 1.77 | -6.84 – 0.10 | -1.90 | 0.057 |
| C2 | -8.64 | 2.03 | -12.63 – -4.66 | -4.25 | <0.001 |
| C3 | -11.83 | 1.76 | -15.29 – -8.38 | -6.71 | <0.001 |
| C4 | -4.93 | 1.74 | -8.34 – -1.52 | -2.84 | 0.005 |
| C5 | -8.09 | 1.72 | -11.46 – -4.71 | -4.70 | <0.001 |
| C6 | -0.84 | 2.00 | -4.76 – 3.08 | -0.42 | 0.675 |
| C7 | 12.51 | 2.03 | 8.54 – 16.48 | 6.18 | <0.001 |
| C8 | 19.62 | 2.08 | 15.55 – 23.70 | 9.43 | <0.001 |
| C9 | 11.82 | 1.87 | 8.16 – 15.49 | 6.32 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 2.02 | 0.043 |
| CNS Score c * C1 | 0.24 | 0.10 | 0.03 – 0.44 | 2.29 | 0.022 |
| CNS Score c * C2 | -0.37 | 0.12 | -0.61 – -0.12 | -2.95 | 0.003 |
| CNS Score c * C3 | 0.04 | 0.10 | -0.17 – 0.24 | 0.34 | 0.734 |
| CNS Score c * C4 | -0.16 | 0.10 | -0.36 – 0.04 | -1.61 | 0.107 |
| CNS Score c * C5 | -0.13 | 0.11 | -0.34 – 0.08 | -1.22 | 0.224 |
| CNS Score c * C6 | -0.01 | 0.11 | -0.23 – 0.21 | -0.09 | 0.930 |
| CNS Score c * C7 | 0.15 | 0.12 | -0.09 – 0.39 | 1.21 | 0.227 |
| CNS Score c * C8 | 0.23 | 0.12 | -0.02 – 0.47 | 1.84 | 0.067 |
| CNS Score c * C9 | 0.08 | 0.12 | -0.15 – 0.31 | 0.72 | 0.472 |
| Random Effects | |||||
| σ2 | 905.12 | ||||
| τ00 id | 527.19 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.255 / 0.529 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict the benefit-risk difference, over and above climate change method contrasts?
modA.914 <- lmer(BRDiff ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 +(1|id), data = L)
summary(modA.914)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 +
## CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30315.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0407 -0.5521 0.0426 0.5749 3.1791
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 441 21.00
## Residual 1016 31.87
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.73297 0.88408 1019.14407 29.107 < 2e-16 ***
## CCBelief_Score.c 0.51455 0.03758 1024.58438 13.693 < 2e-16 ***
## C1 -13.87528 1.76502 2505.39277 -7.861 5.60e-15 ***
## C2 -19.05907 2.04925 2627.34172 -9.301 < 2e-16 ***
## C3 -17.60710 1.81527 2520.62979 -9.699 < 2e-16 ***
## C4 -8.30169 1.81173 2516.62952 -4.582 4.83e-06 ***
## C5 -11.79535 1.79606 2514.86953 -6.567 6.20e-11 ***
## C6 -1.43291 2.08301 2627.66246 -0.688 0.4916
## C7 22.29106 2.04805 2623.88916 10.884 < 2e-16 ***
## C8 30.20208 2.09456 2629.49223 14.419 < 2e-16 ***
## C9 26.75941 1.77676 2512.46341 15.061 < 2e-16 ***
## CCBelief_Score.c:C1 -0.06219 0.07459 2506.42187 -0.834 0.4045
## CCBelief_Score.c:C2 -0.48178 0.08155 2618.88446 -5.907 3.92e-09 ***
## CCBelief_Score.c:C3 0.02147 0.07666 2519.97611 0.280 0.7794
## CCBelief_Score.c:C4 -0.08658 0.07290 2493.04610 -1.188 0.2350
## CCBelief_Score.c:C5 0.04612 0.07896 2534.07771 0.584 0.5593
## CCBelief_Score.c:C6 -0.01734 0.09199 2634.89402 -0.188 0.8505
## CCBelief_Score.c:C7 0.38924 0.08856 2632.59432 4.395 1.15e-05 ***
## CCBelief_Score.c:C8 0.16784 0.09045 2633.07147 1.856 0.0636 .
## CCBelief_Score.c:C9 0.08541 0.07807 2531.89488 1.094 0.2740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.914,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.73 | 0.88 | 24.00 – 27.47 | 29.11 | <0.001 |
| CCBelief Score c | 0.51 | 0.04 | 0.44 – 0.59 | 13.69 | <0.001 |
| C1 | -13.88 | 1.77 | -17.34 – -10.41 | -7.86 | <0.001 |
| C2 | -19.06 | 2.05 | -23.08 – -15.04 | -9.30 | <0.001 |
| C3 | -17.61 | 1.82 | -21.17 – -14.05 | -9.70 | <0.001 |
| C4 | -8.30 | 1.81 | -11.85 – -4.75 | -4.58 | <0.001 |
| C5 | -11.80 | 1.80 | -15.32 – -8.27 | -6.57 | <0.001 |
| C6 | -1.43 | 2.08 | -5.52 – 2.65 | -0.69 | 0.492 |
| C7 | 22.29 | 2.05 | 18.28 – 26.31 | 10.88 | <0.001 |
| C8 | 30.20 | 2.09 | 26.10 – 34.31 | 14.42 | <0.001 |
| C9 | 26.76 | 1.78 | 23.28 – 30.24 | 15.06 | <0.001 |
| CCBelief Score c * C1 | -0.06 | 0.07 | -0.21 – 0.08 | -0.83 | 0.405 |
| CCBelief Score c * C2 | -0.48 | 0.08 | -0.64 – -0.32 | -5.91 | <0.001 |
| CCBelief Score c * C3 | 0.02 | 0.08 | -0.13 – 0.17 | 0.28 | 0.779 |
| CCBelief Score c * C4 | -0.09 | 0.07 | -0.23 – 0.06 | -1.19 | 0.235 |
| CCBelief Score c * C5 | 0.05 | 0.08 | -0.11 – 0.20 | 0.58 | 0.559 |
| CCBelief Score c * C6 | -0.02 | 0.09 | -0.20 – 0.16 | -0.19 | 0.851 |
| CCBelief Score c * C7 | 0.39 | 0.09 | 0.22 – 0.56 | 4.40 | <0.001 |
| CCBelief Score c * C8 | 0.17 | 0.09 | -0.01 – 0.35 | 1.86 | 0.064 |
| CCBelief Score c * C9 | 0.09 | 0.08 | -0.07 – 0.24 | 1.09 | 0.274 |
| Random Effects | |||||
| σ2 | 1015.87 | ||||
| τ00 id | 441.04 | ||||
| ICC | 0.30 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.248 / 0.476 | ||||
Q.2 (CLIMATE CHANGE BELIEF) How does climate change belief depend on naturalness perception in predicting the benefit-risk difference, over and above climate change method contrasts?
modA.9145 <- lmer(BRDiff ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9145)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c *
## C2 + CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 29984.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5446 -0.5604 0.0221 0.5776 2.9929
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 400.7 20.02
## Residual 901.3 30.02
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.559e+01 8.390e-01 1.020e+03 30.494
## CCBelief_Score.c 4.755e-01 3.582e-02 1.038e+03 13.276
## Naturalness.c 6.728e-01 3.582e-02 2.952e+03 18.785
## C1 -3.844e+00 1.749e+00 2.563e+03 -2.197
## C2 -9.475e+00 2.006e+00 2.665e+03 -4.724
## C3 -1.199e+01 1.738e+00 2.527e+03 -6.898
## C4 -4.752e+00 1.719e+00 2.511e+03 -2.764
## C5 -8.622e+00 1.701e+00 2.514e+03 -5.067
## C6 -6.393e-01 1.965e+00 2.621e+03 -0.325
## C7 1.305e+01 1.996e+00 2.640e+03 6.538
## C8 2.015e+01 2.049e+00 2.664e+03 9.833
## C9 1.220e+01 1.845e+00 2.617e+03 6.611
## CCBelief_Score.c:Naturalness.c -3.625e-03 1.352e-03 2.962e+03 -2.680
## CCBelief_Score.c:C1 -9.586e-02 7.329e-02 2.555e+03 -1.308
## CCBelief_Score.c:C2 -4.529e-01 7.885e-02 2.633e+03 -5.744
## CCBelief_Score.c:C3 1.644e-02 7.273e-02 2.517e+03 0.226
## CCBelief_Score.c:C4 -8.259e-02 6.906e-02 2.483e+03 -1.196
## CCBelief_Score.c:C5 2.473e-02 7.473e-02 2.522e+03 0.331
## CCBelief_Score.c:C6 -6.164e-02 8.694e-02 2.648e+03 -0.709
## CCBelief_Score.c:C7 3.919e-01 8.565e-02 2.660e+03 4.575
## CCBelief_Score.c:C8 1.758e-01 8.683e-02 2.671e+03 2.025
## CCBelief_Score.c:C9 1.848e-01 8.129e-02 2.673e+03 2.274
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 0.02809 *
## C2 2.43e-06 ***
## C3 6.63e-12 ***
## C4 0.00575 **
## C5 4.33e-07 ***
## C6 0.74490
## C7 7.48e-11 ***
## C8 < 2e-16 ***
## C9 4.61e-11 ***
## CCBelief_Score.c:Naturalness.c 0.00739 **
## CCBelief_Score.c:C1 0.19100
## CCBelief_Score.c:C2 1.03e-08 ***
## CCBelief_Score.c:C3 0.82121
## CCBelief_Score.c:C4 0.23185
## CCBelief_Score.c:C5 0.74071
## CCBelief_Score.c:C6 0.47833
## CCBelief_Score.c:C7 4.97e-06 ***
## CCBelief_Score.c:C8 0.04297 *
## CCBelief_Score.c:C9 0.02307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9145,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.59 | 0.84 | 23.94 – 27.23 | 30.49 | <0.001 |
| CCBelief Score c | 0.48 | 0.04 | 0.41 – 0.55 | 13.28 | <0.001 |
| Naturalness c | 0.67 | 0.04 | 0.60 – 0.74 | 18.79 | <0.001 |
| C1 | -3.84 | 1.75 | -7.27 – -0.41 | -2.20 | 0.028 |
| C2 | -9.48 | 2.01 | -13.41 – -5.54 | -4.72 | <0.001 |
| C3 | -11.99 | 1.74 | -15.40 – -8.58 | -6.90 | <0.001 |
| C4 | -4.75 | 1.72 | -8.12 – -1.38 | -2.76 | 0.006 |
| C5 | -8.62 | 1.70 | -11.96 – -5.29 | -5.07 | <0.001 |
| C6 | -0.64 | 1.96 | -4.49 – 3.21 | -0.33 | 0.745 |
| C7 | 13.05 | 2.00 | 9.14 – 16.97 | 6.54 | <0.001 |
| C8 | 20.15 | 2.05 | 16.13 – 24.16 | 9.83 | <0.001 |
| C9 | 12.20 | 1.85 | 8.58 – 15.82 | 6.61 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.01 – -0.00 | -2.68 | 0.007 |
| CCBelief Score c * C1 | -0.10 | 0.07 | -0.24 – 0.05 | -1.31 | 0.191 |
| CCBelief Score c * C2 | -0.45 | 0.08 | -0.61 – -0.30 | -5.74 | <0.001 |
| CCBelief Score c * C3 | 0.02 | 0.07 | -0.13 – 0.16 | 0.23 | 0.821 |
| CCBelief Score c * C4 | -0.08 | 0.07 | -0.22 – 0.05 | -1.20 | 0.232 |
| CCBelief Score c * C5 | 0.02 | 0.07 | -0.12 – 0.17 | 0.33 | 0.741 |
| CCBelief Score c * C6 | -0.06 | 0.09 | -0.23 – 0.11 | -0.71 | 0.478 |
| CCBelief Score c * C7 | 0.39 | 0.09 | 0.22 – 0.56 | 4.58 | <0.001 |
| CCBelief Score c * C8 | 0.18 | 0.09 | 0.01 – 0.35 | 2.03 | 0.043 |
| CCBelief Score c * C9 | 0.18 | 0.08 | 0.03 – 0.34 | 2.27 | 0.023 |
| Random Effects | |||||
| σ2 | 901.30 | ||||
| τ00 id | 400.69 | ||||
| ICC | 0.31 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.325 / 0.533 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict the benefit-risk difference, over and above climate change method contrasts?
