Climate Change Methods - Master’s Analysis
Sarah Gonzalez Coffin
2024-03-06
I. Dataset
CC <- read.csv("CCM_Oct 29,2023.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))II. Demographics
#Number of responses (rows)
nrow(CC)## [1] 1007#Age range
range(CC$Dem_Age, na.rm = T)## [1] 18 93#Average age
mean(CC$Dem_Age, na.rm = T)## [1] 45.40321#Standard deviation of age
sd(CC$Dem_Age, na.rm = T)## [1] 16.20402#Gender frequencies
table(CC$Dem_Gen)##
## 1 2 3
## 507 488 12#Ethnicity
table(CC$Dem_Ethnicity)##
## 1 2 3 4 5 6 7
## 61 129 44 1 4 758 10CC$Ethnicity <- NA
CC$Ethnicity[CC$Dem_Ethnicity == 1] <- 'Asian'
CC$Ethnicity[CC$Dem_Ethnicity == 2] <- 'Black'
CC$Ethnicity[CC$Dem_Ethnicity == 3] <- 'Hispanic'
CC$Ethnicity[CC$Dem_Ethnicity == 4] <- 'Nat Amer'
CC$Ethnicity[CC$Dem_Ethnicity == 5] <- 'Nat Pac'
CC$Ethnicity[CC$Dem_Ethnicity == 6] <- 'White'
CC$Ethnicity[CC$Dem_Ethnicity == 7] <- 'Other'
describe(CC$Dem_Ethnicity)## CC$Dem_Ethnicity
## n missing distinct Info Mean Gmd
## 1007 0 7 0.571 5.058 1.518
##
## Value 1 2 3 4 5 6 7
## Frequency 61 129 44 1 4 758 10
## Proportion 0.061 0.128 0.044 0.001 0.004 0.753 0.010
##
## For the frequency table, variable is rounded to the nearest 0# Education: Please indicate the highest level of education you have completed (1 = Elementary/Grammar School, 2 = Middle School, 3 = High School or Equivalent, 4 = Vocational/Technical School (2 years), 5 = Some College, 6 = College or University (4 years), 7 = Master's Degree (MS, MA, MBA, etc.), 8 = Doctoral Degree (PhD), 9 = Professional Degree (MD, JD, etc.).
CC$EdNum <- as.numeric(as.character(CC$Dem_Edu))
CC$EDU <- factor(CC$EdNum, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
labels = c("Elementary/Grammar School", "Middle School", "High School or Equivalent", "Vocational/Technical School (2 years)", "Some College", "College or University (4 years)", "Master's Degree (MS, MA, MBA, etc.)", "Doctoral Degree (PhD)", "Professional Degree (MD, JD, etc.)", "Other"))
table(CC$EDU)##
## Elementary/Grammar School Middle School
## 0 5
## High School or Equivalent Vocational/Technical School (2 years)
## 134 60
## Some College College or University (4 years)
## 233 405
## Master's Degree (MS, MA, MBA, etc.) Doctoral Degree (PhD)
## 126 19
## Professional Degree (MD, JD, etc.) Other
## 22 3describe(CC$EdNum)## CC$EdNum
## n missing distinct Info Mean Gmd
## 1007 0 9 0.918 5.471 1.475
##
## Value 2 3 4 5 6 7 8 9 10
## Frequency 5 134 60 233 405 126 19 22 3
## Proportion 0.005 0.133 0.060 0.231 0.402 0.125 0.019 0.022 0.003
##
## For the frequency table, variable is rounded to the nearest 0length(CC$EdNum)## [1] 1007#Gender
CC$Dem_Gender <- as.numeric(as.character(CC$Dem_Gen))
describe(CC$Dem_Gen)## CC$Dem_Gen
## n missing distinct Info Mean Gmd
## 1007 0 3 0.759 1.508 0.524
##
## Value 1 2 3
## Frequency 507 488 12
## Proportion 0.503 0.485 0.012
##
## For the frequency table, variable is rounded to the nearest 0#Age
CC$Demograph_Age <- as.numeric(as.character(CC$Dem_Age))
describe(CC$Demograph_Age)## CC$Demograph_Age
## n missing distinct Info Mean Gmd .05 .10
## 997 10 67 1 45.4 18.66 21 24
## .25 .50 .75 .90 .95
## 31 44 59 67 71
##
## lowest : 18 19 20 21 22, highest: 80 81 82 91 93range(CC$Demograph_Age ,na.rm = T)## [1] 18 93#Political Orientation
##"Which of the following describes your political orientation?"
CC$polOR <- factor(CC$PI_Orientation, levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Strongly Conservative", "Moderately Conservative", "Slightly Conservative", "Neither Conservative Nor Liberal", "Slightly Liberal", "Moderately Liberal", "Strongly Liberal"))
table(CC$polOR)##
## Strongly Conservative Moderately Conservative
## 62 102
## Slightly Conservative Neither Conservative Nor Liberal
## 72 183
## Slightly Liberal Moderately Liberal
## 125 239
## Strongly Liberal
## 224#Socioeconomic Status
describe(CC$Dem_SES)## CC$Dem_SES
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 10 0.985 6.274 2.76 2 3
## .25 .50 .75 .90 .95
## 4 7 8 9 10
##
## Value 1 2 3 4 5 6 7 8 9 10
## Frequency 35 40 91 97 99 108 183 148 122 83
## Proportion 0.035 0.040 0.090 0.096 0.098 0.107 0.182 0.147 0.121 0.083
##
## For the frequency table, variable is rounded to the nearest 0sd(CC$Dem_SES, na.rm = TRUE)## [1] 2.432426CC$SES <- factor(CC$Dem_SES, levels = c(1, 2, 3, 4, 5, 6, 7,8, 9, 10),
labels = c("Under $10,000", "$10,000-$19,999", "$20,000-$29,999", "$30,000-$39,999", "$40,000-$49,999", "$50,000-$74,999", "$75,000-$99,999", "$100,000-$149,999", "$150,000 or more", "Prefer not to say"))
table(CC$SES)##
## Under $10,000 $10,000-$19,999 $20,000-$29,999 $30,000-$39,999
## 35 40 91 97
## $40,000-$49,999 $50,000-$74,999 $75,000-$99,999 $100,000-$149,999
## 99 108 183 148
## $150,000 or more Prefer not to say
## 122 83hist(CC$Dem_SES, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
# Living Environment (1 = Urban; 2 = Suburban; 3 = Rural)
describe(CC$Dem_Living)## CC$Dem_Living
## n missing distinct Info Mean Gmd
## 1007 0 3 0.824 1.916 0.7145
##
## Value 1 2 3
## Frequency 280 532 195
## Proportion 0.278 0.528 0.194
##
## For the frequency table, variable is rounded to the nearest 0III. Scales
a. Aversion to Tampering with Nature
#Aversion to Tampering with Nature
#Aversion to Tampering with Nature Item Definitions
CC$ATNS_1 <- as.numeric(as.character(CC$ATNS_1_36))
CC$ATNS_2 <- as.numeric(as.character(CC$ATNS_1_37))
CC$ATNS_3 <- as.numeric(as.character(CC$ATNS_1_38))
CC$ATNS_4 <- as.numeric(as.character(CC$ATNS_1_39))
CC$ATNS_5 <- as.numeric(as.character(CC$ATNS_1_40))
#Recode item 2
CC$ATNS_2R <- (100- CC$ATNS_2)
#Aversion to Tampering with Nature Scale Descriptives (No reversed codes)
describe(CC$ATNS_1)## CC$ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.999 49.63 30.88 5.00 15.00
## .25 .50 .75 .90 .95
## 27.25 50.00 70.00 88.00 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100sd(CC$ATNS_1)## [1] NArange(CC$ATNS_1, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_2)## CC$ATNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.999 42.53 32.5 0.0 2.0
## .25 .50 .75 .90 .95
## 19.5 41.0 63.0 82.0 91.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100sd(CC$ATNS_2)## [1] 28.28759range(CC$ATNS_2, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_3)## CC$ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1005 2 101 0.999 49.43 32.8 0.0 10.4
## .25 .50 .75 .90 .95
## 27.0 50.0 70.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(CC$ATNS_3)## [1] NArange(CC$ATNS_3, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_4)## CC$ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.998 61.51 30.6 12 21
## .25 .50 .75 .90 .95
## 45 64 82 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(CC$ATNS_4)## [1] 26.89782range(CC$ATNS_4, na.rm=TRUE)## [1] 0 100describe(CC$ATNS_5)## CC$ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 101 0.999 54.71 33.05 3.3 13.0
## .25 .50 .75 .90 .95
## 32.0 57.0 76.0 96.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(CC$ATNS_5)## [1] 28.8092range(CC$ATNS_5, na.rm=TRUE)## [1] 0 100#Aversion to Tampering with Nature Scale Histograms by Item (No reversed codes)
hist(CC$ATNS_1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks.')
hist(CC$ATNS_2, main = 'ATNS #2: People who say we shouldn’t tamper with nature are just being naïve.')
hist(CC$ATNS_3, main = 'ATNS #3: Human beings have no right to meddle with the natural environment.')
hist(CC$ATNS_4, main = 'ATNS #4: I would prefer to live in a world where humans leave nature alone.')
hist(CC$ATNS_5, main = 'ATNS #5: Altering nature will be our downfall as a species.')
#Cronbach's Alpha (4 and 5 reverse coded)
CC$ATNS_Scale <- data.frame(CC$ATNS_1, CC$ATNS_2R, CC$ATNS_3, CC$ATNS_4, CC$ATNS_5)
CC$ATNS_Score <- rowMeans(CC [, c("ATNS_1", "ATNS_2R", "ATNS_3", "ATNS_4", "ATNS_5")], na.rm=TRUE)
psych::alpha(CC$ATNS_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.81 0.49 4.8 0.0085 55 21 0.52
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.83 0.85
## Duhachek 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.ATNS_1 0.85 0.85 0.81 0.58 5.6 0.0079 0.0053 0.58
## CC.ATNS_2R 0.81 0.81 0.78 0.51 4.2 0.0098 0.0211 0.51
## CC.ATNS_3 0.76 0.76 0.72 0.44 3.2 0.0122 0.0164 0.45
## CC.ATNS_4 0.77 0.77 0.73 0.45 3.3 0.0118 0.0156 0.45
## CC.ATNS_5 0.78 0.78 0.75 0.46 3.5 0.0115 0.0252 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ATNS_1 1006 0.62 0.63 0.47 0.43 50 27
## CC.ATNS_2R 1007 0.74 0.74 0.64 0.58 57 28
## CC.ATNS_3 1005 0.84 0.84 0.81 0.73 49 29
## CC.ATNS_4 1007 0.83 0.83 0.80 0.72 62 27
## CC.ATNS_5 1007 0.81 0.81 0.75 0.69 55 29describe(CC$ATNS_Scale)## CC$ATNS_Scale
##
## 5 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.999 49.63 30.88 5.00 15.00
## .25 .50 .75 .90 .95
## 27.25 50.00 70.00 88.00 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_2R
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.999 57.47 32.5 9.0 18.0
## .25 .50 .75 .90 .95
## 37.0 59.0 80.5 98.0 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1005 2 101 0.999 49.43 32.8 0.0 10.4
## .25 .50 .75 .90 .95
## 27.0 50.0 70.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 99 0.998 61.51 30.6 12 21
## .25 .50 .75 .90 .95
## 45 64 82 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 101 0.999 54.71 33.05 3.3 13.0
## .25 .50 .75 .90 .95
## 32.0 57.0 76.0 96.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$ATNS_Score)## [1] 21.4981#Pearsons r Correlation
correlation <- cor(CC$ATNS_Scale, method = 'pearson')
correlation## CC.ATNS_1 CC.ATNS_2R CC.ATNS_3 CC.ATNS_4 CC.ATNS_5
## CC.ATNS_1 1 NA NA NA NA
## CC.ATNS_2R NA 1.0000000 NA 0.5384829 0.4918514
## CC.ATNS_3 NA NA 1 NA NA
## CC.ATNS_4 NA 0.5384829 NA 1.0000000 0.6127687
## CC.ATNS_5 NA 0.4918514 NA 0.6127687 1.0000000b. 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.i. Descriptives
# Define Variables
CC$Ben_AFSCS <- CC$Ben_AFSCS_18
CC$Ben_BIO <- CC$Ben_BIO_18
CC$Ben_BECCS <- CC$Ben_BECCS_18
CC$Ben_DACCS <- CC$Ben_DACCS_18
CC$Ben_EW <- CC$Ben_EW_18
CC$Ben_OF <- CC$Ben_OF_18
CC$Ben_BF <- CC$Ben_BF_18
CC$Ben_NE <- CC$Ben_NE_18
CC$Ben_SE <- CC$Ben_SE_18
CC$Ben_WE <- CC$Ben_WE_18
#Descriptives
describe(CC$Ben_AFSCS)## CC$Ben_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 80 0.999 68.42 26.15 22.1 37.0
## .25 .50 .75 .90 .95
## 55.5 72.0 85.0 97.0 100.0
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100sd(CC$Ben_AFSCS, na.rm = TRUE)## [1] 23.72132hist(CC$Ben_AFSCS)
describe(CC$Ben_BIO)## CC$Ben_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 85 0.999 53.47 29.28 6.10 20.00
## .25 .50 .75 .90 .95
## 33.00 56.50 72.25 86.00 92.45
##
## lowest : 0 1 3 5 7, highest: 95 97 98 99 100sd(CC$Ben_BIO, na.rm = TRUE)## [1] 25.6215hist(CC$Ben_BIO)
describe(CC$Ben_BECCS) ## CC$Ben_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.999 55 29.13 10.00 18.00
## .25 .50 .75 .90 .95
## 36.00 57.00 74.75 88.00 95.00
##
## lowest : 0 1 3 6 7, highest: 94 95 96 97 100sd(CC$Ben_BECCS, na.rm = TRUE)## [1] 25.51696hist(CC$Ben_BECCS)
describe(CC$Ben_DACCS)## CC$Ben_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 89 0.999 55.35 30.26 3.0 15.0
## .25 .50 .75 .90 .95
## 37.0 59.0 75.0 90.0 99.4
##
## lowest : 0 1 2 3 5, highest: 93 95 96 98 100sd(CC$Ben_DACCS, na.rm = TRUE)## [1] 26.63817hist(CC$Ben_DACCS)
describe(CC$Ben_EW)## CC$Ben_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.999 52.15 27.98 0.0 13.8
## .25 .50 .75 .90 .95
## 37.0 55.0 70.0 81.2 90.0
##
## lowest : 0 3 4 5 6, highest: 95 96 97 99 100sd(CC$Ben_EW, na.rm = TRUE)## [1] 24.84342hist(CC$Ben_EW)
describe(CC$Ben_OF)## CC$Ben_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 54.54 28.93 7.6 17.0
## .25 .50 .75 .90 .95
## 36.0 58.0 74.5 86.0 91.7
##
## lowest : 0 2 4 5 7, highest: 92 93 95 96 100sd(CC$Ben_OF, na.rm = TRUE)## [1] 25.43145hist(CC$Ben_OF)
describe(CC$Ben_BF)## CC$Ben_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 51.92 30.47 3.05 10.00
## .25 .50 .75 .90 .95
## 34.00 57.00 70.00 85.00 94.30
##
## lowest : 0 1 2 5 6, highest: 93 95 96 97 100sd(CC$Ben_BF, na.rm = TRUE)## [1] 26.71672hist(CC$Ben_BF)
describe(CC$Ben_NE)## CC$Ben_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.999 60.18 30.92 0.0 19.2
## .25 .50 .75 .90 .95
## 44.0 66.0 80.0 92.4 98.4
##
## lowest : 0 6 9 10 11, highest: 94 95 97 98 100sd(CC$Ben_NE, na.rm = TRUE)## [1] 27.56813hist(CC$Ben_NE)
describe(CC$Ben_SE)## CC$Ben_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.998 66.31 29.55 10.4 25.0
## .25 .50 .75 .90 .95
## 50.0 71.0 86.0 100.0 100.0
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100sd(CC$Ben_SE, na.rm = TRUE)## [1] 26.49281hist(CC$Ben_SE)
describe(CC$Ben_WE) ## CC$Ben_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.998 64.88 28.99 9.6 25.0
## .25 .50 .75 .90 .95
## 51.0 68.0 85.0 100.0 100.0
##
## lowest : 0 5 6 8 10, highest: 96 97 98 99 100sd(CC$Ben_WE, na.rm = TRUE)## [1] 26.12863hist(CC$Ben_WE)
ii. Score(s) & Scale(s)
# Note: Benefit Scores & scales not present because measure is one item.)c. Climate Change Belief
i. Descriptives
#Climate Change Belief Item Definitions
CC$CCB1 <- as.numeric(as.character(CC$CCB_1_48))
CC$CCB2 <- as.numeric(as.character(CC$CCB_1_49))
CC$CCB3 <- as.numeric(as.character(CC$CCB_1_50))
CC$CCB4 <- as.numeric(as.character(CC$CCB_1_51))
#Climate Change Belief Descriptives
describe(CC$CCB1)## CC$CCB1
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 73 0.859 86.91 19.52 33.25 58.00
## .25 .50 .75 .90 .95
## 83.25 100.00 100.00 100.00 100.00
##
## lowest : 0 8 11 13 15, highest: 96 97 98 99 100range(CC$CCB1, na.rm=TRUE)## [1] 0 100sd(CC$CCB1, na.rm=TRUE)## [1] 21.93689describe(CC$CCB2)## CC$CCB2
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 87 0.89 83.6 23.83 19 50
## .25 .50 .75 .90 .95
## 79 98 100 100 100
##
## lowest : 0 3 5 7 8, highest: 96 97 98 99 100range(CC$CCB2, na.rm=TRUE)## [1] 0 100sd(CC$CCB2, na.rm=TRUE)## [1] 25.67106describe(CC$CCB3)## CC$CCB3
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 89 0.936 79.65 27.61 4 35
## .25 .50 .75 .90 .95
## 70 91 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(CC$CCB3, na.rm=TRUE)## [1] 0 100sd(CC$CCB3, na.rm=TRUE)## [1] 28.29363describe(CC$CCB4)## CC$CCB4
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 86 0.98 76.35 27.33 15 40
## .25 .50 .75 .90 .95
## 65 85 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(CC$CCB4, na.rm=TRUE)## [1] 0 100sd(CC$CCB4, na.rm=TRUE)## [1] 26.23131#Climate Change Belief Histograms
hist(CC$CCB1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(CC$CCB2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(CC$CCB3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(CC$CCB4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
ii. Score(s) & Scale(s)
#Score & Scale
CC$CCB_Score <- rowMeans(CC[, c('CCB1', 'CCB2', 'CCB3','CCB4')], na.rm=T)
describe(CC$CCB_Score)## CC$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 250 0.987 81.61 23.23 25.00 47.05
## .25 .50 .75 .90 .95
## 75.00 91.25 98.88 100.00 100.00
##
## lowest : 0 2 3.75 4 4.75 , highest: 99 99.25 99.5 99.75 100CC$CCB_Scale <- data.frame(CC$CCB_1_48, CC$CCB_1_49, CC$CCB_1_50, CC$CCB_1_51)
describe(CC$CCB_Scale)## CC$CCB_Scale
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.CCB_1_48
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 73 0.859 86.91 19.52 33.25 58.00
## .25 .50 .75 .90 .95
## 83.25 100.00 100.00 100.00 100.00
##
## lowest : 0 8 11 13 15, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_49
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 87 0.89 83.6 23.83 19 50
## .25 .50 .75 .90 .95
## 79 98 100 100 100
##
## lowest : 0 3 5 7 8, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_50
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 89 0.936 79.65 27.61 4 35
## .25 .50 .75 .90 .95
## 70 91 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CCB_1_51
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 86 0.98 76.35 27.33 15 40
## .25 .50 .75 .90 .95
## 65 85 100 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Cronbach's Alpha
psych::alpha(CC$CCB_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.93 0.8 16 0.0031 82 24 0.8
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.94
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CCB_1_48 0.93 0.93 0.90 0.82 13.4 0.0038 0.0013 0.82
## CC.CCB_1_49 0.90 0.91 0.87 0.77 9.8 0.0049 0.0033 0.78
## CC.CCB_1_50 0.91 0.92 0.89 0.78 11.0 0.0048 0.0069 0.78
## CC.CCB_1_51 0.93 0.94 0.92 0.83 15.1 0.0036 0.0024 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CCB_1_48 1006 0.90 0.91 0.87 0.84 87 22
## CC.CCB_1_49 1006 0.95 0.95 0.94 0.91 84 26
## CC.CCB_1_50 1007 0.94 0.93 0.91 0.88 80 28
## CC.CCB_1_51 1007 0.90 0.89 0.84 0.82 76 26#Correlation CCB
cor(CC$CCB_Scale, use= "complete.obs")## CC.CCB_1_48 CC.CCB_1_49 CC.CCB_1_50 CC.CCB_1_51
## CC.CCB_1_48 1.0000000 0.8696179 0.7786829 0.7037607
## CC.CCB_1_49 0.8696179 1.0000000 0.8542424 0.7818553
## CC.CCB_1_50 0.7786829 0.8542424 1.0000000 0.8154086
## CC.CCB_1_51 0.7037607 0.7818553 0.8154086 1.0000000d. Connectedness to Nature
i. Descriptives
#Connectedness to Nature Item Definitions
CC$CNS_1 <- as.numeric(as.character(CC$CNS_1_47))
CC$CNS_2 <- as.numeric(as.character(CC$CNS_1_48))
CC$CNS_3 <- as.numeric(as.character(CC$CNS_1_49))
CC$CNS_4 <- as.numeric(as.character(CC$CNS_1_50))
CC$CNS_5 <- as.numeric(as.character(CC$CNS_1_51))
#Descriptives
describe(CC$CNS_1)## CC$CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 97 0.998 66.82 27.67 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100range(CC$CNS_1, na.rm=TRUE)## [1] 0 100describe(CC$CNS_2)## CC$CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 94 0.995 73.34 24.81 25.0 43.6
## .25 .50 .75 .90 .95
## 62.0 78.0 90.5 100.0 100.0
##
## lowest : 0 5 7 8 10, highest: 96 97 98 99 100range(CC$CNS_2, na.rm=TRUE)## [1] 0 100describe(CC$CNS_3)## CC$CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 98 0.996 65.79 32.06 0 17
## .25 .50 .75 .90 .95
## 51 70 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(CC$CNS_3, na.rm=TRUE)## [1] 0 100describe(CC$CNS_4)## CC$CNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.996 39.73 36.96 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 33.00 67.75 89.50 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(CC$CNS_4, na.rm=TRUE)## [1] 0 100describe(CC$CNS_5)## CC$CNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 98 0.999 49.45 34.86 0.0 4.6
## .25 .50 .75 .90 .95
## 23.0 51.0 72.5 90.0 100.0
##
## lowest : 0 1 3 4 5, highest: 95 97 98 99 100range(CC$CNS_5, na.rm=TRUE)## [1] 0 100#Histograms
hist(CC$CNS_1, main = 'I often feel a sense of oneness with the natural world around me.')
hist(CC$CNS_2, main = 'I think of the natural world as a community to which I belong.')
hist(CC$CNS_3, main = 'I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’.')
hist(CC$CNS_4, main = 'My personal welfare is independent of the welfare of the natural world.')
hist(CC$CNS_5, main = 'When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.')
#Recode items 4 and 5
CC$CNS_4R <- (100 - CC$CNS_4)
CC$CNS_5R <- (100 - CC$CNS_5)ii. Score(s) & Scale(s)
#Score & Scale
CC$CNS_Score <- rowMeans(CC [, c("CNS_1", "CNS_2", "CNS_3", "CNS_4R", "CNS_5R")], na.rm=TRUE)
describe(CC$CNS_Score)## CC$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 322 1 63.36 18.72 35.00 43.12
## .25 .50 .75 .90 .95
## 52.90 63.00 74.60 84.88 91.80
##
## lowest : 0 8.6 10 12.8 16 , highest: 97.8 98.2 98.6 99.6 100CC$CNS_Scale2 <- data.frame(CC$CNS_1, CC$CNS_2, CC$CNS_3, CC$CNS_4R, CC$CNS_5R)
psych::alpha(CC$CNS_Scale2)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CNS_Scale2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.54 0.59 0.63 0.22 1.4 0.024 63 17 0.081
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.5 0.54 0.59
## Duhachek 0.5 0.54 0.59
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CNS_1 0.38 0.41 0.44 0.15 0.70 0.032 0.045 0.081
## CC.CNS_2 0.39 0.41 0.45 0.15 0.70 0.032 0.053 0.071
## CC.CNS_3 0.42 0.46 0.51 0.17 0.85 0.031 0.066 0.068
## CC.CNS_4R 0.63 0.66 0.67 0.33 1.93 0.020 0.092 0.314
## CC.CNS_5R 0.58 0.64 0.66 0.30 1.75 0.023 0.108 0.311
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CNS_1 1007 0.70 0.75 0.75 0.494 67 25
## CC.CNS_2 1007 0.70 0.75 0.74 0.511 73 23
## CC.CNS_3 1006 0.68 0.70 0.63 0.411 66 29
## CC.CNS_4R 1006 0.47 0.41 0.13 0.096 60 33
## CC.CNS_5R 1007 0.50 0.45 0.18 0.157 51 30describe(CC$CNS_Scale2)## CC$CNS_Scale2
##
## 5 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 97 0.998 66.82 27.67 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 94 0.995 73.34 24.81 25.0 43.6
## .25 .50 .75 .90 .95
## 62.0 78.0 90.5 100.0 100.0
##
## lowest : 0 5 7 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 98 0.996 65.79 32.06 0 17
## .25 .50 .75 .90 .95
## 51 70 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_4R
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 100 0.996 60.27 36.96 0.00 10.50
## .25 .50 .75 .90 .95
## 32.25 67.00 86.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_5R
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 98 0.999 50.55 34.86 0.0 10.0
## .25 .50 .75 .90 .95
## 27.5 49.0 77.0 95.4 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------## Drop reverse coded items
CC$CNS_Scale <- data.frame(CC$CNS_1, CC$CNS_2, CC$CNS_3)
psych::alpha(CC$CNS_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.82 0.76 0.6 4.5 0.01 69 22 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.81 0.83
## Duhachek 0.79 0.81 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CNS_1 0.69 0.70 0.54 0.54 2.4 0.019 NA 0.54
## CC.CNS_2 0.73 0.73 0.58 0.58 2.7 0.017 NA 0.58
## CC.CNS_3 0.80 0.80 0.67 0.67 4.1 0.012 NA 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CNS_1 1007 0.87 0.88 0.79 0.70 67 25
## CC.CNS_2 1007 0.84 0.86 0.77 0.68 73 23
## CC.CNS_3 1006 0.85 0.83 0.67 0.61 66 29describe(CC$CNS_Scale)## CC$CNS_Scale
##
## 3 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 97 0.998 66.82 27.67 16 33
## .25 .50 .75 .90 .95
## 51 70 85 100 100
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 94 0.995 73.34 24.81 25.0 43.6
## .25 .50 .75 .90 .95
## 62.0 78.0 90.5 100.0 100.0
##
## lowest : 0 5 7 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 98 0.996 65.79 32.06 0 17
## .25 .50 .75 .90 .95
## 51 70 87 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Correlation CCB
cor(CC$CNS_Scale, use= "complete.obs")## CC.CNS_1 CC.CNS_2 CC.CNS_3
## CC.CNS_1 1.0000000 0.6736904 0.5782495
## CC.CNS_2 0.6736904 1.0000000 0.5437057
## CC.CNS_3 0.5782495 0.5437057 1.0000000e. Control
# Control was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented perception of control over the technology rated.
## 1. We have control over the processes in this method.i. Descriptives
# Define Variables
CC$Control_AFSCS <- CC$Risk_AFSCS_34
CC$Control_BIO <- CC$Risk_BIO_34
CC$Control_BECCS <- CC$Risk_BECCS_34
CC$Control_DACCS <- CC$Risk_DACCS_34
CC$Control_EW <- CC$Risk_EW_34
CC$Control_OF <- CC$Risk_OF_34
CC$Control_BF <- CC$Risk_BF_34
CC$Control_NE <- CC$Risk_NE_34
CC$Control_SE <- CC$Risk_SE_34
CC$Control_WE <- CC$Risk_WE_34
# Descriptives
describe(CC$Control_AFSCS)## CC$Control_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 71 0.997 74.48 22.1 36.0 50.2
## .25 .50 .75 .90 .95
## 65.0 77.0 88.0 100.0 100.0
##
## lowest : 0 5 7 8 20, highest: 96 97 98 99 100sd(CC$Control_AFSCS, na.rm = TRUE)## [1] 20.53265hist(CC$Control_AFSCS)
describe(CC$Control_BIO)## CC$Control_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 77 0.999 68.99 24.04 29.55 41.10
## .25 .50 .75 .90 .95
## 54.00 71.50 85.00 96.00 100.00
##
## lowest : 0 5 9 14 16, highest: 95 96 98 99 100sd(CC$Control_BIO, na.rm = TRUE)## [1] 21.35812hist(CC$Control_BIO)
describe(CC$Control_BECCS)## CC$Control_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 79 0.999 62.23 26.17 20 30
## .25 .50 .75 .90 .95
## 47 65 78 90 100
##
## lowest : 0 10 12 13 15, highest: 95 96 98 99 100sd(CC$Control_BECCS, na.rm = TRUE)## [1] 23.21631hist(CC$Control_BECCS)
describe(CC$Control_DACCS)## CC$Control_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 87 0.999 56.9 28.7 14.0 21.6
## .25 .50 .75 .90 .95
## 40.0 57.0 75.0 90.4 100.0
##
## lowest : 0 1 8 9 10, highest: 94 95 97 99 100sd(CC$Control_DACCS, na.rm = TRUE)## [1] 25.17765hist(CC$Control_DACCS)
describe(CC$Control_EW) ## CC$Control_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 82 0.999 54.69 26.2 14.0 25.0
## .25 .50 .75 .90 .95
## 39.0 55.0 71.0 85.6 92.0
##
## lowest : 0 9 10 12 13, highest: 92 94 95 99 100sd(CC$Control_EW, na.rm = TRUE)## [1] 23.03235hist(CC$Control_EW) 
describe(CC$Control_OF)## CC$Control_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 1 47.47 30.01 5.0 12.0
## .25 .50 .75 .90 .95
## 27.5 47.0 67.5 82.0 91.0
##
## lowest : 0 1 2 3 4, highest: 94 95 98 99 100sd(CC$Control_OF, na.rm = TRUE)## [1] 26.10864hist(CC$Control_OF)
describe(CC$Control_BF)## CC$Control_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 65 0.996 77.06 21.54 36.70 50.70
## .25 .50 .75 .90 .95
## 68.75 80.00 94.00 100.00 100.00
##
## lowest : 0 5 15 25 29, highest: 96 97 98 99 100sd(CC$Control_BF, na.rm = TRUE)## [1] 19.74781hist(CC$Control_BF)
describe(CC$Control_NE)## CC$Control_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 73 0.999 66.94 27.69 20 33
## .25 .50 .75 .90 .95
## 51 71 86 98 100
##
## lowest : 0 5 7 9 10, highest: 95 96 98 99 100sd(CC$Control_NE, na.rm = TRUE)## [1] 24.68996hist(CC$Control_NE)
describe(CC$Control_SE)## CC$Control_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 67 0.992 76.01 25.42 28.4 39.4
## .25 .50 .75 .90 .95
## 63.0 82.0 95.0 100.0 100.0
##
## lowest : 0 7 9 10 13, highest: 96 97 98 99 100sd(CC$Control_SE, na.rm = TRUE)## [1] 23.73107hist(CC$Control_SE)
describe(CC$Control_WE)## CC$Control_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 68 0.997 71.25 27.61 20.8 34.6
## .25 .50 .75 .90 .95
## 58.0 79.0 90.0 100.0 100.0
##
## lowest : 0 4 10 12 15, highest: 96 97 98 99 100sd(CC$Control_WE, na.rm = TRUE)## [1] 25.13561hist(CC$Control_WE)
ii. Score(s) & Scale(s)
# Note: Control scores & scales not present because measure is one item.)f. Familiarity
# Familiarity was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented participant familiarity with the technology rated.
## 1. This is familiar.i. Descriptives
#Define Variables
CC$Familiar_AFSCS <- CC$Risk_AFSCS_31
CC$Familiar_BIO <- CC$Risk_BIO_31
CC$Familiar_BECCS <- CC$Risk_BECCS_31
CC$Familiar_DACCS <- CC$Risk_DACCS_31
CC$Familiar_EW <- CC$Risk_EW_31
CC$Familiar_OF <- CC$Risk_OF_31
CC$Familiar_BF <- CC$Risk_BF_31
CC$Familiar_NE <- CC$Risk_NE_31
CC$Familiar_SE <- CC$Risk_SE_31
CC$Familiar_WE <- CC$Risk_WE_31
# Descriptives
describe(CC$Familiar_AFSCS)## CC$Familiar_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 91 0.997 62.7 34.53 3 12
## .25 .50 .75 .90 .95
## 42 67 89 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(CC$Familiar_AFSCS, na.rm = TRUE)## [1] 30.60777hist(CC$Familiar_AFSCS)
describe(CC$Familiar_BIO)## CC$Familiar_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 81 0.993 27.79 29.57 0.00 0.00
## .25 .50 .75 .90 .95
## 4.75 20.00 44.00 68.90 82.00
##
## lowest : 0 1 2 3 4, highest: 92 93 94 95 100sd(CC$Familiar_BIO, na.rm = TRUE)## [1] 27.00687hist(CC$Familiar_BIO)
describe(CC$Familiar_BECCS)## CC$Familiar_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.994 29.64 30.74 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 21.00 50.00 73.00 83.55
##
## lowest : 0 1 2 3 4, highest: 91 92 94 98 100sd(CC$Familiar_BECCS, na.rm = TRUE)## [1] 27.82hist(CC$Familiar_BECCS)
describe(CC$Familiar_DACCS)## CC$Familiar_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.992 26.05 27.55 0.0 0.0
## .25 .50 .75 .90 .95
## 4.5 20.0 42.0 65.0 75.0
##
## lowest : 0 1 2 3 4, highest: 89 90 93 99 100sd(CC$Familiar_DACCS, na.rm = TRUE)## [1] 25.08586hist(CC$Familiar_DACCS)
describe(CC$Familiar_EW)## CC$Familiar_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 76 0.98 22.5 25.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 17.0 35.5 60.0 70.0
##
## lowest : 0 1 2 3 4, highest: 79 80 87 90 91sd(CC$Familiar_EW, na.rm = TRUE)## [1] 23.20217hist(CC$Familiar_EW)
describe(CC$Familiar_OF)## CC$Familiar_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 76 0.992 25.62 27.66 0.0 0.0
## .25 .50 .75 .90 .95
## 4.0 18.0 40.5 62.8 76.0
##
## lowest : 0 1 2 3 4, highest: 85 86 87 89 100sd(CC$Familiar_OF, na.rm = TRUE)## [1] 25.34433hist(CC$Familiar_OF)
describe(CC$Familiar_BF)## CC$Familiar_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 87 0.999 57.92 32.61 0.0 18.0
## .25 .50 .75 .90 .95
## 36.0 61.0 81.0 93.3 100.0
##
## lowest : 0 1 5 6 8, highest: 95 96 98 99 100sd(CC$Familiar_BF, na.rm = TRUE)## [1] 28.59492hist(CC$Familiar_BF)
describe(CC$Familiar_NE)## CC$Familiar_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 82 0.998 69.17 29.4 14.8 30.6
## .25 .50 .75 .90 .95
## 53.0 75.0 90.0 100.0 100.0
##
## lowest : 0 2 3 4 6, highest: 95 97 98 99 100sd(CC$Familiar_NE, na.rm = TRUE)## [1] 26.59004hist(CC$Familiar_NE)
describe(CC$Familiar_SE)## CC$Familiar_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 49 0.941 87.95 15.68 52.2 65.2
## .25 .50 .75 .90 .95
## 82.0 94.0 100.0 100.0 100.0
##
## lowest : 0 18 35 41 45, highest: 96 97 98 99 100sd(CC$Familiar_SE, na.rm = TRUE)## [1] 16.02333hist(CC$Familiar_SE)
describe(CC$Familiar_WE)## CC$Familiar_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 61 0.982 81.79 20.9 41.6 55.0
## .25 .50 .75 .90 .95
## 75.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 3 13 19, highest: 96 97 98 99 100sd(CC$Familiar_WE, na.rm = TRUE)## [1] 20.79082hist(CC$Familiar_WE)
ii. Score(s) & Scale(s)
# Note: Familiarity scores & scales not present because measure is one item.)g. Ideology
i. Descriptives
ii. Score(s) & Scale(s)
#Political Orientation
##Which of the following best describes your political orientation? ( 1 = Strongly Conservative to 7 = Strongly Liberal)
library(dplyr)
describe(CC$PI_Orientation)## CC$PI_Orientation
## n missing distinct Info Mean Gmd
## 1007 0 7 0.966 4.807 2.078
##
## Value 1 2 3 4 5 6 7
## Frequency 62 102 72 183 125 239 224
## Proportion 0.062 0.101 0.071 0.182 0.124 0.237 0.222
##
## For the frequency table, variable is rounded to the nearest 0CC$Orientation <- ifelse(CC$PI_Orientation == 1, 3,
ifelse(CC$PI_Orientation == 2, 2,
ifelse(CC$PI_Orientation == 3, 1,
ifelse(CC$PI_Orientation == 4, 0,
ifelse(CC$PI_Orientation == 5, -1,
ifelse(CC$PI_Orientation == 6, -2,
ifelse(CC$PI_Orientation == 7, -3, NA)))))))
describe(CC$Orientation)## CC$Orientation
## n missing distinct Info Mean Gmd
## 1007 0 7 0.966 -0.8073 2.078
##
## Value -3 -2 -1 0 1 2 3
## Frequency 224 239 125 183 72 102 62
## Proportion 0.222 0.237 0.124 0.182 0.071 0.101 0.062
##
## For the frequency table, variable is rounded to the nearest 0hist(CC$Orientation , main = 'Political Orientation (Liberal to Conservative)')
#Political Party Identification
##Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what? (1 = Republican, 2 = Democrat, 3 = Independent, 4 = Other (write-in), 5 = No Preference)
describe(CC$PP_Party)## CC$PP_Party
## n missing distinct Info Mean Gmd
## 1006 1 5 0.854 2.252 0.9154
##
## Value 1 2 3 4 5
## Frequency 176 497 272 25 36
## Proportion 0.175 0.494 0.270 0.025 0.036
##
## For the frequency table, variable is rounded to the nearest 0CC$Party <- as.numeric(as.character(CC$PP_Party))
CC$DemStrength <- as.numeric(as.character(CC$PP_DemStrength))
CC$RepStrength <- as.numeric(as.character(CC$PP_RepStrength))
CC$PartyClose <- as.numeric(as.character(CC$PP_CloserTo))
# Recode Party
CC$PartyFull <- NA
CC$PartyFull[CC$DemStrength == 1] <- -3
CC$PartyFull[CC$DemStrength == 2] <- -2
CC$PartyFull[CC$PartyClose == 1] <- -1
CC$PartyFull[CC$PartyClose == 3] <- 0
CC$PartyFull[CC$PartyClose == 2] <- 1
CC$PartyFull[CC$RepStrength == 2] <- 2
CC$PartyFull[CC$RepStrength == 1] <- 3
describe(CC$PartyFull)## CC$PartyFull
## n missing distinct Info Mean Gmd
## 1006 1 7 0.956 -0.9284 2.211
##
## Value -3 -2 -1 0 1 2 3
## Frequency 317 180 122 148 63 92 84
## Proportion 0.315 0.179 0.121 0.147 0.063 0.091 0.083
##
## For the frequency table, variable is rounded to the nearest 0hist(CC$PartyFull , main = 'Party Identification')
# Correlation between party and orientation
cor.test(CC$PartyFull, CC$Orientation)##
## Pearson's product-moment correlation
##
## data: CC$PartyFull and CC$Orientation
## t = 48.246, df = 1004, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8162098 0.8535616
## sample estimates:
## cor
## 0.8358505#New Variable: Ideology
CC$Ideology <- rowMeans(CC[, c('PartyFull', 'Orientation')], na.rm=T)
describe(CC$Ideology)## CC$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 1007 0 13 0.987 -0.8684 2.067 -3.0 -3.0
## .25 .50 .75 .90 .95
## -2.5 -1.5 0.0 2.0 2.5
##
## Value -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
## Frequency 168 140 120 103 70 41 116 20 42 51 53
## Proportion 0.167 0.139 0.119 0.102 0.070 0.041 0.115 0.020 0.042 0.051 0.053
##
## Value 2.5 3.0
## Frequency 38 45
## Proportion 0.038 0.045
##
## For the frequency table, variable is rounded to the nearest 0hist(CC$Ideology)
h. Individualism/Collectivism
#Individualism and Collectivism Scale (Code adapted from J.Cole Collectivism Study)
#Individualism and collectivism were each measured with 4 items (for a total of 8 items) on a 1-7 scale of agreement (0 = 'Strongly disagree' to 100 = 'Strongly agree').
##Collectivism Items
###Individualism/Collectivism Item #3 (C): It is important to me to think of myself as a member of my religious, national, or ethnic group.
###Individualism/Collectivism Item #4 (C): Learning about the traditions, values, and beliefs of my family is important to me.
###Individualism/Collectivism Item #7 (C): In the end, a person feels closest to members of their own religious, national, or ethnic group.
###Individualism/Collectivism Item #8 (C): It is important to me to respect decisions made by my family.
##Individualism Items
###Individualism/Collectivism Item #1 (I): It is important to me to develop my own personal style.
###Individualism/Collectivism Item #2 (I): It is better for me to follow my own ideas than to follow those of anyone else.
###Individualism/Collectivism Item #5 (I): I enjoy being unique and different from others in many respects.
###Individualism/Collectivism Item #6 (I): My personal achievements and accomplishments are very important to who I am.
