Goal: to provide a pilot data overview for the Protein Perceptions naturalness project. The variables of main focus are perceptions of naturalness and risk, as well as endorsement for burger types. Six burgers were included in pilot data collection, with each participant being randomly assigned a survey regarding 2 of the 6 total burger types. Final data will be aggregated for a cross random effects analysis to account for relationships between key variables for all 6 proteins.
PP <- read.csv("Proteins_Full2.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))##Demographics
#Number of responses (rows)
nrow(PP)## [1] 115#Age range
range(PP$Dem_2_Age, na.rm = T)## [1] 19 77#Average age
mean(PP$Dem_2_Age, na.rm = T)## [1] 39.92233#Standard deviation of age
sd(PP$Dem_2_Age, na.rm = T)## [1] 12.06899#Gender frequencies
table(PP$Dem_1_Gen)##
## 1 2 3
## 65 37 2#Ethnicity frequencies
table(PP$Dem_6_Ethnicity)##
## 1 2 3 4 6
## 5 23 4 5 67#Naturalness by Burger Type
#1. GFFB Naturalness Descriptives
#Reverse Code Items 2 and 3
PP$Nat_1_GFFB <- PP$GFFB_Naturalness_30
PP$Nat_2R_GFFB <- (102-PP$GFFB_Naturalness_31)
PP$Nat_3R_GFFB <- (102-PP$GFFB_Naturalness_35)
PP$Nat_2_GFFB <- PP$GFFB_Naturalness_31
PP$Nat_3_GFFB <- PP$GFFB_Naturalness_35
#GFFB Naturalness Scale
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
#GFFB Cronbach's alpha for naturalness scale
psych::alpha(data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)): 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 ( PP.Nat_1_GFFB ) 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(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.17 0.17 0.32 0.064 0.21 0.13 51 21 0.18
##
## lower alpha upper 95% confidence boundaries
## -0.09 0.17 0.43
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_GFFB 0.50 0.50 0.34 0.34 1.01 0.093 NA 0.34
## PP.Nat_2R_GFFB -0.98 -0.98 -0.33 -0.33 -0.49 0.368 NA -0.33
## PP.Nat_3R_GFFB 0.31 0.31 0.18 0.18 0.45 0.128 NA 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_GFFB 53 0.47 0.47 0.06 -0.0868 56 34
## PP.Nat_2R_GFFB 53 0.83 0.83 0.71 0.4490 46 34
## PP.Nat_3R_GFFB 53 0.55 0.55 0.27 0.0048 53 34hist(PP$Naturalness_Score_GFFB, main = 'GFFB Naturalness Scale Score')
#Correlation
cor.plot(PP$Naturalness_Scale_GFFB, labels = c('1','2','3'), main = "Correlation Between GFFB Naturalness Items")
#Individual GFFB Naturalness Items
#Item 1
psych::describe(PP$Nat_1_GFFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 55.7 34.23 66 57.05 40.03 0 100 100 -0.33 -1.37 4.7range(PP$Nat_1_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_1_GFFB, main = 'GFFB Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(PP$Nat_2R_GFFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 45.64 34.09 45 44.37 45.96 2 102 100 0.19 -1.39 4.68range(PP$Nat_2R_GFFB, na.rm=TRUE)## [1] 2 102hist(PP$Nat_2R_GFFB, main = 'GFFB Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(PP$Nat_3R_GFFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 52.7 33.94 47 52.79 41.51 2 102 100 0.04 -1.46 4.66range(PP$Nat_3R_GFFB, na.rm=TRUE)## [1] 2 102hist(PP$Nat_3R_GFFB, main = 'GFFB Naturalness Item #3: "This relies on science-based technology."')
#2. GFPRB Naturalness Descriptives
#Reverse Code Items 2 and 3
PP$Nat_1_GFPRB <- PP$GFPRB_Naturalness_30
PP$Nat_2R_GFPRB <- (102-PP$GFPRB_Naturalness_31)
PP$Nat_3R_GFPRB <- (102-PP$GFPRB_Naturalness_35)
PP$Nat_2_GFPRB <- PP$GFPRB_Naturalness_31
PP$Nat_3_GFPRB <- PP$GFPRB_Naturalness_35
#GFPRB Naturalness Scale
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
#GFPRB Cronbach's alpha for naturalness scale
psych::alpha(data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB,
## PP$Nat_3R_GFPRB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.53 0.49 0.58 0.24 0.95 0.069 60 21 0.25
##
## lower alpha upper 95% confidence boundaries
## 0.4 0.53 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Nat_1_GFPRB 0.75 0.75 0.60 0.60 3.06 0.046 NA
## PP.Nat_2R_GFPRB -0.29 -0.32 -0.14 -0.14 -0.24 0.226 NA
## PP.Nat_3R_GFPRB 0.38 0.40 0.25 0.25 0.68 0.107 NA
## med.r
## PP.Nat_1_GFPRB 0.60
## PP.Nat_2R_GFPRB -0.14
## PP.Nat_3R_GFPRB 0.25
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_GFPRB 52 0.42 0.53 0.20 0.064 73 22
## PP.Nat_2R_GFPRB 53 0.91 0.88 0.83 0.687 54 32
## PP.Nat_3R_GFPRB 53 0.77 0.70 0.58 0.376 53 32hist(PP$Naturalness_Score_GFPRB, main = 'GFPRB Naturalness Scale Score')
#Correlation
cor.plot(PP$Naturalness_Scale_GFPRB, labels = c('1','2','3'), main = "Correlation Between GFPRB Naturalness Items")
#Individual GFPRB Naturalness Items
#Item 1
psych::describe(PP$Nat_1_GFPRB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 52 72.77 22.26 77.5 75.43 15.57 0 100 100 -1.15 1.17 3.09range(PP$Nat_1_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_1_GFPRB, main = 'GFPRB Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(PP$Nat_2_GFPRB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 48.09 31.95 58 48.14 40.03 0 100 100 -0.12 -1.45 4.39range(PP$Nat_2_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_2_GFPRB, main = 'GFPRB Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(PP$Nat_3_GFPRB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 49.4 32.15 57 49.7 40.03 0 100 100 -0.15 -1.41 4.42range(PP$Nat_3_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_3_GFPRB, main = 'GFPRB Naturalness Item #3: "This relies on science-based technology."')
#3. CBB Naturalness Descriptives
#Reverse Code Items 2 and 3
PP$Nat_1_CBB <- PP$CBB_Naturalness_30
PP$Nat_2R_CBB <- (102-PP$CBB_Naturalness_31)
PP$Nat_3R_CBB <- (102-PP$CBB_Naturalness_35)
PP$Nat_2_CBB <- PP$CBB_Naturalness_31
PP$Nat_3_CBB <- PP$CBB_Naturalness_35
#CBB Naturalness Scale
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
#CBB Cronbach's alpha for naturalness scale
psych::alpha(data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.Nat_1_CBB ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.1 0.12 0.09 0.042 0.13 0.14 35 18 0.029
##
## lower alpha upper 95% confidence boundaries
## -0.18 0.1 0.38
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Nat_1_CBB 0.240 0.242 0.138 0.138 0.319 0.14 NA
## PP.Nat_2R_CBB -0.084 -0.088 -0.042 -0.042 -0.081 0.19 NA
## PP.Nat_3R_CBB 0.056 0.056 0.029 0.029 0.060 0.17 NA
## med.r
## PP.Nat_1_CBB 0.138
## PP.Nat_2R_CBB -0.042
## PP.Nat_3R_CBB 0.029
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_CBB 45 0.63 0.55 -0.018 -0.0053 44 34
## PP.Nat_2R_CBB 45 0.63 0.65 0.345 0.1077 35 29
## PP.Nat_3R_CBB 45 0.52 0.61 0.216 0.0571 28 25hist(PP$Naturalness_Score_CBB, main = 'CBB Naturalness Scale Score')
#Correlation
cor.plot(PP$Naturalness_Scale_CBB, labels = c('1','2','3'), main = "Correlation Between CBB Naturalness Items")
#Individual CBB Naturalness Items
#Item 1
psych::describe(PP$Nat_1_CBB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 45 43.62 33.85 39 42.65 44.48 0 100 100 0.07 -1.54 5.05range(PP$Nat_1_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_1_CBB, main = 'CBB Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(PP$Nat_2_CBB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 45 67 29.07 74 69.89 23.72 3 100 97 -0.79 -0.53 4.33range(PP$Nat_2_CBB, na.rm=TRUE)## [1] 3 100hist(PP$Nat_2_CBB, main = 'CBB Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(PP$Nat_3_CBB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 45 74.22 25.25 79 77.76 22.24 0 100 100 -1.16 0.73 3.76range(PP$Nat_3_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_3_CBB, main = 'CBB Naturalness Item #3: "This relies on science-based technology."')
#4. PBPB Naturalness Descriptives
#Reverse Code Items 2 and 3
PP$Nat_1_PBPB <- PP$PBPB_Naturalness_30
PP$Nat_2R_PBPB <- (102-PP$PBPB_Naturalness_31)
PP$Nat_3R_PBPB <- (102-PP$PBPB_Naturalness_35)
PP$Nat_2_PBPB <- PP$PBPB_Naturalness_31
PP$Nat_3_PBPB <- PP$PBPB_Naturalness_35
#PBPB Naturalness Scale
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
#PBPB Cronbach's alpha for naturalness scale
psych::alpha(data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)): 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 ( PP.Nat_1_PBPB ) 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(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.044 0.018 0.13 0.006 0.018 0.15 51 16 -0.12
##
## lower alpha upper 95% confidence boundaries
## -0.25 0.04 0.34
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_PBPB 0.52 0.53 0.36 0.36 1.12 0.087 NA 0.36
## PP.Nat_2R_PBPB -0.58 -0.58 -0.22 -0.22 -0.37 0.293 NA -0.22
## PP.Nat_3R_PBPB -0.26 -0.27 -0.12 -0.12 -0.21 0.232 NA -0.12
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_PBPB 50 0.35 0.38 -0.47 -0.20 72 27
## PP.Nat_2R_PBPB 50 0.76 0.71 0.60 0.18 49 31
## PP.Nat_3R_PBPB 50 0.62 0.65 0.48 0.14 32 25hist(PP$Naturalness_Score_PBPB, main = 'PBPB Naturalness Scale Score')
#Correlation
cor.plot(PP$Naturalness_Scale_PBPB, labels = c('1','2','3'), main = "Correlation Between PBPB Naturalness Items")
#Individual PBPB Naturalness Items
#Item 1
psych::describe(PP$Nat_1_PBPB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 72.18 26.74 77 76.25 22.24 0 100 100 -1.17 0.71 3.78range(PP$Nat_1_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_1_PBPB, main = 'PBPB Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(PP$Nat_2_PBPB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 53.06 31.39 61.5 53.4 36.32 0 100 100 -0.22 -1.32 4.44range(PP$Nat_2_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_2_PBPB, main = 'PBPB Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(PP$Nat_3_PBPB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 69.78 25.37 74 73.05 19.27 3 100 97 -1.02 0.49 3.59range(PP$Nat_3_PBPB, na.rm=TRUE)## [1] 3 100hist(PP$Nat_3_PBPB, main = 'PBPB Naturalness Item #3: "This relies on science-based technology."')
#5. PBFB Naturalness Descriptives
#Reverse Code Items 2 and 3
PP$Nat_1_PBFB <- PP$PBFB_Naturalness_30
PP$Nat_2R_PBFB <- (102-PP$PBFB_Naturalness_31)
PP$Nat_3R_PBFB <- (102-PP$PBFB_Naturalness_35)
PP$Nat_2_PBFB <- PP$PBFB_Naturalness_31
PP$Nat_3_PBFB <- PP$PBFB_Naturalness_35
#PBFB Naturalness Scale
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
#PBFB Cronbach's alpha for naturalness scale
psych::alpha(data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.Nat_1_PBFB ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.28 0.32 0.38 0.13 0.47 0.12 42 16 -0.037
##
## lower alpha upper 95% confidence boundaries
## 0.04 0.28 0.52
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Nat_1_PBFB 0.718 0.719 0.561 0.561 2.557 0.052 NA
## PP.Nat_2R_PBFB -0.268 -0.272 -0.120 -0.120 -0.214 0.233 NA
## PP.Nat_3R_PBFB -0.077 -0.077 -0.037 -0.037 -0.072 0.200 NA
## med.r
## PP.Nat_1_PBFB 0.561
## PP.Nat_2R_PBFB -0.120
## PP.Nat_3R_PBFB -0.037
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_PBFB 60 0.49 0.43 -0.12 -0.087 56 27
## PP.Nat_2R_PBFB 60 0.76 0.78 0.69 0.355 36 25
## PP.Nat_3R_PBFB 60 0.70 0.74 0.63 0.292 35 23hist(PP$Naturalness_Score_PBFB, main = 'PBFB Naturalness Scale Score')
#Correlation
cor.plot(PP$Naturalness_Scale_PBFB, labels = c('1','2','3'), main = "Correlation Between PBFB Naturalness Items")
#Individual PBPB Naturalness Items
#Item 1
psych::describe(PP$Nat_1_PBFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 55.58 27.45 56 56.44 27.43 0 100 100 -0.26 -0.79 3.54range(PP$Nat_1_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_1_PBFB, main = 'PBFB Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(PP$Nat_2_PBFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 66.42 24.69 70 68.46 21.5 0 100 100 -0.71 0.12 3.19range(PP$Nat_2_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_2_PBFB, main = 'PBFB Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(PP$Nat_3_PBFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 67.03 23.16 71 68.69 22.24 0 100 100 -0.73 0.4 2.99range(PP$Nat_3_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_3_PBFB, main = 'PBFB Naturalness Item #3: "This relies on science-based technology."')
#6. VB Naturalness Descriptives
#Reverse Code Items 2 and 3
PP$Nat_1_VB <- PP$VB_Naturalness_30
PP$Nat_2R_VB <- (102-PP$VB_Naturalness_31)
PP$Nat_3R_VB <- (102-PP$VB_Naturalness_35)
PP$Nat_2_VB <- PP$VB_Naturalness_31
PP$Nat_3_VB <- PP$VB_Naturalness_35
#VB Naturalness Scale
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
#VB Cronbach's alpha for naturalness scale
psych::alpha(data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)): 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 ( PP.Nat_1_VB ) 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(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.2 0.14 0.25 0.05 0.16 0.12 50 17 -0.082
##
## lower alpha upper 95% confidence boundaries
## -0.04 0.2 0.44
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_VB 0.63 0.63 0.463 0.463 1.72 0.069 NA 0.463
## PP.Nat_2R_VB -0.59 -0.60 -0.231 -0.231 -0.37 0.293 NA -0.231
## PP.Nat_3R_VB -0.17 -0.18 -0.082 -0.082 -0.15 0.214 NA -0.082
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_VB 56 0.30 0.38 -0.29 -0.18 69 24
## PP.Nat_2R_VB 55 0.80 0.76 0.66 0.34 45 30
## PP.Nat_3R_VB 56 0.71 0.68 0.53 0.23 37 28hist(PP$Naturalness_Score_VB, main = 'VB Naturalness Scale Score')
#Correlation
cor.plot(PP$Naturalness_Scale_VB, labels = c('1','2','3'), main = "Correlation Between VB Naturalness Items")
#Individual VB Naturalness Items
#Item 1
psych::describe(PP$Nat_1_VB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 56 68.68 24.43 72 70.83 20.76 0 100 100 -0.72 -0.07 3.26range(PP$Nat_1_VB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_1_VB, main = 'VB Naturalness Item #1: "This is natural."')
#Item 2 (Not reverse coded)
psych::describe(PP$Nat_2_VB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 55 56.84 30.46 65 57.73 35.58 0 100 100 -0.26 -1.19 4.11range(PP$Nat_2_VB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_2_VB, main = 'VB Naturalness Item #2: "This involves humans altering naturally occurring processes."')
#Item 3 (Not reverse coded)
psych::describe(PP$Nat_3_VB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 56 65.25 28.39 76 67.65 25.2 0 100 100 -0.7 -0.65 3.79range(PP$Nat_3_VB, na.rm=TRUE)## [1] 0 100hist(PP$Nat_3_VB, main = 'VB Naturalness Item #3: "This relies on science-based technology."')
#Risk Perception by Burger Type 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.
#1. GFFB Risk Descriptives
PP$Risk_1_GFFB <- PP$GFFB_Risk_32
PP$Risk_2_GFFB <- PP$GFFB_Risk_35
#GFFB Risk Scale
PP$Risk_Score_GFFB <- rowMeans(PP [, c("Risk_1_GFFB", "Risk_2_GFFB")], na.rm=TRUE)
PP$Risk_Scale_GFFB <- data.frame(PP$Risk_1_GFFB, PP$Risk_2_GFFB)
#GFFB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_GFFB, PP$Risk_2_GFFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_GFFB, PP$Risk_2_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.71 0.71 4.9 0.032 48 32 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.77 0.83 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_GFFB 0.68 0.71 0.5 0.71 2.4 NA 0 0.71
## PP.Risk_2_GFFB 0.74 0.71 0.5 0.71 2.4 NA 0 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_GFFB 53 0.92 0.92 0.78 0.71 47 34
## PP.Risk_2_GFFB 53 0.93 0.92 0.78 0.71 49 35hist(PP$Risk_Score_GFFB, main = 'GFFB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_GFFB, labels = c('1','2'), main = "Correlation Between GFFB Risk Items")
#Individual GFFB Risk Items
#Item 1
psych::describe(PP$Risk_1_GFFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 46.91 34.16 49 46.35 45.96 0 100 100 0.05 -1.41 4.69range(PP$Risk_1_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_GFFB, main = 'GFFB Risk Item #1: "This is risky to deploy.')
#Item 2
psych::describe(PP$Risk_2_GFFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 48.74 35.42 47 48.63 47.44 0 100 100 -0.05 -1.58 4.86range(PP$Risk_2_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_GFFB, main = 'GFFB Risk Item #2: "This is frightening."')
#2. GFPRB Risk Descriptives
PP$Risk_1_GFPRB <- PP$GFPRB_Risk_32
PP$Risk_2_GFPRB <- PP$GFPRB_Risk_35
#GFPRB Risk Scale
PP$Risk_Score_GFPRB <- rowMeans(PP [, c("Risk_1_GFPRB", "Risk_2_GFPRB")], na.rm=TRUE)
PP$Risk_Scale_GFPRB <- data.frame(PP$Risk_1_GFPRB, PP$Risk_2_GFPRB)
#GFPRB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_GFPRB, PP$Risk_2_GFPRB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_GFPRB, PP$Risk_2_GFPRB))
##
## 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 31 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
## PP.Risk_1_GFPRB 0.82 0.84 0.7 0.84 5.1 NA 0 0.84
## PP.Risk_2_GFPRB 0.86 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
## PP.Risk_1_GFPRB 53 0.96 0.96 0.88 0.84 46 32
## PP.Risk_2_GFPRB 53 0.96 0.96 0.88 0.84 47 33hist(PP$Risk_Score_GFPRB, main = 'GFFB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_GFPRB, labels = c('1','2'), main = "Correlation Between GFPRB Risk Items")
#Individual GFPRB Risk Items
#Item 1
psych::describe(PP$Risk_1_GFPRB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 46.49 32.44 52 46.28 43 0 100 100 -0.01 -1.49 4.46range(PP$Risk_1_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_GFPRB, main = 'GFPRB Risk Item #1: "This is risky to deploy.')
#Item 2
psych::describe(PP$Risk_2_GFPRB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 46.98 33.21 50 46.63 44.48 0 100 100 -0.02 -1.5 4.56range(PP$Risk_2_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_GFPRB, main = 'GFPRB Risk Item #2: "This is frightening."')