modA.916 <- lmer(BRDiff ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)
summary(modA.916)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30523
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0020 -0.5453 0.0412 0.5735 3.0893
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 589.2 24.27
## Residual 1030.0 32.09
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.585e+01 9.665e-01 1.016e+03 26.745 < 2e-16 ***
## Collectivism_Score.c -3.640e-02 4.058e-02 1.020e+03 -0.897 0.3700
## C1 -1.405e+01 1.795e+00 2.438e+03 -7.827 7.41e-15 ***
## C2 -1.906e+01 2.092e+00 2.551e+03 -9.111 < 2e-16 ***
## C3 -1.786e+01 1.847e+00 2.452e+03 -9.672 < 2e-16 ***
## C4 -8.711e+00 1.839e+00 2.448e+03 -4.736 2.30e-06 ***
## C5 -1.118e+01 1.832e+00 2.446e+03 -6.100 1.23e-09 ***
## C6 -1.668e+00 2.124e+00 2.553e+03 -0.785 0.4323
## C7 2.286e+01 2.092e+00 2.549e+03 10.925 < 2e-16 ***
## C8 3.015e+01 2.135e+00 2.553e+03 14.120 < 2e-16 ***
## C9 2.702e+01 1.805e+00 2.441e+03 14.967 < 2e-16 ***
## Collectivism_Score.c:C1 -2.320e-02 8.176e-02 2.477e+03 -0.284 0.7767
## Collectivism_Score.c:C2 5.489e-02 8.461e-02 2.548e+03 0.649 0.5166
## Collectivism_Score.c:C3 -5.806e-04 7.829e-02 2.457e+03 -0.007 0.9941
## Collectivism_Score.c:C4 5.309e-02 7.770e-02 2.453e+03 0.683 0.4945
## Collectivism_Score.c:C5 1.087e-01 7.459e-02 2.435e+03 1.458 0.1450
## Collectivism_Score.c:C6 3.354e-02 8.947e-02 2.556e+03 0.375 0.7078
## Collectivism_Score.c:C7 -2.041e-01 9.069e-02 2.556e+03 -2.251 0.0245 *
## Collectivism_Score.c:C8 -2.426e-02 8.990e-02 2.561e+03 -0.270 0.7873
## Collectivism_Score.c:C9 -8.700e-02 7.703e-02 2.452e+03 -1.129 0.2588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.916,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.85 | 0.97 | 23.95 – 27.74 | 26.75 | <0.001 |
| Collectivism Score c | -0.04 | 0.04 | -0.12 – 0.04 | -0.90 | 0.370 |
| C1 | -14.05 | 1.79 | -17.56 – -10.53 | -7.83 | <0.001 |
| C2 | -19.06 | 2.09 | -23.16 – -14.96 | -9.11 | <0.001 |
| C3 | -17.86 | 1.85 | -21.48 – -14.24 | -9.67 | <0.001 |
| C4 | -8.71 | 1.84 | -12.32 – -5.10 | -4.74 | <0.001 |
| C5 | -11.18 | 1.83 | -14.77 – -7.58 | -6.10 | <0.001 |
| C6 | -1.67 | 2.12 | -5.83 – 2.50 | -0.79 | 0.432 |
| C7 | 22.86 | 2.09 | 18.75 – 26.96 | 10.92 | <0.001 |
| C8 | 30.15 | 2.14 | 25.96 – 34.33 | 14.12 | <0.001 |
| C9 | 27.02 | 1.81 | 23.48 – 30.56 | 14.97 | <0.001 |
| Collectivism Score c * C1 | -0.02 | 0.08 | -0.18 – 0.14 | -0.28 | 0.777 |
| Collectivism Score c * C2 | 0.05 | 0.08 | -0.11 – 0.22 | 0.65 | 0.517 |
| Collectivism Score c * C3 | -0.00 | 0.08 | -0.15 – 0.15 | -0.01 | 0.994 |
| Collectivism Score c * C4 | 0.05 | 0.08 | -0.10 – 0.21 | 0.68 | 0.494 |
| Collectivism Score c * C5 | 0.11 | 0.07 | -0.04 – 0.26 | 1.46 | 0.145 |
| Collectivism Score c * C6 | 0.03 | 0.09 | -0.14 – 0.21 | 0.37 | 0.708 |
| Collectivism Score c * C7 | -0.20 | 0.09 | -0.38 – -0.03 | -2.25 | 0.024 |
| Collectivism Score c * C8 | -0.02 | 0.09 | -0.20 – 0.15 | -0.27 | 0.787 |
| Collectivism Score c * C9 | -0.09 | 0.08 | -0.24 – 0.06 | -1.13 | 0.259 |
| Random Effects | |||||
| σ2 | 1030.04 | ||||
| τ00 id | 589.21 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.165 / 0.469 | ||||
Q.2 (COLLECTIVISM) How does collectivism depend on naturalness perception in predicting the benefit-risk difference, over and above climate change method contrasts?
modA.9166 <- lmer(BRDiff ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9166)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c * C1 +
## Collectivism_Score.c * C2 + Collectivism_Score.c * C3 + Collectivism_Score.c *
## C4 + Collectivism_Score.c * C5 + Collectivism_Score.c * C6 +
## Collectivism_Score.c * C7 + Collectivism_Score.c * C8 + Collectivism_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30184
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4954 -0.5367 0.0288 0.5655 2.9099
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 535.9 23.15
## Residual 910.1 30.17
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.560e+01 9.170e-01 1.014e+03 27.919
## Collectivism_Score.c -2.947e-02 3.851e-02 1.019e+03 -0.765
## Naturalness.c 7.091e-01 3.673e-02 2.895e+03 19.307
## C1 -3.407e+00 1.777e+00 2.492e+03 -1.917
## C2 -8.816e+00 2.041e+00 2.581e+03 -4.320
## C3 -1.184e+01 1.765e+00 2.457e+03 -6.706
## C4 -4.926e+00 1.742e+00 2.440e+03 -2.829
## C5 -7.698e+00 1.734e+00 2.444e+03 -4.440
## C6 -9.659e-01 1.999e+00 2.542e+03 -0.483
## C7 1.302e+01 2.034e+00 2.563e+03 6.403
## C8 1.932e+01 2.086e+00 2.585e+03 9.262
## C9 1.173e+01 1.878e+00 2.540e+03 6.247
## Collectivism_Score.c:Naturalness.c 1.583e-03 1.442e-03 2.889e+03 1.098
## Collectivism_Score.c:C1 -2.167e-02 8.106e-02 2.534e+03 -0.267
## Collectivism_Score.c:C2 3.997e-02 8.245e-02 2.593e+03 0.485
## Collectivism_Score.c:C3 4.065e-02 7.474e-02 2.450e+03 0.544
## Collectivism_Score.c:C4 7.501e-02 7.337e-02 2.446e+03 1.022
## Collectivism_Score.c:C5 1.612e-01 7.089e-02 2.430e+03 2.273
## Collectivism_Score.c:C6 1.976e-02 8.423e-02 2.544e+03 0.235
## Collectivism_Score.c:C7 -2.407e-01 8.850e-02 2.586e+03 -2.719
## Collectivism_Score.c:C8 -8.815e-02 8.838e-02 2.589e+03 -0.997
## Collectivism_Score.c:C9 -6.420e-02 7.898e-02 2.534e+03 -0.813
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.44422
## Naturalness.c < 2e-16 ***
## C1 0.05538 .
## C2 1.62e-05 ***
## C3 2.48e-11 ***
## C4 0.00471 **
## C5 9.38e-06 ***
## C6 0.62903
## C7 1.81e-10 ***
## C8 < 2e-16 ***
## C9 4.88e-10 ***
## Collectivism_Score.c:Naturalness.c 0.27229
## Collectivism_Score.c:C1 0.78925
## Collectivism_Score.c:C2 0.62789
## Collectivism_Score.c:C3 0.58657
## Collectivism_Score.c:C4 0.30672
## Collectivism_Score.c:C5 0.02310 *
## Collectivism_Score.c:C6 0.81455
## Collectivism_Score.c:C7 0.00658 **
## Collectivism_Score.c:C8 0.31868
## Collectivism_Score.c:C9 0.41638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9166,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.60 | 0.92 | 23.80 – 27.40 | 27.92 | <0.001 |
| Collectivism Score c | -0.03 | 0.04 | -0.10 – 0.05 | -0.77 | 0.444 |
| Naturalness c | 0.71 | 0.04 | 0.64 – 0.78 | 19.31 | <0.001 |
| C1 | -3.41 | 1.78 | -6.89 – 0.08 | -1.92 | 0.055 |
| C2 | -8.82 | 2.04 | -12.82 – -4.81 | -4.32 | <0.001 |
| C3 | -11.84 | 1.77 | -15.30 – -8.38 | -6.71 | <0.001 |
| C4 | -4.93 | 1.74 | -8.34 – -1.51 | -2.83 | 0.005 |
| C5 | -7.70 | 1.73 | -11.10 – -4.30 | -4.44 | <0.001 |
| C6 | -0.97 | 2.00 | -4.89 – 2.95 | -0.48 | 0.629 |
| C7 | 13.02 | 2.03 | 9.03 – 17.01 | 6.40 | <0.001 |
| C8 | 19.32 | 2.09 | 15.23 – 23.41 | 9.26 | <0.001 |
| C9 | 11.73 | 1.88 | 8.05 – 15.41 | 6.25 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.10 | 0.272 |
| Collectivism Score c * C1 | -0.02 | 0.08 | -0.18 – 0.14 | -0.27 | 0.789 |
| Collectivism Score c * C2 | 0.04 | 0.08 | -0.12 – 0.20 | 0.48 | 0.628 |
| Collectivism Score c * C3 | 0.04 | 0.07 | -0.11 – 0.19 | 0.54 | 0.587 |
| Collectivism Score c * C4 | 0.08 | 0.07 | -0.07 – 0.22 | 1.02 | 0.307 |
| Collectivism Score c * C5 | 0.16 | 0.07 | 0.02 – 0.30 | 2.27 | 0.023 |
| Collectivism Score c * C6 | 0.02 | 0.08 | -0.15 – 0.18 | 0.23 | 0.815 |
| Collectivism Score c * C7 | -0.24 | 0.09 | -0.41 – -0.07 | -2.72 | 0.007 |
| Collectivism Score c * C8 | -0.09 | 0.09 | -0.26 – 0.09 | -1.00 | 0.319 |
| Collectivism Score c * C9 | -0.06 | 0.08 | -0.22 – 0.09 | -0.81 | 0.416 |
| Random Effects | |||||
| σ2 | 910.13 | ||||
| τ00 id | 535.87 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.249 / 0.527 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict the benefit-risk difference, over and above climate change method contrasts?
modA.917 <- lmer(BRDiff ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.917)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C6 + Individualism_Score.c * C7 + Individualism_Score.c *
## C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30509.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9148 -0.5435 0.0469 0.5673 3.3342
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 585.9 24.21
## Residual 1028.7 32.07
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.85656 0.96465 1018.56175 26.804 < 2e-16 ***
## Individualism_Score.c 0.07607 0.05730 1023.65525 1.327 0.18464
## C1 -14.09011 1.79384 2441.20741 -7.855 5.95e-15 ***
## C2 -18.49949 2.09470 2553.94019 -8.832 < 2e-16 ***
## C3 -17.90662 1.84588 2454.58973 -9.701 < 2e-16 ***
## C4 -8.62947 1.83670 2449.61778 -4.698 2.77e-06 ***
## C5 -11.33797 1.82359 2448.95430 -6.217 5.92e-10 ***
## C6 -1.54721 2.12688 2555.87686 -0.727 0.46701
## C7 22.41130 2.08629 2552.05956 10.742 < 2e-16 ***
## C8 30.30384 2.13638 2555.62671 14.185 < 2e-16 ***
## C9 26.87813 1.80374 2443.68235 14.901 < 2e-16 ***
## Individualism_Score.c:C1 -0.04787 0.10630 2440.42634 -0.450 0.65252
## Individualism_Score.c:C2 -0.37170 0.11823 2551.00130 -3.144 0.00169 **
## Individualism_Score.c:C3 -0.08378 0.11387 2474.63843 -0.736 0.46195
## Individualism_Score.c:C4 0.04282 0.10965 2453.54669 0.391 0.69614
## Individualism_Score.c:C5 0.07321 0.10923 2452.78669 0.670 0.50279
## Individualism_Score.c:C6 0.05733 0.13136 2561.57579 0.436 0.66255
## Individualism_Score.c:C7 0.02378 0.13167 2561.86819 0.181 0.85668
## Individualism_Score.c:C8 0.09655 0.12171 2554.43105 0.793 0.42768
## Individualism_Score.c:C9 -0.01260 0.10528 2440.55024 -0.120 0.90473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.917,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.86 | 0.96 | 23.97 – 27.75 | 26.80 | <0.001 |
| Individualism Score c | 0.08 | 0.06 | -0.04 – 0.19 | 1.33 | 0.184 |
| C1 | -14.09 | 1.79 | -17.61 – -10.57 | -7.85 | <0.001 |
| C2 | -18.50 | 2.09 | -22.61 – -14.39 | -8.83 | <0.001 |
| C3 | -17.91 | 1.85 | -21.53 – -14.29 | -9.70 | <0.001 |
| C4 | -8.63 | 1.84 | -12.23 – -5.03 | -4.70 | <0.001 |
| C5 | -11.34 | 1.82 | -14.91 – -7.76 | -6.22 | <0.001 |
| C6 | -1.55 | 2.13 | -5.72 – 2.62 | -0.73 | 0.467 |
| C7 | 22.41 | 2.09 | 18.32 – 26.50 | 10.74 | <0.001 |
| C8 | 30.30 | 2.14 | 26.11 – 34.49 | 14.18 | <0.001 |
| C9 | 26.88 | 1.80 | 23.34 – 30.41 | 14.90 | <0.001 |
|
Individualism Score c * C1 |
-0.05 | 0.11 | -0.26 – 0.16 | -0.45 | 0.653 |
|
Individualism Score c * C2 |
-0.37 | 0.12 | -0.60 – -0.14 | -3.14 | 0.002 |
|
Individualism Score c * C3 |
-0.08 | 0.11 | -0.31 – 0.14 | -0.74 | 0.462 |
|
Individualism Score c * C4 |
0.04 | 0.11 | -0.17 – 0.26 | 0.39 | 0.696 |
|
Individualism Score c * C5 |
0.07 | 0.11 | -0.14 – 0.29 | 0.67 | 0.503 |
|
Individualism Score c * C6 |
0.06 | 0.13 | -0.20 – 0.31 | 0.44 | 0.663 |
|
Individualism Score c * C7 |
0.02 | 0.13 | -0.23 – 0.28 | 0.18 | 0.857 |
|
Individualism Score c * C8 |
0.10 | 0.12 | -0.14 – 0.34 | 0.79 | 0.428 |
|
Individualism Score c * C9 |
-0.01 | 0.11 | -0.22 – 0.19 | -0.12 | 0.905 |
| Random Effects | |||||
| σ2 | 1028.74 | ||||
| τ00 id | 585.94 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.167 / 0.469 | ||||
Q.2 (INDIVIDUALISM) How does individualism depend on naturalness perception in predicting the benefit-risk difference, over and above climate change method contrasts?
