Climate Change Methods - Spring 2022
Sarah Coffin
5/21/2022
#Dataset
CC <- read.csv("Climate Change Methods_Clean_May 21, 2022.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))Participants
#Number of responses (rows)
nrow(CC)## [1] 1033#Age range
range(CC$Dem_Age, na.rm = T)## [1] 0 236#Average age
mean(CC$Dem_Age, na.rm = T)## [1] 45.63805#Standard deviation of age
sd(CC$Dem_Age, na.rm = T)## [1] 17.30648#Gender frequencies
table(CC$Dem_Gen)##
## 1 2 3
## 520 501 12#Ethnicity
table(CC$Dem_Ethnicity)##
## 1 2 3 4 5 6 7
## 61 130 45 2 4 778 13CC$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
## 1033 0 7 0.571 5.076 1.505
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 61 130 45 2 4 778 13
## Proportion 0.059 0.126 0.044 0.002 0.004 0.753 0.013#Gender
CC$Dem_Gender <- as.numeric(as.character(CC$Dem_Gen))
describe(CC$Dem_Gen)## CC$Dem_Gen
## n missing distinct Info Mean Gmd
## 1033 0 3 0.758 1.508 0.5234
##
## Value 1 2 3
## Frequency 520 501 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
## 1025 8 69 1 45.64 19.05 21 24
## .25 .50 .75 .90 .95
## 31 44 60 67 71
##
## lowest : 0 18 19 20 21, highest: 81 82 91 93 236range(CC$Demograph_Age ,na.rm = T)## [1] 0 236#Political Orientation
##"Which of the following describes your political orientation?"
CC$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
## 64 104
## Slightly Conservative Neither Conservative Nor Liberal
## 74 187
## Slightly Liberal Moderately Liberal
## 127 249
## Strongly Liberal
## 228Scales
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
## 1032 1 101 0.999 49.67 31 5.00 15.00
## .25 .50 .75 .90 .95
## 27.75 50.00 70.00 89.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 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
## 1033 0 99 0.999 42.42 32.52 0.0 2.0
## .25 .50 .75 .90 .95
## 19.0 41.0 63.0 81.8 91.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100sd(CC$ATNS_2)## [1] 28.30727range(CC$ATNS_2, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_3)## CC$ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1031 2 101 0.999 49.79 32.83 0.0 11.0
## .25 .50 .75 .90 .95
## 27.5 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
## 1033 0 99 0.998 61.63 30.62 12.0 21.2
## .25 .50 .75 .90 .95
## 45.0 64.0 82.0 100.0 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(CC$ATNS_4)## [1] 26.92168range(CC$ATNS_4, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_5)## CC$ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 101 0.999 54.7 33.13 3 13
## .25 .50 .75 .90 .95
## 32 57 76 96 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(CC$ATNS_5)## [1] 28.87365range(CC$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 0.0084 55 22 0.51
##
## 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 0.0078 0.0057 0.58
## CC.ATNS_2R 0.81 0.81 0.78 0.51 4.2 0.0097 0.0212 0.51
## CC.ATNS_3 0.76 0.76 0.72 0.44 3.2 0.0121 0.0163 0.45
## CC.ATNS_4 0.77 0.77 0.73 0.45 3.3 0.0118 0.0153 0.45
## CC.ATNS_5 0.78 0.78 0.75 0.46 3.5 0.0113 0.0253 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ATNS_1 1032 0.62 0.63 0.47 0.43 50 27
## CC.ATNS_2R 1033 0.74 0.74 0.63 0.58 58 28
## CC.ATNS_3 1031 0.84 0.84 0.81 0.73 50 29
## CC.ATNS_4 1033 0.83 0.83 0.80 0.72 62 27
## CC.ATNS_5 1033 0.81 0.81 0.75 0.68 55 29describe(CC$ATNS_Scale)## CC$ATNS_Scale
##
## 5 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 101 0.999 49.67 31 5.00 15.00
## .25 .50 .75 .90 .95
## 27.75 50.00 70.00 89.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_2R
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 99 0.999 57.58 32.52 9.0 18.2
## .25 .50 .75 .90 .95
## 37.0 59.0 81.0 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
## 1031 2 101 0.999 49.79 32.83 0.0 11.0
## .25 .50 .75 .90 .95
## 27.5 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
## 1033 0 99 0.998 61.63 30.62 12.0 21.2
## .25 .50 .75 .90 .95
## 45.0 64.0 82.0 100.0 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 101 0.999 54.7 33.13 3 13
## .25 .50 .75 .90 .95
## 32 57 76 96 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------Benefit
# 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
## 350 683 80 0.999 68.37 25.96 22.45 37.00
## .25 .50 .75 .90 .95
## 55.25 72.00 85.00 97.00 100.00
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100sd(CC$Ben_AFSCS, na.rm = TRUE)## [1] 23.554hist(CC$Ben_AFSCS)
describe(CC$Ben_BIO)## CC$Ben_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 86 0.999 53.24 29.51 4.6 19.0
## .25 .50 .75 .90 .95
## 32.0 56.0 72.0 86.0 92.2
##
## lowest : 0 1 3 5 7, highest: 95 97 98 99 100sd(CC$Ben_BIO, na.rm = TRUE)## [1] 25.81268hist(CC$Ben_BIO)
describe(CC$Ben_BECCS) ## CC$Ben_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 87 0.999 55.15 29.13 9.90 18.00
## .25 .50 .75 .90 .95
## 36.75 57.00 75.00 88.00 95.05
##
## lowest : 0 1 3 6 7, highest: 94 95 96 97 100sd(CC$Ben_BECCS, na.rm = TRUE)## [1] 25.54434hist(CC$Ben_BECCS)
describe(CC$Ben_DACCS)## CC$Ben_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 90 0.999 55.49 30.48 3.0 15.0
## .25 .50 .75 .90 .95
## 37.0 59.5 75.0 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 93 95 96 98 100sd(CC$Ben_DACCS, na.rm = TRUE)## [1] 26.83216hist(CC$Ben_DACCS)
describe(CC$Ben_EW)## CC$Ben_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 83 0.999 52.29 27.87 0.0 13.8
## .25 .50 .75 .90 .95
## 37.0 55.0 70.0 81.2 89.8
##
## lowest : 0 3 4 5 6, highest: 95 96 97 99 100sd(CC$Ben_EW, na.rm = TRUE)## [1] 24.76007hist(CC$Ben_EW)
describe(CC$Ben_OF)## CC$Ben_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 82 0.999 54.46 28.91 6.50 17.00
## .25 .50 .75 .90 .95
## 35.75 58.00 73.25 86.00 91.25
##
## lowest : 0 2 4 5 7, highest: 92 93 95 96 100sd(CC$Ben_OF, na.rm = TRUE)## [1] 25.41991hist(CC$Ben_OF)
describe(CC$Ben_BF)## CC$Ben_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 82 0.999 52.29 30.31 4.25 10.50
## .25 .50 .75 .90 .95
## 34.00 58.00 70.00 85.00 95.25
##
## lowest : 0 1 2 5 6, highest: 93 95 96 97 100sd(CC$Ben_BF, na.rm = TRUE)## [1] 26.6036hist(CC$Ben_BF)
describe(CC$Ben_NE)## CC$Ben_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 78 0.999 59.96 31.22 0 18
## .25 .50 .75 .90 .95
## 43 66 80 93 100
##
## lowest : 0 6 9 10 11, highest: 94 95 97 98 100sd(CC$Ben_NE, na.rm = TRUE)## [1] 27.81378hist(CC$Ben_NE)
describe(CC$Ben_SE)## CC$Ben_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 76 0.998 66.01 29.55 10.0 25.0
## .25 .50 .75 .90 .95
## 50.0 70.0 85.0 99.8 100.0
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100sd(CC$Ben_SE, na.rm = TRUE)## [1] 26.51654hist(CC$Ben_SE)
describe(CC$Ben_WE) ## CC$Ben_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 71 0.998 65.06 28.81 10.3 25.0
## .25 .50 .75 .90 .95
## 52.0 68.0 85.0 100.0 100.0
##
## lowest : 0 5 6 8 10, highest: 96 97 98 99 100sd(CC$Ben_WE, na.rm = TRUE)## [1] 25.98025hist(CC$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
## 1032 1 73 0.857 86.88 19.61 33.00 58.00
## .25 .50 .75 .90 .95
## 83.75 100.00 100.00 100.00 100.00
##
## 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] 22.10478describe(CC$CCB2)## CC$CCB2
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 87 0.889 83.6 23.87 18.55 50.00
## .25 .50 .75 .90 .95
## 79.00 98.00 100.00 100.00 100.00
##
## 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] 25.75413describe(CC$CCB3)## CC$CCB3
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 89 0.937 79.64 27.63 1.6 35.0
## .25 .50 .75 .90 .95
## 70.0 91.0 100.0 100.0 100.0
##
## 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] 28.35675describe(CC$CCB4)## CC$CCB4
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 87 0.979 76.36 27.45 14.2 40.0
## .25 .50 .75 .90 .95
## 65.0 85.0 100.0 100.0 100.0
##
## 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] 26.39739#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$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 251 0.987 81.61 23.3 24.70 47.35
## .25 .50 .75 .90 .95
## 75.00 91.25 99.00 100.00 100.00
##
## lowest : 0.00 2.00 3.75 4.00 4.75, highest: 99.00 99.25 99.50 99.75 100.00CC$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 1033 Observations
## --------------------------------------------------------------------------------
## CC.CCB_1_48
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 73 0.857 86.88 19.61 33.00 58.00
## .25 .50 .75 .90 .95
## 83.75 100.00 100.00 100.00 100.00
##
## lowest : 0 8 11 13 15, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_49
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 87 0.889 83.6 23.87 18.55 50.00
## .25 .50 .75 .90 .95
## 79.00 98.00 100.00 100.00 100.00
##
## lowest : 0 3 5 7 8, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_50
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 89 0.937 79.64 27.63 1.6 35.0
## .25 .50 .75 .90 .95
## 70.0 91.0 100.0 100.0 100.0
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_51
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 87 0.979 76.36 27.45 14.2 40.0
## .25 .50 .75 .90 .95
## 65.0 85.0 100.0 100.0 100.0
##
## 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 0.003 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 0.0037 0.0012 0.82
## CC.CCB_1_49 0.90 0.91 0.88 0.77 9.9 0.0048 0.0032 0.78
## CC.CCB_1_50 0.91 0.92 0.90 0.79 11.1 0.0047 0.0070 0.78
## CC.CCB_1_51 0.93 0.94 0.92 0.83 15.0 0.0036 0.0025 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CCB_1_48 1032 0.90 0.91 0.88 0.84 87 22
## CC.CCB_1_49 1032 0.95 0.95 0.94 0.90 84 26
## CC.CCB_1_50 1033 0.94 0.93 0.91 0.88 80 28
## CC.CCB_1_51 1033 0.90 0.90 0.84 0.82 76 26#Correlation CCB
cor(CC$CCB_Scale, use= "complete.obs")## CC.CCB_1_48 CC.CCB_1_49 CC.CCB_1_50 CC.CCB_1_51
## CC.CCB_1_48 1.0000000 0.8728811 0.7770843 0.7064467
## CC.CCB_1_49 0.8728811 1.0000000 0.8495833 0.7822191
## CC.CCB_1_50 0.7770843 0.8495833 1.0000000 0.8189666
## CC.CCB_1_51 0.7064467 0.7822191 0.8189666 1.0000000Connectedness to Nature
Descriptives
#Connectedness to Nature Item Definitions
CC$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$CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 97 0.998 67.03 27.56 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100range(CC$CNS_1, na.rm=TRUE)## [1] 0 100describe(CC$CNS_2)## CC$CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 95 0.995 73.43 24.82 25 44
## .25 .50 .75 .90 .95
## 62 78 91 100 100
##
## lowest : 0 5 7 8 9, highest: 96 97 98 99 100range(CC$CNS_2, na.rm=TRUE)## [1] 0 100describe(CC$CNS_3)## CC$CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 98 0.996 65.94 32.07 0.0 17.0
## .25 .50 .75 .90 .95
## 51.0 70.5 87.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(CC$CNS_3, na.rm=TRUE)## [1] 0 100describe(CC$CNS_4)## CC$CNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 100 0.996 39.75 37.06 0 0
## .25 .50 .75 .90 .95
## 13 33 68 90 100
##
## 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$CNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 98 0.999 49.59 34.86 0 5
## .25 .50 .75 .90 .95
## 23 51 72 90 100
##
## lowest : 0 1 3 4 5, highest: 95 97 98 99 100range(CC$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
## 1033 0 323 1 63.42 18.67 35.00 43.20
## .25 .50 .75 .90 .95
## 53.00 63.00 74.60 84.96 91.80
##
## lowest : 0.0 8.6 10.0 12.8 16.0, highest: 98.2 98.6 99.2 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 0.58 0.63 0.22 1.4 0.024 63 17 0.078
##
## lower alpha upper 95% confidence boundaries
## 0.49 0.54 0.58
##
## 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 0.69 0.032 0.046 0.078
## CC.CNS_2 0.38 0.41 0.44 0.15 0.69 0.032 0.054 0.068
## CC.CNS_3 0.41 0.45 0.51 0.17 0.82 0.031 0.068 0.064
## CC.CNS_4R 0.62 0.66 0.67 0.32 1.92 0.020 0.093 0.313
## CC.CNS_5R 0.58 0.63 0.66 0.30 1.74 0.023 0.109 0.308
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CNS_1 1033 0.70 0.75 0.75 0.491 67 25
## CC.CNS_2 1033 0.69 0.75 0.74 0.506 73 23
## CC.CNS_3 1032 0.68 0.70 0.64 0.412 66 29
## CC.CNS_4R 1032 0.47 0.41 0.13 0.092 60 33
## CC.CNS_5R 1033 0.50 0.45 0.18 0.152 50 30describe(CC$CNS_Scale2)## CC$CNS_Scale2
##
## 5 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 97 0.998 67.03 27.56 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
## 1033 0 95 0.995 73.43 24.82 25 44
## .25 .50 .75 .90 .95
## 62 78 91 100 100
##
## lowest : 0 5 7 8 9, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 98 0.996 65.94 32.07 0.0 17.0
## .25 .50 .75 .90 .95
## 51.0 70.5 87.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_4R
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 100 0.996 60.25 37.06 0 10
## .25 .50 .75 .90 .95
## 32 67 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_5R
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 98 0.999 50.41 34.86 0 10
## .25 .50 .75 .90 .95
## 28 49 77 95 100
##
## 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 0.018 NA 0.54
## CC.CNS_2 0.73 0.73 0.58 0.58 2.8 0.017 NA 0.58
## CC.CNS_3 0.80 0.81 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 1033 0.87 0.88 0.80 0.71 67 25
## CC.CNS_2 1033 0.84 0.86 0.76 0.68 73 23
## CC.CNS_3 1032 0.85 0.83 0.67 0.61 66 29describe(CC$CNS_Scale)## CC$CNS_Scale
##
## 3 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1033 0 97 0.998 67.03 27.56 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
## 1033 0 95 0.995 73.43 24.82 25 44
## .25 .50 .75 .90 .95
## 62 78 91 100 100
##
## lowest : 0 5 7 8 9, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1032 1 98 0.996 65.94 32.07 0.0 17.0
## .25 .50 .75 .90 .95
## 51.0 70.5 87.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Correlation CCB
cor(CC$CNS_Scale, use= "complete.obs")## CC.CNS_1 CC.CNS_2 CC.CNS_3
## CC.CNS_1 1.0000000 0.6748648 0.5798941
## CC.CNS_2 0.6748648 1.0000000 0.5429202
## CC.CNS_3 0.5798941 0.5429202 1.0000000Control
# Control was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented perception of control over the technology rated.
## 1. We have control over the processes in this method.Descriptives
# Define Variables
CC$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$Control_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 72 0.997 74.43 22.03 36 50
## .25 .50 .75 .90 .95
## 65 77 88 100 100
##
## lowest : 0 5 7 8 20, highest: 96 97 98 99 100sd(CC$Control_AFSCS, na.rm = TRUE)## [1] 20.45079hist(CC$Control_AFSCS)
describe(CC$Control_BIO)## CC$Control_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 77 0.999 68.98 23.94 29.8 41.6
## .25 .50 .75 .90 .95
## 54.0 71.0 85.0 96.0 100.0
##
## lowest : 0 5 9 14 16, highest: 95 96 98 99 100sd(CC$Control_BIO, na.rm = TRUE)## [1] 21.26588hist(CC$Control_BIO)
describe(CC$Control_BECCS)## CC$Control_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 82 0.999 62.12 26.5 19.0 30.0
## .25 .50 .75 .90 .95
## 47.0 65.0 78.0 90.1 100.0
##
## lowest : 0 2 5 10 12, highest: 95 96 98 99 100sd(CC$Control_BECCS, na.rm = TRUE)## [1] 23.53716hist(CC$Control_BECCS)
describe(CC$Control_DACCS)## CC$Control_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 89 0.999 57.52 29.06 13.70 21.70
## .25 .50 .75 .90 .95
## 40.25 58.00 75.00 92.30 100.00
##
## lowest : 0 1 8 9 10, highest: 94 95 97 99 100sd(CC$Control_DACCS, na.rm = TRUE)## [1] 25.50693hist(CC$Control_DACCS)
describe(CC$Control_EW) ## CC$Control_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 83 0.999 54.8 26.32 14 25
## .25 .50 .75 .90 .95
## 39 55 71 86 92
##
## lowest : 0 9 10 12 13, highest: 93 94 95 99 100sd(CC$Control_EW, na.rm = TRUE)## [1] 23.13068hist(CC$Control_EW) 
describe(CC$Control_OF)## CC$Control_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 90 1 47.47 29.86 5.00 12.50
## .25 .50 .75 .90 .95
## 27.00 47.50 67.25 81.50 91.00
##
## lowest : 0 1 2 3 4, highest: 94 95 98 99 100sd(CC$Control_OF, na.rm = TRUE)## [1] 25.98562hist(CC$Control_OF)
describe(CC$Control_BF)## CC$Control_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 65 0.996 77.22 21.51 37.5 50.5
## .25 .50 .75 .90 .95
## 69.0 80.0 94.0 100.0 100.0
##
## lowest : 0 5 15 25 29, highest: 96 97 98 99 100sd(CC$Control_BF, na.rm = TRUE)## [1] 19.74204hist(CC$Control_BF)
describe(CC$Control_NE)## CC$Control_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 73 0.999 66.63 27.96 20 32
## .25 .50 .75 .90 .95
## 51 71 86 98 100
##
## lowest : 0 5 7 9 10, highest: 95 96 98 99 100sd(CC$Control_NE, na.rm = TRUE)## [1] 24.96451hist(CC$Control_NE)
describe(CC$Control_SE)## CC$Control_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 67 0.993 75.93 25.56 26.8 39.2
## .25 .50 .75 .90 .95
## 63.0 82.0 95.0 100.0 100.0
##
## lowest : 0 7 9 10 13, highest: 96 97 98 99 100sd(CC$Control_SE, na.rm = TRUE)## [1] 23.96366hist(CC$Control_SE)
describe(CC$Control_WE)## CC$Control_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 68 0.997 71.05 27.65 21.1 34.2
## .25 .50 .75 .90 .95
## 56.0 79.0 90.0 100.0 100.0
##
## lowest : 0 4 10 12 15, highest: 96 97 98 99 100sd(CC$Control_WE, na.rm = TRUE)## [1] 25.10143hist(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$Familiar_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 91 0.997 62.74 34.66 3.0 11.9
## .25 .50 .75 .90 .95
## 42.0 67.0 89.0 100.0 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(CC$Familiar_AFSCS, na.rm = TRUE)## [1] 30.73106hist(CC$Familiar_AFSCS)
describe(CC$Familiar_BIO)## CC$Familiar_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 81 0.993 27.51 29.44 0.0 0.0
## .25 .50 .75 .90 .95
## 4.0 20.0 43.0 68.4 82.0
##
## lowest : 0 1 2 3 4, highest: 92 93 94 95 100sd(CC$Familiar_BIO, na.rm = TRUE)## [1] 26.93655hist(CC$Familiar_BIO)
describe(CC$Familiar_BECCS)## CC$Familiar_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 87 0.994 29.66 30.65 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 22.00 48.50 73.00 84.05
##
## lowest : 0 1 2 3 4, highest: 91 92 94 98 100sd(CC$Familiar_BECCS, na.rm = TRUE)## [1] 27.77227hist(CC$Familiar_BECCS)
describe(CC$Familiar_DACCS)## CC$Familiar_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 83 0.992 26.66 28.24 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 20.00 42.00 65.30 76.15
##
## lowest : 0 1 2 3 4, highest: 89 90 93 99 100sd(CC$Familiar_DACCS, na.rm = TRUE)## [1] 25.76452hist(CC$Familiar_DACCS)
describe(CC$Familiar_EW)## CC$Familiar_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 76 0.98 22.48 25.13 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 17.0 36.0 60.0 69.8
##
## lowest : 0 1 2 3 4, highest: 79 80 87 90 91sd(CC$Familiar_EW, na.rm = TRUE)## [1] 23.12228hist(CC$Familiar_EW)
describe(CC$Familiar_OF)## CC$Familiar_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 76 0.992 25.51 27.56 0 0
## .25 .50 .75 .90 .95
## 4 18 40 63 76
##
## lowest : 0 1 2 3 4, highest: 85 86 87 89 100sd(CC$Familiar_OF, na.rm = TRUE)## [1] 25.28677hist(CC$Familiar_OF)
describe(CC$Familiar_BF)## CC$Familiar_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 87 0.999 58.37 32.5 0.00 18.00
## .25 .50 .75 .90 .95
## 36.75 61.00 81.00 93.50 100.00
##
## lowest : 0 1 5 6 8, highest: 95 96 98 99 100sd(CC$Familiar_BF, na.rm = TRUE)## [1] 28.51731hist(CC$Familiar_BF)
describe(CC$Familiar_NE)## CC$Familiar_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 82 0.997 69.35 29.29 15 31
## .25 .50 .75 .90 .95
## 54 75 90 100 100
##
## lowest : 0 2 3 4 6, highest: 95 97 98 99 100sd(CC$Familiar_NE, na.rm = TRUE)## [1] 26.50786hist(CC$Familiar_NE)
describe(CC$Familiar_SE)## CC$Familiar_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 51 0.942 87.65 16.08 51.6 63.2
## .25 .50 .75 .90 .95
## 82.0 94.0 100.0 100.0 100.0
##
## lowest : 0 18 35 38 41, highest: 96 97 98 99 100sd(CC$Familiar_SE, na.rm = TRUE)## [1] 16.36918hist(CC$Familiar_SE)
describe(CC$Familiar_WE)## CC$Familiar_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 63 0.983 81.66 20.96 40.2 55.0
## .25 .50 .75 .90 .95
## 75.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 3 13 19, highest: 96 97 98 99 100sd(CC$Familiar_WE, na.rm = TRUE)## [1] 20.81041hist(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$PI_Orientation
## n missing distinct Info Mean Gmd
## 1033 0 7 0.966 4.808 2.076
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 64 104 74 187 127 249 228
## Proportion 0.062 0.101 0.072 0.181 0.123 0.241 0.221CC$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$Orientation
## n missing distinct Info Mean Gmd
## 1033 0 7 0.966 4.808 2.076
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 64 104 74 187 127 249 228
## Proportion 0.062 0.101 0.072 0.181 0.123 0.241 0.221hist(CC$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
## 1032 1 5 0.856 2.252 0.9224
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 183 508 277 26 38
## Proportion 0.177 0.492 0.268 0.025 0.037CC$Party <- as.numeric(as.character(CC$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$PartyFull
## n missing distinct Info Mean Gmd
## 1032 1 7 0.957 -0.9205 2.216
##
## lowest : -3 -2 -1 0 1, highest: -1 0 1 2 3
##
## Value -3 -2 -1 0 1 2 3
## Frequency 324 184 126 152 63 96 87
## Proportion 0.314 0.178 0.122 0.147 0.061 0.093 0.084hist(CC$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
## 1033 0 13 0.87 1.947 0.5695 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: 3.0 3.5 4.0 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 4.0
## Frequency 1 4 4 11 54 242 502 137 64 11 1
## Proportion 0.001 0.004 0.004 0.011 0.052 0.234 0.486 0.133 0.062 0.011 0.001
##
## Value 5.0 6.0
## Frequency 1 1
## Proportion 0.001 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$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.69 0.4 2.6 0.014 71 17 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.69 0.72 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_1 0.56 0.56 0.48 0.30 1.3 0.024 0.010 0.32
## CC.Ind_2 0.75 0.75 0.68 0.50 3.0 0.014 0.015 0.47
## CC.Ind_5 0.61 0.61 0.54 0.34 1.6 0.021 0.020 0.37
## CC.Ind_6 0.70 0.70 0.64 0.44 2.3 0.017 0.029 0.37
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 1033 0.83 0.84 0.80 0.67 74 22
## CC.Ind_2 1033 0.64 0.63 0.42 0.35 67 23
## CC.Ind_5 1033 0.79 0.79 0.72 0.59 72 22
## CC.Ind_6 1033 0.70 0.69 0.53 0.44 70 24hist(CC$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 4.7 0.0088 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 0.50 3.0 0.0136 0.0174 0.43
## CC.Ind_4 0.76 0.76 0.69 0.52 3.2 0.0126 0.0073 0.53
## CC.Ind_7 0.82 0.82 0.76 0.60 4.5 0.0099 0.0038 0.62
## CC.Ind_8 0.78 0.78 0.72 0.55 3.6 0.0115 0.0102 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 1033 0.86 0.85 0.78 0.71 43 32
## CC.Ind_4 1033 0.83 0.83 0.77 0.69 62 29
## CC.Ind_7 1032 0.75 0.75 0.62 0.57 53 28
## CC.Ind_8 1033 0.80 0.81 0.72 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 0.014 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 0.48 0.30 1.3 0.024 0.010 0.32
## CC.Ind_2 0.75 0.75 0.68 0.50 3.0 0.014 0.015 0.47
## CC.Ind_5 0.61 0.61 0.54 0.34 1.6 0.021 0.020 0.37
## CC.Ind_6 0.70 0.70 0.64 0.44 2.3 0.017 0.029 0.37
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 1033 0.83 0.84 0.80 0.67 74 22
## CC.Ind_2 1033 0.64 0.63 0.42 0.35 67 23
## CC.Ind_5 1033 0.79 0.79 0.72 0.59 72 22
## CC.Ind_6 1033 0.70 0.69 0.53 0.44 70 24CC$CollScale <- data.frame(CC$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$CollScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.8 0.54 4.7 0.0088 54 24 0.56
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_3 0.75 0.75 0.69 0.50 3.0 0.0136 0.0174 0.43
## CC.Ind_4 0.76 0.76 0.69 0.52 3.2 0.0126 0.0073 0.53
## CC.Ind_7 0.82 0.82 0.76 0.60 4.5 0.0099 0.0038 0.62
## CC.Ind_8 0.78 0.78 0.72 0.55 3.6 0.0115 0.0102 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 1033 0.86 0.85 0.78 0.71 43 32
## CC.Ind_4 1033 0.83 0.83 0.77 0.69 62 29
## CC.Ind_7 1032 0.75 0.75 0.62 0.57 53 28
## CC.Ind_8 1033 0.80 0.81 0.72 0.64 58 28Naturalness
# Naturalness was rated on a four item scale (0 = Strongly disagree to 100 = Strongly agree) and a mean score was calculated to represent naturalness perception of the technology rated.
## 1. This is natural
## 2. This involves humans altering naturally occurring processes (Reverse code)
## 3. This relies on science-based technology (Reverse code)
## 4. This is artificial (Reverse code)Descriptives
#Define Variables
CC$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$Nat_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 78 0.991 74.83 27.28 19.45 36.90
## .25 .50 .75 .90 .95
## 60.25 83.00 95.00 100.00 100.00
##
## lowest : 0 3 6 7 10, highest: 96 97 98 99 100describe(CC$Nat_2R_AFSCS)## CC$Nat_2R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 95 0.999 53.13 35.12 0.00 14.00
## .25 .50 .75 .90 .95
## 30.00 50.00 82.75 97.10 100.00
##
## lowest : 0 2 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_3R_AFSCS)## CC$Nat_3R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 89 0.999 39.57 33.51 0.00 2.90
## .25 .50 .75 .90 .95
## 16.00 35.00 60.75 86.20 95.10
##
## lowest : 0 1 2 3 4, highest: 93 94 96 97 100describe(CC$Nat_4R_AFSCS)## CC$Nat_4R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 77 0.99 79.29 25.84 23.45 41.00
## .25 .50 .75 .90 .95
## 65.25 91.00 99.00 100.00 100.00
##
## lowest : 0 4 6 7 12, highest: 96 97 98 99 100describe(CC$Nat_1_BIO)## CC$Nat_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 91 0.999 45.56 31.28 0 5
## .25 .50 .75 .90 .95
## 25 46 64 84 96
##
## lowest : 0 2 3 4 5, highest: 90 95 96 97 100describe(CC$Nat_2R_BIO) ## CC$Nat_2R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 77 0.999 36.78 27.56 0.0 5.0
## .25 .50 .75 .90 .95
## 20.0 35.0 49.0 71.2 85.0
##
## lowest : 0 2 3 5 6, highest: 93 95 96 98 100describe(CC$Nat_3R_BIO)## CC$Nat_3R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 69 0.993 23.72 23.37 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 20.0 35.0 49.4 70.0
##
## lowest : 0 1 2 3 5, highest: 87 88 95 97 100describe(CC$Nat_4R_BIO) ## CC$Nat_4R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 96 0.999 49.98 35.8 0.0 7.6
## .25 .50 .75 .90 .95
## 25.0 49.0 78.0 96.4 100.0
##
## lowest : 0 1 4 5 6, highest: 95 96 97 99 100describe(CC$Nat_1_BECCS) ## CC$Nat_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 89 0.999 43.48 29.15 0.00 6.00
## .25 .50 .75 .90 .95
## 25.00 45.00 60.00 76.00 88.05
##
## lowest : 0 1 2 3 4, highest: 90 93 96 99 100describe(CC$Nat_2R_BECCS) ## CC$Nat_2R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 72 0.997 30.68 25.04 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 30.00 44.25 60.00 75.00
##
## lowest : 0 1 2 3 4, highest: 85 89 90 93 100describe(CC$Nat_3R_BECCS) ## CC$Nat_3R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 70 0.99 22.78 22.63 0.00 0.00
## .25 .50 .75 .90 .95
## 4.00 20.00 35.00 48.00 63.05
##
## lowest : 0 1 2 3 4, highest: 90 92 95 98 100describe(CC$Nat_4R_BECCS)## CC$Nat_4R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 90 0.999 42.06 31.49 0.0 6.0
## .25 .50 .75 .90 .95
## 20.0 40.0 62.0 81.1 93.0
##
## lowest : 0 2 3 4 5, highest: 93 94 95 98 100describe(CC$Nat_1_DACCS)## CC$Nat_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 83 0.996 29.52 27.63 0.00 0.00
## .25 .50 .75 .90 .95
## 10.00 25.00 41.00 65.00 82.15
##
## lowest : 0 1 3 4 5, highest: 94 95 97 98 100describe(CC$Nat_2R_DACCS) ## CC$Nat_2R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 81 0.996 28.75 27.75 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 24.0 40.0 70.3 81.6
##
## lowest : 0 1 2 3 4, highest: 87 90 91 99 100describe(CC$Nat_3R_DACCS)## CC$Nat_3R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 60 0.976 16.52 18.49 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 12.00 26.00 40.00 48.15
##
## lowest : 0 1 3 4 5, highest: 81 83 85 93 100describe(CC$Nat_4R_DACCS)## CC$Nat_4R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 81 0.995 28.65 27.29 0.00 0.00
## .25 .50 .75 .90 .95
## 10.00 23.00 42.00 63.30 82.15
##
## lowest : 0 1 3 4 5, highest: 88 89 95 98 100describe(CC$Nat_1_EW)## CC$Nat_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 93 0.999 45.92 31.11 0 7
## .25 .50 .75 .90 .95
## 25 50 67 81 89
##
## lowest : 0 1 2 3 4, highest: 91 92 95 98 100describe(CC$Nat_2R_EW)## CC$Nat_2R_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 76 0.995 27.12 24.96 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 40.0 58.6 75.0
##
## lowest : 0 1 2 3 4, highest: 85 90 92 93 100describe(CC$Nat_3R_EW)## CC$Nat_3R_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 70 0.994 25.78 24.03 0 0
## .25 .50 .75 .90 .95
## 8 24 39 50 70
##
## lowest : 0 1 2 3 4, highest: 86 88 90 95 100describe(CC$Nat_4R_EW)## CC$Nat_4R_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 84 0.999 44.47 31.96 0.0 6.4
## .25 .50 .75 .90 .95
## 22.0 44.0 67.0 80.0 92.6
##
## lowest : 0 4 5 6 7, highest: 91 93 94 98 100describe(CC$Nat_1_OF)## CC$Nat_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 83 0.999 40.43 31.1 0.00 1.00
## .25 .50 .75 .90 .95
## 18.00 39.00 59.25 78.50 87.00
##
## lowest : 0 2 4 5 6, highest: 88 90 92 93 100describe(CC$Nat_2R_OF)## CC$Nat_2R_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 66 0.996 22.35 21.52 0.00 0.00
## .25 .50 .75 .90 .95
## 7.00 19.00 32.00 46.00 60.25
##
## lowest : 0 1 3 4 5, highest: 80 81 82 89 100describe(CC$Nat_3R_OF)## CC$Nat_3R_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 67 0.996 26.12 23.98 0.00 0.00
## .25 .50 .75 .90 .95
## 10.00 23.00 36.00 58.00 68.25
##
## lowest : 0 1 2 3 4, highest: 79 80 90 91 100describe(CC$Nat_4R_OF)## CC$Nat_4R_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 87 0.999 38.43 30.1 0.00 5.00
## .25 .50 .75 .90 .95
## 16.75 37.00 55.00 76.50 91.25
##
## lowest : 0 2 3 4 5, highest: 92 93 95 99 100describe(CC$Nat_1_BF)## CC$Nat_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 88 0.999 53.11 31.82 2.0 13.5
## .25 .50 .75 .90 .95
## 35.0 53.0 75.0 91.0 100.0
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100describe(CC$Nat_2R_BF)## CC$Nat_2R_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 80 0.998 37.99 30.34 0.0 0.0
## .25 .50 .75 .90 .95
## 19.5 35.0 54.0 79.5 91.0
##
## lowest : 0 2 3 4 5, highest: 91 95 96 99 100describe(CC$Nat_3R_BF)## CC$Nat_3R_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 59 0.987 17.67 18.15 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 15.00 27.00 39.00 46.25
##
## lowest : 0 1 2 3 4, highest: 68 75 77 81 85describe(CC$Nat_4R_BF)## CC$Nat_4R_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 85 0.999 49.22 33.39 1.75 10.00
## .25 .50 .75 .90 .95
## 26.75 49.00 74.00 90.50 99.25
##
## lowest : 0 1 2 4 5, highest: 95 96 98 99 100describe(CC$Nat_1_NE)## CC$Nat_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 80 0.995 30.82 28.99 0 0
## .25 .50 .75 .90 .95
## 8 26 49 67 80
##
## lowest : 0 1 2 3 4, highest: 89 90 93 95 100describe(CC$Nat_2R_NE)## CC$Nat_2R_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 72 0.994 30.07 30.73 0 0
## .25 .50 .75 .90 .95
## 7 23 43 80 95
##
## lowest : 0 2 3 4 5, highest: 94 95 98 99 100describe(CC$Nat_3R_NE)## CC$Nat_3R_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 48 0.931 11.08 14.64 0 0
## .25 .50 .75 .90 .95
## 0 6 17 33 43
##
## lowest : 0 1 2 3 4, highest: 49 50 64 92 100describe(CC$Nat_4R_NE)## CC$Nat_4R_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 79 0.996 32.08 30.61 0 0
## .25 .50 .75 .90 .95
## 9 27 48 77 90
##
## lowest : 0 3 4 5 6, highest: 92 95 96 99 100describe(CC$Nat_1_SE)## CC$Nat_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 75 0.991 72.85 29.21 10.6 30.2
## .25 .50 .75 .90 .95
## 60.0 80.0 95.0 100.0 100.0
##
## lowest : 0 1 4 6 10, highest: 95 97 98 99 100describe(CC$Nat_2R_SE)## CC$Nat_2R_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 79 0.989 66.11 36.13 4.6 18.0
## .25 .50 .75 .90 .95
## 38.0 78.0 95.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_3R_SE)## CC$Nat_3R_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 52 0.964 14.63 17.95 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 10.0 22.0 38.6 50.0
##
## lowest : 0 1 2 3 4, highest: 70 71 76 80 93describe(CC$Nat_4R_SE)## CC$Nat_4R_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 76 0.997 66.13 33.03 9.8 21.6
## .25 .50 .75 .90 .95
## 47.0 74.0 92.0 100.0 100.0
##
## lowest : 0 5 7 8 11, highest: 96 97 98 99 100describe(CC$Nat_1_WE)## CC$Nat_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 78 0.994 69.52 31.09 9.1 21.2
## .25 .50 .75 .90 .95
## 54.5 78.0 91.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_2R_WE)## CC$Nat_2R_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 84 0.992 63.87 35.49 10.0 20.0
## .25 .50 .75 .90 .95
## 38.0 72.0 93.5 100.0 100.0
##
## lowest : 0 1 5 8 10, highest: 96 97 98 99 100describe(CC$Nat_3R_WE)## CC$Nat_3R_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 63 0.988 20.82 22.27 0 0
## .25 .50 .75 .90 .95
## 2 17 30 47 65
##
## lowest : 0 1 2 3 4, highest: 85 88 90 94 100describe(CC$Nat_4R_WE)## CC$Nat_4R_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 78 0.997 62.65 35.13 5.0 13.4
## .25 .50 .75 .90 .95
## 41.0 68.0 90.0 100.0 100.0
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100sd(CC$Nat_1_AFSCS, na.rm = TRUE)## [1] 25.43286sd(CC$Nat_2R_AFSCS, na.rm = TRUE)## [1] 30.48964sd(CC$Nat_3R_AFSCS, na.rm = TRUE)## [1] 29.56135sd(CC$Nat_4R_AFSCS, na.rm = TRUE)## [1] 25.13828sd(CC$Nat_1_BIO, na.rm = TRUE)## [1] 27.31186sd(CC$Nat_2R_BIO, na.rm = TRUE)## [1] 24.53143sd(CC$Nat_3R_BIO, na.rm = TRUE)## [1] 21.85822sd(CC$Nat_4R_BIO, na.rm = TRUE) ## [1] 31.06943sd(CC$Nat_1_BECCS, na.rm = TRUE) ## [1] 25.53642sd(CC$Nat_2R_BECCS, na.rm = TRUE) ## [1] 22.56763sd(CC$Nat_3R_BECCS, na.rm = TRUE) ## [1] 20.86114sd(CC$Nat_4R_BECCS, na.rm = TRUE)## [1] 27.54009sd(CC$Nat_1_DACCS, na.rm = TRUE)## [1] 25.07632sd(CC$Nat_2R_DACCS, na.rm = TRUE) ## [1] 25.6886sd(CC$Nat_3R_DACCS, na.rm = TRUE)## [1] 17.64158sd(CC$Nat_4R_DACCS)## [1] NAsd(CC$Nat_1_EW, na.rm = TRUE)## [1] 27.05301sd(CC$Nat_2R_EW, na.rm = TRUE)## [1] 22.74157sd(CC$Nat_3R_EW, na.rm = TRUE)## [1] 21.88575sd(CC$Nat_4R_EW, na.rm = TRUE)## [1] 27.74531sd(CC$Nat_1_OF, na.rm = TRUE)## [1] 27.0995sd(CC$Nat_2R_OF, na.rm = TRUE)## [1] 20.3552sd(CC$Nat_3R_OF, na.rm = TRUE)## [1] 22.13675sd(CC$Nat_4R_OF, na.rm = TRUE)## [1] 26.58775sd(CC$Nat_1_BF, na.rm = TRUE)## [1] 27.71169sd(CC$Nat_2R_BF, na.rm = TRUE)## [1] 26.88231sd(CC$Nat_3R_BF, na.rm = TRUE)## [1] 16.85631sd(CC$Nat_4R_BF, na.rm = TRUE)## [1] 28.94767sd(CC$Nat_1_NE, na.rm = TRUE)## [1] 25.87979sd(CC$Nat_2R_NE, na.rm = TRUE)## [1] 28.46893sd(CC$Nat_3R_NE, na.rm = TRUE)## [1] 15.27055sd(CC$Nat_4R_NE, na.rm = TRUE)## [1] 27.61447sd(CC$Nat_1_SE, na.rm = TRUE)## [1] 27.29524sd(CC$Nat_2R_SE, na.rm = TRUE)## [1] 32.35417sd(CC$Nat_3R_SE, na.rm = TRUE)## [1] 17.77715sd(CC$Nat_4R_SE, na.rm = TRUE)## [1] 29.37358sd(CC$Nat_1_WE, na.rm = TRUE)## [1] 28.45846sd(CC$Nat_2R_WE, na.rm = TRUE)## [1] 31.25356sd(CC$Nat_3R_WE, na.rm = TRUE)## [1] 21.16047sd(CC$Nat_4R_WE, na.rm = TRUE)## [1] 30.9186hist(CC$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$Nat_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 198 1 61.71 22.46 26.09 35.92
## .25 .50 .75 .90 .95
## 48.38 62.75 74.94 87.50 94.55
##
## lowest : 0.00 7.00 8.00 11.00 11.75, highest: 98.00 98.75 99.50 99.75 100.00describe(CC$Nat_Scale_AFSCS)## CC$Nat_Scale_AFSCS
##
## 4 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 78 0.991 74.83 27.28 19.45 36.90
## .25 .50 .75 .90 .95
## 60.25 83.00 95.00 100.00 100.00
##
## lowest : 0 3 6 7 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 95 0.999 53.13 35.12 0.00 14.00
## .25 .50 .75 .90 .95
## 30.00 50.00 82.75 97.10 100.00
##
## lowest : 0 2 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 89 0.999 39.57 33.51 0.00 2.90
## .25 .50 .75 .90 .95
## 16.00 35.00 60.75 86.20 95.10
##
## lowest : 0 1 2 3 4, highest: 93 94 96 97 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 77 0.99 79.29 25.84 23.45 41.00
## .25 .50 .75 .90 .95
## 65.25 91.00 99.00 100.00 100.00
##
## lowest : 0 4 6 7 12, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_AFSCS, na.rm = TRUE)## [1] 19.85926describe(CC$Nat_Score_BIO)## CC$Nat_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 182 1 39.01 20.91 5.75 12.70
## .25 .50 .75 .90 .95
## 27.00 39.00 50.75 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 91 0.999 45.56 31.28 0 5
## .25 .50 .75 .90 .95
## 25 46 64 84 96
##
## lowest : 0 2 3 4 5, highest: 90 95 96 97 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 77 0.999 36.78 27.56 0.0 5.0
## .25 .50 .75 .90 .95
## 20.0 35.0 49.0 71.2 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
## 337 696 69 0.993 23.72 23.37 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 20.0 35.0 49.4 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
## 337 696 96 0.999 49.98 35.8 0.0 7.6
## .25 .50 .75 .90 .95
## 25.0 49.0 78.0 96.4 100.0
##
## lowest : 0 1 4 5 6, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BIO, na.rm = TRUE)## [1] 18.52405describe(CC$Nat_Score_BECCS)## CC$Nat_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 181 1 34.75 18.68 6.25 12.25
## .25 .50 .75 .90 .95
## 24.94 33.75 46.06 53.80 61.26
##
## lowest : 0.00 2.25 2.50 3.00 4.50, highest: 75.00 76.25 77.50 78.75 79.00describe(CC$Nat_Scale_BECCS)## CC$Nat_Scale_BECCS
##
## 4 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 89 0.999 43.48 29.15 0.00 6.00
## .25 .50 .75 .90 .95
## 25.00 45.00 60.00 76.00 88.05
##
## lowest : 0 1 2 3 4, highest: 90 93 96 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 72 0.997 30.68 25.04 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 30.00 44.25 60.00 75.00
##
## lowest : 0 1 2 3 4, highest: 85 89 90 93 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 70 0.99 22.78 22.63 0.00 0.00
## .25 .50 .75 .90 .95
## 4.00 20.00 35.00 48.00 63.05
##
## lowest : 0 1 2 3 4, highest: 90 92 95 98 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 90 0.999 42.06 31.49 0.0 6.0
## .25 .50 .75 .90 .95
## 20.0 40.0 62.0 81.1 93.0
##
## lowest : 0 2 3 4 5, highest: 93 94 95 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BECCS, na.rm = TRUE)## [1] 16.49646describe(CC$Nat_Score_DACCS)## CC$Nat_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 156 0.999 25.86 19.07 0.00 2.50
## .25 .50 .75 .90 .95
## 13.62 25.00 36.12 48.23 58.50
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 83 0.996 29.52 27.63 0.00 0.00
## .25 .50 .75 .90 .95
## 10.00 25.00 41.00 65.00 82.15
##
## lowest : 0 1 3 4 5, highest: 94 95 97 98 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 81 0.996 28.75 27.75 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 24.0 40.0 70.3 81.6
##
## lowest : 0 1 2 3 4, highest: 87 90 91 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 60 0.976 16.52 18.49 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 12.00 26.00 40.00 48.15
##
## lowest : 0 1 3 4 5, highest: 81 83 85 93 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 81 0.995 28.65 27.29 0.00 0.00
## .25 .50 .75 .90 .95
## 10.00 23.00 42.00 63.30 82.15
##
## lowest : 0 1 3 4 5, highest: 88 89 95 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_DACCS, na.rm = TRUE)## [1] 17.03858describe(CC$Nat_Score_EW)## CC$Nat_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 190 1 35.82 20.63 5.30 13.00
## .25 .50 .75 .90 .95
## 22.50 36.00 49.00 57.75 66.05
##
## lowest : 0.00 0.50 0.75 2.25 2.50, highest: 75.00 76.75 78.50 78.75 87.50describe(CC$Nat_Scale_EW)## CC$Nat_Scale_EW
##
## 4 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 93 0.999 45.92 31.11 0 7
## .25 .50 .75 .90 .95
## 25 50 67 81 89
##
## lowest : 0 1 2 3 4, highest: 91 92 95 98 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 76 0.995 27.12 24.96 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 40.0 58.6 75.0
##
## lowest : 0 1 2 3 4, highest: 85 90 92 93 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 70 0.994 25.78 24.03 0 0
## .25 .50 .75 .90 .95
## 8 24 39 50 70
##
## lowest : 0 1 2 3 4, highest: 86 88 90 95 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 84 0.999 44.47 31.96 0.0 6.4
## .25 .50 .75 .90 .95
## 22.0 44.0 67.0 80.0 92.6
##
## lowest : 0 4 5 6 7, highest: 91 93 94 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_EW, na.rm = TRUE)## [1] 18.13118describe(CC$Nat_Score_OF)## CC$Nat_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 169 1 31.83 19.75 4.50 8.50
## .25 .50 .75 .90 .95
## 20.00 31.25 42.50 54.50 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 83 0.999 40.43 31.1 0.00 1.00
## .25 .50 .75 .90 .95
## 18.00 39.00 59.25 78.50 87.00
##
## lowest : 0 2 4 5 6, highest: 88 90 92 93 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 66 0.996 22.35 21.52 0.00 0.00
## .25 .50 .75 .90 .95
## 7.00 19.00 32.00 46.00 60.25
##
## lowest : 0 1 3 4 5, highest: 80 81 82 89 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 67 0.996 26.12 23.98 0.00 0.00
## .25 .50 .75 .90 .95
## 10.00 23.00 36.00 58.00 68.25
##
## lowest : 0 1 2 3 4, highest: 79 80 90 91 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 87 0.999 38.43 30.1 0.00 5.00
## .25 .50 .75 .90 .95
## 16.75 37.00 55.00 76.50 91.25
##
## lowest : 0 2 3 4 5, highest: 92 93 95 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_OF, na.rm = TRUE)## [1] 17.41912describe(CC$Nat_Score_BF)## CC$Nat_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 150 1 39.5 19.96 8.188 15.500
## .25 .50 .75 .90 .95
## 27.188 39.750 50.312 60.500 69.875
##
## lowest : 0.00 0.25 1.00 1.50 2.00, highest: 72.50 73.00 74.25 75.00 86.75describe(CC$Nat_Scale_BF)## CC$Nat_Scale_BF
##
## 4 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 88 0.999 53.11 31.82 2.0 13.5
## .25 .50 .75 .90 .95
## 35.0 53.0 75.0 91.0 100.0
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 80 0.998 37.99 30.34 0.0 0.0
## .25 .50 .75 .90 .95
## 19.5 35.0 54.0 79.5 91.0
##
## lowest : 0 2 3 4 5, highest: 91 95 96 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 59 0.987 17.67 18.15 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 15.00 27.00 39.00 46.25
##
## lowest : 0 1 2 3 4, highest: 68 75 77 81 85
## --------------------------------------------------------------------------------
## CC.Nat_4R_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 85 0.999 49.22 33.39 1.75 10.00
## .25 .50 .75 .90 .95
## 26.75 49.00 74.00 90.50 99.25
##
## lowest : 0 1 2 4 5, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BF, na.rm = TRUE)## [1] 17.61328describe(CC$Nat_Score_NE)## CC$Nat_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 141 0.999 26.01 19.32 0.00 2.50
## .25 .50 .75 .90 .95
## 13.25 25.00 37.75 48.25 55.50
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 80 0.995 30.82 28.99 0 0
## .25 .50 .75 .90 .95
## 8 26 49 67 80
##
## lowest : 0 1 2 3 4, highest: 89 90 93 95 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 72 0.994 30.07 30.73 0 0
## .25 .50 .75 .90 .95
## 7 23 43 80 95
##
## lowest : 0 2 3 4 5, highest: 94 95 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 48 0.931 11.08 14.64 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
## 261 772 79 0.996 32.08 30.61 0 0
## .25 .50 .75 .90 .95
## 9 27 48 77 90
##
## lowest : 0 3 4 5 6, highest: 92 95 96 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_NE, na.rm = TRUE)## [1] 17.09411describe(CC$Nat_Score_SE)## CC$Nat_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 146 1 54.93 20.65 24.05 31.25
## .25 .50 .75 .90 .95
## 41.75 54.75 69.50 75.00 82.60
##
## lowest : 0.00 2.00 5.50 8.75 14.50, highest: 87.25 87.50 90.00 92.00 94.00describe(CC$Nat_Scale_SE)## CC$Nat_Scale_SE
##
## 4 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 75 0.991 72.85 29.21 10.6 30.2
## .25 .50 .75 .90 .95
## 60.0 80.0 95.0 100.0 100.0
##
## lowest : 0 1 4 6 10, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 79 0.989 66.11 36.13 4.6 18.0
## .25 .50 .75 .90 .95
## 38.0 78.0 95.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 52 0.964 14.63 17.95 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 10.0 22.0 38.6 50.0
##
## lowest : 0 1 2 3 4, highest: 70 71 76 80 93
## --------------------------------------------------------------------------------
## CC.Nat_4R_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 76 0.997 66.13 33.03 9.8 21.6
## .25 .50 .75 .90 .95
## 47.0 74.0 92.0 100.0 100.0
##
## lowest : 0 5 7 8 11, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_SE, na.rm = TRUE)## [1] 18.23051describe(CC$Nat_Score_WE)## CC$Nat_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 149 1 54.22 21.36 22.35 26.60
## .25 .50 .75 .90 .95
## 42.50 54.75 69.50 75.00 80.17
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 78 0.994 69.52 31.09 9.1 21.2
## .25 .50 .75 .90 .95
## 54.5 78.0 91.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 84 0.992 63.87 35.49 10.0 20.0
## .25 .50 .75 .90 .95
## 38.0 72.0 93.5 100.0 100.0
##
## lowest : 0 1 5 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 63 0.988 20.82 22.27 0 0
## .25 .50 .75 .90 .95
## 2 17 30 47 65
##
## lowest : 0 1 2 3 4, highest: 85 88 90 94 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 78 0.997 62.65 35.13 5.0 13.4
## .25 .50 .75 .90 .95
## 41.0 68.0 90.0 100.0 100.0
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_WE, na.rm = TRUE)## [1] 18.78584#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$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$Nat_4R_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.68 0.69 0.67 0.36 2.3 0.016 62 20 0.35
##
## lower alpha upper 95% confidence boundaries
## 0.65 0.68 0.71
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_AFSCS 0.59 0.60 0.54 0.33 1.5 0.022 0.0312 0.23
## CC.Nat_2R_AFSCS 0.56 0.58 0.55 0.31 1.4 0.025 0.0682 0.23
## CC.Nat_3R_AFSCS 0.77 0.77 0.70 0.53 3.4 0.013 0.0052 0.54
## CC.Nat_4R_AFSCS 0.52 0.52 0.46 0.26 1.1 0.026 0.0331 0.23
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_AFSCS 350 0.72 0.75 0.66 0.51 75 25
## CC.Nat_2R_AFSCS 350 0.79 0.77 0.66 0.55 53 30
## CC.Nat_3R_AFSCS 350 0.57 0.54 0.27 0.23 40 30
## CC.Nat_4R_AFSCS 350 0.80 0.82 0.78 0.63 79 25hist(CC$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 0.63 0.3 1.7 0.016 39 19 0.3
##
## lower alpha upper 95% confidence boundaries
## 0.62 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 0.50 0.42 0.25 1.00 0.025 0.014 0.22
## CC.Nat_2R_BIO 0.60 0.57 0.57 0.31 1.32 0.020 0.087 0.22
## CC.Nat_3R_BIO 0.73 0.72 0.66 0.47 2.61 0.014 0.021 0.38
## CC.Nat_4R_BIO 0.43 0.42 0.36 0.20 0.73 0.030 0.028 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BIO 337 0.78 0.75 0.69 0.55 46 27
## CC.Nat_2R_BIO 337 0.67 0.69 0.50 0.42 37 25
## CC.Nat_3R_BIO 337 0.46 0.52 0.23 0.18 24 22
## CC.Nat_4R_BIO 337 0.85 0.81 0.77 0.62 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$Nat_4R_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.61 0.59 0.61 0.26 1.4 0.019 35 16 0.23
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.61 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BECCS 0.41 0.41 0.35 0.19 0.69 0.031 0.028 0.168
## CC.Nat_2R_BECCS 0.54 0.50 0.55 0.25 1.02 0.023 0.148 0.032
## CC.Nat_3R_BECCS 0.72 0.71 0.67 0.45 2.47 0.015 0.046 0.363
## CC.Nat_4R_BECCS 0.37 0.37 0.30 0.16 0.59 0.033 0.018 0.168
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BECCS 340 0.79 0.76 0.72 0.546 43 26
## CC.Nat_2R_BECCS 340 0.66 0.68 0.47 0.391 31 23
## CC.Nat_3R_BECCS 340 0.40 0.46 0.12 0.089 23 21
## CC.Nat_4R_BECCS 340 0.82 0.78 0.77 0.578 42 28hist(CC$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
## 0.69 0.68 0.67 0.34 2.1 0.015 26 17 0.32
##
## lower alpha upper 95% confidence boundaries
## 0.66 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_DACCS 0.52 0.51 0.43 0.26 1.05 0.024 0.016 0.25
## CC.Nat_2R_DACCS 0.67 0.65 0.64 0.38 1.83 0.016 0.082 0.25
## CC.Nat_3R_DACCS 0.75 0.75 0.70 0.50 3.01 0.014 0.032 0.40
## CC.Nat_4R_DACCS 0.50 0.49 0.41 0.24 0.95 0.025 0.020 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_DACCS 358 0.83 0.80 0.78 0.63 30 25
## CC.Nat_2R_DACCS 358 0.70 0.68 0.48 0.42 29 26
## CC.Nat_3R_DACCS 358 0.47 0.55 0.27 0.23 17 18
## CC.Nat_4R_DACCS 358 0.84 0.82 0.80 0.66 29 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$Nat_4R_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.68 0.7 0.35 2.2 0.015 36 18 0.32
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_EW 0.57 0.57 0.51 0.31 1.33 0.022 0.033 0.22
## CC.Nat_2R_EW 0.62 0.59 0.64 0.32 1.42 0.020 0.144 0.18
## CC.Nat_3R_EW 0.80 0.79 0.76 0.56 3.79 0.011 0.031 0.52
## CC.Nat_4R_EW 0.46 0.46 0.41 0.22 0.86 0.029 0.035 0.22
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_EW 345 0.80 0.76 0.74 0.58 46 27
## CC.Nat_2R_EW 345 0.73 0.75 0.60 0.52 27 23
## CC.Nat_3R_EW 345 0.45 0.50 0.22 0.17 26 22
## CC.Nat_4R_EW 345 0.88 0.85 0.86 0.72 44 28hist(CC$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 0.68 0.68 0.35 2.1 0.015 32 17 0.31
##
## lower alpha upper 95% confidence boundaries
## 0.66 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 0.55 0.56 0.48 0.29 1.25 0.024 0.022 0.22
## CC.Nat_2R_OF 0.62 0.60 0.62 0.34 1.51 0.020 0.115 0.20
## CC.Nat_3R_OF 0.77 0.77 0.72 0.53 3.32 0.012 0.031 0.46
## CC.Nat_4R_OF 0.47 0.48 0.41 0.23 0.91 0.029 0.023 0.22
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_OF 336 0.81 0.77 0.73 0.58 40 27
## CC.Nat_2R_OF 336 0.69 0.73 0.56 0.48 22 20
## CC.Nat_3R_OF 336 0.49 0.53 0.24 0.20 26 22
## CC.Nat_4R_OF 336 0.86 0.83 0.83 0.68 38 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$Nat_4R_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.58 0.61 0.26 1.4 0.017 39 18 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.6 0.63 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BF 0.45 0.42 0.37 0.19 0.72 0.026 0.040 0.138
## CC.Nat_2R_BF 0.54 0.45 0.54 0.22 0.82 0.020 0.180 0.027
## CC.Nat_3R_BF 0.74 0.74 0.69 0.49 2.83 0.014 0.036 0.413
## CC.Nat_4R_BF 0.36 0.31 0.29 0.13 0.46 0.031 0.045 0.138
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BF 256 0.79 0.74 0.703 0.547 53 28
## CC.Nat_2R_BF 256 0.72 0.71 0.522 0.436 38 27
## CC.Nat_3R_BF 256 0.27 0.41 0.061 0.033 18 17
## CC.Nat_4R_BF 256 0.85 0.80 0.800 0.642 49 29hist(CC$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 0.27 1.5 0.017 26 17 0.2
##
## lower alpha upper 95% confidence boundaries
## 0.59 0.63 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.Nat_1_NE 0.41 0.39 0.32 0.18 0.64 0.028 0.020 0.15
## CC.Nat_2R_NE 0.65 0.60 0.62 0.33 1.49 0.015 0.119 0.15
## CC.Nat_3R_NE 0.70 0.70 0.68 0.44 2.35 0.017 0.065 0.33
## CC.Nat_4R_NE 0.34 0.33 0.26 0.14 0.49 0.032 0.011 0.12
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_NE 261 0.81 0.78 0.77 0.59 31 26
## CC.Nat_2R_NE 261 0.66 0.61 0.35 0.30 30 28
## CC.Nat_3R_NE 261 0.35 0.49 0.16 0.13 11 15
## CC.Nat_4R_NE 261 0.85 0.82 0.84 0.65 32 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$Nat_4R_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.59 0.58 0.57 0.26 1.4 0.02 55 18 0.23
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.59 0.63
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_SE 0.43 0.45 0.36 0.21 0.82 0.029 0.0036 0.24
## CC.Nat_2R_SE 0.61 0.58 0.56 0.31 1.38 0.018 0.0719 0.26
## CC.Nat_3R_SE 0.61 0.63 0.58 0.36 1.68 0.022 0.0462 0.24
## CC.Nat_4R_SE 0.35 0.35 0.27 0.15 0.53 0.032 0.0055 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_SE 253 0.74 0.72 0.64 0.48 73 27
## CC.Nat_2R_SE 253 0.66 0.61 0.34 0.28 66 32
## CC.Nat_3R_SE 253 0.44 0.56 0.28 0.22 15 18
## CC.Nat_4R_SE 253 0.80 0.79 0.75 0.55 66 29hist(CC$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$Nat_4R_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.58 0.53 0.58 0.22 1.1 0.019 54 19 0.17
##
## lower alpha upper 95% confidence boundaries
## 0.54 0.58 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_WE 0.34 0.28 0.27 0.117 0.40 0.033 0.051 0.024
## CC.Nat_2R_WE 0.54 0.47 0.55 0.230 0.90 0.022 0.178 0.024
## CC.Nat_3R_WE 0.72 0.73 0.69 0.469 2.65 0.015 0.046 0.374
## CC.Nat_4R_WE 0.25 0.19 0.19 0.074 0.24 0.037 0.045 -0.047
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_WE 263 0.79 0.77 0.76 0.559 70 28
## CC.Nat_2R_WE 263 0.68 0.64 0.40 0.334 64 31
## CC.Nat_3R_WE 263 0.25 0.36 -0.03 -0.029 21 21
## CC.Nat_4R_WE 263 0.84 0.82 0.84 0.629 63 31hist(CC$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
## 350 683 67 0.982 77.99 25.42 24.45 41.80
## .25 .50 .75 .90 .95
## 68.00 84.50 100.00 100.00 100.00
##
## 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
## 350 683 69 0.987 74.02 29.26 4.0 27.7
## .25 .50 .75 .90 .95
## 63.0 81.5 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
## 337 696 86 0.999 55.92 31.35 0 12
## .25 .50 .75 .90 .95
## 39 59 76 91 100
##
## 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
## 337 696 90 0.999 51.48 33.96 0.0 4.2
## .25 .50 .75 .90 .95
## 30.0 54.0 75.0 90.4 100.0
##
## 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
## 340 693 86 0.999 55.83 32.3 0.0 10.9
## .25 .50 .75 .90 .95
## 37.0 60.0 75.0 93.0 100.0
##
## 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
## 340 693 87 0.998 51.32 33.91 0.00 0.90
## .25 .50 .75 .90 .95
## 29.00 54.00 73.25 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
## 358 675 88 0.998 55.01 33.93 0.00 5.70
## .25 .50 .75 .90 .95
## 35.25 60.00 75.00 98.30 100.00
##
## 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
## 358 675 91 0.999 51.75 34.09 0.00 1.70
## .25 .50 .75 .90 .95
## 30.00 55.00 74.75 90.00 100.00
##
## 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
## 345 688 94 0.999 50.4 33.79 0 4
## .25 .50 .75 .90 .95
## 27 51 72 90 100
##
## 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
## 345 688 88 0.998 48.39 34.97 0 0
## .25 .50 .75 .90 .95
## 25 50 73 90 100
##
## 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
## 336 697 91 0.999 52.79 35.2 0.00 5.00
## .25 .50 .75 .90 .95
## 27.75 59.00 75.00 94.00 100.00
##
## 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
## 336 697 89 0.998 49.14 35.41 0.00 0.00
## .25 .50 .75 .90 .95
## 20.00 53.50 74.25 89.50 98.00
##
## 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
## 256 777 78 0.999 63.82 29.18 9.75 21.00
## .25 .50 .75 .90 .95
## 50.75 70.00 82.00 95.50 100.00
##
## 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
## 256 777 82 0.999 59.1 31.27 0.00 13.50
## .25 .50 .75 .90 .95
## 46.00 62.00 79.25 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
## 261 772 86 0.997 49.56 39.2 0 0
## .25 .50 .75 .90 .95
## 17 53 80 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
## 261 772 86 0.997 51.79 37.88 0 0
## .25 .50 .75 .90 .95
## 24 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
## 253 780 57 0.956 82.8 22.5 35 52
## .25 .50 .75 .90 .95
## 75 91 100 100 100
##
## 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
## 253 780 66 0.964 76.62 29.3 3.2 30.0
## .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
## 263 770 64 0.989 76.49 25.88 21.2 41.0
## .25 .50 .75 .90 .95
## 68.5 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
## 263 770 66 0.989 73.27 29.79 2.5 25.0
## .25 .50 .75 .90 .95
## 60.0 80.0 97.0 100.0 100.0
##
## lowest : 0 2 7 10 11, highest: 96 97 98 99 100sd(CC$Support1_AFSCS, na.rm = TRUE)## [1] 24.55699sd(CC$Support2_AFSCS, na.rm = TRUE)## [1] 28.03461sd(CC$Support1_BIO, na.rm = TRUE)## [1] 27.62384sd(CC$Support2_BIO, na.rm = TRUE)## [1] 29.6643sd(CC$Support1_BECCS, na.rm = TRUE)## [1] 28.47284sd(CC$Support2_BECCS, na.rm = TRUE)## [1] 29.67555sd(CC$Support1_DACCS, na.rm = TRUE)## [1] 29.83761sd(CC$Support2_DACCS, na.rm = TRUE)## [1] 29.7729sd(CC$Support1_EW, na.rm = TRUE)## [1] 29.4364sd(CC$Support2_EW, na.rm = TRUE)## [1] 30.37366sd(CC$Support1_OF, na.rm = TRUE)## [1] 30.76413sd(CC$Support2_OF, na.rm = TRUE)## [1] 30.90659sd(CC$Support1_BF, na.rm = TRUE)## [1] 26.19582sd(CC$Support2_BF, na.rm = TRUE)## [1] 27.79847sd(CC$Support1_NE, na.rm = TRUE)## [1] 34.03823sd(CC$Support2_NE, na.rm = TRUE)## [1] 33.00463sd(CC$Support1_SE, na.rm = TRUE)## [1] 22.90163sd(CC$Support2_SE, na.rm = TRUE)## [1] 28.60121sd(CC$Support1_WE, na.rm = TRUE)## [1] 24.75049sd(CC$Support2_WE, na.rm = TRUE)## [1] 28.4126hist(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
## 350 683 115 0.992 76.01 25.29 31.12 47.80
## .25 .50 .75 .90 .95
## 62.50 81.00 95.00 100.00 100.00
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 67 0.982 77.99 25.42 24.45 41.80
## .25 .50 .75 .90 .95
## 68.00 84.50 100.00 100.00 100.00
##
## lowest : 0 1 4 9 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 69 0.987 74.02 29.26 4.0 27.7
## .25 .50 .75 .90 .95
## 63.0 81.5 96.0 100.0 100.0
##
## lowest : 0 2 4 5 7, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_AFSCS, na.rm = TRUE)## [1] 23.4927describe(CC$Support_Score_BIO)## CC$Support_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 142 0.999 53.7 30.11 0.8 13.3
## .25 .50 .75 .90 .95
## 36.5 54.5 74.5 87.2 95.9
##
## lowest : 0.0 1.0 2.5 3.5 5.0, highest: 94.0 95.0 95.5 97.5 100.0describe(CC$Support_Scale_BIO)## CC$Support_Scale_BIO
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 86 0.999 55.92 31.35 0 12
## .25 .50 .75 .90 .95
## 39 59 76 91 100
##
## lowest : 0 4 5 6 7, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 90 0.999 51.48 33.96 0.0 4.2
## .25 .50 .75 .90 .95
## 30.0 54.0 75.0 90.4 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BIO, na.rm = TRUE)## [1] 26.4761describe(CC$Support_Score_BECCS)## CC$Support_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 136 0.999 53.58 30.93 0.00 9.90
## .25 .50 .75 .90 .95
## 36.00 55.00 74.50 85.05 100.00
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 86 0.999 55.83 32.3 0.0 10.9
## .25 .50 .75 .90 .95
## 37.0 60.0 75.0 93.0 100.0
##
## lowest : 0 1 2 4 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 87 0.998 51.32 33.91 0.00 0.90
## .25 .50 .75 .90 .95
## 29.00 54.00 73.25 89.00 100.00
##
## lowest : 0 1 3 4 5, highest: 93 94 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BECCS, na.rm = TRUE)## [1] 27.26706describe(CC$Support_Score_DACCS)## CC$Support_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 149 0.999 53.38 32.3 0.00 5.85
## .25 .50 .75 .90 .95
## 35.62 56.00 74.00 90.00 100.00
##
## lowest : 0.0 0.5 1.0 2.0 2.5, highest: 96.5 97.0 98.5 99.5 100.0describe(CC$Support_Scale_DACCS)## CC$Support_Scale_DACCS
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 88 0.998 55.01 33.93 0.00 5.70
## .25 .50 .75 .90 .95
## 35.25 60.00 75.00 98.30 100.00
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 91 0.999 51.75 34.09 0.00 1.70
## .25 .50 .75 .90 .95
## 30.00 55.00 74.75 90.00 100.00
##
## lowest : 0 1 2 4 5, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_DACCS, na.rm = TRUE)## [1] 28.35202describe(CC$Support_Score_EW)## CC$Support_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 143 0.999 49.4 31.85 0.0 8.0
## .25 .50 .75 .90 .95
## 29.5 50.5 68.5 87.8 97.5
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 94.5 95.0 95.5 98.0 100.0describe(CC$Support_Scale_EW)## CC$Support_Scale_EW
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 94 0.999 50.4 33.79 0 4
## .25 .50 .75 .90 .95
## 27 51 72 90 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 88 0.998 48.39 34.97 0 0
## .25 .50 .75 .90 .95
## 25 50 73 90 100
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_EW, na.rm = TRUE)## [1] 27.83398describe(CC$Support_Score_OF)## CC$Support_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 145 0.999 50.96 33.13 0.00 5.25
## .25 .50 .75 .90 .95
## 27.38 54.50 73.62 89.00 95.00
##
## lowest : 0.0 0.5 2.0 3.0 3.5, highest: 95.0 95.5 97.0 97.5 100.0describe(CC$Support_Scale_OF)## CC$Support_Scale_OF
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 91 0.999 52.79 35.2 0.00 5.00
## .25 .50 .75 .90 .95
## 27.75 59.00 75.00 94.00 100.00
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------
## CC.Support2_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 89 0.998 49.14 35.41 0.00 0.00
## .25 .50 .75 .90 .95
## 20.00 53.50 74.25 89.50 98.00
##
## lowest : 0 1 3 4 5, highest: 93 94 95 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_OF, na.rm = TRUE)## [1] 28.94031describe(CC$Support_Score_BF)## CC$Support_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 121 1 61.46 27.66 10.0 22.5
## .25 .50 .75 .90 .95
## 50.0 65.0 78.5 92.5 100.0
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 78 0.999 63.82 29.18 9.75 21.00