#3. CBB Risk Descriptives
PP$Risk_1_CBB <- PP$CBB_Risk_32
PP$Risk_2_CBB <- PP$CBB_Risk_35
#CBB Risk Scale
PP$Risk_Score_CBB <- rowMeans(PP [, c("Risk_1_CBB", "Risk_2_CBB")], na.rm=TRUE)
PP$Risk_Scale_CBB <- data.frame(PP$Risk_1_CBB, PP$Risk_2_CBB)
#CBB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_CBB, PP$Risk_2_CBB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_CBB, PP$Risk_2_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.72 0.72 5.2 0.03 53 29 0.72
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.84 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_CBB 0.72 0.72 0.52 0.72 2.6 NA 0 0.72
## PP.Risk_2_CBB 0.72 0.72 0.52 0.72 2.6 NA 0 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_CBB 44 0.93 0.93 0.79 0.72 52 32
## PP.Risk_2_CBB 44 0.93 0.93 0.79 0.72 53 32hist(PP$Risk_Score_CBB, main = 'CBB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_CBB, labels = c('1','2'), main = "Correlation Between CBB Risk Items")
#Individual CBB Risk Items
#Item 1
psych::describe(PP$Risk_1_CBB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 44 51.95 31.62 56 52.14 40.77 0 100 100 -0.07 -1.35 4.77range(PP$Risk_1_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_CBB, main = 'CBB Risk Item #1: "This is risky to deploy.')
#Item 2
psych::describe(PP$Risk_2_CBB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 44 53.48 31.76 64 54.36 33.36 0 100 100 -0.29 -1.3 4.79range(PP$Risk_2_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_CBB, main = 'CBB Risk Item #2: "This is frightening."')
#4. PBPB Risk Descriptives
PP$Risk_1_PBPB <- PP$PBPB_Risk_32
PP$Risk_2_PBPB <- PP$PBPB_Risk_35
#PBPB Risk Scale
PP$Risk_Score_PBPB <- rowMeans(PP [, c("Risk_1_PBPB", "Risk_2_PBPB")], na.rm=TRUE)
PP$Risk_Scale_PBPB <- data.frame(PP$Risk_1_PBPB, PP$Risk_2_PBPB)
#PBPB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_PBPB, PP$Risk_2_PBPB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_PBPB, PP$Risk_2_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.86 0.86 12 0.014 45 31 0.86
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.93 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_PBPB 0.85 0.86 0.74 0.86 6.2 NA 0 0.86
## PP.Risk_2_PBPB 0.87 0.86 0.74 0.86 6.2 NA 0 0.86
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_PBPB 50 0.96 0.96 0.9 0.86 46 32
## PP.Risk_2_PBPB 50 0.97 0.96 0.9 0.86 44 33hist(PP$Risk_Score_PBPB, main = 'PBPB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_PBPB, labels = c('1','2'), main = "Correlation Between PBPB Risk Items")
#Individual PBPB Risk Items
#Item 1
psych::describe(PP$Risk_1_PBPB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 45.82 32.4 53 45.6 43.74 0 100 100 -0.01 -1.52 4.58range(PP$Risk_1_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_PBPB, main = 'PBPB Risk Item #1: "This is risky to deploy.')
#Item 2
psych::describe(PP$Risk_2_PBPB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 44.34 32.7 52.5 44 45.96 0 100 100 -0.02 -1.57 4.63range(PP$Risk_2_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_PBPB, main = 'PBPB Risk Item #2: "This is frightening."')
#5. PBFB Risk Descriptives
PP$Risk_1_PBFB <- PP$PBFB_Risk_32
PP$Risk_2_PBFB <- PP$PBFB_Risk_35
#PBFB Risk Scale
PP$Risk_Score_PBFB <- rowMeans(PP [, c("Risk_1_PBFB", "Risk_2_PBFB")], na.rm=TRUE)
PP$Risk_Scale_PBFB <- data.frame(PP$Risk_1_PBFB, PP$Risk_2_PBFB)
#PBFB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_PBFB, PP$Risk_2_PBFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_PBFB, PP$Risk_2_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.75 0.75 6 0.027 48 28 0.75
##
## lower alpha upper 95% confidence boundaries
## 0.8 0.86 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_PBFB 0.78 0.75 0.56 0.75 3 NA 0 0.75
## PP.Risk_2_PBFB 0.72 0.75 0.56 0.75 3 NA 0 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_PBFB 60 0.94 0.94 0.81 0.75 49 31
## PP.Risk_2_PBFB 60 0.93 0.94 0.81 0.75 48 30hist(PP$Risk_Score_PBFB, main = 'PBFB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_PBFB, labels = c('1','2'), main = "Correlation Between PBFB Risk Items")
#Individual PBPB Risk Items
#Item 1
psych::describe(PP$Risk_1_PBFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 49.07 30.98 53 49.25 37.06 0 100 100 -0.12 -1.21 4range(PP$Risk_1_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_PBFB, main = 'PBFB Risk Item #1: "This is risky to deploy.')
#Item 2
psych::describe(PP$Risk_2_PBFB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 47.57 29.6 45 47.5 34.84 0 100 100 0 -1.1 3.82range(PP$Risk_2_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_PBFB, main = 'PBFB Risk Item #2: "This is frightening."')
#6. VB Risk Descriptives
PP$Risk_1_VB <- PP$VB_Risk_32
PP$Risk_2_VB <- PP$VB_Risk_35
#VB Risk Scale
PP$Risk_Score_VB <- rowMeans(PP [, c("Risk_1_VB", "Risk_2_VB")], na.rm=TRUE)
PP$Risk_Scale_VB <- data.frame(PP$Risk_1_VB, PP$Risk_2_VB)
#VB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_VB, PP$Risk_2_VB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_VB, PP$Risk_2_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.73 0.73 5.4 0.029 37 30 0.73
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.84 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_VB 0.68 0.73 0.53 0.73 2.7 NA 0 0.73
## PP.Risk_2_VB 0.79 0.73 0.53 0.73 2.7 NA 0 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_VB 56 0.92 0.93 0.8 0.73 36 31
## PP.Risk_2_VB 56 0.94 0.93 0.8 0.73 37 34hist(PP$Risk_Score_VB, main = 'VB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_VB, labels = c('1','2'), main = "Correlation Between VB Risk Items")
#Individual VB Risk Items
#Item 1
psych::describe(PP$Risk_1_VB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 56 35.79 31.19 29 33.74 42.25 0 100 100 0.42 -1.22 4.17range(PP$Risk_1_VB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_VB, main = 'VB Risk Item #1: "This is risky to deploy.')
#Item 2
psych::describe(PP$Risk_2_VB)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 56 37.38 33.74 24.5 35.41 36.32 0 100 100 0.36 -1.43 4.51range(PP$Risk_2_VB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_VB, main = 'VB Risk Item #2: "This is frightening."')
##Individual Difference Measures
##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
PP$ENV_1 <- as.numeric(as.character(PP$Environ_1_18))
PP$ENV_2 <- as.numeric(as.character(PP$Environ_1_19))
PP$ENV_3 <- as.numeric(as.character(PP$Environ_1_26))
#Environmentalism Descriptives
psych::describe(PP$ENV_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 72 24.89 76 75.32 22.24 0 100 100 -1.07 0.73 2.43range(PP$ENV_1, na.rm=TRUE)## [1] 0 100psych::describe(PP$ENV_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 71.63 24.57 75 74.94 22.24 0 100 100 -1.11 0.86 2.4range(PP$ENV_2, na.rm=TRUE)## [1] 0 100psych::describe(PP$ENV_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 73.34 24.35 78 76.87 23.72 0 100 100 -1.16 1.16 2.38range(PP$ENV_3, na.rm=TRUE)## [1] 0 100#Environmentalism Scale Histograms by Item
hist(PP$ENV_1, main = 'ENV #1: Protecting the environment, preserving nature')
hist(PP$ENV_2, main = 'ENV #2: Unity with nature')
hist(PP$ENV_3, main = 'ENV #3: Respecting the earth, harmony with other species')
#Cronbach's Alpha
PP$ENVS_Score <- rowMeans(PP [, c("ENV_1", "ENV_2", "ENV_3")], na.rm=TRUE)
PP$ENV_Scale <- data.frame(PP$ENV_1, PP$ENV_2, PP$ENV_3)
psych::alpha(PP$ENV_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$ENV_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.89 0.79 11 0.013 72 23 0.81
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.92 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.ENV_1 0.89 0.89 0.81 0.81 8.5 0.020 NA 0.81
## PP.ENV_2 0.89 0.89 0.81 0.81 8.4 0.020 NA 0.81
## PP.ENV_3 0.86 0.86 0.75 0.75 6.1 0.026 NA 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.ENV_1 105 0.92 0.92 0.86 0.82 72 25
## PP.ENV_2 105 0.92 0.92 0.86 0.82 72 25
## PP.ENV_3 105 0.94 0.94 0.90 0.86 73 24#Correlation ENV Scale
cor.plot(PP$ENV_Scale, labels = c('1','2','3'), main = "Correlations Between Environmentalism Scale Items")
##Aversion to Tampering with Nature Scale
Aversion to tampering with nature was measured on with 5 items a 1-00 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’).
ATNS Item 1: People who push for technological fixes to environmental problems are underestimating the risks. ATNS Item 2: People who say we shouldn’t tamper with nature are just being naïve. ATNS Item 3: Human beings have no right to meddle with the natural environment. ATNS Item 4: I would prefer to live in a world where humans leave nature alone. ATNS Item 5: Altering nature will be our downfall as a species.
#Aversion to Tampering with Nature Item Definitions
PP$ATNS_1 <- as.numeric(as.character(PP$ATNS_1_1))
PP$ATNS_2 <- as.numeric(as.character(PP$ATNS_1_2))
PP$ATNS_3 <- as.numeric(as.character(PP$ATNS_1_3))
PP$ATNS_4 <- as.numeric(as.character(PP$ATNS_1_8))
PP$ATNS_5 <- as.numeric(as.character(PP$ATNS_1_9))
#Recode item 2
PP$ATNS_2R <- (102- PP$ATNS_2)
#Aversion to Tampering with Nature Scale Descriptives (No reversed codes)
psych::describe(PP$ATNS_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 60.34 27 65 62 26.69 0 100 100 -0.49 -0.56 2.64range(PP$ATNS_1, na.rm=TRUE)## [1] 0 100psych::describe(PP$ATNS_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 50.88 31.16 51 51.46 40.03 0 100 100 -0.09 -1.3 3.04range(PP$ATNS_2, na.rm=TRUE)## [1] 0 100psych::describe(PP$ATNS_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 104 67.07 26.04 72 69.45 28.17 2 100 98 -0.69 -0.32 2.55range(PP$ATNS_3, na.rm=TRUE)## [1] 2 100psych::describe(PP$ATNS_4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 73.96 21.8 78 75.98 23.72 4 100 96 -0.81 0.27 2.13range(PP$ATNS_4, na.rm=TRUE)## [1] 4 100psych::describe(PP$ATNS_5)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 69.61 24.78 74 72.46 20.76 0 100 100 -0.97 0.38 2.42range(PP$ATNS_5, na.rm=TRUE)## [1] 0 100#Aversion to Tampering with Nature Scale Histograms by Item (No reversed codes)
hist(PP$ATNS_1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks."')
hist(PP$ATNS_2, main = 'ATNS #2: "People who say we shouldn’t tamper with nature are just being naïve."')
hist(PP$ATNS_3, main = 'ATNS #3: "Human beings have no right to meddle with the natural environment."')
hist(PP$ATNS_4, main = 'ATNS #4: "I would prefer to live in a world where humans leave nature alone."')
hist(PP$ATNS_5, main = 'ATNS #5: "Altering nature will be our downfall as a species."')
#Cronbach's Alpha (4 and 5 reverse coded)
PP$ATNS_Scale <- data.frame(PP$ATNS_1, PP$ATNS_2R, PP$ATNS_3, PP$ATNS_4, PP$ATNS_5)
PP$ATNS_Score <- rowMeans(PP [, c("ATNS_1", "ATNS_2R", "ATNS_3", "ATNS_4", "ATNS_5")], na.rm=TRUE)
psych::alpha(PP$ATNS_Scale)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(PP$ATNS_Scale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.ATNS_2R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = PP$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.52 0.57 0.64 0.21 1.3 0.073 64 15 0.29
##
## lower alpha upper 95% confidence boundaries
## 0.37 0.52 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.ATNS_1 0.43 0.49 0.52 0.19 0.96 0.089 0.082 0.17
## PP.ATNS_2R 0.75 0.76 0.72 0.44 3.12 0.037 0.014 0.48
## PP.ATNS_3 0.22 0.32 0.40 0.10 0.47 0.123 0.094 0.13
## PP.ATNS_4 0.35 0.41 0.51 0.15 0.69 0.103 0.116 0.13
## PP.ATNS_5 0.38 0.46 0.56 0.18 0.85 0.099 0.155 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.ATNS_1 105 0.62 0.64 0.57 0.32 60 27
## PP.ATNS_2R 105 0.24 0.16 -0.15 -0.17 51 31
## PP.ATNS_3 104 0.81 0.82 0.81 0.62 67 26
## PP.ATNS_4 105 0.70 0.74 0.66 0.50 74 22
## PP.ATNS_5 105 0.67 0.68 0.54 0.42 70 25describe(PP$ATNS_Scale)## PP$ATNS_Scale
##
## 5 Variables 115 Observations
## --------------------------------------------------------------------------------
## PP.ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 105 10 56 0.999 60.34 30.7 5.2 24.4
## .25 .50 .75 .90 .95
## 42.0 65.0 81.0 93.4 99.8
##
## lowest : 0 2 5 6 20, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------
## PP.ATNS_2R
## n missing distinct Info Mean Gmd .05 .10
## 105 10 64 0.999 51.12 36.04 8.0 11.0
## .25 .50 .75 .90 .95
## 24.0 51.0 78.0 97.2 102.0
##
## lowest : 2 4 8 9 10, highest: 96 98 99 101 102
## --------------------------------------------------------------------------------
## PP.ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 104 11 57 0.998 67.07 29.27 20.15 28.90
## .25 .50 .75 .90 .95
## 51.75 72.00 87.25 100.00 100.00
##
## lowest : 2 4 5 6 7, highest: 93 94 96 97 100
## --------------------------------------------------------------------------------
## PP.ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 105 10 54 0.997 73.96 24.29 37.6 42.8
## .25 .50 .75 .90 .95
## 59.0 78.0 92.0 100.0 100.0
##
## lowest : 4 5 31 32 35, highest: 95 96 97 99 100
## --------------------------------------------------------------------------------
## PP.ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 105 10 53 0.998 69.61 27.11 16.8 35.2
## .25 .50 .75 .90 .95
## 59.0 74.0 88.0 100.0 100.0
##
## lowest : 0 3 4 7 13, highest: 93 94 96 98 100
## --------------------------------------------------------------------------------#Correlation ATNS
cor.plot(PP$ATNS_Scale, labels = c('1','2','3','4','5'), main = "Correlations Between Aversion to Tampering with Nature Scale Items")
##Connectedness to Nature Scale
Connectedness to nature was measured on with 5 items a 1-00 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’). CNS Item 1: I often feel a sense of oneness with the natural world around me. CNS Item 2: I think of the natural world as a community to which I belong. CNS Item 3: I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’. CNS Item 4: My personal welfare is independent of the welfare of the natural world. CNS Item 5: When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.
#Connectedness to Nature Item Definitions
PP$CNS_1 <- as.numeric(as.character(PP$CNS_1_1))
PP$CNS_2 <- as.numeric(as.character(PP$CNS_1_2))
PP$CNS_3 <- as.numeric(as.character(PP$CNS_1_3))
PP$CNS_4 <- as.numeric(as.character(PP$CNS_1_8))
PP$CNS_5 <- as.numeric(as.character(PP$CNS_1_9))
#Recode items 4 and 5
PP$CNS_4R <- (102 - PP$CNS_4)
PP$CNS_5R <- (102 - PP$CNS_5)
#Connectedness to Nature Descriptives (No reversed codes)
psych::describe(PP$CNS_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 69.77 22.77 74 71.78 23.72 0 100 100 -0.84 0.51 2.22range(PP$CNS_1, na.rm=TRUE)## [1] 0 100psych::describe(PP$CNS_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 74.3 21.71 79 76.59 19.27 6 100 94 -0.84 0.09 2.12range(PP$CNS_2, na.rm=TRUE)## [1] 6 100psych::describe(PP$CNS_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 72.79 20.23 74 74.29 20.76 9 100 91 -0.53 -0.19 1.97range(PP$CNS_3, na.rm=TRUE)## [1] 9 100psych::describe(PP$CNS_4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 59.37 29.63 66 61.73 26.69 0 100 100 -0.68 -0.6 2.89range(PP$CNS_4, na.rm=TRUE)## [1] 0 100psych::describe(PP$CNS_5R)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 42.27 28.85 35 40.22 29.65 2 102 100 0.56 -0.68 2.82range(PP$CNS_5, na.rm=TRUE)## [1] 0 100#Connectedness to Nature Scale Histograms by Item (No reversed codes)
hist(PP$CNS_1, main = 'CNS #1: I often feel a sense of oneness with the natural world around me."')
hist(PP$CNS_2, main = 'CNS #2: I think of the natural world as a community to which I belong. "')
hist(PP$CNS_3, main = 'CNS #3: I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’. "')
hist(PP$CNS_4, main = 'CNS #4: My personal welfare is independent of the welfare of the natural world."')
hist(PP$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)
PP$CNS_Scale <- data.frame(PP$CNS_1, PP$CNS_2, PP$CNS_3, PP$CNS_4R, PP$CNS_5R)
PP$CNS_Score <- rowMeans(PP [, c("CNS_1", "CNS_2", "CNS_3", "CNS_4R", "CNS_5R")], na.rm=TRUE)
describe(PP$CNS_Scale)## PP$CNS_Scale
##
## 5 Variables 115 Observations
## --------------------------------------------------------------------------------
## PP.CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 105 10 51 0.998 69.77 25.22 31.4 39.8
## .25 .50 .75 .90 .95
## 53.0 74.0 86.0 99.6 100.0
##
## lowest : 0 3 9 11 24, highest: 91 96 97 99 100
## --------------------------------------------------------------------------------
## PP.CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 105 10 49 0.996 74.3 24.06 34 41
## .25 .50 .75 .90 .95
## 63 79 90 100 100
##
## lowest : 6 18 24 30 32, highest: 93 95 97 98 100
## --------------------------------------------------------------------------------
## PP.CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 105 10 46 0.998 72.79 22.89 36.2 49.4
## .25 .50 .75 .90 .95
## 62.0 74.0 89.0 100.0 100.0
##
## lowest : 9 27 28 33 36, highest: 93 94 95 99 100
## --------------------------------------------------------------------------------
## PP.CNS_4R
## n missing distinct Info Mean Gmd .05 .10
## 105 10 57 0.999 42.63 33.18 3.4 9.8
## .25 .50 .75 .90 .95
## 21.0 36.0 59.0 94.2 102.0
##
## lowest : 2 3 5 6 7, highest: 91 93 95 100 102
## --------------------------------------------------------------------------------
## PP.CNS_5R
## n missing distinct Info Mean Gmd .05 .10
## 105 10 55 0.999 42.27 32.64 2.2 7.4
## .25 .50 .75 .90 .95
## 22.0 35.0 59.0 88.0 101.6
##
## lowest : 2 3 7 8 10, highest: 92 93 94 100 102
## --------------------------------------------------------------------------------psych::alpha(PP$CNS_Scale)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(PP$CNS_Scale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.CNS_4R PP.CNS_5R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = PP$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.24 0.32 0.49 0.087 0.48 0.12 60 12 -0.12
##
## lower alpha upper 95% confidence boundaries
## 0 0.24 0.47
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.CNS_1 0.167 0.171 0.32 0.049 0.207 0.128 0.10 -0.12
## PP.CNS_2 0.045 0.067 0.32 0.018 0.072 0.150 0.14 -0.18
## PP.CNS_3 0.060 0.064 0.27 0.017 0.068 0.146 0.11 -0.12
## PP.CNS_4R 0.298 0.425 0.54 0.156 0.739 0.113 0.18 0.17
## PP.CNS_5R 0.390 0.496 0.56 0.197 0.984 0.099 0.15 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.CNS_1 105 0.49 0.61 0.566 0.143 70 23
## PP.CNS_2 105 0.59 0.68 0.599 0.279 74 22
## PP.CNS_3 105 0.57 0.68 0.664 0.278 73 20
## PP.CNS_4R 105 0.50 0.36 0.051 0.026 43 30
## PP.CNS_5R 105 0.40 0.27 -0.058 -0.067 42 29#Correlation CNS Scale
cor.plot(PP$CNS_Scale, labels = c('1','2','3','4','5'), main = "Correlations Between Connectedness to Nature Scale Items")
##Climate Change Belief Scale
Climate change beliefs were measured on with 5 items a 1-7 scale of agreement (0 = ‘Strongly disagree’ to 100 = ‘Strongly agree’). CCB Item 1: Climate change is happening. CCB Item 2: Climate change poses a risk to human health, safety, and prosperity. CCB Item 3: Human activity is largely responsible for recent climate change. CCB Item 4: Reducing greenhouse gas emissions will reduce global warming and climate change.
