Validation Climate Anxiety Scale: Full analysis
Marlis C. Wullenkord
2021-09-02
1 Preparations
1.1 Loading basic packages
library(psych)
library(MASS)
library(rcompanion)##
## Attaching package: 'rcompanion'## The following object is masked from 'package:psych':
##
## philibrary(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit1.2 Reading in data
data <- read.csv("1134.csv", header = TRUE, sep = ";") 1.3 Looking at the data
names (data) #see names in data## [1] "ï..case" "SERIAL" "REF" "QUESTNNR" "MODE"
## [6] "sj01" "sj02" "sj03" "sj04" "sj05"
## [11] "sj06" "sj07" "sj08" "sdo01" "sdo02"
## [16] "sdo03" "sdo04" "sdo05" "sdo06" "sdo07"
## [21] "sdo08" "sdo09" "nd01" "nd02" "nd03"
## [26] "nd04" "nd05" "nd06" "nd07" "nd08"
## [31] "nd09" "nd10" "rwa01" "rwa02" "rwa03"
## [36] "rwa04" "rwa05" "rwa06" "rwa07" "rwa08"
## [41] "rwa09" "rwa10" "rwa11" "rwa12" "gender"
## [46] "age" "edu" "SD03_08" "income" "pol.orient"
## [51] "SD08_01" "asimp01" "asimp02" "asimp03" "asimp04"
## [56] "asimp05" "asimp06" "asimp07" "asimp08" "asimp09"
## [61] "asimp10" "asimp11" "aspro01" "aspro02" "aspro03"
## [66] "aspro04" "aspro05" "aspro06" "aspro07" "aspro08"
## [71] "aspro09" "aspro10" "aspro11" "asimp12" "asimp13"
## [76] "asimp14" "asimp15" "asimp16" "asimp17" "asimp18"
## [81] "asimp19" "asimp20" "asimp21" "asimp22" "aspro12"
## [86] "aspro13" "aspro14" "aspro15" "aspro16" "aspro17"
## [91] "aspro18" "aspro19" "aspro20" "aspro21" "aspro22"
## [96] "sp11" "sp02" "sp24" "sp07" "sp21"
## [101] "sp05" "sp26" "sp14" "sp27" "sp18"
## [106] "sp20" "sp01" "sp23" "sp09" "sp08"
## [111] "sp03" "sp17" "sp19" "sp04" "sp25"
## [116] "k01" "sp22" "sp13" "sp06" "sp28"
## [121] "sp16" "sp10" "sp29" "sp15" "sp30"
## [126] "sp12" "k02" "gb01" "gb03" "gb05"
## [131] "gb07" "gb09" "gb11" "gb13" "gb15"
## [136] "gb17" "gb19" "gb02" "gb04" "gb06"
## [141] "gb08" "gb10" "gb12" "gb14" "gb16"
## [146] "gb18" "gb20" "OR03" "cas01" "cas02"
## [151] "cas03" "cas04" "cas05" "cas06" "cas07"
## [156] "cas08" "cas09" "cas10" "cas11" "cas12"
## [161] "cas13" "cas14" "ID01_RV1" "ID02_01" "ID02_03"
## [166] "phq01" "phq02" "phq03" "phq04" "phq05"
## [171] "ps01" "ps02" "int01" "int02" "int03"
## [176] "mp" "TIME001" "TIME002" "TIME004" "TIME005"
## [181] "TIME006" "TIME007" "TIME008" "TIME009" "TIME010"
## [186] "TIME011" "TIME012" "TIME013" "TIME014" "TIME015"
## [191] "TIME016" "TIME017" "TIME018" "TIME_SUM" "MAILSENT"
## [196] "FINISHED" "Q_VIEWER" "LASTPAGE" "MAXPAGE" "MISSING"
## [201] "MISSREL" "TIME_RSI" "DEG_TIME"str(data)## 'data.frame': 1134 obs. of 203 variables:
## $ ï..case : int 175 183 184 188 190 195 200 214 223 229 ...
## $ SERIAL : logi NA NA NA NA NA NA ...
## $ REF : logi NA NA NA NA NA NA ...
## $ QUESTNNR : chr "base" "base" "base" "base" ...
## $ MODE : chr "interview" "interview" "interview" "interview" ...
## $ sj01 : int 5 3 4 4 1 4 3 4 4 6 ...
## $ sj02 : int 7 5 5 5 4 6 4 3 1 6 ...
## $ sj03 : int 2 5 3 4 7 5 4 6 4 1 ...
## $ sj04 : int 7 4 4 4 1 7 1 6 1 5 ...
## $ sj05 : int 5 5 5 5 1 5 1 1 1 6 ...
## $ sj06 : int 6 2 5 3 1 4 7 7 1 6 ...
## $ sj07 : int 1 4 5 4 7 5 6 7 4 3 ...
## $ sj08 : int 5 1 4 3 1 4 2 6 1 6 ...
## $ sdo01 : int 3 1 4 4 1 2 1 5 1 2 ...
## $ sdo02 : int 5 1 5 6 1 6 1 5 7 3 ...
## $ sdo03 : int 7 7 4 5 7 7 7 5 7 6 ...
## $ sdo04 : int 7 7 6 7 7 7 7 5 1 7 ...
## $ sdo05 : int 2 1 4 2 1 2 2 7 1 3 ...
## $ sdo06 : int 1 1 4 3 4 6 2 5 1 5 ...
## $ sdo07 : int 6 7 5 5 4 6 6 5 7 6 ...
## $ sdo08 : int 6 7 6 6 7 7 7 5 7 7 ...
## $ sdo09 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ nd01 : int 1 3 1 4 1 1 1 1 1 4 ...
## $ nd02 : int 3 4 1 6 1 2 1 3 7 2 ...
## $ nd03 : int 7 6 7 6 7 7 7 7 7 4 ...
## $ nd04 : int 3 2 1 4 1 1 1 2 1 3 ...
## $ nd05 : int 7 7 6 6 7 7 7 6 7 7 ...
## $ nd06 : int 6 4 7 6 7 7 7 6 1 6 ...
## $ nd07 : int 7 3 1 2 1 1 1 6 1 3 ...
## $ nd08 : int 5 4 2 2 1 1 1 3 1 2 ...
## $ nd09 : int 6 5 7 4 7 7 7 7 7 6 ...
## $ nd10 : int 2 7 6 5 7 7 1 6 1 4 ...
## $ rwa01 : int 6 5 7 5 7 6 7 5 7 5 ...
## $ rwa02 : int 2 1 5 3 1 7 4 7 1 4 ...
## $ rwa03 : int 7 7 6 7 7 7 7 7 7 7 ...
## $ rwa04 : int 1 4 6 5 1 6 1 5 1 4 ...
## $ rwa05 : int 7 7 5 3 7 3 7 2 4 7 ...
## $ rwa06 : int 5 3 4 4 3 7 3 7 1 4 ...
## $ rwa07 : int 7 7 2 7 7 7 6 6 7 6 ...
## $ rwa08 : int 4 4 3 3 2 6 7 7 1 4 ...
## $ rwa09 : int 7 7 5 5 7 6 1 2 7 5 ...
## $ rwa10 : int 4 3 5 5 1 7 6 7 3 5 ...
## $ rwa11 : int 1 5 3 3 4 2 1 4 3 3 ...
## $ rwa12 : int 4 3 4 2 1 6 2 7 1 3 ...
## $ gender : int 1 1 1 1 1 1 1 1 1 1 ...
## $ age : int 59 57 57 51 59 59 59 51 59 50 ...
## $ edu : int 6 5 7 4 5 5 5 5 7 7 ...
## $ SD03_08 : logi NA NA NA NA NA NA ...
## $ income : int 4 3 8 2 5 5 3 3 1 10 ...
## $ pol.orient: int 49 11 55 18 13 10 51 56 3 61 ...
## $ SD08_01 : chr "01-apr" "01-apr" "01-apr" "01-apr" ...
## $ asimp01 : int 4 7 7 6 4 7 1 7 1 7 ...
## $ asimp02 : int 4 6 5 6 4 6 1 5 1 6 ...
## $ asimp03 : int 4 5 4 5 4 7 4 4 1 4 ...
## $ asimp04 : int 3 4 5 6 7 7 1 3 1 5 ...
## $ asimp05 : int 3 5 3 5 1 5 1 3 7 4 ...
## $ asimp06 : int 1 3 2 5 1 5 1 4 1 2 ...
## $ asimp07 : int 7 7 7 7 7 7 7 4 7 6 ...
## $ asimp08 : int 1 3 4 4 1 5 1 4 1 3 ...
## $ asimp09 : int 4 7 7 7 7 7 7 5 7 7 ...
## $ asimp10 : int 5 7 6 6 5 7 1 4 7 6 ...
## $ asimp11 : int 1 1 1 6 1 5 1 3 1 2 ...
## $ aspro01 : int 4 7 7 6 3 6 1 7 1 6 ...
## $ aspro02 : int 4 6 2 5 1 5 1 5 1 6 ...
## $ aspro03 : int 4 6 6 6 3 5 1 4 1 5 ...
## $ aspro04 : int 3 4 7 5 1 7 1 3 1 6 ...
## $ aspro05 : int 4 6 3 5 1 5 1 3 7 6 ...
## $ aspro06 : int 1 4 1 5 7 5 1 4 1 5 ...
## $ aspro07 : int 7 3 4 6 1 6 1 7 7 6 ...
## $ aspro08 : int 1 5 3 7 1 4 1 4 1 3 ...
## $ aspro09 : int 6 6 7 7 1 7 7 5 7 7 ...
## $ aspro10 : int 6 6 4 5 1 7 1 4 7 6 ...
## $ aspro11 : int 1 1 1 5 1 3 1 3 1 1 ...
## $ asimp12 : int 4 6 7 6 6 6 5 5 1 7 ...
## $ asimp13 : int 5 6 4 5 6 6 1 5 1 6 ...
## $ asimp14 : int 4 5 6 6 1 5 6 5 1 5 ...
## $ asimp15 : int 3 6 7 6 4 3 5 2 7 7 ...
## $ asimp16 : int 4 6 6 6 2 4 1 4 4 4 ...
## $ asimp17 : int 5 4 1 5 1 4 1 4 1 4 ...
## $ asimp18 : int 7 7 7 6 7 7 7 4 7 7 ...
## $ asimp19 : int 1 3 1 4 1 4 1 5 1 4 ...
## $ asimp20 : int 7 7 7 6 6 6 5 5 7 7 ...
## $ asimp21 : int 7 7 5 6 7 6 6 5 7 7 ...
## $ asimp22 : int 1 3 1 6 1 3 1 3 1 1 ...
## $ aspro12 : int 5 5 7 5 4 5 1 4 1 7 ...
## $ aspro13 : int 5 4 1 4 6 6 1 5 1 6 ...
## $ aspro14 : int 4 6 6 4 1 3 1 5 1 6 ...
## $ aspro15 : int 6 3 7 2 1 5 7 2 7 7 ...
## $ aspro16 : int 7 5 5 4 2 5 1 4 4 5 ...
## $ aspro17 : int 4 4 1 5 1 5 2 4 1 4 ...
## $ aspro18 : int 7 4 4 5 1 6 1 4 7 5 ...
## $ aspro19 : int 1 4 1 1 1 3 1 5 1 5 ...
## $ aspro20 : int 4 6 5 4 1 4 1 4 7 4 ...
## $ aspro21 : int 7 6 4 5 1 5 1 5 7 6 ...
## $ aspro22 : int 1 4 1 5 1 2 1 3 1 2 ...
## $ sp11 : int 5 4 5 6 1 2 1 6 4 5 ...
## $ sp02 : int 1 3 6 6 4 2 1 4 3 1 ...
## $ sp24 : int 4 1 3 5 6 3 7 1 1 4 ...
## $ sp07 : int 2 3 3 6 4 2 1 4 1 1 ...
## [list output truncated]2 Calculating variables
2.1 Self-protection/denial
#recode items
data$sp23.i <- recode (data$sp23, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$sp24.i <- recode (data$sp24, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
#self-protection overall
data$sp <- rowMeans (data [c("sp01", "sp02", "sp03", "sp04", "sp05", "sp06", "sp07",
"sp08", "sp09", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15",
"sp19", "sp20", "sp21", "sp22",
"sp16", "sp17", "sp18",
"sp23.i", "sp24.i", "sp25", "sp26",
"sp27", "sp28", "sp29", "sp30")])
#rationalization
data$sp.1 <- rowMeans (data [c("sp01", "sp02", "sp03", "sp04", "sp05", "sp06", "sp07")])
#avoidance
data$sp.2 <- rowMeans (data [c("sp08", "sp09", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15")])
#denial of personal outcome severity
data$sp.3 <- rowMeans (data [c("sp19", "sp20", "sp21", "sp22")])
#denial of global outcome severity
data$sp.4 <- rowMeans (data [c("sp16", "sp17", "sp18")])
#denial of guilt
data$sp.5 <- rowMeans (data [c("sp23.i", "sp24.i", "sp25", "sp26")])
#literal denial
data$sp.6 <- rowMeans (data [c("sp27", "sp28", "sp29", "sp30")]) 2.2 Basic psychological needs
#recode items