#Individualism (Items 1,2,5,6)
CC$Ind_1 <- as.numeric(as.character(CC$Individualism_54))
CC$Ind_2 <- as.numeric(as.character(CC$Individualism_55))
CC$Ind_5 <- as.numeric(as.character(CC$Individualism_58))
CC$Ind_6 <- as.numeric(as.character(CC$Individualism_59))
CC$Individualism_Score <- rowMeans(CC[, c('Ind_1', 'Ind_2', 'Ind_5','Ind_6')], na.rm=T)
#Collectivism (Items 3,4,7,8)
CC$Ind_3 <- as.numeric(as.character(CC$Individualism_56))
CC$Ind_4 <- as.numeric(as.character(CC$Individualism_57))
CC$Ind_7 <- as.numeric(as.character(CC$Individualism_60))
CC$Ind_8 <- as.numeric(as.character(CC$Individualism_69))
CC$Collectivism_Score <- rowMeans(CC[, c('Ind_3', 'Ind_4', 'Ind_7','Ind_8')], na.rm=T)
#Individualism Alpha and Histogram (4 items)
psych::alpha(data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5,CC$Ind_6))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5, CC$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.69 0.4 2.6 0.015 71 17 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.72 0.75
## Duhachek 0.69 0.72 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_1 0.56 0.56 0.47 0.30 1.3 0.024 0.010 0.31
## CC.Ind_2 0.75 0.75 0.69 0.50 3.0 0.014 0.015 0.48
## CC.Ind_5 0.61 0.61 0.54 0.34 1.6 0.022 0.021 0.36
## CC.Ind_6 0.69 0.70 0.64 0.44 2.3 0.017 0.031 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 1007 0.83 0.84 0.80 0.67 74 22
## CC.Ind_2 1007 0.64 0.63 0.41 0.35 67 23
## CC.Ind_5 1007 0.78 0.79 0.72 0.59 72 22
## CC.Ind_6 1007 0.70 0.70 0.53 0.45 70 23hist(CC$Individualism_Score , main = 'Individualism Score')
#Collectivism Alpha and Histogram (4 items)
psych::alpha(data.frame(CC$Ind_3, CC$Ind_4, CC$Ind_7, CC$Ind_8))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Ind_3, CC$Ind_4, CC$Ind_7, CC$Ind_8))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.8 0.54 4.8 0.0089 54 24 0.56
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.83 0.84
## Duhachek 0.81 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_3 0.75 0.75 0.69 0.51 3.1 0.014 0.0156 0.44
## CC.Ind_4 0.76 0.76 0.69 0.52 3.2 0.013 0.0070 0.53
## CC.Ind_7 0.82 0.82 0.76 0.60 4.5 0.010 0.0037 0.62
## CC.Ind_8 0.79 0.79 0.73 0.55 3.7 0.011 0.0095 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 1007 0.86 0.85 0.78 0.71 44 32
## CC.Ind_4 1007 0.83 0.84 0.77 0.69 62 29
## CC.Ind_7 1006 0.76 0.76 0.63 0.57 53 28
## CC.Ind_8 1007 0.80 0.80 0.71 0.64 58 28hist(CC$Collectivism_Score , main = 'Collectivism Score')
#Cronbachs Alpha for Individualism and Collectivism scales
CC$IndScale <- data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5, CC$Ind_6)
psych::alpha(CC$IndScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$IndScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.69 0.4 2.6 0.015 71 17 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.72 0.75
## Duhachek 0.69 0.72 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_1 0.56 0.56 0.47 0.30 1.3 0.024 0.010 0.31
## CC.Ind_2 0.75 0.75 0.69 0.50 3.0 0.014 0.015 0.48
## CC.Ind_5 0.61 0.61 0.54 0.34 1.6 0.022 0.021 0.36
## CC.Ind_6 0.69 0.70 0.64 0.44 2.3 0.017 0.031 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 1007 0.83 0.84 0.80 0.67 74 22
## CC.Ind_2 1007 0.64 0.63 0.41 0.35 67 23
## CC.Ind_5 1007 0.78 0.79 0.72 0.59 72 22
## CC.Ind_6 1007 0.70 0.70 0.53 0.45 70 23CC$CollScale <- data.frame(CC$Ind_3, CC$Ind_4, CC$Ind_7, CC$Ind_8)
psych::alpha(CC$CollScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$CollScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.8 0.54 4.8 0.0089 54 24 0.56
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.83 0.84
## Duhachek 0.81 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_3 0.75 0.75 0.69 0.51 3.1 0.014 0.0156 0.44
## CC.Ind_4 0.76 0.76 0.69 0.52 3.2 0.013 0.0070 0.53
## CC.Ind_7 0.82 0.82 0.76 0.60 4.5 0.010 0.0037 0.62
## CC.Ind_8 0.79 0.79 0.73 0.55 3.7 0.011 0.0095 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 1007 0.86 0.85 0.78 0.71 44 32
## CC.Ind_4 1007 0.83 0.84 0.77 0.69 62 29
## CC.Ind_7 1006 0.76 0.76 0.63 0.57 53 28
## CC.Ind_8 1007 0.80 0.80 0.71 0.64 58 28i. Naturalness
# 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)i. Descriptives
#Define Variables
CC$Nat_1_AFSCS <- CC$Naturalness_AFSCS_30
CC$Nat_2R_AFSCS <- (100-CC$Naturalness_AFSCS_31)
CC$Nat_3R_AFSCS <- (100-CC$Naturalness_AFSCS_35)
CC$Nat_4R_AFSCS <- (100-CC$Naturalness_AFSCS_36)
CC$Nat_1_BIO <- CC$Naturalness_BIO_30
CC$Nat_2R_BIO <- (100-CC$Naturalness_BIO_31)
CC$Nat_3R_BIO <- (100-CC$Naturalness_BIO_35)
CC$Nat_4R_BIO <- (100-CC$Naturalness_BIO_36)
CC$Nat_1_BECCS <- CC$Naturalness_BECCS_30
CC$Nat_2R_BECCS <- (100-CC$Naturalness_BECCS_31)
CC$Nat_3R_BECCS <- (100-CC$Naturalness_BECCS_35)
CC$Nat_4R_BECCS <- (100-CC$Naturalness_BECCS_36)
CC$Nat_1_DACCS <- CC$Naturalness_DACCS_30
CC$Nat_2R_DACCS <- (100-CC$Naturalness_DACCS_31)
CC$Nat_3R_DACCS <- (100-CC$Naturalness_DACCS_35)
CC$Nat_4R_DACCS <- (100-CC$Naturalness_DACCS_36)
CC$Nat_1_EW <- CC$Naturalness_EW_30
CC$Nat_2R_EW <- (100-CC$Naturalness_EW_31)
CC$Nat_3R_EW <- (100-CC$Naturalness_EW_35)
CC$Nat_4R_EW <- (100-CC$Naturalness_EW_36)
CC$Nat_1_OF <- CC$Naturalness_OF_30
CC$Nat_2R_OF <- (100-CC$Naturalness_OF_31)
CC$Nat_3R_OF <- (100-CC$Naturalness_OF_35)
CC$Nat_4R_OF <- (100-CC$Naturalness_OF_36)
CC$Nat_1_BF <- CC$Naturalness_BF_30
CC$Nat_2R_BF <- (100-CC$Naturalness_BF_31)
CC$Nat_3R_BF <- (100-CC$Naturalness_BF_35)
CC$Nat_4R_BF <- (100-CC$Naturalness_BF_36)
CC$Nat_1_NE <- CC$Naturalness_NE_30
CC$Nat_2R_NE <- (100-CC$Naturalness_NE_31)
CC$Nat_3R_NE <- (100-CC$Naturalness_NE_35)
CC$Nat_4R_NE <- (100-CC$Naturalness_NE_36)
CC$Nat_1_SE <- CC$Naturalness_SE_30
CC$Nat_2R_SE <- (100-CC$Naturalness_SE_31)
CC$Nat_3R_SE <- (100-CC$Naturalness_SE_35)
CC$Nat_4R_SE <- (100-CC$Naturalness_SE_36)
CC$Nat_1_WE <- CC$Naturalness_WE_30
CC$Nat_2R_WE <- (100-CC$Naturalness_WE_31)
CC$Nat_3R_WE <- (100-CC$Naturalness_WE_35)
CC$Nat_4R_WE <- (100-CC$Naturalness_WE_36)
# Descriptives
describe(CC$Nat_1_AFSCS)## CC$Nat_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.991 74.92 27.28 19.1 37.0
## .25 .50 .75 .90 .95
## 60.5 83.0 95.0 100.0 100.0
##
## lowest : 0 3 6 7 10, highest: 96 97 98 99 100describe(CC$Nat_2R_AFSCS)## CC$Nat_2R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 95 0.999 53.22 35.19 0.0 14.0
## .25 .50 .75 .90 .95
## 30.0 50.0 82.5 97.8 100.0
##
## lowest : 0 2 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_3R_AFSCS)## CC$Nat_3R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 89 0.999 39.48 33.58 0.0 2.2
## .25 .50 .75 .90 .95
## 15.5 35.0 60.5 86.0 95.8
##
## lowest : 0 1 2 3 4, highest: 93 94 96 97 100describe(CC$Nat_4R_AFSCS)## CC$Nat_4R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.99 79.59 25.6 23.1 41.2
## .25 .50 .75 .90 .95
## 67.0 91.0 99.0 100.0 100.0
##
## lowest : 0 4 6 7 12, highest: 96 97 98 99 100describe(CC$Nat_1_BIO)## CC$Nat_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 0.999 45.61 31.02 0.0 6.1
## .25 .50 .75 .90 .95
## 25.0 46.0 64.0 83.9 96.0
##
## lowest : 0 2 3 4 5, highest: 90 95 96 97 100describe(CC$Nat_2R_BIO) ## CC$Nat_2R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 77 0.999 37.07 27.62 0.0 5.0
## .25 .50 .75 .90 .95
## 20.0 35.0 49.0 72.7 85.0
##
## lowest : 0 2 3 5 6, highest: 93 95 96 98 100describe(CC$Nat_3R_BIO)## CC$Nat_3R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 69 0.993 23.95 23.47 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 20.5 35.0 49.9 70.0
##
## lowest : 0 1 2 3 5, highest: 87 88 95 97 100describe(CC$Nat_4R_BIO) ## CC$Nat_4R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 96 0.999 49.87 35.67 0.0 8.0
## .25 .50 .75 .90 .95
## 25.0 49.0 78.0 95.9 100.0
##
## lowest : 0 1 4 5 6, highest: 95 96 97 99 100describe(CC$Nat_1_BECCS) ## CC$Nat_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 43.48 29.31 0.00 6.90
## .25 .50 .75 .90 .95
## 25.00 44.00 61.00 76.20 88.55
##
## lowest : 0 1 2 3 4, highest: 90 93 96 99 100describe(CC$Nat_2R_BECCS) ## CC$Nat_2R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 72 0.997 30.43 24.88 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 30.00 44.00 60.00 73.65
##
## lowest : 0 1 2 3 4, highest: 85 89 90 93 100describe(CC$Nat_3R_BECCS) ## CC$Nat_3R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 69 0.991 22.77 22.39 0.00 0.00
## .25 .50 .75 .90 .95
## 4.25 20.00 35.00 48.00 61.65
##
## lowest : 0 1 2 3 4, highest: 80 90 92 98 100describe(CC$Nat_4R_BECCS)## CC$Nat_4R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 41.83 31.45 0.0 6.0
## .25 .50 .75 .90 .95
## 20.0 39.0 60.0 82.1 93.0
##
## lowest : 0 2 3 4 5, highest: 93 94 95 98 100describe(CC$Nat_1_DACCS)## CC$Nat_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.996 29.22 27.38 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 25.0 41.0 63.4 81.7
##
## lowest : 0 1 3 4 5, highest: 94 95 97 98 100describe(CC$Nat_2R_DACCS) ## CC$Nat_2R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 78 0.995 27.79 26.85 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 23.0 39.0 66.8 78.7
##
## lowest : 0 1 2 3 4, highest: 87 90 91 99 100describe(CC$Nat_3R_DACCS)## CC$Nat_3R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 60 0.976 16.62 18.66 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 12.0 26.0 40.4 48.7
##
## lowest : 0 1 3 4 5, highest: 81 83 85 93 100describe(CC$Nat_4R_DACCS)## CC$Nat_4R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 79 0.995 28.49 27.02 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 41.5 63.0 82.0
##
## lowest : 0 1 3 4 5, highest: 88 89 95 98 100describe(CC$Nat_1_EW)## CC$Nat_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 91 0.999 46.07 31.02 0.0 7.0
## .25 .50 .75 .90 .95
## 25.5 50.0 67.0 81.0 89.0
##
## lowest : 0 2 3 4 5, highest: 91 92 95 98 100describe(CC$Nat_2R_EW)## CC$Nat_2R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 75 0.995 27.06 24.88 0.0 0.0
## .25 .50 .75 .90 .95
## 9.5 23.0 40.0 58.2 75.0
##
## lowest : 0 1 2 3 4, highest: 85 90 92 93 100describe(CC$Nat_3R_EW)## CC$Nat_3R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 70 0.994 25.62 23.99 0 0
## .25 .50 .75 .90 .95
## 7 24 39 50 70
##
## lowest : 0 1 2 3 4, highest: 86 88 90 95 100describe(CC$Nat_4R_EW)## CC$Nat_4R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 84 0.999 44.61 31.96 0 7
## .25 .50 .75 .90 .95
## 22 44 67 80 93
##
## lowest : 0 4 5 6 7, highest: 91 93 94 98 100describe(CC$Nat_1_OF)## CC$Nat_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 40.43 31.15 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 39.0 59.5 78.4 86.7
##
## lowest : 0 2 4 5 6, highest: 88 90 92 93 100describe(CC$Nat_2R_OF)## CC$Nat_2R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 65 0.996 22.48 21.68 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 19.0 32.0 46.0 60.7
##
## lowest : 0 1 3 4 5, highest: 80 81 82 89 100describe(CC$Nat_3R_OF)## CC$Nat_3R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 66 0.996 25.66 23.54 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 36.0 56.0 66.7
##
## lowest : 0 1 2 3 4, highest: 79 80 90 91 100describe(CC$Nat_4R_OF)## CC$Nat_4R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 87 0.999 38.51 30.22 0.0 5.0
## .25 .50 .75 .90 .95
## 16.5 38.0 55.0 77.4 91.7
##
## lowest : 0 2 3 4 5, highest: 92 93 95 99 100describe(CC$Nat_1_BF)## CC$Nat_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 88 0.999 52.38 31.87 2.00 12.40
## .25 .50 .75 .90 .95
## 34.00 51.00 75.00 90.60 99.65
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100describe(CC$Nat_2R_BF)## CC$Nat_2R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 79 0.998 38.07 30.26 0.00 0.00
## .25 .50 .75 .90 .95
## 20.00 35.00 54.00 79.30 90.65
##
## lowest : 0 2 3 4 5, highest: 90 91 95 99 100describe(CC$Nat_3R_BF)## CC$Nat_3R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 59 0.988 17.91 18.34 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 15.00 28.00 39.00 46.65
##
## lowest : 0 1 2 3 4, highest: 68 75 77 81 85describe(CC$Nat_4R_BF)## CC$Nat_4R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 84 0.999 48.67 33.41 1.35 10.00
## .25 .50 .75 .90 .95
## 26.00 49.00 73.25 90.30 98.65
##
## lowest : 0 1 2 4 5, highest: 95 96 98 99 100describe(CC$Nat_1_NE)## CC$Nat_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 80 0.995 31 28.97 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 27.0 49.0 68.2 80.4
##
## lowest : 0 1 2 3 4, highest: 89 90 93 95 100describe(CC$Nat_2R_NE)## CC$Nat_2R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 72 0.994 29.96 30.59 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 23.0 43.0 77.6 95.0
##
## lowest : 0 2 3 4 5, highest: 94 95 98 99 100describe(CC$Nat_3R_NE)## CC$Nat_3R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 48 0.931 11.18 14.75 0 0
## .25 .50 .75 .90 .95
## 0 6 17 33 43
##
## lowest : 0 1 2 3 4, highest: 49 50 64 92 100describe(CC$Nat_4R_NE)## CC$Nat_4R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.996 32.51 30.64 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 27.0 48.0 77.4 90.0
##
## lowest : 0 4 5 6 7, highest: 92 95 96 99 100describe(CC$Nat_1_SE)## CC$Nat_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.991 72.97 29.25 10.2 30.4
## .25 .50 .75 .90 .95
## 60.0 80.0 95.0 100.0 100.0
##
## lowest : 0 1 4 6 10, highest: 95 97 98 99 100describe(CC$Nat_2R_SE)## CC$Nat_2R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 77 0.989 66.22 35.98 6.2 18.4
## .25 .50 .75 .90 .95
## 38.0 78.0 96.0 100.0 100.0
##
## lowest : 0 1 5 6 7, highest: 96 97 98 99 100describe(CC$Nat_3R_SE)## CC$Nat_3R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 52 0.964 14.8 18.23 0 0
## .25 .50 .75 .90 .95
## 0 10 22 39 50
##
## lowest : 0 1 2 3 4, highest: 70 71 76 80 93describe(CC$Nat_4R_SE)## CC$Nat_4R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 76 0.997 66.2 33.02 11.0 22.2
## .25 .50 .75 .90 .95
## 47.0 74.0 92.0 100.0 100.0
##
## lowest : 0 5 7 8 11, highest: 96 97 98 99 100describe(CC$Nat_1_WE)## CC$Nat_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.993 70 30.85 9.8 21.6
## .25 .50 .75 .90 .95
## 57.0 78.0 92.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100describe(CC$Nat_2R_WE)## CC$Nat_2R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.991 63.74 35.55 10 20
## .25 .50 .75 .90 .95
## 38 72 93 100 100
##
## lowest : 0 1 5 8 10, highest: 96 97 98 99 100describe(CC$Nat_3R_WE)## CC$Nat_3R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.987 20.75 22.27 0.0 0.0
## .25 .50 .75 .90 .95
## 2.0 17.0 30.0 47.0 65.8
##
## lowest : 0 1 2 3 4, highest: 85 88 90 94 100describe(CC$Nat_4R_WE)## CC$Nat_4R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.997 62.96 35.06 4.8 14.2
## .25 .50 .75 .90 .95
## 42.0 68.0 90.0 100.0 100.0
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100sd(CC$Nat_1_AFSCS, na.rm = TRUE)## [1] 25.49721sd(CC$Nat_2R_AFSCS, na.rm = TRUE)## [1] 30.55137sd(CC$Nat_3R_AFSCS, na.rm = TRUE)## [1] 29.60784sd(CC$Nat_4R_AFSCS, na.rm = TRUE)## [1] 25.03207sd(CC$Nat_1_BIO, na.rm = TRUE)## [1] 27.09083sd(CC$Nat_2R_BIO, na.rm = TRUE)## [1] 24.56568sd(CC$Nat_3R_BIO, na.rm = TRUE)## [1] 21.92187sd(CC$Nat_4R_BIO, na.rm = TRUE) ## [1] 30.9548sd(CC$Nat_1_BECCS, na.rm = TRUE) ## [1] 25.65509sd(CC$Nat_2R_BECCS, na.rm = TRUE) ## [1] 22.39619sd(CC$Nat_3R_BECCS, na.rm = TRUE) ## [1] 20.56689sd(CC$Nat_4R_BECCS, na.rm = TRUE)## [1] 27.55096sd(CC$Nat_1_DACCS, na.rm = TRUE)## [1] 24.84149sd(CC$Nat_2R_DACCS, na.rm = TRUE) ## [1] 24.88956sd(CC$Nat_3R_DACCS, na.rm = TRUE)## [1] 17.79345sd(CC$Nat_4R_DACCS)## [1] NAsd(CC$Nat_1_EW, na.rm = TRUE)## [1] 26.98155sd(CC$Nat_2R_EW, na.rm = TRUE)## [1] 22.69374sd(CC$Nat_3R_EW, na.rm = TRUE)## [1] 21.89062sd(CC$Nat_4R_EW, na.rm = TRUE)## [1] 27.74477sd(CC$Nat_1_OF, na.rm = TRUE)## [1] 27.14107sd(CC$Nat_2R_OF, na.rm = TRUE)## [1] 20.49896sd(CC$Nat_3R_OF, na.rm = TRUE)## [1] 21.76612sd(CC$Nat_4R_OF, na.rm = TRUE)## [1] 26.69147sd(CC$Nat_1_BF, na.rm = TRUE)## [1] 27.74057sd(CC$Nat_2R_BF, na.rm = TRUE)## [1] 26.80887sd(CC$Nat_3R_BF, na.rm = TRUE)## [1] 17.00108sd(CC$Nat_4R_BF, na.rm = TRUE)## [1] 28.97902sd(CC$Nat_1_NE, na.rm = TRUE)## [1] 25.86564sd(CC$Nat_2R_NE, na.rm = TRUE)## [1] 28.30164sd(CC$Nat_3R_NE, na.rm = TRUE)## [1] 15.3576sd(CC$Nat_4R_NE, na.rm = TRUE)## [1] 27.60394sd(CC$Nat_1_SE, na.rm = TRUE)## [1] 27.34702sd(CC$Nat_2R_SE, na.rm = TRUE)## [1] 32.18289sd(CC$Nat_3R_SE, na.rm = TRUE)## [1] 18.00439sd(CC$Nat_4R_SE, na.rm = TRUE)## [1] 29.35406sd(CC$Nat_1_WE, na.rm = TRUE)## [1] 28.31885sd(CC$Nat_2R_WE, na.rm = TRUE)## [1] 31.29357sd(CC$Nat_3R_WE, na.rm = TRUE)## [1] 21.1787sd(CC$Nat_4R_WE, na.rm = TRUE)## [1] 30.8923hist(CC$Nat_1_AFSCS)
hist(CC$Nat_2R_AFSCS)
hist(CC$Nat_3R_AFSCS)
hist(CC$Nat_4R_AFSCS)
hist(CC$Nat_1_BIO)
hist(CC$Nat_2R_BIO) 
hist(CC$Nat_3R_BIO)
hist(CC$Nat_4R_BIO) 
hist(CC$Nat_1_BECCS) 
hist(CC$Nat_2R_BECCS) 
hist(CC$Nat_3R_BECCS) 
hist(CC$Nat_4R_BECCS)
hist(CC$Nat_1_DACCS)
hist(CC$Nat_2R_DACCS) 
hist(CC$Nat_3R_DACCS)
hist(CC$Nat_4R_DACCS)
hist(CC$Nat_1_EW)
hist(CC$Nat_2R_EW)
hist(CC$Nat_3R_EW)
hist(CC$Nat_4R_EW)
hist(CC$Nat_1_OF)
hist(CC$Nat_2R_OF)
hist(CC$Nat_3R_OF)
hist(CC$Nat_4R_OF)
hist(CC$Nat_1_BF)
hist(CC$Nat_2R_BF)
hist(CC$Nat_3R_BF)
hist(CC$Nat_4R_BF)
hist(CC$Nat_1_NE)
hist(CC$Nat_2R_NE)
hist(CC$Nat_3R_NE)
hist(CC$Nat_4R_NE)
hist(CC$Nat_1_SE)
hist(CC$Nat_2R_SE)
hist(CC$Nat_3R_SE)
hist(CC$Nat_4R_SE)
hist(CC$Nat_1_WE)
hist(CC$Nat_2R_WE)
hist(CC$Nat_3R_WE)
hist(CC$Nat_4R_WE)
ii. Score(s) & Scale(s)
# Scores & Scales
CC$Nat_Score_AFSCS <- rowMeans(CC [, c("Nat_1_AFSCS", "Nat_2R_AFSCS", "Nat_3R_AFSCS", "Nat_4R_AFSCS")], na.rm=TRUE)
CC$Nat_Scale_AFSCS <- data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS, CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS)
CC$Nat_Score_BIO <- rowMeans(CC [, c("Nat_1_BIO", "Nat_2R_BIO", "Nat_3R_BIO", "Nat_4R_BIO")], na.rm=TRUE)
CC$Nat_Scale_BIO <- data.frame(CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO, CC$Nat_4R_BIO)
CC$Nat_Score_BECCS <- rowMeans(CC [, c("Nat_1_BECCS", "Nat_2R_BECCS", "Nat_3R_BECCS", "Nat_4R_BECCS")], na.rm=TRUE)
CC$Nat_Scale_BECCS <- data.frame(CC$Nat_1_BECCS, CC$Nat_2R_BECCS, CC$Nat_3R_BECCS, CC$Nat_4R_BECCS)
CC$Nat_Score_DACCS <- rowMeans(CC [, c("Nat_1_DACCS", "Nat_2R_DACCS", "Nat_3R_DACCS", "Nat_4R_DACCS")], na.rm=TRUE)
CC$Nat_Scale_DACCS <- data.frame(CC$Nat_1_DACCS, CC$Nat_2R_DACCS, CC$Nat_3R_DACCS, CC$Nat_4R_DACCS)
CC$Nat_Score_EW <- rowMeans(CC [, c("Nat_1_EW", "Nat_2R_EW", "Nat_3R_EW", "Nat_4R_EW")], na.rm=TRUE)
CC$Nat_Scale_EW <- data.frame(CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW, CC$Nat_4R_EW)
CC$Nat_Score_OF <- rowMeans(CC [, c("Nat_1_OF", "Nat_2R_OF", "Nat_3R_OF", "Nat_4R_OF")], na.rm=TRUE)
CC$Nat_Scale_OF <- data.frame(CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF, CC$Nat_4R_OF)
CC$Nat_Score_BF <- rowMeans(CC [, c("Nat_1_BF", "Nat_2R_BF", "Nat_3R_BF", "Nat_4R_BF")], na.rm=TRUE)
CC$Nat_Scale_BF <- data.frame(CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF, CC$Nat_4R_BF)
CC$Nat_Score_NE <- rowMeans(CC [, c("Nat_1_NE", "Nat_2R_NE", "Nat_3R_NE", "Nat_4R_NE")], na.rm=TRUE)
CC$Nat_Scale_NE <- data.frame(CC$Nat_1_NE, CC$Nat_2R_NE, CC$Nat_3R_NE, CC$Nat_4R_NE)
CC$Nat_Score_SE <- rowMeans(CC [, c("Nat_1_SE", "Nat_2R_SE", "Nat_3R_SE", "Nat_4R_SE")], na.rm=TRUE)
CC$Nat_Scale_SE <- data.frame(CC$Nat_1_SE, CC$Nat_2R_SE, CC$Nat_3R_SE, CC$Nat_4R_SE)
CC$Nat_Score_WE <- rowMeans(CC [, c("Nat_1_WE", "Nat_2R_WE", "Nat_3R_WE", "Nat_4R_WE")], na.rm=TRUE)
CC$Nat_Scale_WE <- data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE, CC$Nat_4R_WE)
# Describe Scores/Scales
describe(CC$Nat_Score_AFSCS)## CC$Nat_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 195 1 61.8 22.29 26.65 36.55
## .25 .50 .75 .90 .95
## 48.88 63.25 74.88 87.20 94.90
##
## lowest : 0 7 8 11 11.75, highest: 98 98.75 99.5 99.75 100describe(CC$Nat_Scale_AFSCS)## CC$Nat_Scale_AFSCS
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.991 74.92 27.28 19.1 37.0
## .25 .50 .75 .90 .95
## 60.5 83.0 95.0 100.0 100.0
##
## lowest : 0 3 6 7 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 95 0.999 53.22 35.19 0.0 14.0
## .25 .50 .75 .90 .95
## 30.0 50.0 82.5 97.8 100.0
##
## lowest : 0 2 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 89 0.999 39.48 33.58 0.0 2.2
## .25 .50 .75 .90 .95
## 15.5 35.0 60.5 86.0 95.8
##
## lowest : 0 1 2 3 4, highest: 93 94 96 97 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 77 0.99 79.59 25.6 23.1 41.2
## .25 .50 .75 .90 .95
## 67.0 91.0 99.0 100.0 100.0
##
## lowest : 0 4 6 7 12, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_AFSCS, na.rm = TRUE)## [1] 19.74064describe(CC$Nat_Score_BIO)## CC$Nat_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 182 1 39.12 20.95 5.75 13.50
## .25 .50 .75 .90 .95
## 26.88 39.25 51.06 63.25 68.75
##
## lowest : 0 0.75 1.75 2.5 2.75 , highest: 76.75 78 87.25 96.5 97.5describe(CC$Nat_Scale_BIO)## CC$Nat_Scale_BIO
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 0.999 45.61 31.02 0.0 6.1
## .25 .50 .75 .90 .95
## 25.0 46.0 64.0 83.9 96.0
##
## lowest : 0 2 3 4 5, highest: 90 95 96 97 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 77 0.999 37.07 27.62 0.0 5.0
## .25 .50 .75 .90 .95
## 20.0 35.0 49.0 72.7 85.0
##
## lowest : 0 2 3 5 6, highest: 93 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 69 0.993 23.95 23.47 0.0 0.0
## .25 .50 .75 .90 .95
## 6.0 20.5 35.0 49.9 70.0
##
## lowest : 0 1 2 3 5, highest: 87 88 95 97 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 96 0.999 49.87 35.67 0.0 8.0
## .25 .50 .75 .90 .95
## 25.0 49.0 78.0 95.9 100.0
##
## lowest : 0 1 4 5 6, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BIO, na.rm = TRUE)## [1] 18.56122describe(CC$Nat_Score_BECCS)## CC$Nat_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 178 1 34.63 18.88 6.25 12.22
## .25 .50 .75 .90 .95
## 24.50 33.75 45.94 54.33 61.39
##
## lowest : 0 2.25 2.5 3 4.5 , highest: 75 76.25 77.5 78.75 79describe(CC$Nat_Scale_BECCS)## CC$Nat_Scale_BECCS
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 43.48 29.31 0.00 6.90
## .25 .50 .75 .90 .95
## 25.00 44.00 61.00 76.20 88.55
##
## lowest : 0 1 2 3 4, highest: 90 93 96 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 72 0.997 30.43 24.88 0.00 0.00
## .25 .50 .75 .90 .95
## 14.00 30.00 44.00 60.00 73.65
##
## lowest : 0 1 2 3 4, highest: 85 89 90 93 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 69 0.991 22.77 22.39 0.00 0.00
## .25 .50 .75 .90 .95
## 4.25 20.00 35.00 48.00 61.65
##
## lowest : 0 1 2 3 4, highest: 80 90 92 98 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 89 0.999 41.83 31.45 0.0 6.0
## .25 .50 .75 .90 .95
## 20.0 39.0 60.0 82.1 93.0
##
## lowest : 0 2 3 4 5, highest: 93 94 95 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BECCS, na.rm = TRUE)## [1] 16.65608describe(CC$Nat_Score_DACCS)## CC$Nat_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 156 0.999 25.53 18.95 0.00 2.50
## .25 .50 .75 .90 .95
## 13.12 24.75 35.75 45.85 56.60
##
## lowest : 0 0.25 0.5 2.5 3.5 , highest: 70.5 70.75 75 75.25 79.25describe(CC$Nat_Scale_DACCS)## CC$Nat_Scale_DACCS
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.996 29.22 27.38 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 25.0 41.0 63.4 81.7
##
## lowest : 0 1 3 4 5, highest: 94 95 97 98 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 78 0.995 27.79 26.85 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 23.0 39.0 66.8 78.7
##
## lowest : 0 1 2 3 4, highest: 87 90 91 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 60 0.976 16.62 18.66 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 12.0 26.0 40.4 48.7
##
## lowest : 0 1 3 4 5, highest: 81 83 85 93 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 79 0.995 28.49 27.02 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 41.5 63.0 82.0
##
## lowest : 0 1 3 4 5, highest: 88 89 95 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_DACCS, na.rm = TRUE)## [1] 16.89449describe(CC$Nat_Score_EW)## CC$Nat_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 187 1 35.84 20.57 5.425 13.000
## .25 .50 .75 .90 .95
## 22.500 36.000 49.125 57.750 65.550
##
## lowest : 0 0.5 0.75 2.25 2.5 , highest: 75 76.75 78.5 78.75 87.5describe(CC$Nat_Scale_EW)## CC$Nat_Scale_EW
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 91 0.999 46.07 31.02 0.0 7.0
## .25 .50 .75 .90 .95
## 25.5 50.0 67.0 81.0 89.0
##
## lowest : 0 2 3 4 5, highest: 91 92 95 98 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 75 0.995 27.06 24.88 0.0 0.0
## .25 .50 .75 .90 .95
## 9.5 23.0 40.0 58.2 75.0
##
## lowest : 0 1 2 3 4, highest: 85 90 92 93 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 70 0.994 25.62 23.99 0 0
## .25 .50 .75 .90 .95
## 7 24 39 50 70
##
## lowest : 0 1 2 3 4, highest: 86 88 90 95 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 84 0.999 44.61 31.96 0 7
## .25 .50 .75 .90 .95
## 22 44 67 80 93
##
## lowest : 0 4 5 6 7, highest: 91 93 94 98 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_EW, na.rm = TRUE)## [1] 18.08834describe(CC$Nat_Score_OF)## CC$Nat_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 166 1 31.77 19.83 4.05 8.50
## .25 .50 .75 .90 .95
## 20.00 31.25 42.50 54.35 61.00
##
## lowest : 0 0.25 1.25 2.5 3 , highest: 73.5 75 80.25 80.5 84.5describe(CC$Nat_Scale_OF)## CC$Nat_Scale_OF
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 40.43 31.15 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 39.0 59.5 78.4 86.7
##
## lowest : 0 2 4 5 6, highest: 88 90 92 93 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 65 0.996 22.48 21.68 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 19.0 32.0 46.0 60.7
##
## lowest : 0 1 3 4 5, highest: 80 81 82 89 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 66 0.996 25.66 23.54 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 23.0 36.0 56.0 66.7
##
## lowest : 0 1 2 3 4, highest: 79 80 90 91 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 87 0.999 38.51 30.22 0.0 5.0
## .25 .50 .75 .90 .95
## 16.5 38.0 55.0 77.4 91.7
##
## lowest : 0 2 3 4 5, highest: 92 93 95 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_OF, na.rm = TRUE)## [1] 17.48905describe(CC$Nat_Score_BF)## CC$Nat_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 147 1 39.26 20.21 8.088 14.450
## .25 .50 .75 .90 .95
## 26.688 39.250 50.062 60.725 70.075
##
## lowest : 0 0.25 1 1.5 2 , highest: 72.5 73 74.25 75 86.75describe(CC$Nat_Scale_BF)## CC$Nat_Scale_BF
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 88 0.999 52.38 31.87 2.00 12.40
## .25 .50 .75 .90 .95
## 34.00 51.00 75.00 90.60 99.65
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 79 0.998 38.07 30.26 0.00 0.00
## .25 .50 .75 .90 .95
## 20.00 35.00 54.00 79.30 90.65
##
## lowest : 0 2 3 4 5, highest: 90 91 95 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 59 0.988 17.91 18.34 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 15.00 28.00 39.00 46.65
##
## lowest : 0 1 2 3 4, highest: 68 75 77 81 85
## --------------------------------------------------------------------------------
## CC.Nat_4R_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 84 0.999 48.67 33.41 1.35 10.00
## .25 .50 .75 .90 .95
## 26.00 49.00 73.25 90.30 98.65
##
## lowest : 0 1 2 4 5, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_BF, na.rm = TRUE)## [1] 17.80321describe(CC$Nat_Score_NE)## CC$Nat_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 139 0.999 26.16 19.39 0.00 2.30
## .25 .50 .75 .90 .95
## 13.50 25.00 38.25 48.25 55.60
##
## lowest : 0 1.25 1.5 2 2.5 , highest: 60.5 63.75 65 69.75 75describe(CC$Nat_Scale_NE)## CC$Nat_Scale_NE
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 80 0.995 31 28.97 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 27.0 49.0 68.2 80.4
##
## lowest : 0 1 2 3 4, highest: 89 90 93 95 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 72 0.994 29.96 30.59 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 23.0 43.0 77.6 95.0
##
## lowest : 0 2 3 4 5, highest: 94 95 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 48 0.931 11.18 14.75 0 0
## .25 .50 .75 .90 .95
## 0 6 17 33 43
##
## lowest : 0 1 2 3 4, highest: 49 50 64 92 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.996 32.51 30.64 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 27.0 48.0 77.4 90.0
##
## lowest : 0 4 5 6 7, highest: 92 95 96 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_NE, na.rm = TRUE)## [1] 17.14904describe(CC$Nat_Score_SE)## CC$Nat_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 143 1 55.05 20.62 24.40 31.25
## .25 .50 .75 .90 .95
## 41.75 54.75 69.75 75.00 83.20
##
## lowest : 0 2 5.5 14.5 16 , highest: 87.25 87.5 90 92 94describe(CC$Nat_Scale_SE)## CC$Nat_Scale_SE
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.991 72.97 29.25 10.2 30.4
## .25 .50 .75 .90 .95
## 60.0 80.0 95.0 100.0 100.0
##
## lowest : 0 1 4 6 10, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 77 0.989 66.22 35.98 6.2 18.4
## .25 .50 .75 .90 .95
## 38.0 78.0 96.0 100.0 100.0
##
## lowest : 0 1 5 6 7, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 52 0.964 14.8 18.23 0 0
## .25 .50 .75 .90 .95
## 0 10 22 39 50
##
## lowest : 0 1 2 3 4, highest: 70 71 76 80 93
## --------------------------------------------------------------------------------
## CC.Nat_4R_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 76 0.997 66.2 33.02 11.0 22.2
## .25 .50 .75 .90 .95
## 47.0 74.0 92.0 100.0 100.0
##
## lowest : 0 5 7 8 11, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_SE, na.rm = TRUE)## [1] 18.17609describe(CC$Nat_Score_WE)## CC$Nat_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 146 1 54.36 21.33 21.80 25.75
## .25 .50 .75 .90 .95
## 42.50 55.00 69.50 75.00 80.30
##
## lowest : 0 6 7.75 15 15.5 , highest: 86.75 90.5 91.5 92 100describe(CC$Nat_Scale_WE)## CC$Nat_Scale_WE
##
## 4 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Nat_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.993 70 30.85 9.8 21.6
## .25 .50 .75 .90 .95
## 57.0 78.0 92.0 100.0 100.0
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_2R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.991 63.74 35.55 10 20
## .25 .50 .75 .90 .95
## 38 72 93 100 100
##
## lowest : 0 1 5 8 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Nat_3R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.987 20.75 22.27 0.0 0.0
## .25 .50 .75 .90 .95
## 2.0 17.0 30.0 47.0 65.8
##
## lowest : 0 1 2 3 4, highest: 85 88 90 94 100
## --------------------------------------------------------------------------------
## CC.Nat_4R_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.997 62.96 35.06 4.8 14.2
## .25 .50 .75 .90 .95
## 42.0 68.0 90.0 100.0 100.0
##
## lowest : 0 3 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Nat_Score_WE, na.rm = TRUE)## [1] 18.78237#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS, CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_AFSCS, CC$Nat_2R_AFSCS,
## CC$Nat_3R_AFSCS, CC$Nat_4R_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.67 0.69 0.66 0.35 2.2 0.017 62 20 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.64 0.67 0.71
## Duhachek 0.64 0.67 0.71
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_AFSCS 0.58 0.59 0.53 0.33 1.5 0.023 0.0318 0.22
## CC.Nat_2R_AFSCS 0.54 0.57 0.54 0.30 1.3 0.026 0.0697 0.22
## CC.Nat_3R_AFSCS 0.76 0.77 0.70 0.53 3.3 0.013 0.0053 0.53
## CC.Nat_4R_AFSCS 0.50 0.51 0.45 0.25 1.0 0.027 0.0337 0.22
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_AFSCS 343 0.72 0.75 0.65 0.50 75 25
## CC.Nat_2R_AFSCS 343 0.79 0.77 0.66 0.54 53 31
## CC.Nat_3R_AFSCS 343 0.56 0.53 0.26 0.22 39 30
## CC.Nat_4R_AFSCS 343 0.80 0.82 0.77 0.63 80 25hist(CC$Nat_Score_AFSCS, main = 'AFSCS Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO, CC$Nat_4R_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_BIO, CC$Nat_2R_BIO, CC$Nat_3R_BIO,
## CC$Nat_4R_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.64 0.64 0.31 1.8 0.016 39 19 0.31
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.63 0.66 0.69
## Duhachek 0.63 0.66 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BIO 0.51 0.51 0.43 0.25 1.03 0.025 0.017 0.24
## CC.Nat_2R_BIO 0.61 0.58 0.58 0.31 1.37 0.020 0.090 0.24
## CC.Nat_3R_BIO 0.73 0.73 0.67 0.48 2.72 0.014 0.021 0.39
## CC.Nat_4R_BIO 0.43 0.42 0.36 0.20 0.73 0.030 0.030 0.14
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BIO 332 0.78 0.75 0.70 0.55 46 27
## CC.Nat_2R_BIO 332 0.68 0.69 0.50 0.43 37 25
## CC.Nat_3R_BIO 332 0.46 0.52 0.23 0.18 24 22
## CC.Nat_4R_BIO 332 0.85 0.82 0.79 0.64 50 31hist(CC$Nat_Score_BIO, main = 'BIO Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_BECCS, CC$Nat_2R_BECCS, CC$Nat_3R_BECCS, CC$Nat_4R_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_BECCS, CC$Nat_2R_BECCS,
## CC$Nat_3R_BECCS, CC$Nat_4R_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.61 0.62 0.28 1.6 0.018 35 17 0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.59 0.63 0.67
## Duhachek 0.59 0.63 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BECCS 0.44 0.44 0.38 0.21 0.79 0.030 0.027 0.195
## CC.Nat_2R_BECCS 0.56 0.52 0.56 0.26 1.07 0.023 0.138 0.052
## CC.Nat_3R_BECCS 0.73 0.72 0.68 0.46 2.58 0.014 0.041 0.380
## CC.Nat_4R_BECCS 0.41 0.41 0.34 0.19 0.68 0.032 0.018 0.195
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BECCS 330 0.79 0.76 0.72 0.55 43 26
## CC.Nat_2R_BECCS 330 0.67 0.70 0.50 0.42 30 22
## CC.Nat_3R_BECCS 330 0.41 0.48 0.16 0.11 23 21
## CC.Nat_4R_BECCS 330 0.82 0.78 0.76 0.59 42 28hist(CC$Nat_Score_BECCS, main = 'BECCS Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_DACCS, CC$Nat_2R_DACCS, CC$Nat_3R_DACCS, CC$Nat_4R_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_DACCS, CC$Nat_2R_DACCS,
## CC$Nat_3R_DACCS, CC$Nat_4R_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.69 0.67 0.35 2.2 0.015 26 17 0.33
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.67 0.7 0.73
## Duhachek 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_DACCS 0.54 0.53 0.45 0.27 1.1 0.024 0.014 0.25
## CC.Nat_2R_DACCS 0.67 0.65 0.63 0.38 1.8 0.017 0.076 0.25
## CC.Nat_3R_DACCS 0.75 0.75 0.70 0.50 3.0 0.014 0.028 0.41
## CC.Nat_4R_DACCS 0.52 0.51 0.43 0.25 1.0 0.025 0.019 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_DACCS 347 0.82 0.80 0.77 0.63 29 25
## CC.Nat_2R_DACCS 347 0.71 0.69 0.50 0.44 28 25
## CC.Nat_3R_DACCS 347 0.49 0.56 0.29 0.25 17 18
## CC.Nat_4R_DACCS 347 0.84 0.82 0.79 0.65 28 25hist(CC$Nat_Score_DACCS, main = 'DACCS Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW, CC$Nat_4R_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_EW, CC$Nat_2R_EW, CC$Nat_3R_EW,
## CC$Nat_4R_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.68 0.7 0.35 2.2 0.015 36 18 0.31
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.67 0.7 0.73
## Duhachek 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_EW 0.57 0.57 0.50 0.30 1.31 0.023 0.033 0.21
## CC.Nat_2R_EW 0.62 0.59 0.64 0.32 1.43 0.020 0.140 0.19
## CC.Nat_3R_EW 0.79 0.79 0.75 0.56 3.75 0.011 0.030 0.51
## CC.Nat_4R_EW 0.46 0.46 0.40 0.22 0.84 0.029 0.034 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_EW 335 0.80 0.76 0.73 0.58 46 27
## CC.Nat_2R_EW 335 0.73 0.74 0.59 0.51 27 23
## CC.Nat_3R_EW 335 0.45 0.50 0.21 0.17 26 22
## CC.Nat_4R_EW 335 0.88 0.85 0.86 0.72 45 28hist(CC$Nat_Score_EW, main = 'EW Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF, CC$Nat_4R_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_OF, CC$Nat_2R_OF, CC$Nat_3R_OF,
## CC$Nat_4R_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.69 0.69 0.35 2.2 0.015 32 17 0.32
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.66 0.69 0.72
## Duhachek 0.67 0.69 0.72
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_OF 0.56 0.57 0.49 0.30 1.31 0.023 0.019 0.24
## CC.Nat_2R_OF 0.63 0.60 0.63 0.34 1.53 0.020 0.115 0.21
## CC.Nat_3R_OF 0.77 0.77 0.73 0.53 3.35 0.012 0.030 0.46
## CC.Nat_4R_OF 0.48 0.49 0.42 0.24 0.95 0.028 0.024 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_OF 327 0.81 0.77 0.73 0.58 40 27
## CC.Nat_2R_OF 327 0.70 0.73 0.57 0.49 22 20
## CC.Nat_3R_OF 327 0.49 0.53 0.26 0.21 26 22
## CC.Nat_4R_OF 327 0.86 0.83 0.83 0.69 39 27hist(CC$Nat_Score_OF, main = 'OF Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF, CC$Nat_4R_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_BF, CC$Nat_2R_BF, CC$Nat_3R_BF,
## CC$Nat_4R_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.65 0.6 0.63 0.27 1.5 0.016 39 18 0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.61 0.65 0.68
## Duhachek 0.61 0.65 0.68
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_BF 0.47 0.44 0.39 0.20 0.77 0.026 0.043 0.139
## CC.Nat_2R_BF 0.55 0.46 0.55 0.22 0.86 0.020 0.176 0.039
## CC.Nat_3R_BF 0.75 0.75 0.71 0.50 3.03 0.013 0.031 0.436
## CC.Nat_4R_BF 0.38 0.34 0.32 0.15 0.51 0.030 0.049 0.139
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_BF 248 0.80 0.74 0.71 0.560 52 28
## CC.Nat_2R_BF 248 0.73 0.72 0.55 0.461 38 27
## CC.Nat_3R_BF 248 0.28 0.41 0.07 0.042 18 17
## CC.Nat_4R_BF 248 0.85 0.81 0.80 0.652 49 29hist(CC$Nat_Score_BF, main = 'BF Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_NE, CC$Nat_2R_NE, CC$Nat_3R_NE, CC$Nat_4R_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_NE, CC$Nat_2R_NE, CC$Nat_3R_NE,
## CC$Nat_4R_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.6 0.62 0.28 1.5 0.017 26 17 0.21
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.6 0.63 0.67
## Duhachek 0.6 0.63 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_NE 0.42 0.40 0.33 0.18 0.66 0.028 0.022 0.15
## CC.Nat_2R_NE 0.65 0.60 0.62 0.33 1.48 0.016 0.123 0.15
## CC.Nat_3R_NE 0.70 0.71 0.69 0.45 2.44 0.017 0.062 0.34
## CC.Nat_4R_NE 0.36 0.34 0.27 0.15 0.51 0.032 0.013 0.11
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_NE 257 0.81 0.78 0.78 0.59 31 26
## CC.Nat_2R_NE 257 0.66 0.62 0.37 0.32 30 28
## CC.Nat_3R_NE 257 0.35 0.48 0.16 0.13 11 15
## CC.Nat_4R_NE 257 0.85 0.82 0.84 0.65 33 28hist(CC$Nat_Score_NE, main = 'NE Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_SE, CC$Nat_2R_SE, CC$Nat_3R_SE, CC$Nat_4R_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_SE, CC$Nat_2R_SE, CC$Nat_3R_SE,
## CC$Nat_4R_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.58 0.58 0.57 0.26 1.4 0.02 55 18 0.22
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.54 0.58 0.62
## Duhachek 0.54 0.58 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_SE 0.43 0.46 0.36 0.22 0.84 0.029 0.0034 0.24
## CC.Nat_2R_SE 0.61 0.58 0.56 0.32 1.39 0.019 0.0689 0.27
## CC.Nat_3R_SE 0.60 0.62 0.57 0.35 1.60 0.022 0.0484 0.24
## CC.Nat_4R_SE 0.34 0.34 0.26 0.15 0.52 0.033 0.0038 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_SE 245 0.73 0.71 0.63 0.46 73 27
## CC.Nat_2R_SE 245 0.65 0.60 0.33 0.27 66 32
## CC.Nat_3R_SE 245 0.45 0.56 0.30 0.23 15 18
## CC.Nat_4R_SE 245 0.80 0.79 0.75 0.55 66 29hist(CC$Nat_Score_SE, main = 'SE Naturalness Scale Score')
psych::alpha(data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE, CC$Nat_4R_WE))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE, : Some items were negatively correlated with the first principal component 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 first principal component and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_WE, CC$Nat_2R_WE, CC$Nat_3R_WE,
## CC$Nat_4R_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.58 0.53 0.58 0.22 1.1 0.02 54 19 0.18
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.54 0.58 0.62
## Duhachek 0.54 0.58 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Nat_1_WE 0.33 0.28 0.27 0.114 0.39 0.033 0.053 0.017
## CC.Nat_2R_WE 0.53 0.47 0.55 0.225 0.87 0.023 0.182 0.017
## CC.Nat_3R_WE 0.73 0.73 0.69 0.476 2.72 0.015 0.044 0.377
## CC.Nat_4R_WE 0.26 0.20 0.20 0.076 0.25 0.038 0.050 -0.051
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_WE 257 0.79 0.77 0.767 0.566 70 28
## CC.Nat_2R_WE 257 0.68 0.64 0.412 0.343 64 31
## CC.Nat_3R_WE 257 0.25 0.35 -0.039 -0.037 21 21
## CC.Nat_4R_WE 257 0.84 0.82 0.834 0.626 63 31hist(CC$Nat_Score_WE, main = 'WE Naturalness Scale Score')
j. Support
# Support was rated on a two item scale (0 = Strongly disagree to 100 = Strongly agree) and a mean score was calculated to represent intent to support of the technology rated, used in this study as a proxy for support.
## 1. I would personally support non-government entities deploying these on a large scale.
## 2. I would personally support spending government tax dollars to deploy these on a large scale. i. Descriptives
# Define Variables
CC$Support1_AFSCS <- as.numeric(as.character(CC$BI_AFSCS_18))
CC$Support2_AFSCS <- as.numeric(as.character(CC$BI_AFSCS_19))
CC$Support1_BIO <- CC$BI_BIO_18
CC$Support2_BIO <- CC$BI_BIO_19
CC$Support1_BECCS <- CC$BI_BECCS_18
CC$Support2_BECCS <- CC$BI_BECCS_19
CC$Support1_DACCS <- CC$BI_DACCS_18
CC$Support2_DACCS <- CC$BI_DACCS_19
CC$Support1_EW <- CC$BI_EW_18
CC$Support2_EW <- CC$BI_EW_19
CC$Support1_OF <- CC$BI_OF_18
CC$Support2_OF <- CC$BI_OF_19
CC$Support1_BF <- CC$BI_BF_18
CC$Support2_BF <- CC$BI_BF_19
CC$Support1_NE <- CC$BI_NE_18
CC$Support2_NE <- CC$BI_NE_19
CC$Support1_SE <- CC$BI_SE_18
CC$Support2_SE <- CC$BI_SE_19
CC$Support1_WE <- CC$BI_WE_18
CC$Support2_WE <- CC$BI_WE_19
# Descriptives
describe(CC$Support1_AFSCS)## CC$Support1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 67 0.98 78.22 25.24 25 42
## .25 .50 .75 .90 .95
## 68 85 100 100 100
##
## lowest : 0 1 4 9 10, highest: 96 97 98 99 100describe(CC$Support2_AFSCS)## CC$Support2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 69 0.987 74.06 29.37 4.0 25.6
## .25 .50 .75 .90 .95
## 63.5 82.0 96.0 100.0 100.0
##
## lowest : 0 2 4 5 7, highest: 95 96 98 99 100describe(CC$Support1_BIO)## CC$Support1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 86 0.999 55.82 31.12 0.00 12.00
## .25 .50 .75 .90 .95
## 39.75 59.00 76.00 90.90 100.00
##
## lowest : 0 4 5 6 7, highest: 95 96 97 98 100describe(CC$Support2_BIO)## CC$Support2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 90 0.999 51.39 33.78 0.0 3.2
## .25 .50 .75 .90 .95
## 30.0 54.0 75.0 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 98 100describe(CC$Support1_BECCS)## CC$Support1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 55.58 32.42 0.00 10.90
## .25 .50 .75 .90 .95
## 36.25 60.00 75.00 93.00 100.00
##
## lowest : 0 1 2 4 5, highest: 94 95 96 98 100describe(CC$Support2_BECCS)## CC$Support2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.998 51.04 34.04 0.00 0.90
## .25 .50 .75 .90 .95
## 28.25 54.00 73.00 89.00 100.00
##
## lowest : 0 1 3 4 5, highest: 93 94 96 98 100describe(CC$Support1_DACCS)## CC$Support1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 86 0.998 54.59 33.75 0.0 5.6
## .25 .50 .75 .90 .95
## 35.0 60.0 75.0 97.4 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100describe(CC$Support2_DACCS)## CC$Support2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 91 0.999 51.18 33.83 0.0 1.6
## .25 .50 .75 .90 .95
## 29.5 55.0 73.5 89.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 97 98 99 100describe(CC$Support1_EW)## CC$Support1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 93 0.998 50.29 33.57 0.0 4.4
## .25 .50 .75 .90 .95
## 27.5 51.0 72.0 90.0 100.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 98 100describe(CC$Support2_EW)## CC$Support2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 87 0.998 48.3 34.89 0.0 0.0
## .25 .50 .75 .90 .95
## 25.0 50.0 72.5 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100describe(CC$Support1_OF)## CC$Support1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 90 0.999 53.27 34.92 0.0 6.0
## .25 .50 .75 .90 .95
## 29.5 59.0 75.0 94.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100describe(CC$Support2_OF)## CC$Support2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.998 49.17 35.2 0.0 0.0
## .25 .50 .75 .90 .95
## 20.0 53.0 74.5 89.0 97.1
##
## lowest : 0 1 3 4 5, highest: 93 94 95 98 100describe(CC$Support1_BF)## CC$Support1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 78 0.999 63.28 29.39 9.35 20.70
## .25 .50 .75 .90 .95
## 50.00 68.50 82.00 95.00 100.00
##
## lowest : 0 4 5 7 8, highest: 94 95 96 98 100describe(CC$Support2_BF)## CC$Support2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 58.35 31.34 0.00 13.00
## .25 .50 .75 .90 .95
## 45.00 61.00 77.25 93.00 100.00
##
## lowest : 0 3 4 5 6, highest: 94 95 96 98 100describe(CC$Support1_NE)## CC$Support1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 49.19 39.16 0 0
## .25 .50 .75 .90 .95
## 15 52 79 95 100
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100describe(CC$Support2_NE)## CC$Support2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 51.91 37.72 0 0
## .25 .50 .75 .90 .95
## 25 55 80 95 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 97 100describe(CC$Support1_SE)## CC$Support1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.956 82.66 22.78 35.0 51.4
## .25 .50 .75 .90 .95
## 75.0 91.0 100.0 100.0 100.0
##
## lowest : 0 1 5 10 14, highest: 96 97 98 99 100describe(CC$Support2_SE)## CC$Support2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 66 0.965 76.36 29.66 2.4 29.4
## .25 .50 .75 .90 .95
## 65.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 2 4 10, highest: 96 97 98 99 100describe(CC$Support1_WE)## CC$Support1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.988 76.82 25.62 22.6 43.0
## .25 .50 .75 .90 .95
## 69.0 81.0 98.0 100.0 100.0
##
## lowest : 0 4 10 17 20, highest: 95 96 98 99 100describe(CC$Support2_WE)## CC$Support2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 65 0.989 73.32 29.82 1.6 24.6
## .25 .50 .75 .90 .95
## 62.0 80.0 97.0 100.0 100.0
##
## lowest : 0 2 7 10 11, highest: 96 97 98 99 100sd(CC$Support1_AFSCS, na.rm = TRUE)## [1] 24.36924sd(CC$Support2_AFSCS, na.rm = TRUE)## [1] 28.19222sd(CC$Support1_BIO, na.rm = TRUE)## [1] 27.4581sd(CC$Support2_BIO, na.rm = TRUE)## [1] 29.52825sd(CC$Support1_BECCS, na.rm = TRUE)## [1] 28.53122sd(CC$Support2_BECCS, na.rm = TRUE)## [1] 29.75296sd(CC$Support1_DACCS, na.rm = TRUE)## [1] 29.6834sd(CC$Support2_DACCS, na.rm = TRUE)## [1] 29.5563sd(CC$Support1_EW, na.rm = TRUE)## [1] 29.25292sd(CC$Support2_EW, na.rm = TRUE)## [1] 30.30295sd(CC$Support1_OF, na.rm = TRUE)## [1] 30.54466sd(CC$Support2_OF, na.rm = TRUE)## [1] 30.72261sd(CC$Support1_BF, na.rm = TRUE)## [1] 26.33298sd(CC$Support2_BF, na.rm = TRUE)## [1] 27.8222sd(CC$Support1_NE, na.rm = TRUE)## [1] 34.00086sd(CC$Support2_NE, na.rm = TRUE)## [1] 32.86786sd(CC$Support1_SE, na.rm = TRUE)## [1] 23.17443sd(CC$Support2_SE, na.rm = TRUE)## [1] 28.89797sd(CC$Support1_WE, na.rm = TRUE)## [1] 24.49376sd(CC$Support2_WE, na.rm = TRUE)## [1] 28.52271hist(CC$Support1_AFSCS)
hist(CC$Support2_AFSCS)
hist(CC$Support1_BIO)
hist(CC$Support2_BIO)
hist(CC$Support1_BECCS)
hist(CC$Support2_BECCS)
hist(CC$Support1_DACCS)
hist(CC$Support2_DACCS)
hist(CC$Support1_EW)
hist(CC$Support2_EW)
hist(CC$Support1_OF)
hist(CC$Support2_OF)
hist(CC$Support1_BF)
hist(CC$Support2_BF)
hist(CC$Support1_NE)
hist(CC$Support2_NE)
hist(CC$Support1_SE)
hist(CC$Support2_SE)
hist(CC$Support1_WE)
hist(CC$Support2_WE)
ii. Score(s) & Scale(s)
# Scores & Scales
CC$Support_Score_AFSCS <- rowMeans(CC [, c("Support1_AFSCS", "Support2_AFSCS")], na.rm=TRUE)
CC$Support_Scale_AFSCS <- data.frame(CC$Support1_AFSCS, CC$Support2_AFSCS)
CC$Support_Score_BIO <- rowMeans(CC [, c("Support1_BIO", "Support2_BIO")], na.rm=TRUE)
CC$Support_Scale_BIO <- data.frame(CC$Support1_BIO, CC$Support2_BIO)
CC$Support_Score_BECCS <- rowMeans(CC [, c("Support1_BECCS", "Support2_BECCS")], na.rm=TRUE)
CC$Support_Scale_BECCS <- data.frame(CC$Support1_BECCS, CC$Support2_BECCS)
CC$Support_Score_DACCS <- rowMeans(CC [, c("Support1_DACCS", "Support2_DACCS")], na.rm=TRUE)
CC$Support_Scale_DACCS <- data.frame(CC$Support1_DACCS, CC$Support2_DACCS)
CC$Support_Score_EW <- rowMeans(CC [, c("Support1_EW", "Support2_EW")], na.rm=TRUE)
CC$Support_Scale_EW <- data.frame(CC$Support1_EW, CC$Support2_EW)
CC$Support_Score_OF <- rowMeans(CC [, c("Support1_OF", "Support2_OF")], na.rm=TRUE)
CC$Support_Scale_OF <- data.frame(CC$Support1_OF, CC$Support2_OF)
CC$Support_Score_BF <- rowMeans(CC [, c("Support1_BF", "Support2_BF")], na.rm=TRUE)
CC$Support_Scale_BF <- data.frame(CC$Support1_BF, CC$Support2_BF)
CC$Support_Score_NE <- rowMeans(CC [, c("Support1_NE", "Support2_NE")], na.rm=TRUE)
CC$Support_Scale_NE <- data.frame(CC$Support1_NE, CC$Support2_NE)
CC$Support_Score_SE <- rowMeans(CC [, c("Support1_SE", "Support2_SE")], na.rm=TRUE)
CC$Support_Scale_SE <- data.frame(CC$Support1_SE, CC$Support2_SE)
CC$Support_Score_WE <- rowMeans(CC [, c("Support1_WE", "Support2_WE")], na.rm=TRUE)
CC$Support_Scale_WE <- data.frame(CC$Support1_WE, CC$Support2_WE)
# Describe Scores/Scales
describe(CC$Support_Score_AFSCS)## CC$Support_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 114 0.991 76.14 25.37 30.25 46.40
## .25 .50 .75 .90 .95
## 62.50 82.00 95.25 100.00 100.00
##
## lowest : 0 4 5 10 12.5, highest: 97 97.5 98 99.5 100describe(CC$Support_Scale_AFSCS)## CC$Support_Scale_AFSCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 67 0.98 78.22 25.24 25 42
## .25 .50 .75 .90 .95
## 68 85 100 100 100
##
## lowest : 0 1 4 9 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 69 0.987 74.06 29.37 4.0 25.6
## .25 .50 .75 .90 .95
## 63.5 82.0 96.0 100.0 100.0
##
## lowest : 0 2 4 5 7, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_AFSCS, na.rm = TRUE)## [1] 23.61434describe(CC$Support_Score_BIO)## CC$Support_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 142 0.999 53.6 29.85 0.55 13.55
## .25 .50 .75 .90 .95
## 36.50 54.25 74.00 87.00 95.22
##
## lowest : 0 1 2.5 3.5 5 , highest: 94 95 95.5 97.5 100describe(CC$Support_Scale_BIO)## CC$Support_Scale_BIO
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 86 0.999 55.82 31.12 0.00 12.00
## .25 .50 .75 .90 .95
## 39.75 59.00 76.00 90.90 100.00
##
## lowest : 0 4 5 6 7, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 90 0.999 51.39 33.78 0.0 3.2
## .25 .50 .75 .90 .95
## 30.0 54.0 75.0 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BIO, na.rm = TRUE)## [1] 26.28137describe(CC$Support_Score_BECCS)## CC$Support_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 136 0.999 53.31 31 0.00 9.90
## .25 .50 .75 .90 .95
## 35.25 55.00 74.25 85.00 98.20
##
## lowest : 0 1 1.5 2 5 , highest: 93 93.5 95 96 100describe(CC$Support_Scale_BECCS)## CC$Support_Scale_BECCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 55.58 32.42 0.00 10.90
## .25 .50 .75 .90 .95
## 36.25 60.00 75.00 93.00 100.00
##
## lowest : 0 1 2 4 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.998 51.04 34.04 0.00 0.90
## .25 .50 .75 .90 .95
## 28.25 54.00 73.00 89.00 100.00
##
## lowest : 0 1 3 4 5, highest: 93 94 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BECCS, na.rm = TRUE)## [1] 27.28703describe(CC$Support_Score_DACCS)## CC$Support_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 148 0.999 52.88 32.02 0.00 5.80
## .25 .50 .75 .90 .95
## 35.50 55.50 73.25 89.40 99.85
##
## lowest : 0 0.5 1 2 2.5 , highest: 96.5 97 98.5 99.5 100describe(CC$Support_Scale_DACCS)## CC$Support_Scale_DACCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 86 0.998 54.59 33.75 0.0 5.6
## .25 .50 .75 .90 .95
## 35.0 60.0 75.0 97.4 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 91 0.999 51.18 33.83 0.0 1.6
## .25 .50 .75 .90 .95
## 29.5 55.0 73.5 89.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_DACCS, na.rm = TRUE)## [1] 28.11655describe(CC$Support_Score_EW)## CC$Support_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 137 0.999 49.29 31.61 0.0 8.0
## .25 .50 .75 .90 .95
## 29.5 50.5 68.5 85.8 98.6
##
## lowest : 0 0.5 1 2 2.5 , highest: 94.5 95 95.5 98 100describe(CC$Support_Scale_EW)## CC$Support_Scale_EW
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 93 0.998 50.29 33.57 0.0 4.4
## .25 .50 .75 .90 .95
## 27.5 51.0 72.0 90.0 100.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 87 0.998 48.3 34.89 0.0 0.0
## .25 .50 .75 .90 .95
## 25.0 50.0 72.5 90.0 100.0
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_EW, na.rm = TRUE)## [1] 27.63767describe(CC$Support_Score_OF)## CC$Support_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 145 0.999 51.22 33 0.00 5.80
## .25 .50 .75 .90 .95
## 27.50 54.50 73.75 89.00 95.00
##
## lowest : 0 0.5 2 3 3.5 , highest: 95 95.5 97 97.5 100describe(CC$Support_Scale_OF)## CC$Support_Scale_OF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 90 0.999 53.27 34.92 0.0 6.0
## .25 .50 .75 .90 .95
## 29.5 59.0 75.0 94.4 100.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------
## CC.Support2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.998 49.17 35.2 0.0 0.0
## .25 .50 .75 .90 .95
## 20.0 53.0 74.5 89.0 97.1
##
## lowest : 0 1 3 4 5, highest: 93 94 95 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_OF, na.rm = TRUE)## [1] 28.83405describe(CC$Support_Score_BF)## CC$Support_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 120 1 60.82 27.72 10.00 22.05
## .25 .50 .75 .90 .95
## 50.00 64.00 78.12 92.00 99.00
##
## lowest : 0 2.5 4 5 7 , highest: 95 96.5 98 99 100describe(CC$Support_Scale_BF)## CC$Support_Scale_BF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 78 0.999 63.28 29.39 9.35 20.70
## .25 .50 .75 .90 .95
## 50.00 68.50 82.00 95.00 100.00
##
## lowest : 0 4 5 7 8, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 58.35 31.34 0.00 13.00
## .25 .50 .75 .90 .95
## 45.00 61.00 77.25 93.00 100.00
##
## lowest : 0 3 4 5 6, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_BF, na.rm = TRUE)## [1] 24.89028describe(CC$Support_Score_NE)## CC$Support_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 122 0.999 50.55 35.85 0.0 0.6
## .25 .50 .75 .90 .95
## 27.5 52.0 76.5 91.1 99.2
##
## lowest : 0 1 2 3 3.5 , highest: 95 95.5 96 99 100describe(CC$Support_Scale_NE)## CC$Support_Scale_NE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 49.19 39.16 0 0
## .25 .50 .75 .90 .95
## 15 52 79 95 100
##
## lowest : 0 1 2 3 5, highest: 94 95 96 98 100
## --------------------------------------------------------------------------------
## CC.Support2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 86 0.997 51.91 37.72 0 0
## .25 .50 .75 .90 .95
## 25 55 80 95 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 97 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_NE, na.rm = TRUE)## [1] 31.10287describe(CC$Support_Score_SE)## CC$Support_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 93 0.979 79.51 24.49 30.4 49.2
## .25 .50 .75 .90 .95
## 68.0 87.5 100.0 100.0 100.0
##
## lowest : 0 0.5 2.5 10 12 , highest: 97.5 98.5 99 99.5 100describe(CC$Support_Scale_SE)## CC$Support_Scale_SE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.956 82.66 22.78 35.0 51.4
## .25 .50 .75 .90 .95
## 75.0 91.0 100.0 100.0 100.0
##
## lowest : 0 1 5 10 14, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 66 0.965 76.36 29.66 2.4 29.4
## .25 .50 .75 .90 .95
## 65.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 2 4 10, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_SE, na.rm = TRUE)## [1] 23.53217describe(CC$Support_Score_WE)## CC$Support_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 99 0.993 75.07 26.24 19.9 42.5
## .25 .50 .75 .90 .95
## 64.0 80.0 95.5 100.0 100.0
##
## lowest : 0 3 10.5 11 15 , highest: 98 98.5 99 99.5 100describe(CC$Support_Scale_WE)## CC$Support_Scale_WE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Support1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 63 0.988 76.82 25.62 22.6 43.0
## .25 .50 .75 .90 .95
## 69.0 81.0 98.0 100.0 100.0
##
## lowest : 0 4 10 17 20, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## CC.Support2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 65 0.989 73.32 29.82 1.6 24.6
## .25 .50 .75 .90 .95
## 62.0 80.0 97.0 100.0 100.0
##
## lowest : 0 2 7 10 11, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------sd(CC$Support_Score_WE, na.rm = TRUE)## [1] 24.65437#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$Support1_AFSCS, CC$Support2_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_AFSCS, CC$Support2_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.76 0.61 0.61 3.2 0.015 76 24 0.61
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.72 0.75 0.78
## Duhachek 0.73 0.75 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_AFSCS 0.53 0.61 0.38 0.61 1.6 NA 0
## CC.Support2_AFSCS 0.71 0.61 0.38 0.61 1.6 NA 0
## med.r
## CC.Support1_AFSCS 0.61
## CC.Support2_AFSCS 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_AFSCS 343 0.88 0.9 0.7 0.61 78 24
## CC.Support2_AFSCS 343 0.91 0.9 0.7 0.61 74 28hist(CC$Support_Score_AFSCS, main = 'AFSCS Support Scale Score')
psych::alpha(data.frame(CC$Support1_BIO, CC$Support2_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_BIO, CC$Support2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.7 0.7 4.7 0.011 54 26 0.7
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.82 0.84
## Duhachek 0.8 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_BIO 0.65 0.7 0.49 0.7 2.3 NA 0 0.7
## CC.Support2_BIO 0.75 0.7 0.49 0.7 2.3 NA 0 0.7
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BIO 332 0.92 0.92 0.77 0.7 56 27
## CC.Support2_BIO 332 0.93 0.92 0.77 0.7 51 30hist(CC$Support_Score_BIO, main = 'BIO Support Scale Score')
psych::alpha(data.frame(CC$Support1_BECCS, CC$Support2_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_BECCS, CC$Support2_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.75 0.75 6.1 0.0089 53 27 0.75
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_BECCS 0.72 0.75 0.57 0.75 3.1 NA 0
## CC.Support2_BECCS 0.79 0.75 0.57 0.75 3.1 NA 0
## med.r
## CC.Support1_BECCS 0.75
## CC.Support2_BECCS 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BECCS 330 0.93 0.94 0.81 0.75 56 29
## CC.Support2_BECCS 330 0.94 0.94 0.81 0.75 51 30hist(CC$Support_Score_BECCS, main = 'BECCS Support Scale Score')
psych::alpha(data.frame(CC$Support1_DACCS, CC$Support2_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_DACCS, CC$Support2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.8 0.8 8.1 0.0069 53 28 0.8
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.9
## Duhachek 0.88 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Support1_DACCS 0.81 0.8 0.64 0.8 4.1 NA 0
## CC.Support2_DACCS 0.80 0.8 0.64 0.8 4.1 NA 0
## med.r
## CC.Support1_DACCS 0.8
## CC.Support2_DACCS 0.8
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_DACCS 347 0.95 0.95 0.85 0.8 55 30
## CC.Support2_DACCS 347 0.95 0.95 0.85 0.8 51 30hist(CC$Support_Score_DACCS, main = 'DACCS Support Scale Score')
psych::alpha(data.frame(CC$Support1_EW, CC$Support2_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_EW, CC$Support2_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.72 0.72 5.2 0.01 49 28 0.72
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.84 0.86
## Duhachek 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_EW 0.70 0.72 0.52 0.72 2.6 NA 0 0.72
## CC.Support2_EW 0.75 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_EW 335 0.93 0.93 0.79 0.72 50 29
## CC.Support2_EW 335 0.93 0.93 0.79 0.72 48 30hist(CC$Support_Score_EW, main = 'EW Support Scale Score')
psych::alpha(data.frame(CC$Support1_OF, CC$Support2_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_OF, CC$Support2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.77 0.77 6.8 0.0081 51 29 0.77
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.87 0.89
## Duhachek 0.86 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_OF 0.77 0.77 0.6 0.77 3.4 NA 0 0.77
## CC.Support2_OF 0.78 0.77 0.6 0.77 3.4 NA 0 0.77
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_OF 327 0.94 0.94 0.83 0.77 53 31
## CC.Support2_OF 327 0.94 0.94 0.83 0.77 49 31hist(CC$Support_Score_OF, main = 'OF Support Scale Score')
psych::alpha(data.frame(CC$Support1_BF, CC$Support2_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_BF, CC$Support2_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.69 0.69 4.4 0.012 61 25 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.82 0.84
## Duhachek 0.79 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_BF 0.65 0.69 0.48 0.69 2.2 NA 0 0.69
## CC.Support2_BF 0.73 0.69 0.48 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_BF 248 0.91 0.92 0.76 0.69 63 26
## CC.Support2_BF 248 0.92 0.92 0.76 0.69 58 28hist(CC$Support_Score_BF, main = 'BF Support Scale Score')
psych::alpha(data.frame(CC$Support1_NE, CC$Support2_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_NE, CC$Support2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.73 0.73 5.4 0.0098 51 31 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.84 0.86
## Duhachek 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_NE 0.76 0.73 0.53 0.73 2.7 NA 0 0.73
## CC.Support2_NE 0.71 0.73 0.53 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_NE 257 0.93 0.93 0.8 0.73 49 34
## CC.Support2_NE 257 0.93 0.93 0.8 0.73 52 33hist(CC$Support_Score_NE, main = 'NE Support Scale Score')
psych::alpha(data.frame(CC$Support1_SE, CC$Support2_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_SE, CC$Support2_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.63 0.63 3.4 0.014 80 24 0.63
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.76 0.79
## Duhachek 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_SE 0.50 0.63 0.4 0.63 1.7 NA 0 0.63
## CC.Support2_SE 0.78 0.63 0.4 0.63 1.7 NA 0 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_SE 245 0.88 0.9 0.72 0.63 83 23
## CC.Support2_SE 245 0.92 0.9 0.72 0.63 76 29hist(CC$Support_Score_SE, main = 'SE Support Scale Score')
psych::alpha(data.frame(CC$Support1_WE, CC$Support2_WE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Support1_WE, CC$Support2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.73 0.73 5.4 0.01 75 25 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.84 0.86
## Duhachek 0.82 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Support1_WE 0.63 0.73 0.53 0.73 2.7 NA 0 0.73
## CC.Support2_WE 0.85 0.73 0.53 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Support1_WE 257 0.92 0.93 0.79 0.73 77 24
## CC.Support2_WE 257 0.94 0.93 0.79 0.73 73 29hist(CC$Support_Score_WE, main = 'WE Support Scale Score')
# Correlation between Support Items Across all 10 Technologies
CC$S1 <- rowMeans(CC [, c("Support1_AFSCS", "Support1_BIO", "Support1_BECCS", "Support1_DACCS", "Support1_EW", "Support1_OF", "Support1_BF", "Support1_NE", "Support1_SE", "Support1_WE")], na.rm=TRUE)
CC$S2 <- rowMeans(CC [, c("Support2_AFSCS", "Support2_BIO", "Support2_BECCS", "Support2_DACCS", "Support2_EW", "Support2_OF", "Support2_BF", "Support2_NE", "Support2_SE", "Support2_WE")], na.rm=TRUE)
cor.test(CC$S1,CC$S2)##
## Pearson's product-moment correlation
##
## data: CC$S1 and CC$S2
## t = 31.906, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6772775 0.7387760
## sample estimates:
## cor
## 0.7093743k. Risk
# Risk was rated on a two item scale (0 = Strongly disagree to 100 = Strongly agree) and a mean score was calculated to represent risk perception of the technology rated.