#Climate Change Belief Item Definitions
PP$CCBelief_1 <- as.numeric(as.character(PP$CCB_1_1))
PP$CCBelief_2 <- as.numeric(as.character(PP$CCB_1_3))
PP$CCBelief_3 <- as.numeric(as.character(PP$CCB_1_4))
PP$CCBelief_4 <- as.numeric(as.character(PP$CCB_1_5))
#Climate Change Belief Descriptives
psych::describe(PP$CCBelief_1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 78.92 22.92 84 82.58 23.72 2 100 98 -1.27 1.18 2.24range(PP$CCBelief_1, na.rm=TRUE)## [1] 2 100psych::describe(PP$CCBelief_2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 77.18 24.45 85 80.61 22.24 2 100 98 -0.99 0.03 2.39range(PP$CCBelief_2, na.rm=TRUE)## [1] 2 100psych::describe(PP$CCBelief_3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 74.82 24.36 80 77.91 25.2 0 100 100 -0.93 0.14 2.38range(PP$CCBelief_3, na.rm=TRUE)## [1] 0 100psych::describe(PP$CCBelief_4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 105 72.9 24.91 78 75.98 25.2 0 100 100 -0.92 0.22 2.43range(PP$CCBelief_4, na.rm=TRUE)## [1] 0 100#Climate Change Belief Histograms
hist(PP$CCBelief_1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(PP$CCBelief_2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(PP$CCBelief_3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(PP$CCBelief_4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
PP$CCBelief_Score <- rowMeans(PP[, c('CCBelief_1', 'CCBelief_2', 'CCBelief_3','CCBelief_4')], na.rm=T)
describe(PP$CCBelief_Score)## PP$CCBelief_Score
## n missing distinct Info Mean Gmd .05 .10
## 105 10 79 0.996 75.96 22.91 40.00 49.75
## .25 .50 .75 .90 .95
## 63.00 80.50 92.25 100.00 100.00
##
## lowest : 2.75 19.00 33.50 37.00 38.00, highest: 97.25 98.50 98.75 99.75 100.00#Cronbach's Alpha
PP$CCB_Scale <- data.frame(PP$CCBelief_1, PP$CCBelief_2, PP$CCBelief_3, PP$CCBelief_4)
psych::alpha(PP$CCB_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.87 0.64 7.1 0.019 76 21 0.64
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.88 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.CCBelief_1 0.83 0.83 0.77 0.62 4.9 0.028 0.003 0.60
## PP.CCBelief_2 0.81 0.81 0.76 0.59 4.3 0.031 0.017 0.58
## PP.CCBelief_3 0.83 0.83 0.80 0.62 4.8 0.029 0.027 0.60
## PP.CCBelief_4 0.89 0.89 0.85 0.73 8.2 0.018 0.003 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.CCBelief_1 105 0.87 0.87 0.84 0.76 79 23
## PP.CCBelief_2 105 0.90 0.90 0.87 0.81 77 24
## PP.CCBelief_3 105 0.87 0.87 0.81 0.76 75 24
## PP.CCBelief_4 105 0.78 0.77 0.65 0.61 73 25#Correlation CCB Scale
cor.plot(PP$CCB_Scale, labels = c('1','2','3','4'), main = "Correlations Between Climate Change Belief Items")
##Individualism Scale ##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)
PP$Ind_1 <- as.numeric(as.character(PP$Individualism_1_19))
PP$Ind_2 <- as.numeric(as.character(PP$Individualism_1_20))
PP$Ind_5 <- as.numeric(as.character(PP$Individualism_1_23))
PP$Ind_6 <- as.numeric(as.character(PP$Individualism_1_24))
PP$Individualism_Score <- rowMeans(PP[, c('Ind_1', 'Ind_2', 'Ind_5','Ind_6')], na.rm=T)
#Collectivism (Items 3,4,7,8)
PP$Ind_3 <- as.numeric(as.character(PP$Individualism_1_21))
PP$Ind_4 <- as.numeric(as.character(PP$Individualism_1_22))
PP$Ind_7 <- as.numeric(as.character(PP$Individualism_1_25))
PP$Ind_8 <- as.numeric(as.character(PP$Individualism_1_34))
PP$Collectivism_Score <- rowMeans(PP[, c('Ind_3', 'Ind_4', 'Ind_7','Ind_8')], na.rm=T)
#Individualism Alpha, Histogram, and Correlation Matrix (4 items)
psych::alpha(data.frame(PP$Ind_1, PP$Ind_2, PP$Ind_5,PP$Ind_6))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Ind_1, PP$Ind_2, PP$Ind_5, PP$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.72 0.46 3.3 0.035 73 16 0.44
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.77 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_1 0.70 0.71 0.62 0.44 2.4 0.047 7.7e-04 0.46
## PP.Ind_2 0.74 0.74 0.66 0.48 2.8 0.043 7.0e-03 0.46
## PP.Ind_5 0.68 0.68 0.58 0.41 2.1 0.052 1.3e-06 0.41
## PP.Ind_6 0.74 0.74 0.66 0.48 2.8 0.042 7.1e-03 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_1 104 0.78 0.78 0.68 0.59 73 22
## PP.Ind_2 105 0.72 0.74 0.60 0.53 74 19
## PP.Ind_5 105 0.82 0.81 0.73 0.64 72 22
## PP.Ind_6 105 0.75 0.74 0.60 0.53 73 22hist(PP$Individualism_Score , main = 'Individualism Score')
PP$IndScale <- data.frame(PP$Ind_1, PP$Ind_2, PP$Ind_5,PP$Ind_6)
psych::alpha(PP$IndScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$IndScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.72 0.46 3.3 0.035 73 16 0.44
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.77 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_1 0.70 0.71 0.62 0.44 2.4 0.047 7.7e-04 0.46
## PP.Ind_2 0.74 0.74 0.66 0.48 2.8 0.043 7.0e-03 0.46
## PP.Ind_5 0.68 0.68 0.58 0.41 2.1 0.052 1.3e-06 0.41
## PP.Ind_6 0.74 0.74 0.66 0.48 2.8 0.042 7.1e-03 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_1 104 0.78 0.78 0.68 0.59 73 22
## PP.Ind_2 105 0.72 0.74 0.60 0.53 74 19
## PP.Ind_5 105 0.82 0.81 0.73 0.64 72 22
## PP.Ind_6 105 0.75 0.74 0.60 0.53 73 22cor.plot(PP$IndScale, labels = c('1','2','3','4'), main = "Correlations Between Individualism Items")
#Collectivism Alpha and Histogram (4 items)
psych::alpha(data.frame(PP$Ind_3, PP$Ind_4, PP$Ind_7, PP$Ind_8))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Ind_3, PP$Ind_4, PP$Ind_7, PP$Ind_8))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.76 0.71 0.44 3.1 0.038 65 20 0.41
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.75 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_3 0.71 0.72 0.64 0.46 2.5 0.047 0.00982 0.41
## PP.Ind_4 0.68 0.68 0.59 0.41 2.1 0.051 0.00080 0.41
## PP.Ind_7 0.70 0.72 0.64 0.46 2.5 0.048 0.00989 0.41
## PP.Ind_8 0.68 0.68 0.59 0.41 2.1 0.052 0.00079 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_3 105 0.77 0.74 0.59 0.52 58 30
## PP.Ind_4 105 0.77 0.78 0.68 0.57 70 26
## PP.Ind_7 105 0.74 0.74 0.60 0.52 62 27
## PP.Ind_8 105 0.76 0.78 0.68 0.58 69 24hist(PP$Collectivism_Score , main = 'Collectivism Score')
PP$CollScale <- data.frame(PP$Ind_3, PP$Ind_4, PP$Ind_7,PP$Ind_8)
psych::alpha(PP$CollScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$CollScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.76 0.71 0.44 3.1 0.038 65 20 0.41
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.75 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_3 0.71 0.72 0.64 0.46 2.5 0.047 0.00982 0.41
## PP.Ind_4 0.68 0.68 0.59 0.41 2.1 0.051 0.00080 0.41
## PP.Ind_7 0.70 0.72 0.64 0.46 2.5 0.048 0.00989 0.41
## PP.Ind_8 0.68 0.68 0.59 0.41 2.1 0.052 0.00079 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_3 105 0.77 0.74 0.59 0.52 58 30
## PP.Ind_4 105 0.77 0.78 0.68 0.57 70 26
## PP.Ind_7 105 0.74 0.74 0.60 0.52 62 27
## PP.Ind_8 105 0.76 0.78 0.68 0.58 69 24cor.plot(PP$CollScale, labels = c('1','2','3','4'), main = "Correlations Between Collectivism 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
PP$ST_1 <- as.numeric(as.character(PP$SystemsHolistic_1))
PP$ST_2 <- as.numeric(as.character(PP$SystemsHolistic_2))
PP$ST_3 <- as.numeric(as.character(PP$SystemsHolistic_3))
PP$ST_4 <- as.numeric(as.character(PP$SystemsHolistic_4))
PP$ST_Score <- rowMeans(PP[, c('ST_1', 'ST_2', 'ST_3','ST_4')], na.rm=T)
#Systems Thinking Histograms
hist(PP$ST_1, main = 'All the Earth’s systems, from the climate to the economy, are interconnected.')
hist(PP$ST_2, main = 'Everything is constantly changing.')
hist(PP$ST_3, main = 'The Earth, including all its inhabitants, is a living system.')
hist(PP$ST_4, main = 'Seemingly small choices we make today can ultimately have major consequences."')
#Holistic Thinking
PP$HT_1 <- as.numeric(as.character(PP$SystemsHolistic_1))
PP$HT_2 <- as.numeric(as.character(PP$SystemsHolistic_2))
PP$HT_3 <- as.numeric(as.character(PP$SystemsHolistic_3))
PP$HT_4 <- as.numeric(as.character(PP$SystemsHolistic_4))
PP$HT_Score <- rowMeans(PP[, c('HT_1', 'HT_2', 'HT_3','HT_4')], na.rm=T)
#Holistic Recode #3
PP$HT_3R <- (102-PP$HT_3)
#Holistic Thinking Histograms (No reverse codes)
hist(PP$HT_1, main = 'Everything in the universe is somehow related to each other.')
hist(PP$HT_2, main = 'It is more desirable to take the middle ground than go to extremes.')
hist(PP$HT_3, main = 'Every phenomenon in the world moves in predictable directions.')
hist(PP$HT_4, main = 'The whole, rather than its parts, should be considered in order to understand a phenomenon.')
#Descriptives by Item
describe(PP$ST_1)## PP$ST_1
## n missing distinct Info Mean Gmd
## 105 10 7 0.955 58.16 1.873
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 3 4 13 15 20 18 32
## Proportion 0.029 0.038 0.124 0.143 0.190 0.171 0.305describe(PP$ST_2)## PP$ST_2
## n missing distinct Info Mean Gmd
## 105 10 7 0.923 58.64 1.545
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 1 8 13 20 21 41
## Proportion 0.010 0.010 0.076 0.124 0.190 0.200 0.390describe(PP$ST_3)## PP$ST_3
## n missing distinct Info Mean Gmd
## 105 10 7 0.903 58.58 1.668
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 2 2 6 17 20 12 46
## Proportion 0.019 0.019 0.057 0.162 0.190 0.114 0.438describe(PP$ST_4)## PP$ST_4
## n missing distinct Info Mean Gmd
## 105 10 7 0.946 58.24 1.9
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 2 8 8 16 18 17 36
## Proportion 0.019 0.076 0.076 0.152 0.171 0.162 0.343describe(PP$HT_1)## PP$HT_1
## n missing distinct Info Mean Gmd
## 105 10 7 0.955 58.16 1.873
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 3 4 13 15 20 18 32
## Proportion 0.029 0.038 0.124 0.143 0.190 0.171 0.305describe(PP$HT_2)## PP$HT_2
## n missing distinct Info Mean Gmd
## 105 10 7 0.923 58.64 1.545
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 1 1 8 13 20 21 41
## Proportion 0.010 0.010 0.076 0.124 0.190 0.200 0.390describe(PP$HT_3R)## PP$HT_3R
## n missing distinct Info Mean Gmd
## 105 10 7 0.903 43.42 1.668
##
## lowest : 42 43 44 45 46, highest: 44 45 46 47 48
##
## Value 42 43 44 45 46 47 48
## Frequency 46 12 20 17 6 2 2
## Proportion 0.438 0.114 0.190 0.162 0.057 0.019 0.019describe(PP$HT_4)## PP$HT_4
## n missing distinct Info Mean Gmd
## 105 10 7 0.946 58.24 1.9
##
## lowest : 54 55 56 57 58, highest: 56 57 58 59 60
##
## Value 54 55 56 57 58 59 60
## Frequency 2 8 8 16 18 17 36
## Proportion 0.019 0.076 0.076 0.152 0.171 0.162 0.343#Systems Thinking Alpha, Histogram, and Correlation (4 items)
psych::alpha(data.frame(PP$ST_1, PP$ST_2, PP$ST_3,PP$ST_4))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$ST_1, PP$ST_2, PP$ST_3, PP$ST_4))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.86 0.65 7.6 0.019 58 1.4 0.67
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.88 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.ST_1 0.90 0.90 0.86 0.75 9.1 0.016 0.00037 0.75
## PP.ST_2 0.83 0.83 0.78 0.63 5.0 0.028 0.00955 0.61
## PP.ST_3 0.82 0.82 0.77 0.60 4.6 0.030 0.01658 0.54
## PP.ST_4 0.83 0.84 0.80 0.63 5.2 0.028 0.01602 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.ST_1 105 0.78 0.78 0.64 0.61 58 1.7
## PP.ST_2 105 0.87 0.88 0.85 0.78 59 1.4
## PP.ST_3 105 0.90 0.90 0.87 0.82 59 1.6
## PP.ST_4 105 0.88 0.88 0.83 0.77 58 1.7
##
## Non missing response frequency for each item
## 54 55 56 57 58 59 60 miss
## PP.ST_1 0.03 0.04 0.12 0.14 0.19 0.17 0.30 0.09
## PP.ST_2 0.01 0.01 0.08 0.12 0.19 0.20 0.39 0.09
## PP.ST_3 0.02 0.02 0.06 0.16 0.19 0.11 0.44 0.09
## PP.ST_4 0.02 0.08 0.08 0.15 0.17 0.16 0.34 0.09hist(PP$ST_Score , main = 'Systems Thinking Score')
PP$STScale <- data.frame(PP$ST_1, PP$ST_2, PP$ST_3, PP$ST_4)
cor.plot(PP$STScale, labels = c('1','2','3','4'), main = "Correlations Between Systems Thinking Items")
#Holistic Thinking Alpha, Histogram, and Correlation (4 items)
psych::alpha(data.frame(PP$HT_1, PP$HT_2, PP$HT_3R, PP$HT_4))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$HT_1, PP$HT_2, PP$HT_3R, PP$HT_4)): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.HT_3R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$HT_1, PP$HT_2, PP$HT_3R, PP$HT_4))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.18 -0.24 0.52 -0.05 -0.19 0.12 55 0.75 -0.045
##
## lower alpha upper 95% confidence boundaries
## -0.42 -0.18 0.06
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.HT_1 -1.45 -1.50 0.293 -0.25 -0.60 0.32 0.754 -0.73
## PP.HT_2 -1.42 -1.73 -0.047 -0.27 -0.63 0.32 0.492 -0.61
## PP.HT_3R 0.82 0.82 0.774 0.60 4.58 0.03 0.017 0.54
## PP.HT_4 -1.95 -2.02 -0.070 -0.29 -0.67 0.40 0.496 -0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.HT_1 105 0.80 0.79 0.63 0.37 58 1.7
## PP.HT_2 105 0.80 0.82 0.88 0.47 59 1.4
## PP.HT_3R 105 -0.61 -0.60 -1.07 -0.82 43 1.6
## PP.HT_4 105 0.85 0.85 0.89 0.47 58 1.7hist(PP$HT_Score , main = 'Holistic Thinking Score')
PP$HTScale <- data.frame(PP$HT_1,PP$HT_2, PP$HT_3R, PP$HT_4)
cor.plot(PP$HTScale, labels = c('1','2','3','4'), main = "Correlations Between Holistic Thinking Items")
#Cronbachs Alpha for Systems/Holistic Thinking full scale (8 items)
PP$STHTScale <- data.frame(PP$ST_1, PP$ST_2, PP$ST_3, PP$ST_4, PP$HT_1,PP$HT_2, PP$HT_3R, PP$HT_4)
psych::alpha(PP$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(PP$STHTScale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.HT_3R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0##
## Reliability analysis
## Call: psych::alpha(x = PP$STHTScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.7 0.33 3.9 0.016 57 1 0.54
##
## lower alpha upper 95% confidence boundaries
## 0.77 0.8 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.ST_1 0.74 0.74 0.66 0.29 2.9 0.0192 0.493 0.61
## PP.ST_2 0.74 0.72 0.66 0.27 2.6 0.0216 0.456 0.54
## PP.ST_3 0.74 0.74 0.67 0.29 2.8 0.0229 0.439 0.54
## PP.ST_4 0.72 0.72 0.65 0.27 2.6 0.0228 0.460 0.54
## PP.HT_1 0.74 0.74 0.66 0.29 2.9 0.0192 0.493 0.61
## PP.HT_2 0.74 0.72 0.66 0.27 2.6 0.0216 0.456 0.54
## PP.HT_3R 0.94 0.94 0.82 0.69 15.5 0.0098 0.027 0.73
## PP.HT_4 0.72 0.72 0.65 0.27 2.6 0.0228 0.460 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.ST_1 105 0.81 0.80 0.73 0.72 58 1.7
## PP.ST_2 105 0.87 0.88 0.82 0.82 59 1.4
## PP.ST_3 105 0.82 0.82 0.75 0.74 59 1.6
## PP.ST_4 105 0.89 0.89 0.83 0.84 58 1.7
## PP.HT_1 105 0.81 0.80 0.73 0.72 58 1.7
## PP.HT_2 105 0.87 0.88 0.82 0.82 59 1.4
## PP.HT_3R 105 -0.82 -0.82 -1.22 -0.87 43 1.6
## PP.HT_4 105 0.89 0.89 0.83 0.84 58 1.7#Correlation Matrix: Individualism and Collectivism Items (8)
cor.plot(PP$STHTScale, labels = c('1','2','3','4','5','6','7','8'), main = "Correlations Between Systems Thinking and Holistic Thinking Items")
##Naturalness Correlations with Individual Differences Measures
#Risk Perception
#Correlation - Risk Perception Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$Risk_Scale_GFFB, PP$Naturalness_Score_GFFB, use="complete.obs")## [,1]
## PP.Risk_1_GFFB -0.4554604
## PP.Risk_2_GFFB -0.5378713 #2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$Risk_Scale_GFPRB, PP$Naturalness_Score_GFPRB, use="complete.obs")## [,1]
## PP.Risk_1_GFPRB -0.6658848
## PP.Risk_2_GFPRB -0.6283318 #3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$Risk_Scale_CBB, PP$Naturalness_Score_CBB, use="complete.obs")## [,1]
## PP.Risk_1_CBB -0.3350653
## PP.Risk_2_CBB -0.1814106 #4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$Risk_Scale_PBPB, PP$Naturalness_Score_PBPB, use="complete.obs")## [,1]
## PP.Risk_1_PBPB -0.4220305
## PP.Risk_2_PBPB -0.4053683 #5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$Risk_Scale_PBFB, PP$Naturalness_Score_PBFB, use="complete.obs")## [,1]
## PP.Risk_1_PBFB -0.1396202
## PP.Risk_2_PBFB -0.1941809 #6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$Risk_Scale_VB, PP$Naturalness_Score_VB, use="complete.obs")## [,1]
## PP.Risk_1_VB -0.3461254
## PP.Risk_2_VB -0.3375328#ENVS/Naturalness Correlations
##Environmentalism
#Correlation - Environmentalism Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$ENVS_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.4200361 plot(PP$ENVS_Score, PP$Naturalness_Score_GFFB, main="Correlation Between Environmentalism and GFFB Naturalness Scales",
xlab="Environmentalism Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$ENVS_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.1446164 plot(PP$ENVS_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between Environmentalism and GFPRB Naturalness Scales",
xlab="Environmentalism Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$ENVS_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.0745823 plot(PP$ENVS_Score, PP$Naturalness_Score_CBB, main="Correlation Between Environmentalism and CBB Naturalness Scales",
xlab="Environmentalism Scale", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$ENVS_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] 0.05170914 plot(PP$ENVS_Score, PP$Naturalness_Score_PBPB, main="Correlation Between Environmentalism and PBPB Naturalness Scales",
xlab="Environmentalism Scale", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$ENVS_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.1774589 plot(PP$ENVS_Score, PP$Naturalness_Score_PBFB, main="Correlation Between Environmentalism and PBFB Naturalness Scales",
xlab="Environmentalism Scale", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$ENVS_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.07424927 plot(PP$ENVS_Score, PP$Naturalness_Score_VB, main="Correlation Between Environmentalism and VB Naturalness Scales",