data$gb11.i <- recode (data$gb11, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb13.i <- recode (data$gb13, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb10.i <- recode (data$gb10, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb12.i <- recode (data$gb12, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb05.i <- recode (data$gb05, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb04.i <- recode (data$gb04, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb06.i <- recode (data$gb06, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb17.i <- recode (data$gb17, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb19.i <- recode (data$gb19, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$gb18.i <- recode (data$gb18, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
#overall basic needs
data$gb <- rowMeans (data [c("gb07", "gb09", "gb08",
"gb01", "gb03", "gb02",
"gb15", "gb14", "gb16",
"gb11.i", "gb13.i", "gb12.i",
"gb05.i", "gb04.i", "gb06.i",
"gb17.i", "gb19.i","gb18.i")])
#relatedness satisfaction
data$gb.rs <- rowMeans (data [c("gb07", "gb09", "gb08")])
#relatedness frustration
data$gb.rf <- rowMeans (data [c("gb11", "gb13", "gb12")])
#autonomy satisfaction
data$gb.as <- rowMeans (data [c("gb01", "gb03", "gb02")])
#autonomy frustration
data$gb.af <- rowMeans (data [c("gb05", "gb04", "gb06")])
#competence satisfaction
data$gb.cs <- rowMeans (data [c("gb15", "gb14", "gb16")])
#competence frustration
data$gb.cf <- rowMeans (data [c("gb17", "gb19","gb18")]) 2.3 Aspirations
#recoding items so that scale range: 0-4
data$asimp01.i <- recode (data$asimp01, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp02.i <- recode (data$asimp02, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp03.i <- recode (data$asimp03, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp04.i <- recode (data$asimp04, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp05.i <- recode (data$asimp05, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp06.i <- recode (data$asimp06, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp07.i <- recode (data$asimp07, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp08.i <- recode (data$asimp08, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp09.i <- recode (data$asimp09, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp10.i <- recode (data$asimp10, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp11.i <- recode (data$asimp11, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp12.i <- recode (data$asimp12, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp13.i <- recode (data$asimp13, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp14.i <- recode (data$asimp14, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp15.i <- recode (data$asimp15, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp16.i <- recode (data$asimp16, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp17.i <- recode (data$asimp17, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp18.i <- recode (data$asimp18, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp19.i <- recode (data$asimp19, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp20.i <- recode (data$asimp20, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp21.i <- recode (data$asimp21, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$asimp22.i <- recode (data$asimp22, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro01.i <- recode (data$aspro01, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro02.i <- recode (data$aspro02, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro03.i <- recode (data$aspro03, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro04.i <- recode (data$aspro04, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro05.i <- recode (data$aspro05, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro06.i <- recode (data$aspro06, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro07.i <- recode (data$aspro07, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro08.i <- recode (data$aspro08, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro09.i <- recode (data$aspro09, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro10.i <- recode (data$aspro10, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro11.i <- recode (data$aspro11, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro12.i <- recode (data$aspro12, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro13.i <- recode (data$aspro13, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro14.i <- recode (data$aspro14, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro15.i <- recode (data$aspro15, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro16.i <- recode (data$aspro16, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro17.i <- recode (data$aspro17, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro18.i <- recode (data$aspro18, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro19.i <- recode (data$aspro19, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro20.i <- recode (data$aspro20, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro21.i <- recode (data$aspro21, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
data$aspro22.i <- recode (data$aspro22, '1=0; 2=1; 3=2; 4=3; 5=4; 6=5; 7=6')
#Affiliation
data$asimp.af <- rowMeans (data [c("asimp01.i", "asimp12.i")])
data$aspro.af <- rowMeans (data [c("aspro01.i", "aspro12.i")])
#Community Feeling
data$asimp.cf <- rowMeans (data [c("asimp02.i", "asimp13.i")])
data$aspro.cf <- rowMeans (data [c("aspro02.i", "aspro13.i")])
#Self-Acceptance
data$asimp.sa <- rowMeans (data [c("asimp10.i", "asimp21.i")])
data$aspro.sa <- rowMeans (data [c("aspro10.i", "aspro21.i")])
#Physical Health
data$asimp.ph <- rowMeans (data [c("asimp07.i", "asimp18.i")])
data$aspro.ph <- rowMeans (data [c("aspro07.i", "aspro18.i")])
#Safety
data$asimp.sf <- rowMeans (data [c("asimp09.i", "asimp20.i")])
data$aspro.sf <- rowMeans (data [c("aspro09.i", "aspro20.i")])
#Financial Success
data$asimp.fs <- rowMeans (data [c("asimp04.i", "asimp15.i")])
data$aspro.fs <- rowMeans (data [c("aspro04.i", "aspro15.i")])
#Image
data$asimp.im <- rowMeans (data [c("asimp06.i", "asimp17.i")])
data$aspro.im <- rowMeans (data [c("aspro06.i", "aspro17.i")])
#Popularity
data$asimp.po <- rowMeans (data [c("asimp08.i", "asimp19.i")])
data$aspro.po <- rowMeans (data [c("aspro08.i", "aspro19.i")])
#Conformity
data$asimp.co <- rowMeans (data [c("asimp03.i", "asimp14.i")])
data$aspro.co <- rowMeans (data [c("aspro03.i", "aspro14.i")])
#Hedonism
data$asimp.he <- rowMeans (data [c("asimp05.i", "asimp16.i")])
data$aspro.he <- rowMeans (data [c("aspro05.i", "aspro16.i")])
#Spirituality
data$asimp.sp <- rowMeans (data [c("asimp11.i", "asimp22.i")])
data$aspro.sp <- rowMeans (data [c("aspro11.i", "aspro22.i")])
#Aspirations importance
data$asimp.all <- ((data$asimp.fs + data$asimp.im + data$asimp.po + data$asimp.co)/4) -
((data$asimp.af + data$asimp.cf + data$asimp.sa + data$asimp.ph + data$asimp.sf)/5)
#Aspirations likelihood
data$aspro.all <- ((data$aspro.fs + data$aspro.im + data$aspro.po + data$aspro.co)/4) -
((data$aspro.af + data$aspro.cf + data$aspro.sa + data$aspro.ph + data$aspro.sf)/5)2.4 System justification
#recode items
data$sj03.i <- recode (data$sj03, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$sj07.i <- recode (data$sj07, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$sj <- rowMeans (data [c("sj01", "sj02", "sj03.i", "sj04",
"sj05", "sj06", "sj07.i", "sj08")]) 2.6 Human dominance over nature
#recode items
data$nd03.i <- recode (data$nd03, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$nd05.i <- recode (data$nd05, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$nd06.i <- recode (data$nd06, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$nd09.i <- recode (data$nd09, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$nd10.i <- recode (data$nd10, '1=7; 2=6; 3=5; 5=3; 6=2; 7=1')
data$nd <- rowMeans (data [c("nd01", "nd02", "nd03.i", "nd04", "nd05.i",
"nd06.i", "nd07", "nd08", "nd09.i", "nd10.i")]) 2.8 Climate anxiety