## .25 .50 .75 .90 .95
## 50.75 70.00 82.00 95.50 100.00
##
## lowest : 0 4 5 7 8, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 82 0.999 59.1 31.27 0.00 13.50
## .25 .50 .75 .90 .95
## 46.00 62.00 79.25 93.00 100.00
##
## lowest : 0 3 4 5 6, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BF, na.rm = TRUE)## [1] 24.86738describe(CC$Support_Score_NE)## CC$Support_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 124 0.999 50.67 35.84 0.0 1.0
## .25 .50 .75 .90 .95
## 27.5 52.0 76.5 92.0 100.0
##
## lowest : 0.0 1.0 2.0 3.0 3.5, highest: 95.0 95.5 96.0 99.0 100.0describe(CC$Support_Scale_NE)## CC$Support_Scale_NE
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 86 0.997 49.56 39.2 0 0
## .25 .50 .75 .90 .95
## 17 53 80 95 100
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 86 0.997 51.79 37.88 0 0
## .25 .50 .75 .90 .95
## 24 55 80 95 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 97 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_NE, na.rm = TRUE)## [1] 31.09265describe(CC$Support_Score_SE)## CC$Support_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 94 0.979 79.71 24.22 31.2 49.5
## .25 .50 .75 .90 .95
## 69.0 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 57 0.956 82.8 22.5 35 52
## .25 .50 .75 .90 .95
## 75 91 100 100 100
##
## lowest : 0 1 5 10 14, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 66 0.964 76.62 29.3 3.2 30.0
## .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$Support_Score_SE, na.rm = TRUE)## [1] 23.29772describe(CC$Support_Score_WE)## CC$Support_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 101 0.993 74.88 26.21 21.10 42.50
## .25 .50 .75 .90 .95
## 63.25 80.00 95.00 100.00 100.00
##
## lowest : 0.0 3.0 10.5 11.0 15.0, highest: 98.0 98.5 99.0 99.5 100.0describe(CC$Support_Scale_WE)## CC$Support_Scale_WE
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Support1_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 64 0.989 76.49 25.88 21.2 41.0
## .25 .50 .75 .90 .95
## 68.5 81.0 98.0 100.0 100.0
##
## lowest : 0 4 10 17 20, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 66 0.989 73.27 29.79 2.5 25.0
## .25 .50 .75 .90 .95
## 60.0 80.0 97.0 100.0 100.0
##
## lowest : 0 2 7 10 11, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_WE, na.rm = TRUE)## [1] 24.54025#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
## 0.74 0.75 0.59 0.59 2.9 0.016 76 23 0.59
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.74 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_AFSCS 0.52 0.59 0.35 0.59 1.5 NA 0
## CC.Support2_AFSCS 0.68 0.59 0.35 0.59 1.5 NA 0
## med.r
## CC.Support1_AFSCS 0.59
## CC.Support2_AFSCS 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_AFSCS 350 0.88 0.89 0.69 0.59 78 25
## CC.Support2_AFSCS 350 0.91 0.89 0.69 0.59 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$Support2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.71 0.71 4.9 0.011 54 26 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_BIO 0.66 0.71 0.5 0.71 2.4 NA 0 0.71
## CC.Support2_BIO 0.76 0.71 0.5 0.71 2.4 NA 0 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BIO 337 0.92 0.92 0.78 0.71 56 28
## CC.Support2_BIO 337 0.93 0.92 0.78 0.71 51 30hist(CC$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 0.76 0.76 6.3 0.0085 54 27 0.76
##
## lower alpha upper 95% confidence boundaries
## 0.85 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 0.73 0.76 0.58 0.76 3.1 NA 0
## CC.Support2_BECCS 0.79 0.76 0.58 0.76 3.1 NA 0
## med.r
## CC.Support1_BECCS 0.76
## CC.Support2_BECCS 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BECCS 340 0.94 0.94 0.82 0.76 56 28
## CC.Support2_BECCS 340 0.94 0.94 0.82 0.76 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$Support2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.81 0.81 8.5 0.0065 53 28 0.81
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_DACCS 0.81 0.81 0.66 0.81 4.3 NA 0
## CC.Support2_DACCS 0.81 0.81 0.66 0.81 4.3 NA 0
## med.r
## CC.Support1_DACCS 0.81
## CC.Support2_DACCS 0.81
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_DACCS 358 0.95 0.95 0.86 0.81 55 30
## CC.Support2_DACCS 358 0.95 0.95 0.86 0.81 52 30hist(CC$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
## 0.85 0.85 0.73 0.73 5.5 0.0096 49 28 0.73
##
## 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.Support1_EW 0.71 0.73 0.54 0.73 2.7 NA 0 0.73
## CC.Support2_EW 0.76 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.Support1_EW 345 0.93 0.93 0.8 0.73 50 29
## CC.Support2_EW 345 0.93 0.93 0.8 0.73 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$Support2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.76 0.76 6.4 0.0084 51 29 0.76
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_OF 0.76 0.76 0.58 0.76 3.2 NA 0 0.76
## CC.Support2_OF 0.77 0.76 0.58 0.76 3.2 NA 0 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_OF 336 0.94 0.94 0.82 0.76 53 31
## CC.Support2_OF 336 0.94 0.94 0.82 0.76 49 31hist(CC$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 0.7 0.7 4.6 0.011 61 25 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_BF 0.66 0.7 0.49 0.7 2.3 NA 0 0.7
## CC.Support2_BF 0.74 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_BF 256 0.92 0.92 0.77 0.7 64 26
## CC.Support2_BF 256 0.93 0.92 0.77 0.7 59 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$Support2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.72 0.72 5.2 0.01 51 31 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_NE 0.74 0.72 0.52 0.72 2.6 NA 0 0.72
## CC.Support2_NE 0.70 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_NE 261 0.93 0.93 0.79 0.72 50 34
## CC.Support2_NE 261 0.93 0.93 0.79 0.72 52 33hist(CC$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 23 0.63
##
## lower alpha upper 95% confidence boundaries
## 0.74 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 0.51 0.63 0.4 0.63 1.7 NA 0 0.63
## CC.Support2_SE 0.79 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 253 0.88 0.9 0.72 0.63 83 23
## CC.Support2_SE 253 0.92 0.9 0.72 0.63 77 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$Support2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.83 0.7 0.7 4.7 0.011 75 25 0.7
##
## lower alpha upper 95% confidence boundaries
## 0.8 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_WE 0.61 0.7 0.49 0.7 2.4 NA 0 0.7
## CC.Support2_WE 0.81 0.7 0.49 0.7 2.4 NA 0 0.7
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_WE 263 0.91 0.92 0.77 0.7 76 25
## CC.Support2_WE 263 0.93 0.92 0.77 0.7 73 28hist(CC$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
## 350 683 66 0.983 19.61 23.65 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 11.0 30.0 52.2 70.0
##
## lowest : 0 1 2 3 4, highest: 80 81 85 86 100describe(CC$Risk_2_AFSCS)## CC$Risk_2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 57 0.934 13.34 19.17 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 3.50 17.00 44.10 60.55
##
## lowest : 0 1 2 3 4, highest: 75 80 85 95 100describe(CC$Risk_1_BIO)## CC$Risk_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 83 0.999 39.28 28.79 0.0 3.6
## .25 .50 .75 .90 .95
## 19.0 40.0 56.0 74.4 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
## 337 696 82 0.992 27.8 28.23 0 0
## .25 .50 .75 .90 .95
## 4 25 47 63 75
##
## 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
## 340 693 86 0.999 45.22 30.92 0.00 5.90
## .25 .50 .75 .90 .95
## 24.75 50.00 64.00 80.00 93.05
##
## 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
## 340 693 81 0.992 31.89 32.03 0 0
## .25 .50 .75 .90 .95
## 5 25 51 75 90
##
## 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
## 358 675 90 0.999 49.88 31.27 0.00 8.40
## .25 .50 .75 .90 .95
## 29.25 52.00 69.75 84.30 95.00
##
## 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
## 358 675 92 0.994 35.56 33.73 0.00 0.00
## .25 .50 .75 .90 .95
## 7.00 32.00 58.75 79.30 89.00
##
## 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
## 345 688 85 0.999 46.13 30.64 4.0 10.0
## .25 .50 .75 .90 .95
## 25.0 50.0 65.0 84.2 92.4
##
## 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
## 345 688 83 0.994 31.43 31.51 0 0
## .25 .50 .75 .90 .95
## 6 25 52 75 85
##
## 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
## 336 697 94 1 54.8 31.28 1.75 15.50
## .25 .50 .75 .90 .95
## 33.00 57.00 75.00 89.50 97.25
##
## 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
## 336 697 88 0.996 38.57 34.03 0.0 0.0
## .25 .50 .75 .90 .95
## 11.0 37.0 63.0 80.5 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
## 256 777 75 0.998 31.8 26.98 0.00 0.00
## .25 .50 .75 .90 .95
## 11.75 29.00 50.00 68.00 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
## 256 777 65 0.983 19.68 22.33 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 13.50 30.25 50.50 63.25
##
## 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
## 261 772 76 0.998 57.13 34.95 4 10
## .25 .50 .75 .90 .95
## 31 63 80 100 100
##
## 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
## 261 772 84 0.998 49.46 37.24 0 3
## .25 .50 .75 .90 .95
## 20 55 76 95 100
##
## 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
## 253 780 54 0.948 13.7 18.54 0 0
## .25 .50 .75 .90 .95
## 0 5 21 40 52
##
## 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
## 253 780 44 0.83 7.194 11.57 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 8.0 24.8 34.8
##
## lowest : 0 1 2 3 4, highest: 64 75 79 88 100describe(CC$Risk_1_WE)## CC$Risk_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 71 0.984 23.22 27.79 0.0 0.0
## .25 .50 .75 .90 .95
## 0.5 13.0 37.5 67.8 79.9
##
## 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
## 263 770 55 0.914 13.97 20.08 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 4.0 19.0 43.0 67.6
##
## lowest : 0 1 2 3 4, highest: 84 89 90 99 100sd(CC$Risk_1_AFSCS, na.rm = TRUE)## [1] 22.91403sd(CC$Risk_2_AFSCS, na.rm = TRUE)## [1] 20.71641sd(CC$Risk_1_BIO, na.rm = TRUE)## [1] 25.10228sd(CC$Risk_2_BIO, na.rm = TRUE)## [1] 25.28385sd(CC$Risk_1_BECCS, na.rm = TRUE)## [1] 26.9803sd(CC$Risk_2_BECCS, na.rm = TRUE)## [1] 28.63165sd(CC$Risk_1_DACCS, na.rm = TRUE)## [1] 27.33425sd(CC$Risk_2_DACCS, na.rm = TRUE)## [1] 29.6358sd(CC$Risk_1_EW, na.rm = TRUE)## [1] 26.69134sd(CC$Risk_2_EW, na.rm = TRUE)## [1] 28.10315sd(CC$Risk_1_OF, na.rm = TRUE)## [1] 27.30675sd(CC$Risk_2_OF, na.rm = TRUE)## [1] 29.71348sd(CC$Risk_1_BF, na.rm = TRUE)## [1] 23.79626sd(CC$Risk_2_BF, na.rm = TRUE)## [1] 21.08743sd(CC$Risk_1_NE, na.rm = TRUE)## [1] 30.53679sd(CC$Risk_2_NE, na.rm = TRUE)## [1] 32.32854sd(CC$Risk_1_SE, na.rm = TRUE)## [1] 19.02075sd(CC$Risk_2_SE, na.rm = TRUE)## [1] 14.92275sd(CC$Risk_1_WE, na.rm = TRUE)## [1] 26.32136sd(CC$Risk_2_WE, na.rm = TRUE)## [1] 21.79981hist(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
## 350 683 105 0.987 16.47 20.51 0.000 0.000
## .25 .50 .75 .90 .95
## 0.625 8.000 24.875 47.650 62.500
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 79.0 80.0 85.0 90.5 100.0describe(CC$Risk_Scale_AFSCS)## CC$Risk_Scale_AFSCS
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 66 0.983 19.61 23.65 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 11.0 30.0 52.2 70.0
##
## lowest : 0 1 2 3 4, highest: 80 81 85 86 100
## --------------------------------------------------------------------------------
## CC.Risk_2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 350 683 57 0.934 13.34 19.17 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 3.50 17.00 44.10 60.55
##
## lowest : 0 1 2 3 4, highest: 75 80 85 95 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_AFSCS, na.rm = TRUE)## [1] 20.42068describe(CC$Risk_Score_BIO)## CC$Risk_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 134 0.999 33.54 26.41 0.0 2.8
## .25 .50 .75 .90 .95
## 12.5 32.5 50.0 63.0 75.3
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 84.0 88.0 90.0 93.0 95.0describe(CC$Risk_Scale_BIO)## CC$Risk_Scale_BIO
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 83 0.999 39.28 28.79 0.0 3.6
## .25 .50 .75 .90 .95
## 19.0 40.0 56.0 74.4 80.0
##
## lowest : 0 1 2 3 4, highest: 86 90 95 96 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 337 696 82 0.992 27.8 28.23 0 0
## .25 .50 .75 .90 .95
## 4 25 47 63 75
##
## lowest : 0 1 2 3 4, highest: 90 92 95 96 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BIO, na.rm = TRUE)## [1] 23.14198describe(CC$Risk_Score_BECCS)## CC$Risk_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 140 0.999 38.55 29.08 0.00 3.50
## .25 .50 .75 .90 .95
## 19.38 37.50 55.00 72.05 86.00
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 86 0.999 45.22 30.92 0.00 5.90
## .25 .50 .75 .90 .95
## 24.75 50.00 64.00 80.00 93.05
##
## lowest : 0 1 4 5 6, highest: 93 94 95 96 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 340 693 81 0.992 31.89 32.03 0 0
## .25 .50 .75 .90 .95
## 5 25 51 75 90
##
## lowest : 0 1 2 3 4, highest: 90 91 92 96 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BECCS, na.rm = TRUE)## [1] 25.57054describe(CC$Risk_Score_DACCS)## CC$Risk_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 154 1 42.72 30.34 0.00 5.50
## .25 .50 .75 .90 .95
## 22.00 45.00 62.50 77.95 89.50
##
## lowest : 0.0 0.5 1.0 2.5 3.0, highest: 95.5 98.0 98.5 99.5 100.0describe(CC$Risk_Scale_DACCS)## CC$Risk_Scale_DACCS
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 90 0.999 49.88 31.27 0.00 8.40
## .25 .50 .75 .90 .95
## 29.25 52.00 69.75 84.30 95.00
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 358 675 92 0.994 35.56 33.73 0.00 0.00
## .25 .50 .75 .90 .95
## 7.00 32.00 58.75 79.30 89.00
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_DACCS, na.rm = TRUE)## [1] 26.44361describe(CC$Risk_Score_EW)## CC$Risk_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 148 1 38.78 28.9 2.5 7.5
## .25 .50 .75 .90 .95
## 18.0 37.5 55.5 75.3 85.0
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 85 0.999 46.13 30.64 4.0 10.0
## .25 .50 .75 .90 .95
## 25.0 50.0 65.0 84.2 92.4
##
## lowest : 0 2 3 4 5, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_EW
## n missing distinct Info Mean Gmd .05 .10
## 345 688 83 0.994 31.43 31.51 0 0
## .25 .50 .75 .90 .95
## 6 25 52 75 85
##
## lowest : 0 1 2 3 4, highest: 92 95 98 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_EW, na.rm = TRUE)## [1] 25.39343describe(CC$Risk_Score_OF)## CC$Risk_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 154 1 46.68 30.59 1.25 10.25
## .25 .50 .75 .90 .95
## 25.50 45.50 66.50 81.75 91.25
##
## lowest : 0.0 0.5 1.5 2.0 5.0, highest: 96.0 97.0 98.5 99.0 100.0describe(CC$Risk_Scale_OF)## CC$Risk_Scale_OF
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 94 1 54.8 31.28 1.75 15.50
## .25 .50 .75 .90 .95
## 33.00 57.00 75.00 89.50 97.25
##
## lowest : 0 1 2 4 7, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------
## CC.Risk_2_OF
## n missing distinct Info Mean Gmd .05 .10
## 336 697 88 0.996 38.57 34.03 0.0 0.0
## .25 .50 .75 .90 .95
## 11.0 37.0 63.0 80.5 90.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_OF, na.rm = TRUE)## [1] 26.55251describe(CC$Risk_Score_BF)## CC$Risk_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 107 0.999 25.74 22.72 0.000 0.500
## .25 .50 .75 .90 .95
## 8.375 21.750 38.750 52.000 60.250
##
## 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 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 256 777 75 0.998 31.8 26.98 0.00 0.00
## .25 .50 .75 .90 .95
## 11.75 29.00 50.00 68.00 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
## 256 777 65 0.983 19.68 22.33 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 13.50 30.25 50.50 63.25
##
## lowest : 0 1 2 3 4, highest: 80 81 87 88 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BF, na.rm = TRUE)## [1] 20.34737describe(CC$Risk_Score_NE)## CC$Risk_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 126 0.999 53.3 34.78 2.5 7.5
## .25 .50 .75 .90 .95
## 25.0 58.5 76.0 92.0 100.0
##
## lowest : 0.0 0.5 1.5 2.0 2.5, highest: 95.5 96.5 98.5 99.0 100.0describe(CC$Risk_Scale_NE)## CC$Risk_Scale_NE
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 76 0.998 57.13 34.95 4 10
## .25 .50 .75 .90 .95
## 31 63 80 100 100
##
## lowest : 0 1 3 4 5, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_NE
## n missing distinct Info Mean Gmd .05 .10
## 261 772 84 0.998 49.46 37.24 0 3
## .25 .50 .75 .90 .95
## 20 55 76 95 100
##
## lowest : 0 1 2 3 4, highest: 93 95 97 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_NE, na.rm = TRUE)## [1] 30.2486describe(CC$Risk_Score_SE)## CC$Risk_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 71 0.958 10.45 14.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.5 15.5 34.0 43.4
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 48.5 51.0 63.0 64.5 78.5describe(CC$Risk_Scale_SE)## CC$Risk_Scale_SE
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 54 0.948 13.7 18.54 0 0
## .25 .50 .75 .90 .95
## 0 5 21 40 52
##
## lowest : 0 1 2 3 4, highest: 67 79 80 82 88
## --------------------------------------------------------------------------------
## CC.Risk_2_SE
## n missing distinct Info Mean Gmd .05 .10
## 253 780 44 0.83 7.194 11.57 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 8.0 24.8 34.8
##
## lowest : 0 1 2 3 4, highest: 64 75 79 88 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_SE, na.rm = TRUE)## [1] 14.54438describe(CC$Risk_Score_WE)## CC$Risk_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 94 0.987 18.59 22.94 0.0 0.0
## .25 .50 .75 .90 .95
## 0.5 11.0 25.5 51.9 65.7
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 89.0 89.5 92.0 98.0 98.5describe(CC$Risk_Scale_WE)## CC$Risk_Scale_WE
##
## 2 Variables 1033 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 71 0.984 23.22 27.79 0.0 0.0
## .25 .50 .75 .90 .95
## 0.5 13.0 37.5 67.8 79.9
##
## lowest : 0 1 2 3 4, highest: 90 91 92 97 100
## --------------------------------------------------------------------------------
## CC.Risk_2_WE
## n missing distinct Info Mean Gmd .05 .10
## 263 770 55 0.914 13.97 20.08 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 4.0 19.0 43.0 67.6
##
## lowest : 0 1 2 3 4, highest: 84 89 90 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_WE, na.rm = TRUE)## [1] 22.66007#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
## 0.86 0.86 0.75 0.75 6.1 0.0089 16 20 0.75
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.86 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 0.75 0.57 0.75 3 NA 0 0.75
## CC.Risk_2_AFSCS 0.68 0.75 0.57 0.75 3 NA 0 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_AFSCS 350 0.94 0.94 0.81 0.75 20 23
## CC.Risk_2_AFSCS 350 0.93 0.94 0.81 0.75 13 21hist(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$Risk_2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.69 0.69 4.4 0.012 34 23 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BIO 0.68 0.69 0.47 0.69 2.2 NA 0 0.69
## CC.Risk_2_BIO 0.69 0.69 0.47 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BIO 337 0.92 0.92 0.76 0.69 39 25
## CC.Risk_2_BIO 337 0.92 0.92 0.76 0.69 28 25hist(CC$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
## 0.82 0.82 0.69 0.69 4.5 0.011 39 26 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.Risk_1_BECCS 0.65 0.69 0.48 0.69 2.2 NA 0 0.69
## CC.Risk_2_BECCS 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.Risk_1_BECCS 340 0.91 0.92 0.76 0.69 45 27
## CC.Risk_2_BECCS 340 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$Risk_2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.72 0.72 5.2 0.01 43 26 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_DACCS 0.67 0.72 0.52 0.72 2.6 NA 0 0.72
## CC.Risk_2_DACCS 0.78 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_DACCS 358 0.92 0.93 0.79 0.72 50 27
## CC.Risk_2_DACCS 358 0.93 0.93 0.79 0.72 36 30hist(CC$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
## 0.84 0.84 0.72 0.72 5.1 0.01 39 25 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.Risk_1_EW 0.68 0.72 0.52 0.72 2.5 NA 0 0.72
## CC.Risk_2_EW 0.76 0.72 0.52 0.72 2.5 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_EW 345 0.92 0.93 0.79 0.72 46 27
## CC.Risk_2_EW 345 0.93 0.93 0.79 0.72 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$Risk_2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.73 0.73 5.5 0.0096 47 27 0.73
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.85 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_OF 0.67 0.73 0.54 0.73 2.8 NA 0 0.73
## CC.Risk_2_OF 0.80 0.73 0.54 0.73 2.8 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_OF 336 0.93 0.93 0.8 0.73 55 27
## CC.Risk_2_OF 336 0.94 0.93 0.8 0.73 39 30hist(CC$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
## 0.78 0.78 0.64 0.64 3.6 0.014 26 20 0.64
##
## lower alpha upper 95% confidence boundaries
## 0.75 0.78 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 0.64 0.41 0.64 1.8 NA 0 0.64
## CC.Risk_2_BF 0.57 0.64 0.41 0.64 1.8 NA 0 0.64
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BF 256 0.92 0.91 0.73 0.64 32 24
## CC.Risk_2_BF 256 0.89 0.91 0.73 0.64 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$Risk_2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.85 0.85 12 0.005 53 30 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_NE 0.8 0.85 0.73 0.85 5.8 NA 0 0.85
## CC.Risk_2_NE 0.9 0.85 0.73 0.85 5.8 NA 0 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_NE 261 0.96 0.96 0.89 0.85 57 31
## CC.Risk_2_NE 261 0.96 0.96 0.89 0.85 49 32hist(CC$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 0.63 0.46 0.46 1.7 0.023 10 15 0.46
##
## lower alpha upper 95% confidence boundaries
## 0.57 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 0.59 0.46 0.21 0.46 0.86 NA 0 0.46
## CC.Risk_2_SE 0.36 0.46 0.21 0.46 0.86 NA 0 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_SE 253 0.89 0.85 0.58 0.46 13.7 19
## CC.Risk_2_SE 253 0.81 0.85 0.58 0.46 7.2 15hist(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$Risk_2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.87 0.77 0.77 6.8 0.0082 19 23 0.77
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_WE 0.93 0.77 0.6 0.77 3.4 NA 0 0.77
## CC.Risk_2_WE 0.64 0.77 0.6 0.77 3.4 NA 0 0.77
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_WE 263 0.95 0.94 0.83 0.77 23 26
## CC.Risk_2_WE 263 0.93 0.94 0.83 0.77 14 22hist(CC$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
## 350 683 85 0.995 70.76 28.76 15.90 31.80
## .25 .50 .75 .90 .95
## 56.25 77.00 91.75 100.00 100.00
##
## 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
## 337 696 91 1 47.78 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
## 340 693 94 0.999 45.15 33 0.00 5.00
## .25 .50 .75 .90 .95
## 21.75 44.00 67.00 85.00 92.15
##
## 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
## 358 675 93 1 45.77 34.78 0.00 5.00
## .25 .50 .75 .90 .95
## 19.00 46.00 70.75 85.00 98.00
##
## 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
## 345 688 92 0.999 43.47 31.34 0.0 5.0
## .25 .50 .75 .90 .95
## 22.0 42.0 63.0 80.0 87.8
##
## 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
## 336 697 88 1 51.06 32.2 5.0 9.5
## .25 .50 .75 .90 .95
## 28.0 53.0 73.0 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
## 256 777 81 0.999 62.13 30.7 8.75 24.00
## .25 .50 .75 .90 .95
## 42.75 66.50 82.25 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
## 261 772 83 0.998 65.22 30.76 11 23
## .25 .50 .75 .90 .95
## 50 71 88 100 100
##
## 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
## 253 780 57 0.978 83.55 19.37 50.0 56.2
## .25 .50 .75 .90 .95
## 74.0 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
## 263 770 53 0.983 82.83 18.92 51 62
## .25 .50 .75 .90 .95
## 74 87 100 100 100
##
## lowest : 0 15 16 19 26, highest: 96 97 98 99 100sd(CC$Und_AFSCS, na.rm = TRUE)## [1] 26.13278sd(CC$Und_BIO, na.rm = TRUE)## [1] 27.80675sd(CC$Und_BECCS, na.rm = TRUE)## [1] 28.61283sd(CC$Und_DACCS, na.rm = TRUE)## [1] 30.14186sd(CC$Und_EW, na.rm = TRUE)## [1] 27.23946sd(CC$Und_OF, na.rm = TRUE)## [1] 27.95346sd(CC$Und_BF, na.rm = TRUE)## [1] 27.17169sd(CC$Und_NE, na.rm = TRUE)## [1] 27.32448sd(CC$Und_SE, na.rm = TRUE)## [1] 18.65695sd(CC$Und_WE, na.rm = TRUE)## [1] 18.27782hist(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
## 350 683 170 1 51.9 38.63 -11.28 1.50
## .25 .50 .75 .90 .95
## 31.62 57.00 80.00 92.00 100.00
##
## 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
## 337 696 184 1 19.7 44.41 -47.5 -27.4
## .25 .50 .75 .90 .95
## -6.0 18.5 45.5 74.2 87.0
##
## 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
## 340 693 191 1 16.6 48.06 -61.08 -36.05
## .25 .50 .75 .90 .95
## -9.25 19.00 47.00 73.15 81.15
##
## lowest : -100.0 -93.0 -92.5 -90.0 -87.0, 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
## 358 675 205 1 12.77 48.7 -60.08 -40.00
## .25 .50 .75 .90 .95
## -14.88 11.75 44.00 68.80 86.15
##
## 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
## 345 688 189 1 13.51 44.37 -55.2 -37.0
## .25 .50 .75 .90 .95
## -10.0 11.0 43.5 62.1 72.8
##
## 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
## 336 697 191 1 7.78 47.63 -70.25 -54.50
## .25 .50 .75 .90 .95
## -16.62 10.00 35.25 62.75 73.00
##
## lowest : -100.0 -99.0 -90.5 -87.5 -87.0, 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
## 256 777 154 1 26.55 41.89 -32.375 -21.500
## .25 .50 .75 .90 .95
## 0.375 25.500 51.750 75.500 85.750
##
## 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
## 261 772 171 1 6.667 55.57 -82.0 -64.0
## .25 .50 .75 .90 .95
## -20.5 3.0 44.5 78.5 86.5
##
## 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
## 253 780 133 0.999 55.56 35.38 -1.2 8.1
## .25 .50 .75 .90 .95
## 33.0 60.5 79.5 95.7 100.0
##
## 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
## 263 770 150 1 46.47 44.44 -30.00 -9.60
## .25 .50 .75 .90 .95
## 23.50 56.00 74.75 90.90 100.00
##
## 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$BRDiff.AFSCS, na.rm = TRUE)## [1] 34.90147sd(CC$BRDiff.BIO, na.rm = TRUE)## [1] 39.21167sd(CC$BRDiff.BECCS, na.rm = TRUE)## [1] 42.59757sd(CC$BRDiff.DACCS, na.rm = TRUE)## [1] 42.81352sd(CC$BRDiff.EW, na.rm = TRUE)## [1] 39.40072sd(CC$BRDiff.OF, na.rm = TRUE)## [1] 42.31704sd(CC$BRDiff.BF, na.rm = TRUE)## [1] 37.03572sd(CC$BRDiff.NE, na.rm = TRUE)## [1] 48.9671sd(CC$BRDiff.SE, na.rm = TRUE)## [1] 31.36065sd(CC$BRDiff.WE, na.rm = TRUE)## [1] 40.7655Familiarity/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
## 350 683 150 0.999 66.75 29.67 15.12 26.50
## .25 .50 .75 .90 .95
## 50.00 71.00 88.50 100.00 100.00
##
## 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
## 337 696 138 1 37.65 27.51 1.0 8.4
## .25 .50 .75 .90 .95
## 18.5 34.5 54.0 73.7 80.0
##
## 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
## 340 693 143 1 37.4 28.27 0.00 4.50
## .25 .50 .75 .90 .95
## 18.38 35.00 52.50 71.55 83.03
##
## 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
## 358 675 149 1 36.21 28.49 0.50 5.00
## .25 .50 .75 .90 .95
## 15.00 33.75 52.50 70.15 81.07
##
## 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
## 345 688 129 1 32.98 25.2 0.0 3.0
## .25 .50 .75 .90 .95
## 16.0 30.5 48.0 63.6 75.3
##
## 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
## 336 697 145 1 38.29 25.47 4.50 7.50
## .25 .50 .75 .90 .95
## 20.00 38.00 52.50 67.50 80.12
##
## 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
## 256 777 119 1 60.25 28.45 13.50 27.00
## .25 .50 .75 .90 .95
## 44.88 61.25 78.50 93.00 99.62
##
## 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
## 261 772 120 1 67.29 26.86 19.5 34.0
## .25 .50 .75 .90 .95
## 51.0 72.5 87.5 95.0 100.0
##
## 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
## 253 780 82 0.987 85.6 16.53 51.3 63.6
## .25 .50 .75 .90 .95
## 78.0 90.0 98.5 100.0 100.0
##
## lowest : 23.0 25.0 33.0 36.0 40.5, 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
## 263 770 89 0.995 82.25 17.68 51.00 63.00
## .25 .50 .75 .90 .95
## 73.25 86.00 94.75 100.00 100.00
##
## 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$FR.AFSCS, na.rm = TRUE)## [1] 26.39931sd(CC$FR.BIO, na.rm = TRUE)## [1] 24.27756sd(CC$FR.BECCS, na.rm = TRUE)## [1] 24.92032sd(CC$FR.DACCS, na.rm = TRUE)## [1] 25.01702sd(CC$FR.EW, na.rm = TRUE)## [1] 22.18373sd(CC$FR.OF, na.rm = TRUE)## [1] 22.352sd(CC$FR.BF, na.rm = TRUE)## [1] 25.06071sd(CC$FR.NE, na.rm = TRUE)## [1] 24.04511sd(CC$FR.SE, na.rm = TRUE)## [1] 15.83442sd(CC$FR.WE, na.rm = TRUE)## [1] 16.89572#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 5.2 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 350 0.94 0.93 0.79 0.72 63 31
## CC.Und_AFSCS 350 0.92 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 337 0.88 0.89 0.67 0.57 28 27
## CC.Und_BIO 337 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
## 0.72 0.72 0.56 0.56 2.6 0.017 37 25 0.56
##
## 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
## CC.Familiar_BECCS 0.55 0.56 0.32 0.56 1.3 NA 0
## CC.Und_BECCS 0.58 0.56 0.32 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 340 0.88 0.88 0.66 0.56 30 28
## CC.Und_BECCS 340 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
## 0.74 0.75 0.6 0.6 3 0.016 36 25 0.6
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.74 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_DACCS 0.51 0.6 0.36 0.6 1.5 NA 0
## CC.Und_DACCS 0.70 0.6 0.36 0.6 1.5 NA 0
## med.r
## CC.Familiar_DACCS 0.6
## CC.Und_DACCS 0.6
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_DACCS 358 0.88 0.89 0.69 0.6 27 26
## CC.Und_DACCS 358 0.91 0.89 0.69 0.6 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 0.47 0.55 0.3 0.55 1.2 NA 0 0.55
## CC.Und_EW 0.65 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 345 0.86 0.88 0.65 0.55 22 23
## CC.Und_EW 345 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
## 0.58 0.58 0.41 0.41 1.4 0.026 38 22 0.41
##
## lower alpha upper 95% confidence boundaries
## 0.53 0.58 0.63
##
## 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 0.37 0.41 0.17 0.41 0.69 NA 0 0.41
## CC.Und_OF 0.45 0.41 0.17 0.41 0.69 NA 0 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_OF 336 0.82 0.84 0.54 0.41 26 25
## CC.Und_OF 336 0.86 0.84 0.54 0.41 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 0.77 0.62 0.62 3.3 0.015 60 25 0.62
##
## lower alpha upper 95% confidence boundaries
## 0.74 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 256 0.91 0.9 0.71 0.62 58 29
## CC.Und_BF 256 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 3 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 0.36 0.6 1.5 NA 0 0.6
## CC.Und_NE 0.61 0.6 0.36 0.6 1.5 NA 0 0.6
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_NE 261 0.89 0.89 0.69 0.6 69 27
## CC.Und_NE 261 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 0.78 0.63 0.63 3.5 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 0.56 0.63 0.4 0.63 1.7 NA 0 0.63
## CC.Und_SE 0.72 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 253 0.89 0.9 0.72 0.63 88 16
## CC.Und_SE 253 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
## 0.61 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 0.43 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 263 0.88 0.86 0.61 0.49 82 21
## CC.Und_WE 263 0.84 0.86 0.61 0.49 83 18Scale Correlations
Naturalness Scales by Technology
#Naturalness Scales by Technology (One scale per technology)
CC$corNat <- data.frame(CC$Nat_Scale_AFSCS, CC$Nat_Scale_BIO, CC$Nat_Scale_BECCS, CC$Nat_Scale_DACCS, CC$Nat_Scale_EW, CC$Nat_Scale_OF, CC$Nat_Scale_BF, CC$Nat_Scale_NE, CC$Nat_Scale_SE, CC$Nat_Scale_WE)
mydata.cor1 = cor(CC$corNat, use = "pairwise.complete.obs")
head(round(mydata.cor1,2))## CC.Nat_1_AFSCS CC.Nat_2R_AFSCS CC.Nat_3R_AFSCS CC.Nat_4R_AFSCS
## CC.Nat_1_AFSCS 1.00 0.46 0.10 0.60
## CC.Nat_2R_AFSCS 0.46 1.00 0.23 0.54
## CC.Nat_3R_AFSCS 0.10 0.23 1.00 0.23
## CC.Nat_4R_AFSCS 0.60 0.54 0.23 1.00
## CC.Nat_1_BIO -0.02 -0.07 -0.02 0.02
## CC.Nat_2R_BIO 0.10 0.30 -0.10 0.15
## CC.Nat_1_BIO CC.Nat_2R_BIO CC.Nat_3R_BIO CC.Nat_4R_BIO
## CC.Nat_1_AFSCS -0.02 0.10 -0.36 -0.15
## CC.Nat_2R_AFSCS -0.07 0.30 -0.24 -0.01
## CC.Nat_3R_AFSCS -0.02 -0.10 0.35 0.11
## CC.Nat_4R_AFSCS 0.02 0.15 -0.09 0.01
## CC.Nat_1_BIO 1.00 0.38 0.06 0.63
## CC.Nat_2R_BIO 0.38 1.00 0.15 0.38
## CC.Nat_1_BECCS CC.Nat_2R_BECCS CC.Nat_3R_BECCS CC.Nat_4R_BECCS
## CC.Nat_1_AFSCS 0.19 0.01 -0.07 0.21
## CC.Nat_2R_AFSCS -0.03 0.17 0.07 0.00
## CC.Nat_3R_AFSCS -0.31 -0.08 0.17 -0.30
## CC.Nat_4R_AFSCS 0.06 0.06 0.07 0.12
## CC.Nat_1_BIO 0.19 0.22 -0.05 0.26
## CC.Nat_2R_BIO 0.01 0.30 -0.06 0.17
## CC.Nat_1_DACCS CC.Nat_2R_DACCS CC.Nat_3R_DACCS CC.Nat_4R_DACCS
## CC.Nat_1_AFSCS 0.09 0.07 -0.02 0.16
## CC.Nat_2R_AFSCS 0.16 0.38 0.04 0.31
## CC.Nat_3R_AFSCS 0.14 0.22 0.05 0.22
## CC.Nat_4R_AFSCS -0.02 0.03 -0.06 0.12
## CC.Nat_1_BIO 0.41 0.05 -0.14 0.31
## CC.Nat_2R_BIO 0.04 0.13 -0.07 0.06
## CC.Nat_1_EW CC.Nat_2R_EW CC.Nat_3R_EW CC.Nat_4R_EW CC.Nat_1_OF
## CC.Nat_1_AFSCS 0.10 -0.04 -0.24 0.02 0.31
## CC.Nat_2R_AFSCS 0.01 0.26 0.13 0.05 0.08
## CC.Nat_3R_AFSCS -0.04 0.20 0.41 0.04 -0.06
## CC.Nat_4R_AFSCS -0.03 0.00 -0.10 0.01 0.18
## CC.Nat_1_BIO 0.27 0.21 0.10 0.37 0.51
## CC.Nat_2R_BIO 0.26 0.36 0.14 0.32 0.23
## CC.Nat_2R_OF CC.Nat_3R_OF CC.Nat_4R_OF CC.Nat_1_BF CC.Nat_2R_BF
## CC.Nat_1_AFSCS 0.09 0.23 0.23 0.03 -0.11
## CC.Nat_2R_AFSCS 0.14 0.13 0.10 -0.13 0.15
## CC.Nat_3R_AFSCS 0.14 0.42 -0.09 -0.17 -0.16
## CC.Nat_4R_AFSCS 0.18 0.07 0.21 -0.03 0.14
## CC.Nat_1_BIO 0.31 -0.24 0.40 0.35 0.07
## CC.Nat_2R_BIO 0.52 0.05 0.31 0.12 0.43
## CC.Nat_3R_BF CC.Nat_4R_BF CC.Nat_1_NE CC.Nat_2R_NE CC.Nat_3R_NE
## CC.Nat_1_AFSCS -0.20 -0.02 0.09 0.01 0.01
## CC.Nat_2R_AFSCS -0.23 0.08 0.05 0.25 0.01
## CC.Nat_3R_AFSCS 0.18 -0.11 -0.24 0.05 0.02
## CC.Nat_4R_AFSCS -0.23 0.10 -0.21 -0.02 -0.08
## CC.Nat_1_BIO -0.17 0.30 0.21 0.02 0.09
## CC.Nat_2R_BIO 0.26 0.20 0.15 0.15 0.22
## CC.Nat_4R_NE CC.Nat_1_SE CC.Nat_2R_SE CC.Nat_3R_SE CC.Nat_4R_SE
## CC.Nat_1_AFSCS -0.04 0.08 0.12 -0.18 0.04
## CC.Nat_2R_AFSCS 0.04 -0.10 0.35 -0.07 0.02
## CC.Nat_3R_AFSCS -0.11 -0.37 0.13 0.22 -0.20
## CC.Nat_4R_AFSCS 0.02 -0.09 0.21 -0.06 -0.03
## CC.Nat_1_BIO 0.15 0.24 0.08 0.04 -0.10
## CC.Nat_2R_BIO 0.13 0.03 0.19 -0.03 0.05
## CC.Nat_1_WE CC.Nat_2R_WE CC.Nat_3R_WE CC.Nat_4R_WE
## CC.Nat_1_AFSCS 0.17 0.30 -0.16 0.13
## CC.Nat_2R_AFSCS -0.04 0.22 0.09 -0.05
## CC.Nat_3R_AFSCS -0.25 -0.08 0.46 -0.29
## CC.Nat_4R_AFSCS 0.13 0.32 -0.17 0.16
## CC.Nat_1_BIO 0.20 0.07 -0.30 0.15
## CC.Nat_2R_BIO -0.09 0.06 -0.06 -0.05library("Hmisc")
mydata.rcorr1 = rcorr(as.matrix(mydata.cor1))
mydata.rcorr1## CC.Nat_1_AFSCS CC.Nat_2R_AFSCS CC.Nat_3R_AFSCS CC.Nat_4R_AFSCS
## CC.Nat_1_AFSCS 1.00 0.60 -0.12 0.80
## CC.Nat_2R_AFSCS 0.60 1.00 0.29 0.70
## CC.Nat_3R_AFSCS -0.12 0.29 1.00 0.10
## CC.Nat_4R_AFSCS 0.80 0.70 0.10 1.00
## CC.Nat_1_BIO -0.04 -0.21 -0.36 -0.05
## CC.Nat_2R_BIO -0.04 0.18 -0.16 0.08
## CC.Nat_3R_BIO -0.65 -0.43 0.43 -0.50
## CC.Nat_4R_BIO -0.28 -0.23 -0.09 -0.17
## CC.Nat_1_BECCS 0.13 -0.23 -0.56 -0.06
## CC.Nat_2R_BECCS -0.09 0.20 -0.17 -0.02
## CC.Nat_3R_BECCS -0.32 -0.07 0.50 -0.23
## CC.Nat_4R_BECCS 0.16 -0.20 -0.57 0.05
## CC.Nat_1_DACCS 0.03 -0.07 -0.08 -0.22
## CC.Nat_2R_DACCS 0.04 0.39 0.23 -0.01
## CC.Nat_3R_DACCS -0.35 -0.21 0.34 -0.32
## CC.Nat_4R_DACCS -0.01 0.08 0.03 -0.13
## CC.Nat_1_EW -0.02 -0.13 -0.10 -0.19
## CC.Nat_2R_EW -0.34 0.09 0.27 -0.23
## CC.Nat_3R_EW -0.56 -0.18 0.61 -0.36
## CC.Nat_4R_EW -0.20 -0.23 -0.09 -0.26
## CC.Nat_1_OF 0.23 -0.08 -0.24 0.02
## CC.Nat_2R_OF -0.06 0.05 0.12 -0.05
## CC.Nat_3R_OF -0.14 0.07 0.69 -0.08
## CC.Nat_4R_OF 0.20 -0.05 -0.21 0.07
## CC.Nat_1_BF -0.07 -0.28 -0.63 -0.18
## CC.Nat_2R_BF -0.27 0.05 -0.38 -0.07
## CC.Nat_3R_BF -0.58 -0.37 0.37 -0.59
## CC.Nat_4R_BF -0.23 -0.18 -0.41 -0.14
## CC.Nat_1_NE -0.12 -0.27 -0.60 -0.42
## CC.Nat_2R_NE -0.15 0.08 -0.15 -0.22
## CC.Nat_3R_NE -0.39 -0.26 0.06 -0.38
## CC.Nat_4R_NE -0.21 -0.25 -0.50 -0.33
## CC.Nat_1_SE 0.03 -0.28 -0.70 -0.25
## CC.Nat_2R_SE 0.11 0.43 0.07 0.19
## CC.Nat_3R_SE -0.62 -0.45 0.31 -0.50
## CC.Nat_4R_SE 0.00 -0.16 -0.54 -0.19
## CC.Nat_1_WE 0.31 -0.21 -0.62 0.19
## CC.Nat_2R_WE 0.41 0.28 -0.27 0.45
## CC.Nat_3R_WE -0.41 -0.06 0.58 -0.39
## CC.Nat_4R_WE 0.26 -0.21 -0.63 0.19
## CC.Nat_1_BIO CC.Nat_2R_BIO CC.Nat_3R_BIO CC.Nat_4R_BIO
## CC.Nat_1_AFSCS -0.04 -0.04 -0.65 -0.28
## CC.Nat_2R_AFSCS -0.21 0.18 -0.43 -0.23
## CC.Nat_3R_AFSCS -0.36 -0.16 0.43 -0.09
## CC.Nat_4R_AFSCS -0.05 0.08 -0.50 -0.17
## CC.Nat_1_BIO 1.00 0.45 -0.14 0.77
## CC.Nat_2R_BIO 0.45 1.00 -0.01 0.47
## CC.Nat_3R_BIO -0.14 -0.01 1.00 0.22
## CC.Nat_4R_BIO 0.77 0.47 0.22 1.00
## CC.Nat_1_BECCS 0.46 0.04 -0.07 0.35
## CC.Nat_2R_BECCS 0.23 0.37 -0.14 0.26
## CC.Nat_3R_BECCS -0.49 -0.19 0.60 -0.24
## CC.Nat_4R_BECCS 0.54 0.13 -0.14 0.54
## CC.Nat_1_DACCS 0.36 -0.05 0.03 0.28
## CC.Nat_2R_DACCS 0.01 0.21 -0.05 -0.07
## CC.Nat_3R_DACCS -0.47 -0.23 0.56 -0.33
## CC.Nat_4R_DACCS 0.26 0.03 0.13 0.27
## CC.Nat_1_EW 0.52 0.34 0.13 0.48
## CC.Nat_2R_EW 0.23 0.51 0.33 0.46
## CC.Nat_3R_EW -0.27 0.05 0.75 0.10
## CC.Nat_4R_EW 0.52 0.39 0.29 0.57
## CC.Nat_1_OF 0.58 0.33 -0.20 0.45
## CC.Nat_2R_OF 0.27 0.52 0.17 0.34
## CC.Nat_3R_OF -0.54 -0.18 0.56 -0.25
## CC.Nat_4R_OF 0.50 0.39 -0.13 0.52
## CC.Nat_1_BF 0.54 0.13 -0.32 0.28
## CC.Nat_2R_BF 0.04 0.47 -0.11 0.02
## CC.Nat_3R_BF -0.43 -0.13 0.67 -0.23
## CC.Nat_4R_BF 0.45 0.28 -0.14 0.36
## CC.Nat_1_NE 0.25 0.02 -0.13 0.09
## CC.Nat_2R_NE -0.23 -0.01 -0.02 -0.17
## CC.Nat_3R_NE -0.17 -0.03 0.37 -0.14
## CC.Nat_4R_NE 0.19 0.01 0.02 0.16
## CC.Nat_1_SE 0.31 0.03 -0.45 0.02
## CC.Nat_2R_SE -0.23 0.03 -0.37 -0.20
## CC.Nat_3R_SE -0.29 -0.32 0.57 -0.06
## CC.Nat_4R_SE -0.10 -0.12 -0.33 -0.17
## CC.Nat_1_WE 0.18 -0.24 -0.53 -0.05
## CC.Nat_2R_WE -0.06 -0.10 -0.52 -0.15
## CC.Nat_3R_WE -0.41 -0.21 0.27 -0.11
## CC.Nat_4R_WE 0.12 -0.24 -0.52 -0.03
## CC.Nat_1_BECCS CC.Nat_2R_BECCS CC.Nat_3R_BECCS CC.Nat_4R_BECCS
## CC.Nat_1_AFSCS 0.13 -0.09 -0.32 0.16
## CC.Nat_2R_AFSCS -0.23 0.20 -0.07 -0.20
## CC.Nat_3R_AFSCS -0.56 -0.17 0.50 -0.57
## CC.Nat_4R_AFSCS -0.06 -0.02 -0.23 0.05
## CC.Nat_1_BIO 0.46 0.23 -0.49 0.54
## CC.Nat_2R_BIO 0.04 0.37 -0.19 0.13
## CC.Nat_3R_BIO -0.07 -0.14 0.60 -0.14
## CC.Nat_4R_BIO 0.35 0.26 -0.24 0.54
## CC.Nat_1_BECCS 1.00 0.19 -0.34 0.85
## CC.Nat_2R_BECCS 0.19 1.00 -0.06 0.23
## CC.Nat_3R_BECCS -0.34 -0.06 1.00 -0.39
## CC.Nat_4R_BECCS 0.85 0.23 -0.39 1.00
## CC.Nat_1_DACCS 0.45 -0.02 -0.30 0.39
## CC.Nat_2R_DACCS -0.10 0.51 0.03 -0.18
## CC.Nat_3R_DACCS -0.30 -0.28 0.72 -0.36
## CC.Nat_4R_DACCS 0.32 -0.06 -0.19 0.26
## CC.Nat_1_EW 0.34 0.09 -0.36 0.30
## CC.Nat_2R_EW -0.01 0.52 0.13 0.07
## CC.Nat_3R_EW -0.35 -0.09 0.78 -0.37
## CC.Nat_4R_EW 0.32 0.20 -0.18 0.31
## CC.Nat_1_OF 0.44 0.20 -0.31 0.45
## CC.Nat_2R_OF 0.14 0.11 0.02 0.18
## CC.Nat_3R_OF -0.40 -0.22 0.76 -0.43
## CC.Nat_4R_OF 0.42 0.23 -0.23 0.53
## CC.Nat_1_BF 0.40 0.28 -0.67 0.30
## CC.Nat_2R_BF -0.05 0.35 -0.30 -0.08
## CC.Nat_3R_BF -0.42 -0.25 0.69 -0.43
## CC.Nat_4R_BF 0.09 0.17 -0.60 0.07
## CC.Nat_1_NE 0.58 -0.13 -0.30 0.34
## CC.Nat_2R_NE 0.12 0.25 0.00 -0.12
## CC.Nat_3R_NE -0.26 -0.17 0.67 -0.25
## CC.Nat_4R_NE 0.57 -0.11 -0.30 0.38
## CC.Nat_1_SE 0.26 0.02 -0.66 0.29
## CC.Nat_2R_SE -0.37 0.21 0.02 -0.27
## CC.Nat_3R_SE -0.33 -0.19 0.52 -0.27
## CC.Nat_4R_SE 0.05 -0.05 -0.43 0.22
## CC.Nat_1_WE 0.34 -0.22 -0.55 0.44
## CC.Nat_2R_WE -0.18 -0.03 -0.39 -0.01
## CC.Nat_3R_WE -0.38 -0.12 0.40 -0.42
## CC.Nat_4R_WE 0.28 -0.11 -0.44 0.43
## CC.Nat_1_DACCS CC.Nat_2R_DACCS CC.Nat_3R_DACCS CC.Nat_4R_DACCS
## CC.Nat_1_AFSCS 0.03 0.04 -0.35 -0.01
## CC.Nat_2R_AFSCS -0.07 0.39 -0.21 0.08
## CC.Nat_3R_AFSCS -0.08 0.23 0.34 0.03
## CC.Nat_4R_AFSCS -0.22 -0.01 -0.32 -0.13
## CC.Nat_1_BIO 0.36 0.01 -0.47 0.26
## CC.Nat_2R_BIO -0.05 0.21 -0.23 0.03
## CC.Nat_3R_BIO 0.03 -0.05 0.56 0.13
## CC.Nat_4R_BIO 0.28 -0.07 -0.33 0.27
## CC.Nat_1_BECCS 0.45 -0.10 -0.30 0.32
## CC.Nat_2R_BECCS -0.02 0.51 -0.28 -0.06
## CC.Nat_3R_BECCS -0.30 0.03 0.72 -0.19
## CC.Nat_4R_BECCS 0.39 -0.18 -0.36 0.26
## CC.Nat_1_DACCS 1.00 0.36 -0.06 0.79
## CC.Nat_2R_DACCS 0.36 1.00 0.10 0.41
## CC.Nat_3R_DACCS -0.06 0.10 1.00 0.07
## CC.Nat_4R_DACCS 0.79 0.41 0.07 1.00
## CC.Nat_1_EW 0.51 0.24 -0.33 0.39
## CC.Nat_2R_EW 0.28 0.59 0.03 0.41
## CC.Nat_3R_EW -0.34 -0.08 0.56 -0.23
## CC.Nat_4R_EW 0.33 0.20 -0.24 0.35
## CC.Nat_1_OF 0.30 0.17 -0.32 0.17
## CC.Nat_2R_OF 0.25 0.25 0.15 0.24
## CC.Nat_3R_OF -0.20 0.07 0.59 -0.18
## CC.Nat_4R_OF 0.10 -0.02 -0.42 -0.01
## CC.Nat_1_BF 0.14 0.04 -0.42 0.23
## CC.Nat_2R_BF -0.13 0.18 -0.09 0.14
## CC.Nat_3R_BF 0.00 0.10 0.84 0.02
## CC.Nat_4R_BF -0.06 -0.02 -0.36 0.20
## CC.Nat_1_NE 0.29 -0.17 -0.26 0.19
## CC.Nat_2R_NE -0.18 0.02 -0.15 -0.15
## CC.Nat_3R_NE -0.28 -0.11 0.64 0.02
## CC.Nat_4R_NE 0.22 -0.28 -0.26 0.28
## CC.Nat_1_SE 0.36 0.01 -0.33 0.24
## CC.Nat_2R_SE -0.26 0.04 -0.13 -0.14
## CC.Nat_3R_SE -0.27 -0.15 0.48 -0.23
## CC.Nat_4R_SE 0.12 -0.06 -0.19 0.18
## CC.Nat_1_WE 0.10 -0.49 -0.31 -0.11
## CC.Nat_2R_WE -0.07 -0.16 -0.28 -0.09
## CC.Nat_3R_WE -0.19 -0.05 0.25 -0.14
## CC.Nat_4R_WE -0.03 -0.49 -0.34 -0.17
## CC.Nat_1_EW CC.Nat_2R_EW CC.Nat_3R_EW CC.Nat_4R_EW CC.Nat_1_OF
## CC.Nat_1_AFSCS -0.02 -0.34 -0.56 -0.20 0.23
## CC.Nat_2R_AFSCS -0.13 0.09 -0.18 -0.23 -0.08
## CC.Nat_3R_AFSCS -0.10 0.27 0.61 -0.09 -0.24
## CC.Nat_4R_AFSCS -0.19 -0.23 -0.36 -0.26 0.02
## CC.Nat_1_BIO 0.52 0.23 -0.27 0.52 0.58
## CC.Nat_2R_BIO 0.34 0.51 0.05 0.39 0.33
## CC.Nat_3R_BIO 0.13 0.33 0.75 0.29 -0.20
## CC.Nat_4R_BIO 0.48 0.46 0.10 0.57 0.45
## CC.Nat_1_BECCS 0.34 -0.01 -0.35 0.32 0.44
## CC.Nat_2R_BECCS 0.09 0.52 -0.09 0.20 0.20
## CC.Nat_3R_BECCS -0.36 0.13 0.78 -0.18 -0.31
## CC.Nat_4R_BECCS 0.30 0.07 -0.37 0.31 0.45
## CC.Nat_1_DACCS 0.51 0.28 -0.34 0.33 0.30
## CC.Nat_2R_DACCS 0.24 0.59 -0.08 0.20 0.17
## CC.Nat_3R_DACCS -0.33 0.03 0.56 -0.24 -0.32
## CC.Nat_4R_DACCS 0.39 0.41 -0.23 0.35 0.17
## CC.Nat_1_EW 1.00 0.49 -0.14 0.88 0.67
## CC.Nat_2R_EW 0.49 1.00 0.30 0.60 0.31
## CC.Nat_3R_EW -0.14 0.30 1.00 0.06 -0.20
## CC.Nat_4R_EW 0.88 0.60 0.06 1.00 0.55
## CC.Nat_1_OF 0.67 0.31 -0.20 0.55 1.00
## CC.Nat_2R_OF 0.30 0.49 0.22 0.25 0.49
## CC.Nat_3R_OF -0.10 0.14 0.72 -0.02 -0.24
## CC.Nat_4R_OF 0.61 0.32 -0.04 0.59 0.86
## CC.Nat_1_BF 0.43 0.07 -0.53 0.43 0.48
## CC.Nat_2R_BF -0.09 0.32 -0.21 0.01 -0.16
## CC.Nat_3R_BF -0.21 0.20 0.67 -0.08 -0.42
## CC.Nat_4R_BF 0.30 0.18 -0.27 0.39 0.20
## CC.Nat_1_NE 0.09 -0.21 -0.23 0.08 0.07
## CC.Nat_2R_NE -0.04 -0.08 -0.03 0.08 -0.31
## CC.Nat_3R_NE -0.33 0.11 0.53 -0.10 -0.26
## CC.Nat_4R_NE 0.02 -0.13 -0.15 0.12 -0.06
## CC.Nat_1_SE 0.33 -0.12 -0.65 0.11 0.25
## CC.Nat_2R_SE -0.41 -0.10 0.01 -0.37 -0.41
## CC.Nat_3R_SE -0.13 0.05 0.63 0.02 -0.36
## CC.Nat_4R_SE 0.09 -0.16 -0.48 -0.03 -0.06
## CC.Nat_1_WE -0.22 -0.56 -0.60 -0.26 -0.01
## CC.Nat_2R_WE -0.39 -0.39 -0.52 -0.35 -0.44
## CC.Nat_3R_WE -0.02 0.27 0.62 -0.01 -0.04
## CC.Nat_4R_WE -0.30 -0.52 -0.57 -0.23 -0.09
## CC.Nat_2R_OF CC.Nat_3R_OF CC.Nat_4R_OF CC.Nat_1_BF CC.Nat_2R_BF
## CC.Nat_1_AFSCS -0.06 -0.14 0.20 -0.07 -0.27
## CC.Nat_2R_AFSCS 0.05 0.07 -0.05 -0.28 0.05
## CC.Nat_3R_AFSCS 0.12 0.69 -0.21 -0.63 -0.38
## CC.Nat_4R_AFSCS -0.05 -0.08 0.07 -0.18 -0.07
## CC.Nat_1_BIO 0.27 -0.54 0.50 0.54 0.04
## CC.Nat_2R_BIO 0.52 -0.18 0.39 0.13 0.47
## CC.Nat_3R_BIO 0.17 0.56 -0.13 -0.32 -0.11
## CC.Nat_4R_BIO 0.34 -0.25 0.52 0.28 0.02
## CC.Nat_1_BECCS 0.14 -0.40 0.42 0.40 -0.05
## CC.Nat_2R_BECCS 0.11 -0.22 0.23 0.28 0.35
## CC.Nat_3R_BECCS 0.02 0.76 -0.23 -0.67 -0.30
## CC.Nat_4R_BECCS 0.18 -0.43 0.53 0.30 -0.08
## CC.Nat_1_DACCS 0.25 -0.20 0.10 0.14 -0.13
## CC.Nat_2R_DACCS 0.25 0.07 -0.02 0.04 0.18
## CC.Nat_3R_DACCS 0.15 0.59 -0.42 -0.42 -0.09
## CC.Nat_4R_DACCS 0.24 -0.18 -0.01 0.23 0.14
## CC.Nat_1_EW 0.30 -0.10 0.61 0.43 -0.09
## CC.Nat_2R_EW 0.49 0.14 0.32 0.07 0.32
## CC.Nat_3R_EW 0.22 0.72 -0.04 -0.53 -0.21
## CC.Nat_4R_EW 0.25 -0.02 0.59 0.43 0.01
## CC.Nat_1_OF 0.49 -0.24 0.86 0.48 -0.16
## CC.Nat_2R_OF 1.00 0.14 0.51 -0.21 0.03
## CC.Nat_3R_OF 0.14 1.00 -0.11 -0.77 -0.39
## CC.Nat_4R_OF 0.51 -0.11 1.00 0.22 -0.25
## CC.Nat_1_BF -0.21 -0.77 0.22 1.00 0.42
## CC.Nat_2R_BF 0.03 -0.39 -0.25 0.42 1.00
## CC.Nat_3R_BF 0.16 0.66 -0.42 -0.42 -0.02
## CC.Nat_4R_BF -0.15 -0.62 0.08 0.82 0.58
## CC.Nat_1_NE -0.08 -0.47 0.05 0.58 0.09
## CC.Nat_2R_NE -0.30 0.02 -0.22 0.03 0.40
## CC.Nat_3R_NE 0.14 0.29 -0.22 -0.31 0.16
## CC.Nat_4R_NE -0.04 -0.48 -0.01 0.51 0.33
## CC.Nat_1_SE 0.05 -0.67 0.18 0.80 0.22
## CC.Nat_2R_SE -0.29 -0.01 -0.28 -0.32 0.35
## CC.Nat_3R_SE -0.04 0.54 -0.23 -0.44 -0.14
## CC.Nat_4R_SE -0.14 -0.43 0.04 0.49 0.27
## CC.Nat_1_WE -0.19 -0.48 -0.02 0.40 -0.07
## CC.Nat_2R_WE -0.39 -0.40 -0.38 -0.02 0.24
## CC.Nat_3R_WE 0.20 0.52 0.05 -0.43 -0.22
## CC.Nat_4R_WE -0.33 -0.44 -0.03 0.38 0.04
## CC.Nat_3R_BF CC.Nat_4R_BF CC.Nat_1_NE CC.Nat_2R_NE CC.Nat_3R_NE
## CC.Nat_1_AFSCS -0.58 -0.23 -0.12 -0.15 -0.39
## CC.Nat_2R_AFSCS -0.37 -0.18 -0.27 0.08 -0.26
## CC.Nat_3R_AFSCS 0.37 -0.41 -0.60 -0.15 0.06
## CC.Nat_4R_AFSCS -0.59 -0.14 -0.42 -0.22 -0.38
## CC.Nat_1_BIO -0.43 0.45 0.25 -0.23 -0.17
## CC.Nat_2R_BIO -0.13 0.28 0.02 -0.01 -0.03
## CC.Nat_3R_BIO 0.67 -0.14 -0.13 -0.02 0.37
## CC.Nat_4R_BIO -0.23 0.36 0.09 -0.17 -0.14
## CC.Nat_1_BECCS -0.42 0.09 0.58 0.12 -0.26
## CC.Nat_2R_BECCS -0.25 0.17 -0.13 0.25 -0.17
## CC.Nat_3R_BECCS 0.69 -0.60 -0.30 0.00 0.67
## CC.Nat_4R_BECCS -0.43 0.07 0.34 -0.12 -0.25
## CC.Nat_1_DACCS 0.00 -0.06 0.29 -0.18 -0.28
## CC.Nat_2R_DACCS 0.10 -0.02 -0.17 0.02 -0.11
## CC.Nat_3R_DACCS 0.84 -0.36 -0.26 -0.15 0.64
## CC.Nat_4R_DACCS 0.02 0.20 0.19 -0.15 0.02
## CC.Nat_1_EW -0.21 0.30 0.09 -0.04 -0.33
## CC.Nat_2R_EW 0.20 0.18 -0.21 -0.08 0.11
## CC.Nat_3R_EW 0.67 -0.27 -0.23 -0.03 0.53
## CC.Nat_4R_EW -0.08 0.39 0.08 0.08 -0.10
## CC.Nat_1_OF -0.42 0.20 0.07 -0.31 -0.26
## CC.Nat_2R_OF 0.16 -0.15 -0.08 -0.30 0.14
## CC.Nat_3R_OF 0.66 -0.62 -0.47 0.02 0.29
## CC.Nat_4R_OF -0.42 0.08 0.05 -0.22 -0.22
## CC.Nat_1_BF -0.42 0.82 0.58 0.03 -0.31
## CC.Nat_2R_BF -0.02 0.58 0.09 0.40 0.16
## CC.Nat_3R_BF 1.00 -0.29 -0.15 -0.03 0.83
## CC.Nat_4R_BF -0.29 1.00 0.32 0.09 -0.13
## CC.Nat_1_NE -0.15 0.32 1.00 0.28 -0.05
## CC.Nat_2R_NE -0.03 0.09 0.28 1.00 -0.16
## CC.Nat_3R_NE 0.83 -0.13 -0.05 -0.16 1.00
## CC.Nat_4R_NE -0.16 0.51 0.87 0.35 -0.03
## CC.Nat_1_SE -0.36 0.55 0.70 -0.10 -0.31
## CC.Nat_2R_SE -0.13 -0.08 -0.35 0.35 -0.05
## CC.Nat_3R_SE 0.79 -0.19 -0.29 0.10 0.64
## CC.Nat_4R_SE -0.22 0.34 0.48 0.00 -0.18
## CC.Nat_1_WE -0.59 0.11 0.49 -0.18 -0.47
## CC.Nat_2R_WE -0.49 0.13 -0.13 0.01 -0.41
## CC.Nat_3R_WE 0.58 -0.27 -0.31 0.01 0.43
## CC.Nat_4R_WE -0.60 0.15 0.39 0.00 -0.41
## CC.Nat_4R_NE CC.Nat_1_SE CC.Nat_2R_SE CC.Nat_3R_SE CC.Nat_4R_SE
## CC.Nat_1_AFSCS -0.21 0.03 0.11 -0.62 0.00
## CC.Nat_2R_AFSCS -0.25 -0.28 0.43 -0.45 -0.16
## CC.Nat_3R_AFSCS -0.50 -0.70 0.07 0.31 -0.54
## CC.Nat_4R_AFSCS -0.33 -0.25 0.19 -0.50 -0.19
## CC.Nat_1_BIO 0.19 0.31 -0.23 -0.29 -0.10
## CC.Nat_2R_BIO 0.01 0.03 0.03 -0.32 -0.12
## CC.Nat_3R_BIO 0.02 -0.45 -0.37 0.57 -0.33
## CC.Nat_4R_BIO 0.16 0.02 -0.20 -0.06 -0.17
## CC.Nat_1_BECCS 0.57 0.26 -0.37 -0.33 0.05
## CC.Nat_2R_BECCS -0.11 0.02 0.21 -0.19 -0.05
## CC.Nat_3R_BECCS -0.30 -0.66 0.02 0.52 -0.43
## CC.Nat_4R_BECCS 0.38 0.29 -0.27 -0.27 0.22
## CC.Nat_1_DACCS 0.22 0.36 -0.26 -0.27 0.12
## CC.Nat_2R_DACCS -0.28 0.01 0.04 -0.15 -0.06
## CC.Nat_3R_DACCS -0.26 -0.33 -0.13 0.48 -0.19
## CC.Nat_4R_DACCS 0.28 0.24 -0.14 -0.23 0.18
## CC.Nat_1_EW 0.02 0.33 -0.41 -0.13 0.09
## CC.Nat_2R_EW -0.13 -0.12 -0.10 0.05 -0.16
## CC.Nat_3R_EW -0.15 -0.65 0.01 0.63 -0.48
## CC.Nat_4R_EW 0.12 0.11 -0.37 0.02 -0.03
## CC.Nat_1_OF -0.06 0.25 -0.41 -0.36 -0.06
## CC.Nat_2R_OF -0.04 0.05 -0.29 -0.04 -0.14
## CC.Nat_3R_OF -0.48 -0.67 -0.01 0.54 -0.43
## CC.Nat_4R_OF -0.01 0.18 -0.28 -0.23 0.04
## CC.Nat_1_BF 0.51 0.80 -0.32 -0.44 0.49
## CC.Nat_2R_BF 0.33 0.22 0.35 -0.14 0.27
## CC.Nat_3R_BF -0.16 -0.36 -0.13 0.79 -0.22
## CC.Nat_4R_BF 0.51 0.55 -0.08 -0.19 0.34
## CC.Nat_1_NE 0.87 0.70 -0.35 -0.29 0.48
## CC.Nat_2R_NE 0.35 -0.10 0.35 0.10 0.00
## CC.Nat_3R_NE -0.03 -0.31 -0.05 0.64 -0.18
## CC.Nat_4R_NE 1.00 0.47 -0.18 -0.13 0.49
## CC.Nat_1_SE 0.47 1.00 0.09 -0.19 0.79
## CC.Nat_2R_SE -0.18 0.09 1.00 -0.03 0.23
## CC.Nat_3R_SE -0.13 -0.19 -0.03 1.00 0.06
## CC.Nat_4R_SE 0.49 0.79 0.23 0.06 1.00
## CC.Nat_1_WE 0.48 0.58 -0.08 -0.53 0.45
## CC.Nat_2R_WE 0.03 0.09 0.60 -0.58 0.23
## CC.Nat_3R_WE -0.28 -0.46 0.05 0.64 -0.26
## CC.Nat_4R_WE 0.44 0.44 0.08 -0.51 0.48
## CC.Nat_1_WE CC.Nat_2R_WE CC.Nat_3R_WE CC.Nat_4R_WE
## CC.Nat_1_AFSCS 0.31 0.41 -0.41 0.26
## CC.Nat_2R_AFSCS -0.21 0.28 -0.06 -0.21
## CC.Nat_3R_AFSCS -0.62 -0.27 0.58 -0.63
## CC.Nat_4R_AFSCS 0.19 0.45 -0.39 0.19
## CC.Nat_1_BIO 0.18 -0.06 -0.41 0.12
## CC.Nat_2R_BIO -0.24 -0.10 -0.21 -0.24
## CC.Nat_3R_BIO -0.53 -0.52 0.27 -0.52
## CC.Nat_4R_BIO -0.05 -0.15 -0.11 -0.03
## CC.Nat_1_BECCS 0.34 -0.18 -0.38 0.28
## CC.Nat_2R_BECCS -0.22 -0.03 -0.12 -0.11
## CC.Nat_3R_BECCS -0.55 -0.39 0.40 -0.44
## CC.Nat_4R_BECCS 0.44 -0.01 -0.42 0.43
## CC.Nat_1_DACCS 0.10 -0.07 -0.19 -0.03
## CC.Nat_2R_DACCS -0.49 -0.16 -0.05 -0.49
## CC.Nat_3R_DACCS -0.31 -0.28 0.25 -0.34
## CC.Nat_4R_DACCS -0.11 -0.09 -0.14 -0.17
## CC.Nat_1_EW -0.22 -0.39 -0.02 -0.30
## CC.Nat_2R_EW -0.56 -0.39 0.27 -0.52
## CC.Nat_3R_EW -0.60 -0.52 0.62 -0.57
## CC.Nat_4R_EW -0.26 -0.35 -0.01 -0.23
## CC.Nat_1_OF -0.01 -0.44 -0.04 -0.09
## CC.Nat_2R_OF -0.19 -0.39 0.20 -0.33
## CC.Nat_3R_OF -0.48 -0.40 0.52 -0.44
## CC.Nat_4R_OF -0.02 -0.38 0.05 -0.03
## CC.Nat_1_BF 0.40 -0.02 -0.43 0.38
## CC.Nat_2R_BF -0.07 0.24 -0.22 0.04
## CC.Nat_3R_BF -0.59 -0.49 0.58 -0.60
## CC.Nat_4R_BF 0.11 0.13 -0.27 0.15
## CC.Nat_1_NE 0.49 -0.13 -0.31 0.39
## CC.Nat_2R_NE -0.18 0.01 0.01 0.00
## CC.Nat_3R_NE -0.47 -0.41 0.43 -0.41
## CC.Nat_4R_NE 0.48 0.03 -0.28 0.44
## CC.Nat_1_SE 0.58 0.09 -0.46 0.44
## CC.Nat_2R_SE -0.08 0.60 0.05 0.08
## CC.Nat_3R_SE -0.53 -0.58 0.64 -0.51
## CC.Nat_4R_SE 0.45 0.23 -0.26 0.48
## CC.Nat_1_WE 1.00 0.53 -0.38 0.93
## CC.Nat_2R_WE 0.53 1.00 -0.40 0.59
## CC.Nat_3R_WE -0.38 -0.40 1.00 -0.35
## CC.Nat_4R_WE 0.93 0.59 -0.35 1.00
##
## n
## CC.Nat_1_AFSCS CC.Nat_2R_AFSCS CC.Nat_3R_AFSCS CC.Nat_4R_AFSCS
## CC.Nat_1_AFSCS 40 40 40 40
## CC.Nat_2R_AFSCS 40 40 40 40
## CC.Nat_3R_AFSCS 40 40 40 40
## CC.Nat_4R_AFSCS 40 40 40 40
## CC.Nat_1_BIO 40 40 40 40
## CC.Nat_2R_BIO 40 40 40 40
## CC.Nat_3R_BIO 40 40 40 40
## CC.Nat_4R_BIO 40 40 40 40
## CC.Nat_1_BECCS 40 40 40 40
## CC.Nat_2R_BECCS 40 40 40 40
## CC.Nat_3R_BECCS 40 40 40 40
## CC.Nat_4R_BECCS 40 40 40 40
## CC.Nat_1_DACCS 40 40 40 40
## CC.Nat_2R_DACCS 40 40 40 40
## CC.Nat_3R_DACCS 40 40 40 40
## CC.Nat_4R_DACCS 40 40 40 40
## CC.Nat_1_EW 40 40 40 40
## CC.Nat_2R_EW 40 40 40 40
## CC.Nat_3R_EW 40 40 40 40
## CC.Nat_4R_EW 40 40 40 40
## CC.Nat_1_OF 40 40 40 40
## CC.Nat_2R_OF 40 40 40 40
## CC.Nat_3R_OF 40 40 40 40
## CC.Nat_4R_OF 40 40 40 40
## CC.Nat_1_BF 28 28 28 28
## CC.Nat_2R_BF 28 28 28 28
## CC.Nat_3R_BF 28 28 28 28
## CC.Nat_4R_BF 28 28 28 28
## CC.Nat_1_NE 28 28 28 28
## CC.Nat_2R_NE 28 28 28 28
## CC.Nat_3R_NE 28 28 28 28
## CC.Nat_4R_NE 28 28 28 28
## CC.Nat_1_SE 28 28 28 28
## CC.Nat_2R_SE 28 28 28 28
## CC.Nat_3R_SE 28 28 28 28
## CC.Nat_4R_SE 28 28 28 28
## CC.Nat_1_WE 28 28 28 28
## CC.Nat_2R_WE 28 28 28 28
## CC.Nat_3R_WE 28 28 28 28
## CC.Nat_4R_WE 28 28 28 28
## CC.Nat_1_BIO CC.Nat_2R_BIO CC.Nat_3R_BIO CC.Nat_4R_BIO
## CC.Nat_1_AFSCS 40 40 40 40
## CC.Nat_2R_AFSCS 40 40 40 40
## CC.Nat_3R_AFSCS 40 40 40 40
## CC.Nat_4R_AFSCS 40 40 40 40
## CC.Nat_1_BIO 40 40 40 40
## CC.Nat_2R_BIO 40 40 40 40
## CC.Nat_3R_BIO 40 40 40 40
## CC.Nat_4R_BIO 40 40 40 40
## CC.Nat_1_BECCS 40 40 40 40
## CC.Nat_2R_BECCS 40 40 40 40
## CC.Nat_3R_BECCS 40 40 40 40
## CC.Nat_4R_BECCS 40 40 40 40
## CC.Nat_1_DACCS 40 40 40 40
## CC.Nat_2R_DACCS 40 40 40 40
## CC.Nat_3R_DACCS 40 40 40 40
## CC.Nat_4R_DACCS 40 40 40 40
## CC.Nat_1_EW 40 40 40 40
## CC.Nat_2R_EW 40 40 40 40
## CC.Nat_3R_EW 40 40 40 40
## CC.Nat_4R_EW 40 40 40 40
## CC.Nat_1_OF 40 40 40 40
## CC.Nat_2R_OF 40 40 40 40
## CC.Nat_3R_OF 40 40 40 40
## CC.Nat_4R_OF 40 40 40 40
## CC.Nat_1_BF 28 28 28 28
## CC.Nat_2R_BF 28 28 28 28
## CC.Nat_3R_BF 28 28 28 28
## CC.Nat_4R_BF 28 28 28 28
## CC.Nat_1_NE 28 28 28 28
## CC.Nat_2R_NE 28 28 28 28
## CC.Nat_3R_NE 28 28 28 28
## CC.Nat_4R_NE 28 28 28 28
## CC.Nat_1_SE 28 28 28 28
## CC.Nat_2R_SE 28 28 28 28
## CC.Nat_3R_SE 28 28 28 28
## CC.Nat_4R_SE 28 28 28 28
## CC.Nat_1_WE 28 28 28 28
## CC.Nat_2R_WE 28 28 28 28
## CC.Nat_3R_WE 28 28 28 28
## CC.Nat_4R_WE 28 28 28 28
## CC.Nat_1_BECCS CC.Nat_2R_BECCS CC.Nat_3R_BECCS CC.Nat_4R_BECCS
## CC.Nat_1_AFSCS 40 40 40 40
## CC.Nat_2R_AFSCS 40 40 40 40
## CC.Nat_3R_AFSCS 40 40 40 40
## CC.Nat_4R_AFSCS 40 40 40 40
## CC.Nat_1_BIO 40 40 40 40
## CC.Nat_2R_BIO 40 40 40 40
## CC.Nat_3R_BIO 40 40 40 40
## CC.Nat_4R_BIO 40 40 40 40
## CC.Nat_1_BECCS 40 40 40 40
## CC.Nat_2R_BECCS 40 40 40 40
## CC.Nat_3R_BECCS 40 40 40 40
## CC.Nat_4R_BECCS 40 40 40 40
## CC.Nat_1_DACCS 40 40 40 40
## CC.Nat_2R_DACCS 40 40 40 40
## CC.Nat_3R_DACCS 40 40 40 40
## CC.Nat_4R_DACCS 40 40 40 40
## CC.Nat_1_EW 40 40 40 40
## CC.Nat_2R_EW 40 40 40 40
## CC.Nat_3R_EW 40 40 40 40
## CC.Nat_4R_EW 40 40 40 40
## CC.Nat_1_OF 40 40 40 40
## CC.Nat_2R_OF 40 40 40 40
## CC.Nat_3R_OF 40 40 40 40
## CC.Nat_4R_OF 40 40 40 40
## CC.Nat_1_BF 28 28 28 28
## CC.Nat_2R_BF 28 28 28 28
## CC.Nat_3R_BF 28 28 28 28
## CC.Nat_4R_BF 28 28 28 28
## CC.Nat_1_NE 28 28 28 28
## CC.Nat_2R_NE 28 28 28 28
## CC.Nat_3R_NE 28 28 28 28
## CC.Nat_4R_NE 28 28 28 28
## CC.Nat_1_SE 28 28 28 28
## CC.Nat_2R_SE 28 28 28 28
## CC.Nat_3R_SE 28 28 28 28
## CC.Nat_4R_SE 28 28 28 28
## CC.Nat_1_WE 28 28 28 28
## CC.Nat_2R_WE 28 28 28 28
## CC.Nat_3R_WE 28 28 28 28
## CC.Nat_4R_WE 28 28 28 28
## CC.Nat_1_DACCS CC.Nat_2R_DACCS CC.Nat_3R_DACCS CC.Nat_4R_DACCS
## CC.Nat_1_AFSCS 40 40 40 40
## CC.Nat_2R_AFSCS 40 40 40 40
## CC.Nat_3R_AFSCS 40 40 40 40
## CC.Nat_4R_AFSCS 40 40 40 40
## CC.Nat_1_BIO 40 40 40 40
## CC.Nat_2R_BIO 40 40 40 40
## CC.Nat_3R_BIO 40 40 40 40
## CC.Nat_4R_BIO 40 40 40 40
## CC.Nat_1_BECCS 40 40 40 40
## CC.Nat_2R_BECCS 40 40 40 40
## CC.Nat_3R_BECCS 40 40 40 40
## CC.Nat_4R_BECCS 40 40 40 40
## CC.Nat_1_DACCS 40 40 40 40
## CC.Nat_2R_DACCS 40 40 40 40
## CC.Nat_3R_DACCS 40 40 40 40
## CC.Nat_4R_DACCS 40 40 40 40
## CC.Nat_1_EW 40 40 40 40
## CC.Nat_2R_EW 40 40 40 40
## CC.Nat_3R_EW 40 40 40 40
## CC.Nat_4R_EW 40 40 40 40
## CC.Nat_1_OF 40 40 40 40
## CC.Nat_2R_OF 40 40 40 40
## CC.Nat_3R_OF 40 40 40 40
## CC.Nat_4R_OF 40 40 40 40
## CC.Nat_1_BF 28 28 28 28
## CC.Nat_2R_BF 28 28 28 28
## CC.Nat_3R_BF 28 28 28 28
## CC.Nat_4R_BF 28 28 28 28
## CC.Nat_1_NE 28 28 28 28
## CC.Nat_2R_NE 28 28 28 28
## CC.Nat_3R_NE 28 28 28 28
## CC.Nat_4R_NE 28 28 28 28
## CC.Nat_1_SE 28 28 28 28
## CC.Nat_2R_SE 28 28 28 28
## CC.Nat_3R_SE 28 28 28 28
## CC.Nat_4R_SE 28 28 28 28
## CC.Nat_1_WE 28 28 28 28
## CC.Nat_2R_WE 28 28 28 28
## CC.Nat_3R_WE 28 28 28 28
## CC.Nat_4R_WE 28 28 28 28
## CC.Nat_1_EW CC.Nat_2R_EW CC.Nat_3R_EW CC.Nat_4R_EW CC.Nat_1_OF
## CC.Nat_1_AFSCS 40 40 40 40 40
## CC.Nat_2R_AFSCS 40 40 40 40 40
## CC.Nat_3R_AFSCS 40 40 40 40 40
## CC.Nat_4R_AFSCS 40 40 40 40 40
## CC.Nat_1_BIO 40 40 40 40 40
## CC.Nat_2R_BIO 40 40 40 40 40
## CC.Nat_3R_BIO 40 40 40 40 40
## CC.Nat_4R_BIO 40 40 40 40 40
## CC.Nat_1_BECCS 40 40 40 40 40
## CC.Nat_2R_BECCS 40 40 40 40 40
## CC.Nat_3R_BECCS 40 40 40 40 40
## CC.Nat_4R_BECCS 40 40 40 40 40
## CC.Nat_1_DACCS 40 40 40 40 40
## CC.Nat_2R_DACCS 40 40 40 40 40
## CC.Nat_3R_DACCS 40 40 40 40 40
## CC.Nat_4R_DACCS 40 40 40 40 40
## CC.Nat_1_EW 40 40 40 40 40
## CC.Nat_2R_EW 40 40 40 40 40
## CC.Nat_3R_EW 40 40 40 40 40
## CC.Nat_4R_EW 40 40 40 40 40
## CC.Nat_1_OF 40 40 40 40 40
## CC.Nat_2R_OF 40 40 40 40 40
## CC.Nat_3R_OF 40 40 40 40 40
## CC.Nat_4R_OF 40 40 40 40 40
## CC.Nat_1_BF 28 28 28 28 28
## CC.Nat_2R_BF 28 28 28 28 28
## CC.Nat_3R_BF 28 28 28 28 28
## CC.Nat_4R_BF 28 28 28 28 28
## CC.Nat_1_NE 28 28 28 28 28
## CC.Nat_2R_NE 28 28 28 28 28
## CC.Nat_3R_NE 28 28 28 28 28
## CC.Nat_4R_NE 28 28 28 28 28
## CC.Nat_1_SE 28 28 28 28 28
## CC.Nat_2R_SE 28 28 28 28 28
## CC.Nat_3R_SE 28 28 28 28 28
## CC.Nat_4R_SE 28 28 28 28 28
## CC.Nat_1_WE 28 28 28 28 28
## CC.Nat_2R_WE 28 28 28 28 28
## CC.Nat_3R_WE 28 28 28 28 28
## CC.Nat_4R_WE 28 28 28 28 28
## CC.Nat_2R_OF CC.Nat_3R_OF CC.Nat_4R_OF CC.Nat_1_BF CC.Nat_2R_BF
## CC.Nat_1_AFSCS 40 40 40 28 28
## CC.Nat_2R_AFSCS 40 40 40 28 28
## CC.Nat_3R_AFSCS 40 40 40 28 28
## CC.Nat_4R_AFSCS 40 40 40 28 28
## CC.Nat_1_BIO 40 40 40 28 28
## CC.Nat_2R_BIO 40 40 40 28 28
## CC.Nat_3R_BIO 40 40 40 28 28
## CC.Nat_4R_BIO 40 40 40 28 28
## CC.Nat_1_BECCS 40 40 40 28 28
## CC.Nat_2R_BECCS 40 40 40 28 28
## CC.Nat_3R_BECCS 40 40 40 28 28
## CC.Nat_4R_BECCS 40 40 40 28 28
## CC.Nat_1_DACCS 40 40 40 28 28
## CC.Nat_2R_DACCS 40 40 40 28 28
## CC.Nat_3R_DACCS 40 40 40 28 28
## CC.Nat_4R_DACCS 40 40 40 28 28
## CC.Nat_1_EW 40 40 40 28 28
## CC.Nat_2R_EW 40 40 40 28 28
## CC.Nat_3R_EW 40 40 40 28 28
## CC.Nat_4R_EW 40 40 40 28 28
## CC.Nat_1_OF 40 40 40 28 28
## CC.Nat_2R_OF 40 40 40 28 28
## CC.Nat_3R_OF 40 40 40 28 28
## CC.Nat_4R_OF 40 40 40 28 28
## CC.Nat_1_BF 28 28 28 28 28
## CC.Nat_2R_BF 28 28 28 28 28
## CC.Nat_3R_BF 28 28 28 28 28
## CC.Nat_4R_BF 28 28 28 28 28
## CC.Nat_1_NE 28 28 28 24 24
## CC.Nat_2R_NE 28 28 28 24 24
## CC.Nat_3R_NE 28 28 28 24 24
## CC.Nat_4R_NE 28 28 28 24 24
## CC.Nat_1_SE 28 28 28 24 24
## CC.Nat_2R_SE 28 28 28 24 24
## CC.Nat_3R_SE 28 28 28 24 24
## CC.Nat_4R_SE 28 28 28 24 24
## CC.Nat_1_WE 28 28 28 24 24
## CC.Nat_2R_WE 28 28 28 24 24
## CC.Nat_3R_WE 28 28 28 24 24
## CC.Nat_4R_WE 28 28 28 24 24
## CC.Nat_3R_BF CC.Nat_4R_BF CC.Nat_1_NE CC.Nat_2R_NE CC.Nat_3R_NE
## CC.Nat_1_AFSCS 28 28 28 28 28
## CC.Nat_2R_AFSCS 28 28 28 28 28
## CC.Nat_3R_AFSCS 28 28 28 28 28
## CC.Nat_4R_AFSCS 28 28 28 28 28
## CC.Nat_1_BIO 28 28 28 28 28
## CC.Nat_2R_BIO 28 28 28 28 28
## CC.Nat_3R_BIO 28 28 28 28 28
## CC.Nat_4R_BIO 28 28 28 28 28
## CC.Nat_1_BECCS 28 28 28 28 28
## CC.Nat_2R_BECCS 28 28 28 28 28
## CC.Nat_3R_BECCS 28 28 28 28 28
## CC.Nat_4R_BECCS 28 28 28 28 28
## CC.Nat_1_DACCS 28 28 28 28 28
## CC.Nat_2R_DACCS 28 28 28 28 28
## CC.Nat_3R_DACCS 28 28 28 28 28
## CC.Nat_4R_DACCS 28 28 28 28 28
## CC.Nat_1_EW 28 28 28 28 28
## CC.Nat_2R_EW 28 28 28 28 28
## CC.Nat_3R_EW 28 28 28 28 28
## CC.Nat_4R_EW 28 28 28 28 28
## CC.Nat_1_OF 28 28 28 28 28
## CC.Nat_2R_OF 28 28 28 28 28
## CC.Nat_3R_OF 28 28 28 28 28
## CC.Nat_4R_OF 28 28 28 28 28
## CC.Nat_1_BF 28 28 24 24 24
## CC.Nat_2R_BF 28 28 24 24 24
## CC.Nat_3R_BF 28 28 24 24 24
## CC.Nat_4R_BF 28 28 24 24 24
## CC.Nat_1_NE 24 24 28 28 28
## CC.Nat_2R_NE 24 24 28 28 28
## CC.Nat_3R_NE 24 24 28 28 28
## CC.Nat_4R_NE 24 24 28 28 28
## CC.Nat_1_SE 24 24 24 24 24
## CC.Nat_2R_SE 24 24 24 24 24
## CC.Nat_3R_SE 24 24 24 24 24
## CC.Nat_4R_SE 24 24 24 24 24
## CC.Nat_1_WE 24 24 24 24 24
## CC.Nat_2R_WE 24 24 24 24 24
## CC.Nat_3R_WE 24 24 24 24 24
## CC.Nat_4R_WE 24 24 24 24 24
## CC.Nat_4R_NE CC.Nat_1_SE CC.Nat_2R_SE CC.Nat_3R_SE CC.Nat_4R_SE
## CC.Nat_1_AFSCS 28 28 28 28 28
## CC.Nat_2R_AFSCS 28 28 28 28 28
## CC.Nat_3R_AFSCS 28 28 28 28 28
## CC.Nat_4R_AFSCS 28 28 28 28 28
## CC.Nat_1_BIO 28 28 28 28 28
## CC.Nat_2R_BIO 28 28 28 28 28
## CC.Nat_3R_BIO 28 28 28 28 28
## CC.Nat_4R_BIO 28 28 28 28 28
## CC.Nat_1_BECCS 28 28 28 28 28
## CC.Nat_2R_BECCS 28 28 28 28 28
## CC.Nat_3R_BECCS 28 28 28 28 28
## CC.Nat_4R_BECCS 28 28 28 28 28
## CC.Nat_1_DACCS 28 28 28 28 28
## CC.Nat_2R_DACCS 28 28 28 28 28
## CC.Nat_3R_DACCS 28 28 28 28 28
## CC.Nat_4R_DACCS 28 28 28 28 28
## CC.Nat_1_EW 28 28 28 28 28
## CC.Nat_2R_EW 28 28 28 28 28
## CC.Nat_3R_EW 28 28 28 28 28
## CC.Nat_4R_EW 28 28 28 28 28
## CC.Nat_1_OF 28 28 28 28 28
## CC.Nat_2R_OF 28 28 28 28 28
## CC.Nat_3R_OF 28 28 28 28 28
## CC.Nat_4R_OF 28 28 28 28 28
## CC.Nat_1_BF 24 24 24 24 24
## CC.Nat_2R_BF 24 24 24 24 24
## CC.Nat_3R_BF 24 24 24 24 24
## CC.Nat_4R_BF 24 24 24 24 24
## CC.Nat_1_NE 28 24 24 24 24
## CC.Nat_2R_NE 28 24 24 24 24
## CC.Nat_3R_NE 28 24 24 24 24
## CC.Nat_4R_NE 28 24 24 24 24
## CC.Nat_1_SE 24 28 28 28 28
## CC.Nat_2R_SE 24 28 28 28 28
## CC.Nat_3R_SE 24 28 28 28 28
## CC.Nat_4R_SE 24 28 28 28 28
## CC.Nat_1_WE 24 24 24 24 24
## CC.Nat_2R_WE 24 24 24 24 24
## CC.Nat_3R_WE 24 24 24 24 24
## CC.Nat_4R_WE 24 24 24 24 24
## CC.Nat_1_WE CC.Nat_2R_WE CC.Nat_3R_WE CC.Nat_4R_WE
## CC.Nat_1_AFSCS 28 28 28 28
## CC.Nat_2R_AFSCS 28 28 28 28
## CC.Nat_3R_AFSCS 28 28 28 28
## CC.Nat_4R_AFSCS 28 28 28 28
## CC.Nat_1_BIO 28 28 28 28
## CC.Nat_2R_BIO 28 28 28 28
## CC.Nat_3R_BIO 28 28 28 28
## CC.Nat_4R_BIO 28 28 28 28
## CC.Nat_1_BECCS 28 28 28 28
## CC.Nat_2R_BECCS 28 28 28 28
## CC.Nat_3R_BECCS 28 28 28 28
## CC.Nat_4R_BECCS 28 28 28 28
## CC.Nat_1_DACCS 28 28 28 28
## CC.Nat_2R_DACCS 28 28 28 28
## CC.Nat_3R_DACCS 28 28 28 28
## CC.Nat_4R_DACCS 28 28 28 28
## CC.Nat_1_EW 28 28 28 28
## CC.Nat_2R_EW 28 28 28 28
## CC.Nat_3R_EW 28 28 28 28
## CC.Nat_4R_EW 28 28 28 28
## CC.Nat_1_OF 28 28 28 28
## CC.Nat_2R_OF 28 28 28 28
## CC.Nat_3R_OF 28 28 