Goal: to provide a pilot data overview for the Climate Change Methods naturalness project. The variables of main focus are perceptions of naturalness and risk, as well as endorsement for climate change methods. Ten methods of climate change technology were included in pilot data collection, with each participant being randomly assigned a survey regarding 3 of the 10 total methods. Final data will be aggregated for a cross random effects analysis to account for relationships between key variables for all 10 methods.
#Climate Change Methods Descriptions
#Climate Change Methods Scale List
CC <- read.csv("ClimateChangeFull.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))#Participants
#Number of responses (rows)
nrow(CC)## [1] 105#Age range
range(CC$Dem_Age, na.rm = T)## [1] 20 85#Average age
mean(CC$Dem_Age, na.rm = T)## [1] 46.67961#Standard deviation of age
sd(CC$Dem_Age, na.rm = T)## [1] 16.9047#Gender frequencies
table(CC$Dem_Gen)##
## 1 2
## 61 42#Ethnicity frequencies
table(CC$Dem_Ethnicity)##
## 1 2 3 4 6
## 3 13 2 4 81#Understanding
Understanding was measured with 1 item on a 0-100 scale ( 0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
Understanding Item 1: I understand how this works.
#AF/SCS Understanding/Familiarity Descriptives
CC$Understanding_AFSCS <- CC$Risk_AFSCS_30
psych::describe(CC$Understanding_AFSCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 64.32 24.78 71 65.4 26.69 0 100 100 -0.46 -0.38 4.45range(CC$Understanding_AFSCS, na.rm=TRUE)## [1] 0 100hist(CC$Understanding_AFSCS, main = 'AFSCS Understanding Item #1: "I understand how this works."')
#Biochar Understanding/Familiarity Descriptives
CC$Understanding_BIO <- CC$Risk_BIO_30
psych::describe(CC$Understanding_BIO)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 61.51 24.87 64 62.35 29.65 12 100 88 -0.22 -1.08 4.09range(CC$Understanding_BIO, na.rm=TRUE)## [1] 12 100hist(CC$Understanding_BIO, main = 'BIO Understanding Item #1: "I understand how this works."')
#BECCS Understanding Descriptives
CC$Understanding_BECCS <- CC$Risk_BECCS_30
psych::describe(CC$Understanding_BECCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 53.53 31.67 60 54.23 36.32 0 100 100 -0.23 -1.28 5.28range(CC$Understanding_BECCS, na.rm=TRUE)## [1] 0 100hist(CC$Understanding_BECCS, main = 'BECCS Understanding Item #1: "I understand how this works."')
#DACCS Understanding Descriptives
CC$Understanding_DACCS <- CC$Risk_DACCS_30
psych::describe(CC$Understanding_DACCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 48.86 27.52 52.5 48.63 31.88 0 100 100 0.08 -0.77 4.59range(CC$Understanding_DACCS, na.rm=TRUE)## [1] 0 100hist(CC$Understanding_DACCS, main = 'DACCS Understanding Item #1: "I understand how this works."')
#EW Understanding Descriptives
CC$Understanding_EW <- CC$Risk_EW_30
psych::describe(CC$Understanding_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 33 51.39 26.19 54 52 28.17 2 100 98 -0.19 -0.84 4.56range(CC$Understanding_EW, na.rm=TRUE)## [1] 2 100hist(CC$Understanding_EW, main = 'EW Understanding Item #1: "I understand how this works."')
#OF Understanding Descriptives
CC$Understanding_OF <- CC$Risk_OF_30
psych::describe(CC$Understanding_OF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 32 47.56 26.22 50.5 48.69 24.46 0 95 95 -0.37 -0.83 4.64range(CC$Understanding_OF, na.rm=TRUE)## [1] 0 95hist(CC$Understanding_OF, main = 'OF Understanding Item #1: "I understand how this works."')
#BF Understanding Descriptives
CC$Understanding_BF <- CC$Risk_BF_30
psych::describe(CC$Understanding_BF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 59.61 28.94 62 61.63 25.2 0 100 100 -0.35 -0.6 6.03range(CC$Understanding_BF, na.rm=TRUE)## [1] 0 100hist(CC$Understanding_BF, main = 'BF Understanding Item #1: "I understand how this works."')
#NE Understanding Descriptives
CC$Understanding_NE <- CC$Risk_NE_30
psych::describe(CC$Understanding_NE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 61.24 28.1 68 62.32 29.65 5 100 95 -0.4 -1.12 5.22range(CC$Understanding_NE, na.rm=TRUE)## [1] 5 100hist(CC$Understanding_NE, main = 'NE Understanding Item #1: "I understand how this works."')
#SE Understanding/Familiarity Descriptives
CC$Understanding_SE <- CC$Risk_SE_30
psych::describe(CC$Understanding_SE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 24 76.71 26.06 80.5 80.35 28.91 14 100 86 -0.95 -0.13 5.32range(CC$Understanding_SE, na.rm=TRUE)## [1] 14 100hist(CC$Understanding_SE, main = 'SE Understanding Item #1: "I understand how this works."')
#WE Understanding Descriptives
CC$Understanding_WE <- CC$Risk_WE_30
psych::describe(CC$Understanding_WE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 63.46 25.41 66.5 64.23 23.72 15 100 85 -0.3 -1.04 4.98range(CC$Understanding_WE, na.rm=TRUE)## [1] 15 100hist(CC$Understanding_WE, main = 'WE Understanding Item #1: "I understand how this works."')
#Familiarity
Familiarity and was measured with 1 item on a 0-100 scale ( 0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’). Familiarity Item 1: This is familiar.
#AF/SCS Familiarity Descriptives
CC$Familiarity_AFSCS <-CC$Risk_AFSCS_31
psych::describe(CC$Familiarity_AFSCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 56.52 27.8 59 56.32 34.1 4 100 96 -0.02 -1.22 4.99range(CC$Familiarity_AFSCS, na.rm=TRUE)## [1] 4 100hist(CC$Familiarity_AFSCS, main = 'AFSCS Familiarity Item #1: "This is familiar."')
#Biochar Familiarity Descriptives
CC$Familiarity_BIO <-CC$Risk_BIO_31
psych::describe(CC$Familiarity_BIO)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 51.14 29.43 47 50.68 31.13 0 100 100 0.25 -1.13 4.84range(CC$Familiarity_BIO, na.rm=TRUE)## [1] 0 100hist(CC$Familiarity_BIO, main = 'BIO Familiarity Item #1: "This is familiar."')
#BECCS Familiarity Descriptives
CC$Familiarity_BECCS <-CC$Risk_BECCS_31
psych::describe(CC$Familiarity_BECCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35 41.97 32.88 31 40.28 37.06 0 100 100 0.36 -1.36 5.56range(CC$Familiarity_BECCS, na.rm=TRUE)## [1] 0 100hist(CC$Familiarity_BECCS, main = 'BECCS Familiarity Item #1: "This is familiar."')
#DACCS Understanding/Familiarity Descriptives
CC$Familiarity_DACCS <-CC$Risk_DACCS_31
psych::describe(CC$Familiarity_DACCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 41.89 28.97 40.5 40.53 29.65 0 100 100 0.28 -0.88 4.83range(CC$Familiarity_DACCS, na.rm=TRUE)## [1] 0 100hist(CC$Familiarity_DACCS, main = 'DACCS Familiarity Item #1: "This is familiar."')
#EW Familiarity Descriptives
CC$Familiarity_EW <-CC$Risk_EW_31
psych::describe(CC$Familiarity_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 33 39.64 29.26 39 38 35.58 1 100 99 0.35 -1.01 5.09range(CC$Familiarity_EW, na.rm=TRUE)## [1] 1 100hist(CC$Familiarity_EW, main = 'EW Familiarity Item #1: "This is familiar."')
#OF Familiarity Descriptives
CC$Familiarity_OF <-CC$Risk_OF_31
psych::describe(CC$Familiarity_OF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 37.03 23.38 39 37 26.69 0 78 78 -0.12 -1.2 4.2range(CC$Familiarity_OF, na.rm=TRUE)## [1] 0 78hist(CC$Familiarity_OF, main = 'OF Familiarity Item #1: "This is familiar."')
#BF Familiarity Descriptives
CC$Familiarity_BF <-CC$Risk_BF_31
psych::describe(CC$Familiarity_BF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 58.57 22.08 60 59.58 14.83 0 99 99 -0.59 0.4 4.6range(CC$Familiarity_BF, na.rm=TRUE)## [1] 0 99hist(CC$Familiarity_BF, main = 'BF Familiarity Item #1: "This is familiar."')
#NE Familiarity Descriptives
CC$Familiarity_NE <-CC$Risk_NE_31
psych::describe(CC$Familiarity_NE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 66.31 24.08 76 68.12 19.27 9 100 91 -0.76 -0.36 4.47range(CC$Familiarity_NE, na.rm=TRUE)## [1] 9 100hist(CC$Familiarity_NE, main = 'NE Familiarity Item #1: "This is familiar."')
#SE Familiarity Descriptives
CC$Familiarity_SE <-CC$Risk_SE_31
psych::describe(CC$Familiarity_SE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 73.04 26.02 74 75.76 32.62 3 100 97 -0.78 -0.02 5.2range(CC$Familiarity_SE, na.rm=TRUE)## [1] 3 100hist(CC$Familiarity_SE, main = 'SE Familiarity Item #1: "This is familiar."')
#WE Familiarity Descriptives
CC$Familiarity_WE <-CC$Risk_WE_31
psych::describe(CC$Familiarity_WE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 62.12 26.92 62.5 63.64 22.98 0 100 100 -0.42 -0.57 5.28range(CC$Familiarity_WE, na.rm=TRUE)## [1] 0 100hist(CC$Familiarity_WE, main = 'WE Familiarity Item #1: "This is familiar."')
#Risk Perception
Risk perception was measured with 2 items on a 0-100 scale ( 0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
Risk Item 1: This is risky to deploy. Risk Item 2: This is frightening.
#AF/SCS Risk Descriptives
#AFSCS Risk Scale
CC$Risk_Score_AFSCS <- rowMeans(CC [, c("Risk_AFSCS_32", "Risk_AFSCS_33")], na.rm=TRUE)
#AFSCS Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_AFSCS_32, CC$Risk_AFSCS_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_AFSCS_32, CC$Risk_AFSCS_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.8 0.8 7.9 0.022 43 31 0.8
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.89 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_AFSCS_32 0.82 0.8 0.64 0.8 4 NA 0 0.8
## CC.Risk_AFSCS_33 0.77 0.8 0.64 0.8 4 NA 0 0.8
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_AFSCS_32 31 0.95 0.95 0.85 0.8 47 33
## CC.Risk_AFSCS_33 31 0.95 0.95 0.85 0.8 39 32hist(CC$Risk_Score_AFSCS, main = 'AF/SCS Risk Scale Score')
#AFSCS Individual Risk Items
psych::describe(CC$Risk_AFSCS_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 46.65 33.09 49 45.88 43 0 100 100 0.07 -1.17 5.94range(CC$Risk_AFSCS_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_AFSCS_32, main = 'AFSCS Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_AFSCS_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 39 32.09 37 36.72 41.51 0 100 100 0.34 -1.1 5.76range(CC$Risk_AFSCS_33, na.rm=TRUE)## [1] 0 100hist(CC$Risk_AFSCS_33, main = 'AFSCS Risk Item #2: "This is frightening."')
#BIOCHAR Risk Descriptives
#BIO Risk Scale
CC$Risk_Score_BIO <- rowMeans(CC [, c("Risk_BIO_32", "Risk_BIO_33")], na.rm=TRUE)
#BIO Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_BIO_32, CC$Risk_BIO_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_BIO_32, CC$Risk_BIO_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.85 0.85 12 0.016 42 28 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.92 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.Risk_BIO_32 0.84 0.85 0.73 0.85 5.8 NA 0 0.85
## CC.Risk_BIO_33 0.87 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_BIO_32 37 0.96 0.96 0.89 0.85 44 29
## CC.Risk_BIO_33 36 0.96 0.96 0.89 0.85 39 29hist(CC$Risk_Score_BIO, main = 'BIO Risk Scale Score')
#BIO Individual Risk Items
psych::describe(CC$Risk_BIO_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 43.78 28.74 45 43.13 29.65 0 100 100 0.1 -1.01 4.72range(CC$Risk_BIO_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_BIO_32, main = 'BIO Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_BIO_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 39.17 29.31 34 37.5 34.84 0 98 98 0.36 -1.05 4.89range(CC$Risk_BIO_33, na.rm=TRUE)## [1] 0 98hist(CC$Risk_BIO_33, main = 'BIO Risk Item #2: "This is frightening."')
#BECCS Risk Descriptives
#BECCS Risk Scale
CC$Risk_Score_BECCS <- rowMeans(CC [, c("Risk_BECCS_32", "Risk_BECCS_33")], na.rm=TRUE)
#BECCS Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_BECCS_32, CC$Risk_BECCS_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_BECCS_32, CC$Risk_BECCS_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.72 0.72 5 0.032 48 24 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.77 0.83 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_BECCS_32 0.75 0.72 0.51 0.72 2.5 NA 0 0.72
## CC.Risk_BECCS_33 0.69 0.72 0.51 0.72 2.5 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_BECCS_32 35 0.93 0.93 0.78 0.72 53 27
## CC.Risk_BECCS_33 36 0.92 0.93 0.78 0.72 43 26hist(CC$Risk_Score_BECCS, main = 'BECCS Risk Scale Score')
#BECCS Individual Risk Items
psych::describe(CC$Risk_BECCS_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35 53.2 26.87 56 54.41 32.62 0 100 100 -0.35 -0.93 4.54range(CC$Risk_BECCS_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_BECCS_32, main = 'BECCS Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_BECCS_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 43.31 25.77 39.5 42.93 25.95 0 99 99 0.11 -0.83 4.3range(CC$Risk_BECCS_33, na.rm=TRUE)## [1] 0 99hist(CC$Risk_BECCS_33, main = 'BECCS Risk Item #2: "This is frightening."')
#DACCS Risk Descriptives
#DACCS Risk Scale
CC$Risk_Score_DACCS <- rowMeans(CC [, c("Risk_DACCS_32", "Risk_DACCS_33")], na.rm=TRUE)
#DACCS Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_DACCS_32, CC$Risk_DACCS_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_DACCS_32, CC$Risk_DACCS_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.79 0.66 0.66 3.8 0.041 61 26 0.66
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.78 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_DACCS_32 0.51 0.66 0.43 0.66 1.9 NA 0 0.66
## CC.Risk_DACCS_33 0.84 0.66 0.43 0.66 1.9 NA 0 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_DACCS_32 36 0.89 0.91 0.74 0.66 64 25
## CC.Risk_DACCS_33 36 0.93 0.91 0.74 0.66 58 33hist(CC$Risk_Score_DACCS, main = 'DACCS Risk Scale Score')
#DACCS Individual Risk Items
psych::describe(CC$Risk_DACCS_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 63.64 25.47 66 65.33 22.24 0 100 100 -0.54 -0.34 4.24range(CC$Risk_DACCS_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_DACCS_32, main = 'DACCS Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_DACCS_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 57.61 32.59 64 59.1 34.84 0 100 100 -0.42 -1.12 5.43range(CC$Risk_DACCS_33, na.rm=TRUE)## [1] 0 100hist(CC$Risk_DACCS_33, main = 'DACCS Risk Item #2: "This is frightening."')
#EW Risk Descriptives
#EW Risk Scale
CC$Risk_Score_EW <- rowMeans(CC [, c("Risk_EW_32", "Risk_EW_33")], na.rm=TRUE)
#EW Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_EW_32, CC$Risk_EW_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_EW_32, CC$Risk_EW_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.46 0.46 0.3 0.3 0.85 0.11 40 23 0.3
##
## lower alpha upper 95% confidence boundaries
## 0.25 0.46 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.Risk_EW_32 0.32 0.3 0.089 0.3 0.43 NA 0 0.3
## CC.Risk_EW_33 0.28 0.3 0.089 0.3 0.43 NA 0 0.3
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_EW_32 33 0.82 0.81 0.44 0.3 45 29
## CC.Risk_EW_33 33 0.79 0.81 0.44 0.3 35 27hist(CC$Risk_Score_EW, main = 'EW Risk Scale Score')
#EW Individual Risk Items
psych::describe(CC$Risk_EW_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 33 45.21 28.91 46 44.74 28.17 0 100 100 0.01 -1.04 5.03range(CC$Risk_EW_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_EW_32, main = 'EW Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_EW_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 33 34.61 27.08 35 33.3 41.51 0 83 83 0.16 -1.31 4.71range(CC$Risk_EW_33, na.rm=TRUE)## [1] 0 83hist(CC$Risk_EW_33, main = 'EW Risk Item #2: "This is frightening."')