modA.9177 <- lmer(BRDiff ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.9177)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c * C1 +
## Individualism_Score.c * C2 + Individualism_Score.c * C3 +
## Individualism_Score.c * C4 + Individualism_Score.c * C5 +
## Individualism_Score.c * C6 + Individualism_Score.c * C7 +
## Individualism_Score.c * C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30167.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4387 -0.5230 0.0317 0.5675 3.0862
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 531.1 23.05
## Residual 909.0 30.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.563e+01 9.142e-01 1.018e+03 28.038
## Individualism_Score.c 9.138e-02 5.430e-02 1.023e+03 1.683
## Naturalness.c 7.024e-01 3.682e-02 2.894e+03 19.079
## C1 -3.566e+00 1.776e+00 2.493e+03 -2.007
## C2 -8.240e+00 2.043e+00 2.582e+03 -4.033
## C3 -1.192e+01 1.764e+00 2.459e+03 -6.757
## C4 -4.840e+00 1.739e+00 2.443e+03 -2.783
## C5 -7.973e+00 1.724e+00 2.448e+03 -4.623
## C6 -8.179e-01 2.002e+00 2.547e+03 -0.409
## C7 1.261e+01 2.028e+00 2.566e+03 6.215
## C8 1.959e+01 2.087e+00 2.589e+03 9.387
## C9 1.170e+01 1.873e+00 2.536e+03 6.247
## Individualism_Score.c:Naturalness.c 3.862e-03 2.079e-03 2.931e+03 1.857
## Individualism_Score.c:C1 -4.785e-03 1.051e-01 2.494e+03 -0.046
## Individualism_Score.c:C2 -3.151e-01 1.157e-01 2.584e+03 -2.724
## Individualism_Score.c:C3 -9.043e-03 1.088e-01 2.478e+03 -0.083
## Individualism_Score.c:C4 8.652e-02 1.040e-01 2.453e+03 0.832
## Individualism_Score.c:C5 1.677e-01 1.036e-01 2.446e+03 1.620
## Individualism_Score.c:C6 7.365e-02 1.236e-01 2.552e+03 0.596
## Individualism_Score.c:C7 -8.299e-02 1.273e-01 2.574e+03 -0.652
## Individualism_Score.c:C8 1.519e-02 1.205e-01 2.596e+03 0.126
## Individualism_Score.c:C9 -1.136e-01 1.095e-01 2.540e+03 -1.037
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.09274 .
## Naturalness.c < 2e-16 ***
## C1 0.04482 *
## C2 5.68e-05 ***
## C3 1.75e-11 ***
## C4 0.00543 **
## C5 3.97e-06 ***
## C6 0.68289
## C7 5.96e-10 ***
## C8 < 2e-16 ***
## C9 4.89e-10 ***
## Individualism_Score.c:Naturalness.c 0.06341 .
## Individualism_Score.c:C1 0.96371
## Individualism_Score.c:C2 0.00649 **
## Individualism_Score.c:C3 0.93374
## Individualism_Score.c:C4 0.40548
## Individualism_Score.c:C5 0.10546
## Individualism_Score.c:C6 0.55136
## Individualism_Score.c:C7 0.51438
## Individualism_Score.c:C8 0.89967
## Individualism_Score.c:C9 0.29974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9177,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.63 | 0.91 | 23.84 – 27.43 | 28.04 | <0.001 |
| Individualism Score c | 0.09 | 0.05 | -0.02 – 0.20 | 1.68 | 0.093 |
| Naturalness c | 0.70 | 0.04 | 0.63 – 0.77 | 19.08 | <0.001 |
| C1 | -3.57 | 1.78 | -7.05 – -0.08 | -2.01 | 0.045 |
| C2 | -8.24 | 2.04 | -12.25 – -4.23 | -4.03 | <0.001 |
| C3 | -11.92 | 1.76 | -15.38 – -8.46 | -6.76 | <0.001 |
| C4 | -4.84 | 1.74 | -8.25 – -1.43 | -2.78 | 0.005 |
| C5 | -7.97 | 1.72 | -11.35 – -4.59 | -4.62 | <0.001 |
| C6 | -0.82 | 2.00 | -4.74 – 3.11 | -0.41 | 0.683 |
| C7 | 12.61 | 2.03 | 8.63 – 16.58 | 6.22 | <0.001 |
| C8 | 19.59 | 2.09 | 15.50 – 23.68 | 9.39 | <0.001 |
| C9 | 11.70 | 1.87 | 8.03 – 15.38 | 6.25 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.01 | 1.86 | 0.063 |
|
Individualism Score c * C1 |
-0.00 | 0.11 | -0.21 – 0.20 | -0.05 | 0.964 |
|
Individualism Score c * C2 |
-0.32 | 0.12 | -0.54 – -0.09 | -2.72 | 0.006 |
|
Individualism Score c * C3 |
-0.01 | 0.11 | -0.22 – 0.20 | -0.08 | 0.934 |
|
Individualism Score c * C4 |
0.09 | 0.10 | -0.12 – 0.29 | 0.83 | 0.405 |
|
Individualism Score c * C5 |
0.17 | 0.10 | -0.04 – 0.37 | 1.62 | 0.105 |
|
Individualism Score c * C6 |
0.07 | 0.12 | -0.17 – 0.32 | 0.60 | 0.551 |
|
Individualism Score c * C7 |
-0.08 | 0.13 | -0.33 – 0.17 | -0.65 | 0.514 |
|
Individualism Score c * C8 |
0.02 | 0.12 | -0.22 – 0.25 | 0.13 | 0.900 |
|
Individualism Score c * C9 |
-0.11 | 0.11 | -0.33 – 0.10 | -1.04 | 0.300 |
| Random Effects | |||||
| σ2 | 908.98 | ||||
| τ00 id | 531.07 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.251 / 0.527 | ||||
Political Ideology
Q.1 (POLITICAL ORIENTATION) How does individualism predict the benefit-risk difference, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.918 <- lmer(BRDiff ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.918)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 +
## Ideology.c * C4 + Ideology.c * C5 + Ideology.c * C6 + Ideology.c *
## C7 + Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30455.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8884 -0.5422 0.0468 0.5726 3.0205
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 584 24.17
## Residual 1035 32.18
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.8207 0.9647 1017.2485 26.766 < 2e-16 ***
## Ideology.c 1.9860 1.6914 1019.4445 1.174 0.241
## C1 -13.9998 1.8008 2444.7078 -7.774 1.11e-14 ***
## C2 -19.0499 2.0919 2556.4924 -9.106 < 2e-16 ***
## C3 -17.8122 1.8514 2458.4257 -9.621 < 2e-16 ***
## C4 -8.5941 1.8416 2451.9061 -4.667 3.23e-06 ***
## C5 -11.3789 1.8342 2450.7388 -6.204 6.45e-10 ***
## C6 -1.5819 2.1267 2556.9068 -0.744 0.457
## C7 22.5066 2.0926 2556.2799 10.755 < 2e-16 ***
## C8 30.1890 2.1396 2559.6403 14.110 < 2e-16 ***
## C9 27.0995 1.8181 2449.7537 14.905 < 2e-16 ***
## Ideology.c:C1 1.0399 3.2031 2459.7429 0.325 0.745
## Ideology.c:C2 0.2817 3.7222 2573.4284 0.076 0.940
## Ideology.c:C3 -0.4996 3.3002 2482.8042 -0.151 0.880
## Ideology.c:C4 -2.9491 3.3059 2484.2353 -0.892 0.372
## Ideology.c:C5 -0.4011 3.1688 2460.1482 -0.127 0.899
## Ideology.c:C6 0.9404 3.5770 2560.3265 0.263 0.793
## Ideology.c:C7 0.1574 3.8684 2567.9686 0.041 0.968
## Ideology.c:C8 2.5642 3.6713 2565.0543 0.698 0.485
## Ideology.c:C9 1.5593 3.2308 2460.4617 0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.918,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.82 | 0.96 | 23.93 – 27.71 | 26.77 | <0.001 |
| Ideology c | 1.99 | 1.69 | -1.33 – 5.30 | 1.17 | 0.240 |
| C1 | -14.00 | 1.80 | -17.53 – -10.47 | -7.77 | <0.001 |
| C2 | -19.05 | 2.09 | -23.15 – -14.95 | -9.11 | <0.001 |
| C3 | -17.81 | 1.85 | -21.44 – -14.18 | -9.62 | <0.001 |
| C4 | -8.59 | 1.84 | -12.21 – -4.98 | -4.67 | <0.001 |
| C5 | -11.38 | 1.83 | -14.98 – -7.78 | -6.20 | <0.001 |
| C6 | -1.58 | 2.13 | -5.75 – 2.59 | -0.74 | 0.457 |
| C7 | 22.51 | 2.09 | 18.40 – 26.61 | 10.76 | <0.001 |
| C8 | 30.19 | 2.14 | 25.99 – 34.38 | 14.11 | <0.001 |
| C9 | 27.10 | 1.82 | 23.53 – 30.66 | 14.91 | <0.001 |
| Ideology c * C1 | 1.04 | 3.20 | -5.24 – 7.32 | 0.32 | 0.745 |
| Ideology c * C2 | 0.28 | 3.72 | -7.02 – 7.58 | 0.08 | 0.940 |
| Ideology c * C3 | -0.50 | 3.30 | -6.97 – 5.97 | -0.15 | 0.880 |
| Ideology c * C4 | -2.95 | 3.31 | -9.43 – 3.53 | -0.89 | 0.372 |
| Ideology c * C5 | -0.40 | 3.17 | -6.61 – 5.81 | -0.13 | 0.899 |
| Ideology c * C6 | 0.94 | 3.58 | -6.07 – 7.95 | 0.26 | 0.793 |
| Ideology c * C7 | 0.16 | 3.87 | -7.43 – 7.74 | 0.04 | 0.968 |
| Ideology c * C8 | 2.56 | 3.67 | -4.63 – 9.76 | 0.70 | 0.485 |
| Ideology c * C9 | 1.56 | 3.23 | -4.78 – 7.89 | 0.48 | 0.629 |
| Random Effects | |||||
| σ2 | 1035.44 | ||||
| τ00 id | 583.96 | ||||
| ICC | 0.36 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.164 / 0.465 | ||||
Q.2 (POLITICAL ORIENTATION) How does individualism depend on naturalness perception in predicting the benefit-risk difference, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.9188 <- lmer(BRDiff ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.9188)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c *
## C3 + Ideology.c * C4 + Ideology.c * C5 + Ideology.c * C6 +
## Ideology.c * C7 + Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30111.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3921 -0.5371 0.0290 0.5757 2.9087
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 531.2 23.05
## Residual 916.1 30.27
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.56220 0.91554 1015.95879 27.920 < 2e-16 ***