xlab="Environmentalism Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Environmentalism
#1. GFFB
cor(PP$ENVS_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.02937379 plot(PP$ENVS_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and Environmentalism",
xlab="Environmentalism Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$ENVS_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.02937379 plot(PP$ENVS_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and Environmentalism",
xlab="Environmentalism Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$ENVS_Score, PP$Nat_1_CBB, use="complete.obs")## [1] 0.1641947 plot(PP$ENVS_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and Environmentalism",
xlab="Environmentalism Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$ENVS_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.4933316 plot(PP$ENVS_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and Environmentalism",
xlab="Environmentalism Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$ENVS_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] 0.3945425 plot(PP$ENVS_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and Environmentalism",
xlab="Environmentalism Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$ENVS_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.1214376 plot(PP$ENVS_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and Environmentalism",
xlab="Environmentalism Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Environmentalism
#1. GFFB
cor(PP$ENVS_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.485828 plot(PP$ENVS_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and Environmentalism",
xlab="Environmentalism Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$ENVS_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] 0.01566231 plot(PP$ENVS_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and Environmentalism",
xlab="Environmentalism Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$ENVS_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] 0.01668158 plot(PP$ENVS_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and Environmentalism",
xlab="Environmentalism Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$ENVS_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] -0.04933061 plot(PP$ENVS_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and Environmentalism",
xlab="Environmentalism Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$ENVS_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.3128651 plot(PP$ENVS_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and Environmentalism",
xlab="Environmentalism Scale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$ENVS_Score, PP$Nat_2R_VB, use="complete.obs")## [1] -0.1110843 plot(PP$ENVS_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and Environmentalism",
xlab="Environmentalism Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Environmentalism
#1. GFFB
cor(PP$ENVS_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] -0.3178536 plot(PP$ENVS_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and Environmentalism",
xlab="Environmentalism Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$ENVS_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] -0.1393938 plot(PP$ENVS_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and Environmentalism",
xlab="Environmentalism Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$ENVS_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.3960431 plot(PP$ENVS_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and Environmentalism",
xlab="Environmentalism Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$ENVS_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.3588735 plot(PP$ENVS_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and Environmentalism",
xlab="Environmentalism Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$ENVS_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.4991476 plot(PP$ENVS_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and Environmentalism",
xlab="Environmentalism Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$ENVS_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.1223758 plot(PP$ENVS_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and Environmentalism",
xlab="Environmentalism Scale", ylab="VB Naturalness Item #3", pch=19)
#ATNS/Naturalness Correlations
#Aversion to Tampering with Nature
#Correlation - Environmentalism Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$ATNS_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] 0.04290389 plot(PP$ATNS_Score, PP$Naturalness_Score_GFFB, main="Correlation Between ATNS and GFFB Naturalness Scales",
xlab="ATNS Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$ATNS_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.1803018 plot(PP$ATNS_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between ATNS and GFPRB Naturalness Scales",
xlab="ATNS Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$ATNS_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.3623529 plot(PP$ATNS_Score, PP$Naturalness_Score_CBB, main="Correlation Between ATNS and CBB Naturalness Scales",
xlab="ATNS Scale", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$ATNS_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] -0.068693 plot(PP$ATNS_Score, PP$Naturalness_Score_PBPB, main="Correlation Between ATNS and PBPB Naturalness Scales",
xlab="ATNS Scale", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$ATNS_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.1679238 plot(PP$ATNS_Score, PP$Naturalness_Score_PBFB, main="Correlation Between ATNS and PBFB Naturalness Scales",
xlab="ATNS Scale", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$ATNS_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.06088496 plot(PP$ATNS_Score, PP$Naturalness_Score_VB, main="Correlation Between ATNS and VB Naturalness Scales",
xlab="ATNS Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Aversion to Tampering with Nature Scale
#1. GFFB
cor(PP$ATNS_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.00856362 plot(PP$ATNS_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and ATNS",
xlab="ATNS Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$ATNS_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.00856362 plot(PP$ATNS_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and ATNS",
xlab="ATNS Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$ATNS_Score, PP$Nat_1_CBB, use="complete.obs")## [1] -0.2705848 plot(PP$ATNS_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and ATNS",
xlab="ATNS Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$ATNS_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.1946937 plot(PP$ATNS_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and ATNS",
xlab="ATNS Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$ATNS_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] -0.04538173 plot(PP$ATNS_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and ATNS",
xlab="ATNS Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$ATNS_Score, PP$Nat_1_VB, use="complete.obs")## [1] -0.004843287 plot(PP$ATNS_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and ATNS",
xlab="ATNS Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Aversion to Tampering to Nature Scale
#1. GFFB
cor(PP$ATNS_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.0227245 plot(PP$ATNS_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and ATNS Scale",
xlab="ATNS Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$ATNS_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] 0.02668842 plot(PP$ATNS_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and ATNS Scale",
xlab="ATNS Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$ATNS_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] -0.2047382 plot(PP$ATNS_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and ATNS Scale",
xlab="ATNS Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$ATNS_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] -0.002176731 plot(PP$ATNS_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and ATNS Scale",
xlab="ATNS Scale Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$ATNS_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.1137831 plot(PP$ATNS_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and ATNS Scale",
xlab="ATNS ScaleScale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$ATNS_Score, PP$Nat_2R_VB, use="complete.obs")## [1] 0.02831801 plot(PP$ATNS_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and ATNS Scale",
xlab="ATNS Scale Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Aversion to Tampering to Nature Scale
#1. GFFB
cor(PP$ATNS_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] 0.09348532 plot(PP$ATNS_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and ATNS",
xlab="ATNS Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$ATNS_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] 0.01004065 plot(PP$ATNS_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and ATNS",
xlab="ATNSm Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$ATNS_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.1630556 plot(PP$ATNS_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and ATNS",
xlab="ATNS Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$ATNS_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.3356528 plot(PP$ATNS_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and ATNS",
xlab= "ATNS Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$ATNS_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.1743142 plot(PP$ATNS_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and ATNS",
xlab="ATNS Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$ATNS_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.1366569 plot(PP$ATNS_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and ATNS",
xlab="ATNS Scale", ylab="VB Naturalness Item #3", pch=19)
#Connectedness to Nature/Naturalness Correlations
#Connectedness to Nature
#Correlation - Connectedness to Nature Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$CNS_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.1672483 plot(PP$CNS_Score, PP$Naturalness_Score_GFFB, main="Correlation Between CNS and GFFB Naturalness Scales",
xlab="CNS Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$CNS_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.4851837 plot(PP$CNS_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between CNS and GFPRB Naturalness Scales",
xlab="CNS Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$CNS_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.2194669 plot(PP$CNS_Score, PP$Naturalness_Score_CBB, main="Correlation Between CNS and CBB Naturalness Scales",
xlab="CNS Scale", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$CNS_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] 0.02988085 plot(PP$CNS_Score, PP$Naturalness_Score_PBPB, main="Correlation Between CNS and PBPB Naturalness Scales",
xlab="CNS Scale", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$CNS_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.1482298 plot(PP$CNS_Score, PP$Naturalness_Score_PBFB, main="Correlation Between CNS and PBFB Naturalness Scales",
xlab="CNS Scale", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$CNS_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] 0.1921644 plot(PP$CNS_Score, PP$Naturalness_Score_VB, main="Correlation Between CNS and VB Naturalness Scales",
xlab="CNS Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Connectedness to Nature Scale
#1. GFFB
cor(PP$CNS_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.3295726 plot(PP$CNS_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and CNS",
xlab="CNS Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$CNS_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.3295726 plot(PP$CNS_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and CNS",
xlab="CNS Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$CNS_Score, PP$Nat_1_CBB, use="complete.obs")## [1] -0.05438292 plot(PP$CNS_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and CNS",
xlab="CNS Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$CNS_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] -0.03451881 plot(PP$CNS_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and CNS",
xlab="CNS Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$CNS_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] -0.08040271 plot(PP$CNS_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and CNS",
xlab="CNS Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$CNS_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.1684939 plot(PP$CNS_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and CNS",
xlab="CNS Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Connectedness to Nature Scale
#1. GFFB
cor(PP$CNS_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.2022239 plot(PP$CNS_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and CNS Scale",
xlab="CNS Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$CNS_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] 0.3139805 plot(PP$CNS_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and CNS Scale",
xlab="CNS Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$CNS_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] -0.240608 plot(PP$CNS_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and CNS Scale",
xlab="CNS Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$CNS_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] 0.2173622 plot(PP$CNS_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and CNS Scale",
xlab="CNS Scale Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$CNS_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.00752778 plot(PP$CNS_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and CNS Scale",
xlab="CNS ScaleScale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$CNS_Score, PP$Nat_2R_VB, use="complete.obs")## [1] 0.1014578 plot(PP$CNS_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and CNS Scale",
xlab="CNS Scale Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Connectedness to Nature Scale
#1. GFFB
cor(PP$CNS_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] 0.2264537 plot(PP$CNS_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and CNS",
xlab="CNS Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$CNS_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] 0.3539681 plot(PP$CNS_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and CNS",
xlab="CNS Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$CNS_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.1113171 plot(PP$CNS_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and CNS",
xlab="CNS Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$CNS_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.1747154 plot(PP$CNS_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and CNS",
xlab= "CNS Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$CNS_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.2055235 plot(PP$CNS_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and CNS",
xlab="CNS Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$CNS_Score, PP$Nat_3R_VB, use="complete.obs")## [1] 0.09804819 plot(PP$CNS_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and CNS",
xlab="CNS Scale", ylab="VB Naturalness Item #3", pch=19)
#Climate Change Belief/Naturalness Correlations
#Correlation - Climate Change Belief Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$CCBelief_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.3027904 plot(PP$CCBelief_Score, PP$Naturalness_Score_GFFB, main="Correlation Between CCB and GFFB Naturalness Scales",
xlab="CCB Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$CCBelief_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.2509665 plot(PP$CCBelief_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between CCB and GFPRB Naturalness Scales",
xlab="CCB Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$CCBelief_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.2244636 plot(PP$CCBelief_Score, PP$Naturalness_Score_CBB, main="Correlation Between CCB and CBB Naturalness Scales",
xlab="CCB Scale", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$CCBelief_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] 0.1499602 plot(PP$CCBelief_Score, PP$Naturalness_Score_PBPB, main="Correlation Between CCB and PBPB Naturalness Scales",
xlab="CCB Scale", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$CCBelief_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.1405876 plot(PP$CCBelief_Score, PP$Naturalness_Score_PBFB, main="Correlation Between CCB and PBFB Naturalness Scales",
xlab="CCB Scale", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$CCBelief_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.007751182 plot(PP$CCBelief_Score, PP$Naturalness_Score_VB, main="Correlation Between CCB and VB Naturalness Scales",
xlab="CCB Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Climate Change Belief
#1. GFFB
cor(PP$CCBelief_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.01870919 plot(PP$CCBelief_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and CCB",
xlab="CCB Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$CCBelief_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.01870919 plot(PP$CCBelief_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and CCB",