#OF Risk Descriptives
#OF Risk Scale
CC$Risk_Score_OF <- rowMeans(CC [, c("Risk_OF_32", "Risk_OF_33")], na.rm=TRUE)
#OF Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_OF_32, CC$Risk_OF_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_OF_32, CC$Risk_OF_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.56 0.56 2.6 0.054 51 24 0.56
##
## lower alpha upper 95% confidence boundaries
## 0.61 0.72 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_OF_32 0.49 0.56 0.32 0.56 1.3 NA 0 0.56
## CC.Risk_OF_33 0.65 0.56 0.32 0.56 1.3 NA 0 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_OF_32 32 0.87 0.88 0.66 0.56 52 26
## CC.Risk_OF_33 32 0.90 0.88 0.66 0.56 51 29hist(CC$Risk_Score_OF, main = 'OF Risk Scale Score')
#OF Individual Risk Items
psych::describe(CC$Risk_OF_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 32 51.69 25.67 48 50.92 25.2 0 100 100 0.32 -0.76 4.54range(CC$Risk_OF_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_OF_32, main = 'OF Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_OF_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 32 50.69 29.48 51.5 50.65 28.91 0 100 100 0.16 -0.91 5.21range(CC$Risk_OF_33, na.rm=TRUE)## [1] 0 100hist(CC$Risk_OF_33, main = 'OF Risk Item #2: "This is frightening."')
#BIOFUEL Risk Descriptives
#BF Risk Scale
CC$Risk_Score_BF <- rowMeans(CC [, c("Risk_BF_32", "Risk_BF_33")], na.rm=TRUE)
#DACCS Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_BF_32, CC$Risk_BF_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_BF_32, CC$Risk_BF_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.79 0.79 7.4 0.023 44 28 0.79
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.88 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_BF_32 0.78 0.79 0.62 0.79 3.7 NA 0 0.79
## CC.Risk_BF_33 0.80 0.79 0.62 0.79 3.7 NA 0 0.79
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_BF_32 23 0.94 0.95 0.84 0.79 49 30
## CC.Risk_BF_33 23 0.95 0.95 0.84 0.79 40 30hist(CC$Risk_Score_BF, main = 'BF Risk Scale Score')
#DACCS Individual Risk Items
psych::describe(CC$Risk_BF_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 49.26 29.61 54 50.11 35.58 0 91 91 -0.26 -1.28 6.17range(CC$Risk_BF_32, na.rm=TRUE)## [1] 0 91hist(CC$Risk_BF_32, main = 'BF Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_BF_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 39.7 30.04 34 38.21 34.1 0 97 97 0.31 -1.2 6.26range(CC$Risk_BF_33, na.rm=TRUE)## [1] 0 97hist(CC$Risk_BF_33, main = 'BF Risk Item #2: "This is frightening."')
#NE Risk Descriptives
#NE Risk Scale
CC$Risk_Score_NE <- rowMeans(CC [, c("Risk_NE_32", "Risk_NE_33")], na.rm=TRUE)
#NE Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_NE_32, CC$Risk_NE_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_NE_32, CC$Risk_NE_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.97 0.97 0.93 0.93 28 0.0067 57 29 0.93
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.97 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_NE_32 0.88 0.93 0.87 0.93 14 NA 0 0.93
## CC.Risk_NE_33 0.99 0.93 0.87 0.93 14 NA 0 0.93
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_NE_32 29 0.98 0.98 0.95 0.93 58 29
## CC.Risk_NE_33 29 0.98 0.98 0.95 0.93 56 31hist(CC$Risk_Score_NE, main = 'NE Risk Scale Score')
#NE Individual Risk Items
psych::describe(CC$Risk_NE_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 57.9 29.04 62 58.88 37.06 1 100 99 -0.33 -1.24 5.39range(CC$Risk_NE_32, na.rm=TRUE)## [1] 1 100hist(CC$Risk_NE_32, main = 'NE Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_NE_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 55.52 30.76 62 56.2 37.06 0 100 100 -0.22 -1.15 5.71range(CC$Risk_NE_33, na.rm=TRUE)## [1] 0 100hist(CC$Risk_NE_33, main = 'NE Risk Item #2: "This is frightening."')
#SE Risk Descriptives
#SE Risk Scale
CC$Risk_Score_SE <- rowMeans(CC [, c("Risk_SE_32", "Risk_SE_33")], na.rm=TRUE)
#DACCS Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_SE_32, CC$Risk_SE_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_SE_32, CC$Risk_SE_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.92 0.92 22 0.0084 33 32 0.92
##
## lower alpha upper 95% confidence boundaries
## 0.94 0.96 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Risk_SE_32 0.92 0.92 0.84 0.92 11 NA 0 0.92
## CC.Risk_SE_33 0.91 0.92 0.84 0.92 11 NA 0 0.92
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_SE_32 25 0.98 0.98 0.94 0.92 35 33
## CC.Risk_SE_33 25 0.98 0.98 0.94 0.92 31 33hist(CC$Risk_Score_SE, main = 'SE Risk Scale Score')
#DACCS Individual Risk Items
psych::describe(CC$Risk_SE_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 34.68 32.73 19 32.67 28.17 0 100 100 0.46 -1.4 6.55range(CC$Risk_SE_32, na.rm=TRUE)## [1] 0 100hist(CC$Risk_SE_32, main = 'SE Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_SE_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 31.44 32.6 17 28.86 25.2 0 100 100 0.63 -1.15 6.52range(CC$Risk_SE_33, na.rm=TRUE)## [1] 0 100hist(CC$Risk_SE_33, main = 'SE Risk Item #2: "This is frightening."')
#WE Risk Descriptives
#WE Risk Scale
CC$Risk_Score_WE <- rowMeans(CC [, c("Risk_WE_32", "Risk_WE_33")], na.rm=TRUE)
#WE Cronbach's alpha for scale
psych::alpha(data.frame(CC$Risk_WE_32, CC$Risk_WE_33))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Risk_WE_32, CC$Risk_WE_33))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.66 0.66 3.8 0.041 36 25 0.66
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.79 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_WE_32 0.56 0.66 0.43 0.66 1.9 NA 0 0.66
## CC.Risk_WE_33 0.77 0.66 0.43 0.66 1.9 NA 0 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Risk_WE_32 26 0.89 0.91 0.74 0.66 35 25
## CC.Risk_WE_33 26 0.92 0.91 0.74 0.66 37 30hist(CC$Risk_Score_WE, main = 'WE Risk Scale Score')
#WE Individual Risk Items
psych::describe(CC$Risk_WE_32)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 34.54 25.39 33 33.95 37.06 0 77 77 0 -1.51 4.98range(CC$Risk_WE_32, na.rm=TRUE)## [1] 0 77hist(CC$Risk_WE_32, main = 'WE Risk Item #1: "This is risky to deploy."')
psych::describe(CC$Risk_WE_33)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 36.77 29.96 34 34.91 31.88 0 97 97 0.46 -1.06 5.88range(CC$Risk_WE_33, na.rm=TRUE)## [1] 0 97hist(CC$Risk_WE_33, main = 'WE Risk Item #2: "This is frightening."')
#Naturalness Descriptives
Naturalness perception was measured for 3 items on a 0-100 scale ( 0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’). Naturalness Item 1: This is natural. Naturalness Item 2: This involves humans altering naturally occurring processes. Naturalness Item 3: This relies on science-based technology.
#AF/SCS Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_AFSCS<- CC$Naturalness_AFSCS_30
CC$Naturalness_2R_AFSCS <- (102-CC$Naturalness_AFSCS_31)
CC$Naturalness_3R_AFSCS <- (102-CC$Naturalness_AFSCS_35)
#AFSCS Naturalness Scale
CC$Naturalness_Score_AFSCS <- rowMeans(CC [, c("Naturalness_1_AFSCS", "Naturalness_2R_AFSCS", "Naturalness_3R_AFSCS")], na.rm=TRUE)
CC$Naturalness_Scale_AFSCS <- data.frame(CC$Naturalness_1_AFSCS, CC$Naturalness_2R_AFSCS, CC$Naturalness_3R_AFSCS)
#AFSCS Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_AFSCS, CC$Naturalness_2R_AFSCS, CC$Naturalness_3R_AFSCS))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Naturalness_1_AFSCS, CC$Naturalness_2R_AFSCS, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Naturalness_1_AFSCS ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_AFSCS, CC$Naturalness_2R_AFSCS,
## CC$Naturalness_3R_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.087 0.076 0.12 0.027 0.082 0.15 48 16 0.036
##
## lower alpha upper 95% confidence boundaries
## -0.21 0.09 0.39
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## CC.Naturalness_1_AFSCS 0.384 0.384 0.238 0.238 0.624 0.12
## CC.Naturalness_2R_AFSCS -0.480 -0.480 -0.194 -0.194 -0.325 0.29
## CC.Naturalness_3R_AFSCS 0.068 0.069 0.036 0.036 0.074 0.18
## var.r med.r
## CC.Naturalness_1_AFSCS NA 0.238
## CC.Naturalness_2R_AFSCS NA -0.194
## CC.Naturalness_3R_AFSCS NA 0.036
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_AFSCS 31 0.46 0.47 -0.19 -0.098 61 27
## CC.Naturalness_2R_AFSCS 31 0.73 0.72 0.56 0.217 41 28
## CC.Naturalness_3R_AFSCS 31 0.59 0.59 0.23 0.040 43 27hist(CC$Naturalness_Score_AFSCS, main = 'AFSCS Naturalness Scale Score')
#Correlation
cor.plot(CC$Naturalness_Scale_AFSCS, labels = c('1','2','3'), main = "Correlation Between AFSCS Naturalness Items")
#Individual AFSCS Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_AFSCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 60.77 26.62 63 62.36 17.79 0 100 100 -0.42 -0.15 4.78range(CC$Naturalness_1_AFSCS, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_AFSCS, main = 'AFSCS Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(CC$Naturalness_AFSCS_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 60.65 28.23 64 62.16 29.65 2 100 98 -0.29 -0.82 5.07range(CC$NNaturalness_AFSCS_31, na.rm=TRUE)## Warning in min(x, na.rm = na.rm): no non-missing arguments to min; returning Inf## Warning in max(x, na.rm = na.rm): no non-missing arguments to max; returning
## -Inf## [1] Inf -Infhist(CC$Naturalness_AFSCS_31, main = 'AFSCS Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_AFSCS_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 59.29 27.27 63 62.56 25.2 0 98 98 -0.92 -0.14 4.9range(CC$Naturalness_AFSCS_35, na.rm=TRUE)## [1] 0 98hist(CC$Naturalness_AFSCS_35, main = 'AFSCS Naturalness Item #3: "This relies on science-based technology."')
#BIOCHAR Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_BIO<- CC$Naturalness_BIO_30
CC$Naturalness_2R_BIO <- abs(CC$Naturalness_BIO_31 -100)
CC$Naturalness_3R_BIO <- abs(CC$Naturalness_BIO_35 -100)
#BIO Naturalness Scale
CC$Naturalness_Score_BIO <- rowMeans(CC [, c("Naturalness_1_BIO", "Naturalness_2R_BIO", "Naturalness_3R_BIO")], na.rm=TRUE)
#BIO Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_BIO, CC$Naturalness_2R_BIO, CC$Naturalness_3R_BIO))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Naturalness_1_BIO, CC$Naturalness_2R_BIO, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Naturalness_1_BIO ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Warning in sqrt(Vtc): NaNs produced##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_BIO, CC$Naturalness_2R_BIO,
## CC$Naturalness_3R_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.43 -0.39 -0.072 -0.1 -0.28 0.24 42 11 -0.18
##
## lower alpha upper 95% confidence boundaries
## -0.91 -0.43 0.05
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## CC.Naturalness_1_BIO 0.42 0.43 0.27 0.27 0.74 0.11
## CC.Naturalness_2R_BIO -0.44 -0.44 -0.18 -0.18 -0.31 0.28
## CC.Naturalness_3R_BIO -1.32 -1.32 -0.40 -0.40 -0.57 0.45
## var.r med.r
## CC.Naturalness_1_BIO NA 0.27
## CC.Naturalness_2R_BIO NA -0.18
## CC.Naturalness_3R_BIO NA -0.40
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_BIO 37 0.30 0.27 NaN -0.370 57 23
## CC.Naturalness_2R_BIO 36 0.58 0.57 NaN -0.124 40 23
## CC.Naturalness_3R_BIO 37 0.67 0.71 NaN 0.088 29 20hist(CC$Naturalness_Score_BIO, main = 'BIO Naturalness Scale Score')
#Individual BIO Naturalness Items
#Item 1 (Not reverse coded)
psych::describe(CC$Naturalness_1_BIO)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 56.54 22.61 61 56.58 17.79 9 100 91 -0.19 -0.7 3.72range(CC$Naturalness_1_BIO, na.rm=TRUE)## [1] 9 100hist(CC$Naturalness_1_BIO, main = 'BIO Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(CC$Naturalness_BIO_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 59.67 23.25 62 60.73 20.76 11 97 86 -0.36 -0.76 3.87range(CC$Naturalness_BIO_31, na.rm=TRUE)## [1] 11 97hist(CC$Naturalness_BIO_31, main = 'BIO Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_BIO_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 70.65 20.47 71 72 22.24 15 100 85 -0.53 -0.11 3.36range(CC$Naturalness_BIO_35, na.rm=TRUE)## [1] 15 100hist(CC$Naturalness_BIO_35, main = 'BIO Naturalness Item #3: "This relies on science-based technology."')
#BECCS Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_BECCS<- CC$Naturalness_BECCS_30
CC$Naturalness_2R_BECCS <- abs(CC$Naturalness_BECCS_31 -100)
CC$Naturalness_3R_BECCS <- abs(CC$Naturalness_BECCS_35 -100)
#BECCS Naturalness Scale
CC$Naturalness_Score_BECCS <- rowMeans(CC [, c("Naturalness_1_BECCS", "Naturalness_2R_BECCS", "Naturalness_3R_BECCS")], na.rm=TRUE)
#BECCS Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_BECCS, CC$Naturalness_2R_BECCS, CC$Naturalness_3R_BECCS))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Naturalness_1_BECCS, CC$Naturalness_2R_BECCS, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Naturalness_1_BECCS ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_BECCS, CC$Naturalness_2R_BECCS,
## CC$Naturalness_3R_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.087 -0.067 0.21 -0.021 -0.063 0.18 41 15 -0.084
##
## lower alpha upper 95% confidence boundaries
## -0.45 -0.09 0.27
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## CC.Naturalness_1_BECCS 0.61 0.61 0.441 0.441 1.58 0.076
## CC.Naturalness_2R_BECCS -1.42 -1.46 -0.422 -0.422 -0.59 0.465
## CC.Naturalness_3R_BECCS -0.18 -0.18 -0.084 -0.084 -0.15 0.231
## var.r med.r
## CC.Naturalness_1_BECCS NA 0.441
## CC.Naturalness_2R_BECCS NA -0.422
## CC.Naturalness_3R_BECCS NA -0.084
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_BECCS 36 0.36 0.29 -0.40 -0.2825 51 29
## CC.Naturalness_2R_BECCS 35 0.80 0.80 0.72 0.2882 39 28
## CC.Naturalness_3R_BECCS 36 0.54 0.60 0.46 0.0079 32 24hist(CC$Naturalness_Score_BECCS, main = 'BECCS Naturalness Scale Score')
#Individual BECCS Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_BECCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 50.81 28.51 51.5 51.4 40.77 0 100 100 -0.06 -1.16 4.75range(CC$Naturalness_1_BECCS, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_BECCS, main = 'BECCS Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(CC$Naturalness_BECCS_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35 60.97 27.92 69 62.41 25.2 2 100 98 -0.48 -0.89 4.72range(CC$Naturalness_BECCS_31, na.rm=TRUE)## [1] 2 100hist(CC$Naturalness_BECCS_31, main = 'BECCS Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_BECCS_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 67.69 24.02 73 70.13 17.79 0 100 100 -0.91 0.58 4range(CC$Naturalness_BECCS_35, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_BECCS_35, main = 'BECCS Naturalness Item #3: "This relies on science-based technology."')