## 1. This is risky to deploy.
## 2. This is frightening.i. Descriptives
# Define Variables
CC$Risk_1_AFSCS <- CC$Risk_AFSCS_32
CC$Risk_2_AFSCS <- CC$Risk_AFSCS_33
CC$Risk_1_BIO <- CC$Risk_BIO_32
CC$Risk_2_BIO <- CC$Risk_BIO_33
CC$Risk_1_BECCS <- CC$Risk_BECCS_32
CC$Risk_2_BECCS <- CC$Risk_BECCS_33
CC$Risk_1_DACCS <- CC$Risk_DACCS_32
CC$Risk_2_DACCS <- CC$Risk_DACCS_33
CC$Risk_1_EW <- CC$Risk_EW_32
CC$Risk_2_EW <- CC$Risk_EW_33
CC$Risk_1_OF <- CC$Risk_OF_32
CC$Risk_2_OF <- CC$Risk_OF_33
CC$Risk_1_BF <- CC$Risk_BF_32
CC$Risk_2_BF <- CC$Risk_BF_33
CC$Risk_1_NE <- CC$Risk_NE_32
CC$Risk_2_NE <- CC$Risk_NE_33
CC$Risk_1_SE <- CC$Risk_SE_32
CC$Risk_2_SE <- CC$Risk_SE_33
CC$Risk_1_WE <- CC$Risk_WE_32
CC$Risk_2_WE <- CC$Risk_WE_33
# Descriptives
describe(CC$Risk_1_AFSCS)## CC$Risk_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 65 0.983 19.29 23.37 0 0
## .25 .50 .75 .90 .95
## 0 11 30 51 70
##
## lowest : 0 1 2 3 4, highest: 79 80 81 85 100describe(CC$Risk_2_AFSCS)## CC$Risk_2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 56 0.933 13.06 18.81 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 16.0 44.0 59.8
##
## lowest : 0 1 2 3 4, highest: 74 75 80 85 100describe(CC$Risk_1_BIO)## CC$Risk_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 83 0.999 39.39 28.83 0.0 4.0
## .25 .50 .75 .90 .95
## 19.0 40.0 56.0 74.9 80.0
##
## lowest : 0 1 2 3 4, highest: 86 90 95 96 100describe(CC$Risk_2_BIO)## CC$Risk_2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 82 0.992 27.98 28.3 0.00 0.00
## .25 .50 .75 .90 .95
## 4.00 25.00 47.25 63.00 75.00
##
## lowest : 0 1 2 3 4, highest: 90 92 95 96 100describe(CC$Risk_1_BECCS)## CC$Risk_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 45.35 30.93 0.00 6.90
## .25 .50 .75 .90 .95
## 24.25 49.50 64.00 80.00 92.55
##
## lowest : 0 1 4 5 6, highest: 93 94 95 96 100describe(CC$Risk_2_BECCS)## CC$Risk_2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 80 0.992 31.86 31.91 0.0 0.0
## .25 .50 .75 .90 .95
## 5.0 25.5 51.0 72.3 90.0
##
## lowest : 0 1 2 3 4, highest: 90 91 92 96 100describe(CC$Risk_1_DACCS)## CC$Risk_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 90 0.999 50.12 31.12 0.0 9.0
## .25 .50 .75 .90 .95
## 30.0 52.0 70.0 84.4 95.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100describe(CC$Risk_2_DACCS)## CC$Risk_2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 92 0.994 35.84 33.73 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 33.0 59.0 79.4 89.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100describe(CC$Risk_1_EW)## CC$Risk_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 85 0.999 45.98 30.54 3.7 10.0
## .25 .50 .75 .90 .95
## 25.0 50.0 64.0 82.6 93.3
##
## lowest : 0 2 3 4 5, highest: 93 94 95 99 100describe(CC$Risk_2_EW)## CC$Risk_2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.994 30.92 31.26 0.0 0.0
## .25 .50 .75 .90 .95
## 5.5 24.0 51.0 74.6 85.0
##
## lowest : 0 1 2 3 4, highest: 92 95 98 99 100describe(CC$Risk_1_OF)## CC$Risk_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 94 0.999 54.49 31.47 1.3 14.6
## .25 .50 .75 .90 .95
## 33.0 57.0 75.0 89.4 97.7
##
## lowest : 0 1 2 4 7, highest: 95 96 97 98 100describe(CC$Risk_2_OF)## CC$Risk_2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.996 38.24 33.86 0.0 0.0
## .25 .50 .75 .90 .95
## 10.5 37.0 62.5 80.0 90.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100describe(CC$Risk_1_BF)## CC$Risk_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 74 0.998 32.13 27.09 0.00 0.00
## .25 .50 .75 .90 .95
## 12.75 30.00 50.00 69.00 74.00
##
## lowest : 0 1 2 4 5, highest: 83 84 87 93 100describe(CC$Risk_2_BF)## CC$Risk_2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 65 0.984 19.96 22.59 0.00 0.00
## .25 .50 .75 .90 .95
## 0.75 14.00 32.00 51.00 63.65
##
## lowest : 0 1 2 3 4, highest: 80 81 87 88 100describe(CC$Risk_1_NE)## CC$Risk_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 76 0.998 56.87 34.87 3.8 10.0
## .25 .50 .75 .90 .95
## 31.0 62.0 80.0 100.0 100.0
##
## lowest : 0 1 3 4 5, highest: 93 94 95 99 100describe(CC$Risk_2_NE)## CC$Risk_2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 84 0.999 48.97 37.15 0.0 2.6
## .25 .50 .75 .90 .95
## 20.0 54.0 76.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 93 95 97 99 100describe(CC$Risk_1_SE)## CC$Risk_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 54 0.945 13.63 18.58 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 5.0 21.0 40.6 52.0
##
## lowest : 0 1 2 3 4, highest: 67 79 80 82 88describe(CC$Risk_2_SE)## CC$Risk_2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 42 0.821 6.735 10.85 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 7.0 22.6 33.8
##
## lowest : 0 1 2 3 4, highest: 51 64 75 79 88describe(CC$Risk_1_WE)## CC$Risk_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.984 23.56 28.04 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 13.0 38.0 68.0 80.2
##
## lowest : 0 1 2 3 4, highest: 90 91 92 97 100describe(CC$Risk_2_WE)## CC$Risk_2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 55 0.915 14.02 20.08 0 0
## .25 .50 .75 .90 .95
## 0 4 19 43 68
##
## lowest : 0 1 2 3 4, highest: 84 89 90 99 100sd(CC$Risk_1_AFSCS, na.rm = TRUE)## [1] 22.70809sd(CC$Risk_2_AFSCS, na.rm = TRUE)## [1] 20.35588sd(CC$Risk_1_BIO, na.rm = TRUE)## [1] 25.12406sd(CC$Risk_2_BIO, na.rm = TRUE)## [1] 25.33067sd(CC$Risk_1_BECCS, na.rm = TRUE)## [1] 26.96612sd(CC$Risk_2_BECCS, na.rm = TRUE)## [1] 28.51304sd(CC$Risk_1_DACCS, na.rm = TRUE)## [1] 27.21191sd(CC$Risk_2_DACCS, na.rm = TRUE)## [1] 29.61833sd(CC$Risk_1_EW, na.rm = TRUE)## [1] 26.62165sd(CC$Risk_2_EW, na.rm = TRUE)## [1] 27.95912sd(CC$Risk_1_OF, na.rm = TRUE)## [1] 27.45413sd(CC$Risk_2_OF, na.rm = TRUE)## [1] 29.57223sd(CC$Risk_1_BF, na.rm = TRUE)## [1] 23.86879sd(CC$Risk_2_BF, na.rm = TRUE)## [1] 21.27785sd(CC$Risk_1_NE, na.rm = TRUE)## [1] 30.46242sd(CC$Risk_2_NE, na.rm = TRUE)## [1] 32.24126sd(CC$Risk_1_SE, na.rm = TRUE)## [1] 19.15884sd(CC$Risk_2_SE, na.rm = TRUE)## [1] 13.82528sd(CC$Risk_1_WE, na.rm = TRUE)## [1] 26.48551sd(CC$Risk_2_WE, na.rm = TRUE)## [1] 21.76884hist(CC$Risk_1_AFSCS)
hist(CC$Risk_2_AFSCS)
hist(CC$Risk_1_BIO)
hist(CC$Risk_2_BIO)
hist(CC$Risk_1_BECCS)
hist(CC$Risk_2_BECCS)
hist(CC$Risk_1_DACCS)
hist(CC$Risk_2_DACCS)
hist(CC$Risk_1_EW)
hist(CC$Risk_2_EW)
hist(CC$Risk_1_OF)
hist(CC$Risk_2_OF)
hist(CC$Risk_1_BF)
hist(CC$Risk_2_BF)
hist(CC$Risk_1_NE)
hist(CC$Risk_2_NE)
hist(CC$Risk_1_SE)
hist(CC$Risk_2_SE)
hist(CC$Risk_1_WE)
hist(CC$Risk_2_WE)
ii. Score(s) & Scale(s)
# Scores & Scales
CC$Risk_Score_AFSCS <- rowMeans(CC [, c("Risk_1_AFSCS", "Risk_2_AFSCS")], na.rm=TRUE)
CC$Risk_Scale_AFSCS <- data.frame(CC$Risk_1_AFSCS, CC$Risk_2_AFSCS)
CC$Risk_Score_BIO <- rowMeans(CC [, c("Risk_1_BIO", "Risk_2_BIO")], na.rm=TRUE)
CC$Risk_Scale_BIO <- data.frame(CC$Risk_1_BIO, CC$Risk_2_BIO)
CC$Risk_Score_BECCS <- rowMeans(CC [, c("Risk_1_BECCS", "Risk_2_BECCS")], na.rm=TRUE)
CC$Risk_Scale_BECCS <- data.frame(CC$Risk_1_BECCS, CC$Risk_2_BECCS)
CC$Risk_Score_DACCS <- rowMeans(CC [, c("Risk_1_DACCS", "Risk_2_DACCS")], na.rm=TRUE)
CC$Risk_Scale_DACCS <- data.frame(CC$Risk_1_DACCS, CC$Risk_2_DACCS)
CC$Risk_Score_EW <- rowMeans(CC [, c("Risk_1_EW", "Risk_2_EW")], na.rm=TRUE)
CC$Risk_Scale_EW <- data.frame(CC$Risk_1_EW, CC$Risk_2_EW)
CC$Risk_Score_OF <- rowMeans(CC [, c("Risk_1_OF", "Risk_2_OF")], na.rm=TRUE)
CC$Risk_Scale_OF <- data.frame(CC$Risk_1_OF, CC$Risk_2_OF)
CC$Risk_Score_BF <- rowMeans(CC [, c("Risk_1_BF", "Risk_2_BF")], na.rm=TRUE)
CC$Risk_Scale_BF <- data.frame(CC$Risk_1_BF, CC$Risk_2_BF)
CC$Risk_Score_NE <- rowMeans(CC [, c("Risk_1_NE", "Risk_2_NE")], na.rm=TRUE)
CC$Risk_Scale_NE <- data.frame(CC$Risk_1_NE, CC$Risk_2_NE)
CC$Risk_Score_SE <- rowMeans(CC [, c("Risk_1_SE", "Risk_2_SE")], na.rm=TRUE)
CC$Risk_Scale_SE <- data.frame(CC$Risk_1_SE, CC$Risk_2_SE)
CC$Risk_Score_WE <- rowMeans(CC [, c("Risk_1_WE", "Risk_2_WE")], na.rm=TRUE)
CC$Risk_Scale_WE <- data.frame(CC$Risk_1_WE, CC$Risk_2_WE)
# Describe Scores/Scales
describe(CC$Risk_Score_AFSCS)## CC$Risk_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 103 0.987 16.18 20.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.5 8.0 24.5 47.3 62.5
##
## lowest : 0 0.5 1 1.5 2 , highest: 78 79 80 85 100describe(CC$Risk_Scale_AFSCS)## CC$Risk_Scale_AFSCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 65 0.983 19.29 23.37 0 0
## .25 .50 .75 .90 .95
## 0 11 30 51 70
##
## lowest : 0 1 2 3 4, highest: 79 80 81 85 100
## --------------------------------------------------------------------------------
## CC.Risk_2_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 56 0.933 13.06 18.81 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 16.0 44.0 59.8
##
## lowest : 0 1 2 3 4, highest: 74 75 80 85 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_AFSCS, na.rm = TRUE)## [1] 20.1135describe(CC$Risk_Score_BIO)## CC$Risk_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 133 1 33.69 26.45 0.00 3.00
## .25 .50 .75 .90 .95
## 12.50 32.50 50.00 63.00 75.67
##
## lowest : 0 0.5 1 1.5 2 , highest: 84 88 90 93 95describe(CC$Risk_Scale_BIO)## CC$Risk_Scale_BIO
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 83 0.999 39.39 28.83 0.0 4.0
## .25 .50 .75 .90 .95
## 19.0 40.0 56.0 74.9 80.0
##
## lowest : 0 1 2 3 4, highest: 86 90 95 96 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 82 0.992 27.98 28.3 0.00 0.00
## .25 .50 .75 .90 .95
## 4.00 25.00 47.25 63.00 75.00
##
## lowest : 0 1 2 3 4, highest: 90 92 95 96 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BIO, na.rm = TRUE)## [1] 23.16999describe(CC$Risk_Score_BECCS)## CC$Risk_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 140 0.999 38.61 28.99 0.00 3.95
## .25 .50 .75 .90 .95
## 19.50 37.75 55.00 72.05 85.55
##
## lowest : 0 0.5 1 2.5 3 , highest: 92.5 93 94 98 100describe(CC$Risk_Scale_BECCS)## CC$Risk_Scale_BECCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 86 0.999 45.35 30.93 0.00 6.90
## .25 .50 .75 .90 .95
## 24.25 49.50 64.00 80.00 92.55
##
## lowest : 0 1 4 5 6, highest: 93 94 95 96 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 80 0.992 31.86 31.91 0.0 0.0
## .25 .50 .75 .90 .95
## 5.0 25.5 51.0 72.3 90.0
##
## lowest : 0 1 2 3 4, highest: 90 91 92 96 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BECCS, na.rm = TRUE)## [1] 25.48881describe(CC$Risk_Score_DACCS)## CC$Risk_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 151 1 42.98 30.22 0.00 5.50
## .25 .50 .75 .90 .95
## 22.25 45.00 62.50 78.10 89.35
##
## lowest : 0 0.5 1 2.5 3 , highest: 95.5 98 98.5 99.5 100describe(CC$Risk_Scale_DACCS)## CC$Risk_Scale_DACCS
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 90 0.999 50.12 31.12 0.0 9.0
## .25 .50 .75 .90 .95
## 30.0 52.0 70.0 84.4 95.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 92 0.994 35.84 33.73 0.0 0.0
## .25 .50 .75 .90 .95
## 7.0 33.0 59.0 79.4 89.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_DACCS, na.rm = TRUE)## [1] 26.347describe(CC$Risk_Score_EW)## CC$Risk_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 146 1 38.45 28.7 2.35 7.50
## .25 .50 .75 .90 .95
## 17.75 37.00 55.00 75.00 85.00
##
## lowest : 0 1 2 2.5 3 , highest: 94 96 97.5 99.5 100describe(CC$Risk_Scale_EW)## CC$Risk_Scale_EW
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 85 0.999 45.98 30.54 3.7 10.0
## .25 .50 .75 .90 .95
## 25.0 50.0 64.0 82.6 93.3
##
## lowest : 0 2 3 4 5, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.994 30.92 31.26 0.0 0.0
## .25 .50 .75 .90 .95
## 5.5 24.0 51.0 74.6 85.0
##
## lowest : 0 1 2 3 4, highest: 92 95 98 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_EW, na.rm = TRUE)## [1] 25.2467describe(CC$Risk_Score_OF)## CC$Risk_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 153 1 46.37 30.58 0.80 10.00
## .25 .50 .75 .90 .95
## 25.25 45.50 66.25 81.50 89.85
##
## lowest : 0 0.5 1.5 2 5 , highest: 96 97 98.5 99 100describe(CC$Risk_Scale_OF)## CC$Risk_Scale_OF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 94 0.999 54.49 31.47 1.3 14.6
## .25 .50 .75 .90 .95
## 33.0 57.0 75.0 89.4 97.7
##
## lowest : 0 1 2 4 7, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------
## CC.Risk_2_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 0.996 38.24 33.86 0.0 0.0
## .25 .50 .75 .90 .95
## 10.5 37.0 62.5 80.0 90.0
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_OF, na.rm = TRUE)## [1] 26.54645describe(CC$Risk_Score_BF)## CC$Risk_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 105 0.999 26.04 22.96 0.000 0.500
## .25 .50 .75 .90 .95
## 8.375 22.250 40.250 52.300 60.650
##
## lowest : 0 0.5 1 1.5 2.5 , highest: 81.5 83.5 85.5 86 100describe(CC$Risk_Scale_BF)## CC$Risk_Scale_BF
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 74 0.998 32.13 27.09 0.00 0.00
## .25 .50 .75 .90 .95
## 12.75 30.00 50.00 69.00 74.00
##
## lowest : 0 1 2 4 5, highest: 83 84 87 93 100
## --------------------------------------------------------------------------------
## CC.Risk_2_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 65 0.984 19.96 22.59 0.00 0.00
## .25 .50 .75 .90 .95
## 0.75 14.00 32.00 51.00 63.65
##
## lowest : 0 1 2 3 4, highest: 80 81 87 88 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_BF, na.rm = TRUE)## [1] 20.52519describe(CC$Risk_Score_NE)## CC$Risk_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 126 1 52.92 34.7 2.4 6.9
## .25 .50 .75 .90 .95
## 25.0 58.0 75.5 91.5 100.0
##
## lowest : 0 0.5 1.5 2 2.5 , highest: 95.5 96.5 98.5 99 100describe(CC$Risk_Scale_NE)## CC$Risk_Scale_NE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 76 0.998 56.87 34.87 3.8 10.0
## .25 .50 .75 .90 .95
## 31.0 62.0 80.0 100.0 100.0
##
## lowest : 0 1 3 4 5, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## CC.Risk_2_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 84 0.999 48.97 37.15 0.0 2.6
## .25 .50 .75 .90 .95
## 20.0 54.0 76.0 93.0 100.0
##
## lowest : 0 1 2 3 4, highest: 93 95 97 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_NE, na.rm = TRUE)## [1] 30.17134describe(CC$Risk_Score_SE)## CC$Risk_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 69 0.955 10.18 13.87 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 15.0 34.0 42.9
##
## lowest : 0 0.5 1 1.5 2 , highest: 45 48.5 51 63 78.5describe(CC$Risk_Scale_SE)## CC$Risk_Scale_SE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 54 0.945 13.63 18.58 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 5.0 21.0 40.6 52.0
##
## lowest : 0 1 2 3 4, highest: 67 79 80 82 88
## --------------------------------------------------------------------------------
## CC.Risk_2_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 42 0.821 6.735 10.85 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 7.0 22.6 33.8
##
## lowest : 0 1 2 3 4, highest: 51 64 75 79 88
## --------------------------------------------------------------------------------sd(CC$Risk_Score_SE, na.rm = TRUE)## [1] 14.22085describe(CC$Risk_Score_WE)## CC$Risk_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 94 0.988 18.79 23.08 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 11.0 25.5 52.2 67.3
##
## lowest : 0 0.5 1 1.5 2 , highest: 89 89.5 92 98 98.5describe(CC$Risk_Scale_WE)## CC$Risk_Scale_WE
##
## 2 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Risk_1_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.984 23.56 28.04 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 13.0 38.0 68.0 80.2
##
## lowest : 0 1 2 3 4, highest: 90 91 92 97 100
## --------------------------------------------------------------------------------
## CC.Risk_2_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 55 0.915 14.02 20.08 0 0
## .25 .50 .75 .90 .95
## 0 4 19 43 68
##
## lowest : 0 1 2 3 4, highest: 84 89 90 99 100
## --------------------------------------------------------------------------------sd(CC$Risk_Score_WE, na.rm = TRUE)## [1] 22.76701#Cronbach's alpha for risk scale
psych::alpha(data.frame(CC$Risk_1_AFSCS, CC$Risk_2_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_AFSCS, CC$Risk_2_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.74 0.74 5.8 0.0093 16 20 0.74
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.85 0.87
## Duhachek 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_AFSCS 0.83 0.74 0.55 0.74 2.9 NA 0 0.74
## CC.Risk_2_AFSCS 0.67 0.74 0.55 0.74 2.9 NA 0 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_AFSCS 343 0.94 0.93 0.81 0.74 19 23
## CC.Risk_2_AFSCS 343 0.93 0.93 0.81 0.74 13 20hist(CC$Risk_Score_AFSCS, main = 'AFSCS Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_BIO, CC$Risk_2_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_BIO, CC$Risk_2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.69 0.69 4.4 0.012 34 23 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.81 0.84
## Duhachek 0.79 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BIO 0.68 0.69 0.47 0.69 2.2 NA 0 0.69
## CC.Risk_2_BIO 0.69 0.69 0.47 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BIO 332 0.92 0.92 0.76 0.69 39 25
## CC.Risk_2_BIO 332 0.92 0.92 0.76 0.69 28 25hist(CC$Risk_Score_BIO, main = 'BIO Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_BECCS, CC$Risk_2_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_BECCS, CC$Risk_2_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.82 0.69 0.69 4.4 0.012 39 25 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.81 0.84
## Duhachek 0.79 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BECCS 0.65 0.69 0.47 0.69 2.2 NA 0 0.69
## CC.Risk_2_BECCS 0.73 0.69 0.47 0.69 2.2 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BECCS 330 0.91 0.92 0.76 0.69 45 27
## CC.Risk_2_BECCS 330 0.92 0.92 0.76 0.69 32 29hist(CC$Risk_Score_BECCS, main = 'BECCS Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_DACCS, CC$Risk_2_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_DACCS, CC$Risk_2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.84 0.72 0.72 5.1 0.01 43 26 0.72
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.83 0.85
## Duhachek 0.81 0.83 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_DACCS 0.66 0.72 0.52 0.72 2.6 NA 0 0.72
## CC.Risk_2_DACCS 0.78 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_DACCS 347 0.92 0.93 0.79 0.72 50 27
## CC.Risk_2_DACCS 347 0.93 0.93 0.79 0.72 36 30hist(CC$Risk_Score_DACCS, main = 'DACCS Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_EW, CC$Risk_2_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_EW, CC$Risk_2_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.71 0.71 4.9 0.011 38 25 0.71
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.83 0.85
## Duhachek 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_EW 0.68 0.71 0.51 0.71 2.5 NA 0 0.71
## CC.Risk_2_EW 0.75 0.71 0.51 0.71 2.5 NA 0 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_EW 335 0.92 0.93 0.78 0.71 46 27
## CC.Risk_2_EW 335 0.93 0.93 0.78 0.71 31 28hist(CC$Risk_Score_EW, main = 'EW Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_OF, CC$Risk_2_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_OF, CC$Risk_2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.73 0.73 5.5 0.0097 46 27 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.84 0.86
## Duhachek 0.83 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_OF 0.68 0.73 0.54 0.73 2.7 NA 0 0.73
## CC.Risk_2_OF 0.79 0.73 0.54 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_OF 327 0.93 0.93 0.8 0.73 54 27
## CC.Risk_2_OF 327 0.94 0.93 0.8 0.73 38 30hist(CC$Risk_Score_OF, main = 'OF Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_BF, CC$Risk_2_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_BF, CC$Risk_2_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.65 0.65 3.8 0.013 26 21 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.79 0.81
## Duhachek 0.76 0.79 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_BF 0.73 0.65 0.43 0.65 1.9 NA 0 0.65
## CC.Risk_2_BF 0.58 0.65 0.43 0.65 1.9 NA 0 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_BF 248 0.92 0.91 0.73 0.65 32 24
## CC.Risk_2_BF 248 0.90 0.91 0.73 0.65 20 21hist(CC$Risk_Score_BF, main = 'BF Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_NE, CC$Risk_2_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_NE, CC$Risk_2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.85 0.85 12 0.0051 53 30 0.85
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_NE 0.81 0.85 0.73 0.85 5.8 NA 0 0.85
## CC.Risk_2_NE 0.90 0.85 0.73 0.85 5.8 NA 0 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_NE 257 0.96 0.96 0.89 0.85 57 30
## CC.Risk_2_NE 257 0.96 0.96 0.89 0.85 49 32hist(CC$Risk_Score_NE, main = 'NE Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_SE, CC$Risk_2_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_SE, CC$Risk_2_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.62 0.64 0.47 0.47 1.8 0.022 10 14 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.57 0.62 0.66
## Duhachek 0.58 0.62 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_SE 0.66 0.47 0.22 0.47 0.9 NA 0 0.47
## CC.Risk_2_SE 0.34 0.47 0.22 0.47 0.9 NA 0 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_SE 245 0.9 0.86 0.59 0.47 13.6 19
## CC.Risk_2_SE 245 0.8 0.86 0.59 0.47 6.7 14hist(CC$Risk_Score_SE, main = 'SE Risk Scale Score')
psych::alpha(data.frame(CC$Risk_1_WE, CC$Risk_2_WE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_1_WE, CC$Risk_2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.88 0.78 0.78 7 0.008 19 23 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.87 0.88
## Duhachek 0.85 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_1_WE 0.95 0.78 0.61 0.78 3.5 NA 0 0.78
## CC.Risk_2_WE 0.64 0.78 0.61 0.78 3.5 NA 0 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_1_WE 257 0.95 0.94 0.83 0.78 24 26
## CC.Risk_2_WE 257 0.93 0.94 0.83 0.78 14 22hist(CC$Risk_Score_WE, main = 'WE Risk Scale Score')
# Correlation between Support Items Across all 10 Technologies
CC$R1 <- rowMeans(CC [, c("Risk_1_AFSCS", "Risk_1_BIO", "Risk_1_BECCS", "Risk_1_DACCS", "Risk_1_EW", "Risk_1_OF", "Risk_1_BF", "Risk_1_NE", "Risk_1_SE", "Risk_1_WE")], na.rm=TRUE)
CC$R2 <- rowMeans(CC [, c("Risk_2_AFSCS", "Risk_2_BIO", "Risk_2_BECCS", "Risk_2_DACCS", "Risk_2_EW", "Risk_2_OF", "Risk_2_BF", "Risk_2_NE", "Risk_2_SE", "Risk_2_WE")], na.rm=TRUE)
cor.test(CC$R1,CC$R2)##
## Pearson's product-moment correlation
##
## data: CC$R1 and CC$R2
## t = 40.056, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7591263 0.8068239
## sample estimates:
## cor
## 0.7841303l. Understanding
# Understanding was rated on a one item scale (0 = Strongly disagree to 100 = Strongly agree) and represented participant understanding of the technology rated.
## 1. I understand how this works.i. Descriptives
# Define Variables
CC$Und_AFSCS <- CC$Risk_AFSCS_30
CC$Und_BIO <- CC$Risk_BIO_30
CC$Und_BECCS <- CC$Risk_BECCS_30
CC$Und_DACCS <- CC$Risk_DACCS_30
CC$Und_EW <- CC$Risk_EW_30
CC$Und_OF <- CC$Risk_OF_30
CC$Und_BF <- CC$Risk_BF_30
CC$Und_NE <- CC$Risk_NE_30
CC$Und_SE <- CC$Risk_SE_30
CC$Und_WE <- CC$Risk_WE_30
# Descriptives
describe(CC$Und_AFSCS)## CC$Und_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 85 0.995 70.96 28.64 17.3 32.0
## .25 .50 .75 .90 .95
## 57.0 77.0 92.0 100.0 100.0
##
## lowest : 0 1 3 5 7, highest: 96 97 98 99 100describe(CC$Und_BIO)## CC$Und_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 1 48.06 32.06 1 10
## .25 .50 .75 .90 .95
## 25 50 70 86 95
##
## lowest : 0 1 2 5 6, highest: 94 95 97 98 100describe(CC$Und_BECCS)## CC$Und_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 93 0.999 45.42 32.95 0.0 5.0
## .25 .50 .75 .90 .95
## 22.0 44.5 67.0 85.0 92.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100describe(CC$Und_DACCS)## CC$Und_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 93 1 45.61 34.44 0.0 5.0
## .25 .50 .75 .90 .95
## 19.0 46.0 70.0 85.0 92.7
##
## lowest : 0 1 2 3 4, highest: 93 95 98 99 100describe(CC$Und_EW)## CC$Und_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 90 0.999 43.44 31.42 0.0 5.0
## .25 .50 .75 .90 .95
## 21.5 41.0 63.0 80.6 88.0
##
## lowest : 0 1 2 3 4, highest: 88 91 93 94 100describe(CC$Und_OF)## CC$Und_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 1 51.18 32.34 5.0 9.0
## .25 .50 .75 .90 .95
## 28.0 53.0 73.5 87.0 94.0
##
## lowest : 0 2 4 5 6, highest: 94 95 97 98 100describe(CC$Und_BF)## CC$Und_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 80 0.999 61.88 30.79 6.75 23.40
## .25 .50 .75 .90 .95
## 42.75 66.00 81.25 99.00 100.00
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100describe(CC$Und_NE)## CC$Und_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.999 65.29 30.66 10.8 23.6
## .25 .50 .75 .90 .95
## 50.0 71.0 88.0 99.4 100.0
##
## lowest : 0 2 3 4 6, highest: 96 97 98 99 100describe(CC$Und_SE)## CC$Und_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.978 83.81 19.14 50.0 56.4
## .25 .50 .75 .90 .95
## 75.0 90.0 100.0 100.0 100.0
##
## lowest : 2 5 21 22 30, highest: 96 97 98 99 100describe(CC$Und_WE)## CC$Und_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 53 0.982 82.82 19.09 51.0 61.2
## .25 .50 .75 .90 .95
## 74.0 87.0 100.0 100.0 100.0
##
## lowest : 0 15 16 19 26, highest: 96 97 98 99 100sd(CC$Und_AFSCS, na.rm = TRUE)## [1] 26.01993sd(CC$Und_BIO, na.rm = TRUE)## [1] 27.80563sd(CC$Und_BECCS, na.rm = TRUE)## [1] 28.56679sd(CC$Und_DACCS, na.rm = TRUE)## [1] 29.85122sd(CC$Und_EW, na.rm = TRUE)## [1] 27.30677sd(CC$Und_OF, na.rm = TRUE)## [1] 28.08305sd(CC$Und_BF, na.rm = TRUE)## [1] 27.24634sd(CC$Und_NE, na.rm = TRUE)## [1] 27.26283sd(CC$Und_SE, na.rm = TRUE)## [1] 18.50476sd(CC$Und_WE, na.rm = TRUE)## [1] 18.43885hist(CC$Und_AFSCS)
hist(CC$Und_BIO)
hist(CC$Und_BECCS)
hist(CC$Und_DACCS)
hist(CC$Und_EW)
hist(CC$Und_OF)
hist(CC$Und_BF)
hist(CC$Und_NE)
hist(CC$Und_SE)
hist(CC$Und_WE)
ii. Score(s) & Scale(s)
# Note: Understanding scores & scales not present because measure is one item.)m. Familiarity/Understanding
i. Descriptives
CC$Fam <- data.frame(CC$Familiar_AFSCS,
CC$Familiar_BIO,
CC$Familiar_BECCS,
CC$Familiar_DACCS,
CC$Familiar_EW,
CC$Familiar_OF,
CC$Familiar_BF,
CC$Familiar_NE,
CC$Familiar_SE,
CC$Familiar_WE)
describe(CC$Fam)## CC$Fam
##
## 10 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Familiar_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 91 0.997 62.7 34.53 3 12
## .25 .50 .75 .90 .95
## 42 67 89 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Familiar_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 81 0.993 27.79 29.57 0.00 0.00
## .25 .50 .75 .90 .95
## 4.75 20.00 44.00 68.90 82.00
##
## lowest : 0 1 2 3 4, highest: 92 93 94 95 100
## --------------------------------------------------------------------------------
## CC.Familiar_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.994 29.64 30.74 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 21.00 50.00 73.00 83.55
##
## lowest : 0 1 2 3 4, highest: 91 92 94 98 100
## --------------------------------------------------------------------------------
## CC.Familiar_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.992 26.05 27.55 0.0 0.0
## .25 .50 .75 .90 .95
## 4.5 20.0 42.0 65.0 75.0
##
## lowest : 0 1 2 3 4, highest: 89 90 93 99 100
## --------------------------------------------------------------------------------
## CC.Familiar_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 76 0.98 22.5 25.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 17.0 35.5 60.0 70.0
##
## lowest : 0 1 2 3 4, highest: 79 80 87 90 91
## --------------------------------------------------------------------------------
## CC.Familiar_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 76 0.992 25.62 27.66 0.0 0.0
## .25 .50 .75 .90 .95
## 4.0 18.0 40.5 62.8 76.0
##
## lowest : 0 1 2 3 4, highest: 85 86 87 89 100
## --------------------------------------------------------------------------------
## CC.Familiar_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 87 0.999 57.92 32.61 0.0 18.0
## .25 .50 .75 .90 .95
## 36.0 61.0 81.0 93.3 100.0
##
## lowest : 0 1 5 6 8, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## CC.Familiar_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 82 0.998 69.17 29.4 14.8 30.6
## .25 .50 .75 .90 .95
## 53.0 75.0 90.0 100.0 100.0
##
## lowest : 0 2 3 4 6, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Familiar_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 49 0.941 87.95 15.68 52.2 65.2
## .25 .50 .75 .90 .95
## 82.0 94.0 100.0 100.0 100.0
##
## lowest : 0 18 35 41 45, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Familiar_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 61 0.982 81.79 20.9 41.6 55.0
## .25 .50 .75 .90 .95
## 75.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 3 13 19, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------CC$Understanding <- data.frame(CC$Und_WE,
CC$Und_SE,
CC$Und_NE,
CC$Und_BF,
CC$Und_OF,
CC$Und_EW,
CC$Und_DACCS,
CC$Und_BECCS,
CC$Und_BIO,
CC$Und_AFSC)
describe(CC$Understanding)## CC$Understanding
##
## 10 Variables 1007 Observations
## --------------------------------------------------------------------------------
## CC.Und_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 53 0.982 82.82 19.09 51.0 61.2
## .25 .50 .75 .90 .95
## 74.0 87.0 100.0 100.0 100.0
##
## lowest : 0 15 16 19 26, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Und_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 56 0.978 83.81 19.14 50.0 56.4
## .25 .50 .75 .90 .95
## 75.0 90.0 100.0 100.0 100.0
##
## lowest : 2 5 21 22 30, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Und_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 83 0.999 65.29 30.66 10.8 23.6
## .25 .50 .75 .90 .95
## 50.0 71.0 88.0 99.4 100.0
##
## lowest : 0 2 3 4 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Und_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 80 0.999 61.88 30.79 6.75 23.40
## .25 .50 .75 .90 .95
## 42.75 66.00 81.25 99.00 100.00
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## CC.Und_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 88 1 51.18 32.34 5.0 9.0
## .25 .50 .75 .90 .95
## 28.0 53.0 73.5 87.0 94.0
##
## lowest : 0 2 4 5 6, highest: 94 95 97 98 100
## --------------------------------------------------------------------------------
## CC.Und_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 90 0.999 43.44 31.42 0.0 5.0
## .25 .50 .75 .90 .95
## 21.5 41.0 63.0 80.6 88.0
##
## lowest : 0 1 2 3 4, highest: 88 91 93 94 100
## --------------------------------------------------------------------------------
## CC.Und_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 93 1 45.61 34.44 0.0 5.0
## .25 .50 .75 .90 .95
## 19.0 46.0 70.0 85.0 92.7
##
## lowest : 0 1 2 3 4, highest: 93 95 98 99 100
## --------------------------------------------------------------------------------
## CC.Und_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 93 0.999 45.42 32.95 0.0 5.0
## .25 .50 .75 .90 .95
## 22.0 44.5 67.0 85.0 92.0
##
## lowest : 0 1 2 4 5, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------
## CC.Und_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 91 1 48.06 32.06 1 10
## .25 .50 .75 .90 .95
## 25 50 70 86 95
##
## lowest : 0 1 2 5 6, highest: 94 95 97 98 100
## --------------------------------------------------------------------------------
## CC.Und_AFSC
## n missing distinct Info Mean Gmd .05 .10
## 343 664 85 0.995 70.96 28.64 17.3 32.0
## .25 .50 .75 .90 .95
## 57.0 77.0 92.0 100.0 100.0
##
## lowest : 0 1 3 5 7, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------#Mean understanding/familiarity scores by technology
CC$FR.AFSCS <- rowMeans(CC [, c("Familiar_AFSCS", "Und_AFSCS")], na.rm=TRUE)
CC$FR.BIO <- rowMeans(CC [, c("Familiar_BIO", "Und_BIO")], na.rm=TRUE)
CC$FR.BECCS <- rowMeans(CC [, c("Familiar_BECCS", "Und_BECCS")], na.rm=TRUE)
CC$FR.DACCS <- rowMeans(CC [, c("Familiar_DACCS", "Und_DACCS")], na.rm=TRUE)
CC$FR.EW <- rowMeans(CC [, c("Familiar_EW", "Und_EW")], na.rm=TRUE)
CC$FR.OF <- rowMeans(CC [, c("Familiar_OF", "Und_OF")], na.rm=TRUE)
CC$FR.BF <- rowMeans(CC [, c("Familiar_BF", "Und_BF")], na.rm=TRUE)
CC$FR.NE <- rowMeans(CC [, c("Familiar_NE", "Und_NE")], na.rm=TRUE)
CC$FR.SE <- rowMeans(CC [, c("Familiar_SE", "Und_SE")], na.rm=TRUE)
CC$FR.WE <- rowMeans(CC [, c("Familiar_WE", "Und_WE")], na.rm=TRUE)
#Descriptives
describe(CC$FR.AFSCS)## CC$FR.AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 149 0.999 66.83 29.53 16.6 26.6
## .25 .50 .75 .90 .95
## 50.0 71.0 88.5 100.0 100.0
##
## lowest : 0 0.5 2.5 3.5 5 , highest: 98 98.5 99 99.5 100describe(CC$FR.BIO)## CC$FR.BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 138 1 37.92 27.57 1.00 9.10
## .25 .50 .75 .90 .95
## 18.88 34.75 54.50 73.95 80.00
##
## lowest : 0 0.5 1 3 3.5 , highest: 92.5 93 93.5 98.5 100describe(CC$FR.BECCS)## CC$FR.BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 142 1 37.53 28.26 0.225 4.500
## .25 .50 .75 .90 .95
## 18.000 35.000 52.500 71.550 82.325
##
## lowest : 0 0.5 1 1.5 2 , highest: 92.5 93.5 95 99.5 100describe(CC$FR.DACCS)## CC$FR.DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 149 1 35.83 27.9 0.50 5.00
## .25 .50 .75 .90 .95
## 15.00 34.00 52.00 69.70 78.55
##
## lowest : 0 0.5 1 2 2.5 , highest: 93 93.5 95 99.5 100describe(CC$FR.EW)## CC$FR.EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 128 1 32.97 25.23 0.0 3.0
## .25 .50 .75 .90 .95
## 16.0 30.5 47.5 64.6 75.5
##
## lowest : 0 1 1.5 2 2.5 , highest: 86 88.5 89.5 95 95.5describe(CC$FR.OF)## CC$FR.OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 145 1 38.4 25.63 4.50 7.50
## .25 .50 .75 .90 .95
## 20.00 38.50 52.50 67.50 80.35
##
## lowest : 0 0.5 1 2 3.5 , highest: 86.5 87 92.5 93 100describe(CC$FR.BF)## CC$FR.BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 117 1 59.9 28.49 12.70 26.70
## .25 .50 .75 .90 .95
## 44.38 61.00 77.50 93.00 99.32
##
## lowest : 0 0.5 2.5 10 11.5, highest: 97.5 98.5 99 99.5 100describe(CC$FR.NE)## CC$FR.NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 120 1 67.23 26.85 19.2 33.6
## .25 .50 .75 .90 .95
## 51.0 72.5 87.0 95.0 99.2
##
## lowest : 0 2.5 3 6 7.5 , highest: 96.5 97 97.5 99 100describe(CC$FR.SE)## CC$FR.SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 78 0.987 85.88 16.23 52.2 63.7
## .25 .50 .75 .90 .95
## 80.0 90.0 98.5 100.0 100.0
##
## lowest : 23 25 33 40.5 42.5, highest: 98 98.5 99 99.5 100describe(CC$FR.WE)## CC$FR.WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 88 0.994 82.31 17.72 50.4 63.0
## .25 .50 .75 .90 .95
## 73.5 86.0 95.0 100.0 100.0
##
## lowest : 1.5 8 16 23.5 34 , highest: 97 97.5 98 99.5 100#SD
sd(CC$FR.AFSCS, na.rm = TRUE)## [1] 26.26246sd(CC$FR.BIO, na.rm = TRUE)## [1] 24.31053sd(CC$FR.BECCS, na.rm = TRUE)## [1] 24.87139sd(CC$FR.DACCS, na.rm = TRUE)## [1] 24.4548sd(CC$FR.EW, na.rm = TRUE)## [1] 22.23358sd(CC$FR.OF, na.rm = TRUE)## [1] 22.49241sd(CC$FR.BF, na.rm = TRUE)## [1] 25.10918sd(CC$FR.NE, na.rm = TRUE)## [1] 24.0517sd(CC$FR.SE, na.rm = TRUE)## [1] 15.59678sd(CC$FR.WE, na.rm = TRUE)## [1] 16.95988#Histograms
hist(CC$FR.AFSCS)
hist(CC$FR.BIO)
hist(CC$FR.BECCS)
hist(CC$FR.DACCS)
hist(CC$FR.EW)
hist(CC$FR.OF)
hist(CC$FR.BF)
hist(CC$FR.NE)
hist(CC$FR.SE)
hist(CC$FR.WE)
ii. Score(s) & Scale(s)
#Scales
CC$FR2.AFSCS <- data.frame(CC$Familiar_AFSCS, CC$Und_AFSCS)
CC$FR2.BIO <- data.frame(CC$Familiar_BIO, CC$Und_BIO)
CC$FR2.BECCS <- data.frame(CC$Familiar_BECCS, CC$Und_BECCS)
CC$FR2.DACCS <- data.frame(CC$Familiar_DACCS, CC$Und_DACCS)
CC$FR2.EW <- data.frame(CC$Familiar_EW, CC$Und_EW)
CC$FR2.OF <- data.frame(CC$Familiar_OF, CC$Und_OF)
CC$FR2.BF <- data.frame(CC$Familiar_BF, CC$Und_BF)
CC$FR2.NE <- data.frame(CC$Familiar_NE, CC$Und_NE)
CC$FR2.SE <- data.frame(CC$Familiar_SE, CC$Und_SE)
CC$FR2.WE <- data.frame(CC$Familiar_WE, CC$Und_WE)
#Alphas
psych::alpha(CC$FR2.AFSCS)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.AFSCS)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.84 0.72 0.72 5.1 0.01 67 26 0.72
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.83 0.85
## Duhachek 0.81 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_AFSCS 0.85 0.72 0.52 0.72 2.6 NA 0
## CC.Und_AFSCS 0.61 0.72 0.52 0.72 2.6 NA 0
## med.r
## CC.Familiar_AFSCS 0.72
## CC.Und_AFSCS 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_AFSCS 343 0.94 0.93 0.79 0.72 63 31
## CC.Und_AFSCS 343 0.91 0.93 0.79 0.72 71 26psych::alpha(CC$FR2.BIO)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.BIO)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.73 0.57 0.57 2.7 0.017 38 24 0.57
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.73 0.76
## Duhachek 0.70 0.73 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_BIO 0.56 0.57 0.33 0.57 1.3 NA 0 0.57
## CC.Und_BIO 0.59 0.57 0.33 0.57 1.3 NA 0 0.57
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_BIO 332 0.88 0.89 0.67 0.57 28 27
## CC.Und_BIO 332 0.89 0.89 0.67 0.57 48 28psych::alpha(CC$FR2.BECCS)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.BECCS)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.71 0.56 0.56 2.5 0.018 38 25 0.56
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.68 0.71 0.75
## Duhachek 0.68 0.71 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_BECCS 0.54 0.56 0.31 0.56 1.3 NA 0
## CC.Und_BECCS 0.57 0.56 0.31 0.56 1.3 NA 0
## med.r
## CC.Familiar_BECCS 0.56
## CC.Und_BECCS 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_BECCS 330 0.88 0.88 0.66 0.56 30 28
## CC.Und_BECCS 330 0.89 0.88 0.66 0.56 45 29psych::alpha(CC$FR2.DACCS)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.DACCS)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.74 0.58 0.58 2.8 0.017 36 24 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.73 0.76
## Duhachek 0.70 0.73 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Familiar_DACCS 0.49 0.58 0.34 0.58 1.4 NA 0
## CC.Und_DACCS 0.69 0.58 0.34 0.58 1.4 NA 0
## med.r
## CC.Familiar_DACCS 0.58
## CC.Und_DACCS 0.58
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_DACCS 347 0.87 0.89 0.68 0.58 26 25
## CC.Und_DACCS 347 0.91 0.89 0.68 0.58 46 30psych::alpha(CC$FR2.EW)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.EW)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.71 0.55 0.55 2.4 0.018 33 22 0.55
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.66 0.7 0.74
## Duhachek 0.67 0.7 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_EW 0.46 0.55 0.3 0.55 1.2 NA 0 0.55
## CC.Und_EW 0.64 0.55 0.3 0.55 1.2 NA 0 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_EW 335 0.86 0.88 0.65 0.55 22 23
## CC.Und_EW 335 0.90 0.88 0.65 0.55 43 27psych::alpha(CC$FR2.OF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.OF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.59 0.59 0.42 0.42 1.4 0.026 38 22 0.42
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.53 0.59 0.63
## Duhachek 0.53 0.59 0.64
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_OF 0.38 0.42 0.17 0.42 0.71 NA 0 0.42
## CC.Und_OF 0.46 0.42 0.17 0.42 0.71 NA 0 0.42
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_OF 327 0.82 0.84 0.54 0.42 26 25
## CC.Und_OF 327 0.86 0.84 0.54 0.42 51 28psych::alpha(CC$FR2.BF)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.BF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.62 0.62 3.2 0.015 60 25 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.76 0.79
## Duhachek 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_BF 0.65 0.62 0.38 0.62 1.6 NA 0 0.62
## CC.Und_BF 0.59 0.62 0.38 0.62 1.6 NA 0 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_BF 248 0.90 0.9 0.71 0.62 58 29
## CC.Und_BF 248 0.89 0.9 0.71 0.62 62 27psych::alpha(CC$FR2.NE)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.NE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.6 0.6 2.9 0.016 67 24 0.6
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.71 0.75 0.78
## Duhachek 0.72 0.75 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_NE 0.58 0.6 0.35 0.6 1.5 NA 0 0.6
## CC.Und_NE 0.61 0.6 0.35 0.6 1.5 NA 0 0.6
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_NE 257 0.89 0.89 0.69 0.6 69 27
## CC.Und_NE 257 0.90 0.89 0.69 0.6 65 27psych::alpha(CC$FR2.SE)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.SE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.63 0.63 3.4 0.014 86 16 0.63
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.74 0.77 0.8
## Duhachek 0.74 0.77 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_SE 0.55 0.63 0.4 0.63 1.7 NA 0 0.63
## CC.Und_SE 0.73 0.63 0.4 0.63 1.7 NA 0 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_SE 245 0.89 0.9 0.72 0.63 88 16
## CC.Und_SE 245 0.92 0.9 0.72 0.63 84 19psych::alpha(CC$FR2.WE)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = CC$FR2.WE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.66 0.49 0.49 1.9 0.021 82 17 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.61 0.66 0.7
## Duhachek 0.62 0.66 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Familiar_WE 0.56 0.49 0.24 0.49 0.97 NA 0 0.49
## CC.Und_WE 0.44 0.49 0.24 0.49 0.97 NA 0 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Familiar_WE 257 0.88 0.86 0.61 0.49 82 21
## CC.Und_WE 257 0.85 0.86 0.61 0.49 83 18# Correlation between Familiarity and Understanding Across all 10 Technologies
CC$FamCor <- rowMeans(CC [, c("Familiar_AFSCS", "Familiar_BIO", "Familiar_BECCS", "Familiar_DACCS", "Familiar_EW", "Familiar_OF", "Familiar_BF", "Familiar_NE", "Familiar_SE", "Familiar_WE")], na.rm=TRUE)
CC$UndCor <- rowMeans(CC [, c("Und_AFSCS", "Und_BIO", "Und_BECCS", "Und_DACCS", "Und_EW", "Und_OF", "Und_BF", "Und_NE", "Und_SE", "Und_WE")], na.rm=TRUE)
cor.test(CC$FamCor,CC$UndCor)##
## Pearson's product-moment correlation
##
## data: CC$FamCor and CC$UndCor
## t = 26.407, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6020578 0.6751138
## sample estimates:
## cor
## 0.6400301IV. Long Form
a. Rename Variables
#Renaming variables to fit pivot_longer command
## Benefit
CC$Ben.AFSCS <- CC$Ben_AFSCS
length(CC$Ben.AFSCS)## [1] 1007CC$Ben.BIO <- CC$Ben_BIO
length(CC$Ben.BIO)## [1] 1007CC$Ben.BECCS <- CC$Ben_BECCS
length(CC$Ben.BECCS) ## [1] 1007CC$Ben.DACCS <- CC$Ben_DACCS
length(CC$Ben.DACCS)## [1] 1007CC$Ben.EW <- CC$Ben_EW
length(CC$Ben.EW) ## [1] 1007CC$Ben.OF <- CC$Ben_OF
length(CC$Ben.OF) ## [1] 1007CC$Ben.BF <- CC$Ben_BF
length(CC$Ben.BF) ## [1] 1007CC$Ben.NE <- CC$Ben_NE
length(CC$Ben.NE) ## [1] 1007CC$Ben.SE <- CC$Ben_SE
length(CC$Ben.SE) ## [1] 1007CC$Ben.WE <- CC$Ben_WE
length(CC$Ben.WE) ## [1] 1007## Control
CC$Control.AFSCS <- CC$Control_AFSCS
length(CC$Control.AFSCS)## [1] 1007CC$Control.BIO <- CC$Control_BIO
length(CC$Control.BIO)## [1] 1007CC$Control.BECCS <- CC$Control_BECCS
length(CC$Control.BECCS)## [1] 1007CC$Control.DACCS <- CC$Control_DACCS
length(CC$Control.DACCS)## [1] 1007CC$Control.EW <- CC$Control_EW
length(CC$Control.EW)## [1] 1007CC$Control.OF <- CC$Control_OF
length(CC$Control.OF)## [1] 1007CC$Control.BF <- CC$Control_BF
length(CC$Control.BF)## [1] 1007CC$Control.NE <- CC$Control_NE
length(CC$Control.NE)## [1] 1007CC$Control.SE <- CC$Control_SE
length(CC$Control.SE)## [1] 1007CC$Control.WE <- CC$Control_WE
length(CC$Control.WE)## [1] 1007## Familiarity
CC$Familiar.AFSCS <- CC$Familiar_AFSCS
length(CC$Familiar.AFSCS)## [1] 1007CC$Familiar.BIO <- CC$Familiar_BIO
length(CC$Familiar.BIO)## [1] 1007CC$Familiar.BECCS <- CC$Familiar_BECCS
length(CC$Familiar.BECCS)## [1] 1007CC$Familiar.DACCS <- CC$Familiar_DACCS
length(CC$Familiar.DACCS)## [1] 1007CC$Familiar.EW <- CC$Familiar_EW
length(CC$Familiar.EW)## [1] 1007CC$Familiar.OF <- CC$Familiar_OF
length(CC$Familiar.OF)## [1] 1007CC$Familiar.BF <- CC$Familiar_BF
length(CC$Familiar.BF)## [1] 1007CC$Familiar.NE <- CC$Familiar_NE
length(CC$Familiar.NE)## [1] 1007CC$Familiar.SE <- CC$Familiar_SE
length(CC$Familiar.SE)## [1] 1007CC$Familiar.WE <- CC$Familiar_WE
length(CC$Familiar.WE)## [1] 1007## Naturalness
CC$Naturalness.AFSCS <- CC$Nat_Score_AFSCS
length(CC$Naturalness.AFSCS)## [1] 1007CC$Naturalness.BIO <- CC$Nat_Score_BIO
length(CC$Naturalness.BIO)## [1] 1007CC$Naturalness.BECCS <- CC$Nat_Score_BECCS
length(CC$Naturalness.BECCS)## [1] 1007CC$Naturalness.DACCS <- CC$Nat_Score_DACCS
length(CC$Naturalness.DACCS)## [1] 1007CC$Naturalness.EW <- CC$Nat_Score_EW
length(CC$Naturalness.EW)## [1] 1007CC$Naturalness.OF <- CC$Nat_Score_OF
length(CC$Naturalness.OF)## [1] 1007CC$Naturalness.BF <- CC$Nat_Score_BF
length(CC$Naturalness.BF)## [1] 1007CC$Naturalness.NE <- CC$Nat_Score_NE
length(CC$Naturalness.NE)## [1] 1007CC$Naturalness.SE <- CC$Nat_Score_SE
length(CC$Naturalness.SE)## [1] 1007CC$Naturalness.WE <- CC$Nat_Score_WE
length(CC$Naturalness.WE)## [1] 1007## Risk
CC$Risk.AFSCS <- CC$Risk_Score_AFSCS
length(CC$Risk.AFSCS)## [1] 1007CC$Risk.BIO <- CC$Risk_Score_BIO
length(CC$Risk.BIO)## [1] 1007CC$Risk.BECCS <- CC$Risk_Score_BECCS
length(CC$Risk.BECCS)## [1] 1007CC$Risk.DACCS <- CC$Risk_Score_DACCS
length(CC$Risk.DACCS)## [1] 1007CC$Risk.EW <- CC$Risk_Score_EW
length(CC$Risk.EW)## [1] 1007CC$Risk.OF <- CC$Risk_Score_OF
length(CC$Risk.OF)## [1] 1007CC$Risk.BF <- CC$Risk_Score_BF
length(CC$Risk.BF)## [1] 1007CC$Risk.NE <- CC$Risk_Score_NE
length(CC$Risk.NE)## [1] 1007CC$Risk.SE <- CC$Risk_Score_SE
length(CC$Risk.SE)## [1] 1007CC$Risk.WE <- CC$Risk_Score_WE
length(CC$Risk.WE)## [1] 1007## Support
CC$Support.AFSCS <- CC$Support_Score_AFSCS
length(CC$Support.AFSCS)## [1] 1007CC$Support.BIO <- CC$Support_Score_BIO
length(CC$Support.BIO)## [1] 1007CC$Support.BECCS <- CC$Support_Score_BECCS
length(CC$Support.BECCS)## [1] 1007CC$Support.DACCS <- CC$Support_Score_DACCS
length(CC$Support.DACCS)## [1] 1007CC$Support.EW <- CC$Support_Score_EW
length(CC$Support.EW)## [1] 1007CC$Support.OF <- CC$Support_Score_OF
length(CC$Support.OF)## [1] 1007CC$Support.BF <- CC$Support_Score_BF
length(CC$Support.BF)## [1] 1007CC$Support.NE <- CC$Support_Score_NE
length(CC$Support.NE)## [1] 1007CC$Support.SE <- CC$Support_Score_SE
length(CC$Support.SE)## [1] 1007CC$Support.WE <- CC$Support_Score_WE
length(CC$Support.WE)## [1] 1007## Understanding
CC$Understanding.AFSCS <- CC$Und_AFSCS
length(CC$Understanding.AFSCS)## [1] 1007CC$Understanding.BIO <- CC$Und_BIO
length(CC$Understanding.BIO)## [1] 1007CC$Understanding.BECCS <- CC$Und_BECCS
length(CC$Understanding.BECCS)## [1] 1007CC$Understanding.DACCS <- CC$Und_DACCS
length(CC$Understanding.DACCS)## [1] 1007CC$Understanding.EW <- CC$Und_EW
length(CC$Understanding.EW)## [1] 1007CC$Understanding.OF <- CC$Und_OF
length(CC$Understanding.OF)## [1] 1007CC$Understanding.BF <- CC$Und_BF
length(CC$Understanding.BF)## [1] 1007CC$Understanding.NE <- CC$Und_NE
length(CC$Understanding.NE)## [1] 1007CC$Understanding.SE <- CC$Und_SE
length(CC$Understanding.SE)## [1] 1007CC$Understanding.WE <- CC$Und_AFSCS
length(CC$Understanding.WE)## [1] 1007## Familiarity/Understanding (Mean)
length(CC$FR.AFSCS)## [1] 1007length(CC$FR.BIO)## [1] 1007length(CC$FR.BECCS)## [1] 1007length(CC$FR.DACCS)## [1] 1007length(CC$FR.EW)## [1] 1007length(CC$FR.OF)## [1] 1007length(CC$FR.BF)## [1] 1007length(CC$FR.NE)## [1] 1007length(CC$FR.SE)## [1] 1007length(CC$FR.WE)## [1] 1007b. Transform: Wide to Long
library(lmerTest)
library(lme4)
#Reshape to long form
CCvector <- c("Ben.AFSCS", "Ben.BIO", "Ben.BECCS", "Ben.DACCS", "Ben.EW", "Ben.OF" , "Ben.BF", "Ben.NE", "Ben.SE", "Ben.WE", "Control.AFSCS" , "Control.BIO" , "Control.BECCS" , "Control.DACCS", "Control.EW", "Control.OF", "Control.BF", "Control.NE", "Control.SE", "Control.WE", "Familiar.AFSCS" , "Familiar.BIO", "Familiar.BECCS" , "Familiar.DACCS", "Familiar.EW", "Familiar.OF", "Familiar.BF", "Familiar.NE", "Familiar.SE", "Familiar.WE", "Naturalness.AFSCS", "Naturalness.BIO" , "Naturalness.BECCS", "Naturalness.DACCS", "Naturalness.EW", "Naturalness.OF", "Naturalness.BF", "Naturalness.NE", "Naturalness.SE", "Naturalness.WE", "Risk.AFSCS", "Risk.BIO", "Risk.BECCS", "Risk.DACCS", "Risk.EW", "Risk.OF", "Risk.BF", "Risk.NE" , "Risk.SE", "Risk.WE", "Support.AFSCS", "Support.BIO", "Support.BECCS" , "Support.DACCS", "Support.EW" , "Support.OF", "Support.BF", "Support.NE", "Support.SE", "Support.WE", "Understanding.AFSCS", "Understanding.BIO", "Understanding.BECCS", "Understanding.DACCS", "Understanding.EW", "Understanding.OF", "Understanding.BF", "Understanding.NE","Understanding.SE","Understanding.WE", "FR.AFSCS", "FR.BIO", "FR.BECCS", "FR.DACCS", "FR.EW", "FR.OF", "FR.BF", "FR.NE", "FR.SE", "FR.WE")
L <- reshape(data = CC,
varying = CCvector,
timevar = "Type",
direction = "long")c. Center Variables
# Describe & Mean Center Long Variables
## By Technology Measures
table(L$Type) ##