28 28
## CC.Nat_4R_OF 28 28 28 28
## CC.Nat_1_BF 24 24 24 24
## CC.Nat_2R_BF 24 24 24 24
## CC.Nat_3R_BF 24 24 24 24
## CC.Nat_4R_BF 24 24 24 24
## CC.Nat_1_NE 24 24 24 24
## CC.Nat_2R_NE 24 24 24 24
## CC.Nat_3R_NE 24 24 24 24
## CC.Nat_4R_NE 24 24 24 24
## CC.Nat_1_SE 24 24 24 24
## CC.Nat_2R_SE 24 24 24 24
## CC.Nat_3R_SE 24 24 24 24
## CC.Nat_4R_SE 24 24 24 24
## CC.Nat_1_WE 28 28 28 28
## CC.Nat_2R_WE 28 28 28 28
## CC.Nat_3R_WE 28 28 28 28
## CC.Nat_4R_WE 28 28 28 28
##
## P
## CC.Nat_1_AFSCS CC.Nat_2R_AFSCS CC.Nat_3R_AFSCS CC.Nat_4R_AFSCS
## CC.Nat_1_AFSCS 0.0000 0.4787 0.0000
## CC.Nat_2R_AFSCS 0.0000 0.0652 0.0000
## CC.Nat_3R_AFSCS 0.4787 0.0652 0.5506
## CC.Nat_4R_AFSCS 0.0000 0.0000 0.5506
## CC.Nat_1_BIO 0.7978 0.2002 0.0228 0.7566
## CC.Nat_2R_BIO 0.8278 0.2684 0.3245 0.6252
## CC.Nat_3R_BIO 0.0000 0.0058 0.0059 0.0010
## CC.Nat_4R_BIO 0.0800 0.1551 0.5869 0.2945
## CC.Nat_1_BECCS 0.4264 0.1582 0.0002 0.7250
## CC.Nat_2R_BECCS 0.5940 0.2201 0.2805 0.9144
## CC.Nat_3R_BECCS 0.0426 0.6729 0.0011 0.1552
## CC.Nat_4R_BECCS 0.3130 0.2053 0.0001 0.7533
## CC.Nat_1_DACCS 0.8473 0.6754 0.6282 0.1767
## CC.Nat_2R_DACCS 0.7848 0.0130 0.1519 0.9581
## CC.Nat_3R_DACCS 0.0253 0.1868 0.0340 0.0474
## CC.Nat_4R_DACCS 0.9325 0.6198 0.8459 0.4213
## CC.Nat_1_EW 0.9070 0.4072 0.5521 0.2448
## CC.Nat_2R_EW 0.0342 0.5907 0.0951 0.1523
## CC.Nat_3R_EW 0.0002 0.2557 0.0000 0.0234
## CC.Nat_4R_EW 0.2165 0.1586 0.6002 0.1039
## CC.Nat_1_OF 0.1495 0.6240 0.1373 0.9164
## CC.Nat_2R_OF 0.7352 0.7803 0.4614 0.7515
## CC.Nat_3R_OF 0.3969 0.6616 0.0000 0.6280
## CC.Nat_4R_OF 0.2091 0.7615 0.1841 0.6497
## CC.Nat_1_BF 0.7120 0.1417 0.0003 0.3482
## CC.Nat_2R_BF 0.1725 0.8023 0.0461 0.7115
## CC.Nat_3R_BF 0.0012 0.0534 0.0552 0.0009
## CC.Nat_4R_BF 0.2356 0.3576 0.0293 0.4707
## CC.Nat_1_NE 0.5357 0.1720 0.0007 0.0246
## CC.Nat_2R_NE 0.4346 0.6848 0.4369 0.2511
## CC.Nat_3R_NE 0.0382 0.1794 0.7640 0.0462
## CC.Nat_4R_NE 0.2791 0.1992 0.0072 0.0867
## CC.Nat_1_SE 0.8716 0.1556 0.0000 0.2023
## CC.Nat_2R_SE 0.5911 0.0213 0.7409 0.3229
## CC.Nat_3R_SE 0.0004 0.0175 0.1040 0.0073
## CC.Nat_4R_SE 0.9910 0.4260 0.0031 0.3221
## CC.Nat_1_WE 0.1140 0.2928 0.0004 0.3328
## CC.Nat_2R_WE 0.0322 0.1491 0.1599 0.0155
## CC.Nat_3R_WE 0.0316 0.7453 0.0012 0.0423
## CC.Nat_4R_WE 0.1737 0.2940 0.0003 0.3280
## CC.Nat_1_BIO CC.Nat_2R_BIO CC.Nat_3R_BIO CC.Nat_4R_BIO
## CC.Nat_1_AFSCS 0.7978 0.8278 0.0000 0.0800
## CC.Nat_2R_AFSCS 0.2002 0.2684 0.0058 0.1551
## CC.Nat_3R_AFSCS 0.0228 0.3245 0.0059 0.5869
## CC.Nat_4R_AFSCS 0.7566 0.6252 0.0010 0.2945
## CC.Nat_1_BIO 0.0035 0.3893 0.0000
## CC.Nat_2R_BIO 0.0035 0.9734 0.0022
## CC.Nat_3R_BIO 0.3893 0.9734 0.1708
## CC.Nat_4R_BIO 0.0000 0.0022 0.1708
## CC.Nat_1_BECCS 0.0029 0.8287 0.6833 0.0287
## CC.Nat_2R_BECCS 0.1622 0.0191 0.3833 0.1112
## CC.Nat_3R_BECCS 0.0014 0.2377 0.0000 0.1415
## CC.Nat_4R_BECCS 0.0003 0.4151 0.4018 0.0003
## CC.Nat_1_DACCS 0.0219 0.7591 0.8308 0.0768
## CC.Nat_2R_DACCS 0.9492 0.1939 0.7456 0.6775
## CC.Nat_3R_DACCS 0.0021 0.1493 0.0001 0.0375
## CC.Nat_4R_DACCS 0.1015 0.8640 0.4310 0.0901
## CC.Nat_1_EW 0.0006 0.0319 0.4343 0.0017
## CC.Nat_2R_EW 0.1523 0.0008 0.0386 0.0031
## CC.Nat_3R_EW 0.0939 0.7773 0.0000 0.5264
## CC.Nat_4R_EW 0.0006 0.0118 0.0730 0.0001
## CC.Nat_1_OF 0.0000 0.0372 0.2153 0.0039
## CC.Nat_2R_OF 0.0962 0.0006 0.2989 0.0302
## CC.Nat_3R_OF 0.0003 0.2593 0.0002 0.1265
## CC.Nat_4R_OF 0.0011 0.0121 0.4252 0.0006
## CC.Nat_1_BF 0.0028 0.5137 0.0950 0.1531
## CC.Nat_2R_BF 0.8377 0.0115 0.5642 0.9233
## CC.Nat_3R_BF 0.0236 0.5106 0.0000 0.2395
## CC.Nat_4R_BF 0.0163 0.1470 0.4834 0.0577
## CC.Nat_1_NE 0.1979 0.9213 0.4966 0.6654
## CC.Nat_2R_NE 0.2329 0.9674 0.9213 0.3964
## CC.Nat_3R_NE 0.3859 0.8637 0.0493 0.4869
## CC.Nat_4R_NE 0.3407 0.9448 0.9274 0.4047
## CC.Nat_1_SE 0.1075 0.8870 0.0170 0.9242
## CC.Nat_2R_SE 0.2290 0.8936 0.0524 0.2961
## CC.Nat_3R_SE 0.1378 0.0929 0.0014 0.7589
## CC.Nat_4R_SE 0.6167 0.5593 0.0854 0.3786
## CC.Nat_1_WE 0.3693 0.2188 0.0038 0.8191
## CC.Nat_2R_WE 0.7470 0.6020 0.0047 0.4495
## CC.Nat_3R_WE 0.0303 0.2937 0.1714 0.5851
## CC.Nat_4R_WE 0.5576 0.2230 0.0048 0.8950
## CC.Nat_1_BECCS CC.Nat_2R_BECCS CC.Nat_3R_BECCS CC.Nat_4R_BECCS
## CC.Nat_1_AFSCS 0.4264 0.5940 0.0426 0.3130
## CC.Nat_2R_AFSCS 0.1582 0.2201 0.6729 0.2053
## CC.Nat_3R_AFSCS 0.0002 0.2805 0.0011 0.0001
## CC.Nat_4R_AFSCS 0.7250 0.9144 0.1552 0.7533
## CC.Nat_1_BIO 0.0029 0.1622 0.0014 0.0003
## CC.Nat_2R_BIO 0.8287 0.0191 0.2377 0.4151
## CC.Nat_3R_BIO 0.6833 0.3833 0.0000 0.4018
## CC.Nat_4R_BIO 0.0287 0.1112 0.1415 0.0003
## CC.Nat_1_BECCS 0.2432 0.0318 0.0000
## CC.Nat_2R_BECCS 0.2432 0.6982 0.1449
## CC.Nat_3R_BECCS 0.0318 0.6982 0.0134
## CC.Nat_4R_BECCS 0.0000 0.1449 0.0134
## CC.Nat_1_DACCS 0.0039 0.9155 0.0607 0.0121
## CC.Nat_2R_DACCS 0.5423 0.0008 0.8636 0.2566
## CC.Nat_3R_DACCS 0.0641 0.0776 0.0000 0.0232
## CC.Nat_4R_DACCS 0.0473 0.7162 0.2279 0.1048
## CC.Nat_1_EW 0.0341 0.5614 0.0228 0.0582
## CC.Nat_2R_EW 0.9422 0.0006 0.4414 0.6587
## CC.Nat_3R_EW 0.0282 0.5708 0.0000 0.0200
## CC.Nat_4R_EW 0.0424 0.2230 0.2673 0.0483
## CC.Nat_1_OF 0.0043 0.2150 0.0538 0.0037
## CC.Nat_2R_OF 0.4009 0.5178 0.9043 0.2682
## CC.Nat_3R_OF 0.0096 0.1717 0.0000 0.0056
## CC.Nat_4R_OF 0.0065 0.1623 0.1556 0.0004
## CC.Nat_1_BF 0.0368 0.1418 0.0000 0.1262
## CC.Nat_2R_BF 0.8035 0.0696 0.1246 0.6700
## CC.Nat_3R_BF 0.0253 0.1964 0.0000 0.0214
## CC.Nat_4R_BF 0.6322 0.3815 0.0007 0.7268
## CC.Nat_1_NE 0.0012 0.5247 0.1269 0.0762
## CC.Nat_2R_NE 0.5278 0.2079 0.9805 0.5589
## CC.Nat_3R_NE 0.1751 0.3871 0.0000 0.1913
## CC.Nat_4R_NE 0.0015 0.5666 0.1233 0.0490
## CC.Nat_1_SE 0.1842 0.9301 0.0001 0.1303
## CC.Nat_2R_SE 0.0544 0.2843 0.9000 0.1675
## CC.Nat_3R_SE 0.0834 0.3342 0.0044 0.1728
## CC.Nat_4R_SE 0.7834 0.7826 0.0213 0.2551
## CC.Nat_1_WE 0.0741 0.2694 0.0027 0.0190
## CC.Nat_2R_WE 0.3510 0.8626 0.0414 0.9612
## CC.Nat_3R_WE 0.0453 0.5377 0.0330 0.0262
## CC.Nat_4R_WE 0.1424 0.5874 0.0179 0.0227
## CC.Nat_1_DACCS CC.Nat_2R_DACCS CC.Nat_3R_DACCS CC.Nat_4R_DACCS
## CC.Nat_1_AFSCS 0.8473 0.7848 0.0253 0.9325
## CC.Nat_2R_AFSCS 0.6754 0.0130 0.1868 0.6198
## CC.Nat_3R_AFSCS 0.6282 0.1519 0.0340 0.8459
## CC.Nat_4R_AFSCS 0.1767 0.9581 0.0474 0.4213
## CC.Nat_1_BIO 0.0219 0.9492 0.0021 0.1015
## CC.Nat_2R_BIO 0.7591 0.1939 0.1493 0.8640
## CC.Nat_3R_BIO 0.8308 0.7456 0.0001 0.4310
## CC.Nat_4R_BIO 0.0768 0.6775 0.0375 0.0901
## CC.Nat_1_BECCS 0.0039 0.5423 0.0641 0.0473
## CC.Nat_2R_BECCS 0.9155 0.0008 0.0776 0.7162
## CC.Nat_3R_BECCS 0.0607 0.8636 0.0000 0.2279
## CC.Nat_4R_BECCS 0.0121 0.2566 0.0232 0.1048
## CC.Nat_1_DACCS 0.0228 0.7250 0.0000
## CC.Nat_2R_DACCS 0.0228 0.5456 0.0091
## CC.Nat_3R_DACCS 0.7250 0.5456 0.6894
## CC.Nat_4R_DACCS 0.0000 0.0091 0.6894
## CC.Nat_1_EW 0.0008 0.1279 0.0384 0.0128
## CC.Nat_2R_EW 0.0753 0.0000 0.8526 0.0093
## CC.Nat_3R_EW 0.0292 0.6397 0.0002 0.1560
## CC.Nat_4R_EW 0.0358 0.2140 0.1340 0.0248
## CC.Nat_1_OF 0.0612 0.2816 0.0446 0.2868
## CC.Nat_2R_OF 0.1148 0.1164 0.3460 0.1425
## CC.Nat_3R_OF 0.2066 0.6475 0.0000 0.2645
## CC.Nat_4R_OF 0.5405 0.9120 0.0064 0.9571
## CC.Nat_1_BF 0.4633 0.8344 0.0263 0.2421
## CC.Nat_2R_BF 0.4949 0.3590 0.6498 0.4856
## CC.Nat_3R_BF 0.9805 0.6275 0.0000 0.9244
## CC.Nat_4R_BF 0.7496 0.9316 0.0621 0.2981
## CC.Nat_1_NE 0.1317 0.3921 0.1876 0.3246
## CC.Nat_2R_NE 0.3673 0.9288 0.4480 0.4431
## CC.Nat_3R_NE 0.1499 0.5637 0.0002 0.9166
## CC.Nat_4R_NE 0.2657 0.1525 0.1778 0.1475
## CC.Nat_1_SE 0.0629 0.9400 0.0833 0.2126
## CC.Nat_2R_SE 0.1749 0.8226 0.5160 0.4894
## CC.Nat_3R_SE 0.1587 0.4409 0.0099 0.2305
## CC.Nat_4R_SE 0.5336 0.7614 0.3276 0.3634
## CC.Nat_1_WE 0.6155 0.0074 0.1068 0.5791
## CC.Nat_2R_WE 0.7107 0.4296 0.1513 0.6412
## CC.Nat_3R_WE 0.3402 0.8165 0.1988 0.4678
## CC.Nat_4R_WE 0.8991 0.0074 0.0748 0.3845
## CC.Nat_1_EW CC.Nat_2R_EW CC.Nat_3R_EW CC.Nat_4R_EW CC.Nat_1_OF
## CC.Nat_1_AFSCS 0.9070 0.0342 0.0002 0.2165 0.1495
## CC.Nat_2R_AFSCS 0.4072 0.5907 0.2557 0.1586 0.6240
## CC.Nat_3R_AFSCS 0.5521 0.0951 0.0000 0.6002 0.1373
## CC.Nat_4R_AFSCS 0.2448 0.1523 0.0234 0.1039 0.9164
## CC.Nat_1_BIO 0.0006 0.1523 0.0939 0.0006 0.0000
## CC.Nat_2R_BIO 0.0319 0.0008 0.7773 0.0118 0.0372
## CC.Nat_3R_BIO 0.4343 0.0386 0.0000 0.0730 0.2153
## CC.Nat_4R_BIO 0.0017 0.0031 0.5264 0.0001 0.0039
## CC.Nat_1_BECCS 0.0341 0.9422 0.0282 0.0424 0.0043
## CC.Nat_2R_BECCS 0.5614 0.0006 0.5708 0.2230 0.2150
## CC.Nat_3R_BECCS 0.0228 0.4414 0.0000 0.2673 0.0538
## CC.Nat_4R_BECCS 0.0582 0.6587 0.0200 0.0483 0.0037
## CC.Nat_1_DACCS 0.0008 0.0753 0.0292 0.0358 0.0612
## CC.Nat_2R_DACCS 0.1279 0.0000 0.6397 0.2140 0.2816
## CC.Nat_3R_DACCS 0.0384 0.8526 0.0002 0.1340 0.0446
## CC.Nat_4R_DACCS 0.0128 0.0093 0.1560 0.0248 0.2868
## CC.Nat_1_EW 0.0013 0.3990 0.0000 0.0000
## CC.Nat_2R_EW 0.0013 0.0616 0.0000 0.0524
## CC.Nat_3R_EW 0.3990 0.0616 0.7165 0.2240
## CC.Nat_4R_EW 0.0000 0.0000 0.7165 0.0002
## CC.Nat_1_OF 0.0000 0.0524 0.2240 0.0002
## CC.Nat_2R_OF 0.0597 0.0012 0.1802 0.1136 0.0012
## CC.Nat_3R_OF 0.5301 0.3792 0.0000 0.9166 0.1408
## CC.Nat_4R_OF 0.0000 0.0441 0.8163 0.0000 0.0000
## CC.Nat_1_BF 0.0237 0.7105 0.0038 0.0229 0.0103
## CC.Nat_2R_BF 0.6462 0.0985 0.2852 0.9497 0.4027
## CC.Nat_3R_BF 0.2755 0.3088 0.0000 0.6818 0.0264
## CC.Nat_4R_BF 0.1164 0.3670 0.1626 0.0395 0.3025
## CC.Nat_1_NE 0.6450 0.2904 0.2296 0.6926 0.7109
## CC.Nat_2R_NE 0.8361 0.6758 0.8888 0.6909 0.1121
## CC.Nat_3R_NE 0.0852 0.5840 0.0041 0.6038 0.1876
## CC.Nat_4R_NE 0.9087 0.5041 0.4371 0.5452 0.7456
## CC.Nat_1_SE 0.0884 0.5552 0.0002 0.5645 0.1939
## CC.Nat_2R_SE 0.0302 0.6077 0.9739 0.0504 0.0296
## CC.Nat_3R_SE 0.5061 0.8163 0.0003 0.9309 0.0624
## CC.Nat_4R_SE 0.6613 0.4067 0.0092 0.8751 0.7531
## CC.Nat_1_WE 0.2543 0.0020 0.0008 0.1846 0.9693
## CC.Nat_2R_WE 0.0402 0.0377 0.0044 0.0686 0.0207
## CC.Nat_3R_WE 0.9009 0.1676 0.0004 0.9670 0.8430
## CC.Nat_4R_WE 0.1170 0.0044 0.0015 0.2287 0.6552
## CC.Nat_2R_OF CC.Nat_3R_OF CC.Nat_4R_OF CC.Nat_1_BF CC.Nat_2R_BF
## CC.Nat_1_AFSCS 0.7352 0.3969 0.2091 0.7120 0.1725
## CC.Nat_2R_AFSCS 0.7803 0.6616 0.7615 0.1417 0.8023
## CC.Nat_3R_AFSCS 0.4614 0.0000 0.1841 0.0003 0.0461
## CC.Nat_4R_AFSCS 0.7515 0.6280 0.6497 0.3482 0.7115
## CC.Nat_1_BIO 0.0962 0.0003 0.0011 0.0028 0.8377
## CC.Nat_2R_BIO 0.0006 0.2593 0.0121 0.5137 0.0115
## CC.Nat_3R_BIO 0.2989 0.0002 0.4252 0.0950 0.5642
## CC.Nat_4R_BIO 0.0302 0.1265 0.0006 0.1531 0.9233
## CC.Nat_1_BECCS 0.4009 0.0096 0.0065 0.0368 0.8035
## CC.Nat_2R_BECCS 0.5178 0.1717 0.1623 0.1418 0.0696
## CC.Nat_3R_BECCS 0.9043 0.0000 0.1556 0.0000 0.1246
## CC.Nat_4R_BECCS 0.2682 0.0056 0.0004 0.1262 0.6700
## CC.Nat_1_DACCS 0.1148 0.2066 0.5405 0.4633 0.4949
## CC.Nat_2R_DACCS 0.1164 0.6475 0.9120 0.8344 0.3590
## CC.Nat_3R_DACCS 0.3460 0.0000 0.0064 0.0263 0.6498
## CC.Nat_4R_DACCS 0.1425 0.2645 0.9571 0.2421 0.4856
## CC.Nat_1_EW 0.0597 0.5301 0.0000 0.0237 0.6462
## CC.Nat_2R_EW 0.0012 0.3792 0.0441 0.7105 0.0985
## CC.Nat_3R_EW 0.1802 0.0000 0.8163 0.0038 0.2852
## CC.Nat_4R_EW 0.1136 0.9166 0.0000 0.0229 0.9497
## CC.Nat_1_OF 0.0012 0.1408 0.0000 0.0103 0.4027
## CC.Nat_2R_OF 0.4047 0.0008 0.2812 0.8985
## CC.Nat_3R_OF 0.4047 0.4830 0.0000 0.0388
## CC.Nat_4R_OF 0.0008 0.4830 0.2671 0.1957
## CC.Nat_1_BF 0.2812 0.0000 0.2671 0.0244
## CC.Nat_2R_BF 0.8985 0.0388 0.1957 0.0244
## CC.Nat_3R_BF 0.4245 0.0001 0.0267 0.0278 0.9200
## CC.Nat_4R_BF 0.4490 0.0005 0.7017 0.0000 0.0013
## CC.Nat_1_NE 0.6971 0.0121 0.8118 0.0032 0.6800
## CC.Nat_2R_NE 0.1259 0.9232 0.2554 0.8985 0.0511
## CC.Nat_3R_NE 0.4837 0.1309 0.2617 0.1414 0.4555
## CC.Nat_4R_NE 0.8405 0.0092 0.9620 0.0111 0.1170
## CC.Nat_1_SE 0.8018 0.0001 0.3690 0.0000 0.2944
## CC.Nat_2R_SE 0.1400 0.9454 0.1511 0.1256 0.0943
## CC.Nat_3R_SE 0.8273 0.0028 0.2304 0.0316 0.5154
## CC.Nat_4R_SE 0.4816 0.0229 0.8541 0.0144 0.2026
## CC.Nat_1_WE 0.3404 0.0105 0.9039 0.0500 0.7436
## CC.Nat_2R_WE 0.0380 0.0359 0.0438 0.9140 0.2548
## CC.Nat_3R_WE 0.3173 0.0043 0.7813 0.0350 0.3071
## CC.Nat_4R_WE 0.0850 0.0183 0.8646 0.0638 0.8693
## CC.Nat_3R_BF CC.Nat_4R_BF CC.Nat_1_NE CC.Nat_2R_NE CC.Nat_3R_NE
## CC.Nat_1_AFSCS 0.0012 0.2356 0.5357 0.4346 0.0382
## CC.Nat_2R_AFSCS 0.0534 0.3576 0.1720 0.6848 0.1794
## CC.Nat_3R_AFSCS 0.0552 0.0293 0.0007 0.4369 0.7640
## CC.Nat_4R_AFSCS 0.0009 0.4707 0.0246 0.2511 0.0462
## CC.Nat_1_BIO 0.0236 0.0163 0.1979 0.2329 0.3859
## CC.Nat_2R_BIO 0.5106 0.1470 0.9213 0.9674 0.8637
## CC.Nat_3R_BIO 0.0000 0.4834 0.4966 0.9213 0.0493
## CC.Nat_4R_BIO 0.2395 0.0577 0.6654 0.3964 0.4869
## CC.Nat_1_BECCS 0.0253 0.6322 0.0012 0.5278 0.1751
## CC.Nat_2R_BECCS 0.1964 0.3815 0.5247 0.2079 0.3871
## CC.Nat_3R_BECCS 0.0000 0.0007 0.1269 0.9805 0.0000
## CC.Nat_4R_BECCS 0.0214 0.7268 0.0762 0.5589 0.1913
## CC.Nat_1_DACCS 0.9805 0.7496 0.1317 0.3673 0.1499
## CC.Nat_2R_DACCS 0.6275 0.9316 0.3921 0.9288 0.5637
## CC.Nat_3R_DACCS 0.0000 0.0621 0.1876 0.4480 0.0002
## CC.Nat_4R_DACCS 0.9244 0.2981 0.3246 0.4431 0.9166
## CC.Nat_1_EW 0.2755 0.1164 0.6450 0.8361 0.0852
## CC.Nat_2R_EW 0.3088 0.3670 0.2904 0.6758 0.5840
## CC.Nat_3R_EW 0.0000 0.1626 0.2296 0.8888 0.0041
## CC.Nat_4R_EW 0.6818 0.0395 0.6926 0.6909 0.6038
## CC.Nat_1_OF 0.0264 0.3025 0.7109 0.1121 0.1876
## CC.Nat_2R_OF 0.4245 0.4490 0.6971 0.1259 0.4837
## CC.Nat_3R_OF 0.0001 0.0005 0.0121 0.9232 0.1309
## CC.Nat_4R_OF 0.0267 0.7017 0.8118 0.2554 0.2617
## CC.Nat_1_BF 0.0278 0.0000 0.0032 0.8985 0.1414
## CC.Nat_2R_BF 0.9200 0.0013 0.6800 0.0511 0.4555
## CC.Nat_3R_BF 0.1397 0.4813 0.8820 0.0000
## CC.Nat_4R_BF 0.1397 0.1305 0.6810 0.5389
## CC.Nat_1_NE 0.4813 0.1305 0.1542 0.7822
## CC.Nat_2R_NE 0.8820 0.6810 0.1542 0.4135
## CC.Nat_3R_NE 0.0000 0.5389 0.7822 0.4135
## CC.Nat_4R_NE 0.4592 0.0105 0.0000 0.0672 0.8663
## CC.Nat_1_SE 0.0806 0.0054 0.0001 0.6303 0.1470
## CC.Nat_2R_SE 0.5515 0.7111 0.0888 0.0952 0.8218
## CC.Nat_3R_SE 0.0000 0.3816 0.1673 0.6498 0.0007
## CC.Nat_4R_SE 0.2988 0.1048 0.0182 0.9866 0.4017
## CC.Nat_1_WE 0.0024 0.6040 0.0157 0.4109 0.0218
## CC.Nat_2R_WE 0.0149 0.5326 0.5371 0.9685 0.0469
## CC.Nat_3R_WE 0.0032 0.1942 0.1440 0.9491 0.0353
## CC.Nat_4R_WE 0.0019 0.4816 0.0628 0.9894 0.0461
## CC.Nat_4R_NE CC.Nat_1_SE CC.Nat_2R_SE CC.Nat_3R_SE CC.Nat_4R_SE
## CC.Nat_1_AFSCS 0.2791 0.8716 0.5911 0.0004 0.9910
## CC.Nat_2R_AFSCS 0.1992 0.1556 0.0213 0.0175 0.4260
## CC.Nat_3R_AFSCS 0.0072 0.0000 0.7409 0.1040 0.0031
## CC.Nat_4R_AFSCS 0.0867 0.2023 0.3229 0.0073 0.3221
## CC.Nat_1_BIO 0.3407 0.1075 0.2290 0.1378 0.6167
## CC.Nat_2R_BIO 0.9448 0.8870 0.8936 0.0929 0.5593
## CC.Nat_3R_BIO 0.9274 0.0170 0.0524 0.0014 0.0854
## CC.Nat_4R_BIO 0.4047 0.9242 0.2961 0.7589 0.3786
## CC.Nat_1_BECCS 0.0015 0.1842 0.0544 0.0834 0.7834
## CC.Nat_2R_BECCS 0.5666 0.9301 0.2843 0.3342 0.7826
## CC.Nat_3R_BECCS 0.1233 0.0001 0.9000 0.0044 0.0213
## CC.Nat_4R_BECCS 0.0490 0.1303 0.1675 0.1728 0.2551
## CC.Nat_1_DACCS 0.2657 0.0629 0.1749 0.1587 0.5336
## CC.Nat_2R_DACCS 0.1525 0.9400 0.8226 0.4409 0.7614
## CC.Nat_3R_DACCS 0.1778 0.0833 0.5160 0.0099 0.3276
## CC.Nat_4R_DACCS 0.1475 0.2126 0.4894 0.2305 0.3634
## CC.Nat_1_EW 0.9087 0.0884 0.0302 0.5061 0.6613
## CC.Nat_2R_EW 0.5041 0.5552 0.6077 0.8163 0.4067
## CC.Nat_3R_EW 0.4371 0.0002 0.9739 0.0003 0.0092
## CC.Nat_4R_EW 0.5452 0.5645 0.0504 0.9309 0.8751
## CC.Nat_1_OF 0.7456 0.1939 0.0296 0.0624 0.7531
## CC.Nat_2R_OF 0.8405 0.8018 0.1400 0.8273 0.4816
## CC.Nat_3R_OF 0.0092 0.0001 0.9454 0.0028 0.0229
## CC.Nat_4R_OF 0.9620 0.3690 0.1511 0.2304 0.8541
## CC.Nat_1_BF 0.0111 0.0000 0.1256 0.0316 0.0144
## CC.Nat_2R_BF 0.1170 0.2944 0.0943 0.5154 0.2026
## CC.Nat_3R_BF 0.4592 0.0806 0.5515 0.0000 0.2988
## CC.Nat_4R_BF 0.0105 0.0054 0.7111 0.3816 0.1048
## CC.Nat_1_NE 0.0000 0.0001 0.0888 0.1673 0.0182
## CC.Nat_2R_NE 0.0672 0.6303 0.0952 0.6498 0.9866
## CC.Nat_3R_NE 0.8663 0.1470 0.8218 0.0007 0.4017
## CC.Nat_4R_NE 0.0213 0.4072 0.5490 0.0144
## CC.Nat_1_SE 0.0213 0.6647 0.3231 0.0000
## CC.Nat_2R_SE 0.4072 0.6647 0.8882 0.2325
## CC.Nat_3R_SE 0.5490 0.3231 0.8882 0.7659
## CC.Nat_4R_SE 0.0144 0.0000 0.2325 0.7659
## CC.Nat_1_WE 0.0174 0.0031 0.6983 0.0075 0.0287
## CC.Nat_2R_WE 0.8754 0.6859 0.0018 0.0031 0.2771
## CC.Nat_3R_WE 0.1874 0.0254 0.8186 0.0008 0.2109
## CC.Nat_4R_WE 0.0323 0.0302 0.7183 0.0117 0.0177
## CC.Nat_1_WE CC.Nat_2R_WE CC.Nat_3R_WE CC.Nat_4R_WE
## CC.Nat_1_AFSCS 0.1140 0.0322 0.0316 0.1737
## CC.Nat_2R_AFSCS 0.2928 0.1491 0.7453 0.2940
## CC.Nat_3R_AFSCS 0.0004 0.1599 0.0012 0.0003
## CC.Nat_4R_AFSCS 0.3328 0.0155 0.0423 0.3280
## CC.Nat_1_BIO 0.3693 0.7470 0.0303 0.5576
## CC.Nat_2R_BIO 0.2188 0.6020 0.2937 0.2230
## CC.Nat_3R_BIO 0.0038 0.0047 0.1714 0.0048
## CC.Nat_4R_BIO 0.8191 0.4495 0.5851 0.8950
## CC.Nat_1_BECCS 0.0741 0.3510 0.0453 0.1424
## CC.Nat_2R_BECCS 0.2694 0.8626 0.5377 0.5874
## CC.Nat_3R_BECCS 0.0027 0.0414 0.0330 0.0179
## CC.Nat_4R_BECCS 0.0190 0.9612 0.0262 0.0227
## CC.Nat_1_DACCS 0.6155 0.7107 0.3402 0.8991
## CC.Nat_2R_DACCS 0.0074 0.4296 0.8165 0.0074
## CC.Nat_3R_DACCS 0.1068 0.1513 0.1988 0.0748
## CC.Nat_4R_DACCS 0.5791 0.6412 0.4678 0.3845
## CC.Nat_1_EW 0.2543 0.0402 0.9009 0.1170
## CC.Nat_2R_EW 0.0020 0.0377 0.1676 0.0044
## CC.Nat_3R_EW 0.0008 0.0044 0.0004 0.0015
## CC.Nat_4R_EW 0.1846 0.0686 0.9670 0.2287
## CC.Nat_1_OF 0.9693 0.0207 0.8430 0.6552
## CC.Nat_2R_OF 0.3404 0.0380 0.3173 0.0850
## CC.Nat_3R_OF 0.0105 0.0359 0.0043 0.0183
## CC.Nat_4R_OF 0.9039 0.0438 0.7813 0.8646
## CC.Nat_1_BF 0.0500 0.9140 0.0350 0.0638
## CC.Nat_2R_BF 0.7436 0.2548 0.3071 0.8693
## CC.Nat_3R_BF 0.0024 0.0149 0.0032 0.0019
## CC.Nat_4R_BF 0.6040 0.5326 0.1942 0.4816
## CC.Nat_1_NE 0.0157 0.5371 0.1440 0.0628
## CC.Nat_2R_NE 0.4109 0.9685 0.9491 0.9894
## CC.Nat_3R_NE 0.0218 0.0469 0.0353 0.0461
## CC.Nat_4R_NE 0.0174 0.8754 0.1874 0.0323
## CC.Nat_1_SE 0.0031 0.6859 0.0254 0.0302
## CC.Nat_2R_SE 0.6983 0.0018 0.8186 0.7183
## CC.Nat_3R_SE 0.0075 0.0031 0.0008 0.0117
## CC.Nat_4R_SE 0.0287 0.2771 0.2109 0.0177
## CC.Nat_1_WE 0.0037 0.0487 0.0000
## CC.Nat_2R_WE 0.0037 0.0372 0.0010
## CC.Nat_3R_WE 0.0487 0.0372 0.0702
## CC.Nat_4R_WE 0.0000 0.0010 0.0702library(corrplot)## corrplot 0.92 loadedcorrplot(mydata.cor1, method="color")
corrplot(mydata.cor1, addCoef.col = 1, number.cex = 0.3, method = 'number')
Individual Differences
#Individual Differences
CC$corID <- data.frame(CC$ATNS_Scale, CC$CCB_Scale, CC$CNS_Scale, CC$IndScale, CC$CollScale, CC$Party, CC$PI_Orientation)
length(CC$ATNS_Scale)## [1] 5length(CC$CCB_Scale)## [1] 4length(CC$CNS_Scale)## [1] 3length(CC$IndScale)## [1] 4length(CC$CollScale)## [1] 4length(CC$PartyFull)## [1] 1033length(CC$Orientation)## [1] 1033mydata.cor2 = cor(CC$corID, use = "pairwise.complete.obs")
head(round(mydata.cor2,2))## CC.ATNS_1 CC.ATNS_2R CC.ATNS_3 CC.ATNS_4 CC.ATNS_5 CC.CCB_1_48
## CC.ATNS_1 1.00 0.26 0.39 0.36 0.41 -0.22
## CC.ATNS_2R 0.26 1.00 0.55 0.54 0.49 -0.02
## CC.ATNS_3 0.39 0.55 1.00 0.70 0.61 -0.01
## CC.ATNS_4 0.36 0.54 0.70 1.00 0.61 0.01
## CC.ATNS_5 0.41 0.49 0.61 0.61 1.00 0.01
## CC.CCB_1_48 -0.22 -0.02 -0.01 0.01 0.01 1.00
## CC.CCB_1_49 CC.CCB_1_50 CC.CCB_1_51 CC.CNS_1 CC.CNS_2 CC.CNS_3
## CC.ATNS_1 -0.20 -0.24 -0.27 0.05 0.01 0.11
## CC.ATNS_2R 0.01 -0.03 -0.03 0.20 0.15 0.18
## CC.ATNS_3 0.03 0.01 -0.01 0.21 0.17 0.28
## CC.ATNS_4 0.06 0.03 0.00 0.29 0.21 0.30
## CC.ATNS_5 0.07 0.04 -0.03 0.23 0.16 0.25
## CC.CCB_1_48 0.87 0.78 0.71 0.21 0.22 0.15
## CC.Ind_1 CC.Ind_2 CC.Ind_5 CC.Ind_6 CC.Ind_3 CC.Ind_4 CC.Ind_7
## CC.ATNS_1 0.03 0.11 0.05 0.08 0.12 0.11 0.13
## CC.ATNS_2R 0.06 0.08 0.05 0.00 0.04 0.05 0.00
## CC.ATNS_3 0.12 0.16 0.06 0.07 0.07 0.08 0.03
## CC.ATNS_4 0.11 0.15 0.07 0.07 0.06 0.09 0.05
## CC.ATNS_5 0.07 0.14 0.05 0.03 0.00 0.03 0.00
## CC.CCB_1_48 0.14 -0.05 0.07 0.07 -0.17 -0.13 -0.16
## CC.Ind_8 CC.Party CC.PI_Orientation
## CC.ATNS_1 0.07 0.02 -0.22
## CC.ATNS_2R 0.05 0.05 0.00
## CC.ATNS_3 0.06 0.01 0.01
## CC.ATNS_4 0.06 0.02 0.02
## CC.ATNS_5 0.01 0.09 0.00
## CC.CCB_1_48 -0.09 0.13 0.56library("Hmisc")
mydata.rcorr2 = rcorr(as.matrix(mydata.cor2))
mydata.rcorr2## CC.ATNS_1 CC.ATNS_2R CC.ATNS_3 CC.ATNS_4 CC.ATNS_5
## CC.ATNS_1 1.00 0.55 0.66 0.62 0.65
## CC.ATNS_2R 0.55 1.00 0.84 0.84 0.80
## CC.ATNS_3 0.66 0.84 1.00 0.94 0.89
## CC.ATNS_4 0.62 0.84 0.94 1.00 0.89
## CC.ATNS_5 0.65 0.80 0.89 0.89 1.00
## CC.CCB_1_48 -0.72 -0.36 -0.40 -0.37 -0.33
## CC.CCB_1_49 -0.70 -0.32 -0.35 -0.31 -0.28
## CC.CCB_1_50 -0.71 -0.33 -0.36 -0.33 -0.29
## CC.CCB_1_51 -0.75 -0.37 -0.40 -0.37 -0.35
## CC.CNS_1 -0.11 0.13 0.13 0.21 0.14
## CC.CNS_2 -0.22 0.01 0.01 0.07 0.01
## CC.CNS_3 0.02 0.14 0.22 0.26 0.18
## CC.Ind_1 -0.12 -0.23 -0.17 -0.19 -0.24
## CC.Ind_2 0.22 0.06 0.14 0.12 0.11
## CC.Ind_5 -0.09 -0.20 -0.18 -0.19 -0.21
## CC.Ind_6 0.03 -0.28 -0.20 -0.21 -0.29
## CC.Ind_3 0.25 -0.08 -0.06 -0.08 -0.17
## CC.Ind_4 0.24 -0.07 -0.05 -0.06 -0.15
## CC.Ind_7 0.30 -0.09 -0.06 -0.07 -0.14
## CC.Ind_8 0.18 -0.09 -0.08 -0.09 -0.18
## CC.Party -0.16 -0.04 -0.11 -0.10 0.02
## CC.PI_Orientation -0.65 -0.25 -0.28 -0.26 -0.22
## CC.CCB_1_48 CC.CCB_1_49 CC.CCB_1_50 CC.CCB_1_51 CC.CNS_1
## CC.ATNS_1 -0.72 -0.70 -0.71 -0.75 -0.11
## CC.ATNS_2R -0.36 -0.32 -0.33 -0.37 0.13
## CC.ATNS_3 -0.40 -0.35 -0.36 -0.40 0.13
## CC.ATNS_4 -0.37 -0.31 -0.33 -0.37 0.21
## CC.ATNS_5 -0.33 -0.28 -0.29 -0.35 0.14
## CC.CCB_1_48 1.00 0.99 0.98 0.96 0.13
## CC.CCB_1_49 0.99 1.00 0.99 0.98 0.15
## CC.CCB_1_50 0.98 0.99 1.00 0.98 0.13
## CC.CCB_1_51 0.96 0.98 0.98 1.00 0.14
## CC.CNS_1 0.13 0.15 0.13 0.14 1.00
## CC.CNS_2 0.21 0.23 0.20 0.22 0.90
## CC.CNS_3 -0.05 -0.02 -0.04 -0.02 0.81
## CC.Ind_1 -0.11 -0.14 -0.16 -0.14 0.00
## CC.Ind_2 -0.39 -0.40 -0.40 -0.41 -0.15
## CC.Ind_5 -0.12 -0.15 -0.16 -0.14 0.02
## CC.Ind_6 -0.32 -0.37 -0.38 -0.35 -0.06
## CC.Ind_3 -0.65 -0.68 -0.68 -0.61 -0.10
## CC.Ind_4 -0.63 -0.66 -0.66 -0.60 0.00
## CC.Ind_7 -0.67 -0.70 -0.70 -0.64 -0.23
## CC.Ind_8 -0.57 -0.60 -0.60 -0.53 -0.02
## CC.Party 0.27 0.28 0.28 0.21 -0.14
## CC.PI_Orientation 0.90 0.92 0.92 0.89 0.14
## CC.CNS_2 CC.CNS_3 CC.Ind_1 CC.Ind_2 CC.Ind_5 CC.Ind_6
## CC.ATNS_1 -0.22 0.02 -0.12 0.22 -0.09 0.03
## CC.ATNS_2R 0.01 0.14 -0.23 0.06 -0.20 -0.28
## CC.ATNS_3 0.01 0.22 -0.17 0.14 -0.18 -0.20
## CC.ATNS_4 0.07 0.26 -0.19 0.12 -0.19 -0.21
## CC.ATNS_5 0.01 0.18 -0.24 0.11 -0.21 -0.29
## CC.CCB_1_48 0.21 -0.05 -0.11 -0.39 -0.12 -0.32
## CC.CCB_1_49 0.23 -0.02 -0.14 -0.40 -0.15 -0.37
## CC.CCB_1_50 0.20 -0.04 -0.16 -0.40 -0.16 -0.38
## CC.CCB_1_51 0.22 -0.02 -0.14 -0.41 -0.14 -0.35
## CC.CNS_1 0.90 0.81 0.00 -0.15 0.02 -0.06
## CC.CNS_2 1.00 0.76 0.03 -0.19 0.04 -0.01
## CC.CNS_3 0.76 1.00 0.01 -0.08 0.02 0.04
## CC.Ind_1 0.03 0.01 1.00 0.51 0.87 0.66
## CC.Ind_2 -0.19 -0.08 0.51 1.00 0.49 0.27
## CC.Ind_5 0.04 0.02 0.87 0.49 1.00 0.56
## CC.Ind_6 -0.01 0.04 0.66 0.27 0.56 1.00
## CC.Ind_3 -0.08 0.08 0.10 -0.03 -0.01 0.50
## CC.Ind_4 0.00 0.17 0.15 -0.05 0.04 0.52
## CC.Ind_7 -0.21 -0.07 0.05 0.05 -0.07 0.43
## CC.Ind_8 -0.02 0.14 0.09 -0.09 -0.03 0.45
## CC.Party -0.17 -0.22 -0.23 -0.02 -0.07 -0.48
## CC.PI_Orientation 0.19 -0.03 -0.12 -0.27 -0.08 -0.42
## CC.Ind_3 CC.Ind_4 CC.Ind_7 CC.Ind_8 CC.Party
## CC.ATNS_1 0.25 0.24 0.30 0.18 -0.16
## CC.ATNS_2R -0.08 -0.07 -0.09 -0.09 -0.04
## CC.ATNS_3 -0.06 -0.05 -0.06 -0.08 -0.11
## CC.ATNS_4 -0.08 -0.06 -0.07 -0.09 -0.10
## CC.ATNS_5 -0.17 -0.15 -0.14 -0.18 0.02
## CC.CCB_1_48 -0.65 -0.63 -0.67 -0.57 0.27
## CC.CCB_1_49 -0.68 -0.66 -0.70 -0.60 0.28
## CC.CCB_1_50 -0.68 -0.66 -0.70 -0.60 0.28
## CC.CCB_1_51 -0.61 -0.60 -0.64 -0.53 0.21
## CC.CNS_1 -0.10 0.00 -0.23 -0.02 -0.14
## CC.CNS_2 -0.08 0.00 -0.21 -0.02 -0.17
## CC.CNS_3 0.08 0.17 -0.07 0.14 -0.22
## CC.Ind_1 0.10 0.15 0.05 0.09 -0.23
## CC.Ind_2 -0.03 -0.05 0.05 -0.09 -0.02
## CC.Ind_5 -0.01 0.04 -0.07 -0.03 -0.07
## CC.Ind_6 0.50 0.52 0.43 0.45 -0.48
## CC.Ind_3 1.00 0.92 0.90 0.88 -0.61
## CC.Ind_4 0.92 1.00 0.80 0.93 -0.61
## CC.Ind_7 0.90 0.80 1.00 0.79 -0.55
## CC.Ind_8 0.88 0.93 0.79 1.00 -0.62
## CC.Party -0.61 -0.61 -0.55 -0.62 1.00
## CC.PI_Orientation -0.79 -0.78 -0.81 -0.74 0.40
## CC.PI_Orientation
## CC.ATNS_1 -0.65
## CC.ATNS_2R -0.25
## CC.ATNS_3 -0.28
## CC.ATNS_4 -0.26
## CC.ATNS_5 -0.22
## CC.CCB_1_48 0.90
## CC.CCB_1_49 0.92
## CC.CCB_1_50 0.92
## CC.CCB_1_51 0.89
## CC.CNS_1 0.14
## CC.CNS_2 0.19
## CC.CNS_3 -0.03
## CC.Ind_1 -0.12
## CC.Ind_2 -0.27
## CC.Ind_5 -0.08
## CC.Ind_6 -0.42
## CC.Ind_3 -0.79
## CC.Ind_4 -0.78
## CC.Ind_7 -0.81
## CC.Ind_8 -0.74
## CC.Party 0.40
## CC.PI_Orientation 1.00
##
## n= 22
##
##
## P
## CC.ATNS_1 CC.ATNS_2R CC.ATNS_3 CC.ATNS_4 CC.ATNS_5
## CC.ATNS_1 0.0077 0.0009 0.0019 0.0010
## CC.ATNS_2R 0.0077 0.0000 0.0000 0.0000
## CC.ATNS_3 0.0009 0.0000 0.0000 0.0000
## CC.ATNS_4 0.0019 0.0000 0.0000 0.0000
## CC.ATNS_5 0.0010 0.0000 0.0000 0.0000
## CC.CCB_1_48 0.0001 0.0960 0.0660 0.0916 0.1318
## CC.CCB_1_49 0.0003 0.1508 0.1145 0.1561 0.2152
## CC.CCB_1_50 0.0002 0.1299 0.1035 0.1355 0.1953
## CC.CCB_1_51 0.0000 0.0917 0.0661 0.0886 0.1088
## CC.CNS_1 0.6106 0.5567 0.5552 0.3416 0.5235
## CC.CNS_2 0.3230 0.9736 0.9673 0.7457 0.9785
## CC.CNS_3 0.9354 0.5295 0.3292 0.2437 0.4109
## CC.Ind_1 0.5882 0.3028 0.4464 0.3986 0.2856
## CC.Ind_2 0.3276 0.8015 0.5234 0.6090 0.6287
## CC.Ind_5 0.6971 0.3781 0.4242 0.3962 0.3442
## CC.Ind_6 0.9100 0.2154 0.3666 0.3369 0.1926
## CC.Ind_3 0.2558 0.7102 0.7813 0.7248 0.4598
## CC.Ind_4 0.2869 0.7422 0.8213 0.7854 0.4996
## CC.Ind_7 0.1802 0.7046 0.7982 0.7587 0.5401
## CC.Ind_8 0.4147 0.7002 0.7256 0.6891 0.4287
## CC.Party 0.4908 0.8488 0.6326 0.6678 0.9167
## CC.PI_Orientation 0.0010 0.2625 0.2023 0.2465 0.3281
## CC.CCB_1_48 CC.CCB_1_49 CC.CCB_1_50 CC.CCB_1_51 CC.CNS_1
## CC.ATNS_1 0.0001 0.0003 0.0002 0.0000 0.6106
## CC.ATNS_2R 0.0960 0.1508 0.1299 0.0917 0.5567
## CC.ATNS_3 0.0660 0.1145 0.1035 0.0661 0.5552
## CC.ATNS_4 0.0916 0.1561 0.1355 0.0886 0.3416
## CC.ATNS_5 0.1318 0.2152 0.1953 0.1088 0.5235
## CC.CCB_1_48 0.0000 0.0000 0.0000 0.5574
## CC.CCB_1_49 0.0000 0.0000 0.0000 0.4933
## CC.CCB_1_50 0.0000 0.0000 0.0000 0.5779
## CC.CCB_1_51 0.0000 0.0000 0.0000 0.5299
## CC.CNS_1 0.5574 0.4933 0.5779 0.5299
## CC.CNS_2 0.3535 0.3109 0.3759 0.3184 0.0000
## CC.CNS_3 0.8393 0.9356 0.8556 0.9218 0.0000
## CC.Ind_1 0.6261 0.5261 0.4790 0.5403 0.9870
## CC.Ind_2 0.0764 0.0643 0.0639 0.0610 0.5074
## CC.Ind_5 0.5909 0.5036 0.4907 0.5229 0.9168
## CC.Ind_6 0.1412 0.0911 0.0770 0.1111 0.8071
## CC.Ind_3 0.0010 0.0005 0.0006 0.0023 0.6650
## CC.Ind_4 0.0017 0.0009 0.0008 0.0034 0.9936
## CC.Ind_7 0.0007 0.0003 0.0003 0.0013 0.3102
## CC.Ind_8 0.0057 0.0034 0.0033 0.0109 0.9176
## CC.Party 0.2293 0.2149 0.2037 0.3397 0.5273
## CC.PI_Orientation 0.0000 0.0000 0.0000 0.0000 0.5303
## CC.CNS_2 CC.CNS_3 CC.Ind_1 CC.Ind_2 CC.Ind_5 CC.Ind_6
## CC.ATNS_1 0.3230 0.9354 0.5882 0.3276 0.6971 0.9100
## CC.ATNS_2R 0.9736 0.5295 0.3028 0.8015 0.3781 0.2154
## CC.ATNS_3 0.9673 0.3292 0.4464 0.5234 0.4242 0.3666
## CC.ATNS_4 0.7457 0.2437 0.3986 0.6090 0.3962 0.3369
## CC.ATNS_5 0.9785 0.4109 0.2856 0.6287 0.3442 0.1926
## CC.CCB_1_48 0.3535 0.8393 0.6261 0.0764 0.5909 0.1412
## CC.CCB_1_49 0.3109 0.9356 0.5261 0.0643 0.5036 0.0911
## CC.CCB_1_50 0.3759 0.8556 0.4790 0.0639 0.4907 0.0770
## CC.CCB_1_51 0.3184 0.9218 0.5403 0.0610 0.5229 0.1111
## CC.CNS_1 0.0000 0.0000 0.9870 0.5074 0.9168 0.8071
## CC.CNS_2 0.0000 0.9047 0.3952 0.8436 0.9757
## CC.CNS_3 0.0000 0.9493 0.7203 0.9210 0.8749
## CC.Ind_1 0.9047 0.9493 0.0158 0.0000 0.0007
## CC.Ind_2 0.3952 0.7203 0.0158 0.0192 0.2312
## CC.Ind_5 0.8436 0.9210 0.0000 0.0192 0.0067
## CC.Ind_6 0.9757 0.8749 0.0007 0.2312 0.0067
## CC.Ind_3 0.7116 0.7256 0.6650 0.8912 0.9482 0.0183
## CC.Ind_4 0.9987 0.4544 0.5008 0.8315 0.8526 0.0129
## CC.Ind_7 0.3555 0.7636 0.8397 0.8102 0.7510 0.0443
## CC.Ind_8 0.9292 0.5356 0.6998 0.6869 0.8913 0.0374
## CC.Party 0.4408 0.3314 0.3008 0.9329 0.7684 0.0246
## CC.PI_Orientation 0.3935 0.9089 0.5870 0.2293 0.7241 0.0501
## CC.Ind_3 CC.Ind_4 CC.Ind_7 CC.Ind_8 CC.Party
## CC.ATNS_1 0.2558 0.2869 0.1802 0.4147 0.4908
## CC.ATNS_2R 0.7102 0.7422 0.7046 0.7002 0.8488
## CC.ATNS_3 0.7813 0.8213 0.7982 0.7256 0.6326
## CC.ATNS_4 0.7248 0.7854 0.7587 0.6891 0.6678
## CC.ATNS_5 0.4598 0.4996 0.5401 0.4287 0.9167
## CC.CCB_1_48 0.0010 0.0017 0.0007 0.0057 0.2293
## CC.CCB_1_49 0.0005 0.0009 0.0003 0.0034 0.2149
## CC.CCB_1_50 0.0006 0.0008 0.0003 0.0033 0.2037
## CC.CCB_1_51 0.0023 0.0034 0.0013 0.0109 0.3397
## CC.CNS_1 0.6650 0.9936 0.3102 0.9176 0.5273
## CC.CNS_2 0.7116 0.9987 0.3555 0.9292 0.4408
## CC.CNS_3 0.7256 0.4544 0.7636 0.5356 0.3314
## CC.Ind_1 0.6650 0.5008 0.8397 0.6998 0.3008
## CC.Ind_2 0.8912 0.8315 0.8102 0.6869 0.9329
## CC.Ind_5 0.9482 0.8526 0.7510 0.8913 0.7684
## CC.Ind_6 0.0183 0.0129 0.0443 0.0374 0.0246
## CC.Ind_3 0.0000 0.0000 0.0000 0.0025
## CC.Ind_4 0.0000 0.0000 0.0000 0.0028
## CC.Ind_7 0.0000 0.0000 0.0000 0.0087
## CC.Ind_8 0.0000 0.0000 0.0000 0.0020
## CC.Party 0.0025 0.0028 0.0087 0.0020
## CC.PI_Orientation 0.0000 0.0000 0.0000 0.0000 0.0658
## CC.PI_Orientation
## CC.ATNS_1 0.0010
## CC.ATNS_2R 0.2625
## CC.ATNS_3 0.2023
## CC.ATNS_4 0.2465
## CC.ATNS_5 0.3281
## CC.CCB_1_48 0.0000
## CC.CCB_1_49 0.0000
## CC.CCB_1_50 0.0000
## CC.CCB_1_51 0.0000
## CC.CNS_1 0.5303
## CC.CNS_2 0.3935
## CC.CNS_3 0.9089
## CC.Ind_1 0.5870
## CC.Ind_2 0.2293
## CC.Ind_5 0.7241
## CC.Ind_6 0.0501
## CC.Ind_3 0.0000
## CC.Ind_4 0.0000
## CC.Ind_7 0.0000
## CC.Ind_8 0.0000
## CC.Party 0.0658
## CC.PI_Orientationlibrary(corrplot)
corrplot(mydata.cor2, method="color")
corrplot(mydata.cor2, addCoef.col = 1, number.cex = 0.3, method = 'number')
Long Form
Rename Variables
#Renaming variables to fit pivot_longer command
## Benefit
CC$Ben.AFSCS <- CC$Ben_AFSCS
length(CC$Ben.AFSCS)## [1] 1033CC$Ben.BIO <- CC$Ben_BIO
length(CC$Ben.BIO)## [1] 1033CC$Ben.BECCS <- CC$Ben_BECCS
length(CC$Ben.BECCS) ## [1] 1033CC$Ben.DACCS <- CC$Ben_DACCS
length(CC$Ben.DACCS)## [1] 1033CC$Ben.EW <- CC$Ben_EW
length(CC$Ben.EW) ## [1] 1033CC$Ben.OF <- CC$Ben_OF
length(CC$Ben.OF) ## [1] 1033CC$Ben.BF <- CC$Ben_BF
length(CC$Ben.BF) ## [1] 1033CC$Ben.NE <- CC$Ben_NE
length(CC$Ben.NE) ## [1] 1033CC$Ben.SE <- CC$Ben_SE
length(CC$Ben.SE) ## [1] 1033CC$Ben.WE <- CC$Ben_WE
length(CC$Ben.WE) ## [1] 1033## Control
CC$Control.AFSCS <- CC$Control_AFSCS
length(CC$Control.AFSCS)## [1] 1033CC$Control.BIO <- CC$Control_BIO
length(CC$Control.BIO)## [1] 1033CC$Control.BECCS <- CC$Control_BECCS
length(CC$Control.BECCS)## [1] 1033CC$Control.DACCS <- CC$Control_DACCS
length(CC$Control.DACCS)## [1] 1033CC$Control.EW <- CC$Control_EW
length(CC$Control.EW)## [1] 1033CC$Control.OF <- CC$Control_OF
length(CC$Control.OF)## [1] 1033CC$Control.BF <- CC$Control_BF
length(CC$Control.BF)## [1] 1033CC$Control.NE <- CC$Control_NE
length(CC$Control.NE)## [1] 1033CC$Control.SE <- CC$Control_SE
length(CC$Control.SE)## [1] 1033CC$Control.WE <- CC$Control_WE
length(CC$Control.WE)## [1] 1033## Familiarity
CC$Familiar.AFSCS <- CC$Familiar_AFSCS
length(CC$Familiar.AFSCS)## [1] 1033CC$Familiar.BIO <- CC$Familiar_BIO
length(CC$Familiar.BIO)## [1] 1033CC$Familiar.BECCS <- CC$Familiar_BECCS
length(CC$Familiar.BECCS)## [1] 1033CC$Familiar.DACCS <- CC$Familiar_DACCS
length(CC$Familiar.DACCS)## [1] 1033CC$Familiar.EW <- CC$Familiar_EW
length(CC$Familiar.EW)## [1] 1033CC$Familiar.OF <- CC$Familiar_OF
length(CC$Familiar.OF)## [1] 1033CC$Familiar.BF <- CC$Familiar_BF
length(CC$Familiar.BF)## [1] 1033CC$Familiar.NE <- CC$Familiar_NE
length(CC$Familiar.NE)## [1] 1033CC$Familiar.SE <- CC$Familiar_SE
length(CC$Familiar.SE)## [1] 1033CC$Familiar.WE <- CC$Familiar_WE
length(CC$Familiar.WE)## [1] 1033## Naturalness
CC$Naturalness.AFSCS <- CC$Nat_Score_AFSCS
length(CC$Naturalness.AFSCS)## [1] 1033CC$Naturalness.BIO <- CC$Nat_Score_BIO
length(CC$Naturalness.BIO)## [1] 1033CC$Naturalness.BECCS <- CC$Nat_Score_BECCS
length(CC$Naturalness.BECCS)## [1] 1033CC$Naturalness.DACCS <- CC$Nat_Score_DACCS
length(CC$Naturalness.DACCS)## [1] 1033CC$Naturalness.EW <- CC$Nat_Score_EW
length(CC$Naturalness.EW)## [1] 1033CC$Naturalness.OF <- CC$Nat_Score_OF
length(CC$Naturalness.OF)## [1] 1033CC$Naturalness.BF <- CC$Nat_Score_BF
length(CC$Naturalness.BF)## [1] 1033CC$Naturalness.NE <- CC$Nat_Score_NE
length(CC$Naturalness.NE)## [1] 1033CC$Naturalness.SE <- CC$Nat_Score_SE
length(CC$Naturalness.SE)## [1] 1033CC$Naturalness.WE <- CC$Nat_Score_WE
length(CC$Naturalness.WE)## [1] 1033## Risk
CC$Risk.AFSCS <- CC$Risk_Score_AFSCS
length(CC$Risk.AFSCS)## [1] 1033CC$Risk.BIO <- CC$Risk_Score_BIO
length(CC$Risk.BIO)## [1] 1033CC$Risk.BECCS <- CC$Risk_Score_BECCS
length(CC$Risk.BECCS)## [1] 1033CC$Risk.DACCS <- CC$Risk_Score_DACCS
length(CC$Risk.DACCS)## [1] 1033CC$Risk.EW <- CC$Risk_Score_EW
length(CC$Risk.EW)## [1] 1033CC$Risk.OF <- CC$Risk_Score_OF
length(CC$Risk.OF)## [1] 1033CC$Risk.BF <- CC$Risk_Score_BF
length(CC$Risk.BF)## [1] 1033CC$Risk.NE <- CC$Risk_Score_NE
length(CC$Risk.NE)## [1] 1033CC$Risk.SE <- CC$Risk_Score_SE
length(CC$Risk.SE)## [1] 1033CC$Risk.WE <- CC$Risk_Score_WE
length(CC$Risk.WE)## [1] 1033## Support
CC$Support.AFSCS <- CC$Support_Score_AFSCS
length(CC$Support.AFSCS)## [1] 1033CC$Support.BIO <- CC$Support_Score_BIO
length(CC$Support.BIO)## [1] 1033CC$Support.BECCS <- CC$Support_Score_BECCS
length(CC$Support.BECCS)## [1] 1033CC$Support.DACCS <- CC$Support_Score_DACCS
length(CC$Support.DACCS)## [1] 1033CC$Support.EW <- CC$Support_Score_EW
length(CC$Support.EW)## [1] 1033CC$Support.OF <- CC$Support_Score_OF
length(CC$Support.OF)## [1] 1033CC$Support.BF <- CC$Support_Score_BF
length(CC$Support.BF)## [1] 1033CC$Support.NE <- CC$Support_Score_NE
length(CC$Support.NE)## [1] 1033CC$Support.SE <- CC$Support_Score_SE
length(CC$Support.SE)## [1] 1033CC$Support.WE <- CC$Support_Score_WE
length(CC$Support.WE)## [1] 1033## Understanding
CC$Understanding.AFSCS <- CC$Und_AFSCS
length(CC$Understanding.AFSCS)## [1] 1033CC$Understanding.BIO <- CC$Und_BIO
length(CC$Understanding.BIO)## [1] 1033CC$Understanding.BECCS <- CC$Und_BECCS
length(CC$Understanding.BECCS)## [1] 1033CC$Understanding.DACCS <- CC$Und_DACCS
length(CC$Understanding.DACCS)## [1] 1033CC$Understanding.EW <- CC$Und_EW
length(CC$Understanding.EW)## [1] 1033CC$Understanding.OF <- CC$Und_OF
length(CC$Understanding.OF)## [1] 1033CC$Understanding.BF <- CC$Und_BF
length(CC$Understanding.BF)## [1] 1033CC$Understanding.NE <- CC$Und_NE
length(CC$Understanding.NE)## [1] 1033CC$Understanding.SE <- CC$Und_SE
length(CC$Understanding.SE)## [1] 1033CC$Understanding.WE <- CC$Und_AFSCS
length(CC$Understanding.WE)## [1] 1033## Familiarity/Understanding (Mean)
length(CC$FR.AFSCS)## [1] 1033length(CC$FR.BIO)## [1] 1033length(CC$FR.BECCS)## [1] 1033length(CC$FR.DACCS)## [1] 1033length(CC$FR.EW)## [1] 1033length(CC$FR.OF)## [1] 1033length(CC$FR.BF)## [1] 1033length(CC$FR.NE)## [1] 1033length(CC$FR.SE)## [1] 1033length(CC$FR.WE)## [1] 1033#Benefit - Risk Difference Score
length(CC$BRDiff.AFSCS)## [1] 1033length(CC$BRDiff.BIO)## [1] 1033length(CC$BRDiff.BECCS)## [1] 1033length(CC$BRDiff.DACCS)## [1] 1033length(CC$BRDiff.EW)## [1] 1033length(CC$BRDiff.OF)## [1] 1033length(CC$BRDiff.BF)## [1] 1033length(CC$BRDiff.NE)## [1] 1033length(CC$BRDiff.SE)## [1] 1033length(CC$BRDiff.WE)## [1] 1033Transform: 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")Mixed Effects Models
Center Variables
# Describe & Mean Center Long Variables
## By Technology Measures
table(L$Type) ##
## AFSCS BECCS BF BIO DACCS EW NE OF SE WE
## 1033 1033 1033 1033 1033 1033 1033 1033 1033 1033describe(L$Ben) ## L$Ben
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 101 0.999 57.98 29.92 5 20
## .25 .50 .75 .90 .95
## 40 61 77 90 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Control) ## L$Control
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 100 0.999 64.83 28.5 17 29
## .25 .50 .75 .90 .95
## 50 69 85 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Familiar) ## L$Familiar
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 101 0.997 46.43 40.01 0 0
## .25 .50 .75 .90 .95
## 13 45 79 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Naturalness) ## L$Naturalness
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 368 1 39.99 24.45 5.00 12.00
## .25 .50 .75 .90 .95
## 24.75 39.00 54.00 70.25 75.00
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 98.00 98.75 99.50 99.75 100.00describe(L$Risk) ## L$Risk
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 201 0.998 33.1 30.8 0.00 0.00
## .25 .50 .75 .90 .95
## 8.25 28.50 52.00 72.60 85.00
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$Support) ## L$Support
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 201 0.999 59.7 32.84 0.45 13.00
## .25 .50 .75 .90 .95
## 42.00 62.50 82.50 99.50 100.00
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$Understanding) ## L$Understanding
## n missing distinct Info Mean Gmd .05 .10
## 3186 7144 101 0.999 57.62 34.31 4 12
## .25 .50 .75 .90 .95
## 34 61 83 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$FR) ## L$FR
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 201 1 52.35 34.45 4.0 11.0
## .25 .50 .75 .90 .95
## 27.5 51.0 78.5 94.5 100.0
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$BRDiff) ## L$BRDiff
## n missing distinct Info Mean Gmd .05 .10
## 3099 7231 366 1 24.88 49.59 -55.00 -31.00
## .25 .50 .75 .90 .95
## -3.25 25.50 58.50 82.00 91.50
##
## lowest : -100.0 -99.0 -93.0 -92.5 -92.0, highest: 98.0 98.5 99.0 99.5 100.0L$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$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 10330 0 368 1 54.69 24.41 18.6 26.0
## .25 .50 .75 .90 .95
## 40.2 54.6 69.0 82.2 92.4
##
## lowest : 0.0 2.0 3.0 4.0 6.4, highest: 97.6 98.8 99.2 99.8 100.0L$ATNS_Score.c <- L$ATNS_Score - 54.69
describe(L$CCB_Score)## L$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 10330 0 251 0.987 81.61 23.28 24.25 47.25
## .25 .50 .75 .90 .95
## 75.00 91.25 99.00 100.00 100.00
##
## lowest : 0.00 2.00 3.75 4.00 4.75, highest: 99.00 99.25 99.50 99.75 100.00L$CCBelief_Score.c <- L$CCB_Score - 81.61
describe(L$CNS_Score)## L$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 10330 0 323 1 63.42 18.65 35.0 43.2
## .25 .50 .75 .90 .95
## 53.0 63.0 74.6 85.0 91.8
##
## lowest : 0.0 8.6 10.0 12.8 16.0, highest: 98.2 98.6 99.2 99.6 100.0L$CNS_Score.c <- L$CNS_Score -63.42
describe(L$Individualism_Score)## L$Individualism_Score
## n missing distinct Info Mean Gmd .05 .10
## 10330 0 267 1 70.81 18.89 40.75 49.75
## .25 .50 .75 .90 .95
## 60.00 71.50 83.75 91.75 96.25
##
## lowest : 0.75 6.00 6.25 6.50 15.50, highest: 99.00 99.25 99.50 99.75 100.00L$Individualism_Score.c <- L$Individualism_Score - 70.81
describe(L$Collectivism_Score)## L$Collectivism_Score
## n missing distinct Info Mean Gmd .05 .10
## 10330 0 342 1 54.17 27.2 12.75 21.50
## .25 .50 .75 .90 .95
## 38.25 54.50 72.00 85.50 93.25
##
## lowest : 0.00 0.25 0.50 1.00 1.75, highest: 98.25 98.50 99.50 99.75 100.00L$Collectivism_Score.c <- L$Collectivism_Score - 54.17
describe(L$Ideology)## L$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 10330 0 13 0.87 1.947 0.569 1.0 1.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.0 2.5 3.0
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 3.0 3.5 4.0 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
## Frequency 10 40 40 110 540 2420 5020 1370 640 110 10
## Proportion 0.001 0.004 0.004 0.011 0.052 0.234 0.486 0.133 0.062 0.011 0.001
##
## Value 5.0 6.0
## Frequency 10 10
## Proportion 0.001 0.001L$Ideology.c <- L$Ideology - 1.947Contrast Codes
#C1. DACCS vs. Grand Mean
L$C1 <- (0)*(L$Type == 'AFSCS') + (0)*(L$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. Biofuel vs. Grand Mean
L$C2 <- (0)*(L$Type == 'AFSCS') + (0)*(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$Type == 'WE')
#C3. Nuclear Energy vs. Grand Mean
L$C3 <- (0)*(L$Type == 'AFSCS') + (0)*(L$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')
#C4. BECCS vs. Grand Mean
L$C4 <- (0)*(L$Type == 'AFSCS') + (0)*(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$Type == 'AFSCS') + (0)*(L$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. Ocean fertilization vs. Grand Mean
L$C6 <- (0)*(L$Type == 'AFSCS') + (0)*(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$Type == 'WE')
#C7. Solar Energy vs. Grand Mean
L$C7 <- (0)*(L$Type == 'AFSCS') + (0)*(L$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')
#C8. Afforestation/reforestation and Soil Carbon Sequestration vs. Grand Mean
L$C8 <- (1)*(L$Type == 'AFSCS') + (0)*(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$Type == 'WE')
#C9. Wind Energy vs. Grand Mean
L$C9 <- (0)*(L$Type == 'AFSCS') + (0)*(L$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')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: 28385.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4352 -0.5146 0.0631 0.5681 3.1759
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 287.9 16.97
## Residual 378.5 19.46
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.2626 1.2720 3086.2930 41.874 < 2e-16 ***
## C1 1.9219 1.6360 2478.3405 1.175 0.240217
## C2 -2.2926 1.7654 2441.2064 -1.299 0.194184
## C3 6.2690 1.7668 2472.4609 3.548 0.000395 ***
## C4 2.1007 1.6569 2478.2035 1.268 0.204973
## C5 -0.4471 1.6575 2495.9323 -0.270 0.787385
## C6 0.5182 1.6618 2479.0967 0.312 0.755189
## C7 13.7277 1.7846 2477.1998 7.692 2.07e-14 ***
## C8 15.3323 1.6432 2473.6132 9.330 < 2e-16 ***
## C9 12.5640 1.7683 2486.0313 7.105 1.56e-12 ***
## ---
## 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 -0.664
## C2 -0.588 0.471
## C3 -0.597 0.478 0.388
## C4 -0.654 0.509 0.467 0.469
## C5 -0.660 0.516 0.469 0.478 0.508
## C6 -0.652 0.506 0.457 0.481 0.499 0.503
## C7 -0.592 0.471 0.385 0.391 0.469 0.472 0.462
## C8 -0.659 0.512 0.468 0.466 0.503 0.509 0.506 0.478
## C9 -0.601 0.486 0.391 0.398 0.475 0.476 0.473 0.395 0.480tab_model(modA.4,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.26 | 1.27 | 50.77 – 55.76 | 41.87 | <0.001 |
| C1 | 1.92 | 1.64 | -1.29 – 5.13 | 1.17 | 0.240 |
| C2 | -2.29 | 1.77 | -5.75 – 1.17 | -1.30 | 0.194 |
| C3 | 6.27 | 1.77 | 2.80 – 9.73 | 3.55 | <0.001 |
| C4 | 2.10 | 1.66 | -1.15 – 5.35 | 1.27 | 0.205 |
| C5 | -0.45 | 1.66 | -3.70 – 2.80 | -0.27 | 0.787 |
| C6 | 0.52 | 1.66 | -2.74 – 3.78 | 0.31 | 0.755 |
| C7 | 13.73 | 1.78 | 10.23 – 17.23 | 7.69 | <0.001 |
| C8 | 15.33 | 1.64 | 12.11 – 18.55 | 9.33 | <0.001 |
| C9 | 12.56 | 1.77 | 9.10 – 16.03 | 7.11 | <0.001 |
| Random Effects | |||||
| σ2 | 378.52 | ||||
| τ00 id | 287.94 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.463 | ||||
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: 28646.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10563 -0.53235 0.05168 0.61065 2.84417
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 277.1 16.65
## Residual 421.3 20.53
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.9787 0.6358 1032.0000 91.19 <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 | 56.73 – 59.23 | 91.19 | <0.001 |
| Random Effects | |||||
| σ2 | 421.30 | ||||
| τ00 id | 277.14 | ||||
| ICC | 0.40 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.397 | ||||
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: 31282.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9132 -0.5398 0.0435 0.5736 3.1191
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 589.9 24.29
## Residual 1029.7 32.09
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.318 2.037 3078.449 8.992 < 2e-16 ***
## C1 -6.048 2.671 2553.236 -2.264 0.0236 *
## C2 6.112 2.885 2506.924 2.119 0.0342 *
## C3 -12.037 2.885 2541.897 -4.172 3.12e-05 ***
## C4 -1.060 2.705 2553.147 -0.392 0.6953
## C5 -4.087 2.705 2573.268 -1.511 0.1309
## C6 -10.680 2.713 2553.934 -3.936 8.49e-05 ***
## C7 37.559 2.914 2547.501 12.890 < 2e-16 ***
## C8 34.209 2.683 2547.793 12.750 < 2e-16 ***
## C9 30.287 2.887 2557.379 10.493 < 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 -0.676
## C2 -0.601 0.472
## C3 -0.610 0.478 0.393
## C4 -0.667 0.509 0.467 0.469
## C5 -0.672 0.515 0.469 0.477 0.508
## C6 -0.665 0.506 0.458 0.479 0.499 0.503
## C7 -0.605 0.472 0.390 0.396 0.468 0.471 0.462
## C8 -0.672 0.512 0.468 0.467 0.503 0.509 0.505 0.477
## C9 -0.614 0.485 0.396 0.402 0.475 0.476 0.473 0.398 0.480tab_model(modA.5,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 18.32 | 2.04 | 14.32 – 22.31 | 8.99 | <0.001 |
| C1 | -6.05 | 2.67 | -11.28 – -0.81 | -2.26 | 0.024 |
| C2 | 6.11 | 2.88 | 0.46 – 11.77 | 2.12 | 0.034 |
| C3 | -12.04 | 2.88 | -17.69 – -6.38 | -4.17 | <0.001 |
| C4 | -1.06 | 2.71 | -6.36 – 4.24 | -0.39 | 0.695 |
| C5 | -4.09 | 2.70 | -9.39 – 1.22 | -1.51 | 0.131 |
| C6 | -10.68 | 2.71 | -16.00 – -5.36 | -3.94 | <0.001 |
| C7 | 37.56 | 2.91 | 31.85 – 43.27 | 12.89 | <0.001 |
| C8 | 34.21 | 2.68 | 28.95 – 39.47 | 12.75 | <0.001 |
| C9 | 30.29 | 2.89 | 24.63 – 35.95 | 10.49 | <0.001 |
| Random Effects | |||||
| σ2 | 1029.74 | ||||
| τ00 id | 589.87 | ||||
| ICC | 0.36 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.468 | ||||
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: 31994.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4838 -0.5754 0.0260 0.6643 2.7425
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 535.2 23.14
## Residual 1380.2 37.15
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 24.8814 0.9816 1032.0000 25.35 <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) | 24.88 | 0.98 | 22.96 – 26.81 | 25.35 | <0.001 |
| Random Effects | |||||
| σ2 | 1380.17 | ||||
| τ00 id | 535.24 | ||||
| ICC | 0.28 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| 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: 28667.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5108 -0.6389 -0.0497 0.6138 3.4708
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.3 13.54
## Residual 477.5 21.85
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.4596 1.3392 3070.9606 20.504 < 2e-16 ***
## C1 -0.9268 1.7912 2660.2651 -0.517 0.60492
## C2 29.9723 1.9376 2600.4444 15.469 < 2e-16 ***
## C3 42.1717 1.9356 2639.3012 21.787 < 2e-16 ***
## C4 2.8784 1.8141 2660.2697 1.587 0.11271
## C5 -5.6227 1.8128 2682.9563 -3.102 0.00194 **
## C6 -2.2287 1.8194 2660.7957 -1.225 0.22070
## C7 60.8342 1.9546 2646.0179 31.123 < 2e-16 ***
## C8 35.7122 1.7996 2653.9923 19.844 < 2e-16 ***
## C9 54.2108 1.9357 2656.9459 28.005 < 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 -0.690
## C2 -0.617 0.472
## C3 -0.624 0.477 0.400
## C4 -0.680 0.509 0.467 0.469
## C5 -0.685 0.514 0.469 0.476 0.507
## C6 -0.678 0.506 0.459 0.477 0.499 0.503
## C7 -0.619 0.471 0.397 0.402 0.468 0.471 0.463
## C8 -0.685 0.512 0.469 0.468 0.504 0.508 0.505 0.476
## C9 -0.628 0.483 0.403 0.408 0.474 0.475 0.472 0.404 0.478tab_model(modA.7,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 27.46 | 1.34 | 24.83 – 30.09 | 20.50 | <0.001 |
| C1 | -0.93 | 1.79 | -4.44 – 2.59 | -0.52 | 0.605 |
| C2 | 29.97 | 1.94 | 26.17 – 33.77 | 15.47 | <0.001 |
| C3 | 42.17 | 1.94 | 38.38 – 45.97 | 21.79 | <0.001 |
| C4 | 2.88 | 1.81 | -0.68 – 6.44 | 1.59 | 0.113 |
| C5 | -5.62 | 1.81 | -9.18 – -2.07 | -3.10 | 0.002 |
| C6 | -2.23 | 1.82 | -5.80 – 1.34 | -1.22 | 0.221 |
| C7 | 60.83 | 1.95 | 57.00 – 64.67 | 31.12 | <0.001 |
| C8 | 35.71 | 1.80 | 32.18 – 39.24 | 19.84 | <0.001 |
| C9 | 54.21 | 1.94 | 50.42 – 58.01 | 28.01 | <0.001 |
| Random Effects | |||||
| σ2 | 477.53 | ||||
| τ00 id | 183.26 | ||||
| ICC | 0.28 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.461 / 0.610 | ||||
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: 30804.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.33181 -0.95891 -0.04099 0.93431 1.53669
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0 0.00
## Residual 1215 34.86
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.4288 0.6262 3098.0000 74.14 <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) | 46.43 | 0.63 | 45.20 – 47.66 | 74.14 | <0.001 |
| Random Effects | |||||
| σ2 | 1215.31 | ||||
| τ00 id | 0.00 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.000 / NA | ||||
anova(modC.7, modA.7)## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce qFamiliarity/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: 27833.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0364 -0.5885 -0.0151 0.5956 3.1054
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 206.2 14.36
## Residual 331.1 18.20
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.4355 1.1648 3080.8192 32.140 < 2e-16 ***
## C1 -1.2691 1.5193 2532.2689 -0.835 0.40361
## C2 22.4050 1.6405 2488.8121 13.657 < 2e-16 ***
## C3 29.9624 1.6409 2522.6960 18.259 < 2e-16 ***
## C4 0.2945 1.5387 2532.1634 0.191 0.84823
## C5 -4.9841 1.5388 2551.5791 -3.239 0.00121 **
## C6 0.7724 1.5433 2532.9878 0.501 0.61676
## C7 48.3596 1.6574 2528.0376 29.178 < 2e-16 ***
## C8 29.8444 1.5261 2527.0387 19.556 < 2e-16 ***
## C9 44.9082 1.6420 2537.6100 27.350 < 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 -0.673
## C2 -0.598 0.472
## C3 -0.606 0.478 0.392
## C4 -0.664 0.509 0.467 0.469
## C5 -0.669 0.515 0.469 0.478 0.508
## C6 -0.661 0.506 0.457 0.480 0.499 0.503
## C7 -0.601 0.471 0.389 0.394 0.469 0.471 0.462
## C8 -0.668 0.512 0.468 0.467 0.503 0.509 0.505 0.477
## C9 -0.610 0.485 0.395 0.401 0.475 0.476 0.473 0.397 0.480tab_model(modA.6,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.44 | 1.16 | 35.15 – 39.72 | 32.14 | <0.001 |
| C1 | -1.27 | 1.52 | -4.25 – 1.71 | -0.84 | 0.404 |
| C2 | 22.40 | 1.64 | 19.19 – 25.62 | 13.66 | <0.001 |
| C3 | 29.96 | 1.64 | 26.74 – 33.18 | 18.26 | <0.001 |
| C4 | 0.29 | 1.54 | -2.72 – 3.31 | 0.19 | 0.848 |
| C5 | -4.98 | 1.54 | -8.00 – -1.97 | -3.24 | 0.001 |
| C6 | 0.77 | 1.54 | -2.25 – 3.80 | 0.50 | 0.617 |
| C7 | 48.36 | 1.66 | 45.11 – 51.61 | 29.18 | <0.001 |
| C8 | 29.84 | 1.53 | 26.85 – 32.84 | 19.56 | <0.001 |
| C9 | 44.91 | 1.64 | 41.69 – 48.13 | 27.35 | <0.001 |
| Random Effects | |||||
| σ2 | 331.14 | ||||
| τ00 id | 206.19 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.401 / 0.631 | ||||
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: 29821.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94080 -0.79495 -0.03855 0.83631 1.81154
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 74.84 8.651
## Residual 816.06 28.567
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.3541 0.5795 1032.0000 90.35 <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) | 52.35 | 0.58 | 51.22 – 53.49 | 90.35 | <0.001 |
| Random Effects | |||||
| σ2 | 816.06 | ||||
| τ00 id | 74.84 | ||||
| ICC | 0.08 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| 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: 26542.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5590 -0.6146 -0.0188 0.6110 3.4214
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.07 8.128
## Residual 255.98 15.999
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.7758 0.9529 3071.0207 40.694 < 2e-16 ***
## C1 -13.0130 1.2917 2757.1250 -10.074 < 2e-16 ***
## C2 0.6991 1.3996 2685.1901 0.499 0.61748
## C3 -13.0158 1.3968 2725.9700 -9.318 < 2e-16 ***
## C4 -3.8060 1.3082 2757.2346 -2.909 0.00365 **
## C5 -3.0749 1.3065 2781.1438 -2.354 0.01866 *
## C6 -6.8214 1.3121 2757.4352 -5.199 2.15e-07 ***
## C7 16.4597 1.4103 2733.5148 11.671 < 2e-16 ***
## C8 23.0426 1.2980 2750.3673 17.752 < 2e-16 ***
## C9 15.4236 1.3962 2744.8882 11.047 < 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 -0.699
## C2 -0.628 0.472
## C3 -0.635 0.477 0.407
## C4 -0.690 0.509 0.467 0.469
## C5 -0.694 0.514 0.469 0.475 0.506
## C6 -0.687 0.506 0.461 0.475 0.499 0.503
## C7 -0.629 0.471 0.404 0.408 0.467 0.470 0.463
## C8 -0.694 0.512 0.469 0.469 0.504 0.508 0.505 0.474
## C9 -0.638 0.482 0.409 0.414 0.473 0.474 0.472 0.410 0.477tab_model(modA.2,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 38.78 | 0.95 | 36.91 – 40.64 | 40.69 | <0.001 |
| C1 | -13.01 | 1.29 | -15.55 – -10.48 | -10.07 | <0.001 |
| C2 | 0.70 | 1.40 | -2.05 – 3.44 | 0.50 | 0.617 |
| C3 | -13.02 | 1.40 | -15.75 – -10.28 | -9.32 | <0.001 |
| C4 | -3.81 | 1.31 | -6.37 – -1.24 | -2.91 | 0.004 |
| C5 | -3.07 | 1.31 | -5.64 – -0.51 | -2.35 | 0.019 |
| C6 | -6.82 | 1.31 | -9.39 – -4.25 | -5.20 | <0.001 |
| C7 | 16.46 | 1.41 | 13.69 – 19.22 | 11.67 | <0.001 |
| C8 | 23.04 | 1.30 | 20.50 – 25.59 | 17.75 | <0.001 |
| C9 | 15.42 | 1.40 | 12.69 – 18.16 | 11.05 | <0.001 |
| Random Effects | |||||
| σ2 | 255.98 | ||||
| τ00 id | 66.07 | ||||
| ICC | 0.21 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.307 / 0.449 | ||||
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: 27767.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17900 -0.67989 -0.04767 0.61274 3.03592
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 52.78 7.265
## Residual 408.92 20.222
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.9898 0.4278 1032.0000 93.47 <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) | 39.99 | 0.43 | 39.15 – 40.83 | 93.47 | <0.001 |
| Random Effects | |||||
| σ2 | 408.92 | ||||
| τ00 id | 52.78 | ||||
| ICC | 0.11 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.114 | ||||
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: 28183.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5937 -0.6121 -0.0692 0.5627 3.6688
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 186.0 13.64
## Residual 392.4 19.81
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.824 1.236 3073.917 28.180 < 2e-16 ***
## C1 8.104 1.637 2603.391 4.950 7.88e-07 ***
## C2 -8.219 1.769 2550.596 -4.645 3.57e-06 ***
## C3 18.477 1.769 2587.689 10.447 < 2e-16 ***
## C4 3.297 1.658 2603.343 1.988 0.0469 *
## C5 3.725 1.657 2624.855 2.247 0.0247 *
## C6 11.433 1.663 2604.021 6.876 7.70e-12 ***
## C7 -23.874 1.786 2593.853 -13.366 < 2e-16 ***
## C8 -18.746 1.645 2597.511 -11.398 < 2e-16 ***
## C9 -17.561 1.769 2604.314 -9.926 < 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 -0.683
## C2 -0.609 0.472
## C3 -0.617 0.477 0.396
## C4 -0.674 0.509 0.467 0.469
## C5 -0.679 0.515 0.469 0.477 0.507
## C6 -0.672 0.506 0.458 0.478 0.499 0.503
## C7 -0.612 0.472 0.393 0.399 0.468 0.471 0.463
## C8 -0.679 0.512 0.468 0.468 0.504 0.508 0.505 0.476
## C9 -0.621 0.484 0.399 0.405 0.474 0.475 0.473 0.401 0.479tab_model(modA.3,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.82 | 1.24 | 32.40 – 37.25 | 28.18 | <0.001 |
| C1 | 8.10 | 1.64 | 4.89 – 11.31 | 4.95 | <0.001 |
| C2 | -8.22 | 1.77 | -11.69 – -4.75 | -4.65 | <0.001 |
| C3 | 18.48 | 1.77 | 15.01 – 21.94 | 10.45 | <0.001 |
| C4 | 3.30 | 1.66 | 0.05 – 6.55 | 1.99 | 0.047 |
| C5 | 3.72 | 1.66 | 0.48 – 6.97 | 2.25 | 0.025 |
| C6 | 11.43 | 1.66 | 8.17 – 14.69 | 6.88 | <0.001 |
| C7 | -23.87 | 1.79 | -27.38 – -20.37 | -13.37 | <0.001 |
| C8 | -18.75 | 1.64 | -21.97 – -15.52 | -11.40 | <0.001 |
| C9 | -17.56 | 1.77 | -21.03 – -14.09 | -9.93 | <0.001 |
| Random Effects | |||||
| σ2 | 392.45 | ||||
| τ00 id | 186.03 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.474 | ||||
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: 29129.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0387 -0.7433 -0.1324 0.6451 2.7981
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.3 13.20
## Residual 569.7 23.87
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 33.0973 0.5938 1032.0000 55.74 <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) | 33.10 | 0.59 | 31.93 – 34.26 | 55.74 | <0.001 |
| Random Effects | |||||
| σ2 | 569.75 | ||||
| τ00 id | 174.31 | ||||
| ICC | 0.23 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.234 | ||||
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: 28616.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2167 -0.5115 0.0664 0.5574 3.0875
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 310.2 17.61
## Residual 408.0 20.20
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.92592 1.32047 3086.28324 40.081 < 2e-16 ***
## C1 -0.03794 1.69842 2477.86487 -0.022 0.982180
## C2 7.05492 1.83271 2440.69740 3.849 0.000121 ***
## C3 -1.81584 1.83417 2471.97653 -0.990 0.322267
## C4 1.72122 1.72013 2477.72780 1.001 0.317099
## C5 -2.64670 1.72070 2495.47117 -1.538 0.124138
## C6 -2.83635 1.72523 2478.62149 -1.644 0.100294
## C7 27.07239 1.85271 2476.71954 14.612 < 2e-16 ***
## C8 23.25155 1.70592 2473.13367 13.630 < 2e-16 ***
## C9 22.68855 1.83576 2485.55818 12.359 < 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 -0.664
## C2 -0.588 0.471
## C3 -0.597 0.478 0.388
## C4 -0.654 0.509 0.467 0.469
## C5 -0.660 0.516 0.469 0.478 0.508
## C6 -0.652 0.506 0.457 0.481 0.499 0.503
## C7 -0.592 0.471 0.385 0.391 0.469 0.472 0.462
## C8 -0.659 0.512 0.468 0.466 0.503 0.509 0.506 0.478
## C9 -0.601 0.486 0.391 0.398 0.475 0.476 0.473 0.395 0.480tab_model(modA.1,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 52.93 | 1.32 | 50.34 – 55.52 | 40.08 | <0.001 |
| C1 | -0.04 | 1.70 | -3.37 – 3.29 | -0.02 | 0.982 |
| C2 | 7.05 | 1.83 | 3.46 – 10.65 | 3.85 | <0.001 |
| C3 | -1.82 | 1.83 | -5.41 – 1.78 | -0.99 | 0.322 |
| C4 | 1.72 | 1.72 | -1.65 – 5.09 | 1.00 | 0.317 |
| C5 | -2.65 | 1.72 | -6.02 – 0.73 | -1.54 | 0.124 |
| C6 | -2.84 | 1.73 | -6.22 – 0.55 | -1.64 | 0.100 |
| C7 | 27.07 | 1.85 | 23.44 – 30.71 | 14.61 | <0.001 |
| C8 | 23.25 | 1.71 | 19.91 – 26.60 | 13.63 | <0.001 |
| C9 | 22.69 | 1.84 | 19.09 – 26.29 | 12.36 | <0.001 |
| Random Effects | |||||
| σ2 | 407.95 | ||||
| τ00 id | 310.24 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.148 / 0.516 | ||||
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: 29317.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73404 -0.56652 0.07057 0.65903 2.41427
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 290.7 17.05
## Residual 547.6 23.40
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.6997 0.6768 1031.9998 88.2 <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) | 59.70 | 0.68 | 58.37 – 61.03 | 88.21 | <0.001 |
| Random Effects | |||||
| σ2 | 547.60 | ||||
| τ00 id | 290.68 | ||||
| ICC | 0.35 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.347 | ||||
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: 29215.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9395 -0.5457 0.0369 0.5813 3.2128
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 380.7 19.51
## Residual 355.5 18.85
## Number of obs: 3186, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.603 1.287 3166.241 36.977 <2e-16 ***
## C1 -1.984 1.608 2519.768 -1.233 0.2175
## C2 14.904 1.721 2441.244 8.658 <2e-16 ***
## C3 17.557 1.725 2471.167 10.177 <2e-16 ***
## C4 -2.573 1.628 2516.214 -1.580 0.1142
## C5 -4.333 1.630 2534.479 -2.658 0.0079 **
## C6 3.731 1.633 2517.827 2.285 0.0224 *
## C7 35.499 1.741 2469.807 20.389 <2e-16 ***
## C8 23.527 1.622 2538.539 14.508 <2e-16 ***
## C9 23.527 1.622 2538.539 14.508 <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 -0.644
## C2 -0.573 0.474
## C3 -0.581 0.481 0.391
## C4 -0.635 0.508 0.469 0.471
## C5 -0.641 0.517 0.472 0.481 0.508
## C6 -0.633 0.505 0.459 0.484 0.498 0.503
## C7 -0.577 0.475 0.390 0.396 0.472 0.476 0.466
## C8 -0.638 0.508 0.470 0.468 0.499 0.506 0.503 0.482
## C9 -0.638 0.508 0.470 0.468 0.499 0.506 0.503 0.482 0.614tab_model(modA.8,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 47.60 | 1.29 | 45.08 – 50.13 | 36.98 | <0.001 |
| C1 | -1.98 | 1.61 | -5.14 – 1.17 | -1.23 | 0.218 |
| C2 | 14.90 | 1.72 | 11.53 – 18.28 | 8.66 | <0.001 |
| C3 | 17.56 | 1.73 | 14.17 – 20.94 | 10.18 | <0.001 |
| C4 | -2.57 | 1.63 | -5.77 – 0.62 | -1.58 | 0.114 |
| C5 | -4.33 | 1.63 | -7.53 – -1.14 | -2.66 | 0.008 |
| C6 | 3.73 | 1.63 | 0.53 – 6.93 | 2.28 | 0.022 |
| C7 | 35.50 | 1.74 | 32.08 – 38.91 | 20.39 | <0.001 |
| C8 | 23.53 | 1.62 | 20.35 – 26.71 | 14.51 | <0.001 |
| C9 | 23.53 | 1.62 | 20.35 – 26.71 | 14.51 | <0.001 |
| Random Effects | |||||
| σ2 | 355.47 | ||||
| τ00 id | 380.68 | ||||
| ICC | 0.52 | ||||
| N id | 1033 | ||||
| Observations | 3186 | ||||
| Marginal R2 / Conditional R2 | 0.186 / 0.607 | ||||
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: 30217.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.02681 -0.64709 0.08625 0.61808 2.86728
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 363.3 19.06
## Residual 536.5 23.16
## Number of obs: 3186, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.8517 0.7248 1017.7886 78.44 <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) | 56.85 | 0.72 | 55.43 – 58.27 | 78.44 | <0.001 |
| Random Effects | |||||
| σ2 | 536.47 | ||||
| τ00 id | 363.27 | ||||
| ICC | 0.40 | ||||
| N id | 1033 | ||||
| Observations | 3186 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.404 | ||||
anova(modC.8, modA.8)## refitting model(s) with ML (instead of REML)Mixed Models
Support
Q.1: (SIMPLE MODEL) How do burger contrasts predict support?