#DACCS Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_DACCS<- CC$Naturalness_DACCS_30
CC$Naturalness_2R_DACCS <- abs(CC$Naturalness_DACCS_31 -100)
CC$Naturalness_3R_DACCS <- abs(CC$Naturalness_DACCS_35 -100)
#DACCS Naturalness Scale
CC$Naturalness_Score_DACCS <- rowMeans(CC [, c("Naturalness_1_DACCS", "Naturalness_2R_DACCS", "Naturalness_3R_DACCS")], na.rm=TRUE)
#DACCS Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_DACCS, CC$Naturalness_2R_DACCS, CC$Naturalness_3R_DACCS))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Naturalness_1_DACCS, CC$Naturalness_2R_DACCS, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Naturalness_3R_DACCS ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_DACCS, CC$Naturalness_2R_DACCS,
## CC$Naturalness_3R_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.096 0.11 0.088 0.038 0.12 0.15 34 15 0.0091
##
## lower alpha upper 95% confidence boundaries
## -0.2 0.1 0.39
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## CC.Naturalness_1_DACCS 0.018 0.018 0.0091 0.0091 0.018 0.19
## CC.Naturalness_2R_DACCS -0.113 -0.116 -0.0549 -0.0549 -0.104 0.21
## CC.Naturalness_3R_DACCS 0.261 0.275 0.1594 0.1594 0.379 0.14
## var.r med.r
## CC.Naturalness_1_DACCS NA 0.0091
## CC.Naturalness_2R_DACCS NA -0.0549
## CC.Naturalness_3R_DACCS NA 0.1594
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_DACCS 36 0.71 0.61 0.25 0.064 45 29
## CC.Naturalness_2R_DACCS 36 0.58 0.65 0.36 0.134 27 20
## CC.Naturalness_3R_DACCS 36 0.50 0.53 -0.08 -0.037 30 23hist(CC$Naturalness_Score_DACCS, main = 'DACCS Naturalness Scale Score')
#Individual DACCS Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_DACCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 45.08 28.96 48.5 43.93 37.81 0 100 100 0.26 -1.02 4.83range(CC$Naturalness_1_DACCS, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_DACCS, main = 'DACCS Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_DACCS_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 73.17 20.43 73 74.47 17.79 29 100 71 -0.28 -0.76 3.41range(CC$Naturalness_DACCS_31, na.rm=TRUE)## [1] 29 100hist(CC$Naturalness_DACCS_31, main = 'DACCS Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_DACCS_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 70.08 23.31 69 72.07 18.53 0 100 100 -0.68 0.47 3.89range(CC$Naturalness_DACCS_35, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_DACCS_35, main = 'DACCS Naturalness Item #3: "This relies on science-based technology."')
#EW Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_EW<- CC$Naturalness_EW_30
CC$Naturalness_2R_EW <- abs(CC$Naturalness_EW_31 -100)
CC$Naturalness_3R_EW <- abs(CC$Naturalness_EW_35 -100)
#EW Naturalness Scale
CC$Naturalness_Score_EW <- rowMeans(CC [, c("Naturalness_1_EW", "Naturalness_2R_EW", "Naturalness_3R_EW")], na.rm=TRUE)
#EW Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_EW, CC$Naturalness_2R_EW, CC$Naturalness_3R_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_EW, CC$Naturalness_2R_EW,
## CC$Naturalness_3R_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.53 0.56 0.61 0.3 1.3 0.084 41 20 0.15
##
## lower alpha upper 95% confidence boundaries
## 0.37 0.53 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Naturalness_1_EW 0.842 0.842 0.727 0.727 5.321 0.031 NA
## CC.Naturalness_2R_EW 0.018 0.018 0.009 0.009 0.018 0.190 NA
## CC.Naturalness_3R_EW 0.258 0.260 0.150 0.150 0.352 0.144 NA
## med.r
## CC.Naturalness_1_EW 0.727
## CC.Naturalness_2R_EW 0.009
## CC.Naturalness_3R_EW 0.150
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_EW 34 0.57 0.53 0.12 0.085 52 30
## CC.Naturalness_2R_EW 34 0.84 0.86 0.84 0.593 39 27
## CC.Naturalness_3R_EW 34 0.77 0.79 0.75 0.454 33 27hist(CC$Naturalness_Score_EW, main = 'EW Naturalness Scale Score')
#Individual EW Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 52.09 30.36 52.5 52.39 36.32 1 100 99 -0.12 -1.15 5.21range(CC$Naturalness_1_EW, na.rm=TRUE)## [1] 1 100hist(CC$Naturalness_1_EW, main = 'EW Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_EW_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 61.15 26.67 66 63.11 19.27 0 100 100 -0.71 -0.22 4.57range(CC$Naturalness_EW_31, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_EW_31, main = 'EW Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_EW_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 67.06 27.08 74 70.21 23.72 0 100 100 -1.01 0.23 4.64range(CC$Naturalness_EW_35, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_EW_35, main = 'EW Naturalness Item #3: "This relies on science-based technology."')
#OF Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_OF <- CC$Naturalness_OF_30
CC$Naturalness_2R_OF <- abs(CC$Naturalness_OF_31 -100)
CC$Naturalness_3R_OF <- abs(CC$Naturalness_OF_35 -100)
#OF Naturalness Scale
CC$Naturalness_Score_OF <- rowMeans(CC [, c("Naturalness_1_OF", "Naturalness_2R_OF", "Naturalness_3R_OF")], na.rm=TRUE)
#OF Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_OF, CC$Naturalness_2R_OF, CC$Naturalness_3R_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_OF, CC$Naturalness_2R_OF,
## CC$Naturalness_3R_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.52 0.54 0.59 0.28 1.2 0.085 43 20 0.098
##
## lower alpha upper 95% confidence boundaries
## 0.36 0.52 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Naturalness_1_OF 0.844 0.844 0.730 0.730 5.399 0.031 NA
## CC.Naturalness_2R_OF 0.044 0.044 0.023 0.023 0.046 0.186 NA
## CC.Naturalness_3R_OF 0.178 0.179 0.098 0.098 0.218 0.160 NA
## med.r
## CC.Naturalness_1_OF 0.730
## CC.Naturalness_2R_OF 0.023
## CC.Naturalness_3R_OF 0.098
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_OF 31 0.54 0.52 0.081 0.065 46 30
## CC.Naturalness_2R_OF 32 0.83 0.84 0.819 0.560 42 27
## CC.Naturalness_3R_OF 32 0.79 0.81 0.771 0.483 43 28hist(CC$Naturalness_Score_OF, main = 'OF Naturalness Scale Score')
#Individual OF Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_OF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 45.81 29.99 41 44.8 34.1 0 100 100 0.19 -1.14 5.39range(CC$Naturalness_1_OF, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_OF, main = 'OF Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_OF_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 32 57.69 27.14 63.5 59.46 19.27 0 100 100 -0.63 -0.5 4.8range(CC$Naturalness_OF_31, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_OF_31, main = 'OF Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_OF_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 32 57.38 27.54 65 58.92 21.5 0 100 100 -0.56 -0.83 4.87range(CC$Naturalness_OF_35, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_OF_35, main = 'OF Naturalness Item #3: "This relies on science-based technology."')
#BIOFUEL Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_BF<- CC$Naturalness_BF_30
CC$Naturalness_2R_BF <- abs(CC$Naturalness_BF_31 -100)
CC$Naturalness_3R_BF <- abs(CC$Naturalness_BF_35 -100)
#DACCS Naturalness Scale
CC$Naturalness_Score_BF <- rowMeans(CC [, c("Naturalness_1_BF", "Naturalness_2R_BF", "Naturalness_3R_BF")], na.rm=TRUE)
#BF Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_BF, CC$Naturalness_2R_BF, CC$Naturalness_3R_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_BF, CC$Naturalness_2R_BF,
## CC$Naturalness_3R_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.39 0.41 0.32 0.19 0.69 0.098 38 18 0.19
##
## lower alpha upper 95% confidence boundaries
## 0.2 0.39 0.59
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Naturalness_1_BF 0.25 0.27 0.16 0.16 0.37 0.13 NA
## CC.Naturalness_2R_BF 0.32 0.35 0.21 0.21 0.54 0.12 NA
## CC.Naturalness_3R_BF 0.32 0.32 0.19 0.19 0.48 0.13 NA
## med.r
## CC.Naturalness_1_BF 0.16
## CC.Naturalness_2R_BF 0.21
## CC.Naturalness_3R_BF 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_BF 23 0.74 0.69 0.42 0.26 58 31
## CC.Naturalness_2R_BF 23 0.72 0.66 0.35 0.23 34 31
## CC.Naturalness_3R_BF 23 0.56 0.67 0.37 0.24 20 19hist(CC$Naturalness_Score_BF, main = 'BF Naturalness Scale Score')
#Individual BF Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_BF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 58.04 30.85 66 59.79 26.69 0 100 100 -0.58 -0.87 6.43range(CC$Naturalness_1_BF, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_BF, main = 'BF Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_BF_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 65.74 30.64 72 68.58 31.13 2 100 98 -0.64 -0.89 6.39range(CC$Naturalness_BF_31, na.rm=TRUE)## [1] 2 100hist(CC$Naturalness_BF_31, main = 'BF Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_BF_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 79.7 19.44 79 82.11 23.72 33 100 67 -0.81 -0.23 4.05range(CC$Naturalness_BF_35, na.rm=TRUE)## [1] 33 100hist(CC$Naturalness_BF_35, main = 'BF Naturalness Item #3: "This relies on science-based technology."')
#NE Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_NE<- CC$Naturalness_NE_30
CC$Naturalness_2R_NE <- abs(CC$Naturalness_NE_31 -100)
CC$Naturalness_3R_NE <- abs(CC$Naturalness_NE_35 -100)
#NE Naturalness Scale
CC$Naturalness_Score_NE <- rowMeans(CC [, c("Naturalness_1_NE", "Naturalness_2R_NE", "Naturalness_3R_NE")], na.rm=TRUE)
#NE Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_NE, CC$Naturalness_2R_NE, CC$Naturalness_3R_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_NE, CC$Naturalness_2R_NE,
## CC$Naturalness_3R_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.41 0.43 0.4 0.2 0.77 0.1 38 17 0.13
##
## lower alpha upper 95% confidence boundaries
## 0.21 0.41 0.61
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Naturalness_1_NE 0.624 0.624 0.454 0.454 1.661 0.073 NA
## CC.Naturalness_2R_NE 0.048 0.048 0.025 0.025 0.051 0.182 NA
## CC.Naturalness_3R_NE 0.233 0.237 0.134 0.134 0.310 0.147 NA
## med.r
## CC.Naturalness_1_NE 0.454
## CC.Naturalness_2R_NE 0.025
## CC.Naturalness_3R_NE 0.134
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_NE 29 0.63 0.56 0.14 0.093 47 29
## CC.Naturalness_2R_NE 29 0.74 0.77 0.63 0.387 37 24
## CC.Naturalness_3R_NE 29 0.68 0.72 0.53 0.290 30 24hist(CC$Naturalness_Score_NE, main = 'NE Naturalness Scale Score')
#Individual DACCS Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_NE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 47.38 28.96 43 46.68 35.58 4 100 96 0.22 -1.23 5.38range(CC$Naturalness_1_NE, na.rm=TRUE)## [1] 4 100hist(CC$Naturalness_1_NE, main = 'NE Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_DACCS_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 73.17 20.43 73 74.47 17.79 29 100 71 -0.28 -0.76 3.41range(CC$Naturalness_NE_31, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_NE_31, main = 'NE Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_NE_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 70.38 23.82 74 72.8 20.76 1 100 99 -1.13 1.02 4.42range(CC$Naturalness_NE_35, na.rm=TRUE)## [1] 1 100hist(CC$Naturalness_NE_35, main = 'NE Naturalness Item #3: "This relies on science-based technology."')
#SE Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_SE<- CC$Naturalness_SE_30
CC$Naturalness_2R_SE <- abs(CC$Naturalness_SE_31 -100)
CC$Naturalness_3R_SE <- abs(CC$Naturalness_SE_35 -100)
#SE Naturalness Scale
CC$Naturalness_Score_SE <- rowMeans(CC [, c("Naturalness_1_SE", "Naturalness_2R_SE", "Naturalness_3R_SE")], na.rm=TRUE)
#SE Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_SE, CC$Naturalness_2R_SE, CC$Naturalness_3R_SE))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Naturalness_1_SE, CC$Naturalness_2R_SE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Naturalness_1_SE ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_SE, CC$Naturalness_2R_SE,
## CC$Naturalness_3R_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.1 -0.16 0.015 -0.048 -0.14 0.18 48 17 0.034
##
## lower alpha upper 95% confidence boundaries
## -0.46 -0.1 0.25
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## CC.Naturalness_1_SE 0.294 0.294 0.172 0.172 0.416 0.14
## CC.Naturalness_2R_SE -1.061 -1.079 -0.350 -0.350 -0.519 0.40
## CC.Naturalness_3R_SE 0.066 0.067 0.034 0.034 0.071 0.18
## var.r med.r
## CC.Naturalness_1_SE NA 0.172
## CC.Naturalness_2R_SE NA -0.350
## CC.Naturalness_3R_SE NA 0.034
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_SE 25 0.34 0.42 -0.9 -0.20 70 27
## CC.Naturalness_2R_SE 25 0.76 0.73 1.2 0.19 45 33
## CC.Naturalness_3R_SE 25 0.54 0.50 -0.1 -0.09 30 31hist(CC$Naturalness_Score_SE, main = 'SE Naturalness Scale Score')
#Individual DACCS Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_SE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 69.64 27.09 73 72.43 25.2 0 100 100 -0.88 -0.03 5.42range(CC$Naturalness_1_SE, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_SE, main = 'SE Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_SE_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 55.4 32.68 57 56.33 44.48 1 100 99 -0.07 -1.41 6.54range(CC$Naturalness_SE_31, na.rm=TRUE)## [1] 1 100hist(CC$Naturalness_SE_31, main = 'SE Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_SE_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 69.6 31.31 72 73.33 32.62 0 100 100 -0.89 -0.26 6.26range(CC$Naturalness_SE_35, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_SE_35, main = 'SE Naturalness Item #3: "This relies on science-based technology."')
#WE Naturalness Descriptives
#Reverse Code Items 2 and 3
CC$Naturalness_1_WE<- CC$Naturalness_WE_30
CC$Naturalness_2R_WE <- abs(CC$Naturalness_WE_31 -100)
CC$Naturalness_3R_WE <- abs(CC$Naturalness_WE_35 -100)
#WE Naturalness Scale
CC$Naturalness_Score_WE <- rowMeans(CC [, c("Naturalness_1_WE", "Naturalness_2R_WE", "Naturalness_3R_WE")], na.rm=TRUE)
#WE Cronbach's alpha for naturalness scale
psych::alpha(data.frame(CC$Naturalness_1_WE, CC$Naturalness_2R_WE, CC$Naturalness_3R_WE))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Naturalness_1_WE, CC$Naturalness_2R_WE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Naturalness_3R_WE ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Naturalness_1_WE, CC$Naturalness_2R_WE,
## CC$Naturalness_3R_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -1.4 -1.2 0.03 -0.23 -0.55 0.38 53 12 -0.22
##
## lower alpha upper 95% confidence boundaries
## -2.12 -1.37 -0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Naturalness_1_WE -0.56 -0.56 -0.22 -0.22 -0.36 0.303 NA
## CC.Naturalness_2R_WE -7.58 -7.70 -0.79 -0.79 -0.89 1.659 NA
## CC.Naturalness_3R_WE 0.50 0.50 0.33 0.33 1.00 0.097 NA
## med.r
## CC.Naturalness_1_WE -0.22
## CC.Naturalness_2R_WE -0.79
## CC.Naturalness_3R_WE 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Naturalness_1_WE 26 0.477 0.4211 0.89 -0.40 61 31
## CC.Naturalness_2R_WE 26 0.853 0.8700 1.05 0.21 57 27
## CC.Naturalness_3R_WE 26 -0.052 -0.0097 -1.72 -0.65 42 29hist(CC$Naturalness_Score_WE, main = 'WE Naturalness Scale Score')
#Individual WE Naturalness Items
#Item 1
psych::describe(CC$Naturalness_1_WE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 60.92 31.01 64.5 62.82 29.65 0 100 100 -0.51 -0.94 6.08range(CC$Naturalness_1_WE, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_1_WE, main = 'WE Naturalness Item #1: "This is natural."')