xlab="CCB Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$CCBelief_Score, PP$Nat_1_CBB, use="complete.obs")## [1] -0.04686755 plot(PP$CCBelief_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and CCB",
xlab="CCB Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$CCBelief_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.2658654 plot(PP$CCBelief_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and CCB",
xlab="CCB Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$CCBelief_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] 0.2200274 plot(PP$CCBelief_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and CCB",
xlab="CCB Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$CCBelief_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.3349562 plot(PP$CCBelief_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and CNS",
xlab="CCB Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Climate Change Belief Scale
#1. GFFB
cor(PP$CCBelief_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.4210733 plot(PP$CCBelief_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and CCB Scale",
xlab="CCB Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$CCBelief_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] 0.1364657 plot(PP$CCBelief_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and CCB Scale",
xlab="CCB Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$CCBelief_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] -0.09825971 plot(PP$CCBelief_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and CCB Scale",
xlab="CCB Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$CCBelief_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] 0.1943077 plot(PP$CCBelief_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and CCB Scale",
xlab="CCB Scale Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$CCBelief_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.1517014 plot(PP$CCBelief_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and CCCB Scale",
xlab="CCB ScaleScale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$CCBelief_Score, PP$Nat_2R_VB, use="complete.obs")## [1] -0.07067231 plot(PP$CCBelief_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and CCB Scale",
xlab="CCB Scale Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Climate Change Belief Scale
#1. GFFB
cor(PP$CCBelief_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] -0.1177224 plot(PP$CCBelief_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and CCB",
xlab="CCB Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$CCBelief_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] 0.01309672 plot(PP$CCBelief_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and CCB",
xlab="CCB Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$CCBelief_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.2957607 plot(PP$CCBelief_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and CCB",
xlab="CCB Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$CCBelief_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.230227 plot(PP$CCBelief_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and CCB",
xlab= "CCB Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$CCBelief_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.3891721 plot(PP$CCBelief_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and CCB",
xlab="CCB Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$CCBelief_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.2266098 plot(PP$CCBelief_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and CCB",
xlab="CCB Scale", ylab="VB Naturalness Item #3", pch=19)
#Individualism/Naturalness Correlations
#Correlation - Individualism Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$Individualism_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.2239533 plot(PP$Individualism_Score, PP$Naturalness_Score_GFFB, main="Correlation Between Individualism and GFFB Naturalness Scales",
xlab="Individualism Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$Individualism_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.0550101 plot(PP$Individualism_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between Individualism and GFPRB Naturalness Scales",
xlab="Individualism Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$Individualism_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.44175 plot(PP$Individualism_Score, PP$Naturalness_Score_CBB, main="Correlation Between Individualism and CBB Naturalness Scales",
xlab="Individualism Scale", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$Individualism_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] -0.01104635 plot(PP$Individualism_Score, PP$Naturalness_Score_PBPB, main="Correlation Between Individualism and PBPB Naturalness Scales",
xlab="Individualism Scale", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$Individualism_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.2881681 plot(PP$Individualism_Score, PP$Naturalness_Score_PBFB, main="Correlation Between Individualism and PBFB Naturalness Scales",
xlab="Individualism Scale", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$Individualism_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.1552394 plot(PP$Individualism_Score, PP$Naturalness_Score_VB, main="Correlation Between Individualism and VB Naturalness Scales",
xlab="Individualism Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Individualism Scale
#1. GFFB
cor(PP$Individualism_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.2637115 plot(PP$Individualism_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and Individualism Scale",
xlab="IND Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$Individualism_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.2637115 plot(PP$Individualism_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and Individualism Scale",
xlab="IND Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$Individualism_Score, PP$Nat_1_CBB, use="complete.obs")## [1] -0.1620996 plot(PP$Individualism_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and Individualism Scale",
xlab="IND Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$Individualism_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.4393399 plot(PP$Individualism_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and Individualism Scale",
xlab="IND Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$Individualism_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] 0.1266517 plot(PP$Individualism_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and Individualism Scale",
xlab="IND Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$Individualism_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.1323967 plot(PP$Individualism_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and Individualism Scale",
xlab="IND Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Individualism Scale
#1. GFFB
cor(PP$Individualism_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.2777159 plot(PP$Individualism_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and IND Scale",
xlab="IND Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$Individualism_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] -0.01073339 plot(PP$Individualism_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and IND Scale",
xlab="IND Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$Individualism_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] -0.2956445 plot(PP$Individualism_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and IND Scale",
xlab="IND Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$Individualism_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] -0.1545008 plot(PP$Individualism_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and IND Scale",
xlab="IND Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$Individualism_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.2882167 plot(PP$Individualism_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and IND Scale",
xlab="IND ScaleScale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$Individualism_Score, PP$Nat_2R_VB, use="complete.obs")## [1] -0.08929135 plot(PP$Individualism_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and IND Scale",
xlab="IND Scale Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Individualism Scale
#1. GFFB
cor(PP$Individualism_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] -0.4008802 plot(PP$Individualism_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and Individualism",
xlab="IND Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$Individualism_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] -0.2987333 plot(PP$Individualism_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and Individualism",
xlab="IND Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$Individualism_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.3706834 plot(PP$Individualism_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and Individualism",
xlab="IND Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$Individualism_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.2933803 plot(PP$Individualism_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and Individualism",
xlab= "IND Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$Individualism_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.4405149 plot(PP$Individualism_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and Individualism",
xlab="IND Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$Individualism_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.3011725 plot(PP$Individualism_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and Individualism",
xlab="IND Scale", ylab="VB Naturalness Item #3", pch=19)
#Collectivism/Naturalness Correlations
#Correlation - Collectivism Scale, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$Collectivism_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.1182927 plot(PP$Collectivism_Score, PP$Naturalness_Score_GFFB, main="Correlation Between Collectivism and GFFB Naturalness Scales",
xlab="Collectivism Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$Collectivism_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] -0.1438231 plot(PP$Collectivism_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between Collectivism and GFPRB Naturalness Scales",
xlab="Collectivism Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$Collectivism_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.01211381 plot(PP$Collectivism_Score, PP$Naturalness_Score_CBB, main="Correlation Between Collectivism and CBB Naturalness Scales",
xlab="Collectivism Scale", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$Collectivism_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] 0.05511321 plot(PP$Collectivism_Score, PP$Naturalness_Score_PBPB, main="Correlation Between Collectivism and PBPB Naturalness Scales",
xlab="Collectivism Scale", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$Collectivism_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.07770604 plot(PP$Collectivism_Score, PP$Naturalness_Score_PBFB, main="Correlation Between Collectivism and PBFB Naturalness Scales",
xlab="Collectivism Scale", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$Collectivism_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.2115697 plot(PP$Collectivism_Score, PP$Naturalness_Score_VB, main="Correlation Between Collectivism and VB Naturalness Scales",
xlab="Collectivism Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Collectivism Scale
#1. GFFB
cor(PP$Collectivism_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.4524201 plot(PP$Collectivism_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and Collectivism Scale",
xlab="Collectivism Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$Collectivism_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] 0.4524201 plot(PP$Collectivism_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and Collectivism Scale",
xlab="Collectivism Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$Collectivism_Score, PP$Nat_1_CBB, use="complete.obs")## [1] 0.200486 plot(PP$Collectivism_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and Collectivism Scale",
xlab="Collectivism Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$Collectivism_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.4203608 plot(PP$Collectivism_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and Collectivism Scale",
xlab="Collectivism Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$Collectivism_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] 0.08275208 plot(PP$Collectivism_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and Collectivism Scale",
xlab="Collectivism Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$Collectivism_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.03957689 plot(PP$Collectivism_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and Collectivism Scale",
xlab="Collectivism Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Collectivism Scale
#1. GFFB
cor(PP$Collectivism_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.2100618 plot(PP$Collectivism_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and Collectivism Scale",
xlab="Collectivism Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$Collectivism_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] -0.2729313 plot(PP$Collectivism_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and Collectivism Scale",
xlab="Collectivism Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$Collectivism_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] 0.01861201 plot(PP$Collectivism_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and Collectivism Scale",
xlab="Collectivism Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$Collectivism_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] -0.2333794 plot(PP$Collectivism_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and Collectivism Scale",
xlab="Collectivism Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$Collectivism_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.1424306 plot(PP$Collectivism_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and Collectivism Scale",
xlab="Collectivism ScaleScale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$Collectivism_Score, PP$Nat_2R_VB, use="complete.obs")## [1] -0.1124313 plot(PP$Collectivism_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and Collectivism Scale",
xlab="Collectivism Scale Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Individualism Scale
#1. GFFB
cor(PP$Collectivism_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] -0.4638958 plot(PP$Collectivism_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and Collectivism",
xlab="Collectivism Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$Collectivism_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] -0.3506179 plot(PP$Collectivism_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and Collectivism",
xlab="Collectivism Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$Collectivism_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.3156394 plot(PP$Collectivism_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and Collectivism",
xlab="Collectivism Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$Collectivism_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.04757559 plot(PP$Collectivism_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and Collectivism",
xlab= "Collectivism Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$Collectivism_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.1069208 plot(PP$Collectivism_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and Collectivism",
xlab="Collectivism Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$Collectivism_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.3010127 plot(PP$Collectivism_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and Collectivism",
xlab="Collectivism Scale", ylab="VB Naturalness Item #3", pch=19)
#Systems Thinking/Naturalness Correlations
#Correlation - Systems Thinking, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$ST_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.1778856 plot(PP$ST_Score, PP$Naturalness_Score_GFFB, main="Correlation Between Systems Thinking and GFFB Naturalness Scales",
xlab="Systems Thinking Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$ST_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.2974051 plot(PP$ST_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between Systems Thinking and GFPRB Naturalness Scales",
xlab="Systems Thinking Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$ST_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.4926093 plot(PP$ST_Score, PP$Naturalness_Score_CBB, main="Correlation Between Systems Thinking and CBB Naturalness Scales",
xlab="Systems Thinking", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$ST_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] -0.01449606 plot(PP$ST_Score, PP$Naturalness_Score_PBPB, main="Correlation Between Systems Thinking and PBPB Naturalness Scales",