## AFSCS BECCS BF BIO DACCS EW NE OF SE WE
## 1007 1007 1007 1007 1007 1007 1007 1007 1007 1007describe(L$Ben) ## L$Ben
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 101 0.999 57.98 29.92 5 20
## .25 .50 .75 .90 .95
## 40 61 77 90 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(L$Ben, na.rm = TRUE)## [1] 26.41802describe(L$Control) ## L$Control
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 100 0.999 64.82 28.44 17 29
## .25 .50 .75 .90 .95
## 50 69 85 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Familiar) ## L$Familiar
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 101 0.997 46.4 39.99 0 0
## .25 .50 .75 .90 .95
## 13 45 79 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Naturalness) ## L$Naturalness
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 366 1 39.98 24.53 5.00 12.00
## .25 .50 .75 .90 .95
## 24.75 39.00 54.25 70.25 75.00
##
## lowest : 0 0.25 0.5 0.75 1 , highest: 98 98.75 99.5 99.75 100sd(L$Naturalness, na.rm = TRUE)## [1] 21.54695describe(L$Risk)## L$Risk
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 201 0.998 33.04 30.73 0.0 0.0
## .25 .50 .75 .90 .95
## 8.0 28.5 52.0 72.5 84.5
##
## lowest : 0 0.5 1 1.5 2 , highest: 98 98.5 99 99.5 100sd(L$Risk, na.rm = TRUE)## [1] 27.20861describe(L$Support) ## L$Support
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 201 0.999 59.57 32.84 0.0 13.0
## .25 .50 .75 .90 .95
## 41.5 62.5 82.5 99.0 100.0
##
## lowest : 0 0.5 1 1.5 2 , highest: 98 98.5 99 99.5 100sd(L$Support, na.rm = TRUE)## [1] 28.93482describe(L$Understanding) ## L$Understanding
## n missing distinct Info Mean Gmd .05 .10
## 3107 6963 101 0.999 57.74 34.27 4 12
## .25 .50 .75 .90 .95
## 34 61 83 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$FR) ## L$FR
## n missing distinct Info Mean Gmd .05 .10
## 3021 7049 201 1 52.39 34.4 4.0 11.0
## .25 .50 .75 .90 .95
## 27.5 51.0 78.0 94.0 100.0
##
## lowest : 0 0.5 1 1.5 2 , highest: 98 98.5 99 99.5 100cor.test (L$Familiar, L$Understanding)##
## Pearson's product-moment correlation
##
## data: L$Familiar and L$Understanding
## t = 47.117, df = 2852, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6404416 0.6817284
## sample estimates:
## cor
## 0.6615861L$Benefit.c <- L$Ben - 57.98
L$Control.c <- L$Control - 64.82
L$Familiarity <- L$Familiar
L$Familiarity.c <- L$Familiarity - 46.40
L$Naturalness.c <- L$Naturalness - 39.98
L$Risk.c <- L$Risk - 33.04
L$Support.c <- L$Support - 59.57
L$Understanding.c <- L$Understanding - 57.74
L$FR.c <- L$FR - 52.39
## Individual Difference Measures
describe(L$Dem_Age)## L$Dem_Age
## n missing distinct Info Mean Gmd .05 .10
## 9970 100 67 1 45.4 18.64 21 24
## .25 .50 .75 .90 .95
## 31 44 59 67 71
##
## lowest : 18 19 20 21 22, highest: 80 81 82 91 93L$Age.c <- L$Dem_Age - 45.4
describe(L$ATNS_Score)## L$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 366 1 54.56 24.38 18.8 26.0
## .25 .50 .75 .90 .95
## 40.2 54.4 69.0 82.0 92.4
##
## lowest : 0 2 3 4 6.4 , highest: 97.6 98.8 99.2 99.8 100L$ATNS_Score.c <- L$ATNS_Score - 54.56
describe(L$CCB_Score)## L$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 250 0.987 81.61 23.21 25.00 46.75
## .25 .50 .75 .90 .95
## 75.00 91.25 99.00 100.00 100.00
##
## lowest : 0 2 3.75 4 4.75 , highest: 99 99.25 99.5 99.75 100L$CCBelief_Score.c <- L$CCB_Score - 81.61
describe(L$CNS_Score)## L$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 322 1 63.36 18.7 35.0 43.0
## .25 .50 .75 .90 .95
## 52.8 63.0 74.6 85.0 91.8
##
## lowest : 0 8.6 10 12.8 16 , highest: 97.8 98.2 98.6 99.6 100L$CNS_Score.c <- L$CNS_Score -63.36
describe(L$Individualism_Score)## L$Individualism_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 266 1 70.77 18.9 40.25 50.00
## .25 .50 .75 .90 .95
## 60.00 71.50 83.75 91.75 95.75
##
## lowest : 0.75 6 6.25 6.5 15.5 , highest: 99 99.25 99.5 99.75 100L$Individualism_Score.c <- L$Individualism_Score - 70.77
describe(L$Collectivism_Score)## L$Collectivism_Score
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 341 1 54.19 27.22 12.75 21.50
## .25 .50 .75 .90 .95
## 38.50 54.50 72.00 86.00 93.25
##
## lowest : 0 0.25 0.5 1 1.75 , highest: 98.25 98.5 99.5 99.75 100L$Collectivism_Score.c <- L$Collectivism_Score - 54.19
describe(L$Ideology)## L$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 10070 0 13 0.987 -0.8684 2.065 -3.0 -3.0
## .25 .50 .75 .90 .95
## -2.5 -1.5 0.0 2.0 2.5
##
## Value -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
## Frequency 1680 1400 1200 1030 700 410 1160 200 420 510 530
## Proportion 0.167 0.139 0.119 0.102 0.070 0.041 0.115 0.020 0.042 0.051 0.053
##
## Value 2.5 3.0
## Frequency 380 450
## Proportion 0.038 0.045
##
## For the frequency table, variable is rounded to the nearest 0L$Ideology.c <- L$Ideology - 1.94
describe(CC$Dem_SES)## CC$Dem_SES
## n missing distinct Info Mean Gmd .05 .10
## 1006 1 10 0.985 6.274 2.76 2 3
## .25 .50 .75 .90 .95
## 4 7 8 9 10
##
## Value 1 2 3 4 5 6 7 8 9 10
## Frequency 35 40 91 97 99 108 183 148 122 83
## Proportion 0.035 0.040 0.090 0.096 0.098 0.107 0.182 0.147 0.121 0.083
##
## For the frequency table, variable is rounded to the nearest 0L$SES.c <- L$Dem_SE-6.274
describe(CC$EdNum)## CC$EdNum
## n missing distinct Info Mean Gmd
## 1007 0 9 0.918 5.471 1.475
##
## Value 2 3 4 5 6 7 8 9 10
## Frequency 5 134 60 233 405 126 19 22 3
## Proportion 0.005 0.133 0.060 0.231 0.402 0.125 0.019 0.022 0.003
##
## For the frequency table, variable is rounded to the nearest 0L$EDU.c <- L$EdNum - 5.471d. Contrast Code - Carbon Dioxide Removal vs Renewable Energy
# Renewable
L$Renewable <- (-1/2)*(L$Type == 'AFSCS') + (-1/2)*(L$Type == 'BIO') + (-1/2)*(L$Type == 'BECCS') + (-1/2)*(L$Type == 'DACCS') + (-1/2)*(L$Type == 'EW') + (-1/2)*(L$Type == 'OF') + (1/2)*(L$Type == 'BF') + (1/2)*(L$Type == 'NE') + (1/2)*(L$Type == 'SE') + (1/2)*(L$Type == 'WE')e. Contrast Codes (Deviation Coding)
# These codes compare the mean of each technology to the grand mean of all technologies on whatever the outcome variable is (e.g., support)
#1. Direct air capture and carbon sequestration vs. Grand mean
L$DACCS <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') +(1)*(L$Type == 'DACCS') +(0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C2. Nuclear Energy vs. Grand Mean
L$NE <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (1)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C3. Ocean Fertilization vs. Grand Mean
L$OF <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (1)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C4. BECCS vs. Grand Mean
L$BECCS <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (1)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C5. Enhanced Weathering vs. Grand Mean
L$EW <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (1)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C6. Biofuel vs. Grand Mean
L$BF <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (1)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C7. Wind Energy vs. Grand Mean
L$WE <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (1)*(L$Type == 'WE')
#C8. Solar Energy vs. Grand Mean
L$SE <- (0)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (1)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C9. Afforestation/reforestation and Soil Carbon Sequestration vs. Grand Mean
L$AFSCS <- (1)*(L$Type == 'AFSCS') + (-1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (0)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')
#C10. Biochar vs. Grand Mean
L$BIO <- (0)*(L$Type == 'AFSCS') + (1)*(L$Type == 'BIO') + (0)*(L$Type == 'BECCS') + (0)*(L$Type == 'DACCS') + (0)*(L$Type == 'EW') + (0)*(L$Type == 'OF') + (-1)*(L$Type == 'BF') + (0)*(L$Type == 'NE') + (0)*(L$Type == 'SE') + (0)*(L$Type == 'WE')f. Demographics
# Assuming 'gender' is a factor variable with levels: 1 = man, 2 = woman, 3 = nonbinary
# Convert 'gender' to a factor variable if it's not already
L$Gender <- factor(L$Dem_Gen)
# Create contrasts
contrasts(L$Gender) <- cbind(
"Man vs Woman" = c(1, -1, 0), # Contrast comparing men to women
"Man&Woman vs Nonbinary" = c(1/2, 1/2, -1) # Contrast comparing men and women combined to nonbinary
)
# Check orthogonality
round(cor(contrasts(L$Gender)), 10) # Should return an identity matrix## Man vs Woman Man&Woman vs Nonbinary
## Man vs Woman 1 0
## Man&Woman vs Nonbinary 0 1# Now, 'gender' has orthogonal contrast codes
# Ethnicity
# Create Ethnicity variable based on Dem_Ethnicity in data 'L'
L$Ethnicity <- NA
L$Ethnicity[L$Dem_Ethnicity == 1] <- 'Asian'
L$Ethnicity[L$Dem_Ethnicity == 2] <- 'Black'
L$Ethnicity[L$Dem_Ethnicity == 3] <- 'Hispanic'
L$Ethnicity[L$Dem_Ethnicity == 4] <- 'Nat Amer'
L$Ethnicity[L$Dem_Ethnicity == 5] <- 'Nat Pac'
L$Ethnicity[L$Dem_Ethnicity == 6] <- 'White'
L$Ethnicity[L$Dem_Ethnicity == 7] <- 'Other'
# Convert 'Ethnicity' to a factor variable if it's not already
L$Ethnicity <- factor(L$Ethnicity)
# Create dummy variables for each ethnic group compared to White
L$Ethnicity_Asian <- ifelse(L$Ethnicity == "Asian", 1, 0)
L$Ethnicity_Black <- ifelse(L$Ethnicity == "Black", 1, 0)
L$Ethnicity_Hispanic <- ifelse(L$Ethnicity == "Hispanic", 1, 0)
L$Ethnicity_NatAmer <- ifelse(L$Ethnicity == "Nat Amer", 1, 0)
L$Ethnicity_NatPac <- ifelse(L$Ethnicity == "Nat Pac", 1, 0)
L$Ethnicity_Other <- ifelse(L$Ethnicity == "Other", 1, 0)
# Remove dummy variable for White ethnicity
L$Ethnicity_White <- NULL
# Now, 'Ethnicity_Asian', 'Ethnicity_Black', 'Ethnicity_Hispanic', 'Ethnicity_NatAmer', 'Ethnicity_NatPac', and 'Ethnicity_Other' are the dummy variablesV. Correlations
a. Tech by Variables: Benefit, Familiarity/Understanding, Naturalness, Risk, and Support
L$corR <- data.frame(L$Ben, L$FR, L$Naturalness, L$Risk, L$Support)
mydata.cor11 = cor(L$corR, use = "pairwise.complete.obs")
head(round(mydata.cor11,2))## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 1.00 0.24 0.26 -0.33 0.63
## L.FR 0.24 1.00 0.38 -0.34 0.38
## L.Naturalness 0.26 0.38 1.00 -0.49 0.43
## L.Risk -0.33 -0.34 -0.49 1.00 -0.61
## L.Support 0.63 0.38 0.43 -0.61 1.00library("Hmisc")
mydata.rcorr11 = rcorr(as.matrix(mydata.cor11))
mydata.rcorr11## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 1.00 0.35 0.45 -0.75 0.85
## L.FR 0.35 1.00 0.61 -0.74 0.59
## L.Naturalness 0.45 0.61 1.00 -0.87 0.69
## L.Risk -0.75 -0.74 -0.87 1.00 -0.95
## L.Support 0.85 0.59 0.69 -0.95 1.00
##
## n= 5
##
##
## P
## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 0.5605 0.4480 0.1424 0.0682
## L.FR 0.5605 0.2730 0.1540 0.2901
## L.Naturalness 0.4480 0.2730 0.0574 0.1931
## L.Risk 0.1424 0.1540 0.0574 0.0140
## L.Support 0.0682 0.2901 0.1931 0.0140library(corrplot)## corrplot 0.92 loadedcorrplot(mydata.cor11, method="color")
corrplot(mydata.cor11, addCoef.col = 1, number.cex = 0.3, method = 'number')
b. Individual Differences
#Individual Differences
L$corID <- data.frame(L$ATNS_Score, L$CCB_Score, L$CNS_Score, L$Individualism_Score, L$Collectivism_Score, L$Ideology)
mydata.cor2 = cor(L$corID, use = "pairwise.complete.obs")
head(round(mydata.cor2,2))## L.ATNS_Score L.CCB_Score L.CNS_Score
## L.ATNS_Score 1.00 -0.05 0.29
## L.CCB_Score -0.05 1.00 0.30
## L.CNS_Score 0.29 0.30 1.00
## L.Individualism_Score 0.14 0.03 0.12
## L.Collectivism_Score 0.09 -0.19 -0.02
## L.Ideology 0.04 -0.64 -0.30
## L.Individualism_Score L.Collectivism_Score L.Ideology
## L.ATNS_Score 0.14 0.09 0.04
## L.CCB_Score 0.03 -0.19 -0.64
## L.CNS_Score 0.12 -0.02 -0.30
## L.Individualism_Score 1.00 0.21 -0.01
## L.Collectivism_Score 0.21 1.00 0.32
## L.Ideology -0.01 0.32 1.00library("Hmisc")
mydata.rcorr2 = rcorr(as.matrix(mydata.cor2))
mydata.rcorr2## L.ATNS_Score L.CCB_Score L.CNS_Score
## L.ATNS_Score 1.00 -0.15 0.26
## L.CCB_Score -0.15 1.00 0.57
## L.CNS_Score 0.26 0.57 1.00
## L.Individualism_Score -0.07 -0.05 -0.08
## L.Collectivism_Score -0.16 -0.61 -0.52
## L.Ideology -0.05 -0.95 -0.72
## L.Individualism_Score L.Collectivism_Score L.Ideology
## L.ATNS_Score -0.07 -0.16 -0.05
## L.CCB_Score -0.05 -0.61 -0.95
## L.CNS_Score -0.08 -0.52 -0.72
## L.Individualism_Score 1.00 0.10 -0.11
## L.Collectivism_Score 0.10 1.00 0.57
## L.Ideology -0.11 0.57 1.00
##
## n= 6
##
##
## P
## L.ATNS_Score L.CCB_Score L.CNS_Score
## L.ATNS_Score 0.7701 0.6158
## L.CCB_Score 0.7701 0.2376
## L.CNS_Score 0.6158 0.2376
## L.Individualism_Score 0.8997 0.9277 0.8832
## L.Collectivism_Score 0.7594 0.1966 0.2922
## L.Ideology 0.9263 0.0037 0.1084
## L.Individualism_Score L.Collectivism_Score L.Ideology
## L.ATNS_Score 0.8997 0.7594 0.9263
## L.CCB_Score 0.9277 0.1966 0.0037
## L.CNS_Score 0.8832 0.2922 0.1084
## L.Individualism_Score 0.8473 0.8427
## L.Collectivism_Score 0.8473 0.2347
## L.Ideology 0.8427 0.2347library(corrplot)
corrplot(mydata.cor2, method="color")
corrplot(mydata.cor2, addCoef.col = 1, number.cex = 0.3, method = 'number')
##c. Zero Order Correlations
# Age and Gender
cor.test(CC$Dem_Age, CC$Dem_Gen)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Dem_Gen
## t = -2.8079, df = 995, p-value = 0.005084
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1499265 -0.0267263
## sample estimates:
## cor
## -0.08866549# Age and Education
cor.test(CC$Dem_Age, CC$Dem_Edu)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Dem_Edu
## t = 5.0156, df = 995, p-value = 6.258e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09588088 0.21700305
## sample estimates:
## cor
## 0.1570324# Age and SES
cor.test(CC$Dem_Age, CC$Dem_SES)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Dem_SES
## t = 1.8151, df = 994, p-value = 0.06982
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.004659095 0.119167081
## sample estimates:
## cor
## 0.05747504# Age and Aversion to Tampering with Nature
cor.test(CC$Dem_Age, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$ATNS_Score
## t = 1.0732, df = 995, p-value = 0.2834
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02814267 0.09588696
## sample estimates:
## cor
## 0.03400306# Age and Climate Change Belief
cor.test(CC$Dem_Age, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$CCB_Score
## t = -5.3, df = 995, p-value = 1.427e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2254643 -0.1046881
## sample estimates:
## cor
## -0.1656975# Age and Connectedness to Nature
cor.test(CC$Dem_Age, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$CNS_Score
## t = 3.1178, df = 995, p-value = 0.001874
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03649872 0.15947457
## sample estimates:
## cor
## 0.09836215# Age and Political Ideology
cor.test(CC$Dem_Age, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Ideology
## t = 4.8249, df = 995, p-value = 1.62e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08995803 0.21130261
## sample estimates:
## cor
## 0.1511999# Gender and Education
cor.test(CC$Dem_Gen, CC$Dem_Edu)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$Dem_Edu
## t = 1.045, df = 1005, p-value = 0.2963
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02889094 0.09452976
## sample estimates:
## cor
## 0.032945# Gender and SES
cor.test(CC$Dem_Gen, CC$Dem_SES)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$Dem_SES
## t = 1.3017, df = 1004, p-value = 0.1933
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02081245 0.10259577
## sample estimates:
## cor
## 0.04104821# Gender and Aversion to Tampering with Nature
cor.test(CC$Dem_Gen, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$ATNS_Score
## t = -6.7495, df = 1005, p-value = 2.503e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2665865 -0.1483704
## sample estimates:
## cor
## -0.2082389# Gender and Climate Change Belief
cor.test(CC$Dem_Gen, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$CCB_Score
## t = -2.534, df = 1005, p-value = 0.01143
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14076367 -0.01799082
## sample estimates:
## cor
## -0.07967941# Gender and Connectedness to Nature
cor.test(CC$Dem_Gen, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$CNS_Score
## t = -5.1191, df = 1005, p-value = 3.677e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.21903313 -0.09860699
## sample estimates:
## cor
## -0.159413# Gender and Political Ideology
cor.test(CC$Dem_Gen, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$Ideology
## t = 1.9133, df = 1005, p-value = 0.05599
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00153865 0.12156892
## sample estimates:
## cor
## 0.06024422# Education and SES
cor.test(CC$Dem_Edu, CC$Dem_SES)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$Dem_SES
## t = 11.4, df = 1004, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2826379 0.3921347
## sample estimates:
## cor
## 0.3385318# Education and Aversion to Tampering with Nature
cor.test(CC$Dem_Edu, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$ATNS_Score
## t = -2.4982, df = 1005, p-value = 0.01264
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13965980 -0.01686516
## sample estimates:
## cor
## -0.07856046# Education and Climate Change Belief
cor.test(CC$Dem_Edu, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$CCB_Score
## t = 1.3042, df = 1005, p-value = 0.1925
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02072462 0.10262170
## sample estimates:
## cor
## 0.04110515# Education and Connectedness to Nature
cor.test(CC$Dem_Edu, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$CNS_Score
## t = 1.7004, df = 1005, p-value = 0.08936
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.008242423 0.114958753
## sample estimates:
## cor
## 0.053562# Education and Political Ideology
cor.test(CC$Dem_Edu, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$Ideology
## t = -2.6518, df = 1005, p-value = 0.008132
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14439085 -0.02169181
## sample estimates:
## cor
## -0.08335725# SES and Aversion to Tampering with Nature
cor.test(CC$Dem_SES, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$ATNS_Score
## t = -1.7618, df = 1004, p-value = 0.07842
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.116921365 0.006314827
## sample estimates:
## cor
## -0.0555147# SES and Climate Change Belief
cor.test(CC$Dem_SES, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$CCB_Score
## t = -2.7951, df = 1004, p-value = 0.005287
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14887180 -0.02620697
## sample estimates:
## cor
## -0.08787249# SES and Connectedness to Nature
cor.test(CC$Dem_SES, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$CNS_Score
## t = -1.5389, df = 1004, p-value = 0.1241
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1099887 0.0133372
## sample estimates:
## cor
## -0.04851065# SES and Political Ideology
cor.test(CC$Dem_SES, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$Ideology
## t = 3.4441, df = 1004, p-value = 0.0005966
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0465622 0.1687399
## sample estimates:
## cor
## 0.1080591# ATNS and Climate Change Belief
cor.test(CC$ATNS_Score, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$ATNS_Score and CC$CCB_Score
## t = -1.5637, df = 1005, p-value = 0.1182
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11070453 0.01255104
## sample estimates:
## cor
## -0.04926431# ATNS and Connectedness to Nature
cor.test(CC$ATNS_Score, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$ATNS_Score and CC$CNS_Score
## t = 9.6442, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2334665 0.3465914
## sample estimates:
## cor
## 0.291046# ATNS and Political Ideology
cor.test(CC$ATNS_Score, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$ATNS_Score and CC$Ideology
## t = 1.2313, df = 1005, p-value = 0.2185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02302278 0.10034613
## sample estimates:
## cor
## 0.03880957# Climate Change Belief and Connectedness to Nature
cor.test(CC$CCB_Score, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$CCB_Score and CC$CNS_Score
## t = 10.087, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2460271 0.3582627
## sample estimates:
## cor
## 0.303196# Climate Change Belief and Political Ideology
cor.test(CC$CCB_Score, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$CCB_Score and CC$Ideology
## t = -26.269, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6732828 -0.5999135
## sample estimates:
## cor
## -0.6380441# Connectedness to Nature and Political Ideology
cor.test(CC$CNS_Score, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$CNS_Score and CC$Ideology
## t = -9.8754, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3527090 -0.2400456
## sample estimates:
## cor
## -0.2974123VI. Violin Plots
# Support
library(ggplot2)
# Create a violin plot of means
ggplot(L, aes(x = "", y = Support)) +
geom_violin(fill = "darkred", color = "darkred") +
geom_point(aes(y = mean(Support)), color = "black", size = 1, shape = 18) +
labs(title = "Means for Support",
y = "Support") +
theme_minimal()## Warning: Removed 7049 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 10070 rows containing missing values (`geom_point()`).
# Naturalness
library(ggplot2)
# Create a violin plot of means
ggplot(L, aes(x = "", y = Naturalness)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Naturalness)), color = "black", size = 1, shape = 18) +
labs(title = "Means for Naturalness",
y = "Naturalness") +
theme_minimal()## Warning: Removed 7049 rows containing non-finite values (`stat_ydensity()`).
## Removed 10070 rows containing missing values (`geom_point()`).
# Risk
library(ggplot2)
# Create a violin plot of means
ggplot(L, aes(x = "", y = Risk)) +
geom_violin(fill = "navajowhite3", color = "navajowhite3") +
geom_point(aes(y = mean(Risk)), color = "black", size = 1, shape = 18) +
labs(title = "Means for Risk",
y = "Risk") +
theme_minimal()## Warning: Removed 7049 rows containing non-finite values (`stat_ydensity()`).
## Removed 10070 rows containing missing values (`geom_point()`).
# Benefit
library(ggplot2)
# Create a violin plot of means
ggplot(L, aes(x = "", y = Ben)) +
geom_violin(fill = "steelblue4", color = "steelblue4") +
geom_point(aes(y = mean(Ben)), color = "black", size = 1, shape = 18) +
labs(title = "Means for Benefit",
y = "Benefit") +
theme_minimal()## Warning: Removed 7049 rows containing non-finite values (`stat_ydensity()`).
## Removed 10070 rows containing missing values (`geom_point()`).
a. Naturalness
#DACCS
# Naturalness
library(ggplot2)
# AFSCS
ggplot(CC, aes(x = "", y = Nat_Score_AFSCS)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_AFSCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Afforestation/Reforestation",
y = "Naturalness") +
theme_minimal()## Warning: Removed 664 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_AFSCS)## CC$Nat_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 195 1 61.8 22.29 26.65 36.55
## .25 .50 .75 .90 .95
## 48.88 63.25 74.88 87.20 94.90
##
## lowest : 0 7 8 11 11.75, highest: 98 98.75 99.5 99.75 100sd(CC$Nat_Score_AFSCS, na.rm = TRUE)## [1] 19.74064# Biochar
ggplot(CC, aes(x = "", y = Nat_Score_BIO)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_BIO)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Biochar",
y = "Naturalness") +
theme_minimal()## Warning: Removed 675 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_BIO)## CC$Nat_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 182 1 39.12 20.95 5.75 13.50
## .25 .50 .75 .90 .95
## 26.88 39.25 51.06 63.25 68.75
##
## lowest : 0 0.75 1.75 2.5 2.75 , highest: 76.75 78 87.25 96.5 97.5sd(CC$Nat_Score_BIO, na.rm = TRUE)## [1] 18.56122# Bioenergy Carbon Capture
ggplot(CC, aes(x = "", y = Nat_Score_BECCS)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_BECCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Bioenergy and Carbon Capture",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 677 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_BECCS)## CC$Nat_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 178 1 34.63 18.88 6.25 12.22
## .25 .50 .75 .90 .95
## 24.50 33.75 45.94 54.33 61.39
##
## lowest : 0 2.25 2.5 3 4.5 , highest: 75 76.25 77.5 78.75 79sd(CC$Nat_Score_BECCS, na.rm = TRUE)## [1] 16.65608# Direct Air Capture
ggplot(CC, aes(x = "", y = Nat_Score_DACCS)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_DACCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Direct Air Capture",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 660 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_DACCS)## CC$Nat_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 156 0.999 25.53 18.95 0.00 2.50
## .25 .50 .75 .90 .95
## 13.12 24.75 35.75 45.85 56.60
##
## lowest : 0 0.25 0.5 2.5 3.5 , highest: 70.5 70.75 75 75.25 79.25sd(CC$Nat_Score_DACCS, na.rm = TRUE)## [1] 16.89449# Enhanced Weathering
ggplot(CC, aes(x = "", y = Nat_Score_EW)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_EW)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Enhanced Weathering",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 672 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_EW)## CC$Nat_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 187 1 35.84 20.57 5.425 13.000
## .25 .50 .75 .90 .95
## 22.500 36.000 49.125 57.750 65.550
##
## lowest : 0 0.5 0.75 2.25 2.5 , highest: 75 76.75 78.5 78.75 87.5sd(CC$Nat_Score_EW, na.rm = TRUE)## [1] 18.08834# Ocean Fertilization
ggplot(CC, aes(x = "", y = Nat_Score_OF)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_OF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Ocean Fertilization",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 680 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_OF)## CC$Nat_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 166 1 31.77 19.83 4.05 8.50
## .25 .50 .75 .90 .95
## 20.00 31.25 42.50 54.35 61.00
##
## lowest : 0 0.25 1.25 2.5 3 , highest: 73.5 75 80.25 80.5 84.5sd(CC$Nat_Score_OF, na.rm = TRUE)## [1] 17.48905# Biofuel
ggplot(CC, aes(x = "", y = Nat_Score_BF)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_BF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Biofuel",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 759 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_BF)## CC$Nat_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 147 1 39.26 20.21 8.088 14.450
## .25 .50 .75 .90 .95
## 26.688 39.250 50.062 60.725 70.075
##
## lowest : 0 0.25 1 1.5 2 , highest: 72.5 73 74.25 75 86.75sd(CC$Nat_Score_BF, na.rm = TRUE)## [1] 17.80321# Nuclear Energy
ggplot(CC, aes(x = "", y = Nat_Score_NE)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_NE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Nuclear Energy",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_NE)## CC$Nat_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 139 0.999 26.16 19.39 0.00 2.30
## .25 .50 .75 .90 .95
## 13.50 25.00 38.25 48.25 55.60
##
## lowest : 0 1.25 1.5 2 2.5 , highest: 60.5 63.75 65 69.75 75sd(CC$Nat_Score_NE, na.rm = TRUE)## [1] 17.14904# Solar Energy
ggplot(CC, aes(x = "", y = Nat_Score_SE)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_SE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Solar Energy",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 762 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_SE)## CC$Nat_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 143 1 55.05 20.62 24.40 31.25
## .25 .50 .75 .90 .95
## 41.75 54.75 69.75 75.00 83.20
##
## lowest : 0 2 5.5 14.5 16 , highest: 87.25 87.5 90 92 94sd(CC$Nat_Score_SE, na.rm = TRUE)## [1] 18.17609# Wind Energy
ggplot(CC, aes(x = "", y = Nat_Score_WE)) +
geom_violin(fill = "darkseagreen4", color = "darkseagreen4") +
geom_point(aes(y = mean(Nat_Score_WE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Naturalness for Wind Energy",
y = "Naturalness") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Nat_Score_WE)## CC$Nat_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 146 1 54.36 21.33 21.80 25.75
## .25 .50 .75 .90 .95
## 42.50 55.00 69.50 75.00 80.30
##
## lowest : 0 6 7.75 15 15.5 , highest: 86.75 90.5 91.5 92 100sd(CC$Nat_Score_WE, na.rm = TRUE)## [1] 18.78237b. Support
# Support
# AFSCS
ggplot(CC, aes(x = "", y = Support_Score_AFSCS)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_AFSCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Afforestation/Reforestation",
y = "Support") +
theme_minimal()## Warning: Removed 664 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_AFSCS)## CC$Support_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 114 0.991 76.14 25.37 30.25 46.40
## .25 .50 .75 .90 .95
## 62.50 82.00 95.25 100.00 100.00
##
## lowest : 0 4 5 10 12.5, highest: 97 97.5 98 99.5 100sd(CC$Support_Score_AFSCS, na.rm = TRUE)## [1] 23.61434# Biochar
ggplot(CC, aes(x = "", y = Support_Score_BIO)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_BIO)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Biochar",
y = "Support") +
theme_minimal()## Warning: Removed 675 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_BIO)## CC$Support_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 142 0.999 53.6 29.85 0.55 13.55
## .25 .50 .75 .90 .95
## 36.50 54.25 74.00 87.00 95.22
##
## lowest : 0 1 2.5 3.5 5 , highest: 94 95 95.5 97.5 100sd(CC$Support_Score_BIO, na.rm = TRUE)## [1] 26.28137# Bioenergy Carbon Capture
ggplot(CC, aes(x = "", y = Support_Score_BECCS)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_BECCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Bioenergy and Carbon Capture",
y = "Support") +
theme_minimal()## Warning: Removed 677 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_BECCS)## CC$Support_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 136 0.999 53.31 31 0.00 9.90
## .25 .50 .75 .90 .95
## 35.25 55.00 74.25 85.00 98.20
##
## lowest : 0 1 1.5 2 5 , highest: 93 93.5 95 96 100sd(CC$Support_Score_BECCS, na.rm = TRUE)## [1] 27.28703# Direct Air Capture
ggplot(CC, aes(x = "", y = Support_Score_DACCS)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_DACCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Direct Air Capture",
y = "Support") +
theme_minimal()## Warning: Removed 660 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_DACCS)## CC$Support_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 148 0.999 52.88 32.02 0.00 5.80
## .25 .50 .75 .90 .95
## 35.50 55.50 73.25 89.40 99.85
##
## lowest : 0 0.5 1 2 2.5 , highest: 96.5 97 98.5 99.5 100sd(CC$Support_Score_DACCS, na.rm = TRUE)## [1] 28.11655# Enhanced Weathering
ggplot(CC, aes(x = "", y = Support_Score_EW)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_EW)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Enhanced Weathering",
y = "Support") +
theme_minimal()## Warning: Removed 672 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_EW)## CC$Support_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 137 0.999 49.29 31.61 0.0 8.0
## .25 .50 .75 .90 .95
## 29.5 50.5 68.5 85.8 98.6
##
## lowest : 0 0.5 1 2 2.5 , highest: 94.5 95 95.5 98 100sd(CC$Support_Score_EW, na.rm = TRUE)## [1] 27.63767# Ocean Fertilization
ggplot(CC, aes(x = "", y = Support_Score_OF)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_OF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Ocean Fertilization",
y = "Support") +
theme_minimal()## Warning: Removed 680 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_OF)## CC$Support_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 145 0.999 51.22 33 0.00 5.80
## .25 .50 .75 .90 .95
## 27.50 54.50 73.75 89.00 95.00
##
## lowest : 0 0.5 2 3 3.5 , highest: 95 95.5 97 97.5 100sd(CC$Support_Score_OF, na.rm = TRUE)## [1] 28.83405# Biofuel
ggplot(CC, aes(x = "", y = Support_Score_BF)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_BF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Biofuel",
y = "Support") +
theme_minimal()## Warning: Removed 759 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_BF)## CC$Support_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 120 1 60.82 27.72 10.00 22.05
## .25 .50 .75 .90 .95
## 50.00 64.00 78.12 92.00 99.00
##
## lowest : 0 2.5 4 5 7 , highest: 95 96.5 98 99 100sd(CC$Support_Score_BF, na.rm = TRUE)## [1] 24.89028# Nuclear Energy
ggplot(CC, aes(x = "", y = Support_Score_NE)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_NE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Nuclear Energy",
y = "Support") +
theme_minimal()## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_NE)## CC$Support_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 122 0.999 50.55 35.85 0.0 0.6
## .25 .50 .75 .90 .95
## 27.5 52.0 76.5 91.1 99.2
##
## lowest : 0 1 2 3 3.5 , highest: 95 95.5 96 99 100sd(CC$Support_Score_NE, na.rm = TRUE)## [1] 31.10287# Solar Energy
ggplot(CC, aes(x = "", y = Support_Score_SE)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_SE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Solar Energy",
y = "Support") +
theme_minimal()## Warning: Removed 762 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_SE)## CC$Support_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 93 0.979 79.51 24.49 30.4 49.2
## .25 .50 .75 .90 .95
## 68.0 87.5 100.0 100.0 100.0
##
## lowest : 0 0.5 2.5 10 12 , highest: 97.5 98.5 99 99.5 100sd(CC$Support_Score_SE, na.rm = TRUE)## [1] 23.53217# Wind Energy
ggplot(CC, aes(x = "", y = Support_Score_WE)) +
geom_violin(fill = "purple", color = "purple") +
geom_point(aes(y = mean(Support_Score_WE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Support for Wind Energy",
y = "Support") +
theme_minimal()## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
describe(CC$Support_Score_WE)## CC$Support_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 99 0.993 75.07 26.24 19.9 42.5
## .25 .50 .75 .90 .95
## 64.0 80.0 95.5 100.0 100.0
##
## lowest : 0 3 10.5 11 15 , highest: 98 98.5 99 99.5 100sd(CC$Support_Score_WE, na.rm = TRUE)## [1] 24.65437c. Risk
# Risk
# Describe Scores/Scales
describe(CC$Risk_Score_AFSCS)## CC$Risk_Score_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 103 0.987 16.18 20.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.5 8.0 24.5 47.3 62.5
##
## lowest : 0 0.5 1 1.5 2 , highest: 78 79 80 85 100describe(CC$Risk_Score_BIO)## CC$Risk_Score_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 133 1 33.69 26.45 0.00 3.00
## .25 .50 .75 .90 .95
## 12.50 32.50 50.00 63.00 75.67
##
## lowest : 0 0.5 1 1.5 2 , highest: 84 88 90 93 95describe(CC$Risk_Score_BECCS)## CC$Risk_Score_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 140 0.999 38.61 28.99 0.00 3.95
## .25 .50 .75 .90 .95
## 19.50 37.75 55.00 72.05 85.55
##
## lowest : 0 0.5 1 2.5 3 , highest: 92.5 93 94 98 100describe(CC$Risk_Score_DACCS)## CC$Risk_Score_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 151 1 42.98 30.22 0.00 5.50
## .25 .50 .75 .90 .95
## 22.25 45.00 62.50 78.10 89.35
##
## lowest : 0 0.5 1 2.5 3 , highest: 95.5 98 98.5 99.5 100describe(CC$Risk_Score_EW)## CC$Risk_Score_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 146 1 38.45 28.7 2.35 7.50
## .25 .50 .75 .90 .95
## 17.75 37.00 55.00 75.00 85.00
##
## lowest : 0 1 2 2.5 3 , highest: 94 96 97.5 99.5 100describe(CC$Risk_Score_OF)## CC$Risk_Score_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 153 1 46.37 30.58 0.80 10.00
## .25 .50 .75 .90 .95
## 25.25 45.50 66.25 81.50 89.85
##
## lowest : 0 0.5 1.5 2 5 , highest: 96 97 98.5 99 100describe(CC$Risk_Score_BF)## CC$Risk_Score_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 105 0.999 26.04 22.96 0.000 0.500
## .25 .50 .75 .90 .95
## 8.375 22.250 40.250 52.300 60.650
##
## lowest : 0 0.5 1 1.5 2.5 , highest: 81.5 83.5 85.5 86 100describe(CC$Risk_Score_NE)## CC$Risk_Score_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 126 1 52.92 34.7 2.4 6.9
## .25 .50 .75 .90 .95
## 25.0 58.0 75.5 91.5 100.0
##
## lowest : 0 0.5 1.5 2 2.5 , highest: 95.5 96.5 98.5 99 100describe(CC$Risk_Score_SE)## CC$Risk_Score_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 69 0.955 10.18 13.87 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 3.0 15.0 34.0 42.9
##
## lowest : 0 0.5 1 1.5 2 , highest: 45 48.5 51 63 78.5describe(CC$Risk_Score_WE)## CC$Risk_Score_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 94 0.988 18.79 23.08 0.0 0.0
## .25 .50 .75 .90 .95
## 1.0 11.0 25.5 52.2 67.3
##
## lowest : 0 0.5 1 1.5 2 , highest: 89 89.5 92 98 98.5sd(CC$Risk_Score_AFSCS, na.rm = TRUE)## [1] 20.1135sd(CC$Risk_Score_BIO, na.rm = TRUE)## [1] 23.16999sd(CC$Risk_Score_BECCS, na.rm = TRUE)## [1] 25.48881sd(CC$Risk_Score_DACCS, na.rm = TRUE)## [1] 26.347sd(CC$Risk_Score_EW, na.rm = TRUE)## [1] 25.2467sd(CC$Risk_Score_OF, na.rm = TRUE)## [1] 26.54645sd(CC$Risk_Score_BF, na.rm = TRUE)## [1] 20.52519sd(CC$Risk_Score_NE, na.rm = TRUE)## [1] 30.17134sd(CC$Risk_Score_SE, na.rm = TRUE)## [1] 14.22085sd(CC$Risk_Score_WE, na.rm = TRUE)## [1] 22.76701# AFSCS
ggplot(CC, aes(x = "", y = Risk_Score_AFSCS)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_AFSCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Afforestation/Reforestation",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 664 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1007 rows containing missing values (`geom_point()`).
# Biochar
ggplot(CC, aes(x = "", y = Risk_Score_BIO)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_BIO)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Biochar",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 675 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Bioenergy Carbon Capture
ggplot(CC, aes(x = "", y = Risk_Score_BECCS)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_BECCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Bioenergy and Carbon Capture",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 677 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Direct Air Capture
ggplot(CC, aes(x = "", y = Risk_Score_DACCS)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_DACCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Direct Air Capture",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 660 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Enhanced Weathering
ggplot(CC, aes(x = "", y = Risk_Score_EW)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_EW)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Enhanced Weathering",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 672 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Ocean Fertilization
ggplot(CC, aes(x = "", y = Risk_Score_OF)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_OF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Ocean Fertilization",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 680 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Biofuel
ggplot(CC, aes(x = "", y = Risk_Score_BF)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_BF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Biofuel",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 759 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Nuclear Energy
ggplot(CC, aes(x = "", y = Risk_Score_NE)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_NE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Nuclear Energy",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Solar Energy
ggplot(CC, aes(x = "", y = Risk_Score_SE)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_SE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Solar Energy",
y = "Risk") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 762 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Wind Energy
ggplot(CC, aes(x = "", y = Risk_Score_WE)) +
geom_violin(fill = "red", color = "red") +
geom_point(aes(y = mean(Risk_Score_WE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Risk for Wind Energy",
y = "Support") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
d. Benefit
# Benefit
#Descriptives
describe(CC$Ben_AFSCS)## CC$Ben_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 80 0.999 68.42 26.15 22.1 37.0
## .25 .50 .75 .90 .95
## 55.5 72.0 85.0 97.0 100.0
##
## lowest : 0 1 5 10 12, highest: 96 97 98 99 100describe(CC$Ben_BIO)## CC$Ben_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 85 0.999 53.47 29.28 6.10 20.00
## .25 .50 .75 .90 .95
## 33.00 56.50 72.25 86.00 92.45
##
## lowest : 0 1 3 5 7, highest: 95 97 98 99 100describe(CC$Ben_BECCS) ## CC$Ben_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.999 55 29.13 10.00 18.00
## .25 .50 .75 .90 .95
## 36.00 57.00 74.75 88.00 95.00
##
## lowest : 0 1 3 6 7, highest: 94 95 96 97 100describe(CC$Ben_DACCS)## CC$Ben_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 89 0.999 55.35 30.26 3.0 15.0
## .25 .50 .75 .90 .95
## 37.0 59.0 75.0 90.0 99.4
##
## lowest : 0 1 2 3 5, highest: 93 95 96 98 100describe(CC$Ben_EW)## CC$Ben_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 83 0.999 52.15 27.98 0.0 13.8
## .25 .50 .75 .90 .95
## 37.0 55.0 70.0 81.2 90.0
##
## lowest : 0 3 4 5 6, highest: 95 96 97 99 100describe(CC$Ben_OF)## CC$Ben_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 82 0.999 54.54 28.93 7.6 17.0
## .25 .50 .75 .90 .95
## 36.0 58.0 74.5 86.0 91.7
##
## lowest : 0 2 4 5 7, highest: 92 93 95 96 100describe(CC$Ben_BF)## CC$Ben_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 82 0.999 51.92 30.47 3.05 10.00
## .25 .50 .75 .90 .95
## 34.00 57.00 70.00 85.00 94.30
##
## lowest : 0 1 2 5 6, highest: 93 95 96 97 100describe(CC$Ben_NE)## CC$Ben_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 78 0.999 60.18 30.92 0.0 19.2
## .25 .50 .75 .90 .95
## 44.0 66.0 80.0 92.4 98.4
##
## lowest : 0 6 9 10 11, highest: 94 95 97 98 100describe(CC$Ben_SE)## CC$Ben_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 75 0.998 66.31 29.55 10.4 25.0
## .25 .50 .75 .90 .95
## 50.0 71.0 86.0 100.0 100.0
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100describe(CC$Ben_WE) ## CC$Ben_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 71 0.998 64.88 28.99 9.6 25.0
## .25 .50 .75 .90 .95
## 51.0 68.0 85.0 100.0 100.0
##
## lowest : 0 5 6 8 10, highest: 96 97 98 99 100sd(CC$Ben_AFSCS, na.rm = TRUE)## [1] 23.72132sd(CC$Ben_BIO, na.rm = TRUE)## [1] 25.6215sd(CC$Ben_BECCS, na.rm = TRUE)## [1] 25.51696sd(CC$Ben_DACCS, na.rm = TRUE)## [1] 26.63817sd(CC$Ben_EW, na.rm = TRUE)## [1] 24.84342sd(CC$Ben_OF, na.rm = TRUE)## [1] 25.43145sd(CC$Ben_BF, na.rm = TRUE)## [1] 26.71672sd(CC$Ben_NE, na.rm = TRUE)## [1] 27.56813sd(CC$Ben_SE, na.rm = TRUE)## [1] 26.49281sd(CC$Ben_WE, na.rm = TRUE)## [1] 26.12863# AFSCS
ggplot(CC, aes(x = "", y = Ben_AFSCS)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_AFSCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Afforestation/Reforestation",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 664 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1007 rows containing missing values (`geom_point()`).
# Biochar
ggplot(CC, aes(x = "", y = Ben_BIO)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_BIO)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Biochar",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 675 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Bioenergy Carbon Capture
ggplot(CC, aes(x = "", y = Ben_BECCS)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_BECCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Bioenergy and Carbon Capture",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 677 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Direct Air Capture
ggplot(CC, aes(x = "", y = Ben_DACCS)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_DACCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Direct Air Capture",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 660 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Enhanced Weathering
ggplot(CC, aes(x = "", y = Ben_EW)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_EW)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Enhanced Weathering",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 672 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Ocean Fertilization
ggplot(CC, aes(x = "", y = Ben_OF)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_OF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Ocean Fertilization",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 680 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Biofuel
ggplot(CC, aes(x = "", y = Ben_BF)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_BF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Biofuel",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 759 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Nuclear Energy
ggplot(CC, aes(x = "", y = Ben_NE)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_NE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Nuclear Energy",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Solar Energy
ggplot(CC, aes(x = "", y = Ben_SE)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_SE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Solar Energy",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 762 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Wind Energy
ggplot(CC, aes(x = "", y = Ben_WE)) +
geom_violin(fill = "royalblue", color = "royalblue") +
geom_point(aes(y = mean(Ben_WE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Benefit for Wind Energy",
y = "Benefit") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
e. Familiarity
#Descriptives
describe(CC$Familiar_AFSCS)## CC$Familiar_AFSCS
## n missing distinct Info Mean Gmd .05 .10
## 343 664 91 0.997 62.7 34.53 3 12
## .25 .50 .75 .90 .95
## 42 67 89 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100describe(CC$Familiar_BIO)## CC$Familiar_BIO
## n missing distinct Info Mean Gmd .05 .10
## 332 675 81 0.993 27.79 29.57 0.00 0.00
## .25 .50 .75 .90 .95
## 4.75 20.00 44.00 68.90 82.00
##
## lowest : 0 1 2 3 4, highest: 92 93 94 95 100describe(CC$Familiar_BECCS)## CC$Familiar_BECCS
## n missing distinct Info Mean Gmd .05 .10
## 330 677 87 0.994 29.64 30.74 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 21.00 50.00 73.00 83.55
##
## lowest : 0 1 2 3 4, highest: 91 92 94 98 100describe(CC$Familiar_DACCS)## CC$Familiar_DACCS
## n missing distinct Info Mean Gmd .05 .10
## 347 660 82 0.992 26.05 27.55 0.0 0.0
## .25 .50 .75 .90 .95
## 4.5 20.0 42.0 65.0 75.0
##
## lowest : 0 1 2 3 4, highest: 89 90 93 99 100describe(CC$Familiar_EW)## CC$Familiar_EW
## n missing distinct Info Mean Gmd .05 .10
## 335 672 76 0.98 22.5 25.19 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 17.0 35.5 60.0 70.0
##
## lowest : 0 1 2 3 4, highest: 79 80 87 90 91describe(CC$Familiar_OF)## CC$Familiar_OF
## n missing distinct Info Mean Gmd .05 .10
## 327 680 76 0.992 25.62 27.66 0.0 0.0
## .25 .50 .75 .90 .95
## 4.0 18.0 40.5 62.8 76.0
##
## lowest : 0 1 2 3 4, highest: 85 86 87 89 100describe(CC$Familiar_BF)## CC$Familiar_BF
## n missing distinct Info Mean Gmd .05 .10
## 248 759 87 0.999 57.92 32.61 0.0 18.0
## .25 .50 .75 .90 .95
## 36.0 61.0 81.0 93.3 100.0
##
## lowest : 0 1 5 6 8, highest: 95 96 98 99 100describe(CC$Familiar_NE)## CC$Familiar_NE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 82 0.998 69.17 29.4 14.8 30.6
## .25 .50 .75 .90 .95
## 53.0 75.0 90.0 100.0 100.0
##
## lowest : 0 2 3 4 6, highest: 95 97 98 99 100describe(CC$Familiar_SE)## CC$Familiar_SE
## n missing distinct Info Mean Gmd .05 .10
## 245 762 49 0.941 87.95 15.68 52.2 65.2
## .25 .50 .75 .90 .95
## 82.0 94.0 100.0 100.0 100.0
##
## lowest : 0 18 35 41 45, highest: 96 97 98 99 100describe(CC$Familiar_WE)## CC$Familiar_WE
## n missing distinct Info Mean Gmd .05 .10
## 257 750 61 0.982 81.79 20.9 41.6 55.0
## .25 .50 .75 .90 .95
## 75.0 87.0 100.0 100.0 100.0
##
## lowest : 0 1 3 13 19, highest: 96 97 98 99 100#SD
sd(CC$Familiar_AFSCS, na.rm = TRUE)## [1] 30.60777sd(CC$Familiar_BIO, na.rm = TRUE)## [1] 27.00687sd(CC$Familiar_BECCS, na.rm = TRUE)## [1] 27.82sd(CC$Familiar_DACCS, na.rm = TRUE)## [1] 25.08586sd(CC$Familiar_EW, na.rm = TRUE)## [1] 23.20217sd(CC$Familiar_OF, na.rm = TRUE)## [1] 25.34433sd(CC$Familiar_BF, na.rm = TRUE)## [1] 28.59492sd(CC$Familiar_NE, na.rm = TRUE)## [1] 26.59004sd(CC$Familiar_SE, na.rm = TRUE)## [1] 16.02333sd(CC$Familiar_WE, na.rm = TRUE)## [1] 20.79082# AFSCS
ggplot(CC, aes(x = "", y = Familiar_AFSCS)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_AFSCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Afforestation/Reforestation",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 664 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1007 rows containing missing values (`geom_point()`).