## Ideology.c 2.41832 1.60711 1021.83410 1.505 0.13270
## Naturalness.c 0.70879 0.03687 2899.39072 19.226 < 2e-16 ***
## C1 -3.33516 1.78486 2498.58710 -1.869 0.06180 .
## C2 -8.66767 2.04214 2587.10921 -4.244 2.27e-05 ***
## C3 -11.85475 1.77050 2462.37460 -6.696 2.65e-11 ***
## C4 -4.80507 1.74490 2444.14888 -2.754 0.00593 **
## C5 -8.01659 1.73639 2450.54853 -4.617 4.10e-06 ***
## C6 -0.88124 2.00343 2547.39741 -0.440 0.66007
## C7 12.70657 2.03722 2570.49081 6.237 5.19e-10 ***
## C8 19.45456 2.09094 2591.78139 9.304 < 2e-16 ***
## C9 11.64949 1.88966 2545.95853 6.165 8.18e-10 ***
## Ideology.c:Naturalness.c 0.02127 0.06521 2856.71685 0.326 0.74431
## Ideology.c:C1 0.75303 3.13143 2481.21488 0.240 0.80998
## Ideology.c:C2 2.53630 3.69575 2679.40777 0.686 0.49260
## Ideology.c:C3 -0.26573 3.14185 2485.27290 -0.085 0.93260
## Ideology.c:C4 -2.08615 3.13092 2496.02309 -0.666 0.50528
## Ideology.c:C5 -1.81604 2.98806 2450.84414 -0.608 0.54340
## Ideology.c:C6 0.10060 3.37207 2544.75807 0.030 0.97620
## Ideology.c:C7 1.46074 3.78908 2613.84612 0.386 0.69989
## Ideology.c:C8 2.28133 3.57400 2581.25891 0.638 0.52333
## Ideology.c:C9 -1.12997 3.31369 2574.00086 -0.341 0.73313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9188,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 25.56 | 0.92 | 23.77 – 27.36 | 27.92 | <0.001 |
| Ideology c | 2.42 | 1.61 | -0.73 – 5.57 | 1.50 | 0.132 |
| Naturalness c | 0.71 | 0.04 | 0.64 – 0.78 | 19.23 | <0.001 |
| C1 | -3.34 | 1.78 | -6.83 – 0.16 | -1.87 | 0.062 |
| C2 | -8.67 | 2.04 | -12.67 – -4.66 | -4.24 | <0.001 |
| C3 | -11.85 | 1.77 | -15.33 – -8.38 | -6.70 | <0.001 |
| C4 | -4.81 | 1.74 | -8.23 – -1.38 | -2.75 | 0.006 |
| C5 | -8.02 | 1.74 | -11.42 – -4.61 | -4.62 | <0.001 |
| C6 | -0.88 | 2.00 | -4.81 – 3.05 | -0.44 | 0.660 |
| C7 | 12.71 | 2.04 | 8.71 – 16.70 | 6.24 | <0.001 |
| C8 | 19.45 | 2.09 | 15.35 – 23.55 | 9.30 | <0.001 |
| C9 | 11.65 | 1.89 | 7.94 – 15.35 | 6.16 | <0.001 |
|
Ideology c * Naturalness c |
0.02 | 0.07 | -0.11 – 0.15 | 0.33 | 0.744 |
| Ideology c * C1 | 0.75 | 3.13 | -5.39 – 6.89 | 0.24 | 0.810 |
| Ideology c * C2 | 2.54 | 3.70 | -4.71 – 9.78 | 0.69 | 0.493 |
| Ideology c * C3 | -0.27 | 3.14 | -6.43 – 5.89 | -0.08 | 0.933 |
| Ideology c * C4 | -2.09 | 3.13 | -8.23 – 4.05 | -0.67 | 0.505 |
| Ideology c * C5 | -1.82 | 2.99 | -7.67 – 4.04 | -0.61 | 0.543 |
| Ideology c * C6 | 0.10 | 3.37 | -6.51 – 6.71 | 0.03 | 0.976 |
| Ideology c * C7 | 1.46 | 3.79 | -5.97 – 8.89 | 0.39 | 0.700 |
| Ideology c * C8 | 2.28 | 3.57 | -4.73 – 9.29 | 0.64 | 0.523 |
| Ideology c * C9 | -1.13 | 3.31 | -7.63 – 5.37 | -0.34 | 0.733 |
| Random Effects | |||||
| σ2 | 916.10 | ||||
| τ00 id | 531.21 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.247 / 0.523 | ||||
Familiarity/Understanding (Mean score)
Q.1 How do climate change method contrasts predict familiarity/understanding?
modA.920 <- lmer(FR ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(modA.920)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27119.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0412 -0.5869 -0.0111 0.5966 3.1013
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.7 14.31
## Residual 329.5 18.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.4788 0.5608 1019.6835 97.137 < 2e-16 ***
## C1 -18.6588 1.0176 2429.9000 -18.337 < 2e-16 ***
## C2 12.8814 1.1847 2540.1072 10.873 < 2e-16 ***
## C3 -16.1090 1.0468 2442.9867 -15.389 < 2e-16 ***
## C4 -16.5934 1.0420 2438.5014 -15.925 < 2e-16 ***
## C5 -22.1040 1.0346 2436.3623 -21.364 < 2e-16 ***
## C6 5.1720 1.2045 2541.1519 4.294 1.82e-05 ***
## C7 27.7437 1.1846 2538.2041 23.421 < 2e-16 ***
## C8 31.6044 1.2114 2541.7653 26.090 < 2e-16 ***
## C9 12.8248 1.0232 2432.9494 12.534 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4 C5 C6 C7 C8
## C1 -0.030
## C2 0.024 -0.093
## C3 -0.017 -0.113 -0.076
## C4 -0.019 -0.110 -0.099 -0.117
## C5 -0.022 -0.106 -0.087 -0.115 -0.108
## C6 0.033 -0.095 -0.169 -0.110 -0.098 -0.098
## C7 0.024 -0.082 -0.167 -0.095 -0.093 -0.098 -0.168
## C8 0.036 -0.104 -0.169 -0.105 -0.095 -0.098 -0.170 -0.169
## C9 -0.027 -0.109 -0.109 -0.109 -0.116 -0.110 -0.094 -0.090 -0.083tab_model(modA.920,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.48 | 0.56 | 53.38 – 55.58 | 97.14 | <0.001 |
| C1 | -18.66 | 1.02 | -20.65 – -16.66 | -18.34 | <0.001 |
| C2 | 12.88 | 1.18 | 10.56 – 15.20 | 10.87 | <0.001 |
| C3 | -16.11 | 1.05 | -18.16 – -14.06 | -15.39 | <0.001 |
| C4 | -16.59 | 1.04 | -18.64 – -14.55 | -15.93 | <0.001 |
| C5 | -22.10 | 1.03 | -24.13 – -20.08 | -21.36 | <0.001 |
| C6 | 5.17 | 1.20 | 2.81 – 7.53 | 4.29 | <0.001 |
| C7 | 27.74 | 1.18 | 25.42 – 30.07 | 23.42 | <0.001 |
| C8 | 31.60 | 1.21 | 29.23 – 33.98 | 26.09 | <0.001 |
| C9 | 12.82 | 1.02 | 10.82 – 14.83 | 12.53 | <0.001 |
| Random Effects | |||||
| σ2 | 329.45 | ||||
| τ00 id | 204.67 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.403 / 0.632 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict familiarity/understanding, over and above climate change method contrasts?
modA.921 <- lmer(FR ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)
summary(modA.921)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 +
## ATNS_Score.c * C4 + ATNS_Score.c * C5 + ATNS_Score.c * C6 +
## ATNS_Score.c * C7 + ATNS_Score.c * C8 + ATNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27138.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8477 -0.5853 -0.0075 0.5996 2.9912
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 202.0 14.21
## Residual 328.6 18.13
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.44594 0.55840 1015.80657 97.504 < 2e-16 ***
## ATNS_Score.c -0.07283 0.02599 1016.90238 -2.802 0.00518 **
## C1 -18.62310 1.01595 2421.07890 -18.331 < 2e-16 ***
## C2 12.91255 1.18287 2531.88233 10.916 < 2e-16 ***
## C3 -16.12189 1.04659 2434.93899 -15.404 < 2e-16 ***
## C4 -16.69598 1.04182 2433.39446 -16.026 < 2e-16 ***
## C5 -22.03961 1.03345 2430.01241 -21.326 < 2e-16 ***
## C6 5.13503 1.20331 2533.24466 4.267 2.05e-05 ***
## C7 27.76291 1.18334 2530.57035 23.461 < 2e-16 ***
## C8 31.65122 1.21053 2535.14160 26.147 < 2e-16 ***
## C9 12.77171 1.02188 2425.25000 12.498 < 2e-16 ***
## ATNS_Score.c:C1 0.06039 0.04791 2432.53962 1.261 0.20760
## ATNS_Score.c:C2 -0.08955 0.05533 2532.37450 -1.618 0.10568
## ATNS_Score.c:C3 0.07389 0.05062 2451.78661 1.460 0.14454
## ATNS_Score.c:C4 -0.07478 0.04658 2418.70829 -1.605 0.10855
## ATNS_Score.c:C5 0.03996 0.04707 2423.35536 0.849 0.39598
## ATNS_Score.c:C6 -0.04744 0.05550 2535.49970 -0.855 0.39273
## ATNS_Score.c:C7 0.07819 0.05568 2539.86532 1.404 0.16039
## ATNS_Score.c:C8 0.03434 0.05599 2534.44754 0.613 0.53966
## ATNS_Score.c:C9 -0.11122 0.04752 2424.75377 -2.340 0.01935 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.921,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.45 | 0.56 | 53.35 – 55.54 | 97.50 | <0.001 |
| ATNS Score c | -0.07 | 0.03 | -0.12 – -0.02 | -2.80 | 0.005 |
| C1 | -18.62 | 1.02 | -20.62 – -16.63 | -18.33 | <0.001 |
| C2 | 12.91 | 1.18 | 10.59 – 15.23 | 10.92 | <0.001 |
| C3 | -16.12 | 1.05 | -18.17 – -14.07 | -15.40 | <0.001 |
| C4 | -16.70 | 1.04 | -18.74 – -14.65 | -16.03 | <0.001 |
| C5 | -22.04 | 1.03 | -24.07 – -20.01 | -21.33 | <0.001 |
| C6 | 5.14 | 1.20 | 2.78 – 7.49 | 4.27 | <0.001 |
| C7 | 27.76 | 1.18 | 25.44 – 30.08 | 23.46 | <0.001 |
| C8 | 31.65 | 1.21 | 29.28 – 34.02 | 26.15 | <0.001 |
| C9 | 12.77 | 1.02 | 10.77 – 14.78 | 12.50 | <0.001 |
| ATNS Score c * C1 | 0.06 | 0.05 | -0.03 – 0.15 | 1.26 | 0.208 |
| ATNS Score c * C2 | -0.09 | 0.06 | -0.20 – 0.02 | -1.62 | 0.106 |
| ATNS Score c * C3 | 0.07 | 0.05 | -0.03 – 0.17 | 1.46 | 0.145 |
| ATNS Score c * C4 | -0.07 | 0.05 | -0.17 – 0.02 | -1.61 | 0.109 |
| ATNS Score c * C5 | 0.04 | 0.05 | -0.05 – 0.13 | 0.85 | 0.396 |
| ATNS Score c * C6 | -0.05 | 0.06 | -0.16 – 0.06 | -0.85 | 0.393 |
| ATNS Score c * C7 | 0.08 | 0.06 | -0.03 – 0.19 | 1.40 | 0.160 |
| ATNS Score c * C8 | 0.03 | 0.06 | -0.08 – 0.14 | 0.61 | 0.540 |
| ATNS Score c * C9 | -0.11 | 0.05 | -0.20 – -0.02 | -2.34 | 0.019 |
| Random Effects | |||||
| σ2 | 328.58 | ||||
| τ00 id | 202.04 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.408 / 0.633 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on perceptions of naturalness in predicting familiarity/understanding, over and above climate change method contrasts?
modA.9213 <- lmer(FR ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + ATNS_Score.c*C6 + ATNS_Score.c*C7 + ATNS_Score.c*C8 + ATNS_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9213)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + ATNS_Score.c * C1 + ATNS_Score.c * C2 +
## ATNS_Score.c * C3 + ATNS_Score.c * C4 + ATNS_Score.c * C5 +
## ATNS_Score.c * C6 + ATNS_Score.c * C7 + ATNS_Score.c * C8 +
## ATNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26950.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8111 -0.5690 -0.0025 0.5935 3.2482
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 210.9 14.52
## Residual 297.4 17.24
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.432e+01 5.573e-01 1.012e+03 97.474 < 2e-16