xlab="Systems Thinking", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$ST_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.3262814 plot(PP$ST_Score, PP$Naturalness_Score_PBFB, main="Correlation Between Systems Thinking and PBFB Naturalness Scales",
xlab="Systems Thinking", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$ST_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.02588182 plot(PP$ST_Score, PP$Naturalness_Score_VB, main="Correlation Between Systems Thinking and VB Naturalness Scales",
xlab="Systems Thinking Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Systems Thinking Scale
#1. GFFB
cor(PP$ST_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.1273476 plot(PP$ST_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$ST_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.1273476 plot(PP$ST_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$ST_Score, PP$Nat_1_CBB, use="complete.obs")## [1] -0.2591229 plot(PP$ST_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$ST_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.1127269 plot(PP$ST_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$ST_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] 0.1142938 plot(PP$ST_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$ST_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.04189862 plot(PP$ST_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Systems Thinking Scale
#1. GFFB
cor(PP$ST_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.14821 plot(PP$ST_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$ST_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] 0.2819284 plot(PP$ST_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and Systems Thinking Scale",
xlab="Systems Thinking Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$ST_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] -0.3096436 plot(PP$ST_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$ST_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] 0.1490989 plot(PP$ST_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and Systems Thinking Scale",
xlab="Systems Thinking Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$ST_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.2690046 plot(PP$ST_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and Systems Thinking Scale",
xlab="Systems Thinking ScaleScale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$ST_Score, PP$Nat_2R_VB, use="complete.obs")## [1] 0.05649654 plot(PP$ST_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and Systems Thinking Scale",
xlab="Systems Thinking Scale Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Systems Thinking Scale
#1. GFFB
cor(PP$ST_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] -0.05142814 plot(PP$ST_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and Systems Thinking",
xlab="Systems Thinking Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$ST_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] -0.005062278 plot(PP$ST_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and Systems Thinking",
xlab="Systems Thinking Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$ST_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.3313847 plot(PP$ST_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and Systems Thinking",
xlab="Systems Thinking Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$ST_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.3314487 plot(PP$ST_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and Systems Thinking",
xlab= "Systems Thinking Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$ST_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.5256942 plot(PP$ST_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and Systems Thinking",
xlab="Systems Thinking Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$ST_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.1432221 plot(PP$ST_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and Systems Thinking",
xlab="Systems Thinking Scale", ylab="VB Naturalness Item #3", pch=19)
#Holistic Thinking/Naturalness Correlations
#Correlation - Holistic Thinking, Naturalness Scale
#1. GFFB
PP$Naturalness_Score_GFFB <- rowMeans(PP [, c("Nat_1_GFFB", "Nat_2R_GFFB", "Nat_3R_GFFB")], na.rm=TRUE)
PP$Naturalness_Scale_GFFB <- data.frame(PP$Nat_1_GFFB, PP$Nat_2R_GFFB, PP$Nat_3R_GFFB)
cor(PP$HT_Score, PP$Naturalness_Score_GFFB, use="complete.obs")## [1] -0.1778856 plot(PP$HT_Score, PP$Naturalness_Score_GFFB, main="Correlation Between Holistic Thinking and GFFB Naturalness Scales",
xlab="Holistic Thinking Scale", ylab="GFFB Naturalness Scale", pch=19)
#2. GFPRB
PP$Naturalness_Score_GFPRB <- rowMeans(PP [, c("Nat_1_GFPRB", "Nat_2R_GFPRB", "Nat_3R_GFPRB")], na.rm=TRUE)
PP$Naturalness_Scale_GFPRB <- data.frame(PP$Nat_1_GFPRB, PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB)
cor(PP$HT_Score, PP$Naturalness_Score_GFPRB, use="complete.obs")## [1] 0.2974051 plot(PP$HT_Score, PP$Naturalness_Score_GFPRB, main="Correlation Between Holistic Thinking and GFPRB Naturalness Scales",
xlab="Holistic Thinking Scale", ylab="GFPRB Naturalness Scale", pch=19)
#3. CBB
PP$Naturalness_Score_CBB <- rowMeans(PP [, c("Nat_1_CBB", "Nat_2R_CBB", "Nat_3R_CBB")], na.rm=TRUE)
PP$Naturalness_Scale_CBB <- data.frame(PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB)
cor(PP$HT_Score, PP$Naturalness_Score_CBB, use="complete.obs")## [1] -0.4926093 plot(PP$HT_Score, PP$Naturalness_Score_CBB, main="Correlation Between Holistic Thinking and CBB Naturalness Scales",
xlab="Holistic Thinking", ylab="CBB Naturalness Scale", pch=19)
#4. PBPB
PP$Naturalness_Score_PBPB <- rowMeans(PP [, c("Nat_1_PBPB", "Nat_2R_PBPB", "Nat_3R_PBPB")], na.rm=TRUE)
PP$Naturalness_Scale_PBPB <- data.frame(PP$Nat_1_PBPB, PP$Nat_2R_PBPB, PP$Nat_3R_PBPB)
cor(PP$HT_Score, PP$Naturalness_Score_PBPB, use="complete.obs")## [1] -0.01449606 plot(PP$HT_Score, PP$Naturalness_Score_PBPB, main="Correlation Between Holistic Thinking and PBPB Naturalness Scales",
xlab="Holistic Thinking", ylab="PBPB Naturalness Scale", pch=19)
#5. PBFB
PP$Naturalness_Score_PBFB <- rowMeans(PP [, c("Nat_1_PBFB", "Nat_2R_PBFB", "Nat_3R_PBFB")], na.rm=TRUE)
PP$Naturalness_Scale_PBFB <- data.frame(PP$Nat_1_PBFB, PP$Nat_2R_PBFB, PP$Nat_3R_PBFB)
cor(PP$HT_Score, PP$Naturalness_Score_PBFB, use="complete.obs")## [1] -0.3262814 plot(PP$HT_Score, PP$Naturalness_Score_PBFB, main="Correlation Between Holistic Thinking and PBFB Naturalness Scales",
xlab="Holistic Thinking", ylab="PBFB Naturalness Scale", pch=19)
#6. VB
PP$Naturalness_Score_VB <- rowMeans(PP [, c("Nat_1_VB", "Nat_2R_VB", "Nat_3R_VB")], na.rm=TRUE)
PP$Naturalness_Scale_VB <- data.frame(PP$Nat_1_VB, PP$Nat_2R_VB, PP$Nat_3R_VB)
cor(PP$HT_Score, PP$Naturalness_Score_VB, use="complete.obs")## [1] -0.02588182 plot(PP$HT_Score, PP$Naturalness_Score_VB, main="Correlation Between Holistic Thinking and VB Naturalness Scales",
xlab="Holistic Thinking Scale", ylab="VB Naturalness Scale", pch=19)
#Naturalness Item 1, Holistic Thinking Scale
#1. GFFB
cor(PP$HT_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.1273476 plot(PP$HT_Score, PP$Nat_1_GFFB, main="Correlation Between GFFB Naturalness Item #1 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="GFFB Naturalness Item #1", pch=19)
#2. GFPRB
cor(PP$HT_Score, PP$Nat_1_GFFB, use="complete.obs")## [1] -0.1273476 plot(PP$HT_Score, PP$Nat_1_GFFB, main="Correlation Between GFPRB Naturalness Item #1 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="GFFB Naturalness Item #1", pch=19)
#3. CBB
cor(PP$HT_Score, PP$Nat_1_CBB, use="complete.obs")## [1] -0.2591229 plot(PP$HT_Score, PP$Nat_1_CBB, main="Correlation Between CCB Naturalness Item #1 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="CCB Naturalness Item #1", pch=19)
#4. PBPB
cor(PP$HT_Score, PP$Nat_1_PBPB, use="complete.obs")## [1] 0.1127269 plot(PP$HT_Score, PP$Nat_1_PBPB, main="Correlation Between PBPB Naturalness Item #1 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="PBPB Naturalness Item #1", pch=19)
#5. PBFB
cor(PP$HT_Score, PP$Nat_1_PBFB, use="complete.obs")## [1] 0.1142938 plot(PP$HT_Score, PP$Nat_1_PBFB, main="Correlation Between PBFB Naturalness Item #1 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="PBFB Naturalness Item #1", pch=19)
#6. VB
cor(PP$HT_Score, PP$Nat_1_VB, use="complete.obs")## [1] 0.04189862 plot(PP$HT_Score, PP$Nat_1_VB, main="Correlation Between VB Naturalness Item #1 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="VB Naturalness Item #1", pch=19)
#Naturalness Item 2, Holistic Thinking Scale
#1. GFFB
cor(PP$HT_Score, PP$Nat_2R_GFFB, use="complete.obs")## [1] -0.14821 plot(PP$HT_Score, PP$Nat_2R_GFFB, main="Correlation Between GFFB Naturalness Item #2 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="GFFB Naturalness Item #2", pch=19)
#2. GFPRB
cor(PP$HT_Score, PP$Nat_2R_GFPRB, use="complete.obs")## [1] 0.2819284 plot(PP$HT_Score, PP$Nat_2R_GFPRB, main="Correlation Between GFPRB Naturalness Item #2 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale Scale", ylab="GFFB Naturalness Item #2", pch=19)
#3. CBB
cor(PP$HT_Score, PP$Nat_2R_CBB, use="complete.obs")## [1] -0.3096436 plot(PP$HT_Score, PP$Nat_2R_CBB, main="Correlation Between CBB Naturalness Item #2 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="CBB Naturalness Item #2", pch=19)
#4. PBPB
cor(PP$HT_Score, PP$Nat_2R_PBPB, use="complete.obs")## [1] 0.1490989 plot(PP$HT_Score, PP$Nat_2R_PBPB, main="Correlation Between PBPB Naturalness Item #2 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="PBPB Naturalness Item #2", pch=19)
#5. PBFB
cor(PP$HT_Score, PP$Nat_2R_PBFB, use="complete.obs")## [1] -0.2690046 plot(PP$HT_Score, PP$Nat_2R_PBFB, main="Correlation Between PBFB Naturalness Item #2 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="PBFB Naturalness Item #2", pch=19)
#6. VB
cor(PP$HT_Score, PP$Nat_2R_VB, use="complete.obs")## [1] 0.05649654 plot(PP$HT_Score, PP$Nat_2R_VB, main="Correlation Between VB Naturalness Item #2 and Holistic Thinking Scale",
xlab="Holistic Thinking Scale", ylab="VB Naturalness Item #2", pch=19)
#Naturalness Item 3, Holistic Thinking Scale
#1. GFFB
cor(PP$HT_Score, PP$Nat_3R_GFFB, use="complete.obs")## [1] -0.05142814 plot(PP$HT_Score, PP$Nat_3R_GFFB, main="Correlation Between GFFB Naturalness Item #3 and Holistic Thinking",
xlab="Holistic Thinking Scale", ylab="GFFB Naturalness Item #3", pch=19)
#2. GFPRB
cor(PP$HT_Score, PP$Nat_3R_GFPRB, use="complete.obs")## [1] -0.005062278 plot(PP$HT_Score, PP$Nat_3R_GFPRB, main="Correlation Between GFPRB Naturalness Item #3 and Holistic Thinking",
xlab="Holistic Thinking Scale", ylab="GFPRB Naturalness Item #3", pch=19)
#3. CBB
cor(PP$HT_Score, PP$Nat_3R_CBB, use="complete.obs")## [1] -0.3313847 plot(PP$HT_Score, PP$Nat_3R_CBB, main="Correlation Between CCB Naturalness Item #3 and Holistic Thinking",
xlab="Holistic Thinking Scale", ylab="CCB Naturalness Item #3", pch=19)
#4. PBPB
cor(PP$HT_Score, PP$Nat_3R_PBPB, use="complete.obs")## [1] -0.3314487 plot(PP$HT_Score, PP$Nat_3R_PBPB, main="Correlation Between PBPB Naturalness Item #3 and Holistic Thinking",
xlab= "Systems Thinking Scale", ylab="PBPB Naturalness Item #3", pch=19)
#5. PBFB
cor(PP$HT_Score, PP$Nat_3R_PBFB, use="complete.obs")## [1] -0.5256942 plot(PP$HT_Score, PP$Nat_3R_PBFB, main="Correlation Between PBFB Naturalness Item #3 and Holistic Thinking",
xlab="Holistic Thinking Scale", ylab="PBFB Naturalness Item #3", pch=19)
#6. VB
cor(PP$HT_Score, PP$Nat_3R_VB, use="complete.obs")## [1] -0.1432221 plot(PP$HT_Score, PP$Nat_3R_VB, main="Correlation Between VB Naturalness Item #3 and Holistic Thinking",
xlab="Holistic Thinking Scale", ylab="VB Naturalness Item #3", pch=19)
#Correlations
#Naturalness aggregated variables consist of the first method participants saw on the survey (randomly assigned)
PP$Nat_1_Aggregated<- PP$Nat_1_Aggregated
PP$Nat_2_Aggregated <- (102-PP$Nat_2_Aggregated)
PP$Nat_3_Aggregated <- (102-PP$Nat_3_Aggregated)
PP$Nat_Score_Aggregated <- rowMeans(PP [, c("Nat_1_Aggregated", "Nat_2_Aggregated", "Nat_3_Aggregated")], na.rm=TRUE)
PP$NatAg_Scale <- data.frame(PP$Nat_1_Aggregated, PP$Nat_2_Aggregated, PP$Nat_3_Aggregated)
#Cronbach's alpha for aggregated naturalness scale
psych::alpha(data.frame(PP$Nat_1_Aggregated, PP$Nat_2_Aggregated, PP$Nat_3_Aggregated))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_Aggregated, PP$Nat_2_Aggregated, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.Nat_1_Aggregated ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_Aggregated, PP$Nat_2_Aggregated,
## PP$Nat_3_Aggregated))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.37 0.37 0.42 0.16 0.58 0.1 50 20 0.14
##
## lower alpha upper 95% confidence boundaries
## 0.17 0.37 0.57
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Nat_1_Aggregated 0.67 0.67 0.51 0.51 2.05 0.061 NA
## PP.Nat_2_Aggregated -0.37 -0.37 -0.16 -0.16 -0.27 0.256 NA
## PP.Nat_3_Aggregated 0.24 0.24 0.14 0.14 0.31 0.142 NA
## med.r
## PP.Nat_1_Aggregated 0.51
## PP.Nat_2_Aggregated -0.16
## PP.Nat_3_Aggregated 0.14
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_Aggregated 107 0.50 0.49 0.05 -0.0085 64 30
## PP.Nat_2_Aggregated 106 0.83 0.82 0.73 0.4899 45 30
## PP.Nat_3_Aggregated 107 0.67 0.68 0.51 0.2355 42 29hist(PP$Nat_Score_Aggregated, main = 'Aggregated Naturalness Scale Score')
#Risk aggregated variables consist of the first method participants saw on the survey (randomly assigned)
PP$Risk_1_AG <- PP$Risk_1_Aggregated
PP$Risk_2_AG <- PP$Risk_2_Aggregated
PP$Risk_3_AG <- PP$Risk_3_Aggregated
PP$Risk_4_AG <- PP$Risk_4_Aggregated
PP$Risk_Score_Aggregated <- rowMeans(PP [, c("Risk_1_AG", "Risk_2_AG", "Risk_3_AG", "Risk_4_AG")], na.rm=TRUE)
PP$RiskAg_Scale <- data.frame(PP$Risk_1_AG, PP$Risk_2_AG, PP$Risk_3_AG, PP$Risk_4_AG)
psych::alpha(data.frame(PP$Risk_1_AG, PP$Risk_2_AG, PP$Risk_3_AG, PP$Risk_4_AG))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_AG, PP$Risk_2_AG, PP$Risk_3_AG,
## PP$Risk_4_AG))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.91 0.74 12 0.012 46 29 0.76
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.92 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_AG 0.89 0.89 0.85 0.72 7.9 0.019 0.00654 0.74
## PP.Risk_2_AG 0.88 0.88 0.85 0.72 7.6 0.019 0.00589 0.72
## PP.Risk_3_AG 0.91 0.91 0.87 0.77 10.1 0.015 0.00057 0.78
## PP.Risk_4_AG 0.91 0.91 0.87 0.77 9.8 0.015 0.00139 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_AG 107 0.91 0.92 0.88 0.85 44 32
## PP.Risk_2_AG 107 0.92 0.92 0.89 0.86 45 32
## PP.Risk_3_AG 106 0.87 0.88 0.82 0.78 46 31
## PP.Risk_4_AG 107 0.89 0.88 0.83 0.79 48 34hist(PP$Risk_Score_Aggregated, main = 'Aggregated Risk Scale Score')
#Understanding aggregated variable consist of the first method participants saw on the survey (randomly assigned)
PP$Understanding_1_AG <- PP$Understanding_1_Aggregated
#Familiarity aggregated variable consist of the first method participants saw on the survey (randomly assigned)
PP$Familiarity_1_AG <-PP$Familiarity_1_Aggregated
#Control aggregated variable consist of the first method participants saw on the survey (randomly assigned)
PP$Control_1_AG <-PP$Control_1_Aggregated
#Benefits aggregated variable consist of the first method participants saw on the survey (randomly assigned)
PP$Benefit_1_AG <-PP$Benefit_1_Aggregated
PP$Benefit_2_AG <-PP$Benefit_2_Aggregated
PP$Benefit_3_AG <-PP$Benefit_3_Aggregated
PP$Ben_Score_Aggregated <- rowMeans(PP [, c("Benefit_1_AG", "Benefit_2_AG", "Benefit_3_AG")], na.rm=TRUE)
PP$BenAg_Scale <- data.frame(PP$Benefit_1_AG, PP$Benefit_2_AG, PP$Benefit_3_AG)
psych::alpha(data.frame(PP$Benefit_1_AG, PP$Benefit_2_AG, PP$Benefit_3_AG))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_AG, PP$Benefit_2_AG,
## PP$Benefit_3_AG))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.9 0.81 13 0.012 64 26 0.81
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.93 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Benefit_1_AG 0.86 0.86 0.76 0.76 6.3 0.026 NA 0.76
## PP.Benefit_2_AG 0.89 0.90 0.81 0.81 8.5 0.020 NA 0.81
## PP.Benefit_3_AG 0.92 0.92 0.85 0.85 11.7 0.015 NA 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_AG 89 0.95 0.95 0.92 0.89 64 27
## PP.Benefit_2_AG 89 0.93 0.93 0.89 0.85 64 28
## PP.Benefit_3_AG 89 0.92 0.92 0.84 0.81 64 28hist(PP$Ben_Score_Aggregated, main = 'Aggregated Benefit Scale Score')
#Behavioral Intent aggregated variables consist of the first method participants saw on the survey (randomly assigned)
PP$BI_1_AG <-PP$Behav_Intent_1_Aggregated
PP$BI_2_AG <-PP$Behav_Intent_2_Aggregated
PP$BI_3_AG <-PP$Behav_Intent_3_Aggregated
PP$BI_4_AG <-PP$Behav_Intent_4_Aggregated
PP$BI_Score_Aggregated <- rowMeans(PP [, c("BI_1_AG", "BI_2_AG", "BI_3_AG", "BI_4_AG")], na.rm=TRUE)
PP$BIAg_Scale <- data.frame(PP$BI_1_AG, PP$BI_2_AG, PP$BI_3_AG, PP$BI_4_AG)
psych::alpha(data.frame(PP$Benefit_1_AG, PP$Benefit_2_AG, PP$Benefit_3_AG))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_AG, PP$Benefit_2_AG,
## PP$Benefit_3_AG))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.9 0.81 13 0.012 64 26 0.81
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.93 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Benefit_1_AG 0.86 0.86 0.76 0.76 6.3 0.026 NA 0.76