#Do burger 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: 28616.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2167 -0.5115 0.0664 0.5574 3.0875
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 310.2 17.61
## Residual 408.0 20.20
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.92592 1.32047 3086.28324 40.081 < 2e-16 ***
## C1 -0.03794 1.69842 2477.86487 -0.022 0.982180
## C2 7.05492 1.83271 2440.69740 3.849 0.000121 ***
## C3 -1.81584 1.83417 2471.97653 -0.990 0.322267
## C4 1.72122 1.72013 2477.72780 1.001 0.317099
## C5 -2.64670 1.72070 2495.47117 -1.538 0.124138
## C6 -2.83635 1.72523 2478.62149 -1.644 0.100294
## C7 27.07239 1.85271 2476.71954 14.612 < 2e-16 ***
## C8 23.25155 1.70592 2473.13367 13.630 < 2e-16 ***
## C9 22.68855 1.83576 2485.55818 12.359 < 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 -0.664
## C2 -0.588 0.471
## C3 -0.597 0.478 0.388
## C4 -0.654 0.509 0.467 0.469
## C5 -0.660 0.516 0.469 0.478 0.508
## C6 -0.652 0.506 0.457 0.481 0.499 0.503
## C7 -0.592 0.471 0.385 0.391 0.469 0.472 0.462
## C8 -0.659 0.512 0.468 0.466 0.503 0.509 0.506 0.478
## C9 -0.601 0.486 0.391 0.398 0.475 0.476 0.473 0.395 0.480tab_model(modA.71,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 52.93 | 1.32 | 50.34 – 55.52 | 40.08 | <0.001 |
| C1 | -0.04 | 1.70 | -3.37 – 3.29 | -0.02 | 0.982 |
| C2 | 7.05 | 1.83 | 3.46 – 10.65 | 3.85 | <0.001 |
| C3 | -1.82 | 1.83 | -5.41 – 1.78 | -0.99 | 0.322 |
| C4 | 1.72 | 1.72 | -1.65 – 5.09 | 1.00 | 0.317 |
| C5 | -2.65 | 1.72 | -6.02 – 0.73 | -1.54 | 0.124 |
| C6 | -2.84 | 1.73 | -6.22 – 0.55 | -1.64 | 0.100 |
| C7 | 27.07 | 1.85 | 23.44 – 30.71 | 14.61 | <0.001 |
| C8 | 23.25 | 1.71 | 19.91 – 26.60 | 13.63 | <0.001 |
| C9 | 22.69 | 1.84 | 19.09 – 26.29 | 12.36 | <0.001 |
| Random Effects | |||||
| σ2 | 407.95 | ||||
| τ00 id | 310.24 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.148 / 0.516 | ||||
Q.2: Does naturalness predict support, over and above burger contrasts?
#Does naturalness predict support?
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: 28268.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4910 -0.5370 0.0329 0.5471 3.2926
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 295.0 17.18
## Residual 356.9 18.89
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.52996 1.24722 3087.00469 42.920 < 2e-16 ***
## Naturalness.c 0.45035 0.02323 2868.37846 19.387 < 2e-16 ***
## C1 5.78473 1.62105 2468.31920 3.569 0.000366 ***
## C2 6.64433 1.71873 2420.99352 3.866 0.000114 ***
## C3 4.09693 1.74683 2465.44214 2.345 0.019088 *
## C4 3.33378 1.61543 2453.90143 2.064 0.039150 *
## C5 -1.24857 1.61566 2472.47592 -0.773 0.439718
## C6 0.11066 1.62536 2456.62988 0.068 0.945723
## C7 19.49870 1.78155 2489.48852 10.945 < 2e-16 ***
## C8 12.78046 1.68925 2509.67281 7.566 5.38e-14 ***
## C9 15.71514 1.75968 2487.09403 8.931 < 2e-16 ***
## ---
## 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 0.026
## C1 -0.643 0.185
## C2 -0.584 -0.012 0.461
## C3 -0.579 0.173 0.494 0.379
## C4 -0.648 0.050 0.509 0.465 0.470
## C5 -0.653 0.043 0.514 0.468 0.478 0.509
## C6 -0.642 0.094 0.512 0.453 0.488 0.500 0.504
## C7 -0.579 -0.220 0.411 0.377 0.337 0.446 0.450 0.428
## C8 -0.628 -0.321 0.417 0.447 0.379 0.460 0.468 0.447 0.513
## C9 -0.589 -0.206 0.429 0.384 0.347 0.454 0.457 0.442 0.421
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.511tab_model(modA.7,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.53 | 1.25 | 51.08 – 55.98 | 42.92 | <0.001 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.50 | 19.39 | <0.001 |
| C1 | 5.78 | 1.62 | 2.61 – 8.96 | 3.57 | <0.001 |
| C2 | 6.64 | 1.72 | 3.27 – 10.01 | 3.87 | <0.001 |
| C3 | 4.10 | 1.75 | 0.67 – 7.52 | 2.35 | 0.019 |
| C4 | 3.33 | 1.62 | 0.17 – 6.50 | 2.06 | 0.039 |
| C5 | -1.25 | 1.62 | -4.42 – 1.92 | -0.77 | 0.440 |
| C6 | 0.11 | 1.63 | -3.08 – 3.30 | 0.07 | 0.946 |
| C7 | 19.50 | 1.78 | 16.01 – 22.99 | 10.94 | <0.001 |
| C8 | 12.78 | 1.69 | 9.47 – 16.09 | 7.57 | <0.001 |
| C9 | 15.72 | 1.76 | 12.26 – 19.17 | 8.93 | <0.001 |
| Random Effects | |||||
| σ2 | 356.87 | ||||
| τ00 id | 295.02 | ||||
| ICC | 0.45 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.575 | ||||
Q.3: Does perceived naturalness predict support, over and above risk perception and burger contrasts?
#Does naturalness predict support, over and above risk perception?
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: 27337.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6560 -0.4992 0.0365 0.5223 3.9479
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 246.0 15.68
## Residual 253.4 15.92
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.31967 1.07184 3086.73504 50.679 < 2e-16 ***
## Naturalness.c 0.19192 0.02127 2856.43872 9.022 < 2e-16 ***
## Risk.c -0.55186 0.01663 2996.44355 -33.190 < 2e-16 ***
## C1 6.73346 1.37370 2425.14465 4.902 1.01e-06 ***
## C2 2.21646 1.46123 2391.37126 1.517 0.12944
## C3 10.75468 1.49319 2426.54599 7.202 7.85e-13 ***
## C4 4.04074 1.36851 2411.79880 2.953 0.00318 **
## C5 -0.15398 1.36925 2428.21065 -0.112 0.91047
## C6 4.39688 1.38295 2412.73150 3.179 0.00149 **
## C7 10.60035 1.53370 2469.36992 6.912 6.08e-12 ***
## C8 8.19726 1.43877 2476.93574 5.697 1.36e-08 ***
## C9 9.63641 1.50278 2471.06194 6.412 1.71e-10 ***
## ---
## 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 0.016
## Risk.c -0.023 0.366
## C1 -0.633 0.165 -0.021
## C2 -0.574 0.022 0.090 0.457
## C3 -0.561 0.112 -0.133 0.493 0.360
## C4 -0.638 0.041 -0.014 0.509 0.462 0.467
## C5 -0.644 0.032 -0.022 0.515 0.463 0.477 0.510
## C6 -0.628 0.052 -0.094 0.512 0.440 0.494 0.499 0.504
## C7 -0.565 -0.139 0.176 0.400 0.383 0.302 0.436 0.439 0.403
## C8 -0.618 -0.264 0.097 0.412 0.451 0.360 0.456 0.463 0.433
## C9 -0.578 -0.146 0.124 0.423 0.389 0.322 0.449 0.450 0.425
## C7 C8
## Naturlnss.c
## Risk.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 0.520
## C9 0.431 0.518tab_model(modA.9,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.32 | 1.07 | 52.22 – 56.42 | 50.68 | <0.001 |
| Naturalness c | 0.19 | 0.02 | 0.15 – 0.23 | 9.02 | <0.001 |
| Risk c | -0.55 | 0.02 | -0.58 – -0.52 | -33.19 | <0.001 |
| C1 | 6.73 | 1.37 | 4.04 – 9.43 | 4.90 | <0.001 |
| C2 | 2.22 | 1.46 | -0.65 – 5.08 | 1.52 | 0.129 |
| C3 | 10.75 | 1.49 | 7.83 – 13.68 | 7.20 | <0.001 |
| C4 | 4.04 | 1.37 | 1.36 – 6.72 | 2.95 | 0.003 |
| C5 | -0.15 | 1.37 | -2.84 – 2.53 | -0.11 | 0.910 |
| C6 | 4.40 | 1.38 | 1.69 – 7.11 | 3.18 | 0.001 |
| C7 | 10.60 | 1.53 | 7.59 – 13.61 | 6.91 | <0.001 |
| C8 | 8.20 | 1.44 | 5.38 – 11.02 | 5.70 | <0.001 |
| C9 | 9.64 | 1.50 | 6.69 – 12.58 | 6.41 | <0.001 |
| Random Effects | |||||
| σ2 | 253.39 | ||||
| τ00 id | 245.99 | ||||
| ICC | 0.49 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.405 / 0.698 | ||||
Q.4: Does perceived benefit predict behavioral intent, over and above naturalness and burger contrasts?
#Does perceived benefit predict behavioral intent, over and above naturalness and burger 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: 27042.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8153 -0.5005 0.0110 0.5069 4.1441
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 169.3 13.01
## Residual 251.0 15.84
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.09373 1.02480 3081.19354 54.736 < 2e-16 ***
## Naturalness.c 0.31063 0.01952 2924.30679 15.913 < 2e-16 ***
## Benefit.c 0.57390 0.01470 3084.10134 39.048 < 2e-16 ***
## C1 2.85165 1.35163 2517.97955 2.110 0.035 *
## C2 8.11019 1.43255 2465.56344 5.661 1.68e-08 ***
## C3 -1.43221 1.46103 2515.59778 -0.980 0.327
## C4 1.52706 1.34592 2503.15348 1.135 0.257
## C5 -1.52057 1.34498 2523.04742 -1.131 0.258
## C6 -1.10090 1.35380 2504.32595 -0.813 0.416
## C7 13.94600 1.48933 2553.76062 9.364 < 2e-16 ***
## C8 7.18639 1.41245 2577.67497 5.088 3.88e-07 ***
## C9 10.59315 1.47013 2549.10069 7.206 7.58e-13 ***
## ---
## 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 0.063 -0.184
## C1 -0.654 0.191 -0.055
## C2 -0.591 -0.016 0.025 0.459
## C3 -0.591 0.186 -0.095 0.496 0.378
## C4 -0.658 0.055 -0.032 0.510 0.464 0.471
## C5 -0.662 0.043 -0.003 0.513 0.467 0.476 0.509
## C6 -0.652 0.096 -0.023 0.513 0.453 0.487 0.501 0.504
## C7 -0.591 -0.196 -0.094 0.415 0.376 0.346 0.447 0.449 0.429
## C8 -0.640 -0.292 -0.100 0.421 0.442 0.387 0.461 0.466 0.447
## C9 -0.601 -0.184 -0.087 0.432 0.384 0.356 0.455 0.455 0.443
## C7 C8
## Naturlnss.c
## Benefit.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 0.516
## C9 0.428 0.514tab_model(modA.10,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.09 | 1.02 | 54.08 – 58.10 | 54.74 | <0.001 |
| Naturalness c | 0.31 | 0.02 | 0.27 – 0.35 | 15.91 | <0.001 |
| Benefit c | 0.57 | 0.01 | 0.55 – 0.60 | 39.05 | <0.001 |
| C1 | 2.85 | 1.35 | 0.20 – 5.50 | 2.11 | 0.035 |
| C2 | 8.11 | 1.43 | 5.30 – 10.92 | 5.66 | <0.001 |
| C3 | -1.43 | 1.46 | -4.30 – 1.43 | -0.98 | 0.327 |
| C4 | 1.53 | 1.35 | -1.11 – 4.17 | 1.13 | 0.257 |
| C5 | -1.52 | 1.34 | -4.16 – 1.12 | -1.13 | 0.258 |
| C6 | -1.10 | 1.35 | -3.76 – 1.55 | -0.81 | 0.416 |
| C7 | 13.95 | 1.49 | 11.03 – 16.87 | 9.36 | <0.001 |
| C8 | 7.19 | 1.41 | 4.42 – 9.96 | 5.09 | <0.001 |
| C9 | 10.59 | 1.47 | 7.71 – 13.48 | 7.21 | <0.001 |
| Random Effects | |||||
| σ2 | 250.99 | ||||
| τ00 id | 169.32 | ||||
| ICC | 0.40 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.489 / 0.695 | ||||
Q.5: Does perceived benefit predict support, over and above perceived risk, naturalness, and burger contrasts?
#Does perceived benefit predict support, over and above perceived risk, naturalness, and burger 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: 26363.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4649 -0.5066 0.0307 0.5050 3.7502
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 148.2 12.17
## Residual 195.9 14.00
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.16585 0.91664 3083.19538 61.273 < 2e-16 ***
## Naturalness.c 0.14554 0.01840 2931.07853 7.910 3.62e-15 ***
## Risk.c -0.41178 0.01484 3066.01967 -27.747 < 2e-16 ***
## Benefit.c 0.46785 0.01368 3085.49633 34.191 < 2e-16 ***
## C1 4.17712 1.20001 2480.02663 3.481 0.000508 ***
## C2 4.58609 1.27661 2446.05124 3.592 0.000334 ***
## C3 4.63514 1.31389 2487.91499 3.528 0.000427 ***
## C4 2.43944 1.19426 2465.95256 2.043 0.041195 *
## C5 -0.61225 1.19365 2484.82056 -0.513 0.608055
## C6 2.42408 1.20753 2465.17012 2.007 0.044808 *
## C7 8.29217 1.33739 2531.99423 6.200 6.56e-10 ***
## C8 4.81081 1.25673 2546.50435 3.828 0.000132 ***
## C9 7.05879 1.31098 2532.49899 5.384 7.94e-08 ***
## ---
## 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) | 56.17 | 0.92 | 54.37 – 57.96 | 61.27 | <0.001 |
| Naturalness c | 0.15 | 0.02 | 0.11 – 0.18 | 7.91 | <0.001 |
| Risk c | -0.41 | 0.01 | -0.44 – -0.38 | -27.75 | <0.001 |
| Benefit c | 0.47 | 0.01 | 0.44 – 0.49 | 34.19 | <0.001 |
| C1 | 4.18 | 1.20 | 1.82 – 6.53 | 3.48 | 0.001 |
| C2 | 4.59 | 1.28 | 2.08 – 7.09 | 3.59 | <0.001 |
| C3 | 4.64 | 1.31 | 2.06 – 7.21 | 3.53 | <0.001 |
| C4 | 2.44 | 1.19 | 0.10 – 4.78 | 2.04 | 0.041 |
| C5 | -0.61 | 1.19 | -2.95 – 1.73 | -0.51 | 0.608 |
| C6 | 2.42 | 1.21 | 0.06 – 4.79 | 2.01 | 0.045 |
| C7 | 8.29 | 1.34 | 5.67 – 10.91 | 6.20 | <0.001 |
| C8 | 4.81 | 1.26 | 2.35 – 7.27 | 3.83 | <0.001 |
| C9 | 7.06 | 1.31 | 4.49 – 9.63 | 5.38 | <0.001 |
| Random Effects | |||||
| σ2 | 195.90 | ||||
| τ00 id | 148.21 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.578 / 0.760 | ||||
Q.6: Does perceived familiarity/understanding predict support, over and above perceived benefit, risk, naturalness, and burger contrasts?
#How does perceived benefit and naturalness predict behavioral intent?
modA.115 <- lmer(Support ~ FR.c + Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 + C9 + (1|id), data = L)
summary(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 | id)
## Data: L
##
## REML criterion at convergence: 26330.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4947 -0.5050 0.0353 0.5155 3.6622
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.2 12.05
## Residual 194.0 13.93
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.46660 0.93402 3081.89495 61.526 < 2e-16 ***
## FR.c 0.09334 0.01481 3084.87128 6.304 3.30e-10 ***
## Naturalness.c 0.12290 0.01864 2966.56654 6.593 5.07e-11 ***
## Risk.c -0.40054 0.01486 3067.65237 -26.961 < 2e-16 ***
## Benefit.c 0.45952 0.01367 3084.03234 33.625 < 2e-16 ***
## C1 3.93027 1.19422 2484.15782 3.291 0.00101 **
## C2 2.59125 1.30875 2501.71789 1.980 0.04782 *
## C3 1.38554 1.40441 2630.37661 0.987 0.32395
## C4 2.31086 1.18805 2468.26054 1.945 0.05188 .
## C5 -0.25711 1.18862 2490.15240 -0.216 0.82877
## C6 2.08530 1.20229 2469.41951 1.734 0.08297 .
## C7 4.54122 1.45711 2659.06106 3.117 0.00185 **
## C8 2.89030 1.28651 2584.28299 2.247 0.02475 *
## C9 3.52728 1.41916 2648.09658 2.485 0.01300 *
## ---
## 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 ittab_model(modA.115,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.47 | 0.93 | 55.64 – 59.30 | 61.53 | <0.001 |
| FR c | 0.09 | 0.01 | 0.06 – 0.12 | 6.30 | <0.001 |
| Naturalness c | 0.12 | 0.02 | 0.09 – 0.16 | 6.59 | <0.001 |
| Risk c | -0.40 | 0.01 | -0.43 – -0.37 | -26.96 | <0.001 |
| Benefit c | 0.46 | 0.01 | 0.43 – 0.49 | 33.62 | <0.001 |
| C1 | 3.93 | 1.19 | 1.59 – 6.27 | 3.29 | 0.001 |
| C2 | 2.59 | 1.31 | 0.03 – 5.16 | 1.98 | 0.048 |
| C3 | 1.39 | 1.40 | -1.37 – 4.14 | 0.99 | 0.324 |
| C4 | 2.31 | 1.19 | -0.02 – 4.64 | 1.95 | 0.052 |
| C5 | -0.26 | 1.19 | -2.59 – 2.07 | -0.22 | 0.829 |
| C6 | 2.09 | 1.20 | -0.27 – 4.44 | 1.73 | 0.083 |
| C7 | 4.54 | 1.46 | 1.68 – 7.40 | 3.12 | 0.002 |
| C8 | 2.89 | 1.29 | 0.37 – 5.41 | 2.25 | 0.025 |
| C9 | 3.53 | 1.42 | 0.74 – 6.31 | 2.49 | 0.013 |
| Random Effects | |||||
| σ2 | 193.96 | ||||
| τ00 id | 145.15 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.583 / 0.761 | ||||
Naturalness
Q.1: (SIMPLE MODEL) How do burger contrasts predict naturalness perception?
#How do burger 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: 26542.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5590 -0.6146 -0.0188 0.6110 3.4214
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.07 8.128
## Residual 255.98 15.999
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.7758 0.9529 3071.0207 40.694 < 2e-16 ***
## C1 -13.0130 1.2917 2757.1250 -10.074 < 2e-16 ***
## C2 0.6991 1.3996 2685.1901 0.499 0.61748
## C3 -13.0158 1.3968 2725.9700 -9.318 < 2e-16 ***
## C4 -3.8060 1.3082 2757.2346 -2.909 0.00365 **
## C5 -3.0749 1.3065 2781.1438 -2.354 0.01866 *
## C6 -6.8214 1.3121 2757.4352 -5.199 2.15e-07 ***
## C7 16.4597 1.4103 2733.5148 11.671 < 2e-16 ***
## C8 23.0426 1.2980 2750.3673 17.752 < 2e-16 ***
## C9 15.4236 1.3962 2744.8882 11.047 < 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 -0.699
## C2 -0.628 0.472
## C3 -0.635 0.477 0.407
## C4 -0.690 0.509 0.467 0.469
## C5 -0.694 0.514 0.469 0.475 0.506
## C6 -0.687 0.506 0.461 0.475 0.499 0.503
## C7 -0.629 0.471 0.404 0.408 0.467 0.470 0.463
## C8 -0.694 0.512 0.469 0.469 0.504 0.508 0.505 0.474
## C9 -0.638 0.482 0.409 0.414 0.473 0.474 0.472 0.410 0.477tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 38.78 | 0.95 | 36.91 – 40.64 | 40.69 | <0.001 |
| C1 | -13.01 | 1.29 | -15.55 – -10.48 | -10.07 | <0.001 |
| C2 | 0.70 | 1.40 | -2.05 – 3.44 | 0.50 | 0.617 |
| C3 | -13.02 | 1.40 | -15.75 – -10.28 | -9.32 | <0.001 |
| C4 | -3.81 | 1.31 | -6.37 – -1.24 | -2.91 | 0.004 |
| C5 | -3.07 | 1.31 | -5.64 – -0.51 | -2.35 | 0.019 |
| C6 | -6.82 | 1.31 | -9.39 – -4.25 | -5.20 | <0.001 |
| C7 | 16.46 | 1.41 | 13.69 – 19.22 | 11.67 | <0.001 |
| C8 | 23.04 | 1.30 | 20.50 – 25.59 | 17.75 | <0.001 |
| C9 | 15.42 | 1.40 | 12.69 – 18.16 | 11.05 | <0.001 |
| Random Effects | |||||
| σ2 | 255.98 | ||||
| τ00 id | 66.07 | ||||
| ICC | 0.21 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.307 / 0.449 | ||||
Q.2: Does understanding/familiarity (mean score) predict naturalness perception, over and above burger contrasts?
#Does understanding/familiarity (mean score) predict naturalness perception, over and above burger 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: 26364.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6736 -0.6024 -0.0028 0.5820 3.4842
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 75.4 8.683
## Residual 232.8 15.258
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.65810 0.94636 3075.48603 44.019 < 2e-16 ***
## FR.c 0.19635 0.01406 2981.22493 13.968 < 2e-16 ***
## C1 -12.73469 1.24262 2692.27004 -10.248 < 2e-16 ***
## C2 -3.68111 1.38155 2680.94339 -2.664 0.00776 **
## C3 -18.88601 1.40714 2764.76443 -13.422 < 2e-16 ***
## C4 -3.76718 1.25837 2692.33909 -2.994 0.00278 **
## C5 -2.09696 1.25901 2720.37648 -1.666 0.09591 .
## C6 -6.91316 1.26209 2692.81978 -5.478 4.71e-08 ***
## C7 7.06883 1.51620 2877.02147 4.662 3.27e-06 ***
## C8 17.27510 1.31610 2800.86277 13.126 < 2e-16 ***
## C9 6.65527 1.48328 2865.37825 4.487 7.51e-06 ***
## ---
## 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 0.221
## C1 -0.674 0.015
## C2 -0.641 -0.229 0.456
## C3 -0.651 -0.298 0.451 0.443
## C4 -0.668 -0.002 0.509 0.455 0.448
## C5 -0.659 0.055 0.514 0.443 0.437 0.506
## C6 -0.667 -0.008 0.506 0.450 0.457 0.499 0.501
## C7 -0.643 -0.447 0.415 0.450 0.479 0.419 0.395 0.418
## C8 -0.708 -0.317 0.481 0.505 0.519 0.479 0.464 0.482 0.545
## C9 -0.652 -0.425 0.430 0.454 0.481 0.429 0.406 0.431 0.519 0.545tab_model(modA.94,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 41.66 | 0.95 | 39.80 – 43.51 | 44.02 | <0.001 |
| FR c | 0.20 | 0.01 | 0.17 – 0.22 | 13.97 | <0.001 |
| C1 | -12.73 | 1.24 | -15.17 – -10.30 | -10.25 | <0.001 |
| C2 | -3.68 | 1.38 | -6.39 – -0.97 | -2.66 | 0.008 |
| C3 | -18.89 | 1.41 | -21.65 – -16.13 | -13.42 | <0.001 |
| C4 | -3.77 | 1.26 | -6.23 – -1.30 | -2.99 | 0.003 |
| C5 | -2.10 | 1.26 | -4.57 – 0.37 | -1.67 | 0.096 |
| C6 | -6.91 | 1.26 | -9.39 – -4.44 | -5.48 | <0.001 |
| C7 | 7.07 | 1.52 | 4.10 – 10.04 | 4.66 | <0.001 |
| C8 | 17.28 | 1.32 | 14.69 – 19.86 | 13.13 | <0.001 |
| C9 | 6.66 | 1.48 | 3.75 – 9.56 | 4.49 | <0.001 |
| Random Effects | |||||
| σ2 | 232.80 | ||||
| τ00 id | 75.40 | ||||
| ICC | 0.24 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.346 / 0.506 | ||||
Q.3: Does familiarity predict naturalness perception, over and above familiarity and burger 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: 24884.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7110 -0.6046 0.0022 0.5710 3.4483
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 78.33 8.85
## Residual 226.70 15.06
## Number of obs: 2929, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.01787 0.94747 2903.17266 44.347 < 2e-16 ***
## Familiarity.c 0.14100 0.01508 2914.45656 9.353 < 2e-16 ***
## Understanding.c 0.04953 0.01509 2750.41478 3.283 0.001040 **
## C1 -12.84913 1.23304 2543.70412 -10.421 < 2e-16 ***
## C2 -4.34262 1.38383 2488.27858 -3.138 0.001720 **
## C3 -19.95013 1.43350 2594.44614 -13.917 < 2e-16 ***
## C4 -4.11954 1.25042 2546.41950 -3.295 0.000999 ***
## C5 -2.23247 1.24960 2571.41221 -1.787 0.074128 .
## C6 -6.78354 1.25467 2550.11694 -5.407 7.02e-08 ***
## C7 6.11241 1.54410 2698.69182 3.959 7.74e-05 ***
## C8 16.77315 1.31793 2642.26883 12.727 < 2e-16 ***
## C9 5.91420 2.03277 2706.76727 2.909 0.003650 **
## ---
## 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 0.212
## Undrstndng. -0.007 -0.555
## C1 -0.668 0.000 0.015
## C2 -0.647 -0.237 0.019 0.452
## C3 -0.657 -0.340 0.061 0.440 0.461
## C4 -0.668 -0.051 0.050 0.508 0.458 0.449
## C5 -0.651 0.040 0.013 0.514 0.436 0.422 0.504
## C6 -0.652 0.052 -0.059 0.504 0.435 0.430 0.494 0.501
## C7 -0.648 -0.400 -0.019 0.405 0.468 0.507 0.420 0.381 0.392
## C8 -0.711 -0.257 -0.043 0.476 0.516 0.531 0.480 0.457 0.469
## C9 -0.488 -0.280 0.030 0.310 0.348 0.373 0.317 0.292 0.301
## C7 C8
## Familirty.c
## Undrstndng.
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 0.556
## C9 0.393 0.443tab_model(modA.9433,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 42.02 | 0.95 | 40.16 – 43.88 | 44.35 | <0.001 |
| Familiarity c | 0.14 | 0.02 | 0.11 – 0.17 | 9.35 | <0.001 |
| Understanding c | 0.05 | 0.02 | 0.02 – 0.08 | 3.28 | 0.001 |
| C1 | -12.85 | 1.23 | -15.27 – -10.43 | -10.42 | <0.001 |
| C2 | -4.34 | 1.38 | -7.06 – -1.63 | -3.14 | 0.002 |
| C3 | -19.95 | 1.43 | -22.76 – -17.14 | -13.92 | <0.001 |
| C4 | -4.12 | 1.25 | -6.57 – -1.67 | -3.29 | 0.001 |
| C5 | -2.23 | 1.25 | -4.68 – 0.22 | -1.79 | 0.074 |
| C6 | -6.78 | 1.25 | -9.24 – -4.32 | -5.41 | <0.001 |
| C7 | 6.11 | 1.54 | 3.08 – 9.14 | 3.96 | <0.001 |
| C8 | 16.77 | 1.32 | 14.19 – 19.36 | 12.73 | <0.001 |
| C9 | 5.91 | 2.03 | 1.93 – 9.90 | 2.91 | 0.004 |
| Random Effects | |||||
| σ2 | 226.70 | ||||
| τ00 id | 78.33 | ||||
| ICC | 0.26 | ||||
| N id | 1033 | ||||
| Observations | 2929 | ||||
| Marginal R2 / Conditional R2 | 0.341 / 0.510 | ||||
Risk
Q.1: (SIMPLE MODEL) How do burger contrasts predict risk perception?
#Does naturalness 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: 28183.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5937 -0.6121 -0.0692 0.5627 3.6688
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 186.0 13.64
## Residual 392.4 19.81
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.824 1.236 3073.917 28.180 < 2e-16 ***
## C1 8.104 1.637 2603.391 4.950 7.88e-07 ***
## C2 -8.219 1.769 2550.596 -4.645 3.57e-06 ***
## C3 18.477 1.769 2587.689 10.447 < 2e-16 ***
## C4 3.297 1.658 2603.343 1.988 0.0469 *
## C5 3.725 1.657 2624.855 2.247 0.0247 *
## C6 11.433 1.663 2604.021 6.876 7.70e-12 ***
## C7 -23.874 1.786 2593.853 -13.366 < 2e-16 ***
## C8 -18.746 1.645 2597.511 -11.398 < 2e-16 ***
## C9 -17.561 1.769 2604.314 -9.926 < 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 -0.683
## C2 -0.609 0.472
## C3 -0.617 0.477 0.396
## C4 -0.674 0.509 0.467 0.469
## C5 -0.679 0.515 0.469 0.477 0.507
## C6 -0.672 0.506 0.458 0.478 0.499 0.503
## C7 -0.612 0.472 0.393 0.399 0.468 0.471 0.463
## C8 -0.679 0.512 0.468 0.468 0.504 0.508 0.505 0.476
## C9 -0.621 0.484 0.399 0.405 0.474 0.475 0.473 0.401 0.479tab_model(modA.82,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.82 | 1.24 | 32.40 – 37.25 | 28.18 | <0.001 |
| C1 | 8.10 | 1.64 | 4.89 – 11.31 | 4.95 | <0.001 |
| C2 | -8.22 | 1.77 | -11.69 – -4.75 | -4.65 | <0.001 |
| C3 | 18.48 | 1.77 | 15.01 – 21.94 | 10.45 | <0.001 |
| C4 | 3.30 | 1.66 | 0.05 – 6.55 | 1.99 | 0.047 |
| C5 | 3.72 | 1.66 | 0.48 – 6.97 | 2.25 | 0.025 |
| C6 | 11.43 | 1.66 | 8.17 – 14.69 | 6.88 | <0.001 |
| C7 | -23.87 | 1.79 | -27.38 – -20.37 | -13.37 | <0.001 |
| C8 | -18.75 | 1.64 | -21.97 – -15.52 | -11.40 | <0.001 |
| C9 | -17.56 | 1.77 | -21.03 – -14.09 | -9.93 | <0.001 |
| Random Effects | |||||
| σ2 | 392.45 | ||||
| τ00 id | 186.03 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.474 | ||||
Q.2: Does naturalness predict risk perception, over and above burger 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: 27768.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3450 -0.6028 -0.0208 0.5695 3.6980
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.6 13.44
## Residual 333.5 18.26
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.29243 1.15344 3076.08607 29.731 < 2e-16 ***
## Naturalness.c -0.46313 0.02175 2999.47898 -21.298 < 2e-16 ***
## C1 2.03764 1.54266 2581.00506 1.321 0.187
## C2 -7.87191 1.63865 2518.37582 -4.804 1.65e-06 ***
## C3 12.35689 1.66272 2570.48308 7.432 1.45e-13 ***
## C4 1.52699 1.53812 2564.68416 0.993 0.321
## C5 2.22684 1.53727 2587.15427 1.449 0.148
## C6 8.24283 1.54744 2567.52291 5.327 1.09e-07 ***
## C7 -16.17286 1.69427 2598.33439 -9.546 < 2e-16 ***
## C8 -8.10285 1.60514 2627.19172 -5.048 4.77e-07 ***
## C9 -10.54300 1.67360 2596.25957 -6.300 3.49e-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 0.025
## C1 -0.662 0.182
## C2 -0.604 -0.011 0.462
## C3 -0.599 0.170 0.494 0.387
## C4 -0.667 0.051 0.509 0.466 0.470
## C5 -0.672 0.043 0.514 0.468 0.477 0.509
## C6 -0.661 0.093 0.512 0.455 0.486 0.501 0.504
## C7 -0.598 -0.215 0.414 0.384 0.345 0.446 0.451 0.430
## C8 -0.647 -0.315 0.420 0.448 0.384 0.461 0.469 0.448 0.510
## C9 -0.608 -0.202 0.430 0.391 0.354 0.454 0.457 0.442 0.425
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.509tab_model(modA.8,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.29 | 1.15 | 32.03 – 36.55 | 29.73 | <0.001 |
| Naturalness c | -0.46 | 0.02 | -0.51 – -0.42 | -21.30 | <0.001 |
| C1 | 2.04 | 1.54 | -0.99 – 5.06 | 1.32 | 0.187 |
| C2 | -7.87 | 1.64 | -11.08 – -4.66 | -4.80 | <0.001 |
| C3 | 12.36 | 1.66 | 9.10 – 15.62 | 7.43 | <0.001 |
| C4 | 1.53 | 1.54 | -1.49 – 4.54 | 0.99 | 0.321 |
| C5 | 2.23 | 1.54 | -0.79 – 5.24 | 1.45 | 0.148 |
| C6 | 8.24 | 1.55 | 5.21 – 11.28 | 5.33 | <0.001 |
| C7 | -16.17 | 1.69 | -19.49 – -12.85 | -9.55 | <0.001 |
| C8 | -8.10 | 1.61 | -11.25 – -4.96 | -5.05 | <0.001 |
| C9 | -10.54 | 1.67 | -13.82 – -7.26 | -6.30 | <0.001 |
| Random Effects | |||||
| σ2 | 333.49 | ||||
| τ00 id | 180.57 | ||||
| ICC | 0.35 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.314 / 0.555 | ||||
Q.3: Does benefit predict risk perception, over and above burger 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: 27868.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4788 -0.6142 -0.0510 0.5897 3.5700
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 177.5 13.32
## Residual 348.8 18.68
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 33.43858 1.17513 3074.71974 28.455 < 2e-16 ***
## Benefit.c -0.30329 0.01647 3037.67164 -18.414 < 2e-16 ***
## C1 8.66339 1.54800 2581.39265 5.597 2.42e-08 ***
## C2 -8.86224 1.67266 2532.74825 -5.298 1.27e-07 ***
## C3 20.35550 1.67513 2570.00083 12.152 < 2e-16 ***
## C4 3.86402 1.56781 2581.98392 2.465 0.0138 *
## C5 3.51145 1.56702 2602.18582 2.241 0.0251 *
## C6 11.56279 1.57208 2581.73373 7.355 2.55e-13 ***
## C7 -19.78631 1.70320 2599.06517 -11.617 < 2e-16 ***
## C8 -14.16561 1.57503 2605.54589 -8.994 < 2e-16 ***
## C9 -13.88157 1.68503 2604.52362 -8.238 2.74e-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 0.066
## C1 -0.681 -0.021
## C2 -0.604 0.020 0.471
## C3 -0.616 -0.062 0.478 0.393
## C4 -0.671 -0.022 0.509 0.466 0.470
## C5 -0.675 0.006 0.515 0.469 0.476 0.507
## C6 -0.668 -0.007 0.506 0.458 0.478 0.499 0.503
## C7 -0.611 -0.131 0.470 0.386 0.401 0.467 0.466 0.459
## C8 -0.677 -0.160 0.508 0.459 0.471 0.500 0.501 0.500 0.487
## C9 -0.620 -0.122 0.483 0.392 0.407 0.473 0.471 0.470 0.410 0.489tab_model(modA.88,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 33.44 | 1.18 | 31.13 – 35.74 | 28.46 | <0.001 |
| Benefit c | -0.30 | 0.02 | -0.34 – -0.27 | -18.41 | <0.001 |
| C1 | 8.66 | 1.55 | 5.63 – 11.70 | 5.60 | <0.001 |
| C2 | -8.86 | 1.67 | -12.14 – -5.58 | -5.30 | <0.001 |
| C3 | 20.36 | 1.68 | 17.07 – 23.64 | 12.15 | <0.001 |
| C4 | 3.86 | 1.57 | 0.79 – 6.94 | 2.46 | 0.014 |
| C5 | 3.51 | 1.57 | 0.44 – 6.58 | 2.24 | 0.025 |
| C6 | 11.56 | 1.57 | 8.48 – 14.65 | 7.36 | <0.001 |
| C7 | -19.79 | 1.70 | -23.13 – -16.45 | -11.62 | <0.001 |
| C8 | -14.17 | 1.58 | -17.25 – -11.08 | -8.99 | <0.001 |
| C9 | -13.88 | 1.69 | -17.19 – -10.58 | -8.24 | <0.001 |
| Random Effects | |||||
| σ2 | 348.84 | ||||
| τ00 id | 177.53 | ||||
| ICC | 0.34 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.302 / 0.537 | ||||
Q.4: Does benefit predict risk perception, over and above naturalness and burger 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: 27538.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1310 -0.5882 -0.0140 0.5795 3.7415
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 176.6 13.29
## Residual 304.6 17.45
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 33.23517 1.11184 3076.99253 29.892 < 2e-16 ***
## Naturalness.c -0.40203 0.02126 2969.99565 -18.909 < 2e-16 ***
## Benefit.c -0.24957 0.01591 3067.90331 -15.689 < 2e-16 ***
## C1 3.28213 1.48056 2558.92030 2.217 0.026724 *
## C2 -8.46539 1.57022 2500.90707 -5.391 7.65e-08 ***
## C3 14.68630 1.60047 2553.74422 9.176 < 2e-16 ***
## C4 2.21565 1.47457 2543.46635 1.503 0.133073
## C5 2.23734 1.47318 2564.72057 1.519 0.128957
## C6 8.74500 1.48319 2544.59873 5.896 4.22e-09 ***
## C7 -13.81213 1.63071 2593.09324 -8.470 < 2e-16 ***
## C8 -5.75077 1.54605 2620.15997 -3.720 0.000204 ***
## C9 -8.46345 1.60978 2588.58288 -5.258 1.58e-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 0.013
## Benefit.c 0.063 -0.185
## C1 -0.661 0.190 -0.055
## C2 -0.598 -0.015 0.024 0.459
## C3 -0.598 0.185 -0.095 0.496 0.381
## C4 -0.665 0.055 -0.032 0.510 0.464 0.471
## C5 -0.668 0.043 -0.003 0.513 0.468 0.475 0.509
## C6 -0.659 0.096 -0.024 0.513 0.454 0.486 0.501 0.504
## C7 -0.597 -0.194 -0.092 0.416 0.379 0.349 0.447 0.449 0.430
## C8 -0.646 -0.290 -0.098 0.422 0.443 0.389 0.462 0.467 0.448
## C9 -0.607 -0.182 -0.085 0.432 0.386 0.358 0.455 0.455 0.443
## C7 C8
## Naturlnss.c
## Benefit.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 0.515
## C9 0.429 0.514tab_model(modA.99,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 33.24 | 1.11 | 31.06 – 35.42 | 29.89 | <0.001 |
| Naturalness c | -0.40 | 0.02 | -0.44 – -0.36 | -18.91 | <0.001 |
| Benefit c | -0.25 | 0.02 | -0.28 – -0.22 | -15.69 | <0.001 |
| C1 | 3.28 | 1.48 | 0.38 – 6.19 | 2.22 | 0.027 |
| C2 | -8.47 | 1.57 | -11.54 – -5.39 | -5.39 | <0.001 |
| C3 | 14.69 | 1.60 | 11.55 – 17.82 | 9.18 | <0.001 |
| C4 | 2.22 | 1.47 | -0.68 – 5.11 | 1.50 | 0.133 |
| C5 | 2.24 | 1.47 | -0.65 – 5.13 | 1.52 | 0.129 |
| C6 | 8.74 | 1.48 | 5.84 – 11.65 | 5.90 | <0.001 |
| C7 | -13.81 | 1.63 | -17.01 – -10.61 | -8.47 | <0.001 |
| C8 | -5.75 | 1.55 | -8.78 – -2.72 | -3.72 | <0.001 |
| C9 | -8.46 | 1.61 | -11.62 – -5.31 | -5.26 | <0.001 |
| Random Effects | |||||
| σ2 | 304.62 | ||||
| τ00 id | 176.64 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.365 / 0.598 | ||||
Q.5: Does understanding/familiarity predict risk perception, over and above naturalness, benefit, and burger 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: 27501
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5699 -0.5970 -0.0079 0.5663 3.9521
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 175.3 13.24
## Residual 299.9 17.32
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.59616 1.13160 3078.18873 27.922 < 2e-16 ***
## Naturalness.c -0.36859 0.02171 3011.64513 -16.981 < 2e-16 ***
## Benefit.c -0.23540 0.01594 3066.22913 -14.763 < 2e-16 ***
## FR.c -0.11774 0.01776 3070.86974 -6.628 4.01e-11 ***
## C1 3.53283 1.47007 2558.55122 2.403 0.016324 *
## C2 -5.82553 1.60868 2562.33574 -3.621 0.000299 ***
## C3 18.55381 1.69267 2688.77738 10.961 < 2e-16 ***
## C4 2.33245 1.46372 2541.62702 1.594 0.111170
## C5 1.75395 1.46401 2565.49882 1.198 0.231012
## C6 9.04213 1.47287 2544.02515 6.139 9.60e-10 ***
## C7 -8.87417 1.78175 2732.00635 -4.981 6.73e-07 ***
## C8 -3.23870 1.58082 2658.70462 -2.049 0.040584 *
## C9 -3.88311 1.74131 2717.29803 -2.230 0.025830 *
## ---
## 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) | 31.60 | 1.13 | 29.38 – 33.81 | 27.92 | <0.001 |
| Naturalness c | -0.37 | 0.02 | -0.41 – -0.33 | -16.98 | <0.001 |
| Benefit c | -0.24 | 0.02 | -0.27 – -0.20 | -14.76 | <0.001 |
| FR c | -0.12 | 0.02 | -0.15 – -0.08 | -6.63 | <0.001 |
| C1 | 3.53 | 1.47 | 0.65 – 6.42 | 2.40 | 0.016 |
| C2 | -5.83 | 1.61 | -8.98 – -2.67 | -3.62 | <0.001 |
| C3 | 18.55 | 1.69 | 15.23 – 21.87 | 10.96 | <0.001 |
| C4 | 2.33 | 1.46 | -0.54 – 5.20 | 1.59 | 0.111 |
| C5 | 1.75 | 1.46 | -1.12 – 4.62 | 1.20 | 0.231 |
| C6 | 9.04 | 1.47 | 6.15 – 11.93 | 6.14 | <0.001 |
| C7 | -8.87 | 1.78 | -12.37 – -5.38 | -4.98 | <0.001 |
| C8 | -3.24 | 1.58 | -6.34 – -0.14 | -2.05 | 0.041 |
| C9 | -3.88 | 1.74 | -7.30 – -0.47 | -2.23 | 0.026 |
| Random Effects | |||||
| σ2 | 299.93 | ||||
| τ00 id | 175.34 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.372 / 0.604 | ||||
Benefit
Q.1: (SIMPLE MODEL) How do burger contrasts predict perceived benefit?
#How do burger 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: 28385.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4352 -0.5146 0.0631 0.5681 3.1759
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 287.9 16.97
## Residual 378.5 19.46
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.2626 1.2720 3086.2930 41.874 < 2e-16 ***
## C1 1.9219 1.6360 2478.3405 1.175 0.240217
## C2 -2.2926 1.7654 2441.2064 -1.299 0.194184
## C3 6.2690 1.7668 2472.4609 3.548 0.000395 ***
## C4 2.1007 1.6569 2478.2035 1.268 0.204973
## C5 -0.4471 1.6575 2495.9323 -0.270 0.787385
## C6 0.5182 1.6618 2479.0967 0.312 0.755189
## C7 13.7277 1.7846 2477.1998 7.692 2.07e-14 ***
## C8 15.3323 1.6432 2473.6132 9.330 < 2e-16 ***
## C9 12.5640 1.7683 2486.0313 7.105 1.56e-12 ***
## ---
## 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 -0.664
## C2 -0.588 0.471
## C3 -0.597 0.478 0.388
## C4 -0.654 0.509 0.467 0.469
## C5 -0.660 0.516 0.469 0.478 0.508
## C6 -0.652 0.506 0.457 0.481 0.499 0.503
## C7 -0.592 0.471 0.385 0.391 0.469 0.472 0.462
## C8 -0.659 0.512 0.468 0.466 0.503 0.509 0.506 0.478
## C9 -0.601 0.486 0.391 0.398 0.475 0.476 0.473 0.395 0.480tab_model(modA.109,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.26 | 1.27 | 50.77 – 55.76 | 41.87 | <0.001 |
| C1 | 1.92 | 1.64 | -1.29 – 5.13 | 1.17 | 0.240 |
| C2 | -2.29 | 1.77 | -5.75 – 1.17 | -1.30 | 0.194 |
| C3 | 6.27 | 1.77 | 2.80 – 9.73 | 3.55 | <0.001 |
| C4 | 2.10 | 1.66 | -1.15 – 5.35 | 1.27 | 0.205 |
| C5 | -0.45 | 1.66 | -3.70 – 2.80 | -0.27 | 0.787 |
| C6 | 0.52 | 1.66 | -2.74 – 3.78 | 0.31 | 0.755 |
| C7 | 13.73 | 1.78 | 10.23 – 17.23 | 7.69 | <0.001 |
| C8 | 15.33 | 1.64 | 12.11 – 18.55 | 9.33 | <0.001 |
| C9 | 12.56 | 1.77 | 9.10 – 16.03 | 7.11 | <0.001 |
| Random Effects | |||||
| σ2 | 378.52 | ||||
| τ00 id | 287.94 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.463 | ||||
Q.2: How does naturalness predict benefit, over and above burger contrasts?
#How does naturalness predict benefit, over and above burger 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: 28285.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4947 -0.5159 0.0513 0.5638 3.2861
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 273.0 16.52
## Residual 368.1 19.19
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.5969 1.2513 3084.7037 42.833 < 2e-16 ***
## Naturalness.c 0.2422 0.0234 2905.9943 10.350 < 2e-16 ***
## C1 5.0536 1.6401 2495.6541 3.081 0.00208 **
## C2 -2.4841 1.7397 2444.4560 -1.428 0.15344
## C3 9.4326 1.7674 2491.1110 5.337 1.03e-07 ***
## C4 2.9426 1.6346 2480.6457 1.800 0.07195 .
## C5 0.2737 1.6345 2500.3070 0.167 0.86704
## C6 2.1167 1.6446 2483.4312 1.287 0.19820
## C7 9.6348 1.8021 2516.2787 5.346 9.78e-08 ***
## C8 9.6878 1.7084 2538.5723 5.671 1.58e-08 ***
## C9 8.7829 1.7800 2513.9333 4.934 8.58e-07 ***
## ---
## 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 0.026
## C1 -0.649 0.184
## C2 -0.589 -0.011 0.461
## C3 -0.584 0.173 0.494 0.381
## C4 -0.653 0.050 0.509 0.466 0.470
## C5 -0.659 0.043 0.514 0.468 0.478 0.509
## C6 -0.648 0.094 0.512 0.454 0.487 0.500 0.504
## C7 -0.584 -0.219 0.412 0.379 0.339 0.446 0.450 0.429
## C8 -0.634 -0.319 0.418 0.447 0.380 0.460 0.468 0.447 0.512
## C9 -0.594 -0.205 0.429 0.386 0.348 0.454 0.457 0.442 0.422
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.511tab_model(modA.110,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.60 | 1.25 | 51.14 – 56.05 | 42.83 | <0.001 |
| Naturalness c | 0.24 | 0.02 | 0.20 – 0.29 | 10.35 | <0.001 |
| C1 | 5.05 | 1.64 | 1.84 – 8.27 | 3.08 | 0.002 |
| C2 | -2.48 | 1.74 | -5.90 – 0.93 | -1.43 | 0.153 |
| C3 | 9.43 | 1.77 | 5.97 – 12.90 | 5.34 | <0.001 |
| C4 | 2.94 | 1.63 | -0.26 – 6.15 | 1.80 | 0.072 |
| C5 | 0.27 | 1.63 | -2.93 – 3.48 | 0.17 | 0.867 |
| C6 | 2.12 | 1.64 | -1.11 – 5.34 | 1.29 | 0.198 |
| C7 | 9.63 | 1.80 | 6.10 – 13.17 | 5.35 | <0.001 |
| C8 | 9.69 | 1.71 | 6.34 – 13.04 | 5.67 | <0.001 |
| C9 | 8.78 | 1.78 | 5.29 – 12.27 | 4.93 | <0.001 |
| Random Effects | |||||
| σ2 | 368.12 | ||||
| τ00 id | 272.99 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.081 / 0.472 | ||||
Q.3: How does risk perception predict benefit, over and above burger contrasts?
#How does risk perception predict benefit, over and above burger 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: 28062
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5844 -0.5162 0.0703 0.5377 3.2324
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270 16.43
## Residual 336 18.33
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.93335 1.20635 3086.66586 44.708 < 2e-16 ***
## Risk.c -0.32691 0.01752 3019.27710 -18.662 < 2e-16 ***
## C1 4.47443 1.55032 2462.79760 2.886 0.003934 **
## C2 -5.03787 1.67241 2435.16333 -3.012 0.002619 **
## C3 12.19283 1.69767 2469.43822 7.182 9.05e-13 ***
## C4 3.04083 1.56478 2460.53646 1.943 0.052095 .
## C5 0.65497 1.56572 2477.88385 0.418 0.675749
## C6 4.09879 1.58040 2461.73406 2.594 0.009557 **
## C7 5.88082 1.73648 2514.00898 3.387 0.000718 ***
## C8 9.06979 1.58697 2511.21104 5.715 1.23e-08 ***
## C9 6.61597 1.69950 2524.09029 3.893 0.000102 ***
## ---
## 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 -0.030
## C1 -0.655 -0.088
## C2 -0.585 0.087 0.460
## C3 -0.578 -0.187 0.484 0.363
## C4 -0.650 -0.032 0.510 0.462 0.467
## C5 -0.655 -0.037 0.517 0.463 0.476 0.509
## C6 -0.641 -0.122 0.511 0.441 0.492 0.498 0.503
## C7 -0.578 0.243 0.434 0.393 0.327 0.447 0.448 0.415
## C8 -0.647 0.212 0.479 0.474 0.408 0.485 0.489 0.465 0.505
## C9 -0.593 0.188 0.459 0.399 0.348 0.460 0.460 0.439 0.421 0.501tab_model(modA.113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.93 | 1.21 | 51.57 – 56.30 | 44.71 | <0.001 |
| Risk c | -0.33 | 0.02 | -0.36 – -0.29 | -18.66 | <0.001 |
| C1 | 4.47 | 1.55 | 1.43 – 7.51 | 2.89 | 0.004 |
| C2 | -5.04 | 1.67 | -8.32 – -1.76 | -3.01 | 0.003 |
| C3 | 12.19 | 1.70 | 8.86 – 15.52 | 7.18 | <0.001 |
| C4 | 3.04 | 1.56 | -0.03 – 6.11 | 1.94 | 0.052 |
| C5 | 0.65 | 1.57 | -2.41 – 3.72 | 0.42 | 0.676 |
| C6 | 4.10 | 1.58 | 1.00 – 7.20 | 2.59 | 0.010 |
| C7 | 5.88 | 1.74 | 2.48 – 9.29 | 3.39 | 0.001 |
| C8 | 9.07 | 1.59 | 5.96 – 12.18 | 5.72 | <0.001 |
| C9 | 6.62 | 1.70 | 3.28 – 9.95 | 3.89 | <0.001 |
| Random Effects | |||||
| σ2 | 335.97 | ||||
| τ00 id | 270.03 | ||||
| ICC | 0.45 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.139 / 0.523 | ||||
Q.4: How does risk perception predict benefit, over and above naturalness and burger contrasts?
#How does risk perception predict benefit, over and above naturalness and burger 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: 28049.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5963 -0.5130 0.0709 0.5396 3.3272
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.1 16.25
## Residual 335.9 18.33
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.01209 1.20332 3085.20501 44.886 < 2e-16 ***
## Naturalness.c 0.10218 0.02410 2930.45877 4.241 2.30e-05 ***
## Risk.c -0.29801 0.01876 3049.36000 -15.888 < 2e-16 ***
## C1 5.57325 1.57023 2476.91282 3.549 0.000393 ***
## C2 -4.87253 1.67139 2436.66483 -2.915 0.003586 **
## C3 13.00875 1.70681 2475.74578 7.622 3.54e-14 ***
## C4 3.31573 1.56469 2462.29336 2.119 0.034183 *
## C5 0.86453 1.56505 2480.79702 0.552 0.580723
## C6 4.46195 1.58118 2463.26181 2.822 0.004812 **
## C7 4.84568 1.75168 2522.40855 2.766 0.005711 **
## C8 7.24554 1.64299 2532.93865 4.410 1.08e-05 ***
## C9 5.55228 1.71632 2523.88645 3.235 0.001232 **
## ---
## 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 0.016
## Risk.c -0.022 0.363
## C1 -0.645 0.164 -0.022
## C2 -0.586 0.022 0.089 0.457
## C3 -0.573 0.112 -0.133 0.493 0.363
## C4 -0.650 0.041 -0.015 0.509 0.462 0.468
## C5 -0.655 0.031 -0.023 0.515 0.464 0.477 0.509
## C6 -0.640 0.053 -0.094 0.512 0.441 0.494 0.499 0.504
## C7 -0.576 -0.139 0.173 0.401 0.386 0.306 0.436 0.439 0.404
## C8 -0.630 -0.262 0.095 0.413 0.452 0.362 0.457 0.463 0.434
## C9 -0.590 -0.147 0.120 0.424 0.391 0.326 0.449 0.451 0.426
## C7 C8
## Naturlnss.c
## Risk.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 0.519
## C9 0.433 0.516tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.01 | 1.20 | 51.65 – 56.37 | 44.89 | <0.001 |
| Naturalness c | 0.10 | 0.02 | 0.05 – 0.15 | 4.24 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.33 – -0.26 | -15.89 | <0.001 |
| C1 | 5.57 | 1.57 | 2.49 – 8.65 | 3.55 | <0.001 |
| C2 | -4.87 | 1.67 | -8.15 – -1.60 | -2.92 | 0.004 |
| C3 | 13.01 | 1.71 | 9.66 – 16.36 | 7.62 | <0.001 |
| C4 | 3.32 | 1.56 | 0.25 – 6.38 | 2.12 | 0.034 |
| C5 | 0.86 | 1.57 | -2.20 – 3.93 | 0.55 | 0.581 |
| C6 | 4.46 | 1.58 | 1.36 – 7.56 | 2.82 | 0.005 |
| C7 | 4.85 | 1.75 | 1.41 – 8.28 | 2.77 | 0.006 |
| C8 | 7.25 | 1.64 | 4.02 – 10.47 | 4.41 | <0.001 |
| C9 | 5.55 | 1.72 | 2.19 – 8.92 | 3.23 | 0.001 |
| Random Effects | |||||
| σ2 | 335.87 | ||||
| τ00 id | 264.09 | ||||
| ICC | 0.44 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.143 / 0.520 | ||||
Q.5: How does risk perception predict benefit, over and above naturalness and burger contrasts?
#How does risk perception predict benefit, over and above naturalness and burger 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: 28049.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5963 -0.5130 0.0709 0.5396 3.3272
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.1 16.25
## Residual 335.9 18.33
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.01209 1.20332 3085.20501 44.886 < 2e-16 ***
## Naturalness.c 0.10218 0.02410 2930.45877 4.241 2.30e-05 ***
## Risk.c -0.29801 0.01876 3049.36000 -15.888 < 2e-16 ***
## C1 5.57325 1.57023 2476.91282 3.549 0.000393 ***
## C2 -4.87253 1.67139 2436.66483 -2.915 0.003586 **
## C3 13.00875 1.70681 2475.74578 7.622 3.54e-14 ***
## C4 3.31573 1.56469 2462.29336 2.119 0.034183 *
## C5 0.86453 1.56505 2480.79702 0.552 0.580723
## C6 4.46195 1.58118 2463.26181 2.822 0.004812 **
## C7 4.84568 1.75168 2522.40855 2.766 0.005711 **
## C8 7.24554 1.64299 2532.93865 4.410 1.08e-05 ***
## C9 5.55228 1.71632 2523.88645 3.235 0.001232 **
## ---
## 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 0.016
## Risk.c -0.022 0.363
## C1 -0.645 0.164 -0.022
## C2 -0.586 0.022 0.089 0.457
## C3 -0.573 0.112 -0.133 0.493 0.363
## C4 -0.650 0.041 -0.015 0.509 0.462 0.468
## C5 -0.655 0.031 -0.023 0.515 0.464 0.477 0.509
## C6 -0.640 0.053 -0.094 0.512 0.441 0.494 0.499 0.504
## C7 -0.576 -0.139 0.173 0.401 0.386 0.306 0.436 0.439 0.404
## C8 -0.630 -0.262 0.095 0.413 0.452 0.362 0.457 0.463 0.434
## C9 -0.590 -0.147 0.120 0.424 0.391 0.326 0.449 0.451 0.426
## C7 C8
## Naturlnss.c
## Risk.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8 0.519
## C9 0.433 0.516tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.01 | 1.20 | 51.65 – 56.37 | 44.89 | <0.001 |
| Naturalness c | 0.10 | 0.02 | 0.05 – 0.15 | 4.24 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.33 – -0.26 | -15.89 | <0.001 |
| C1 | 5.57 | 1.57 | 2.49 – 8.65 | 3.55 | <0.001 |
| C2 | -4.87 | 1.67 | -8.15 – -1.60 | -2.92 | 0.004 |
| C3 | 13.01 | 1.71 | 9.66 – 16.36 | 7.62 | <0.001 |
| C4 | 3.32 | 1.56 | 0.25 – 6.38 | 2.12 | 0.034 |
| C5 | 0.86 | 1.57 | -2.20 – 3.93 | 0.55 | 0.581 |
| C6 | 4.46 | 1.58 | 1.36 – 7.56 | 2.82 | 0.005 |
| C7 | 4.85 | 1.75 | 1.41 – 8.28 | 2.77 | 0.006 |
| C8 | 7.25 | 1.64 | 4.02 – 10.47 | 4.41 | <0.001 |
| C9 | 5.55 | 1.72 | 2.19 – 8.92 | 3.23 | 0.001 |
| Random Effects | |||||
| σ2 | 335.87 | ||||
| τ00 id | 264.09 | ||||
| ICC | 0.44 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.143 / 0.520 | ||||
Q.6: How does understanding/familiarity predict benefit, over and above risk perception, naturalness, and burger contrasts?
#How does risk perception predict benefit, over and above naturalness and burger 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: 28024.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6238 -0.4969 0.0684 0.5460 3.3548
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 262.3 16.20
## Residual 332.3 18.23
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 55.55666 1.22914 3084.03789 45.200 < 2e-16 ***
## FR.c 0.10808 0.01940 3084.05680 5.571 2.75e-08 ***
## Naturalness.c 0.07481 0.02447 2959.79018 3.057 0.002253 **
## Risk.c -0.28213 0.01888 3050.69201 -14.941 < 2e-16 ***
## C1 5.22992 1.56334 2477.37051 3.345 0.000834 ***
## C2 -7.13880 1.71168 2483.36115 -4.171 3.14e-05 ***
## C3 9.12031 1.83570 2620.99576 4.968 7.19e-07 ***
## C4 3.13705 1.55695 2460.71958 2.015 0.044027 *
## C5 1.27136 1.55869 2481.79753 0.816 0.414775
## C6 4.02070 1.57499 2463.61964 2.553 0.010745 *
## C7 0.45922 1.91243 2648.25084 0.240 0.810251
## C8 4.95338 1.68559 2569.99972 2.939 0.003326 **
## C9 1.41057 1.86236 2637.38011 0.757 0.448873
## ---
## 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) | 55.56 | 1.23 | 53.15 – 57.97 | 45.20 | <0.001 |
| FR c | 0.11 | 0.02 | 0.07 – 0.15 | 5.57 | <0.001 |
| Naturalness c | 0.07 | 0.02 | 0.03 – 0.12 | 3.06 | 0.002 |
| Risk c | -0.28 | 0.02 | -0.32 – -0.25 | -14.94 | <0.001 |
| C1 | 5.23 | 1.56 | 2.16 – 8.30 | 3.35 | 0.001 |
| C2 | -7.14 | 1.71 | -10.49 – -3.78 | -4.17 | <0.001 |
| C3 | 9.12 | 1.84 | 5.52 – 12.72 | 4.97 | <0.001 |
| C4 | 3.14 | 1.56 | 0.08 – 6.19 | 2.01 | 0.044 |
| C5 | 1.27 | 1.56 | -1.78 – 4.33 | 0.82 | 0.415 |
| C6 | 4.02 | 1.57 | 0.93 – 7.11 | 2.55 | 0.011 |
| C7 | 0.46 | 1.91 | -3.29 – 4.21 | 0.24 | 0.810 |
| C8 | 4.95 | 1.69 | 1.65 – 8.26 | 2.94 | 0.003 |
| C9 | 1.41 | 1.86 | -2.24 – 5.06 | 0.76 | 0.449 |
| Random Effects | |||||
| σ2 | 332.32 | ||||
| τ00 id | 262.34 | ||||
| ICC | 0.44 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.149 / 0.525 | ||||
Difference Benefit - Risk
Q.1: (SIMPLE MODEL) How do burger contrasts predict the difference between perceived benefit and risk?
#How do burger 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: 31282.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9132 -0.5398 0.0435 0.5736 3.1191
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 589.9 24.29
## Residual 1029.7 32.09
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.318 2.037 3078.449 8.992 < 2e-16 ***
## C1 -6.048 2.671 2553.236 -2.264 0.0236 *
## C2 6.112 2.885 2506.924 2.119 0.0342 *
## C3 -12.037 2.885 2541.897 -4.172 3.12e-05 ***
## C4 -1.060 2.705 2553.147 -0.392 0.6953
## C5 -4.087 2.705 2573.268 -1.511 0.1309
## C6 -10.680 2.713 2553.934 -3.936 8.49e-05 ***
## C7 37.559 2.914 2547.501 12.890 < 2e-16 ***
## C8 34.209 2.683 2547.793 12.750 < 2e-16 ***
## C9 30.287 2.887 2557.379 10.493 < 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 -0.676
## C2 -0.601 0.472
## C3 -0.610 0.478 0.393
## C4 -0.667 0.509 0.467 0.469
## C5 -0.672 0.515 0.469 0.477 0.508
## C6 -0.665 0.506 0.458 0.479 0.499 0.503
## C7 -0.605 0.472 0.390 0.396 0.468 0.471 0.462
## C8 -0.672 0.512 0.468 0.467 0.503 0.509 0.505 0.477
## C9 -0.614 0.485 0.396 0.402 0.475 0.476 0.473 0.398 0.480tab_model(modA.118,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 18.32 | 2.04 | 14.32 – 22.31 | 8.99 | <0.001 |
| C1 | -6.05 | 2.67 | -11.28 – -0.81 | -2.26 | 0.024 |
| C2 | 6.11 | 2.88 | 0.46 – 11.77 | 2.12 | 0.034 |
| C3 | -12.04 | 2.88 | -17.69 – -6.38 | -4.17 | <0.001 |
| C4 | -1.06 | 2.71 | -6.36 – 4.24 | -0.39 | 0.695 |
| C5 | -4.09 | 2.70 | -9.39 – 1.22 | -1.51 | 0.131 |
| C6 | -10.68 | 2.71 | -16.00 – -5.36 | -3.94 | <0.001 |
| C7 | 37.56 | 2.91 | 31.85 – 43.27 | 12.89 | <0.001 |
| C8 | 34.21 | 2.68 | 28.95 – 39.47 | 12.75 | <0.001 |
| C9 | 30.29 | 2.89 | 24.63 – 35.95 | 10.49 | <0.001 |
| Random Effects | |||||
| σ2 | 1029.74 | ||||
| τ00 id | 589.87 | ||||
| ICC | 0.36 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.468 | ||||
Q.2: How does naturalness predict the difference between perceived benefit and risk, over and above burger contrasts?
#How does naturalness predict the difference between benefit and risk?
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: 30923.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3839 -0.5378 0.0273 0.5733 2.9244
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 537.7 23.19
## Residual 909.5 30.16
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.25369 1.92102 3078.44031 10.023 < 2e-16 ***
## Naturalness.c 0.71052 0.03616 2976.55787 19.650 < 2e-16 ***
## C1 3.15701 2.55627 2557.96972 1.235 0.2169
## C2 5.52176 2.71422 2498.65290 2.034 0.0420 *
## C3 -2.76117 2.75503 2549.29480 -1.002 0.3163
## C4 1.49296 2.54846 2541.98321 0.586 0.5580
## C5 -1.91667 2.54743 2563.68677 -0.752 0.4519
## C6 -5.95497 2.56394 2544.81839 -2.323 0.0203 *
## C7 25.66827 2.80783 2576.43380 9.142 < 2e-16 ***
## C8 17.72567 2.66063 2603.35082 6.662 3.28e-11 ***
## C9 19.29346 2.77353 2574.27060 6.956 4.41e-12 ***
## ---
## 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 0.025
## C1 -0.659 0.183
## C2 -0.600 -0.011 0.462
## C3 -0.595 0.171 0.494 0.385
## C4 -0.663 0.050 0.509 0.466 0.470
## C5 -0.669 0.043 0.514 0.468 0.477 0.509
## C6 -0.658 0.093 0.512 0.455 0.486 0.501 0.504
## C7 -0.594 -0.216 0.413 0.383 0.343 0.446 0.451 0.429
## C8 -0.644 -0.316 0.419 0.448 0.383 0.461 0.469 0.448 0.510
## C9 -0.605 -0.203 0.430 0.389 0.353 0.454 0.457 0.442 0.424
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.510tab_model(modA.11,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 19.25 | 1.92 | 15.49 – 23.02 | 10.02 | <0.001 |
| Naturalness c | 0.71 | 0.04 | 0.64 – 0.78 | 19.65 | <0.001 |
| C1 | 3.16 | 2.56 | -1.86 – 8.17 | 1.24 | 0.217 |
| C2 | 5.52 | 2.71 | 0.20 – 10.84 | 2.03 | 0.042 |
| C3 | -2.76 | 2.76 | -8.16 – 2.64 | -1.00 | 0.316 |
| C4 | 1.49 | 2.55 | -3.50 – 6.49 | 0.59 | 0.558 |
| C5 | -1.92 | 2.55 | -6.91 – 3.08 | -0.75 | 0.452 |
| C6 | -5.95 | 2.56 | -10.98 – -0.93 | -2.32 | 0.020 |
| C7 | 25.67 | 2.81 | 20.16 – 31.17 | 9.14 | <0.001 |
| C8 | 17.73 | 2.66 | 12.51 – 22.94 | 6.66 | <0.001 |
| C9 | 19.29 | 2.77 | 13.86 – 24.73 | 6.96 | <0.001 |
| Random Effects | |||||
| σ2 | 909.51 | ||||
| τ00 id | 537.72 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.247 / 0.527 | ||||
Familiarity/Understanding (Mean score)
Q.1: (SIMPLE MODEL) How do burger contrasts predict understanding and familiarity (mean score)?
#How do burger 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: 27833.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0364 -0.5885 -0.0151 0.5956 3.1054
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 206.2 14.36
## Residual 331.1 18.20
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.4355 1.1648 3080.8192 32.140 < 2e-16 ***
## C1 -1.2691 1.5193 2532.2689 -0.835 0.40361
## C2 22.4050 1.6405 2488.8121 13.657 < 2e-16 ***
## C3 29.9624 1.6409 2522.6960 18.259 < 2e-16 ***
## C4 0.2945 1.5387 2532.1634 0.191 0.84823
## C5 -4.9841 1.5388 2551.5791 -3.239 0.00121 **
## C6 0.7724 1.5433 2532.9878 0.501 0.61676
## C7 48.3596 1.6574 2528.0376 29.178 < 2e-16 ***
## C8 29.8444 1.5261 2527.0387 19.556 < 2e-16 ***
## C9 44.9082 1.6420 2537.6100 27.350 < 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 -0.673
## C2 -0.598 0.472
## C3 -0.606 0.478 0.392
## C4 -0.664 0.509 0.467 0.469
## C5 -0.669 0.515 0.469 0.478 0.508
## C6 -0.661 0.506 0.457 0.480 0.499 0.503
## C7 -0.601 0.471 0.389 0.394 0.469 0.471 0.462
## C8 -0.668 0.512 0.468 0.467 0.503 0.509 0.505 0.477
## C9 -0.610 0.485 0.395 0.401 0.475 0.476 0.473 0.397 0.480tab_model(modA.12,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.44 | 1.16 | 35.15 – 39.72 | 32.14 | <0.001 |
| C1 | -1.27 | 1.52 | -4.25 – 1.71 | -0.84 | 0.404 |
| C2 | 22.40 | 1.64 | 19.19 – 25.62 | 13.66 | <0.001 |
| C3 | 29.96 | 1.64 | 26.74 – 33.18 | 18.26 | <0.001 |
| C4 | 0.29 | 1.54 | -2.72 – 3.31 | 0.19 | 0.848 |
| C5 | -4.98 | 1.54 | -8.00 – -1.97 | -3.24 | 0.001 |
| C6 | 0.77 | 1.54 | -2.25 – 3.80 | 0.50 | 0.617 |
| C7 | 48.36 | 1.66 | 45.11 – 51.61 | 29.18 | <0.001 |
| C8 | 29.84 | 1.53 | 26.85 – 32.84 | 19.56 | <0.001 |
| C9 | 44.91 | 1.64 | 41.69 – 48.13 | 27.35 | <0.001 |
| Random Effects | |||||
| σ2 | 331.14 | ||||
| τ00 id | 206.19 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.401 / 0.631 | ||||
Q.2: How does naturalness predict understanding and familiarity (mean score), over and above burger contrasts?