## ATNS_Score.c -5.733e-02 2.595e-02 1.015e+03 -2.209 0.0274
## Naturalness.c 3.151e-01 2.142e-02 2.829e+03 14.709 < 2e-16
## C1 -1.386e+01 1.024e+00 2.441e+03 -13.533 < 2e-16
## C2 1.752e+01 1.175e+00 2.527e+03 14.914 < 2e-16
## C3 -1.353e+01 1.016e+00 2.408e+03 -13.312 < 2e-16
## C4 -1.497e+01 1.004e+00 2.397e+03 -14.911 < 2e-16
## C5 -2.061e+01 9.933e-01 2.396e+03 -20.748 < 2e-16
## C6 5.458e+00 1.152e+00 2.488e+03 4.737 2.29e-06
## C7 2.340e+01 1.171e+00 2.508e+03 19.987 < 2e-16
## C8 2.684e+01 1.205e+00 2.535e+03 22.275 < 2e-16
## C9 6.006e+00 1.080e+00 2.484e+03 5.560 2.98e-08
## ATNS_Score.c:Naturalness.c -3.418e-04 8.464e-04 2.835e+03 -0.404 0.6864
## ATNS_Score.c:C1 3.338e-02 4.782e-02 2.433e+03 0.698 0.4852
## ATNS_Score.c:C2 -6.569e-02 5.449e-02 2.530e+03 -1.206 0.2281
## ATNS_Score.c:C3 9.525e-02 4.873e-02 2.410e+03 1.955 0.0507
## ATNS_Score.c:C4 -3.813e-02 4.491e-02 2.383e+03 -0.849 0.3959
## ATNS_Score.c:C5 5.922e-02 4.532e-02 2.390e+03 1.307 0.1914
## ATNS_Score.c:C6 -6.583e-02 5.314e-02 2.491e+03 -1.239 0.2156
## ATNS_Score.c:C7 7.105e-02 5.487e-02 2.531e+03 1.295 0.1955
## ATNS_Score.c:C8 -5.512e-03 5.537e-02 2.528e+03 -0.100 0.9207
## ATNS_Score.c:C9 -1.145e-01 4.904e-02 2.444e+03 -2.336 0.0196
##
## (Intercept) ***
## ATNS_Score.c *
## Naturalness.c ***
## C1 ***
## C2 ***
## C3 ***
## C4 ***
## C5 ***
## C6 ***
## C7 ***
## C8 ***
## C9 ***
## ATNS_Score.c:Naturalness.c
## ATNS_Score.c:C1
## ATNS_Score.c:C2
## ATNS_Score.c:C3 .
## ATNS_Score.c:C4
## ATNS_Score.c:C5
## ATNS_Score.c:C6
## ATNS_Score.c:C7
## ATNS_Score.c:C8
## ATNS_Score.c:C9 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9213,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.32 | 0.56 | 53.23 – 55.41 | 97.47 | <0.001 |
| ATNS Score c | -0.06 | 0.03 | -0.11 – -0.01 | -2.21 | 0.027 |
| Naturalness c | 0.32 | 0.02 | 0.27 – 0.36 | 14.71 | <0.001 |
| C1 | -13.86 | 1.02 | -15.87 – -11.85 | -13.53 | <0.001 |
| C2 | 17.52 | 1.17 | 15.21 – 19.82 | 14.91 | <0.001 |
| C3 | -13.53 | 1.02 | -15.52 – -11.53 | -13.31 | <0.001 |
| C4 | -14.97 | 1.00 | -16.93 – -13.00 | -14.91 | <0.001 |
| C5 | -20.61 | 0.99 | -22.56 – -18.66 | -20.75 | <0.001 |
| C6 | 5.46 | 1.15 | 3.20 – 7.72 | 4.74 | <0.001 |
| C7 | 23.40 | 1.17 | 21.10 – 25.69 | 19.99 | <0.001 |
| C8 | 26.84 | 1.20 | 24.48 – 29.20 | 22.27 | <0.001 |
| C9 | 6.01 | 1.08 | 3.89 – 8.12 | 5.56 | <0.001 |
|
ATNS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.40 | 0.686 |
| ATNS Score c * C1 | 0.03 | 0.05 | -0.06 – 0.13 | 0.70 | 0.485 |
| ATNS Score c * C2 | -0.07 | 0.05 | -0.17 – 0.04 | -1.21 | 0.228 |
| ATNS Score c * C3 | 0.10 | 0.05 | -0.00 – 0.19 | 1.95 | 0.051 |
| ATNS Score c * C4 | -0.04 | 0.04 | -0.13 – 0.05 | -0.85 | 0.396 |
| ATNS Score c * C5 | 0.06 | 0.05 | -0.03 – 0.15 | 1.31 | 0.191 |
| ATNS Score c * C6 | -0.07 | 0.05 | -0.17 – 0.04 | -1.24 | 0.216 |
| ATNS Score c * C7 | 0.07 | 0.05 | -0.04 – 0.18 | 1.29 | 0.195 |
| ATNS Score c * C8 | -0.01 | 0.06 | -0.11 – 0.10 | -0.10 | 0.921 |
| ATNS Score c * C9 | -0.11 | 0.05 | -0.21 – -0.02 | -2.34 | 0.020 |
| Random Effects | |||||
| σ2 | 297.35 | ||||
| τ00 id | 210.91 | ||||
| ICC | 0.41 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.438 / 0.671 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict familiarity/understanding, over and above climate change method contrasts?
modA.923 <- lmer(FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.923)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c * C6 +
## CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27130.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9338 -0.5868 -0.0118 0.5966 3.0822
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.6 14.30
## Residual 327.0 18.08
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.449e+01 5.603e-01 1.018e+03 97.242 < 2e-16 ***
## CNS_Score.c 7.218e-02 3.359e-02 1.020e+03 2.149 0.03189 *
## C1 -1.862e+01 1.015e+00 2.421e+03 -18.355 < 2e-16 ***
## C2 1.288e+01 1.181e+00 2.531e+03 10.902 < 2e-16 ***
## C3 -1.608e+01 1.045e+00 2.434e+03 -15.383 < 2e-16 ***
## C4 -1.667e+01 1.040e+00 2.430e+03 -16.026 < 2e-16 ***
## C5 -2.211e+01 1.032e+00 2.427e+03 -21.428 < 2e-16 ***
## C6 5.506e+00 1.205e+00 2.529e+03 4.571 5.08e-06 ***
## C7 2.752e+01 1.182e+00 2.526e+03 23.283 < 2e-16 ***
## C8 3.162e+01 1.208e+00 2.532e+03 26.180 < 2e-16 ***
## C9 1.272e+01 1.021e+00 2.422e+03 12.451 < 2e-16 ***
## CNS_Score.c:C1 -3.690e-02 5.897e-02 2.411e+03 -0.626 0.53152
## CNS_Score.c:C2 -1.273e-01 7.308e-02 2.539e+03 -1.742 0.08157 .
## CNS_Score.c:C3 8.545e-03 6.229e-02 2.430e+03 0.137 0.89090
## CNS_Score.c:C4 -1.290e-01 6.012e-02 2.425e+03 -2.145 0.03205 *
## CNS_Score.c:C5 -1.165e-01 6.383e-02 2.443e+03 -1.825 0.06806 .
## CNS_Score.c:C6 1.797e-01 6.902e-02 2.531e+03 2.604 0.00926 **
## CNS_Score.c:C7 1.756e-01 7.151e-02 2.532e+03 2.455 0.01414 *
## CNS_Score.c:C8 3.698e-02 7.287e-02 2.532e+03 0.507 0.61193
## CNS_Score.c:C9 4.567e-02 6.438e-02 2.445e+03 0.709 0.47816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.923,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.49 | 0.56 | 53.39 – 55.59 | 97.24 | <0.001 |
| CNS Score c | 0.07 | 0.03 | 0.01 – 0.14 | 2.15 | 0.032 |
| C1 | -18.62 | 1.01 | -20.61 – -16.63 | -18.35 | <0.001 |
| C2 | 12.88 | 1.18 | 10.56 – 15.20 | 10.90 | <0.001 |
| C3 | -16.08 | 1.05 | -18.13 – -14.03 | -15.38 | <0.001 |
| C4 | -16.67 | 1.04 | -18.71 – -14.63 | -16.03 | <0.001 |
| C5 | -22.11 | 1.03 | -24.13 – -20.09 | -21.43 | <0.001 |
| C6 | 5.51 | 1.20 | 3.14 – 7.87 | 4.57 | <0.001 |
| C7 | 27.52 | 1.18 | 25.21 – 29.84 | 23.28 | <0.001 |
| C8 | 31.62 | 1.21 | 29.25 – 33.99 | 26.18 | <0.001 |
| C9 | 12.72 | 1.02 | 10.71 – 14.72 | 12.45 | <0.001 |
| CNS Score c * C1 | -0.04 | 0.06 | -0.15 – 0.08 | -0.63 | 0.532 |
| CNS Score c * C2 | -0.13 | 0.07 | -0.27 – 0.02 | -1.74 | 0.082 |
| CNS Score c * C3 | 0.01 | 0.06 | -0.11 – 0.13 | 0.14 | 0.891 |
| CNS Score c * C4 | -0.13 | 0.06 | -0.25 – -0.01 | -2.15 | 0.032 |
| CNS Score c * C5 | -0.12 | 0.06 | -0.24 – 0.01 | -1.83 | 0.068 |
| CNS Score c * C6 | 0.18 | 0.07 | 0.04 – 0.32 | 2.60 | 0.009 |
| CNS Score c * C7 | 0.18 | 0.07 | 0.04 – 0.32 | 2.46 | 0.014 |
| CNS Score c * C8 | 0.04 | 0.07 | -0.11 – 0.18 | 0.51 | 0.612 |
| CNS Score c * C9 | 0.05 | 0.06 | -0.08 – 0.17 | 0.71 | 0.478 |
| Random Effects | |||||
| σ2 | 327.05 | ||||
| τ00 id | 204.62 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.407 / 0.635 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting familiarity/understanding, over and above climate change method contrasts?
modA.923 <- lmer(FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + CNS_Score.c*C6 + CNS_Score.c*C7 + CNS_Score.c*C8 + CNS_Score.c*C9 + (1|id), data = L)
summary(modA.923)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + CNS_Score.c * C6 +
## CNS_Score.c * C7 + CNS_Score.c * C8 + CNS_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27130.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9338 -0.5868 -0.0118 0.5966 3.0822
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.6 14.30
## Residual 327.0 18.08
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.449e+01 5.603e-01 1.018e+03 97.242 < 2e-16 ***
## CNS_Score.c 7.218e-02 3.359e-02 1.020e+03 2.149 0.03189 *
## C1 -1.862e+01 1.015e+00 2.421e+03 -18.355 < 2e-16 ***
## C2 1.288e+01 1.181e+00 2.531e+03 10.902 < 2e-16 ***
## C3 -1.608e+01 1.045e+00 2.434e+03 -15.383 < 2e-16 ***
## C4 -1.667e+01 1.040e+00 2.430e+03 -16.026 < 2e-16 ***
## C5 -2.211e+01 1.032e+00 2.427e+03 -21.428 < 2e-16 ***
## C6 5.506e+00 1.205e+00 2.529e+03 4.571 5.08e-06 ***
## C7 2.752e+01 1.182e+00 2.526e+03 23.283 < 2e-16 ***
## C8 3.162e+01 1.208e+00 2.532e+03 26.180 < 2e-16 ***
## C9 1.272e+01 1.021e+00 2.422e+03 12.451 < 2e-16 ***
## CNS_Score.c:C1 -3.690e-02 5.897e-02 2.411e+03 -0.626 0.53152
## CNS_Score.c:C2 -1.273e-01 7.308e-02 2.539e+03 -1.742 0.08157 .
## CNS_Score.c:C3 8.545e-03 6.229e-02 2.430e+03 0.137 0.89090
## CNS_Score.c:C4 -1.290e-01 6.012e-02 2.425e+03 -2.145 0.03205 *
## CNS_Score.c:C5 -1.165e-01 6.383e-02 2.443e+03 -1.825 0.06806 .
## CNS_Score.c:C6 1.797e-01 6.902e-02 2.531e+03 2.604 0.00926 **
## CNS_Score.c:C7 1.756e-01 7.151e-02 2.532e+03 2.455 0.01414 *
## CNS_Score.c:C8 3.698e-02 7.287e-02 2.532e+03 0.507 0.61193
## CNS_Score.c:C9 4.567e-02 6.438e-02 2.445e+03 0.709 0.47816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.923,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.49 | 0.56 | 53.39 – 55.59 | 97.24 | <0.001 |
| CNS Score c | 0.07 | 0.03 | 0.01 – 0.14 | 2.15 | 0.032 |
| C1 | -18.62 | 1.01 | -20.61 – -16.63 | -18.35 | <0.001 |
| C2 | 12.88 | 1.18 | 10.56 – 15.20 | 10.90 | <0.001 |
| C3 | -16.08 | 1.05 | -18.13 – -14.03 | -15.38 | <0.001 |
| C4 | -16.67 | 1.04 | -18.71 – -14.63 | -16.03 | <0.001 |
| C5 | -22.11 | 1.03 | -24.13 – -20.09 | -21.43 | <0.001 |
| C6 | 5.51 | 1.20 | 3.14 – 7.87 | 4.57 | <0.001 |
| C7 | 27.52 | 1.18 | 25.21 – 29.84 | 23.28 | <0.001 |
| C8 | 31.62 | 1.21 | 29.25 – 33.99 | 26.18 | <0.001 |
| C9 | 12.72 | 1.02 | 10.71 – 14.72 | 12.45 | <0.001 |
| CNS Score c * C1 | -0.04 | 0.06 | -0.15 – 0.08 | -0.63 | 0.532 |
| CNS Score c * C2 | -0.13 | 0.07 | -0.27 – 0.02 | -1.74 | 0.082 |
| CNS Score c * C3 | 0.01 | 0.06 | -0.11 – 0.13 | 0.14 | 0.891 |
| CNS Score c * C4 | -0.13 | 0.06 | -0.25 – -0.01 | -2.15 | 0.032 |
| CNS Score c * C5 | -0.12 | 0.06 | -0.24 – 0.01 | -1.83 | 0.068 |
| CNS Score c * C6 | 0.18 | 0.07 | 0.04 – 0.32 | 2.60 | 0.009 |
| CNS Score c * C7 | 0.18 | 0.07 | 0.04 – 0.32 | 2.46 | 0.014 |
| CNS Score c * C8 | 0.04 | 0.07 | -0.11 – 0.18 | 0.51 | 0.612 |
| CNS Score c * C9 | 0.05 | 0.06 | -0.08 – 0.17 | 0.71 | 0.478 |
| Random Effects | |||||
| σ2 | 327.05 | ||||
| τ00 id | 204.62 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.407 / 0.635 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict familiarity/understanding, over and above climate change method contrasts?
