Tendency to Perceive Technology as Natural Scale
Sarah Gonzalez Coffin | University of Colorado Boulder
2023-11-22
Participants
#Data File
N <- read.csv("full.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))
#Sample Size: Number of participants (rows)
nrow(N)## [1] 40Demographics
#Age range
range(N$Age, na.rm = T)## [1] 17 94#Average age
mean(N$Age, na.rm = T)## [1] 36.65789#Standard deviation of age
sd(N$Age, na.rm = T)## [1] 18.76062#Gender frequencies
table(N$Gender)##
## 1 2 3
## 22 15 2#Ethnicity
table(N$Ethnicity)##
## 1 2 3 6 7
## 1 2 6 29 1describe(N$Ethnicity)## N$Ethnicity
## n missing distinct Info Mean Gmd
## 39 1 5 0.585 5.231 1.341
##
## Value 1 2 3 6 7
## Frequency 1 2 6 29 1
## Proportion 0.026 0.051 0.154 0.744 0.026
##
## For the frequency table, variable is rounded to the nearest 0# Education: Please indicate the highest level of education you have completed (1 = Elementary/Grammar School, 2 = Middle School, 3 = High School or Equivalent, 4 = Vocational/Technical School (2 years), 5 = Some College, 6 = College or University (4 years), 7 = Master's Degree (MS, MA, MBA, etc.), 8 = Doctoral Degree (PhD), 9 = Professional Degree (MD, JD, etc.).
N$EdNum <- as.numeric(as.character(N$EDU))
N$EDU1 <- factor(N$EdNum, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
labels = c("Elementary/Grammar School", "Middle School", "High School or Equivalent", "Vocational/Technical School (2 years)", "Some College", "College or University (4 years)", "Master's Degree (MS, MA, MBA, etc.)", "Doctoral Degree (PhD)", "Doctoral Degree (PhD)", "Other"))
table(N$EDU1)##
## Elementary/Grammar School Middle School
## 0 0
## High School or Equivalent Vocational/Technical School (2 years)
## 4 0
## Some College College or University (4 years)
## 8 15
## Master's Degree (MS, MA, MBA, etc.) Doctoral Degree (PhD)
## 9 3
## Other
## 0describe(N$EdNum)## N$EdNum
## n missing distinct Info Mean Gmd
## 39 1 5 0.921 5.872 1.398
##
## Value 3 5 6 7 8
## Frequency 4 8 15 9 3
## Proportion 0.103 0.205 0.385 0.231 0.077
##
## For the frequency table, variable is rounded to the nearest 0hist(N$EdNum)
#Socioeconomic Status
describe(N$SES)## N$SES
## n missing distinct Info Mean Gmd .05 .10
## 39 1 10 0.973 5.769 2.777 1.0 1.8
## .25 .50 .75 .90 .95
## 5.0 6.0 7.5 9.0 9.0
##
## Value 1 2 3 4 5 6 7 8 9 10
## Frequency 4 1 1 3 10 3 7 4 5 1
## Proportion 0.103 0.026 0.026 0.077 0.256 0.077 0.179 0.103 0.128 0.026
##
## For the frequency table, variable is rounded to the nearest 0sd(N$SES, na.rm = TRUE)## [1] 2.454443N$SES1 <- factor(N$SES, levels = c(2, 3, 4, 5, 6, 7,8, 9, 10),
labels = c("Under $10,000", "$10,000-$19,999", "$20,000-$29,999", "$30,000-$39,999", "$40,000-$49,999", "$50,000-$74,999", "$75,000-$99,999", "$100,000-$149,999", "$150,000 or more"))
table(N$SES1)##
## Under $10,000 $10,000-$19,999 $20,000-$29,999 $30,000-$39,999
## 1 1 3 10
## $40,000-$49,999 $50,000-$74,999 $75,000-$99,999 $100,000-$149,999
## 3 7 4 5
## $150,000 or more
## 1hist(N$SES, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
# Living Environment (1 = urban, 2 = suburban, 3 = rural)
table(N$Region)##
## 1 2 3
## 9 24 6#Political Ideology
##Which of the following best describes your political orientation? ( 1 = Strongly Conservative to 7 = Strongly Liberal)
describe(N$ContoLib)## N$ContoLib
## n missing distinct Info Mean Gmd
## 39 1 7 0.956 5.128 1.919
##
## Value 1 2 3 4 5 6 7
## Frequency 2 2 2 6 9 7 11
## Proportion 0.051 0.051 0.051 0.154 0.231 0.179 0.282
##
## For the frequency table, variable is rounded to the nearest 0N$IDEO = as.numeric(recode_factor(N$ContoLib,'1'= "3",'2'= "2",'3'= "1",
'4'= "0",'5'= "-1", '6'= "-2", '7'= "-3"))
describe(N$IDEO)## N$IDEO
## n missing distinct Info Mean Gmd
## 39 1 7 0.956 5.128 1.919
##
## Value 1 2 3 4 5 6 7
## Frequency 2 2 2 6 9 7 11
## Proportion 0.051 0.051 0.051 0.154 0.231 0.179 0.282
##
## For the frequency table, variable is rounded to the nearest 0hist(N$IDEO , main = 'Political Ideology')
Tendency to Perceive Technology as Natural Scale
#Recode 6-12 coding scheme to traditional 1-7 Likert Scale
N$TPTNS_1RECODE = recode(N$TPTNS_1,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'= 6, '12'=7)
N$TPTNS_2RECODE = recode(N$TPTNS_2,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_3RRECODE = recode(N$TPTNS_3R,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_4RECODE = recode(N$TPTNS_4,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_5RRECODE = recode(N$TPTNS_5R,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_6RECODE = recode(N$TPTNS_6,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_7RECODE = recode(N$TPTNS_7R,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_8RECODE = recode(N$TPTNS_8,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_9RRECODE = recode(N$TPTNS_9R,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_10RECODE = recode(N$TPTNS_10,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_11RECODE = recode(N$TPTNS_11,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_12RRECODE = recode(N$TPTNS_12R,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_13RECODE = recode(N$TPTNS_13,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_14RECODE = recode(N$TPTNS_14,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
N$TPTNS_15RRECODE = recode(N$TPTNS_15R,'6'=1,'7'=2,'8'=3,
'9'=4,'10'= 5, '11'=6, '12'=7)
#Now make sure that all items on the TPTNS are numeric and rename them