#How does naturalness predict understanding and familiarity (mean score), over and above burger 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: 27620.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9332 -0.5708 0.0035 0.5958 3.1829
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 215.1 14.67
## Residual 298.8 17.29
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.86146 1.12370 3083.93555 33.694 < 2e-16 ***
## Naturalness.c 0.31895 0.02104 2915.99710 15.159 < 2e-16 ***
## C1 2.86101 1.47604 2503.54599 1.938 0.05270 .
## C2 22.12320 1.56588 2451.28025 14.128 < 2e-16 ***
## C3 34.14049 1.59064 2498.50822 21.463 < 2e-16 ***
## C4 1.44047 1.47118 2488.38909 0.979 0.32761
## C5 -4.04661 1.47106 2508.33734 -2.751 0.00599 **
## C6 2.87890 1.48018 2491.18639 1.945 0.05189 .
## C7 43.01292 1.62180 2523.96397 26.522 < 2e-16 ***
## C8 22.46214 1.53738 2546.85026 14.611 < 2e-16 ***
## C9 39.96053 1.60193 2521.63681 24.945 < 2e-16 ***
## ---
## 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 0.026
## C1 -0.650 0.184
## C2 -0.591 -0.011 0.461
## C3 -0.586 0.172 0.494 0.381
## C4 -0.655 0.050 0.509 0.466 0.470
## C5 -0.660 0.043 0.514 0.468 0.478 0.509
## C6 -0.649 0.094 0.512 0.454 0.487 0.500 0.504
## C7 -0.586 -0.219 0.412 0.379 0.339 0.446 0.451 0.429
## C8 -0.635 -0.319 0.418 0.447 0.381 0.460 0.468 0.447 0.512
## C9 -0.596 -0.205 0.429 0.386 0.349 0.454 0.457 0.442 0.422
## C8
## Naturlnss.c
## C1
## C2
## C3
## C4
## C5
## C6
## C7
## C8
## C9 0.511tab_model(modA.130,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.86 | 1.12 | 35.66 – 40.06 | 33.69 | <0.001 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 15.16 | <0.001 |
| C1 | 2.86 | 1.48 | -0.03 – 5.76 | 1.94 | 0.053 |
| C2 | 22.12 | 1.57 | 19.05 – 25.19 | 14.13 | <0.001 |
| C3 | 34.14 | 1.59 | 31.02 – 37.26 | 21.46 | <0.001 |
| C4 | 1.44 | 1.47 | -1.44 – 4.33 | 0.98 | 0.328 |
| C5 | -4.05 | 1.47 | -6.93 – -1.16 | -2.75 | 0.006 |
| C6 | 2.88 | 1.48 | -0.02 – 5.78 | 1.94 | 0.052 |
| C7 | 43.01 | 1.62 | 39.83 – 46.19 | 26.52 | <0.001 |
| C8 | 22.46 | 1.54 | 19.45 – 25.48 | 14.61 | <0.001 |
| C9 | 39.96 | 1.60 | 36.82 – 43.10 | 24.95 | <0.001 |
| Random Effects | |||||
| σ2 | 298.82 | ||||
| τ00 id | 215.08 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.433 / 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 burger 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: 28535.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3853 -0.5260 0.0613 0.5519 3.3065
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 281.7 16.78
## Residual 401.2 20.03
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.99434 1.29842 3074.08141 40.814 < 2e-16 ***
## ATNS_Score.c -0.32679 0.06158 3070.04696 -5.307 1.2e-07 ***
## C1 -0.06312 1.67974 2489.22507 -0.038 0.97003
## C2 7.07550 1.81445 2449.77228 3.900 9.9e-05 ***
## C3 -1.71983 1.81443 2482.22669 -0.948 0.34329
## C4 1.29903 1.70250 2490.69563 0.763 0.44553
## C5 -2.71709 1.70194 2509.43067 -1.596 0.11051
## C6 -2.59911 1.70792 2490.69883 -1.522 0.12819
## C7 27.07067 1.83284 2488.84837 14.770 < 2e-16 ***
## C8 23.12991 1.68736 2484.78774 13.708 < 2e-16 ***
## C9 22.48551 1.81626 2495.81469 12.380 < 2e-16 ***
## ATNS_Score.c:C1 0.07410 0.07919 2497.55754 0.936 0.34954
## ATNS_Score.c:C2 0.22537 0.08472 2441.57218 2.660 0.00786 **
## ATNS_Score.c:C3 -0.14390 0.08484 2471.47535 -1.696 0.08998 .
## ATNS_Score.c:C4 -0.04968 0.07848 2491.18034 -0.633 0.52675
## ATNS_Score.c:C5 0.03260 0.07928 2515.99561 0.411 0.68099
## ATNS_Score.c:C6 -0.02016 0.08215 2514.33556 -0.245 0.80616
## ATNS_Score.c:C7 0.26055 0.08576 2497.49501 3.038 0.00241 **
## ATNS_Score.c:C8 0.15914 0.07936 2476.88798 2.005 0.04503 *
## ATNS_Score.c:C9 0.25165 0.08659 2527.38638 2.906 0.00369 **
## ---
## 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) | 52.99 | 1.30 | 50.45 – 55.54 | 40.81 | <0.001 |
| ATNS Score c | -0.33 | 0.06 | -0.45 – -0.21 | -5.31 | <0.001 |
| C1 | -0.06 | 1.68 | -3.36 – 3.23 | -0.04 | 0.970 |
| C2 | 7.08 | 1.81 | 3.52 – 10.63 | 3.90 | <0.001 |
| C3 | -1.72 | 1.81 | -5.28 – 1.84 | -0.95 | 0.343 |
| C4 | 1.30 | 1.70 | -2.04 – 4.64 | 0.76 | 0.446 |
| C5 | -2.72 | 1.70 | -6.05 – 0.62 | -1.60 | 0.110 |
| C6 | -2.60 | 1.71 | -5.95 – 0.75 | -1.52 | 0.128 |
| C7 | 27.07 | 1.83 | 23.48 – 30.66 | 14.77 | <0.001 |
| C8 | 23.13 | 1.69 | 19.82 – 26.44 | 13.71 | <0.001 |
| C9 | 22.49 | 1.82 | 18.92 – 26.05 | 12.38 | <0.001 |
| ATNS Score c * C1 | 0.07 | 0.08 | -0.08 – 0.23 | 0.94 | 0.350 |
| ATNS Score c * C2 | 0.23 | 0.08 | 0.06 – 0.39 | 2.66 | 0.008 |
| ATNS Score c * C3 | -0.14 | 0.08 | -0.31 – 0.02 | -1.70 | 0.090 |
| ATNS Score c * C4 | -0.05 | 0.08 | -0.20 – 0.10 | -0.63 | 0.527 |
| ATNS Score c * C5 | 0.03 | 0.08 | -0.12 – 0.19 | 0.41 | 0.681 |
| ATNS Score c * C6 | -0.02 | 0.08 | -0.18 – 0.14 | -0.25 | 0.806 |
| ATNS Score c * C7 | 0.26 | 0.09 | 0.09 – 0.43 | 3.04 | 0.002 |
| ATNS Score c * C8 | 0.16 | 0.08 | 0.00 – 0.31 | 2.01 | 0.045 |
| ATNS Score c * C9 | 0.25 | 0.09 | 0.08 – 0.42 | 2.91 | 0.004 |
| Random Effects | |||||
| σ2 | 401.22 | ||||
| τ00 id | 281.71 | ||||
| ICC | 0.41 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.192 / 0.525 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on naturalness in predicting support, over and above burger 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)
summary(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: 28212.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4672 -0.5382 0.0299 0.5400 3.4201
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 271.1 16.46
## Residual 353.4 18.80
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.357e+01 1.231e+00 3.075e+03 43.523 < 2e-16 ***
## ATNS_Score.c -3.117e-01 5.835e-02 3.072e+03 -5.342 9.84e-08 ***
## Naturalness.c 4.324e-01 2.315e-02 2.882e+03 18.682 < 2e-16 ***
## C1 5.529e+00 1.609e+00 2.478e+03 3.436 0.00060 ***
## C2 6.675e+00 1.708e+00 2.428e+03 3.909 9.53e-05 ***
## C3 3.936e+00 1.734e+00 2.474e+03 2.270 0.02332 *
## C4 2.938e+00 1.605e+00 2.465e+03 1.831 0.06729 .
## C5 -1.375e+00 1.604e+00 2.484e+03 -0.858 0.39122
## C6 1.733e-01 1.615e+00 2.467e+03 0.107 0.91452
## C7 1.977e+01 1.770e+00 2.502e+03 11.171 < 2e-16 ***
## C8 1.309e+01 1.677e+00 2.521e+03 7.804 8.69e-15 ***
## C9 1.583e+01 1.747e+00 2.495e+03 9.058 < 2e-16 ***
## ATNS_Score.c:C1 4.949e-02 7.457e-02 2.474e+03 0.664 0.50696
## ATNS_Score.c:C2 2.131e-01 7.973e-02 2.420e+03 2.672 0.00758 **
## ATNS_Score.c:C3 -9.533e-02 7.990e-02 2.448e+03 -1.193 0.23294
## ATNS_Score.c:C4 5.633e-03 7.395e-02 2.465e+03 0.076 0.93928
## ATNS_Score.c:C5 7.359e-02 7.468e-02 2.488e+03 0.985 0.32454
## ATNS_Score.c:C6 2.355e-02 7.739e-02 2.487e+03 0.304 0.76087
## ATNS_Score.c:C7 2.072e-01 8.079e-02 2.476e+03 2.565 0.01038 *
## ATNS_Score.c:C8 1.499e-01 7.470e-02 2.453e+03 2.007 0.04491 *
## ATNS_Score.c:C9 2.357e-01 8.154e-02 2.503e+03 2.891 0.00387 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 21 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.89012,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.57 | 1.23 | 51.16 – 55.98 | 43.52 | <0.001 |
| ATNS Score c | -0.31 | 0.06 | -0.43 – -0.20 | -5.34 | <0.001 |
| Naturalness c | 0.43 | 0.02 | 0.39 – 0.48 | 18.68 | <0.001 |
| C1 | 5.53 | 1.61 | 2.37 – 8.68 | 3.44 | 0.001 |
| C2 | 6.68 | 1.71 | 3.33 – 10.02 | 3.91 | <0.001 |
| C3 | 3.94 | 1.73 | 0.54 – 7.34 | 2.27 | 0.023 |
| C4 | 2.94 | 1.60 | -0.21 – 6.08 | 1.83 | 0.067 |
| C5 | -1.38 | 1.60 | -4.52 – 1.77 | -0.86 | 0.391 |
| C6 | 0.17 | 1.61 | -2.99 – 3.34 | 0.11 | 0.915 |
| C7 | 19.77 | 1.77 | 16.30 – 23.24 | 11.17 | <0.001 |
| C8 | 13.09 | 1.68 | 9.80 – 16.38 | 7.80 | <0.001 |
| C9 | 15.83 | 1.75 | 12.40 – 19.25 | 9.06 | <0.001 |
| ATNS Score c * C1 | 0.05 | 0.07 | -0.10 – 0.20 | 0.66 | 0.507 |
| ATNS Score c * C2 | 0.21 | 0.08 | 0.06 – 0.37 | 2.67 | 0.008 |
| ATNS Score c * C3 | -0.10 | 0.08 | -0.25 – 0.06 | -1.19 | 0.233 |
| ATNS Score c * C4 | 0.01 | 0.07 | -0.14 – 0.15 | 0.08 | 0.939 |
| ATNS Score c * C5 | 0.07 | 0.07 | -0.07 – 0.22 | 0.99 | 0.325 |
| ATNS Score c * C6 | 0.02 | 0.08 | -0.13 – 0.18 | 0.30 | 0.761 |
| ATNS Score c * C7 | 0.21 | 0.08 | 0.05 – 0.37 | 2.57 | 0.010 |
| ATNS Score c * C8 | 0.15 | 0.07 | 0.00 – 0.30 | 2.01 | 0.045 |
| ATNS Score c * C9 | 0.24 | 0.08 | 0.08 – 0.40 | 2.89 | 0.004 |
| Random Effects | |||||
| σ2 | 353.36 | ||||
| τ00 id | 271.07 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.261 / 0.582 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict support, over and above burger 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: 28586.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3539 -0.5268 0.0600 0.5757 3.2149
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 304.7 17.46
## Residual 401.4 20.03
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.91841 1.30975 3076.09535 40.403 < 2e-16 ***
## CNS_Score.c 0.13498 0.07836 3076.96324 1.723 0.0851 .
## C1 0.15224 1.68494 2469.24404 0.090 0.9280
## C2 7.16794 1.82264 2431.49181 3.933 8.63e-05 ***
## C3 -1.67912 1.81998 2464.15114 -0.923 0.3563
## C4 1.74744 1.70788 2469.72198 1.023 0.3063
## C5 -2.79368 1.70732 2486.86040 -1.636 0.1019
## C6 -2.91837 1.71225 2469.91604 -1.704 0.0884 .
## C7 27.05592 1.83849 2469.20022 14.716 < 2e-16 ***
## C8 23.01295 1.69358 2463.02421 13.588 < 2e-16 ***
## C9 22.42331 1.82248 2475.74259 12.304 < 2e-16 ***
## CNS_Score.c:C1 0.03002 0.09910 2459.02241 0.303 0.7620
## CNS_Score.c:C2 0.02890 0.10592 2418.85562 0.273 0.7850
## CNS_Score.c:C3 -0.45326 0.11055 2431.51337 -4.100 4.26e-05 ***
## CNS_Score.c:C4 -0.07508 0.10053 2479.28504 -0.747 0.4552
## CNS_Score.c:C5 -0.04777 0.10476 2503.99225 -0.456 0.6484
## CNS_Score.c:C6 -0.11194 0.10188 2454.82905 -1.099 0.2720
## CNS_Score.c:C7 0.23955 0.11155 2493.77712 2.147 0.0319 *
## CNS_Score.c:C8 0.17188 0.10447 2484.54913 1.645 0.1001
## CNS_Score.c:C9 0.22703 0.11025 2510.02866 2.059 0.0396 *
## ---
## 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) | 52.92 | 1.31 | 50.35 – 55.49 | 40.40 | <0.001 |
| CNS Score c | 0.13 | 0.08 | -0.02 – 0.29 | 1.72 | 0.085 |
| C1 | 0.15 | 1.68 | -3.15 – 3.46 | 0.09 | 0.928 |
| C2 | 7.17 | 1.82 | 3.59 – 10.74 | 3.93 | <0.001 |
| C3 | -1.68 | 1.82 | -5.25 – 1.89 | -0.92 | 0.356 |
| C4 | 1.75 | 1.71 | -1.60 – 5.10 | 1.02 | 0.306 |
| C5 | -2.79 | 1.71 | -6.14 – 0.55 | -1.64 | 0.102 |
| C6 | -2.92 | 1.71 | -6.28 – 0.44 | -1.70 | 0.088 |
| C7 | 27.06 | 1.84 | 23.45 – 30.66 | 14.72 | <0.001 |
| C8 | 23.01 | 1.69 | 19.69 – 26.33 | 13.59 | <0.001 |
| C9 | 22.42 | 1.82 | 18.85 – 26.00 | 12.30 | <0.001 |
| CNS Score c * C1 | 0.03 | 0.10 | -0.16 – 0.22 | 0.30 | 0.762 |
| CNS Score c * C2 | 0.03 | 0.11 | -0.18 – 0.24 | 0.27 | 0.785 |
| CNS Score c * C3 | -0.45 | 0.11 | -0.67 – -0.24 | -4.10 | <0.001 |
| CNS Score c * C4 | -0.08 | 0.10 | -0.27 – 0.12 | -0.75 | 0.455 |
| CNS Score c * C5 | -0.05 | 0.10 | -0.25 – 0.16 | -0.46 | 0.648 |
| CNS Score c * C6 | -0.11 | 0.10 | -0.31 – 0.09 | -1.10 | 0.272 |
| CNS Score c * C7 | 0.24 | 0.11 | 0.02 – 0.46 | 2.15 | 0.032 |
| CNS Score c * C8 | 0.17 | 0.10 | -0.03 – 0.38 | 1.65 | 0.100 |
| CNS Score c * C9 | 0.23 | 0.11 | 0.01 – 0.44 | 2.06 | 0.040 |
| Random Effects | |||||
| σ2 | 401.36 | ||||
| τ00 id | 304.69 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.165 / 0.525 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting support, over and above burger 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: 28251.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6080 -0.5275 0.0386 0.5454 3.3443
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 292.7 17.11
## Residual 350.1 18.71
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.346e+01 1.237e+00 3.076e+03 43.212 < 2e-16 ***
## CNS_Score.c 1.417e-01 7.410e-02 3.077e+03 1.912 0.056002 .
## Naturalness.c 4.352e-01 2.316e-02 2.854e+03 18.792 < 2e-16 ***
## C1 5.835e+00 1.606e+00 2.452e+03 3.632 0.000287 ***
## C2 6.711e+00 1.708e+00 2.406e+03 3.930 8.74e-05 ***
## C3 4.319e+00 1.733e+00 2.451e+03 2.493 0.012750 *
## C4 3.398e+00 1.603e+00 2.439e+03 2.120 0.034094 *
## C5 -1.310e+00 1.602e+00 2.457e+03 -0.818 0.413485
## C6 1.148e-01 1.612e+00 2.441e+03 0.071 0.943232
## C7 1.961e+01 1.767e+00 2.476e+03 11.101 < 2e-16 ***
## C8 1.294e+01 1.676e+00 2.493e+03 7.718 1.70e-14 ***
## C9 1.561e+01 1.745e+00 2.470e+03 8.946 < 2e-16 ***
## CNS_Score.c:Naturalness.c 4.852e-03 1.297e-03 2.898e+03 3.740 0.000187 ***
## CNS_Score.c:C1 1.249e-01 9.473e-02 2.454e+03 1.319 0.187351
## CNS_Score.c:C2 1.253e-02 9.924e-02 2.396e+03 0.126 0.899509
## CNS_Score.c:C3 -3.027e-01 1.045e-01 2.412e+03 -2.897 0.003799 **
## CNS_Score.c:C4 -3.430e-02 9.441e-02 2.446e+03 -0.363 0.716441
## CNS_Score.c:C5 -5.844e-03 9.827e-02 2.471e+03 -0.059 0.952587
## CNS_Score.c:C6 -4.259e-02 9.571e-02 2.421e+03 -0.445 0.656384
## CNS_Score.c:C7 1.064e-01 1.070e-01 2.494e+03 0.995 0.319926
## CNS_Score.c:C8 3.594e-02 1.039e-01 2.548e+03 0.346 0.729479
## CNS_Score.c:C9 7.816e-02 1.059e-01 2.515e+03 0.738 0.460402
## ---
## 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) | 53.46 | 1.24 | 51.04 – 55.89 | 43.21 | <0.001 |
| CNS Score c | 0.14 | 0.07 | -0.00 – 0.29 | 1.91 | 0.056 |
| Naturalness c | 0.44 | 0.02 | 0.39 – 0.48 | 18.79 | <0.001 |
| C1 | 5.83 | 1.61 | 2.68 – 8.98 | 3.63 | <0.001 |
| C2 | 6.71 | 1.71 | 3.36 – 10.06 | 3.93 | <0.001 |
| C3 | 4.32 | 1.73 | 0.92 – 7.72 | 2.49 | 0.013 |
| C4 | 3.40 | 1.60 | 0.26 – 6.54 | 2.12 | 0.034 |
| C5 | -1.31 | 1.60 | -4.45 – 1.83 | -0.82 | 0.413 |
| C6 | 0.11 | 1.61 | -3.05 – 3.28 | 0.07 | 0.943 |
| C7 | 19.61 | 1.77 | 16.15 – 23.08 | 11.10 | <0.001 |
| C8 | 12.94 | 1.68 | 9.65 – 16.22 | 7.72 | <0.001 |
| C9 | 15.61 | 1.74 | 12.19 – 19.03 | 8.95 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 3.74 | <0.001 |
| CNS Score c * C1 | 0.12 | 0.09 | -0.06 – 0.31 | 1.32 | 0.187 |
| CNS Score c * C2 | 0.01 | 0.10 | -0.18 – 0.21 | 0.13 | 0.900 |
| CNS Score c * C3 | -0.30 | 0.10 | -0.51 – -0.10 | -2.90 | 0.004 |
| CNS Score c * C4 | -0.03 | 0.09 | -0.22 – 0.15 | -0.36 | 0.716 |
| CNS Score c * C5 | -0.01 | 0.10 | -0.20 – 0.19 | -0.06 | 0.953 |
| CNS Score c * C6 | -0.04 | 0.10 | -0.23 – 0.15 | -0.44 | 0.656 |
| CNS Score c * C7 | 0.11 | 0.11 | -0.10 – 0.32 | 0.99 | 0.320 |
| CNS Score c * C8 | 0.04 | 0.10 | -0.17 – 0.24 | 0.35 | 0.729 |
| CNS Score c * C9 | 0.08 | 0.11 | -0.13 – 0.29 | 0.74 | 0.460 |
| Random Effects | |||||
| σ2 | 350.11 | ||||
| τ00 id | 292.72 | ||||
| ICC | 0.46 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.239 / 0.586 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict support, over and above burger 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: 28239.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4995 -0.5198 0.0487 0.5808 3.3752
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 186.3 13.65
## Residual 396.4 19.91
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.298e+01 1.241e+00 3.064e+03 42.671 < 2e-16 ***
## CCBelief_Score.c 4.668e-01 5.287e-02 3.062e+03 8.828 < 2e-16 ***
## C1 3.843e-02 1.645e+00 2.596e+03 0.023 0.981367
## C2 7.180e+00 1.779e+00 2.543e+03 4.037 5.57e-05 ***
## C3 -2.185e+00 1.778e+00 2.581e+03 -1.229 0.219238
## C4 1.956e+00 1.669e+00 2.595e+03 1.172 0.241328
## C5 -3.271e+00 1.667e+00 2.617e+03 -1.963 0.049783 *
## C6 -2.603e+00 1.671e+00 2.596e+03 -1.558 0.119445
## C7 2.672e+01 1.795e+00 2.586e+03 14.884 < 2e-16 ***
## C8 2.296e+01 1.654e+00 2.592e+03 13.882 < 2e-16 ***
## C9 2.255e+01 1.778e+00 2.597e+03 12.684 < 2e-16 ***
## CCBelief_Score.c:C1 -9.954e-03 6.960e-02 2.614e+03 -0.143 0.886291
## CCBelief_Score.c:C2 -1.969e-02 7.678e-02 2.549e+03 -0.256 0.797621
## CCBelief_Score.c:C3 -4.242e-01 7.187e-02 2.555e+03 -5.903 4.04e-09 ***
## CCBelief_Score.c:C4 -2.929e-03 6.843e-02 2.602e+03 -0.043 0.965859
## CCBelief_Score.c:C5 -4.028e-03 7.233e-02 2.609e+03 -0.056 0.955595
## CCBelief_Score.c:C6 -1.100e-02 7.015e-02 2.568e+03 -0.157 0.875431
## CCBelief_Score.c:C7 1.729e-01 7.740e-02 2.570e+03 2.234 0.025558 *
## CCBelief_Score.c:C8 9.141e-02 7.221e-02 2.631e+03 1.266 0.205671
## CCBelief_Score.c:C9 2.570e-01 7.716e-02 2.650e+03 3.330 0.000881 ***
## ---
## 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) | 52.98 | 1.24 | 50.54 – 55.41 | 42.67 | <0.001 |
| CCBelief Score c | 0.47 | 0.05 | 0.36 – 0.57 | 8.83 | <0.001 |
| C1 | 0.04 | 1.65 | -3.19 – 3.26 | 0.02 | 0.981 |
| C2 | 7.18 | 1.78 | 3.69 – 10.67 | 4.04 | <0.001 |
| C3 | -2.19 | 1.78 | -5.67 – 1.30 | -1.23 | 0.219 |
| C4 | 1.96 | 1.67 | -1.32 – 5.23 | 1.17 | 0.241 |
| C5 | -3.27 | 1.67 | -6.54 – -0.00 | -1.96 | 0.050 |
| C6 | -2.60 | 1.67 | -5.88 – 0.67 | -1.56 | 0.119 |
| C7 | 26.72 | 1.80 | 23.20 – 30.24 | 14.88 | <0.001 |
| C8 | 22.96 | 1.65 | 19.72 – 26.20 | 13.88 | <0.001 |
| C9 | 22.55 | 1.78 | 19.06 – 26.04 | 12.68 | <0.001 |
| CCBelief Score c * C1 | -0.01 | 0.07 | -0.15 – 0.13 | -0.14 | 0.886 |
| CCBelief Score c * C2 | -0.02 | 0.08 | -0.17 – 0.13 | -0.26 | 0.798 |
| CCBelief Score c * C3 | -0.42 | 0.07 | -0.57 – -0.28 | -5.90 | <0.001 |
| CCBelief Score c * C4 | -0.00 | 0.07 | -0.14 – 0.13 | -0.04 | 0.966 |
| CCBelief Score c * C5 | -0.00 | 0.07 | -0.15 – 0.14 | -0.06 | 0.956 |
| CCBelief Score c * C6 | -0.01 | 0.07 | -0.15 – 0.13 | -0.16 | 0.875 |
| CCBelief Score c * C7 | 0.17 | 0.08 | 0.02 – 0.32 | 2.23 | 0.026 |
| CCBelief Score c * C8 | 0.09 | 0.07 | -0.05 – 0.23 | 1.27 | 0.206 |
| CCBelief Score c * C9 | 0.26 | 0.08 | 0.11 – 0.41 | 3.33 | 0.001 |
| Random Effects | |||||
| σ2 | 396.45 | ||||
| τ00 id | 186.30 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.310 / 0.531 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perception sof naturalness in predicting support, over and above burger 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)
summary(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: 27913.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7959 -0.5379 0.0537 0.5435 3.1141
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.4 13.43
## Residual 347.9 18.65
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.357e+01 1.174e+00 3.064e+03 45.624
## CCBelief_Score.c 4.269e-01 5.028e-02 3.068e+03 8.491
## Naturalness.c 4.214e-01 2.225e-02 2.997e+03 18.942
## C1 5.438e+00 1.574e+00 2.585e+03 3.454
## C2 6.829e+00 1.672e+00 2.518e+03 4.085
## C3 3.259e+00 1.699e+00 2.578e+03 1.918
## C4 3.460e+00 1.571e+00 2.566e+03 2.201
## C5 -1.935e+00 1.569e+00 2.590e+03 -1.233
## C6 1.389e-01 1.579e+00 2.570e+03 0.088
## C7 1.974e+01 1.728e+00 2.598e+03 11.422
## C8 1.317e+01 1.639e+00 2.633e+03 8.039
## C9 1.606e+01 1.707e+00 2.596e+03 9.404
## CCBelief_Score.c:Naturalness.c -1.096e-03 8.424e-04 3.013e+03 -1.301
## CCBelief_Score.c:C1 8.459e-03 6.608e-02 2.575e+03 0.128
## CCBelief_Score.c:C2 -2.709e-02 7.217e-02 2.524e+03 -0.375
## CCBelief_Score.c:C3 -3.773e-01 6.807e-02 2.518e+03 -5.543
## CCBelief_Score.c:C4 2.410e-02 6.436e-02 2.572e+03 0.375
## CCBelief_Score.c:C5 6.236e-03 6.802e-02 2.578e+03 0.092
## CCBelief_Score.c:C6 7.768e-03 6.598e-02 2.536e+03 0.118
## CCBelief_Score.c:C7 1.802e-01 7.402e-02 2.620e+03 2.435
## CCBelief_Score.c:C8 1.385e-01 7.232e-02 2.745e+03 1.915
## CCBelief_Score.c:C9 2.622e-01 7.412e-02 2.685e+03 3.537
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 0.000561 ***
## C2 4.55e-05 ***
## C3 0.055253 .
## C4 0.027792 *
## C5 0.217547
## C6 0.929919
## C7 < 2e-16 ***
## C8 1.35e-15 ***
## C9 < 2e-16 ***
## CCBelief_Score.c:Naturalness.c 0.193201
## CCBelief_Score.c:C1 0.898148
## CCBelief_Score.c:C2 0.707399
## CCBelief_Score.c:C3 3.28e-08 ***
## CCBelief_Score.c:C4 0.708053
## CCBelief_Score.c:C5 0.926969
## CCBelief_Score.c:C6 0.906289
## CCBelief_Score.c:C7 0.014958 *
## CCBelief_Score.c:C8 0.055578 .
## CCBelief_Score.c:C9 0.000412 ***
## ---
## 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.89614,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.57 | 1.17 | 51.27 – 55.88 | 45.62 | <0.001 |
| CCBelief Score c | 0.43 | 0.05 | 0.33 – 0.53 | 8.49 | <0.001 |
| Naturalness c | 0.42 | 0.02 | 0.38 – 0.47 | 18.94 | <0.001 |
| C1 | 5.44 | 1.57 | 2.35 – 8.53 | 3.45 | 0.001 |
| C2 | 6.83 | 1.67 | 3.55 – 10.11 | 4.08 | <0.001 |
| C3 | 3.26 | 1.70 | -0.07 – 6.59 | 1.92 | 0.055 |
| C4 | 3.46 | 1.57 | 0.38 – 6.54 | 2.20 | 0.028 |
| C5 | -1.94 | 1.57 | -5.01 – 1.14 | -1.23 | 0.218 |
| C6 | 0.14 | 1.58 | -2.96 – 3.23 | 0.09 | 0.930 |
| C7 | 19.74 | 1.73 | 16.35 – 23.13 | 11.42 | <0.001 |
| C8 | 13.17 | 1.64 | 9.96 – 16.39 | 8.04 | <0.001 |
| C9 | 16.06 | 1.71 | 12.71 – 19.40 | 9.40 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.30 | 0.193 |
| CCBelief Score c * C1 | 0.01 | 0.07 | -0.12 – 0.14 | 0.13 | 0.898 |
| CCBelief Score c * C2 | -0.03 | 0.07 | -0.17 – 0.11 | -0.38 | 0.707 |
| CCBelief Score c * C3 | -0.38 | 0.07 | -0.51 – -0.24 | -5.54 | <0.001 |
| CCBelief Score c * C4 | 0.02 | 0.06 | -0.10 – 0.15 | 0.37 | 0.708 |
| CCBelief Score c * C5 | 0.01 | 0.07 | -0.13 – 0.14 | 0.09 | 0.927 |
| CCBelief Score c * C6 | 0.01 | 0.07 | -0.12 – 0.14 | 0.12 | 0.906 |
| CCBelief Score c * C7 | 0.18 | 0.07 | 0.04 – 0.33 | 2.43 | 0.015 |
| CCBelief Score c * C8 | 0.14 | 0.07 | -0.00 – 0.28 | 1.92 | 0.056 |
| CCBelief Score c * C9 | 0.26 | 0.07 | 0.12 – 0.41 | 3.54 | <0.001 |
| Random Effects | |||||
| σ2 | 347.86 | ||||
| τ00 id | 180.38 | ||||
| ICC | 0.34 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.376 / 0.589 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict support, over and above burger 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: 28632.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1105 -0.5127 0.0579 0.5730 3.2750
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.2 17.58
## Residual 406.0 20.15
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.294e+01 1.319e+00 3.076e+03 40.145 < 2e-16 ***
## Collectivism_Score.c -6.134e-02 5.164e-02 3.076e+03 -1.188 0.234997
## C1 -6.214e-02 1.696e+00 2.469e+03 -0.037 0.970772
## C2 6.913e+00 1.831e+00 2.433e+03 3.776 0.000163 ***
## C3 -1.691e+00 1.833e+00 2.462e+03 -0.923 0.356342
## C4 1.732e+00 1.719e+00 2.471e+03 1.007 0.313984
## C5 -2.653e+00 1.723e+00 2.488e+03 -1.540 0.123761
## C6 -2.869e+00 1.723e+00 2.469e+03 -1.665 0.095946 .
## C7 2.701e+01 1.850e+00 2.468e+03 14.602 < 2e-16 ***
## C8 2.334e+01 1.704e+00 2.464e+03 13.698 < 2e-16 ***
## C9 2.284e+01 1.835e+00 2.476e+03 12.451 < 2e-16 ***
## Collectivism_Score.c:C1 -7.867e-02 7.124e-02 2.456e+03 -1.104 0.269593
## Collectivism_Score.c:C2 1.343e-01 7.438e-02 2.414e+03 1.806 0.071065 .
## Collectivism_Score.c:C3 1.088e-01 7.206e-02 2.411e+03 1.509 0.131306
## Collectivism_Score.c:C4 -3.798e-03 6.948e-02 2.453e+03 -0.055 0.956419
## Collectivism_Score.c:C5 3.163e-02 6.825e-02 2.467e+03 0.463 0.643076
## Collectivism_Score.c:C6 -9.485e-03 7.002e-02 2.462e+03 -0.135 0.892258
## Collectivism_Score.c:C7 -5.896e-02 7.530e-02 2.482e+03 -0.783 0.433713
## Collectivism_Score.c:C8 -8.561e-02 6.964e-02 2.481e+03 -1.229 0.219089
## Collectivism_Score.c:C9 -9.947e-02 7.654e-02 2.506e+03 -1.300 0.193849
## ---
## 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) | 52.94 | 1.32 | 50.36 – 55.53 | 40.14 | <0.001 |
| Collectivism Score c | -0.06 | 0.05 | -0.16 – 0.04 | -1.19 | 0.235 |
| C1 | -0.06 | 1.70 | -3.39 – 3.26 | -0.04 | 0.971 |
| C2 | 6.91 | 1.83 | 3.32 – 10.50 | 3.78 | <0.001 |
| C3 | -1.69 | 1.83 | -5.29 – 1.90 | -0.92 | 0.356 |
| C4 | 1.73 | 1.72 | -1.64 – 5.10 | 1.01 | 0.314 |
| C5 | -2.65 | 1.72 | -6.03 – 0.73 | -1.54 | 0.124 |
| C6 | -2.87 | 1.72 | -6.25 – 0.51 | -1.67 | 0.096 |
| C7 | 27.01 | 1.85 | 23.39 – 30.64 | 14.60 | <0.001 |
| C8 | 23.34 | 1.70 | 20.00 – 26.68 | 13.70 | <0.001 |
| C9 | 22.84 | 1.83 | 19.24 – 26.44 | 12.45 | <0.001 |
| Collectivism Score c * C1 | -0.08 | 0.07 | -0.22 – 0.06 | -1.10 | 0.270 |
| Collectivism Score c * C2 | 0.13 | 0.07 | -0.01 – 0.28 | 1.81 | 0.071 |
| Collectivism Score c * C3 | 0.11 | 0.07 | -0.03 – 0.25 | 1.51 | 0.131 |
| Collectivism Score c * C4 | -0.00 | 0.07 | -0.14 – 0.13 | -0.05 | 0.956 |
| Collectivism Score c * C5 | 0.03 | 0.07 | -0.10 – 0.17 | 0.46 | 0.643 |
| Collectivism Score c * C6 | -0.01 | 0.07 | -0.15 – 0.13 | -0.14 | 0.892 |
| Collectivism Score c * C7 | -0.06 | 0.08 | -0.21 – 0.09 | -0.78 | 0.434 |
| Collectivism Score c * C8 | -0.09 | 0.07 | -0.22 – 0.05 | -1.23 | 0.219 |
| Collectivism Score c * C9 | -0.10 | 0.08 | -0.25 – 0.05 | -1.30 | 0.194 |
| Random Effects | |||||
| σ2 | 406.05 | ||||
| τ00 id | 309.23 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.154 / 0.520 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting support, over and above burger 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)
summary(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: 28294.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4611 -0.5356 0.0275 0.5521 3.3558
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.9 17.17
## Residual 354.6 18.83
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.353e+01 1.245e+00 3.076e+03 42.985
## Collectivism_Score.c -6.459e-02 4.888e-02 3.070e+03 -1.322
## Naturalness.c 4.489e-01 2.325e-02 2.861e+03 19.308
## C1 5.736e+00 1.617e+00 2.457e+03 3.547
## C2 6.512e+00 1.716e+00 2.411e+03 3.796
## C3 4.131e+00 1.744e+00 2.453e+03 2.368
## C4 3.356e+00 1.614e+00 2.445e+03 2.080
## C5 -1.110e+00 1.618e+00 2.463e+03 -0.686
## C6 1.539e-01 1.622e+00 2.447e+03 0.095
## C7 1.945e+01 1.778e+00 2.478e+03 10.938
## C8 1.293e+01 1.690e+00 2.502e+03 7.655
## C9 1.592e+01 1.757e+00 2.475e+03 9.061
## Collectivism_Score.c:Naturalness.c 1.253e-03 9.132e-04 2.856e+03 1.372
## Collectivism_Score.c:C1 -6.603e-02 6.813e-02 2.444e+03 -0.969
## Collectivism_Score.c:C2 1.378e-01 6.970e-02 2.395e+03 1.976
## Collectivism_Score.c:C3 1.094e-01 6.848e-02 2.407e+03 1.598
## Collectivism_Score.c:C4 1.565e-02 6.515e-02 2.428e+03 0.240
## Collectivism_Score.c:C5 7.303e-02 6.411e-02 2.440e+03 1.139
## Collectivism_Score.c:C6 3.228e-02 6.587e-02 2.434e+03 0.490
## Collectivism_Score.c:C7 -9.111e-02 7.284e-02 2.500e+03 -1.251
## Collectivism_Score.c:C8 -6.400e-02 6.892e-02 2.523e+03 -0.929
## Collectivism_Score.c:C9 -1.254e-01 7.382e-02 2.524e+03 -1.699
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.186407
## Naturalness.c < 2e-16 ***
## C1 0.000397 ***
## C2 0.000151 ***
## C3 0.017956 *
## C4 0.037657 *
## C5 0.492621
## C6 0.924405
## C7 < 2e-16 ***
## C8 2.75e-14 ***
## C9 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.170097
## Collectivism_Score.c:C1 0.332514
## Collectivism_Score.c:C2 0.048227 *
## Collectivism_Score.c:C3 0.110217
## Collectivism_Score.c:C4 0.810122
## Collectivism_Score.c:C5 0.254804
## Collectivism_Score.c:C6 0.624162
## Collectivism_Score.c:C7 0.211158
## Collectivism_Score.c:C8 0.353130
## Collectivism_Score.c:C9 0.089398 .
## ---
## 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.89516,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.53 | 1.25 | 51.09 – 55.97 | 42.98 | <0.001 |
| Collectivism Score c | -0.06 | 0.05 | -0.16 – 0.03 | -1.32 | 0.186 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.49 | 19.31 | <0.001 |
| C1 | 5.74 | 1.62 | 2.57 – 8.91 | 3.55 | <0.001 |
| C2 | 6.51 | 1.72 | 3.15 – 9.88 | 3.80 | <0.001 |
| C3 | 4.13 | 1.74 | 0.71 – 7.55 | 2.37 | 0.018 |
| C4 | 3.36 | 1.61 | 0.19 – 6.52 | 2.08 | 0.038 |
| C5 | -1.11 | 1.62 | -4.28 – 2.06 | -0.69 | 0.493 |
| C6 | 0.15 | 1.62 | -3.03 – 3.33 | 0.09 | 0.924 |
| C7 | 19.45 | 1.78 | 15.96 – 22.93 | 10.94 | <0.001 |
| C8 | 12.93 | 1.69 | 9.62 – 16.25 | 7.65 | <0.001 |
| C9 | 15.92 | 1.76 | 12.48 – 19.37 | 9.06 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.37 | 0.170 |
| Collectivism Score c * C1 | -0.07 | 0.07 | -0.20 – 0.07 | -0.97 | 0.332 |
| Collectivism Score c * C2 | 0.14 | 0.07 | 0.00 – 0.27 | 1.98 | 0.048 |
| Collectivism Score c * C3 | 0.11 | 0.07 | -0.02 – 0.24 | 1.60 | 0.110 |
| Collectivism Score c * C4 | 0.02 | 0.07 | -0.11 – 0.14 | 0.24 | 0.810 |
| Collectivism Score c * C5 | 0.07 | 0.06 | -0.05 – 0.20 | 1.14 | 0.255 |
| Collectivism Score c * C6 | 0.03 | 0.07 | -0.10 – 0.16 | 0.49 | 0.624 |
| Collectivism Score c * C7 | -0.09 | 0.07 | -0.23 – 0.05 | -1.25 | 0.211 |
| Collectivism Score c * C8 | -0.06 | 0.07 | -0.20 – 0.07 | -0.93 | 0.353 |
| Collectivism Score c * C9 | -0.13 | 0.07 | -0.27 – 0.02 | -1.70 | 0.089 |
| Random Effects | |||||
| σ2 | 354.59 | ||||
| τ00 id | 294.94 | ||||
| ICC | 0.45 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.230 / 0.580 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict support, over and above burger 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: 28638
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2069 -0.5225 0.0663 0.5541 3.1744
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.4 17.59
## Residual 408.2 20.20
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.288e+01 1.322e+00 3.076e+03 39.984 < 2e-16 ***
## Individualism_Score.c 9.909e-02 7.604e-02 3.079e+03 1.303 0.192625
## C1 -4.420e-04 1.701e+00 2.472e+03 0.000 0.999793
## C2 7.144e+00 1.837e+00 2.433e+03 3.889 0.000104 ***
## C3 -1.595e+00 1.841e+00 2.466e+03 -0.867 0.386262
## C4 1.740e+00 1.723e+00 2.470e+03 1.010 0.312628
## C5 -2.557e+00 1.723e+00 2.490e+03 -1.484 0.138026
## C6 -2.804e+00 1.728e+00 2.471e+03 -1.623 0.104658
## C7 2.721e+01 1.856e+00 2.469e+03 14.661 < 2e-16 ***
## C8 2.328e+01 1.708e+00 2.466e+03 13.627 < 2e-16 ***
## C9 2.272e+01 1.837e+00 2.476e+03 12.367 < 2e-16 ***
## Individualism_Score.c:C1 -8.756e-02 9.848e-02 2.448e+03 -0.889 0.374005
## Individualism_Score.c:C2 -9.637e-03 1.114e-01 2.475e+03 -0.086 0.931088
## Individualism_Score.c:C3 -1.715e-01 1.030e-01 2.416e+03 -1.665 0.095971 .
## Individualism_Score.c:C4 -9.414e-02 1.006e-01 2.461e+03 -0.936 0.349376
## Individualism_Score.c:C5 7.772e-02 1.012e-01 2.494e+03 0.768 0.442408
## Individualism_Score.c:C6 -8.086e-02 1.031e-01 2.481e+03 -0.785 0.432748
## Individualism_Score.c:C7 1.875e-02 1.052e-01 2.453e+03 0.178 0.858558
## Individualism_Score.c:C8 -4.303e-02 9.892e-02 2.469e+03 -0.435 0.663597
## Individualism_Score.c:C9 2.830e-02 1.124e-01 2.496e+03 0.252 0.801238
## ---
## 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) | 52.88 | 1.32 | 50.28 – 55.47 | 39.98 | <0.001 |
| Individualism Score c | 0.10 | 0.08 | -0.05 – 0.25 | 1.30 | 0.193 |
| C1 | -0.00 | 1.70 | -3.33 – 3.33 | -0.00 | 1.000 |
| C2 | 7.14 | 1.84 | 3.54 – 10.75 | 3.89 | <0.001 |
| C3 | -1.60 | 1.84 | -5.21 – 2.01 | -0.87 | 0.386 |
| C4 | 1.74 | 1.72 | -1.64 – 5.12 | 1.01 | 0.313 |
| C5 | -2.56 | 1.72 | -5.94 – 0.82 | -1.48 | 0.138 |
| C6 | -2.80 | 1.73 | -6.19 – 0.58 | -1.62 | 0.105 |
| C7 | 27.21 | 1.86 | 23.57 – 30.84 | 14.66 | <0.001 |
| C8 | 23.28 | 1.71 | 19.93 – 26.63 | 13.63 | <0.001 |
| C9 | 22.72 | 1.84 | 19.12 – 26.32 | 12.37 | <0.001 |
|
Individualism Score c * C1 |
-0.09 | 0.10 | -0.28 – 0.11 | -0.89 | 0.374 |
|
Individualism Score c * C2 |
-0.01 | 0.11 | -0.23 – 0.21 | -0.09 | 0.931 |
|
Individualism Score c * C3 |
-0.17 | 0.10 | -0.37 – 0.03 | -1.67 | 0.096 |
|
Individualism Score c * C4 |
-0.09 | 0.10 | -0.29 – 0.10 | -0.94 | 0.349 |
|
Individualism Score c * C5 |
0.08 | 0.10 | -0.12 – 0.28 | 0.77 | 0.442 |
|
Individualism Score c * C6 |
-0.08 | 0.10 | -0.28 – 0.12 | -0.78 | 0.433 |
|
Individualism Score c * C7 |
0.02 | 0.11 | -0.19 – 0.23 | 0.18 | 0.859 |
|
Individualism Score c * C8 |
-0.04 | 0.10 | -0.24 – 0.15 | -0.44 | 0.664 |
|
Individualism Score c * C9 |
0.03 | 0.11 | -0.19 – 0.25 | 0.25 | 0.801 |
| Random Effects | |||||
| σ2 | 408.17 | ||||
| τ00 id | 309.41 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.151 / 0.517 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting support, over and above burger 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: 28295.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4915 -0.5314 0.0361 0.5467 3.2588
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 293.9 17.14
## Residual 356.2 18.87
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.348e+01 1.248e+00 3.076e+03 42.854
## Individualism_Score.c 8.933e-02 7.181e-02 3.077e+03 1.244
## Naturalness.c 4.478e-01 2.340e-02 2.859e+03 19.137
## C1 5.763e+00 1.621e+00 2.460e+03 3.554
## C2 6.750e+00 1.721e+00 2.412e+03 3.921
## C3 4.294e+00 1.751e+00 2.456e+03 2.452
## C4 3.382e+00 1.616e+00 2.445e+03 2.093
## C5 -1.089e+00 1.617e+00 2.466e+03 -0.674
## C6 2.010e-01 1.626e+00 2.447e+03 0.124
## C7 1.967e+01 1.784e+00 2.480e+03 11.026
## C8 1.287e+01 1.692e+00 2.499e+03 7.608
## C9 1.578e+01 1.760e+00 2.475e+03 8.965
## Individualism_Score.c:Naturalness.c 2.367e-03 1.323e-03 2.906e+03 1.788
## Individualism_Score.c:C1 -5.490e-02 9.373e-02 2.431e+03 -0.586
## Individualism_Score.c:C2 1.523e-02 1.045e-01 2.454e+03 0.146
## Individualism_Score.c:C3 -1.161e-01 9.802e-02 2.404e+03 -1.184
## Individualism_Score.c:C4 -4.754e-02 9.439e-02 2.434e+03 -0.504
## Individualism_Score.c:C5 1.552e-01 9.499e-02 2.464e+03 1.634
## Individualism_Score.c:C6 -1.197e-02 9.700e-02 2.452e+03 -0.123
## Individualism_Score.c:C7 -8.479e-03 1.022e-01 2.485e+03 -0.083
## Individualism_Score.c:C8 -8.753e-02 9.832e-02 2.522e+03 -0.890
## Individualism_Score.c:C9 -2.153e-02 1.074e-01 2.500e+03 -0.200
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.213583
## Naturalness.c < 2e-16 ***
## C1 0.000386 ***
## C2 9.05e-05 ***
## C3 0.014270 *
## C4 0.036485 *
## C5 0.500614
## C6 0.901628
## C7 < 2e-16 ***
## C8 3.92e-14 ***
## C9 < 2e-16 ***
## Individualism_Score.c:Naturalness.c 0.073825 .
## Individualism_Score.c:C1 0.558134
## Individualism_Score.c:C2 0.884077
## Individualism_Score.c:C3 0.236341
## Individualism_Score.c:C4 0.614544
## Individualism_Score.c:C5 0.102349
## Individualism_Score.c:C6 0.901770
## Individualism_Score.c:C7 0.933877
## Individualism_Score.c:C8 0.373392
## Individualism_Score.c:C9 0.841151
## ---
## 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) | 53.48 | 1.25 | 51.03 – 55.93 | 42.85 | <0.001 |
| Individualism Score c | 0.09 | 0.07 | -0.05 – 0.23 | 1.24 | 0.214 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.49 | 19.14 | <0.001 |
| C1 | 5.76 | 1.62 | 2.58 – 8.94 | 3.55 | <0.001 |
| C2 | 6.75 | 1.72 | 3.37 – 10.13 | 3.92 | <0.001 |
| C3 | 4.29 | 1.75 | 0.86 – 7.73 | 2.45 | 0.014 |
| C4 | 3.38 | 1.62 | 0.21 – 6.55 | 2.09 | 0.036 |
| C5 | -1.09 | 1.62 | -4.26 – 2.08 | -0.67 | 0.501 |
| C6 | 0.20 | 1.63 | -2.99 – 3.39 | 0.12 | 0.902 |
| C7 | 19.67 | 1.78 | 16.17 – 23.17 | 11.03 | <0.001 |
| C8 | 12.87 | 1.69 | 9.55 – 16.19 | 7.61 | <0.001 |
| C9 | 15.78 | 1.76 | 12.33 – 19.23 | 8.97 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.79 | 0.074 |
|
Individualism Score c * C1 |
-0.05 | 0.09 | -0.24 – 0.13 | -0.59 | 0.558 |
|
Individualism Score c * C2 |
0.02 | 0.10 | -0.19 – 0.22 | 0.15 | 0.884 |
|
Individualism Score c * C3 |
-0.12 | 0.10 | -0.31 – 0.08 | -1.18 | 0.236 |
|
Individualism Score c * C4 |
-0.05 | 0.09 | -0.23 – 0.14 | -0.50 | 0.615 |
|
Individualism Score c * C5 |
0.16 | 0.09 | -0.03 – 0.34 | 1.63 | 0.102 |
|
Individualism Score c * C6 |
-0.01 | 0.10 | -0.20 – 0.18 | -0.12 | 0.902 |
|
Individualism Score c * C7 |
-0.01 | 0.10 | -0.21 – 0.19 | -0.08 | 0.934 |
|
Individualism Score c * C8 |
-0.09 | 0.10 | -0.28 – 0.11 | -0.89 | 0.373 |
|
Individualism Score c * C9 |
-0.02 | 0.11 | -0.23 – 0.19 | -0.20 | 0.841 |
| Random Effects | |||||
| σ2 | 356.24 | ||||
| τ00 id | 293.91 | ||||
| ICC | 0.45 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.228 / 0.577 | ||||
Political Ideology
Q.1 (Ideology) How does ideology predict support, over and above burger 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: 28572.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2025 -0.5236 0.0630 0.5577 3.1998
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 310.0 17.61
## Residual 408.3 20.21
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.92894 1.32113 3076.18636 40.063 < 2e-16 ***
## Ideology.c 1.21438 2.21726 3078.79529 0.548 0.583942
## C1 -0.03011 1.69965 2470.76869 -0.018 0.985867
## C2 7.07741 1.83382 2433.59670 3.859 0.000117 ***
## C3 -1.78097 1.83536 2465.54936 -0.970 0.331961
## C4 1.76355 1.72121 2470.55329 1.025 0.305652
## C5 -2.71849 1.72485 2488.99373 -1.576 0.115135
## C6 -2.74828 1.72744 2473.29880 -1.591 0.111748
## C7 27.03613 1.85370 2469.13103 14.585 < 2e-16 ***
## C8 23.34695 1.71144 2469.35634 13.642 < 2e-16 ***
## C9 22.64593 1.83677 2477.73739 12.329 < 2e-16 ***
## Ideology.c:C1 0.91804 2.88266 2439.56147 0.318 0.750157
## Ideology.c:C2 -2.99822 3.04639 2383.79617 -0.984 0.325123
## Ideology.c:C3 -3.15838 3.18877 2467.91908 -0.990 0.322041
## Ideology.c:C4 -2.68595 2.99533 2494.59317 -0.897 0.369960
## Ideology.c:C5 0.09953 2.96106 2541.01535 0.034 0.973187
## Ideology.c:C6 -3.95289 2.99263 2479.72515 -1.321 0.186665
## Ideology.c:C7 2.54151 3.12690 2520.20311 0.813 0.416415
## Ideology.c:C8 1.28967 2.90882 2451.17218 0.443 0.657539
## Ideology.c:C9 -1.73559 3.26000 2488.84424 -0.532 0.594504
## ---
## 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) | 52.93 | 1.32 | 50.34 – 55.52 | 40.06 | <0.001 |
| Ideology c | 1.21 | 2.22 | -3.13 – 5.56 | 0.55 | 0.584 |
| C1 | -0.03 | 1.70 | -3.36 – 3.30 | -0.02 | 0.986 |
| C2 | 7.08 | 1.83 | 3.48 – 10.67 | 3.86 | <0.001 |
| C3 | -1.78 | 1.84 | -5.38 – 1.82 | -0.97 | 0.332 |
| C4 | 1.76 | 1.72 | -1.61 – 5.14 | 1.02 | 0.306 |
| C5 | -2.72 | 1.72 | -6.10 – 0.66 | -1.58 | 0.115 |
| C6 | -2.75 | 1.73 | -6.14 – 0.64 | -1.59 | 0.112 |
| C7 | 27.04 | 1.85 | 23.40 – 30.67 | 14.58 | <0.001 |
| C8 | 23.35 | 1.71 | 19.99 – 26.70 | 13.64 | <0.001 |
| C9 | 22.65 | 1.84 | 19.04 – 26.25 | 12.33 | <0.001 |
| Ideology c * C1 | 0.92 | 2.88 | -4.73 – 6.57 | 0.32 | 0.750 |
| Ideology c * C2 | -3.00 | 3.05 | -8.97 – 2.97 | -0.98 | 0.325 |
| Ideology c * C3 | -3.16 | 3.19 | -9.41 – 3.09 | -0.99 | 0.322 |
| Ideology c * C4 | -2.69 | 3.00 | -8.56 – 3.19 | -0.90 | 0.370 |
| Ideology c * C5 | 0.10 | 2.96 | -5.71 – 5.91 | 0.03 | 0.973 |
| Ideology c * C6 | -3.95 | 2.99 | -9.82 – 1.91 | -1.32 | 0.187 |
| Ideology c * C7 | 2.54 | 3.13 | -3.59 – 8.67 | 0.81 | 0.416 |
| Ideology c * C8 | 1.29 | 2.91 | -4.41 – 6.99 | 0.44 | 0.658 |
| Ideology c * C9 | -1.74 | 3.26 | -8.13 – 4.66 | -0.53 | 0.594 |
| Random Effects | |||||
| σ2 | 408.30 | ||||
| τ00 id | 310.02 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.149 / 0.517 | ||||
Q.2 (Ideology) Does ideology depend on perceptions of naturalness in predicting support, over and above burger 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: 28230.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4993 -0.5404 0.0294 0.5461 3.2895
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.3 17.16
## Residual 357.5 18.91
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.53220 1.24833 3075.87309 42.883 < 2e-16 ***
## Ideology.c 2.11624 2.09641 3075.63466 1.009 0.312835
## Naturalness.c 0.45157 0.02341 2863.96353 19.292 < 2e-16 ***
## C1 5.81083 1.62430 2462.43905 3.577 0.000354 ***
## C2 6.65931 1.72076 2414.04895 3.870 0.000112 ***
## C3 4.13156 1.74868 2457.96630 2.363 0.018221 *
## C4 3.36549 1.61704 2445.79651 2.081 0.037514 *
## C5 -1.26171 1.62101 2466.68082 -0.778 0.436437
## C6 0.20796 1.62847 2451.14910 0.128 0.898395
## C7 19.44652 1.78360 2481.40787 10.903 < 2e-16 ***
## C8 12.78731 1.69654 2506.88540 7.537 6.66e-14 ***
## C9 15.63982 1.76205 2478.61055 8.876 < 2e-16 ***
## Ideology.c:Naturalness.c -0.01574 0.04085 2862.37665 -0.385 0.700044
## Ideology.c:C1 -0.45095 2.73322 2414.96576 -0.165 0.868967
## Ideology.c:C2 -4.06008 2.85758 2367.50977 -1.421 0.155505
## Ideology.c:C3 -2.73538 3.06008 2500.12871 -0.894 0.371465
## Ideology.c:C4 -2.67678 2.81327 2474.41346 -0.951 0.341452
## Ideology.c:C5 -1.44754 2.78094 2516.64399 -0.521 0.602746
## Ideology.c:C6 -4.66143 2.81441 2457.36972 -1.656 0.097794 .
## Ideology.c:C7 2.56949 3.00041 2528.98427 0.856 0.391868
## Ideology.c:C8 -0.15224 2.87464 2522.89220 -0.053 0.957767
## Ideology.c:C9 -0.95514 3.15112 2519.02767 -0.303 0.761831
## ---
## 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) | 53.53 | 1.25 | 51.08 – 55.98 | 42.88 | <0.001 |
| Ideology c | 2.12 | 2.10 | -1.99 – 6.23 | 1.01 | 0.313 |
| Naturalness c | 0.45 | 0.02 | 0.41 – 0.50 | 19.29 | <0.001 |
| C1 | 5.81 | 1.62 | 2.63 – 9.00 | 3.58 | <0.001 |
| C2 | 6.66 | 1.72 | 3.29 – 10.03 | 3.87 | <0.001 |
| C3 | 4.13 | 1.75 | 0.70 – 7.56 | 2.36 | 0.018 |
| C4 | 3.37 | 1.62 | 0.19 – 6.54 | 2.08 | 0.037 |
| C5 | -1.26 | 1.62 | -4.44 – 1.92 | -0.78 | 0.436 |
| C6 | 0.21 | 1.63 | -2.99 – 3.40 | 0.13 | 0.898 |
| C7 | 19.45 | 1.78 | 15.95 – 22.94 | 10.90 | <0.001 |
| C8 | 12.79 | 1.70 | 9.46 – 16.11 | 7.54 | <0.001 |
| C9 | 15.64 | 1.76 | 12.18 – 19.09 | 8.88 | <0.001 |
|
Ideology c * Naturalness c |
-0.02 | 0.04 | -0.10 – 0.06 | -0.39 | 0.700 |
| Ideology c * C1 | -0.45 | 2.73 | -5.81 – 4.91 | -0.16 | 0.869 |
| Ideology c * C2 | -4.06 | 2.86 | -9.66 – 1.54 | -1.42 | 0.155 |
| Ideology c * C3 | -2.74 | 3.06 | -8.74 – 3.26 | -0.89 | 0.371 |
| Ideology c * C4 | -2.68 | 2.81 | -8.19 – 2.84 | -0.95 | 0.341 |
| Ideology c * C5 | -1.45 | 2.78 | -6.90 – 4.01 | -0.52 | 0.603 |
| Ideology c * C6 | -4.66 | 2.81 | -10.18 – 0.86 | -1.66 | 0.098 |
| Ideology c * C7 | 2.57 | 3.00 | -3.31 – 8.45 | 0.86 | 0.392 |
| Ideology c * C8 | -0.15 | 2.87 | -5.79 – 5.48 | -0.05 | 0.958 |
| Ideology c * C9 | -0.96 | 3.15 | -7.13 – 5.22 | -0.30 | 0.762 |
| Random Effects | |||||
| σ2 | 357.49 | ||||
| τ00 id | 294.35 | ||||
| ICC | 0.45 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.575 | ||||
Naturalness
Q.1 How do burger 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: 26542.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5590 -0.6146 -0.0188 0.6110 3.4214
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.07 8.128
## Residual 255.98 15.999
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.7758 0.9529 3071.0207 40.694 < 2e-16 ***
## C1 -13.0130 1.2917 2757.1250 -10.074 < 2e-16 ***
## C2 0.6991 1.3996 2685.1901 0.499 0.61748
## C3 -13.0158 1.3968 2725.9700 -9.318 < 2e-16 ***
## C4 -3.8060 1.3082 2757.2346 -2.909 0.00365 **
## C5 -3.0749 1.3065 2781.1438 -2.354 0.01866 *
## C6 -6.8214 1.3121 2757.4352 -5.199 2.15e-07 ***
## C7 16.4597 1.4103 2733.5148 11.671 < 2e-16 ***
## C8 23.0426 1.2980 2750.3673 17.752 < 2e-16 ***
## C9 15.4236 1.3962 2744.8882 11.047 < 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 -0.699
## C2 -0.628 0.472
## C3 -0.635 0.477 0.407
## C4 -0.690 0.509 0.467 0.469
## C5 -0.694 0.514 0.469 0.475 0.506
## C6 -0.687 0.506 0.461 0.475 0.499 0.503
## C7 -0.629 0.471 0.404 0.408 0.467 0.470 0.463
## C8 -0.694 0.512 0.469 0.469 0.504 0.508 0.505 0.474
## C9 -0.638 0.482 0.409 0.414 0.473 0.474 0.472 0.410 0.477tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 38.78 | 0.95 | 36.91 – 40.64 | 40.69 | <0.001 |
| C1 | -13.01 | 1.29 | -15.55 – -10.48 | -10.07 | <0.001 |
| C2 | 0.70 | 1.40 | -2.05 – 3.44 | 0.50 | 0.617 |
| C3 | -13.02 | 1.40 | -15.75 – -10.28 | -9.32 | <0.001 |
| C4 | -3.81 | 1.31 | -6.37 – -1.24 | -2.91 | 0.004 |
| C5 | -3.07 | 1.31 | -5.64 – -0.51 | -2.35 | 0.019 |
| C6 | -6.82 | 1.31 | -9.39 – -4.25 | -5.20 | <0.001 |
| C7 | 16.46 | 1.41 | 13.69 – 19.22 | 11.67 | <0.001 |
| C8 | 23.04 | 1.30 | 20.50 – 25.59 | 17.75 | <0.001 |
| C9 | 15.42 | 1.40 | 12.69 – 18.16 | 11.05 | <0.001 |
| Random Effects | |||||
| σ2 | 255.98 | ||||
| τ00 id | 66.07 | ||||
| ICC | 0.21 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.307 / 0.449 | ||||
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 burger 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: 26541.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3063 -0.6148 -0.0208 0.6025 3.4403
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 64.34 8.021
## Residual 253.80 15.931
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.877e+01 9.478e-01 3.061e+03 40.910 < 2e-16 ***
## ATNS_Score.c -1.562e-02 4.501e-02 3.058e+03 -0.347 0.72860
## C1 -1.301e+01 1.285e+00 2.751e+03 -10.121 < 2e-16 ***
## C2 7.323e-01 1.394e+00 2.678e+03 0.525 0.59941
## C3 -1.295e+01 1.390e+00 2.719e+03 -9.314 < 2e-16 ***
## C4 -3.979e+00 1.303e+00 2.751e+03 -3.055 0.00228 **
## C5 -3.071e+00 1.300e+00 2.776e+03 -2.362 0.01823 *
## C6 -6.660e+00 1.307e+00 2.752e+03 -5.096 3.7e-07 ***
## C7 1.653e+01 1.404e+00 2.728e+03 11.779 < 2e-16 ***
## C8 2.303e+01 1.292e+00 2.744e+03 17.830 < 2e-16 ***
## C9 1.539e+01 1.390e+00 2.737e+03 11.074 < 2e-16 ***
## ATNS_Score.c:C1 3.113e-02 6.057e-02 2.758e+03 0.514 0.60727
## ATNS_Score.c:C2 2.748e-02 6.513e-02 2.667e+03 0.422 0.67314
## ATNS_Score.c:C3 -1.323e-01 6.506e-02 2.705e+03 -2.033 0.04216 *
## ATNS_Score.c:C4 -1.465e-01 6.005e-02 2.751e+03 -2.439 0.01479 *
## ATNS_Score.c:C5 -1.202e-01 6.053e-02 2.784e+03 -1.986 0.04712 *
## ATNS_Score.c:C6 -1.320e-01 6.274e-02 2.780e+03 -2.104 0.03548 *
## ATNS_Score.c:C7 1.050e-01 6.563e-02 2.738e+03 1.599 0.10989
## ATNS_Score.c:C8 7.223e-03 6.079e-02 2.734e+03 0.119 0.90543
## ATNS_Score.c:C9 -2.059e-03 6.610e-02 2.776e+03 -0.031 0.97515
## ---
## 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) | 38.77 | 0.95 | 36.92 – 40.63 | 40.91 | <0.001 |
| ATNS Score c | -0.02 | 0.05 | -0.10 – 0.07 | -0.35 | 0.729 |
| C1 | -13.01 | 1.29 | -15.53 – -10.49 | -10.12 | <0.001 |
| C2 | 0.73 | 1.39 | -2.00 – 3.47 | 0.53 | 0.599 |
| C3 | -12.95 | 1.39 | -15.67 – -10.22 | -9.31 | <0.001 |
| C4 | -3.98 | 1.30 | -6.53 – -1.43 | -3.05 | 0.002 |
| C5 | -3.07 | 1.30 | -5.62 – -0.52 | -2.36 | 0.018 |
| C6 | -6.66 | 1.31 | -9.22 – -4.10 | -5.10 | <0.001 |
| C7 | 16.53 | 1.40 | 13.78 – 19.29 | 11.78 | <0.001 |
| C8 | 23.03 | 1.29 | 20.50 – 25.57 | 17.83 | <0.001 |
| C9 | 15.39 | 1.39 | 12.67 – 18.12 | 11.07 | <0.001 |
| ATNS Score c * C1 | 0.03 | 0.06 | -0.09 – 0.15 | 0.51 | 0.607 |
| ATNS Score c * C2 | 0.03 | 0.07 | -0.10 – 0.16 | 0.42 | 0.673 |
| ATNS Score c * C3 | -0.13 | 0.07 | -0.26 – -0.00 | -2.03 | 0.042 |
| ATNS Score c * C4 | -0.15 | 0.06 | -0.26 – -0.03 | -2.44 | 0.015 |
| ATNS Score c * C5 | -0.12 | 0.06 | -0.24 – -0.00 | -1.99 | 0.047 |
| ATNS Score c * C6 | -0.13 | 0.06 | -0.25 – -0.01 | -2.10 | 0.035 |
| ATNS Score c * C7 | 0.10 | 0.07 | -0.02 – 0.23 | 1.60 | 0.110 |
| ATNS Score c * C8 | 0.01 | 0.06 | -0.11 – 0.13 | 0.12 | 0.905 |
| ATNS Score c * C9 | -0.00 | 0.07 | -0.13 – 0.13 | -0.03 | 0.975 |
| Random Effects | |||||
| σ2 | 253.80 | ||||
| τ00 id | 64.34 | ||||
| ICC | 0.20 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.316 / 0.454 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict naturalness perception, over and above burger 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: 26555.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5906 -0.6116 -0.0123 0.6007 3.4726
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.97 8.122
## Residual 254.51 15.953
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.877e+01 9.505e-01 3.061e+03 40.787 < 2e-16 ***
## CNS_Score.c 2.931e-02 5.683e-02 3.062e+03 0.516 0.60612
## C1 -1.295e+01 1.288e+00 2.747e+03 -10.053 < 2e-16 ***
## C2 7.607e-01 1.399e+00 2.676e+03 0.544 0.58680
## C3 -1.294e+01 1.393e+00 2.717e+03 -9.288 < 2e-16 ***
## C4 -3.817e+00 1.306e+00 2.748e+03 -2.923 0.00349 **
## C5 -3.097e+00 1.303e+00 2.771e+03 -2.376 0.01755 *
## C6 -6.897e+00 1.309e+00 2.748e+03 -5.268 1.48e-07 ***
## C7 1.644e+01 1.407e+00 2.725e+03 11.687 < 2e-16 ***
## C8 2.301e+01 1.296e+00 2.739e+03 17.760 < 2e-16 ***
## C9 1.529e+01 1.394e+00 2.734e+03 10.967 < 2e-16 ***
## CNS_Score.c:C1 -5.713e-02 7.586e-02 2.730e+03 -0.753 0.45141
## CNS_Score.c:C2 1.037e-03 8.143e-02 2.652e+03 0.013 0.98984
## CNS_Score.c:C3 -2.423e-01 8.489e-02 2.674e+03 -2.855 0.00434 **
## CNS_Score.c:C4 -6.524e-02 7.681e-02 2.758e+03 -0.849 0.39573
## CNS_Score.c:C5 -8.041e-02 7.985e-02 2.792e+03 -1.007 0.31403
## CNS_Score.c:C6 -1.273e-01 7.802e-02 2.726e+03 -1.632 0.10276
## CNS_Score.c:C7 1.164e-01 8.517e-02 2.756e+03 1.367 0.17172
## CNS_Score.c:C8 -1.863e-03 7.978e-02 2.764e+03 -0.023 0.98138
## CNS_Score.c:C9 1.150e-01 8.406e-02 2.776e+03 1.368 0.17151
## ---
## 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) | 38.77 | 0.95 | 36.91 – 40.63 | 40.79 | <0.001 |
| CNS Score c | 0.03 | 0.06 | -0.08 – 0.14 | 0.52 | 0.606 |
| C1 | -12.95 | 1.29 | -15.48 – -10.43 | -10.05 | <0.001 |
| C2 | 0.76 | 1.40 | -1.98 – 3.50 | 0.54 | 0.587 |
| C3 | -12.94 | 1.39 | -15.67 – -10.21 | -9.29 | <0.001 |
| C4 | -3.82 | 1.31 | -6.38 – -1.26 | -2.92 | 0.003 |
| C5 | -3.10 | 1.30 | -5.65 – -0.54 | -2.38 | 0.018 |
| C6 | -6.90 | 1.31 | -9.46 – -4.33 | -5.27 | <0.001 |
| C7 | 16.44 | 1.41 | 13.68 – 19.20 | 11.69 | <0.001 |
| C8 | 23.01 | 1.30 | 20.47 – 25.55 | 17.76 | <0.001 |
| C9 | 15.29 | 1.39 | 12.55 – 18.02 | 10.97 | <0.001 |
| CNS Score c * C1 | -0.06 | 0.08 | -0.21 – 0.09 | -0.75 | 0.451 |
| CNS Score c * C2 | 0.00 | 0.08 | -0.16 – 0.16 | 0.01 | 0.990 |
| CNS Score c * C3 | -0.24 | 0.08 | -0.41 – -0.08 | -2.85 | 0.004 |
| CNS Score c * C4 | -0.07 | 0.08 | -0.22 – 0.09 | -0.85 | 0.396 |
| CNS Score c * C5 | -0.08 | 0.08 | -0.24 – 0.08 | -1.01 | 0.314 |
| CNS Score c * C6 | -0.13 | 0.08 | -0.28 – 0.03 | -1.63 | 0.103 |
| CNS Score c * C7 | 0.12 | 0.09 | -0.05 – 0.28 | 1.37 | 0.172 |
| CNS Score c * C8 | -0.00 | 0.08 | -0.16 – 0.15 | -0.02 | 0.981 |
| CNS Score c * C9 | 0.11 | 0.08 | -0.05 – 0.28 | 1.37 | 0.171 |
| Random Effects | |||||
| σ2 | 254.51 | ||||
| τ00 id | 65.97 | ||||
| ICC | 0.21 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.311 / 0.453 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict naturalness perception, over and above burger 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: 26568.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3234 -0.6178 -0.0149 0.5976 3.6270
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.38 8.086
## Residual 255.54 15.986
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.875e+01 9.518e-01 3.061e+03 40.710 < 2e-16 ***
## CCBelief_Score.c 7.258e-02 4.054e-02 3.060e+03 1.790 0.07349 .
## C1 -1.299e+01 1.290e+00 2.749e+03 -10.063 < 2e-16 ***
## C2 6.936e-01 1.398e+00 2.676e+03 0.496 0.61998
## C3 -1.305e+01 1.396e+00 2.718e+03 -9.349 < 2e-16 ***
## C4 -3.778e+00 1.309e+00 2.747e+03 -2.887 0.00393 **
## C5 -3.121e+00 1.306e+00 2.772e+03 -2.389 0.01695 *
## C6 -6.790e+00 1.311e+00 2.749e+03 -5.180 2.38e-07 ***
## C7 1.642e+01 1.409e+00 2.724e+03 11.657 < 2e-16 ***
## C8 2.307e+01 1.297e+00 2.744e+03 17.781 < 2e-16 ***
## C9 1.547e+01 1.395e+00 2.736e+03 11.089 < 2e-16 ***
## CCBelief_Score.c:C1 -6.528e-02 5.455e-02 2.767e+03 -1.197 0.23153
## CCBelief_Score.c:C2 7.071e-03 6.036e-02 2.683e+03 0.117 0.90675
## CCBelief_Score.c:C3 -1.237e-01 5.648e-02 2.687e+03 -2.191 0.02855 *
## CCBelief_Score.c:C4 -6.015e-02 5.366e-02 2.755e+03 -1.121 0.26236
## CCBelief_Score.c:C5 -1.557e-02 5.670e-02 2.760e+03 -0.275 0.78366
## CCBelief_Score.c:C6 -4.860e-02 5.509e-02 2.716e+03 -0.882 0.37775
## CCBelief_Score.c:C7 2.645e-02 6.079e-02 2.707e+03 0.435 0.66351
## CCBelief_Score.c:C8 -2.759e-02 5.656e-02 2.785e+03 -0.488 0.62569
## CCBelief_Score.c:C9 4.902e-02 6.040e-02 2.792e+03 0.812 0.41706
## ---
## 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) | 38.75 | 0.95 | 36.88 – 40.61 | 40.71 | <0.001 |
| CCBelief Score c | 0.07 | 0.04 | -0.01 – 0.15 | 1.79 | 0.073 |
| C1 | -12.99 | 1.29 | -15.52 – -10.46 | -10.06 | <0.001 |
| C2 | 0.69 | 1.40 | -2.05 – 3.44 | 0.50 | 0.620 |
| C3 | -13.05 | 1.40 | -15.79 – -10.32 | -9.35 | <0.001 |
| C4 | -3.78 | 1.31 | -6.34 – -1.21 | -2.89 | 0.004 |
| C5 | -3.12 | 1.31 | -5.68 – -0.56 | -2.39 | 0.017 |
| C6 | -6.79 | 1.31 | -9.36 – -4.22 | -5.18 | <0.001 |
| C7 | 16.42 | 1.41 | 13.66 – 19.19 | 11.66 | <0.001 |
| C8 | 23.07 | 1.30 | 20.53 – 25.61 | 17.78 | <0.001 |
| C9 | 15.47 | 1.39 | 12.73 – 18.20 | 11.09 | <0.001 |
| CCBelief Score c * C1 | -0.07 | 0.05 | -0.17 – 0.04 | -1.20 | 0.232 |
| CCBelief Score c * C2 | 0.01 | 0.06 | -0.11 – 0.13 | 0.12 | 0.907 |
| CCBelief Score c * C3 | -0.12 | 0.06 | -0.23 – -0.01 | -2.19 | 0.029 |
| CCBelief Score c * C4 | -0.06 | 0.05 | -0.17 – 0.05 | -1.12 | 0.262 |
| CCBelief Score c * C5 | -0.02 | 0.06 | -0.13 – 0.10 | -0.27 | 0.784 |
| CCBelief Score c * C6 | -0.05 | 0.06 | -0.16 – 0.06 | -0.88 | 0.378 |
| CCBelief Score c * C7 | 0.03 | 0.06 | -0.09 – 0.15 | 0.44 | 0.664 |
| CCBelief Score c * C8 | -0.03 | 0.06 | -0.14 – 0.08 | -0.49 | 0.626 |
| CCBelief Score c * C9 | 0.05 | 0.06 | -0.07 – 0.17 | 0.81 | 0.417 |
| Random Effects | |||||
| σ2 | 255.54 | ||||
| τ00 id | 65.38 | ||||
| ICC | 0.20 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.311 / 0.451 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict naturalness perception, over and above burger 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: 26574.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5409 -0.6176 -0.0206 0.6037 3.3935
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.74 8.169
## Residual 255.18 15.974
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.876e+01 9.531e-01 3.061e+03 40.665 < 2e-16 ***
## Collectivism_Score.c 1.635e-02 3.707e-02 3.072e+03 0.441 0.65919
## C1 -1.299e+01 1.291e+00 2.745e+03 -10.059 < 2e-16 ***
## C2 6.988e-01 1.400e+00 2.675e+03 0.499 0.61757
## C3 -1.290e+01 1.398e+00 2.713e+03 -9.229 < 2e-16 ***
## C4 -3.741e+00 1.309e+00 2.748e+03 -2.858 0.00429 **
## C5 -3.215e+00 1.309e+00 2.771e+03 -2.455 0.01414 *
## C6 -6.844e+00 1.312e+00 2.745e+03 -5.218 1.94e-07 ***
## C7 1.650e+01 1.410e+00 2.722e+03 11.703 < 2e-16 ***
## C8 2.317e+01 1.298e+00 2.738e+03 17.851 < 2e-16 ***
## C9 1.541e+01 1.397e+00 2.733e+03 11.033 < 2e-16 ***
## Collectivism_Score.c:C1 2.066e-03 5.432e-02 2.724e+03 0.038 0.96966
## Collectivism_Score.c:C2 1.110e-03 5.696e-02 2.647e+03 0.019 0.98445
## Collectivism_Score.c:C3 3.149e-02 5.520e-02 2.641e+03 0.570 0.56844
## Collectivism_Score.c:C4 -4.882e-02 5.298e-02 2.723e+03 -0.922 0.35685
## Collectivism_Score.c:C5 -7.903e-02 5.198e-02 2.739e+03 -1.520 0.12853
## Collectivism_Score.c:C6 -5.744e-02 5.335e-02 2.735e+03 -1.077 0.28175
## Collectivism_Score.c:C7 1.450e-02 5.731e-02 2.735e+03 0.253 0.80029
## Collectivism_Score.c:C8 -1.157e-01 5.298e-02 2.756e+03 -2.183 0.02911 *
## Collectivism_Score.c:C9 -5.044e-03 5.812e-02 2.770e+03 -0.087 0.93085
## ---
## 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) | 38.76 | 0.95 | 36.89 – 40.63 | 40.67 | <0.001 |
| Collectivism Score c | 0.02 | 0.04 | -0.06 – 0.09 | 0.44 | 0.659 |
| C1 | -12.99 | 1.29 | -15.52 – -10.46 | -10.06 | <0.001 |
| C2 | 0.70 | 1.40 | -2.05 – 3.44 | 0.50 | 0.618 |
| C3 | -12.90 | 1.40 | -15.64 – -10.16 | -9.23 | <0.001 |
| C4 | -3.74 | 1.31 | -6.31 – -1.17 | -2.86 | 0.004 |
| C5 | -3.21 | 1.31 | -5.78 – -0.65 | -2.46 | 0.014 |
| C6 | -6.84 | 1.31 | -9.42 – -4.27 | -5.22 | <0.001 |
| C7 | 16.50 | 1.41 | 13.73 – 19.26 | 11.70 | <0.001 |
| C8 | 23.17 | 1.30 | 20.63 – 25.72 | 17.85 | <0.001 |
| C9 | 15.41 | 1.40 | 12.67 – 18.15 | 11.03 | <0.001 |
| Collectivism Score c * C1 | 0.00 | 0.05 | -0.10 – 0.11 | 0.04 | 0.970 |
| Collectivism Score c * C2 | 0.00 | 0.06 | -0.11 – 0.11 | 0.02 | 0.984 |
| Collectivism Score c * C3 | 0.03 | 0.06 | -0.08 – 0.14 | 0.57 | 0.568 |
| Collectivism Score c * C4 | -0.05 | 0.05 | -0.15 – 0.06 | -0.92 | 0.357 |
| Collectivism Score c * C5 | -0.08 | 0.05 | -0.18 – 0.02 | -1.52 | 0.129 |
| Collectivism Score c * C6 | -0.06 | 0.05 | -0.16 – 0.05 | -1.08 | 0.282 |
| Collectivism Score c * C7 | 0.01 | 0.06 | -0.10 – 0.13 | 0.25 | 0.800 |
| Collectivism Score c * C8 | -0.12 | 0.05 | -0.22 – -0.01 | -2.18 | 0.029 |
| Collectivism Score c * C9 | -0.01 | 0.06 | -0.12 – 0.11 | -0.09 | 0.931 |
| Random Effects | |||||
| σ2 | 255.18 | ||||
| τ00 id | 66.74 | ||||
| ICC | 0.21 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.308 / 0.452 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict naturalness perception, over and above burger 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: 26571.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5454 -0.6265 -0.0179 0.6034 3.3751
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.62 8.101
## Residual 256.29 16.009
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.872e+01 9.545e-01 3.061e+03 40.562 < 2e-16 ***