modA.924 <- lmer(FR ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)
summary(modA.924)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 +
## C9 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + CCBelief_Score.c *
## C6 + CCBelief_Score.c * C7 + CCBelief_Score.c * C8 + CCBelief_Score.c *
## C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27141.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0540 -0.5796 -0.0068 0.6002 3.1052
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 203.8 14.28
## Residual 327.9 18.11
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.447e+01 5.598e-01 1.017e+03 97.301 < 2e-16 ***
## CCBelief_Score.c 4.238e-02 2.379e-02 1.021e+03 1.782 0.07511 .
## C1 -1.865e+01 1.015e+00 2.419e+03 -18.373 < 2e-16 ***
## C2 1.281e+01 1.183e+00 2.531e+03 10.829 < 2e-16 ***
## C3 -1.610e+01 1.045e+00 2.433e+03 -15.417 < 2e-16 ***
## C4 -1.667e+01 1.042e+00 2.429e+03 -15.992 < 2e-16 ***
## C5 -2.197e+01 1.033e+00 2.428e+03 -21.255 < 2e-16 ***
## C6 5.145e+00 1.202e+00 2.531e+03 4.280 1.94e-05 ***
## C7 2.776e+01 1.182e+00 2.527e+03 23.488 < 2e-16 ***
## C8 3.169e+01 1.209e+00 2.532e+03 26.213 < 2e-16 ***
## C9 1.272e+01 1.022e+00 2.426e+03 12.447 < 2e-16 ***
## CCBelief_Score.c:C1 7.846e-03 4.291e-02 2.421e+03 0.183 0.85494
## CCBelief_Score.c:C2 -4.562e-02 4.706e-02 2.524e+03 -0.969 0.33241
## CCBelief_Score.c:C3 -1.117e-02 4.412e-02 2.432e+03 -0.253 0.80020
## CCBelief_Score.c:C4 -6.334e-02 4.192e-02 2.409e+03 -1.511 0.13094
## CCBelief_Score.c:C5 -1.379e-01 4.546e-02 2.446e+03 -3.034 0.00244 **
## CCBelief_Score.c:C6 5.830e-02 5.310e-02 2.537e+03 1.098 0.27233
## CCBelief_Score.c:C7 1.186e-01 5.112e-02 2.536e+03 2.320 0.02040 *
## CCBelief_Score.c:C8 -1.074e-02 5.221e-02 2.536e+03 -0.206 0.83696
## CCBelief_Score.c:C9 9.335e-02 4.494e-02 2.443e+03 2.077 0.03787 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.924,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.47 | 0.56 | 53.37 – 55.56 | 97.30 | <0.001 |
| CCBelief Score c | 0.04 | 0.02 | -0.00 – 0.09 | 1.78 | 0.075 |
| C1 | -18.65 | 1.02 | -20.65 – -16.66 | -18.37 | <0.001 |
| C2 | 12.81 | 1.18 | 10.49 – 15.13 | 10.83 | <0.001 |
| C3 | -16.10 | 1.04 | -18.15 – -14.06 | -15.42 | <0.001 |
| C4 | -16.67 | 1.04 | -18.72 – -14.63 | -15.99 | <0.001 |
| C5 | -21.97 | 1.03 | -23.99 – -19.94 | -21.26 | <0.001 |
| C6 | 5.14 | 1.20 | 2.79 – 7.50 | 4.28 | <0.001 |
| C7 | 27.76 | 1.18 | 25.44 – 30.08 | 23.49 | <0.001 |
| C8 | 31.69 | 1.21 | 29.32 – 34.06 | 26.21 | <0.001 |
| C9 | 12.72 | 1.02 | 10.72 – 14.73 | 12.45 | <0.001 |
| CCBelief Score c * C1 | 0.01 | 0.04 | -0.08 – 0.09 | 0.18 | 0.855 |
| CCBelief Score c * C2 | -0.05 | 0.05 | -0.14 – 0.05 | -0.97 | 0.332 |
| CCBelief Score c * C3 | -0.01 | 0.04 | -0.10 – 0.08 | -0.25 | 0.800 |
| CCBelief Score c * C4 | -0.06 | 0.04 | -0.15 – 0.02 | -1.51 | 0.131 |
| CCBelief Score c * C5 | -0.14 | 0.05 | -0.23 – -0.05 | -3.03 | 0.002 |
| CCBelief Score c * C6 | 0.06 | 0.05 | -0.05 – 0.16 | 1.10 | 0.272 |
| CCBelief Score c * C7 | 0.12 | 0.05 | 0.02 – 0.22 | 2.32 | 0.020 |
| CCBelief Score c * C8 | -0.01 | 0.05 | -0.11 – 0.09 | -0.21 | 0.837 |
| CCBelief Score c * C9 | 0.09 | 0.04 | 0.01 – 0.18 | 2.08 | 0.038 |
| Random Effects | |||||
| σ2 | 327.90 | ||||
| τ00 id | 203.84 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.407 / 0.634 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perceptinos of naturalness in predicting familiarity/understanding, over and above climate change method contrasts?
modA.9245 <- lmer(FR ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + CCBelief_Score.c*C6 + CCBelief_Score.c*C7 + CCBelief_Score.c*C8 + CCBelief_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9245)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## C6 + C7 + C8 + C9 + CCBelief_Score.c * C1 + CCBelief_Score.c *
## C2 + CCBelief_Score.c * C3 + CCBelief_Score.c * C4 + CCBelief_Score.c *
## C5 + CCBelief_Score.c * C6 + CCBelief_Score.c * C7 + CCBelief_Score.c *
## C8 + CCBelief_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26952.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0453 -0.5663 -0.0022 0.5792 3.1989
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 212.7 14.58
## Residual 296.7 17.23
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.437e+01 5.585e-01 1.014e+03 97.341
## CCBelief_Score.c 2.718e-02 2.381e-02 1.029e+03 1.141
## Naturalness.c 3.124e-01 2.133e-02 2.831e+03 14.647
## C1 -1.396e+01 1.023e+00 2.440e+03 -13.652
## C2 1.737e+01 1.177e+00 2.531e+03 14.763
## C3 -1.349e+01 1.015e+00 2.407e+03 -13.299
## C4 -1.501e+01 1.003e+00 2.392e+03 -14.962
## C5 -2.053e+01 9.928e-01 2.396e+03 -20.677
## C6 5.477e+00 1.151e+00 2.490e+03 4.759
## C7 2.341e+01 1.170e+00 2.507e+03 20.006
## C8 2.697e+01 1.202e+00 2.529e+03 22.432
## C9 5.982e+00 1.081e+00 2.489e+03 5.536
## CCBelief_Score.c:Naturalness.c -5.081e-04 8.060e-04 2.850e+03 -0.630
## CCBelief_Score.c:C1 1.162e-02 4.283e-02 2.434e+03 0.271
## CCBelief_Score.c:C2 -1.803e-02 4.621e-02 2.502e+03 -0.390
## CCBelief_Score.c:C3 -8.841e-03 4.245e-02 2.399e+03 -0.208
## CCBelief_Score.c:C4 -5.594e-02 4.025e-02 2.370e+03 -1.390
## CCBelief_Score.c:C5 -1.395e-01 4.362e-02 2.403e+03 -3.199
## CCBelief_Score.c:C6 3.999e-02 5.098e-02 2.517e+03 0.784
## CCBelief_Score.c:C7 1.065e-01 5.025e-02 2.527e+03 2.119
## CCBelief_Score.c:C8 -2.091e-02 5.096e-02 2.537e+03 -0.410
## CCBelief_Score.c:C9 1.080e-01 4.772e-02 2.542e+03 2.264
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 0.2540
## Naturalness.c < 2e-16 ***
## C1 < 2e-16 ***
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 < 2e-16 ***
## C5 < 2e-16 ***
## C6 2.06e-06 ***
## C7 < 2e-16 ***
## C8 < 2e-16 ***
## C9 3.42e-08 ***
## CCBelief_Score.c:Naturalness.c 0.5285
## CCBelief_Score.c:C1 0.7862
## CCBelief_Score.c:C2 0.6964
## CCBelief_Score.c:C3 0.8350
## CCBelief_Score.c:C4 0.1647
## CCBelief_Score.c:C5 0.0014 **
## CCBelief_Score.c:C6 0.4329
## CCBelief_Score.c:C7 0.0342 *
## CCBelief_Score.c:C8 0.6817
## CCBelief_Score.c:C9 0.0236 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9245,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.37 | 0.56 | 53.27 – 55.46 | 97.34 | <0.001 |
| CCBelief Score c | 0.03 | 0.02 | -0.02 – 0.07 | 1.14 | 0.254 |
| Naturalness c | 0.31 | 0.02 | 0.27 – 0.35 | 14.65 | <0.001 |
| C1 | -13.96 | 1.02 | -15.96 – -11.95 | -13.65 | <0.001 |
| C2 | 17.37 | 1.18 | 15.07 – 19.68 | 14.76 | <0.001 |
| C3 | -13.49 | 1.01 | -15.48 – -11.50 | -13.30 | <0.001 |
| C4 | -15.01 | 1.00 | -16.97 – -13.04 | -14.96 | <0.001 |
| C5 | -20.53 | 0.99 | -22.48 – -18.58 | -20.68 | <0.001 |
| C6 | 5.48 | 1.15 | 3.22 – 7.73 | 4.76 | <0.001 |
| C7 | 23.41 | 1.17 | 21.12 – 25.71 | 20.01 | <0.001 |
| C8 | 26.97 | 1.20 | 24.61 – 29.32 | 22.43 | <0.001 |
| C9 | 5.98 | 1.08 | 3.86 – 8.10 | 5.54 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.63 | 0.528 |
| CCBelief Score c * C1 | 0.01 | 0.04 | -0.07 – 0.10 | 0.27 | 0.786 |
| CCBelief Score c * C2 | -0.02 | 0.05 | -0.11 – 0.07 | -0.39 | 0.696 |
| CCBelief Score c * C3 | -0.01 | 0.04 | -0.09 – 0.07 | -0.21 | 0.835 |
| CCBelief Score c * C4 | -0.06 | 0.04 | -0.13 – 0.02 | -1.39 | 0.165 |
| CCBelief Score c * C5 | -0.14 | 0.04 | -0.23 – -0.05 | -3.20 | 0.001 |
| CCBelief Score c * C6 | 0.04 | 0.05 | -0.06 – 0.14 | 0.78 | 0.433 |
| CCBelief Score c * C7 | 0.11 | 0.05 | 0.01 – 0.21 | 2.12 | 0.034 |
| CCBelief Score c * C8 | -0.02 | 0.05 | -0.12 – 0.08 | -0.41 | 0.682 |
| CCBelief Score c * C9 | 0.11 | 0.05 | 0.01 – 0.20 | 2.26 | 0.024 |
| Random Effects | |||||
| σ2 | 296.71 | ||||
| τ00 id | 212.65 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.437 / 0.672 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict familiarity/understanding, over and above climate change method contrasts?
modA.926 <- lmer(FR ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)
summary(modA.926)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27137.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0741 -0.5856 -0.0118 0.5959 3.1429
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 207.4 14.40
## Residual 325.7 18.05
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.449e+01 5.624e-01 1.018e+03 96.887 < 2e-16 ***
## Collectivism_Score.c -6.731e-03 2.361e-02 1.022e+03 -0.285 0.775676
## C1 -1.869e+01 1.013e+00 2.414e+03 -18.449 < 2e-16 ***
## C2 1.269e+01 1.182e+00 2.523e+03 10.741 < 2e-16 ***
## C3 -1.613e+01 1.042e+00 2.428e+03 -15.471 < 2e-16 ***
## C4 -1.667e+01 1.038e+00 2.424e+03 -16.058 < 2e-16 ***
## C5 -2.190e+01 1.034e+00 2.422e+03 -21.174 < 2e-16 ***
## C6 5.098e+00 1.200e+00 2.525e+03 4.249 2.22e-05 ***
## C7 2.785e+01 1.182e+00 2.522e+03 23.564 < 2e-16 ***
## C8 3.162e+01 1.206e+00 2.525e+03 26.217 < 2e-16 ***
## C9 1.295e+01 1.019e+00 2.417e+03 12.706 < 2e-16 ***
## Collectivism_Score.c:C1 2.183e-02 4.616e-02 2.452e+03 0.473 0.636344
## Collectivism_Score.c:C2 -1.035e-01 4.779e-02 2.521e+03 -2.165 0.030518 *
## Collectivism_Score.c:C3 -4.458e-02 4.419e-02 2.433e+03 -1.009 0.313175
## Collectivism_Score.c:C4 5.096e-02 4.385e-02 2.428e+03 1.162 0.245324
## Collectivism_Score.c:C5 1.030e-01 4.210e-02 2.411e+03 2.447 0.014471 *
## Collectivism_Score.c:C6 6.400e-02 5.055e-02 2.528e+03 1.266 0.205551
## Collectivism_Score.c:C7 -5.507e-02 5.123e-02 2.528e+03 -1.075 0.282586
## Collectivism_Score.c:C8 5.185e-02 5.079e-02 2.533e+03 1.021 0.307398
## Collectivism_Score.c:C9 -1.498e-01 4.348e-02 2.428e+03 -3.444 0.000582 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.926,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.49 | 0.56 | 53.39 – 55.59 | 96.89 | <0.001 |
| Collectivism Score c | -0.01 | 0.02 | -0.05 – 0.04 | -0.29 | 0.776 |
| C1 | -18.69 | 1.01 | -20.67 – -16.70 | -18.45 | <0.001 |
| C2 | 12.69 | 1.18 | 10.38 – 15.01 | 10.74 | <0.001 |
| C3 | -16.13 | 1.04 | -18.17 – -14.08 | -15.47 | <0.001 |
| C4 | -16.67 | 1.04 | -18.71 – -14.64 | -16.06 | <0.001 |
| C5 | -21.90 | 1.03 | -23.92 – -19.87 | -21.17 | <0.001 |
| C6 | 5.10 | 1.20 | 2.75 – 7.45 | 4.25 | <0.001 |
| C7 | 27.85 | 1.18 | 25.53 – 30.17 | 23.56 | <0.001 |
| C8 | 31.62 | 1.21 | 29.26 – 33.99 | 26.22 | <0.001 |
| C9 | 12.95 | 1.02 | 10.95 – 14.94 | 12.71 | <0.001 |
| Collectivism Score c * C1 | 0.02 | 0.05 | -0.07 – 0.11 | 0.47 | 0.636 |
| Collectivism Score c * C2 | -0.10 | 0.05 | -0.20 – -0.01 | -2.16 | 0.031 |
| Collectivism Score c * C3 | -0.04 | 0.04 | -0.13 – 0.04 | -1.01 | 0.313 |
| Collectivism Score c * C4 | 0.05 | 0.04 | -0.04 – 0.14 | 1.16 | 0.245 |
| Collectivism Score c * C5 | 0.10 | 0.04 | 0.02 – 0.19 | 2.45 | 0.014 |
| Collectivism Score c * C6 | 0.06 | 0.05 | -0.04 – 0.16 | 1.27 | 0.206 |
| Collectivism Score c * C7 | -0.06 | 0.05 | -0.16 – 0.05 | -1.07 | 0.283 |
| Collectivism Score c * C8 | 0.05 | 0.05 | -0.05 – 0.15 | 1.02 | 0.307 |
| Collectivism Score c * C9 | -0.15 | 0.04 | -0.24 – -0.06 | -3.44 | 0.001 |
| Random Effects | |||||
| σ2 | 325.73 | ||||
| τ00 id | 207.36 | ||||
| ICC | 0.39 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.406 / 0.637 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting familiarity/understanding, over and above climate change method contrasts?