N$T1 <- as.numeric(as.character(N$TPTNS_1RECODE))
N$T2 <- as.numeric(as.character(N$TPTNS_2RECODE))
N$T3R <- as.numeric(as.character(N$TPTNS_3RRECODE))
N$T4 <- as.numeric(as.character(N$TPTNS_4RECODE))
N$T5R <- as.numeric(as.character(N$TPTNS_5RRECODE))
N$T6 <- as.numeric(as.character(N$TPTNS_6RECODE))
N$T7R <- as.numeric(as.character(N$TPTNS_7RECODE))
N$T8 <- as.numeric(as.character(N$TPTNS_8RECODE))
N$T9R <- as.numeric(as.character(N$TPTNS_9RRECODE))
N$T10 <- as.numeric(as.character(N$TPTNS_10RECODE))
N$T11 <- as.numeric(as.character(N$TPTNS_11RECODE))
N$T12R <- as.numeric(as.character(N$TPTNS_12RRECODE))
N$T13 <- as.numeric(as.character(N$TPTNS_13RECODE))
N$T14 <- as.numeric(as.character(N$TPTNS_14RECODE))
N$T15R <- as.numeric(as.character(N$TPTNS_15RRECODE))
#Histograms
hist(N$T1, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T2, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T3R, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T4, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T5R, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T6, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T7R, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T8, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T9R, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T10, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T11, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T12R, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T13, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T14, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
hist(N$T15R, na.rm = TRUE)## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter## Warning in axis(1, ...): "na.rm" is not a graphical parameter## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter
#Tendency to Perceive Technology as Natural Score
N$TPTNS_Score <- rowMeans(N [, c("T1", "T2", "T3R", "T4", "T5R", "T6", "T7R", "T8", "T9R", "T10", "T11", "T12R", "T13", "T14", "T15R")], na.rm=TRUE)
describe(N$TPTNS_Score)## N$TPTNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 40 0 30 0.998 3.732 0.9832 2.383 2.593
## .25 .50 .75 .90 .95
## 3.283 3.833 4.017 4.940 5.410
##
## lowest : 1.66667 2.06667 2.4 2.53333 2.6
## highest: 4.6 4.93333 5 5.4 5.6sd(N$TPTNS_Score, na.rm= TRUE)## [1] 0.8821739hist(N$TPTNS_Score)
# TPTNS Scale
N$TPTNS_Scale <- data.frame(N$T1, N$T2, N$T3R, N$T4, N$T5R, N$T6, N$T7R, N$T8, N$T9R, N$T10, N$T11, N$T12R, N$T13, N$T14, N$T15R)
describe(N$TPTNS_Scale)## N$TPTNS_Scale
##
## 15 Variables 40 Observations
## --------------------------------------------------------------------------------
## N.T1
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.45 2.018
##
## Value 1 2 3 4 5 6 7
## Frequency 4 13 5 6 5 5 2
## Proportion 0.100 0.325 0.125 0.150 0.125 0.125 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T2
## n missing distinct Info Mean Gmd
## 40 0 6 0.924 4.925 1.429
##
## Value 2 3 4 5 6 7
## Frequency 4 1 7 12 14 2
## Proportion 0.100 0.025 0.175 0.300 0.350 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T3R
## n missing distinct Info Mean Gmd
## 40 0 4 0.885 2.25 0.9795
##
## Value 1 2 3 4
## Frequency 8 18 10 4
## Proportion 0.20 0.45 0.25 0.10
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T4
## n missing distinct Info Mean Gmd
## 40 0 6 0.963 4.5 1.944
##
## Value 2 3 4 5 6 7
## Frequency 7 5 9 4 10 5
## Proportion 0.175 0.125 0.225 0.100 0.250 0.125
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T5R
## n missing distinct Info Mean Gmd
## 40 0 7 0.942 3.075 1.786
##
## Value 1 2 3 4 5 6 7
## Frequency 5 14 7 8 1 3 2
## Proportion 0.125 0.350 0.175 0.200 0.025 0.075 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T6
## n missing distinct Info Mean Gmd
## 40 0 7 0.951 4.75 2.005
##
## Value 1 2 3 4 5 6 7
## Frequency 3 4 3 3 9 13 5
## Proportion 0.075 0.100 0.075 0.075 0.225 0.325 0.125
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T7R
## n missing distinct Info Mean Gmd
## 40 0 7 0.962 4.5 1.946
##
## Value 1 2 3 4 5 6 7
## Frequency 2 4 5 9 5 11 4
## Proportion 0.050 0.100 0.125 0.225 0.125 0.275 0.100
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T8
## n missing distinct Info Mean Gmd
## 40 0 7 0.947 4.45 1.992
##
## Value 1 2 3 4 5 6 7
## Frequency 3 5 4 5 7 14 2
## Proportion 0.075 0.125 0.100 0.125 0.175 0.350 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T9R
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.675 1.722
##
## Value 1 2 3 4 5 6 7
## Frequency 2 7 12 8 4 6 1
## Proportion 0.050 0.175 0.300 0.200 0.100 0.150 0.025
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T10
## n missing distinct Info Mean Gmd
## 40 0 6 0.947 4.5 1.638
##
## Value 2 3 4 5 6 7
## Frequency 7 1 10 11 9 2
## Proportion 0.175 0.025 0.250 0.275 0.225 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T11
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.575 2.206
##
## Value 1 2 3 4 5 6 7
## Frequency 4 13 6 3 6 3 5
## Proportion 0.100 0.325 0.150 0.075 0.150 0.075 0.125
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T12R
## n missing distinct Info Mean Gmd
## 40 0 6 0.93 2.9 1.869
##
## Value 1 2 3 4 6 7
## Frequency 7 15 9 3 2 4
## Proportion 0.175 0.375 0.225 0.075 0.050 0.100
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T13
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.4 2.351