#Item 2 (not reverse coded)
psych::describe(CC$Naturalness_WE_31)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 42.96 26.7 51.5 42.64 30.39 0 100 100 -0.01 -0.93 5.24range(CC$Naturalness_WE_31, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_WE_31, main = 'WE Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Naturalness_WE_35)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 58.38 28.58 67.5 59.91 23.72 0 100 100 -0.6 -0.66 5.61range(CC$Naturalness_WE_35, na.rm=TRUE)## [1] 0 100hist(CC$Naturalness_WE_35, main = 'WE Naturalness Item #3: "This relies on science-based technology."')
#Benefit
#Perceived benefit of climate methods was measured with 1 item on a 0-100 scale ( 0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
#Benefit Item 1: This is likely to lead to achieving carbon neutral climate goals.
#AF/SCS Benefit Descriptives
CC$Benefit_AFSCS <- CC$Ben_AFSCS_18
psych::describe(CC$Benefit_AFSCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 63.16 25.36 65 65.28 19.27 0 100 100 -0.76 0.29 4.55range(CC$Benefit_AFSCS, na.rm=TRUE)## [1] 0 100hist(CC$Benefit_AFSCS, main = 'AFSCS Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#BIO Benefit Descriptives
CC$Benefit_BIO <- CC$Ben_BIO_18
psych::describe(CC$Benefit_BIO)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 62.14 25.77 66 63.81 22.24 3 100 97 -0.62 -0.44 4.24range(CC$Benefit_BIO, na.rm=TRUE)## [1] 3 100hist(CC$Benefit_BIO, main = 'BIO Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#BECCS Benefit Descriptives
CC$Benefit_BECCS <- CC$Ben_BECCS_18
psych::describe(CC$Benefit_BECCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 58.85 21.85 63 60.32 18.53 8 97 89 -0.56 -0.16 3.75range(CC$Benefit_BECCS, na.rm=TRUE)## [1] 8 97hist(CC$Benefit_BECCS, main = 'BECCS Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#DACCS Benefit Descriptives
CC$Benefit_DACCS <- CC$Ben_DACCS_18
psych::describe(CC$Benefit_DACCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 68.44 23.25 69 70.47 17.79 0 100 100 -0.66 0.52 3.88range(CC$Benefit_DACCS, na.rm=TRUE)## [1] 0 100hist(CC$Benefit_DACCS, main = 'DACCS Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#EW Benefit Descriptives
CC$Benefit_EW <- CC$Ben_EW_18
psych::describe(CC$Benefit_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 62.62 26.3 66 64.5 22.24 0 100 100 -0.54 -0.38 4.51range(CC$Benefit_EW, na.rm=TRUE)## [1] 0 100hist(CC$Benefit_EW, main = 'EW Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#OF Benefit Descriptives
CC$Benefit_EW <- CC$Ben_EW_18
psych::describe(CC$Benefit_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 62.62 26.3 66 64.5 22.24 0 100 100 -0.54 -0.38 4.51range(CC$Benefit_EW, na.rm=TRUE)## [1] 0 100hist(CC$Benefit_EW, main = 'EW Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#BF Benefit Descriptives
CC$Benefit_BF <- CC$Ben_BF_18
psych::describe(CC$Benefit_BF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 72.65 21.27 70 73.74 25.2 35 100 65 -0.16 -1.19 4.44range(CC$Benefit_BF, na.rm=TRUE)## [1] 35 100hist(CC$Benefit_BF, main = 'BF Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#NE Benefit Descriptives
CC$Benefit_NE <- CC$Ben_NE_18
psych::describe(CC$Benefit_NE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 66.93 19.82 70 68.24 22.24 20 100 80 -0.56 -0.15 3.68range(CC$Benefit_NE, na.rm=TRUE)## [1] 20 100hist(CC$Benefit_NE, main = 'NE Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#SE Benefit Descriptives
CC$Benefit_SE <- CC$Ben_SE_18
psych::describe(CC$Benefit_SE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 66.6 23.68 70 68.24 25.2 0 100 100 -0.62 0.49 4.74range(CC$Benefit_SE, na.rm=TRUE)## [1] 0 100hist(CC$Benefit_SE, main = 'SE Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#WE Benefit Descriptives
CC$Benefit_WE <- CC$Ben_WE_18
psych::describe(CC$Benefit_WE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 61.92 23.4 65 63.33 14.83 0 100 100 -0.72 0.35 4.68range(CC$Benefit_WE, na.rm=TRUE)## [1] 0 100hist(CC$Benefit_WE, main = 'WE Benefit Item #1: "This is likely to lead to achieving carbon neutral climate goals."')
#Behavioral Intent
#Behavioral intent was measured with 2 items on a 0-100 scale ( 0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
#Behavioral Intent Item 1: I would personally support non-government entities deploying these on a large scale. #Behavioral Intent Item 2: I would personally support spending government tax dollars to deploy these on a large scale.
#AF/SCS Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_AFSCS <- CC$BI_AFSCS_18
psych::describe(CC$BehavInt1_AFSCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 60.71 29.59 57 63.08 29.65 0 100 100 -0.46 -0.65 5.31range(CC$BehavInt1_AFSCS, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt1_AFSCS, main = 'AFSCS Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_AFSCS <- CC$BI_AFSCS_19
psych::describe(CC$BehavInt2_AFSCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 55.35 29.72 56 56.76 31.13 0 100 100 -0.31 -0.84 5.34range(CC$BehavInt2_AFSCS, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt2_AFSCS, main = 'AFSCS Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#AF/SCS Behavioral Intent Scale
CC$BI_Score_AFSCS <- rowMeans(CC [, c("BehavInt1_AFSCS", "BehavInt2_AFSCS")], na.rm=TRUE)
#AF/SCS Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_AFSCS, CC$BehavInt2_AFSCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_AFSCS, CC$BehavInt2_AFSCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.83 0.83 9.5 0.019 58 28 0.83
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.9 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.BehavInt1_AFSCS 0.82 0.83 0.68 0.83 4.7 NA 0
## CC.BehavInt2_AFSCS 0.83 0.83 0.68 0.83 4.7 NA 0
## med.r
## CC.BehavInt1_AFSCS 0.83
## CC.BehavInt2_AFSCS 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_AFSCS 31 0.96 0.96 0.87 0.83 61 30
## CC.BehavInt2_AFSCS 31 0.96 0.96 0.87 0.83 55 30hist(CC$BI_Score_AFSCS, main = 'AFSCS Behavioral Intent Scale Score')
#BIO Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_BIO <- CC$BI_BIO_18
psych::describe(CC$BehavInt1_BIO)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 66.14 24.18 68.5 67.77 25.2 11 100 89 -0.52 -0.7 4.03range(CC$BehavInt1_BIO, na.rm=TRUE)## [1] 11 100hist(CC$BehavInt1_BIO, main = 'BIO Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_BIO <- CC$BI_BIO_19
psych::describe(CC$BehavInt2_BIO)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 60.35 26.04 64 60.97 25.2 15 100 85 -0.27 -1.05 4.28range(CC$BehavInt2_BIO, na.rm=TRUE)## [1] 15 100hist(CC$BehavInt2_BIO, main = 'BIO Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#BIO Behavioral Intent Scale
CC$BI_Score_BIO <- rowMeans(CC [, c("BehavInt1_BIO", "BehavInt2_BIO")], na.rm=TRUE)
#BIO Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_BIO, CC$BehavInt2_BIO))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_BIO, CC$BehavInt2_BIO))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.74 0.74 5.6 0.03 63 23 0.74
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.85 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_BIO 0.68 0.74 0.54 0.74 2.8 NA 0 0.74
## CC.BehavInt2_BIO 0.79 0.74 0.54 0.74 2.8 NA 0 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_BIO 36 0.92 0.93 0.8 0.74 66 24
## CC.BehavInt2_BIO 37 0.93 0.93 0.8 0.74 60 26hist(CC$BI_Score_BIO, main = 'BIO Naturalness Scale Score')
#BECCS Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_BECCS <- CC$BI_BACCS_18
psych::describe(CC$BehavInt1_BECCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35 59.46 25.12 61 60.62 26.69 7 100 93 -0.47 -0.71 4.25range(CC$BehavInt1_BECCS, na.rm=TRUE)## [1] 7 100hist(CC$BehavInt1_BECCS, main = 'BECCS Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_BECCS <- CC$BI_BACCS_19
psych::describe(CC$BehavInt2_BECCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35 54.94 31.63 65 55.9 34.1 0 100 100 -0.33 -1.32 5.35range(CC$BehavInt2_BECCS, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt2_BECCS, main = 'BECCS Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#BIO Behavioral Intent Scale
CC$BI_Score_BECCS <- rowMeans(CC [, c("BehavInt1_BECCS", "BehavInt2_BECCS")], na.rm=TRUE)
#BIO Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_BECCS, CC$BehavInt2_BECCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_BECCS, CC$BehavInt2_BECCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.72 0.56 0.56 2.6 0.055 57 25 0.56
##
## lower alpha upper 95% confidence boundaries
## 0.6 0.71 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.BehavInt1_BECCS 0.45 0.56 0.32 0.56 1.3 NA 0
## CC.BehavInt2_BECCS 0.71 0.56 0.32 0.56 1.3 NA 0
## med.r
## CC.BehavInt1_BECCS 0.56
## CC.BehavInt2_BECCS 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_BECCS 35 0.85 0.88 0.66 0.56 59 25
## CC.BehavInt2_BECCS 35 0.91 0.88 0.66 0.56 55 32hist(CC$BI_Score_BECCS, main = 'BECCS Naturalness Scale Score')
#DACCS Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_DACCS <- CC$BI_DACCS_18
psych::describe(CC$BehavInt1_DACCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 58.33 25.28 59.5 59.07 22.24 0 100 100 -0.37 -0.43 4.21range(CC$BehavInt1_DACCS, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt1_DACCS, main = 'DACCS Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_DACCS <- CC$BI_DACCS_19
psych::describe(CC$BehavInt2_DACCS)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 51.14 27.16 53.5 51.83 30.39 0 100 100 -0.37 -0.63 4.53range(CC$BehavInt2_DACCS, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt2_DACCS, main = 'DACCS Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#DACCS Behavioral Intent Scale
CC$BI_Score_DACCS <- rowMeans(CC [, c("BehavInt1_DACCS", "BehavInt2_DACCS")], na.rm=TRUE)
#DACCS Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_DACCS, CC$BehavInt2_DACCS))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_DACCS, CC$BehavInt2_DACCS))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.64 0.65 0.48 0.48 1.8 0.069 55 23 0.48
##
## lower alpha upper 95% confidence boundaries
## 0.51 0.64 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.BehavInt1_DACCS 0.44 0.48 0.23 0.48 0.91 NA 0
## CC.BehavInt2_DACCS 0.51 0.48 0.23 0.48 0.91 NA 0
## med.r
## CC.BehavInt1_DACCS 0.48
## CC.BehavInt2_DACCS 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_DACCS 36 0.85 0.86 0.59 0.48 58 25
## CC.BehavInt2_DACCS 36 0.87 0.86 0.59 0.48 51 27hist(CC$BI_Score_DACCS, main = 'DACCS Naturalness Scale Score')
#EW Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_EW <- CC$BI_EW_18
psych::describe(CC$BehavInt1_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 67.21 27.63 73 70.46 25.95 2 100 98 -0.85 -0.02 4.74range(CC$BehavInt1_EW, na.rm=TRUE)## [1] 2 100hist(CC$BehavInt1_EW, main = 'EW Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_EW <- CC$BI_EW_19
psych::describe(CC$BehavInt2_EW)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 34 59.24 30.05 68.5 60.71 27.43 2 100 98 -0.46 -0.97 5.15range(CC$BehavInt2_EW, na.rm=TRUE)## [1] 2 100hist(CC$BehavInt2_EW, main = 'EW Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#EW Behavioral Intent Scale
CC$BI_Score_EW <- rowMeans(CC [, c("BehavInt1_EW", "BehavInt2_EW")], na.rm=TRUE)
#EW Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_EW, CC$BehavInt2_EW))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_EW, CC$BehavInt2_EW))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.7 0.7 4.7 0.034 63 27 0.7
##
## lower alpha upper 95% confidence boundaries
## 0.75 0.82 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_EW 0.64 0.7 0.49 0.7 2.3 NA 0 0.7
## CC.BehavInt2_EW 0.76 0.7 0.49 0.7 2.3 NA 0 0.7
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_EW 34 0.92 0.92 0.77 0.7 67 28
## CC.BehavInt2_EW 34 0.93 0.92 0.77 0.7 59 30hist(CC$BI_Score_EW, main = 'EW Naturalness Scale Score')
#OF Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_OF <- CC$BI_OF_18
psych::describe(CC$BehavInt1_OF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 47.16 22.58 48 47.92 28.17 0 84 84 -0.23 -1.07 4.06range(CC$BehavInt1_OF, na.rm=TRUE)## [1] 0 84hist(CC$BehavInt1_OF, main = 'OF Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_OF <- CC$BI_OF_19
psych::describe(CC$BehavInt2_OF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 47.42 22.97 51 48.68 25.2 0 86 86 -0.39 -0.75 4.13range(CC$BehavInt2_OF, na.rm=TRUE)## [1] 0 86hist(CC$BehavInt2_OF, main = 'OF Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#OF Behavioral Intent Scale
CC$BI_Score_OF <- rowMeans(CC [, c("BehavInt1_OF", "BehavInt2_OF")], na.rm=TRUE)
#OF Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_OF, CC$BehavInt2_OF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_OF, CC$BehavInt2_OF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.84 0.84 10 0.017 47 22 0.84
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.91 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_OF 0.82 0.84 0.7 0.84 5.1 NA 0 0.84
## CC.BehavInt2_OF 0.85 0.84 0.7 0.84 5.1 NA 0 0.84
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_OF 31 0.96 0.96 0.88 0.84 47 23
## CC.BehavInt2_OF 31 0.96 0.96 0.88 0.84 47 23hist(CC$BI_Score_OF, main = 'OF Naturalness Scale Score')
#BF Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_BF <- CC$BI_BF_18
psych::describe(CC$BehavInt1_BF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 66.61 23.76 70 68.37 26.69 0 100 100 -0.84 0.51 4.95range(CC$BehavInt1_BF, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt1_BF, main = 'BF Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_BF <- CC$BI_BF_19
psych::describe(CC$BehavInt2_BF)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 57.39 26.28 63 59.84 28.17 0 95 95 -0.65 -0.4 5.48range(CC$BehavInt2_BF, na.rm=TRUE)## [1] 0 95hist(CC$BehavInt2_BF, main = 'BF Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#BIO Behavioral Intent Scale
CC$BI_Score_BF <- rowMeans(CC [, c("BehavInt1_BF", "BehavInt2_BF")], na.rm=TRUE)
#BIO Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_BF, CC$BehavInt2_BF))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_BF, CC$BehavInt2_BF))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.44 0.44 0.28 0.28 0.79 0.11 62 20 0.28
##
## lower alpha upper 95% confidence boundaries
## 0.23 0.44 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_BF 0.26 0.28 0.08 0.28 0.4 NA 0 0.28
## CC.BehavInt2_BF 0.31 0.28 0.08 0.28 0.4 NA 0 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_BF 23 0.78 0.8 0.43 0.28 67 24
## CC.BehavInt2_BF 23 0.82 0.8 0.43 0.28 57 26hist(CC$BI_Score_BF, main = 'BF Naturalness Scale Score')
#NE Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_NE <- CC$BI_NE_18
psych::describe(CC$BehavInt1_NE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 56.07 28.34 62 56.88 25.2 0 100 100 -0.47 -0.98 5.26range(CC$BehavInt1_NE, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt1_NE, main = 'NE Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_NE <- CC$BI_NE_19
psych::describe(CC$BehavInt2_NE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 29 49.69 31.1 51 49.8 35.58 0 100 100 -0.05 -1.33 5.77range(CC$BehavInt2_NE, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt2_NE, main = 'NE Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#NE Behavioral Intent Scale
CC$BI_Score_NE <- rowMeans(CC [, c("BehavInt1_NE", "BehavInt2_NE")], na.rm=TRUE)
#NE Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_NE, CC$BehavInt2_NE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_NE, CC$BehavInt2_NE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.82 0.82 8.9 0.02 53 28 0.82
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.9 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_NE 0.74 0.82 0.67 0.82 4.5 NA 0 0.82
## CC.BehavInt2_NE 0.90 0.82 0.67 0.82 4.5 NA 0 0.82
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_NE 29 0.95 0.95 0.86 0.82 56 28
## CC.BehavInt2_NE 29 0.96 0.95 0.86 0.82 50 31hist(CC$BI_Score_NE, main = 'NE Naturalness Scale Score')
#SE Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_SE <- CC$BI_SE_18
psych::describe(CC$BehavInt1_SE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 68.2 25.14 72 71.62 13.34 0 100 100 -1.28 1.66 5.03range(CC$BehavInt1_SE, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt1_SE, main = 'SE Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_SE <- CC$BI_SE_19
psych::describe(CC$BehavInt2_SE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 65.24 26.81 68 67.9 17.79 2 100 98 -0.88 0.23 5.36range(CC$BehavInt2_SE, na.rm=TRUE)## [1] 2 100hist(CC$BehavInt2_SE, main = 'SE Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#SE Behavioral Intent Scale
CC$BI_Score_SE <- rowMeans(CC [, c("BehavInt1_SE", "BehavInt2_SE")], na.rm=TRUE)
#SE Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_SE, CC$BehavInt2_SE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_SE, CC$BehavInt2_SE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.85 0.85 11 0.016 67 25 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.92 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_SE 0.79 0.85 0.72 0.85 5.5 NA 0 0.85
## CC.BehavInt2_SE 0.90 0.85 0.72 0.85 5.5 NA 0 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_SE 25 0.96 0.96 0.88 0.85 68 25
## CC.BehavInt2_SE 25 0.96 0.96 0.88 0.85 65 27hist(CC$BI_Score_SE, main = 'SE Naturalness Scale Score')
#WE Behavioral Intent Descriptives
#Item 1
CC$BehavInt1_WE <- CC$BI_WI_18
psych::describe(CC$BehavInt1_WE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 59.85 29.68 62.5 61.64 34.84 0 100 100 -0.45 -0.83 5.82range(CC$BehavInt1_WE, na.rm=TRUE)## [1] 0 100hist(CC$BehavInt1_WE, main = 'WE Behavioral Item #1: "I would personally support non-government entities deploying these on a large scale."')