## Individualism_Score.c 4.695e-02 5.472e-02 3.066e+03 0.858 0.39095
## C1 -1.294e+01 1.293e+00 2.750e+03 -10.004 < 2e-16 ***
## C2 7.352e-01 1.403e+00 2.677e+03 0.524 0.60035
## C3 -1.298e+01 1.402e+00 2.720e+03 -9.257 < 2e-16 ***
## C4 -3.768e+00 1.310e+00 2.749e+03 -2.876 0.00406 **
## C5 -3.022e+00 1.308e+00 2.775e+03 -2.310 0.02097 *
## C6 -6.825e+00 1.314e+00 2.749e+03 -5.194 2.21e-07 ***
## C7 1.651e+01 1.413e+00 2.725e+03 11.691 < 2e-16 ***
## C8 2.310e+01 1.300e+00 2.742e+03 17.771 < 2e-16 ***
## C9 1.545e+01 1.398e+00 2.735e+03 11.057 < 2e-16 ***
## Individualism_Score.c:C1 -1.069e-02 7.506e-02 2.717e+03 -0.142 0.88673
## Individualism_Score.c:C2 -8.477e-02 8.478e-02 2.732e+03 -1.000 0.31744
## Individualism_Score.c:C3 -4.237e-02 7.881e-02 2.647e+03 -0.538 0.59084
## Individualism_Score.c:C4 -1.007e-01 7.657e-02 2.735e+03 -1.315 0.18848
## Individualism_Score.c:C5 -1.628e-01 7.678e-02 2.779e+03 -2.120 0.03413 *
## Individualism_Score.c:C6 -1.440e-01 7.831e-02 2.762e+03 -1.838 0.06610 .
## Individualism_Score.c:C7 -6.910e-02 8.023e-02 2.700e+03 -0.861 0.38920
## Individualism_Score.c:C8 -6.171e-02 7.526e-02 2.743e+03 -0.820 0.41230
## Individualism_Score.c:C9 -9.616e-03 8.535e-02 2.761e+03 -0.113 0.91030
## ---
## 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) | 38.72 | 0.95 | 36.84 – 40.59 | 40.56 | <0.001 |
| Individualism Score c | 0.05 | 0.05 | -0.06 – 0.15 | 0.86 | 0.391 |
| C1 | -12.94 | 1.29 | -15.47 – -10.40 | -10.00 | <0.001 |
| C2 | 0.74 | 1.40 | -2.02 – 3.49 | 0.52 | 0.600 |
| C3 | -12.98 | 1.40 | -15.73 – -10.23 | -9.26 | <0.001 |
| C4 | -3.77 | 1.31 | -6.34 – -1.20 | -2.88 | 0.004 |
| C5 | -3.02 | 1.31 | -5.59 – -0.46 | -2.31 | 0.021 |
| C6 | -6.82 | 1.31 | -9.40 – -4.25 | -5.19 | <0.001 |
| C7 | 16.51 | 1.41 | 13.75 – 19.28 | 11.69 | <0.001 |
| C8 | 23.10 | 1.30 | 20.55 – 25.65 | 17.77 | <0.001 |
| C9 | 15.45 | 1.40 | 12.71 – 18.19 | 11.06 | <0.001 |
|
Individualism Score c * C1 |
-0.01 | 0.08 | -0.16 – 0.14 | -0.14 | 0.887 |
|
Individualism Score c * C2 |
-0.08 | 0.08 | -0.25 – 0.08 | -1.00 | 0.317 |
|
Individualism Score c * C3 |
-0.04 | 0.08 | -0.20 – 0.11 | -0.54 | 0.591 |
|
Individualism Score c * C4 |
-0.10 | 0.08 | -0.25 – 0.05 | -1.32 | 0.188 |
|
Individualism Score c * C5 |
-0.16 | 0.08 | -0.31 – -0.01 | -2.12 | 0.034 |
|
Individualism Score c * C6 |
-0.14 | 0.08 | -0.30 – 0.01 | -1.84 | 0.066 |
|
Individualism Score c * C7 |
-0.07 | 0.08 | -0.23 – 0.09 | -0.86 | 0.389 |
|
Individualism Score c * C8 |
-0.06 | 0.08 | -0.21 – 0.09 | -0.82 | 0.412 |
|
Individualism Score c * C9 |
-0.01 | 0.09 | -0.18 – 0.16 | -0.11 | 0.910 |
| Random Effects | |||||
| σ2 | 256.29 | ||||
| τ00 id | 65.62 | ||||
| ICC | 0.20 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.308 / 0.449 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does individualism predict naturalness perception, over and above burger 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: 26502.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5567 -0.6135 -0.0214 0.6081 3.4284
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 66.7 8.167
## Residual 255.5 15.984
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.7576 0.9528 3061.0689 40.677 < 2e-16 ***
## Ideology.c -1.9985 1.5923 3069.4145 -1.255 0.20952
## C1 -12.9916 1.2914 2745.4549 -10.060 < 2e-16 ***
## C2 0.7309 1.3990 2674.3033 0.522 0.60141
## C3 -12.9976 1.3962 2715.3581 -9.309 < 2e-16 ***
## C4 -3.7752 1.3077 2745.8373 -2.887 0.00392 **
## C5 -3.1326 1.3083 2770.6013 -2.394 0.01671 *
## C6 -6.8001 1.3122 2747.6846 -5.182 2.35e-07 ***
## C7 16.4688 1.4096 2722.0428 11.683 < 2e-16 ***
## C8 23.1879 1.3005 2743.6560 17.830 < 2e-16 ***
## C9 15.4442 1.3956 2733.3881 11.066 < 2e-16 ***
## Ideology.c:C1 2.4187 2.1969 2697.0800 1.101 0.27101
## Ideology.c:C2 2.0755 2.3348 2599.2181 0.889 0.37413
## Ideology.c:C3 -0.9274 2.4260 2709.4717 -0.382 0.70228
## Ideology.c:C4 0.3840 2.2719 2763.0783 0.169 0.86580
## Ideology.c:C5 3.1428 2.2356 2830.0089 1.406 0.15989
## Ideology.c:C6 1.3414 2.2726 2748.1375 0.590 0.55507
## Ideology.c:C7 0.4123 2.3674 2779.8006 0.174 0.86176
## Ideology.c:C8 3.8165 2.2144 2714.8400 1.724 0.08490 .
## Ideology.c:C9 -1.4934 2.4747 2746.0827 -0.603 0.54624
## ---
## 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) | 38.76 | 0.95 | 36.89 – 40.63 | 40.68 | <0.001 |
| Ideology c | -2.00 | 1.59 | -5.12 – 1.12 | -1.26 | 0.210 |
| C1 | -12.99 | 1.29 | -15.52 – -10.46 | -10.06 | <0.001 |
| C2 | 0.73 | 1.40 | -2.01 – 3.47 | 0.52 | 0.601 |
| C3 | -13.00 | 1.40 | -15.74 – -10.26 | -9.31 | <0.001 |
| C4 | -3.78 | 1.31 | -6.34 – -1.21 | -2.89 | 0.004 |
| C5 | -3.13 | 1.31 | -5.70 – -0.57 | -2.39 | 0.017 |
| C6 | -6.80 | 1.31 | -9.37 – -4.23 | -5.18 | <0.001 |
| C7 | 16.47 | 1.41 | 13.70 – 19.23 | 11.68 | <0.001 |
| C8 | 23.19 | 1.30 | 20.64 – 25.74 | 17.83 | <0.001 |
| C9 | 15.44 | 1.40 | 12.71 – 18.18 | 11.07 | <0.001 |
| Ideology c * C1 | 2.42 | 2.20 | -1.89 – 6.73 | 1.10 | 0.271 |
| Ideology c * C2 | 2.08 | 2.33 | -2.50 – 6.65 | 0.89 | 0.374 |
| Ideology c * C3 | -0.93 | 2.43 | -5.68 – 3.83 | -0.38 | 0.702 |
| Ideology c * C4 | 0.38 | 2.27 | -4.07 – 4.84 | 0.17 | 0.866 |
| Ideology c * C5 | 3.14 | 2.24 | -1.24 – 7.53 | 1.41 | 0.160 |
| Ideology c * C6 | 1.34 | 2.27 | -3.11 – 5.80 | 0.59 | 0.555 |
| Ideology c * C7 | 0.41 | 2.37 | -4.23 – 5.05 | 0.17 | 0.862 |
| Ideology c * C8 | 3.82 | 2.21 | -0.53 – 8.16 | 1.72 | 0.085 |
| Ideology c * C9 | -1.49 | 2.47 | -6.35 – 3.36 | -0.60 | 0.546 |
| Random Effects | |||||
| σ2 | 255.49 | ||||
| τ00 id | 66.70 | ||||
| ICC | 0.21 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.308 / 0.452 | ||||
Risk
Q.1 How do burger 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: 28183.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5937 -0.6121 -0.0692 0.5627 3.6688
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 186.0 13.64
## Residual 392.4 19.81
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.824 1.236 3073.917 28.180 < 2e-16 ***
## C1 8.104 1.637 2603.391 4.950 7.88e-07 ***
## C2 -8.219 1.769 2550.596 -4.645 3.57e-06 ***
## C3 18.477 1.769 2587.689 10.447 < 2e-16 ***
## C4 3.297 1.658 2603.343 1.988 0.0469 *
## C5 3.725 1.657 2624.855 2.247 0.0247 *
## C6 11.433 1.663 2604.021 6.876 7.70e-12 ***
## C7 -23.874 1.786 2593.853 -13.366 < 2e-16 ***
## C8 -18.746 1.645 2597.511 -11.398 < 2e-16 ***
## C9 -17.561 1.769 2604.314 -9.926 < 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 -0.683
## C2 -0.609 0.472
## C3 -0.617 0.477 0.396
## C4 -0.674 0.509 0.467 0.469
## C5 -0.679 0.515 0.469 0.477 0.507
## C6 -0.672 0.506 0.458 0.478 0.499 0.503
## C7 -0.612 0.472 0.393 0.399 0.468 0.471 0.463
## C8 -0.679 0.512 0.468 0.468 0.504 0.508 0.505 0.476
## C9 -0.621 0.484 0.399 0.405 0.474 0.475 0.473 0.401 0.479tab_model(modA.860,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.82 | 1.24 | 32.40 – 37.25 | 28.18 | <0.001 |
| C1 | 8.10 | 1.64 | 4.89 – 11.31 | 4.95 | <0.001 |
| C2 | -8.22 | 1.77 | -11.69 – -4.75 | -4.65 | <0.001 |
| C3 | 18.48 | 1.77 | 15.01 – 21.94 | 10.45 | <0.001 |
| C4 | 3.30 | 1.66 | 0.05 – 6.55 | 1.99 | 0.047 |
| C5 | 3.72 | 1.66 | 0.48 – 6.97 | 2.25 | 0.025 |
| C6 | 11.43 | 1.66 | 8.17 – 14.69 | 6.88 | <0.001 |
| C7 | -23.87 | 1.79 | -27.38 – -20.37 | -13.37 | <0.001 |
| C8 | -18.75 | 1.64 | -21.97 – -15.52 | -11.40 | <0.001 |
| C9 | -17.56 | 1.77 | -21.03 – -14.09 | -9.93 | <0.001 |
| Random Effects | |||||
| σ2 | 392.45 | ||||
| τ00 id | 186.03 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.474 | ||||
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 burger 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: 28035.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0817 -0.5962 -0.0880 0.5990 3.6467
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 149.3 12.22
## Residual 384.9 19.62
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.66063 1.20349 3060.91068 28.800 < 2e-16 ***
## ATNS_Score.c 0.30499 0.05714 3056.52744 5.338 1.01e-07 ***
## C1 8.24676 1.60878 2646.42703 5.126 3.17e-07 ***
## C2 -8.05956 1.74156 2587.10498 -4.628 3.88e-06 ***
## C3 18.50040 1.73879 2626.01259 10.640 < 2e-16 ***
## C4 3.94656 1.63048 2647.70979 2.420 0.01557 *
## C5 3.88599 1.62840 2670.88110 2.386 0.01708 *
## C6 11.44672 1.63567 2647.74251 6.998 3.27e-12 ***
## C7 -23.83729 1.75586 2634.21857 -13.576 < 2e-16 ***
## C8 -18.50720 1.61645 2640.65645 -11.449 < 2e-16 ***
## C9 -17.16386 1.73937 2642.95054 -9.868 < 2e-16 ***
## ATNS_Score.c:C1 0.04772 0.07582 2654.62169 0.629 0.52912
## ATNS_Score.c:C2 -0.09832 0.08135 2576.64767 -1.209 0.22692
## ATNS_Score.c:C3 0.09307 0.08134 2612.54887 1.144 0.25264
## ATNS_Score.c:C4 0.17255 0.07516 2647.80078 2.296 0.02177 *
## ATNS_Score.c:C5 0.08889 0.07583 2678.35521 1.172 0.24123
## ATNS_Score.c:C6 -0.03127 0.07858 2675.58841 -0.398 0.69068
## ATNS_Score.c:C7 -0.24261 0.08212 2644.27231 -2.954 0.00316 **
## ATNS_Score.c:C8 -0.10444 0.07605 2630.90897 -1.373 0.16978
## ATNS_Score.c:C9 -0.22353 0.08279 2680.18831 -2.700 0.00698 **
## ---
## 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) | 34.66 | 1.20 | 32.30 – 37.02 | 28.80 | <0.001 |
| ATNS Score c | 0.30 | 0.06 | 0.19 – 0.42 | 5.34 | <0.001 |
| C1 | 8.25 | 1.61 | 5.09 – 11.40 | 5.13 | <0.001 |
| C2 | -8.06 | 1.74 | -11.47 – -4.64 | -4.63 | <0.001 |
| C3 | 18.50 | 1.74 | 15.09 – 21.91 | 10.64 | <0.001 |
| C4 | 3.95 | 1.63 | 0.75 – 7.14 | 2.42 | 0.016 |
| C5 | 3.89 | 1.63 | 0.69 – 7.08 | 2.39 | 0.017 |
| C6 | 11.45 | 1.64 | 8.24 – 14.65 | 7.00 | <0.001 |
| C7 | -23.84 | 1.76 | -27.28 – -20.39 | -13.58 | <0.001 |
| C8 | -18.51 | 1.62 | -21.68 – -15.34 | -11.45 | <0.001 |
| C9 | -17.16 | 1.74 | -20.57 – -13.75 | -9.87 | <0.001 |
| ATNS Score c * C1 | 0.05 | 0.08 | -0.10 – 0.20 | 0.63 | 0.529 |
| ATNS Score c * C2 | -0.10 | 0.08 | -0.26 – 0.06 | -1.21 | 0.227 |
| ATNS Score c * C3 | 0.09 | 0.08 | -0.07 – 0.25 | 1.14 | 0.253 |
| ATNS Score c * C4 | 0.17 | 0.08 | 0.03 – 0.32 | 2.30 | 0.022 |
| ATNS Score c * C5 | 0.09 | 0.08 | -0.06 – 0.24 | 1.17 | 0.241 |
| ATNS Score c * C6 | -0.03 | 0.08 | -0.19 – 0.12 | -0.40 | 0.691 |
| ATNS Score c * C7 | -0.24 | 0.08 | -0.40 – -0.08 | -2.95 | 0.003 |
| ATNS Score c * C8 | -0.10 | 0.08 | -0.25 – 0.04 | -1.37 | 0.170 |
| ATNS Score c * C9 | -0.22 | 0.08 | -0.39 – -0.06 | -2.70 | 0.007 |
| Random Effects | |||||
| σ2 | 384.85 | ||||
| τ00 id | 149.30 | ||||
| ICC | 0.28 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.287 / 0.486 | ||||
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 burger 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)
summary(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: 27625.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0861 -0.6013 -0.0388 0.5636 3.6593
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.2 12.05
## Residual 326.8 18.08
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.413e+01 1.122e+00 3.061e+03 30.426 < 2e-16
## ATNS_Score.c 2.830e-01 5.328e-02 3.057e+03 5.313 1.16e-07
## Naturalness.c -4.296e-01 2.142e-02 3.027e+03 -20.055 < 2e-16
## C1 2.703e+00 1.516e+00 2.624e+03 1.783 0.0748
## C2 -7.692e+00 1.612e+00 2.553e+03 -4.770 1.94e-06
## C3 1.256e+01 1.634e+00 2.608e+03 7.685 2.15e-14
## C4 1.821e+00 1.513e+00 2.606e+03 1.203 0.2290
## C5 2.212e+00 1.511e+00 2.630e+03 1.464 0.1432
## C6 8.270e+00 1.522e+00 2.608e+03 5.434 6.02e-08
## C7 -1.643e+01 1.665e+00 2.641e+03 -9.864 < 2e-16
## C8 -8.637e+00 1.577e+00 2.670e+03 -5.476 4.75e-08
## C9 -1.067e+01 1.645e+00 2.632e+03 -6.489 1.03e-10
## ATNS_Score.c:Naturalness.c -5.040e-03 8.460e-04 3.025e+03 -5.957 2.86e-09
## ATNS_Score.c:C1 -6.218e-03 7.123e-02 2.618e+03 -0.087 0.9304
## ATNS_Score.c:C2 -8.730e-02 7.531e-02 2.543e+03 -1.159 0.2464
## ATNS_Score.c:C3 -2.862e-02 7.622e-02 2.591e+03 -0.375 0.7074
## ATNS_Score.c:C4 8.532e-02 6.981e-02 2.603e+03 1.222 0.2217
## ATNS_Score.c:C5 1.670e-02 7.041e-02 2.634e+03 0.237 0.8126
## ATNS_Score.c:C6 -1.089e-01 7.298e-02 2.629e+03 -1.492 0.1357
## ATNS_Score.c:C7 -1.081e-01 7.750e-02 2.650e+03 -1.395 0.1630
## ATNS_Score.c:C8 1.826e-02 7.316e-02 2.644e+03 0.250 0.8029
## ATNS_Score.c:C9 -1.314e-01 7.804e-02 2.680e+03 -1.684 0.0923
##
## (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 ittab_model(modA.8617,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.13 | 1.12 | 31.93 – 36.33 | 30.43 | <0.001 |
| ATNS Score c | 0.28 | 0.05 | 0.18 – 0.39 | 5.31 | <0.001 |
| Naturalness c | -0.43 | 0.02 | -0.47 – -0.39 | -20.05 | <0.001 |
| C1 | 2.70 | 1.52 | -0.27 – 5.68 | 1.78 | 0.075 |
| C2 | -7.69 | 1.61 | -10.85 – -4.53 | -4.77 | <0.001 |
| C3 | 12.56 | 1.63 | 9.35 – 15.76 | 7.69 | <0.001 |
| C4 | 1.82 | 1.51 | -1.15 – 4.79 | 1.20 | 0.229 |
| C5 | 2.21 | 1.51 | -0.75 – 5.17 | 1.46 | 0.143 |
| C6 | 8.27 | 1.52 | 5.29 – 11.25 | 5.43 | <0.001 |
| C7 | -16.43 | 1.67 | -19.69 – -13.16 | -9.86 | <0.001 |
| C8 | -8.64 | 1.58 | -11.73 – -5.54 | -5.48 | <0.001 |
| C9 | -10.67 | 1.64 | -13.90 – -7.45 | -6.49 | <0.001 |
|
ATNS Score c * Naturalness c |
-0.01 | 0.00 | -0.01 – -0.00 | -5.96 | <0.001 |
| ATNS Score c * C1 | -0.01 | 0.07 | -0.15 – 0.13 | -0.09 | 0.930 |
| ATNS Score c * C2 | -0.09 | 0.08 | -0.23 – 0.06 | -1.16 | 0.246 |
| ATNS Score c * C3 | -0.03 | 0.08 | -0.18 – 0.12 | -0.38 | 0.707 |
| ATNS Score c * C4 | 0.09 | 0.07 | -0.05 – 0.22 | 1.22 | 0.222 |
| ATNS Score c * C5 | 0.02 | 0.07 | -0.12 – 0.15 | 0.24 | 0.813 |
| ATNS Score c * C6 | -0.11 | 0.07 | -0.25 – 0.03 | -1.49 | 0.136 |
| ATNS Score c * C7 | -0.11 | 0.08 | -0.26 – 0.04 | -1.40 | 0.163 |
| ATNS Score c * C8 | 0.02 | 0.07 | -0.13 – 0.16 | 0.25 | 0.803 |
| ATNS Score c * C9 | -0.13 | 0.08 | -0.28 – 0.02 | -1.68 | 0.092 |
| Random Effects | |||||
| σ2 | 326.77 | ||||
| τ00 id | 145.16 | ||||
| ICC | 0.31 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.372 / 0.565 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict risk perception, over and above burger 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: 28176.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9898 -0.6068 -0.0694 0.5815 4.0009
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.9 13.56
## Residual 388.8 19.72
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.483e+01 1.230e+00 3.064e+03 28.315 < 2e-16 ***
## CNS_Score.c 1.685e-02 7.357e-02 3.066e+03 0.229 0.81882
## C1 7.998e+00 1.630e+00 2.595e+03 4.908 9.78e-07 ***
## C2 -8.189e+00 1.766e+00 2.542e+03 -4.638 3.70e-06 ***
## C3 1.838e+01 1.761e+00 2.580e+03 10.440 < 2e-16 ***
## C4 3.421e+00 1.652e+00 2.596e+03 2.071 0.03843 *
## C5 3.785e+00 1.650e+00 2.616e+03 2.294 0.02189 *
## C6 1.152e+01 1.656e+00 2.596e+03 6.955 4.45e-12 ***
## C7 -2.388e+01 1.778e+00 2.587e+03 -13.427 < 2e-16 ***
## C8 -1.859e+01 1.638e+00 2.588e+03 -11.348 < 2e-16 ***
## C9 -1.727e+01 1.762e+00 2.595e+03 -9.800 < 2e-16 ***
## CNS_Score.c:C1 -4.606e-02 9.589e-02 2.581e+03 -0.480 0.63104
## CNS_Score.c:C2 2.259e-03 1.027e-01 2.524e+03 0.022 0.98244
## CNS_Score.c:C3 2.762e-01 1.071e-01 2.541e+03 2.579 0.00996 **
## CNS_Score.c:C4 1.501e-01 9.719e-02 2.606e+03 1.545 0.12251
## CNS_Score.c:C5 3.710e-02 1.012e-01 2.636e+03 0.367 0.71387
## CNS_Score.c:C6 7.050e-02 9.860e-02 2.577e+03 0.715 0.47462
## CNS_Score.c:C7 -1.992e-01 1.078e-01 2.616e+03 -1.848 0.06476 .
## CNS_Score.c:C8 -1.575e-01 1.010e-01 2.612e+03 -1.559 0.11904
## CNS_Score.c:C9 -2.942e-01 1.065e-01 2.634e+03 -2.763 0.00576 **
## ---
## 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) | 34.83 | 1.23 | 32.42 – 37.24 | 28.32 | <0.001 |
| CNS Score c | 0.02 | 0.07 | -0.13 – 0.16 | 0.23 | 0.819 |
| C1 | 8.00 | 1.63 | 4.80 – 11.19 | 4.91 | <0.001 |
| C2 | -8.19 | 1.77 | -11.65 – -4.73 | -4.64 | <0.001 |
| C3 | 18.38 | 1.76 | 14.93 – 21.84 | 10.44 | <0.001 |
| C4 | 3.42 | 1.65 | 0.18 – 6.66 | 2.07 | 0.038 |
| C5 | 3.78 | 1.65 | 0.55 – 7.02 | 2.29 | 0.022 |
| C6 | 11.52 | 1.66 | 8.27 – 14.76 | 6.95 | <0.001 |
| C7 | -23.88 | 1.78 | -27.36 – -20.39 | -13.43 | <0.001 |
| C8 | -18.59 | 1.64 | -21.80 – -15.38 | -11.35 | <0.001 |
| C9 | -17.27 | 1.76 | -20.73 – -13.82 | -9.80 | <0.001 |
| CNS Score c * C1 | -0.05 | 0.10 | -0.23 – 0.14 | -0.48 | 0.631 |
| CNS Score c * C2 | 0.00 | 0.10 | -0.20 – 0.20 | 0.02 | 0.982 |
| CNS Score c * C3 | 0.28 | 0.11 | 0.07 – 0.49 | 2.58 | 0.010 |
| CNS Score c * C4 | 0.15 | 0.10 | -0.04 – 0.34 | 1.54 | 0.122 |
| CNS Score c * C5 | 0.04 | 0.10 | -0.16 – 0.24 | 0.37 | 0.714 |
| CNS Score c * C6 | 0.07 | 0.10 | -0.12 – 0.26 | 0.72 | 0.475 |
| CNS Score c * C7 | -0.20 | 0.11 | -0.41 – 0.01 | -1.85 | 0.065 |
| CNS Score c * C8 | -0.16 | 0.10 | -0.36 – 0.04 | -1.56 | 0.119 |
| CNS Score c * C9 | -0.29 | 0.11 | -0.50 – -0.09 | -2.76 | 0.006 |
| Random Effects | |||||
| σ2 | 388.78 | ||||
| τ00 id | 183.95 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.234 / 0.480 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting risk perception, over and above burger 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: 27783.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8170 -0.6029 -0.0225 0.5633 3.9329
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 179.0 13.38
## Residual 331.6 18.21
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.433e+01 1.150e+00 3.065e+03 29.847 < 2e-16 ***
## CNS_Score.c 2.103e-02 6.885e-02 3.067e+03 0.305 0.7600
## Naturalness.c -4.527e-01 2.180e-02 2.991e+03 -20.767 < 2e-16 ***
## C1 2.052e+00 1.538e+00 2.571e+03 1.334 0.1823
## C2 -7.818e+00 1.638e+00 2.509e+03 -4.772 1.92e-06 ***
## C3 1.227e+01 1.660e+00 2.562e+03 7.394 1.93e-13 ***
## C4 1.636e+00 1.535e+00 2.556e+03 1.066 0.2866
## C5 2.250e+00 1.534e+00 2.578e+03 1.467 0.1424
## C6 8.277e+00 1.544e+00 2.558e+03 5.359 9.11e-08 ***
## C7 -1.629e+01 1.690e+00 2.590e+03 -9.636 < 2e-16 ***
## C8 -8.218e+00 1.602e+00 2.616e+03 -5.129 3.13e-07 ***
## C9 -1.041e+01 1.670e+00 2.585e+03 -6.232 5.35e-10 ***
## CNS_Score.c:Naturalness.c -2.533e-03 1.218e-03 3.022e+03 -2.079 0.0377 *
## CNS_Score.c:C1 -1.097e-01 9.071e-02 2.570e+03 -1.209 0.2267
## CNS_Score.c:C2 7.499e-03 9.524e-02 2.495e+03 0.079 0.9373
## CNS_Score.c:C3 1.422e-01 1.002e-01 2.515e+03 1.419 0.1559
## CNS_Score.c:C4 1.118e-01 9.043e-02 2.563e+03 1.237 0.2164
## CNS_Score.c:C5 -6.248e-03 9.403e-02 2.594e+03 -0.066 0.9470
## CNS_Score.c:C6 5.746e-03 9.176e-02 2.533e+03 0.063 0.9501
## CNS_Score.c:C7 -1.058e-01 1.023e-01 2.612e+03 -1.034 0.3011
## CNS_Score.c:C8 -8.657e-02 9.915e-02 2.677e+03 -0.873 0.3827
## CNS_Score.c:C9 -1.876e-01 1.011e-01 2.636e+03 -1.855 0.0637 .
## ---
## 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) | 34.33 | 1.15 | 32.07 – 36.58 | 29.85 | <0.001 |
| CNS Score c | 0.02 | 0.07 | -0.11 – 0.16 | 0.31 | 0.760 |
| Naturalness c | -0.45 | 0.02 | -0.50 – -0.41 | -20.77 | <0.001 |
| C1 | 2.05 | 1.54 | -0.96 – 5.07 | 1.33 | 0.182 |
| C2 | -7.82 | 1.64 | -11.03 – -4.61 | -4.77 | <0.001 |
| C3 | 12.27 | 1.66 | 9.02 – 15.52 | 7.39 | <0.001 |
| C4 | 1.64 | 1.54 | -1.37 – 4.65 | 1.07 | 0.287 |
| C5 | 2.25 | 1.53 | -0.76 – 5.26 | 1.47 | 0.142 |
| C6 | 8.28 | 1.54 | 5.25 – 11.30 | 5.36 | <0.001 |
| C7 | -16.29 | 1.69 | -19.60 – -12.97 | -9.64 | <0.001 |
| C8 | -8.22 | 1.60 | -11.36 – -5.08 | -5.13 | <0.001 |
| C9 | -10.41 | 1.67 | -13.68 – -7.13 | -6.23 | <0.001 |
|
CNS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – -0.00 | -2.08 | 0.038 |
| CNS Score c * C1 | -0.11 | 0.09 | -0.29 – 0.07 | -1.21 | 0.227 |
| CNS Score c * C2 | 0.01 | 0.10 | -0.18 – 0.19 | 0.08 | 0.937 |
| CNS Score c * C3 | 0.14 | 0.10 | -0.05 – 0.34 | 1.42 | 0.156 |
| CNS Score c * C4 | 0.11 | 0.09 | -0.07 – 0.29 | 1.24 | 0.216 |
| CNS Score c * C5 | -0.01 | 0.09 | -0.19 – 0.18 | -0.07 | 0.947 |
| CNS Score c * C6 | 0.01 | 0.09 | -0.17 – 0.19 | 0.06 | 0.950 |
| CNS Score c * C7 | -0.11 | 0.10 | -0.31 – 0.09 | -1.03 | 0.301 |
| CNS Score c * C8 | -0.09 | 0.10 | -0.28 – 0.11 | -0.87 | 0.383 |
| CNS Score c * C9 | -0.19 | 0.10 | -0.39 – 0.01 | -1.86 | 0.064 |
| Random Effects | |||||
| σ2 | 331.61 | ||||
| τ00 id | 178.95 | ||||
| ICC | 0.35 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.320 / 0.558 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict risk perception, over and above burger 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: 28128.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7588 -0.6103 -0.0588 0.5796 3.6839
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 171 13.08
## Residual 387 19.67
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.481e+01 1.220e+00 3.062e+03 28.529 < 2e-16 ***
## CCBelief_Score.c -1.775e-01 5.196e-02 3.061e+03 -3.415 0.000646 ***
## C1 8.114e+00 1.622e+00 2.612e+03 5.003 6.02e-07 ***
## C2 -8.208e+00 1.753e+00 2.557e+03 -4.681 3.01e-06 ***
## C3 1.874e+01 1.753e+00 2.595e+03 10.688 < 2e-16 ***
## C4 3.157e+00 1.645e+00 2.611e+03 1.919 0.055054 .
## C5 3.935e+00 1.643e+00 2.634e+03 2.395 0.016680 *
## C6 1.141e+01 1.647e+00 2.612e+03 6.926 5.43e-12 ***
## C7 -2.386e+01 1.770e+00 2.600e+03 -13.482 < 2e-16 ***
## C8 -1.863e+01 1.630e+00 2.608e+03 -11.425 < 2e-16 ***
## C9 -1.753e+01 1.752e+00 2.611e+03 -10.002 < 2e-16 ***
## CCBelief_Score.c:C1 -1.506e-03 6.860e-02 2.630e+03 -0.022 0.982486
## CCBelief_Score.c:C2 4.436e-02 7.569e-02 2.563e+03 0.586 0.557939
## CCBelief_Score.c:C3 3.036e-01 7.085e-02 2.569e+03 4.285 1.89e-05 ***
## CCBelief_Score.c:C4 3.012e-02 6.745e-02 2.618e+03 0.447 0.655203
## CCBelief_Score.c:C5 7.575e-03 7.129e-02 2.625e+03 0.106 0.915389
## CCBelief_Score.c:C6 -3.371e-03 6.915e-02 2.583e+03 -0.049 0.961126
## CCBelief_Score.c:C7 4.910e-02 7.630e-02 2.585e+03 0.643 0.520008
## CCBelief_Score.c:C8 -3.558e-02 7.117e-02 2.647e+03 -0.500 0.617144
## CCBelief_Score.c:C9 -2.216e-01 7.604e-02 2.665e+03 -2.914 0.003596 **
## ---
## 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) | 34.81 | 1.22 | 32.42 – 37.20 | 28.53 | <0.001 |
| CCBelief Score c | -0.18 | 0.05 | -0.28 – -0.08 | -3.42 | 0.001 |
| C1 | 8.11 | 1.62 | 4.93 – 11.29 | 5.00 | <0.001 |
| C2 | -8.21 | 1.75 | -11.65 – -4.77 | -4.68 | <0.001 |
| C3 | 18.74 | 1.75 | 15.30 – 22.17 | 10.69 | <0.001 |
| C4 | 3.16 | 1.64 | -0.07 – 6.38 | 1.92 | 0.055 |
| C5 | 3.93 | 1.64 | 0.71 – 7.16 | 2.40 | 0.017 |
| C6 | 11.41 | 1.65 | 8.18 – 14.64 | 6.93 | <0.001 |
| C7 | -23.86 | 1.77 | -27.33 – -20.39 | -13.48 | <0.001 |
| C8 | -18.63 | 1.63 | -21.82 – -15.43 | -11.43 | <0.001 |
| C9 | -17.53 | 1.75 | -20.96 – -14.09 | -10.00 | <0.001 |
| CCBelief Score c * C1 | -0.00 | 0.07 | -0.14 – 0.13 | -0.02 | 0.982 |
| CCBelief Score c * C2 | 0.04 | 0.08 | -0.10 – 0.19 | 0.59 | 0.558 |
| CCBelief Score c * C3 | 0.30 | 0.07 | 0.16 – 0.44 | 4.29 | <0.001 |
| CCBelief Score c * C4 | 0.03 | 0.07 | -0.10 – 0.16 | 0.45 | 0.655 |
| CCBelief Score c * C5 | 0.01 | 0.07 | -0.13 – 0.15 | 0.11 | 0.915 |
| CCBelief Score c * C6 | -0.00 | 0.07 | -0.14 – 0.13 | -0.05 | 0.961 |
| CCBelief Score c * C7 | 0.05 | 0.08 | -0.10 – 0.20 | 0.64 | 0.520 |
| CCBelief Score c * C8 | -0.04 | 0.07 | -0.18 – 0.10 | -0.50 | 0.617 |
| CCBelief Score c * C9 | -0.22 | 0.08 | -0.37 – -0.07 | -2.91 | 0.004 |
| Random Effects | |||||
| σ2 | 387.00 | ||||
| τ00 id | 171.03 | ||||
| ICC | 0.31 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.255 / 0.483 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perceptions of naturalness in predicting risk perception, over and above burger 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)
summary(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: 27738.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3605 -0.6101 -0.0086 0.5647 3.7196
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 168.6 12.98
## Residual 329.5 18.15
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.423e+01 1.141e+00 3.064e+03 29.991
## CCBelief_Score.c -1.350e-01 4.887e-02 3.067e+03 -2.763
## Naturalness.c -4.496e-01 2.163e-02 3.001e+03 -20.786
## C1 2.283e+00 1.531e+00 2.588e+03 1.491
## C2 -7.889e+00 1.626e+00 2.521e+03 -4.851
## C3 1.288e+01 1.653e+00 2.581e+03 7.791
## C4 1.491e+00 1.529e+00 2.570e+03 0.976
## C5 2.473e+00 1.526e+00 2.594e+03 1.620
## C6 8.350e+00 1.536e+00 2.573e+03 5.437
## C7 -1.642e+01 1.681e+00 2.601e+03 -9.767
## C8 -8.262e+00 1.594e+00 2.637e+03 -5.183
## C9 -1.073e+01 1.661e+00 2.599e+03 -6.462
## CCBelief_Score.c:Naturalness.c 1.184e-03 8.190e-04 3.015e+03 1.446
## CCBelief_Score.c:C1 -2.205e-02 6.427e-02 2.579e+03 -0.343
## CCBelief_Score.c:C2 4.964e-02 7.021e-02 2.527e+03 0.707
## CCBelief_Score.c:C3 2.554e-01 6.622e-02 2.522e+03 3.857
## CCBelief_Score.c:C4 2.612e-03 6.261e-02 2.576e+03 0.042
## CCBelief_Score.c:C5 -2.905e-03 6.617e-02 2.582e+03 -0.044
## CCBelief_Score.c:C6 -2.198e-02 6.418e-02 2.540e+03 -0.342
## CCBelief_Score.c:C7 3.741e-02 7.200e-02 2.623e+03 0.520
## CCBelief_Score.c:C8 -8.780e-02 7.033e-02 2.749e+03 -1.248
## CCBelief_Score.c:C9 -2.230e-01 7.209e-02 2.689e+03 -3.093
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 0.005764 **
## Naturalness.c < 2e-16 ***
## C1 0.136177
## C2 1.30e-06 ***
## C3 9.55e-15 ***
## C4 0.329382
## C5 0.105319
## C6 5.91e-08 ***
## C7 < 2e-16 ***
## C8 2.34e-07 ***
## C9 1.23e-10 ***
## CCBelief_Score.c:Naturalness.c 0.148232
## CCBelief_Score.c:C1 0.731609
## CCBelief_Score.c:C2 0.479572
## CCBelief_Score.c:C3 0.000117 ***
## CCBelief_Score.c:C4 0.966724
## CCBelief_Score.c:C5 0.964989
## CCBelief_Score.c:C6 0.732027
## CCBelief_Score.c:C7 0.603418
## CCBelief_Score.c:C8 0.211980
## CCBelief_Score.c:C9 0.002002 **
## ---
## 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.8649,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.23 | 1.14 | 31.99 – 36.47 | 29.99 | <0.001 |
| CCBelief Score c | -0.14 | 0.05 | -0.23 – -0.04 | -2.76 | 0.006 |
| Naturalness c | -0.45 | 0.02 | -0.49 – -0.41 | -20.79 | <0.001 |
| C1 | 2.28 | 1.53 | -0.72 – 5.29 | 1.49 | 0.136 |
| C2 | -7.89 | 1.63 | -11.08 – -4.70 | -4.85 | <0.001 |
| C3 | 12.88 | 1.65 | 9.64 – 16.12 | 7.79 | <0.001 |
| C4 | 1.49 | 1.53 | -1.51 – 4.49 | 0.98 | 0.329 |
| C5 | 2.47 | 1.53 | -0.52 – 5.47 | 1.62 | 0.105 |
| C6 | 8.35 | 1.54 | 5.34 – 11.36 | 5.44 | <0.001 |
| C7 | -16.42 | 1.68 | -19.72 – -13.12 | -9.77 | <0.001 |
| C8 | -8.26 | 1.59 | -11.39 – -5.14 | -5.18 | <0.001 |
| C9 | -10.73 | 1.66 | -13.99 – -7.48 | -6.46 | <0.001 |
|
CCBelief Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.45 | 0.148 |
| CCBelief Score c * C1 | -0.02 | 0.06 | -0.15 – 0.10 | -0.34 | 0.732 |
| CCBelief Score c * C2 | 0.05 | 0.07 | -0.09 – 0.19 | 0.71 | 0.480 |
| CCBelief Score c * C3 | 0.26 | 0.07 | 0.13 – 0.39 | 3.86 | <0.001 |
| CCBelief Score c * C4 | 0.00 | 0.06 | -0.12 – 0.13 | 0.04 | 0.967 |
| CCBelief Score c * C5 | -0.00 | 0.07 | -0.13 – 0.13 | -0.04 | 0.965 |
| CCBelief Score c * C6 | -0.02 | 0.06 | -0.15 – 0.10 | -0.34 | 0.732 |
| CCBelief Score c * C7 | 0.04 | 0.07 | -0.10 – 0.18 | 0.52 | 0.603 |
| CCBelief Score c * C8 | -0.09 | 0.07 | -0.23 – 0.05 | -1.25 | 0.212 |
| CCBelief Score c * C9 | -0.22 | 0.07 | -0.36 – -0.08 | -3.09 | 0.002 |
| Random Effects | |||||
| σ2 | 329.49 | ||||
| τ00 id | 168.56 | ||||
| ICC | 0.34 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.339 / 0.562 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict risk perception, over and above burger 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: 28196.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6533 -0.6158 -0.0667 0.5643 3.6878
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 181.0 13.45
## Residual 392.3 19.81
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.479e+01 1.234e+00 3.063e+03 28.194 < 2e-16 ***
## Collectivism_Score.c 6.601e-02 4.811e-02 3.077e+03 1.372 0.1702
## C1 8.155e+00 1.636e+00 2.602e+03 4.984 6.63e-07 ***
## C2 -8.229e+00 1.769e+00 2.549e+03 -4.650 3.48e-06 ***
## C3 1.836e+01 1.770e+00 2.584e+03 10.375 < 2e-16 ***
## C4 3.324e+00 1.659e+00 2.605e+03 2.004 0.0452 *
## C5 3.752e+00 1.661e+00 2.625e+03 2.259 0.0240 *
## C6 1.155e+01 1.662e+00 2.602e+03 6.947 4.71e-12 ***
## C7 -2.384e+01 1.785e+00 2.592e+03 -13.354 < 2e-16 ***
## C8 -1.878e+01 1.645e+00 2.596e+03 -11.418 < 2e-16 ***
## C9 -1.766e+01 1.770e+00 2.602e+03 -9.981 < 2e-16 ***
## Collectivism_Score.c:C1 1.082e-01 6.878e-02 2.584e+03 1.573 0.1159
## Collectivism_Score.c:C2 2.883e-02 7.195e-02 2.526e+03 0.401 0.6887
## Collectivism_Score.c:C3 -1.189e-01 6.971e-02 2.521e+03 -1.705 0.0883 .
## Collectivism_Score.c:C4 -9.289e-04 6.709e-02 2.583e+03 -0.014 0.9890
## Collectivism_Score.c:C5 -3.047e-02 6.586e-02 2.598e+03 -0.463 0.6437
## Collectivism_Score.c:C6 7.374e-02 6.758e-02 2.593e+03 1.091 0.2753
## Collectivism_Score.c:C7 3.681e-03 7.262e-02 2.607e+03 0.051 0.9596
## Collectivism_Score.c:C8 4.559e-02 6.716e-02 2.614e+03 0.679 0.4973
## Collectivism_Score.c:C9 9.351e-02 7.374e-02 2.637e+03 1.268 0.2049
## ---
## 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) | 34.79 | 1.23 | 32.37 – 37.21 | 28.19 | <0.001 |
| Collectivism Score c | 0.07 | 0.05 | -0.03 – 0.16 | 1.37 | 0.170 |
| C1 | 8.16 | 1.64 | 4.95 – 11.36 | 4.98 | <0.001 |
| C2 | -8.23 | 1.77 | -11.70 – -4.76 | -4.65 | <0.001 |
| C3 | 18.36 | 1.77 | 14.89 – 21.83 | 10.37 | <0.001 |
| C4 | 3.32 | 1.66 | 0.07 – 6.58 | 2.00 | 0.045 |
| C5 | 3.75 | 1.66 | 0.49 – 7.01 | 2.26 | 0.024 |
| C6 | 11.55 | 1.66 | 8.29 – 14.80 | 6.95 | <0.001 |
| C7 | -23.84 | 1.79 | -27.34 – -20.34 | -13.35 | <0.001 |
| C8 | -18.78 | 1.64 | -22.00 – -15.55 | -11.42 | <0.001 |
| C9 | -17.66 | 1.77 | -21.13 – -14.19 | -9.98 | <0.001 |
| Collectivism Score c * C1 | 0.11 | 0.07 | -0.03 – 0.24 | 1.57 | 0.116 |
| Collectivism Score c * C2 | 0.03 | 0.07 | -0.11 – 0.17 | 0.40 | 0.689 |
| Collectivism Score c * C3 | -0.12 | 0.07 | -0.26 – 0.02 | -1.71 | 0.088 |
| Collectivism Score c * C4 | -0.00 | 0.07 | -0.13 – 0.13 | -0.01 | 0.989 |
| Collectivism Score c * C5 | -0.03 | 0.07 | -0.16 – 0.10 | -0.46 | 0.644 |
| Collectivism Score c * C6 | 0.07 | 0.07 | -0.06 – 0.21 | 1.09 | 0.275 |
| Collectivism Score c * C7 | 0.00 | 0.07 | -0.14 – 0.15 | 0.05 | 0.960 |
| Collectivism Score c * C8 | 0.05 | 0.07 | -0.09 – 0.18 | 0.68 | 0.497 |
| Collectivism Score c * C9 | 0.09 | 0.07 | -0.05 – 0.24 | 1.27 | 0.205 |
| Random Effects | |||||
| σ2 | 392.32 | ||||
| τ00 id | 181.00 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.233 / 0.475 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting risk perception, over and above burger 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)
summary(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: 27793.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3527 -0.6028 -0.0219 0.5631 3.7108
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 176.0 13.27
## Residual 333.3 18.26
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.426e+01 1.152e+00 3.064e+03 29.756
## Collectivism_Score.c 7.122e-02 4.500e-02 3.076e+03 1.583
## Naturalness.c -4.623e-01 2.177e-02 2.999e+03 -21.234
## C1 2.101e+00 1.542e+00 2.578e+03 1.363
## C2 -7.888e+00 1.639e+00 2.515e+03 -4.814
## C3 1.231e+01 1.663e+00 2.565e+03 7.402
## C4 1.557e+00 1.539e+00 2.564e+03 1.012
## C5 2.139e+00 1.541e+00 2.587e+03 1.387
## C6 8.304e+00 1.547e+00 2.566e+03 5.368
## C7 -1.615e+01 1.694e+00 2.595e+03 -9.536
## C8 -8.146e+00 1.608e+00 2.628e+03 -5.067
## C9 -1.068e+01 1.674e+00 2.592e+03 -6.382
## Collectivism_Score.c:Naturalness.c -6.341e-04 8.553e-04 2.994e+03 -0.741
## Collectivism_Score.c:C1 1.022e-01 6.498e-02 2.561e+03 1.573
## Collectivism_Score.c:C2 2.731e-02 6.662e-02 2.494e+03 0.410
## Collectivism_Score.c:C3 -1.097e-01 6.541e-02 2.509e+03 -1.678
## Collectivism_Score.c:C4 -2.090e-02 6.217e-02 2.543e+03 -0.336
## Collectivism_Score.c:C5 -6.941e-02 6.116e-02 2.557e+03 -1.135
## Collectivism_Score.c:C6 3.756e-02 6.285e-02 2.550e+03 0.598
## Collectivism_Score.c:C7 2.178e-02 6.933e-02 2.619e+03 0.314
## Collectivism_Score.c:C8 8.520e-03 6.552e-02 2.651e+03 0.130
## Collectivism_Score.c:C9 1.061e-01 7.019e-02 2.648e+03 1.512
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.1136
## Naturalness.c < 2e-16 ***
## C1 0.1730
## C2 1.57e-06 ***
## C3 1.81e-13 ***
## C4 0.3119
## C5 0.1654
## C6 8.69e-08 ***
## C7 < 2e-16 ***
## C8 4.33e-07 ***
## C9 2.06e-10 ***
## Collectivism_Score.c:Naturalness.c 0.4585
## Collectivism_Score.c:C1 0.1157
## Collectivism_Score.c:C2 0.6818
## Collectivism_Score.c:C3 0.0935 .
## Collectivism_Score.c:C4 0.7368
## Collectivism_Score.c:C5 0.2565
## Collectivism_Score.c:C6 0.5501
## Collectivism_Score.c:C7 0.7534
## Collectivism_Score.c:C8 0.8966
## Collectivism_Score.c:C9 0.1306
## ---
## 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.8665,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 34.26 | 1.15 | 32.01 – 36.52 | 29.76 | <0.001 |
| Collectivism Score c | 0.07 | 0.05 | -0.02 – 0.16 | 1.58 | 0.114 |
| Naturalness c | -0.46 | 0.02 | -0.50 – -0.42 | -21.23 | <0.001 |
| C1 | 2.10 | 1.54 | -0.92 – 5.12 | 1.36 | 0.173 |
| C2 | -7.89 | 1.64 | -11.10 – -4.68 | -4.81 | <0.001 |
| C3 | 12.31 | 1.66 | 9.05 – 15.57 | 7.40 | <0.001 |
| C4 | 1.56 | 1.54 | -1.46 – 4.57 | 1.01 | 0.312 |
| C5 | 2.14 | 1.54 | -0.88 – 5.16 | 1.39 | 0.165 |
| C6 | 8.30 | 1.55 | 5.27 – 11.34 | 5.37 | <0.001 |
| C7 | -16.15 | 1.69 | -19.47 – -12.83 | -9.54 | <0.001 |
| C8 | -8.15 | 1.61 | -11.30 – -4.99 | -5.07 | <0.001 |
| C9 | -10.68 | 1.67 | -13.97 – -7.40 | -6.38 | <0.001 |
|
Collectivism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.74 | 0.459 |
| Collectivism Score c * C1 | 0.10 | 0.06 | -0.03 – 0.23 | 1.57 | 0.116 |
| Collectivism Score c * C2 | 0.03 | 0.07 | -0.10 – 0.16 | 0.41 | 0.682 |
| Collectivism Score c * C3 | -0.11 | 0.07 | -0.24 – 0.02 | -1.68 | 0.094 |
| Collectivism Score c * C4 | -0.02 | 0.06 | -0.14 – 0.10 | -0.34 | 0.737 |
| Collectivism Score c * C5 | -0.07 | 0.06 | -0.19 – 0.05 | -1.13 | 0.257 |
| Collectivism Score c * C6 | 0.04 | 0.06 | -0.09 – 0.16 | 0.60 | 0.550 |
| Collectivism Score c * C7 | 0.02 | 0.07 | -0.11 – 0.16 | 0.31 | 0.753 |
| Collectivism Score c * C8 | 0.01 | 0.07 | -0.12 – 0.14 | 0.13 | 0.897 |
| Collectivism Score c * C9 | 0.11 | 0.07 | -0.03 – 0.24 | 1.51 | 0.131 |
| Random Effects | |||||
| σ2 | 333.29 | ||||
| τ00 id | 175.97 | ||||
| ICC | 0.35 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.321 / 0.556 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict risk perception, over and above burger 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: 28207.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7700 -0.6067 -0.0709 0.5828 3.6594
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 186.1 13.64
## Residual 392.5 19.81
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.94212 1.23789 3063.60706 28.227 < 2e-16 ***
## Individualism_Score.c -0.11101 0.07106 3071.07651 -1.562 0.11835
## C1 7.98539 1.63895 2596.44562 4.872 1.17e-06 ***
## C2 -8.36427 1.77354 2542.71589 -4.716 2.53e-06 ***
## C3 18.08629 1.77495 2581.25156 10.190 < 2e-16 ***
## C4 3.18505 1.66018 2595.41936 1.919 0.05516 .
## C5 3.58419 1.65946 2619.17891 2.160 0.03087 *
## C6 11.33848 1.66500 2595.70747 6.810 1.21e-11 ***
## C7 -24.05104 1.78875 2585.48781 -13.446 < 2e-16 ***
## C8 -18.88382 1.64659 2589.41217 -11.468 < 2e-16 ***
## C9 -17.65197 1.77045 2594.37567 -9.970 < 2e-16 ***
## Individualism_Score.c:C1 0.12769 0.09500 2567.76302 1.344 0.17901
## Individualism_Score.c:C2 0.09300 0.10738 2592.40292 0.866 0.38656
## Individualism_Score.c:C3 0.27879 0.09950 2519.43890 2.802 0.00512 **
## Individualism_Score.c:C4 0.13386 0.09698 2583.50123 1.380 0.16760
## Individualism_Score.c:C5 0.12391 0.09741 2622.93036 1.272 0.20350
## Individualism_Score.c:C6 0.12111 0.09928 2607.17961 1.220 0.22259
## Individualism_Score.c:C7 0.05749 0.10150 2565.00599 0.566 0.57119
## Individualism_Score.c:C8 0.17414 0.09535 2591.78656 1.826 0.06792 .
## Individualism_Score.c:C9 0.07592 0.10822 2618.14926 0.702 0.48302
## ---
## 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) | 34.94 | 1.24 | 32.51 – 37.37 | 28.23 | <0.001 |
| Individualism Score c | -0.11 | 0.07 | -0.25 – 0.03 | -1.56 | 0.118 |
| C1 | 7.99 | 1.64 | 4.77 – 11.20 | 4.87 | <0.001 |
| C2 | -8.36 | 1.77 | -11.84 – -4.89 | -4.72 | <0.001 |
| C3 | 18.09 | 1.77 | 14.61 – 21.57 | 10.19 | <0.001 |
| C4 | 3.19 | 1.66 | -0.07 – 6.44 | 1.92 | 0.055 |
| C5 | 3.58 | 1.66 | 0.33 – 6.84 | 2.16 | 0.031 |
| C6 | 11.34 | 1.67 | 8.07 – 14.60 | 6.81 | <0.001 |
| C7 | -24.05 | 1.79 | -27.56 – -20.54 | -13.45 | <0.001 |
| C8 | -18.88 | 1.65 | -22.11 – -15.66 | -11.47 | <0.001 |
| C9 | -17.65 | 1.77 | -21.12 – -14.18 | -9.97 | <0.001 |
|
Individualism Score c * C1 |
0.13 | 0.09 | -0.06 – 0.31 | 1.34 | 0.179 |
|
Individualism Score c * C2 |
0.09 | 0.11 | -0.12 – 0.30 | 0.87 | 0.387 |
|
Individualism Score c * C3 |
0.28 | 0.10 | 0.08 – 0.47 | 2.80 | 0.005 |
|
Individualism Score c * C4 |
0.13 | 0.10 | -0.06 – 0.32 | 1.38 | 0.168 |
|
Individualism Score c * C5 |
0.12 | 0.10 | -0.07 – 0.31 | 1.27 | 0.203 |
|
Individualism Score c * C6 |
0.12 | 0.10 | -0.07 – 0.32 | 1.22 | 0.223 |
|
Individualism Score c * C7 |
0.06 | 0.10 | -0.14 – 0.26 | 0.57 | 0.571 |
|
Individualism Score c * C8 |
0.17 | 0.10 | -0.01 – 0.36 | 1.83 | 0.068 |
|
Individualism Score c * C9 |
0.08 | 0.11 | -0.14 – 0.29 | 0.70 | 0.483 |
| Random Effects | |||||
| σ2 | 392.51 | ||||
| τ00 id | 186.09 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.227 / 0.475 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting risk perception, over and above burger 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: 27798.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3734 -0.6091 -0.0185 0.5621 3.6866
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 179.9 13.41
## Residual 333.1 18.25
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.442e+01 1.155e+00 3.065e+03 29.814
## Individualism_Score.c -9.830e-02 6.632e-02 3.072e+03 -1.482
## Naturalness.c -4.577e-01 2.191e-02 2.990e+03 -20.889
## C1 2.014e+00 1.543e+00 2.573e+03 1.305
## C2 -8.057e+00 1.642e+00 2.510e+03 -4.908
## C3 1.202e+01 1.667e+00 2.562e+03 7.212
## C4 1.378e+00 1.539e+00 2.556e+03 0.896
## C5 2.000e+00 1.539e+00 2.581e+03 1.300
## C6 8.084e+00 1.549e+00 2.558e+03 5.220
## C7 -1.645e+01 1.697e+00 2.589e+03 -9.695
## C8 -8.383e+00 1.608e+00 2.616e+03 -5.213
## C9 -1.071e+01 1.674e+00 2.584e+03 -6.396
## Individualism_Score.c:Naturalness.c -2.872e-03 1.236e-03 3.024e+03 -2.323
## Individualism_Score.c:C1 8.589e-02 8.932e-02 2.538e+03 0.962
## Individualism_Score.c:C2 6.361e-02 9.946e-02 2.559e+03 0.639
## Individualism_Score.c:C3 2.164e-01 9.351e-02 2.498e+03 2.314
## Individualism_Score.c:C4 8.035e-02 8.993e-02 2.543e+03 0.894
## Individualism_Score.c:C5 3.882e-02 9.040e-02 2.579e+03 0.429
## Individualism_Score.c:C6 4.100e-02 9.236e-02 2.564e+03 0.444
## Individualism_Score.c:C7 8.991e-02 9.720e-02 2.592e+03 0.925
## Individualism_Score.c:C8 2.255e-01 9.338e-02 2.640e+03 2.415
## Individualism_Score.c:C9 1.276e-01 1.021e-01 2.613e+03 1.249
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.1384
## Naturalness.c < 2e-16 ***
## C1 0.1921
## C2 9.79e-07 ***
## C3 7.25e-13 ***
## C4 0.3706
## C5 0.1937
## C6 1.93e-07 ***
## C7 < 2e-16 ***
## C8 2.00e-07 ***
## C9 1.89e-10 ***
## Individualism_Score.c:Naturalness.c 0.0202 *
## Individualism_Score.c:C1 0.3364
## Individualism_Score.c:C2 0.5226
## Individualism_Score.c:C3 0.0207 *
## Individualism_Score.c:C4 0.3717
## Individualism_Score.c:C5 0.6677
## Individualism_Score.c:C6 0.6572
## Individualism_Score.c:C7 0.3551
## Individualism_Score.c:C8 0.0158 *
## Individualism_Score.c:C9 0.2118
## ---
## 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) | 34.42 | 1.15 | 32.16 – 36.69 | 29.81 | <0.001 |
| Individualism Score c | -0.10 | 0.07 | -0.23 – 0.03 | -1.48 | 0.138 |
| Naturalness c | -0.46 | 0.02 | -0.50 – -0.41 | -20.89 | <0.001 |
| C1 | 2.01 | 1.54 | -1.01 – 5.04 | 1.30 | 0.192 |
| C2 | -8.06 | 1.64 | -11.28 – -4.84 | -4.91 | <0.001 |
| C3 | 12.02 | 1.67 | 8.75 – 15.29 | 7.21 | <0.001 |
| C4 | 1.38 | 1.54 | -1.64 – 4.40 | 0.90 | 0.371 |
| C5 | 2.00 | 1.54 | -1.02 – 5.02 | 1.30 | 0.194 |
| C6 | 8.08 | 1.55 | 5.05 – 11.12 | 5.22 | <0.001 |
| C7 | -16.45 | 1.70 | -19.78 – -13.12 | -9.69 | <0.001 |
| C8 | -8.38 | 1.61 | -11.54 – -5.23 | -5.21 | <0.001 |
| C9 | -10.71 | 1.67 | -13.99 – -7.43 | -6.40 | <0.001 |
|
Individualism Score c * Naturalness c |
-0.00 | 0.00 | -0.01 – -0.00 | -2.32 | 0.020 |
|
Individualism Score c * C1 |
0.09 | 0.09 | -0.09 – 0.26 | 0.96 | 0.336 |
|
Individualism Score c * C2 |
0.06 | 0.10 | -0.13 – 0.26 | 0.64 | 0.523 |
|
Individualism Score c * C3 |
0.22 | 0.09 | 0.03 – 0.40 | 2.31 | 0.021 |
|
Individualism Score c * C4 |
0.08 | 0.09 | -0.10 – 0.26 | 0.89 | 0.372 |
|
Individualism Score c * C5 |
0.04 | 0.09 | -0.14 – 0.22 | 0.43 | 0.668 |
|
Individualism Score c * C6 |
0.04 | 0.09 | -0.14 – 0.22 | 0.44 | 0.657 |
|
Individualism Score c * C7 |
0.09 | 0.10 | -0.10 – 0.28 | 0.92 | 0.355 |
|
Individualism Score c * C8 |
0.23 | 0.09 | 0.04 – 0.41 | 2.42 | 0.016 |
|
Individualism Score c * C9 |
0.13 | 0.10 | -0.07 – 0.33 | 1.25 | 0.212 |
| Random Effects | |||||
| σ2 | 333.12 | ||||
| τ00 id | 179.94 | ||||
| ICC | 0.35 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.316 / 0.556 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does ideology predict risk perception, over and above burger 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: 28137.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6001 -0.6111 -0.0695 0.5652 3.6344
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.7 13.56
## Residual 393.4 19.83
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.7923 1.2359 3063.6533 28.152 < 2e-16 ***
## Ideology.c -3.3398 2.0690 3073.8641 -1.614 0.1066
## C1 8.0833 1.6386 2598.6780 4.933 8.60e-07 ***
## C2 -8.2099 1.7709 2545.5662 -4.636 3.73e-06 ***
## C3 18.5136 1.7701 2583.3796 10.459 < 2e-16 ***
## C4 3.3412 1.6594 2598.6995 2.014 0.0442 *
## C5 3.8757 1.6616 2621.1413 2.333 0.0197 *
## C6 11.4818 1.6652 2601.2557 6.895 6.73e-12 ***
## C7 -23.8334 1.7875 2588.4834 -13.334 < 2e-16 ***
## C8 -18.7521 1.6500 2597.0099 -11.365 < 2e-16 ***
## C9 -17.5216 1.7705 2598.8239 -9.896 < 2e-16 ***
## Ideology.c:C1 -0.1485 2.7828 2558.2343 -0.053 0.9574
## Ideology.c:C2 -1.4045 2.9480 2482.4186 -0.476 0.6338
## Ideology.c:C3 5.6565 3.0752 2582.4842 1.839 0.0660 .
## Ideology.c:C4 3.1744 2.8851 2621.6357 1.100 0.2713
## Ideology.c:C5 0.3445 2.8463 2680.2277 0.121 0.9037
## Ideology.c:C6 0.7916 2.8842 2605.6474 0.274 0.7838
## Ideology.c:C7 1.6466 3.0089 2645.8546 0.547 0.5843
## Ideology.c:C8 1.4292 2.8067 2573.1500 0.509 0.6107
## Ideology.c:C9 2.8474 3.1410 2611.3341 0.907 0.3647
## ---
## 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) | 34.79 | 1.24 | 32.37 – 37.22 | 28.15 | <0.001 |
| Ideology c | -3.34 | 2.07 | -7.40 – 0.72 | -1.61 | 0.107 |
| C1 | 8.08 | 1.64 | 4.87 – 11.30 | 4.93 | <0.001 |
| C2 | -8.21 | 1.77 | -11.68 – -4.74 | -4.64 | <0.001 |
| C3 | 18.51 | 1.77 | 15.04 – 21.98 | 10.46 | <0.001 |
| C4 | 3.34 | 1.66 | 0.09 – 6.59 | 2.01 | 0.044 |
| C5 | 3.88 | 1.66 | 0.62 – 7.13 | 2.33 | 0.020 |
| C6 | 11.48 | 1.67 | 8.22 – 14.75 | 6.90 | <0.001 |
| C7 | -23.83 | 1.79 | -27.34 – -20.33 | -13.33 | <0.001 |
| C8 | -18.75 | 1.65 | -21.99 – -15.52 | -11.36 | <0.001 |
| C9 | -17.52 | 1.77 | -20.99 – -14.05 | -9.90 | <0.001 |
| Ideology c * C1 | -0.15 | 2.78 | -5.60 – 5.31 | -0.05 | 0.957 |
| Ideology c * C2 | -1.40 | 2.95 | -7.18 – 4.38 | -0.48 | 0.634 |
| Ideology c * C3 | 5.66 | 3.08 | -0.37 – 11.69 | 1.84 | 0.066 |
| Ideology c * C4 | 3.17 | 2.89 | -2.48 – 8.83 | 1.10 | 0.271 |
| Ideology c * C5 | 0.34 | 2.85 | -5.24 – 5.93 | 0.12 | 0.904 |
| Ideology c * C6 | 0.79 | 2.88 | -4.86 – 6.45 | 0.27 | 0.784 |
| Ideology c * C7 | 1.65 | 3.01 | -4.25 – 7.55 | 0.55 | 0.584 |
| Ideology c * C8 | 1.43 | 2.81 | -4.07 – 6.93 | 0.51 | 0.611 |
| Ideology c * C9 | 2.85 | 3.14 | -3.31 – 9.01 | 0.91 | 0.365 |
| Random Effects | |||||
| σ2 | 393.37 | ||||
| τ00 id | 183.75 | ||||
| ICC | 0.32 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.228 / 0.474 | ||||
Q.2 (POLITICAL IDEOLOGY) Does political ideology depend on perceptions of naturalness in predicting risk perception, over and above burger 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: 27744.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2973 -0.6120 -0.0234 0.5683 3.6528
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 177.8 13.34
## Residual 334.4 18.29
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.428e+01 1.153e+00 3.068e+03 29.725 < 2e-16 ***
## Ideology.c -2.554e+00 1.152e+00 1.929e+03 -2.217 0.0267 *
## Naturalness.c -4.624e-01 2.186e-02 3.000e+03 -21.149 < 2e-16 ***
## C1 2.022e+00 1.545e+00 2.580e+03 1.309 0.1908
## C2 -7.878e+00 1.640e+00 2.515e+03 -4.804 1.65e-06 ***
## C3 1.238e+01 1.664e+00 2.568e+03 7.443 1.33e-13 ***
## C4 1.553e+00 1.539e+00 2.562e+03 1.009 0.3131
## C5 2.321e+00 1.542e+00 2.587e+03 1.506 0.1322
## C6 8.273e+00 1.549e+00 2.565e+03 5.342 9.99e-08 ***
## C7 -1.617e+01 1.696e+00 2.597e+03 -9.532 < 2e-16 ***
## C8 -8.159e+00 1.607e+00 2.626e+03 -5.078 4.08e-07 ***
## C9 -1.053e+01 1.675e+00 2.593e+03 -6.285 3.83e-10 ***
## Ideology.c:Naturalness.c -3.946e-03 3.479e-02 2.894e+03 -0.113 0.9097
## Ideology.c:C1 -6.835e-01 2.139e+00 2.537e+03 -0.320 0.7494
## Ideology.c:C2 -1.936e+00 2.319e+00 2.588e+03 -0.835 0.4039
## Ideology.c:C3 3.333e+00 2.522e+00 2.651e+03 1.322 0.1864
## Ideology.c:C4 1.378e+00 2.191e+00 2.635e+03 0.629 0.5293
## Ideology.c:C5 6.443e-02 2.101e+00 2.601e+03 0.031 0.9755
## ---
## 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) | 34.28 | 1.15 | 32.02 – 36.54 | 29.72 | <0.001 |
| Ideology c | -2.55 | 1.15 | -4.81 – -0.30 | -2.22 | 0.027 |
| Naturalness c | -0.46 | 0.02 | -0.51 – -0.42 | -21.15 | <0.001 |
| C1 | 2.02 | 1.54 | -1.01 – 5.05 | 1.31 | 0.191 |
| C2 | -7.88 | 1.64 | -11.09 – -4.66 | -4.80 | <0.001 |
| C3 | 12.38 | 1.66 | 9.12 – 15.65 | 7.44 | <0.001 |
| C4 | 1.55 | 1.54 | -1.46 – 4.57 | 1.01 | 0.313 |
| C5 | 2.32 | 1.54 | -0.70 – 5.34 | 1.51 | 0.132 |
| C6 | 8.27 | 1.55 | 5.24 – 11.31 | 5.34 | <0.001 |
| C7 | -16.17 | 1.70 | -19.49 – -12.84 | -9.53 | <0.001 |
| C8 | -8.16 | 1.61 | -11.31 – -5.01 | -5.08 | <0.001 |
| C9 | -10.53 | 1.68 | -13.81 – -7.24 | -6.29 | <0.001 |
|
Ideology c * Naturalness c |
-0.00 | 0.03 | -0.07 – 0.06 | -0.11 | 0.910 |
| Ideology c * C1 | -0.68 | 2.14 | -4.88 – 3.51 | -0.32 | 0.749 |
| Ideology c * C2 | -1.94 | 2.32 | -6.48 – 2.61 | -0.83 | 0.404 |
| Ideology c * C3 | 3.33 | 2.52 | -1.61 – 8.28 | 1.32 | 0.186 |
| Ideology c * C4 | 1.38 | 2.19 | -2.92 – 5.67 | 0.63 | 0.529 |
| Ideology c * C5 | 0.06 | 2.10 | -4.06 – 4.18 | 0.03 | 0.976 |
| Random Effects | |||||
| σ2 | 334.36 | ||||
| τ00 id | 177.85 | ||||
| ICC | 0.35 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.316 / 0.554 | ||||
Benefit
Q.1 How do burger 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: 28385.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4352 -0.5146 0.0631 0.5681 3.1759
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 287.9 16.97
## Residual 378.5 19.46
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.2626 1.2720 3086.2930 41.874 < 2e-16 ***
## C1 1.9219 1.6360 2478.3405 1.175 0.240217
## C2 -2.2926 1.7654 2441.2064 -1.299 0.194184
## C3 6.2690 1.7668 2472.4609 3.548 0.000395 ***
## C4 2.1007 1.6569 2478.2035 1.268 0.204973
## C5 -0.4471 1.6575 2495.9323 -0.270 0.787385
## C6 0.5182 1.6618 2479.0967 0.312 0.755189
## C7 13.7277 1.7846 2477.1998 7.692 2.07e-14 ***
## C8 15.3323 1.6432 2473.6132 9.330 < 2e-16 ***
## C9 12.5640 1.7683 2486.0313 7.105 1.56e-12 ***
## ---
## 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 -0.664
## C2 -0.588 0.471
## C3 -0.597 0.478 0.388
## C4 -0.654 0.509 0.467 0.469
## C5 -0.660 0.516 0.469 0.478 0.508
## C6 -0.652 0.506 0.457 0.481 0.499 0.503
## C7 -0.592 0.471 0.385 0.391 0.469 0.472 0.462
## C8 -0.659 0.512 0.468 0.466 0.503 0.509 0.506 0.478
## C9 -0.601 0.486 0.391 0.398 0.475 0.476 0.473 0.395 0.480tab_model(modA.870,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.26 | 1.27 | 50.77 – 55.76 | 41.87 | <0.001 |
| C1 | 1.92 | 1.64 | -1.29 – 5.13 | 1.17 | 0.240 |
| C2 | -2.29 | 1.77 | -5.75 – 1.17 | -1.30 | 0.194 |
| C3 | 6.27 | 1.77 | 2.80 – 9.73 | 3.55 | <0.001 |
| C4 | 2.10 | 1.66 | -1.15 – 5.35 | 1.27 | 0.205 |
| C5 | -0.45 | 1.66 | -3.70 – 2.80 | -0.27 | 0.787 |
| C6 | 0.52 | 1.66 | -2.74 – 3.78 | 0.31 | 0.755 |
| C7 | 13.73 | 1.78 | 10.23 – 17.23 | 7.69 | <0.001 |
| C8 | 15.33 | 1.64 | 12.11 – 18.55 | 9.33 | <0.001 |
| C9 | 12.56 | 1.77 | 9.10 – 16.03 | 7.11 | <0.001 |
| Random Effects | |||||
| σ2 | 378.52 | ||||
| τ00 id | 287.94 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.463 | ||||
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 burger 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: 28380.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2517 -0.5192 0.0541 0.5725 3.1078
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 282.9 16.82
## Residual 375.3 19.37
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.26347 1.26555 3075.95046 42.087 < 2e-16 ***
## ATNS_Score.c -0.10266 0.06001 3072.44457 -1.711 0.087269 .
## C1 1.93764 1.62867 2470.46478 1.190 0.234278
## C2 -2.24415 1.75889 2433.21751 -1.276 0.202117
## C3 6.40337 1.75920 2464.70324 3.640 0.000278 ***
## C4 1.88813 1.65076 2471.93450 1.144 0.252821
## C5 -0.42858 1.65039 2490.02902 -0.260 0.795130
## C6 0.67062 1.65601 2471.93501 0.405 0.685544
## C7 13.80576 1.77712 2471.10220 7.769 1.15e-14 ***
## C8 15.30440 1.63602 2466.20404 9.355 < 2e-16 ***
## C9 12.54239 1.76112 2477.82184 7.122 1.39e-12 ***
## ATNS_Score.c:C1 0.03031 0.07679 2478.70795 0.395 0.693060
## ATNS_Score.c:C2 0.09028 0.08212 2425.31790 1.099 0.271716
## ATNS_Score.c:C3 -0.22777 0.08225 2454.32826 -2.769 0.005660 **
## ATNS_Score.c:C4 -0.12849 0.07610 2472.44934 -1.689 0.091436 .
## ATNS_Score.c:C5 -0.03810 0.07688 2496.42855 -0.496 0.620232
## ATNS_Score.c:C6 -0.07631 0.07966 2494.89206 -0.958 0.338201
## ATNS_Score.c:C7 0.09025 0.08316 2479.51818 1.085 0.277895
## ATNS_Score.c:C8 0.05005 0.07694 2458.56897 0.651 0.515415
## ATNS_Score.c:C9 0.01064 0.08398 2508.49644 0.127 0.899148
## ---
## 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) | 53.26 | 1.27 | 50.78 – 55.74 | 42.09 | <0.001 |
| ATNS Score c | -0.10 | 0.06 | -0.22 – 0.02 | -1.71 | 0.087 |
| C1 | 1.94 | 1.63 | -1.26 – 5.13 | 1.19 | 0.234 |
| C2 | -2.24 | 1.76 | -5.69 – 1.20 | -1.28 | 0.202 |
| C3 | 6.40 | 1.76 | 2.95 – 9.85 | 3.64 | <0.001 |
| C4 | 1.89 | 1.65 | -1.35 – 5.12 | 1.14 | 0.253 |
| C5 | -0.43 | 1.65 | -3.66 – 2.81 | -0.26 | 0.795 |
| C6 | 0.67 | 1.66 | -2.58 – 3.92 | 0.40 | 0.686 |
| C7 | 13.81 | 1.78 | 10.32 – 17.29 | 7.77 | <0.001 |
| C8 | 15.30 | 1.64 | 12.10 – 18.51 | 9.35 | <0.001 |
| C9 | 12.54 | 1.76 | 9.09 – 16.00 | 7.12 | <0.001 |
| ATNS Score c * C1 | 0.03 | 0.08 | -0.12 – 0.18 | 0.39 | 0.693 |
| ATNS Score c * C2 | 0.09 | 0.08 | -0.07 – 0.25 | 1.10 | 0.272 |
| ATNS Score c * C3 | -0.23 | 0.08 | -0.39 – -0.07 | -2.77 | 0.006 |
| ATNS Score c * C4 | -0.13 | 0.08 | -0.28 – 0.02 | -1.69 | 0.091 |
| ATNS Score c * C5 | -0.04 | 0.08 | -0.19 – 0.11 | -0.50 | 0.620 |
| ATNS Score c * C6 | -0.08 | 0.08 | -0.23 – 0.08 | -0.96 | 0.338 |
| ATNS Score c * C7 | 0.09 | 0.08 | -0.07 – 0.25 | 1.09 | 0.278 |
| ATNS Score c * C8 | 0.05 | 0.08 | -0.10 – 0.20 | 0.65 | 0.515 |
| ATNS Score c * C9 | 0.01 | 0.08 | -0.15 – 0.18 | 0.13 | 0.899 |
| Random Effects | |||||
| σ2 | 375.31 | ||||
| τ00 id | 282.91 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.069 / 0.469 | ||||
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 burger 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)
summary(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: 28301.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4988 -0.5172 0.0558 0.5599 3.3991
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.8 16.36
## Residual 366.6 19.15
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.360e+01 1.247e+00 3.073e+03 42.987 < 2e-16
## ATNS_Score.c -9.218e-02 5.917e-02 3.070e+03 -1.558 0.11936
## Naturalness.c 2.271e-01 2.357e-02 2.894e+03 9.632 < 2e-16
## C1 4.836e+00 1.637e+00 2.487e+03 2.954 0.00316
## C2 -2.447e+00 1.737e+00 2.434e+03 -1.409 0.15895
## C3 9.435e+00 1.763e+00 2.481e+03 5.350 9.58e-08
## C4 2.814e+00 1.633e+00 2.471e+03 1.723 0.08494
## C5 3.088e-01 1.631e+00 2.491e+03 0.189 0.84985
## C6 2.208e+00 1.642e+00 2.473e+03 1.345 0.17889
## C7 9.854e+00 1.799e+00 2.510e+03 5.477 4.76e-08
## C8 1.000e+01 1.706e+00 2.529e+03 5.863 5.13e-09
## C9 8.997e+00 1.776e+00 2.501e+03 5.065 4.39e-07
## ATNS_Score.c:Naturalness.c 1.461e-03 9.309e-04 2.890e+03 1.570 0.11652
## ATNS_Score.c:C1 3.917e-02 7.688e-02 2.483e+03 0.510 0.61043
## ATNS_Score.c:C2 8.356e-02 8.108e-02 2.426e+03 1.031 0.30287
## ATNS_Score.c:C3 -1.801e-01 8.221e-02 2.467e+03 -2.190 0.02861
## ATNS_Score.c:C4 -9.052e-02 7.531e-02 2.469e+03 -1.202 0.22949
## ATNS_Score.c:C5 -8.036e-03 7.604e-02 2.494e+03 -0.106 0.91584
## ATNS_Score.c:C6 -4.443e-02 7.880e-02 2.491e+03 -0.564 0.57296
## ATNS_Score.c:C7 3.706e-02 8.377e-02 2.519e+03 0.442 0.65826
## ATNS_Score.c:C8 1.185e-02 7.904e-02 2.506e+03 0.150 0.88085
## ATNS_Score.c:C9 -2.063e-02 8.444e-02 2.545e+03 -0.244 0.80700
##
## (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 ittab_model(modA.8715,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.60 | 1.25 | 51.15 – 56.04 | 42.99 | <0.001 |
| ATNS Score c | -0.09 | 0.06 | -0.21 – 0.02 | -1.56 | 0.119 |
| Naturalness c | 0.23 | 0.02 | 0.18 – 0.27 | 9.63 | <0.001 |
| C1 | 4.84 | 1.64 | 1.63 – 8.05 | 2.95 | 0.003 |
| C2 | -2.45 | 1.74 | -5.85 – 0.96 | -1.41 | 0.159 |
| C3 | 9.44 | 1.76 | 5.98 – 12.89 | 5.35 | <0.001 |
| C4 | 2.81 | 1.63 | -0.39 – 6.02 | 1.72 | 0.085 |
| C5 | 0.31 | 1.63 | -2.89 – 3.51 | 0.19 | 0.850 |
| C6 | 2.21 | 1.64 | -1.01 – 5.43 | 1.34 | 0.179 |
| C7 | 9.85 | 1.80 | 6.33 – 13.38 | 5.48 | <0.001 |
| C8 | 10.00 | 1.71 | 6.66 – 13.34 | 5.86 | <0.001 |
| C9 | 9.00 | 1.78 | 5.51 – 12.48 | 5.06 | <0.001 |
|
ATNS Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.57 | 0.117 |
| ATNS Score c * C1 | 0.04 | 0.08 | -0.11 – 0.19 | 0.51 | 0.610 |
| ATNS Score c * C2 | 0.08 | 0.08 | -0.08 – 0.24 | 1.03 | 0.303 |
| ATNS Score c * C3 | -0.18 | 0.08 | -0.34 – -0.02 | -2.19 | 0.029 |
| ATNS Score c * C4 | -0.09 | 0.08 | -0.24 – 0.06 | -1.20 | 0.229 |
| ATNS Score c * C5 | -0.01 | 0.08 | -0.16 – 0.14 | -0.11 | 0.916 |
| ATNS Score c * C6 | -0.04 | 0.08 | -0.20 – 0.11 | -0.56 | 0.573 |
| ATNS Score c * C7 | 0.04 | 0.08 | -0.13 – 0.20 | 0.44 | 0.658 |
| ATNS Score c * C8 | 0.01 | 0.08 | -0.14 – 0.17 | 0.15 | 0.881 |
| ATNS Score c * C9 | -0.02 | 0.08 | -0.19 – 0.14 | -0.24 | 0.807 |
| Random Effects | |||||
| σ2 | 366.58 | ||||
| τ00 id | 267.75 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.094 / 0.476 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict benefit perception, over and above burger 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: 28383
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3185 -0.5144 0.0599 0.5659 3.1878
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 284.4 16.86
## Residual 375.9 19.39
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.28941 1.26711 3076.01365 42.056 < 2e-16 ***