data$cas <- rowMeans (data [c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12", "cas13")]) 2.9 Anxiety and depressiveness
data$phq <- rowMeans (data [c("phq01", "phq02", "phq03", "phq04")])
#depressiveness
data$phq.d <- rowMeans (data [c("phq01", "phq02")])
#anxiety
data$phq.a <- rowMeans (data [c("phq03", "phq04")]) 2.10 Policy support
#define missings
is.na(data$ps01) <- which(data$ps01== -9)
is.na(data$ps02) <- which(data$ps02== -9)
data$ps <- rowMeans (data [c("ps01", "ps02")]) 2.11 Intentions
data$int <- rowMeans (data [c("int01", "int02", "int03")]) 2.12 Gender
#create dummy variable
data$gender.d <- recode (data$gender, '1=0; 2=1')3 Transforming variables
Transformed variables that should be used are: sp.3.tt, sp.4.tt, sp.5.t, sp.6.tt, cas.tt, phq.tt ## Self-protection/denial {.tabset} ### Square-root transformations
data$sp.5.t = sqrt(data$sp.5)3.0.1 Log transformations
data$sp.3.tt = log(data$sp.3)
data$sp.4.tt = log(data$sp.4)
data$sp.6.tt = log(data$sp.6)3.1 Climate anxiety
3.1.1 Log transformations
data$cas.tt = log(data$cas) 3.2 Anxiety and depressiveness
3.2.1 Log transformations
data$phq.tt = log(data$phq) 4 Distributions and descriptives
Average time [minutes] to answer the questionnaire
mean(data$TIME_SUM)/60 ## [1] 19.47379sd(data$TIME_SUM)/60 ## [1] 7.439106median(data$TIME_SUM)/60## [1] 18.33333min(data$TIME_SUM)/60 ## [1] 3.566667max(data$TIME_SUM)/60 ## [1] 54.633334.1 Self-protection/denial
subset.sp <- subset (data, select = c(sp, sp.1, sp.2, sp.3, sp.4, sp.5, sp.6))
describe (subset.sp)## vars n mean sd median trimmed mad min max range skew kurtosis se
## sp 1 1134 3.01 1.09 2.87 2.94 1.14 1 6.6 5.6 0.57 -0.06 0.03
## sp.1 2 1134 3.22 1.48 3.14 3.12 1.48 1 7.0 6.0 0.52 -0.24 0.04
## sp.2 3 1134 3.07 1.21 3.12 3.05 1.30 1 7.0 6.0 0.13 -0.45 0.04
## sp.3 4 1134 2.39 1.27 2.00 2.24 1.48 1 7.0 6.0 0.91 0.46 0.04
## sp.4 5 1134 2.63 1.64 2.00 2.40 1.48 1 7.0 6.0 0.96 0.08 0.05
## sp.5 6 1134 3.92 1.38 3.75 3.86 1.48 1 7.0 6.0 0.39 -0.39 0.04
## sp.6 7 1134 2.51 1.59 2.00 2.26 1.48 1 7.0 6.0 1.08 0.39 0.054.1.1 Overall
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp)
qqnorm(data$sp,
ylab="Sample Quantiles for sp")
qqline(data$sp,
col="red")
describe(data$sp)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.01 1.09 2.87 2.94 1.14 1 6.6 5.6 0.57 -0.06 0.03shapiro.test (data$sp)##
## Shapiro-Wilk normality test
##
## data: data$sp
## W = 0.97151, p-value = 3.865e-14par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.1.2 Rationalization
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.1)
qqnorm(data$sp.1,
ylab="Sample Quantiles for sp.1")
qqline(data$sp.1,
col="red")
describe(data$sp.1)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.22 1.48 3.14 3.12 1.48 1 7 6 0.52 -0.24 0.04shapiro.test (data$sp.1)##
## Shapiro-Wilk normality test
##
## data: data$sp.1
## W = 0.96323, p-value = 2.531e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.1.3 Avoidance
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.2)
qqnorm(data$sp.2,
ylab="Sample Quantiles for sp.2")
qqline(data$sp.2,
col="red")
describe(data$sp.2)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.07 1.21 3.12 3.05 1.3 1 7 6 0.13 -0.45 0.04shapiro.test (data$sp.2)##
## Shapiro-Wilk normality test
##
## data: data$sp.2
## W = 0.97783, p-value = 3.676e-12par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.1.4 Denial of personal outcome severity
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.3)
qqnorm(data$sp.3,
ylab="Sample Quantiles for sp.3")
qqline(data$sp.3,
col="red")
describe(data$sp.3)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.39 1.27 2 2.24 1.48 1 7 6 0.91 0.46 0.04shapiro.test (data$sp.3)##
## Shapiro-Wilk normality test
##
## data: data$sp.3
## W = 0.90464, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.1.5 Denial of global outcome severity
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.4)
qqnorm(data$sp.4,
ylab="Sample Quantiles for sp.4")
qqline(data$sp.4,
col="red")
describe(data$sp.4)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.63 1.64 2 2.4 1.48 1 7 6 0.96 0.08 0.05shapiro.test (data$sp.4)##
## Shapiro-Wilk normality test
##
## data: data$sp.4
## W = 0.87318, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.1.6 Denial of guilt
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.5)
qqnorm(data$sp.5,
ylab="Sample Quantiles for sp.5")
qqline(data$sp.5,
col="red")
describe(data$sp.5)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.92 1.38 3.75 3.86 1.48 1 7 6 0.39 -0.39 0.04shapiro.test (data$sp.5)##
## Shapiro-Wilk normality test
##
## data: data$sp.5
## W = 0.97391, p-value = 1.989e-13par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.1.7 Literal denial
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.6)
qqnorm(data$sp.6,
ylab="Sample Quantiles for sp.6")
qqline(data$sp.6,
col="red")
describe(data$sp.6)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.51 1.59 2 2.26 1.48 1 7 6 1.08 0.39 0.05shapiro.test (data$sp.6)##
## Shapiro-Wilk normality test
##
## data: data$sp.6
## W = 0.85904, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.2 Self-protection/denial - square-root transformed
4.2.1 Denial of guilt
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.5.t)
qqnorm(data$sp.5.t,
ylab="Sample Quantiles for sp.5.t")
qqline(data$sp.5.t,
col="red")
describe(data$sp.5.t)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.95 0.35 1.94 1.95 0.36 1 2.65 1.65 -0.04 -0.35 0.01shapiro.test (data$sp.5.t)##
## Shapiro-Wilk normality test
##
## data: data$sp.5.t
## W = 0.98737, p-value = 2.508e-08par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.3 Self-protection/denial - log transformed
4.3.1 Denial of personal outcome severity
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.3.tt)
qqnorm(data$sp.3.tt,
ylab="Sample Quantiles for sp.3.tt")
qqline(data$sp.3.tt,
col="red")
describe(data$sp.3.tt)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0.73 0.53 0.69 0.72 0.7 0 1.95 1.95 0.07 -1.13 0.02shapiro.test (data$sp.3.tt)##
## Shapiro-Wilk normality test
##
## data: data$sp.3.tt
## W = 0.93692, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.3.2 Denial of global outcome severity
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.4.tt)
qqnorm(data$sp.4.tt,
ylab="Sample Quantiles for sp.4.tt")
qqline(data$sp.4.tt,
col="red")
describe(data$sp.4.tt)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0.78 0.61 0.69 0.75 0.9 0 1.95 1.95 0.17 -1.23 0.02shapiro.test (data$sp.4.tt)##
## Shapiro-Wilk normality test
##
## data: data$sp.4.tt
## W = 0.91777, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.3.3 Literal denial
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sp.6.tt)
qqnorm(data$sp.6.tt,
ylab="Sample Quantiles for sp.6.tt")
qqline(data$sp.6.tt,
col="red")
describe(data$sp.6.tt)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0.73 0.61 0.69 0.69 0.83 0 1.95 1.95 0.25 -1.17 0.02shapiro.test (data$sp.6.tt)##
## Shapiro-Wilk normality test
##
## data: data$sp.6.tt
## W = 0.91232, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.4 Basic psychological needs
subset.gb <- subset (data, select = c(gb.rs, gb.rf, gb.as, gb.af, gb.cs, gb.cf))
describe (subset.gb)## vars n mean sd median trimmed mad min max range skew kurtosis se
## gb.rs 1 1134 5.38 1.19 5.67 5.48 0.99 1 7 6 -0.87 0.91 0.04
## gb.rf 2 1134 2.69 1.46 2.33 2.54 1.98 1 7 6 0.67 -0.31 0.04
## gb.as 3 1134 5.03 1.13 5.00 5.09 0.99 1 7 6 -0.53 0.16 0.03
## gb.af 4 1134 3.14 1.47 3.00 3.07 1.48 1 7 6 0.32 -0.67 0.04
## gb.cs 5 1134 4.37 1.32 4.33 4.41 1.48 1 7 6 -0.26 -0.22 0.04
## gb.cf 6 1134 2.70 1.38 2.50 2.59 1.73 1 7 6 0.56 -0.50 0.044.4.1 Relatedness satisfaction
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$gb.rs)
qqnorm(data$gb.rs,
ylab="Sample Quantiles for gb.rs")
qqline(data$gb.rs,
col="red")
describe(data$gb.rs)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 5.38 1.19 5.67 5.48 0.99 1 7 6 -0.87 0.91 0.04shapiro.test (data$gb.rs)##
## Shapiro-Wilk normality test
##
## data: data$gb.rs
## W = 0.93772, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.4.2 Relatedness frustration
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$gb.rf)