#Item 2
CC$BehavInt2_WE <- CC$BI_WI_19
psych::describe(CC$BehavInt2_WE)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 26 53.27 27.09 52.5 54.27 34.84 0 97 97 -0.26 -0.86 5.31range(CC$BehavInt2_WE, na.rm=TRUE)## [1] 0 97hist(CC$BehavInt2_WE, main = 'WE Behavioral Item #2: "I would personally support spending government tax dollars to deploy these on a large scale."')
#WE Behavioral Intent Scale
CC$BI_Score_WE <- rowMeans(CC [, c("BehavInt1_WE", "BehavInt2_WE")], na.rm=TRUE)
#WE Cronbach's alpha for behavioral intent scale
psych::alpha(data.frame(CC$BehavInt1_WE, CC$BehavInt2_WE))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$BehavInt1_WE, CC$BehavInt2_WE))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.84 0.84 10 0.017 57 27 0.84
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.91 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.BehavInt1_WE 0.92 0.84 0.71 0.84 5.2 NA 0 0.84
## CC.BehavInt2_WE 0.77 0.84 0.71 0.84 5.2 NA 0 0.84
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.BehavInt1_WE 26 0.96 0.96 0.88 0.84 60 30
## CC.BehavInt2_WE 26 0.96 0.96 0.88 0.84 53 27hist(CC$BI_Score_WE, main = 'WE Naturalness Scale Score')
#Environmentalism Scale
#Environmentalism was measured on with 3 items a 1-00 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
#ENV Item 1: Protecting the environment, preserving nature #ENV Item 2: Unity with nature #ENV Item 3: Respecting the earth, harmony with other species
#Environmentalism Item Definitions
CC$ENV_1 <- as.numeric(as.character(CC$Environ_GP_1))
CC$ENV_2 <- as.numeric(as.character(CC$Environ_GP_2))
CC$ENV_3 <- as.numeric(as.character(CC$Environ_GP_3))
#Environmentalism Descriptives
psych::describe(CC$ENV_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 83.16 1.76 83 83.36 2.97 79 85 6 -0.62 -0.46 0.17range(CC$ENV_1, na.rm=TRUE)## [1] 79 85psych::describe(CC$ENV_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 83 1.77 83 83.14 2.97 79 85 6 -0.43 -0.8 0.17range(CC$ENV_2, na.rm=TRUE)## [1] 79 85psych::describe(CC$ENV_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 83.09 1.79 83 83.29 2.97 79 85 6 -0.62 -0.55 0.18range(CC$ENV_3, na.rm=TRUE)## [1] 79 85#Environmentalism Scale Histograms by Item
hist(CC$ENV_1, main = 'ENV #1: Protecting the environment, preserving nature')
hist(CC$ENV_2, main = 'ENV #2: Unity with nature')
hist(CC$ENV_3, main = 'ENV #3: Respecting the earth, harmony with other species')
#Cronbach's Alpha
CC$ENVS_Score <- rowMeans(CC [, c("ENV_1", "ENV_2", "ENV_3")], na.rm=TRUE)
CC$ENV_Scale <- data.frame(CC$ENV_1, CC$ENV_2, CC$ENV_3)
psych::alpha(CC$ENV_Scale)##
## Reliability analysis
## Call: psych::alpha(x = CC$ENV_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.92 0.84 16 0.01 83 1.7 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.92 0.94 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.ENV_1 0.92 0.92 0.85 0.85 11.2 0.016 NA 0.85
## CC.ENV_2 0.90 0.90 0.82 0.82 8.9 0.020 NA 0.82
## CC.ENV_3 0.93 0.93 0.86 0.86 12.5 0.014 NA 0.86
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ENV_1 103 0.94 0.94 0.90 0.87 83 1.8
## CC.ENV_2 103 0.96 0.96 0.93 0.90 83 1.8
## CC.ENV_3 103 0.94 0.94 0.89 0.86 83 1.8
##
## Non missing response frequency for each item
## 79 80 81 82 83 84 85 miss
## CC.ENV_1 0.05 0.04 0.07 0.18 0.23 0.07 0.36 0.02
## CC.ENV_2 0.05 0.02 0.15 0.20 0.15 0.13 0.31 0.02
## CC.ENV_3 0.06 0.03 0.10 0.18 0.17 0.15 0.32 0.02#Correlation ENV Scale
cor.plot(CC$ENV_Scale, labels = c('1','2','3'), main = "Correlations Between Environmentalism Scale Items")
#Aversion to Tampering with Nature Scale
#Aversion to tampering with nature was measured on with 5 items a 1-00 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
#ATNS Item 1: People who push for technological fixes to environmental problems are underestimating the risks. #ATNS Item 2: People who say we shouldn’t tamper with nature are just being naïve. #ATNS Item 3: Human beings have no right to meddle with the natural environment. #ATNS Item 4: I would prefer to live in a world where humans leave nature alone. #ATNS Item 5: Altering nature will be our downfall as a species.
#Aversion to Tampering with Nature Item Definitions
CC$ATNS_1 <- as.numeric(as.character(CC$ATNS_1_1))
CC$ATNS_2 <- as.numeric(as.character(CC$ATNS_1_2))
CC$ATNS_3 <- as.numeric(as.character(CC$ATNS_1_3))
CC$ATNS_4 <- as.numeric(as.character(CC$ATNS_1_4))
CC$ATNS_5 <- as.numeric(as.character(CC$ATNS_1_5))
#Recode item 2
CC$ATNS_2R <- (102- CC$ATNS_2)
#Aversion to Tampering with Nature Scale Descriptives (No reversed codes)
psych::describe(CC$ATNS_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 37.5 1.54 37 37.53 1.48 34 40 6 -0.09 -0.44 0.15range(CC$ATNS_1, na.rm=TRUE)## [1] 34 40psych::describe(CC$ATNS_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 36.66 1.91 37 36.58 1.48 34 40 6 0.15 -0.96 0.19range(CC$ATNS_2, na.rm=TRUE)## [1] 34 40psych::describe(CC$ATNS_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 37.64 1.71 38 37.76 1.48 34 40 6 -0.42 -0.61 0.17range(CC$ATNS_3, na.rm=TRUE)## [1] 34 40psych::describe(CC$ATNS_4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 37.89 1.74 38 38.05 1.48 34 40 6 -0.53 -0.51 0.17range(CC$ATNS_4, na.rm=TRUE)## [1] 34 40psych::describe(CC$ATNS_5)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 37.73 1.74 38 37.86 1.48 34 40 6 -0.33 -0.66 0.17range(CC$ATNS_5, na.rm=TRUE)## [1] 34 40#Aversion to Tampering with Nature Scale Histograms by Item (No reversed codes)
hist(CC$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$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.72 0.72 0.34 2.6 0.046 43 1.2 0.47
##
## lower alpha upper 95% confidence boundaries
## 0.62 0.71 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.ATNS_1 0.63 0.64 0.65 0.31 1.8 0.060 0.064 0.35
## CC.ATNS_2R 0.80 0.80 0.76 0.50 4.1 0.031 0.002 0.50
## CC.ATNS_3 0.62 0.64 0.61 0.30 1.7 0.061 0.038 0.33
## CC.ATNS_4 0.60 0.61 0.61 0.28 1.6 0.066 0.056 0.33
## CC.ATNS_5 0.60 0.62 0.62 0.29 1.6 0.066 0.072 0.30
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ATNS_1 103 0.71 0.73 0.64 0.54 38 1.5
## CC.ATNS_2R 103 0.43 0.40 0.15 0.11 65 1.9
## CC.ATNS_3 103 0.73 0.75 0.70 0.55 38 1.7
## CC.ATNS_4 103 0.78 0.78 0.72 0.61 38 1.7
## CC.ATNS_5 103 0.78 0.77 0.70 0.61 38 1.7describe(CC$ATNS_Scale)## CC$ATNS_Scale
##
## 5 Variables 105 Observations
## --------------------------------------------------------------------------------
## CC.ATNS_1
## n missing distinct Info Mean Gmd
## 103 2 7 0.957 37.5 1.718
##
## lowest : 34 35 36 37 38, highest: 36 37 38 39 40
##
## Value 34 35 36 37 38 39 40
## Frequency 4 4 18 26 27 9 15
## Proportion 0.039 0.039 0.175 0.252 0.262 0.087 0.146
## --------------------------------------------------------------------------------
## CC.ATNS_2R
## n missing distinct Info Mean Gmd
## 103 2 7 0.969 65.34 2.17
##
## lowest : 62 63 64 65 66, highest: 64 65 66 67 68
##
## Value 62 63 64 65 66 67 68
## Frequency 12 5 16 24 16 9 21
## Proportion 0.117 0.049 0.155 0.233 0.155 0.087 0.204
## --------------------------------------------------------------------------------
## CC.ATNS_3
## n missing distinct Info Mean Gmd
## 103 2 7 0.969 37.64 1.923
##
## lowest : 34 35 36 37 38, highest: 36 37 38 39 40
##
## Value 34 35 36 37 38 39 40
## Frequency 7 4 14 22 19 21 16
## Proportion 0.068 0.039 0.136 0.214 0.184 0.204 0.155
## --------------------------------------------------------------------------------
## CC.ATNS_4
## n missing distinct Info Mean Gmd
## 103 2 7 0.964 37.89 1.947
##
## lowest : 34 35 36 37 38, highest: 36 37 38 39 40
##
## Value 34 35 36 37 38 39 40
## Frequency 7 1 13 22 16 20 24
## Proportion 0.068 0.010 0.126 0.214 0.155 0.194 0.233
## --------------------------------------------------------------------------------
## CC.ATNS_5
## n missing distinct Info Mean Gmd
## 103 2 7 0.961 37.73 1.951
##
## lowest : 34 35 36 37 38, highest: 36 37 38 39 40
##
## Value 34 35 36 37 38 39 40
## Frequency 6 5 11 25 22 10 24
## Proportion 0.058 0.049 0.107 0.243 0.214 0.097 0.233
## --------------------------------------------------------------------------------#Correlation ATNS
cor.plot(CC$ATNS_Scale, labels = c('1','2','3','4','5'), main = "Correlations Between Aversion to Tampering with Nature Scale Items")
#Connectedness to Nature Scale
#Connectedness to nature was measured on with 5 items a 1-00 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’). #CNS Item 1: I often feel a sense of oneness with the natural world around me. #CNS Item 2: I think of the natural world as a community to which I belong. #CNS Item 3: I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’. #CNS Item 4: My personal welfare is independent of the welfare of the natural world. #CNS Item 5: When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.
#Connectedness to Nature Item Definitions
CC$CNS_1 <- as.numeric(as.character(CC$CNS_1_1))
CC$CNS_2 <- as.numeric(as.character(CC$CNS_1_2))
CC$CNS_3 <- as.numeric(as.character(CC$CNS_1_3))
CC$CNS_4 <- as.numeric(as.character(CC$CNS_1_4))
CC$CNS_5 <- as.numeric(as.character(CC$CNS_1_5))
#Recode items 4 and 5
CC$CNS_4R <- (102 - CC$CNS_4)
CC$CNS_5R <- (102 - CC$CNS_5)
#Connectedness to Nature Descriptives (No reversed codes)
psych::describe(CC$CNS_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 32.83 1.64 33 32.93 1.48 29 35 6 -0.39 -0.45 0.16range(CC$CNS_1, na.rm=TRUE)## [1] 29 35psych::describe(CC$CNS_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 33.16 1.55 33 33.29 1.48 29 35 6 -0.54 -0.38 0.15range(CC$CNS_2, na.rm=TRUE)## [1] 29 35psych::describe(CC$CNS_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 33.24 1.52 33 33.37 1.48 29 35 6 -0.54 -0.52 0.15range(CC$CNS_3, na.rm=TRUE)## [1] 29 35psych::describe(CC$CNS_4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 32.26 1.88 32 32.33 1.48 29 35 6 -0.24 -0.92 0.19range(CC$CNS_4, na.rm=TRUE)## [1] 29 35psych::describe(CC$CNS_5R)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 69.36 1.61 69 69.29 1.48 67 73 6 0.18 -0.74 0.16range(CC$CNS_5, na.rm=TRUE)## [1] 29 35#Connectedness to Nature Scale Histograms by Item (No reversed codes)
hist(CC$CNS_1, main = 'CNS #1: I often feel a sense of oneness with the natural world around me."')
hist(CC$CNS_2, main = 'CNS #2: I think of the natural world as a community to which I belong. "')
hist(CC$CNS_3, main = 'CNS #3: I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’. "')
hist(CC$CNS_4, main = 'CNS #4: My personal welfare is independent of the welfare of the natural world."')
hist(CC$CNS_5, main = 'CNS #5: When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature."')