##
## Value 1 2 3 4 5 6 7
## Frequency 8 12 4 2 3 8 3
## Proportion 0.200 0.300 0.100 0.050 0.075 0.200 0.075
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T14
## n missing distinct Info Mean Gmd
## 40 0 7 0.934 2.775 1.714
##
## Value 1 2 3 4 5 6 7
## Frequency 8 15 5 6 3 2 1
## Proportion 0.200 0.375 0.125 0.150 0.075 0.050 0.025
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T15R
## n missing distinct Info Mean Gmd
## 40 0 7 0.955 3.25 2.056
##
## Value 1 2 3 4 5 6 7
## Frequency 6 13 6 4 4 5 2
## Proportion 0.150 0.325 0.150 0.100 0.100 0.125 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------psych::alpha(N$TPTNS_Scale)## Warning in psych::alpha(N$TPTNS_Scale): Some items were negatively correlated with the first principal component and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( N.T3R ) were negatively correlated with the first principal component and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = N$TPTNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.8 0.9 0.21 4.1 0.042 3.7 0.88 0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.71 0.81 0.89
## Duhachek 0.73 0.81 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## N.T1 0.78 0.78 0.87 0.20 3.5 0.049 0.052 0.24
## N.T2 0.80 0.79 0.88 0.21 3.8 0.045 0.057 0.26
## N.T3R 0.83 0.83 0.90 0.26 4.9 0.040 0.037 0.27
## N.T4 0.78 0.78 0.88 0.20 3.5 0.049 0.051 0.24
## N.T5R 0.82 0.81 0.89 0.23 4.1 0.041 0.054 0.27
## N.T6 0.80 0.79 0.88 0.21 3.8 0.044 0.051 0.26
## N.T7R 0.79 0.78 0.88 0.20 3.5 0.048 0.055 0.22
## N.T8 0.80 0.80 0.88 0.22 3.9 0.044 0.052 0.25
## N.T9R 0.80 0.79 0.89 0.21 3.7 0.045 0.056 0.26
## N.T10 0.79 0.77 0.87 0.20 3.4 0.048 0.050 0.24
## N.T11 0.79 0.78 0.88 0.20 3.6 0.048 0.052 0.25
## N.T12R 0.80 0.79 0.88 0.21 3.8 0.045 0.052 0.25
## N.T13 0.83 0.82 0.90 0.24 4.5 0.038 0.047 0.27
## N.T14 0.80 0.79 0.89 0.21 3.7 0.046 0.057 0.25
## N.T15R 0.79 0.79 0.88 0.21 3.7 0.046 0.054 0.25
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## N.T1 40 0.72 0.71 0.707 0.644 3.5 1.8
## N.T2 40 0.51 0.55 0.512 0.429 4.9 1.3
## N.T3R 40 -0.12 -0.03 -0.105 -0.182 2.2 0.9
## N.T4 40 0.71 0.69 0.688 0.638 4.5 1.7
## N.T5R 40 0.31 0.36 0.310 0.189 3.1 1.6
## N.T6 40 0.53 0.55 0.548 0.425 4.8 1.8
## N.T7R 40 0.67 0.68 0.664 0.595 4.5 1.7
## N.T8 40 0.50 0.48 0.462 0.387 4.5 1.8
## N.T9R 40 0.54 0.57 0.532 0.446 3.7 1.5
## N.T10 40 0.71 0.72 0.729 0.645 4.5 1.5
## N.T11 40 0.67 0.63 0.621 0.570 3.6 2.0
## N.T12R 40 0.56 0.52 0.493 0.454 2.9 1.8
## N.T13 40 0.22 0.16 0.086 0.058 3.4 2.1
## N.T14 40 0.58 0.59 0.543 0.495 2.8 1.6
## N.T15R 40 0.61 0.58 0.544 0.506 3.2 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## N.T1 0.10 0.32 0.12 0.15 0.12 0.12 0.05 0
## N.T2 0.00 0.10 0.03 0.17 0.30 0.35 0.05 0
## N.T3R 0.20 0.45 0.25 0.10 0.00 0.00 0.00 0
## N.T4 0.00 0.17 0.12 0.22 0.10 0.25 0.12 0
## N.T5R 0.12 0.35 0.17 0.20 0.03 0.07 0.05 0
## N.T6 0.07 0.10 0.07 0.07 0.22 0.32 0.12 0
## N.T7R 0.05 0.10 0.12 0.22 0.12 0.28 0.10 0
## N.T8 0.07 0.12 0.10 0.12 0.17 0.35 0.05 0
## N.T9R 0.05 0.17 0.30 0.20 0.10 0.15 0.03 0
## N.T10 0.00 0.17 0.03 0.25 0.28 0.22 0.05 0
## N.T11 0.10 0.32 0.15 0.07 0.15 0.07 0.12 0
## N.T12R 0.17 0.38 0.22 0.07 0.00 0.05 0.10 0
## N.T13 0.20 0.30 0.10 0.05 0.07 0.20 0.07 0
## N.T14 0.20 0.38 0.12 0.15 0.07 0.05 0.03 0
## N.T15R 0.15 0.32 0.15 0.10 0.10 0.12 0.05 0# Reliability if items 2, 3, and 13 were removed
N$TPTNS_Scale2 <- data.frame(N$T1,N$T4, N$T5R, N$T6, N$T7R, N$T8, N$T9R, N$T10, N$T11, N$T12R, N$T14, N$T15R)
describe(N$TPTNS_Scale2)## N$TPTNS_Scale2
##
## 12 Variables 40 Observations
## --------------------------------------------------------------------------------
## N.T1
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.45 2.018
##
## Value 1 2 3 4 5 6 7
## Frequency 4 13 5 6 5 5 2
## Proportion 0.100 0.325 0.125 0.150 0.125 0.125 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T4
## n missing distinct Info Mean Gmd
## 40 0 6 0.963 4.5 1.944
##
## Value 2 3 4 5 6 7
## Frequency 7 5 9 4 10 5
## Proportion 0.175 0.125 0.225 0.100 0.250 0.125
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T5R
## n missing distinct Info Mean Gmd
## 40 0 7 0.942 3.075 1.786
##
## Value 1 2 3 4 5 6 7
## Frequency 5 14 7 8 1 3 2
## Proportion 0.125 0.350 0.175 0.200 0.025 0.075 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T6
## n missing distinct Info Mean Gmd
## 40 0 7 0.951 4.75 2.005
##
## Value 1 2 3 4 5 6 7
## Frequency 3 4 3 3 9 13 5
## Proportion 0.075 0.100 0.075 0.075 0.225 0.325 0.125
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T7R
## n missing distinct Info Mean Gmd
## 40 0 7 0.962 4.5 1.946
##
## Value 1 2 3 4 5 6 7
## Frequency 2 4 5 9 5 11 4
## Proportion 0.050 0.100 0.125 0.225 0.125 0.275 0.100
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T8
## n missing distinct Info Mean Gmd
## 40 0 7 0.947 4.45 1.992
##
## Value 1 2 3 4 5 6 7
## Frequency 3 5 4 5 7 14 2
## Proportion 0.075 0.125 0.100 0.125 0.175 0.350 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T9R
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.675 1.722
##
## Value 1 2 3 4 5 6 7
## Frequency 2 7 12 8 4 6 1
## Proportion 0.050 0.175 0.300 0.200 0.100 0.150 0.025
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T10
## n missing distinct Info Mean Gmd
## 40 0 6 0.947 4.5 1.638
##
## Value 2 3 4 5 6 7
## Frequency 7 1 10 11 9 2
## Proportion 0.175 0.025 0.250 0.275 0.225 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T11
## n missing distinct Info Mean Gmd
## 40 0 7 0.956 3.575 2.206
##
## Value 1 2 3 4 5 6 7
## Frequency 4 13 6 3 6 3 5
## Proportion 0.100 0.325 0.150 0.075 0.150 0.075 0.125
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T12R