qqnorm(data$gb.rf,
ylab="Sample Quantiles for gb.rf")
qqline(data$gb.rf,
col="red")
describe(data$gb.rf)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.69 1.46 2.33 2.54 1.98 1 7 6 0.67 -0.31 0.04shapiro.test (data$gb.rf)##
## Shapiro-Wilk normality test
##
## data: data$gb.rf
## W = 0.92029, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.4.3 Autonomy satisfaction
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$gb.as)
qqnorm(data$gb.as,
ylab="Sample Quantiles for gb.as")
qqline(data$gb.as,
col="red")
describe(data$gb.as)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 5.03 1.13 5 5.09 0.99 1 7 6 -0.53 0.16 0.03shapiro.test (data$gb.as)##
## Shapiro-Wilk normality test
##
## data: data$gb.as
## W = 0.97132, p-value = 3.412e-14par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.4.4 Autonomy frustration
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$gb.af)
qqnorm(data$gb.af,
ylab="Sample Quantiles for gb.af")
qqline(data$gb.af,
col="red")
describe(data$gb.af)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.14 1.47 3 3.07 1.48 1 7 6 0.32 -0.67 0.04shapiro.test (data$gb.af)##
## Shapiro-Wilk normality test
##
## data: data$gb.af
## W = 0.96159, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.4.5 Competence satisfaction
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$gb.cs)
qqnorm(data$gb.cs,
ylab="Sample Quantiles for gb.cs")
qqline(data$gb.cs,
col="red")
describe(data$gb.cs)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 4.37 1.32 4.33 4.41 1.48 1 7 6 -0.26 -0.22 0.04shapiro.test (data$gb.cs)##
## Shapiro-Wilk normality test
##
## data: data$gb.cs
## W = 0.98286, p-value = 2.652e-10par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.4.6 Competence frustration
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$gb.cf)
qqnorm(data$gb.cf,
ylab="Sample Quantiles for gb.cf")
qqline(data$gb.cf,
col="red")
describe(data$gb.cf)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.7 1.38 2.5 2.59 1.73 1 7 6 0.56 -0.5 0.04shapiro.test (data$gb.cf)##
## Shapiro-Wilk normality test
##
## data: data$gb.cf
## W = 0.93473, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.5 Aspirations
subset.as <- subset (data, select = c(asimp.all, aspro.all,
asimp.af, aspro.af, #Affiliation
asimp.cf, aspro.cf, #Community Feeling
asimp.sa, aspro.sa, #Self-Acceptance
asimp.ph, aspro.ph, #Physical Health
asimp.sf, aspro.sf, #Safety
asimp.fs, aspro.fs, #Financial Success
asimp.im, aspro.im, #Image
asimp.po, aspro.po, #Popularity
asimp.co, aspro.co, #Conformity
asimp.he, aspro.he, #Hedonism
asimp.sp, aspro.sp #Spirituality
))
describe (subset.as)## vars n mean sd median trimmed mad min max range skew
## asimp.all 1 1134 -2.03 1.15 -2.01 -2.03 1.17 -5.38 1.15 6.53 -0.03
## aspro.all 2 1134 -1.22 0.97 -1.09 -1.18 0.98 -4.10 1.85 5.95 -0.29
## asimp.af 3 1134 4.62 1.25 5.00 4.76 1.48 0.00 6.00 6.00 -0.96
## aspro.af 4 1134 4.02 1.42 4.00 4.13 1.48 0.00 6.00 6.00 -0.62
## asimp.cf 5 1134 3.83 1.46 4.00 3.92 1.48 0.00 6.00 6.00 -0.53
## aspro.cf 6 1134 3.31 1.50 3.50 3.36 1.48 0.00 6.00 6.00 -0.27
## asimp.sa 7 1134 4.77 1.08 5.00 4.90 1.48 0.00 6.00 6.00 -0.90
## aspro.sa 8 1134 3.94 1.30 4.00 3.99 1.48 0.00 6.00 6.00 -0.38
## asimp.ph 9 1134 5.06 1.13 5.50 5.25 0.74 0.00 6.00 6.00 -1.51
## aspro.ph 10 1134 3.58 1.57 4.00 3.68 1.48 0.00 6.00 6.00 -0.54
## asimp.sf 11 1134 5.09 1.00 5.50 5.25 0.74 0.00 6.00 6.00 -1.22
## aspro.sf 12 1134 4.35 1.26 4.50 4.46 1.48 0.00 6.00 6.00 -0.80
## asimp.fs 13 1134 3.73 1.58 4.00 3.83 1.48 0.00 6.00 6.00 -0.49
## aspro.fs 14 1134 2.84 1.71 3.00 2.84 2.22 0.00 6.00 6.00 -0.06
## asimp.im 15 1134 1.68 1.47 1.50 1.54 2.22 0.00 6.00 6.00 0.60
## aspro.im 16 1134 2.11 1.55 2.00 2.01 1.48 0.00 6.00 6.00 0.41
## asimp.po 17 1134 1.71 1.51 1.50 1.54 1.48 0.00 6.00 6.00 0.71
## aspro.po 18 1134 2.08 1.57 2.00 1.96 1.48 0.00 6.00 6.00 0.44
## asimp.co 19 1134 3.46 1.34 3.50 3.51 1.48 0.00 6.00 6.00 -0.37
## aspro.co 20 1134 3.46 1.34 3.50 3.52 1.48 0.00 6.00 6.00 -0.46
## asimp.he 21 1134 3.32 1.45 3.50 3.36 1.48 0.00 6.00 6.00 -0.27
## aspro.he 22 1134 3.08 1.52 3.00 3.10 1.48 0.00 6.00 6.00 -0.12
## asimp.sp 23 1134 1.37 1.78 0.50 1.05 0.74 0.00 6.00 6.00 1.15
## aspro.sp 24 1134 1.50 1.84 0.50 1.20 0.74 0.00 6.00 6.00 1.02
## kurtosis se
## asimp.all -0.34 0.03
## aspro.all -0.06 0.03
## asimp.af 0.77 0.04
## aspro.af 0.04 0.04
## asimp.cf -0.14 0.04
## aspro.cf -0.45 0.04
## asimp.sa 0.65 0.03
## aspro.sa -0.28 0.04
## asimp.ph 2.60 0.03
## aspro.ph -0.30 0.05
## asimp.sf 1.36 0.03
## aspro.sf 0.37 0.04
## asimp.fs -0.41 0.05
## aspro.fs -0.87 0.05
## asimp.im -0.46 0.04
## aspro.im -0.46 0.05
## asimp.po -0.21 0.04
## aspro.po -0.47 0.05
## asimp.co -0.07 0.04
## aspro.co 0.06 0.04
## asimp.he -0.33 0.04
## aspro.he -0.62 0.05
## asimp.sp 0.15 0.05
## aspro.sp -0.15 0.054.5.1 Aspirations importance
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$asimp.all)
qqnorm(data$asimp.all,
ylab="Sample Quantiles for asimp.all")
qqline(data$asimp.all,
col="red")
describe(data$asimp.all)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1134 -2.03 1.15 -2.01 -2.03 1.17 -5.38 1.15 6.53 -0.03 -0.34
## se
## X1 0.03shapiro.test (data$asimp.all)##
## Shapiro-Wilk normality test
##
## data: data$asimp.all
## W = 0.99742, p-value = 0.06707par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.5.2 Aspirations likelihood
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$aspro.all)
qqnorm(data$aspro.all,
ylab="Sample Quantiles for aspro.all")
qqline(data$aspro.all,
col="red")
describe(data$aspro.all)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 -1.22 0.97 -1.09 -1.18 0.98 -4.1 1.85 5.95 -0.29 -0.06 0.03shapiro.test (data$aspro.all)##
## Shapiro-Wilk normality test
##
## data: data$aspro.all
## W = 0.9888, p-value = 1.301e-07par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.6 System justification
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sj)
qqnorm(data$sj,
ylab="Sample Quantiles for sj")
qqline(data$sj,
col="red")
describe(data$sj)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.93 1.1 4 3.96 1.11 1 7 6 -0.26 -0.2 0.03shapiro.test (data$sj)##
## Shapiro-Wilk normality test
##
## data: data$sj
## W = 0.99183, p-value = 6.332e-06par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.7 Social dominance orientation
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$sdo)
qqnorm(data$sdo,
ylab="Sample Quantiles for sdo")
qqline(data$sdo,
col="red")
describe(data$sdo)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.95 1.02 3 2.93 1.11 1 6.62 5.62 0.27 0.06 0.03shapiro.test (data$sdo)##
## Shapiro-Wilk normality test
##
## data: data$sdo
## W = 0.98512, p-value = 2.342e-09par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.8 Human dominance over nature
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$nd)
qqnorm(data$nd,
ylab="Sample Quantiles for nd")
qqline(data$nd,
col="red")
describe(data$nd)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.54 1.14 2.4 2.47 1.26 1 7 6 0.59 -0.16 0.03shapiro.test (data$nd)##
## Shapiro-Wilk normality test
##
## data: data$nd
## W = 0.95141, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.9 Right-wing authoritarianism
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$rwa)
qqnorm(data$rwa,
ylab="Sample Quantiles for rwa")
qqline(data$rwa,
col="red")
describe(data$rwa)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.55 1.01 3.67 3.57 0.99 1 6.5 5.5 -0.16 -0.41 0.03shapiro.test (data$rwa)##
## Shapiro-Wilk normality test
##
## data: data$rwa
## W = 0.99012, p-value = 6.51e-07par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.10 Climate anxiety
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas)
qqnorm(data$cas,
ylab="Sample Quantiles for cas")
qqline(data$cas,
col="red")
describe(data$cas)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.86 0.91 1.58 1.72 0.86 1 7 6 1.62 3.71 0.03shapiro.test (data$cas)##
## Shapiro-Wilk normality test
##
## data: data$cas
## W = 0.84225, p-value < 2.2e-16boxplot(data$cas, las=2)
par(mfrow = c(1, 1)) # Return plotting panel to 1 section