#Cronbach's Alpha (4 and 5 reverse coded)
CC$CNS_Scale <- data.frame(CC$CNS_1, CC$CNS_2, CC$CNS_3, CC$CNS_4R, CC$CNS_5R)
CC$CNS_Score <- rowMeans(CC [, c("CNS_1", "CNS_2", "CNS_3", "CNS_4R", "CNS_5R")], na.rm=TRUE)
describe(CC$CNS_Scale)## CC$CNS_Scale
##
## 5 Variables 105 Observations
## --------------------------------------------------------------------------------
## CC.CNS_1
## n missing distinct Info Mean Gmd
## 103 2 7 0.961 32.83 1.829
##
## lowest : 29 30 31 32 33, highest: 31 32 33 34 35
##
## Value 29 30 31 32 33 34 35
## Frequency 5 2 14 21 26 13 22
## Proportion 0.049 0.019 0.136 0.204 0.252 0.126 0.214
## --------------------------------------------------------------------------------
## CC.CNS_2
## n missing distinct Info Mean Gmd
## 103 2 7 0.954 33.16 1.718
##
## lowest : 29 30 31 32 33, highest: 31 32 33 34 35
##
## Value 29 30 31 32 33 34 35
## Frequency 2 4 9 17 27 17 27
## Proportion 0.019 0.039 0.087 0.165 0.262 0.165 0.262
## --------------------------------------------------------------------------------
## CC.CNS_3
## n missing distinct Info Mean Gmd
## 103 2 7 0.948 33.24 1.685
##
## lowest : 29 30 31 32 33, highest: 31 32 33 34 35
##
## Value 29 30 31 32 33 34 35
## Frequency 1 4 11 12 29 17 29
## Proportion 0.010 0.039 0.107 0.117 0.282 0.165 0.282
## --------------------------------------------------------------------------------
## CC.CNS_4R
## n missing distinct Info Mean Gmd
## 103 2 7 0.971 69.74 2.131
##
## lowest : 67 68 69 70 71, highest: 69 70 71 72 73
##
## Value 67 68 69 70 71 72 73
## Frequency 15 15 16 26 11 7 13
## Proportion 0.146 0.146 0.155 0.252 0.107 0.068 0.126
## --------------------------------------------------------------------------------
## CC.CNS_5R
## n missing distinct Info Mean Gmd
## 103 2 7 0.966 69.36 1.824
##
## lowest : 67 68 69 70 71, highest: 69 70 71 72 73
##
## Value 67 68 69 70 71 72 73
## Frequency 18 13 24 23 15 7 3
## Proportion 0.175 0.126 0.233 0.223 0.146 0.068 0.029
## --------------------------------------------------------------------------------psych::alpha(CC$CNS_Scale)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(CC$CNS_Scale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.CNS_4R CC.CNS_5R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = CC$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.26 0.31 0.53 0.083 0.45 0.12 48 0.83 -0.15
##
## lower alpha upper 95% confidence boundaries
## 0.03 0.26 0.5
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.CNS_1 0.0855 0.110 0.40 0.0300 0.124 0.154 0.16 -0.15
## CC.CNS_2 -0.0038 0.018 0.24 0.0045 0.018 0.167 0.12 -0.15
## CC.CNS_3 -0.1205 -0.108 0.21 -0.0251 -0.098 0.187 0.15 -0.22
## CC.CNS_4R 0.4691 0.483 0.64 0.1893 0.934 0.082 0.24 0.21
## CC.CNS_5R 0.4565 0.522 0.64 0.2145 1.092 0.090 0.21 0.20
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CNS_1 103 0.61 0.64 0.57 0.26 33 1.6
## CC.CNS_2 103 0.66 0.70 0.75 0.36 33 1.5
## CC.CNS_3 103 0.73 0.77 0.81 0.47 33 1.5
## CC.CNS_4R 103 0.34 0.27 -0.10 -0.12 70 1.9
## CC.CNS_5R 103 0.24 0.21 -0.14 -0.16 69 1.6#Correlation CNS Scale
cor.plot(CC$CNS_Scale, labels = c('1','2','3','4','5'), main = "Correlations Between Connectedness to Nature Scale Items")
#Climate Change Belief Scale
#Climate change beliefs were measured on with 5 items a 1-7 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’). #CCB Item 1: Climate change is happening. #CCB Item 2: Climate change poses a risk to human health, safety, and prosperity. #CCB Item 3: Human activity is largely responsible for recent climate change. #CCB Item 4: Reducing greenhouse gas emissions will reduce global warming and climate change.
#Climate Change Belief Item Definitions
CC$CCBelief_1 <- as.numeric(as.character(CC$CCB_1_1))
CC$CCBelief_2 <- as.numeric(as.character(CC$CCB_1_2))
CC$CCBelief_3 <- as.numeric(as.character(CC$CCB_1_3))
CC$CCBelief_4 <- as.numeric(as.character(CC$CCB_1_4))
#Climate Change Belief Descriptives
psych::describe(CC$CCBelief_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 41.46 1.66 42 41.66 1.48 37 43 6 -0.83 -0.12 0.16range(CC$CCBelief_1, na.rm=TRUE)## [1] 37 43psych::describe(CC$CCBelief_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 102 41.36 1.68 42 41.57 1.48 37 43 6 -0.78 -0.34 0.17range(CC$CCBelief_2, na.rm=TRUE)## [1] 37 43psych::describe(CC$CCBelief_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 41.26 1.74 42 41.51 1.48 37 43 6 -0.91 -0.04 0.17range(CC$CCBelief_3, na.rm=TRUE)## [1] 37 43psych::describe(CC$CCBelief_4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 40.91 1.74 41 41.07 1.48 37 43 6 -0.49 -0.56 0.17range(CC$CCBelief_4, na.rm=TRUE)## [1] 37 43#Climate Change Belief Histograms
hist(CC$CCBelief_1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(CC$CCBelief_2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(CC$CCBelief_3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(CC$CCBelief_4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
CC$CCBelief_Score <- rowMeans(CC[, c('CCBelief_1', 'CCBelief_2', 'CCBelief_3','CCBelief_4')], na.rm=T)
describe(CC$CCBelief_Score)## CC$CCBelief_Score
## n missing distinct Info Mean Gmd .05 .10
## 103 2 23 0.991 41.25 1.689 38.77 39.25
## .25 .50 .75 .90 .95
## 40.00 41.75 42.50 43.00 43.00
##
## lowest : 37.00000 37.50000 37.75000 38.50000 38.75000
## highest: 42.25000 42.50000 42.66667 42.75000 43.00000#Cronbach's Alpha
CC$CCB_Scale <- data.frame(CC$CCB_1_1, CC$CCB_1_2, CC$CCB_1_3, CC$CCB_1_4)
psych::alpha(CC$CCB_Scale)##
## Reliability analysis
## Call: psych::alpha(x = CC$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.89 0.71 9.7 0.015 41 1.5 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.91 0.94
##
## 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_1 0.89 0.89 0.84 0.72 7.9 0.019 0.0022 0.75
## CC.CCB_1_2 0.86 0.86 0.82 0.68 6.3 0.023 0.0048 0.68
## CC.CCB_1_3 0.87 0.87 0.83 0.69 6.6 0.023 0.0082 0.67
## CC.CCB_1_4 0.90 0.90 0.86 0.74 8.6 0.018 0.0028 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.CCB_1_1 103 0.87 0.87 0.81 0.76 41 1.7
## CC.CCB_1_2 102 0.91 0.91 0.88 0.83 41 1.7
## CC.CCB_1_3 103 0.90 0.90 0.86 0.82 41 1.7
## CC.CCB_1_4 103 0.86 0.85 0.78 0.74 41 1.7
##
## Non missing response frequency for each item
## 37 38 39 40 41 42 43 miss
## CC.CCB_1_1 0.04 0.00 0.09 0.17 0.15 0.15 0.41 0.02
## CC.CCB_1_2 0.03 0.04 0.08 0.16 0.15 0.19 0.36 0.03
## CC.CCB_1_3 0.06 0.03 0.06 0.17 0.14 0.24 0.31 0.02
## CC.CCB_1_4 0.06 0.03 0.10 0.24 0.16 0.17 0.25 0.02#Correlation CCB Scale
cor.plot(CC$CCB_Scale, labels = c('1','2','3','4'), main = "Correlations Between Climate Change Belief Items")
#Individualism/Collectivism Scale
#Individualism and collectivism were each measured with 4 items (for a total of 8 items) on a 1-7 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
#Collectivism Items ##Individualism/Collectivism Item #3 (C): It is important to me to think of myself as a member of my religious, national, or ethnic group. ##Individualism/Collectivism Item #4 (C): Learning about the traditions, values, and beliefs of my family is important to me. ##Individualism/Collectivism Item #7 (C): In the end, a person feels closest to members of their own religious, national, or ethnic group. ##Individualism/Collectivism Item #8 (C): It is important to me to respect decisions made by my family.
#Individualism Items ##Individualism/Collectivism Item #1 (I): It is important to me to develop my own personal style. ##Individualism/Collectivism Item #2 (I): It is better for me to follow my own ideas than to follow those of anyone else.
##Individualism/Collectivism Item #5 (I): I enjoy being unique and different from others in many respects. ##Individualism/Collectivism Item #6 (I): My personal achievements and accomplishments are very important to who I am.
#Individualism and Collectivism Scale (Code adapted from J.Cole Collectivism Study)
#Individualism (Items 1,2,5,6)
CC$Ind_1 <- as.numeric(as.character(CC$Individualism_1))
CC$Ind_2 <- as.numeric(as.character(CC$Individualism_2))
CC$Ind_5 <- as.numeric(as.character(CC$Individualism_5))
CC$Ind_6 <- as.numeric(as.character(CC$Individualism_6))
CC$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_3))
CC$Ind_4 <- as.numeric(as.character(CC$Individualism_4))
CC$Ind_7 <- as.numeric(as.character(CC$Individualism_7))
CC$Ind_8 <- as.numeric(as.character(CC$Individualism_8))
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$Ind_6))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_5, CC$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.83 0.6 6.1 0.023 58 1.3 0.6
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.86 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.Ind_1 0.78 0.78 0.71 0.54 3.5 0.037 0.0096 0.50
## CC.Ind_2 0.87 0.88 0.83 0.70 7.0 0.022 0.0019 0.71
## CC.Ind_5 0.80 0.80 0.75 0.57 4.1 0.034 0.0152 0.54
## CC.Ind_6 0.81 0.81 0.76 0.59 4.4 0.032 0.0161 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 103 0.89 0.89 0.87 0.80 58 1.4
## CC.Ind_2 103 0.75 0.75 0.60 0.56 58 1.5
## CC.Ind_5 103 0.86 0.86 0.81 0.74 58 1.5
## CC.Ind_6 103 0.85 0.85 0.78 0.71 58 1.7
##
## Non missing response frequency for each item
## 54 55 56 57 58 59 60 miss
## CC.Ind_1 0.02 0.03 0.03 0.19 0.20 0.25 0.27 0.02
## CC.Ind_2 0.02 0.03 0.11 0.22 0.20 0.18 0.23 0.02
## CC.Ind_5 0.02 0.02 0.14 0.20 0.19 0.19 0.23 0.02
## CC.Ind_6 0.04 0.06 0.06 0.21 0.17 0.19 0.26 0.02hist(CC$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))##
## 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
## 0.82 0.82 0.8 0.54 4.7 0.029 58 1.4 0.51
##
## lower alpha upper 95% confidence boundaries
## 0.76 0.82 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.Ind_3 0.78 0.78 0.72 0.54 3.5 0.038 0.0143 0.50
## CC.Ind_4 0.76 0.76 0.69 0.51 3.1 0.041 0.0134 0.44
## CC.Ind_7 0.77 0.78 0.72 0.55 3.6 0.039 0.0137 0.52
## CC.Ind_8 0.79 0.79 0.72 0.56 3.8 0.035 0.0058 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_3 103 0.83 0.81 0.72 0.64 57 1.9
## CC.Ind_4 103 0.82 0.83 0.77 0.69 58 1.6
## CC.Ind_7 103 0.80 0.80 0.71 0.64 58 1.6
## CC.Ind_8 103 0.78 0.79 0.70 0.61 58 1.6
##
## Non missing response frequency for each item
## 54 55 56 57 58 59 60 miss
## CC.Ind_3 0.11 0.12 0.13 0.18 0.18 0.10 0.18 0.02
## CC.Ind_4 0.01 0.07 0.07 0.24 0.19 0.14 0.28 0.02
## CC.Ind_7 0.02 0.08 0.11 0.27 0.19 0.14 0.19 0.02
## CC.Ind_8 0.02 0.06 0.10 0.26 0.20 0.11 0.25 0.02hist(CC$Collectivism_Score , main = 'Collectivism Score')
#Cronbachs Alpha for Individualism/Collectivism full scale (8 items)
CC$ICScale <- data.frame(CC$Ind_1, CC$Ind_2, CC$Ind_3, CC$Ind_4, CC$Ind_5,CC$Ind_6, CC$Ind_7, CC$Ind_8)
psych::alpha(CC$ICScale)##
## Reliability analysis
## Call: psych::alpha(x = CC$ICScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.9 0.48 7.4 0.019 58 1.2 0.48
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.88 0.91
##
## 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 0.85 0.85 0.87 0.45 5.8 0.023 0.018 0.44
## CC.Ind_2 0.87 0.88 0.89 0.51 7.2 0.019 0.018 0.50
## CC.Ind_3 0.88 0.88 0.88 0.52 7.5 0.018 0.011 0.50
## CC.Ind_4 0.86 0.86 0.88 0.47 6.2 0.022 0.024 0.47
## CC.Ind_5 0.86 0.87 0.88 0.48 6.5 0.021 0.018 0.48
## CC.Ind_6 0.85 0.86 0.88 0.46 6.0 0.023 0.022 0.44
## CC.Ind_7 0.86 0.87 0.88 0.48 6.5 0.021 0.026 0.50
## CC.Ind_8 0.86 0.86 0.88 0.47 6.2 0.022 0.024 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Ind_1 103 0.82 0.83 0.82 0.76 58 1.4
## CC.Ind_2 103 0.63 0.64 0.57 0.52 58 1.5
## CC.Ind_3 103 0.63 0.61 0.56 0.49 57 1.9
## CC.Ind_4 103 0.78 0.77 0.74 0.70 58 1.6
## CC.Ind_5 103 0.72 0.73 0.70 0.63 58 1.5
## CC.Ind_6 103 0.80 0.81 0.78 0.73 58 1.7
## CC.Ind_7 103 0.75 0.74 0.70 0.66 58 1.6
## CC.Ind_8 103 0.77 0.77 0.73 0.69 58 1.6
##
## Non missing response frequency for each item
## 54 55 56 57 58 59 60 miss
## CC.Ind_1 0.02 0.03 0.03 0.19 0.20 0.25 0.27 0.02
## CC.Ind_2 0.02 0.03 0.11 0.22 0.20 0.18 0.23 0.02
## CC.Ind_3 0.11 0.12 0.13 0.18 0.18 0.10 0.18 0.02
## CC.Ind_4 0.01 0.07 0.07 0.24 0.19 0.14 0.28 0.02
## CC.Ind_5 0.02 0.02 0.14 0.20 0.19 0.19 0.23 0.02
## CC.Ind_6 0.04 0.06 0.06 0.21 0.17 0.19 0.26 0.02
## CC.Ind_7 0.02 0.08 0.11 0.27 0.19 0.14 0.19 0.02
## CC.Ind_8 0.02 0.06 0.10 0.26 0.20 0.11 0.25 0.02#Correlation Matrix: Individualism and Collectivism Items (8)
cor.plot(CC$ICScale, labels = c('1','2','3','4','5','6','7','8'), main = "Correlations Between Individualistic/Collectivist Items")
#Systems/Holistic Thinking
ST Item #1: All the Earth’s systems, from the climate to the economy, are interconnected. ST Item #2: Everything is constantly changing. ST Item #3: The Earth, including all its inhabitants, is a living system. ST Item #4: Seemingly small choices we make today can ultimately have major consequences.
HT Item #1: Everything in the universe is somehow related to each other. HT Item #2: It is more desirable to take the middle ground than go to extremes. HT Item #3: Every phenomenon in the world moves in predictable directions. HT Item #4: The whole, rather than its parts, should be considered in order to understand a phenomenon.
#Order of Items on Full Scale #Systems Thinking (15 items full scale) ST Item #1 = #5 ST Item #2 = #6 ST Item #3 = #10 ST Item #4 = #13
#Holistic Thinking (24 items full scale) HT Item #1 = #1 (Factor 1: Causality) HT Item #2 = #7 (Factor 2: Attitude Toward Contradictions) HT Item #3 (Reverse coded) = #13 (Factor 3: Perception of Change) HT Item #4 = #19 (Factor 4: Locus of Attention)
#Systems Thinking
CC$ST_1 <- as.numeric(as.character(CC$SystemsHolistic_1))
CC$ST_2 <- as.numeric(as.character(CC$SystemsHolistic_2))
CC$ST_3 <- as.numeric(as.character(CC$SystemsHolistic_3))
CC$ST_4 <- as.numeric(as.character(CC$SystemsHolistic_4))
CC$ST_Score <- rowMeans(CC[, c('ST_1', 'ST_2', 'ST_3','ST_4')], na.rm=T)
#Systems Thinking Histograms
hist(CC$ST_1, main = 'All the Earth’s systems, from the climate to the economy, are interconnected.')
hist(CC$ST_2, main = 'Everything is constantly changing.')
hist(CC$ST_3, main = 'The Earth, including all its inhabitants, is a living system.')
hist(CC$ST_4, main = 'Seemingly small choices we make today can ultimately have major consequences."')
#Holistic Thinking
CC$HT_1 <- as.numeric(as.character(CC$SystemsHolistic_1))
CC$HT_2 <- as.numeric(as.character(CC$SystemsHolistic_2))
CC$HT_3 <- as.numeric(as.character(CC$SystemsHolistic_3))
CC$HT_4 <- as.numeric(as.character(CC$SystemsHolistic_4))
CC$HT_Score <- rowMeans(CC[, c('HT_1', 'HT_2', 'HT_3','HT_4')], na.rm=T)
#Holistic Recode #3
CC$HT_3R <- abs(CC$HT_3 -100)
#Holistic Thinking Histograms (No reverse codes)
hist(CC$HT_1, main = 'Everything in the universe is somehow related to each other.')
hist(CC$HT_2, main = 'It is more desirable to take the middle ground than go to extremes.')
hist(CC$HT_3, main = 'Every phenomenon in the world moves in predictable directions.')
hist(CC$HT_4, main = 'The whole, rather than its parts, should be considered in order to understand a phenomenon.')