## CNS_Score.c 0.07548 0.07581 3076.89360 0.996 0.319532
## C1 2.00138 1.63052 2470.77505 1.227 0.219770
## C2 -2.20240 1.76379 2432.94439 -1.249 0.211903
## C3 6.24767 1.76120 2465.62738 3.547 0.000396 ***
## C4 2.06903 1.65272 2471.25304 1.252 0.210727
## C5 -0.51240 1.65217 2488.40892 -0.310 0.756484
## C6 0.46797 1.65695 2471.44577 0.282 0.777636
## C7 13.69206 1.77910 2470.68447 7.696 2.01e-14 ***
## C8 15.19604 1.63888 2464.54999 9.272 < 2e-16 ***
## C9 12.44228 1.76361 2477.23342 7.055 2.23e-12 ***
## CNS_Score.c:C1 0.17072 0.09590 2460.53535 1.780 0.075156 .
## CNS_Score.c:C2 0.06593 0.10250 2420.28217 0.643 0.520162
## CNS_Score.c:C3 -0.23306 0.10698 2432.96049 -2.179 0.029460 *
## CNS_Score.c:C4 -0.02636 0.09728 2480.81756 -0.271 0.786428
## CNS_Score.c:C5 -0.09519 0.10137 2505.55186 -0.939 0.347815
## CNS_Score.c:C6 0.04411 0.09859 2456.34057 0.447 0.654627
## CNS_Score.c:C7 0.23075 0.10795 2495.27939 2.138 0.032641 *
## CNS_Score.c:C8 0.10357 0.10110 2486.08465 1.024 0.305702
## CNS_Score.c:C9 0.11948 0.10669 2511.54125 1.120 0.262893
## ---
## 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) | 53.29 | 1.27 | 50.80 – 55.77 | 42.06 | <0.001 |
| CNS Score c | 0.08 | 0.08 | -0.07 – 0.22 | 1.00 | 0.320 |
| C1 | 2.00 | 1.63 | -1.20 – 5.20 | 1.23 | 0.220 |
| C2 | -2.20 | 1.76 | -5.66 – 1.26 | -1.25 | 0.212 |
| C3 | 6.25 | 1.76 | 2.79 – 9.70 | 3.55 | <0.001 |
| C4 | 2.07 | 1.65 | -1.17 – 5.31 | 1.25 | 0.211 |
| C5 | -0.51 | 1.65 | -3.75 – 2.73 | -0.31 | 0.756 |
| C6 | 0.47 | 1.66 | -2.78 – 3.72 | 0.28 | 0.778 |
| C7 | 13.69 | 1.78 | 10.20 – 17.18 | 7.70 | <0.001 |
| C8 | 15.20 | 1.64 | 11.98 – 18.41 | 9.27 | <0.001 |
| C9 | 12.44 | 1.76 | 8.98 – 15.90 | 7.06 | <0.001 |
| CNS Score c * C1 | 0.17 | 0.10 | -0.02 – 0.36 | 1.78 | 0.075 |
| CNS Score c * C2 | 0.07 | 0.10 | -0.14 – 0.27 | 0.64 | 0.520 |
| CNS Score c * C3 | -0.23 | 0.11 | -0.44 – -0.02 | -2.18 | 0.029 |
| CNS Score c * C4 | -0.03 | 0.10 | -0.22 – 0.16 | -0.27 | 0.786 |
| CNS Score c * C5 | -0.10 | 0.10 | -0.29 – 0.10 | -0.94 | 0.348 |
| CNS Score c * C6 | 0.04 | 0.10 | -0.15 – 0.24 | 0.45 | 0.655 |
| CNS Score c * C7 | 0.23 | 0.11 | 0.02 – 0.44 | 2.14 | 0.033 |
| CNS Score c * C8 | 0.10 | 0.10 | -0.09 – 0.30 | 1.02 | 0.306 |
| CNS Score c * C9 | 0.12 | 0.11 | -0.09 – 0.33 | 1.12 | 0.263 |
| Random Effects | |||||
| σ2 | 375.95 | ||||
| τ00 id | 284.40 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.065 / 0.468 | ||||
Q.2 (CONNECTEDNESS TO NATURE) How does connectedness to nature depend on naturalness perception in predicting benefit perception, over and above burger 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: 28298.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6524 -0.5134 0.0564 0.5630 3.2770
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270.5 16.45
## Residual 365.9 19.13
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.360e+01 1.247e+00 3.073e+03 42.972 < 2e-16 ***
## CNS_Score.c 7.406e-02 7.469e-02 3.075e+03 0.992 0.32152
## Naturalness.c 2.353e-01 2.346e-02 2.898e+03 10.031 < 2e-16 ***
## C1 5.050e+00 1.635e+00 2.485e+03 3.088 0.00204 **
## C2 -2.409e+00 1.739e+00 2.434e+03 -1.385 0.16603
## C3 9.403e+00 1.764e+00 2.482e+03 5.331 1.07e-07 ***
## C4 2.916e+00 1.631e+00 2.471e+03 1.787 0.07404 .
## C5 2.338e-01 1.630e+00 2.491e+03 0.143 0.88600
## C6 2.091e+00 1.641e+00 2.473e+03 1.274 0.20278
## C7 9.675e+00 1.798e+00 2.508e+03 5.382 8.06e-08 ***
## C8 9.728e+00 1.705e+00 2.527e+03 5.705 1.30e-08 ***
## C9 8.756e+00 1.776e+00 2.503e+03 4.931 8.72e-07 ***
## CNS_Score.c:Naturalness.c 1.519e-03 1.313e-03 2.939e+03 1.156 0.24762
## CNS_Score.c:C1 2.069e-01 9.642e-02 2.486e+03 2.146 0.03196 *
## CNS_Score.c:C2 6.085e-02 1.011e-01 2.423e+03 0.602 0.54716
## CNS_Score.c:C3 -1.618e-01 1.064e-01 2.441e+03 -1.521 0.12840
## CNS_Score.c:C4 -6.836e-03 9.611e-02 2.478e+03 -0.071 0.94330
## CNS_Score.c:C5 -7.215e-02 1.000e-01 2.505e+03 -0.721 0.47070
## CNS_Score.c:C6 7.845e-02 9.746e-02 2.452e+03 0.805 0.42091
## CNS_Score.c:C7 1.783e-01 1.089e-01 2.527e+03 1.638 0.10152
## CNS_Score.c:C8 6.175e-02 1.057e-01 2.584e+03 0.584 0.55906
## CNS_Score.c:C9 6.313e-02 1.077e-01 2.549e+03 0.586 0.55780
## ---
## 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) | 53.60 | 1.25 | 51.16 – 56.05 | 42.97 | <0.001 |
| CNS Score c | 0.07 | 0.07 | -0.07 – 0.22 | 0.99 | 0.322 |
| Naturalness c | 0.24 | 0.02 | 0.19 – 0.28 | 10.03 | <0.001 |
| C1 | 5.05 | 1.64 | 1.84 – 8.26 | 3.09 | 0.002 |
| C2 | -2.41 | 1.74 | -5.82 – 1.00 | -1.39 | 0.166 |
| C3 | 9.40 | 1.76 | 5.94 – 12.86 | 5.33 | <0.001 |
| C4 | 2.92 | 1.63 | -0.28 – 6.11 | 1.79 | 0.074 |
| C5 | 0.23 | 1.63 | -2.96 – 3.43 | 0.14 | 0.886 |
| C6 | 2.09 | 1.64 | -1.13 – 5.31 | 1.27 | 0.203 |
| C7 | 9.68 | 1.80 | 6.15 – 13.20 | 5.38 | <0.001 |
| C8 | 9.73 | 1.71 | 6.38 – 13.07 | 5.70 | <0.001 |
| C9 | 8.76 | 1.78 | 5.27 – 12.24 | 4.93 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.16 | 0.248 |
| CNS Score c * C1 | 0.21 | 0.10 | 0.02 – 0.40 | 2.15 | 0.032 |
| CNS Score c * C2 | 0.06 | 0.10 | -0.14 – 0.26 | 0.60 | 0.547 |
| CNS Score c * C3 | -0.16 | 0.11 | -0.37 – 0.05 | -1.52 | 0.128 |
| CNS Score c * C4 | -0.01 | 0.10 | -0.20 – 0.18 | -0.07 | 0.943 |
| CNS Score c * C5 | -0.07 | 0.10 | -0.27 – 0.12 | -0.72 | 0.471 |
| CNS Score c * C6 | 0.08 | 0.10 | -0.11 – 0.27 | 0.80 | 0.421 |
| CNS Score c * C7 | 0.18 | 0.11 | -0.04 – 0.39 | 1.64 | 0.101 |
| CNS Score c * C8 | 0.06 | 0.11 | -0.15 – 0.27 | 0.58 | 0.559 |
| CNS Score c * C9 | 0.06 | 0.11 | -0.15 – 0.27 | 0.59 | 0.558 |
| Random Effects | |||||
| σ2 | 365.93 | ||||
| τ00 id | 270.47 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.091 / 0.477 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict benefit perception, over and above burger 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: 28211.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5642 -0.5111 0.0627 0.5664 3.0778
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 223.8 14.96
## Residual 373.8 19.33
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.321e+01 1.233e+00 3.070e+03 43.150 < 2e-16 ***
## CCBelief_Score.c 2.679e-01 5.251e-02 3.068e+03 5.102 3.56e-07 ***
## C1 2.018e+00 1.612e+00 2.534e+03 1.252 0.210857
## C2 -2.178e+00 1.741e+00 2.488e+03 -1.251 0.211181
## C3 6.353e+00 1.742e+00 2.524e+03 3.646 0.000272 ***
## C4 2.325e+00 1.635e+00 2.532e+03 1.422 0.155103
## C5 -7.561e-01 1.634e+00 2.553e+03 -0.463 0.643588
## C6 7.551e-01 1.638e+00 2.534e+03 0.461 0.644817
## C7 1.345e+01 1.759e+00 2.528e+03 7.644 2.96e-14 ***
## C8 1.528e+01 1.621e+00 2.530e+03 9.428 < 2e-16 ***
## C9 1.257e+01 1.742e+00 2.538e+03 7.212 7.23e-13 ***
## CCBelief_Score.c:C1 -8.853e-03 6.823e-02 2.551e+03 -0.130 0.896768
## CCBelief_Score.c:C2 6.324e-02 7.518e-02 2.494e+03 0.841 0.400292
## CCBelief_Score.c:C3 -1.074e-01 7.038e-02 2.501e+03 -1.526 0.127216
## CCBelief_Score.c:C4 2.753e-02 6.706e-02 2.538e+03 0.410 0.681506
## CCBelief_Score.c:C5 1.232e-01 7.090e-02 2.546e+03 1.738 0.082346 .
## CCBelief_Score.c:C6 1.017e-01 6.871e-02 2.508e+03 1.480 0.138945
## CCBelief_Score.c:C7 2.759e-01 7.582e-02 2.514e+03 3.639 0.000279 ***
## CCBelief_Score.c:C8 1.102e-01 7.081e-02 2.566e+03 1.557 0.119616
## CCBelief_Score.c:C9 2.336e-01 7.569e-02 2.587e+03 3.087 0.002047 **
## ---
## 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) | 53.21 | 1.23 | 50.79 – 55.62 | 43.15 | <0.001 |
| CCBelief Score c | 0.27 | 0.05 | 0.16 – 0.37 | 5.10 | <0.001 |
| C1 | 2.02 | 1.61 | -1.14 – 5.18 | 1.25 | 0.211 |
| C2 | -2.18 | 1.74 | -5.59 – 1.24 | -1.25 | 0.211 |
| C3 | 6.35 | 1.74 | 2.94 – 9.77 | 3.65 | <0.001 |
| C4 | 2.33 | 1.64 | -0.88 – 5.53 | 1.42 | 0.155 |
| C5 | -0.76 | 1.63 | -3.96 – 2.45 | -0.46 | 0.644 |
| C6 | 0.76 | 1.64 | -2.46 – 3.97 | 0.46 | 0.645 |
| C7 | 13.45 | 1.76 | 10.00 – 16.90 | 7.64 | <0.001 |
| C8 | 15.28 | 1.62 | 12.10 – 18.46 | 9.43 | <0.001 |
| C9 | 12.57 | 1.74 | 9.15 – 15.98 | 7.21 | <0.001 |
| CCBelief Score c * C1 | -0.01 | 0.07 | -0.14 – 0.12 | -0.13 | 0.897 |
| CCBelief Score c * C2 | 0.06 | 0.08 | -0.08 – 0.21 | 0.84 | 0.400 |
| CCBelief Score c * C3 | -0.11 | 0.07 | -0.25 – 0.03 | -1.53 | 0.127 |
| CCBelief Score c * C4 | 0.03 | 0.07 | -0.10 – 0.16 | 0.41 | 0.681 |
| CCBelief Score c * C5 | 0.12 | 0.07 | -0.02 – 0.26 | 1.74 | 0.082 |
| CCBelief Score c * C6 | 0.10 | 0.07 | -0.03 – 0.24 | 1.48 | 0.139 |
| CCBelief Score c * C7 | 0.28 | 0.08 | 0.13 – 0.42 | 3.64 | <0.001 |
| CCBelief Score c * C8 | 0.11 | 0.07 | -0.03 – 0.25 | 1.56 | 0.120 |
| CCBelief Score c * C9 | 0.23 | 0.08 | 0.09 – 0.38 | 3.09 | 0.002 |
| Random Effects | |||||
| σ2 | 373.80 | ||||
| τ00 id | 223.77 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.154 / 0.471 | ||||
Q.2 (CLIMATE CHANGE BELIEF) How does climate change belief depend on naturalness perception in predicting benefit perception, over and above burger 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)
summary(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: 28123.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5879 -0.5213 0.0626 0.5664 3.4720
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 210.0 14.49
## Residual 364.3 19.09
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.363e+01 1.214e+00 3.067e+03 44.187
## CCBelief_Score.c 2.329e-01 5.198e-02 3.069e+03 4.480
## Naturalness.c 2.243e-01 2.296e-02 2.971e+03 9.771
## C1 4.808e+00 1.618e+00 2.555e+03 2.972
## C2 -2.313e+00 1.717e+00 2.492e+03 -1.347
## C3 9.076e+00 1.746e+00 2.550e+03 5.197
## C4 3.015e+00 1.615e+00 2.537e+03 1.868
## C5 -1.322e-01 1.612e+00 2.560e+03 -0.082
## C6 2.167e+00 1.622e+00 2.540e+03 1.336
## C7 9.691e+00 1.776e+00 2.570e+03 5.456
## C8 9.974e+00 1.684e+00 2.602e+03 5.921
## C9 9.090e+00 1.755e+00 2.568e+03 5.180
## CCBelief_Score.c:Naturalness.c -2.548e-03 8.695e-04 2.988e+03 -2.930
## CCBelief_Score.c:C1 -1.857e-02 6.789e-02 2.546e+03 -0.273
## CCBelief_Score.c:C2 5.757e-02 7.412e-02 2.498e+03 0.777
## CCBelief_Score.c:C3 -9.923e-02 6.992e-02 2.493e+03 -1.419
## CCBelief_Score.c:C4 4.154e-02 6.613e-02 2.543e+03 0.628
## CCBelief_Score.c:C5 1.281e-01 6.990e-02 2.550e+03 1.833
## CCBelief_Score.c:C6 1.096e-01 6.777e-02 2.508e+03 1.617
## CCBelief_Score.c:C7 3.152e-01 7.609e-02 2.592e+03 4.143
## CCBelief_Score.c:C8 1.956e-01 7.441e-02 2.713e+03 2.629
## CCBelief_Score.c:C9 2.719e-01 7.623e-02 2.656e+03 3.567
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 7.73e-06 ***
## Naturalness.c < 2e-16 ***
## C1 0.002986 **
## C2 0.178034
## C3 2.18e-07 ***
## C4 0.061937 .
## C5 0.934651
## C6 0.181621
## C7 5.34e-08 ***
## C8 3.62e-09 ***
## C9 2.39e-07 ***
## CCBelief_Score.c:Naturalness.c 0.003413 **
## CCBelief_Score.c:C1 0.784504
## CCBelief_Score.c:C2 0.437464
## CCBelief_Score.c:C3 0.155943
## CCBelief_Score.c:C4 0.529950
## CCBelief_Score.c:C5 0.066893 .
## CCBelief_Score.c:C6 0.105996
## CCBelief_Score.c:C7 3.54e-05 ***
## CCBelief_Score.c:C8 0.008609 **
## CCBelief_Score.c:C9 0.000367 ***
## ---
## 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.8746,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.63 | 1.21 | 51.25 – 56.01 | 44.19 | <0.001 |
| CCBelief Score c | 0.23 | 0.05 | 0.13 – 0.33 | 4.48 | <0.001 |
| Naturalness c | 0.22 | 0.02 | 0.18 – 0.27 | 9.77 | <0.001 |
| C1 | 4.81 | 1.62 | 1.64 – 7.98 | 2.97 | 0.003 |
| C2 | -2.31 | 1.72 | -5.68 – 1.05 | -1.35 | 0.178 |
| C3 | 9.08 | 1.75 | 5.65 – 12.50 | 5.20 | <0.001 |
| C4 | 3.02 | 1.61 | -0.15 – 6.18 | 1.87 | 0.062 |
| C5 | -0.13 | 1.61 | -3.29 – 3.03 | -0.08 | 0.935 |
| C6 | 2.17 | 1.62 | -1.01 – 5.35 | 1.34 | 0.182 |
| C7 | 9.69 | 1.78 | 6.21 – 13.17 | 5.46 | <0.001 |
| C8 | 9.97 | 1.68 | 6.67 – 13.28 | 5.92 | <0.001 |
| C9 | 9.09 | 1.75 | 5.65 – 12.53 | 5.18 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – -0.00 | -2.93 | 0.003 |
| CCBelief Score c * C1 | -0.02 | 0.07 | -0.15 – 0.11 | -0.27 | 0.785 |
| CCBelief Score c * C2 | 0.06 | 0.07 | -0.09 – 0.20 | 0.78 | 0.437 |
| CCBelief Score c * C3 | -0.10 | 0.07 | -0.24 – 0.04 | -1.42 | 0.156 |
| CCBelief Score c * C4 | 0.04 | 0.07 | -0.09 – 0.17 | 0.63 | 0.530 |
| CCBelief Score c * C5 | 0.13 | 0.07 | -0.01 – 0.27 | 1.83 | 0.067 |
| CCBelief Score c * C6 | 0.11 | 0.07 | -0.02 – 0.24 | 1.62 | 0.106 |
| CCBelief Score c * C7 | 0.32 | 0.08 | 0.17 – 0.46 | 4.14 | <0.001 |
| CCBelief Score c * C8 | 0.20 | 0.07 | 0.05 – 0.34 | 2.63 | 0.009 |
| CCBelief Score c * C9 | 0.27 | 0.08 | 0.12 – 0.42 | 3.57 | <0.001 |
| Random Effects | |||||
| σ2 | 364.29 | ||||
| τ00 id | 209.98 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.180 / 0.480 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict benefit perception, over and above burger 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: 28402.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5304 -0.5137 0.0636 0.5579 3.2779
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 290.0 17.03
## Residual 375.7 19.38
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.316e+01 1.271e+00 3.076e+03 41.844 < 2e-16 ***
## Collectivism_Score.c 1.186e-01 4.976e-02 3.076e+03 2.383 0.017226 *
## C1 2.037e+00 1.632e+00 2.465e+03 1.248 0.212044
## C2 -2.245e+00 1.762e+00 2.429e+03 -1.275 0.202607
## C3 6.253e+00 1.764e+00 2.458e+03 3.544 0.000401 ***
## C4 2.122e+00 1.655e+00 2.467e+03 1.282 0.199873
## C5 -1.164e-01 1.658e+00 2.484e+03 -0.070 0.944030
## C6 6.502e-01 1.658e+00 2.465e+03 0.392 0.694946
## C7 1.377e+01 1.780e+00 2.464e+03 7.736 1.48e-14 ***
## C8 1.550e+01 1.640e+00 2.460e+03 9.449 < 2e-16 ***
## C9 1.286e+01 1.766e+00 2.473e+03 7.286 4.26e-13 ***
## Collectivism_Score.c:C1 -1.786e-02 6.856e-02 2.452e+03 -0.261 0.794466
## Collectivism_Score.c:C2 -2.529e-02 7.158e-02 2.411e+03 -0.353 0.723890
## Collectivism_Score.c:C3 -1.372e-01 6.935e-02 2.408e+03 -1.979 0.047915 *
## Collectivism_Score.c:C4 -3.625e-02 6.687e-02 2.450e+03 -0.542 0.587810
## Collectivism_Score.c:C5 3.826e-03 6.569e-02 2.463e+03 0.058 0.953564
## Collectivism_Score.c:C6 -3.741e-02 6.739e-02 2.458e+03 -0.555 0.578794
## Collectivism_Score.c:C7 -1.325e-01 7.247e-02 2.479e+03 -1.829 0.067558 .
## Collectivism_Score.c:C8 -1.418e-01 6.702e-02 2.477e+03 -2.116 0.034466 *
## Collectivism_Score.c:C9 -2.024e-01 7.366e-02 2.503e+03 -2.747 0.006056 **
## ---
## 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) | 53.16 | 1.27 | 50.67 – 55.66 | 41.84 | <0.001 |
| Collectivism Score c | 0.12 | 0.05 | 0.02 – 0.22 | 2.38 | 0.017 |
| C1 | 2.04 | 1.63 | -1.16 – 5.24 | 1.25 | 0.212 |
| C2 | -2.25 | 1.76 | -5.70 – 1.21 | -1.27 | 0.203 |
| C3 | 6.25 | 1.76 | 2.79 – 9.71 | 3.54 | <0.001 |
| C4 | 2.12 | 1.65 | -1.12 – 5.37 | 1.28 | 0.200 |
| C5 | -0.12 | 1.66 | -3.37 – 3.13 | -0.07 | 0.944 |
| C6 | 0.65 | 1.66 | -2.60 – 3.90 | 0.39 | 0.695 |
| C7 | 13.77 | 1.78 | 10.28 – 17.26 | 7.74 | <0.001 |
| C8 | 15.50 | 1.64 | 12.28 – 18.71 | 9.45 | <0.001 |
| C9 | 12.86 | 1.77 | 9.40 – 16.33 | 7.29 | <0.001 |
| Collectivism Score c * C1 | -0.02 | 0.07 | -0.15 – 0.12 | -0.26 | 0.794 |
| Collectivism Score c * C2 | -0.03 | 0.07 | -0.17 – 0.12 | -0.35 | 0.724 |
| Collectivism Score c * C3 | -0.14 | 0.07 | -0.27 – -0.00 | -1.98 | 0.048 |
| Collectivism Score c * C4 | -0.04 | 0.07 | -0.17 – 0.09 | -0.54 | 0.588 |
| Collectivism Score c * C5 | 0.00 | 0.07 | -0.12 – 0.13 | 0.06 | 0.954 |
| Collectivism Score c * C6 | -0.04 | 0.07 | -0.17 – 0.09 | -0.56 | 0.579 |
| Collectivism Score c * C7 | -0.13 | 0.07 | -0.27 – 0.01 | -1.83 | 0.068 |
| Collectivism Score c * C8 | -0.14 | 0.07 | -0.27 – -0.01 | -2.12 | 0.034 |
| Collectivism Score c * C9 | -0.20 | 0.07 | -0.35 – -0.06 | -2.75 | 0.006 |
| Random Effects | |||||
| σ2 | 375.73 | ||||
| τ00 id | 290.01 | ||||
| ICC | 0.44 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.059 / 0.469 | ||||
Q.2 (COLLECTIVISM) How does collectivism depend on naturalness perception in predicting benefit perception, over and above burger 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)
summary(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: 28311.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5198 -0.5157 0.0629 0.5611 3.2931
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 274.2 16.56
## Residual 365.2 19.11
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.349e+01 1.249e+00 3.074e+03 42.816
## Collectivism_Score.c 1.169e-01 4.898e-02 3.074e+03 2.387
## Naturalness.c 2.440e-01 2.342e-02 2.896e+03 10.418
## C1 5.187e+00 1.636e+00 2.482e+03 3.171
## C2 -2.430e+00 1.736e+00 2.431e+03 -1.400
## C3 9.394e+00 1.764e+00 2.476e+03 5.325
## C4 2.984e+00 1.632e+00 2.469e+03 1.828
## C5 7.047e-01 1.636e+00 2.488e+03 0.431
## C6 2.318e+00 1.641e+00 2.471e+03 1.413
## C7 9.634e+00 1.798e+00 2.502e+03 5.359
## C8 9.845e+00 1.708e+00 2.528e+03 5.764
## C9 9.071e+00 1.777e+00 2.499e+03 5.105
## Collectivism_Score.c:Naturalness.c 1.002e-03 9.199e-04 2.891e+03 1.090
## Collectivism_Score.c:C1 -5.043e-03 6.890e-02 2.468e+03 -0.073
## Collectivism_Score.c:C2 -2.470e-02 7.053e-02 2.414e+03 -0.350
## Collectivism_Score.c:C3 -1.314e-01 6.927e-02 2.428e+03 -1.897
## Collectivism_Score.c:C4 -2.398e-02 6.589e-02 2.451e+03 -0.364
## Collectivism_Score.c:C5 2.883e-02 6.484e-02 2.463e+03 0.445
## Collectivism_Score.c:C6 -1.258e-02 6.662e-02 2.457e+03 -0.189
## Collectivism_Score.c:C7 -1.558e-01 7.363e-02 2.525e+03 -2.116
## Collectivism_Score.c:C8 -1.358e-01 6.965e-02 2.550e+03 -1.950
## Collectivism_Score.c:C9 -2.201e-01 7.460e-02 2.550e+03 -2.951
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.01706 *
## Naturalness.c < 2e-16 ***
## C1 0.00154 **
## C2 0.16163
## C3 1.10e-07 ***
## C4 0.06762 .
## C5 0.66659
## C6 0.15789
## C7 9.11e-08 ***
## C8 9.19e-09 ***
## C9 3.55e-07 ***
## Collectivism_Score.c:Naturalness.c 0.27601
## Collectivism_Score.c:C1 0.94165
## Collectivism_Score.c:C2 0.72620
## Collectivism_Score.c:C3 0.05798 .
## Collectivism_Score.c:C4 0.71597
## Collectivism_Score.c:C5 0.65660
## Collectivism_Score.c:C6 0.85026
## Collectivism_Score.c:C7 0.03446 *
## Collectivism_Score.c:C8 0.05126 .
## Collectivism_Score.c:C9 0.00320 **
## ---
## 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.8766,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.49 | 1.25 | 51.04 – 55.94 | 42.82 | <0.001 |
| Collectivism Score c | 0.12 | 0.05 | 0.02 – 0.21 | 2.39 | 0.017 |
| Naturalness c | 0.24 | 0.02 | 0.20 – 0.29 | 10.42 | <0.001 |
| C1 | 5.19 | 1.64 | 1.98 – 8.39 | 3.17 | 0.002 |
| C2 | -2.43 | 1.74 | -5.83 – 0.97 | -1.40 | 0.162 |
| C3 | 9.39 | 1.76 | 5.94 – 12.85 | 5.33 | <0.001 |
| C4 | 2.98 | 1.63 | -0.22 – 6.18 | 1.83 | 0.068 |
| C5 | 0.70 | 1.64 | -2.50 – 3.91 | 0.43 | 0.667 |
| C6 | 2.32 | 1.64 | -0.90 – 5.53 | 1.41 | 0.158 |
| C7 | 9.63 | 1.80 | 6.11 – 13.16 | 5.36 | <0.001 |
| C8 | 9.85 | 1.71 | 6.50 – 13.19 | 5.76 | <0.001 |
| C9 | 9.07 | 1.78 | 5.59 – 12.55 | 5.11 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.09 | 0.276 |
| Collectivism Score c * C1 | -0.01 | 0.07 | -0.14 – 0.13 | -0.07 | 0.942 |
| Collectivism Score c * C2 | -0.02 | 0.07 | -0.16 – 0.11 | -0.35 | 0.726 |
| Collectivism Score c * C3 | -0.13 | 0.07 | -0.27 – 0.00 | -1.90 | 0.058 |
| Collectivism Score c * C4 | -0.02 | 0.07 | -0.15 – 0.11 | -0.36 | 0.716 |
| Collectivism Score c * C5 | 0.03 | 0.06 | -0.10 – 0.16 | 0.44 | 0.657 |
| Collectivism Score c * C6 | -0.01 | 0.07 | -0.14 – 0.12 | -0.19 | 0.850 |
| Collectivism Score c * C7 | -0.16 | 0.07 | -0.30 – -0.01 | -2.12 | 0.034 |
| Collectivism Score c * C8 | -0.14 | 0.07 | -0.27 – 0.00 | -1.95 | 0.051 |
| Collectivism Score c * C9 | -0.22 | 0.07 | -0.37 – -0.07 | -2.95 | 0.003 |
| Random Effects | |||||
| σ2 | 365.24 | ||||
| τ00 id | 274.25 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.087 / 0.479 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict benefit perception, over and above burger 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: 28395.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2935 -0.5153 0.0657 0.5694 3.2392
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 287.7 16.96
## Residual 376.6 19.41
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.317e+01 1.271e+00 3.076e+03 41.820 < 2e-16 ***
## Individualism_Score.c 1.885e-01 7.310e-02 3.079e+03 2.579 0.009960 **
## C1 2.006e+00 1.634e+00 2.469e+03 1.228 0.219654
## C2 -2.155e+00 1.765e+00 2.431e+03 -1.221 0.222180
## C3 6.689e+00 1.769e+00 2.464e+03 3.782 0.000159 ***
## C4 2.276e+00 1.655e+00 2.468e+03 1.375 0.169218
## C5 -3.223e-01 1.656e+00 2.488e+03 -0.195 0.845688
## C6 5.620e-01 1.660e+00 2.469e+03 0.339 0.734962
## C7 1.387e+01 1.783e+00 2.467e+03 7.779 1.07e-14 ***
## C8 1.537e+01 1.641e+00 2.463e+03 9.365 < 2e-16 ***
## C9 1.265e+01 1.765e+00 2.474e+03 7.164 1.03e-12 ***
## Individualism_Score.c:C1 -1.178e-01 9.461e-02 2.446e+03 -1.245 0.213117
## Individualism_Score.c:C2 -7.596e-02 1.071e-01 2.473e+03 -0.710 0.478046
## Individualism_Score.c:C3 -3.112e-01 9.896e-02 2.414e+03 -3.145 0.001682 **
## Individualism_Score.c:C4 -1.730e-03 9.664e-02 2.459e+03 -0.018 0.985721
## Individualism_Score.c:C5 -4.278e-03 9.720e-02 2.491e+03 -0.044 0.964894
## Individualism_Score.c:C6 -1.934e-01 9.901e-02 2.478e+03 -1.953 0.050945 .
## Individualism_Score.c:C7 -7.689e-02 1.011e-01 2.451e+03 -0.761 0.446995
## Individualism_Score.c:C8 -5.946e-02 9.504e-02 2.467e+03 -0.626 0.531651
## Individualism_Score.c:C9 -9.913e-02 1.080e-01 2.494e+03 -0.918 0.358693
## ---
## 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) | 53.17 | 1.27 | 50.67 – 55.66 | 41.82 | <0.001 |
| Individualism Score c | 0.19 | 0.07 | 0.05 – 0.33 | 2.58 | 0.010 |
| C1 | 2.01 | 1.63 | -1.20 – 5.21 | 1.23 | 0.220 |
| C2 | -2.16 | 1.77 | -5.62 – 1.31 | -1.22 | 0.222 |
| C3 | 6.69 | 1.77 | 3.22 – 10.16 | 3.78 | <0.001 |
| C4 | 2.28 | 1.65 | -0.97 – 5.52 | 1.38 | 0.169 |
| C5 | -0.32 | 1.66 | -3.57 – 2.92 | -0.19 | 0.846 |
| C6 | 0.56 | 1.66 | -2.69 – 3.82 | 0.34 | 0.735 |
| C7 | 13.87 | 1.78 | 10.37 – 17.36 | 7.78 | <0.001 |
| C8 | 15.37 | 1.64 | 12.15 – 18.59 | 9.37 | <0.001 |
| C9 | 12.65 | 1.77 | 9.18 – 16.11 | 7.16 | <0.001 |
|
Individualism Score c * C1 |
-0.12 | 0.09 | -0.30 – 0.07 | -1.25 | 0.213 |
|
Individualism Score c * C2 |
-0.08 | 0.11 | -0.29 – 0.13 | -0.71 | 0.478 |
|
Individualism Score c * C3 |
-0.31 | 0.10 | -0.51 – -0.12 | -3.14 | 0.002 |
|
Individualism Score c * C4 |
-0.00 | 0.10 | -0.19 – 0.19 | -0.02 | 0.986 |
|
Individualism Score c * C5 |
-0.00 | 0.10 | -0.19 – 0.19 | -0.04 | 0.965 |
|
Individualism Score c * C6 |
-0.19 | 0.10 | -0.39 – 0.00 | -1.95 | 0.051 |
|
Individualism Score c * C7 |
-0.08 | 0.10 | -0.28 – 0.12 | -0.76 | 0.447 |
|
Individualism Score c * C8 |
-0.06 | 0.10 | -0.25 – 0.13 | -0.63 | 0.532 |
|
Individualism Score c * C9 |
-0.10 | 0.11 | -0.31 – 0.11 | -0.92 | 0.359 |
| Random Effects | |||||
| σ2 | 376.56 | ||||
| τ00 id | 287.65 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.061 / 0.468 | ||||
Q.2 (INDIVIDUALISM) How does individualism depend on naturalness perception in predicting benefit perception, over and above burger 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: 28304.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4940 -0.5186 0.0466 0.5651 3.2979
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 272.2 16.50
## Residual 366.2 19.14
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.351e+01 1.251e+00 3.073e+03 42.787
## Individualism_Score.c 1.813e-01 7.192e-02 3.077e+03 2.521
## Naturalness.c 2.422e-01 2.354e-02 2.895e+03 10.288
## C1 5.122e+00 1.638e+00 2.486e+03 3.128
## C2 -2.341e+00 1.739e+00 2.434e+03 -1.346
## C3 9.850e+00 1.769e+00 2.480e+03 5.568
## C4 3.130e+00 1.633e+00 2.470e+03 1.917
## C5 4.230e-01 1.633e+00 2.492e+03 0.259
## C6 2.191e+00 1.643e+00 2.472e+03 1.334
## C7 9.768e+00 1.802e+00 2.505e+03 5.422
## C8 9.726e+00 1.708e+00 2.526e+03 5.693
## C9 8.860e+00 1.777e+00 2.500e+03 4.985
## Individualism_Score.c:Naturalness.c 9.055e-04 1.331e-03 2.939e+03 0.680
## Individualism_Score.c:C1 -1.027e-01 9.470e-02 2.454e+03 -1.085
## Individualism_Score.c:C2 -5.962e-02 1.055e-01 2.478e+03 -0.565
## Individualism_Score.c:C3 -2.853e-01 9.906e-02 2.425e+03 -2.880
## Individualism_Score.c:C4 2.296e-02 9.536e-02 2.458e+03 0.241
## Individualism_Score.c:C5 3.716e-02 9.594e-02 2.490e+03 0.387
## Individualism_Score.c:C6 -1.571e-01 9.799e-02 2.477e+03 -1.603
## Individualism_Score.c:C7 -8.356e-02 1.032e-01 2.510e+03 -0.810
## Individualism_Score.c:C8 -7.275e-02 9.926e-02 2.549e+03 -0.733
## Individualism_Score.c:C9 -1.183e-01 1.085e-01 2.526e+03 -1.091
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.01176 *
## Naturalness.c < 2e-16 ***
## C1 0.00178 **
## C2 0.17847
## C3 2.85e-08 ***
## C4 0.05536 .
## C5 0.79565
## C6 0.18238
## C7 6.45e-08 ***
## C8 1.39e-08 ***
## C9 6.61e-07 ***
## Individualism_Score.c:Naturalness.c 0.49631
## Individualism_Score.c:C1 0.27808
## Individualism_Score.c:C2 0.57208
## Individualism_Score.c:C3 0.00402 **
## Individualism_Score.c:C4 0.80975
## Individualism_Score.c:C5 0.69858
## Individualism_Score.c:C6 0.10903
## Individualism_Score.c:C7 0.41820
## Individualism_Score.c:C8 0.46368
## Individualism_Score.c:C9 0.27542
## ---
## 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) | 53.51 | 1.25 | 51.05 – 55.96 | 42.79 | <0.001 |
| Individualism Score c | 0.18 | 0.07 | 0.04 – 0.32 | 2.52 | 0.012 |
| Naturalness c | 0.24 | 0.02 | 0.20 – 0.29 | 10.29 | <0.001 |
| C1 | 5.12 | 1.64 | 1.91 – 8.33 | 3.13 | 0.002 |
| C2 | -2.34 | 1.74 | -5.75 – 1.07 | -1.35 | 0.178 |
| C3 | 9.85 | 1.77 | 6.38 – 13.32 | 5.57 | <0.001 |
| C4 | 3.13 | 1.63 | -0.07 – 6.33 | 1.92 | 0.055 |
| C5 | 0.42 | 1.63 | -2.78 – 3.63 | 0.26 | 0.796 |
| C6 | 2.19 | 1.64 | -1.03 – 5.41 | 1.33 | 0.182 |
| C7 | 9.77 | 1.80 | 6.24 – 13.30 | 5.42 | <0.001 |
| C8 | 9.73 | 1.71 | 6.38 – 13.08 | 5.69 | <0.001 |
| C9 | 8.86 | 1.78 | 5.38 – 12.34 | 4.99 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.68 | 0.496 |
|
Individualism Score c * C1 |
-0.10 | 0.09 | -0.29 – 0.08 | -1.08 | 0.278 |
|
Individualism Score c * C2 |
-0.06 | 0.11 | -0.27 – 0.15 | -0.57 | 0.572 |
|
Individualism Score c * C3 |
-0.29 | 0.10 | -0.48 – -0.09 | -2.88 | 0.004 |
|
Individualism Score c * C4 |
0.02 | 0.10 | -0.16 – 0.21 | 0.24 | 0.810 |
|
Individualism Score c * C5 |
0.04 | 0.10 | -0.15 – 0.23 | 0.39 | 0.699 |
|
Individualism Score c * C6 |
-0.16 | 0.10 | -0.35 – 0.04 | -1.60 | 0.109 |
|
Individualism Score c * C7 |
-0.08 | 0.10 | -0.29 – 0.12 | -0.81 | 0.418 |
|
Individualism Score c * C8 |
-0.07 | 0.10 | -0.27 – 0.12 | -0.73 | 0.464 |
|
Individualism Score c * C9 |
-0.12 | 0.11 | -0.33 – 0.09 | -1.09 | 0.275 |
| Random Effects | |||||
| σ2 | 366.17 | ||||
| τ00 id | 272.25 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.088 / 0.477 | ||||
Political Ideology
Q.1 (POLITICAL ORIENTATION) How does political ideology predict benefit perception, over and above burger 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: 28339.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4483 -0.5138 0.0590 0.5715 3.1841
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 289.0 17.00
## Residual 377.9 19.44
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 53.2345 1.2720 3076.3606 41.851 < 2e-16 ***
## Ideology.c -3.9022 2.1349 3078.7384 -1.828 0.067675 .
## C1 1.9326 1.6355 2469.3516 1.182 0.237459
## C2 -2.2482 1.7646 2432.4210 -1.274 0.202764
## C3 6.2866 1.7661 2464.2547 3.560 0.000379 ***
## C4 2.1629 1.6563 2469.1337 1.306 0.191707
## C5 -0.3374 1.6598 2487.4967 -0.203 0.838924
## C6 0.5391 1.6623 2471.8772 0.324 0.745750
## C7 13.7295 1.7838 2467.8127 7.697 2.0e-14 ***
## C8 15.3943 1.6469 2467.9445 9.348 < 2e-16 ***
## C9 12.6051 1.7675 2476.3847 7.132 1.3e-12 ***
## Ideology.c:C1 3.8965 2.7739 2438.3025 1.405 0.160229
## Ideology.c:C2 1.7752 2.9313 2382.8518 0.606 0.544828
## Ideology.c:C3 8.3186 3.0685 2466.6597 2.711 0.006754 **
## Ideology.c:C4 2.0512 2.8824 2493.1478 0.712 0.476761
## Ideology.c:C5 2.2857 2.8495 2539.3477 0.802 0.422536
## Ideology.c:C6 3.1636 2.8797 2478.3174 1.099 0.272058
## Ideology.c:C7 5.3134 3.0090 2518.7281 1.766 0.077549 .
## Ideology.c:C8 4.3633 2.7990 2449.8562 1.559 0.119158
## Ideology.c:C9 6.1618 3.1371 2487.4574 1.964 0.049620 *
## ---
## 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) | 53.23 | 1.27 | 50.74 – 55.73 | 41.85 | <0.001 |
| Ideology c | -3.90 | 2.13 | -8.09 – 0.28 | -1.83 | 0.068 |
| C1 | 1.93 | 1.64 | -1.27 – 5.14 | 1.18 | 0.237 |
| C2 | -2.25 | 1.76 | -5.71 – 1.21 | -1.27 | 0.203 |
| C3 | 6.29 | 1.77 | 2.82 – 9.75 | 3.56 | <0.001 |
| C4 | 2.16 | 1.66 | -1.08 – 5.41 | 1.31 | 0.192 |
| C5 | -0.34 | 1.66 | -3.59 – 2.92 | -0.20 | 0.839 |
| C6 | 0.54 | 1.66 | -2.72 – 3.80 | 0.32 | 0.746 |
| C7 | 13.73 | 1.78 | 10.23 – 17.23 | 7.70 | <0.001 |
| C8 | 15.39 | 1.65 | 12.17 – 18.62 | 9.35 | <0.001 |
| C9 | 12.61 | 1.77 | 9.14 – 16.07 | 7.13 | <0.001 |
| Ideology c * C1 | 3.90 | 2.77 | -1.54 – 9.34 | 1.40 | 0.160 |
| Ideology c * C2 | 1.78 | 2.93 | -3.97 – 7.52 | 0.61 | 0.545 |
| Ideology c * C3 | 8.32 | 3.07 | 2.30 – 14.34 | 2.71 | 0.007 |
| Ideology c * C4 | 2.05 | 2.88 | -3.60 – 7.70 | 0.71 | 0.477 |
| Ideology c * C5 | 2.29 | 2.85 | -3.30 – 7.87 | 0.80 | 0.423 |
| Ideology c * C6 | 3.16 | 2.88 | -2.48 – 8.81 | 1.10 | 0.272 |
| Ideology c * C7 | 5.31 | 3.01 | -0.59 – 11.21 | 1.77 | 0.078 |
| Ideology c * C8 | 4.36 | 2.80 | -1.12 – 9.85 | 1.56 | 0.119 |
| Ideology c * C9 | 6.16 | 3.14 | 0.01 – 12.31 | 1.96 | 0.050 |
| Random Effects | |||||
| σ2 | 377.88 | ||||
| τ00 id | 289.02 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.056 / 0.465 | ||||
Q.2 (POLITICAL ORIENTATION) How does political ideology depend on naturalness perception in predicting benefit perception, over and above burger 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: 28262.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5068 -0.5151 0.0534 0.5610 3.2928
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 273.9 16.55
## Residual 367.6 19.17
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 43.90473 1.54687 3040.11677 28.383 < 2e-16 ***
## Ideology.c -1.14684 2.19192 3055.33274 -0.523 0.60087
## Naturalness 0.24306 0.02353 2902.21313 10.330 < 2e-16 ***
## C1 5.02278 1.64046 2489.60790 3.062 0.00222 **
## C2 -2.50697 1.73880 2437.60047 -1.442 0.14949
## C3 9.43929 1.76651 2484.26613 5.343 9.95e-08 ***
## C4 2.94621 1.63374 2473.66349 1.803 0.07145 .
## C5 0.36389 1.63724 2494.81094 0.222 0.82413
## C6 2.07318 1.64382 2476.09265 1.261 0.20736
## C7 9.59908 1.80174 2510.19853 5.328 1.08e-07 ***
## C8 9.63885 1.70823 2532.39610 5.643 1.86e-08 ***
## C9 8.76169 1.77976 2506.64787 4.923 9.08e-07 ***
## Ideology.c:Naturalness 0.02043 0.03728 2783.69564 0.548 0.58360
## Ideology.c:C1 0.27946 2.26923 2453.94004 0.123 0.90200
## Ideology.c:C2 -1.47001 2.46317 2497.86473 -0.597 0.55070
## Ideology.c:C3 6.27311 2.68284 2555.93123 2.338 0.01945 *
## Ideology.c:C4 -1.05943 2.33027 2541.63172 -0.455 0.64941
## Ideology.c:C5 -1.81166 2.23267 2510.29251 -0.811 0.41719
## ---
## 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) | 43.90 | 1.55 | 40.87 – 46.94 | 28.38 | <0.001 |
| Ideology c | -1.15 | 2.19 | -5.44 – 3.15 | -0.52 | 0.601 |
| Naturalness | 0.24 | 0.02 | 0.20 – 0.29 | 10.33 | <0.001 |
| C1 | 5.02 | 1.64 | 1.81 – 8.24 | 3.06 | 0.002 |
| C2 | -2.51 | 1.74 | -5.92 – 0.90 | -1.44 | 0.149 |
| C3 | 9.44 | 1.77 | 5.98 – 12.90 | 5.34 | <0.001 |
| C4 | 2.95 | 1.63 | -0.26 – 6.15 | 1.80 | 0.071 |
| C5 | 0.36 | 1.64 | -2.85 – 3.57 | 0.22 | 0.824 |
| C6 | 2.07 | 1.64 | -1.15 – 5.30 | 1.26 | 0.207 |
| C7 | 9.60 | 1.80 | 6.07 – 13.13 | 5.33 | <0.001 |
| C8 | 9.64 | 1.71 | 6.29 – 12.99 | 5.64 | <0.001 |
| C9 | 8.76 | 1.78 | 5.27 – 12.25 | 4.92 | <0.001 |
| Ideology c * Naturalness | 0.02 | 0.04 | -0.05 – 0.09 | 0.55 | 0.584 |
| Ideology c * C1 | 0.28 | 2.27 | -4.17 – 4.73 | 0.12 | 0.902 |
| Ideology c * C2 | -1.47 | 2.46 | -6.30 – 3.36 | -0.60 | 0.551 |
| Ideology c * C3 | 6.27 | 2.68 | 1.01 – 11.53 | 2.34 | 0.019 |
| Ideology c * C4 | -1.06 | 2.33 | -5.63 – 3.51 | -0.45 | 0.649 |
| Ideology c * C5 | -1.81 | 2.23 | -6.19 – 2.57 | -0.81 | 0.417 |
| Random Effects | |||||
| σ2 | 367.58 | ||||
| τ00 id | 273.89 | ||||
| ICC | 0.43 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.082 / 0.474 | ||||
Difference Benefit - Risk
Q.1 How do burger 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: 31282.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9132 -0.5398 0.0435 0.5736 3.1191
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 589.9 24.29
## Residual 1029.7 32.09
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.318 2.037 3078.449 8.992 < 2e-16 ***
## C1 -6.048 2.671 2553.236 -2.264 0.0236 *
## C2 6.112 2.885 2506.924 2.119 0.0342 *
## C3 -12.037 2.885 2541.897 -4.172 3.12e-05 ***
## C4 -1.060 2.705 2553.147 -0.392 0.6953
## C5 -4.087 2.705 2573.268 -1.511 0.1309
## C6 -10.680 2.713 2553.934 -3.936 8.49e-05 ***
## C7 37.559 2.914 2547.501 12.890 < 2e-16 ***
## C8 34.209 2.683 2547.793 12.750 < 2e-16 ***
## C9 30.287 2.887 2557.379 10.493 < 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 -0.676
## C2 -0.601 0.472
## C3 -0.610 0.478 0.393
## C4 -0.667 0.509 0.467 0.469
## C5 -0.672 0.515 0.469 0.477 0.508
## C6 -0.665 0.506 0.458 0.479 0.499 0.503
## C7 -0.605 0.472 0.390 0.396 0.468 0.471 0.462
## C8 -0.672 0.512 0.468 0.467 0.503 0.509 0.505 0.477
## C9 -0.614 0.485 0.396 0.402 0.475 0.476 0.473 0.398 0.480tab_model(modA.910,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 18.32 | 2.04 | 14.32 – 22.31 | 8.99 | <0.001 |
| C1 | -6.05 | 2.67 | -11.28 – -0.81 | -2.26 | 0.024 |
| C2 | 6.11 | 2.88 | 0.46 – 11.77 | 2.12 | 0.034 |
| C3 | -12.04 | 2.88 | -17.69 – -6.38 | -4.17 | <0.001 |
| C4 | -1.06 | 2.71 | -6.36 – 4.24 | -0.39 | 0.695 |
| C5 | -4.09 | 2.70 | -9.39 – 1.22 | -1.51 | 0.131 |
| C6 | -10.68 | 2.71 | -16.00 – -5.36 | -3.94 | <0.001 |
| C7 | 37.56 | 2.91 | 31.85 – 43.27 | 12.89 | <0.001 |
| C8 | 34.21 | 2.68 | 28.95 – 39.47 | 12.75 | <0.001 |
| C9 | 30.29 | 2.89 | 24.63 – 35.95 | 10.49 | <0.001 |
| Random Effects | |||||
| σ2 | 1029.74 | ||||
| τ00 id | 589.87 | ||||
| ICC | 0.36 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.468 | ||||
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 burger 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: 31172.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1075 -0.5493 0.0386 0.5796 3.4080
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 519.7 22.80
## Residual 1009.9 31.78
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.842e+01 1.997e+00 3.066e+03 9.223 < 2e-16 ***
## ATNS_Score.c -4.169e-01 9.479e-02 3.061e+03 -4.398 1.13e-05 ***
## C1 -6.113e+00 2.635e+00 2.572e+03 -2.320 0.02041 *
## C2 6.080e+00 2.849e+00 2.523e+03 2.134 0.03292 *
## C3 -1.185e+01 2.846e+00 2.559e+03 -4.165 3.22e-05 ***
## C4 -1.842e+00 2.670e+00 2.574e+03 -0.690 0.49035
## C5 -4.174e+00 2.668e+00 2.595e+03 -1.565 0.11781
## C6 -1.044e+01 2.679e+00 2.574e+03 -3.896 0.00010 ***
## C7 3.760e+01 2.875e+00 2.567e+03 13.079 < 2e-16 ***
## C8 3.402e+01 2.647e+00 2.567e+03 12.852 < 2e-16 ***
## C9 2.995e+01 2.848e+00 2.575e+03 10.516 < 2e-16 ***
## ATNS_Score.c:C1 1.184e-04 1.242e-01 2.581e+03 0.001 0.99924
## ATNS_Score.c:C2 1.924e-01 1.330e-01 2.513e+03 1.446 0.14834
## ATNS_Score.c:C3 -3.093e-01 1.331e-01 2.547e+03 -2.323 0.02024 *
## ATNS_Score.c:C4 -2.881e-01 1.231e-01 2.574e+03 -2.341 0.01933 *
## ATNS_Score.c:C5 -1.116e-01 1.243e-01 2.602e+03 -0.899 0.36900
## ATNS_Score.c:C6 -4.456e-02 1.288e-01 2.600e+03 -0.346 0.72930
## ATNS_Score.c:C7 3.476e-01 1.345e-01 2.576e+03 2.584 0.00981 **
## ATNS_Score.c:C8 1.612e-01 1.245e-01 2.558e+03 1.295 0.19555
## ATNS_Score.c:C9 2.409e-01 1.357e-01 2.610e+03 1.776 0.07590 .
## ---
## 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) | 18.42 | 2.00 | 14.51 – 22.34 | 9.22 | <0.001 |
| ATNS Score c | -0.42 | 0.09 | -0.60 – -0.23 | -4.40 | <0.001 |
| C1 | -6.11 | 2.63 | -11.28 – -0.95 | -2.32 | 0.020 |
| C2 | 6.08 | 2.85 | 0.49 – 11.67 | 2.13 | 0.033 |
| C3 | -11.85 | 2.85 | -17.44 – -6.27 | -4.16 | <0.001 |
| C4 | -1.84 | 2.67 | -7.08 – 3.39 | -0.69 | 0.490 |
| C5 | -4.17 | 2.67 | -9.41 – 1.06 | -1.56 | 0.118 |
| C6 | -10.44 | 2.68 | -15.69 – -5.18 | -3.90 | <0.001 |
| C7 | 37.60 | 2.87 | 31.96 – 43.24 | 13.08 | <0.001 |
| C8 | 34.02 | 2.65 | 28.83 – 39.21 | 12.85 | <0.001 |
| C9 | 29.95 | 2.85 | 24.37 – 35.54 | 10.52 | <0.001 |
| ATNS Score c * C1 | 0.00 | 0.12 | -0.24 – 0.24 | 0.00 | 0.999 |
| ATNS Score c * C2 | 0.19 | 0.13 | -0.07 – 0.45 | 1.45 | 0.148 |
| ATNS Score c * C3 | -0.31 | 0.13 | -0.57 – -0.05 | -2.32 | 0.020 |
| ATNS Score c * C4 | -0.29 | 0.12 | -0.53 – -0.05 | -2.34 | 0.019 |
| ATNS Score c * C5 | -0.11 | 0.12 | -0.36 – 0.13 | -0.90 | 0.369 |
| ATNS Score c * C6 | -0.04 | 0.13 | -0.30 – 0.21 | -0.35 | 0.729 |
| ATNS Score c * C7 | 0.35 | 0.13 | 0.08 – 0.61 | 2.58 | 0.010 |
| ATNS Score c * C8 | 0.16 | 0.12 | -0.08 – 0.41 | 1.29 | 0.196 |
| ATNS Score c * C9 | 0.24 | 0.14 | -0.03 – 0.51 | 1.78 | 0.076 |
| Random Effects | |||||
| σ2 | 1009.86 | ||||
| τ00 id | 519.72 | ||||
| ICC | 0.34 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.213 / 0.480 | ||||
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 burger 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)
summary(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: 30828
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0294 -0.5419 0.0292 0.5884 3.1934
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 465.9 21.59
## Residual 897.2 29.95
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.938e+01 1.885e+00 3.064e+03 10.283 < 2e-16
## ATNS_Score.c -3.819e-01 8.950e-02 3.060e+03 -4.268 2.04e-05
## Naturalness.c 6.642e-01 3.593e-02 2.994e+03 18.485 < 2e-16
## C1 2.344e+00 2.528e+00 2.582e+03 0.927 0.3538
## C2 5.444e+00 2.686e+00 2.516e+03 2.026 0.0428
## C3 -2.857e+00 2.724e+00 2.568e+03 -1.049 0.2945
## C4 1.145e+00 2.523e+00 2.564e+03 0.454 0.6500
## C5 -1.822e+00 2.519e+00 2.587e+03 -0.723 0.4696
## C6 -5.774e+00 2.537e+00 2.566e+03 -2.276 0.0229
## C7 2.606e+01 2.777e+00 2.601e+03 9.383 < 2e-16
## C8 1.856e+01 2.631e+00 2.626e+03 7.055 2.20e-12
## C9 1.965e+01 2.742e+00 2.592e+03 7.165 1.01e-12
## ATNS_Score.c:Naturalness.c 6.766e-03 1.419e-03 2.991e+03 4.768 1.95e-06
## ATNS_Score.c:C1 6.274e-02 1.187e-01 2.576e+03 0.528 0.5973
## ATNS_Score.c:C2 1.724e-01 1.254e-01 2.506e+03 1.374 0.1695
## ATNS_Score.c:C3 -1.358e-01 1.270e-01 2.553e+03 -1.069 0.2851
## ATNS_Score.c:C4 -1.630e-01 1.164e-01 2.561e+03 -1.400 0.1615
## ATNS_Score.c:C5 -9.886e-03 1.174e-01 2.591e+03 -0.084 0.9329
## ATNS_Score.c:C6 6.754e-02 1.217e-01 2.586e+03 0.555 0.5789
## ATNS_Score.c:C7 1.507e-01 1.293e-01 2.610e+03 1.166 0.2438
## ATNS_Score.c:C8 -7.320e-03 1.220e-01 2.601e+03 -0.060 0.9522
## ATNS_Score.c:C9 1.127e-01 1.302e-01 2.639e+03 0.865 0.3870
##
## (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 ittab_model(modA.9114,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 19.38 | 1.88 | 15.69 – 23.08 | 10.28 | <0.001 |
| ATNS Score c | -0.38 | 0.09 | -0.56 – -0.21 | -4.27 | <0.001 |
| Naturalness c | 0.66 | 0.04 | 0.59 – 0.73 | 18.48 | <0.001 |
| C1 | 2.34 | 2.53 | -2.61 – 7.30 | 0.93 | 0.354 |
| C2 | 5.44 | 2.69 | 0.18 – 10.71 | 2.03 | 0.043 |
| C3 | -2.86 | 2.72 | -8.20 – 2.48 | -1.05 | 0.294 |
| C4 | 1.14 | 2.52 | -3.80 – 6.09 | 0.45 | 0.650 |
| C5 | -1.82 | 2.52 | -6.76 – 3.12 | -0.72 | 0.470 |
| C6 | -5.77 | 2.54 | -10.75 – -0.80 | -2.28 | 0.023 |
| C7 | 26.06 | 2.78 | 20.61 – 31.50 | 9.38 | <0.001 |
| C8 | 18.56 | 2.63 | 13.40 – 23.72 | 7.06 | <0.001 |
| C9 | 19.65 | 2.74 | 14.27 – 25.03 | 7.17 | <0.001 |
|
ATNS Score c * Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 4.77 | <0.001 |
| ATNS Score c * C1 | 0.06 | 0.12 | -0.17 – 0.30 | 0.53 | 0.597 |
| ATNS Score c * C2 | 0.17 | 0.13 | -0.07 – 0.42 | 1.37 | 0.169 |
| ATNS Score c * C3 | -0.14 | 0.13 | -0.38 – 0.11 | -1.07 | 0.285 |
| ATNS Score c * C4 | -0.16 | 0.12 | -0.39 – 0.07 | -1.40 | 0.161 |
| ATNS Score c * C5 | -0.01 | 0.12 | -0.24 – 0.22 | -0.08 | 0.933 |
| ATNS Score c * C6 | 0.07 | 0.12 | -0.17 – 0.31 | 0.56 | 0.579 |
| ATNS Score c * C7 | 0.15 | 0.13 | -0.10 – 0.40 | 1.17 | 0.244 |
| ATNS Score c * C8 | -0.01 | 0.12 | -0.25 – 0.23 | -0.06 | 0.952 |
| ATNS Score c * C9 | 0.11 | 0.13 | -0.14 – 0.37 | 0.87 | 0.387 |
| Random Effects | |||||
| σ2 | 897.22 | ||||
| τ00 id | 465.91 | ||||
| ICC | 0.34 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.293 / 0.535 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict the benefit-risk difference, over and above burger 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: 31258.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3377 -0.5396 0.0339 0.5751 3.2496
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 582.1 24.13
## Residual 1017.5 31.90
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.33700 2.02514 3068.19713 9.055 < 2e-16 ***
## CNS_Score.c 0.05276 0.12114 3069.82526 0.435 0.66323
## C1 -5.85715 2.65547 2544.32399 -2.206 0.02749 *
## C2 6.18443 2.87521 2497.52032 2.151 0.03158 *
## C3 -11.95391 2.86880 2533.76239 -4.167 3.19e-05 ***
## C4 -1.19882 2.69159 2544.82786 -0.445 0.65607
## C5 -4.21313 2.68959 2564.33313 -1.566 0.11737
## C6 -10.80225 2.69847 2544.90438 -4.003 6.43e-05 ***
## C7 37.52857 2.89760 2539.77735 12.952 < 2e-16 ***
## C8 33.91791 2.66948 2537.35178 12.706 < 2e-16 ***
## C9 29.86713 2.87191 2547.21347 10.400 < 2e-16 ***
## CNS_Score.c:C1 0.22489 0.15622 2531.97868 1.440 0.15011
## CNS_Score.c:C2 0.06786 0.16714 2481.92765 0.406 0.68476
## CNS_Score.c:C3 -0.50506 0.17439 2497.03597 -2.896 0.00381 **
## CNS_Score.c:C4 -0.16800 0.15840 2554.93757 -1.061 0.28898
## CNS_Score.c:C5 -0.12035 0.16496 2583.25351 -0.730 0.46570
## CNS_Score.c:C6 -0.02266 0.16063 2527.47601 -0.141 0.88783
## CNS_Score.c:C7 0.43562 0.17571 2567.11685 2.479 0.01323 *
## CNS_Score.c:C8 0.26426 0.16459 2560.71281 1.606 0.10850
## CNS_Score.c:C9 0.42349 0.17360 2585.06140 2.439 0.01477 *
## ---
## 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) | 18.34 | 2.03 | 14.37 – 22.31 | 9.05 | <0.001 |
| CNS Score c | 0.05 | 0.12 | -0.18 – 0.29 | 0.44 | 0.663 |
| C1 | -5.86 | 2.66 | -11.06 – -0.65 | -2.21 | 0.027 |
| C2 | 6.18 | 2.88 | 0.55 – 11.82 | 2.15 | 0.032 |
| C3 | -11.95 | 2.87 | -17.58 – -6.33 | -4.17 | <0.001 |
| C4 | -1.20 | 2.69 | -6.48 – 4.08 | -0.45 | 0.656 |
| C5 | -4.21 | 2.69 | -9.49 – 1.06 | -1.57 | 0.117 |
| C6 | -10.80 | 2.70 | -16.09 – -5.51 | -4.00 | <0.001 |
| C7 | 37.53 | 2.90 | 31.85 – 43.21 | 12.95 | <0.001 |
| C8 | 33.92 | 2.67 | 28.68 – 39.15 | 12.71 | <0.001 |
| C9 | 29.87 | 2.87 | 24.24 – 35.50 | 10.40 | <0.001 |
| CNS Score c * C1 | 0.22 | 0.16 | -0.08 – 0.53 | 1.44 | 0.150 |
| CNS Score c * C2 | 0.07 | 0.17 | -0.26 – 0.40 | 0.41 | 0.685 |
| CNS Score c * C3 | -0.51 | 0.17 | -0.85 – -0.16 | -2.90 | 0.004 |
| CNS Score c * C4 | -0.17 | 0.16 | -0.48 – 0.14 | -1.06 | 0.289 |
| CNS Score c * C5 | -0.12 | 0.16 | -0.44 – 0.20 | -0.73 | 0.466 |
| CNS Score c * C6 | -0.02 | 0.16 | -0.34 – 0.29 | -0.14 | 0.888 |
| CNS Score c * C7 | 0.44 | 0.18 | 0.09 – 0.78 | 2.48 | 0.013 |
| CNS Score c * C8 | 0.26 | 0.16 | -0.06 – 0.59 | 1.61 | 0.108 |
| CNS Score c * C9 | 0.42 | 0.17 | 0.08 – 0.76 | 2.44 | 0.015 |
| Random Effects | |||||
| σ2 | 1017.55 | ||||
| τ00 id | 582.15 | ||||
| ICC | 0.36 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.175 / 0.475 | ||||
Q.2 (CONNECTEDNESS TO NATURE) How does connectedness to nature depend on naturalness perception in predicting the benefit-risk difference, over and above burger 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: 30919.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3731 -0.5294 0.0164 0.5731 3.0596
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 533.3 23.09
## Residual 901.1 30.02
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.922e+01 1.913e+00 3.067e+03 10.049 < 2e-16 ***
## CNS_Score.c 4.781e-02 1.145e-01 3.069e+03 0.418 0.6763
## Naturalness.c 6.933e-01 3.619e-02 2.967e+03 19.155 < 2e-16 ***
## C1 3.140e+00 2.545e+00 2.546e+03 1.234 0.2174
## C2 5.551e+00 2.709e+00 2.487e+03 2.049 0.0405 *
## C3 -2.700e+00 2.745e+00 2.539e+03 -0.983 0.3255
## C4 1.370e+00 2.540e+00 2.531e+03 0.539 0.5897
## C5 -1.977e+00 2.537e+00 2.553e+03 -0.779 0.4358
## C6 -6.005e+00 2.555e+00 2.533e+03 -2.351 0.0188 *
## C7 2.582e+01 2.797e+00 2.567e+03 9.234 < 2e-16 ***
## C8 1.788e+01 2.652e+00 2.590e+03 6.743 1.91e-11 ***
## C9 1.913e+01 2.763e+00 2.561e+03 6.924 5.52e-12 ***
## CNS_Score.c:Naturalness.c 3.935e-03 2.024e-03 3.001e+03 1.944 0.0520 .
## CNS_Score.c:C1 3.213e-01 1.501e-01 2.546e+03 2.141 0.0324 *
## CNS_Score.c:C2 5.611e-02 1.575e-01 2.474e+03 0.356 0.7216
## CNS_Score.c:C3 -3.008e-01 1.657e-01 2.494e+03 -1.815 0.0696 .
## CNS_Score.c:C4 -1.125e-01 1.496e-01 2.538e+03 -0.752 0.4521
## CNS_Score.c:C5 -5.659e-02 1.556e-01 2.568e+03 -0.364 0.7161
## CNS_Score.c:C6 7.641e-02 1.518e-01 2.509e+03 0.503 0.6147
## CNS_Score.c:C7 2.893e-01 1.693e-01 2.588e+03 1.709 0.0876 .
## CNS_Score.c:C8 1.549e-01 1.642e-01 2.650e+03 0.944 0.3453
## CNS_Score.c:C9 2.624e-01 1.674e-01 2.611e+03 1.567 0.1172
## ---
## 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) | 19.22 | 1.91 | 15.47 – 22.97 | 10.05 | <0.001 |
| CNS Score c | 0.05 | 0.11 | -0.18 – 0.27 | 0.42 | 0.676 |
| Naturalness c | 0.69 | 0.04 | 0.62 – 0.76 | 19.16 | <0.001 |
| C1 | 3.14 | 2.54 | -1.85 – 8.13 | 1.23 | 0.217 |
| C2 | 5.55 | 2.71 | 0.24 – 10.86 | 2.05 | 0.041 |
| C3 | -2.70 | 2.75 | -8.08 – 2.68 | -0.98 | 0.325 |
| C4 | 1.37 | 2.54 | -3.61 – 6.35 | 0.54 | 0.590 |
| C5 | -1.98 | 2.54 | -6.95 – 3.00 | -0.78 | 0.436 |
| C6 | -6.00 | 2.55 | -11.01 – -1.00 | -2.35 | 0.019 |
| C7 | 25.82 | 2.80 | 20.34 – 31.31 | 9.23 | <0.001 |
| C8 | 17.88 | 2.65 | 12.68 – 23.08 | 6.74 | <0.001 |
| C9 | 19.13 | 2.76 | 13.71 – 24.55 | 6.92 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.01 | 1.94 | 0.052 |
| CNS Score c * C1 | 0.32 | 0.15 | 0.03 – 0.62 | 2.14 | 0.032 |
| CNS Score c * C2 | 0.06 | 0.16 | -0.25 – 0.36 | 0.36 | 0.722 |
| CNS Score c * C3 | -0.30 | 0.17 | -0.63 – 0.02 | -1.82 | 0.070 |
| CNS Score c * C4 | -0.11 | 0.15 | -0.41 – 0.18 | -0.75 | 0.452 |
| CNS Score c * C5 | -0.06 | 0.16 | -0.36 – 0.25 | -0.36 | 0.716 |
| CNS Score c * C6 | 0.08 | 0.15 | -0.22 – 0.37 | 0.50 | 0.615 |
| CNS Score c * C7 | 0.29 | 0.17 | -0.04 – 0.62 | 1.71 | 0.088 |
| CNS Score c * C8 | 0.15 | 0.16 | -0.17 – 0.48 | 0.94 | 0.345 |
| CNS Score c * C9 | 0.26 | 0.17 | -0.07 – 0.59 | 1.57 | 0.117 |
| Random Effects | |||||
| σ2 | 901.06 | ||||
| τ00 id | 533.27 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.256 / 0.532 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict the benefit-risk difference, over and above burger 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: 31093.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0602 -0.5522 0.0396 0.5793 3.1982
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 449 21.19
## Residual 1013 31.83
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.838e+01 1.974e+00 3.063e+03 9.311 < 2e-16 ***
## CCBelief_Score.c 4.460e-01 8.409e-02 3.061e+03 5.304 1.21e-07 ***
## C1 -6.039e+00 2.624e+00 2.613e+03 -2.302 0.021438 *
## C2 6.188e+00 2.837e+00 2.558e+03 2.181 0.029269 *
## C3 -1.229e+01 2.836e+00 2.597e+03 -4.332 1.53e-05 ***
## C4 -7.918e-01 2.661e+00 2.612e+03 -0.298 0.766079
## C5 -4.756e+00 2.658e+00 2.635e+03 -1.790 0.073630 .
## C6 -1.055e+01 2.665e+00 2.613e+03 -3.957 7.78e-05 ***
## C7 3.718e+01 2.863e+00 2.602e+03 12.985 < 2e-16 ***
## C8 3.385e+01 2.638e+00 2.609e+03 12.834 < 2e-16 ***
## C9 3.002e+01 2.835e+00 2.613e+03 10.588 < 2e-16 ***
## CCBelief_Score.c:C1 -6.950e-03 1.110e-01 2.631e+03 -0.063 0.950078
## CCBelief_Score.c:C2 1.886e-02 1.225e-01 2.564e+03 0.154 0.877592
## CCBelief_Score.c:C3 -4.109e-01 1.146e-01 2.570e+03 -3.585 0.000344 ***
## CCBelief_Score.c:C4 -5.134e-03 1.091e-01 2.619e+03 -0.047 0.962480
## CCBelief_Score.c:C5 1.129e-01 1.153e-01 2.626e+03 0.979 0.327582
## CCBelief_Score.c:C6 1.054e-01 1.119e-01 2.585e+03 0.942 0.346071
## CCBelief_Score.c:C7 2.279e-01 1.235e-01 2.586e+03 1.846 0.064958 .
## CCBelief_Score.c:C8 1.471e-01 1.152e-01 2.648e+03 1.278 0.201456
## CCBelief_Score.c:C9 4.516e-01 1.230e-01 2.666e+03 3.670 0.000247 ***
## ---
## 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) | 18.38 | 1.97 | 14.51 – 22.25 | 9.31 | <0.001 |
| CCBelief Score c | 0.45 | 0.08 | 0.28 – 0.61 | 5.30 | <0.001 |
| C1 | -6.04 | 2.62 | -11.18 – -0.89 | -2.30 | 0.021 |
| C2 | 6.19 | 2.84 | 0.63 – 11.75 | 2.18 | 0.029 |
| C3 | -12.29 | 2.84 | -17.85 – -6.73 | -4.33 | <0.001 |
| C4 | -0.79 | 2.66 | -6.01 – 4.43 | -0.30 | 0.766 |
| C5 | -4.76 | 2.66 | -9.97 – 0.45 | -1.79 | 0.074 |
| C6 | -10.55 | 2.67 | -15.77 – -5.32 | -3.96 | <0.001 |
| C7 | 37.18 | 2.86 | 31.57 – 42.79 | 12.99 | <0.001 |
| C8 | 33.85 | 2.64 | 28.68 – 39.02 | 12.83 | <0.001 |
| C9 | 30.02 | 2.84 | 24.46 – 35.58 | 10.59 | <0.001 |
| CCBelief Score c * C1 | -0.01 | 0.11 | -0.22 – 0.21 | -0.06 | 0.950 |
| CCBelief Score c * C2 | 0.02 | 0.12 | -0.22 – 0.26 | 0.15 | 0.878 |
| CCBelief Score c * C3 | -0.41 | 0.11 | -0.64 – -0.19 | -3.58 | <0.001 |
| CCBelief Score c * C4 | -0.01 | 0.11 | -0.22 – 0.21 | -0.05 | 0.962 |
| CCBelief Score c * C5 | 0.11 | 0.12 | -0.11 – 0.34 | 0.98 | 0.328 |
| CCBelief Score c * C6 | 0.11 | 0.11 | -0.11 – 0.32 | 0.94 | 0.346 |
| CCBelief Score c * C7 | 0.23 | 0.12 | -0.01 – 0.47 | 1.85 | 0.065 |
| CCBelief Score c * C8 | 0.15 | 0.12 | -0.08 – 0.37 | 1.28 | 0.201 |
| CCBelief Score c * C9 | 0.45 | 0.12 | 0.21 – 0.69 | 3.67 | <0.001 |
| Random Effects | |||||
| σ2 | 1012.88 | ||||
| τ00 id | 449.00 | ||||
| ICC | 0.31 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.247 / 0.478 | ||||
Q.2 (CLIMATE CHANGE BELIEF) How does climate change belief depend on naturalness perception in predicting the benefit-risk difference, over and above burger 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)
summary(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: 30748.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5645 -0.5566 0.0185 0.5764 3.0210
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 408.4 20.21
## Residual 896.9 29.95
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.945e+01 1.864e+00 3.062e+03 10.437
## CCBelief_Score.c 3.681e-01 7.979e-02 3.066e+03 4.613
## Naturalness.c 6.755e-01 3.536e-02 3.025e+03 19.102
## C1 2.538e+00 2.515e+00 2.621e+03 1.009
## C2 5.689e+00 2.672e+00 2.550e+03 2.129
## C3 -3.784e+00 2.715e+00 2.611e+03 -1.394
## C4 1.502e+00 2.511e+00 2.602e+03 0.598
## C5 -2.724e+00 2.506e+00 2.626e+03 -1.087
## C6 -6.160e+00 2.522e+00 2.605e+03 -2.442
## C7 2.599e+01 2.761e+00 2.632e+03 9.415
## C8 1.809e+01 2.616e+00 2.670e+03 6.913
## C9 1.962e+01 2.727e+00 2.630e+03 7.192
## CCBelief_Score.c:Naturalness.c -3.919e-03 1.339e-03 3.037e+03 -2.927
## CCBelief_Score.c:C1 -1.353e-04 1.056e-01 2.611e+03 -0.001
## CCBelief_Score.c:C2 7.212e-03 1.154e-01 2.556e+03 0.063
## CCBelief_Score.c:C3 -3.573e-01 1.088e-01 2.549e+03 -3.283
## CCBelief_Score.c:C4 3.649e-02 1.028e-01 2.608e+03 0.355
## CCBelief_Score.c:C5 1.268e-01 1.087e-01 2.613e+03 1.167
## CCBelief_Score.c:C6 1.326e-01 1.055e-01 2.570e+03 1.257
## CCBelief_Score.c:C7 2.793e-01 1.182e-01 2.652e+03 2.363
## CCBelief_Score.c:C8 2.874e-01 1.153e-01 2.782e+03 2.492
## CCBelief_Score.c:C9 4.953e-01 1.183e-01 2.720e+03 4.188
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 4.13e-06 ***
## Naturalness.c < 2e-16 ***
## C1 0.31295
## C2 0.03338 *
## C3 0.16352
## C4 0.54981
## C5 0.27721
## C6 0.01467 *
## C7 < 2e-16 ***
## C8 5.90e-12 ***
## C9 8.26e-13 ***
## CCBelief_Score.c:Naturalness.c 0.00344 **
## CCBelief_Score.c:C1 0.99898
## CCBelief_Score.c:C2 0.95015
## CCBelief_Score.c:C3 0.00104 **
## CCBelief_Score.c:C4 0.72272
## CCBelief_Score.c:C5 0.24349
## CCBelief_Score.c:C6 0.20877
## CCBelief_Score.c:C7 0.01822 *
## CCBelief_Score.c:C8 0.01275 *
## CCBelief_Score.c:C9 2.91e-05 ***
## ---
## 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.9145,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 19.45 | 1.86 | 15.80 – 23.11 | 10.44 | <0.001 |
| CCBelief Score c | 0.37 | 0.08 | 0.21 – 0.52 | 4.61 | <0.001 |
| Naturalness c | 0.68 | 0.04 | 0.61 – 0.74 | 19.10 | <0.001 |
| C1 | 2.54 | 2.52 | -2.39 – 7.47 | 1.01 | 0.313 |
| C2 | 5.69 | 2.67 | 0.45 – 10.93 | 2.13 | 0.033 |
| C3 | -3.78 | 2.72 | -9.11 – 1.54 | -1.39 | 0.164 |
| C4 | 1.50 | 2.51 | -3.42 – 6.43 | 0.60 | 0.550 |
| C5 | -2.72 | 2.51 | -7.64 – 2.19 | -1.09 | 0.277 |
| C6 | -6.16 | 2.52 | -11.11 – -1.21 | -2.44 | 0.015 |
| C7 | 25.99 | 2.76 | 20.58 – 31.40 | 9.41 | <0.001 |
| C8 | 18.09 | 2.62 | 12.96 – 23.22 | 6.91 | <0.001 |
| C9 | 19.62 | 2.73 | 14.27 – 24.96 | 7.19 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.01 – -0.00 | -2.93 | 0.003 |
| CCBelief Score c * C1 | -0.00 | 0.11 | -0.21 – 0.21 | -0.00 | 0.999 |
| CCBelief Score c * C2 | 0.01 | 0.12 | -0.22 – 0.23 | 0.06 | 0.950 |
| CCBelief Score c * C3 | -0.36 | 0.11 | -0.57 – -0.14 | -3.28 | 0.001 |
| CCBelief Score c * C4 | 0.04 | 0.10 | -0.17 – 0.24 | 0.35 | 0.723 |
| CCBelief Score c * C5 | 0.13 | 0.11 | -0.09 – 0.34 | 1.17 | 0.243 |
| CCBelief Score c * C6 | 0.13 | 0.11 | -0.07 – 0.34 | 1.26 | 0.209 |
| CCBelief Score c * C7 | 0.28 | 0.12 | 0.05 – 0.51 | 2.36 | 0.018 |
| CCBelief Score c * C8 | 0.29 | 0.12 | 0.06 – 0.51 | 2.49 | 0.013 |
| CCBelief Score c * C9 | 0.50 | 0.12 | 0.26 – 0.73 | 4.19 | <0.001 |
| Random Effects | |||||
| σ2 | 896.92 | ||||
| τ00 id | 408.45 | ||||
| ICC | 0.31 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.325 / 0.536 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict the benefit-risk difference, over and above burger 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: 31301
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0425 -0.5419 0.0425 0.5765 3.1185
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 593.4 24.36
## Residual 1027.3 32.05
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.23090 2.03830 3068.32735 8.944 < 2e-16 ***