prop.table (table (data$cas>=2)) #people scoring 2 or higher##
## FALSE TRUE
## 0.6340388 0.3659612prop.table (table (data$cas>=3)) #people scoring 3 or higher##
## FALSE TRUE
## 0.8791887 0.12081134.10.1 Climate anxiety - log transformed
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas.tt)
qqnorm(data$cas.tt,
ylab="Sample Quantiles for cas.tt")
qqline(data$cas.tt,
col="red")
describe(data$cas.tt)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0.52 0.43 0.46 0.48 0.52 0 1.95 1.95 0.54 -0.51 0.01shapiro.test (data$cas.tt)##
## Shapiro-Wilk normality test
##
## data: data$cas.tt
## W = 0.93255, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.10.2 Climate anxiety - individual items
4.10.2.1 cas01
Thinking about climate change makes it difficult for me to concentrate. Über den Klimawandel nachzudenken bereitet mir Konzentrationsschwierigkeiten.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas01)
qqnorm(data$cas01,
ylab="Sample Quantiles for cas01")
qqline(data$cas01,
col="red")
boxplot(data$cas01, las=2)
describe(data$cas01)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.85 1.28 1 1.61 0 1 7 6 1.51 1.72 0.04shapiro.test (data$cas01)##
## Shapiro-Wilk normality test
##
## data: data$cas01
## W = 0.70511, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.2 cas02
Thinking about climate change makes it difficult for me to sleep. Über den Klimawandel nachzudenken bereitet mir Schlafschwierigkeiten.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas02)
qqnorm(data$cas02,
ylab="Sample Quantiles for cas02")
qqline(data$cas02,
col="red")
boxplot(data$cas02, las=2)
describe(data$cas02)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.76 1.27 1 1.49 0 1 7 6 1.73 2.38 0.04shapiro.test (data$cas02)##
## Shapiro-Wilk normality test
##
## data: data$cas02
## W = 0.65649, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.3 cas03
I have nightmares about climate change. Ich habe Albträume über den Klimawandel.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas03)
qqnorm(data$cas03,
ylab="Sample Quantiles for cas03")
qqline(data$cas03,
col="red")
boxplot(data$cas03, las=2)
describe(data$cas03)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.47 1.04 1 1.19 0 1 7 6 2.58 6.75 0.03shapiro.test (data$cas03)##
## Shapiro-Wilk normality test
##
## data: data$cas03
## W = 0.51652, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.4 cas04
I find myself crying because of climate change. Ich ertappe mich dabei, dass ich wegen des Klimawandels weine.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas04)
qqnorm(data$cas04,
ylab="Sample Quantiles for cas04")
qqline(data$cas04,
col="red")
boxplot(data$cas04, las=2)
describe(data$cas04)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.4 1.02 1 1.12 0 1 7 6 2.87 8.33 0.03shapiro.test (data$cas04)##
## Shapiro-Wilk normality test
##
## data: data$cas04
## W = 0.46067, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.5 cas05
I think, “why can’t I handle climate change better?”. Ich frage mich, warum ich nicht besser mit dem Klimawandel umgehen kann.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas05)
qqnorm(data$cas05,
ylab="Sample Quantiles for cas05")
qqline(data$cas05,
col="red")
boxplot(data$cas05, las=2)
describe(data$cas05)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.47 1.62 2 2.28 1.48 1 7 6 0.73 -0.58 0.05shapiro.test (data$cas05)##
## Shapiro-Wilk normality test
##
## data: data$cas05
## W = 0.82538, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.6 cas06
I go away by myself and think about why I feel this way about climate change. Ich nehme mir bewusst Zeit, um über meine Gefühle bezüglich des Klimawandels nachzudenken.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas06)
qqnorm(data$cas06,
ylab="Sample Quantiles for cas06")
qqline(data$cas06,
col="red")
boxplot(data$cas06, las=2)
describe(data$cas06)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.91 1.81 3 2.76 2.97 1 7 6 0.4 -1.13 0.05shapiro.test (data$cas06)##
## Shapiro-Wilk normality test
##
## data: data$cas06
## W = 0.86398, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.7 cas07
I write down my thoughts about climate change and analyze them. Ich schreibe meine Gedanken über den Klimawandel auf und analysiere sie.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas07)
qqnorm(data$cas07,
ylab="Sample Quantiles for cas07")
qqline(data$cas07,
col="red")
boxplot(data$cas07, las=2)
describe(data$cas07)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.42 1.04 1 1.13 0 1 7 6 2.93 9.06 0.03shapiro.test (data$cas07)##
## Shapiro-Wilk normality test
##
## data: data$cas07
## W = 0.4699, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.8 cas08
I think, “why do I react to climate change this way?”. Ich frage mich, warum ich so und nicht anders auf den Klimawandel reagiere.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas08)
qqnorm(data$cas08,
ylab="Sample Quantiles for cas08")
qqline(data$cas08,
col="red")
boxplot(data$cas08, las=2)
describe(data$cas08)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.42 1.55 2 2.23 1.48 1 7 6 0.71 -0.56 0.05shapiro.test (data$cas08)##
## Shapiro-Wilk normality test
##
## data: data$cas08
## W = 0.82267, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.9 cas09
My concerns about climate change make it hard for me to have fun with my family or friends. Meine Sorgen um den Klimawandel erschweren es mir, Spaß mit meiner Familie und mit Freund*innen zu haben.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas09)
qqnorm(data$cas09,
ylab="Sample Quantiles for cas09")
qqline(data$cas09,
col="red")
boxplot(data$cas09, las=2)
describe(data$cas09)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.87 1.29 1 1.62 0 1 7 6 1.47 1.48 0.04shapiro.test (data$cas09)##
## Shapiro-Wilk normality test
##
## data: data$cas09
## W = 0.70934, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.10 cas10
I have problems balancing my concerns about sustainability with the needs of my family. Ich habe Schwierigkeiten, meine Sorgen um Nachhaltigkeit mit den Bedürfnissen meiner Familie unter einen Hut zu kriegen.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas10)
qqnorm(data$cas10,
ylab="Sample Quantiles for cas10")
qqline(data$cas10,
col="red")
boxplot(data$cas10, las=2)
describe(data$cas10)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 2.39 1.63 2 2.16 1.48 1 7 6 0.94 -0.18 0.05shapiro.test (data$cas10)##
## Shapiro-Wilk normality test
##
## data: data$cas10
## W = 0.81025, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.11 cas11
My concerns about climate change interfere with my ability to get work or school assignments done. Meine Sorgen um den Klimawandel beeinträchtigen meine Fähigkeit, Arbeits- oder Schulaufgaben zu bewältigen.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas11)
qqnorm(data$cas11,
ylab="Sample Quantiles for cas11")
qqline(data$cas11,
col="red")
boxplot(data$cas11, las=2)
describe(data$cas11)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.64 1.14 1 1.38 0 1 7 6 1.91 3.31 0.03shapiro.test (data$cas11)##
## Shapiro-Wilk normality test
##
## data: data$cas11
## W = 0.62295, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.12 cas12
My concerns about climate change undermine my ability to work to my potential. Meine Sorgen um den Klimawandel untergraben meine Fähigkeit, mein volles Potential auszuschöpfen/zu entfalten.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas12)
qqnorm(data$cas12,
ylab="Sample Quantiles for cas12")
qqline(data$cas12,
col="red")
boxplot(data$cas12, las=2)
describe(data$cas12)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.81 1.25 1 1.57 0 1 7 6 1.56 1.94 0.04shapiro.test (data$cas12)##
## Shapiro-Wilk normality test
##
## data: data$cas12
## W = 0.6902, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.10.2.13 cas13
My friends say I think about climate change too much. Meine Freund*innen sagen, dass ich zu viel über den Klimawandel nachdenke.