#Descriptives by Item
describe(CC$ST_1)## CC$ST_1
## n missing distinct Info Mean Gmd
## 103 2 6 0.938 58.41 1.634
##
## lowest : 55 56 57 58 59, highest: 56 57 58 59 60
##
## Value 55 56 57 58 59 60
## Frequency 5 6 16 26 15 35
## Proportion 0.049 0.058 0.155 0.252 0.146 0.340describe(CC$ST_2)## CC$ST_2
## n missing distinct Info Mean Gmd
## 103 2 7 0.938 58.54 1.512
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 2 3 20 19 24 34
## Proportion 0.010 0.019 0.029 0.194 0.184 0.233 0.330describe(CC$ST_3)## CC$ST_3
## n missing distinct Info Mean Gmd
## 103 2 7 0.923 58.62 1.566
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 2 5 18 15 22 40
## Proportion 0.010 0.019 0.049 0.175 0.146 0.214 0.388describe(CC$ST_4)## CC$ST_4
## n missing distinct Info Mean Gmd
## 103 2 7 0.924 58.63 1.55
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 2 5 16 18 21 40
## Proportion 0.010 0.019 0.049 0.155 0.175 0.204 0.388describe(CC$HT_1)## CC$HT_1
## n missing distinct Info Mean Gmd
## 103 2 6 0.938 58.41 1.634
##
## lowest : 55 56 57 58 59, highest: 56 57 58 59 60
##
## Value 55 56 57 58 59 60
## Frequency 5 6 16 26 15 35
## Proportion 0.049 0.058 0.155 0.252 0.146 0.340describe(CC$HT_2)## CC$HT_2
## n missing distinct Info Mean Gmd
## 103 2 7 0.938 58.54 1.512
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 2 3 20 19 24 34
## Proportion 0.010 0.019 0.029 0.194 0.184 0.233 0.330describe(CC$HT_3R)## CC$HT_3R
## n missing distinct Info Mean Gmd
## 103 2 7 0.923 41.38 1.566
##
## lowest : 40 41 42 43 44, highest: 42 43 44 45 46
##
## Value 40 41 42 43 44 45 46
## Frequency 40 22 15 18 5 2 1
## Proportion 0.388 0.214 0.146 0.175 0.049 0.019 0.010describe(CC$HT_4)## CC$HT_4
## n missing distinct Info Mean Gmd
## 103 2 7 0.924 58.63 1.55
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 2 5 16 18 21 40
## Proportion 0.010 0.019 0.049 0.155 0.175 0.204 0.388#Systems Thinking Alpha and Histogram (4 items)
psych::alpha(data.frame(CC$ST_1, CC$ST_2, CC$ST_3,CC$ST_4))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$ST_1, CC$ST_2, CC$ST_3, CC$ST_4))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.9 0.87 0.68 8.5 0.017 59 1.3 0.7
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.89 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.ST_1 0.88 0.88 0.83 0.70 7.1 0.021 0.00223 0.71
## CC.ST_2 0.85 0.85 0.80 0.66 5.7 0.026 0.00732 0.70
## CC.ST_3 0.84 0.84 0.79 0.64 5.4 0.027 0.00632 0.65
## CC.ST_4 0.89 0.89 0.84 0.72 7.7 0.019 0.00063 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ST_1 103 0.85 0.85 0.78 0.73 58 1.5
## CC.ST_2 103 0.89 0.89 0.85 0.80 59 1.4
## CC.ST_3 103 0.91 0.91 0.87 0.82 59 1.4
## CC.ST_4 103 0.84 0.84 0.76 0.71 59 1.4
##
## Non missing response frequency for each item
## 54 55 56 57 58 59 60 miss
## CC.ST_1 0.00 0.05 0.06 0.16 0.25 0.15 0.34 0.02
## CC.ST_2 0.01 0.02 0.03 0.19 0.18 0.23 0.33 0.02
## CC.ST_3 0.01 0.02 0.05 0.17 0.15 0.21 0.39 0.02
## CC.ST_4 0.01 0.02 0.05 0.16 0.17 0.20 0.39 0.02hist(CC$ST_Score , main = 'Systems Thinking Score')
#Holistic Thinking Alpha and Histogram (4 items)
psych::alpha(data.frame(CC$HT_1, CC$HT_2, CC$HT_3R, CC$HT_4))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$HT_1, CC$HT_2, CC$HT_3R, CC$HT_4)): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.HT_3R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$HT_1, CC$HT_2, CC$HT_3R, CC$HT_4))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.2 -0.18 0.56 -0.039 -0.15 0.13 54 0.67 -0.071
##
## lower alpha upper 95% confidence boundaries
## -0.45 -0.2 0.06
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.HT_1 -1.85 -1.73 0.120 -0.27 -0.63 0.410 0.6388 -0.71
## CC.HT_2 -1.95 -1.98 -0.046 -0.28 -0.66 0.421 0.5308 -0.70
## CC.HT_3R 0.84 0.84 0.792 0.64 5.38 0.027 0.0063 0.65
## CC.HT_4 -1.48 -1.44 0.230 -0.24 -0.59 0.357 0.6905 -0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.HT_1 103 0.84 0.84 0.81 0.47 58 1.5
## CC.HT_2 103 0.86 0.86 0.90 0.55 59 1.4
## CC.HT_3R 103 -0.61 -0.62 -1.05 -0.82 41 1.4
## CC.HT_4 103 0.80 0.80 0.74 0.40 59 1.4hist(CC$HT_Score , main = 'Holistic Thinking Score')
#Cronbachs Alpha for Systems/Holistic Thinking full scale (8 items)
CC$STHTScale <- data.frame(CC$ST_1, CC$ST_2, CC$ST_3, CC$ST_4, CC$HT_1,CC$HT_2, CC$HT_3R, CC$HT_4)
psych::alpha(CC$STHTScale)## Number of categories should be increased in order to count frequencies.## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.stats, I could not find the RMSEA upper bound . Sorry about that## Warning in psych::alpha(CC$STHTScale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.HT_3R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0##
## Reliability analysis
## Call: psych::alpha(x = CC$STHTScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.71 0.35 4.2 0.016 56 0.94 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CC.ST_1 0.74 0.74 0.67 0.29 2.9 0.0210 0.489 0.65
## CC.ST_2 0.73 0.74 0.66 0.28 2.8 0.0219 0.476 0.65
## CC.ST_3 0.75 0.75 0.68 0.30 3.1 0.0226 0.458 0.65
## CC.ST_4 0.75 0.75 0.67 0.30 3.0 0.0202 0.492 0.70
## CC.HT_1 0.74 0.74 0.67 0.29 2.9 0.0210 0.489 0.65
## CC.HT_2 0.73 0.74 0.66 0.28 2.8 0.0219 0.476 0.65
## CC.HT_3R 0.95 0.95 0.83 0.72 17.6 0.0088 0.018 0.71
## CC.HT_4 0.75 0.75 0.67 0.30 3.0 0.0202 0.492 0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.ST_1 103 0.87 0.87 0.80 0.81 58 1.5
## CC.ST_2 103 0.90 0.91 0.85 0.86 59 1.4
## CC.ST_3 103 0.82 0.82 0.75 0.74 59 1.4
## CC.ST_4 103 0.84 0.84 0.78 0.77 59 1.4
## CC.HT_1 103 0.87 0.87 0.80 0.81 58 1.5
## CC.HT_2 103 0.90 0.91 0.85 0.86 59 1.4
## CC.HT_3R 103 -0.82 -0.82 -1.21 -0.87 41 1.4
## CC.HT_4 103 0.84 0.84 0.78 0.77 59 1.4#Correlation Matrix: Individualism and Collectivism Items (8)
cor.plot(CC$STHTScale, labels = c('1','2','3','4','5','6','7','8'), main = "Correlations Between Systems Thinking and Holistic Thinking Items")
#Correlations - Aggregated Naturalness to Individual Difference Measures
#Naturalness - Deeper Examination
#By looking at naturalness across 10 technologies, questions of power were raised in our initial discussions of the data. Further examinations have been conducted below looking at 1) alpha values of naturalness items for first technologies seen across ALL tech (n=100). The first technology naturalness ratings were used, rather than all three participants saw, because we wanted to look at items with a larger sample of 100, while also maintaining the randomness of methods seen.
#Define new variables
CC$Nat_1_Aggregated<- CC$Nat_1
CC$Nat_2_Aggregated <- (102-CC$Nat_2)
CC$Nat_3_Aggregated <- (102-CC$Nat_3)
CC$Nat_Score_Aggregated <- rowMeans(CC [, c("Nat_1_Aggregated", "Nat_2_Aggregated", "Nat_3_Aggregated")], na.rm=TRUE)
CC$NatAg_Scale <- data.frame(CC$Nat_1, CC$Nat_2, CC$Nat_3)
#Cronbach's alpha for aggregated naturalness scale
psych::alpha(data.frame(CC$Nat_1_Aggregated, CC$Nat_2_Aggregated, CC$Nat_3_Aggregated))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(CC$Nat_1_Aggregated, CC$Nat_2_Aggregated, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( CC.Nat_1_Aggregated ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(CC$Nat_1_Aggregated, CC$Nat_2_Aggregated,
## CC$Nat_3_Aggregated))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.26 0.29 0.37 0.12 0.4 0.13 44 17 -0.027
##
## lower alpha upper 95% confidence boundaries
## 0.01 0.26 0.52
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## CC.Nat_1_Aggregated 0.705 0.707 0.546 0.546 2.409 0.057 NA
## CC.Nat_2_Aggregated -0.390 -0.393 -0.164 -0.164 -0.282 0.269 NA
## CC.Nat_3_Aggregated -0.055 -0.055 -0.027 -0.027 -0.052 0.206 NA
## med.r
## CC.Nat_1_Aggregated 0.546
## CC.Nat_2_Aggregated -0.164
## CC.Nat_3_Aggregated -0.027
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CC.Nat_1_Aggregated 103 0.46 0.42 -0.14 -0.11 52 29
## CC.Nat_2_Aggregated 103 0.78 0.79 0.70 0.37 44 27
## CC.Nat_3_Aggregated 104 0.68 0.72 0.60 0.26 37 25hist(CC$Nat_Score_Aggregated, main = 'Aggregated Naturalness Scale Score')
#Individual AFSCS Naturalness Items
#Item 1
psych::describe(CC$Nat_1_Aggregated)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 51.91 28.74 54 52.17 37.06 0 100 100 -0.06 -1.03 2.83range(CC$Nat_1_Aggregated, na.rm=TRUE)## [1] 0 100hist(CC$Nat_1_Aggregated, main = '"This is natural."')
#Item 2 (Not reverse coded)
psych::describe(CC$Nat_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 58.14 27.3 63 59.53 22.24 0 100 100 -0.47 -0.55 2.69range(CC$Nat_2, na.rm=TRUE)## [1] 0 100hist(CC$Nat_2, main = '"This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(CC$Nat_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 104 65.19 25.34 71 68.12 19.27 0 100 100 -1.02 0.6 2.48range(CC$Nat_3, na.rm=TRUE)## [1] 0 100hist(CC$Nat_3, main = '"This relies on science-based technology."')
#Naturalness Correlation
cor.plot(CC$NatAg_Scale, labels = c('1','2','3'), main = "Correlation Between Aggregated Nat Items")
#Naturalness Correlation to Individual Difference Measures
#Environmentalism
cor(CC$ENVS_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.05507577plot(CC$ENVS_Score, CC$Nat_Score_Aggregated, main="Correlation Between Environmentalism and Naturalness Scales",
xlab="Environmentalism Scale", ylab="Naturalness Scale", pch=19)
#Correlation between environmentalism scale score and naturalness scale score negatively correlated. The following tests the relationship between individual naturalness items and Environmentalism scale. While we do see an incredibly slight negative correlation between reverse coded naturalness items (2, 3) and Environmentalism scale, the correlation between the core "This is natural" item (1) has an incredibly small positive correlation.