## n missing distinct Info Mean Gmd
## 40 0 6 0.93 2.9 1.869
##
## Value 1 2 3 4 6 7
## Frequency 7 15 9 3 2 4
## Proportion 0.175 0.375 0.225 0.075 0.050 0.100
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T14
## n missing distinct Info Mean Gmd
## 40 0 7 0.934 2.775 1.714
##
## Value 1 2 3 4 5 6 7
## Frequency 8 15 5 6 3 2 1
## Proportion 0.200 0.375 0.125 0.150 0.075 0.050 0.025
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.T15R
## n missing distinct Info Mean Gmd
## 40 0 7 0.955 3.25 2.056
##
## Value 1 2 3 4 5 6 7
## Frequency 6 13 6 4 4 5 2
## Proportion 0.150 0.325 0.150 0.100 0.100 0.125 0.050
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------psych::alpha(N$TPTNS_Scale2)##
## Reliability analysis
## Call: psych::alpha(x = N$TPTNS_Scale2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.9 0.31 5.3 0.037 3.8 1 0.32
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.75 0.84 0.90
## Duhachek 0.77 0.84 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## N.T1 0.82 0.82 0.88 0.29 4.5 0.043 0.032 0.32
## N.T4 0.82 0.82 0.88 0.29 4.5 0.043 0.030 0.32
## N.T5R 0.85 0.85 0.90 0.34 5.8 0.035 0.023 0.35
## N.T6 0.83 0.83 0.88 0.31 4.9 0.040 0.027 0.33
## N.T7R 0.82 0.82 0.88 0.29 4.6 0.042 0.033 0.32
## N.T8 0.83 0.83 0.88 0.31 5.0 0.040 0.029 0.34
## N.T9R 0.83 0.83 0.89 0.31 4.9 0.040 0.033 0.34
## N.T10 0.82 0.82 0.87 0.29 4.4 0.043 0.027 0.29
## N.T11 0.82 0.82 0.89 0.29 4.6 0.043 0.030 0.29
## N.T12R 0.83 0.84 0.89 0.32 5.1 0.039 0.026 0.34
## N.T14 0.83 0.83 0.90 0.31 4.9 0.040 0.033 0.32
## N.T15R 0.83 0.83 0.89 0.31 4.9 0.040 0.031 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## N.T1 40 0.71 0.70 0.68 0.63 3.5 1.8
## N.T4 40 0.71 0.71 0.70 0.63 4.5 1.7
## N.T5R 40 0.30 0.31 0.25 0.17 3.1 1.6
## N.T6 40 0.58 0.59 0.58 0.47 4.8 1.8
## N.T7R 40 0.69 0.69 0.66 0.60 4.5 1.7
## N.T8 40 0.56 0.56 0.53 0.45 4.5 1.8
## N.T9R 40 0.55 0.57 0.52 0.46 3.7 1.5
## N.T10 40 0.72 0.74 0.74 0.66 4.5 1.5
## N.T11 40 0.70 0.69 0.66 0.60 3.6 2.0
## N.T12R 40 0.53 0.51 0.48 0.42 2.9 1.8
## N.T14 40 0.59 0.59 0.54 0.49 2.8 1.6
## N.T15R 40 0.57 0.56 0.52 0.46 3.2 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## N.T1 0.10 0.32 0.12 0.15 0.12 0.12 0.05 0
## N.T4 0.00 0.17 0.12 0.22 0.10 0.25 0.12 0
## N.T5R 0.12 0.35 0.17 0.20 0.03 0.07 0.05 0
## N.T6 0.07 0.10 0.07 0.07 0.22 0.32 0.12 0
## N.T7R 0.05 0.10 0.12 0.22 0.12 0.28 0.10 0
## N.T8 0.07 0.12 0.10 0.12 0.17 0.35 0.05 0
## N.T9R 0.05 0.17 0.30 0.20 0.10 0.15 0.03 0
## N.T10 0.00 0.17 0.03 0.25 0.28 0.22 0.05 0
## N.T11 0.10 0.32 0.15 0.07 0.15 0.07 0.12 0
## N.T12R 0.17 0.38 0.22 0.07 0.00 0.05 0.10 0
## N.T14 0.20 0.38 0.12 0.15 0.07 0.05 0.03 0
## N.T15R 0.15 0.32 0.15 0.10 0.10 0.12 0.05 0Aversion to Tampering with Nature
#Aversion to Tampering with Nature
#Aversion to Tampering with Nature Item Definitions
N$A1 <- as.numeric(as.character(N$ATNS_1))
N$A2 <- as.numeric(as.character(N$ATNS_2))
N$A3 <- as.numeric(as.character(N$ATNS_3))
N$A4 <- as.numeric(as.character(N$ATNS_4))
N$A5R <- as.numeric(as.character(N$ATNS_5R))
#Aversion to Tampering with Nature Scale Descriptives (No reversed codes)
describe(N$A1)## N$A1
## n missing distinct Info Mean Gmd
## 39 1 7 0.93 9.744 1.609
##
## Value 6 7 8 9 10 11 12
## Frequency 1 4 1 7 15 7 4
## Proportion 0.026 0.103 0.026 0.179 0.385 0.179 0.103
##
## For the frequency table, variable is rounded to the nearest 0sd(N$A1)## [1] NArange(N$A1, na.rm=TRUE)## [1] 6 12describe(N$A2)## N$A2
## n missing distinct Info Mean Gmd
## 39 1 7 0.957 9.385 1.738
##
## Value 6 7 8 9 10 11 12
## Frequency 1 4 7 6 12 6 3
## Proportion 0.026 0.103 0.179 0.154 0.308 0.154 0.077
##
## For the frequency table, variable is rounded to the nearest 0sd(N$A2)## [1] NArange(N$A2, na.rm=TRUE)## [1] 6 12describe(N$A3)## N$A3
## n missing distinct Info Mean Gmd
## 39 1 7 0.949 8.718 1.598
##
## Value 6 7 8 9 10 11 12
## Frequency 1 8 10 6 11 2 1
## Proportion 0.026 0.205 0.256 0.154 0.282 0.051 0.026
##
## For the frequency table, variable is rounded to the nearest 0sd(N$A3)## [1] NArange(N$A3, na.rm=TRUE)## [1] 6 12describe(N$A4)## N$A4
## n missing distinct Info Mean Gmd
## 39 1 7 0.962 9 1.781
##
## Value 6 7 8 9 10 11 12
## Frequency 1 7 9 5 9 7 1
## Proportion 0.026 0.179 0.231 0.128 0.231 0.179 0.026
##
## For the frequency table, variable is rounded to the nearest 0sd(N$A4)## [1] NArange(N$A4, na.rm=TRUE)## [1] 6 12describe(N$A5R)## N$A5R
## n missing distinct Info Mean Gmd
## 39 1 6 0.96 9.282 1.657
##
## Value 7 8 9 10 11 12
## Frequency 4 10 8 7 8 2
## Proportion 0.103 0.256 0.205 0.179 0.205 0.051
##
## For the frequency table, variable is rounded to the nearest 0sd(N$A5R)## [1] NArange(N$A5R, na.rm=TRUE)## [1] 7 12#Aversion to Tampering with Nature Scale Histograms by Item (No reversed codes)
hist(N$A1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks.')
hist(N$A2, main = 'ATNS #2: People who say we shouldn’t tamper with nature are just being naïve.')
hist(N$A3, main = 'ATNS #3: Human beings have no right to meddle with the natural environment.')
hist(N$A4, main = 'ATNS #4: I would prefer to live in a world where humans leave nature alone.')
hist(N$A5R, main = 'ATNS #5: Altering nature will be our downfall as a species.')