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$cas13)
qqnorm(data$cas13,
ylab="Sample Quantiles for cas13")
qqline(data$cas13,
col="red")
boxplot(data$cas13, las=2)
describe(data$cas13)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.84 1.34 1 1.58 0 1 7 6 1.56 1.61 0.04shapiro.test (data$cas13)##
## Shapiro-Wilk normality test
##
## data: data$cas13
## W = 0.68319, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section
4.11 Anxiety and depressiveness
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$phq)
qqnorm(data$phq,
ylab="Sample Quantiles for phq")
qqline(data$phq,
col="red")
describe(data$phq)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 1.68 0.74 1.5 1.55 0.74 1 4 3 1.29 1.17 0.02shapiro.test (data$phq)##
## Shapiro-Wilk normality test
##
## data: data$phq
## W = 0.83667, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.11.1 Anxiety and depressiveness - log transformed
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$phq.tt)
qqnorm(data$phq.tt,
ylab="Sample Quantiles for phq.tt")
qqline(data$phq.tt,
col="red")
describe(data$phq.tt)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0.43 0.4 0.41 0.39 0.6 0 1.39 1.39 0.57 -0.63 0.01shapiro.test (data$phq.tt)##
## Shapiro-Wilk normality test
##
## data: data$phq.tt
## W = 0.89581, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.12 Policy support
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$ps)
qqnorm(data$ps,
ylab="Sample Quantiles for ps")
qqline(data$ps,
col="red")
describe(data$ps)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 71.43 21.06 74 73.18 22.24 1 101 100 -0.84 0.75 0.64shapiro.test (data$ps)##
## Shapiro-Wilk normality test
##
## data: data$ps
## W = 0.94441, p-value < 2.2e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.13 Intentions
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$int)
qqnorm(data$int,
ylab="Sample Quantiles for int")
qqline(data$int,
col="red")
describe(data$int)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 3.54 1.28 3.33 3.5 1.48 1 7 6 0.29 -0.2 0.04shapiro.test (data$int)##
## Shapiro-Wilk normality test
##
## data: data$int
## W = 0.98011, p-value = 2.335e-11par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.14 Socio-demographics
subset.sd <- subset (data, select = c(age, income, pol.orient))
describe (subset.sd)## vars n mean sd median trimmed mad min max range skew
## age 1 1134 43.73 13.96 44 43.80 17.79 18 69 51 -0.06
## income 2 1134 4.90 2.85 4 4.64 2.97 1 11 10 0.73
## pol.orient 3 1134 44.66 19.34 48 44.68 14.83 1 101 100 0.00
## kurtosis se
## age -1.10 0.41
## income -0.34 0.08
## pol.orient 0.20 0.574.14.1 Age
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$age)
qqnorm(data$age,
ylab="Sample Quantiles for age")
qqline(data$age,
col="red")
describe(data$age)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 43.73 13.96 44 43.8 17.79 18 69 51 -0.06 -1.1 0.41shapiro.test (data$age)##
## Shapiro-Wilk normality test
##
## data: data$age
## W = 0.96462, p-value = 5.558e-16par(mfrow = c(1, 1)) # Return plotting panel to 1 section4.14.2 Gender
table (data$gender.d)##
## 0 1
## 564 570prop.table (table (data$gender.d))##
## 0 1
## 0.4973545 0.50264554.14.3 Income
table (data$income)##
## 1 2 3 4 5 6 7 8 9 10 11
## 98 138 180 184 157 106 65 42 33 46 85prop.table (table (data$income))##
## 1 2 3 4 5 6 7
## 0.08641975 0.12169312 0.15873016 0.16225750 0.13844797 0.09347443 0.05731922
## 8 9 10 11
## 0.03703704 0.02910053 0.04056437 0.074955914.14.4 Education
table (data$edu)##
## 1 2 3 4 5 6 7 8
## 5 3 60 177 263 286 338 2prop.table (table (data$edu))##
## 1 2 3 4 5 6
## 0.004409171 0.002645503 0.052910053 0.156084656 0.231922399 0.252204586
## 7 8
## 0.298059965 0.0017636684.14.5 Political orientation
par(mfrow = c(1, 2)) # Split the plotting panel into a 1 x 2 grid
plotNormalHistogram(data$pol.orient)
qqnorm(data$pol.orient,
ylab="Sample Quantiles for pol.orient")
qqline(data$pol.orient,
col="red")
describe(data$pol.orient)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 44.66 19.34 48 44.68 14.83 1 101 100 0 0.2 0.57shapiro.test (data$pol.orient)##
## Shapiro-Wilk normality test
##
## data: data$pol.orient
## W = 0.97699, p-value = 1.908e-12par(mfrow = c(1, 1)) # Return plotting panel to 1 section5 Correlations
All variables
corr.test (data [,c("sp", "sp.1", "sp.2", "sp.3", "sp.4", "sp.5", "sp.6",
"gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af", "gb.cf",
"asimp.all", "aspro.all",
"sj", "sdo", "nd", "rwa",
"cas",
"phq",
"ps", "int",
"age", "income", "pol.orient")],
method = "spearman")## Call:corr.test(x = data[, c("sp", "sp.1", "sp.2", "sp.3", "sp.4",
## "sp.5", "sp.6", "gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af",
## "gb.cf", "asimp.all", "aspro.all", "sj", "sdo", "nd", "rwa",
## "cas", "phq", "ps", "int", "age", "income", "pol.orient")],
## method = "spearman")
## Correlation matrix
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 gb.rs gb.as gb.cs gb.rf
## sp 1.00 0.87 0.67 0.79 0.85 0.64 0.81 -0.14 -0.06 -0.02 0.11
## sp.1 0.87 1.00 0.46 0.61 0.65 0.55 0.61 -0.14 -0.04 -0.03 0.06
## sp.2 0.67 0.46 1.00 0.47 0.44 0.12 0.39 -0.12 -0.10 -0.05 0.21
## sp.3 0.79 0.61 0.47 1.00 0.73 0.46 0.65 -0.14 -0.07 0.01 0.11
## sp.4 0.85 0.65 0.44 0.73 1.00 0.57 0.83 -0.09 -0.04 0.03 0.09
## sp.5 0.64 0.55 0.12 0.46 0.57 1.00 0.53 -0.02 0.07 0.04 -0.10
## sp.6 0.81 0.61 0.39 0.65 0.83 0.53 1.00 -0.10 -0.05 0.00 0.09
## gb.rs -0.14 -0.14 -0.12 -0.14 -0.09 -0.02 -0.10 1.00 0.48 0.31 -0.38
## gb.as -0.06 -0.04 -0.10 -0.07 -0.04 0.07 -0.05 0.48 1.00 0.31 -0.33
## gb.cs -0.02 -0.03 -0.05 0.01 0.03 0.04 0.00 0.31 0.31 1.00 -0.01
## gb.rf 0.11 0.06 0.21 0.11 0.09 -0.10 0.09 -0.38 -0.33 -0.01 1.00
## gb.af 0.09 0.07 0.14 0.05 0.07 -0.07 0.08 -0.27 -0.47 0.04 0.63
## gb.cf 0.05 0.03 0.18 0.05 0.03 -0.18 0.03 -0.32 -0.39 -0.10 0.66
## asimp.all 0.27 0.19 0.29 0.28 0.27 0.03 0.22 -0.16 -0.13 0.12 0.21
## aspro.all 0.23 0.21 0.23 0.23 0.18 0.02 0.16 -0.20 -0.19 0.03 0.24
## sj -0.14 -0.15 -0.03 0.01 -0.11 -0.19 -0.15 0.13 0.07 0.11 -0.10
## sdo 0.41 0.35 0.24 0.37 0.37 0.26 0.38 -0.10 -0.05 0.04 0.07
## nd 0.41 0.35 0.26 0.40 0.39 0.23 0.36 -0.07 -0.04 0.04 0.06
## rwa 0.42 0.34 0.27 0.31 0.38 0.32 0.39 -0.03 0.04 0.04 0.05
## cas -0.09 -0.13 0.20 -0.01 -0.12 -0.37 -0.09 -0.12 -0.14 0.02 0.29
## phq -0.09 -0.08 0.07 -0.09 -0.12 -0.20 -0.11 -0.27 -0.34 -0.18 0.45
## ps -0.53 -0.41 -0.27 -0.46 -0.54 -0.40 -0.52 0.07 0.08 -0.03 -0.04
## int -0.46 -0.44 -0.22 -0.32 -0.39 -0.38 -0.33 0.06 0.03 0.14 0.12
## age 0.05 0.05 0.01 0.01 0.01 0.14 0.02 0.05 0.16 -0.11 -0.20
## income 0.01 0.02 -0.03 0.03 0.03 0.00 0.02 0.05 -0.01 0.11 -0.10
## pol.orient 0.38 0.34 0.14 0.29 0.36 0.32 0.35 -0.04 -0.01 -0.01 0.00
## gb.af gb.cf asimp.all aspro.all sj sdo nd rwa cas phq
## sp 0.09 0.05 0.27 0.23 -0.14 0.41 0.41 0.42 -0.09 -0.09
## sp.1 0.07 0.03 0.19 0.21 -0.15 0.35 0.35 0.34 -0.13 -0.08
## sp.2 0.14 0.18 0.29 0.23 -0.03 0.24 0.26 0.27 0.20 0.07
## sp.3 0.05 0.05 0.28 0.23 0.01 0.37 0.40 0.31 -0.01 -0.09
## sp.4 0.07 0.03 0.27 0.18 -0.11 0.37 0.39 0.38 -0.12 -0.12
## sp.5 -0.07 -0.18 0.03 0.02 -0.19 0.26 0.23 0.32 -0.37 -0.20
## sp.6 0.08 0.03 0.22 0.16 -0.15 0.38 0.36 0.39 -0.09 -0.11
## gb.rs -0.27 -0.32 -0.16 -0.20 0.13 -0.10 -0.07 -0.03 -0.12 -0.27
## gb.as -0.47 -0.39 -0.13 -0.19 0.07 -0.05 -0.04 0.04 -0.14 -0.34
## gb.cs 0.04 -0.10 0.12 0.03 0.11 0.04 0.04 0.04 0.02 -0.18
## gb.rf 0.63 0.66 0.21 0.24 -0.10 0.07 0.06 0.05 0.29 0.45
## gb.af 1.00 0.62 0.15 0.23 -0.14 0.06 0.06 -0.01 0.21 0.48
## gb.cf 0.62 1.00 0.19 0.23 -0.05 -0.01 0.08 -0.07 0.31 0.56
## asimp.all 0.15 0.19 1.00 0.61 0.08 0.31 0.33 0.26 0.18 0.04
## aspro.all 0.23 0.23 0.61 1.00 -0.04 0.24 0.22 0.18 0.14 0.14
## sj -0.14 -0.05 0.08 -0.04 1.00 0.06 