## PP.Benefit_2_AG 0.89 0.90 0.81 0.81 8.5 0.020 NA 0.81
## PP.Benefit_3_AG 0.92 0.92 0.85 0.85 11.7 0.015 NA 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_AG 89 0.95 0.95 0.92 0.89 64 27
## PP.Benefit_2_AG 89 0.93 0.93 0.89 0.85 64 28
## PP.Benefit_3_AG 89 0.92 0.92 0.84 0.81 64 28hist(PP$BI_Score_Aggregated, main = 'Aggregated Behavioral Intent Scale Score')
#Correlation Matrix Data Frame
PP$corrdatascales <- data.frame(PP$Nat_1_Aggregated, PP$Nat_2_Aggregated, PP$Nat_3_Aggregated, PP$Risk_1_AG, PP$Risk_2_AG, PP$Risk_3_AG,PP$Risk_4_AG, PP$Understanding_1_AG,PP$Familiarity_1_AG,PP$Control_1_AG, PP$Benefit_1_AG,PP$Benefit_2_AG, PP$Benefit_3_AG, PP$BI_1_AG, PP$BI_2_AG, PP$BI_3_AG, PP$BI_4_AG, PP$BI_2_AG, PP$ENVS_Score, PP$ATNS_Score, PP$CNS_Score,PP$CCBelief_Score, PP$Collectivism_Score, PP$Individualism_Score, PP$ST_Score, PP$HT_Score)
mydata.corscale = cor(PP$corrdatascales,use="complete.obs")
head(round(mydata.corscale,2))## PP.Nat_1_Aggregated PP.Nat_2_Aggregated PP.Nat_3_Aggregated
## PP.Nat_1_Aggregated 1.00 0.18 -0.18
## PP.Nat_2_Aggregated 0.18 1.00 0.48
## PP.Nat_3_Aggregated -0.18 0.48 1.00
## PP.Risk_1_AG -0.10 -0.51 -0.16
## PP.Risk_2_AG 0.05 -0.45 -0.23
## PP.Risk_3_AG 0.10 -0.46 -0.28
## PP.Risk_1_AG PP.Risk_2_AG PP.Risk_3_AG PP.Risk_4_AG
## PP.Nat_1_Aggregated -0.10 0.05 0.10 -0.06
## PP.Nat_2_Aggregated -0.51 -0.45 -0.46 -0.42
## PP.Nat_3_Aggregated -0.16 -0.23 -0.28 -0.08
## PP.Risk_1_AG 1.00 0.77 0.70 0.77
## PP.Risk_2_AG 0.77 1.00 0.74 0.70
## PP.Risk_3_AG 0.70 0.74 1.00 0.57
## PP.Understanding_1_AG PP.Familiarity_1_AG PP.Control_1_AG
## PP.Nat_1_Aggregated 0.10 0.52 0.32
## PP.Nat_2_Aggregated 0.04 0.02 -0.16
## PP.Nat_3_Aggregated 0.21 -0.11 -0.34
## PP.Risk_1_AG -0.04 0.10 0.04
## PP.Risk_2_AG -0.19 0.16 0.24
## PP.Risk_3_AG 0.02 0.29 0.17
## PP.Benefit_1_AG PP.Benefit_2_AG PP.Benefit_3_AG PP.BI_1_AG
## PP.Nat_1_Aggregated 0.57 0.64 0.48 0.62
## PP.Nat_2_Aggregated 0.19 0.06 0.03 0.20
## PP.Nat_3_Aggregated -0.34 -0.42 -0.45 -0.34
## PP.Risk_1_AG -0.32 -0.26 -0.21 -0.29
## PP.Risk_2_AG -0.11 -0.09 -0.12 -0.17
## PP.Risk_3_AG -0.10 -0.07 -0.09 -0.15
## PP.BI_2_AG PP.BI_3_AG PP.BI_4_AG PP.BI_2_AG.1 PP.ENVS_Score
## PP.Nat_1_Aggregated 0.58 0.62 0.64 0.58 0.32
## PP.Nat_2_Aggregated 0.17 0.18 0.15 0.17 -0.23
## PP.Nat_3_Aggregated -0.24 -0.37 -0.38 -0.24 -0.45
## PP.Risk_1_AG -0.24 -0.30 -0.31 -0.24 0.11
## PP.Risk_2_AG -0.09 -0.13 -0.13 -0.09 0.16
## PP.Risk_3_AG -0.06 -0.16 -0.13 -0.06 0.11
## PP.ATNS_Score PP.CNS_Score PP.CCBelief_Score
## PP.Nat_1_Aggregated 0.17 0.04 0.18
## PP.Nat_2_Aggregated 0.10 0.07 -0.05
## PP.Nat_3_Aggregated 0.04 0.09 -0.15
## PP.Risk_1_AG 0.12 -0.14 0.14
## PP.Risk_2_AG 0.05 -0.16 0.09
## PP.Risk_3_AG 0.00 -0.19 0.13
## PP.Collectivism_Score PP.Individualism_Score PP.ST_Score
## PP.Nat_1_Aggregated 0.22 0.26 0.05
## PP.Nat_2_Aggregated -0.32 -0.16 -0.03
## PP.Nat_3_Aggregated -0.34 -0.44 -0.13
## PP.Risk_1_AG 0.36 0.13 -0.06
## PP.Risk_2_AG 0.29 0.08 -0.14
## PP.Risk_3_AG 0.28 0.01 -0.15
## PP.HT_Score
## PP.Nat_1_Aggregated 0.05
## PP.Nat_2_Aggregated -0.03
## PP.Nat_3_Aggregated -0.13
## PP.Risk_1_AG -0.06
## PP.Risk_2_AG -0.14
## PP.Risk_3_AG -0.15library("Hmisc")
mydata.rcorrscale = rcorr(as.matrix(PP$corrdatascale))
mydata.rcorrscale## PP.Nat_1_Aggregated PP.Nat_2_Aggregated
## PP.Nat_1_Aggregated 1.00 0.13
## PP.Nat_2_Aggregated 0.13 1.00
## PP.Nat_3_Aggregated -0.16 0.50
## PP.Risk_1_AG -0.18 -0.47
## PP.Risk_2_AG -0.01 -0.44
## PP.Risk_3_AG 0.01 -0.44
## PP.Risk_4_AG -0.12 -0.43
## PP.Understanding_1_AG 0.15 0.03
## PP.Familiarity_1_AG 0.55 -0.03
## PP.Control_1_AG 0.36 -0.12
## PP.Benefit_1_AG 0.58 0.19
## PP.Benefit_2_AG 0.64 0.06
## PP.Benefit_3_AG 0.49 0.04
## PP.BI_1_AG 0.63 0.20
## PP.BI_2_AG 0.59 0.17
## PP.BI_3_AG 0.62 0.18
## PP.BI_4_AG 0.64 0.16
## PP.BI_2_AG.1 0.59 0.17
## PP.ENVS_Score 0.30 -0.18
## PP.ATNS_Score 0.15 0.08
## PP.CNS_Score 0.05 0.12
## PP.CCBelief_Score 0.22 0.00
## PP.Collectivism_Score 0.14 -0.26
## PP.Individualism_Score 0.23 -0.19
## PP.ST_Score 0.06 0.01
## PP.HT_Score 0.06 0.01
## PP.Nat_3_Aggregated PP.Risk_1_AG PP.Risk_2_AG
## PP.Nat_1_Aggregated -0.16 -0.18 -0.01
## PP.Nat_2_Aggregated 0.50 -0.47 -0.44
## PP.Nat_3_Aggregated 1.00 -0.17 -0.21
## PP.Risk_1_AG -0.17 1.00 0.78
## PP.Risk_2_AG -0.21 0.78 1.00
## PP.Risk_3_AG -0.24 0.72 0.79
## PP.Risk_4_AG -0.11 0.79 0.74
## PP.Understanding_1_AG 0.17 -0.10 -0.18
## PP.Familiarity_1_AG -0.09 -0.01 0.08
## PP.Control_1_AG -0.31 -0.06 0.10
## PP.Benefit_1_AG -0.33 -0.33 -0.12
## PP.Benefit_2_AG -0.41 -0.27 -0.10
## PP.Benefit_3_AG -0.44 -0.22 -0.13
## PP.BI_1_AG -0.33 -0.31 -0.18
## PP.BI_2_AG -0.23 -0.26 -0.11
## PP.BI_3_AG -0.37 -0.31 -0.14
## PP.BI_4_AG -0.37 -0.32 -0.14
## PP.BI_2_AG.1 -0.23 -0.26 -0.11
## PP.ENVS_Score -0.41 0.08 0.13
## PP.ATNS_Score 0.05 0.09 0.03
## PP.CNS_Score 0.11 -0.17 -0.20
## PP.CCBelief_Score -0.15 0.02 -0.02
## PP.Collectivism_Score -0.31 0.35 0.32
## PP.Individualism_Score -0.46 0.11 0.08
## PP.ST_Score -0.13 -0.09 -0.18
## PP.HT_Score -0.13 -0.09 -0.18
## PP.Risk_3_AG PP.Risk_4_AG PP.Understanding_1_AG
## PP.Nat_1_Aggregated 0.01 -0.12 0.15
## PP.Nat_2_Aggregated -0.44 -0.43 0.03
## PP.Nat_3_Aggregated -0.24 -0.11 0.17
## PP.Risk_1_AG 0.72 0.79 -0.10
## PP.Risk_2_AG 0.79 0.74 -0.18
## PP.Risk_3_AG 1.00 0.64 -0.01
## PP.Risk_4_AG 0.64 1.00 -0.08
## PP.Understanding_1_AG -0.01 -0.08 1.00
## PP.Familiarity_1_AG 0.18 -0.01 0.04
## PP.Control_1_AG 0.04 0.03 -0.14
## PP.Benefit_1_AG -0.11 -0.29 -0.03
## PP.Benefit_2_AG -0.08 -0.19 0.01
## PP.Benefit_3_AG -0.10 -0.19 -0.01
## PP.BI_1_AG -0.16 -0.28 0.07
## PP.BI_2_AG -0.08 -0.21 0.05
## PP.BI_3_AG -0.17 -0.31 0.02
## PP.BI_4_AG -0.13 -0.28 0.03
## PP.BI_2_AG.1 -0.08 -0.21 0.05
## PP.ENVS_Score 0.09 0.13 0.02
## PP.ATNS_Score -0.02 0.04 -0.02
## PP.CNS_Score -0.24 -0.16 0.16
## PP.CCBelief_Score 0.00 0.01 0.00
## PP.Collectivism_Score 0.32 0.32 0.00
## PP.Individualism_Score 0.03 0.04 0.01
## PP.ST_Score -0.20 -0.19 -0.08
## PP.HT_Score -0.20 -0.19 -0.08
## PP.Familiarity_1_AG PP.Control_1_AG PP.Benefit_1_AG
## PP.Nat_1_Aggregated 0.55 0.36 0.58
## PP.Nat_2_Aggregated -0.03 -0.12 0.19
## PP.Nat_3_Aggregated -0.09 -0.31 -0.33
## PP.Risk_1_AG -0.01 -0.06 -0.33
## PP.Risk_2_AG 0.08 0.10 -0.12
## PP.Risk_3_AG 0.18 0.04 -0.11
## PP.Risk_4_AG -0.01 0.03 -0.29
## PP.Understanding_1_AG 0.04 -0.14 -0.03
## PP.Familiarity_1_AG 1.00 0.33 0.39
## PP.Control_1_AG 0.33 1.00 0.36
## PP.Benefit_1_AG 0.39 0.36 1.00
## PP.Benefit_2_AG 0.40 0.47 0.85
## PP.Benefit_3_AG 0.34 0.33 0.81
## PP.BI_1_AG 0.41 0.30 0.87
## PP.BI_2_AG 0.53 0.41 0.79
## PP.BI_3_AG 0.38 0.34 0.86
## PP.BI_4_AG 0.38 0.37 0.88
## PP.BI_2_AG.1 0.53 0.41 0.79
## PP.ENVS_Score 0.15 0.35 0.37
## PP.ATNS_Score 0.23 0.31 0.21
## PP.CNS_Score 0.05 0.22 0.11
## PP.CCBelief_Score 0.17 0.39 0.29
## PP.Collectivism_Score -0.02 0.19 0.31
## PP.Individualism_Score 0.19 0.38 0.46
## PP.ST_Score 0.04 0.25 0.17
## PP.HT_Score 0.04 0.25 0.17
## PP.Benefit_2_AG PP.Benefit_3_AG PP.BI_1_AG PP.BI_2_AG
## PP.Nat_1_Aggregated 0.64 0.49 0.63 0.59
## PP.Nat_2_Aggregated 0.06 0.04 0.20 0.17
## PP.Nat_3_Aggregated -0.41 -0.44 -0.33 -0.23
## PP.Risk_1_AG -0.27 -0.22 -0.31 -0.26
## PP.Risk_2_AG -0.10 -0.13 -0.18 -0.11
## PP.Risk_3_AG -0.08 -0.10 -0.16 -0.08
## PP.Risk_4_AG -0.19 -0.19 -0.28 -0.21
## PP.Understanding_1_AG 0.01 -0.01 0.07 0.05
## PP.Familiarity_1_AG 0.40 0.34 0.41 0.53
## PP.Control_1_AG 0.47 0.33 0.30 0.41
## PP.Benefit_1_AG 0.85 0.81 0.87 0.79
## PP.Benefit_2_AG 1.00 0.76 0.86 0.77
## PP.Benefit_3_AG 0.76 1.00 0.80 0.71
## PP.BI_1_AG 0.86 0.80 1.00 0.88
## PP.BI_2_AG 0.77 0.71 0.88 1.00
## PP.BI_3_AG 0.85 0.83 0.93 0.83
## PP.BI_4_AG 0.86 0.81 0.94 0.86
## PP.BI_2_AG.1 0.77 0.71 0.88 1.00
## PP.ENVS_Score 0.45 0.46 0.36 0.31
## PP.ATNS_Score 0.19 0.19 0.15 0.24
## PP.CNS_Score 0.18 0.19 0.11 0.18
## PP.CCBelief_Score 0.32 0.28 0.22 0.26
## PP.Collectivism_Score 0.36 0.30 0.27 0.21
## PP.Individualism_Score 0.53 0.49 0.47 0.39
## PP.ST_Score 0.22 0.14 0.10 0.11
## PP.HT_Score 0.22 0.14 0.10 0.11
## PP.BI_3_AG PP.BI_4_AG PP.BI_2_AG.1 PP.ENVS_Score
## PP.Nat_1_Aggregated 0.62 0.64 0.59 0.30
## PP.Nat_2_Aggregated 0.18 0.16 0.17 -0.18
## PP.Nat_3_Aggregated -0.37 -0.37 -0.23 -0.41
## PP.Risk_1_AG -0.31 -0.32 -0.26 0.08
## PP.Risk_2_AG -0.14 -0.14 -0.11 0.13
## PP.Risk_3_AG -0.17 -0.13 -0.08 0.09
## PP.Risk_4_AG -0.31 -0.28 -0.21 0.13
## PP.Understanding_1_AG 0.02 0.03 0.05 0.02
## PP.Familiarity_1_AG 0.38 0.38 0.53 0.15
## PP.Control_1_AG 0.34 0.37 0.41 0.35
## PP.Benefit_1_AG 0.86 0.88 0.79 0.37
## PP.Benefit_2_AG 0.85 0.86 0.77 0.45
## PP.Benefit_3_AG 0.83 0.81 0.71 0.46
## PP.BI_1_AG 0.93 0.94 0.88 0.36
## PP.BI_2_AG 0.83 0.86 1.00 0.31
## PP.BI_3_AG 1.00 0.95 0.83 0.41
## PP.BI_4_AG 0.95 1.00 0.86 0.42
## PP.BI_2_AG.1 0.83 0.86 1.00 0.31
## PP.ENVS_Score 0.41 0.42 0.31 1.00
## PP.ATNS_Score 0.18 0.19 0.24 0.21
## PP.CNS_Score 0.13 0.11 0.18 0.40
## PP.CCBelief_Score 0.25 0.22 0.26 0.58
## PP.Collectivism_Score 0.29 0.30 0.21 0.31
## PP.Individualism_Score 0.52 0.49 0.39 0.56
## PP.ST_Score 0.17 0.12 0.11 0.36
## PP.HT_Score 0.17 0.12 0.11 0.36
## PP.ATNS_Score PP.CNS_Score PP.CCBelief_Score
## PP.Nat_1_Aggregated 0.15 0.05 0.22
## PP.Nat_2_Aggregated 0.08 0.12 0.00
## PP.Nat_3_Aggregated 0.05 0.11 -0.15
## PP.Risk_1_AG 0.09 -0.17 0.02
## PP.Risk_2_AG 0.03 -0.20 -0.02
## PP.Risk_3_AG -0.02 -0.24 0.00
## PP.Risk_4_AG 0.04 -0.16 0.01
## PP.Understanding_1_AG -0.02 0.16 0.00
## PP.Familiarity_1_AG 0.23 0.05 0.17
## PP.Control_1_AG 0.31 0.22 0.39
## PP.Benefit_1_AG 0.21 0.11 0.29
## PP.Benefit_2_AG 0.19 0.18 0.32
## PP.Benefit_3_AG 0.19 0.19 0.28
## PP.BI_1_AG 0.15 0.11 0.22
## PP.BI_2_AG 0.24 0.18 0.26
## PP.BI_3_AG 0.18 0.13 0.25
## PP.BI_4_AG 0.19 0.11 0.22
## PP.BI_2_AG.1 0.24 0.18 0.26
## PP.ENVS_Score 0.21 0.40 0.58
## PP.ATNS_Score 1.00 0.38 0.35
## PP.CNS_Score 0.38 1.00 0.47
## PP.CCBelief_Score 0.35 0.47 1.00
## PP.Collectivism_Score 0.13 -0.14 0.23
## PP.Individualism_Score 0.33 0.23 0.46
## PP.ST_Score 0.27 0.39 0.49
## PP.HT_Score 0.27 0.39 0.49
## PP.Collectivism_Score PP.Individualism_Score PP.ST_Score
## PP.Nat_1_Aggregated 0.14 0.23 0.06
## PP.Nat_2_Aggregated -0.26 -0.19 0.01
## PP.Nat_3_Aggregated -0.31 -0.46 -0.13
## PP.Risk_1_AG 0.35 0.11 -0.09
## PP.Risk_2_AG 0.32 0.08 -0.18
## PP.Risk_3_AG 0.32 0.03 -0.20
## PP.Risk_4_AG 0.32 0.04 -0.19
## PP.Understanding_1_AG 0.00 0.01 -0.08
## PP.Familiarity_1_AG -0.02 0.19 0.04
## PP.Control_1_AG 0.19 0.38 0.25
## PP.Benefit_1_AG 0.31 0.46 0.17
## PP.Benefit_2_AG 0.36 0.53 0.22
## PP.Benefit_3_AG 0.30 0.49 0.14
## PP.BI_1_AG 0.27 0.47 0.10
## PP.BI_2_AG 0.21 0.39 0.11
## PP.BI_3_AG 0.29 0.52 0.17
## PP.BI_4_AG 0.30 0.49 0.12
## PP.BI_2_AG.1 0.21 0.39 0.11
## PP.ENVS_Score 0.31 0.56 0.36
## PP.ATNS_Score 0.13 0.33 0.27
## PP.CNS_Score -0.14 0.23 0.39
## PP.CCBelief_Score 0.23 0.46 0.49
## PP.Collectivism_Score 1.00 0.51 0.18
## PP.Individualism_Score 0.51 1.00 0.45
## PP.ST_Score 0.18 0.45 1.00
## PP.HT_Score 0.18 0.45 1.00
## PP.HT_Score
## PP.Nat_1_Aggregated 0.06
## PP.Nat_2_Aggregated 0.01
## PP.Nat_3_Aggregated -0.13
## PP.Risk_1_AG -0.09
## PP.Risk_2_AG -0.18
## PP.Risk_3_AG -0.20
## PP.Risk_4_AG -0.19
## PP.Understanding_1_AG -0.08
## PP.Familiarity_1_AG 0.04
## PP.Control_1_AG 0.25
## PP.Benefit_1_AG 0.17
## PP.Benefit_2_AG 0.22
## PP.Benefit_3_AG 0.14
## PP.BI_1_AG 0.10
## PP.BI_2_AG 0.11
## PP.BI_3_AG 0.17
## PP.BI_4_AG 0.12
## PP.BI_2_AG.1 0.11
## PP.ENVS_Score 0.36
## PP.ATNS_Score 0.27
## PP.CNS_Score 0.39
## PP.CCBelief_Score 0.49
## PP.Collectivism_Score 0.18
## PP.Individualism_Score 0.45
## PP.ST_Score 1.00
## PP.HT_Score 1.00
##
## n
## PP.Nat_1_Aggregated PP.Nat_2_Aggregated
## PP.Nat_1_Aggregated 107 106
## PP.Nat_2_Aggregated 106 106
## PP.Nat_3_Aggregated 107 106
## PP.Risk_1_AG 107 106
## PP.Risk_2_AG 107 106
## PP.Risk_3_AG 106 105
## PP.Risk_4_AG 107 106
## PP.Understanding_1_AG 104 103
## PP.Familiarity_1_AG 107 106
## PP.Control_1_AG 107 106
## PP.Benefit_1_AG 89 89
## PP.Benefit_2_AG 89 89
## PP.Benefit_3_AG 89 89
## PP.BI_1_AG 89 89
## PP.BI_2_AG 89 89
## PP.BI_3_AG 89 89
## PP.BI_4_AG 89 89
## PP.BI_2_AG.1 89 89
## PP.ENVS_Score 105 104
## PP.ATNS_Score 105 104
## PP.CNS_Score 105 104
## PP.CCBelief_Score 105 104
## PP.Collectivism_Score 105 104
## PP.Individualism_Score 105 104
## PP.ST_Score 105 104
## PP.HT_Score 105 104
## PP.Nat_3_Aggregated PP.Risk_1_AG PP.Risk_2_AG
## PP.Nat_1_Aggregated 107 107 107
## PP.Nat_2_Aggregated 106 106 106
## PP.Nat_3_Aggregated 107 107 107
## PP.Risk_1_AG 107 107 107
## PP.Risk_2_AG 107 107 107
## PP.Risk_3_AG 106 106 106
## PP.Risk_4_AG 107 107 107
## PP.Understanding_1_AG 104 104 104
## PP.Familiarity_1_AG 107 107 107
## PP.Control_1_AG 107 107 107
## PP.Benefit_1_AG 89 89 89
## PP.Benefit_2_AG 89 89 89
## PP.Benefit_3_AG 89 89 89
## PP.BI_1_AG 89 89 89
## PP.BI_2_AG 89 89 89
## PP.BI_3_AG 89 89 89
## PP.BI_4_AG 89 89 89
## PP.BI_2_AG.1 89 89 89
## PP.ENVS_Score 105 105 105
## PP.ATNS_Score 105 105 105
## PP.CNS_Score 105 105 105
## PP.CCBelief_Score 105 105 105
## PP.Collectivism_Score 105 105 105
## PP.Individualism_Score 105 105 105
## PP.ST_Score 105 105 105
## PP.HT_Score 105 105 105
## PP.Risk_3_AG PP.Risk_4_AG PP.Understanding_1_AG
## PP.Nat_1_Aggregated 106 107 104
## PP.Nat_2_Aggregated 105 106 103
## PP.Nat_3_Aggregated 106 107 104
## PP.Risk_1_AG 106 107 104
## PP.Risk_2_AG 106 107 104
## PP.Risk_3_AG 106 106 104
## PP.Risk_4_AG 106 107 104
## PP.Understanding_1_AG 104 104 107
## PP.Familiarity_1_AG 106 107 104
## PP.Control_1_AG 106 107 104
## PP.Benefit_1_AG 88 89 88
## PP.Benefit_2_AG 88 89 88
## PP.Benefit_3_AG 88 89 88
## PP.BI_1_AG 88 89 88
## PP.BI_2_AG 88 89 88
## PP.BI_3_AG 88 89 88
## PP.BI_4_AG 88 89 88
## PP.BI_2_AG.1 88 89 88
## PP.ENVS_Score 104 105 102
## PP.ATNS_Score 104 105 102
## PP.CNS_Score 104 105 102
## PP.CCBelief_Score 104 105 102
## PP.Collectivism_Score 104 105 102
## PP.Individualism_Score 104 105 102
## PP.ST_Score 104 105 102
## PP.HT_Score 104 105 102
## PP.Familiarity_1_AG PP.Control_1_AG PP.Benefit_1_AG
## PP.Nat_1_Aggregated 107 107 89
## PP.Nat_2_Aggregated 106 106 89
## PP.Nat_3_Aggregated 107 107 89
## PP.Risk_1_AG 107 107 89
## PP.Risk_2_AG 107 107 89
## PP.Risk_3_AG 106 106 88
## PP.Risk_4_AG 107 107 89
## PP.Understanding_1_AG 104 104 88
## PP.Familiarity_1_AG 107 107 89
## PP.Control_1_AG 107 107 89
## PP.Benefit_1_AG 89 89 89
## PP.Benefit_2_AG 89 89 89
## PP.Benefit_3_AG 89 89 89
## PP.BI_1_AG 89 89 89
## PP.BI_2_AG 89 89 89
## PP.BI_3_AG 89 89 89
## PP.BI_4_AG 89 89 89
## PP.BI_2_AG.1 89 89 89
## PP.ENVS_Score 105 105 87
## PP.ATNS_Score 105 105 87
## PP.CNS_Score 105 105 87
## PP.CCBelief_Score 105 105 87
## PP.Collectivism_Score 105 105 87
## PP.Individualism_Score 105 105 87
## PP.ST_Score 105 105 87
## PP.HT_Score 105 105 87
## PP.Benefit_2_AG PP.Benefit_3_AG PP.BI_1_AG PP.BI_2_AG
## PP.Nat_1_Aggregated 89 89 89 89
## PP.Nat_2_Aggregated 89 89 89 89
## PP.Nat_3_Aggregated 89 89 89 89
## PP.Risk_1_AG 89 89 89 89
## PP.Risk_2_AG 89 89 89 89
## PP.Risk_3_AG 88 88 88 88
## PP.Risk_4_AG 89 89 89 89
## PP.Understanding_1_AG 88 88 88 88
## PP.Familiarity_1_AG 89 89 89 89
## PP.Control_1_AG 89 89 89 89
## PP.Benefit_1_AG 89 89 89 89
## PP.Benefit_2_AG 89 89 89 89
## PP.Benefit_3_AG 89 89 89 89
## PP.BI_1_AG 89 89 89 89
## PP.BI_2_AG 89 89 89 89
## PP.BI_3_AG 89 89 89 89
## PP.BI_4_AG 89 89 89 89
## PP.BI_2_AG.1 89 89 89 89
## PP.ENVS_Score 87 87 87 87
## PP.ATNS_Score 87 87 87 87
## PP.CNS_Score 87 87 87 87
## PP.CCBelief_Score 87 87 87 87
## PP.Collectivism_Score 87 87 87 87
## PP.Individualism_Score 87 87 87 87
## PP.ST_Score 87 87 87 87
## PP.HT_Score 87 87 87 87
## PP.BI_3_AG PP.BI_4_AG PP.BI_2_AG.1 PP.ENVS_Score
## PP.Nat_1_Aggregated 89 89 89 105
## PP.Nat_2_Aggregated 89 89 89 104
## PP.Nat_3_Aggregated 89 89 89 105
## PP.Risk_1_AG 89 89 89 105
## PP.Risk_2_AG 89 89 89 105
## PP.Risk_3_AG 88 88 88 104
## PP.Risk_4_AG 89 89 89 105
## PP.Understanding_1_AG 88 88 88 102
## PP.Familiarity_1_AG 89 89 89 105
## PP.Control_1_AG 89 89 89 105
## PP.Benefit_1_AG 89 89 89 87
## PP.Benefit_2_AG 89 89 89 87
## PP.Benefit_3_AG 89 89 89 87
## PP.BI_1_AG 89 89 89 87
## PP.BI_2_AG 89 89 89 87
## PP.BI_3_AG 89 89 89 87
## PP.BI_4_AG 89 89 89 87
## PP.BI_2_AG.1 89 89 89 87
## PP.ENVS_Score 87 87 87 105
## PP.ATNS_Score 87 87 87 105
## PP.CNS_Score 87 87 87 105
## PP.CCBelief_Score 87 87 87 105
## PP.Collectivism_Score 87 87 87 105
## PP.Individualism_Score 87 87 87 105
## PP.ST_Score 87 87 87 105
## PP.HT_Score 87 87 87 105
## PP.ATNS_Score PP.CNS_Score PP.CCBelief_Score
## PP.Nat_1_Aggregated 105 105 105
## PP.Nat_2_Aggregated 104 104 104
## PP.Nat_3_Aggregated 105 105 105
## PP.Risk_1_AG 105 105 105
## PP.Risk_2_AG 105 105 105
## PP.Risk_3_AG 104 104 104
## PP.Risk_4_AG 105 105 105
## PP.Understanding_1_AG 102 102 102
## PP.Familiarity_1_AG 105 105 105
## PP.Control_1_AG 105 105 105
## PP.Benefit_1_AG 87 87 87
## PP.Benefit_2_AG 87 87 87
## PP.Benefit_3_AG 87 87 87
## PP.BI_1_AG 87 87 87
## PP.BI_2_AG 87 87 87
## PP.BI_3_AG 87 87 87
## PP.BI_4_AG 87 87 87
## PP.BI_2_AG.1 87 87 87
## PP.ENVS_Score 105 105 105
## PP.ATNS_Score 105 105 105
## PP.CNS_Score 105 105 105
## PP.CCBelief_Score 105 105 105
## PP.Collectivism_Score 105 105 105
## PP.Individualism_Score 105 105 105
## PP.ST_Score 105 105 105
## PP.HT_Score 105 105 105
## PP.Collectivism_Score PP.Individualism_Score PP.ST_Score
## PP.Nat_1_Aggregated 105 105 105
## PP.Nat_2_Aggregated 104 104 104
## PP.Nat_3_Aggregated 105 105 105
## PP.Risk_1_AG 105 105 105
## PP.Risk_2_AG 105 105 105
## PP.Risk_3_AG 104 104 104
## PP.Risk_4_AG 105 105 105