# Biochar
ggplot(CC, aes(x = "", y = Familiar_BIO)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_BIO)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Biochar",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 675 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Bioenergy Carbon Capture
ggplot(CC, aes(x = "", y = Familiar_BECCS)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_BECCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Bioenergy and Carbon Capture",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 677 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Direct Air Capture
ggplot(CC, aes(x = "", y = Familiar_DACCS)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_DACCS)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Direct Air Capture",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 660 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Enhanced Weathering
ggplot(CC, aes(x = "", y = Familiar_EW)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_EW)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Enhanced Weathering",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 672 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Ocean Fertilization
ggplot(CC, aes(x = "", y = Familiar_OF)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_OF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Ocean Fertilization",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 680 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Biofuel
ggplot(CC, aes(x = "", y = Familiar_BF)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_BF)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Biofuel",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 759 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Nuclear Energy
ggplot(CC, aes(x = "", y = Familiar_NE)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_NE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Nuclear Energy",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Solar Energy
ggplot(CC, aes(x = "", y = Familiar_SE)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_SE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Solar Energy",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 762 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
# Wind Energy
ggplot(CC, aes(x = "", y = Familiar_WE)) +
geom_violin(fill = "slategray3", color = "slategray3") +
geom_point(aes(y = mean(Familiar_WE)), color = "white", size = 3, shape = 18) +
labs(title = "Means for Familiarity for Wind Energy",
y = "Familiarity") +
theme_minimal() +
coord_cartesian(ylim = c(0, 100))## Warning: Removed 750 rows containing non-finite values (`stat_ydensity()`).
## Removed 1007 rows containing missing values (`geom_point()`).
VII. Mixed Models
NEW ANALYSIS (POST-MASTERS, March 6, 2024)
Section 1: Concept model
## Model 1: Support ~ Nat + Age + Gender + Ethnicity, + Ideology + ATNS + ATNS*Nat + Ideology*Nat
mod101010 <- lmer(Support ~ Naturalness.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c*Naturalness.c + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(mod101010)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Age.c + Gender + Ethnicity_Asian +
## Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer +
## Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c * Naturalness.c +
## Ideology.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27073.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5504 -0.5310 0.0434 0.5511 3.3395
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213.60 14.615
## Type (Intercept) 50.97 7.139
## Residual 351.32 18.744
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.986e+01 3.068e+00 2.829e+01 16.249 7.03e-16
## Naturalness.c 4.160e-01 3.417e-02 2.729e+03 12.176 < 2e-16
## Age.c -1.297e-01 3.712e-02 9.876e+02 -3.495 0.000496
## GenderMan vs Woman -1.705e-01 6.010e-01 9.882e+02 -0.284 0.776755
## GenderMan&Woman vs Nonbinary 6.847e-01 3.562e+00 9.806e+02 0.192 0.847624
## Ethnicity_Asian -2.552e+00 2.485e+00 9.826e+02 -1.027 0.304743
## Ethnicity_Black -4.861e+00 1.787e+00 9.829e+02 -2.720 0.006638
## Ethnicity_Hispanic -1.188e+00 2.855e+00 9.820e+02 -0.416 0.677552
## Ethnicity_NatAmer 5.754e+00 1.827e+01 9.841e+02 0.315 0.752794
## Ethnicity_NatPac -5.689e-01 9.165e+00 9.865e+02 -0.062 0.950513
## Ethnicity_Other 8.375e+00 5.812e+00 9.825e+02 1.441 0.149938
## ATNS_Score.c -1.997e-01 2.774e-02 9.889e+02 -7.196 1.22e-12
## Ideology.c -3.827e+00 3.209e-01 9.838e+02 -11.925 < 2e-16
## Naturalness.c:ATNS_Score.c 5.322e-03 7.665e-04 2.747e+03 6.943 4.78e-12
## Naturalness.c:Ideology.c 5.927e-04 9.682e-03 2.677e+03 0.061 0.951197
##
## (Intercept) ***
## Naturalness.c ***
## Age.c ***
## GenderMan vs Woman
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black **
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## ATNS_Score.c ***
## Ideology.c ***
## Naturalness.c:ATNS_Score.c ***
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(mod101010,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 49.86 | 3.07 | 43.84 – 55.88 | 16.25 | <0.001 |
| Naturalness c | 0.42 | 0.03 | 0.35 – 0.48 | 12.18 | <0.001 |
| Age c | -0.13 | 0.04 | -0.20 – -0.06 | -3.49 | <0.001 |
| GenderMan vs Woman | -0.17 | 0.60 | -1.35 – 1.01 | -0.28 | 0.777 |
|
GenderMan&Woman vs Nonbinary |
0.68 | 3.56 | -6.30 – 7.67 | 0.19 | 0.848 |
| Ethnicity Asian | -2.55 | 2.49 | -7.42 – 2.32 | -1.03 | 0.305 |
| Ethnicity Black | -4.86 | 1.79 | -8.37 – -1.36 | -2.72 | 0.007 |
| Ethnicity Hispanic | -1.19 | 2.86 | -6.79 – 4.41 | -0.42 | 0.677 |
| Ethnicity NatAmer | 5.75 | 18.27 | -30.06 – 41.57 | 0.32 | 0.753 |
| Ethnicity NatPac | -0.57 | 9.16 | -18.54 – 17.40 | -0.06 | 0.951 |
| Ethnicity Other | 8.38 | 5.81 | -3.02 – 19.77 | 1.44 | 0.150 |
| ATNS Score c | -0.20 | 0.03 | -0.25 – -0.15 | -7.20 | <0.001 |
| Ideology c | -3.83 | 0.32 | -4.46 – -3.20 | -11.92 | <0.001 |
|
Naturalness c × ATNS Score c |
0.01 | 0.00 | 0.00 – 0.01 | 6.94 | <0.001 |
|
Naturalness c × Ideology c |
0.00 | 0.01 | -0.02 – 0.02 | 0.06 | 0.951 |
| Random Effects | |||||
| σ2 | 351.32 | ||||
| τ00 id | 213.60 | ||||
| τ00 Type | 50.97 | ||||
| ICC | 0.43 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.237 / 0.565 | ||||
## Model 2: Support ~ Nat + Ben + Risk + Age + Gender + Ethnicity, + Ideology + ATNS + ATNS*Risk + ATNS*Ben + ATNS*Nat + Ideology*Nat
mod101011 <- lmer(Support ~ Naturalness.c + Benefit.c + Risk.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c*Naturalness.c + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(mod101011)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + Risk.c + Age.c + Gender +
## Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic +
## Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other +
## ATNS_Score.c * Naturalness.c + Ideology.c * Naturalness.c +
## (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25347.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7138 -0.4924 0.0303 0.5320 4.0800
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 125.044 11.182
## Type (Intercept) 8.437 2.905
## Residual 194.204 13.936
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.338e+01 1.825e+00 1.067e+02 29.243 < 2e-16
## Naturalness.c 1.303e-01 2.634e-02 2.714e+03 4.948 7.97e-07
## Benefit.c 4.518e-01 1.370e-02 2.970e+03 32.979 < 2e-16
## Risk.c -3.958e-01 1.508e-02 2.807e+03 -26.246 < 2e-16
## Age.c -4.575e-02 2.817e-02 9.843e+02 -1.624 0.10469
## GenderMan vs Woman 1.430e-02 4.558e-01 9.826e+02 0.031 0.97498
## GenderMan&Woman vs Nonbinary 4.593e-01 2.699e+00 9.729e+02 0.170 0.86492
## Ethnicity_Asian -2.289e+00 1.883e+00 9.745e+02 -1.216 0.22441
## Ethnicity_Black -4.344e+00 1.355e+00 9.775e+02 -3.206 0.00139
## Ethnicity_Hispanic -1.644e+00 2.163e+00 9.738e+02 -0.760 0.44744
## Ethnicity_NatAmer 1.226e+01 1.384e+01 9.761e+02 0.886 0.37581
## Ethnicity_NatPac 7.565e+00 6.944e+00 9.786e+02 1.089 0.27621
## Ethnicity_Other -4.297e-01 4.407e+00 9.767e+02 -0.098 0.92235
## ATNS_Score.c -5.561e-02 2.130e-02 1.020e+03 -2.611 0.00917
## Ideology.c -2.491e+00 2.448e-01 9.941e+02 -10.174 < 2e-16
## Naturalness.c:ATNS_Score.c 2.454e-03 5.772e-04 2.728e+03 4.251 2.20e-05
## Naturalness.c:Ideology.c -4.178e-03 7.221e-03 2.658e+03 -0.579 0.56295
##
## (Intercept) ***
## Naturalness.c ***
## Benefit.c ***
## Risk.c ***
## Age.c
## GenderMan vs Woman
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black **
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## ATNS_Score.c **
## Ideology.c ***
## Naturalness.c:ATNS_Score.c ***
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 17 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(mod101011,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.38 | 1.83 | 49.80 – 56.95 | 29.24 | <0.001 |
| Naturalness c | 0.13 | 0.03 | 0.08 – 0.18 | 4.95 | <0.001 |
| Benefit c | 0.45 | 0.01 | 0.42 – 0.48 | 32.98 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.25 | <0.001 |
| Age c | -0.05 | 0.03 | -0.10 – 0.01 | -1.62 | 0.104 |
| GenderMan vs Woman | 0.01 | 0.46 | -0.88 – 0.91 | 0.03 | 0.975 |
|
GenderMan&Woman vs Nonbinary |
0.46 | 2.70 | -4.83 – 5.75 | 0.17 | 0.865 |
| Ethnicity Asian | -2.29 | 1.88 | -5.98 – 1.40 | -1.22 | 0.224 |
| Ethnicity Black | -4.34 | 1.36 | -7.00 – -1.69 | -3.21 | 0.001 |
| Ethnicity Hispanic | -1.64 | 2.16 | -5.89 – 2.60 | -0.76 | 0.447 |
| Ethnicity NatAmer | 12.26 | 13.84 | -14.87 – 39.39 | 0.89 | 0.376 |
| Ethnicity NatPac | 7.56 | 6.94 | -6.05 – 21.18 | 1.09 | 0.276 |
| Ethnicity Other | -0.43 | 4.41 | -9.07 – 8.21 | -0.10 | 0.922 |
| ATNS Score c | -0.06 | 0.02 | -0.10 – -0.01 | -2.61 | 0.009 |
| Ideology c | -2.49 | 0.24 | -2.97 – -2.01 | -10.17 | <0.001 |
|
Naturalness c × ATNS Score c |
0.00 | 0.00 | 0.00 – 0.00 | 4.25 | <0.001 |
|
Naturalness c × Ideology c |
-0.00 | 0.01 | -0.02 – 0.01 | -0.58 | 0.563 |
| Random Effects | |||||
| σ2 | 194.20 | ||||
| τ00 id | 125.04 | ||||
| τ00 Type | 8.44 | ||||
| ICC | 0.41 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.596 / 0.761 | ||||
# Model 3: Risk ~ Nat + Ben + Age + Gender + Ethnicity, + Ideology + ATNS + ATNS*Ben + ATNS*Nat + Ideology*Nat
mod101012 <- lmer(Risk ~ Naturalness.c + Benefit.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c*Naturalness.c + ATNS_Score.c*Benefit.c + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(mod101012)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + Benefit.c + Age.c + Gender + Ethnicity_Asian +
## Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer +
## Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c * Naturalness.c +
## ATNS_Score.c * Benefit.c + Ideology.c * Naturalness.c + (1 |
## id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 26479.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7920 -0.6013 -0.0118 0.5727 3.7146
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 143.80 11.992
## Type (Intercept) 75.21 8.673
## Residual 299.84 17.316
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.186e+01 3.274e+00 1.740e+01 9.733 1.86e-08
## Naturalness.c -4.005e-01 3.137e-02 2.786e+03 -12.765 < 2e-16
## Benefit.c -2.282e-01 1.597e-02 2.944e+03 -14.292 < 2e-16
## Age.c 4.001e-02 3.195e-02 9.775e+02 1.253 0.21067
## GenderMan vs Woman 1.358e+00 5.163e-01 9.739e+02 2.630 0.00866
## GenderMan&Woman vs Nonbinary 3.021e+00 3.059e+00 9.660e+02 0.988 0.32362
## Ethnicity_Asian 1.751e+00 2.134e+00 9.677e+02 0.820 0.41221
## Ethnicity_Black 4.020e+00 1.535e+00 9.694e+02 2.619 0.00897
## Ethnicity_Hispanic -1.531e+00 2.452e+00 9.673e+02 -0.625 0.53240
## Ethnicity_NatAmer -7.664e+00 1.569e+01 9.705e+02 -0.488 0.62541
## Ethnicity_NatPac 7.809e+00 7.872e+00 9.722e+02 0.992 0.32142
## Ethnicity_Other -9.125e+00 4.994e+00 9.688e+02 -1.827 0.06796
## ATNS_Score.c 2.093e-01 2.390e-02 9.807e+02 8.759 < 2e-16
## Ideology.c 4.590e-01 2.777e-01 9.868e+02 1.653 0.09864
## Naturalness.c:ATNS_Score.c -5.328e-03 7.264e-04 2.809e+03 -7.335 2.88e-13
## Benefit.c:ATNS_Score.c 1.055e-03 6.403e-04 2.938e+03 1.648 0.09937
## Naturalness.c:Ideology.c -8.140e-03 8.827e-03 2.744e+03 -0.922 0.35653
##
## (Intercept) ***
## Naturalness.c ***
## Benefit.c ***
## Age.c
## GenderMan vs Woman **
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black **
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other .
## ATNS_Score.c ***
## Ideology.c .
## Naturalness.c:ATNS_Score.c ***
## Benefit.c:ATNS_Score.c .
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 17 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(mod101012,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 31.86 | 3.27 | 25.44 – 38.28 | 9.73 | <0.001 |
| Naturalness c | -0.40 | 0.03 | -0.46 – -0.34 | -12.76 | <0.001 |
| Benefit c | -0.23 | 0.02 | -0.26 – -0.20 | -14.29 | <0.001 |
| Age c | 0.04 | 0.03 | -0.02 – 0.10 | 1.25 | 0.210 |
| GenderMan vs Woman | 1.36 | 0.52 | 0.35 – 2.37 | 2.63 | 0.009 |
|
GenderMan&Woman vs Nonbinary |
3.02 | 3.06 | -2.98 – 9.02 | 0.99 | 0.323 |
| Ethnicity Asian | 1.75 | 2.13 | -2.43 – 5.93 | 0.82 | 0.412 |
| Ethnicity Black | 4.02 | 1.54 | 1.01 – 7.03 | 2.62 | 0.009 |
| Ethnicity Hispanic | -1.53 | 2.45 | -6.34 – 3.28 | -0.62 | 0.532 |
| Ethnicity NatAmer | -7.66 | 15.69 | -38.43 – 23.11 | -0.49 | 0.625 |
| Ethnicity NatPac | 7.81 | 7.87 | -7.63 – 23.24 | 0.99 | 0.321 |
| Ethnicity Other | -9.13 | 4.99 | -18.92 – 0.67 | -1.83 | 0.068 |
| ATNS Score c | 0.21 | 0.02 | 0.16 – 0.26 | 8.76 | <0.001 |
| Ideology c | 0.46 | 0.28 | -0.09 – 1.00 | 1.65 | 0.098 |
|
Naturalness c × ATNS Score c |
-0.01 | 0.00 | -0.01 – -0.00 | -7.34 | <0.001 |
| Benefit c × ATNS Score c | 0.00 | 0.00 | -0.00 – 0.00 | 1.65 | 0.099 |
|
Naturalness c × Ideology c |
-0.01 | 0.01 | -0.03 – 0.01 | -0.92 | 0.357 |
| Random Effects | |||||
| σ2 | 299.84 | ||||
| τ00 id | 143.80 | ||||
| τ00 Type | 75.21 | ||||
| ICC | 0.42 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.265 / 0.575 | ||||
# Model 4: Ben ~ Nat + Risk + Age + Gender + Ethnicity, + Ideology + ATNS + ATNS*Risk + ATNS*Nat + Ideology*Nat
mod101013 <- lmer(Ben ~ Naturalness.c + Risk.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c*Naturalness.c + ATNS_Score.c*Risk.c + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(mod101013)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + Risk.c + Age.c + Gender + Ethnicity_Asian +
## Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer +
## Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c * Naturalness.c +
## ATNS_Score.c * Risk.c + Ideology.c * Naturalness.c + (1 |
## id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27081.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5569 -0.5223 0.0692 0.5399 3.4714
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 241.86 15.552
## Type (Intercept) 21.22 4.607
## Residual 339.11 18.415
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.056e+01 2.601e+00 7.558e+01 19.439 < 2e-16
## Naturalness.c 1.028e-01 3.507e-02 2.704e+03 2.931 0.00340
## Risk.c -2.899e-01 1.961e-02 2.849e+03 -14.782 < 2e-16
## Age.c -1.078e-01 3.847e-02 9.832e+02 -2.803 0.00516
## GenderMan vs Woman 9.635e-01 6.231e-01 9.848e+02 1.546 0.12234
## GenderMan&Woman vs Nonbinary 3.175e+00 3.691e+00 9.752e+02 0.860 0.38976
## Ethnicity_Asian 1.281e+00 2.574e+00 9.769e+02 0.498 0.61878
## Ethnicity_Black 2.768e+00 1.854e+00 9.814e+02 1.493 0.13579
## Ethnicity_Hispanic -6.896e-01 2.958e+00 9.765e+02 -0.233 0.81571
## Ethnicity_NatAmer -1.822e+01 1.892e+01 9.782e+02 -0.963 0.33585
## Ethnicity_NatPac -6.557e+00 9.494e+00 9.808e+02 -0.691 0.48994
## Ethnicity_Other 6.335e+00 6.025e+00 9.787e+02 1.051 0.29331
## ATNS_Score.c -4.146e-02 2.917e-02 1.027e+03 -1.421 0.15551
## Ideology.c -1.875e+00 3.330e-01 9.828e+02 -5.630 2.35e-08
## Naturalness.c:ATNS_Score.c 1.097e-03 9.058e-04 2.779e+03 1.211 0.22588
## Risk.c:ATNS_Score.c 1.659e-03 7.830e-04 2.918e+03 2.119 0.03415
## Naturalness.c:Ideology.c -1.626e-03 9.599e-03 2.624e+03 -0.169 0.86552
##
## (Intercept) ***
## Naturalness.c **
## Risk.c ***
## Age.c **
## GenderMan vs Woman
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## ATNS_Score.c
## Ideology.c ***
## Naturalness.c:ATNS_Score.c
## Risk.c:ATNS_Score.c *
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 17 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(mod101013,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.56 | 2.60 | 45.46 – 55.66 | 19.44 | <0.001 |
| Naturalness c | 0.10 | 0.04 | 0.03 – 0.17 | 2.93 | 0.003 |
| Risk c | -0.29 | 0.02 | -0.33 – -0.25 | -14.78 | <0.001 |
| Age c | -0.11 | 0.04 | -0.18 – -0.03 | -2.80 | 0.005 |
| GenderMan vs Woman | 0.96 | 0.62 | -0.26 – 2.19 | 1.55 | 0.122 |
|
GenderMan&Woman vs Nonbinary |
3.18 | 3.69 | -4.06 – 10.41 | 0.86 | 0.390 |
| Ethnicity Asian | 1.28 | 2.57 | -3.77 – 6.33 | 0.50 | 0.619 |
| Ethnicity Black | 2.77 | 1.85 | -0.87 – 6.40 | 1.49 | 0.136 |
| Ethnicity Hispanic | -0.69 | 2.96 | -6.49 – 5.11 | -0.23 | 0.816 |
| Ethnicity NatAmer | -18.22 | 18.92 | -55.32 – 18.88 | -0.96 | 0.336 |
| Ethnicity NatPac | -6.56 | 9.49 | -25.17 – 12.06 | -0.69 | 0.490 |
| Ethnicity Other | 6.33 | 6.02 | -5.48 – 18.15 | 1.05 | 0.293 |
| ATNS Score c | -0.04 | 0.03 | -0.10 – 0.02 | -1.42 | 0.155 |
| Ideology c | -1.87 | 0.33 | -2.53 – -1.22 | -5.63 | <0.001 |
|
Naturalness c × ATNS Score c |
0.00 | 0.00 | -0.00 – 0.00 | 1.21 | 0.226 |
| Risk c × ATNS Score c | 0.00 | 0.00 | 0.00 – 0.00 | 2.12 | 0.034 |
|
Naturalness c × Ideology c |
-0.00 | 0.01 | -0.02 – 0.02 | -0.17 | 0.866 |
| Random Effects | |||||
| σ2 | 339.11 | ||||
| τ00 id | 241.86 | ||||
| τ00 Type | 21.22 | ||||
| ICC | 0.44 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.528 | ||||
Section 2: Where does naturalness come from?
mod101014 <- lmer(Naturalness ~ Familiarity.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + (1 | id) + (1|Type), data = L)
summary(mod101014)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Familiarity.c + Age.c + Gender + Ethnicity_Asian +
## Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer +
## Ethnicity_NatPac + Ethnicity_Other + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25480.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4869 -0.6164 -0.0097 0.6038 3.3709
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 68.3 8.265
## Type (Intercept) 108.3 10.405
## Residual 237.0 15.394
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.42644 3.51223 11.45331 11.225 1.58e-07
## Familiarity.c 0.16997 0.01269 2941.35167 13.394 < 2e-16
## Age.c -0.07695 0.02455 983.34593 -3.134 0.001776
## GenderMan vs Woman 1.39967 0.39160 997.67155 3.574 0.000368
## GenderMan&Woman vs Nonbinary 0.85461 2.37264 979.31514 0.360 0.718780
## Ethnicity_Asian -1.34202 1.65754 981.28224 -0.810 0.418340
## Ethnicity_Black 0.74261 1.18187 983.59710 0.628 0.529930
## Ethnicity_Hispanic -0.90958 1.90485 982.28579 -0.478 0.633109
## Ethnicity_NatAmer 15.26006 12.17427 981.12406 1.253 0.210334
## Ethnicity_NatPac 2.51732 6.10813 986.41524 0.412 0.680336
## Ethnicity_Other 0.02663 3.87135 982.14556 0.007 0.994513
##
## (Intercept) ***
## Familiarity.c ***
## Age.c **
## GenderMan vs Woman ***
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Fmlrt. Age.c GndMvW GM&WvN Ethn_A Ethn_B Ethn_H Eth_NA
## Familirty.c -0.012
## Age.c 0.024 0.049
## GndrMnvsWmn -0.001 0.113 -0.060
## GndrMn&WmvN -0.326 0.007 -0.115 0.002
## Ethncty_Asn -0.023 -0.024 0.132 -0.016 -0.038
## Ethncty_Blc -0.050 0.037 0.136 -0.041 0.001 0.118
## Ethncty_Hsp -0.047 -0.031 0.119 -0.057 0.050 0.077 0.104
## Ethncty_NtA -0.005 0.003 -0.048 0.036 0.002 0.003 0.006 0.001
## Ethncty_NtP -0.006 -0.007 0.043 0.029 -0.011 0.026 0.033 0.020 0.002
## Ethncty_Oth -0.011 0.014 0.038 0.018 -0.014 0.036 0.049 0.028 0.003
## Eth_NP
## Familirty.c
## Age.c
## GndrMnvsWmn
## GndrMn&WmvN
## Ethncty_Asn
## Ethncty_Blc
## Ethncty_Hsp
## Ethncty_NtA
## Ethncty_NtP
## Ethncty_Oth 0.010tab_model(mod101014,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.43 | 3.51 | 32.54 – 46.31 | 11.23 | <0.001 |
| Familiarity c | 0.17 | 0.01 | 0.15 – 0.19 | 13.39 | <0.001 |
| Age c | -0.08 | 0.02 | -0.13 – -0.03 | -3.13 | 0.002 |
| GenderMan vs Woman | 1.40 | 0.39 | 0.63 – 2.17 | 3.57 | <0.001 |
|
GenderMan&Woman vs Nonbinary |
0.85 | 2.37 | -3.80 – 5.51 | 0.36 | 0.719 |
| Ethnicity Asian | -1.34 | 1.66 | -4.59 – 1.91 | -0.81 | 0.418 |
| Ethnicity Black | 0.74 | 1.18 | -1.57 – 3.06 | 0.63 | 0.530 |
| Ethnicity Hispanic | -0.91 | 1.90 | -4.64 – 2.83 | -0.48 | 0.633 |
| Ethnicity NatAmer | 15.26 | 12.17 | -8.61 – 39.13 | 1.25 | 0.210 |
| Ethnicity NatPac | 2.52 | 6.11 | -9.46 – 14.49 | 0.41 | 0.680 |
| Ethnicity Other | 0.03 | 3.87 | -7.56 – 7.62 | 0.01 | 0.995 |
| Random Effects | |||||
| σ2 | 236.98 | ||||
| τ00 id | 68.30 | ||||
| τ00 Type | 108.27 | ||||
| ICC | 0.43 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.084 / 0.475 | ||||
Section 3: Moderators
Aversion to Tampering
# Looks at interactions from Section 1
# Interaction Plots: ATNS, Political Ideology, Climate Change Belief
## ATNS
## Model 2: Support ~ Nat + Ben + Risk + Age + Gender + Ethnicity, + Ideology + ATNS + ATNS*Risk + ATNS*Ben + ATNS*Nat + Ideology*Nat
mod101011 <- lmer(Support ~ Naturalness.c + Benefit.c + Risk.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + ATNS_Score.c*Naturalness.c + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(mod101011)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + Risk.c + Age.c + Gender +
## Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic +
## Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other +
## ATNS_Score.c * Naturalness.c + Ideology.c * Naturalness.c +
## (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25347.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7138 -0.4924 0.0303 0.5320 4.0800
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 125.044 11.182
## Type (Intercept) 8.437 2.905
## Residual 194.204 13.936
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.338e+01 1.825e+00 1.067e+02 29.243 < 2e-16
## Naturalness.c 1.303e-01 2.634e-02 2.714e+03 4.948 7.97e-07
## Benefit.c 4.518e-01 1.370e-02 2.970e+03 32.979 < 2e-16
## Risk.c -3.958e-01 1.508e-02 2.807e+03 -26.246 < 2e-16
## Age.c -4.575e-02 2.817e-02 9.843e+02 -1.624 0.10469
## GenderMan vs Woman 1.430e-02 4.558e-01 9.826e+02 0.031 0.97498
## GenderMan&Woman vs Nonbinary 4.593e-01 2.699e+00 9.729e+02 0.170 0.86492
## Ethnicity_Asian -2.289e+00 1.883e+00 9.745e+02 -1.216 0.22441
## Ethnicity_Black -4.344e+00 1.355e+00 9.775e+02 -3.206 0.00139
## Ethnicity_Hispanic -1.644e+00 2.163e+00 9.738e+02 -0.760 0.44744
## Ethnicity_NatAmer 1.226e+01 1.384e+01 9.761e+02 0.886 0.37581
## Ethnicity_NatPac 7.565e+00 6.944e+00 9.786e+02 1.089 0.27621
## Ethnicity_Other -4.297e-01 4.407e+00 9.767e+02 -0.098 0.92235
## ATNS_Score.c -5.561e-02 2.130e-02 1.020e+03 -2.611 0.00917
## Ideology.c -2.491e+00 2.448e-01 9.941e+02 -10.174 < 2e-16
## Naturalness.c:ATNS_Score.c 2.454e-03 5.772e-04 2.728e+03 4.251 2.20e-05
## Naturalness.c:Ideology.c -4.178e-03 7.221e-03 2.658e+03 -0.579 0.56295
##
## (Intercept) ***
## Naturalness.c ***
## Benefit.c ***
## Risk.c ***
## Age.c
## GenderMan vs Woman
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black **
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## ATNS_Score.c **
## Ideology.c ***
## Naturalness.c:ATNS_Score.c ***
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 17 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(mod101011,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 53.38 | 1.83 | 49.80 – 56.95 | 29.24 | <0.001 |
| Naturalness c | 0.13 | 0.03 | 0.08 – 0.18 | 4.95 | <0.001 |
| Benefit c | 0.45 | 0.01 | 0.42 – 0.48 | 32.98 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.25 | <0.001 |
| Age c | -0.05 | 0.03 | -0.10 – 0.01 | -1.62 | 0.104 |
| GenderMan vs Woman | 0.01 | 0.46 | -0.88 – 0.91 | 0.03 | 0.975 |
|
GenderMan&Woman vs Nonbinary |
0.46 | 2.70 | -4.83 – 5.75 | 0.17 | 0.865 |
| Ethnicity Asian | -2.29 | 1.88 | -5.98 – 1.40 | -1.22 | 0.224 |
| Ethnicity Black | -4.34 | 1.36 | -7.00 – -1.69 | -3.21 | 0.001 |
| Ethnicity Hispanic | -1.64 | 2.16 | -5.89 – 2.60 | -0.76 | 0.447 |
| Ethnicity NatAmer | 12.26 | 13.84 | -14.87 – 39.39 | 0.89 | 0.376 |
| Ethnicity NatPac | 7.56 | 6.94 | -6.05 – 21.18 | 1.09 | 0.276 |
| Ethnicity Other | -0.43 | 4.41 | -9.07 – 8.21 | -0.10 | 0.922 |
| ATNS Score c | -0.06 | 0.02 | -0.10 – -0.01 | -2.61 | 0.009 |
| Ideology c | -2.49 | 0.24 | -2.97 – -2.01 | -10.17 | <0.001 |
|
Naturalness c × ATNS Score c |
0.00 | 0.00 | 0.00 – 0.00 | 4.25 | <0.001 |
|
Naturalness c × Ideology c |
-0.00 | 0.01 | -0.02 – 0.01 | -0.58 | 0.563 |
| Random Effects | |||||
| σ2 | 194.20 | ||||
| τ00 id | 125.04 | ||||
| τ00 Type | 8.44 | ||||
| ICC | 0.41 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.596 / 0.761 | ||||
confint(mod101011)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 10.323895804 11.867791425
## .sig02 1.730179826 4.770330870
## .sigma 13.499808096 14.368449583
## (Intercept) 49.837531244 56.917178339
## Naturalness.c 0.078991170 0.182356825
## Benefit.c 0.425208681 0.479079608
## Risk.c -0.425561197 -0.366160518
## Age.c -0.100626300 0.009231650
## GenderMan vs Woman -0.874821962 0.902506459
## GenderMan&Woman vs Nonbinary -4.803066764 5.721547101
## Ethnicity_Asian -5.956204961 1.385492151
## Ethnicity_Black -6.984768815 -1.700420834
## Ethnicity_Hispanic -5.860972095 2.573307363
## Ethnicity_NatAmer -14.711287798 39.248261942
## Ethnicity_NatPac -5.967715777 21.107377583
## Ethnicity_Other -9.021423123 8.162366952
## ATNS_Score.c -0.097052851 -0.013960376
## Ideology.c -2.966990500 -2.012174025
## Naturalness.c:ATNS_Score.c 0.001321252 0.003580691
## Naturalness.c:Ideology.c -0.018305710 0.009958888library (ggplot2)
# Interaction Plot
# +1 SD, -1 SD Aversion to Tampering with Nature Score
L$ATNS_sd <- sd(L$ATNS_Score, na.rm = TRUE)
L$MinusOneSD <- (L$ATNS_Score.c + L$ATNS_sd)
L$PlusOneSD <- (L$ATNS_Score.c - L$ATNS_sd)
L$MinusOneSD <- as.numeric(as.character(L$MinusOneSD))
L$PlusOneSD <- as.numeric(as.character(L$PlusOneSD))
#Look at coeffients for interaction at +1/-1 SD
M.MinusOne <- lmer(Support ~ MinusOneSD*Naturalness.c + Benefit.c + Risk.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingM.PlusOne <- lmer(Support ~ PlusOneSD*Naturalness.c*Naturalness.c + Benefit.c + Risk.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other + Ideology.c*Naturalness.c + (1 | id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(M.MinusOne)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ MinusOneSD * Naturalness.c + Benefit.c + Risk.c + Age.c +
## Gender + Ethnicity_Asian + Ethnicity_Black + Ethnicity_Hispanic +
## Ethnicity_NatAmer + Ethnicity_NatPac + Ethnicity_Other +
## Ideology.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25347.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7138 -0.4924 0.0303 0.5320 4.0800
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 125.044 11.182
## Type (Intercept) 8.437 2.905
## Residual 194.204 13.936
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.457e+01 1.887e+00 1.205e+02 28.915 < 2e-16
## MinusOneSD -5.561e-02 2.130e-02 1.020e+03 -2.611 0.00917
## Naturalness.c 7.759e-02 2.901e-02 2.668e+03 2.675 0.00752
## Benefit.c 4.518e-01 1.370e-02 2.970e+03 32.979 < 2e-16
## Risk.c -3.958e-01 1.508e-02 2.807e+03 -26.246 < 2e-16
## Age.c -4.575e-02 2.817e-02 9.843e+02 -1.624 0.10469
## GenderMan vs Woman 1.430e-02 4.558e-01 9.826e+02 0.031 0.97498
## GenderMan&Woman vs Nonbinary 4.593e-01 2.699e+00 9.729e+02 0.170 0.86492
## Ethnicity_Asian -2.289e+00 1.883e+00 9.745e+02 -1.216 0.22441
## Ethnicity_Black -4.344e+00 1.355e+00 9.775e+02 -3.206 0.00139
## Ethnicity_Hispanic -1.644e+00 2.163e+00 9.738e+02 -0.760 0.44744
## Ethnicity_NatAmer 1.226e+01 1.384e+01 9.761e+02 0.886 0.37581
## Ethnicity_NatPac 7.565e+00 6.944e+00 9.786e+02 1.089 0.27621
## Ethnicity_Other -4.297e-01 4.407e+00 9.767e+02 -0.098 0.92235
## Ideology.c -2.491e+00 2.448e-01 9.941e+02 -10.174 < 2e-16
## MinusOneSD:Naturalness.c 2.454e-03 5.772e-04 2.728e+03 4.251 2.2e-05
## Naturalness.c:Ideology.c -4.178e-03 7.221e-03 2.658e+03 -0.579 0.56295
##
## (Intercept) ***
## MinusOneSD **
## Naturalness.c **
## Benefit.c ***
## Risk.c ***
## Age.c
## GenderMan vs Woman
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black **
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## Ideology.c ***
## MinusOneSD:Naturalness.c ***
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 17 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(M.MinusOne,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.57 | 1.89 | 50.87 – 58.27 | 28.91 | <0.001 |
| MinusOneSD | -0.06 | 0.02 | -0.10 – -0.01 | -2.61 | 0.009 |
| Naturalness c | 0.08 | 0.03 | 0.02 – 0.13 | 2.68 | 0.008 |
| Benefit c | 0.45 | 0.01 | 0.42 – 0.48 | 32.98 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.25 | <0.001 |
| Age c | -0.05 | 0.03 | -0.10 – 0.01 | -1.62 | 0.104 |
| GenderMan vs Woman | 0.01 | 0.46 | -0.88 – 0.91 | 0.03 | 0.975 |
|
GenderMan&Woman vs Nonbinary |
0.46 | 2.70 | -4.83 – 5.75 | 0.17 | 0.865 |
| Ethnicity Asian | -2.29 | 1.88 | -5.98 – 1.40 | -1.22 | 0.224 |
| Ethnicity Black | -4.34 | 1.36 | -7.00 – -1.69 | -3.21 | 0.001 |
| Ethnicity Hispanic | -1.64 | 2.16 | -5.89 – 2.60 | -0.76 | 0.447 |
| Ethnicity NatAmer | 12.26 | 13.84 | -14.87 – 39.39 | 0.89 | 0.376 |
| Ethnicity NatPac | 7.56 | 6.94 | -6.05 – 21.18 | 1.09 | 0.276 |
| Ethnicity Other | -0.43 | 4.41 | -9.07 – 8.21 | -0.10 | 0.922 |
| Ideology c | -2.49 | 0.24 | -2.97 – -2.01 | -10.17 | <0.001 |
|
MinusOneSD × Naturalness c |
0.00 | 0.00 | 0.00 – 0.00 | 4.25 | <0.001 |
|
Naturalness c × Ideology c |
-0.00 | 0.01 | -0.02 – 0.01 | -0.58 | 0.563 |
| Random Effects | |||||
| σ2 | 194.20 | ||||
| τ00 id | 125.04 | ||||
| τ00 Type | 8.44 | ||||
| ICC | 0.41 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.596 / 0.761 | ||||
summary(M.PlusOne)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ PlusOneSD * Naturalness.c * Naturalness.c + Benefit.c +
## Risk.c + Age.c + Gender + Ethnicity_Asian + Ethnicity_Black +
## Ethnicity_Hispanic + Ethnicity_NatAmer + Ethnicity_NatPac +
## Ethnicity_Other + Ideology.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25347.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7138 -0.4924 0.0303 0.5320 4.0800
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 125.044 11.182
## Type (Intercept) 8.437 2.905
## Residual 194.204 13.936
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.218e+01 1.876e+00 1.181e+02 27.812 < 2e-16
## PlusOneSD -5.561e-02 2.130e-02 1.020e+03 -2.611 0.00917
## Naturalness.c 1.831e-01 2.923e-02 2.774e+03 6.264 4.35e-10
## Benefit.c 4.518e-01 1.370e-02 2.970e+03 32.979 < 2e-16
## Risk.c -3.958e-01 1.508e-02 2.807e+03 -26.246 < 2e-16
## Age.c -4.575e-02 2.817e-02 9.843e+02 -1.624 0.10469
## GenderMan vs Woman 1.430e-02 4.558e-01 9.826e+02 0.031 0.97498
## GenderMan&Woman vs Nonbinary 4.593e-01 2.699e+00 9.729e+02 0.170 0.86492
## Ethnicity_Asian -2.289e+00 1.883e+00 9.745e+02 -1.216 0.22441
## Ethnicity_Black -4.344e+00 1.355e+00 9.775e+02 -3.206 0.00139
## Ethnicity_Hispanic -1.644e+00 2.163e+00 9.738e+02 -0.760 0.44744
## Ethnicity_NatAmer 1.226e+01 1.384e+01 9.761e+02 0.886 0.37581
## Ethnicity_NatPac 7.565e+00 6.944e+00 9.786e+02 1.089 0.27621
## Ethnicity_Other -4.297e-01 4.407e+00 9.767e+02 -0.098 0.92235
## Ideology.c -2.491e+00 2.448e-01 9.941e+02 -10.174 < 2e-16
## PlusOneSD:Naturalness.c 2.454e-03 5.772e-04 2.728e+03 4.251 2.20e-05
## Naturalness.c:Ideology.c -4.178e-03 7.221e-03 2.658e+03 -0.579 0.56295
##
## (Intercept) ***
## PlusOneSD **
## Naturalness.c ***
## Benefit.c ***
## Risk.c ***
## Age.c
## GenderMan vs Woman
## GenderMan&Woman vs Nonbinary
## Ethnicity_Asian
## Ethnicity_Black **
## Ethnicity_Hispanic
## Ethnicity_NatAmer
## Ethnicity_NatPac
## Ethnicity_Other
## Ideology.c ***
## PlusOneSD:Naturalness.c ***
## Naturalness.c:Ideology.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 17 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(M.PlusOne,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 52.18 | 1.88 | 48.50 – 55.86 | 27.81 | <0.001 |
| PlusOneSD | -0.06 | 0.02 | -0.10 – -0.01 | -2.61 | 0.009 |
| Naturalness c | 0.18 | 0.03 | 0.13 – 0.24 | 6.26 | <0.001 |
| Benefit c | 0.45 | 0.01 | 0.42 – 0.48 | 32.98 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.25 | <0.001 |
| Age c | -0.05 | 0.03 | -0.10 – 0.01 | -1.62 | 0.104 |
| GenderMan vs Woman | 0.01 | 0.46 | -0.88 – 0.91 | 0.03 | 0.975 |
|
GenderMan&Woman vs Nonbinary |
0.46 | 2.70 | -4.83 – 5.75 | 0.17 | 0.865 |
| Ethnicity Asian | -2.29 | 1.88 | -5.98 – 1.40 | -1.22 | 0.224 |
| Ethnicity Black | -4.34 | 1.36 | -7.00 – -1.69 | -3.21 | 0.001 |
| Ethnicity Hispanic | -1.64 | 2.16 | -5.89 – 2.60 | -0.76 | 0.447 |
| Ethnicity NatAmer | 12.26 | 13.84 | -14.87 – 39.39 | 0.89 | 0.376 |
| Ethnicity NatPac | 7.56 | 6.94 | -6.05 – 21.18 | 1.09 | 0.276 |
| Ethnicity Other | -0.43 | 4.41 | -9.07 – 8.21 | -0.10 | 0.922 |
| Ideology c | -2.49 | 0.24 | -2.97 – -2.01 | -10.17 | <0.001 |
| PlusOneSD × Naturalness c | 0.00 | 0.00 | 0.00 – 0.00 | 4.25 | <0.001 |
|
Naturalness c × Ideology c |
-0.00 | 0.01 | -0.02 – 0.01 | -0.58 | 0.563 |
| Random Effects | |||||
| σ2 | 194.20 | ||||
| τ00 id | 125.04 | ||||
| τ00 Type | 8.44 | ||||
| ICC | 0.41 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.596 / 0.761 | ||||
# Extract predicted values from the models
L$M.PlusOne.pred <- predict(M.PlusOne, allow.new.levels = TRUE, newdata = L)
L$M.MinusOne.pred <- predict(M.MinusOne, allow.new.levels = TRUE, newdata = L)
# Plot the predicted values against the original data
library(ggplot2)
#create plot
p <- plot_model(mod101011, type = "pred",
terms = c("Naturalness.c", "ATNS_Score.c [-21.50, 21.50]")) +
ggtitle("") +
ylab("Support (0-100)") +
xlab("Naturalness 0-100 (Mean Centered at 39.98)") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.background = element_rect(fill = "white", color = "white"))
p <- p + labs(color = "Aversion to Tampering with Nature")
(mod101011 <- p +
scale_color_manual(labels = c("-1 SD", "+1 SD"),
values = c("blue", "red")) +
scale_fill_manual(values = c("blue", "red")) +
scale_y_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100), limits = c(20, 100)) +
scale_x_continuous(breaks = c(-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50),
limits = c(-50, 50)))## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.## Warning: Removed 6 rows containing missing values (`geom_line()`).
Political Ideology
Climate Change Belief
Section 4: Relationships
##c. Zero Order Correlations
# Age and Gender
cor.test(CC$Dem_Age, CC$Dem_Gen)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Dem_Gen
## t = -2.8079, df = 995, p-value = 0.005084
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1499265 -0.0267263
## sample estimates:
## cor
## -0.08866549# Age and Education
cor.test(CC$Dem_Age, CC$Dem_Edu)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Dem_Edu
## t = 5.0156, df = 995, p-value = 6.258e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09588088 0.21700305
## sample estimates:
## cor
## 0.1570324# Age and SES
cor.test(CC$Dem_Age, CC$Dem_SES)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Dem_SES
## t = 1.8151, df = 994, p-value = 0.06982
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.004659095 0.119167081
## sample estimates:
## cor
## 0.05747504# Age and Aversion to Tampering with Nature
cor.test(CC$Dem_Age, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$ATNS_Score
## t = 1.0732, df = 995, p-value = 0.2834
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02814267 0.09588696
## sample estimates:
## cor
## 0.03400306# Age and Climate Change Belief
cor.test(CC$Dem_Age, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$CCB_Score
## t = -5.3, df = 995, p-value = 1.427e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2254643 -0.1046881
## sample estimates:
## cor
## -0.1656975# Age and Connectedness to Nature
cor.test(CC$Dem_Age, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$CNS_Score
## t = 3.1178, df = 995, p-value = 0.001874
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03649872 0.15947457
## sample estimates:
## cor
## 0.09836215# Age and Political Ideology
cor.test(CC$Dem_Age, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Age and CC$Ideology
## t = 4.8249, df = 995, p-value = 1.62e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08995803 0.21130261
## sample estimates:
## cor
## 0.1511999# Gender and Education
cor.test(CC$Dem_Gen, CC$Dem_Edu)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$Dem_Edu
## t = 1.045, df = 1005, p-value = 0.2963
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02889094 0.09452976
## sample estimates:
## cor
## 0.032945# Gender and SES
cor.test(CC$Dem_Gen, CC$Dem_SES)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$Dem_SES
## t = 1.3017, df = 1004, p-value = 0.1933
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02081245 0.10259577
## sample estimates:
## cor
## 0.04104821# Gender and Aversion to Tampering with Nature
cor.test(CC$Dem_Gen, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$ATNS_Score
## t = -6.7495, df = 1005, p-value = 2.503e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2665865 -0.1483704
## sample estimates:
## cor
## -0.2082389# Gender and Climate Change Belief
cor.test(CC$Dem_Gen, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$CCB_Score
## t = -2.534, df = 1005, p-value = 0.01143
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14076367 -0.01799082
## sample estimates:
## cor
## -0.07967941# Gender and Connectedness to Nature
cor.test(CC$Dem_Gen, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$CNS_Score
## t = -5.1191, df = 1005, p-value = 3.677e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.21903313 -0.09860699
## sample estimates:
## cor
## -0.159413# Gender and Political Ideology
cor.test(CC$Dem_Gen, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Gen and CC$Ideology
## t = 1.9133, df = 1005, p-value = 0.05599
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00153865 0.12156892
## sample estimates:
## cor
## 0.06024422# Education and SES
cor.test(CC$Dem_Edu, CC$Dem_SES)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$Dem_SES
## t = 11.4, df = 1004, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2826379 0.3921347
## sample estimates:
## cor
## 0.3385318# Education and Aversion to Tampering with Nature
cor.test(CC$Dem_Edu, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$ATNS_Score
## t = -2.4982, df = 1005, p-value = 0.01264
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13965980 -0.01686516
## sample estimates:
## cor
## -0.07856046# Education and Climate Change Belief
cor.test(CC$Dem_Edu, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$CCB_Score
## t = 1.3042, df = 1005, p-value = 0.1925
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02072462 0.10262170
## sample estimates:
## cor
## 0.04110515# Education and Connectedness to Nature
cor.test(CC$Dem_Edu, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$CNS_Score
## t = 1.7004, df = 1005, p-value = 0.08936
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.008242423 0.114958753
## sample estimates:
## cor
## 0.053562# Education and Political Ideology
cor.test(CC$Dem_Edu, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_Edu and CC$Ideology
## t = -2.6518, df = 1005, p-value = 0.008132
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14439085 -0.02169181
## sample estimates:
## cor
## -0.08335725# SES and Aversion to Tampering with Nature
cor.test(CC$Dem_SES, CC$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$ATNS_Score
## t = -1.7618, df = 1004, p-value = 0.07842
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.116921365 0.006314827
## sample estimates:
## cor
## -0.0555147# SES and Climate Change Belief
cor.test(CC$Dem_SES, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$CCB_Score
## t = -2.7951, df = 1004, p-value = 0.005287
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14887180 -0.02620697
## sample estimates:
## cor
## -0.08787249# SES and Connectedness to Nature
cor.test(CC$Dem_SES, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$CNS_Score
## t = -1.5389, df = 1004, p-value = 0.1241
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1099887 0.0133372
## sample estimates:
## cor
## -0.04851065# SES and Political Ideology
cor.test(CC$Dem_SES, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$Dem_SES and CC$Ideology
## t = 3.4441, df = 1004, p-value = 0.0005966
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0465622 0.1687399
## sample estimates:
## cor
## 0.1080591# ATNS and Climate Change Belief
cor.test(CC$ATNS_Score, CC$CCB_Score)##
## Pearson's product-moment correlation
##
## data: CC$ATNS_Score and CC$CCB_Score
## t = -1.5637, df = 1005, p-value = 0.1182
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11070453 0.01255104
## sample estimates:
## cor
## -0.04926431# ATNS and Connectedness to Nature
cor.test(CC$ATNS_Score, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$ATNS_Score and CC$CNS_Score
## t = 9.6442, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2334665 0.3465914
## sample estimates:
## cor
## 0.291046# ATNS and Political Ideology
cor.test(CC$ATNS_Score, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$ATNS_Score and CC$Ideology
## t = 1.2313, df = 1005, p-value = 0.2185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02302278 0.10034613
## sample estimates:
## cor
## 0.03880957# Climate Change Belief and Connectedness to Nature
cor.test(CC$CCB_Score, CC$CNS_Score)##
## Pearson's product-moment correlation
##
## data: CC$CCB_Score and CC$CNS_Score
## t = 10.087, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2460271 0.3582627
## sample estimates:
## cor
## 0.303196# Climate Change Belief and Political Ideology
cor.test(CC$CCB_Score, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$CCB_Score and CC$Ideology
## t = -26.269, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6732828 -0.5999135
## sample estimates:
## cor
## -0.6380441# Connectedness to Nature and Political Ideology
cor.test(CC$CNS_Score, CC$Ideology)##
## Pearson's product-moment correlation
##
## data: CC$CNS_Score and CC$Ideology
## t = -9.8754, df = 1005, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3527090 -0.2400456
## sample estimates:
## cor
## -0.2974123Support
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict support?
modA.71 <- lmer(Support ~ (1|id) + (1|Type), data = L)
summary(modA.71)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27956.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2256 -0.5082 0.0638 0.5568 3.1006
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.7 17.60
## Type (Intercept) 144.0 12.00
## Residual 406.3 20.16
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.228 3.852 9.333 15.63 5.23e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modA.71,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.23 | 3.85 | 52.67 – 67.78 | 15.63 | <0.001 |
| Random Effects | |||||
| σ2 | 406.28 | ||||
| τ00 id | 309.67 | ||||
| τ00 Type | 143.96 | ||||
| ICC | 0.53 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.528 | ||||
Q.2: Does naturalness predict support, over and above climate change method contrasts?
modA.7 <- lmer(Support ~ Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27614.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4977 -0.5399 0.0304 0.5444 3.2984
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.31 17.155
## Type (Intercept) 48.25 6.946
## Residual 356.41 18.879
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.005e+01 2.289e+00 9.775e+00 26.24 2.17e-10 ***
## Naturalness.c 4.524e-01 2.331e-02 2.667e+03 19.41 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Naturlnss.c -0.004tab_model(modA.7,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.05 | 2.29 | 55.56 – 64.54 | 26.24 | <0.001 |
| Naturalness c | 0.45 | 0.02 | 0.41 – 0.50 | 19.41 | <0.001 |
| Random Effects | |||||
| σ2 | 356.41 | ||||
| τ00 id | 294.31 | ||||
| τ00 Type | 48.25 | ||||
| ICC | 0.49 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.120 / 0.551 | ||||
Q.3: Does perceived risk predict support, over and above climate change method contrasts?
modA.966 <- lmer(Support ~ Risk.c + Benefit.c + (1|id) + (1|Type), data = L)
summary(modA.966)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Risk.c + Benefit.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25787
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7027 -0.4973 0.0319 0.5133 4.1021
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 144.99 12.041
## Type (Intercept) 11.95 3.457
## Residual 200.80 14.170
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.82464 1.18608 10.58526 50.44 5.88e-14 ***
## Risk.c -0.45542 0.01418 2627.49269 -32.11 < 2e-16 ***
## Benefit.c 0.47641 0.01387 3015.94636 34.34 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c
## Risk.c 0.006
## Benefit.c 0.000 0.321tab_model(modA.966,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.82 | 1.19 | 57.50 – 62.15 | 50.44 | <0.001 |
| Risk c | -0.46 | 0.01 | -0.48 – -0.43 | -32.11 | <0.001 |
| Benefit c | 0.48 | 0.01 | 0.45 – 0.50 | 34.34 | <0.001 |
| Random Effects | |||||
| σ2 | 200.80 | ||||
| τ00 id | 144.99 | ||||
| τ00 Type | 11.95 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.537 / 0.740 | ||||
Q.4: Does perceived benefit predict support, over and above perceived risk, naturalness, and climate change method contrasts?
modA.101 <- lmer(Support ~ Naturalness.c + Risk.c + Benefit.c + (1|id) + (1|Type), 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 + (1 | id) + (1 |
## Type)
## Data: L
##
## REML criterion at convergence: 25731.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4519 -0.5042 0.0334 0.5033 3.7828
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.694 12.070
## Type (Intercept) 7.085 2.662
## Residual 195.724 13.990
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.78924 0.95878 11.87834 62.360 2.58e-16 ***
## Naturalness.c 0.14536 0.01829 2352.66247 7.947 2.93e-15 ***
## Risk.c -0.41733 0.01488 2817.01224 -28.053 < 2e-16 ***
## Benefit.c 0.46739 0.01378 3013.44379 33.922 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c
## Naturlnss.c -0.003
## Risk.c 0.006 0.347
## Benefit.c 0.000 -0.081 0.274tab_model(modA.101,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.79 | 0.96 | 57.91 – 61.67 | 62.36 | <0.001 |
| Naturalness c | 0.15 | 0.02 | 0.11 – 0.18 | 7.95 | <0.001 |
| Risk c | -0.42 | 0.01 | -0.45 – -0.39 | -28.05 | <0.001 |
| Benefit c | 0.47 | 0.01 | 0.44 – 0.49 | 33.92 | <0.001 |
| Random Effects | |||||
| σ2 | 195.72 | ||||
| τ00 id | 145.69 | ||||
| τ00 Type | 7.09 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.557 / 0.751 | ||||
Q.5: Does perceived familiarity/understanding predict support, over and above climate change method contrasts?
modA.11566 <- lmer(Support ~ FR.c + Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.11566)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ FR.c + Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27483.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5353 -0.5089 0.0419 0.5623 3.2725
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.96 16.851
## Type (Intercept) 15.75 3.968
## Residual 341.13 18.470
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.960e+01 1.405e+00 1.083e+01 42.43 2.19e-13 ***
## FR.c 2.326e-01 1.921e-02 1.313e+03 12.11 < 2e-16 ***
## Naturalness.c 3.823e-01 2.349e-02 2.306e+03 16.27 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c
## FR.c -0.025
## Naturlnss.c 0.002 -0.288tab_model(modA.11566,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.60 | 1.40 | 56.85 – 62.36 | 42.43 | <0.001 |
| FR c | 0.23 | 0.02 | 0.19 – 0.27 | 12.11 | <0.001 |
| Naturalness c | 0.38 | 0.02 | 0.34 – 0.43 | 16.27 | <0.001 |
| Random Effects | |||||
| σ2 | 341.13 | ||||
| τ00 id | 283.96 | ||||
| τ00 Type | 15.75 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.199 / 0.573 | ||||
Q.6: Does perceived familiarity/understanding predict support, over and above perceived benefit, risk, naturalness, and climate change method contrasts?
modA.115 <- lmer(Support ~ Naturalness.c + Risk.c + Benefit.c + FR.c + (1 | id) + (1|Type), data = L)
summary(modA.115)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Risk.c + Benefit.c + FR.c + (1 | id) +
## (1 | Type)
## Data: L
##
## REML criterion at convergence: 25688.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4781 -0.5076 0.0331 0.5082 3.6633
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 143.318 11.972
## Type (Intercept) 1.574 1.255
## Residual 193.465 13.909
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.59967 0.60414 19.26152 98.652 < 2e-16 ***
## Naturalness.c 0.12016 0.01814 1255.89944 6.624 5.17e-11 ***
## Risk.c -0.40623 0.01470 2105.58261 -27.641 < 2e-16 ***
## Benefit.c 0.45814 0.01371 2936.19308 33.426 < 2e-16 ***
## FR.c 0.10385 0.01342 204.82794 7.736 4.58e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c Bnft.c
## Naturlnss.c 0.004
## Risk.c 0.002 0.346
## Benefit.c 0.003 -0.056 0.250
## FR.c -0.028 -0.234 0.148 -0.112tab_model(modA.115,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.60 | 0.60 | 58.42 – 60.78 | 98.65 | <0.001 |
| Naturalness c | 0.12 | 0.02 | 0.08 – 0.16 | 6.62 | <0.001 |
| Risk c | -0.41 | 0.01 | -0.44 – -0.38 | -27.64 | <0.001 |
| Benefit c | 0.46 | 0.01 | 0.43 – 0.49 | 33.43 | <0.001 |
| FR c | 0.10 | 0.01 | 0.08 – 0.13 | 7.74 | <0.001 |
| Random Effects | |||||
| σ2 | 193.46 | ||||
| τ00 id | 143.32 | ||||
| τ00 Type | 1.57 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.579 / 0.759 | ||||
Q.7: How does benefit predict support?
modA.715656 <- lmer(Support ~ Benefit.c + (1|id) + (1|Type), data = L)
summary(modA.715656)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Benefit.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 26643.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8395 -0.5101 0.0306 0.5117 4.2361
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 162.32 12.740
## Type (Intercept) 75.73 8.702
## Residual 278.89 16.700
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.008e+01 2.798e+00 9.302e+00 21.47 3.05e-09 ***
## Benefit.c 6.188e-01 1.513e-02 2.997e+03 40.90 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Benefit.c -0.001tab_model(modA.715656,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.08 | 2.80 | 54.59 – 65.57 | 21.47 | <0.001 |
| Benefit c | 0.62 | 0.02 | 0.59 – 0.65 | 40.90 | <0.001 |
| Random Effects | |||||
| σ2 | 278.89 | ||||
| τ00 id | 162.32 | ||||
| τ00 Type | 75.73 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.341 / 0.644 | ||||
Q.8: How does risk predict support?
modA.7156566 <- lmer(Support ~ Risk.c + (1|id) + (1|Type), data = L)
summary(modA.7156566)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Risk.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 26760.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6382 -0.4865 0.0528 0.5307 3.9976
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 249.98 15.811
## Type (Intercept) 24.19 4.918
## Residual 258.86 16.089
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.84827 1.65969 10.57827 36.06 2.04e-12 ***
## Risk.c -0.61121 0.01572 2715.53500 -38.88 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Risk.c 0.006tab_model(modA.7156566,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.85 | 1.66 | 56.59 – 63.10 | 36.06 | <0.001 |
| Risk c | -0.61 | 0.02 | -0.64 – -0.58 | -38.88 | <0.001 |
| Random Effects | |||||
| σ2 | 258.86 | ||||
| τ00 id | 249.98 | ||||
| τ00 Type | 24.19 | ||||
| ICC | 0.51 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.342 / 0.680 | ||||
Naturalness
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict naturalness perception?
modA.89 <- lmer(Naturalness ~ (1|id) + (1|Type), data = L)
summary(modA.89)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25951.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5568 -0.6137 -0.0214 0.6137 3.4134
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.94 8.121
## Type (Intercept) 159.37 12.624
## Residual 256.45 16.014
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.352 4.011 9.059 10.06 3.24e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 4.01 | 32.49 – 48.22 | 10.06 | <0.001 |
| Random Effects | |||||
| σ2 | 256.45 | ||||
| τ00 id | 65.94 | ||||
| τ00 Type | 159.37 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.468 | ||||
Q.2: Do risks and benefits predict naturalness perception, over and above climate change method contrasts?