## Collectivism_Score.c 0.04778 0.07961 3078.88135 0.600 0.5484
## C1 -5.95530 2.67043 2541.45385 -2.230 0.0258 *
## C2 6.17731 2.88566 2496.39127 2.141 0.0324 *
## C3 -11.89238 2.88747 2529.27492 -4.119 3.93e-05 ***
## C4 -1.04275 2.70770 2544.18635 -0.385 0.7002
## C5 -3.73089 2.71197 2563.51159 -1.376 0.1690
## C6 -10.61721 2.71267 2541.84003 -3.914 9.32e-05 ***
## C7 37.60129 2.91352 2536.01823 12.906 < 2e-16 ***
## C8 34.42700 2.68378 2536.01442 12.828 < 2e-16 ***
## C9 30.71540 2.88858 2545.63150 10.633 < 2e-16 ***
## Collectivism_Score.c:C1 -0.11881 0.11223 2525.71325 -1.059 0.2899
## Collectivism_Score.c:C2 -0.05590 0.11729 2475.30518 -0.477 0.6337
## Collectivism_Score.c:C3 -0.01571 0.11364 2471.30900 -0.138 0.8901
## Collectivism_Score.c:C4 -0.03066 0.10946 2523.86807 -0.280 0.7794
## Collectivism_Score.c:C5 0.03911 0.10749 2538.69683 0.364 0.7160
## Collectivism_Score.c:C6 -0.10396 0.11029 2533.71809 -0.943 0.3459
## Collectivism_Score.c:C7 -0.12859 0.11854 2551.12297 -1.085 0.2781
## Collectivism_Score.c:C8 -0.18116 0.10964 2553.83618 -1.652 0.0986 .
## Collectivism_Score.c:C9 -0.28501 0.12043 2578.71645 -2.367 0.0180 *
## ---
## 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) | 18.23 | 2.04 | 14.23 – 22.23 | 8.94 | <0.001 |
| Collectivism Score c | 0.05 | 0.08 | -0.11 – 0.20 | 0.60 | 0.548 |
| C1 | -5.96 | 2.67 | -11.19 – -0.72 | -2.23 | 0.026 |
| C2 | 6.18 | 2.89 | 0.52 – 11.84 | 2.14 | 0.032 |
| C3 | -11.89 | 2.89 | -17.55 – -6.23 | -4.12 | <0.001 |
| C4 | -1.04 | 2.71 | -6.35 – 4.27 | -0.39 | 0.700 |
| C5 | -3.73 | 2.71 | -9.05 – 1.59 | -1.38 | 0.169 |
| C6 | -10.62 | 2.71 | -15.94 – -5.30 | -3.91 | <0.001 |
| C7 | 37.60 | 2.91 | 31.89 – 43.31 | 12.91 | <0.001 |
| C8 | 34.43 | 2.68 | 29.16 – 39.69 | 12.83 | <0.001 |
| C9 | 30.72 | 2.89 | 25.05 – 36.38 | 10.63 | <0.001 |
| Collectivism Score c * C1 | -0.12 | 0.11 | -0.34 – 0.10 | -1.06 | 0.290 |
| Collectivism Score c * C2 | -0.06 | 0.12 | -0.29 – 0.17 | -0.48 | 0.634 |
| Collectivism Score c * C3 | -0.02 | 0.11 | -0.24 – 0.21 | -0.14 | 0.890 |
| Collectivism Score c * C4 | -0.03 | 0.11 | -0.25 – 0.18 | -0.28 | 0.779 |
| Collectivism Score c * C5 | 0.04 | 0.11 | -0.17 – 0.25 | 0.36 | 0.716 |
| Collectivism Score c * C6 | -0.10 | 0.11 | -0.32 – 0.11 | -0.94 | 0.346 |
| Collectivism Score c * C7 | -0.13 | 0.12 | -0.36 – 0.10 | -1.08 | 0.278 |
| Collectivism Score c * C8 | -0.18 | 0.11 | -0.40 – 0.03 | -1.65 | 0.099 |
| Collectivism Score c * C9 | -0.29 | 0.12 | -0.52 – -0.05 | -2.37 | 0.018 |
| Random Effects | |||||
| σ2 | 1027.34 | ||||
| τ00 id | 593.39 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.165 / 0.471 | ||||
Q.2 (COLLECTIVISM) How does collectivism depend on naturalness perception in predicting the benefit-risk difference, over and above burger 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)
summary(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: 30949.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5184 -0.5354 0.0242 0.5642 2.9212
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 540.4 23.25
## Residual 906.3 30.10
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.915e+01 1.921e+00 3.067e+03 9.971
## Collectivism_Score.c 4.160e-02 7.515e-02 3.077e+03 0.554
## Naturalness.c 7.118e-01 3.624e-02 2.968e+03 19.639
## C1 3.253e+00 2.554e+00 2.545e+03 1.274
## C2 5.597e+00 2.713e+00 2.487e+03 2.063
## C3 -2.716e+00 2.755e+00 2.535e+03 -0.986
## C4 1.527e+00 2.550e+00 2.531e+03 0.599
## C5 -1.351e+00 2.554e+00 2.553e+03 -0.529
## C6 -5.778e+00 2.563e+00 2.533e+03 -2.255
## C7 2.565e+01 2.806e+00 2.563e+03 9.141
## C8 1.794e+01 2.665e+00 2.594e+03 6.731
## C9 1.974e+01 2.774e+00 2.560e+03 7.117
## Collectivism_Score.c:Naturalness.c 1.708e-03 1.424e-03 2.963e+03 1.200
## Collectivism_Score.c:C1 -1.006e-01 1.076e-01 2.529e+03 -0.935
## Collectivism_Score.c:C2 -5.331e-02 1.103e-01 2.467e+03 -0.483
## Collectivism_Score.c:C3 -1.761e-02 1.083e-01 2.481e+03 -0.163
## Collectivism_Score.c:C4 1.541e-03 1.030e-01 2.511e+03 0.015
## Collectivism_Score.c:C5 1.035e-01 1.013e-01 2.525e+03 1.022
## Collectivism_Score.c:C6 -4.360e-02 1.041e-01 2.518e+03 -0.419
## Collectivism_Score.c:C7 -1.736e-01 1.149e-01 2.587e+03 -1.511
## Collectivism_Score.c:C8 -1.394e-01 1.086e-01 2.617e+03 -1.283
## Collectivism_Score.c:C9 -3.182e-01 1.164e-01 2.615e+03 -2.735
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.57993
## Naturalness.c < 2e-16 ***
## C1 0.20289
## C2 0.03925 *
## C3 0.32436
## C4 0.54937
## C5 0.59696
## C6 0.02424 *
## C7 < 2e-16 ***
## C8 2.06e-11 ***
## C9 1.42e-12 ***
## Collectivism_Score.c:Naturalness.c 0.23042
## Collectivism_Score.c:C1 0.35013
## Collectivism_Score.c:C2 0.62889
## Collectivism_Score.c:C3 0.87087
## Collectivism_Score.c:C4 0.98806
## Collectivism_Score.c:C5 0.30690
## Collectivism_Score.c:C6 0.67535
## Collectivism_Score.c:C7 0.13092
## Collectivism_Score.c:C8 0.19958
## Collectivism_Score.c:C9 0.00628 **
## ---
## 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.9166,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 19.15 | 1.92 | 15.39 – 22.92 | 9.97 | <0.001 |
| Collectivism Score c | 0.04 | 0.08 | -0.11 – 0.19 | 0.55 | 0.580 |
| Naturalness c | 0.71 | 0.04 | 0.64 – 0.78 | 19.64 | <0.001 |
| C1 | 3.25 | 2.55 | -1.75 – 8.26 | 1.27 | 0.203 |
| C2 | 5.60 | 2.71 | 0.28 – 10.92 | 2.06 | 0.039 |
| C3 | -2.72 | 2.76 | -8.12 – 2.69 | -0.99 | 0.324 |
| C4 | 1.53 | 2.55 | -3.47 – 6.53 | 0.60 | 0.549 |
| C5 | -1.35 | 2.55 | -6.36 – 3.66 | -0.53 | 0.597 |
| C6 | -5.78 | 2.56 | -10.80 – -0.75 | -2.25 | 0.024 |
| C7 | 25.65 | 2.81 | 20.15 – 31.16 | 9.14 | <0.001 |
| C8 | 17.94 | 2.66 | 12.71 – 23.16 | 6.73 | <0.001 |
| C9 | 19.74 | 2.77 | 14.30 – 25.18 | 7.12 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.20 | 0.230 |
| Collectivism Score c * C1 | -0.10 | 0.11 | -0.31 – 0.11 | -0.93 | 0.350 |
| Collectivism Score c * C2 | -0.05 | 0.11 | -0.27 – 0.16 | -0.48 | 0.629 |
| Collectivism Score c * C3 | -0.02 | 0.11 | -0.23 – 0.19 | -0.16 | 0.871 |
| Collectivism Score c * C4 | 0.00 | 0.10 | -0.20 – 0.20 | 0.01 | 0.988 |
| Collectivism Score c * C5 | 0.10 | 0.10 | -0.10 – 0.30 | 1.02 | 0.307 |
| Collectivism Score c * C6 | -0.04 | 0.10 | -0.25 – 0.16 | -0.42 | 0.675 |
| Collectivism Score c * C7 | -0.17 | 0.11 | -0.40 – 0.05 | -1.51 | 0.131 |
| Collectivism Score c * C8 | -0.14 | 0.11 | -0.35 – 0.07 | -1.28 | 0.200 |
| Collectivism Score c * C9 | -0.32 | 0.12 | -0.55 – -0.09 | -2.74 | 0.006 |
| Random Effects | |||||
| σ2 | 906.27 | ||||
| τ00 id | 540.41 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.530 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict the benefit-risk difference, over and above burger 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: 31288.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9615 -0.5434 0.0455 0.5695 3.3463
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 591.4 24.32
## Residual 1025.8 32.03
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.0957 2.0378 3068.2587 8.880 < 2e-16 ***
## Individualism_Score.c 0.2935 0.1170 3074.9481 2.507 0.012211 *
## C1 -5.8328 2.6694 2544.3378 -2.185 0.028976 *
## C2 6.4065 2.8865 2497.3109 2.219 0.026543 *
## C3 -11.2422 2.8904 2533.4822 -3.890 0.000103 ***
## C4 -0.7577 2.7039 2543.2513 -0.280 0.779317
## C5 -3.8110 2.7037 2565.5953 -1.410 0.158793
## C6 -10.5256 2.7118 2543.7188 -3.881 0.000107 ***
## C7 37.8854 2.9130 2537.2695 13.006 < 2e-16 ***
## C8 34.3960 2.6816 2537.7570 12.827 < 2e-16 ***
## C9 30.4728 2.8836 2545.6713 10.568 < 2e-16 ***
## Individualism_Score.c:C1 -0.2319 0.1547 2517.6763 -1.499 0.133899
## Individualism_Score.c:C2 -0.1644 0.1749 2543.8761 -0.940 0.347315
## Individualism_Score.c:C3 -0.5773 0.1619 2476.3711 -3.566 0.000369 ***
## Individualism_Score.c:C4 -0.1271 0.1579 2532.2790 -0.805 0.420857
## Individualism_Score.c:C5 -0.1237 0.1587 2569.1258 -0.780 0.435712
## Individualism_Score.c:C6 -0.3056 0.1617 2554.5028 -1.890 0.058922 .
## Individualism_Score.c:C7 -0.1358 0.1653 2518.7100 -0.822 0.411130
## Individualism_Score.c:C8 -0.2317 0.1553 2540.5450 -1.492 0.135869
## Individualism_Score.c:C9 -0.1673 0.1763 2567.9226 -0.949 0.342693
## ---
## 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) | 18.10 | 2.04 | 14.10 – 22.09 | 8.88 | <0.001 |
| Individualism Score c | 0.29 | 0.12 | 0.06 – 0.52 | 2.51 | 0.012 |
| C1 | -5.83 | 2.67 | -11.07 – -0.60 | -2.19 | 0.029 |
| C2 | 6.41 | 2.89 | 0.75 – 12.07 | 2.22 | 0.027 |
| C3 | -11.24 | 2.89 | -16.91 – -5.58 | -3.89 | <0.001 |
| C4 | -0.76 | 2.70 | -6.06 – 4.54 | -0.28 | 0.779 |
| C5 | -3.81 | 2.70 | -9.11 – 1.49 | -1.41 | 0.159 |
| C6 | -10.53 | 2.71 | -15.84 – -5.21 | -3.88 | <0.001 |
| C7 | 37.89 | 2.91 | 32.17 – 43.60 | 13.01 | <0.001 |
| C8 | 34.40 | 2.68 | 29.14 – 39.65 | 12.83 | <0.001 |
| C9 | 30.47 | 2.88 | 24.82 – 36.13 | 10.57 | <0.001 |
|
Individualism Score c * C1 |
-0.23 | 0.15 | -0.54 – 0.07 | -1.50 | 0.134 |
|
Individualism Score c * C2 |
-0.16 | 0.17 | -0.51 – 0.18 | -0.94 | 0.347 |
|
Individualism Score c * C3 |
-0.58 | 0.16 | -0.89 – -0.26 | -3.57 | <0.001 |
|
Individualism Score c * C4 |
-0.13 | 0.16 | -0.44 – 0.18 | -0.81 | 0.421 |
|
Individualism Score c * C5 |
-0.12 | 0.16 | -0.43 – 0.19 | -0.78 | 0.436 |
|
Individualism Score c * C6 |
-0.31 | 0.16 | -0.62 – 0.01 | -1.89 | 0.059 |
|
Individualism Score c * C7 |
-0.14 | 0.17 | -0.46 – 0.19 | -0.82 | 0.411 |
|
Individualism Score c * C8 |
-0.23 | 0.16 | -0.54 – 0.07 | -1.49 | 0.136 |
|
Individualism Score c * C9 |
-0.17 | 0.18 | -0.51 – 0.18 | -0.95 | 0.343 |
| Random Effects | |||||
| σ2 | 1025.85 | ||||
| τ00 id | 591.40 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.167 / 0.472 | ||||
Q.2 (INDIVIDUALISM) How does individualism depend on naturalness perception in predicting the benefit-risk difference, over and above burger 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: 30933.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4199 -0.5223 0.0274 0.5693 3.1047
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 537.2 23.18
## Residual 904.6 30.08
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.903e+01 1.920e+00 3.067e+03 9.909
## Individualism_Score.c 2.746e-01 1.103e-01 3.074e+03 2.489
## Naturalness.c 7.053e-01 3.638e-02 2.965e+03 19.389
## C1 3.263e+00 2.552e+00 2.548e+03 1.278
## C2 5.858e+00 2.714e+00 2.488e+03 2.159
## C3 -2.019e+00 2.757e+00 2.539e+03 -0.732
## C4 1.840e+00 2.545e+00 2.531e+03 0.723
## C5 -1.533e+00 2.545e+00 2.555e+03 -0.602
## C6 -5.708e+00 2.561e+00 2.533e+03 -2.229
## C7 2.608e+01 2.807e+00 2.565e+03 9.291
## C8 1.805e+01 2.661e+00 2.590e+03 6.783
## C9 1.954e+01 2.769e+00 2.560e+03 7.057
## Individualism_Score.c:Naturalness.c 3.744e-03 2.054e-03 3.003e+03 1.823
## Individualism_Score.c:C1 -1.784e-01 1.477e-01 2.514e+03 -1.208
## Individualism_Score.c:C2 -1.188e-01 1.645e-01 2.536e+03 -0.722
## Individualism_Score.c:C3 -4.924e-01 1.546e-01 2.477e+03 -3.186
## Individualism_Score.c:C4 -5.055e-02 1.487e-01 2.519e+03 -0.340
## Individualism_Score.c:C5 2.150e-03 1.495e-01 2.554e+03 0.014
## Individualism_Score.c:C6 -1.909e-01 1.528e-01 2.539e+03 -1.250
## Individualism_Score.c:C7 -1.735e-01 1.608e-01 2.569e+03 -1.079
## Individualism_Score.c:C8 -2.950e-01 1.545e-01 2.614e+03 -1.909
## Individualism_Score.c:C9 -2.389e-01 1.690e-01 2.589e+03 -1.414
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.01287 *
## Naturalness.c < 2e-16 ***
## C1 0.20130
## C2 0.03097 *
## C3 0.46412
## C4 0.46972
## C5 0.54696
## C6 0.02590 *
## C7 < 2e-16 ***
## C8 1.45e-11 ***
## C9 2.18e-12 ***
## Individualism_Score.c:Naturalness.c 0.06837 .
## Individualism_Score.c:C1 0.22722
## Individualism_Score.c:C2 0.47010
## Individualism_Score.c:C3 0.00146 **
## Individualism_Score.c:C4 0.73393
## Individualism_Score.c:C5 0.98853
## Individualism_Score.c:C6 0.21151
## Individualism_Score.c:C7 0.28057
## Individualism_Score.c:C8 0.05636 .
## Individualism_Score.c:C9 0.15743
## ---
## 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) | 19.03 | 1.92 | 15.26 – 22.79 | 9.91 | <0.001 |
| Individualism Score c | 0.27 | 0.11 | 0.06 – 0.49 | 2.49 | 0.013 |
| Naturalness c | 0.71 | 0.04 | 0.63 – 0.78 | 19.39 | <0.001 |
| C1 | 3.26 | 2.55 | -1.74 – 8.27 | 1.28 | 0.201 |
| C2 | 5.86 | 2.71 | 0.54 – 11.18 | 2.16 | 0.031 |
| C3 | -2.02 | 2.76 | -7.43 – 3.39 | -0.73 | 0.464 |
| C4 | 1.84 | 2.55 | -3.15 – 6.83 | 0.72 | 0.470 |
| C5 | -1.53 | 2.55 | -6.52 – 3.46 | -0.60 | 0.547 |
| C6 | -5.71 | 2.56 | -10.73 – -0.69 | -2.23 | 0.026 |
| C7 | 26.08 | 2.81 | 20.58 – 31.59 | 9.29 | <0.001 |
| C8 | 18.05 | 2.66 | 12.83 – 23.26 | 6.78 | <0.001 |
| C9 | 19.54 | 2.77 | 14.11 – 24.97 | 7.06 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.01 | 1.82 | 0.068 |
|
Individualism Score c * C1 |
-0.18 | 0.15 | -0.47 – 0.11 | -1.21 | 0.227 |
|
Individualism Score c * C2 |
-0.12 | 0.16 | -0.44 – 0.20 | -0.72 | 0.470 |
|
Individualism Score c * C3 |
-0.49 | 0.15 | -0.80 – -0.19 | -3.19 | 0.001 |
|
Individualism Score c * C4 |
-0.05 | 0.15 | -0.34 – 0.24 | -0.34 | 0.734 |
|
Individualism Score c * C5 |
0.00 | 0.15 | -0.29 – 0.30 | 0.01 | 0.989 |
|
Individualism Score c * C6 |
-0.19 | 0.15 | -0.49 – 0.11 | -1.25 | 0.211 |
|
Individualism Score c * C7 |
-0.17 | 0.16 | -0.49 – 0.14 | -1.08 | 0.281 |
|
Individualism Score c * C8 |
-0.29 | 0.15 | -0.60 – 0.01 | -1.91 | 0.056 |
|
Individualism Score c * C9 |
-0.24 | 0.17 | -0.57 – 0.09 | -1.41 | 0.157 |
| Random Effects | |||||
| σ2 | 904.60 | ||||
| τ00 id | 537.22 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.251 / 0.530 | ||||
Political Ideology
Q.1 (POLITICAL ORIENTATION) How does individualism predict the benefit-risk difference, over and above burger 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: 31235.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9114 -0.5381 0.0444 0.5710 3.0414
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 589.4 24.28
## Residual 1032.9 32.14
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.3142 2.0400 3068.3188 8.978 < 2e-16 ***
## Ideology.c -0.5207 3.4182 3076.9725 -0.152 0.8789
## C1 -6.0079 2.6755 2546.1899 -2.246 0.0248 *
## C2 6.1514 2.8895 2499.8743 2.129 0.0334 *
## C3 -12.0443 2.8897 2535.5097 -4.168 3.17e-05 ***
## C4 -1.0371 2.7095 2546.1005 -0.383 0.7019
## C5 -4.1194 2.7140 2567.0261 -1.518 0.1292
## C6 -10.6856 2.7191 2548.7856 -3.930 8.73e-05 ***
## C7 37.5256 2.9183 2539.9758 12.859 < 2e-16 ***
## C8 34.2757 2.6942 2544.6166 12.722 < 2e-16 ***
## C9 30.2994 2.8910 2549.6648 10.481 < 2e-16 ***
## Ideology.c:C1 3.9436 4.5413 2509.4696 0.868 0.3853
## Ideology.c:C2 3.0957 4.8059 2442.0972 0.644 0.5195
## Ideology.c:C3 2.6452 5.0203 2536.1361 0.527 0.5983
## Ideology.c:C4 -0.9968 4.7125 2569.9352 -0.212 0.8325
## Ideology.c:C5 1.9100 4.6532 2623.7823 0.410 0.6815
## Ideology.c:C6 2.2428 4.7099 2554.2088 0.476 0.6340
## Ideology.c:C7 3.5917 4.9166 2595.2950 0.731 0.4651
## Ideology.c:C8 2.8187 4.5812 2523.0553 0.615 0.5384
## Ideology.c:C9 3.3360 5.1298 2561.7095 0.650 0.5155
## ---
## 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) | 18.31 | 2.04 | 14.31 – 22.31 | 8.98 | <0.001 |
| Ideology c | -0.52 | 3.42 | -7.22 – 6.18 | -0.15 | 0.879 |
| C1 | -6.01 | 2.68 | -11.25 – -0.76 | -2.25 | 0.025 |
| C2 | 6.15 | 2.89 | 0.49 – 11.82 | 2.13 | 0.033 |
| C3 | -12.04 | 2.89 | -17.71 – -6.38 | -4.17 | <0.001 |
| C4 | -1.04 | 2.71 | -6.35 – 4.28 | -0.38 | 0.702 |
| C5 | -4.12 | 2.71 | -9.44 – 1.20 | -1.52 | 0.129 |
| C6 | -10.69 | 2.72 | -16.02 – -5.35 | -3.93 | <0.001 |
| C7 | 37.53 | 2.92 | 31.80 – 43.25 | 12.86 | <0.001 |
| C8 | 34.28 | 2.69 | 28.99 – 39.56 | 12.72 | <0.001 |
| C9 | 30.30 | 2.89 | 24.63 – 35.97 | 10.48 | <0.001 |
| Ideology c * C1 | 3.94 | 4.54 | -4.96 – 12.85 | 0.87 | 0.385 |
| Ideology c * C2 | 3.10 | 4.81 | -6.33 – 12.52 | 0.64 | 0.520 |
| Ideology c * C3 | 2.65 | 5.02 | -7.20 – 12.49 | 0.53 | 0.598 |
| Ideology c * C4 | -1.00 | 4.71 | -10.24 – 8.24 | -0.21 | 0.832 |
| Ideology c * C5 | 1.91 | 4.65 | -7.21 – 11.03 | 0.41 | 0.681 |
| Ideology c * C6 | 2.24 | 4.71 | -6.99 – 11.48 | 0.48 | 0.634 |
| Ideology c * C7 | 3.59 | 4.92 | -6.05 – 13.23 | 0.73 | 0.465 |
| Ideology c * C8 | 2.82 | 4.58 | -6.16 – 11.80 | 0.62 | 0.538 |
| Ideology c * C9 | 3.34 | 5.13 | -6.72 – 13.39 | 0.65 | 0.516 |
| Random Effects | |||||
| σ2 | 1032.94 | ||||
| τ00 id | 589.37 | ||||
| ICC | 0.36 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.164 / 0.467 | ||||
Q.2 (POLITICAL ORIENTATION) How does individualism depend on naturalness perception in predicting the benefit-risk difference, over and above burger 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: 30879.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3829 -0.5347 0.0271 0.5763 2.9198
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 536.6 23.16
## Residual 912.4 30.21
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.27726 1.92394 3067.23856 10.020 < 2e-16 ***
## Ideology.c 0.93376 3.22455 3075.50658 0.290 0.7722
## Naturalness.c 0.71238 0.03645 2971.10346 19.544 < 2e-16 ***
## C1 3.18736 2.56278 2551.87725 1.244 0.2137
## C2 5.51687 2.71900 2491.38974 2.029 0.0426 *
## C3 -2.75510 2.75957 2541.39894 -0.998 0.3182
## C4 1.49880 2.55255 2533.51128 0.587 0.5571
## C5 -1.90367 2.55722 2557.84391 -0.744 0.4567
## C6 -5.96926 2.57021 2539.18042 -2.322 0.0203 *
## C7 25.60447 2.81274 2568.05189 9.103 < 2e-16 ***
## C8 17.61135 2.67328 2600.97612 6.588 5.38e-11 ***
## C9 19.26580 2.77896 2565.47228 6.933 5.20e-12 ***
## Ideology.c:Naturalness.c 0.01622 0.06362 2967.11621 0.255 0.7988
## Ideology.c:C1 2.24927 4.31849 2495.93671 0.521 0.6025
## Ideology.c:C2 1.41184 4.52158 2434.95695 0.312 0.7549
## Ideology.c:C3 3.87465 4.82339 2584.12513 0.803 0.4219
## Ideology.c:C4 -0.94968 4.43729 2562.64263 -0.214 0.8305
## Ideology.c:C5 -0.45034 4.38063 2613.62580 -0.103 0.9181
## Ideology.c:C6 1.34408 4.44128 2544.14130 0.303 0.7622
## Ideology.c:C7 3.08152 4.72504 2620.33342 0.652 0.5144
## Ideology.c:C8 -0.29586 4.52768 2615.47604 -0.065 0.9479
## Ideology.c:C9 3.93880 4.96386 2609.18935 0.793 0.4276
## ---
## 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) | 19.28 | 1.92 | 15.50 – 23.05 | 10.02 | <0.001 |
| Ideology c | 0.93 | 3.22 | -5.39 – 7.26 | 0.29 | 0.772 |
| Naturalness c | 0.71 | 0.04 | 0.64 – 0.78 | 19.54 | <0.001 |
| C1 | 3.19 | 2.56 | -1.84 – 8.21 | 1.24 | 0.214 |
| C2 | 5.52 | 2.72 | 0.19 – 10.85 | 2.03 | 0.043 |
| C3 | -2.76 | 2.76 | -8.17 – 2.66 | -1.00 | 0.318 |
| C4 | 1.50 | 2.55 | -3.51 – 6.50 | 0.59 | 0.557 |
| C5 | -1.90 | 2.56 | -6.92 – 3.11 | -0.74 | 0.457 |
| C6 | -5.97 | 2.57 | -11.01 – -0.93 | -2.32 | 0.020 |
| C7 | 25.60 | 2.81 | 20.09 – 31.12 | 9.10 | <0.001 |
| C8 | 17.61 | 2.67 | 12.37 – 22.85 | 6.59 | <0.001 |
| C9 | 19.27 | 2.78 | 13.82 – 24.71 | 6.93 | <0.001 |
|
Ideology c * Naturalness c |
0.02 | 0.06 | -0.11 – 0.14 | 0.25 | 0.799 |
| Ideology c * C1 | 2.25 | 4.32 | -6.22 – 10.72 | 0.52 | 0.603 |
| Ideology c * C2 | 1.41 | 4.52 | -7.45 – 10.28 | 0.31 | 0.755 |
| Ideology c * C3 | 3.87 | 4.82 | -5.58 – 13.33 | 0.80 | 0.422 |
| Ideology c * C4 | -0.95 | 4.44 | -9.65 – 7.75 | -0.21 | 0.831 |
| Ideology c * C5 | -0.45 | 4.38 | -9.04 – 8.14 | -0.10 | 0.918 |
| Ideology c * C6 | 1.34 | 4.44 | -7.36 – 10.05 | 0.30 | 0.762 |
| Ideology c * C7 | 3.08 | 4.73 | -6.18 – 12.35 | 0.65 | 0.514 |
| Ideology c * C8 | -0.30 | 4.53 | -9.17 – 8.58 | -0.07 | 0.948 |
| Ideology c * C9 | 3.94 | 4.96 | -5.79 – 13.67 | 0.79 | 0.428 |
| Random Effects | |||||
| σ2 | 912.36 | ||||
| τ00 id | 536.59 | ||||
| ICC | 0.37 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.247 / 0.526 | ||||
Familiarity/Understanding (Mean score)
Q.1 How do burger 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: 27833.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0364 -0.5885 -0.0151 0.5956 3.1054
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 206.2 14.36
## Residual 331.1 18.20
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.4355 1.1648 3080.8192 32.140 < 2e-16 ***
## C1 -1.2691 1.5193 2532.2689 -0.835 0.40361
## C2 22.4050 1.6405 2488.8121 13.657 < 2e-16 ***
## C3 29.9624 1.6409 2522.6960 18.259 < 2e-16 ***
## C4 0.2945 1.5387 2532.1634 0.191 0.84823
## C5 -4.9841 1.5388 2551.5791 -3.239 0.00121 **
## C6 0.7724 1.5433 2532.9878 0.501 0.61676
## C7 48.3596 1.6574 2528.0376 29.178 < 2e-16 ***
## C8 29.8444 1.5261 2527.0387 19.556 < 2e-16 ***
## C9 44.9082 1.6420 2537.6100 27.350 < 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 -0.673
## C2 -0.598 0.472
## C3 -0.606 0.478 0.392
## C4 -0.664 0.509 0.467 0.469
## C5 -0.669 0.515 0.469 0.478 0.508
## C6 -0.661 0.506 0.457 0.480 0.499 0.503
## C7 -0.601 0.471 0.389 0.394 0.469 0.471 0.462
## C8 -0.668 0.512 0.468 0.467 0.503 0.509 0.505 0.477
## C9 -0.610 0.485 0.395 0.401 0.475 0.476 0.473 0.397 0.480tab_model(modA.920,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.44 | 1.16 | 35.15 – 39.72 | 32.14 | <0.001 |
| C1 | -1.27 | 1.52 | -4.25 – 1.71 | -0.84 | 0.404 |
| C2 | 22.40 | 1.64 | 19.19 – 25.62 | 13.66 | <0.001 |
| C3 | 29.96 | 1.64 | 26.74 – 33.18 | 18.26 | <0.001 |
| C4 | 0.29 | 1.54 | -2.72 – 3.31 | 0.19 | 0.848 |
| C5 | -4.98 | 1.54 | -8.00 – -1.97 | -3.24 | 0.001 |
| C6 | 0.77 | 1.54 | -2.25 – 3.80 | 0.50 | 0.617 |
| C7 | 48.36 | 1.66 | 45.11 – 51.61 | 29.18 | <0.001 |
| C8 | 29.84 | 1.53 | 26.85 – 32.84 | 19.56 | <0.001 |
| C9 | 44.91 | 1.64 | 41.69 – 48.13 | 27.35 | <0.001 |
| Random Effects | |||||
| σ2 | 331.14 | ||||
| τ00 id | 206.19 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.401 / 0.631 | ||||
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 burger 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: 27847.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8756 -0.5873 -0.0089 0.5948 2.9958
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 203.0 14.25
## Residual 330.4 18.18
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.741e+01 1.162e+00 3.070e+03 32.186 < 2e-16 ***
## ATNS_Score.c -3.266e-02 5.514e-02 3.066e+03 -0.592 0.55370
## C1 -1.235e+00 1.517e+00 2.525e+03 -0.814 0.41560
## C2 2.234e+01 1.639e+00 2.481e+03 13.627 < 2e-16 ***
## C3 3.000e+01 1.639e+00 2.515e+03 18.307 < 2e-16 ***
## C4 1.788e-01 1.538e+00 2.526e+03 0.116 0.90744
## C5 -4.909e+00 1.537e+00 2.546e+03 -3.194 0.00142 **
## C6 7.552e-01 1.542e+00 2.526e+03 0.490 0.62444
## C7 4.842e+01 1.655e+00 2.522e+03 29.248 < 2e-16 ***
## C8 2.983e+01 1.524e+00 2.520e+03 19.572 < 2e-16 ***
## C9 4.490e+01 1.640e+00 2.529e+03 27.377 < 2e-16 ***
## ATNS_Score.c:C1 1.023e-02 7.152e-02 2.533e+03 0.143 0.88624
## ATNS_Score.c:C2 -9.474e-02 7.655e-02 2.472e+03 -1.238 0.21596
## ATNS_Score.c:C3 -1.124e-01 7.663e-02 2.504e+03 -1.467 0.14259
## ATNS_Score.c:C4 -1.273e-01 7.088e-02 2.526e+03 -1.796 0.07259 .
## ATNS_Score.c:C5 -5.841e-03 7.158e-02 2.553e+03 -0.082 0.93497
## ATNS_Score.c:C6 2.745e-02 7.417e-02 2.551e+03 0.370 0.71139
## ATNS_Score.c:C7 -2.266e-02 7.745e-02 2.531e+03 -0.293 0.76987
## ATNS_Score.c:C8 -1.549e-01 7.168e-02 2.511e+03 -2.161 0.03083 *
## ATNS_Score.c:C9 4.076e-02 7.817e-02 2.563e+03 0.521 0.60208
## ---
## 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) | 37.41 | 1.16 | 35.13 – 39.68 | 32.19 | <0.001 |
| ATNS Score c | -0.03 | 0.06 | -0.14 – 0.08 | -0.59 | 0.554 |
| C1 | -1.24 | 1.52 | -4.21 – 1.74 | -0.81 | 0.416 |
| C2 | 22.34 | 1.64 | 19.13 – 25.56 | 13.63 | <0.001 |
| C3 | 30.00 | 1.64 | 26.79 – 33.22 | 18.31 | <0.001 |
| C4 | 0.18 | 1.54 | -2.84 – 3.19 | 0.12 | 0.907 |
| C5 | -4.91 | 1.54 | -7.92 – -1.90 | -3.19 | 0.001 |
| C6 | 0.76 | 1.54 | -2.27 – 3.78 | 0.49 | 0.624 |
| C7 | 48.42 | 1.66 | 45.17 – 51.66 | 29.25 | <0.001 |
| C8 | 29.83 | 1.52 | 26.84 – 32.82 | 19.57 | <0.001 |
| C9 | 44.90 | 1.64 | 41.69 – 48.12 | 27.38 | <0.001 |
| ATNS Score c * C1 | 0.01 | 0.07 | -0.13 – 0.15 | 0.14 | 0.886 |
| ATNS Score c * C2 | -0.09 | 0.08 | -0.24 – 0.06 | -1.24 | 0.216 |
| ATNS Score c * C3 | -0.11 | 0.08 | -0.26 – 0.04 | -1.47 | 0.143 |
| ATNS Score c * C4 | -0.13 | 0.07 | -0.27 – 0.01 | -1.80 | 0.073 |
| ATNS Score c * C5 | -0.01 | 0.07 | -0.15 – 0.13 | -0.08 | 0.935 |
| ATNS Score c * C6 | 0.03 | 0.07 | -0.12 – 0.17 | 0.37 | 0.711 |
| ATNS Score c * C7 | -0.02 | 0.08 | -0.17 – 0.13 | -0.29 | 0.770 |
| ATNS Score c * C8 | -0.15 | 0.07 | -0.30 – -0.01 | -2.16 | 0.031 |
| ATNS Score c * C9 | 0.04 | 0.08 | -0.11 – 0.19 | 0.52 | 0.602 |
| Random Effects | |||||
| σ2 | 330.44 | ||||
| τ00 id | 202.98 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.406 / 0.632 | ||||
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 burger 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)
summary(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: 27649.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7998 -0.5764 -0.0006 0.5957 3.2179
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 212.0 14.56
## Residual 298.5 17.28
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.783e+01 1.122e+00 3.072e+03 33.718 < 2e-16
## ATNS_Score.c -2.280e-02 5.324e-02 3.069e+03 -0.428 0.66846
## Naturalness.c 3.189e-01 2.123e-02 2.904e+03 15.023 < 2e-16
## C1 2.904e+00 1.476e+00 2.496e+03 1.968 0.04918
## C2 2.206e+01 1.566e+00 2.442e+03 14.087 < 2e-16
## C3 3.414e+01 1.590e+00 2.489e+03 21.474 < 2e-16
## C4 1.355e+00 1.472e+00 2.480e+03 0.921 0.35729
## C5 -3.996e+00 1.470e+00 2.500e+03 -2.718 0.00662
## C6 2.797e+00 1.480e+00 2.482e+03 1.889 0.05895
## C7 4.306e+01 1.622e+00 2.519e+03 26.551 < 2e-16
## C8 2.245e+01 1.537e+00 2.538e+03 14.605 < 2e-16
## C9 3.998e+01 1.601e+00 2.510e+03 24.966 < 2e-16
## ATNS_Score.c:Naturalness.c -4.178e-04 8.383e-04 2.900e+03 -0.498 0.61823
## ATNS_Score.c:C1 -1.476e-02 6.931e-02 2.491e+03 -0.213 0.83139
## ATNS_Score.c:C2 -1.055e-01 7.311e-02 2.433e+03 -1.443 0.14912
## ATNS_Score.c:C3 -8.559e-02 7.412e-02 2.475e+03 -1.155 0.24827
## ATNS_Score.c:C4 -8.570e-02 6.789e-02 2.477e+03 -1.262 0.20696
## ATNS_Score.c:C5 2.013e-02 6.855e-02 2.503e+03 0.294 0.76898
## ATNS_Score.c:C6 5.716e-02 7.104e-02 2.500e+03 0.805 0.42111
## ATNS_Score.c:C7 -5.253e-02 7.551e-02 2.528e+03 -0.696 0.48674
## ATNS_Score.c:C8 -1.502e-01 7.125e-02 2.515e+03 -2.108 0.03517
## ATNS_Score.c:C9 4.009e-02 7.612e-02 2.553e+03 0.527 0.59850
##
## (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 ittab_model(modA.9213,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.83 | 1.12 | 35.63 – 40.02 | 33.72 | <0.001 |
| ATNS Score c | -0.02 | 0.05 | -0.13 – 0.08 | -0.43 | 0.668 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 15.02 | <0.001 |
| C1 | 2.90 | 1.48 | 0.01 – 5.80 | 1.97 | 0.049 |
| C2 | 22.06 | 1.57 | 18.99 – 25.13 | 14.09 | <0.001 |
| C3 | 34.14 | 1.59 | 31.02 – 37.26 | 21.47 | <0.001 |
| C4 | 1.36 | 1.47 | -1.53 – 4.24 | 0.92 | 0.357 |
| C5 | -4.00 | 1.47 | -6.88 – -1.11 | -2.72 | 0.007 |
| C6 | 2.80 | 1.48 | -0.11 – 5.70 | 1.89 | 0.059 |
| C7 | 43.06 | 1.62 | 39.88 – 46.24 | 26.55 | <0.001 |
| C8 | 22.45 | 1.54 | 19.44 – 25.47 | 14.61 | <0.001 |
| C9 | 39.98 | 1.60 | 36.84 – 43.12 | 24.97 | <0.001 |
|
ATNS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.50 | 0.618 |
| ATNS Score c * C1 | -0.01 | 0.07 | -0.15 – 0.12 | -0.21 | 0.831 |
| ATNS Score c * C2 | -0.11 | 0.07 | -0.25 – 0.04 | -1.44 | 0.149 |
| ATNS Score c * C3 | -0.09 | 0.07 | -0.23 – 0.06 | -1.15 | 0.248 |
| ATNS Score c * C4 | -0.09 | 0.07 | -0.22 – 0.05 | -1.26 | 0.207 |
| ATNS Score c * C5 | 0.02 | 0.07 | -0.11 – 0.15 | 0.29 | 0.769 |
| ATNS Score c * C6 | 0.06 | 0.07 | -0.08 – 0.20 | 0.80 | 0.421 |
| ATNS Score c * C7 | -0.05 | 0.08 | -0.20 – 0.10 | -0.70 | 0.487 |
| ATNS Score c * C8 | -0.15 | 0.07 | -0.29 – -0.01 | -2.11 | 0.035 |
| ATNS Score c * C9 | 0.04 | 0.08 | -0.11 – 0.19 | 0.53 | 0.598 |
| Random Effects | |||||
| σ2 | 298.54 | ||||
| τ00 id | 212.03 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.437 / 0.671 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict familiarity/understanding, over and above burger 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: 27841.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9235 -0.5821 -0.0116 0.5938 3.0997
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 206.2 14.36
## Residual 328.9 18.14
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.43448 1.16184 3070.81038 32.220 <2e-16 ***
## CNS_Score.c 0.02699 0.06950 3072.25188 0.388 0.6978
## C1 -1.22854 1.51488 2521.45660 -0.811 0.4175
## C2 22.75354 1.63971 2477.75289 13.877 <2e-16 ***
## C3 29.97399 1.63646 2512.83655 18.316 <2e-16 ***
## C4 0.22353 1.53549 2521.95285 0.146 0.8843
## C5 -4.97592 1.53456 2540.68248 -3.243 0.0012 **
## C6 0.78738 1.53942 2522.07155 0.511 0.6091
## C7 48.36798 1.65296 2518.52711 29.261 <2e-16 ***
## C8 29.74845 1.52280 2514.72858 19.535 <2e-16 ***
## C9 44.67518 1.63840 2525.67049 27.268 <2e-16 ***
## CNS_Score.c:C1 0.02096 0.08911 2509.82640 0.235 0.8141
## CNS_Score.c:C2 0.20635 0.09531 2463.16545 2.165 0.0305 *
## CNS_Score.c:C3 -0.07776 0.09945 2477.44671 -0.782 0.4343
## CNS_Score.c:C4 -0.08575 0.09037 2531.90252 -0.949 0.3428
## CNS_Score.c:C5 -0.06859 0.09413 2559.03204 -0.729 0.4663
## CNS_Score.c:C6 0.03775 0.09162 2505.41908 0.412 0.6804
## CNS_Score.c:C7 0.08251 0.10025 2544.97315 0.823 0.4106
## CNS_Score.c:C8 0.08789 0.09391 2537.51472 0.936 0.3494
## CNS_Score.c:C9 0.22495 0.09906 2562.37506 2.271 0.0232 *
## ---
## 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) | 37.43 | 1.16 | 35.16 – 39.71 | 32.22 | <0.001 |
| CNS Score c | 0.03 | 0.07 | -0.11 – 0.16 | 0.39 | 0.698 |
| C1 | -1.23 | 1.51 | -4.20 – 1.74 | -0.81 | 0.417 |
| C2 | 22.75 | 1.64 | 19.54 – 25.97 | 13.88 | <0.001 |
| C3 | 29.97 | 1.64 | 26.77 – 33.18 | 18.32 | <0.001 |
| C4 | 0.22 | 1.54 | -2.79 – 3.23 | 0.15 | 0.884 |
| C5 | -4.98 | 1.53 | -7.98 – -1.97 | -3.24 | 0.001 |
| C6 | 0.79 | 1.54 | -2.23 – 3.81 | 0.51 | 0.609 |
| C7 | 48.37 | 1.65 | 45.13 – 51.61 | 29.26 | <0.001 |
| C8 | 29.75 | 1.52 | 26.76 – 32.73 | 19.54 | <0.001 |
| C9 | 44.68 | 1.64 | 41.46 – 47.89 | 27.27 | <0.001 |
| CNS Score c * C1 | 0.02 | 0.09 | -0.15 – 0.20 | 0.24 | 0.814 |
| CNS Score c * C2 | 0.21 | 0.10 | 0.02 – 0.39 | 2.17 | 0.030 |
| CNS Score c * C3 | -0.08 | 0.10 | -0.27 – 0.12 | -0.78 | 0.434 |
| CNS Score c * C4 | -0.09 | 0.09 | -0.26 – 0.09 | -0.95 | 0.343 |
| CNS Score c * C5 | -0.07 | 0.09 | -0.25 – 0.12 | -0.73 | 0.466 |
| CNS Score c * C6 | 0.04 | 0.09 | -0.14 – 0.22 | 0.41 | 0.680 |
| CNS Score c * C7 | 0.08 | 0.10 | -0.11 – 0.28 | 0.82 | 0.411 |
| CNS Score c * C8 | 0.09 | 0.09 | -0.10 – 0.27 | 0.94 | 0.349 |
| CNS Score c * C9 | 0.22 | 0.10 | 0.03 – 0.42 | 2.27 | 0.023 |
| Random Effects | |||||
| σ2 | 328.92 | ||||
| τ00 id | 206.18 | ||||
| ICC | 0.39 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.405 / 0.634 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger 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: 27841.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9235 -0.5821 -0.0116 0.5938 3.0997
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 206.2 14.36
## Residual 328.9 18.14
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.43448 1.16184 3070.81038 32.220 <2e-16 ***
## CNS_Score.c 0.02699 0.06950 3072.25188 0.388 0.6978
## C1 -1.22854 1.51488 2521.45660 -0.811 0.4175
## C2 22.75354 1.63971 2477.75289 13.877 <2e-16 ***
## C3 29.97399 1.63646 2512.83655 18.316 <2e-16 ***
## C4 0.22353 1.53549 2521.95285 0.146 0.8843
## C5 -4.97592 1.53456 2540.68248 -3.243 0.0012 **
## C6 0.78738 1.53942 2522.07155 0.511 0.6091
## C7 48.36798 1.65296 2518.52711 29.261 <2e-16 ***
## C8 29.74845 1.52280 2514.72858 19.535 <2e-16 ***
## C9 44.67518 1.63840 2525.67049 27.268 <2e-16 ***
## CNS_Score.c:C1 0.02096 0.08911 2509.82640 0.235 0.8141
## CNS_Score.c:C2 0.20635 0.09531 2463.16545 2.165 0.0305 *
## CNS_Score.c:C3 -0.07776 0.09945 2477.44671 -0.782 0.4343
## CNS_Score.c:C4 -0.08575 0.09037 2531.90252 -0.949 0.3428
## CNS_Score.c:C5 -0.06859 0.09413 2559.03204 -0.729 0.4663
## CNS_Score.c:C6 0.03775 0.09162 2505.41908 0.412 0.6804
## CNS_Score.c:C7 0.08251 0.10025 2544.97315 0.823 0.4106
## CNS_Score.c:C8 0.08789 0.09391 2537.51472 0.936 0.3494
## CNS_Score.c:C9 0.22495 0.09906 2562.37506 2.271 0.0232 *
## ---
## 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) | 37.43 | 1.16 | 35.16 – 39.71 | 32.22 | <0.001 |
| CNS Score c | 0.03 | 0.07 | -0.11 – 0.16 | 0.39 | 0.698 |
| C1 | -1.23 | 1.51 | -4.20 – 1.74 | -0.81 | 0.417 |
| C2 | 22.75 | 1.64 | 19.54 – 25.97 | 13.88 | <0.001 |
| C3 | 29.97 | 1.64 | 26.77 – 33.18 | 18.32 | <0.001 |
| C4 | 0.22 | 1.54 | -2.79 – 3.23 | 0.15 | 0.884 |
| C5 | -4.98 | 1.53 | -7.98 – -1.97 | -3.24 | 0.001 |
| C6 | 0.79 | 1.54 | -2.23 – 3.81 | 0.51 | 0.609 |
| C7 | 48.37 | 1.65 | 45.13 – 51.61 | 29.26 | <0.001 |
| C8 | 29.75 | 1.52 | 26.76 – 32.73 | 19.54 | <0.001 |
| C9 | 44.68 | 1.64 | 41.46 – 47.89 | 27.27 | <0.001 |
| CNS Score c * C1 | 0.02 | 0.09 | -0.15 – 0.20 | 0.24 | 0.814 |
| CNS Score c * C2 | 0.21 | 0.10 | 0.02 – 0.39 | 2.17 | 0.030 |
| CNS Score c * C3 | -0.08 | 0.10 | -0.27 – 0.12 | -0.78 | 0.434 |
| CNS Score c * C4 | -0.09 | 0.09 | -0.26 – 0.09 | -0.95 | 0.343 |
| CNS Score c * C5 | -0.07 | 0.09 | -0.25 – 0.12 | -0.73 | 0.466 |
| CNS Score c * C6 | 0.04 | 0.09 | -0.14 – 0.22 | 0.41 | 0.680 |
| CNS Score c * C7 | 0.08 | 0.10 | -0.11 – 0.28 | 0.82 | 0.411 |
| CNS Score c * C8 | 0.09 | 0.09 | -0.10 – 0.27 | 0.94 | 0.349 |
| CNS Score c * C9 | 0.22 | 0.10 | 0.03 – 0.42 | 2.27 | 0.023 |
| Random Effects | |||||
| σ2 | 328.92 | ||||
| τ00 id | 206.18 | ||||
| ICC | 0.39 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.405 / 0.634 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict familiarity/understanding, over and above burger 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: 27851.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0505 -0.5810 -0.0164 0.5989 3.1079
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 205.2 14.33
## Residual 329.7 18.16
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.747e+01 1.163e+00 3.071e+03 32.225 < 2e-16 ***
## CCBelief_Score.c 2.546e-02 4.951e-02 3.069e+03 0.514 0.60707
## C1 -1.304e+00 1.516e+00 2.523e+03 -0.860 0.38992
## C2 2.234e+01 1.638e+00 2.479e+03 13.640 < 2e-16 ***
## C3 2.983e+01 1.639e+00 2.514e+03 18.201 < 2e-16 ***
## C4 2.071e-01 1.538e+00 2.521e+03 0.135 0.89288
## C5 -4.874e+00 1.537e+00 2.542e+03 -3.172 0.00153 **
## C6 7.406e-01 1.540e+00 2.523e+03 0.481 0.63075
## C7 4.839e+01 1.654e+00 2.518e+03 29.251 < 2e-16 ***
## C8 2.969e+01 1.524e+00 2.520e+03 19.480 < 2e-16 ***
## C9 4.490e+01 1.639e+00 2.528e+03 27.395 < 2e-16 ***
## CCBelief_Score.c:C1 4.298e-02 6.418e-02 2.540e+03 0.670 0.50314
## CCBelief_Score.c:C2 4.991e-02 7.071e-02 2.485e+03 0.706 0.48030
## CCBelief_Score.c:C3 -4.128e-02 6.619e-02 2.491e+03 -0.624 0.53294
## CCBelief_Score.c:C4 -2.779e-02 6.308e-02 2.528e+03 -0.440 0.65962
## CCBelief_Score.c:C5 -1.166e-01 6.669e-02 2.535e+03 -1.749 0.08047 .
## CCBelief_Score.c:C6 -2.856e-03 6.462e-02 2.498e+03 -0.044 0.96475
## CCBelief_Score.c:C7 5.867e-03 7.131e-02 2.504e+03 0.082 0.93444
## CCBelief_Score.c:C8 1.138e-01 6.661e-02 2.555e+03 1.708 0.08778 .
## CCBelief_Score.c:C9 1.396e-01 7.121e-02 2.577e+03 1.961 0.05004 .
## ---
## 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) | 37.47 | 1.16 | 35.19 – 39.74 | 32.23 | <0.001 |
| CCBelief Score c | 0.03 | 0.05 | -0.07 – 0.12 | 0.51 | 0.607 |
| C1 | -1.30 | 1.52 | -4.28 – 1.67 | -0.86 | 0.390 |
| C2 | 22.34 | 1.64 | 19.13 – 25.55 | 13.64 | <0.001 |
| C3 | 29.83 | 1.64 | 26.61 – 33.04 | 18.20 | <0.001 |
| C4 | 0.21 | 1.54 | -2.81 – 3.22 | 0.13 | 0.893 |
| C5 | -4.87 | 1.54 | -7.89 – -1.86 | -3.17 | 0.002 |
| C6 | 0.74 | 1.54 | -2.28 – 3.76 | 0.48 | 0.631 |
| C7 | 48.39 | 1.65 | 45.15 – 51.64 | 29.25 | <0.001 |
| C8 | 29.69 | 1.52 | 26.70 – 32.68 | 19.48 | <0.001 |
| C9 | 44.90 | 1.64 | 41.68 – 48.11 | 27.39 | <0.001 |
| CCBelief Score c * C1 | 0.04 | 0.06 | -0.08 – 0.17 | 0.67 | 0.503 |
| CCBelief Score c * C2 | 0.05 | 0.07 | -0.09 – 0.19 | 0.71 | 0.480 |
| CCBelief Score c * C3 | -0.04 | 0.07 | -0.17 – 0.09 | -0.62 | 0.533 |
| CCBelief Score c * C4 | -0.03 | 0.06 | -0.15 – 0.10 | -0.44 | 0.660 |
| CCBelief Score c * C5 | -0.12 | 0.07 | -0.25 – 0.01 | -1.75 | 0.080 |
| CCBelief Score c * C6 | -0.00 | 0.06 | -0.13 – 0.12 | -0.04 | 0.965 |
| CCBelief Score c * C7 | 0.01 | 0.07 | -0.13 – 0.15 | 0.08 | 0.934 |
| CCBelief Score c * C8 | 0.11 | 0.07 | -0.02 – 0.24 | 1.71 | 0.088 |
| CCBelief Score c * C9 | 0.14 | 0.07 | -0.00 – 0.28 | 1.96 | 0.050 |
| Random Effects | |||||
| σ2 | 329.75 | ||||
| τ00 id | 205.22 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.405 / 0.633 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perceptinos of naturalness in predicting familiarity/understanding, over and above burger 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)
summary(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: 27654.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0405 -0.5710 -0.0053 0.5821 3.1860
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 214.3 14.64
## Residual 297.9 17.26
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.792e+01 1.123e+00 3.073e+03 33.767
## CCBelief_Score.c -1.525e-03 4.810e-02 3.074e+03 -0.032
## Naturalness.c 3.163e-01 2.111e-02 2.904e+03 14.980
## C1 2.766e+00 1.475e+00 2.496e+03 1.875
## C2 2.206e+01 1.564e+00 2.441e+03 14.106
## C3 3.395e+01 1.592e+00 2.495e+03 21.322
## C4 1.319e+00 1.472e+00 2.479e+03 0.896
## C5 -3.945e+00 1.470e+00 2.500e+03 -2.683
## C6 2.807e+00 1.479e+00 2.482e+03 1.898
## C7 4.310e+01 1.620e+00 2.513e+03 26.606
## C8 2.234e+01 1.537e+00 2.541e+03 14.534
## C9 3.998e+01 1.600e+00 2.510e+03 24.978
## CCBelief_Score.c:Naturalness.c -3.219e-04 8.002e-04 2.926e+03 -0.402
## CCBelief_Score.c:C1 6.362e-02 6.190e-02 2.487e+03 1.028
## CCBelief_Score.c:C2 4.204e-02 6.753e-02 2.446e+03 0.623
## CCBelief_Score.c:C3 -2.193e-03 6.369e-02 2.443e+03 -0.034
## CCBelief_Score.c:C4 -7.340e-03 6.029e-02 2.485e+03 -0.122
## CCBelief_Score.c:C5 -1.059e-01 6.373e-02 2.492e+03 -1.662
## CCBelief_Score.c:C6 1.131e-02 6.175e-02 2.454e+03 0.183
## CCBelief_Score.c:C7 6.148e-03 6.943e-02 2.536e+03 0.089
## CCBelief_Score.c:C8 1.334e-01 6.805e-02 2.647e+03 1.960
## CCBelief_Score.c:C9 1.366e-01 6.964e-02 2.594e+03 1.962
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 0.97471
## Naturalness.c < 2e-16 ***
## C1 0.06089 .
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 0.37019
## C5 0.00735 **
## C6 0.05782 .
## C7 < 2e-16 ***
## C8 < 2e-16 ***
## C9 < 2e-16 ***
## CCBelief_Score.c:Naturalness.c 0.68749
## CCBelief_Score.c:C1 0.30413
## CCBelief_Score.c:C2 0.53365
## CCBelief_Score.c:C3 0.97254
## CCBelief_Score.c:C4 0.90311
## CCBelief_Score.c:C5 0.09659 .
## CCBelief_Score.c:C6 0.85474
## CCBelief_Score.c:C7 0.92944
## CCBelief_Score.c:C8 0.05013 .
## CCBelief_Score.c:C9 0.04988 *
## ---
## 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.9245,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.92 | 1.12 | 35.71 – 40.12 | 33.77 | <0.001 |
| CCBelief Score c | -0.00 | 0.05 | -0.10 – 0.09 | -0.03 | 0.975 |
| Naturalness c | 0.32 | 0.02 | 0.27 – 0.36 | 14.98 | <0.001 |
| C1 | 2.77 | 1.48 | -0.13 – 5.66 | 1.88 | 0.061 |
| C2 | 22.06 | 1.56 | 19.00 – 25.13 | 14.11 | <0.001 |
| C3 | 33.95 | 1.59 | 30.83 – 37.07 | 21.32 | <0.001 |
| C4 | 1.32 | 1.47 | -1.57 – 4.21 | 0.90 | 0.370 |
| C5 | -3.94 | 1.47 | -6.83 – -1.06 | -2.68 | 0.007 |
| C6 | 2.81 | 1.48 | -0.09 – 5.71 | 1.90 | 0.058 |
| C7 | 43.10 | 1.62 | 39.93 – 46.28 | 26.61 | <0.001 |
| C8 | 22.34 | 1.54 | 19.33 – 25.36 | 14.53 | <0.001 |
| C9 | 39.98 | 1.60 | 36.84 – 43.11 | 24.98 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.40 | 0.687 |
| CCBelief Score c * C1 | 0.06 | 0.06 | -0.06 – 0.18 | 1.03 | 0.304 |
| CCBelief Score c * C2 | 0.04 | 0.07 | -0.09 – 0.17 | 0.62 | 0.534 |
| CCBelief Score c * C3 | -0.00 | 0.06 | -0.13 – 0.12 | -0.03 | 0.973 |
| CCBelief Score c * C4 | -0.01 | 0.06 | -0.13 – 0.11 | -0.12 | 0.903 |
| CCBelief Score c * C5 | -0.11 | 0.06 | -0.23 – 0.02 | -1.66 | 0.097 |
| CCBelief Score c * C6 | 0.01 | 0.06 | -0.11 – 0.13 | 0.18 | 0.855 |
| CCBelief Score c * C7 | 0.01 | 0.07 | -0.13 – 0.14 | 0.09 | 0.929 |
| CCBelief Score c * C8 | 0.13 | 0.07 | -0.00 – 0.27 | 1.96 | 0.050 |
| CCBelief Score c * C9 | 0.14 | 0.07 | 0.00 – 0.27 | 1.96 | 0.050 |
| Random Effects | |||||
| σ2 | 297.88 | ||||
| τ00 id | 214.33 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.436 / 0.672 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict familiarity/understanding, over and above burger 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: 27844.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0693 -0.5867 -0.0156 0.5943 3.1507
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 208.9 14.45
## Residual 327.2 18.09
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.737e+01 1.162e+00 3.071e+03 32.164 < 2e-16 ***
## Collectivism_Score.c 6.263e-02 4.542e-02 3.079e+03 1.379 0.16800
## C1 -1.225e+00 1.513e+00 2.516e+03 -0.810 0.41822
## C2 2.240e+01 1.634e+00 2.474e+03 13.708 < 2e-16 ***
## C3 2.986e+01 1.636e+00 2.506e+03 18.256 < 2e-16 ***
## C4 2.970e-01 1.534e+00 2.519e+03 0.194 0.84646
## C5 -4.679e+00 1.537e+00 2.537e+03 -3.045 0.00235 **
## C6 8.344e-01 1.537e+00 2.517e+03 0.543 0.58718
## C7 4.845e+01 1.650e+00 2.512e+03 29.355 < 2e-16 ***
## C8 3.010e+01 1.520e+00 2.511e+03 19.801 < 2e-16 ***
## C9 4.508e+01 1.636e+00 2.522e+03 27.551 < 2e-16 ***
## Collectivism_Score.c:C1 -5.038e-02 6.357e-02 2.501e+03 -0.793 0.42807
## Collectivism_Score.c:C2 -4.138e-03 6.641e-02 2.454e+03 -0.062 0.95032
## Collectivism_Score.c:C3 -1.697e-01 6.434e-02 2.451e+03 -2.638 0.00840 **
## Collectivism_Score.c:C4 -3.232e-02 6.200e-02 2.499e+03 -0.521 0.60219
## Collectivism_Score.c:C5 3.353e-02 6.089e-02 2.514e+03 0.551 0.58191
## Collectivism_Score.c:C6 -1.129e-01 6.247e-02 2.509e+03 -1.807 0.07090 .
## Collectivism_Score.c:C7 -1.417e-02 6.716e-02 2.527e+03 -0.211 0.83297
## Collectivism_Score.c:C8 -2.291e-01 6.211e-02 2.528e+03 -3.689 0.00023 ***
## Collectivism_Score.c:C9 -1.274e-01 6.824e-02 2.554e+03 -1.866 0.06210 .
## ---
## 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) | 37.37 | 1.16 | 35.09 – 39.65 | 32.16 | <0.001 |
| Collectivism Score c | 0.06 | 0.05 | -0.03 – 0.15 | 1.38 | 0.168 |
| C1 | -1.22 | 1.51 | -4.19 – 1.74 | -0.81 | 0.418 |
| C2 | 22.40 | 1.63 | 19.20 – 25.60 | 13.71 | <0.001 |
| C3 | 29.86 | 1.64 | 26.65 – 33.06 | 18.26 | <0.001 |
| C4 | 0.30 | 1.53 | -2.71 – 3.30 | 0.19 | 0.846 |
| C5 | -4.68 | 1.54 | -7.69 – -1.67 | -3.05 | 0.002 |
| C6 | 0.83 | 1.54 | -2.18 – 3.85 | 0.54 | 0.587 |
| C7 | 48.45 | 1.65 | 45.21 – 51.68 | 29.36 | <0.001 |
| C8 | 30.10 | 1.52 | 27.12 – 33.08 | 19.80 | <0.001 |
| C9 | 45.08 | 1.64 | 41.87 – 48.29 | 27.55 | <0.001 |
| Collectivism Score c * C1 | -0.05 | 0.06 | -0.18 – 0.07 | -0.79 | 0.428 |
| Collectivism Score c * C2 | -0.00 | 0.07 | -0.13 – 0.13 | -0.06 | 0.950 |
| Collectivism Score c * C3 | -0.17 | 0.06 | -0.30 – -0.04 | -2.64 | 0.008 |
| Collectivism Score c * C4 | -0.03 | 0.06 | -0.15 – 0.09 | -0.52 | 0.602 |
| Collectivism Score c * C5 | 0.03 | 0.06 | -0.09 – 0.15 | 0.55 | 0.582 |
| Collectivism Score c * C6 | -0.11 | 0.06 | -0.24 – 0.01 | -1.81 | 0.071 |
| Collectivism Score c * C7 | -0.01 | 0.07 | -0.15 – 0.12 | -0.21 | 0.833 |
| Collectivism Score c * C8 | -0.23 | 0.06 | -0.35 – -0.11 | -3.69 | <0.001 |
| Collectivism Score c * C9 | -0.13 | 0.07 | -0.26 – 0.01 | -1.87 | 0.062 |
| Random Effects | |||||
| σ2 | 327.24 | ||||
| τ00 id | 208.85 | ||||
| ICC | 0.39 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.405 / 0.637 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger 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)
summary(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: 27642.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9074 -0.5703 -0.0019 0.5798 3.3091
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 217.8 14.76
## Residual 295.0 17.18
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.779e+01 1.121e+00 3.073e+03 33.717
## Collectivism_Score.c 6.082e-02 4.393e-02 3.075e+03 1.385
## Naturalness.c 3.179e-01 2.102e-02 2.902e+03 15.121
## C1 2.882e+00 1.469e+00 2.487e+03 1.962
## C2 2.212e+01 1.559e+00 2.436e+03 14.189
## C3 3.397e+01 1.584e+00 2.481e+03 21.437
## C4 1.440e+00 1.466e+00 2.474e+03 0.982
## C5 -3.650e+00 1.469e+00 2.494e+03 -2.485
## C6 2.988e+00 1.474e+00 2.476e+03 2.028
## C7 4.310e+01 1.615e+00 2.507e+03 26.693
## C8 2.276e+01 1.534e+00 2.533e+03 14.839
## C9 4.018e+01 1.596e+00 2.504e+03 25.175
## Collectivism_Score.c:Naturalness.c 8.692e-04 8.257e-04 2.897e+03 1.053
## Collectivism_Score.c:C1 -4.320e-02 6.189e-02 2.473e+03 -0.698
## Collectivism_Score.c:C2 -1.708e-03 6.335e-02 2.419e+03 -0.027
## Collectivism_Score.c:C3 -1.697e-01 6.223e-02 2.432e+03 -2.727
## Collectivism_Score.c:C4 -1.605e-02 5.919e-02 2.456e+03 -0.271
## Collectivism_Score.c:C5 6.132e-02 5.825e-02 2.468e+03 1.053
## Collectivism_Score.c:C6 -8.582e-02 5.984e-02 2.462e+03 -1.434
## Collectivism_Score.c:C7 -3.591e-02 6.614e-02 2.530e+03 -0.543
## Collectivism_Score.c:C8 -2.144e-01 6.255e-02 2.555e+03 -3.427
## Collectivism_Score.c:C9 -1.481e-01 6.700e-02 2.556e+03 -2.211
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.16630
## Naturalness.c < 2e-16 ***
## C1 0.04988 *
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 0.32604
## C5 0.01304 *
## C6 0.04269 *
## C7 < 2e-16 ***
## C8 < 2e-16 ***
## C9 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.29258
## Collectivism_Score.c:C1 0.48521
## Collectivism_Score.c:C2 0.97850
## Collectivism_Score.c:C3 0.00644 **
## Collectivism_Score.c:C4 0.78636
## Collectivism_Score.c:C5 0.29251
## Collectivism_Score.c:C6 0.15168
## Collectivism_Score.c:C7 0.58720
## Collectivism_Score.c:C8 0.00062 ***
## Collectivism_Score.c:C9 0.02713 *
## ---
## 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.9267,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 37.79 | 1.12 | 35.59 – 39.99 | 33.72 | <0.001 |
| Collectivism Score c | 0.06 | 0.04 | -0.03 – 0.15 | 1.38 | 0.166 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 15.12 | <0.001 |
| C1 | 2.88 | 1.47 | 0.00 – 5.76 | 1.96 | 0.050 |
| C2 | 22.12 | 1.56 | 19.07 – 25.18 | 14.19 | <0.001 |
| C3 | 33.97 | 1.58 | 30.86 – 37.07 | 21.44 | <0.001 |
| C4 | 1.44 | 1.47 | -1.43 – 4.31 | 0.98 | 0.326 |
| C5 | -3.65 | 1.47 | -6.53 – -0.77 | -2.48 | 0.013 |
| C6 | 2.99 | 1.47 | 0.10 – 5.88 | 2.03 | 0.043 |
| C7 | 43.10 | 1.61 | 39.93 – 46.26 | 26.69 | <0.001 |
| C8 | 22.76 | 1.53 | 19.76 – 25.77 | 14.84 | <0.001 |
| C9 | 40.18 | 1.60 | 37.05 – 43.31 | 25.18 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.05 | 0.293 |
| Collectivism Score c * C1 | -0.04 | 0.06 | -0.16 – 0.08 | -0.70 | 0.485 |
| Collectivism Score c * C2 | -0.00 | 0.06 | -0.13 – 0.12 | -0.03 | 0.978 |
| Collectivism Score c * C3 | -0.17 | 0.06 | -0.29 – -0.05 | -2.73 | 0.006 |
| Collectivism Score c * C4 | -0.02 | 0.06 | -0.13 – 0.10 | -0.27 | 0.786 |
| Collectivism Score c * C5 | 0.06 | 0.06 | -0.05 – 0.18 | 1.05 | 0.292 |
| Collectivism Score c * C6 | -0.09 | 0.06 | -0.20 – 0.03 | -1.43 | 0.152 |
| Collectivism Score c * C7 | -0.04 | 0.07 | -0.17 – 0.09 | -0.54 | 0.587 |
| Collectivism Score c * C8 | -0.21 | 0.06 | -0.34 – -0.09 | -3.43 | 0.001 |
| Collectivism Score c * C9 | -0.15 | 0.07 | -0.28 – -0.02 | -2.21 | 0.027 |
| Random Effects | |||||
| σ2 | 295.04 | ||||
| τ00 id | 217.76 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.436 / 0.675 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict familiarity/understanding, over and above burger 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: 27831.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0365 -0.5832 -0.0267 0.5918 3.0679
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.2 14.29
## Residual 328.5 18.12
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.15170 1.16178 3070.40068 31.978 < 2e-16 ***
## Individualism_Score.c 0.34006 0.06675 3076.41862 5.095 3.70e-07 ***
## C1 -0.97959 1.51481 2525.28741 -0.647 0.51790
## C2 22.78230 1.63757 2480.84668 13.912 < 2e-16 ***
## C3 30.45246 1.64010 2515.96643 18.567 < 2e-16 ***
## C4 0.60660 1.53440 2524.18742 0.395 0.69263
## C5 -4.64983 1.53446 2545.88727 -3.030 0.00247 **
## C6 1.04457 1.53887 2524.71393 0.679 0.49733
## C7 48.73084 1.65299 2519.57367 29.480 < 2e-16 ***
## C8 30.15360 1.52166 2518.90781 19.816 < 2e-16 ***
## C9 45.14152 1.63636 2527.75085 27.586 < 2e-16 ***
## Individualism_Score.c:C1 -0.26990 0.08775 2499.50346 -3.076 0.00212 **
## Individualism_Score.c:C2 -0.19853 0.09925 2526.03066 -2.000 0.04557 *
## Individualism_Score.c:C3 -0.40247 0.09184 2460.84074 -4.383 1.22e-05 ***
## Individualism_Score.c:C4 -0.25831 0.08961 2513.61644 -2.883 0.00398 **
## Individualism_Score.c:C5 -0.21198 0.09008 2549.31641 -2.353 0.01869 *
## Individualism_Score.c:C6 -0.22321 0.09178 2535.18534 -2.432 0.01508 *
## Individualism_Score.c:C7 -0.20703 0.09376 2501.79734 -2.208 0.02734 *
## Individualism_Score.c:C8 -0.34986 0.08812 2521.81931 -3.970 7.38e-05 ***
## Individualism_Score.c:C9 -0.20431 0.10007 2549.32736 -2.042 0.04130 *
## ---
## 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) | 37.15 | 1.16 | 34.87 – 39.43 | 31.98 | <0.001 |
| Individualism Score c | 0.34 | 0.07 | 0.21 – 0.47 | 5.09 | <0.001 |
| C1 | -0.98 | 1.51 | -3.95 – 1.99 | -0.65 | 0.518 |
| C2 | 22.78 | 1.64 | 19.57 – 25.99 | 13.91 | <0.001 |
| C3 | 30.45 | 1.64 | 27.24 – 33.67 | 18.57 | <0.001 |
| C4 | 0.61 | 1.53 | -2.40 – 3.62 | 0.40 | 0.693 |
| C5 | -4.65 | 1.53 | -7.66 – -1.64 | -3.03 | 0.002 |
| C6 | 1.04 | 1.54 | -1.97 – 4.06 | 0.68 | 0.497 |
| C7 | 48.73 | 1.65 | 45.49 – 51.97 | 29.48 | <0.001 |
| C8 | 30.15 | 1.52 | 27.17 – 33.14 | 19.82 | <0.001 |
| C9 | 45.14 | 1.64 | 41.93 – 48.35 | 27.59 | <0.001 |
|
Individualism Score c * C1 |
-0.27 | 0.09 | -0.44 – -0.10 | -3.08 | 0.002 |
|
Individualism Score c * C2 |
-0.20 | 0.10 | -0.39 – -0.00 | -2.00 | 0.046 |
|
Individualism Score c * C3 |
-0.40 | 0.09 | -0.58 – -0.22 | -4.38 | <0.001 |
|
Individualism Score c * C4 |
-0.26 | 0.09 | -0.43 – -0.08 | -2.88 | 0.004 |
|
Individualism Score c * C5 |
-0.21 | 0.09 | -0.39 – -0.04 | -2.35 | 0.019 |
|
Individualism Score c * C6 |
-0.22 | 0.09 | -0.40 – -0.04 | -2.43 | 0.015 |
|
Individualism Score c * C7 |
-0.21 | 0.09 | -0.39 – -0.02 | -2.21 | 0.027 |
|
Individualism Score c * C8 |
-0.35 | 0.09 | -0.52 – -0.18 | -3.97 | <0.001 |
|
Individualism Score c * C9 |
-0.20 | 0.10 | -0.40 – -0.01 | -2.04 | 0.041 |
| Random Effects | |||||
| σ2 | 328.51 | ||||
| τ00 id | 204.17 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.408 / 0.635 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger 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: 27627.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9663 -0.5705 0.0013 0.5920 3.3093
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213 14.60
## Residual 296 17.21
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.761e+01 1.120e+00 3.073e+03 33.572
## Individualism_Score.c 3.280e-01 6.443e-02 3.077e+03 5.091
## Naturalness.c 3.217e-01 2.112e-02 2.906e+03 15.235
## C1 3.153e+00 1.471e+00 2.495e+03 2.144
## C2 2.246e+01 1.562e+00 2.442e+03 14.373
## C3 3.464e+01 1.589e+00 2.489e+03 21.803
## C4 1.722e+00 1.466e+00 2.479e+03 1.174
## C5 -3.763e+00 1.467e+00 2.501e+03 -2.565
## C6 3.131e+00 1.475e+00 2.481e+03 2.122
## C7 4.330e+01 1.618e+00 2.514e+03 26.764
## C8 2.268e+01 1.534e+00 2.535e+03 14.785
## C9 4.014e+01 1.596e+00 2.509e+03 25.153
## Individualism_Score.c:Naturalness.c -8.300e-04 1.193e-03 2.950e+03 -0.695
## Individualism_Score.c:C1 -2.798e-01 8.506e-02 2.463e+03 -3.289
## Individualism_Score.c:C2 -1.743e-01 9.477e-02 2.487e+03 -1.839
## Individualism_Score.c:C3 -3.974e-01 8.898e-02 2.433e+03 -4.467
## Individualism_Score.c:C4 -2.347e-01 8.565e-02 2.467e+03 -2.740
## Individualism_Score.c:C5 -1.667e-01 8.617e-02 2.500e+03 -1.935
## Individualism_Score.c:C6 -1.900e-01 8.800e-02 2.486e+03 -2.160
## Individualism_Score.c:C7 -1.728e-01 9.268e-02 2.518e+03 -1.864
## Individualism_Score.c:C8 -3.190e-01 8.912e-02 2.559e+03 -3.579
## Individualism_Score.c:C9 -2.026e-01 9.742e-02 2.535e+03 -2.080
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 3.77e-07 ***
## Naturalness.c < 2e-16 ***
## C1 0.032137 *
## C2 < 2e-16 ***
## C3 < 2e-16 ***
## C4 0.240476
## C5 0.010366 *
## C6 0.033922 *
## C7 < 2e-16 ***
## C8 < 2e-16 ***
## C9 < 2e-16 ***
## Individualism_Score.c:Naturalness.c 0.486817
## Individualism_Score.c:C1 0.001018 **
## Individualism_Score.c:C2 0.066013 .
## Individualism_Score.c:C3 8.31e-06 ***
## Individualism_Score.c:C4 0.006189 **
## Individualism_Score.c:C5 0.053131 .
## Individualism_Score.c:C6 0.030903 *
## Individualism_Score.c:C7 0.062427 .
## Individualism_Score.c:C8 0.000351 ***
## Individualism_Score.c:C9 0.037662 *
## ---
## 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) | 37.61 | 1.12 | 35.42 – 39.81 | 33.57 | <0.001 |
| Individualism Score c | 0.33 | 0.06 | 0.20 – 0.45 | 5.09 | <0.001 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 15.23 | <0.001 |
| C1 | 3.15 | 1.47 | 0.27 – 6.04 | 2.14 | 0.032 |
| C2 | 22.46 | 1.56 | 19.39 – 25.52 | 14.37 | <0.001 |
| C3 | 34.64 | 1.59 | 31.52 – 37.75 | 21.80 | <0.001 |
| C4 | 1.72 | 1.47 | -1.15 – 4.60 | 1.17 | 0.240 |
| C5 | -3.76 | 1.47 | -6.64 – -0.89 | -2.57 | 0.010 |
| C6 | 3.13 | 1.48 | 0.24 – 6.02 | 2.12 | 0.034 |
| C7 | 43.30 | 1.62 | 40.13 – 46.47 | 26.76 | <0.001 |
| C8 | 22.68 | 1.53 | 19.67 – 25.69 | 14.79 | <0.001 |
| C9 | 40.14 | 1.60 | 37.02 – 43.27 | 25.15 | <0.001 |
|
Individualism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.70 | 0.487 |
|
Individualism Score c * C1 |
-0.28 | 0.09 | -0.45 – -0.11 | -3.29 | 0.001 |
|
Individualism Score c * C2 |
-0.17 | 0.09 | -0.36 – 0.01 | -1.84 | 0.066 |
|
Individualism Score c * C3 |
-0.40 | 0.09 | -0.57 – -0.22 | -4.47 | <0.001 |
|
Individualism Score c * C4 |
-0.23 | 0.09 | -0.40 – -0.07 | -2.74 | 0.006 |
|
Individualism Score c * C5 |
-0.17 | 0.09 | -0.34 – 0.00 | -1.93 | 0.053 |
|
Individualism Score c * C6 |
-0.19 | 0.09 | -0.36 – -0.02 | -2.16 | 0.031 |
|
Individualism Score c * C7 |
-0.17 | 0.09 | -0.35 – 0.01 | -1.86 | 0.062 |
|
Individualism Score c * C8 |
-0.32 | 0.09 | -0.49 – -0.14 | -3.58 | <0.001 |
|
Individualism Score c * C9 |
-0.20 | 0.10 | -0.39 – -0.01 | -2.08 | 0.038 |
| Random Effects | |||||
| σ2 | 296.04 | ||||
| τ00 id | 213.03 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.440 / 0.674 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does political ideology predict familiarity/understanding, over and above burger 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: 27786.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9981 -0.5913 -0.0133 0.5982 3.0535
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 205.6 14.34
## Residual 330.9 18.19
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.3930 1.1643 3070.7080 32.116 < 2e-16 ***
## Ideology.c -3.0735 1.9518 3078.0949 -1.575 0.11542
## C1 -1.2746 1.5190 2523.9573 -0.839 0.40149
## C2 22.4607 1.6400 2480.4876 13.695 < 2e-16 ***
## C3 30.0174 1.6405 2515.0658 18.298 < 2e-16 ***
## C4 0.3474 1.5383 2523.8258 0.226 0.82134
## C5 -4.7546 1.5411 2544.0292 -3.085 0.00206 **
## C6 0.8301 1.5438 2526.5432 0.538 0.59084
## C7 48.4017 1.6568 2519.2596 29.214 < 2e-16 ***
## C8 29.9469 1.5296 2522.4301 19.578 < 2e-16 ***
## C9 44.9294 1.6414 2528.6358 27.372 < 2e-16 ***
## Ideology.c:C1 -1.5345 2.5777 2488.8949 -0.595 0.55170
## Ideology.c:C2 2.8620 2.7267 2425.1022 1.050 0.29400
## Ideology.c:C3 4.2922 2.8501 2516.2712 1.506 0.13220
## Ideology.c:C4 0.3290 2.6760 2547.8431 0.123 0.90216
## Ideology.c:C5 -1.3360 2.6432 2599.4965 -0.505 0.61330
## Ideology.c:C6 0.4832 2.6742 2532.3245 0.181 0.85662
## Ideology.c:C7 1.1543 2.7923 2573.4315 0.413 0.67937
## Ideology.c:C8 3.0049 2.6006 2501.8874 1.155 0.24801
## Ideology.c:C9 2.7123 2.9127 2540.4253 0.931 0.35185
## ---
## 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) | 37.39 | 1.16 | 35.11 – 39.68 | 32.12 | <0.001 |
| Ideology c | -3.07 | 1.95 | -6.90 – 0.75 | -1.57 | 0.115 |
| C1 | -1.27 | 1.52 | -4.25 – 1.70 | -0.84 | 0.401 |
| C2 | 22.46 | 1.64 | 19.25 – 25.68 | 13.70 | <0.001 |
| C3 | 30.02 | 1.64 | 26.80 – 33.23 | 18.30 | <0.001 |
| C4 | 0.35 | 1.54 | -2.67 – 3.36 | 0.23 | 0.821 |
| C5 | -4.75 | 1.54 | -7.78 – -1.73 | -3.09 | 0.002 |
| C6 | 0.83 | 1.54 | -2.20 – 3.86 | 0.54 | 0.591 |
| C7 | 48.40 | 1.66 | 45.15 – 51.65 | 29.21 | <0.001 |
| C8 | 29.95 | 1.53 | 26.95 – 32.95 | 19.58 | <0.001 |
| C9 | 44.93 | 1.64 | 41.71 – 48.15 | 27.37 | <0.001 |
| Ideology c * C1 | -1.53 | 2.58 | -6.59 – 3.52 | -0.60 | 0.552 |
| Ideology c * C2 | 2.86 | 2.73 | -2.48 – 8.21 | 1.05 | 0.294 |
| Ideology c * C3 | 4.29 | 2.85 | -1.30 – 9.88 | 1.51 | 0.132 |
| Ideology c * C4 | 0.33 | 2.68 | -4.92 – 5.58 | 0.12 | 0.902 |
| Ideology c * C5 | -1.34 | 2.64 | -6.52 – 3.85 | -0.51 | 0.613 |
| Ideology c * C6 | 0.48 | 2.67 | -4.76 – 5.73 | 0.18 | 0.857 |
| Ideology c * C7 | 1.15 | 2.79 | -4.32 – 6.63 | 0.41 | 0.679 |
| Ideology c * C8 | 3.00 | 2.60 | -2.09 – 8.10 | 1.16 | 0.248 |
| Ideology c * C9 | 2.71 | 2.91 | -3.00 – 8.42 | 0.93 | 0.352 |
| Random Effects | |||||
| σ2 | 330.85 | ||||
| τ00 id | 205.62 | ||||
| ICC | 0.38 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.404 / 0.632 | ||||
Q.2 (POLITICAL IDEOLOGY) Does political ideology depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger 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: 27569
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9454 -0.5764 -0.0018 0.5955 3.0866
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 215.2 14.67
## Residual 297.1 17.24
## Number of obs: 3099, groups: id, 1033
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.76430 1.12182 3073.01012 33.663 < 2e-16 ***
## Ideology.c -2.55277 1.88240 3076.98713 -1.356 0.17516
## Naturalness.c 0.32667 0.02114 2907.39233 15.455 < 2e-16 ***
## C1 3.04378 1.47401 2493.86754 2.065 0.03903 *
## C2 22.24077 1.56239 2440.81989 14.235 < 2e-16 ***
## C3 34.25565 1.58697 2487.32263 21.586 < 2e-16 ***
## C4 1.51454 1.46769 2476.44455 1.032 0.30221
## C5 -3.66804 1.47095 2498.75771 -2.494 0.01271 *
## C6 3.02971 1.47798 2481.96637 2.050 0.04048 *
## C7 42.94118 1.61825 2512.13257 26.536 < 2e-16 ***
## C8 22.45510 1.53881 2540.43087 14.593 < 2e-16 ***
## C9 39.83707 1.59874 2509.38307 24.918 < 2e-16 ***
## Ideology.c:Naturalness.c -0.09705 0.03689 2904.74914 -2.631 0.00856 **
## Ideology.c:C1 -3.33032 2.48163 2442.95137 -1.342 0.17972
## Ideology.c:C2 2.23183 2.59592 2390.38357 0.860 0.39002
## Ideology.c:C3 3.33912 2.77584 2530.12710 1.203 0.22912
## Ideology.c:C4 0.11667 2.55264 2505.57409 0.046 0.96355
## Ideology.c:C5 -2.41656 2.52211 2551.32650 -0.958 0.33808
## Ideology.c:C6 -0.39977 2.55416 2487.82772 -0.157 0.87564
## Ideology.c:C7 2.32676 2.72081 2561.89234 0.855 0.39254
## Ideology.c:C8 3.81552 2.60693 2556.08427 1.464 0.14342
## Ideology.c:C9 4.83275 2.85780 2551.39152 1.691 0.09094 .
## ---
## 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) | 37.76 | 1.12 | 35.56 – 39.96 | 33.66 | <0.001 |
| Ideology c | -2.55 | 1.88 | -6.24 – 1.14 | -1.36 | 0.175 |
| Naturalness c | 0.33 | 0.02 | 0.29 – 0.37 | 15.45 | <0.001 |
| C1 | 3.04 | 1.47 | 0.15 – 5.93 | 2.06 | 0.039 |
| C2 | 22.24 | 1.56 | 19.18 – 25.30 | 14.24 | <0.001 |
| C3 | 34.26 | 1.59 | 31.14 – 37.37 | 21.59 | <0.001 |
| C4 | 1.51 | 1.47 | -1.36 – 4.39 | 1.03 | 0.302 |
| C5 | -3.67 | 1.47 | -6.55 – -0.78 | -2.49 | 0.013 |
| C6 | 3.03 | 1.48 | 0.13 – 5.93 | 2.05 | 0.040 |
| C7 | 42.94 | 1.62 | 39.77 – 46.11 | 26.54 | <0.001 |
| C8 | 22.46 | 1.54 | 19.44 – 25.47 | 14.59 | <0.001 |
| C9 | 39.84 | 1.60 | 36.70 – 42.97 | 24.92 | <0.001 |
|
Ideology c * Naturalness c |
-0.10 | 0.04 | -0.17 – -0.02 | -2.63 | 0.009 |
| Ideology c * C1 | -3.33 | 2.48 | -8.20 – 1.54 | -1.34 | 0.180 |
| Ideology c * C2 | 2.23 | 2.60 | -2.86 – 7.32 | 0.86 | 0.390 |
| Ideology c * C3 | 3.34 | 2.78 | -2.10 – 8.78 | 1.20 | 0.229 |
| Ideology c * C4 | 0.12 | 2.55 | -4.89 – 5.12 | 0.05 | 0.964 |
| Ideology c * C5 | -2.42 | 2.52 | -7.36 – 2.53 | -0.96 | 0.338 |
| Ideology c * C6 | -0.40 | 2.55 | -5.41 – 4.61 | -0.16 | 0.876 |
| Ideology c * C7 | 2.33 | 2.72 | -3.01 – 7.66 | 0.86 | 0.393 |
| Ideology c * C8 | 3.82 | 2.61 | -1.30 – 8.93 | 1.46 | 0.143 |
| Ideology c * C9 | 4.83 | 2.86 | -0.77 – 10.44 | 1.69 | 0.091 |
| Random Effects | |||||
| σ2 | 297.12 | ||||
| τ00 id | 215.23 | ||||
| ICC | 0.42 | ||||
| N id | 1033 | ||||
| Observations | 3099 | ||||
| Marginal R2 / Conditional R2 | 0.436 / 0.673 | ||||