cor(CC$ENVS_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] 0.1392068cor(CC$ENVS_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.09146713cor(CC$ENVS_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.186224#Aversion to Tampering with Nature
cor(CC$ATNS_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.09768477plot(CC$ATNS_Score, CC$Nat_Score_Aggregated, main="Correlation Between Aversion to Tampering with Nature and Naturalness Scales",
xlab="ATNS Scale", ylab="Naturalness Scale", pch=19)
cor(CC$ATNS_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] -0.03073172cor(CC$ATNS_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.08765947cor(CC$ATNS_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.08501642#Connectedness to Nature
cor(CC$CNS_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.06590193plot(CC$CNS_Score, CC$Nat_Score_Aggregated, main="Correlation Between Connectedness to Nature and Naturalness Scales",
xlab="CNS Scale", ylab="Naturalness Scale", pch=19)
cor(CC$CNS_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] -0.01626357cor(CC$CNS_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] 0.01474817cor(CC$CNS_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.1257454#Climate Change Belief
cor(CC$CCBelief_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.1014121plot(CC$CCBelief_Score, CC$Nat_Score_Aggregated, main="Correlation Between Climate Change Belief and Naturalness Scales",
xlab="CC Belief Scale", ylab="Naturalness Scale", pch=19)
cor(CC$CCBelief_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] 0.1574064cor(CC$CCBelief_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.05898942cor(CC$CCBelief_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.322444#Individualism
cor(CC$Individualism_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.09937843plot(CC$Individualism_Score, CC$Nat_Score_Aggregated, main="Correlation Between Individualism and Naturalness Scales",
xlab="Individualism Scale", ylab="Naturalness Scale", pch=19)
cor(CC$Individualism_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] 0.0997064cor(CC$Individualism_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.08352061cor(CC$Individualism_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.2384698#Collectivism
cor(CC$Collectivism_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.08822026plot(CC$Collectivism_Score, CC$Nat_Score_Aggregated, main="Correlation Between Collectivism and Naturalness Scales",
xlab="Collectivism Scale", ylab="Naturalness Scale", pch=19)
cor(CC$Collectivism_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] 0.1009656cor(CC$Collectivism_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.02915125cor(CC$Collectivism_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.273157#Systems Thinking
CC$ST_Score## [1] 57.00 59.00 59.50 59.50 59.25 59.50 58.75 60.00 56.00 59.50 59.75 56.25
## [13] 59.25 59.25 58.50 58.25 57.75 58.75 60.00 57.50 60.00 60.00 57.75 57.00
## [25] 58.75 58.00 55.50 55.75 59.00 59.00 59.00 59.75 NaN 60.00 57.00 59.00
## [37] 60.00 57.75 59.25 59.75 56.25 NaN 59.00 56.75 59.00 59.75 58.75 58.75
## [49] 60.00 59.25 58.50 60.00 59.50 57.50 58.25 59.75 57.00 60.00 57.50 56.50
## [61] 57.75 57.00 58.75 58.75 59.00 59.25 60.00 57.00 59.50 57.00 58.25 58.75
## [73] 59.50 59.75 60.00 59.75 57.50 59.50 59.50 54.50 59.25 57.25 58.75 60.00
## [85] 57.75 57.75 60.00 59.75 59.75 59.75 57.00 58.75 57.00 59.75 59.75 57.25
## [97] 60.00 57.25 59.25 58.00 57.00 58.50 57.75 57.75 57.25cor(CC$ST_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.06958118plot(CC$ST_Score, CC$Nat_Score_Aggregated, main="Correlation Between Systems Thinking and Naturalness Scales",
xlab="Systems Thinking Scale", ylab="Naturalness Scale", pch=19)
cor(CC$ST_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] 0.0930561cor(CC$ST_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.06468005cor(CC$ST_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.1745634#Holistic Thinking
CC$HT_Score## [1] 57.00 59.00 59.50 59.50 59.25 59.50 58.75 60.00 56.00 59.50 59.75 56.25
## [13] 59.25 59.25 58.50 58.25 57.75 58.75 60.00 57.50 60.00 60.00 57.75 57.00
## [25] 58.75 58.00 55.50 55.75 59.00 59.00 59.00 59.75 NaN 60.00 57.00 59.00
## [37] 60.00 57.75 59.25 59.75 56.25 NaN 59.00 56.75 59.00 59.75 58.75 58.75
## [49] 60.00 59.25 58.50 60.00 59.50 57.50 58.25 59.75 57.00 60.00 57.50 56.50
## [61] 57.75 57.00 58.75 58.75 59.00 59.25 60.00 57.00 59.50 57.00 58.25 58.75
## [73] 59.50 59.75 60.00 59.75 57.50 59.50 59.50 54.50 59.25 57.25 58.75 60.00
## [85] 57.75 57.75 60.00 59.75 59.75 59.75 57.00 58.75 57.00 59.75 59.75 57.25
## [97] 60.00 57.25 59.25 58.00 57.00 58.50 57.75 57.75 57.25cor(CC$HT_Score, CC$Nat_Score_Aggregated, use="complete.obs")## [1] -0.06958118plot(CC$HT_Score, CC$Nat_Score_Aggregated, main="Correlation Between Holistic Thinking and Naturalness Scales",
xlab="Holistic Thinking Scale", ylab="Naturalness Scale", pch=19)
cor(CC$HT_Score, CC$Nat_1_Aggregated, use="complete.obs")## [1] 0.0930561cor(CC$HT_Score, CC$Nat_2_Aggregated, use="complete.obs")## [1] -0.06468005cor(CC$HT_Score, CC$Nat_3_Aggregated, use="complete.obs")## [1] -0.1745634#Demographics
#Ethnicity
CC$Ethnicity <- NA
CC$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$Dem_Ethnicity
## n missing distinct Info Mean Gmd
## 103 2 5 0.512 5.214 1.292
##
## lowest : 1 2 3 4 6, highest: 1 2 3 4 6
##
## Value 1 2 3 4 6
## Frequency 3 13 2 4 81
## Proportion 0.029 0.126 0.019 0.039 0.786#Gender
CC$Dem_Gender <- as.numeric(as.character(CC$Dem_Gen))
describe(CC$Dem_Gen)## CC$Dem_Gen
## n missing distinct Info Mean Gmd
## 103 2 2 0.725 1.408 0.4877
##
## Value 1 2
## Frequency 61 42
## Proportion 0.592 0.408#Age
CC$Demograph_Age <- as.numeric(as.character(CC$Dem_Age))
describe(CC$Demograph_Age)## CC$Demograph_Age
## n missing distinct Info Mean Gmd .05 .10
## 103 2 52 0.999 46.68 19.3 25.0 27.4
## .25 .50 .75 .90 .95
## 32.0 42.0 61.0 72.0 75.8
##
## lowest : 20 21 23 24 25, highest: 76 77 78 80 85range(CC$Demograph_Age ,na.rm = T)## [1] 20 85#Predictions - Risk/Naturalness by Method Type
#1. #AFSCS
CC$Risk_Score_AFSCS <- rowMeans(CC [, c("Risk_AFSCS_32", "Risk_AFSCS_33")], na.rm=TRUE)
CC$Naturalness_Score_AFSCS <- rowMeans(CC [, c("Naturalness_1_AFSCS", "Naturalness_2R_AFSCS", "Naturalness_3R_AFSCS")], na.rm=TRUE)
m.a1 <- lm(CC$Risk_Score_AFSCS ~ Naturalness_Score_AFSCS, data = CC)
mcSummary(m.a1)## lm(formula = CC$Risk_Score_AFSCS ~ Naturalness_Score_AFSCS, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 425.575 1 425.575 0.015 0.437 0.514
## Error 28231.199 29 973.490
## Corr Total 28656.774 30 955.226
##
## RMSE AdjEtaSq
## 31.201 -0.019
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 53.986 17.79 3.035 8964.737 0.241 NA 17.601 90.371
## Naturalness_Score_AFSCS -0.231 0.35 -0.661 425.575 0.015 NA -0.947 0.484
## p
## (Intercept) 0.005
## Naturalness_Score_AFSCS 0.514 m.c1 <- lm(Risk_Score_AFSCS ~ 1, data = CC)
mcSummary(m.c1)## lm(formula = Risk_Score_AFSCS ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 28656.77 30 955.226
## Corr Total 28656.77 30 955.226
##
## RMSE AdjEtaSq
## 30.907 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 42.823 5.551 7.714 56846.98 0.665 NA 31.486 54.159 0 modelCompare(m.c1, m.a1)## SSE (Compact) = 28656.77
## SSE (Augmented) = 28231.2
## Delta R-Squared = 0.01485076
## Partial Eta-Squared (PRE) = 0.01485076
## F(1,29) = 0.4371643, p = 0.5137134 #2. Biochar
m.a2 <- lm(CC$Risk_Score_BIO ~ Naturalness_Score_BIO, data = CC)
mcSummary(m.a2)## lm(formula = CC$Risk_Score_BIO ~ Naturalness_Score_BIO, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 4650.578 1 4650.578 0.165 6.936 0.012
## Error 23466.124 35 670.461
## Corr Total 28116.703 36 781.020
##
## RMSE AdjEtaSq
## 25.893 0.142
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 84.482 16.727 5.051 17102.505 0.422 NA 50.524 118.440
## Naturalness_Score_BIO -1.011 0.384 -2.634 4650.578 0.165 NA -1.791 -0.232
## p
## (Intercept) 0.000
## Naturalness_Score_BIO 0.012 m.c2 <- lm(Risk_Score_BIO ~ 1, data = CC)
mcSummary(m.c2)## lm(formula = Risk_Score_BIO ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.0 0 Inf 0
## Error 28116.7 36 781.02
## Corr Total 28116.7 36 781.02
##
## RMSE AdjEtaSq
## 27.947 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 41.878 4.594 9.115 64890.55 0.698 NA 32.56 51.196 0 modelCompare(m.c2, m.a2)## SSE (Compact) = 28116.7
## SSE (Augmented) = 23466.12
## Delta R-Squared = 0.1654027
## Partial Eta-Squared (PRE) = 0.1654027
## F(1,35) = 6.936392, p = 0.01249091 #3. BECCS
m.a3 <- lm(CC$Risk_Score_BECCS ~ CC$Naturalness_Score_BECCS)
mcSummary(m.a3)## lm(formula = CC$Risk_Score_BECCS ~ CC$Naturalness_Score_BECCS)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 741.767 1 741.767 0.036 1.283 0.265
## Error 19652.420 34 578.012
## Corr Total 20394.188 35 582.691
##
## RMSE AdjEtaSq
## 24.042 0.008
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5
## (Intercept) 60.805 11.751 5.175 15477.553 0.441 NA 36.925
## CC$Naturalness_Score_BECCS -0.307 0.271 -1.133 741.767 0.036 NA -0.857
## CI_97.5 p
## (Intercept) 84.685 0.000
## CC$Naturalness_Score_BECCS 0.243 0.265 m.c3 <- lm(CC$Risk_Score_BECCS ~ 1, data = CC)
mcSummary(m.c3)## lm(formula = CC$Risk_Score_BECCS ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 20394.19 35 582.691
## Corr Total 20394.19 35 582.691
##
## RMSE AdjEtaSq
## 24.139 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 48.292 4.023 12.003 83955.06 0.805 NA 40.124 56.459 0 modelCompare(m.c3, m.a3)## SSE (Compact) = 20394.19
## SSE (Augmented) = 19652.42
## Delta R-Squared = 0.03637151
## Partial Eta-Squared (PRE) = 0.03637151
## F(1,34) = 1.283307, p = 0.2652107 #4. DACCS
m.a4 <- lm(CC$Risk_Score_DACCS ~ Naturalness_Score_DACCS, data = CC)
mcSummary(m.a4)## lm(formula = CC$Risk_Score_DACCS ~ Naturalness_Score_DACCS, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 869.682 1 869.682 0.036 1.251 0.271
## Error 23628.005 34 694.941
## Corr Total 24497.688 35 699.934
##
## RMSE AdjEtaSq
## 26.362 0.007
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 49.049 11.242 4.363 13229.567 0.359 NA 26.203 71.895
## Naturalness_Score_DACCS 0.341 0.305 1.119 869.682 0.036 NA -0.278 0.961
## p
## (Intercept) 0.000
## Naturalness_Score_DACCS 0.271 m.c4 <- lm(Risk_Score_DACCS ~ 1, data = CC)
mcSummary(m.c4)## lm(formula = Risk_Score_DACCS ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 24497.69 35 699.934
## Corr Total 24497.69 35 699.934
##
## RMSE AdjEtaSq
## 26.456 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 60.625 4.409 13.749 132314.1 0.844 NA 51.673 69.577 0 modelCompare(m.c4, m.a4)## SSE (Compact) = 24497.69
## SSE (Augmented) = 23628.01
## Delta R-Squared = 0.03550059
## Partial Eta-Squared (PRE) = 0.03550059
## F(1,34) = 1.251447, p = 0.2711179 #5. EW
m.a5 <- lm(Risk_Score_EW ~ Naturalness_Score_EW, data = CC)
mcSummary(m.a5)## lm(formula = Risk_Score_EW ~ Naturalness_Score_EW, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 394.22 1 394.220 0.024 0.769 0.387
## Error 15901.01 31 512.936
## Corr Total 16295.23 32 509.226
##
## RMSE AdjEtaSq
## 22.648 -0.007
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 46.949 8.946 5.248 14126.43 0.470 NA 28.703 65.196
## Naturalness_Score_EW -0.171 0.196 -0.877 394.22 0.024 NA -0.570 0.227
## p
## (Intercept) 0.000
## Naturalness_Score_EW 0.387 m.c5 <- lm(Risk_Score_EW ~ 1, data = CC)
mcSummary(m.c5)## lm(formula = Risk_Score_EW ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 16295.23 32 509.226
## Corr Total 16295.23 32 509.226
##
## RMSE AdjEtaSq
## 22.566 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 39.909 3.928 10.16 52560.27 0.763 NA 31.908 47.911 0 modelCompare(m.c5, m.a5)## SSE (Compact) = 16295.23
## SSE (Augmented) = 15901.01
## Delta R-Squared = 0.02419239
## Partial Eta-Squared (PRE) = 0.02419239
## F(1,31) = 0.7685572, p = 0.387407 #6. OF
m.a6 <- lm(Risk_Score_OF ~ Naturalness_Score_OF, data = CC)
mcSummary(m.a6)## lm(formula = Risk_Score_OF ~ Naturalness_Score_OF, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 1478.978 1 1478.978 0.08 2.611 0.117
## Error 16990.897 30 566.363
## Corr Total 18469.875 31 595.802
##
## RMSE AdjEtaSq
## 23.798 0.049
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 66.024 10.099 6.538 24206.468 0.588 NA 45.399 86.649
## Naturalness_Score_OF -0.342 0.211 -1.616 1478.978 0.080 NA -0.773 0.090
## p
## (Intercept) 0.000
## Naturalness_Score_OF 0.117 m.c6 <- lm(Risk_Score_OF ~ 1, data = CC)
mcSummary(m.c6)## lm(formula = Risk_Score_OF ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 18469.88 31 595.802
## Corr Total 18469.88 31 595.802
##
## RMSE AdjEtaSq
## 24.409 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 51.188 4.315 11.863 83845.12 0.819 NA 42.387 59.988 0 modelCompare(m.c6, m.a6)## SSE (Compact) = 18469.88
## SSE (Augmented) = 16990.9
## Delta R-Squared = 0.08007514
## Partial Eta-Squared (PRE) = 0.08007514
## F(1,30) = 2.611359, p = 0.11657 #7. BF
m.a7 <- lm(Risk_Score_BF ~ Naturalness_Score_BF, data = CC)
mcSummary(m.a7)## lm(formula = Risk_Score_BF ~ Naturalness_Score_BF, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 315.29 1 315.290 0.018 0.386 0.541
## Error 17174.95 21 817.855
## Corr Total 17490.24 22 795.011
##
## RMSE AdjEtaSq
## 28.598 -0.029
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 52.165 13.742 3.796 11785.40 0.407 NA 23.587 80.743
## Naturalness_Score_BF -0.205 0.330 -0.621 315.29 0.018 NA -0.891 0.481
## p
## (Intercept) 0.001
## Naturalness_Score_BF 0.541 m.c7 <- lm(Risk_Score_BF ~ 1, data = CC)
mcSummary(m.c7)## lm(formula = Risk_Score_BF ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 17490.24 22 795.011
## Corr Total 17490.24 22 795.011
##
## RMSE AdjEtaSq
## 28.196 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 44.478 5.879 7.565 45501.26 0.722 NA 32.285 56.671 0 modelCompare(m.c7, m.a7)## SSE (Compact) = 17490.24
## SSE (Augmented) = 17174.95
## Delta R-Squared = 0.01802662
## Partial Eta-Squared (PRE) = 0.01802662
## F(1,21) = 0.3855084, p = 0.541351 #8. NE
m.a8 <- lm(Risk_Score_NE ~ Naturalness_Score_NE, data = CC)
mcSummary(m.a8)## lm(formula = Risk_Score_NE ~ Naturalness_Score_NE, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 7836.382 1 7836.382 0.324 12.924 0.001
## Error 16370.877 27 606.329
## Corr Total 24207.259 28 864.545
##
## RMSE AdjEtaSq
## 24.624 0.299
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 93.415 11.188 8.350 42271.582 0.721 NA 70.460 116.371
## Naturalness_Score_NE -0.963 0.268 -3.595 7836.382 0.324 NA -1.513 -0.413
## p
## (Intercept) 0.000
## Naturalness_Score_NE 0.001 m.c8 <- lm(Risk_Score_NE ~ 1, data = CC)
mcSummary(m.c8)## lm(formula = Risk_Score_NE ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 24207.26 28 864.545
## Corr Total 24207.26 28 864.545
##
## RMSE AdjEtaSq
## 29.403 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 56.707 5.46 10.386 93254.49 0.794 NA 45.523 67.891 0 modelCompare(m.c8, m.a8)## SSE (Compact) = 24207.26
## SSE (Augmented) = 16370.88
## Delta R-Squared = 0.3237203
## Partial Eta-Squared (PRE) = 0.3237203
## F(1,27) = 12.92431, p = 0.001278274 #9. SE
m.a9 <- lm(Risk_Score_SE ~ Naturalness_Score_SE, data = CC)
mcSummary(m.a9)## lm(formula = Risk_Score_SE ~ Naturalness_Score_SE, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 1401.16 1 1401.160 0.057 1.392 0.25
## Error 23151.50 23 1006.587
## Corr Total 24552.66 24 1023.028
##
## RMSE AdjEtaSq
## 31.727 0.016
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 54.730 19.432 2.816 7984.775 0.256 NA 14.532 94.928
## Naturalness_Score_SE -0.449 0.381 -1.180 1401.160 0.057 NA -1.238 0.339
## p
## (Intercept) 0.01
## Naturalness_Score_SE 0.25 m.c9 <- lm(Risk_Score_SE ~ 1, data = CC)
mcSummary(m.c9)## lm(formula = Risk_Score_SE ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 24552.66 24 1023.028
## Corr Total 24552.66 24 1023.028
##
## RMSE AdjEtaSq
## 31.985 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 33.06 6.397 5.168 27324.09 0.527 NA 19.857 46.263 0 modelCompare(m.c9, m.a9)## SSE (Compact) = 24552.66
## SSE (Augmented) = 23151.5
## Delta R-Squared = 0.05706756
## Partial Eta-Squared (PRE) = 0.05706756
## F(1,23) = 1.391992, p = 0.2501288 #10. WE
m.a10 <- lm(Risk_Score_WE ~ Naturalness_Score_WE, data = CC)
mcSummary(m.a10)## lm(formula = Risk_Score_WE ~ Naturalness_Score_WE, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 4238.864 1 4238.864 0.267 8.741 0.007
## Error 11638.521 24 484.938
## Corr Total 15877.385 25 635.095
##
## RMSE AdjEtaSq
## 22.021 0.236
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 93.220 19.944 4.674 10594.341 0.477 NA 52.058 134.383
## Naturalness_Score_WE -1.082 0.366 -2.957 4238.864 0.267 NA -1.838 -0.327
## p
## (Intercept) 0.000
## Naturalness_Score_WE 0.007 m.c10 <- lm(Risk_Score_WE ~ 1, data = CC)
mcSummary(m.c10)## lm(formula = Risk_Score_WE ~ 1, data = CC)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 15877.39 25 635.095
## Corr Total 15877.39 25 635.095
##
## RMSE AdjEtaSq
## 25.201 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 35.654 4.942 7.214 33051.11 0.675 NA 25.475 45.833 0 modelCompare(m.c10, m.a10)## SSE (Compact) = 15877.38
## SSE (Augmented) = 11638.52
## Delta R-Squared = 0.2669749
## Partial Eta-Squared (PRE) = 0.2669749
## F(1,24) = 8.741035, p = 0.006879434