#Note: Understanding/familiarity mean score taken from two item measure.
modA.946611 <- lmer(Naturalness ~ Risk.c + Benefit.c + (1 | id) + (1|Type), data = L)
summary(modA.946611)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Risk.c + Benefit.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25548.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6055 -0.6051 0.0034 0.5958 3.3347
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.46 8.091
## Type (Intercept) 82.75 9.097
## Residual 218.85 14.794
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.18486 2.90064 9.06458 13.854 2.09e-07 ***
## Risk.c -0.24539 0.01371 2963.81148 -17.896 < 2e-16 ***
## Benefit.c 0.06804 0.01307 2778.57351 5.206 2.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c
## Risk.c 0.003
## Benefit.c 0.000 0.312tab_model(modA.946611,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.18 | 2.90 | 34.50 – 45.87 | 13.85 | <0.001 |
| Risk c | -0.25 | 0.01 | -0.27 – -0.22 | -17.90 | <0.001 |
| Benefit c | 0.07 | 0.01 | 0.04 – 0.09 | 5.21 | <0.001 |
| Random Effects | |||||
| σ2 | 218.85 | ||||
| τ00 id | 65.46 | ||||
| τ00 Type | 82.75 | ||||
| ICC | 0.40 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.132 / 0.482 | ||||
Q.3: Does risk, benefit, and familiarity/understanding predict naturalness perception, over and above understanding and climate change method contrasts?
modA.9433 <- lmer(Naturalness ~ Risk.c + Benefit.c + FR.c + (1 | id) + (1|Type), data = L)
summary(modA.9433)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Risk.c + Benefit.c + FR.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25461.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6946 -0.5940 0.0134 0.5731 3.3437
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 70.91 8.421
## Type (Intercept) 62.77 7.923
## Residual 207.88 14.418
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.91859 2.53341 9.09074 15.757 6.57e-08 ***
## Risk.c -0.22394 0.01375 2973.01620 -16.288 < 2e-16 ***
## Benefit.c 0.05265 0.01301 2821.08592 4.047 5.33e-05 ***
## FR.c 0.13698 0.01393 2865.09972 9.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c Bnft.c
## Risk.c 0.001
## Benefit.c 0.001 0.286
## FR.c -0.011 0.179 -0.112tab_model(modA.9433,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.92 | 2.53 | 34.95 – 44.89 | 15.76 | <0.001 |
| Risk c | -0.22 | 0.01 | -0.25 – -0.20 | -16.29 | <0.001 |
| Benefit c | 0.05 | 0.01 | 0.03 – 0.08 | 4.05 | <0.001 |
| FR c | 0.14 | 0.01 | 0.11 – 0.16 | 9.84 | <0.001 |
| Random Effects | |||||
| σ2 | 207.88 | ||||
| τ00 id | 70.91 | ||||
| τ00 Type | 62.77 | ||||
| ICC | 0.39 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.192 / 0.508 | ||||
Q.4: Does understanding/familiarity (mean score) predict naturalness perception, over and above climate change method contrasts?
#Note: Understanding/familiarity mean score taken from two item measure.
modA.94 <- lmer(Naturalness ~ FR.c + (1 | id) + (1|Type), data = L)
summary(modA.94)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ FR.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25776.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6685 -0.6030 -0.0056 0.5873 3.4752
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 75.33 8.679
## Type (Intercept) 107.08 10.348
## Residual 233.47 15.280
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.994e+01 3.296e+00 9.077e+00 12.12 6.55e-07 ***
## FR.c 1.971e-01 1.424e-02 2.900e+03 13.84 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## FR.c -0.009tab_model(modA.94,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.94 | 3.30 | 33.48 – 46.41 | 12.12 | <0.001 |
| FR c | 0.20 | 0.01 | 0.17 – 0.23 | 13.84 | <0.001 |
| Random Effects | |||||
| σ2 | 233.47 | ||||
| τ00 id | 75.33 | ||||
| τ00 Type | 107.08 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.077 / 0.482 | ||||
Q.5: Nat ~ Risk
#Note: Understanding/familiarity mean score taken from two item measure.
modA.9466 <- lmer(Naturalness ~ Risk.c + (1 | id) + (1|Type), data = L)
summary(modA.9466)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Risk.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25568.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8316 -0.6073 0.0142 0.5880 3.2699
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 69.11 8.313
## Type (Intercept) 83.74 9.151
## Residual 218.81 14.792
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.18716 2.91829 9.07280 13.77 2.18e-07 ***
## Risk.c -0.26845 0.01309 2987.14042 -20.51 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Risk.c 0.003tab_model(modA.9466,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.19 | 2.92 | 34.47 – 45.91 | 13.77 | <0.001 |
| Risk c | -0.27 | 0.01 | -0.29 – -0.24 | -20.51 | <0.001 |
| Random Effects | |||||
| σ2 | 218.81 | ||||
| τ00 id | 69.11 | ||||
| τ00 Type | 83.74 | ||||
| ICC | 0.41 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.126 / 0.485 | ||||
Q.6: Nat ~ Ben
modA.94667 <- lmer(Naturalness ~ Benefit.c + (1 | id) + (1|Type), data = L)
summary(modA.94667)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Benefit.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25842.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3270 -0.6200 -0.0147 0.6070 3.3697
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 59.91 7.74
## Type (Intercept) 142.52 11.94
## Residual 249.34 15.79
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.032e+01 3.794e+00 9.055e+00 10.63 2.05e-06 ***
## Benefit.c 1.409e-01 1.295e-02 2.734e+03 10.88 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Benefit.c -0.001tab_model(modA.94667,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.32 | 3.79 | 32.88 – 47.76 | 10.63 | <0.001 |
| Benefit c | 0.14 | 0.01 | 0.12 – 0.17 | 10.88 | <0.001 |
| Random Effects | |||||
| σ2 | 249.34 | ||||
| τ00 id | 59.91 | ||||
| τ00 Type | 142.52 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.030 / 0.465 | ||||
Risk
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict risk perception?
modA.82 <- lmer(Risk ~ (1|id) + (1|Type), data = L)
summary(modA.82)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27537.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5722 -0.6107 -0.0700 0.5559 3.6678
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Type (Intercept) 197.0 14.04
## Residual 391.9 19.80
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.429 4.474 9.129 7.249 4.48e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modA.82,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.43 | 4.47 | 23.66 – 41.20 | 7.25 | <0.001 |
| Random Effects | |||||
| σ2 | 391.88 | ||||
| τ00 id | 183.48 | ||||
| τ00 Type | 197.00 | ||||
| ICC | 0.49 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.493 | ||||
Q.2: Does benefit predict risk perception, over and above climate change method contrasts?
modA.88 <- lmer(Risk ~ Benefit.c + (1|id) + (1|Type), data = L)
summary(modA.88)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27226.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4624 -0.6151 -0.0514 0.5896 3.5658
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.2 13.20
## Type (Intercept) 164.0 12.81
## Residual 348.6 18.67
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.50204 4.08567 9.14213 7.955 2.11e-05 ***
## Benefit.c -0.30388 0.01662 2966.54852 -18.286 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Benefit.c -0.001tab_model(modA.88,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.50 | 4.09 | 24.49 – 40.51 | 7.96 | <0.001 |
| Benefit c | -0.30 | 0.02 | -0.34 – -0.27 | -18.29 | <0.001 |
| Random Effects | |||||
| σ2 | 348.56 | ||||
| τ00 id | 174.17 | ||||
| τ00 Type | 164.02 | ||||
| ICC | 0.49 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.086 / 0.536 | ||||
Q.3: Does naturalness predict risk perception, over and above climate change method contrasts?
modA.8 <- lmer(Risk ~ Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27132.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3202 -0.6043 -0.0232 0.5679 3.6956
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.42 13.432
## Type (Intercept) 74.95 8.657
## Residual 333.16 18.253
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.6060 2.7905 9.2052 11.69 7.92e-07 ***
## Naturalness.c -0.4630 0.0219 2895.6104 -21.14 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Naturlnss.c -0.003tab_model(modA.8,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.61 | 2.79 | 27.13 – 38.08 | 11.68 | <0.001 |
| Naturalness c | -0.46 | 0.02 | -0.51 – -0.42 | -21.14 | <0.001 |
| Random Effects | |||||
| σ2 | 333.16 | ||||
| τ00 id | 180.42 | ||||
| τ00 Type | 74.95 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.516 | ||||
Q.4: Does benefit predict risk perception, over and above naturalness and climate change method contrasts?
modA.99 <- lmer(Risk ~ Naturalness.c + Benefit.c + (1|id) + (1|Type), data = L)
summary(modA.99)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + Benefit.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 26907
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1055 -0.5880 -0.0152 0.5804 3.7304
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 176.06 13.269
## Type (Intercept) 72.26 8.501
## Residual 304.37 17.446
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.64204 2.73937 9.22291 11.92 6.57e-07 ***
## Naturalness.c -0.40073 0.02143 2876.23838 -18.70 < 2e-16 ***
## Benefit.c -0.24992 0.01608 2998.12337 -15.54 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln.
## Naturlnss.c -0.003
## Benefit.c -0.001 -0.188tab_model(modA.99,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.64 | 2.74 | 27.27 – 38.01 | 11.92 | <0.001 |
| Naturalness c | -0.40 | 0.02 | -0.44 – -0.36 | -18.70 | <0.001 |
| Benefit c | -0.25 | 0.02 | -0.28 – -0.22 | -15.54 | <0.001 |
| Random Effects | |||||
| σ2 | 304.37 | ||||
| τ00 id | 176.06 | ||||
| τ00 Type | 72.26 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.211 / 0.566 | ||||
Q.5: Does familiarity/understanding predict risk perception, over and above climate change method contrasts?
modA.8666 <- lmer(Risk ~ FR.c + (1|id) + (1|Type), data = L)
summary(modA.8666)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ FR.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27384.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4712 -0.5859 -0.0533 0.5354 4.1431
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 187.2 13.68
## Type (Intercept) 127.6 11.30
## Residual 365.8 19.13
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.92751 3.61576 9.16767 9.107 6.83e-06 ***
## FR.c -0.24003 0.01871 2956.34056 -12.832 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## FR.c -0.011tab_model(modA.8666,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.93 | 3.62 | 25.84 – 40.02 | 9.11 | <0.001 |
| FR c | -0.24 | 0.02 | -0.28 – -0.20 | -12.83 | <0.001 |
| Random Effects | |||||
| σ2 | 365.84 | ||||
| τ00 id | 187.23 | ||||
| τ00 Type | 127.63 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.070 / 0.500 | ||||
Q.6: Does understanding/familiarity predict risk perception, over and above naturalness, benefit, and climate change method contrasts?
modA.100 <- lmer(Risk ~ Naturalness.c + Benefit.c + FR.c + (1|id) + (1|Type), 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 + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 26868.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5461 -0.6065 -0.0057 0.5658 3.9443
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.46 13.21
## Type (Intercept) 60.06 7.75
## Residual 299.78 17.31
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.87341 2.50624 9.26184 13.117 2.72e-07 ***
## Naturalness.c -0.36707 0.02188 2926.84707 -16.780 < 2e-16 ***
## Benefit.c -0.23538 0.01612 2994.55937 -14.603 < 2e-16 ***
## FR.c -0.11990 0.01789 2869.59603 -6.704 2.44e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c
## Naturlnss.c 0.000
## Benefit.c 0.001 -0.150
## FR.c -0.014 -0.236 -0.135tab_model(modA.100,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.87 | 2.51 | 27.96 – 37.79 | 13.12 | <0.001 |
| Naturalness c | -0.37 | 0.02 | -0.41 – -0.32 | -16.78 | <0.001 |
| Benefit c | -0.24 | 0.02 | -0.27 – -0.20 | -14.60 | <0.001 |
| FR c | -0.12 | 0.02 | -0.15 – -0.08 | -6.70 | <0.001 |
| Random Effects | |||||
| σ2 | 299.78 | ||||
| τ00 id | 174.46 | ||||
| τ00 Type | 60.06 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.243 / 0.575 | ||||
Benefit
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict perceived benefit?
modA.109 <- lmer(Ben ~ (1|id) + (1|Type), data = L)
summary(modA.109)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27740.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4202 -0.5167 0.0697 0.5663 3.1505
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.16 16.827
## Type (Intercept) 41.71 6.458
## Residual 381.93 19.543
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.21 2.14 10.05 27.2 9.62e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modA.109,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.21 | 2.14 | 54.02 – 62.41 | 27.20 | <0.001 |
| Random Effects | |||||
| σ2 | 381.93 | ||||
| τ00 id | 283.16 | ||||
| τ00 Type | 41.71 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.460 | ||||
Q.2: How does risk perception predict benefit, over and above climate change method contrasts?
modA.113 <- lmer(Ben ~ Risk.c + (1|id) + (1|Type), data = L)
summary(modA.113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27419.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5527 -0.5112 0.0725 0.5394 3.2070
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.69 16.269
## Type (Intercept) 22.33 4.726
## Residual 339.24 18.419
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.0172 1.6158 10.6968 35.91 1.69e-12 ***
## Risk.c -0.3283 0.0176 2605.9916 -18.65 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Risk.c 0.006tab_model(modA.113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.02 | 1.62 | 54.85 – 61.19 | 35.91 | <0.001 |
| Risk c | -0.33 | 0.02 | -0.36 – -0.29 | -18.65 | <0.001 |
| Random Effects | |||||
| σ2 | 339.24 | ||||
| τ00 id | 264.69 | ||||
| τ00 Type | 22.33 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.113 / 0.520 | ||||
Q.3: How does naturalness predict benefit, over and above climate change method contrasts?
modA.110 <- lmer(Ben ~ Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.110)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27635.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4640 -0.5131 0.0589 0.5595 3.2689
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 266.46 16.324
## Type (Intercept) 19.33 4.396
## Residual 371.96 19.286
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.1167 1.5241 10.8684 38.13 6.34e-13 ***
## Naturalness.c 0.2509 0.0233 2056.6656 10.77 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Naturlnss.c -0.005tab_model(modA.110,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.12 | 1.52 | 55.13 – 61.11 | 38.13 | <0.001 |
| Naturalness c | 0.25 | 0.02 | 0.21 – 0.30 | 10.77 | <0.001 |
| Random Effects | |||||
| σ2 | 371.96 | ||||
| τ00 id | 266.46 | ||||
| τ00 Type | 19.33 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.043 / 0.459 | ||||
Q.4: How does famililiarity/understanding predict benefit, over and above climate change method contrasts?
modA.11766 <- lmer(Ben ~ FR.c + (1|id) + (1|Type), data = L)
summary(modA.11766)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ FR.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27642.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5108 -0.4992 0.0584 0.5578 3.1241
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 279.61 16.722
## Type (Intercept) 14.98 3.871
## Residual 368.02 19.184
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.81785 1.37893 11.60654 41.93 4.92e-14 ***
## FR.c 0.19946 0.01886 943.69013 10.58 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## FR.c -0.026tab_model(modA.11766,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.82 | 1.38 | 55.11 – 60.52 | 41.93 | <0.001 |
| FR c | 0.20 | 0.02 | 0.16 – 0.24 | 10.58 | <0.001 |
| Random Effects | |||||
| σ2 | 368.02 | ||||
| τ00 id | 279.61 | ||||
| τ00 Type | 14.98 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.051 / 0.473 | ||||
Q.5: How does risk perception predict benefit, over and above naturalness and climate change method contrasts?
modA.114 <- lmer(Ben ~ Risk.c + Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27405.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5761 -0.5129 0.0682 0.5435 3.3057
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 257.94 16.060
## Type (Intercept) 21.07 4.591
## Residual 339.31 18.420
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.99758 1.57423 10.68237 36.842 1.33e-12 ***
## Risk.c -0.29694 0.01894 2934.73084 -15.678 < 2e-16 ***
## Naturalness.c 0.10627 0.02418 2672.23334 4.394 1.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c
## Risk.c 0.005
## Naturlnss.c -0.003 0.376tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.00 | 1.57 | 54.91 – 61.08 | 36.84 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.33 – -0.26 | -15.68 | <0.001 |
| Naturalness c | 0.11 | 0.02 | 0.06 – 0.15 | 4.39 | <0.001 |
| Random Effects | |||||
| σ2 | 339.31 | ||||
| τ00 id | 257.94 | ||||
| τ00 Type | 21.07 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.125 / 0.520 | ||||
Q.6: How does understanding/familiarity predict benefit, over and above risk perception, naturalness, and climate change method contrasts?
modA.117 <- lmer(Ben ~ Risk.c + Naturalness.c + FR.c + (1|id) + (1|Type), data = L)
summary(modA.117)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + Naturalness.c + FR.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27380.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6109 -0.5016 0.0707 0.5471 3.3377
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 257.03 16.03
## Type (Intercept) 16.89 4.11
## Residual 335.50 18.32
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.80706 1.43490 10.95546 40.286 2.93e-13 ***
## Risk.c -0.27937 0.01907 2943.22940 -14.648 < 2e-16 ***
## Naturalness.c 0.07695 0.02457 2725.39995 3.132 0.00175 **
## FR.c 0.10810 0.01918 1574.87965 5.636 2.05e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c Ntrln.
## Risk.c 0.001
## Naturlnss.c 0.002 0.332
## FR.c -0.024 0.160 -0.211tab_model(modA.117,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.81 | 1.43 | 54.99 – 60.62 | 40.29 | <0.001 |
| Risk c | -0.28 | 0.02 | -0.32 – -0.24 | -14.65 | <0.001 |
| Naturalness c | 0.08 | 0.02 | 0.03 – 0.13 | 3.13 | 0.002 |
| FR c | 0.11 | 0.02 | 0.07 – 0.15 | 5.64 | <0.001 |
| Random Effects | |||||
| σ2 | 335.50 | ||||
| τ00 id | 257.03 | ||||
| τ00 Type | 16.89 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.146 / 0.530 | ||||
Familiarity/Understanding (Mean score)
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict understanding and familiarity (mean score)?
modA.12 <- lmer(FR ~ (1|id) + (1|Type), data = L)
summary(modA.12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27196.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0398 -0.5867 -0.0115 0.5977 3.0985
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.6 14.31
## Type (Intercept) 418.7 20.46
## Residual 329.5 18.15
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.473 6.495 9.081 8.387 1.43e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modA.12,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.47 | 6.49 | 41.74 – 67.21 | 8.39 | <0.001 |
| Random Effects | |||||
| σ2 | 329.46 | ||||
| τ00 id | 204.65 | ||||
| τ00 Type | 418.66 | ||||
| ICC | 0.65 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.654 | ||||
Q.2: How does naturalness predict understanding and familiarity (mean score), over and above climate change method contrasts?
modA.130 <- lmer(FR ~ Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.130)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 26990.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9390 -0.5692 0.0046 0.5977 3.1914
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213.5 14.61
## Type (Intercept) 323.8 17.99
## Residual 297.7 17.25
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.435e+01 5.718e+00 9.101e+00 9.506 5.03e-06 ***
## Naturalness.c 3.171e-01 2.123e-02 2.851e+03 14.937 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Naturlnss.c -0.001tab_model(modA.130,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.35 | 5.72 | 43.14 – 65.56 | 9.51 | <0.001 |
| Naturalness c | 0.32 | 0.02 | 0.28 – 0.36 | 14.94 | <0.001 |
| Random Effects | |||||
| σ2 | 297.71 | ||||
| τ00 id | 213.52 | ||||
| τ00 Type | 323.77 | ||||
| ICC | 0.64 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.053 / 0.662 | ||||
Moderators of Naturalness and Support
Age
modA.110000006 <- lmer(Support ~ Age.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.110000006)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Age.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27316.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5224 -0.5295 0.0384 0.5436 3.2562
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.06 16.824
## Type (Intercept) 48.23 6.945
## Residual 354.72 18.834
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.018e+01 2.287e+00 9.738e+00 26.317 2.25e-10 ***
## Age.c -2.010e-01 3.926e-02 9.989e+02 -5.121 3.65e-07 ***
## Naturalness.c 4.400e-01 2.340e-02 2.641e+03 18.802 < 2e-16 ***
## Age.c:Naturalness.c 6.204e-03 1.213e-03 2.628e+03 5.115 3.36e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Age.c Ntrln.
## Age.c -0.001
## Naturlnss.c -0.004 0.054
## Ag.c:Ntrln. 0.014 -0.028 -0.060tab_model(modA.110000006,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.18 | 2.29 | 55.69 – 64.66 | 26.32 | <0.001 |
| Age c | -0.20 | 0.04 | -0.28 – -0.12 | -5.12 | <0.001 |
| Naturalness c | 0.44 | 0.02 | 0.39 – 0.49 | 18.80 | <0.001 |
| Age c × Naturalness c | 0.01 | 0.00 | 0.00 – 0.01 | 5.12 | <0.001 |
| Random Effects | |||||
| σ2 | 354.72 | ||||
| τ00 id | 283.06 | ||||
| τ00 Type | 48.23 | ||||
| ICC | 0.48 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.555 | ||||
confint(modA.110000006)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 15.761013795 17.906616783
## .sig02 4.366448360 11.154730613
## .sigma 18.255668956 19.428164355
## (Intercept) 55.501268970 64.857800112
## Age.c -0.277955673 -0.124039823
## Naturalness.c 0.394469793 0.487140614
## Age.c:Naturalness.c 0.003827503 0.008583623library (ggplot2)
# Interaction Plot
# +1 SD, -1 SD Aversion to Tampering with Nature Score
L$Age_sd <- sd(L$Dem_Age, na.rm = TRUE)
L$MinusOneSDAge <- (L$Age.c + L$Age_sd)
L$PlusOneSDAge <- (L$Age.c - L$Age_sd)
L$MinusOneSDAge <- as.numeric(as.character(L$MinusOneSDAge))
L$PlusOneSDAge <- as.numeric(as.character(L$PlusOneSDAge))
#Look at coeffients for interaction at +1/-1 SD
M.MinusOneAge <- lmer(Support ~ MinusOneSDAge*Naturalness.c + (1|id) + (1|Type), data = L)
M.PlusOneAge <- lmer(Support ~ PlusOneSDAge*Naturalness.c + (1|id) + (1|Type), data = L)
summary(M.MinusOneAge)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ MinusOneSDAge * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27316.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5224 -0.5295 0.0384 0.5436 3.2562
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.06 16.824
## Type (Intercept) 48.23 6.945
## Residual 354.72 18.834
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.343e+01 2.374e+00 1.131e+01 26.722 1.42e-11
## MinusOneSDAge -2.010e-01 3.926e-02 9.989e+02 -5.121 3.65e-07
## Naturalness.c 3.396e-01 3.145e-02 2.681e+03 10.798 < 2e-16
## MinusOneSDAge:Naturalness.c 6.204e-03 1.213e-03 2.628e+03 5.115 3.36e-07
##
## (Intercept) ***
## MinusOneSDAge ***
## Naturalness.c ***
## MinusOneSDAge:Naturalness.c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) MnOSDA Ntrln.
## MinusOnSDAg -0.268
## Naturlnss.c -0.027 0.058
## MnsOnSDA:N. 0.021 -0.028 -0.669tab_model(M.MinusOneAge,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.43 | 2.37 | 58.78 – 68.09 | 26.72 | <0.001 |
| MinusOneSDAge | -0.20 | 0.04 | -0.28 – -0.12 | -5.12 | <0.001 |
| Naturalness c | 0.34 | 0.03 | 0.28 – 0.40 | 10.80 | <0.001 |
|
MinusOneSDAge × Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 5.12 | <0.001 |
| Random Effects | |||||
| σ2 | 354.72 | ||||
| τ00 id | 283.06 | ||||
| τ00 Type | 48.23 | ||||
| ICC | 0.48 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.555 | ||||
summary(M.PlusOneAge)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ PlusOneSDAge * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27316.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5224 -0.5295 0.0384 0.5436 3.2562
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.06 16.824
## Type (Intercept) 48.23 6.945
## Residual 354.72 18.834
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.692e+01 2.373e+00 1.129e+01 23.987 4.82e-11
## PlusOneSDAge -2.010e-01 3.926e-02 9.989e+02 -5.121 3.65e-07
## Naturalness.c 5.405e-01 2.964e-02 2.702e+03 18.239 < 2e-16
## PlusOneSDAge:Naturalness.c 6.204e-03 1.213e-03 2.628e+03 5.115 3.36e-07
##
## (Intercept) ***
## PlusOneSDAge ***
## Naturalness.c ***
## PlusOneSDAge:Naturalness.c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PlOSDA Ntrln.
## PlusOneSDAg 0.267
## Naturlnss.c 0.012 0.024
## PlsOnSDA:N. 0.005 -0.028 0.615tab_model(M.PlusOneAge,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.92 | 2.37 | 52.27 – 61.57 | 23.99 | <0.001 |
| PlusOneSDAge | -0.20 | 0.04 | -0.28 – -0.12 | -5.12 | <0.001 |
| Naturalness c | 0.54 | 0.03 | 0.48 – 0.60 | 18.24 | <0.001 |
|
PlusOneSDAge × Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 5.12 | <0.001 |
| Random Effects | |||||
| σ2 | 354.72 | ||||
| τ00 id | 283.06 | ||||
| τ00 Type | 48.23 | ||||
| ICC | 0.48 | ||||
| N id | 997 | ||||
| N Type | 10 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.555 | ||||
# Extract predicted values from the models
L$M.PlusOne.predAge <- predict(M.PlusOneAge, allow.new.levels = TRUE, newdata = L)
L$M.MinusOne.predAge <- predict(M.MinusOneAge, allow.new.levels = TRUE, newdata = L)
# Plot the predicted values against the original data
library(ggplot2)
# Create plot of aversion to tampering with nature interacting with naturalness in predicting support of technologies.
m.w4 <- lmer(Support ~ Age.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary (m.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Age.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27316.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5224 -0.5295 0.0384 0.5436 3.2562
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.06 16.824
## Type (Intercept) 48.23 6.945
## Residual 354.72 18.834
## Number of obs: 2991, groups: id, 997; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.018e+01 2.287e+00 9.738e+00 26.317 2.25e-10 ***
## Age.c -2.010e-01 3.926e-02 9.989e+02 -5.121 3.65e-07 ***
## Naturalness.c 4.400e-01 2.340e-02 2.641e+03 18.802 < 2e-16 ***
## Age.c:Naturalness.c 6.204e-03 1.213e-03 2.628e+03 5.115 3.36e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Age.c Ntrln.
## Age.c -0.001
## Naturlnss.c -0.004 0.054
## Ag.c:Ntrln. 0.014 -0.028 -0.060#create plot
p2 <- plot_model(m.w4, type = "pred",
terms = c("Naturalness.c", "Age.c [-21.50, 21.50]")) +
ggtitle("") +
ylab("Support (0-100)") +
xlab("Naturalness 0-100 (Mean Centered at 39.98)") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.background = element_rect(fill = "white", color = "white"))
p2 <- p2 + labs(color = "Age")
(p.w4 <- p2 +
scale_color_manual(labels = c("-1 SD", "+1 SD"),
values = c("blue", "red")) +
scale_fill_manual(values = c("blue", "red")) +
scale_y_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100), limits = c(20, 100)) +
scale_x_continuous(breaks = c(-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50),
limits = c(-50, 50)))## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.## Warning: Removed 6 rows containing missing values (`geom_line()`).
Aversion to Tampering with Nature (SUPPORT)
modA.11 <- lmer(Support ~ ATNS_Score.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ ATNS_Score.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5823 -0.5342 0.0326 0.5438 3.3820
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.70 16.362
## Type (Intercept) 49.01 7.001
## Residual 350.10 18.711
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.020e+01 2.299e+00 9.670e+00 26.185 2.65e-10
## ATNS_Score.c -2.189e-01 2.880e-02 1.008e+03 -7.601 6.71e-14
## Naturalness.c 4.282e-01 2.311e-02 2.697e+03 18.529 < 2e-16
## ATNS_Score.c:Naturalness.c 5.545e-03 7.726e-04 2.708e+03 7.177 9.16e-13
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## ATNS_Score.c:Naturalness.c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. Ntrln.
## ATNS_Scor.c 0.000
## Naturlnss.c -0.005 0.041
## ATNS_Sc.:N. 0.009 0.033 -0.097tab_model(modA.11,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.20 | 2.30 | 55.69 – 64.71 | 26.19 | <0.001 |
| ATNS Score c | -0.22 | 0.03 | -0.28 – -0.16 | -7.60 | <0.001 |
| Naturalness c | 0.43 | 0.02 | 0.38 – 0.47 | 18.53 | <0.001 |
|
ATNS Score c × Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 7.18 | <0.001 |
| Random Effects | |||||
| σ2 | 350.10 | ||||
| τ00 id | 267.70 | ||||
| τ00 Type | 49.01 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.161 / 0.560 | ||||
confint(modA.11)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 15.317619663 17.422919792
## .sig02 4.407071920 11.237385560
## .sigma 18.139326047 19.298179967
## (Intercept) 55.499601239 64.909641145
## ATNS_Score.c -0.275367807 -0.162433732
## Naturalness.c 0.383173112 0.474627385
## ATNS_Score.c:Naturalness.c 0.004031156 0.007060033library (ggplot2)
# Interaction Plot
# +1 SD, -1 SD Aversion to Tampering with Nature Score
L$ATNS_sd <- sd(L$ATNS_Score, na.rm = TRUE)
L$MinusOneSD <- (L$ATNS_Score.c + L$ATNS_sd)
L$PlusOneSD <- (L$ATNS_Score.c - L$ATNS_sd)
L$MinusOneSD <- as.numeric(as.character(L$MinusOneSD))
L$PlusOneSD <- as.numeric(as.character(L$PlusOneSD))
#Look at coeffients for interaction at +1/-1 SD
M.MinusOne <- lmer(Support ~ MinusOneSD*Naturalness.c + (1|id) + (1|Type), data = L)
M.PlusOne <- lmer(Support ~ PlusOneSD*Naturalness.c + (1|id) + (1|Type), data = L)
summary(M.MinusOne)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ MinusOneSD * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5823 -0.5342 0.0326 0.5438 3.3820
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.70 16.362
## Type (Intercept) 49.01 7.001
## Residual 350.10 18.711
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.491e+01 2.381e+00 1.112e+01 27.262 1.55e-11 ***
## MinusOneSD -2.189e-01 2.880e-02 1.008e+03 -7.601 6.71e-14 ***
## Naturalness.c 3.090e-01 2.974e-02 2.742e+03 10.391 < 2e-16 ***
## MinusOneSD:Naturalness.c 5.545e-03 7.726e-04 2.708e+03 7.177 9.16e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) MnsOSD Ntrln.
## MinusOneSD -0.260
## Naturlnss.c -0.012 0.013
## MnsOnSD:Nt. 0.000 0.033 -0.634tab_model(M.MinusOne,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 64.91 | 2.38 | 60.24 – 69.57 | 27.26 | <0.001 |
| MinusOneSD | -0.22 | 0.03 | -0.28 – -0.16 | -7.60 | <0.001 |
| Naturalness c | 0.31 | 0.03 | 0.25 – 0.37 | 10.39 | <0.001 |
|
MinusOneSD × Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 7.18 | <0.001 |
| Random Effects | |||||
| σ2 | 350.10 | ||||
| τ00 id | 267.70 | ||||
| τ00 Type | 49.01 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.161 / 0.560 | ||||
summary(M.PlusOne)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ PlusOneSD * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5823 -0.5342 0.0326 0.5438 3.3820
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.70 16.362
## Type (Intercept) 49.01 7.001
## Residual 350.10 18.711
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.550e+01 2.381e+00 1.112e+01 23.308 8.54e-11 ***
## PlusOneSD -2.189e-01 2.880e-02 1.008e+03 -7.601 6.71e-14 ***
## Naturalness.c 5.473e-01 2.711e-02 2.753e+03 20.190 < 2e-16 ***
## PlusOneSD:Naturalness.c 5.545e-03 7.726e-04 2.708e+03 7.177 9.16e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PlsOSD Ntrln.
## PlusOneSD 0.260
## Naturlnss.c 0.016 0.055
## PlsOnSD:Nt. 0.017 0.033 0.529tab_model(M.PlusOne,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 55.50 | 2.38 | 50.83 – 60.16 | 23.31 | <0.001 |
| PlusOneSD | -0.22 | 0.03 | -0.28 – -0.16 | -7.60 | <0.001 |
| Naturalness c | 0.55 | 0.03 | 0.49 – 0.60 | 20.19 | <0.001 |
| PlusOneSD × Naturalness c | 0.01 | 0.00 | 0.00 – 0.01 | 7.18 | <0.001 |
| Random Effects | |||||
| σ2 | 350.10 | ||||
| τ00 id | 267.70 | ||||
| τ00 Type | 49.01 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.161 / 0.560 | ||||
# Extract predicted values from the models
L$M.PlusOne.pred <- predict(M.PlusOne, allow.new.levels = TRUE, newdata = L)
L$M.MinusOne.pred <- predict(M.MinusOne, allow.new.levels = TRUE, newdata = L)
# Plot the predicted values against the original data
library(ggplot2)
# Create plot of aversion to tampering with nature interacting with naturalness in predicting support of technologies.
m.w3 <- lmer(Support ~ ATNS_Score.c*Naturalness.c + Renewable + (1|id) + (1|Type), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary (m.w3 )## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ ATNS_Score.c * Naturalness.c + Renewable + (1 | id) +
## (1 | Type)
## Data: L
##
## REML criterion at convergence: 27513.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5763 -0.5315 0.0339 0.5414 3.3866
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 267.69 16.361
## Type (Intercept) 36.64 6.053
## Residual 350.11 18.711
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.100e+01 2.053e+00 8.725e+00 29.716 4.51e-10
## ATNS_Score.c -2.189e-01 2.880e-02 1.008e+03 -7.600 6.73e-14
## Naturalness.c 4.289e-01 2.306e-02 2.574e+03 18.596 < 2e-16
## Renewable 7.829e+00 3.975e+00 7.672e+00 1.969 0.086
## ATNS_Score.c:Naturalness.c 5.541e-03 7.726e-04 2.708e+03 7.172 9.46e-13
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## Renewable .
## ATNS_Score.c:Naturalness.c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. Ntrln. Renwbl
## ATNS_Scor.c 0.000
## Naturlnss.c -0.011 0.040
## Renewable 0.198 -0.001 -0.032
## ATNS_Sc.:N. 0.009 0.033 -0.097 -0.003
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling#create plot
p <- plot_model(m.w3, type = "pred",
terms = c("Naturalness.c", "ATNS_Score.c [-21.50, 21.50]")) +
ggtitle("") +
ylab("Support (0-100)") +
xlab("Naturalness 0-100 (Mean Centered at 39.98)") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.background = element_rect(fill = "white", color = "white"))
p <- p + labs(color = "Aversion to Tampering with Nature")
(p.w3 <- p +
scale_color_manual(labels = c("-1 SD", "+1 SD"),
values = c("blue", "red")) +
scale_fill_manual(values = c("blue", "red")) +
scale_y_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100), limits = c(20, 100)) +
scale_x_continuous(breaks = c(-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50),
limits = c(-50, 50)))## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.## Warning: Removed 6 rows containing missing values (`geom_line()`).
Aversion to Tampering with Nature (Naturalness)
modA.11bonus1 <- lmer(Naturalness ~ ATNS_Score.c + (1|id) + (1|Type), data = L)
summary(modA.11bonus1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ ATNS_Score.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 25948.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6435 -0.6157 -0.0282 0.6100 3.4151
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 64.68 8.042
## Type (Intercept) 159.19 12.617
## Residual 256.46 16.014
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.35200 4.00877 9.05756 10.066 3.23e-06 ***
## ATNS_Score.c -0.05509 0.01798 1004.29852 -3.064 0.00224 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ATNS_Scor.c 0.000tab_model(modA.11bonus1,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 4.01 | 32.49 – 48.21 | 10.07 | <0.001 |
| ATNS Score c | -0.06 | 0.02 | -0.09 – -0.02 | -3.06 | 0.002 |
| Random Effects | |||||
| σ2 | 256.46 | ||||
| τ00 id | 64.68 | ||||
| τ00 Type | 159.19 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.003 / 0.468 | ||||
confint(modA.11bonus1)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 7.13820769 8.90749908
## .sig02 8.13023791 20.02938788
## .sigma 15.53139665 16.52310933
## (Intercept) 32.12396760 48.58112423
## ATNS_Score.c -0.09033827 -0.01983642Aversion to Tampering with Nature (Risk and Benefit)
modA.11bonus2 <- lmer(Risk ~ Benefit.c + ATNS_Score.c + (1|id) + (1|Type), data = L)
summary(modA.11bonus2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + ATNS_Score.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27128.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7076 -0.6119 -0.0453 0.5879 3.4449
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 144.7 12.03
## Type (Intercept) 164.8 12.84
## Residual 349.1 18.69
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.49797 4.09190 9.11306 7.942 2.18e-05 ***
## Benefit.c -0.28730 0.01633 2924.27674 -17.595 < 2e-16 ***
## ATNS_Score.c 0.24953 0.02379 1002.03616 10.491 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c
## Benefit.c -0.001
## ATNS_Scor.c 0.000 0.083tab_model(modA.11bonus2,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.50 | 4.09 | 24.47 – 40.52 | 7.94 | <0.001 |
| Benefit c | -0.29 | 0.02 | -0.32 – -0.26 | -17.60 | <0.001 |
| ATNS Score c | 0.25 | 0.02 | 0.20 – 0.30 | 10.49 | <0.001 |
| Random Effects | |||||
| σ2 | 349.14 | ||||
| τ00 id | 144.73 | ||||
| τ00 Type | 164.82 | ||||
| ICC | 0.47 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.125 / 0.536 | ||||
confint(modA.11bonus2)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 11.0197075 13.0285790
## .sig02 8.2549457 20.4036871
## .sigma 18.1176554 19.2774155
## (Intercept) 24.0993735 40.8943026
## Benefit.c -0.3195323 -0.2551751
## ATNS_Score.c 0.2028875 0.2961552modA.11bonus3 <- lmer(Ben ~ Risk.c + ATNS_Score.c + (1|id) + (1|Type), data = L)
summary(modA.11bonus3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + ATNS_Score.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27423.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5244 -0.5180 0.0706 0.5399 3.2272
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.68 16.269
## Type (Intercept) 22.35 4.727
## Residual 339.26 18.419
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.01896 1.61624 10.69756 35.898 1.7e-12 ***
## Risk.c -0.32524 0.01788 2550.23120 -18.190 < 2e-16 ***
## ATNS_Score.c -0.02795 0.02896 1053.05934 -0.965 0.335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c
## Risk.c 0.006
## ATNS_Scor.c -0.001 -0.176tab_model(modA.11bonus3,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.02 | 1.62 | 54.85 – 61.19 | 35.90 | <0.001 |
| Risk c | -0.33 | 0.02 | -0.36 – -0.29 | -18.19 | <0.001 |
| ATNS Score c | -0.03 | 0.03 | -0.08 – 0.03 | -0.96 | 0.335 |
| Random Effects | |||||
| σ2 | 339.26 | ||||
| τ00 id | 264.68 | ||||
| τ00 Type | 22.35 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.115 / 0.520 | ||||
confint(modA.11bonus3)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 15.23823277 17.31582126
## .sig02 2.91314320 7.67587677
## .sigma 17.86024733 19.00089858
## (Intercept) 54.72365934 61.31496696
## Risk.c -0.36021878 -0.29020449
## ATNS_Score.c -0.08470647 0.02883807Climate Change Belief
modA.110000000 <- lmer(Support ~ CCBelief_Score.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.110000000)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CCBelief_Score.c * Naturalness.c + (1 | id) + (1 |
## Type)
## Data: L
##
## REML criterion at convergence: 27302.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7501 -0.5443 0.0452 0.5458 3.1976
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.61 13.439
## Type (Intercept) 49.47 7.034
## Residual 355.73 18.861
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.002e+01 2.291e+00 9.382e+00 26.201 4.3e-10
## CCBelief_Score.c 4.583e-01 2.326e-02 1.019e+03 19.701 < 2e-16
## Naturalness.c 4.371e-01 2.249e-02 2.820e+03 19.440 < 2e-16
## CCBelief_Score.c:Naturalness.c 1.463e-03 7.159e-04 2.797e+03 2.043 0.0411
##
## (Intercept) ***
## CCBelief_Score.c ***
## Naturalness.c ***
## CCBelief_Score.c:Naturalness.c *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. Ntrln.
## CCBlf_Scr.c -0.001
## Naturlnss.c -0.004 -0.036
## CCBlf_S.:N. -0.009 0.096 0.036tab_model(modA.110000000,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.02 | 2.29 | 55.53 – 64.51 | 26.20 | <0.001 |
| CCBelief Score c | 0.46 | 0.02 | 0.41 – 0.50 | 19.70 | <0.001 |
| Naturalness c | 0.44 | 0.02 | 0.39 – 0.48 | 19.44 | <0.001 |
|
CCBelief Score c × Naturalness c |
0.00 | 0.00 | 0.00 – 0.00 | 2.04 | 0.041 |
| Random Effects | |||||
| σ2 | 355.73 | ||||
| τ00 id | 180.61 | ||||
| τ00 Type | 49.47 | ||||
| ICC | 0.39 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.268 / 0.555 | ||||
confint(modA.110000000)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 1.242458e+01 14.45374878
## .sig02 4.429316e+00 11.27627061
## .sigma 1.828471e+01 19.45315696
## (Intercept) 5.532953e+01 64.71510403
## CCBelief_Score.c 4.127114e-01 0.50392343
## Naturalness.c 3.933521e-01 0.48228787
## CCBelief_Score.c:Naturalness.c 6.038998e-05 0.00286732library (ggplot2)
# Interaction Plot
L$CCBelief_Score.c <- L$CCB_Score - 81.61
# +1 SD, -1 SD Aversion to Tampering with Nature Score
L$CCB_sd <- sd(L$CCB_Score, na.rm = TRUE)
L$MinusOneSDCCB <- (L$CCBelief_Score.c + L$CCB_sd)
L$PlusOneSDCCB <- (L$CCBelief_Score.c - L$CCB_sd)
L$MinusOneSDCCB <- as.numeric(as.character(L$MinusOneSDCCB))
L$PlusOneSDCCB <- as.numeric(as.character(L$PlusOneSDCCB))
#Look at coeffients for interaction at +1/-1 SD
M.MinusOneCCB <- lmer(Support ~ MinusOneSDCCB*Naturalness.c + (1|id) + (1|Type), data = L)
M.PlusOneCCB <- lmer(Support ~ PlusOneSDCCB*Naturalness.c + (1|id) + (1|Type), data = L)
summary(M.MinusOneCCB)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ MinusOneSDCCB * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27302.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7501 -0.5443 0.0452 0.5458 3.1976
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.61 13.439
## Type (Intercept) 49.47 7.034
## Residual 355.73 18.861
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.922e+01 2.356e+00 1.049e+01 20.893 6.86e-10 ***
## MinusOneSDCCB 4.583e-01 2.326e-02 1.019e+03 19.701 < 2e-16 ***
## Naturalness.c 4.027e-01 2.762e-02 2.877e+03 14.578 < 2e-16 ***
## MinusOneSDCCB:Naturalness.c 1.463e-03 7.159e-04 2.797e+03 2.043 0.0411 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) MnOSDCCB Ntrln.
## MinsOnSDCCB -0.233
## Naturlnss.c 0.023 -0.088
## MnOSDCCB:N. -0.031 0.096 -0.582tab_model(M.MinusOneCCB,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 49.22 | 2.36 | 44.60 – 53.84 | 20.89 | <0.001 |
| MinusOneSDCCB | 0.46 | 0.02 | 0.41 – 0.50 | 19.70 | <0.001 |
| Naturalness c | 0.40 | 0.03 | 0.35 – 0.46 | 14.58 | <0.001 |
|
MinusOneSDCCB × Naturalness c |
0.00 | 0.00 | 0.00 – 0.00 | 2.04 | 0.041 |
| Random Effects | |||||
| σ2 | 355.73 | ||||
| τ00 id | 180.61 | ||||
| τ00 Type | 49.47 | ||||
| ICC | 0.39 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.268 / 0.555 | ||||
summary(M.PlusOneCCB)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ PlusOneSDCCB * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27302.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7501 -0.5443 0.0452 0.5458 3.1976
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.61 13.439
## Type (Intercept) 49.47 7.034
## Residual 355.73 18.861
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.082e+01 2.355e+00 1.048e+01 30.072 1.63e-11 ***
## PlusOneSDCCB 4.583e-01 2.326e-02 1.019e+03 19.701 < 2e-16 ***
## Naturalness.c 4.716e-01 2.859e-02 2.850e+03 16.494 < 2e-16 ***
## PlusOneSDCCB:Naturalness.c 1.463e-03 7.159e-04 2.797e+03 2.043 0.0411 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PlOSDCCB Ntrln.
## PlusOnSDCCB 0.232
## Naturlnss.c -0.001 0.029
## PlOSDCCB:N. 0.014 0.096 0.618tab_model(M.PlusOneCCB,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 70.82 | 2.35 | 66.20 – 75.44 | 30.07 | <0.001 |
| PlusOneSDCCB | 0.46 | 0.02 | 0.41 – 0.50 | 19.70 | <0.001 |
| Naturalness c | 0.47 | 0.03 | 0.42 – 0.53 | 16.49 | <0.001 |
|
PlusOneSDCCB × Naturalness c |
0.00 | 0.00 | 0.00 – 0.00 | 2.04 | 0.041 |
| Random Effects | |||||
| σ2 | 355.73 | ||||
| τ00 id | 180.61 | ||||
| τ00 Type | 49.47 | ||||
| ICC | 0.39 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.268 / 0.555 | ||||
# Extract predicted values from the models
L$M.PlusOne.predCCB <- predict(M.PlusOneCCB, allow.new.levels = TRUE, newdata = L)
L$M.MinusOne.predCCB <- predict(M.MinusOneCCB, allow.new.levels = TRUE, newdata = L)
# Plot the predicted values against the original data
library(ggplot2)
# Create plot of aversion to tampering with nature interacting with naturalness in predicting support of technologies.
m.w5 <- lmer(Support ~ CCBelief_Score.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary (m.w5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CCBelief_Score.c * Naturalness.c + (1 | id) + (1 |
## Type)
## Data: L
##
## REML criterion at convergence: 27302.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7501 -0.5443 0.0452 0.5458 3.1976
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 180.61 13.439
## Type (Intercept) 49.47 7.034
## Residual 355.73 18.861
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.002e+01 2.291e+00 9.382e+00 26.201 4.3e-10
## CCBelief_Score.c 4.583e-01 2.326e-02 1.019e+03 19.701 < 2e-16
## Naturalness.c 4.371e-01 2.249e-02 2.820e+03 19.440 < 2e-16
## CCBelief_Score.c:Naturalness.c 1.463e-03 7.159e-04 2.797e+03 2.043 0.0411
##
## (Intercept) ***
## CCBelief_Score.c ***
## Naturalness.c ***
## CCBelief_Score.c:Naturalness.c *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. Ntrln.
## CCBlf_Scr.c -0.001
## Naturlnss.c -0.004 -0.036
## CCBlf_S.:N. -0.009 0.096 0.036#create plot
p3 <- plot_model(m.w5, type = "pred",
terms = c("Naturalness.c", "CCBelief_Score.c [-40, 40]")) +
ggtitle("") +
ylab("Support (0-100)") +
xlab("Naturalness 0-100 (Mean Centered at 39.98)") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
legend.background = element_rect(fill = "white", color = "white"))
p3 <- p3 + labs(color = "Climate Change Belief")
(p.w5 <- p3 +
scale_color_manual(labels = c("-1 SD", "+1 SD"),
values = c("blue", "red")) +
scale_fill_manual(values = c("blue", "red")) +
scale_y_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100), limits = c(0, 120)) +
scale_x_continuous(breaks = c(-80, -70, -60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60, 70, 80),
limits = c(-80, 80)))## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
#### Connectedness to Nature
modA.110000004 <- lmer(Support ~ CNS_Score.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.110000004)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CNS_Score.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27587.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6228 -0.5329 0.0359 0.5409 3.2918
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 291.57 17.075
## Type (Intercept) 47.21 6.871
## Residual 351.15 18.739
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.004e+01 2.265e+00 9.789e+00 26.511 1.92e-10 ***
## CNS_Score.c 1.547e-01 3.820e-02 1.005e+03 4.049 5.53e-05 ***
## Naturalness.c 4.439e-01 2.320e-02 2.667e+03 19.129 < 2e-16 ***
## CNS_Score.c:Naturalness.c 5.645e-03 1.061e-03 2.676e+03 5.323 1.11e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. Ntrln.
## CNS_Score.c 0.000
## Naturlnss.c -0.004 0.004
## CNS_Scr.:N. -0.001 0.033 -0.074tab_model(modA.110000004,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.04 | 2.26 | 55.60 – 64.48 | 26.51 | <0.001 |
| CNS Score c | 0.15 | 0.04 | 0.08 – 0.23 | 4.05 | <0.001 |
| Naturalness c | 0.44 | 0.02 | 0.40 – 0.49 | 19.13 | <0.001 |
|
CNS Score c × Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 5.32 | <0.001 |
| Random Effects | |||||
| σ2 | 351.15 | ||||
| τ00 id | 291.57 | ||||
| τ00 Type | 47.21 | ||||
| ICC | 0.49 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.133 / 0.559 | ||||
confint(modA.110000004)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 16.015230624 18.155036999
## .sig02 4.322601518 11.036899204
## .sigma 18.166204575 19.327118916
## (Intercept) 55.412998414 64.678985977
## CNS_Score.c 0.079820684 0.229586755
## Naturalness.c 0.398710835 0.490546405
## CNS_Score.c:Naturalness.c 0.003565125 0.007725237Political 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.110000008 <- lmer(Support ~ Ideology.c + (1|id) + (1|Type), data = L)
summary(modA.110000008)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Ideology.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27809
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2823 -0.5220 0.0622 0.5711 3.2584
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 248.8 15.77
## Type (Intercept) 143.4 11.97
## Residual 406.4 20.16
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.3557 3.9505 10.4052 12.24 1.65e-07 ***
## Ideology.c -4.2278 0.3346 1003.6802 -12.64 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Ideology.c 0.238tab_model(modA.110000008,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 48.36 | 3.95 | 40.61 – 56.10 | 12.24 | <0.001 |
| Ideology c | -4.23 | 0.33 | -4.88 – -3.57 | -12.64 | <0.001 |
| Random Effects | |||||
| σ2 | 406.35 | ||||
| τ00 id | 248.85 | ||||
| τ00 Type | 143.39 | ||||
| ICC | 0.49 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.071 / 0.527 | ||||
confint(modA.110000008)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 14.680827 16.882318
## .sig02 7.689003 19.054431
## .sigma 19.549949 20.798930
## (Intercept) 40.308511 56.406339
## Ideology.c -4.883933 -3.571767Political 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.110000005 <- lmer(Support ~ Ideology.c*Naturalness.c + (1|id) + (1|Type), data = L)
summary(modA.110000005)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Ideology.c * Naturalness.c + (1 | id) + (1 | Type)
## Data: L
##
## REML criterion at convergence: 27479.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5615 -0.5381 0.0376 0.5465 3.1855
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 240.07 15.494
## Type (Intercept) 49.72 7.051
## Residual 356.56 18.883
## Number of obs: 3021, groups: id, 1007; Type, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.883e+01 2.481e+00 1.276e+01 19.677 6.33e-11 ***
## Ideology.c -3.998e+00 3.237e-01 1.005e+03 -12.352 < 2e-16 ***
## Naturalness.c 4.394e-01 3.439e-02 2.747e+03 12.778 < 2e-16 ***
## Ideology.c:Naturalness.c -9.597e-04 9.746e-03 2.677e+03 -0.098 0.922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. Ntrln.