#Cronbach's Alpha (4 and 5 reverse coded)
N$ATNS_Scale <- data.frame(N$A1, N$A2, N$A3, N$A4, N$A5R)
N$ATNS_Score <- rowMeans(N [, c("A1", "A2", "A3", "A4", "A5R")], na.rm=TRUE)
psych::alpha(N$ATNS_Scale)##
## Reliability analysis
## Call: psych::alpha(x = N$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.74 0.76 0.36 2.9 0.065 9.2 1 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.59 0.74 0.85
## Duhachek 0.61 0.74 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## N.A1 0.71 0.71 0.73 0.38 2.4 0.076 0.054 0.36
## N.A2 0.76 0.76 0.77 0.45 3.2 0.063 0.034 0.42
## N.A3 0.60 0.60 0.57 0.27 1.5 0.103 0.018 0.30
## N.A4 0.63 0.63 0.60 0.30 1.7 0.096 0.022 0.35
## N.A5R 0.74 0.75 0.74 0.42 2.9 0.067 0.045 0.40
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## N.A1 39 0.68 0.68 0.54 0.47 9.7 1.5
## N.A2 39 0.57 0.56 0.37 0.32 9.4 1.5
## N.A3 39 0.85 0.86 0.88 0.74 8.7 1.4
## N.A4 39 0.81 0.81 0.82 0.66 9.0 1.6
## N.A5R 39 0.59 0.60 0.44 0.37 9.3 1.5
##
## Non missing response frequency for each item
## 6 7 8 9 10 11 12 miss
## N.A1 0.03 0.10 0.03 0.18 0.38 0.18 0.10 0.03
## N.A2 0.03 0.10 0.18 0.15 0.31 0.15 0.08 0.03
## N.A3 0.03 0.21 0.26 0.15 0.28 0.05 0.03 0.03
## N.A4 0.03 0.18 0.23 0.13 0.23 0.18 0.03 0.03
## N.A5R 0.00 0.10 0.26 0.21 0.18 0.21 0.05 0.03describe(N$ATNS_Score)## N$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 39 1 20 0.996 9.226 1.202 7.78 7.96
## .25 .50 .75 .90 .95
## 8.50 9.20 10.00 10.64 10.82
##
## Value 7.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4
## Frequency 1 1 2 2 1 3 3 2 4 4 2
## Proportion 0.026 0.026 0.051 0.051 0.026 0.077 0.077 0.051 0.103 0.103 0.051
##
## Value 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.4
## Frequency 1 1 3 2 2 1 2 1 1
## Proportion 0.026 0.026 0.077 0.051 0.051 0.026 0.051 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 0sd(N$ATNS_Score, na.rm=TRUE)## [1] 1.041436hist(N$ATNS_Score)
# Climate Change Belief
#Climate Change Belief Item Definitions
N$C1 <- as.numeric(as.character(N$CCB_1))
N$C2 <- as.numeric(as.character(N$CCB_2))
N$C3 <- as.numeric(as.character(N$CCB_3))
N$C4 <- as.numeric(as.character(N$CCB_4))
#Climate Change Belief Descriptives
describe(N$C1)## N$C1
## n missing distinct Info Mean Gmd
## 39 1 4 0.833 11.18 1.039
##
## Value 7 10 11 12
## Frequency 2 3 16 18
## Proportion 0.051 0.077 0.410 0.462
##
## For the frequency table, variable is rounded to the nearest 0range(N$C1, na.rm=TRUE)## [1] 7 12sd(N$C1, na.rm=TRUE)## [1] 1.166908describe(N$C2)## N$C2
## n missing distinct Info Mean Gmd
## 39 1 4 0.827 11.21 1.08
##
## Value 7 10 11 12
## Frequency 2 4 13 20
## Proportion 0.051 0.103 0.333 0.513
##
## For the frequency table, variable is rounded to the nearest 0range(N$C2, na.rm=TRUE)## [1] 7 12sd(N$C2, na.rm=TRUE)## [1] 1.196035describe(N$C3)## N$C3
## n missing distinct Info Mean Gmd
## 39 1 6 0.883 10.97 1.247
##
## Value 7 8 9 10 11 12
## Frequency 1 2 1 5 14 16
## Proportion 0.026 0.051 0.026 0.128 0.359 0.410
##
## For the frequency table, variable is rounded to the nearest 0range(N$C3, na.rm=TRUE)## [1] 7 12sd(N$C3, na.rm=TRUE)## [1] 1.245776describe(N$C4)## N$C4
## n missing distinct Info Mean Gmd
## 39 1 5 0.879 10.79 1.142
##
## Value 8 9 10 11 12
## Frequency 3 1 7 18 10
## Proportion 0.077 0.026 0.179 0.462 0.256
##
## For the frequency table, variable is rounded to the nearest 0range(N$C4, na.rm=TRUE)## [1] 8 12sd(N$C4, na.rm=TRUE)## [1] 1.104524#Climate Change Belief Histograms
hist(N$C1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(N$C2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(N$C3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(N$C4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
#Score & Scale
N$CCB_Score <- rowMeans(N [, c('C1', 'C2', 'C3','C4')], na.rm=T)
describe(N$CCB_Score)## N$CCB_Score
## n missing distinct Info Mean Gmd .05 .10
## 39 1 11 0.976 11.04 1.078 8.85 10.00
## .25 .50 .75 .90 .95
## 10.75 11.25 11.75 12.00 12.00
##
## Value 7.25 7.50 9.00 10.00 10.50 10.75 11.00 11.25 11.50 11.75 12.00
## Frequency 1 1 1 2 3 4 7 1 6 4 9
## Proportion 0.026 0.026 0.026 0.051 0.077 0.103 0.179 0.026 0.154 0.103 0.231
##
## For the frequency table, variable is rounded to the nearest 0hist(N$CCB_Score)
sd(N$CCB_Score, na.rm = TRUE)## [1] 1.101044N$CCB_Scale <- data.frame(N$CCB_1, N$CCB_2, N$CCB_3, N$CCB_4)