modA.9267 <- lmer(FR ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + Collectivism_Score.c*C6 + Collectivism_Score.c*C7 + Collectivism_Score.c*C8 + Collectivism_Score.c*C9 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9267)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + Collectivism_Score.c * C6 + Collectivism_Score.c *
## C7 + Collectivism_Score.c * C8 + Collectivism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26943.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9164 -0.5685 0.0014 0.5842 3.3166
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 216.0 14.70
## Residual 294.1 17.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.438e+01 5.606e-01 1.013e+03 97.010
## Collectivism_Score.c -3.588e-03 2.354e-02 1.018e+03 -0.152
## Naturalness.c 3.149e-01 2.123e-02 2.829e+03 14.831
## C1 -1.396e+01 1.019e+00 2.436e+03 -13.698
## C2 1.726e+01 1.172e+00 2.518e+03 14.730
## C3 -1.345e+01 1.011e+00 2.403e+03 -13.299
## C4 -1.499e+01 9.975e-01 2.387e+03 -15.025
## C5 -2.038e+01 9.930e-01 2.390e+03 -20.525
## C6 5.399e+00 1.147e+00 2.480e+03 4.706
## C7 2.347e+01 1.168e+00 2.500e+03 20.105
## C8 2.681e+01 1.198e+00 2.521e+03 22.383
## C9 6.185e+00 1.078e+00 2.481e+03 5.740
## Collectivism_Score.c:Naturalness.c 8.720e-04 8.334e-04 2.822e+03 1.046
## Collectivism_Score.c:C1 2.394e-02 4.650e-02 2.475e+03 0.515
## Collectivism_Score.c:C2 -1.073e-01 4.735e-02 2.530e+03 -2.266
## Collectivism_Score.c:C3 -2.454e-02 4.281e-02 2.396e+03 -0.573
## Collectivism_Score.c:C4 6.308e-02 4.203e-02 2.392e+03 1.501
## Collectivism_Score.c:C5 1.270e-01 4.060e-02 2.378e+03 3.128
## Collectivism_Score.c:C6 5.946e-02 4.833e-02 2.483e+03 1.230
## Collectivism_Score.c:C7 -7.728e-02 5.082e-02 2.523e+03 -1.521
## Collectivism_Score.c:C8 2.224e-02 5.076e-02 2.526e+03 0.438
## Collectivism_Score.c:C9 -1.439e-01 4.531e-02 2.475e+03 -3.176
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.87887
## Naturalness.c < 2e-16 ***
## C1 < 2e-16 ***
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 < 2e-16 ***
## C5 < 2e-16 ***
## C6 2.66e-06 ***
## C7 < 2e-16 ***
## C8 < 2e-16 ***
## C9 1.06e-08 ***
## Collectivism_Score.c:Naturalness.c 0.29550
## Collectivism_Score.c:C1 0.60679
## Collectivism_Score.c:C2 0.02351 *
## Collectivism_Score.c:C3 0.56658
## Collectivism_Score.c:C4 0.13348
## Collectivism_Score.c:C5 0.00178 **
## Collectivism_Score.c:C6 0.21872
## Collectivism_Score.c:C7 0.12849
## Collectivism_Score.c:C8 0.66136
## Collectivism_Score.c:C9 0.00151 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9267,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.38 | 0.56 | 53.28 – 55.48 | 97.01 | <0.001 |
| Collectivism Score c | -0.00 | 0.02 | -0.05 – 0.04 | -0.15 | 0.879 |
| Naturalness c | 0.31 | 0.02 | 0.27 – 0.36 | 14.83 | <0.001 |
| C1 | -13.96 | 1.02 | -15.95 – -11.96 | -13.70 | <0.001 |
| C2 | 17.26 | 1.17 | 14.96 – 19.56 | 14.73 | <0.001 |
| C3 | -13.45 | 1.01 | -15.43 – -11.47 | -13.30 | <0.001 |
| C4 | -14.99 | 1.00 | -16.94 – -13.03 | -15.03 | <0.001 |
| C5 | -20.38 | 0.99 | -22.33 – -18.43 | -20.53 | <0.001 |
| C6 | 5.40 | 1.15 | 3.15 – 7.65 | 4.71 | <0.001 |
| C7 | 23.47 | 1.17 | 21.18 – 25.76 | 20.11 | <0.001 |
| C8 | 26.81 | 1.20 | 24.46 – 29.15 | 22.38 | <0.001 |
| C9 | 6.19 | 1.08 | 4.07 – 8.30 | 5.74 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.05 | 0.295 |
| Collectivism Score c * C1 | 0.02 | 0.05 | -0.07 – 0.12 | 0.51 | 0.607 |
| Collectivism Score c * C2 | -0.11 | 0.05 | -0.20 – -0.01 | -2.27 | 0.023 |
| Collectivism Score c * C3 | -0.02 | 0.04 | -0.11 – 0.06 | -0.57 | 0.567 |
| Collectivism Score c * C4 | 0.06 | 0.04 | -0.02 – 0.15 | 1.50 | 0.133 |
| Collectivism Score c * C5 | 0.13 | 0.04 | 0.05 – 0.21 | 3.13 | 0.002 |
| Collectivism Score c * C6 | 0.06 | 0.05 | -0.04 – 0.15 | 1.23 | 0.219 |
| Collectivism Score c * C7 | -0.08 | 0.05 | -0.18 – 0.02 | -1.52 | 0.128 |
| Collectivism Score c * C8 | 0.02 | 0.05 | -0.08 – 0.12 | 0.44 | 0.661 |
| Collectivism Score c * C9 | -0.14 | 0.05 | -0.23 – -0.06 | -3.18 | 0.002 |
| Random Effects | |||||
| σ2 | 294.10 | ||||
| τ00 id | 215.98 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.437 / 0.675 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict familiarity/understanding, over and above climate change method contrasts?
modA.927 <- lmer(FR ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.927)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 +
## C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C6 + Individualism_Score.c * C7 + Individualism_Score.c *
## C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27119.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0442 -0.5819 -0.0200 0.5925 3.0607
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 203.0 14.25
## Residual 326.2 18.06
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.450e+01 5.587e-01 1.019e+03 97.555 < 2e-16 ***
## Individualism_Score.c 1.055e-01 3.319e-02 1.024e+03 3.180 0.00152 **
## C1 -1.869e+01 1.013e+00 2.421e+03 -18.445 < 2e-16 ***
## C2 1.306e+01 1.184e+00 2.530e+03 11.034 < 2e-16 ***
## C3 -1.614e+01 1.043e+00 2.434e+03 -15.481 < 2e-16 ***
## C4 -1.660e+01 1.037e+00 2.429e+03 -15.997 < 2e-16 ***
## C5 -2.208e+01 1.030e+00 2.428e+03 -21.436 < 2e-16 ***
## C6 5.271e+00 1.202e+00 2.532e+03 4.385 1.21e-05 ***
## C7 2.767e+01 1.179e+00 2.529e+03 23.461 < 2e-16 ***
## C8 3.169e+01 1.208e+00 2.532e+03 26.240 < 2e-16 ***
## C9 1.283e+01 1.019e+00 2.423e+03 12.598 < 2e-16 ***
## Individualism_Score.c:C1 -7.194e-02 6.004e-02 2.420e+03 -1.198 0.23096
## Individualism_Score.c:C2 -1.661e-01 6.683e-02 2.528e+03 -2.486 0.01299 *
## Individualism_Score.c:C3 2.015e-02 6.433e-02 2.453e+03 0.313 0.75417
## Individualism_Score.c:C4 -3.411e-02 6.193e-02 2.433e+03 -0.551 0.58184
## Individualism_Score.c:C5 2.418e-02 6.170e-02 2.432e+03 0.392 0.69521
## Individualism_Score.c:C6 5.047e-02 7.426e-02 2.538e+03 0.680 0.49676
## Individualism_Score.c:C7 7.987e-03 7.444e-02 2.538e+03 0.107 0.91456
## Individualism_Score.c:C8 3.214e-02 6.880e-02 2.531e+03 0.467 0.64046
## Individualism_Score.c:C9 -1.156e-01 5.947e-02 2.421e+03 -1.944 0.05201 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.927,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.50 | 0.56 | 53.41 – 55.60 | 97.56 | <0.001 |
| Individualism Score c | 0.11 | 0.03 | 0.04 – 0.17 | 3.18 | 0.001 |
| C1 | -18.69 | 1.01 | -20.68 – -16.70 | -18.45 | <0.001 |
| C2 | 13.06 | 1.18 | 10.74 – 15.39 | 11.03 | <0.001 |
| C3 | -16.14 | 1.04 | -18.19 – -14.10 | -15.48 | <0.001 |
| C4 | -16.60 | 1.04 | -18.63 – -14.56 | -16.00 | <0.001 |
| C5 | -22.08 | 1.03 | -24.10 – -20.06 | -21.44 | <0.001 |
| C6 | 5.27 | 1.20 | 2.91 – 7.63 | 4.38 | <0.001 |
| C7 | 27.67 | 1.18 | 25.35 – 29.98 | 23.46 | <0.001 |
| C8 | 31.69 | 1.21 | 29.32 – 34.06 | 26.24 | <0.001 |
| C9 | 12.83 | 1.02 | 10.84 – 14.83 | 12.60 | <0.001 |
|
Individualism Score c * C1 |
-0.07 | 0.06 | -0.19 – 0.05 | -1.20 | 0.231 |
|
Individualism Score c * C2 |
-0.17 | 0.07 | -0.30 – -0.04 | -2.49 | 0.013 |
|
Individualism Score c * C3 |
0.02 | 0.06 | -0.11 – 0.15 | 0.31 | 0.754 |
|
Individualism Score c * C4 |
-0.03 | 0.06 | -0.16 – 0.09 | -0.55 | 0.582 |
|
Individualism Score c * C5 |
0.02 | 0.06 | -0.10 – 0.15 | 0.39 | 0.695 |
|
Individualism Score c * C6 |
0.05 | 0.07 | -0.10 – 0.20 | 0.68 | 0.497 |
|
Individualism Score c * C7 |
0.01 | 0.07 | -0.14 – 0.15 | 0.11 | 0.915 |
|
Individualism Score c * C8 |
0.03 | 0.07 | -0.10 – 0.17 | 0.47 | 0.640 |
|
Individualism Score c * C9 |
-0.12 | 0.06 | -0.23 – 0.00 | -1.94 | 0.052 |
| Random Effects | |||||
| σ2 | 326.23 | ||||
| τ00 id | 202.98 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.410 / 0.636 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting familiarity/understanding, over and above climate change method contrasts?