## Ideology.c 0.366
## Naturlnss.c 0.019 0.043
## Idlgy.c:Nt. 0.017 0.024 0.744tab_model(modA.110000005,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 48.83 | 2.48 | 43.96 – 53.69 | 19.68 | <0.001 |
| Ideology c | -4.00 | 0.32 | -4.63 – -3.36 | -12.35 | <0.001 |
| Naturalness c | 0.44 | 0.03 | 0.37 – 0.51 | 12.78 | <0.001 |
|
Ideology c × Naturalness c |
-0.00 | 0.01 | -0.02 – 0.02 | -0.10 | 0.922 |
| Random Effects | |||||
| σ2 | 356.56 | ||||
| τ00 id | 240.07 | ||||
| τ00 Type | 49.72 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| N Type | 10 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.191 / 0.554 | ||||
confint(modA.110000005)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 14.45709623 16.54447978
## .sig02 4.43895051 11.31323294
## .sigma 18.30560202 19.47555921
## (Intercept) 43.82387221 53.83201673
## Ideology.c -4.63220885 -3.36313938
## Naturalness.c 0.37242512 0.50775201
## Ideology.c:Naturalness.c -0.02008768 0.01815798#VIII. Deviation Coded Models
Support
Set #1
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict support?
modA.71444 <- lmer(Support ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.71444)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 27888.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2299 -0.5111 0.0607 0.5552 3.1022
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.7 17.60
## Residual 406.2 20.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.2345 0.6670 1016.3399 90.313 < 2e-16 ***
## DACCS -7.7095 1.1375 2380.1041 -6.777 1.54e-11 ***
## NE -9.3404 1.3268 2482.4889 -7.040 2.48e-12 ***
## OF -9.9708 1.1705 2392.1093 -8.519 < 2e-16 ***
## BECCS -5.8768 1.1650 2387.8451 -5.045 4.89e-07 ***
## EW -10.0224 1.1568 2385.9393 -8.664 < 2e-16 ***
## BF -0.7135 1.3490 2483.2374 -0.529 0.597
## WE 15.6042 1.3266 2480.5218 11.763 < 2e-16 ***
## SE 19.4142 1.3567 2483.7684 14.310 < 2e-16 ***
## AFSCS 15.9948 1.1439 2382.9907 13.983 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BF WE SE
## DACCS -0.028
## NE 0.023 -0.092
## OF -0.016 -0.115 -0.073
## BECCS -0.018 -0.111 -0.098 -0.119
## EW -0.021 -0.107 -0.085 -0.116 -0.109
## BF 0.030 -0.094 -0.171 -0.110 -0.096 -0.097
## WE 0.022 -0.080 -0.170 -0.094 -0.092 -0.097 -0.171
## SE 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.173 -0.172
## AFSCS -0.025 -0.111 -0.109 -0.110 -0.118 -0.112 -0.092 -0.088 -0.081tab_model(modA.71444,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.23 | 0.67 | 58.93 – 61.54 | 90.31 | <0.001 |
| DACCS | -7.71 | 1.14 | -9.94 – -5.48 | -6.78 | <0.001 |
| NE | -9.34 | 1.33 | -11.94 – -6.74 | -7.04 | <0.001 |
| OF | -9.97 | 1.17 | -12.27 – -7.68 | -8.52 | <0.001 |
| BECCS | -5.88 | 1.16 | -8.16 – -3.59 | -5.04 | <0.001 |
| EW | -10.02 | 1.16 | -12.29 – -7.75 | -8.66 | <0.001 |
| BF | -0.71 | 1.35 | -3.36 – 1.93 | -0.53 | 0.597 |
| WE | 15.60 | 1.33 | 13.00 – 18.21 | 11.76 | <0.001 |
| SE | 19.41 | 1.36 | 16.75 – 22.07 | 14.31 | <0.001 |
| AFSCS | 15.99 | 1.14 | 13.75 – 18.24 | 13.98 | <0.001 |
| Random Effects | |||||
| σ2 | 406.25 | ||||
| τ00 id | 309.74 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.150 / 0.517 | ||||
Q.2: Does naturalness predict support, over and above climate change method contrasts?
modA.7444 <- lmer(Support ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BF +
## WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27555.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5008 -0.5399 0.0313 0.5419 3.2984
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.4 17.16
## Residual 356.4 18.88
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.06679 0.64251 1015.86154 93.488 < 2e-16 ***
## Naturalness.c 0.44546 0.02349 2795.60249 18.961 < 2e-16 ***
## DACCS -0.99636 1.12542 2415.54374 -0.885 0.37607
## NE -2.77864 1.29367 2496.67041 -2.148 0.03182 *
## OF -6.26532 1.11661 2383.86521 -5.611 2.24e-08 ***
## BECCS -3.49410 1.10092 2369.62156 -3.174 0.00152 **
## EW -7.91475 1.09171 2372.39823 -7.250 5.62e-13 ***
## BF -0.28951 1.26777 2460.26449 -0.228 0.81939
## WE 9.42505 1.28868 2480.04872 7.314 3.49e-13 ***
## SE 12.62230 1.32421 2499.36105 9.532 < 2e-16 ***
## AFSCS 6.37199 1.18798 2455.76383 5.364 8.92e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. DACCS NE OF BECCS EW BF WE
## Naturlnss.c -0.014
## DACCS -0.030 0.315
## NE 0.017 0.267 0.001
## OF -0.018 0.176 -0.052 -0.021
## BECCS -0.018 0.113 -0.070 -0.063 -0.097
## EW -0.021 0.100 -0.069 -0.054 -0.097 -0.097
## BF 0.029 0.019 -0.083 -0.161 -0.105 -0.093 -0.094
## WE 0.024 -0.254 -0.152 -0.227 -0.133 -0.116 -0.118 -0.171
## SE 0.035 -0.271 -0.179 -0.233 -0.146 -0.119 -0.120 -0.172 -0.092
## AFSCS -0.016 -0.427 -0.230 -0.209 -0.173 -0.154 -0.144 -0.091 0.032
## SE
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BF
## WE
## SE
## AFSCS 0.046tab_model(modA.7444,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.07 | 0.64 | 58.81 – 61.33 | 93.49 | <0.001 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.49 | 18.96 | <0.001 |
| DACCS | -1.00 | 1.13 | -3.20 – 1.21 | -0.89 | 0.376 |
| NE | -2.78 | 1.29 | -5.32 – -0.24 | -2.15 | 0.032 |
| OF | -6.27 | 1.12 | -8.45 – -4.08 | -5.61 | <0.001 |
| BECCS | -3.49 | 1.10 | -5.65 – -1.34 | -3.17 | 0.002 |
| EW | -7.91 | 1.09 | -10.06 – -5.77 | -7.25 | <0.001 |
| BF | -0.29 | 1.27 | -2.78 – 2.20 | -0.23 | 0.819 |
| WE | 9.43 | 1.29 | 6.90 – 11.95 | 7.31 | <0.001 |
| SE | 12.62 | 1.32 | 10.03 – 15.22 | 9.53 | <0.001 |
| AFSCS | 6.37 | 1.19 | 4.04 – 8.70 | 5.36 | <0.001 |
| Random Effects | |||||
| σ2 | 356.37 | ||||
| τ00 id | 294.39 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.224 / 0.575 | ||||
Q.3: Nat Ben Risk Predicting Support
modA.74445511 <- lmer(Support ~ Naturalness.c + Benefit.c + Risk.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + Risk.c + DACCS + NE + OF +
## BECCS + EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25689.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4550 -0.5095 0.0335 0.5025 3.7798
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.6 12.07
## Residual 195.7 13.99
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.81482 0.45918 1003.58733 130.266 < 2e-16 ***
## Naturalness.c 0.14095 0.01857 2863.29821 7.590 4.31e-14 ***
## Benefit.c 0.46634 0.01381 3007.84868 33.764 < 2e-16 ***
## Risk.c -0.41425 0.01501 2990.28692 -27.599 < 2e-16 ***
## DACCS 0.42091 0.83319 2432.47909 0.505 0.6135
## NE 0.55870 0.98316 2540.76780 0.568 0.5699
## OF -1.17842 0.83619 2410.74493 -1.409 0.1589
## BECCS -1.45967 0.81447 2383.74279 -1.792 0.0732 .
## EW -4.42861 0.80929 2389.20448 -5.472 4.91e-08 ***
## BF 0.57440 0.94768 2502.85410 0.606 0.5445
## WE 3.80039 0.95962 2525.91992 3.960 7.69e-05 ***
## SE 4.29501 0.99893 2529.50674 4.300 1.78e-05 ***
## AFSCS 1.20536 0.88309 2486.72827 1.365 0.1724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.74445511,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.81 | 0.46 | 58.91 – 60.72 | 130.27 | <0.001 |
| Naturalness c | 0.14 | 0.02 | 0.10 – 0.18 | 7.59 | <0.001 |
| Benefit c | 0.47 | 0.01 | 0.44 – 0.49 | 33.76 | <0.001 |
| Risk c | -0.41 | 0.02 | -0.44 – -0.38 | -27.60 | <0.001 |
| DACCS | 0.42 | 0.83 | -1.21 – 2.05 | 0.51 | 0.613 |
| NE | 0.56 | 0.98 | -1.37 – 2.49 | 0.57 | 0.570 |
| OF | -1.18 | 0.84 | -2.82 – 0.46 | -1.41 | 0.159 |
| BECCS | -1.46 | 0.81 | -3.06 – 0.14 | -1.79 | 0.073 |
| EW | -4.43 | 0.81 | -6.02 – -2.84 | -5.47 | <0.001 |
| BF | 0.57 | 0.95 | -1.28 – 2.43 | 0.61 | 0.544 |
| WE | 3.80 | 0.96 | 1.92 – 5.68 | 3.96 | <0.001 |
| SE | 4.30 | 1.00 | 2.34 – 6.25 | 4.30 | <0.001 |
| AFSCS | 1.21 | 0.88 | -0.53 – 2.94 | 1.36 | 0.172 |
| Random Effects | |||||
| σ2 | 195.74 | ||||
| τ00 id | 145.65 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.580 / 0.759 | ||||
Q.4: Fam/Und Nat Ben Risk Predicting Support
modA.74445511 <- lmer(Support ~ Naturalness.c + Benefit.c + Risk.c + FR.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + Risk.c + FR.c + DACCS +
## NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25657.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4852 -0.5053 0.0383 0.5115 3.6947
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 143.2 11.96
## Residual 193.5 13.91
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.63520 0.45654 1008.32565 130.623 < 2e-16 ***
## Naturalness.c 0.11838 0.01880 2895.51251 6.296 3.51e-10 ***
## Benefit.c 0.45790 0.01379 3006.62058 33.202 < 2e-16 ***
## Risk.c -0.40292 0.01502 2990.80080 -26.821 < 2e-16 ***
## FR.c 0.09437 0.01499 3006.96174 6.295 3.53e-10 ***
## DACCS 1.69096 0.85251 2466.18843 1.984 0.04742 *
## NE -1.21149 1.01682 2619.16689 -1.191 0.23359
## OF -0.03470 0.85083 2446.50902 -0.041 0.96747
## BECCS -0.10504 0.83785 2438.15755 -0.125 0.90024
## EW -2.55639 0.85779 2515.04425 -2.980 0.00291 **
## BF 0.06658 0.94553 2509.69888 0.070 0.94387
## WE 1.73440 1.00884 2585.03325 1.719 0.08570 .
## SE 1.97172 1.05936 2607.97944 1.861 0.06282 .
## AFSCS 0.75609 0.88075 2487.46739 0.858 0.39072
## ---
## 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.74445511,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.64 | 0.46 | 58.74 – 60.53 | 130.62 | <0.001 |
| Naturalness c | 0.12 | 0.02 | 0.08 – 0.16 | 6.30 | <0.001 |
| Benefit c | 0.46 | 0.01 | 0.43 – 0.48 | 33.20 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.82 | <0.001 |
| FR c | 0.09 | 0.01 | 0.06 – 0.12 | 6.29 | <0.001 |
| DACCS | 1.69 | 0.85 | 0.02 – 3.36 | 1.98 | 0.047 |
| NE | -1.21 | 1.02 | -3.21 – 0.78 | -1.19 | 0.234 |
| OF | -0.03 | 0.85 | -1.70 – 1.63 | -0.04 | 0.967 |
| BECCS | -0.11 | 0.84 | -1.75 – 1.54 | -0.13 | 0.900 |
| EW | -2.56 | 0.86 | -4.24 – -0.87 | -2.98 | 0.003 |
| BF | 0.07 | 0.95 | -1.79 – 1.92 | 0.07 | 0.944 |
| WE | 1.73 | 1.01 | -0.24 – 3.71 | 1.72 | 0.086 |
| SE | 1.97 | 1.06 | -0.11 – 4.05 | 1.86 | 0.063 |
| AFSCS | 0.76 | 0.88 | -0.97 – 2.48 | 0.86 | 0.391 |
| Random Effects | |||||
| σ2 | 193.52 | ||||
| τ00 id | 143.16 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.585 / 0.761 | ||||
Q.5: Does the interaction of naturalness and contrasts predict support, over and above climate change method contrasts?
modA.744455 <- lmer(Support ~ Naturalness.c *(DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS) + (1|id), data = L)
summary(modA.744455)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c * (DACCS + NE + OF + BECCS + EW + BF +
## WE + SE + AFSCS) + (1 | id)
## Data: L
##
## REML criterion at convergence: 27550.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5500 -0.5456 0.0489 0.5393 3.3462
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 297.5 17.25
## Residual 350.5 18.72
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 61.39887 0.69412 1300.42578 88.456 < 2e-16 ***
## Naturalness.c 0.45808 0.02371 2786.57851 19.317 < 2e-16 ***
## DACCS -1.75816 1.40426 2419.27762 -1.252 0.210684
## NE 0.47048 1.58970 2464.95296 0.296 0.767290
## OF -6.36005 1.22448 2378.49507 -5.194 2.23e-07 ***
## BECCS -4.69508 1.16570 2368.90017 -4.028 5.81e-05 ***
## EW -9.01420 1.13312 2360.46374 -7.955 2.75e-15 ***
## BF -1.60046 1.28652 2448.80813 -1.244 0.213610
## WE 9.57640 1.56448 2468.64843 6.121 1.08e-09 ***
## SE 14.81002 1.64916 2481.29782 8.980 < 2e-16 ***
## AFSCS 6.76256 1.58998 2440.04036 4.253 2.19e-05 ***
## Naturalness.c:DACCS 0.02274 0.06412 2448.38813 0.355 0.722889
## Naturalness.c:NE 0.31821 0.07296 2477.62471 4.362 1.34e-05 ***
## Naturalness.c:OF 0.13171 0.06395 2454.35483 2.059 0.039552 *
## Naturalness.c:BECCS 0.01775 0.06659 2460.75868 0.267 0.789793
## Naturalness.c:EW 0.04266 0.06121 2441.99572 0.697 0.485941
## Naturalness.c:BF 0.03273 0.07172 2481.63749 0.456 0.648186
## Naturalness.c:WE -0.11913 0.06791 2523.71340 -1.754 0.079518 .
## Naturalness.c:SE -0.24276 0.07120 2505.32469 -3.409 0.000662 ***
## Naturalness.c:AFSCS -0.09124 0.05734 2525.36213 -1.591 0.111649
## ---
## 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.744455,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 61.40 | 0.69 | 60.04 – 62.76 | 88.46 | <0.001 |
| Naturalness c | 0.46 | 0.02 | 0.41 – 0.50 | 19.32 | <0.001 |
| DACCS | -1.76 | 1.40 | -4.51 – 1.00 | -1.25 | 0.211 |
| NE | 0.47 | 1.59 | -2.65 – 3.59 | 0.30 | 0.767 |
| OF | -6.36 | 1.22 | -8.76 – -3.96 | -5.19 | <0.001 |
| BECCS | -4.70 | 1.17 | -6.98 – -2.41 | -4.03 | <0.001 |
| EW | -9.01 | 1.13 | -11.24 – -6.79 | -7.96 | <0.001 |
| BF | -1.60 | 1.29 | -4.12 – 0.92 | -1.24 | 0.214 |
| WE | 9.58 | 1.56 | 6.51 – 12.64 | 6.12 | <0.001 |
| SE | 14.81 | 1.65 | 11.58 – 18.04 | 8.98 | <0.001 |
| AFSCS | 6.76 | 1.59 | 3.64 – 9.88 | 4.25 | <0.001 |
| Naturalness c × DACCS | 0.02 | 0.06 | -0.10 – 0.15 | 0.35 | 0.723 |
| Naturalness c × NE | 0.32 | 0.07 | 0.18 – 0.46 | 4.36 | <0.001 |
| Naturalness c × OF | 0.13 | 0.06 | 0.01 – 0.26 | 2.06 | 0.040 |
| Naturalness c × BECCS | 0.02 | 0.07 | -0.11 – 0.15 | 0.27 | 0.790 |
| Naturalness c × EW | 0.04 | 0.06 | -0.08 – 0.16 | 0.70 | 0.486 |
| Naturalness c × BF | 0.03 | 0.07 | -0.11 – 0.17 | 0.46 | 0.648 |
| Naturalness c × WE | -0.12 | 0.07 | -0.25 – 0.01 | -1.75 | 0.079 |
| Naturalness c × SE | -0.24 | 0.07 | -0.38 – -0.10 | -3.41 | 0.001 |
| Naturalness c × AFSCS | -0.09 | 0.06 | -0.20 – 0.02 | -1.59 | 0.112 |
| Random Effects | |||||
| σ2 | 350.53 | ||||
| τ00 id | 297.49 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.232 / 0.585 | ||||
Set #2
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict support?
modA.71444777 <- lmer(Support ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.71444777)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 27888.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2299 -0.5111 0.0607 0.5552 3.1022
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.7 17.60
## Residual 406.2 20.16
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.2345 0.6670 1016.3399 90.313 < 2e-16 ***
## DACCS -7.7095 1.1375 2380.1041 -6.777 1.54e-11 ***
## NE -9.3404 1.3268 2482.4889 -7.040 2.48e-12 ***
## OF -9.9708 1.1705 2392.1093 -8.519 < 2e-16 ***
## BECCS -5.8768 1.1650 2387.8451 -5.045 4.89e-07 ***
## EW -10.0224 1.1568 2385.9393 -8.664 < 2e-16 ***
## BIO 0.7135 1.3490 2483.2374 0.529 0.597
## WE 15.6042 1.3266 2480.5218 11.763 < 2e-16 ***
## SE 19.4142 1.3567 2483.7684 14.310 < 2e-16 ***
## AFSCS 15.9948 1.1439 2382.9907 13.983 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BIO WE SE
## DACCS -0.028
## NE 0.023 -0.092
## OF -0.016 -0.115 -0.073
## BECCS -0.018 -0.111 -0.098 -0.119
## EW -0.021 -0.107 -0.085 -0.116 -0.109
## BIO -0.030 0.094 0.171 0.110 0.096 0.097
## WE 0.022 -0.080 -0.170 -0.094 -0.092 -0.097 0.171
## SE 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 0.173 -0.172
## AFSCS -0.025 -0.111 -0.109 -0.110 -0.118 -0.112 0.092 -0.088 -0.081tab_model(modA.71444777,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.23 | 0.67 | 58.93 – 61.54 | 90.31 | <0.001 |
| DACCS | -7.71 | 1.14 | -9.94 – -5.48 | -6.78 | <0.001 |
| NE | -9.34 | 1.33 | -11.94 – -6.74 | -7.04 | <0.001 |
| OF | -9.97 | 1.17 | -12.27 – -7.68 | -8.52 | <0.001 |
| BECCS | -5.88 | 1.16 | -8.16 – -3.59 | -5.04 | <0.001 |
| EW | -10.02 | 1.16 | -12.29 – -7.75 | -8.66 | <0.001 |
| BIO | 0.71 | 1.35 | -1.93 – 3.36 | 0.53 | 0.597 |
| WE | 15.60 | 1.33 | 13.00 – 18.21 | 11.76 | <0.001 |
| SE | 19.41 | 1.36 | 16.75 – 22.07 | 14.31 | <0.001 |
| AFSCS | 15.99 | 1.14 | 13.75 – 18.24 | 13.98 | <0.001 |
| Random Effects | |||||
| σ2 | 406.25 | ||||
| τ00 id | 309.74 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.150 / 0.517 | ||||
Q.2: Does naturalness predict support, over and above climate change method contrasts?
modA.7444777 <- lmer(Support ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444777)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BIO +
## WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27555.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5008 -0.5399 0.0313 0.5419 3.2984
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 294.4 17.16
## Residual 356.4 18.88
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.06679 0.64251 1015.86154 93.488 < 2e-16 ***
## Naturalness.c 0.44546 0.02349 2795.60249 18.961 < 2e-16 ***
## DACCS -0.99636 1.12542 2415.54374 -0.885 0.37607
## NE -2.77864 1.29367 2496.67041 -2.148 0.03182 *
## OF -6.26532 1.11661 2383.86521 -5.611 2.24e-08 ***
## BECCS -3.49410 1.10092 2369.62156 -3.174 0.00152 **
## EW -7.91475 1.09171 2372.39823 -7.250 5.62e-13 ***
## BIO 0.28951 1.26777 2460.26449 0.228 0.81939
## WE 9.42505 1.28868 2480.04872 7.314 3.49e-13 ***
## SE 12.62230 1.32421 2499.36105 9.532 < 2e-16 ***
## AFSCS 6.37199 1.18798 2455.76383 5.364 8.92e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. DACCS NE OF BECCS EW BIO WE
## Naturlnss.c -0.014
## DACCS -0.030 0.315
## NE 0.017 0.267 0.001
## OF -0.018 0.176 -0.052 -0.021
## BECCS -0.018 0.113 -0.070 -0.063 -0.097
## EW -0.021 0.100 -0.069 -0.054 -0.097 -0.097
## BIO -0.029 -0.019 0.083 0.161 0.105 0.093 0.094
## WE 0.024 -0.254 -0.152 -0.227 -0.133 -0.116 -0.118 0.171
## SE 0.035 -0.271 -0.179 -0.233 -0.146 -0.119 -0.120 0.172 -0.092
## AFSCS -0.016 -0.427 -0.230 -0.209 -0.173 -0.154 -0.144 0.091 0.032
## SE
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BIO
## WE
## SE
## AFSCS 0.046tab_model(modA.7444777,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.07 | 0.64 | 58.81 – 61.33 | 93.49 | <0.001 |
| Naturalness c | 0.45 | 0.02 | 0.40 – 0.49 | 18.96 | <0.001 |
| DACCS | -1.00 | 1.13 | -3.20 – 1.21 | -0.89 | 0.376 |
| NE | -2.78 | 1.29 | -5.32 – -0.24 | -2.15 | 0.032 |
| OF | -6.27 | 1.12 | -8.45 – -4.08 | -5.61 | <0.001 |
| BECCS | -3.49 | 1.10 | -5.65 – -1.34 | -3.17 | 0.002 |
| EW | -7.91 | 1.09 | -10.06 – -5.77 | -7.25 | <0.001 |
| BIO | 0.29 | 1.27 | -2.20 – 2.78 | 0.23 | 0.819 |
| WE | 9.43 | 1.29 | 6.90 – 11.95 | 7.31 | <0.001 |
| SE | 12.62 | 1.32 | 10.03 – 15.22 | 9.53 | <0.001 |
| AFSCS | 6.37 | 1.19 | 4.04 – 8.70 | 5.36 | <0.001 |
| Random Effects | |||||
| σ2 | 356.37 | ||||
| τ00 id | 294.39 | ||||
| ICC | 0.45 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.224 / 0.575 | ||||
Q.3: Nat Ben Risk Predicting Support
modA.7444551176 <- lmer(Support ~ Naturalness.c + Benefit.c + Risk.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551176)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + Risk.c + DACCS + NE + OF +
## BECCS + EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25689.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4550 -0.5095 0.0335 0.5025 3.7798
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 145.6 12.07
## Residual 195.7 13.99
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.81482 0.45918 1003.58733 130.266 < 2e-16 ***
## Naturalness.c 0.14095 0.01857 2863.29821 7.590 4.31e-14 ***
## Benefit.c 0.46634 0.01381 3007.84868 33.764 < 2e-16 ***
## Risk.c -0.41425 0.01501 2990.28692 -27.599 < 2e-16 ***
## DACCS 0.42091 0.83319 2432.47909 0.505 0.6135
## NE 0.55870 0.98316 2540.76780 0.568 0.5699
## OF -1.17842 0.83619 2410.74493 -1.409 0.1589
## BECCS -1.45967 0.81447 2383.74279 -1.792 0.0732 .
## EW -4.42861 0.80929 2389.20448 -5.472 4.91e-08 ***
## BIO -0.57440 0.94768 2502.85410 -0.606 0.5445
## WE 3.80039 0.95962 2525.91992 3.960 7.69e-05 ***
## SE 4.29501 0.99893 2529.50674 4.300 1.78e-05 ***
## AFSCS 1.20536 0.88309 2486.72827 1.365 0.1724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.7444551176,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.81 | 0.46 | 58.91 – 60.72 | 130.27 | <0.001 |
| Naturalness c | 0.14 | 0.02 | 0.10 – 0.18 | 7.59 | <0.001 |
| Benefit c | 0.47 | 0.01 | 0.44 – 0.49 | 33.76 | <0.001 |
| Risk c | -0.41 | 0.02 | -0.44 – -0.38 | -27.60 | <0.001 |
| DACCS | 0.42 | 0.83 | -1.21 – 2.05 | 0.51 | 0.613 |
| NE | 0.56 | 0.98 | -1.37 – 2.49 | 0.57 | 0.570 |
| OF | -1.18 | 0.84 | -2.82 – 0.46 | -1.41 | 0.159 |
| BECCS | -1.46 | 0.81 | -3.06 – 0.14 | -1.79 | 0.073 |
| EW | -4.43 | 0.81 | -6.02 – -2.84 | -5.47 | <0.001 |
| BIO | -0.57 | 0.95 | -2.43 – 1.28 | -0.61 | 0.544 |
| WE | 3.80 | 0.96 | 1.92 – 5.68 | 3.96 | <0.001 |
| SE | 4.30 | 1.00 | 2.34 – 6.25 | 4.30 | <0.001 |
| AFSCS | 1.21 | 0.88 | -0.53 – 2.94 | 1.36 | 0.172 |
| Random Effects | |||||
| σ2 | 195.74 | ||||
| τ00 id | 145.65 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.580 / 0.759 | ||||
Q.4: Fam/Und Nat Ben Risk Predicting Support
modA.7444551144 <- lmer(Support ~ Naturalness.c + Benefit.c + Risk.c + FR.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551144)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Benefit.c + Risk.c + FR.c + DACCS +
## NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25657.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4852 -0.5053 0.0383 0.5115 3.6947
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 143.2 11.96
## Residual 193.5 13.91
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.63520 0.45654 1008.32565 130.623 < 2e-16 ***
## Naturalness.c 0.11838 0.01880 2895.51251 6.296 3.51e-10 ***
## Benefit.c 0.45790 0.01379 3006.62058 33.202 < 2e-16 ***
## Risk.c -0.40292 0.01502 2990.80080 -26.821 < 2e-16 ***
## FR.c 0.09437 0.01499 3006.96174 6.295 3.53e-10 ***
## DACCS 1.69096 0.85251 2466.18843 1.984 0.04742 *
## NE -1.21149 1.01682 2619.16689 -1.191 0.23359
## OF -0.03470 0.85083 2446.50902 -0.041 0.96747
## BECCS -0.10504 0.83785 2438.15755 -0.125 0.90024
## EW -2.55639 0.85779 2515.04425 -2.980 0.00291 **
## BIO -0.06658 0.94553 2509.69888 -0.070 0.94387
## WE 1.73440 1.00884 2585.03325 1.719 0.08570 .
## SE 1.97172 1.05936 2607.97944 1.861 0.06282 .
## AFSCS 0.75609 0.88075 2487.46739 0.858 0.39072
## ---
## 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.7444551144,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.64 | 0.46 | 58.74 – 60.53 | 130.62 | <0.001 |
| Naturalness c | 0.12 | 0.02 | 0.08 – 0.16 | 6.30 | <0.001 |
| Benefit c | 0.46 | 0.01 | 0.43 – 0.48 | 33.20 | <0.001 |
| Risk c | -0.40 | 0.02 | -0.43 – -0.37 | -26.82 | <0.001 |
| FR c | 0.09 | 0.01 | 0.06 – 0.12 | 6.29 | <0.001 |
| DACCS | 1.69 | 0.85 | 0.02 – 3.36 | 1.98 | 0.047 |
| NE | -1.21 | 1.02 | -3.21 – 0.78 | -1.19 | 0.234 |
| OF | -0.03 | 0.85 | -1.70 – 1.63 | -0.04 | 0.967 |
| BECCS | -0.11 | 0.84 | -1.75 – 1.54 | -0.13 | 0.900 |
| EW | -2.56 | 0.86 | -4.24 – -0.87 | -2.98 | 0.003 |
| BIO | -0.07 | 0.95 | -1.92 – 1.79 | -0.07 | 0.944 |
| WE | 1.73 | 1.01 | -0.24 – 3.71 | 1.72 | 0.086 |
| SE | 1.97 | 1.06 | -0.11 – 4.05 | 1.86 | 0.063 |
| AFSCS | 0.76 | 0.88 | -0.97 – 2.48 | 0.86 | 0.391 |
| Random Effects | |||||
| σ2 | 193.52 | ||||
| τ00 id | 143.16 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.585 / 0.761 | ||||
Q.5: Does the interaction of naturalness and contrasts predict support, over and above climate change method contrasts?
modA.74445533 <- lmer(Support ~ Naturalness.c *(DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS) + (1|id), data = L)
summary(modA.74445533)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c * (DACCS + NE + OF + BECCS + EW + BIO +
## WE + SE + AFSCS) + (1 | id)
## Data: L
##
## REML criterion at convergence: 27550.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5500 -0.5456 0.0489 0.5393 3.3462
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 297.5 17.25
## Residual 350.5 18.72
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 61.39887 0.69412 1300.42578 88.456 < 2e-16 ***
## Naturalness.c 0.45808 0.02371 2786.57851 19.317 < 2e-16 ***
## DACCS -1.75816 1.40426 2419.27762 -1.252 0.210684
## NE 0.47048 1.58970 2464.95296 0.296 0.767290
## OF -6.36005 1.22448 2378.49507 -5.194 2.23e-07 ***
## BECCS -4.69508 1.16570 2368.90017 -4.028 5.81e-05 ***
## EW -9.01420 1.13312 2360.46374 -7.955 2.75e-15 ***
## BIO 1.60046 1.28652 2448.80813 1.244 0.213610
## WE 9.57640 1.56448 2468.64843 6.121 1.08e-09 ***
## SE 14.81002 1.64916 2481.29782 8.980 < 2e-16 ***
## AFSCS 6.76256 1.58998 2440.04036 4.253 2.19e-05 ***
## Naturalness.c:DACCS 0.02274 0.06412 2448.38813 0.355 0.722889
## Naturalness.c:NE 0.31821 0.07296 2477.62471 4.362 1.34e-05 ***
## Naturalness.c:OF 0.13171 0.06395 2454.35483 2.059 0.039552 *
## Naturalness.c:BECCS 0.01775 0.06659 2460.75868 0.267 0.789793
## Naturalness.c:EW 0.04266 0.06121 2441.99572 0.697 0.485941
## Naturalness.c:BIO -0.03273 0.07172 2481.63749 -0.456 0.648186
## Naturalness.c:WE -0.11913 0.06791 2523.71340 -1.754 0.079518 .
## Naturalness.c:SE -0.24276 0.07120 2505.32469 -3.409 0.000662 ***
## Naturalness.c:AFSCS -0.09124 0.05734 2525.36213 -1.591 0.111649
## ---
## 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.74445533,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 61.40 | 0.69 | 60.04 – 62.76 | 88.46 | <0.001 |
| Naturalness c | 0.46 | 0.02 | 0.41 – 0.50 | 19.32 | <0.001 |
| DACCS | -1.76 | 1.40 | -4.51 – 1.00 | -1.25 | 0.211 |
| NE | 0.47 | 1.59 | -2.65 – 3.59 | 0.30 | 0.767 |
| OF | -6.36 | 1.22 | -8.76 – -3.96 | -5.19 | <0.001 |
| BECCS | -4.70 | 1.17 | -6.98 – -2.41 | -4.03 | <0.001 |
| EW | -9.01 | 1.13 | -11.24 – -6.79 | -7.96 | <0.001 |
| BIO | 1.60 | 1.29 | -0.92 – 4.12 | 1.24 | 0.214 |
| WE | 9.58 | 1.56 | 6.51 – 12.64 | 6.12 | <0.001 |
| SE | 14.81 | 1.65 | 11.58 – 18.04 | 8.98 | <0.001 |
| AFSCS | 6.76 | 1.59 | 3.64 – 9.88 | 4.25 | <0.001 |
| Naturalness c × DACCS | 0.02 | 0.06 | -0.10 – 0.15 | 0.35 | 0.723 |
| Naturalness c × NE | 0.32 | 0.07 | 0.18 – 0.46 | 4.36 | <0.001 |
| Naturalness c × OF | 0.13 | 0.06 | 0.01 – 0.26 | 2.06 | 0.040 |
| Naturalness c × BECCS | 0.02 | 0.07 | -0.11 – 0.15 | 0.27 | 0.790 |
| Naturalness c × EW | 0.04 | 0.06 | -0.08 – 0.16 | 0.70 | 0.486 |
| Naturalness c × BIO | -0.03 | 0.07 | -0.17 – 0.11 | -0.46 | 0.648 |
| Naturalness c × WE | -0.12 | 0.07 | -0.25 – 0.01 | -1.75 | 0.079 |
| Naturalness c × SE | -0.24 | 0.07 | -0.38 – -0.10 | -3.41 | 0.001 |
| Naturalness c × AFSCS | -0.09 | 0.06 | -0.20 – 0.02 | -1.59 | 0.112 |
| Random Effects | |||||
| σ2 | 350.53 | ||||
| τ00 id | 297.49 | ||||
| ICC | 0.46 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.232 / 0.585 | ||||
Naturalness
Set #1
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict naturalness?
modA.71444888 <- lmer(Naturalness ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.71444888)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 25882.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5623 -0.6131 -0.0219 0.6137 3.4124
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.98 8.123
## Residual 256.42 16.013
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.3544 0.3901 1027.4504 103.457 < 2e-16 ***
## DACCS -14.9730 0.8706 2633.0772 -17.198 < 2e-16 ***
## NE -14.4713 1.0060 2757.7982 -14.386 < 2e-16 ***
## OF -8.4667 0.8948 2649.1062 -9.462 < 2e-16 ***
## BECCS -5.4749 0.8910 2644.5116 -6.145 9.20e-10 ***
## EW -4.6006 0.8848 2641.5512 -5.199 2.15e-07 ***
## BF -1.0643 1.0226 2760.2000 -1.041 0.298
## WE 13.9641 1.0059 2756.7292 13.882 < 2e-16 ***
## SE 15.0008 1.0284 2761.1074 14.586 < 2e-16 ***
## AFSCS 21.5355 0.8753 2636.2795 24.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BF WE SE
## DACCS -0.038
## NE 0.033 -0.100
## OF -0.023 -0.107 -0.090
## BECCS -0.025 -0.105 -0.104 -0.111
## EW -0.029 -0.102 -0.096 -0.109 -0.104
## BF 0.043 -0.102 -0.155 -0.112 -0.104 -0.104
## WE 0.033 -0.093 -0.154 -0.102 -0.101 -0.103 -0.155
## SE 0.046 -0.108 -0.156 -0.109 -0.103 -0.105 -0.157 -0.156
## AFSCS -0.035 -0.103 -0.110 -0.104 -0.109 -0.105 -0.101 -0.098 -0.095tab_model(modA.71444888,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 0.39 | 39.59 – 41.12 | 103.46 | <0.001 |
| DACCS | -14.97 | 0.87 | -16.68 – -13.27 | -17.20 | <0.001 |
| NE | -14.47 | 1.01 | -16.44 – -12.50 | -14.39 | <0.001 |
| OF | -8.47 | 0.89 | -10.22 – -6.71 | -9.46 | <0.001 |
| BECCS | -5.47 | 0.89 | -7.22 – -3.73 | -6.15 | <0.001 |
| EW | -4.60 | 0.88 | -6.34 – -2.87 | -5.20 | <0.001 |
| BF | -1.06 | 1.02 | -3.07 – 0.94 | -1.04 | 0.298 |
| WE | 13.96 | 1.01 | 11.99 – 15.94 | 13.88 | <0.001 |
| SE | 15.00 | 1.03 | 12.98 – 17.02 | 14.59 | <0.001 |
| AFSCS | 21.54 | 0.88 | 19.82 – 23.25 | 24.60 | <0.001 |
| Random Effects | |||||
| σ2 | 256.42 | ||||
| τ00 id | 65.98 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.310 / 0.451 | ||||
Q.2: Ben Risk Predicting Nat
modA.74445511844 <- lmer(Naturalness ~ Benefit.c + Risk.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511844)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Benefit.c + Risk.c + DACCS + NE + OF + BECCS +
## EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25485.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6109 -0.6052 0.0023 0.5947 3.3301
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.35 8.084
## Residual 218.92 14.796
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.18822 0.37272 1012.13803 107.825 < 2e-16 ***
## Benefit.c 0.06792 0.01307 2776.17288 5.195 2.19e-07 ***
## Risk.c -0.24329 0.01373 2954.69711 -17.713 < 2e-16 ***
## DACCS -12.15552 0.82072 2625.57996 -14.811 < 2e-16 ***
## NE -9.61496 0.97914 2780.36473 -9.820 < 2e-16 ***
## OF -4.86083 0.84997 2637.69991 -5.719 1.19e-08 ***
## BECCS -3.83540 0.83108 2606.49319 -4.615 4.12e-06 ***
## EW -2.80824 0.82681 2607.92643 -3.396 0.000693 ***
## BF -2.01663 0.96100 2742.70699 -2.098 0.035954 *
## WE 9.90647 0.95514 2772.21830 10.372 < 2e-16 ***
## SE 9.16407 0.99761 2763.71823 9.186 < 2e-16 ***
## AFSCS 16.83548 0.84328 2670.46414 19.964 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c Risk.c DACCS NE OF BECCS EW BF WE
## Benefit.c -0.001
## Risk.c 0.020 0.312
## DACCS -0.040 -0.007 -0.165
## NE 0.024 -0.112 -0.295 -0.045
## OF -0.026 -0.005 -0.200 -0.068 -0.024
## BECCS -0.026 0.018 -0.081 -0.088 -0.075 -0.090
## EW -0.030 0.061 -0.068 -0.085 -0.072 -0.088 -0.095
## BF 0.042 0.121 0.113 -0.113 -0.184 -0.126 -0.107 -0.102
## WE 0.035 -0.033 0.180 -0.122 -0.197 -0.138 -0.116 -0.120 -0.141
## SE 0.049 -0.018 0.265 -0.150 -0.221 -0.161 -0.123 -0.126 -0.131 -0.091
## AFSCS -0.027 -0.077 0.218 -0.142 -0.162 -0.152 -0.130 -0.129 -0.084 -0.037
## SE
## Benefit.c
## Risk.c
## DACCS
## NE
## OF
## BECCS
## EW
## BF
## WE
## SE
## AFSCS -0.012tab_model(modA.74445511844,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.19 | 0.37 | 39.46 – 40.92 | 107.83 | <0.001 |
| Benefit c | 0.07 | 0.01 | 0.04 – 0.09 | 5.20 | <0.001 |
| Risk c | -0.24 | 0.01 | -0.27 – -0.22 | -17.71 | <0.001 |
| DACCS | -12.16 | 0.82 | -13.76 – -10.55 | -14.81 | <0.001 |
| NE | -9.61 | 0.98 | -11.53 – -7.70 | -9.82 | <0.001 |
| OF | -4.86 | 0.85 | -6.53 – -3.19 | -5.72 | <0.001 |
| BECCS | -3.84 | 0.83 | -5.46 – -2.21 | -4.61 | <0.001 |
| EW | -2.81 | 0.83 | -4.43 – -1.19 | -3.40 | 0.001 |
| BF | -2.02 | 0.96 | -3.90 – -0.13 | -2.10 | 0.036 |
| WE | 9.91 | 0.96 | 8.03 – 11.78 | 10.37 | <0.001 |
| SE | 9.16 | 1.00 | 7.21 – 11.12 | 9.19 | <0.001 |
| AFSCS | 16.84 | 0.84 | 15.18 – 18.49 | 19.96 | <0.001 |
| Random Effects | |||||
| σ2 | 218.92 | ||||
| τ00 id | 65.35 | ||||
| ICC | 0.23 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.397 / 0.536 | ||||
Q.3: Fam/Und Ben Risk Predicting Nat
modA.74445511355 <- lmer(Naturalness ~ Benefit.c + Risk.c + FR.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511355)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Benefit.c + Risk.c + FR.c + DACCS + NE + OF + BECCS +
## EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25401.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6979 -0.5923 0.0151 0.5734 3.3383
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 70.72 8.41
## Residual 207.99 14.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.92212 0.37590 1004.38050 106.203 < 2e-16 ***
## Benefit.c 0.05278 0.01301 2818.54215 4.056 5.12e-05 ***
## Risk.c -0.22193 0.01377 2963.77462 -16.116 < 2e-16 ***
## FR.c 0.13557 0.01399 2888.83466 9.691 < 2e-16 ***
## DACCS -9.91189 0.83617 2659.73444 -11.854 < 2e-16 ***
## NE -11.79404 0.98688 2797.98889 -11.951 < 2e-16 ***
## OF -3.02541 0.85314 2644.66651 -3.546 0.000398 ***
## BECCS -1.72916 0.84178 2630.68569 -2.054 0.040058 *
## EW -0.05623 0.85830 2706.19582 -0.066 0.947773
## BF -2.69218 0.94475 2711.19543 -2.850 0.004410 **
## WE 6.53910 0.99725 2807.70108 6.557 6.50e-11 ***
## SE 5.51549 1.04814 2821.99870 5.262 1.53e-07 ***
## AFSCS 15.62517 0.83530 2656.01787 18.706 < 2e-16 ***
## ---
## 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.74445511355,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.92 | 0.38 | 39.19 – 40.66 | 106.20 | <0.001 |
| Benefit c | 0.05 | 0.01 | 0.03 – 0.08 | 4.06 | <0.001 |
| Risk c | -0.22 | 0.01 | -0.25 – -0.19 | -16.12 | <0.001 |
| FR c | 0.14 | 0.01 | 0.11 – 0.16 | 9.69 | <0.001 |
| DACCS | -9.91 | 0.84 | -11.55 – -8.27 | -11.85 | <0.001 |
| NE | -11.79 | 0.99 | -13.73 – -9.86 | -11.95 | <0.001 |
| OF | -3.03 | 0.85 | -4.70 – -1.35 | -3.55 | <0.001 |
| BECCS | -1.73 | 0.84 | -3.38 – -0.08 | -2.05 | 0.040 |
| EW | -0.06 | 0.86 | -1.74 – 1.63 | -0.07 | 0.948 |
| BF | -2.69 | 0.94 | -4.54 – -0.84 | -2.85 | 0.004 |
| WE | 6.54 | 1.00 | 4.58 – 8.49 | 6.56 | <0.001 |
| SE | 5.52 | 1.05 | 3.46 – 7.57 | 5.26 | <0.001 |
| AFSCS | 15.63 | 0.84 | 13.99 – 17.26 | 18.71 | <0.001 |
| Random Effects | |||||
| σ2 | 207.99 | ||||
| τ00 id | 70.72 | ||||
| ICC | 0.25 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.413 / 0.562 | ||||
Set #2
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict naturalness?
modA.7144488812 <- lmer(Naturalness ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7144488812)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE +
## AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25882.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5623 -0.6131 -0.0219 0.6137 3.4124
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.98 8.123
## Residual 256.42 16.013
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.3544 0.3901 1027.4504 103.457 < 2e-16 ***
## DACCS -14.9730 0.8706 2633.0772 -17.198 < 2e-16 ***
## NE -14.4713 1.0060 2757.7982 -14.386 < 2e-16 ***
## OF -8.4667 0.8948 2649.1062 -9.462 < 2e-16 ***
## BECCS -5.4749 0.8910 2644.5116 -6.145 9.20e-10 ***
## EW -4.6006 0.8848 2641.5512 -5.199 2.15e-07 ***
## BIO 1.0643 1.0226 2760.2000 1.041 0.298
## WE 13.9641 1.0059 2756.7292 13.882 < 2e-16 ***
## SE 15.0008 1.0284 2761.1074 14.586 < 2e-16 ***
## AFSCS 21.5355 0.8753 2636.2795 24.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BIO WE SE
## DACCS -0.038
## NE 0.033 -0.100
## OF -0.023 -0.107 -0.090
## BECCS -0.025 -0.105 -0.104 -0.111
## EW -0.029 -0.102 -0.096 -0.109 -0.104
## BIO -0.043 0.102 0.155 0.112 0.104 0.104
## WE 0.033 -0.093 -0.154 -0.102 -0.101 -0.103 0.155
## SE 0.046 -0.108 -0.156 -0.109 -0.103 -0.105 0.157 -0.156
## AFSCS -0.035 -0.103 -0.110 -0.104 -0.109 -0.105 0.101 -0.098 -0.095tab_model(modA.7144488812,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.35 | 0.39 | 39.59 – 41.12 | 103.46 | <0.001 |
| DACCS | -14.97 | 0.87 | -16.68 – -13.27 | -17.20 | <0.001 |
| NE | -14.47 | 1.01 | -16.44 – -12.50 | -14.39 | <0.001 |
| OF | -8.47 | 0.89 | -10.22 – -6.71 | -9.46 | <0.001 |
| BECCS | -5.47 | 0.89 | -7.22 – -3.73 | -6.15 | <0.001 |
| EW | -4.60 | 0.88 | -6.34 – -2.87 | -5.20 | <0.001 |
| BIO | 1.06 | 1.02 | -0.94 – 3.07 | 1.04 | 0.298 |
| WE | 13.96 | 1.01 | 11.99 – 15.94 | 13.88 | <0.001 |
| SE | 15.00 | 1.03 | 12.98 – 17.02 | 14.59 | <0.001 |
| AFSCS | 21.54 | 0.88 | 19.82 – 23.25 | 24.60 | <0.001 |
| Random Effects | |||||
| σ2 | 256.42 | ||||
| τ00 id | 65.98 | ||||
| ICC | 0.20 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.310 / 0.451 | ||||
Q.2: Ben Risk Predicting Nat
modA.7444551184412 <- lmer(Naturalness ~ Benefit.c + Risk.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551184412)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Benefit.c + Risk.c + DACCS + NE + OF + BECCS +
## EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25485.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6109 -0.6052 0.0023 0.5947 3.3301
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 65.35 8.084
## Residual 218.92 14.796
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.18822 0.37272 1012.13803 107.825 < 2e-16 ***
## Benefit.c 0.06792 0.01307 2776.17288 5.195 2.19e-07 ***
## Risk.c -0.24329 0.01373 2954.69711 -17.713 < 2e-16 ***
## DACCS -12.15552 0.82072 2625.57996 -14.811 < 2e-16 ***
## NE -9.61496 0.97914 2780.36473 -9.820 < 2e-16 ***
## OF -4.86083 0.84997 2637.69991 -5.719 1.19e-08 ***
## BECCS -3.83540 0.83108 2606.49319 -4.615 4.12e-06 ***
## EW -2.80824 0.82681 2607.92643 -3.396 0.000693 ***
## BIO 2.01663 0.96100 2742.70699 2.098 0.035954 *
## WE 9.90647 0.95514 2772.21830 10.372 < 2e-16 ***
## SE 9.16407 0.99761 2763.71823 9.186 < 2e-16 ***
## AFSCS 16.83548 0.84328 2670.46414 19.964 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c Risk.c DACCS NE OF BECCS EW BIO WE
## Benefit.c -0.001
## Risk.c 0.020 0.312
## DACCS -0.040 -0.007 -0.165
## NE 0.024 -0.112 -0.295 -0.045
## OF -0.026 -0.005 -0.200 -0.068 -0.024
## BECCS -0.026 0.018 -0.081 -0.088 -0.075 -0.090
## EW -0.030 0.061 -0.068 -0.085 -0.072 -0.088 -0.095
## BIO -0.042 -0.121 -0.113 0.113 0.184 0.126 0.107 0.102
## WE 0.035 -0.033 0.180 -0.122 -0.197 -0.138 -0.116 -0.120 0.141
## SE 0.049 -0.018 0.265 -0.150 -0.221 -0.161 -0.123 -0.126 0.131 -0.091
## AFSCS -0.027 -0.077 0.218 -0.142 -0.162 -0.152 -0.130 -0.129 0.084 -0.037
## SE
## Benefit.c
## Risk.c
## DACCS
## NE
## OF
## BECCS
## EW
## BIO
## WE
## SE
## AFSCS -0.012tab_model(modA.7444551184412,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.19 | 0.37 | 39.46 – 40.92 | 107.83 | <0.001 |
| Benefit c | 0.07 | 0.01 | 0.04 – 0.09 | 5.20 | <0.001 |
| Risk c | -0.24 | 0.01 | -0.27 – -0.22 | -17.71 | <0.001 |
| DACCS | -12.16 | 0.82 | -13.76 – -10.55 | -14.81 | <0.001 |
| NE | -9.61 | 0.98 | -11.53 – -7.70 | -9.82 | <0.001 |
| OF | -4.86 | 0.85 | -6.53 – -3.19 | -5.72 | <0.001 |
| BECCS | -3.84 | 0.83 | -5.46 – -2.21 | -4.61 | <0.001 |
| EW | -2.81 | 0.83 | -4.43 – -1.19 | -3.40 | 0.001 |
| BIO | 2.02 | 0.96 | 0.13 – 3.90 | 2.10 | 0.036 |
| WE | 9.91 | 0.96 | 8.03 – 11.78 | 10.37 | <0.001 |
| SE | 9.16 | 1.00 | 7.21 – 11.12 | 9.19 | <0.001 |
| AFSCS | 16.84 | 0.84 | 15.18 – 18.49 | 19.96 | <0.001 |
| Random Effects | |||||
| σ2 | 218.92 | ||||
| τ00 id | 65.35 | ||||
| ICC | 0.23 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.397 / 0.536 | ||||
Q.3: Fam/Und Ben Risk Predicting Nat
modA.7444551135512 <- lmer(Naturalness ~ Benefit.c + Risk.c + FR.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551135512)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Benefit.c + Risk.c + FR.c + DACCS + NE + OF + BECCS +
## EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 25401.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6979 -0.5923 0.0151 0.5734 3.3383
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 70.72 8.41
## Residual 207.99 14.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.92212 0.37590 1004.38050 106.203 < 2e-16 ***
## Benefit.c 0.05278 0.01301 2818.54215 4.056 5.12e-05 ***
## Risk.c -0.22193 0.01377 2963.77462 -16.116 < 2e-16 ***
## FR.c 0.13557 0.01399 2888.83466 9.691 < 2e-16 ***
## DACCS -9.91189 0.83617 2659.73444 -11.854 < 2e-16 ***
## NE -11.79404 0.98688 2797.98889 -11.951 < 2e-16 ***
## OF -3.02541 0.85314 2644.66651 -3.546 0.000398 ***
## BECCS -1.72916 0.84178 2630.68569 -2.054 0.040058 *
## EW -0.05623 0.85830 2706.19582 -0.066 0.947773
## BIO 2.69218 0.94475 2711.19543 2.850 0.004410 **
## WE 6.53910 0.99725 2807.70108 6.557 6.50e-11 ***
## SE 5.51549 1.04814 2821.99870 5.262 1.53e-07 ***
## AFSCS 15.62517 0.83530 2656.01787 18.706 < 2e-16 ***
## ---
## 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.7444551135512,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 39.92 | 0.38 | 39.19 – 40.66 | 106.20 | <0.001 |
| Benefit c | 0.05 | 0.01 | 0.03 – 0.08 | 4.06 | <0.001 |
| Risk c | -0.22 | 0.01 | -0.25 – -0.19 | -16.12 | <0.001 |
| FR c | 0.14 | 0.01 | 0.11 – 0.16 | 9.69 | <0.001 |
| DACCS | -9.91 | 0.84 | -11.55 – -8.27 | -11.85 | <0.001 |
| NE | -11.79 | 0.99 | -13.73 – -9.86 | -11.95 | <0.001 |
| OF | -3.03 | 0.85 | -4.70 – -1.35 | -3.55 | <0.001 |
| BECCS | -1.73 | 0.84 | -3.38 – -0.08 | -2.05 | 0.040 |
| EW | -0.06 | 0.86 | -1.74 – 1.63 | -0.07 | 0.948 |
| BIO | 2.69 | 0.94 | 0.84 – 4.54 | 2.85 | 0.004 |
| WE | 6.54 | 1.00 | 4.58 – 8.49 | 6.56 | <0.001 |
| SE | 5.52 | 1.05 | 3.46 – 7.57 | 5.26 | <0.001 |
| AFSCS | 15.63 | 0.84 | 13.99 – 17.26 | 18.71 | <0.001 |
| Random Effects | |||||
| σ2 | 207.99 | ||||
| τ00 id | 70.72 | ||||
| ICC | 0.25 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.413 / 0.562 | ||||
Benefit
Set #1
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict benefits?
modA.714448881113 <- lmer(Ben ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.714448881113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 27683.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4246 -0.5150 0.0654 0.5678 3.1565
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.4 16.84
## Residual 381.8 19.54
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.2205 0.6407 1017.4032 90.874 < 2e-16 ***
## DACCS -3.1843 1.1019 2386.9763 -2.890 0.003888 **
## NE 1.4717 1.2849 2490.3918 1.145 0.252168
## OF -4.3571 1.1337 2399.1210 -3.843 0.000125 ***
## BECCS -2.9518 1.1284 2394.8266 -2.616 0.008955 **
## EW -5.5747 1.1205 2392.8913 -4.975 6.98e-07 ***
## BF -7.3526 1.3064 2491.1763 -5.628 2.02e-08 ***
## WE 7.5302 1.2847 2488.4305 5.862 5.19e-09 ***
## SE 8.7549 1.3138 2491.7176 6.664 3.28e-11 ***
## AFSCS 10.4528 1.1080 2389.8849 9.434 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BF WE SE
## DACCS -0.028
## NE 0.023 -0.092
## OF -0.016 -0.115 -0.073
## BECCS -0.018 -0.111 -0.098 -0.118
## EW -0.021 -0.107 -0.085 -0.116 -0.109
## BF 0.031 -0.094 -0.171 -0.110 -0.096 -0.097
## WE 0.023 -0.080 -0.169 -0.094 -0.092 -0.097 -0.171
## SE 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 -0.172 -0.171
## AFSCS -0.026 -0.110 -0.109 -0.110 -0.118 -0.111 -0.093 -0.088 -0.081tab_model(modA.714448881113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.22 | 0.64 | 56.96 – 59.48 | 90.87 | <0.001 |
| DACCS | -3.18 | 1.10 | -5.34 – -1.02 | -2.89 | 0.004 |
| NE | 1.47 | 1.28 | -1.05 – 3.99 | 1.15 | 0.252 |
| OF | -4.36 | 1.13 | -6.58 – -2.13 | -3.84 | <0.001 |
| BECCS | -2.95 | 1.13 | -5.16 – -0.74 | -2.62 | 0.009 |
| EW | -5.57 | 1.12 | -7.77 – -3.38 | -4.98 | <0.001 |
| BF | -7.35 | 1.31 | -9.91 – -4.79 | -5.63 | <0.001 |
| WE | 7.53 | 1.28 | 5.01 – 10.05 | 5.86 | <0.001 |
| SE | 8.75 | 1.31 | 6.18 – 11.33 | 6.66 | <0.001 |
| AFSCS | 10.45 | 1.11 | 8.28 – 12.63 | 9.43 | <0.001 |
| Random Effects | |||||
| σ2 | 381.81 | ||||
| τ00 id | 283.44 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.457 | ||||
Q.2: Risk Predicting Ben
modA.744455118441113 <- lmer(Ben ~ Risk.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.744455118441113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE +
## AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27367.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5767 -0.5166 0.0713 0.5419 3.2054
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.8 16.27
## Residual 339.2 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.01851 0.61451 1017.49951 94.414 < 2e-16 ***
## Risk.c -0.32860 0.01781 2947.72272 -18.451 < 2e-16 ***
## DACCS 0.39429 1.05821 2403.22100 0.373 0.709480
## NE 8.21888 1.26760 2526.86263 6.484 1.07e-10 ***
## OF 0.04484 1.09679 2412.11753 0.041 0.967392
## BECCS -1.12268 1.06990 2384.12634 -1.049 0.294131
## EW -3.70706 1.06259 2384.19348 -3.489 0.000494 ***
## BF -9.19352 1.23778 2475.99282 -7.427 1.52e-13 ***
## WE 2.52342 1.24352 2525.84295 2.029 0.042538 *
## SE 1.72404 1.29829 2514.77457 1.328 0.184322
## AFSCS 5.00702 1.08694 2436.59516 4.607 4.30e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c DACCS NE OF BECCS EW BF WE SE
## Risk.c 0.018
## DACCS -0.030 -0.184
## NE 0.016 -0.289 -0.033
## OF -0.019 -0.218 -0.070 -0.005
## BECCS -0.019 -0.092 -0.092 -0.066 -0.095
## EW -0.022 -0.095 -0.087 -0.053 -0.093 -0.100
## BF 0.031 0.080 -0.107 -0.187 -0.124 -0.103 -0.104
## WE 0.025 0.219 -0.116 -0.222 -0.137 -0.109 -0.115 -0.149
## SE 0.037 0.294 -0.151 -0.243 -0.161 -0.115 -0.120 -0.141 -0.096
## AFSCS -0.019 0.272 -0.155 -0.179 -0.162 -0.138 -0.133 -0.067 -0.023 0.006tab_model(modA.744455118441113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.02 | 0.61 | 56.81 – 59.22 | 94.41 | <0.001 |
| Risk c | -0.33 | 0.02 | -0.36 – -0.29 | -18.45 | <0.001 |
| DACCS | 0.39 | 1.06 | -1.68 – 2.47 | 0.37 | 0.709 |
| NE | 8.22 | 1.27 | 5.73 – 10.70 | 6.48 | <0.001 |
| OF | 0.04 | 1.10 | -2.11 – 2.20 | 0.04 | 0.967 |
| BECCS | -1.12 | 1.07 | -3.22 – 0.98 | -1.05 | 0.294 |
| EW | -3.71 | 1.06 | -5.79 – -1.62 | -3.49 | <0.001 |
| BF | -9.19 | 1.24 | -11.62 – -6.77 | -7.43 | <0.001 |
| WE | 2.52 | 1.24 | 0.09 – 4.96 | 2.03 | 0.043 |
| SE | 1.72 | 1.30 | -0.82 – 4.27 | 1.33 | 0.184 |
| AFSCS | 5.01 | 1.09 | 2.88 – 7.14 | 4.61 | <0.001 |
| Random Effects | |||||
| σ2 | 339.21 | ||||
| τ00 id | 264.75 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.141 / 0.518 | ||||
Q.3: Risk and Nat Predicting Ben
modA.744455113551113 <- lmer(Ben ~ Risk.c + Naturalness.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.744455113551113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + Naturalness.c + DACCS + NE + OF + BECCS + EW +
## BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27354.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5827 -0.5132 0.0670 0.5443 3.3001
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 258.0 16.06
## Residual 339.3 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.99693 0.60905 1012.38721 95.225 < 2e-16 ***
## Risk.c -0.29888 0.01904 2978.41274 -15.697 < 2e-16 ***
## Naturalness.c 0.10591 0.02442 2869.77305 4.337 1.49e-05 ***
## DACCS 1.66980 1.09741 2435.23479 1.522 0.12825
## NE 9.16876 1.28488 2536.11022 7.136 1.25e-12 ***
## OF 0.54201 1.10170 2413.06294 0.492 0.62278
## BECCS -0.72811 1.07300 2385.32406 -0.679 0.49747
## EW -3.38484 1.06447 2388.48994 -3.180 0.00149 **
## BF -8.91009 1.23825 2479.90081 -7.196 8.19e-13 ***
## WE 1.49863 1.26435 2523.79863 1.185 0.23601
## SE 0.73745 1.31638 2528.12689 0.560 0.57539
## AFSCS 3.21040 1.16223 2480.08350 2.762 0.00578 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c Ntrln. DACCS NE OF BECCS EW BF
## Risk.c 0.014
## Naturlnss.c -0.008 0.359
## DACCS -0.032 -0.069 0.268
## NE 0.015 -0.204 0.170 0.013
## OF -0.020 -0.165 0.102 -0.040 0.012
## BECCS -0.020 -0.055 0.085 -0.066 -0.051 -0.086
## EW -0.023 -0.063 0.071 -0.065 -0.041 -0.085 -0.093
## BF 0.031 0.093 0.051 -0.089 -0.175 -0.118 -0.098 -0.100
## WE 0.027 0.133 -0.186 -0.160 -0.246 -0.153 -0.123 -0.126 -0.156
## SE 0.038 0.209 -0.171 -0.189 -0.264 -0.175 -0.128 -0.130 -0.148
## AFSCS -0.015 0.109 -0.356 -0.234 -0.225 -0.187 -0.159 -0.149 -0.081
## WE SE
## Risk.c
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BF
## WE
## SE -0.061
## AFSCS 0.045 0.066tab_model(modA.744455113551113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.00 | 0.61 | 56.80 – 59.19 | 95.22 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.34 – -0.26 | -15.70 | <0.001 |
| Naturalness c | 0.11 | 0.02 | 0.06 – 0.15 | 4.34 | <0.001 |
| DACCS | 1.67 | 1.10 | -0.48 – 3.82 | 1.52 | 0.128 |
| NE | 9.17 | 1.28 | 6.65 – 11.69 | 7.14 | <0.001 |
| OF | 0.54 | 1.10 | -1.62 – 2.70 | 0.49 | 0.623 |
| BECCS | -0.73 | 1.07 | -2.83 – 1.38 | -0.68 | 0.497 |
| EW | -3.38 | 1.06 | -5.47 – -1.30 | -3.18 | 0.001 |
| BF | -8.91 | 1.24 | -11.34 – -6.48 | -7.20 | <0.001 |
| WE | 1.50 | 1.26 | -0.98 – 3.98 | 1.19 | 0.236 |
| SE | 0.74 | 1.32 | -1.84 – 3.32 | 0.56 | 0.575 |
| AFSCS | 3.21 | 1.16 | 0.93 – 5.49 | 2.76 | 0.006 |
| Random Effects | |||||
| σ2 | 339.29 | ||||
| τ00 id | 257.97 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.514 | ||||
Q.4: Risk, Nat, and Fam/Und Predicting Ben
modA.744455113551113 <- lmer(Ben ~ Risk.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.744455113551113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS +
## EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27330.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6121 -0.4980 0.0692 0.5470 3.3281
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 256.9 16.03
## Residual 335.6 18.32
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.79216 0.60827 1016.52016 95.011 < 2e-16 ***
## Risk.c -0.28313 0.01917 2978.72943 -14.770 < 2e-16 ***
## Naturalness.c 0.07893 0.02479 2896.03309 3.184 0.00147 **
## FR.c 0.10753 0.01972 3007.14629 5.452 5.37e-08 ***
## DACCS 3.09707 1.12269 2464.89329 2.759 0.00585 **
## NE 7.06107 1.33519 2615.46207 5.288 1.34e-07 ***
## OF 1.83432 1.12134 2445.74170 1.636 0.10200
## BECCS 0.82275 1.10459 2435.97833 0.745 0.45643
## EW -1.21656 1.13102 2512.02167 -1.076 0.28219
## BF -9.40672 1.23508 2480.43867 -7.616 3.69e-14 ***
## WE -0.86640 1.33062 2580.74909 -0.651 0.51502
## SE -1.91176 1.39701 2604.22658 -1.368 0.17129
## AFSCS 2.67142 1.16040 2478.46802 2.302 0.02141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.744455113551113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.79 | 0.61 | 56.60 – 58.98 | 95.01 | <0.001 |
| Risk c | -0.28 | 0.02 | -0.32 – -0.25 | -14.77 | <0.001 |
| Naturalness c | 0.08 | 0.02 | 0.03 – 0.13 | 3.18 | 0.001 |
| FR c | 0.11 | 0.02 | 0.07 – 0.15 | 5.45 | <0.001 |
| DACCS | 3.10 | 1.12 | 0.90 – 5.30 | 2.76 | 0.006 |
| NE | 7.06 | 1.34 | 4.44 – 9.68 | 5.29 | <0.001 |
| OF | 1.83 | 1.12 | -0.36 – 4.03 | 1.64 | 0.102 |
| BECCS | 0.82 | 1.10 | -1.34 – 2.99 | 0.74 | 0.456 |
| EW | -1.22 | 1.13 | -3.43 – 1.00 | -1.08 | 0.282 |
| BF | -9.41 | 1.24 | -11.83 – -6.99 | -7.62 | <0.001 |
| WE | -0.87 | 1.33 | -3.48 – 1.74 | -0.65 | 0.515 |
| SE | -1.91 | 1.40 | -4.65 – 0.83 | -1.37 | 0.171 |
| AFSCS | 2.67 | 1.16 | 0.40 – 4.95 | 2.30 | 0.021 |
| Random Effects | |||||
| σ2 | 335.56 | ||||
| τ00 id | 256.86 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.151 / 0.519 | ||||
Set #2
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict benefits?