## PP.Understanding_1_AG 102 102 102
## PP.Familiarity_1_AG 105 105 105
## PP.Control_1_AG 105 105 105
## PP.Benefit_1_AG 87 87 87
## PP.Benefit_2_AG 87 87 87
## PP.Benefit_3_AG 87 87 87
## PP.BI_1_AG 87 87 87
## PP.BI_2_AG 87 87 87
## PP.BI_3_AG 87 87 87
## PP.BI_4_AG 87 87 87
## PP.BI_2_AG.1 87 87 87
## PP.ENVS_Score 105 105 105
## PP.ATNS_Score 105 105 105
## PP.CNS_Score 105 105 105
## PP.CCBelief_Score 105 105 105
## PP.Collectivism_Score 105 105 105
## PP.Individualism_Score 105 105 105
## PP.ST_Score 105 105 105
## PP.HT_Score 105 105 105
## PP.HT_Score
## PP.Nat_1_Aggregated 105
## PP.Nat_2_Aggregated 104
## PP.Nat_3_Aggregated 105
## PP.Risk_1_AG 105
## PP.Risk_2_AG 105
## PP.Risk_3_AG 104
## PP.Risk_4_AG 105
## PP.Understanding_1_AG 102
## PP.Familiarity_1_AG 105
## PP.Control_1_AG 105
## PP.Benefit_1_AG 87
## PP.Benefit_2_AG 87
## PP.Benefit_3_AG 87
## PP.BI_1_AG 87
## PP.BI_2_AG 87
## PP.BI_3_AG 87
## PP.BI_4_AG 87
## PP.BI_2_AG.1 87
## PP.ENVS_Score 105
## PP.ATNS_Score 105
## PP.CNS_Score 105
## PP.CCBelief_Score 105
## PP.Collectivism_Score 105
## PP.Individualism_Score 105
## PP.ST_Score 105
## PP.HT_Score 105
##
## P
## PP.Nat_1_Aggregated PP.Nat_2_Aggregated
## PP.Nat_1_Aggregated 0.1677
## PP.Nat_2_Aggregated 0.1677
## PP.Nat_3_Aggregated 0.1055 0.0000
## PP.Risk_1_AG 0.0617 0.0000
## PP.Risk_2_AG 0.8788 0.0000
## PP.Risk_3_AG 0.8798 0.0000
## PP.Risk_4_AG 0.2246 0.0000
## PP.Understanding_1_AG 0.1197 0.8020
## PP.Familiarity_1_AG 0.0000 0.7817
## PP.Control_1_AG 0.0001 0.2383
## PP.Benefit_1_AG 0.0000 0.0724
## PP.Benefit_2_AG 0.0000 0.5682
## PP.Benefit_3_AG 0.0000 0.7280
## PP.BI_1_AG 0.0000 0.0546
## PP.BI_2_AG 0.0000 0.1071
## PP.BI_3_AG 0.0000 0.0856
## PP.BI_4_AG 0.0000 0.1373
## PP.BI_2_AG.1 0.0000 0.1071
## PP.ENVS_Score 0.0018 0.0645
## PP.ATNS_Score 0.1260 0.4028
## PP.CNS_Score 0.5971 0.2359
## PP.CCBelief_Score 0.0254 0.9913
## PP.Collectivism_Score 0.1476 0.0077
## PP.Individualism_Score 0.0170 0.0515
## PP.ST_Score 0.5466 0.9302
## PP.HT_Score 0.5466 0.9302
## PP.Nat_3_Aggregated PP.Risk_1_AG PP.Risk_2_AG
## PP.Nat_1_Aggregated 0.1055 0.0617 0.8788
## PP.Nat_2_Aggregated 0.0000 0.0000 0.0000
## PP.Nat_3_Aggregated 0.0783 0.0308
## PP.Risk_1_AG 0.0783 0.0000
## PP.Risk_2_AG 0.0308 0.0000
## PP.Risk_3_AG 0.0134 0.0000 0.0000
## PP.Risk_4_AG 0.2462 0.0000 0.0000
## PP.Understanding_1_AG 0.0812 0.3264 0.0648
## PP.Familiarity_1_AG 0.3342 0.9395 0.4382
## PP.Control_1_AG 0.0011 0.5650 0.2983
## PP.Benefit_1_AG 0.0016 0.0018 0.2666
## PP.Benefit_2_AG 0.0000 0.0107 0.3502
## PP.Benefit_3_AG 0.0000 0.0348 0.2406
## PP.BI_1_AG 0.0017 0.0036 0.0962
## PP.BI_2_AG 0.0285 0.0129 0.3200
## PP.BI_3_AG 0.0004 0.0029 0.2055
## PP.BI_4_AG 0.0004 0.0020 0.2021
## PP.BI_2_AG.1 0.0285 0.0129 0.3200
## PP.ENVS_Score 0.0000 0.4001 0.1754
## PP.ATNS_Score 0.5913 0.3592 0.7531
## PP.CNS_Score 0.2536 0.0869 0.0382
## PP.CCBelief_Score 0.1208 0.8373 0.8161
## PP.Collectivism_Score 0.0011 0.0002 0.0008
## PP.Individualism_Score 0.0000 0.2762 0.3923
## PP.ST_Score 0.1795 0.3703 0.0627
## PP.HT_Score 0.1795 0.3703 0.0627
## PP.Risk_3_AG PP.Risk_4_AG PP.Understanding_1_AG
## PP.Nat_1_Aggregated 0.8798 0.2246 0.1197
## PP.Nat_2_Aggregated 0.0000 0.0000 0.8020
## PP.Nat_3_Aggregated 0.0134 0.2462 0.0812
## PP.Risk_1_AG 0.0000 0.0000 0.3264
## PP.Risk_2_AG 0.0000 0.0000 0.0648
## PP.Risk_3_AG 0.0000 0.9196
## PP.Risk_4_AG 0.0000 0.4317
## PP.Understanding_1_AG 0.9196 0.4317
## PP.Familiarity_1_AG 0.0727 0.9467 0.7066
## PP.Control_1_AG 0.6671 0.7724 0.1598
## PP.Benefit_1_AG 0.3064 0.0061 0.7821
## PP.Benefit_2_AG 0.4456 0.0815 0.9503
## PP.Benefit_3_AG 0.3738 0.0701 0.9267
## PP.BI_1_AG 0.1443 0.0075 0.5075
## PP.BI_2_AG 0.4757 0.0526 0.6378
## PP.BI_3_AG 0.1184 0.0035 0.8337
## PP.BI_4_AG 0.2105 0.0072 0.7686
## PP.BI_2_AG.1 0.4757 0.0526 0.6378
## PP.ENVS_Score 0.3672 0.1768 0.8316
## PP.ATNS_Score 0.8755 0.7019 0.8419
## PP.CNS_Score 0.0144 0.1135 0.1079
## PP.CCBelief_Score 0.9610 0.9326 0.9770
## PP.Collectivism_Score 0.0011 0.0009 0.9723
## PP.Individualism_Score 0.7970 0.7084 0.8993
## PP.ST_Score 0.0430 0.0508 0.4092
## PP.HT_Score 0.0430 0.0508 0.4092
## PP.Familiarity_1_AG PP.Control_1_AG PP.Benefit_1_AG
## PP.Nat_1_Aggregated 0.0000 0.0001 0.0000
## PP.Nat_2_Aggregated 0.7817 0.2383 0.0724
## PP.Nat_3_Aggregated 0.3342 0.0011 0.0016
## PP.Risk_1_AG 0.9395 0.5650 0.0018
## PP.Risk_2_AG 0.4382 0.2983 0.2666
## PP.Risk_3_AG 0.0727 0.6671 0.3064
## PP.Risk_4_AG 0.9467 0.7724 0.0061
## PP.Understanding_1_AG 0.7066 0.1598 0.7821
## PP.Familiarity_1_AG 0.0005 0.0002
## PP.Control_1_AG 0.0005 0.0006
## PP.Benefit_1_AG 0.0002 0.0006
## PP.Benefit_2_AG 0.0001 0.0000 0.0000
## PP.Benefit_3_AG 0.0012 0.0018 0.0000
## PP.BI_1_AG 0.0000 0.0042 0.0000
## PP.BI_2_AG 0.0000 0.0000 0.0000
## PP.BI_3_AG 0.0002 0.0011 0.0000
## PP.BI_4_AG 0.0003 0.0004 0.0000
## PP.BI_2_AG.1 0.0000 0.0000 0.0000
## PP.ENVS_Score 0.1386 0.0002 0.0003
## PP.ATNS_Score 0.0164 0.0015 0.0494
## PP.CNS_Score 0.6209 0.0220 0.3186
## PP.CCBelief_Score 0.0879 0.0000 0.0059
## PP.Collectivism_Score 0.8489 0.0572 0.0034
## PP.Individualism_Score 0.0475 0.0000 0.0000
## PP.ST_Score 0.6670 0.0102 0.1063
## PP.HT_Score 0.6670 0.0102 0.1063
## PP.Benefit_2_AG PP.Benefit_3_AG PP.BI_1_AG PP.BI_2_AG
## PP.Nat_1_Aggregated 0.0000 0.0000 0.0000 0.0000
## PP.Nat_2_Aggregated 0.5682 0.7280 0.0546 0.1071
## PP.Nat_3_Aggregated 0.0000 0.0000 0.0017 0.0285
## PP.Risk_1_AG 0.0107 0.0348 0.0036 0.0129
## PP.Risk_2_AG 0.3502 0.2406 0.0962 0.3200
## PP.Risk_3_AG 0.4456 0.3738 0.1443 0.4757
## PP.Risk_4_AG 0.0815 0.0701 0.0075 0.0526
## PP.Understanding_1_AG 0.9503 0.9267 0.5075 0.6378
## PP.Familiarity_1_AG 0.0001 0.0012 0.0000 0.0000
## PP.Control_1_AG 0.0000 0.0018 0.0042 0.0000
## PP.Benefit_1_AG 0.0000 0.0000 0.0000 0.0000
## PP.Benefit_2_AG 0.0000 0.0000 0.0000
## PP.Benefit_3_AG 0.0000 0.0000 0.0000
## PP.BI_1_AG 0.0000 0.0000 0.0000
## PP.BI_2_AG 0.0000 0.0000 0.0000
## PP.BI_3_AG 0.0000 0.0000 0.0000 0.0000
## PP.BI_4_AG 0.0000 0.0000 0.0000 0.0000
## PP.BI_2_AG.1 0.0000 0.0000 0.0000 0.0000
## PP.ENVS_Score 0.0000 0.0000 0.0006 0.0032
## PP.ATNS_Score 0.0777 0.0792 0.1710 0.0276
## PP.CNS_Score 0.0867 0.0757 0.3049 0.1028
## PP.CCBelief_Score 0.0027 0.0088 0.0452 0.0153
## PP.Collectivism_Score 0.0006 0.0043 0.0103 0.0469
## PP.Individualism_Score 0.0000 0.0000 0.0000 0.0002
## PP.ST_Score 0.0387 0.1873 0.3747 0.3245
## PP.HT_Score 0.0387 0.1873 0.3747 0.3245
## PP.BI_3_AG PP.BI_4_AG PP.BI_2_AG.1 PP.ENVS_Score
## PP.Nat_1_Aggregated 0.0000 0.0000 0.0000 0.0018
## PP.Nat_2_Aggregated 0.0856 0.1373 0.1071 0.0645
## PP.Nat_3_Aggregated 0.0004 0.0004 0.0285 0.0000
## PP.Risk_1_AG 0.0029 0.0020 0.0129 0.4001
## PP.Risk_2_AG 0.2055 0.2021 0.3200 0.1754
## PP.Risk_3_AG 0.1184 0.2105 0.4757 0.3672
## PP.Risk_4_AG 0.0035 0.0072 0.0526 0.1768
## PP.Understanding_1_AG 0.8337 0.7686 0.6378 0.8316
## PP.Familiarity_1_AG 0.0002 0.0003 0.0000 0.1386
## PP.Control_1_AG 0.0011 0.0004 0.0000 0.0002
## PP.Benefit_1_AG 0.0000 0.0000 0.0000 0.0003
## PP.Benefit_2_AG 0.0000 0.0000 0.0000 0.0000
## PP.Benefit_3_AG 0.0000 0.0000 0.0000 0.0000
## PP.BI_1_AG 0.0000 0.0000 0.0000 0.0006
## PP.BI_2_AG 0.0000 0.0000 0.0000 0.0032
## PP.BI_3_AG 0.0000 0.0000 0.0000
## PP.BI_4_AG 0.0000 0.0000 0.0000
## PP.BI_2_AG.1 0.0000 0.0000 0.0032
## PP.ENVS_Score 0.0000 0.0000 0.0032
## PP.ATNS_Score 0.1036 0.0807 0.0276 0.0307
## PP.CNS_Score 0.2147 0.3219 0.1028 0.0000
## PP.CCBelief_Score 0.0197 0.0410 0.0153 0.0000
## PP.Collectivism_Score 0.0063 0.0044 0.0469 0.0013
## PP.Individualism_Score 0.0000 0.0000 0.0002 0.0000
## PP.ST_Score 0.1074 0.2539 0.3245 0.0002
## PP.HT_Score 0.1074 0.2539 0.3245 0.0002
## PP.ATNS_Score PP.CNS_Score PP.CCBelief_Score
## PP.Nat_1_Aggregated 0.1260 0.5971 0.0254
## PP.Nat_2_Aggregated 0.4028 0.2359 0.9913
## PP.Nat_3_Aggregated 0.5913 0.2536 0.1208
## PP.Risk_1_AG 0.3592 0.0869 0.8373
## PP.Risk_2_AG 0.7531 0.0382 0.8161
## PP.Risk_3_AG 0.8755 0.0144 0.9610
## PP.Risk_4_AG 0.7019 0.1135 0.9326
## PP.Understanding_1_AG 0.8419 0.1079 0.9770
## PP.Familiarity_1_AG 0.0164 0.6209 0.0879
## PP.Control_1_AG 0.0015 0.0220 0.0000
## PP.Benefit_1_AG 0.0494 0.3186 0.0059
## PP.Benefit_2_AG 0.0777 0.0867 0.0027
## PP.Benefit_3_AG 0.0792 0.0757 0.0088
## PP.BI_1_AG 0.1710 0.3049 0.0452
## PP.BI_2_AG 0.0276 0.1028 0.0153
## PP.BI_3_AG 0.1036 0.2147 0.0197
## PP.BI_4_AG 0.0807 0.3219 0.0410
## PP.BI_2_AG.1 0.0276 0.1028 0.0153
## PP.ENVS_Score 0.0307 0.0000 0.0000
## PP.ATNS_Score 0.0000 0.0003
## PP.CNS_Score 0.0000 0.0000
## PP.CCBelief_Score 0.0003 0.0000
## PP.Collectivism_Score 0.1933 0.1418 0.0204
## PP.Individualism_Score 0.0007 0.0181 0.0000
## PP.ST_Score 0.0052 0.0000 0.0000
## PP.HT_Score 0.0052 0.0000 0.0000
## PP.Collectivism_Score PP.Individualism_Score PP.ST_Score
## PP.Nat_1_Aggregated 0.1476 0.0170 0.5466
## PP.Nat_2_Aggregated 0.0077 0.0515 0.9302
## PP.Nat_3_Aggregated 0.0011 0.0000 0.1795
## PP.Risk_1_AG 0.0002 0.2762 0.3703
## PP.Risk_2_AG 0.0008 0.3923 0.0627
## PP.Risk_3_AG 0.0011 0.7970 0.0430
## PP.Risk_4_AG 0.0009 0.7084 0.0508
## PP.Understanding_1_AG 0.9723 0.8993 0.4092
## PP.Familiarity_1_AG 0.8489 0.0475 0.6670
## PP.Control_1_AG 0.0572 0.0000 0.0102
## PP.Benefit_1_AG 0.0034 0.0000 0.1063
## PP.Benefit_2_AG 0.0006 0.0000 0.0387
## PP.Benefit_3_AG 0.0043 0.0000 0.1873
## PP.BI_1_AG 0.0103 0.0000 0.3747
## PP.BI_2_AG 0.0469 0.0002 0.3245
## PP.BI_3_AG 0.0063 0.0000 0.1074
## PP.BI_4_AG 0.0044 0.0000 0.2539
## PP.BI_2_AG.1 0.0469 0.0002 0.3245
## PP.ENVS_Score 0.0013 0.0000 0.0002
## PP.ATNS_Score 0.1933 0.0007 0.0052
## PP.CNS_Score 0.1418 0.0181 0.0000
## PP.CCBelief_Score 0.0204 0.0000 0.0000
## PP.Collectivism_Score 0.0000 0.0633
## PP.Individualism_Score 0.0000 0.0000
## PP.ST_Score 0.0633 0.0000
## PP.HT_Score 0.0633 0.0000 0.0000
## PP.HT_Score
## PP.Nat_1_Aggregated 0.5466
## PP.Nat_2_Aggregated 0.9302
## PP.Nat_3_Aggregated 0.1795
## PP.Risk_1_AG 0.3703
## PP.Risk_2_AG 0.0627
## PP.Risk_3_AG 0.0430
## PP.Risk_4_AG 0.0508
## PP.Understanding_1_AG 0.4092
## PP.Familiarity_1_AG 0.6670
## PP.Control_1_AG 0.0102
## PP.Benefit_1_AG 0.1063
## PP.Benefit_2_AG 0.0387
## PP.Benefit_3_AG 0.1873
## PP.BI_1_AG 0.3747
## PP.BI_2_AG 0.3245
## PP.BI_3_AG 0.1074
## PP.BI_4_AG 0.2539
## PP.BI_2_AG.1 0.3245
## PP.ENVS_Score 0.0002
## PP.ATNS_Score 0.0052
## PP.CNS_Score 0.0000
## PP.CCBelief_Score 0.0000
## PP.Collectivism_Score 0.0633
## PP.Individualism_Score 0.0000
## PP.ST_Score 0.0000
## PP.HT_Scorelibrary(corrplot)## corrplot 0.92 loadedcorrplot(mydata.corscale, method="color")
corrplot(mydata.corscale, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Predictions - Risk/Naturalness by Burger Type
#1. GFFB
m.a1 <- lm(Risk_Score_GFFB ~ Naturalness_Score_GFFB, data = PP)
mcSummary(m.a1)## lm(formula = Risk_Score_GFFB ~ Naturalness_Score_GFFB, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 15567.78 1 15567.779 0.289 20.779 0
## Error 38209.27 51 749.201
## Corr Total 53777.05 52 1034.174
##
## RMSE AdjEtaSq
## 27.372 0.276
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 90.314 10.052 8.985 60483.97 0.613 NA 70.135 110.493 0
## Naturalness_Score_GFFB -0.828 0.182 -4.558 15567.78 0.289 NA -1.192 -0.463 0 m.c1 <- lm(Risk_Score_GFFB ~ 1, data = PP)
mcSummary(m.c1)## lm(formula = Risk_Score_GFFB ~ 1, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 53777.05 52 1034.174
## Corr Total 53777.05 52 1034.174
##
## RMSE AdjEtaSq
## 32.159 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 47.821 4.417 10.826 121201.7 0.693 NA 38.957 56.685 0 modelCompare(m.c1, m.a1)## SSE (Compact) = 53777.05
## SSE (Augmented) = 38209.27
## Delta R-Squared = 0.2894874
## Partial Eta-Squared (PRE) = 0.2894874
## F(1,51) = 20.77917, p = 3.251169e-05 #2. GFPRB
m.a2 <- lm(Risk_Score_GFPRB ~ Naturalness_Score_GFPRB, data = PP)
mcSummary(m.a2)## lm(formula = Risk_Score_GFPRB ~ Naturalness_Score_GFPRB, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 23449.39 1 23449.391 0.456 42.675 0
## Error 28023.91 51 549.488
## Corr Total 51473.30 52 989.871
##
## RMSE AdjEtaSq
## 23.441 0.445
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 106.978 9.768 10.952 65911.06 0.702 NA 87.369 126.588
## Naturalness_Score_GFPRB -1.006 0.154 -6.533 23449.39 0.456 NA -1.315 -0.697
## p
## (Intercept) 0
## Naturalness_Score_GFPRB 0 m.c2 <- lm(Risk_Score_GFPRB ~ 1, data = PP)
mcSummary(m.c2)## lm(formula = Risk_Score_GFPRB ~ 1, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.0 0 Inf 0
## Error 51473.3 52 989.871
## Corr Total 51473.3 52 989.871
##
## RMSE AdjEtaSq
## 31.462 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 46.736 4.322 10.814 115764.7 0.692 NA 38.064 55.408 0 modelCompare(m.c2, m.a2)## SSE (Compact) = 51473.3
## SSE (Augmented) = 28023.91
## Delta R-Squared = 0.4555641
## Partial Eta-Squared (PRE) = 0.4555641
## F(1,51) = 42.67495, p = 2.979068e-08 #3. CBB
m.a3 <- lm(Risk_Score_CBB ~ Naturalness_Score_CBB, data = PP)
mcSummary(m.a3)## lm(formula = Risk_Score_CBB ~ Naturalness_Score_CBB, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 2876.748 1 2876.748 0.077 3.524 0.067
## Error 34287.450 42 816.368
## Corr Total 37164.199 43 864.284
##
## RMSE AdjEtaSq
## 28.572 0.055
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 69.072 9.719 7.107 41228.589 0.546 NA 49.457 88.686
## Naturalness_Score_CBB -0.469 0.250 -1.877 2876.748 0.077 NA -0.973 0.035
## p
## (Intercept) 0.000
## Naturalness_Score_CBB 0.067 m.c3 <- lm(Risk_Score_CBB ~ 1, data = PP)
mcSummary(m.c3)## lm(formula = Risk_Score_CBB ~ 1, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.0 0 Inf 0
## Error 37164.2 43 864.284
## Corr Total 37164.2 43 864.284
##
## RMSE AdjEtaSq
## 29.399 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 52.716 4.432 11.894 122274.6 0.767 NA 43.778 61.654 0 modelCompare(m.c3, m.a3)## SSE (Compact) = 37164.2
## SSE (Augmented) = 34287.45
## Delta R-Squared = 0.07740644
## Partial Eta-Squared (PRE) = 0.07740644
## F(1,42) = 3.523838, p = 0.06744953 #4. PBPB
m.a4 <- lm(Risk_Score_PBPB ~ Naturalness_Score_PBPB, data = PP)
mcSummary(m.a4)## lm(formula = Risk_Score_PBPB ~ Naturalness_Score_PBPB, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 8885.36 1 8885.360 0.184 10.815 0.002
## Error 39436.82 48 821.600
## Corr Total 48322.18 49 986.167
##
## RMSE AdjEtaSq
## 28.664 0.167
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 87.098 13.405 6.498 34687.43 0.468 NA 60.146 114.049
## Naturalness_Score_PBPB -0.822 0.250 -3.289 8885.36 0.184 NA -1.325 -0.319
## p
## (Intercept) 0.000
## Naturalness_Score_PBPB 0.002 m.c4 <- lm(Risk_Score_PBPB ~ 1, data = PP)
mcSummary(m.c4)## lm(formula = Risk_Score_PBPB ~ 1, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 48322.18 49 986.167
## Corr Total 48322.18 49 986.167
##
## RMSE AdjEtaSq
## 31.403 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 45.08 4.441 10.151 101610.3 0.678 NA 36.155 54.005 0 modelCompare(m.c4, m.a4)## SSE (Compact) = 48322.18
## SSE (Augmented) = 39436.82
## Delta R-Squared = 0.1838775
## Partial Eta-Squared (PRE) = 0.1838775
## F(1,48) = 10.8147, p = 0.001889587 #5. PBFB
m.a5 <- lm(Risk_Score_PBFB ~ Naturalness_Score_PBFB, data = PP)
mcSummary(m.a5)## lm(formula = Risk_Score_PBFB ~ Naturalness_Score_PBFB, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 1496.651 1 1496.651 0.032 1.893 0.174
## Error 45856.333 58 790.626
## Corr Total 47352.983 59 802.593
##
## RMSE AdjEtaSq
## 28.118 0.015
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 61.453 10.215 6.016 28616.656 0.384 NA 41.006 81.900
## Naturalness_Score_PBFB -0.312 0.227 -1.376 1496.651 0.032 NA -0.767 0.142
## p
## (Intercept) 0.000
## Naturalness_Score_PBFB 0.174 m.c5 <- lm(Risk_Score_PBFB ~ 1, data = PP)
mcSummary(m.c5)## lm(formula = Risk_Score_PBFB ~ 1, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 47352.98 59 802.593
## Corr Total 47352.98 59 802.593
##
## RMSE AdjEtaSq
## 28.33 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 48.317 3.657 13.211 140070 0.747 NA 40.998 55.635 0 modelCompare(m.c5, m.a5)## SSE (Compact) = 47352.98
## SSE (Augmented) = 45856.33
## Delta R-Squared = 0.03160626
## Partial Eta-Squared (PRE) = 0.03160626
## F(1,58) = 1.892993, p = 0.1741546 #6. VB
m.a6 <- lm(Risk_Score_VB ~ Naturalness_Score_VB, data = PP)
mcSummary(m.a6)## lm(formula = Risk_Score_VB ~ Naturalness_Score_VB, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 6766.344 1 6766.344 0.135 8.416 0.005
## Error 43416.044 54 804.001
## Corr Total 50182.388 55 912.407
##
## RMSE AdjEtaSq
## 28.355 0.119
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5
## (Intercept) 68.995 11.798 5.848 27494.062 0.388 NA 45.340 92.649
## Naturalness_Score_VB -0.645 0.222 -2.901 6766.344 0.135 NA -1.091 -0.199
## p
## (Intercept) 0.000
## Naturalness_Score_VB 0.005 m.c6 <- lm(Risk_Score_VB ~ 1, data = PP)
mcSummary(m.c6)## lm(formula = Risk_Score_VB ~ 1, data = PP)
##
## Omnibus ANOVA
## SS df MS EtaSq F p
## Model 0.00 0 Inf 0
## Error 50182.39 55 912.407
## Corr Total 50182.39 55 912.407
##
## RMSE AdjEtaSq
## 30.206 0
##
## Coefficients
## Est StErr t SSR(3) EtaSq tol CI_2.5 CI_97.5 p
## (Intercept) 36.58 4.036 9.062 74934.86 0.599 NA 28.491 44.67 0 modelCompare(m.c6, m.a6)## SSE (Compact) = 50182.39
## SSE (Augmented) = 43416.04
## Delta R-Squared = 0.134835
## Partial Eta-Squared (PRE) = 0.134835
## F(1,54) = 8.415843, p = 0.005371584