modA.9275 <- lmer(FR ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + Individualism_Score.c*C6 + Individualism_Score.c*C7 + Individualism_Score.c*C8 + Individualism_Score.c*C9 + (1|id), data = L)
summary(modA.9275)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + C6 + C7 + C8 + C9 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + Individualism_Score.c *
## C6 + Individualism_Score.c * C7 + Individualism_Score.c *
## C8 + Individualism_Score.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26924.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9802 -0.5737 0.0031 0.5899 3.2648
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 211.6 14.55
## Residual 294.5 17.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.439e+01 5.569e-01 1.014e+03 97.668
## Individualism_Score.c 1.129e-01 3.307e-02 1.019e+03 3.413
## Naturalness.c 3.180e-01 2.129e-02 2.833e+03 14.938
## C1 -1.390e+01 1.019e+00 2.438e+03 -13.642
## C2 1.774e+01 1.174e+00 2.522e+03 15.109
## C3 -1.348e+01 1.011e+00 2.406e+03 -13.330
## C4 -1.490e+01 9.968e-01 2.391e+03 -14.947
## C5 -2.067e+01 9.886e-01 2.396e+03 -20.906
## C6 5.578e+00 1.149e+00 2.488e+03 4.853
## C7 2.326e+01 1.165e+00 2.506e+03 19.969
## C8 2.686e+01 1.199e+00 2.528e+03 22.401
## C9 6.002e+00 1.075e+00 2.479e+03 5.581
## Individualism_Score.c:Naturalness.c -9.161e-04 1.204e-03 2.876e+03 -0.761
## Individualism_Score.c:C1 -9.400e-02 6.032e-02 2.440e+03 -1.558
## Individualism_Score.c:C2 -1.790e-01 6.645e-02 2.524e+03 -2.693
## Individualism_Score.c:C3 3.039e-02 6.237e-02 2.424e+03 0.487
## Individualism_Score.c:C4 -3.120e-02 5.962e-02 2.401e+03 -0.523
## Individualism_Score.c:C5 5.055e-02 5.936e-02 2.394e+03 0.852
## Individualism_Score.c:C6 5.652e-02 7.099e-02 2.493e+03 0.796
## Individualism_Score.c:C7 -8.108e-03 7.311e-02 2.514e+03 -0.111
## Individualism_Score.c:C8 4.594e-02 6.925e-02 2.536e+03 0.663
## Individualism_Score.c:C9 -1.023e-01 6.288e-02 2.483e+03 -1.627
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.000668 ***
## Naturalness.c < 2e-16 ***
## C1 < 2e-16 ***
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 < 2e-16 ***
## C5 < 2e-16 ***
## C6 1.29e-06 ***
## C7 < 2e-16 ***
## C8 < 2e-16 ***
## C9 2.65e-08 ***
## Individualism_Score.c:Naturalness.c 0.446766
## Individualism_Score.c:C1 0.119280
## Individualism_Score.c:C2 0.007119 **
## Individualism_Score.c:C3 0.626145
## Individualism_Score.c:C4 0.600731
## Individualism_Score.c:C5 0.394546
## Individualism_Score.c:C6 0.426021
## Individualism_Score.c:C7 0.911706
## Individualism_Score.c:C8 0.507143
## Individualism_Score.c:C9 0.103921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9275,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.39 | 0.56 | 53.30 – 55.48 | 97.67 | <0.001 |
| Individualism Score c | 0.11 | 0.03 | 0.05 – 0.18 | 3.41 | 0.001 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 14.94 | <0.001 |
| C1 | -13.90 | 1.02 | -15.90 – -11.90 | -13.64 | <0.001 |
| C2 | 17.74 | 1.17 | 15.44 – 20.04 | 15.11 | <0.001 |
| C3 | -13.48 | 1.01 | -15.46 – -11.50 | -13.33 | <0.001 |
| C4 | -14.90 | 1.00 | -16.85 – -12.94 | -14.95 | <0.001 |
| C5 | -20.67 | 0.99 | -22.61 – -18.73 | -20.91 | <0.001 |
| C6 | 5.58 | 1.15 | 3.32 – 7.83 | 4.85 | <0.001 |
| C7 | 23.26 | 1.16 | 20.98 – 25.55 | 19.97 | <0.001 |
| C8 | 26.86 | 1.20 | 24.51 – 29.21 | 22.40 | <0.001 |
| C9 | 6.00 | 1.08 | 3.89 – 8.11 | 5.58 | <0.001 |
|
Individualism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.76 | 0.447 |
|
Individualism Score c * C1 |
-0.09 | 0.06 | -0.21 – 0.02 | -1.56 | 0.119 |
|
Individualism Score c * C2 |
-0.18 | 0.07 | -0.31 – -0.05 | -2.69 | 0.007 |
|
Individualism Score c * C3 |
0.03 | 0.06 | -0.09 – 0.15 | 0.49 | 0.626 |
|
Individualism Score c * C4 |
-0.03 | 0.06 | -0.15 – 0.09 | -0.52 | 0.601 |
|
Individualism Score c * C5 |
0.05 | 0.06 | -0.07 – 0.17 | 0.85 | 0.395 |
|
Individualism Score c * C6 |
0.06 | 0.07 | -0.08 – 0.20 | 0.80 | 0.426 |
|
Individualism Score c * C7 |
-0.01 | 0.07 | -0.15 – 0.14 | -0.11 | 0.912 |
|
Individualism Score c * C8 |
0.05 | 0.07 | -0.09 – 0.18 | 0.66 | 0.507 |
|
Individualism Score c * C9 |
-0.10 | 0.06 | -0.23 – 0.02 | -1.63 | 0.104 |
| Random Effects | |||||
| σ2 | 294.46 | ||||
| τ00 id | 211.63 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.441 / 0.675 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does political ideology predict familiarity/understanding, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.928 <- lmer(FR ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.928)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 +
## Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c *
## C4 + Ideology.c * C5 + Ideology.c * C6 + Ideology.c * C7 +
## Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27074.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0079 -0.5875 -0.0110 0.6005 3.0505
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 203.9 14.28
## Residual 328.9 18.14
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.4843 0.5603 1018.1554 97.244 < 2e-16 ***
## Ideology.c -2.0856 0.9823 1020.2570 -2.123 0.034 *
## C1 -18.8043 1.0183 2423.0838 -18.466 < 2e-16 ***
## C2 12.8895 1.1839 2531.4970 10.887 < 2e-16 ***
## C3 -16.1147 1.0470 2436.3043 -15.391 < 2e-16 ***
## C4 -16.5885 1.0414 2429.8893 -15.929 < 2e-16 ***
## C5 -21.9216 1.0372 2428.7767 -21.136 < 2e-16 ***
## C6 5.1562 1.2036 2531.7660 4.284 1.9e-05 ***
## C7 27.7051 1.1843 2531.2158 23.395 < 2e-16 ***
## C8 31.6055 1.2109 2534.4250 26.101 < 2e-16 ***
## C9 12.8888 1.0281 2427.9536 12.536 < 2e-16 ***
## Ideology.c:C1 -3.8648 1.8114 2437.9196 -2.134 0.033 *
## Ideology.c:C2 3.2028 2.1068 2548.5452 1.520 0.129
## Ideology.c:C3 -0.7301 1.8666 2460.5939 -0.391 0.696
## Ideology.c:C4 -0.6865 1.8699 2461.8692 -0.367 0.714
## Ideology.c:C5 -2.3553 1.7920 2438.6126 -1.314 0.189
## Ideology.c:C6 1.7774 2.0244 2535.5055 0.878 0.380
## Ideology.c:C7 1.0662 2.1894 2542.6194 0.487 0.626
## Ideology.c:C8 0.2298 2.0779 2540.1642 0.111 0.912
## Ideology.c:C9 1.9378 1.8271 2438.3560 1.061 0.289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.928,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.48 | 0.56 | 53.39 – 55.58 | 97.24 | <0.001 |
| Ideology c | -2.09 | 0.98 | -4.01 – -0.16 | -2.12 | 0.034 |
| C1 | -18.80 | 1.02 | -20.80 – -16.81 | -18.47 | <0.001 |
| C2 | 12.89 | 1.18 | 10.57 – 15.21 | 10.89 | <0.001 |
| C3 | -16.11 | 1.05 | -18.17 – -14.06 | -15.39 | <0.001 |
| C4 | -16.59 | 1.04 | -18.63 – -14.55 | -15.93 | <0.001 |
| C5 | -21.92 | 1.04 | -23.96 – -19.89 | -21.14 | <0.001 |
| C6 | 5.16 | 1.20 | 2.80 – 7.52 | 4.28 | <0.001 |
| C7 | 27.71 | 1.18 | 25.38 – 30.03 | 23.39 | <0.001 |
| C8 | 31.61 | 1.21 | 29.23 – 33.98 | 26.10 | <0.001 |
| C9 | 12.89 | 1.03 | 10.87 – 14.90 | 12.54 | <0.001 |
| Ideology c * C1 | -3.86 | 1.81 | -7.42 – -0.31 | -2.13 | 0.033 |
| Ideology c * C2 | 3.20 | 2.11 | -0.93 – 7.33 | 1.52 | 0.129 |
| Ideology c * C3 | -0.73 | 1.87 | -4.39 – 2.93 | -0.39 | 0.696 |
| Ideology c * C4 | -0.69 | 1.87 | -4.35 – 2.98 | -0.37 | 0.714 |
| Ideology c * C5 | -2.36 | 1.79 | -5.87 – 1.16 | -1.31 | 0.189 |
| Ideology c * C6 | 1.78 | 2.02 | -2.19 – 5.75 | 0.88 | 0.380 |
| Ideology c * C7 | 1.07 | 2.19 | -3.23 – 5.36 | 0.49 | 0.626 |
| Ideology c * C8 | 0.23 | 2.08 | -3.84 – 4.30 | 0.11 | 0.912 |
| Ideology c * C9 | 1.94 | 1.83 | -1.64 – 5.52 | 1.06 | 0.289 |
| Random Effects | |||||
| σ2 | 328.94 | ||||
| τ00 id | 203.92 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.406 / 0.633 | ||||
Q.2 (POLITICAL IDEOLOGY) Does political ideology depend on perceptions of naturalness in predicting familiarity/understanding, over and above climate change method contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.9281 <- lmer(FR ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + Ideology.c*C6 + Ideology.c*C7 + Ideology.c*C8 + Ideology.c*C9 + (1|id), data = L)
summary(modA.9281)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 + C6 +
## C7 + C8 + C9 + Ideology.c * C1 + Ideology.c * C2 + Ideology.c *
## C3 + Ideology.c * C4 + Ideology.c * C5 + Ideology.c * C6 +
## Ideology.c * C7 + Ideology.c * C8 + Ideology.c * C9 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26865.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9582 -0.5714 0.0018 0.5918 3.1024
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213.7 14.62
## Residual 295.6 17.19
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.34197 0.55901 1012.82582 97.211 < 2e-16 ***
## Ideology.c -1.74779 0.98108 1018.17406 -1.782 0.07513 .
## Naturalness.c 0.32237 0.02130 2834.36659 15.133 < 2e-16 ***
## C1 -13.90299 1.02255 2440.87438 -13.596 < 2e-16 ***
## C2 17.53947 1.17180 2523.33612 14.968 < 2e-16 ***
## C3 -13.41566 1.01368 2406.58421 -13.235 < 2e-16 ***
## C4 -14.90366 0.99870 2389.44489 -14.923 < 2e-16 ***
## C5 -20.36526 0.99394 2395.45651 -20.489 < 2e-16 ***
## C6 5.51551 1.14875 2485.29134 4.801 1.67e-06 ***
## C7 23.16407 1.16862 2507.23489 19.822 < 2e-16 ***
## C8 26.71987 1.19991 2527.49031 22.268 < 2e-16 ***
## C9 5.99545 1.08350 2485.33805 5.533 3.47e-08 ***
## Ideology.c:Naturalness.c -0.10444 0.03763 2788.59260 -2.775 0.00555 **
## Ideology.c:C1 -5.45758 1.79346 2424.36541 -3.043 0.00237 **
## Ideology.c:C2 2.26808 2.12445 2614.24787 1.068 0.28579
## Ideology.c:C3 -1.44055 1.79957 2429.59811 -0.800 0.42350
## Ideology.c:C4 -0.83551 1.79365 2439.41520 -0.466 0.64139
## Ideology.c:C5 -3.27654 1.71046 2397.25636 -1.916 0.05554 .
## Ideology.c:C6 1.24590 1.93345 2483.99100 0.644 0.51938
## Ideology.c:C7 3.38287 2.17530 2548.91636 1.555 0.12004
## Ideology.c:C8 1.55803 2.05059 2518.23641 0.760 0.44745
## Ideology.c:C9 3.00702 1.90100 2512.36868 1.582 0.11382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 22 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9281,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.34 | 0.56 | 53.25 – 55.44 | 97.21 | <0.001 |
| Ideology c | -1.75 | 0.98 | -3.67 – 0.18 | -1.78 | 0.075 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 15.13 | <0.001 |
| C1 | -13.90 | 1.02 | -15.91 – -11.90 | -13.60 | <0.001 |
| C2 | 17.54 | 1.17 | 15.24 – 19.84 | 14.97 | <0.001 |
| C3 | -13.42 | 1.01 | -15.40 – -11.43 | -13.23 | <0.001 |
| C4 | -14.90 | 1.00 | -16.86 – -12.95 | -14.92 | <0.001 |
| C5 | -20.37 | 0.99 | -22.31 – -18.42 | -20.49 | <0.001 |
| C6 | 5.52 | 1.15 | 3.26 – 7.77 | 4.80 | <0.001 |
| C7 | 23.16 | 1.17 | 20.87 – 25.46 | 19.82 | <0.001 |
| C8 | 26.72 | 1.20 | 24.37 – 29.07 | 22.27 | <0.001 |
| C9 | 6.00 | 1.08 | 3.87 – 8.12 | 5.53 | <0.001 |
|
Ideology c * Naturalness c |
-0.10 | 0.04 | -0.18 – -0.03 | -2.78 | 0.006 |
| Ideology c * C1 | -5.46 | 1.79 | -8.97 – -1.94 | -3.04 | 0.002 |
| Ideology c * C2 | 2.27 | 2.12 | -1.90 – 6.43 | 1.07 | 0.286 |
| Ideology c * C3 | -1.44 | 1.80 | -4.97 – 2.09 | -0.80 | 0.423 |
| Ideology c * C4 | -0.84 | 1.79 | -4.35 – 2.68 | -0.47 | 0.641 |
| Ideology c * C5 | -3.28 | 1.71 | -6.63 – 0.08 | -1.92 | 0.056 |
| Ideology c * C6 | 1.25 | 1.93 | -2.55 – 5.04 | 0.64 | 0.519 |
| Ideology c * C7 | 3.38 | 2.18 | -0.88 – 7.65 | 1.56 | 0.120 |
| Ideology c * C8 | 1.56 | 2.05 | -2.46 – 5.58 | 0.76 | 0.447 |
| Ideology c * C9 | 3.01 | 1.90 | -0.72 – 6.73 | 1.58 | 0.114 |
| Random Effects | |||||
| σ2 | 295.63 | ||||
| τ00 id | 213.71 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.438 / 0.674 | ||||