modA.7144488811134 <- lmer(Ben ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7144488811134)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 27683.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4246 -0.5150 0.0654 0.5678 3.1565
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.4 16.84
## Residual 381.8 19.54
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.2205 0.6407 1017.4032 90.874 < 2e-16 ***
## DACCS -3.1843 1.1019 2386.9763 -2.890 0.003888 **
## NE 1.4717 1.2849 2490.3918 1.145 0.252168
## OF -4.3571 1.1337 2399.1210 -3.843 0.000125 ***
## BECCS -2.9518 1.1284 2394.8266 -2.616 0.008955 **
## EW -5.5747 1.1205 2392.8913 -4.975 6.98e-07 ***
## BIO 7.3526 1.3064 2491.1763 5.628 2.02e-08 ***
## WE 7.5302 1.2847 2488.4305 5.862 5.19e-09 ***
## SE 8.7549 1.3138 2491.7176 6.664 3.28e-11 ***
## AFSCS 10.4528 1.1080 2389.8849 9.434 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BIO WE SE
## DACCS -0.028
## NE 0.023 -0.092
## OF -0.016 -0.115 -0.073
## BECCS -0.018 -0.111 -0.098 -0.118
## EW -0.021 -0.107 -0.085 -0.116 -0.109
## BIO -0.031 0.094 0.171 0.110 0.096 0.097
## WE 0.023 -0.080 -0.169 -0.094 -0.092 -0.097 0.171
## SE 0.033 -0.103 -0.172 -0.104 -0.093 -0.097 0.172 -0.171
## AFSCS -0.026 -0.110 -0.109 -0.110 -0.118 -0.111 0.093 -0.088 -0.081tab_model(modA.7144488811134,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.22 | 0.64 | 56.96 – 59.48 | 90.87 | <0.001 |
| DACCS | -3.18 | 1.10 | -5.34 – -1.02 | -2.89 | 0.004 |
| NE | 1.47 | 1.28 | -1.05 – 3.99 | 1.15 | 0.252 |
| OF | -4.36 | 1.13 | -6.58 – -2.13 | -3.84 | <0.001 |
| BECCS | -2.95 | 1.13 | -5.16 – -0.74 | -2.62 | 0.009 |
| EW | -5.57 | 1.12 | -7.77 – -3.38 | -4.98 | <0.001 |
| BIO | 7.35 | 1.31 | 4.79 – 9.91 | 5.63 | <0.001 |
| WE | 7.53 | 1.28 | 5.01 – 10.05 | 5.86 | <0.001 |
| SE | 8.75 | 1.31 | 6.18 – 11.33 | 6.66 | <0.001 |
| AFSCS | 10.45 | 1.11 | 8.28 – 12.63 | 9.43 | <0.001 |
| Random Effects | |||||
| σ2 | 381.81 | ||||
| τ00 id | 283.44 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.457 | ||||
Q.2: Risk Predicting Ben
modA.74445511844111345 <- lmer(Ben ~ Risk.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511844111345)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE +
## AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27367.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5767 -0.5166 0.0713 0.5419 3.2054
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.8 16.27
## Residual 339.2 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.01851 0.61451 1017.49951 94.414 < 2e-16 ***
## Risk.c -0.32860 0.01781 2947.72272 -18.451 < 2e-16 ***
## DACCS 0.39429 1.05821 2403.22100 0.373 0.709480
## NE 8.21888 1.26760 2526.86263 6.484 1.07e-10 ***
## OF 0.04484 1.09679 2412.11753 0.041 0.967392
## BECCS -1.12268 1.06990 2384.12634 -1.049 0.294131
## EW -3.70706 1.06259 2384.19348 -3.489 0.000494 ***
## BIO 9.19352 1.23778 2475.99282 7.427 1.52e-13 ***
## WE 2.52342 1.24352 2525.84295 2.029 0.042538 *
## SE 1.72404 1.29829 2514.77457 1.328 0.184322
## AFSCS 5.00702 1.08694 2436.59516 4.607 4.30e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c DACCS NE OF BECCS EW BIO WE SE
## Risk.c 0.018
## DACCS -0.030 -0.184
## NE 0.016 -0.289 -0.033
## OF -0.019 -0.218 -0.070 -0.005
## BECCS -0.019 -0.092 -0.092 -0.066 -0.095
## EW -0.022 -0.095 -0.087 -0.053 -0.093 -0.100
## BIO -0.031 -0.080 0.107 0.187 0.124 0.103 0.104
## WE 0.025 0.219 -0.116 -0.222 -0.137 -0.109 -0.115 0.149
## SE 0.037 0.294 -0.151 -0.243 -0.161 -0.115 -0.120 0.141 -0.096
## AFSCS -0.019 0.272 -0.155 -0.179 -0.162 -0.138 -0.133 0.067 -0.023 0.006tab_model(modA.74445511844111345,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.02 | 0.61 | 56.81 – 59.22 | 94.41 | <0.001 |
| Risk c | -0.33 | 0.02 | -0.36 – -0.29 | -18.45 | <0.001 |
| DACCS | 0.39 | 1.06 | -1.68 – 2.47 | 0.37 | 0.709 |
| NE | 8.22 | 1.27 | 5.73 – 10.70 | 6.48 | <0.001 |
| OF | 0.04 | 1.10 | -2.11 – 2.20 | 0.04 | 0.967 |
| BECCS | -1.12 | 1.07 | -3.22 – 0.98 | -1.05 | 0.294 |
| EW | -3.71 | 1.06 | -5.79 – -1.62 | -3.49 | <0.001 |
| BIO | 9.19 | 1.24 | 6.77 – 11.62 | 7.43 | <0.001 |
| WE | 2.52 | 1.24 | 0.09 – 4.96 | 2.03 | 0.043 |
| SE | 1.72 | 1.30 | -0.82 – 4.27 | 1.33 | 0.184 |
| AFSCS | 5.01 | 1.09 | 2.88 – 7.14 | 4.61 | <0.001 |
| Random Effects | |||||
| σ2 | 339.21 | ||||
| τ00 id | 264.75 | ||||
| ICC | 0.44 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.141 / 0.518 | ||||
Q.3: Risk and Nat Predicting Ben
modA.7444551135511134 <- lmer(Ben ~ Risk.c + Naturalness.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551135511134)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + Naturalness.c + DACCS + NE + OF + BECCS + EW +
## BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27354.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5827 -0.5132 0.0670 0.5443 3.3001
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 258.0 16.06
## Residual 339.3 18.42
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.99693 0.60905 1012.38721 95.225 < 2e-16 ***
## Risk.c -0.29888 0.01904 2978.41274 -15.697 < 2e-16 ***
## Naturalness.c 0.10591 0.02442 2869.77305 4.337 1.49e-05 ***
## DACCS 1.66980 1.09741 2435.23479 1.522 0.12825
## NE 9.16876 1.28488 2536.11022 7.136 1.25e-12 ***
## OF 0.54201 1.10170 2413.06294 0.492 0.62278
## BECCS -0.72811 1.07300 2385.32406 -0.679 0.49747
## EW -3.38484 1.06447 2388.48994 -3.180 0.00149 **
## BIO 8.91009 1.23825 2479.90081 7.196 8.19e-13 ***
## WE 1.49863 1.26435 2523.79863 1.185 0.23601
## SE 0.73745 1.31638 2528.12689 0.560 0.57539
## AFSCS 3.21040 1.16223 2480.08350 2.762 0.00578 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c Ntrln. DACCS NE OF BECCS EW BIO
## Risk.c 0.014
## Naturlnss.c -0.008 0.359
## DACCS -0.032 -0.069 0.268
## NE 0.015 -0.204 0.170 0.013
## OF -0.020 -0.165 0.102 -0.040 0.012
## BECCS -0.020 -0.055 0.085 -0.066 -0.051 -0.086
## EW -0.023 -0.063 0.071 -0.065 -0.041 -0.085 -0.093
## BIO -0.031 -0.093 -0.051 0.089 0.175 0.118 0.098 0.100
## WE 0.027 0.133 -0.186 -0.160 -0.246 -0.153 -0.123 -0.126 0.156
## SE 0.038 0.209 -0.171 -0.189 -0.264 -0.175 -0.128 -0.130 0.148
## AFSCS -0.015 0.109 -0.356 -0.234 -0.225 -0.187 -0.159 -0.149 0.081
## WE SE
## Risk.c
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BIO
## WE
## SE -0.061
## AFSCS 0.045 0.066tab_model(modA.7444551135511134,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.00 | 0.61 | 56.80 – 59.19 | 95.22 | <0.001 |
| Risk c | -0.30 | 0.02 | -0.34 – -0.26 | -15.70 | <0.001 |
| Naturalness c | 0.11 | 0.02 | 0.06 – 0.15 | 4.34 | <0.001 |
| DACCS | 1.67 | 1.10 | -0.48 – 3.82 | 1.52 | 0.128 |
| NE | 9.17 | 1.28 | 6.65 – 11.69 | 7.14 | <0.001 |
| OF | 0.54 | 1.10 | -1.62 – 2.70 | 0.49 | 0.623 |
| BECCS | -0.73 | 1.07 | -2.83 – 1.38 | -0.68 | 0.497 |
| EW | -3.38 | 1.06 | -5.47 – -1.30 | -3.18 | 0.001 |
| BIO | 8.91 | 1.24 | 6.48 – 11.34 | 7.20 | <0.001 |
| WE | 1.50 | 1.26 | -0.98 – 3.98 | 1.19 | 0.236 |
| SE | 0.74 | 1.32 | -1.84 – 3.32 | 0.56 | 0.575 |
| AFSCS | 3.21 | 1.16 | 0.93 – 5.49 | 2.76 | 0.006 |
| Random Effects | |||||
| σ2 | 339.29 | ||||
| τ00 id | 257.97 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.514 | ||||
Q.4: Risk, Nat, and Fam/Und Predicting Ben
modA.7444551135511134 <- lmer(Ben ~ Risk.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551135511134)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS +
## EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27330.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6121 -0.4980 0.0692 0.5470 3.3281
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 256.9 16.03
## Residual 335.6 18.32
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.79216 0.60827 1016.52016 95.011 < 2e-16 ***
## Risk.c -0.28313 0.01917 2978.72943 -14.770 < 2e-16 ***
## Naturalness.c 0.07893 0.02479 2896.03309 3.184 0.00147 **
## FR.c 0.10753 0.01972 3007.14629 5.452 5.37e-08 ***
## DACCS 3.09707 1.12269 2464.89329 2.759 0.00585 **
## NE 7.06107 1.33519 2615.46207 5.288 1.34e-07 ***
## OF 1.83432 1.12134 2445.74170 1.636 0.10200
## BECCS 0.82275 1.10459 2435.97833 0.745 0.45643
## EW -1.21656 1.13102 2512.02167 -1.076 0.28219
## BIO 9.40672 1.23508 2480.43867 7.616 3.69e-14 ***
## WE -0.86640 1.33062 2580.74909 -0.651 0.51502
## SE -1.91176 1.39701 2604.22658 -1.368 0.17129
## AFSCS 2.67142 1.16040 2478.46802 2.302 0.02141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.7444551135511134,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.79 | 0.61 | 56.60 – 58.98 | 95.01 | <0.001 |
| Risk c | -0.28 | 0.02 | -0.32 – -0.25 | -14.77 | <0.001 |
| Naturalness c | 0.08 | 0.02 | 0.03 – 0.13 | 3.18 | 0.001 |
| FR c | 0.11 | 0.02 | 0.07 – 0.15 | 5.45 | <0.001 |
| DACCS | 3.10 | 1.12 | 0.90 – 5.30 | 2.76 | 0.006 |
| NE | 7.06 | 1.34 | 4.44 – 9.68 | 5.29 | <0.001 |
| OF | 1.83 | 1.12 | -0.36 – 4.03 | 1.64 | 0.102 |
| BECCS | 0.82 | 1.10 | -1.34 – 2.99 | 0.74 | 0.456 |
| EW | -1.22 | 1.13 | -3.43 – 1.00 | -1.08 | 0.282 |
| BIO | 9.41 | 1.24 | 6.99 – 11.83 | 7.62 | <0.001 |
| WE | -0.87 | 1.33 | -3.48 – 1.74 | -0.65 | 0.515 |
| SE | -1.91 | 1.40 | -4.65 – 0.83 | -1.37 | 0.171 |
| AFSCS | 2.67 | 1.16 | 0.40 – 4.95 | 2.30 | 0.021 |
| Random Effects | |||||
| σ2 | 335.56 | ||||
| τ00 id | 256.86 | ||||
| ICC | 0.43 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.151 / 0.519 | ||||
Risk
Set #1
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict risks?
modA.71444888111366 <- lmer(Risk ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.71444888111366)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 27466.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5812 -0.6117 -0.0694 0.5565 3.6749
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Residual 391.9 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.4243 0.5609 1018.7068 57.808 < 2e-16 ***
## DACCS 10.8426 1.0990 2495.8856 9.866 < 2e-16 ***
## NE 20.5519 1.2763 2615.0096 16.103 < 2e-16 ***
## OF 13.5376 1.1303 2510.3315 11.977 < 2e-16 ***
## BECCS 5.6756 1.1252 2505.6400 5.044 4.88e-07 ***
## EW 5.7300 1.1173 2503.1806 5.128 3.15e-07 ***
## BF -5.7162 1.2976 2616.5136 -4.405 1.10e-05 ***
## WE -14.9462 1.2762 2613.2863 -11.711 < 2e-16 ***
## SE -21.6013 1.3050 2617.2425 -16.553 < 2e-16 ***
## AFSCS -16.5218 1.1050 2499.0970 -14.952 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BF WE SE
## DACCS -0.033
## NE 0.027 -0.095
## OF -0.019 -0.111 -0.081
## BECCS -0.021 -0.108 -0.100 -0.115
## EW -0.025 -0.105 -0.090 -0.113 -0.107
## BF 0.036 -0.097 -0.164 -0.111 -0.100 -0.100
## WE 0.027 -0.085 -0.163 -0.097 -0.096 -0.100 -0.164
## SE 0.039 -0.105 -0.165 -0.106 -0.097 -0.100 -0.166 -0.165
## AFSCS -0.030 -0.107 -0.109 -0.107 -0.114 -0.109 -0.096 -0.092 -0.087tab_model(modA.71444888111366,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.42 | 0.56 | 31.32 – 33.52 | 57.81 | <0.001 |
| DACCS | 10.84 | 1.10 | 8.69 – 13.00 | 9.87 | <0.001 |
| NE | 20.55 | 1.28 | 18.05 – 23.05 | 16.10 | <0.001 |
| OF | 13.54 | 1.13 | 11.32 – 15.75 | 11.98 | <0.001 |
| BECCS | 5.68 | 1.13 | 3.47 – 7.88 | 5.04 | <0.001 |
| EW | 5.73 | 1.12 | 3.54 – 7.92 | 5.13 | <0.001 |
| BF | -5.72 | 1.30 | -8.26 – -3.17 | -4.41 | <0.001 |
| WE | -14.95 | 1.28 | -17.45 – -12.44 | -11.71 | <0.001 |
| SE | -21.60 | 1.30 | -24.16 – -19.04 | -16.55 | <0.001 |
| AFSCS | -16.52 | 1.11 | -18.69 – -14.36 | -14.95 | <0.001 |
| Random Effects | |||||
| σ2 | 391.88 | ||||
| τ00 id | 183.49 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.472 | ||||
Q.2: Ben Predicting Risk
modA.74445511844111366 <- lmer(Risk ~ Benefit.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511844111366)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE +
## AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27157.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4711 -0.6153 -0.0514 0.5895 3.5733
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.1 13.19
## Residual 348.6 18.67
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.49743 0.53913 1014.83213 60.278 < 2e-16 ***
## Benefit.c -0.30315 0.01662 2963.05117 -18.237 < 2e-16 ***
## DACCS 9.90027 1.04021 2481.76069 9.518 < 2e-16 ***
## NE 21.02454 1.20745 2595.07549 17.412 < 2e-16 ***
## OF 12.23551 1.07085 2500.31944 11.426 < 2e-16 ***
## BECCS 4.75139 1.06481 2488.48710 4.462 8.47e-06 ***
## EW 4.00716 1.06039 2489.75535 3.779 0.000161 ***
## BF -7.87076 1.23312 2612.78201 -6.383 2.05e-10 ***
## WE -12.75032 1.21330 2609.62119 -10.509 < 2e-16 ***
## SE -18.96017 1.24263 2615.52667 -15.258 < 2e-16 ***
## AFSCS -13.37406 1.05882 2513.88731 -12.631 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c DACCS NE OF BECCS EW BF WE SE
## Benefit.c -0.007
## DACCS -0.032 0.050
## NE 0.027 -0.021 -0.096
## OF -0.019 0.066 -0.108 -0.081
## BECCS -0.021 0.047 -0.106 -0.101 -0.112
## EW -0.025 0.089 -0.100 -0.091 -0.107 -0.103
## BF 0.034 0.097 -0.092 -0.167 -0.103 -0.094 -0.090
## WE 0.027 -0.101 -0.089 -0.161 -0.103 -0.099 -0.107 -0.173
## SE 0.039 -0.116 -0.110 -0.162 -0.113 -0.101 -0.109 -0.176 -0.152
## AFSCS -0.028 -0.163 -0.114 -0.104 -0.117 -0.120 -0.122 -0.110 -0.073 -0.066tab_model(modA.74445511844111366,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.50 | 0.54 | 31.44 – 33.55 | 60.28 | <0.001 |
| Benefit c | -0.30 | 0.02 | -0.34 – -0.27 | -18.24 | <0.001 |
| DACCS | 9.90 | 1.04 | 7.86 – 11.94 | 9.52 | <0.001 |
| NE | 21.02 | 1.21 | 18.66 – 23.39 | 17.41 | <0.001 |
| OF | 12.24 | 1.07 | 10.14 – 14.34 | 11.43 | <0.001 |
| BECCS | 4.75 | 1.06 | 2.66 – 6.84 | 4.46 | <0.001 |
| EW | 4.01 | 1.06 | 1.93 – 6.09 | 3.78 | <0.001 |
| BF | -7.87 | 1.23 | -10.29 – -5.45 | -6.38 | <0.001 |
| WE | -12.75 | 1.21 | -15.13 – -10.37 | -10.51 | <0.001 |
| SE | -18.96 | 1.24 | -21.40 – -16.52 | -15.26 | <0.001 |
| AFSCS | -13.37 | 1.06 | -15.45 – -11.30 | -12.63 | <0.001 |
| Random Effects | |||||
| σ2 | 348.59 | ||||
| τ00 id | 174.09 | ||||
| ICC | 0.33 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.302 / 0.535 | ||||
Q.3: Benefit and Nat Predicting Ben
modA.74445511355111366 <- lmer(Risk ~ Benefit.c + Naturalness.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511355111366)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + Naturalness.c + DACCS + NE + OF + BECCS +
## EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 26845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1133 -0.5867 -0.0161 0.5774 3.7285
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 175.8 13.26
## Residual 304.5 17.45
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.63364 0.52678 1011.73074 61.950 < 2e-16 ***
## Benefit.c -0.24981 0.01609 2994.53357 -15.529 < 2e-16 ***
## Naturalness.c -0.39588 0.02151 2892.93622 -18.400 < 2e-16 ***
## DACCS 4.12154 1.02646 2501.49390 4.015 6.11e-05 ***
## NE 15.14426 1.17969 2593.51167 12.837 < 2e-16 ***
## OF 9.09503 1.02001 2471.38800 8.917 < 2e-16 ***
## BECCS 2.76589 1.00587 2451.91888 2.750 0.00601 **
## EW 2.45397 0.99960 2457.63302 2.455 0.01416 *
## BF -7.86802 1.16000 2571.01255 -6.783 1.46e-11 ***
## WE -7.71224 1.17457 2585.57224 -6.566 6.22e-11 ***
## SE -13.35513 1.20699 2607.60143 -11.065 < 2e-16 ***
## AFSCS -5.39958 1.08511 2560.07989 -4.976 6.92e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c Ntrln. DACCS NE OF BECCS EW BF
## Benefit.c -0.004
## Naturlnss.c -0.014 -0.187
## DACCS -0.034 -0.009 0.306
## NE 0.021 -0.071 0.271 -0.004
## OF -0.020 0.035 0.165 -0.052 -0.030
## BECCS -0.021 0.026 0.105 -0.069 -0.067 -0.094
## EW -0.025 0.072 0.083 -0.070 -0.063 -0.092 -0.094
## BF 0.033 0.097 0.001 -0.086 -0.162 -0.101 -0.092 -0.089
## WE 0.028 -0.054 -0.236 -0.153 -0.216 -0.137 -0.120 -0.123 -0.171
## SE 0.039 -0.065 -0.249 -0.177 -0.221 -0.148 -0.123 -0.125 -0.173
## AFSCS -0.019 -0.075 -0.399 -0.223 -0.200 -0.172 -0.153 -0.145 -0.100
## WE SE
## Benefit.c
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BF
## WE
## SE -0.086
## AFSCS 0.031 0.043tab_model(modA.74445511355111366,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.63 | 0.53 | 31.60 – 33.67 | 61.95 | <0.001 |
| Benefit c | -0.25 | 0.02 | -0.28 – -0.22 | -15.53 | <0.001 |
| Naturalness c | -0.40 | 0.02 | -0.44 – -0.35 | -18.40 | <0.001 |
| DACCS | 4.12 | 1.03 | 2.11 – 6.13 | 4.02 | <0.001 |
| NE | 15.14 | 1.18 | 12.83 – 17.46 | 12.84 | <0.001 |
| OF | 9.10 | 1.02 | 7.10 – 11.10 | 8.92 | <0.001 |
| BECCS | 2.77 | 1.01 | 0.79 – 4.74 | 2.75 | 0.006 |
| EW | 2.45 | 1.00 | 0.49 – 4.41 | 2.45 | 0.014 |
| BF | -7.87 | 1.16 | -10.14 – -5.59 | -6.78 | <0.001 |
| WE | -7.71 | 1.17 | -10.02 – -5.41 | -6.57 | <0.001 |
| SE | -13.36 | 1.21 | -15.72 – -10.99 | -11.06 | <0.001 |
| AFSCS | -5.40 | 1.09 | -7.53 – -3.27 | -4.98 | <0.001 |
| Random Effects | |||||
| σ2 | 304.49 | ||||
| τ00 id | 175.78 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.364 / 0.597 | ||||
Q.4: Ben, Nat, and Fam/Und Predicting Risk
modA.74445511355111366 <- lmer(Risk ~ Benefit.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511355111366)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS +
## EW + BF + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 26808.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5539 -0.6058 -0.0052 0.5672 3.9418
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.2 13.20
## Residual 299.9 17.32
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.86499 0.52497 1018.31284 62.603 < 2e-16 ***
## Benefit.c -0.23569 0.01612 2992.23158 -14.620 < 2e-16 ***
## Naturalness.c -0.36268 0.02195 2934.01779 -16.520 < 2e-16 ***
## FR.c -0.11835 0.01802 2992.51333 -6.569 5.96e-11 ***
## DACCS 2.45813 1.04989 2536.25081 2.341 0.0193 *
## NE 17.13221 1.20962 2663.12734 14.163 < 2e-16 ***
## OF 7.52683 1.04019 2512.50253 7.236 6.11e-13 ***
## BECCS 1.01914 1.03321 2506.20951 0.986 0.3240
## EW 0.07287 1.05631 2584.33815 0.069 0.9450
## BF -7.11631 1.15719 2577.56329 -6.150 8.97e-10 ***
## WE -4.99995 1.23706 2653.33980 -4.042 5.45e-05 ***
## SE -10.23919 1.28841 2690.85251 -7.947 2.78e-15 ***
## AFSCS -4.74780 1.08171 2559.01865 -4.389 1.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.74445511355111366,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.86 | 0.52 | 31.84 – 33.89 | 62.60 | <0.001 |
| Benefit c | -0.24 | 0.02 | -0.27 – -0.20 | -14.62 | <0.001 |
| Naturalness c | -0.36 | 0.02 | -0.41 – -0.32 | -16.52 | <0.001 |
| FR c | -0.12 | 0.02 | -0.15 – -0.08 | -6.57 | <0.001 |
| DACCS | 2.46 | 1.05 | 0.40 – 4.52 | 2.34 | 0.019 |
| NE | 17.13 | 1.21 | 14.76 – 19.50 | 14.16 | <0.001 |
| OF | 7.53 | 1.04 | 5.49 – 9.57 | 7.24 | <0.001 |
| BECCS | 1.02 | 1.03 | -1.01 – 3.05 | 0.99 | 0.324 |
| EW | 0.07 | 1.06 | -2.00 – 2.14 | 0.07 | 0.945 |
| BF | -7.12 | 1.16 | -9.39 – -4.85 | -6.15 | <0.001 |
| WE | -5.00 | 1.24 | -7.43 – -2.57 | -4.04 | <0.001 |
| SE | -10.24 | 1.29 | -12.77 – -7.71 | -7.95 | <0.001 |
| AFSCS | -4.75 | 1.08 | -6.87 – -2.63 | -4.39 | <0.001 |
| Random Effects | |||||
| σ2 | 299.90 | ||||
| τ00 id | 174.19 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.371 / 0.602 | ||||
Set #2
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict risks?
modA.714448881113664 <- lmer(Risk ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.714448881113664)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 27466.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5812 -0.6117 -0.0694 0.5565 3.6749
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 183.5 13.55
## Residual 391.9 19.80
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.4243 0.5609 1018.7068 57.808 < 2e-16 ***
## DACCS 10.8426 1.0990 2495.8856 9.866 < 2e-16 ***
## NE 20.5519 1.2763 2615.0096 16.103 < 2e-16 ***
## OF 13.5376 1.1303 2510.3315 11.977 < 2e-16 ***
## BECCS 5.6756 1.1252 2505.6400 5.044 4.88e-07 ***
## EW 5.7300 1.1173 2503.1806 5.128 3.15e-07 ***
## BIO 5.7162 1.2976 2616.5136 4.405 1.10e-05 ***
## WE -14.9462 1.2762 2613.2863 -11.711 < 2e-16 ***
## SE -21.6013 1.3050 2617.2425 -16.553 < 2e-16 ***
## AFSCS -16.5218 1.1050 2499.0970 -14.952 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BIO WE SE
## DACCS -0.033
## NE 0.027 -0.095
## OF -0.019 -0.111 -0.081
## BECCS -0.021 -0.108 -0.100 -0.115
## EW -0.025 -0.105 -0.090 -0.113 -0.107
## BIO -0.036 0.097 0.164 0.111 0.100 0.100
## WE 0.027 -0.085 -0.163 -0.097 -0.096 -0.100 0.164
## SE 0.039 -0.105 -0.165 -0.106 -0.097 -0.100 0.166 -0.165
## AFSCS -0.030 -0.107 -0.109 -0.107 -0.114 -0.109 0.096 -0.092 -0.087tab_model(modA.714448881113664,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.42 | 0.56 | 31.32 – 33.52 | 57.81 | <0.001 |
| DACCS | 10.84 | 1.10 | 8.69 – 13.00 | 9.87 | <0.001 |
| NE | 20.55 | 1.28 | 18.05 – 23.05 | 16.10 | <0.001 |
| OF | 13.54 | 1.13 | 11.32 – 15.75 | 11.98 | <0.001 |
| BECCS | 5.68 | 1.13 | 3.47 – 7.88 | 5.04 | <0.001 |
| EW | 5.73 | 1.12 | 3.54 – 7.92 | 5.13 | <0.001 |
| BIO | 5.72 | 1.30 | 3.17 – 8.26 | 4.41 | <0.001 |
| WE | -14.95 | 1.28 | -17.45 – -12.44 | -11.71 | <0.001 |
| SE | -21.60 | 1.30 | -24.16 – -19.04 | -16.55 | <0.001 |
| AFSCS | -16.52 | 1.11 | -18.69 – -14.36 | -14.95 | <0.001 |
| Random Effects | |||||
| σ2 | 391.88 | ||||
| τ00 id | 183.49 | ||||
| ICC | 0.32 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.225 / 0.472 | ||||
Q.2: Ben Predicting Risk
modA.744455118441113664 <- lmer(Risk ~ Benefit.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.744455118441113664)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + DACCS + NE + OF + BECCS + EW + BIO + WE +
## SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 27157.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4711 -0.6153 -0.0514 0.5895 3.5733
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.1 13.19
## Residual 348.6 18.67
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.49743 0.53913 1014.83213 60.278 < 2e-16 ***
## Benefit.c -0.30315 0.01662 2963.05117 -18.237 < 2e-16 ***
## DACCS 9.90027 1.04021 2481.76069 9.518 < 2e-16 ***
## NE 21.02454 1.20745 2595.07549 17.412 < 2e-16 ***
## OF 12.23551 1.07085 2500.31944 11.426 < 2e-16 ***
## BECCS 4.75139 1.06481 2488.48710 4.462 8.47e-06 ***
## EW 4.00716 1.06039 2489.75535 3.779 0.000161 ***
## BIO 7.87076 1.23312 2612.78201 6.383 2.05e-10 ***
## WE -12.75032 1.21330 2609.62119 -10.509 < 2e-16 ***
## SE -18.96017 1.24263 2615.52667 -15.258 < 2e-16 ***
## AFSCS -13.37406 1.05882 2513.88731 -12.631 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c DACCS NE OF BECCS EW BIO WE SE
## Benefit.c -0.007
## DACCS -0.032 0.050
## NE 0.027 -0.021 -0.096
## OF -0.019 0.066 -0.108 -0.081
## BECCS -0.021 0.047 -0.106 -0.101 -0.112
## EW -0.025 0.089 -0.100 -0.091 -0.107 -0.103
## BIO -0.034 -0.097 0.092 0.167 0.103 0.094 0.090
## WE 0.027 -0.101 -0.089 -0.161 -0.103 -0.099 -0.107 0.173
## SE 0.039 -0.116 -0.110 -0.162 -0.113 -0.101 -0.109 0.176 -0.152
## AFSCS -0.028 -0.163 -0.114 -0.104 -0.117 -0.120 -0.122 0.110 -0.073 -0.066tab_model(modA.744455118441113664,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.50 | 0.54 | 31.44 – 33.55 | 60.28 | <0.001 |
| Benefit c | -0.30 | 0.02 | -0.34 – -0.27 | -18.24 | <0.001 |
| DACCS | 9.90 | 1.04 | 7.86 – 11.94 | 9.52 | <0.001 |
| NE | 21.02 | 1.21 | 18.66 – 23.39 | 17.41 | <0.001 |
| OF | 12.24 | 1.07 | 10.14 – 14.34 | 11.43 | <0.001 |
| BECCS | 4.75 | 1.06 | 2.66 – 6.84 | 4.46 | <0.001 |
| EW | 4.01 | 1.06 | 1.93 – 6.09 | 3.78 | <0.001 |
| BIO | 7.87 | 1.23 | 5.45 – 10.29 | 6.38 | <0.001 |
| WE | -12.75 | 1.21 | -15.13 – -10.37 | -10.51 | <0.001 |
| SE | -18.96 | 1.24 | -21.40 – -16.52 | -15.26 | <0.001 |
| AFSCS | -13.37 | 1.06 | -15.45 – -11.30 | -12.63 | <0.001 |
| Random Effects | |||||
| σ2 | 348.59 | ||||
| τ00 id | 174.09 | ||||
| ICC | 0.33 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.302 / 0.535 | ||||
Q.3: Benefit and Nat Predicting Ben
modA.744455113551113664 <- lmer(Risk ~ Benefit.c + Naturalness.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.744455113551113664)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + Naturalness.c + DACCS + NE + OF + BECCS +
## EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 26845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1133 -0.5867 -0.0161 0.5774 3.7285
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 175.8 13.26
## Residual 304.5 17.45
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.63364 0.52678 1011.73074 61.950 < 2e-16 ***
## Benefit.c -0.24981 0.01609 2994.53357 -15.529 < 2e-16 ***
## Naturalness.c -0.39588 0.02151 2892.93622 -18.400 < 2e-16 ***
## DACCS 4.12154 1.02646 2501.49390 4.015 6.11e-05 ***
## NE 15.14426 1.17969 2593.51167 12.837 < 2e-16 ***
## OF 9.09503 1.02001 2471.38800 8.917 < 2e-16 ***
## BECCS 2.76589 1.00587 2451.91888 2.750 0.00601 **
## EW 2.45397 0.99960 2457.63302 2.455 0.01416 *
## BIO 7.86802 1.16000 2571.01255 6.783 1.46e-11 ***
## WE -7.71224 1.17457 2585.57224 -6.566 6.22e-11 ***
## SE -13.35513 1.20699 2607.60143 -11.065 < 2e-16 ***
## AFSCS -5.39958 1.08511 2560.07989 -4.976 6.92e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c Ntrln. DACCS NE OF BECCS EW BIO
## Benefit.c -0.004
## Naturlnss.c -0.014 -0.187
## DACCS -0.034 -0.009 0.306
## NE 0.021 -0.071 0.271 -0.004
## OF -0.020 0.035 0.165 -0.052 -0.030
## BECCS -0.021 0.026 0.105 -0.069 -0.067 -0.094
## EW -0.025 0.072 0.083 -0.070 -0.063 -0.092 -0.094
## BIO -0.033 -0.097 -0.001 0.086 0.162 0.101 0.092 0.089
## WE 0.028 -0.054 -0.236 -0.153 -0.216 -0.137 -0.120 -0.123 0.171
## SE 0.039 -0.065 -0.249 -0.177 -0.221 -0.148 -0.123 -0.125 0.173
## AFSCS -0.019 -0.075 -0.399 -0.223 -0.200 -0.172 -0.153 -0.145 0.100
## WE SE
## Benefit.c
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BIO
## WE
## SE -0.086
## AFSCS 0.031 0.043tab_model(modA.744455113551113664,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.63 | 0.53 | 31.60 – 33.67 | 61.95 | <0.001 |
| Benefit c | -0.25 | 0.02 | -0.28 – -0.22 | -15.53 | <0.001 |
| Naturalness c | -0.40 | 0.02 | -0.44 – -0.35 | -18.40 | <0.001 |
| DACCS | 4.12 | 1.03 | 2.11 – 6.13 | 4.02 | <0.001 |
| NE | 15.14 | 1.18 | 12.83 – 17.46 | 12.84 | <0.001 |
| OF | 9.10 | 1.02 | 7.10 – 11.10 | 8.92 | <0.001 |
| BECCS | 2.77 | 1.01 | 0.79 – 4.74 | 2.75 | 0.006 |
| EW | 2.45 | 1.00 | 0.49 – 4.41 | 2.45 | 0.014 |
| BIO | 7.87 | 1.16 | 5.59 – 10.14 | 6.78 | <0.001 |
| WE | -7.71 | 1.17 | -10.02 – -5.41 | -6.57 | <0.001 |
| SE | -13.36 | 1.21 | -15.72 – -10.99 | -11.06 | <0.001 |
| AFSCS | -5.40 | 1.09 | -7.53 – -3.27 | -4.98 | <0.001 |
| Random Effects | |||||
| σ2 | 304.49 | ||||
| τ00 id | 175.78 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.364 / 0.597 | ||||
Q.4: Ben, Nat, and Fam/Und Predicting Risk
modA.744455113551113664 <- lmer(Risk ~ Benefit.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.744455113551113664)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + Naturalness.c + FR.c + DACCS + NE + OF + BECCS +
## EW + BIO + WE + SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 26808.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5539 -0.6058 -0.0052 0.5672 3.9418
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 174.2 13.20
## Residual 299.9 17.32
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.86499 0.52497 1018.31284 62.603 < 2e-16 ***
## Benefit.c -0.23569 0.01612 2992.23158 -14.620 < 2e-16 ***
## Naturalness.c -0.36268 0.02195 2934.01779 -16.520 < 2e-16 ***
## FR.c -0.11835 0.01802 2992.51333 -6.569 5.96e-11 ***
## DACCS 2.45813 1.04989 2536.25081 2.341 0.0193 *
## NE 17.13221 1.20962 2663.12734 14.163 < 2e-16 ***
## OF 7.52683 1.04019 2512.50253 7.236 6.11e-13 ***
## BECCS 1.01914 1.03321 2506.20951 0.986 0.3240
## EW 0.07287 1.05631 2584.33815 0.069 0.9450
## BIO 7.11631 1.15719 2577.56329 6.150 8.97e-10 ***
## WE -4.99995 1.23706 2653.33980 -4.042 5.45e-05 ***
## SE -10.23919 1.28841 2690.85251 -7.947 2.78e-15 ***
## AFSCS -4.74780 1.08171 2559.01865 -4.389 1.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.744455113551113664,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 32.86 | 0.52 | 31.84 – 33.89 | 62.60 | <0.001 |
| Benefit c | -0.24 | 0.02 | -0.27 – -0.20 | -14.62 | <0.001 |
| Naturalness c | -0.36 | 0.02 | -0.41 – -0.32 | -16.52 | <0.001 |
| FR c | -0.12 | 0.02 | -0.15 – -0.08 | -6.57 | <0.001 |
| DACCS | 2.46 | 1.05 | 0.40 – 4.52 | 2.34 | 0.019 |
| NE | 17.13 | 1.21 | 14.76 – 19.50 | 14.16 | <0.001 |
| OF | 7.53 | 1.04 | 5.49 – 9.57 | 7.24 | <0.001 |
| BECCS | 1.02 | 1.03 | -1.01 – 3.05 | 0.99 | 0.324 |
| EW | 0.07 | 1.06 | -2.00 – 2.14 | 0.07 | 0.945 |
| BIO | 7.12 | 1.16 | 4.85 – 9.39 | 6.15 | <0.001 |
| WE | -5.00 | 1.24 | -7.43 – -2.57 | -4.04 | <0.001 |
| SE | -10.24 | 1.29 | -12.77 – -7.71 | -7.95 | <0.001 |
| AFSCS | -4.75 | 1.08 | -6.87 – -2.63 | -4.39 | <0.001 |
| Random Effects | |||||
| σ2 | 299.90 | ||||
| τ00 id | 174.19 | ||||
| ICC | 0.37 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.371 / 0.602 | ||||
Familiarity/Understanding
Set #1
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict familiarity and understanding of technology?
modA.7144488811136622 <- lmer(FR ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7144488811136622)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 27119.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0412 -0.5869 -0.0111 0.5966 3.1013
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.7 14.31
## Residual 329.5 18.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.4788 0.5608 1019.6835 97.137 < 2e-16 ***
## DACCS -18.6588 1.0176 2429.9000 -18.337 < 2e-16 ***
## NE 12.8814 1.1847 2540.1072 10.873 < 2e-16 ***
## OF -16.1090 1.0468 2442.9867 -15.389 < 2e-16 ***
## BECCS -16.5934 1.0420 2438.5014 -15.925 < 2e-16 ***
## EW -22.1040 1.0346 2436.3623 -21.364 < 2e-16 ***
## BF 5.1720 1.2045 2541.1519 4.294 1.82e-05 ***
## WE 27.7437 1.1846 2538.2041 23.421 < 2e-16 ***
## SE 31.6044 1.2114 2541.7653 26.090 < 2e-16 ***
## AFSCS 12.8248 1.0232 2432.9494 12.534 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BF WE SE
## DACCS -0.030
## NE 0.024 -0.093
## OF -0.017 -0.113 -0.076
## BECCS -0.019 -0.110 -0.099 -0.117
## EW -0.022 -0.106 -0.087 -0.115 -0.108
## BF 0.033 -0.095 -0.169 -0.110 -0.098 -0.098
## WE 0.024 -0.082 -0.167 -0.095 -0.093 -0.098 -0.168
## SE 0.036 -0.104 -0.169 -0.105 -0.095 -0.098 -0.170 -0.169
## AFSCS -0.027 -0.109 -0.109 -0.109 -0.116 -0.110 -0.094 -0.090 -0.083tab_model(modA.7144488811136622,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.48 | 0.56 | 53.38 – 55.58 | 97.14 | <0.001 |
| DACCS | -18.66 | 1.02 | -20.65 – -16.66 | -18.34 | <0.001 |
| NE | 12.88 | 1.18 | 10.56 – 15.20 | 10.87 | <0.001 |
| OF | -16.11 | 1.05 | -18.16 – -14.06 | -15.39 | <0.001 |
| BECCS | -16.59 | 1.04 | -18.64 – -14.55 | -15.93 | <0.001 |
| EW | -22.10 | 1.03 | -24.13 – -20.08 | -21.36 | <0.001 |
| BF | 5.17 | 1.20 | 2.81 – 7.53 | 4.29 | <0.001 |
| WE | 27.74 | 1.18 | 25.42 – 30.07 | 23.42 | <0.001 |
| SE | 31.60 | 1.21 | 29.23 – 33.98 | 26.09 | <0.001 |
| AFSCS | 12.82 | 1.02 | 10.82 – 14.83 | 12.53 | <0.001 |
| Random Effects | |||||
| σ2 | 329.45 | ||||
| τ00 id | 204.67 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.403 / 0.632 | ||||
Q.2: Nat predicting fam/und
modA.7444551184411136622 <- lmer(FR ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BF + WE + SE + AFSCS + (1|id), data = L)
summary(modA.7444551184411136622)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BF + WE +
## SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 26915.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9409 -0.5696 0.0042 0.5983 3.1937
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213.5 14.61
## Residual 297.7 17.25
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.36006 0.55913 1014.67378 97.223 < 2e-16 ***
## Naturalness.c 0.31552 0.02125 2843.02165 14.848 < 2e-16 ***
## DACCS -13.90200 1.02337 2449.90959 -13.585 < 2e-16 ***
## NE 17.52007 1.17515 2535.98030 14.909 < 2e-16 ***
## OF -13.46299 1.01576 2416.65135 -13.254 < 2e-16 ***
## BECCS -14.89955 1.00166 2401.66368 -14.875 < 2e-16 ***
## EW -20.66833 0.99325 2404.53197 -20.809 < 2e-16 ***
## BF 5.48603 1.15217 2497.99616 4.761 2.03e-06 ***
## WE 23.34344 1.17088 2518.74575 19.937 < 2e-16 ***
## SE 26.81364 1.20286 2538.95709 22.292 < 2e-16 ***
## AFSCS 6.03803 1.07972 2492.29200 5.592 2.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. DACCS NE OF BECCS EW BF WE
## Naturlnss.c -0.014
## DACCS -0.032 0.313
## NE 0.019 0.265 -0.001
## OF -0.019 0.176 -0.052 -0.024
## BECCS -0.019 0.112 -0.069 -0.064 -0.096
## EW -0.022 0.100 -0.069 -0.055 -0.096 -0.096
## BF 0.031 0.019 -0.084 -0.159 -0.105 -0.094 -0.095
## WE 0.026 -0.253 -0.153 -0.225 -0.134 -0.117 -0.119 -0.170
## SE 0.036 -0.269 -0.179 -0.230 -0.146 -0.120 -0.120 -0.171 -0.091
## AFSCS -0.017 -0.425 -0.228 -0.208 -0.172 -0.153 -0.143 -0.092 0.030
## SE
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BF
## WE
## SE
## AFSCS 0.043tab_model(modA.7444551184411136622,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.36 | 0.56 | 53.26 – 55.46 | 97.22 | <0.001 |
| Naturalness c | 0.32 | 0.02 | 0.27 – 0.36 | 14.85 | <0.001 |
| DACCS | -13.90 | 1.02 | -15.91 – -11.90 | -13.58 | <0.001 |
| NE | 17.52 | 1.18 | 15.22 – 19.82 | 14.91 | <0.001 |
| OF | -13.46 | 1.02 | -15.45 – -11.47 | -13.25 | <0.001 |
| BECCS | -14.90 | 1.00 | -16.86 – -12.94 | -14.87 | <0.001 |
| EW | -20.67 | 0.99 | -22.62 – -18.72 | -20.81 | <0.001 |
| BF | 5.49 | 1.15 | 3.23 – 7.75 | 4.76 | <0.001 |
| WE | 23.34 | 1.17 | 21.05 – 25.64 | 19.94 | <0.001 |
| SE | 26.81 | 1.20 | 24.46 – 29.17 | 22.29 | <0.001 |
| AFSCS | 6.04 | 1.08 | 3.92 – 8.16 | 5.59 | <0.001 |
| Random Effects | |||||
| σ2 | 297.73 | ||||
| τ00 id | 213.46 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.434 / 0.670 | ||||
Set #2
Q.1: (SIMPLE MODEL) How do climate change method contrasts predict familiarity and understanding of technology?
modA.71444888111366223 <- lmer(FR ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.71444888111366223)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 27119.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0412 -0.5869 -0.0111 0.5966 3.1013
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 204.7 14.31
## Residual 329.5 18.15
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.4788 0.5608 1019.6835 97.137 < 2e-16 ***
## DACCS -18.6588 1.0176 2429.9000 -18.337 < 2e-16 ***
## NE 12.8814 1.1847 2540.1072 10.873 < 2e-16 ***
## OF -16.1090 1.0468 2442.9867 -15.389 < 2e-16 ***
## BECCS -16.5934 1.0420 2438.5014 -15.925 < 2e-16 ***
## EW -22.1040 1.0346 2436.3623 -21.364 < 2e-16 ***
## BIO -5.1720 1.2045 2541.1519 -4.294 1.82e-05 ***
## WE 27.7437 1.1846 2538.2041 23.421 < 2e-16 ***
## SE 31.6044 1.2114 2541.7653 26.090 < 2e-16 ***
## AFSCS 12.8248 1.0232 2432.9494 12.534 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DACCS NE OF BECCS EW BIO WE SE
## DACCS -0.030
## NE 0.024 -0.093
## OF -0.017 -0.113 -0.076
## BECCS -0.019 -0.110 -0.099 -0.117
## EW -0.022 -0.106 -0.087 -0.115 -0.108
## BIO -0.033 0.095 0.169 0.110 0.098 0.098
## WE 0.024 -0.082 -0.167 -0.095 -0.093 -0.098 0.168
## SE 0.036 -0.104 -0.169 -0.105 -0.095 -0.098 0.170 -0.169
## AFSCS -0.027 -0.109 -0.109 -0.109 -0.116 -0.110 0.094 -0.090 -0.083tab_model(modA.71444888111366223,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.48 | 0.56 | 53.38 – 55.58 | 97.14 | <0.001 |
| DACCS | -18.66 | 1.02 | -20.65 – -16.66 | -18.34 | <0.001 |
| NE | 12.88 | 1.18 | 10.56 – 15.20 | 10.87 | <0.001 |
| OF | -16.11 | 1.05 | -18.16 – -14.06 | -15.39 | <0.001 |
| BECCS | -16.59 | 1.04 | -18.64 – -14.55 | -15.93 | <0.001 |
| EW | -22.10 | 1.03 | -24.13 – -20.08 | -21.36 | <0.001 |
| BIO | -5.17 | 1.20 | -7.53 – -2.81 | -4.29 | <0.001 |
| WE | 27.74 | 1.18 | 25.42 – 30.07 | 23.42 | <0.001 |
| SE | 31.60 | 1.21 | 29.23 – 33.98 | 26.09 | <0.001 |
| AFSCS | 12.82 | 1.02 | 10.82 – 14.83 | 12.53 | <0.001 |
| Random Effects | |||||
| σ2 | 329.45 | ||||
| τ00 id | 204.67 | ||||
| ICC | 0.38 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.403 / 0.632 | ||||
Q.2: Nat predicting fam/und
modA.74445511844111366223 <- lmer(FR ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BIO + WE + SE + AFSCS + (1|id), data = L)
summary(modA.74445511844111366223)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Naturalness.c + DACCS + NE + OF + BECCS + EW + BIO + WE +
## SE + AFSCS + (1 | id)
## Data: L
##
## REML criterion at convergence: 26915.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9409 -0.5696 0.0042 0.5983 3.1937
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 213.5 14.61
## Residual 297.7 17.25
## Number of obs: 3021, groups: id, 1007
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.36006 0.55913 1014.67378 97.223 < 2e-16 ***
## Naturalness.c 0.31552 0.02125 2843.02165 14.848 < 2e-16 ***
## DACCS -13.90200 1.02337 2449.90959 -13.585 < 2e-16 ***
## NE 17.52007 1.17515 2535.98030 14.909 < 2e-16 ***
## OF -13.46299 1.01576 2416.65135 -13.254 < 2e-16 ***
## BECCS -14.89955 1.00166 2401.66368 -14.875 < 2e-16 ***
## EW -20.66833 0.99325 2404.53197 -20.809 < 2e-16 ***
## BIO -5.48603 1.15217 2497.99616 -4.761 2.03e-06 ***
## WE 23.34344 1.17088 2518.74575 19.937 < 2e-16 ***
## SE 26.81364 1.20286 2538.95709 22.292 < 2e-16 ***
## AFSCS 6.03803 1.07972 2492.29200 5.592 2.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. DACCS NE OF BECCS EW BIO WE
## Naturlnss.c -0.014
## DACCS -0.032 0.313
## NE 0.019 0.265 -0.001
## OF -0.019 0.176 -0.052 -0.024
## BECCS -0.019 0.112 -0.069 -0.064 -0.096
## EW -0.022 0.100 -0.069 -0.055 -0.096 -0.096
## BIO -0.031 -0.019 0.084 0.159 0.105 0.094 0.095
## WE 0.026 -0.253 -0.153 -0.225 -0.134 -0.117 -0.119 0.170
## SE 0.036 -0.269 -0.179 -0.230 -0.146 -0.120 -0.120 0.171 -0.091
## AFSCS -0.017 -0.425 -0.228 -0.208 -0.172 -0.153 -0.143 0.092 0.030
## SE
## Naturlnss.c
## DACCS
## NE
## OF
## BECCS
## EW
## BIO
## WE
## SE
## AFSCS 0.043tab_model(modA.74445511844111366223,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.36 | 0.56 | 53.26 – 55.46 | 97.22 | <0.001 |
| Naturalness c | 0.32 | 0.02 | 0.27 – 0.36 | 14.85 | <0.001 |
| DACCS | -13.90 | 1.02 | -15.91 – -11.90 | -13.58 | <0.001 |
| NE | 17.52 | 1.18 | 15.22 – 19.82 | 14.91 | <0.001 |
| OF | -13.46 | 1.02 | -15.45 – -11.47 | -13.25 | <0.001 |
| BECCS | -14.90 | 1.00 | -16.86 – -12.94 | -14.87 | <0.001 |
| EW | -20.67 | 0.99 | -22.62 – -18.72 | -20.81 | <0.001 |
| BIO | -5.49 | 1.15 | -7.75 – -3.23 | -4.76 | <0.001 |
| WE | 23.34 | 1.17 | 21.05 – 25.64 | 19.94 | <0.001 |
| SE | 26.81 | 1.20 | 24.46 – 29.17 | 22.29 | <0.001 |
| AFSCS | 6.04 | 1.08 | 3.92 – 8.16 | 5.59 | <0.001 |
| Random Effects | |||||
| σ2 | 297.73 | ||||
| τ00 id | 213.46 | ||||
| ICC | 0.42 | ||||
| N id | 1007 | ||||
| Observations | 3021 | ||||
| Marginal R2 / Conditional R2 | 0.434 / 0.670 | ||||