0.09 -0.17 0.06 -0.13
## sdo 0.06 -0.01 0.31 0.24 0.06 1.00 0.37 0.45 0.01 -0.11
## nd 0.06 0.08 0.33 0.22 0.09 0.37 1.00 0.30 0.10 -0.05
## rwa -0.01 -0.07 0.26 0.18 -0.17 0.45 0.30 1.00 0.01 -0.09
## cas 0.21 0.31 0.18 0.14 0.06 0.01 0.10 0.01 1.00 0.25
## phq 0.48 0.56 0.04 0.14 -0.13 -0.11 -0.05 -0.09 0.25 1.00
## ps -0.03 0.01 -0.24 -0.15 0.01 -0.35 -0.39 -0.30 0.15 0.09
## int 0.07 0.16 -0.02 -0.07 0.08 -0.23 -0.15 -0.22 0.45 0.14
## age -0.22 -0.29 -0.27 -0.24 -0.06 0.07 -0.02 0.20 0.01 -0.16
## income -0.07 -0.12 0.07 0.05 0.08 0.13 0.05 0.04 -0.03 -0.13
## pol.orient 0.01 -0.08 0.12 0.11 -0.05 0.40 0.23 0.44 -0.12 -0.09
## ps int age income pol.orient
## sp -0.53 -0.46 0.05 0.01 0.38
## sp.1 -0.41 -0.44 0.05 0.02 0.34
## sp.2 -0.27 -0.22 0.01 -0.03 0.14
## sp.3 -0.46 -0.32 0.01 0.03 0.29
## sp.4 -0.54 -0.39 0.01 0.03 0.36
## sp.5 -0.40 -0.38 0.14 0.00 0.32
## sp.6 -0.52 -0.33 0.02 0.02 0.35
## gb.rs 0.07 0.06 0.05 0.05 -0.04
## gb.as 0.08 0.03 0.16 -0.01 -0.01
## gb.cs -0.03 0.14 -0.11 0.11 -0.01
## gb.rf -0.04 0.12 -0.20 -0.10 0.00
## gb.af -0.03 0.07 -0.22 -0.07 0.01
## gb.cf 0.01 0.16 -0.29 -0.12 -0.08
## asimp.all -0.24 -0.02 -0.27 0.07 0.12
## aspro.all -0.15 -0.07 -0.24 0.05 0.11
## sj 0.01 0.08 -0.06 0.08 -0.05
## sdo -0.35 -0.23 0.07 0.13 0.40
## nd -0.39 -0.15 -0.02 0.05 0.23
## rwa -0.30 -0.22 0.20 0.04 0.44
## cas 0.15 0.45 0.01 -0.03 -0.12
## phq 0.09 0.14 -0.16 -0.13 -0.09
## ps 1.00 0.37 0.10 -0.05 -0.36
## int 0.37 1.00 -0.10 -0.03 -0.32
## age 0.10 -0.10 1.00 0.11 0.05
## income -0.05 -0.03 0.11 1.00 0.11
## pol.orient -0.36 -0.32 0.05 0.11 1.00
## Sample Size
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 gb.rs gb.as gb.cs gb.rf gb.af
## sp 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sp.1 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sp.2 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sp.3 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sp.4 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sp.5 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sp.6 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.rs 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.as 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.cs 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.rf 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.af 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.cf 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## asimp.all 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## aspro.all 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sj 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## sdo 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## nd 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## rwa 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## phq 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## ps 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087
## int 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## age 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## income 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## pol.orient 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## gb.cf asimp.all aspro.all sj sdo nd rwa cas phq ps int
## sp 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sp.1 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sp.2 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sp.3 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sp.4 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sp.5 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sp.6 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## gb.rs 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## gb.as 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## gb.cs 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## gb.rf 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## gb.af 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## gb.cf 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## asimp.all 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## aspro.all 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sj 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## sdo 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## nd 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## rwa 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## cas 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## phq 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## ps 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087
## int 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## age 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## income 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## pol.orient 1134 1134 1134 1134 1134 1134 1134 1134 1134 1087 1134
## age income pol.orient
## sp 1134 1134 1134
## sp.1 1134 1134 1134
## sp.2 1134 1134 1134
## sp.3 1134 1134 1134
## sp.4 1134 1134 1134
## sp.5 1134 1134 1134
## sp.6 1134 1134 1134
## gb.rs 1134 1134 1134
## gb.as 1134 1134 1134
## gb.cs 1134 1134 1134
## gb.rf 1134 1134 1134
## gb.af 1134 1134 1134
## gb.cf 1134 1134 1134
## asimp.all 1134 1134 1134
## aspro.all 1134 1134 1134
## sj 1134 1134 1134
## sdo 1134 1134 1134
## nd 1134 1134 1134
## rwa 1134 1134 1134
## cas 1134 1134 1134
## phq 1134 1134 1134
## ps 1087 1087 1087
## int 1134 1134 1134
## age 1134 1134 1134
## income 1134 1134 1134
## pol.orient 1134 1134 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 gb.rs gb.as gb.cs gb.rf gb.af
## sp 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 0.04 0.31
## sp.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00
## sp.2 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.05 1.00 0.00 0.00
## sp.3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 0.02 1.00
## sp.4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 1.00 1.00 0.38 1.00
## sp.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 0.09 1.00
## sp.6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 1.00 1.00 0.28 0.51
## gb.rs 0.00 0.00 0.00 0.00 0.00 0.56 0.00 0.00 0.00 0.00 0.00 0.00
## gb.as 0.03 0.13 0.00 0.02 0.17 0.02 0.08 0.00 0.00 0.00 0.00 0.00
## gb.cs 0.46 0.24 0.09 0.78 0.30 0.14 0.92 0.00 0.00 0.00 1.00 1.00
## gb.rf 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.63 0.00 0.00
## gb.af 0.00 0.02 0.00 0.08 0.01 0.01 0.00 0.00 0.00 0.21 0.00 0.00
## gb.cf 0.09 0.24 0.00 0.07 0.36 0.00 0.28 0.00 0.00 0.00 0.00 0.00
## asimp.all 0.00 0.00 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00
## aspro.all 0.00 0.00 0.00 0.00 0.00 0.51 0.00 0.00 0.00 0.39 0.00 0.00
## sj 0.00 0.00 0.37 0.72 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00
## sdo 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.16 0.02 0.06
## nd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.14 0.14 0.04 0.04
## rwa 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.16 0.14 0.07 0.62
## cas 0.00 0.00 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.58 0.00 0.00
## phq 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## ps 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.01 0.32 0.14 0.25
## int 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.28 0.00 0.00 0.01
## age 0.11 0.08 0.64 0.83 0.84 0.00 0.47 0.09 0.00 0.00 0.00 0.00
## income 0.75 0.49 0.36 0.39 0.27 0.90 0.60 0.11 0.64 0.00 0.00 0.02
## pol.orient 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.76 0.75 0.88 0.80
## gb.cf asimp.all aspro.all sj sdo nd rwa cas phq ps int
## sp 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.41 0.00 0.00
## sp.1 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.78 0.00 0.00
## sp.2 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00
## sp.3 1.