describe(N$CCB_Scale)## N$CCB_Scale
##
## 4 Variables 40 Observations
## --------------------------------------------------------------------------------
## N.CCB_1
## n missing distinct Info Mean Gmd
## 39 1 4 0.833 11.18 1.039
##
## Value 7 10 11 12
## Frequency 2 3 16 18
## Proportion 0.051 0.077 0.410 0.462
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CCB_2
## n missing distinct Info Mean Gmd
## 39 1 4 0.827 11.21 1.08
##
## Value 7 10 11 12
## Frequency 2 4 13 20
## Proportion 0.051 0.103 0.333 0.513
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CCB_3
## n missing distinct Info Mean Gmd
## 39 1 6 0.883 10.97 1.247
##
## Value 7 8 9 10 11 12
## Frequency 1 2 1 5 14 16
## Proportion 0.026 0.051 0.026 0.128 0.359 0.410
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CCB_4
## n missing distinct Info Mean Gmd
## 39 1 5 0.879 10.79 1.142
##
## Value 8 9 10 11 12
## Frequency 3 1 7 18 10
## Proportion 0.077 0.026 0.179 0.462 0.256
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------#Cronbach's Alpha
psych::alpha(N$CCB_Scale)##
## Reliability analysis
## Call: psych::alpha(x = N$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.96 0.83 19 0.013 11 1.1 0.84
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.95 0.97
## Duhachek 0.92 0.95 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## N.CCB_1 0.93 0.93 0.91 0.81 13 0.020 0.0053 0.84
## N.CCB_2 0.92 0.92 0.90 0.80 12 0.021 0.0043 0.84
## N.CCB_3 0.93 0.93 0.93 0.81 13 0.021 0.0201 0.73
## N.CCB_4 0.96 0.96 0.96 0.89 25 0.012 0.0051 0.87
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## N.CCB_1 39 0.95 0.95 0.95 0.90 11 1.2
## N.CCB_2 39 0.96 0.96 0.96 0.92 11 1.2
## N.CCB_3 39 0.95 0.95 0.93 0.91 11 1.2
## N.CCB_4 39 0.88 0.88 0.82 0.79 11 1.1
##
## Non missing response frequency for each item
## 7 8 9 10 11 12 miss
## N.CCB_1 0.05 0.00 0.00 0.08 0.41 0.46 0.03
## N.CCB_2 0.05 0.00 0.00 0.10 0.33 0.51 0.03
## N.CCB_3 0.03 0.05 0.03 0.13 0.36 0.41 0.03
## N.CCB_4 0.00 0.08 0.03 0.18 0.46 0.26 0.03Connectedness to Nature
#Connectedness to Nature Item Definitions
N$CNS1 <- as.numeric(as.character(N$CNS_1))
N$CNS2 <- as.numeric(as.character(N$CNS_2))
N$CNS3 <- as.numeric(as.character(N$CNS_3))
N$CNS4R <- as.numeric(as.character(N$CNS_4R))
N$CNS5R <- as.numeric(as.character(N$CNS_5R))
#Descriptives
describe(N$CNS1)## N$CNS1
## n missing distinct Info Mean Gmd
## 39 1 5 0.908 10.54 1.233
##
## Value 7 9 10 11 12
## Frequency 2 2 14 13 8
## Proportion 0.051 0.051 0.359 0.333 0.205
##
## For the frequency table, variable is rounded to the nearest 0range(N$CNS1, na.rm=TRUE)## [1] 7 12describe(N$CNS2)## N$CNS2
## n missing distinct Info Mean Gmd
## 39 1 6 0.945 10.36 1.622
##
## Value 7 8 9 10 11 12
## Frequency 2 3 4 11 8 11
## Proportion 0.051 0.077 0.103 0.282 0.205 0.282
##
## For the frequency table, variable is rounded to the nearest 0range(N$CNS2, na.rm=TRUE)## [1] 7 12describe(N$CNS3)## N$CNS3
## n missing distinct Info Mean Gmd
## 39 1 7 0.964 10.03 1.906
##
## Value 6 7 8 9 10 11 12
## Frequency 1 3 4 5 8 9 9
## Proportion 0.026 0.077 0.103 0.128 0.205 0.231 0.231
##
## For the frequency table, variable is rounded to the nearest 0range(N$CNS3, na.rm=TRUE)## [1] 6 12describe(N$CNS4R)## N$CNS4R
## n missing distinct Info Mean Gmd
## 39 1 7 0.959 9.641 2.337
##
## Value 6 7 8 9 10 11 12
## Frequency 1 8 6 3 3 7 11
## Proportion 0.026 0.205 0.154 0.077 0.077 0.179 0.282
##
## For the frequency table, variable is rounded to the nearest 0range(N$CNS4R, na.rm=TRUE)## [1] 6 12describe(N$CNS5R)## N$CNS5R
## n missing distinct Info Mean Gmd
## 39 1 6 0.97 9.538 1.825
##
## Value 7 8 9 10 11 12
## Frequency 5 6 8 8 7 5
## Proportion 0.128 0.154 0.205 0.205 0.179 0.128
##
## For the frequency table, variable is rounded to the nearest 0range(N$CNS5R, na.rm=TRUE)## [1] 7 12#Histograms
hist(N$CNS1, main = 'I often feel a sense of oneness with the natural world around me.')
hist(N$CNS2, main = 'I think of the natural world as a community to which I belong.')
hist(N$CNS3, main = 'I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’.')
hist(N$CNS4R, main = 'My personal welfare is independent of the welfare of the natural world.')
hist(N$CNS5R, main = 'When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.')
#Score & Scale
N$CNS_Score <- rowMeans(N [, c("CNS1", "CNS2", "CNS3", "CNS4R", "CNS5R")], na.rm=TRUE)
describe(N$CNS_Score)## N$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 39 1 18 0.995 10.02 1.418 8.00 8.56
## .25 .50 .75 .90 .95
## 9.20 10.00 11.10 11.60 11.80
##
## Value 6.8 8.0 8.4 8.6 9.0 9.2 9.4 9.6 9.8 10.0 10.2
## Frequency 1 2 1 1 3 4 4 2 1 1 3
## Proportion 0.026 0.051 0.026 0.026 0.077 0.103 0.103 0.051 0.026 0.026 0.077
##
## Value 10.4 10.8 11.0 11.2 11.4 11.6 11.8
## Frequency 2 1 3 2 2 3 3
## Proportion 0.051 0.026 0.077 0.051 0.051 0.077 0.077
##
## For the frequency table, variable is rounded to the nearest 0sd(N$CNS_Score, na.rm = TRUE)## [1] 1.23952hist(N$CNS_Score)
N$CNS_Scale <- data.frame(N$CNS1, N$CNS2, N$CNS3, N$CNS4R, N$CNS5R)
psych::alpha(N$CNS_Scale)##
## Reliability analysis
## Call: psych::alpha(x = N$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.84 0.87 0.51 5.2 0.045 10 1.2 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.72 0.82 0.90
## Duhachek 0.73 0.82 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## N.CNS1 0.78 0.80 0.81 0.49 3.9 0.058 0.032 0.46
## N.CNS2 0.74 0.76 0.78 0.44 3.1 0.066 0.040 0.40
## N.CNS3 0.76 0.78 0.80 0.47 3.6 0.059 0.030 0.46
## N.CNS4R 0.82 0.83 0.85 0.55 4.9 0.047 0.068 0.60
## N.CNS5R 0.82 0.86 0.85 0.60 5.9 0.048 0.040 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## N.CNS1 39 0.77 0.81 0.78 0.67 10.5 1.2
## N.CNS2 39 0.87 0.89 0.88 0.79 10.4 1.5
## N.CNS3 39 0.82 0.84 0.83 0.69 10.0 1.7
## N.CNS4R 39 0.76 0.72 0.61 0.55 9.6 2.1
## N.CNS5R 39 0.67 0.65 0.53 0.49 9.5 1.6
##
## Non missing response frequency for each item
## 6 7 8 9 10 11 12 miss
## N.CNS1 0.00 0.05 0.00 0.05 0.36 0.33 0.21 0.03
## N.CNS2 0.00 0.05 0.08 0.10 0.28 0.21 0.28 0.03
## N.CNS3 0.03 0.08 0.10 0.13 0.21 0.23 0.23 0.03
## N.CNS4R 0.03 0.21 0.15 0.08 0.08 0.18 0.28 0.03
## N.CNS5R 0.00 0.13 0.15 0.21 0.21 0.18 0.13 0.03describe(N$CNS_Scale)## N$CNS_Scale
##
## 5 Variables 40 Observations
## --------------------------------------------------------------------------------
## N.CNS1
## n missing distinct Info Mean Gmd
## 39 1 5 0.908 10.54 1.233
##
## Value 7 9 10 11 12
## Frequency 2 2 14 13 8
## Proportion 0.051 0.051 0.359 0.333 0.205
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CNS2
## n missing distinct Info Mean Gmd
## 39 1 6 0.945 10.36 1.622
##
## Value 7 8 9 10 11 12
## Frequency 2 3 4 11 8 11
## Proportion 0.051 0.077 0.103 0.282 0.205 0.282
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CNS3
## n missing distinct Info Mean Gmd
## 39 1 7 0.964 10.03 1.906
##
## Value 6 7 8 9 10 11 12
## Frequency 1 3 4 5 8 9 9
## Proportion 0.026 0.077 0.103 0.128 0.205 0.231 0.231
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CNS4R
## n missing distinct Info Mean Gmd
## 39 1 7 0.959 9.641 2.337
##
## Value 6 7 8 9 10 11 12
## Frequency 1 8 6 3 3 7 11
## Proportion 0.026 0.205 0.154 0.077 0.077 0.179 0.282
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## N.CNS5R
## n missing distinct Info Mean Gmd
## 39 1 6 0.97 9.538 1.825
##
## Value 7 8 9 10 11 12
## Frequency 5 6 8 8 7 5
## Proportion 0.128 0.154 0.205 0.205 0.179 0.128
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------#Correlation CNS
psych::alpha(N$CNS_Scale)##
## Reliability analysis
## Call: psych::alpha(x = N$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.84 0.87 0.51 5.2 0.045 10 1.2 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.72 0.82 0.90
## Duhachek 0.73 0.82 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## N.CNS1 0.78 0.80 0.81 0.49 3.9 0.058 0.032 0.46
## N.CNS2 0.74 0.76 0.78 0.44 3.1 0.066 0.040 0.40
## N.CNS3 0.76 0.78 0.80 0.47 3.6 0.059 0.030 0.46
## N.CNS4R 0.82 0.83 0.85 0.55 4.9 0.047 0.068 0.60
## N.CNS5R 0.82 0.86 0.85 0.60 5.9 0.048 0.040 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## N.CNS1 39 0.77 0.81 0.78 0.67 10.5 1.2
## N.CNS2 39 0.87 0.89 0.88 0.79 10.4 1.5
## N.CNS3 39 0.82 0.84 0.83 0.69 10.0 1.7
## N.CNS4R 39 0.76 0.72 0.61 0.55 9.6 2.1
## N.CNS5R 39 0.67 0.65 0.53 0.49 9.5 1.6
##
## Non missing response frequency for each item
## 6 7 8 9 10 11 12 miss
## N.CNS1 0.00 0.05 0.00 0.05 0.36 0.33 0.21 0.03
## N.CNS2 0.00 0.05 0.08 0.10 0.28 0.21 0.28 0.03
## N.CNS3 0.03 0.08 0.10 0.13 0.21 0.23 0.23 0.03
## N.CNS4R 0.03 0.21 0.15 0.08 0.08 0.18 0.28 0.03
## N.CNS5R 0.00 0.13 0.15 0.21 0.21 0.18 0.13 0.03Correlations Between Scales
length(N$TPTNS_Score)## [1] 40N$cor1 <- data.frame(N$TPTNS_Score, N$CCB_Score, N$CNS_Score, N$ATNS_Score, N$IDEO)
mydata.cor1 = cor(N$cor1, use = "pairwise.complete.obs")
head(round(mydata.cor1,2))## N.TPTNS_Score N.CCB_Score N.CNS_Score N.ATNS_Score N.IDEO
## N.TPTNS_Score 1.00 0.15 0.01 -0.29 0.21
## N.CCB_Score 0.15 1.00 0.54 0.20 0.53
## N.CNS_Score 0.01 0.54 1.00 0.25 0.39
## N.ATNS_Score -0.29 0.20 0.25 1.00 0.10
## N.IDEO 0.21 0.53 0.39 0.10 1.00library("Hmisc")
mydata.rcorr1 = rcorr(as.matrix(mydata.cor1))
mydata.rcorr1## N.TPTNS_Score N.CCB_Score N.CNS_Score N.ATNS_Score N.IDEO
## N.TPTNS_Score 1.00 -0.24 -0.51 -0.89 0.00
## N.CCB_Score -0.24 1.00 0.55 -0.11 0.52
## N.CNS_Score -0.51 0.55 1.00 0.13 0.23
## N.ATNS_Score -0.89 -0.11 0.13 1.00 -0.34
## N.IDEO 0.00 0.52 0.23 -0.34 1.00
##
## n= 5
##
##
## P
## N.TPTNS_Score N.CCB_Score N.CNS_Score N.ATNS_Score N.IDEO
## N.TPTNS_Score 0.6965 0.3778 0.0429 0.9979
## N.CCB_Score 0.6965 0.3388 0.8592 0.3715
## N.CNS_Score 0.3778 0.3388 0.8359 0.7139
## N.ATNS_Score 0.0429 0.8592 0.8359 0.5815
## N.IDEO 0.9979 0.3715 0.7139 0.5815library(corrplot)## corrplot 0.92 loadedcorrplot(mydata.cor1, method="color")
corrplot(mydata.cor1, addCoef.col = 1, number.cex = 0.3, method = 'number')
# Tendency to Perceive Technology as Natural Scale & Aversion to Tampering with Nature
cor.test(N$TPTNS_Score, N$ATNS_Score)##
## Pearson's product-moment correlation
##
## data: N$TPTNS_Score and N$ATNS_Score
## t = -1.8669, df = 37, p-value = 0.06985
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.55733347 0.02435972
## sample estimates:
## cor
## -0.2934125# Tendency to Perceive Technology as Natural Scale & Political Ideology
cor.test(N$TPTNS_Score, N$IDEO)##
## Pearson's product-moment correlation
##
## data: N$TPTNS_Score and N$IDEO
## t = 1.3308, df = 37, p-value = 0.1914
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1091542 0.4958066
## sample estimates:
## cor
## 0.2137235Exploratory Factor Analysis: TPTNS
library(psych)
# TPTNS Scale
N$TPTNS_Scale <- cbind(N$T1, N$T2, N$T3R, N$T4, N$T5R, N$T6, N$T7R, N$T8, N$T9R, N$T10, N$T11, N$T12R, N$T13, N$T14, N$T15R)
# Specify the name of the column corresponding to your items
column_name <- "TPTNS_Scale"
# Create a new data frame "N" with only the specified column
TPTNS <- N[, column_name, drop = FALSE]
print## standardGeneric for "print" defined from package "base"
##
## function (x, ...)
## standardGeneric("print")
## <environment: 0x105578d48>
## Methods may be defined for arguments: x
## Use showMethods(print) for currently available ones.# Check the first few rows of your new data frame
head(TPTNS)# Run factor analysis with 5 factors
fa_result_5factors <- fa(TPTNS, nfactors = 5, rotate = "varimax")## In fa, too many factors requested for this number of variables to use SMC for communality estimates, 1s are used instead## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully# Print factor loadings for 5 factors
print(fa_result_5factors$loadings, digits = 2)##
## Loadings:
## MR1 MR2 MR4 MR3 MR5
## TPTNS_Scale.1 0.27 0.17 0.36 0.89
## TPTNS_Scale.2 0.36 0.20 0.34 0.38
## TPTNS_Scale.3 -0.10 -0.24 -0.21 0.60
## TPTNS_Scale.4 0.66 0.42 0.15 0.14
## TPTNS_Scale.5 0.72 0.11
## TPTNS_Scale.6 0.82 -0.27 0.14
## TPTNS_Scale.7 0.51 0.18 0.40 0.23
## TPTNS_Scale.8 0.54 -0.25 0.50 -0.25
## TPTNS_Scale.9 0.28 0.40 0.50
## TPTNS_Scale.10 0.80 0.13 0.13 0.21
## TPTNS_Scale.11 0.15 0.32 0.73 0.22
## TPTNS_Scale.12 0.59 0.44 0.25
## TPTNS_Scale.13 -0.12 0.58 -0.20
## TPTNS_Scale.14 0.28 0.23 0.31 0.19
## TPTNS_Scale.15 0.29 0.65 0.20
##
## MR1 MR2 MR4 MR3 MR5
## SS loadings 2.80 1.74 1.68 1.43 1.21
## Proportion Var 0.19 0.12 0.11 0.10 0.08
## Cumulative Var 0.19 0.30 0.41 0.51 0.59nvar_fa_result <- length(fa_result_5factors$loadings)
print(nvar_fa_result)## [1] 75# Run factor analysis with 3 factors
fa_result_3factors <- fa(TPTNS, nfactors = 3, rotate = "varimax")## In fa, too many factors requested for this number of variables to use SMC for communality estimates, 1s are used instead# Print factor loadings for 2 factors
print(fa_result_3factors$loadings, digits = 2)##
## Loadings:
## MR1 MR2 MR3
## TPTNS_Scale.1 0.42 0.53 0.19
## TPTNS_Scale.2 0.29 0.23 0.38
## TPTNS_Scale.3 -0.18 -0.36 0.57
## TPTNS_Scale.4 0.57 0.46
## TPTNS_Scale.5 0.77
## TPTNS_Scale.6 0.87 -0.13 0.14
## TPTNS_Scale.7 0.49 0.31 0.27
## TPTNS_Scale.8 0.67 -0.12
## TPTNS_Scale.9 0.32 0.17 0.53
## TPTNS_Scale.10 0.76 0.24 0.17
## TPTNS_Scale.11 0.30 0.64
## TPTNS_Scale.12 0.80
## TPTNS_Scale.13 -0.21 0.49 -0.20
## TPTNS_Scale.14 0.31 0.41 0.14
## TPTNS_Scale.15 0.19 0.63
##
## MR1 MR2 MR3
## SS loadings 3.0 2.73 1.57
## Proportion Var 0.2 0.18 0.10
## Cumulative Var 0.2 0.38 0.49# Run factor analysis with 2 factors
fa_result_2factors <- fa(TPTNS, nfactors = 2, rotate = "varimax")## In fa, too many factors requested for this number of variables to use SMC for communality estimates, 1s are used instead# Print factor loadings for 2 factors
print(fa_result_2factors$loadings, digits = 2)##
## Loadings:
## MR1 MR2
## TPTNS_Scale.1 0.55 0.43
## TPTNS_Scale.2 0.47
## TPTNS_Scale.3 -0.48
## TPTNS_Scale.4 0.61 0.40
## TPTNS_Scale.5 0.33 -0.21
## TPTNS_Scale.6 0.75 -0.16
## TPTNS_Scale.7 0.61 0.19
## TPTNS_Scale.8 0.52 0.10
## TPTNS_Scale.9 0.55
## TPTNS_Scale.10 0.79 0.14
## TPTNS_Scale.11 0.42 0.56
## TPTNS_Scale.12 0.14 0.75
## TPTNS_Scale.13 -0.20 0.55
## TPTNS_Scale.14 0.41 0.33
## TPTNS_Scale.15 0.29 0.59
##
## MR1 MR2
## SS loadings 3.62 2.35
## Proportion Var 0.24 0.16
## Cumulative Var 0.24 0.40