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.32 0.00 0.00
## sp.4 1.00 0.00 0.00 0.03 0.00 0.00 0.00 0.01 0.01 0.00 0.00
## sp.5 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## sp.6 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.02 0.00 0.00
## gb.rs 0.00 0.00 0.00 0.00 0.10 1.00 1.00 0.01 0.00 1.00 1.00
## gb.as 0.00 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 1.00 1.00
## gb.cs 0.06 0.01 1.00 0.02 1.00 1.00 1.00 1.00 0.00 1.00 0.00
## gb.rf 0.00 0.00 0.00 0.08 1.00 1.00 1.00 0.00 0.00 1.00 0.00
## gb.af 0.00 0.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 1.00 1.00
## gb.cf 0.00 0.00 0.00 1.00 1.00 0.99 1.00 0.00 0.00 1.00 0.00
## asimp.all 0.00 0.00 0.00 0.71 0.00 0.00 0.00 0.00 1.00 0.00 1.00
## aspro.all 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00
## sj 0.07 0.01 0.22 0.00 1.00 0.18 0.00 1.00 0.00 1.00 0.90
## sdo 0.83 0.00 0.00 0.03 0.00 0.00 0.00 1.00 0.02 0.00 0.00
## nd 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.12 1.00 0.00 0.00
## rwa 0.02 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.35 0.00 0.00
## cas 0.00 0.00 0.00 0.03 0.76 0.00 0.70 0.00 0.00 0.00 0.00
## phq 0.00 0.23 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.29 0.00
## ps 0.77 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## int 0.00 0.42 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## age 0.00 0.00 0.00 0.05 0.01 0.59 0.00 0.74 0.00 0.00 0.00
## income 0.00 0.01 0.13 0.01 0.00 0.11 0.22 0.28 0.00 0.13 0.27
## pol.orient 0.01 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## age income pol.orient
## sp 1.00 1.00 0.00
## sp.1 1.00 1.00 0.00
## sp.2 1.00 1.00 0.00
## sp.3 1.00 1.00 0.00
## sp.4 1.00 1.00 0.00
## sp.5 0.00 1.00 0.00
## sp.6 1.00 1.00 0.00
## gb.rs 1.00 1.00 1.00
## gb.as 0.00 1.00 1.00
## gb.cs 0.05 0.02 1.00
## gb.rf 0.00 0.11 1.00
## gb.af 0.00 1.00 1.00
## gb.cf 0.00 0.00 1.00
## asimp.all 0.00 1.00 0.01
## aspro.all 0.00 1.00 0.02
## sj 1.00 0.60 1.00
## sdo 1.00 0.00 0.00
## nd 1.00 1.00 0.00
## rwa 0.00 1.00 0.00
## cas 1.00 1.00 0.00
## phq 0.00 0.00 0.26
## ps 0.20 1.00 0.00
## int 0.16 1.00 0.00
## age 0.00 0.03 1.00
## income 0.00 0.00 0.05
## pol.orient 0.07 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("sp", "sp.1", "sp.2", "sp.3", "sp.4", "sp.5", "sp.6",
"gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af", "gb.cf",
"asimp.all", "aspro.all",
"sj", "sdo", "nd", "rwa",
"cas",
"phq",
"ps", "int",
"age", "income", "pol.orient")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 gb.rs gb.s gb.cs gb.rf
## sp 1.00 0.90 0.69 0.83 0.89 0.70 0.86 -0.15 -0.08 0.08 0.17
## sp.1 0.86 1.00 0.50 0.67 0.74 0.64 0.71 -0.18 -0.07 -0.06 0.13
## sp.2 0.60 0.38 1.00 0.50 0.45 0.17 0.41 -0.12 -0.14 -0.09 0.26
## sp.3 0.78 0.59 0.40 1.00 0.77 0.52 0.70 -0.16 -0.09 0.09 0.17
## sp.4 0.86 0.66 0.33 0.70 1.00 0.66 0.89 -0.11 0.07 0.11 0.13
## sp.5 0.62 0.55 0.04 0.41 0.57 1.00 0.62 -0.09 0.14 0.11 -0.14
## sp.6 0.82 0.64 0.28 0.62 0.85 0.54 1.00 -0.11 0.05 0.09 0.13
## gb.rs -0.03 -0.05 0.00 -0.04 0.01 0.05 0.00 1.00 0.52 0.37 -0.41
## gb.as 0.05 0.06 -0.01 0.03 -0.05 0.03 -0.05 0.40 1.00 0.39 -0.36
## gb.cs -0.04 0.07 0.03 -0.04 -0.01 0.01 -0.02 0.25 0.26 1.00 0.08
## gb.rf 0.04 -0.01 0.14 0.04 0.01 -0.04 0.01 -0.28 -0.23 -0.06 1.00
## gb.af 0.02 0.01 0.06 0.00 0.01 -0.02 0.02 -0.17 -0.40 -0.01 0.58
## gb.cf -0.03 -0.04 0.11 -0.02 -0.06 -0.12 0.06 -0.23 -0.30 -0.04 0.60
## asimp.all 0.21 0.13 0.24 0.24 0.17 -0.02 0.13 -0.07 -0.05 0.05 0.13
## aspro.all 0.15 0.13 0.15 0.15 0.09 -0.04 0.07 -0.13 -0.14 -0.03 0.17
## sj -0.10 -0.12 0.04 -0.06 -0.10 -0.16 -0.14 0.08 0.01 0.06 -0.03
## sdo 0.35 0.29 0.18 0.31 0.32 0.23 0.28 -0.01 0.01 0.00 -0.01
## nd 0.35 0.30 0.18 0.35 0.31 0.18 0.27 0.02 0.03 0.00 -0.02
## rwa 0.37 0.30 0.22 0.26 0.32 0.26 0.33 0.03 -0.02 -0.01 -0.01
## cas 0.08 0.02 0.17 -0.02 0.01 -0.26 0.04 -0.01 -0.03 -0.01 0.24
## phq 0.00 0.02 0.02 -0.01 -0.04 -0.11 -0.05 -0.24 -0.29 -0.15 0.43
## ps -0.51 -0.41 -0.19 -0.43 -0.53 -0.38 -0.49 -0.02 0.00 0.03 0.04
## int -0.37 -0.38 -0.14 -0.22 -0.33 -0.35 -0.27 0.01 -0.02 0.11 0.06
## age 0.00 0.00 -0.05 0.05 -0.04 0.08 -0.02 -0.01 0.10 -0.06 -0.13
## income 0.05 0.06 0.03 -0.05 -0.05 0.05 0.05 0.01 0.05 0.04 -0.06
## pol.orient 0.32 0.30 0.10 0.24 0.31 0.25 0.29 0.00 0.03 -0.05 -0.05
## gb.f gb.cf asm. asp. sj sdo nd rwa cas phq ps
## sp 0.16 0.11 0.32 0.27 -0.22 0.46 0.46 0.48 -0.10 -0.13 -0.61
## sp.1 0.14 0.09 0.24 0.25 -0.24 0.40 0.41 0.41 -0.15 -0.11 -0.52
## sp.2 0.22 0.25 0.34 0.28 -0.08 0.28 0.29 0.33 0.31 0.16 -0.30
## sp.3 0.12 0.12 0.35 0.27 0.07 0.42 0.46 0.36 0.16 -0.15 -0.54
## sp.4 0.13 0.07 0.28 0.22 -0.22 0.42 0.43 0.43 -0.15 -0.17 -0.62
## sp.5 -0.13 -0.23 0.09 0.08 -0.26 0.34 0.31 0.37 -0.36 -0.22 -0.50
## sp.6 0.15 -0.06 0.24 0.19 -0.26 0.40 0.39 0.44 -0.12 -0.17 -0.59
## gb.rs -0.30 -0.35 -0.19 -0.24 0.20 -0.14 -0.09 -0.09 -0.13 -0.36 0.11
## gb.as -0.51 -0.41 -0.17 -0.26 0.12 -0.11 -0.08 0.09 -0.17 -0.41 0.11
## gb.cs 0.11 -0.16 0.17 0.09 0.18 0.12 0.12 0.10 0.12 -0.28 -0.09
## gb.rf 0.67 0.68 0.26 0.28 -0.16 0.11 0.12 0.13 0.36 0.53 -0.10
## gb.af 1.00 0.65 0.19 0.29 -0.21 0.10 0.12 -0.07 0.30 0.54 -0.10
## gb.cf 0.56 1.00 0.25 0.28 -0.10 -0.07 0.14 -0.12 0.37 0.63 0.07
## asimp.all 0.07 0.13 1.00 0.65 0.15 0.36 0.38 0.32 0.29 0.09 -0.28
## aspro.all 0.17 0.18 0.58 1.00 -0.09 0.29 0.27 0.24 0.21 0.20 -0.21
## sj -0.08 0.02 0.04 0.03 1.00 0.09 0.15 -0.22 0.14 -0.19 0.12
## sdo -0.01 0.04 0.25 0.18 -0.02 1.00 0.41 0.54 0.10 -0.16 -0.43
## nd 0.00 0.02 0.26 0.15 0.02 0.29 1.00 0.36 0.18 -0.11 -0.47
## rwa 0.05 0.00 0.21 0.13 -0.10 0.43 0.25 1.00 0.09 -0.11 -0.36
## cas 0.18 0.26 0.16 0.11 0.03 -0.03 0.04 -0.01 1.00 0.32 0.18
## phq 0.45 0.54 -0.03 0.08 -0.06 -0.04 0.02 0.01 0.18 1.00 0.15
## ps 0.04 -0.04 -0.17 -0.09 -0.01 -0.32 -0.34 -0.25 0.06 0.04 1.00
## int 0.01 0.10 0.04 -0.01 0.03 -0.19 -0.09 -0.16 0.38 0.04 0.35
## age -0.16 -0.24 -0.22 -0.20 -0.01 0.02 0.04 0.14 0.04 -0.07 0.03
## income -0.02 -0.06 0.00 -0.02 0.00 0.06 -0.01 -0.04 0.02 -0.06 0.01
## pol.orient -0.04 0.02 0.07 0.07 0.01 0.36 0.18 0.40 0.01 0.02 -0.30
## int age incm pl.r
## sp -0.52 0.10 -0.07 0.43
## sp.1 -0.51 0.11 -0.06 0.41
## sp.2 -0.29 0.06 -0.11 0.20
## sp.3 -0.38 -0.06 0.07 0.34
## sp.4 -0.47 0.07 0.07 0.41
## sp.5 -0.45 0.19 -0.06 0.37
## sp.6 -0.41 0.09 -0.07 0.41
## gb.rs 0.13 0.08 0.11 -0.12
## gb.as 0.11 0.20 -0.07 -0.09
## gb.cs 0.21 -0.16 0.15 0.06
## gb.rf 0.18 -0.24 -0.17 0.07
## gb.af 0.12 -0.25 -0.13 0.08
## gb.cf 0.20 -0.34 -0.17 -0.10
## asimp.all -0.08 -0.32 0.11 0.17
## aspro.all -0.12 -0.30 0.10 0.18
## sj 0.15 -0.12 0.12 -0.10
## sdo -0.32 0.12 0.16 0.46
## nd -0.24 -0.08 0.10 0.31
## rwa -0.29 0.25 0.07 0.50
## cas 0.48 -0.08 -0.09 -0.12
## phq 0.16 -0.18 -0.17 -0.10
## ps 0.47 0.14 -0.10 -0.41
## int 1.00 -0.15 -0.09 -0.38
## age -0.05 1.00 0.15 0.11
## income 0.02 0.04 1.00 0.14
## pol.orient -0.24 -0.01 0.04 1.006 Reliability
6.1 Self-protection/denial
6.1.1 Overall
alpha (data[c("sp01", "sp02", "sp03", "sp04", "sp05", "sp06", "sp07",
"sp08", "sp09", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15",
"sp19", "sp20", "sp21", "sp22",
"sp16", "sp17", "sp18",
"sp23.i", "sp24.i", "sp25", "sp26",
"sp27", "sp28", "sp29", "sp30")])##
## Reliability analysis
## Call: alpha(x = data[c("sp01", "sp02", "sp03", "sp04", "sp05", "sp06",
## "sp07", "sp08", "sp09", "sp10", "sp11", "sp12", "sp13", "sp14",
## "sp15", "sp19", "sp20", "sp21", "sp22", "sp16", "sp17", "sp18",
## "sp23.i", "sp24.i", "sp25", "sp26", "sp27", "sp28", "sp29",
## "sp30")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.97 0.4 20 0.002 3 1.1 0.4
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp01 0.95 0.95 0.97 0.40 19 0.0020 0.043 0.39
## sp02 0.95 0.95 0.97 0.39 19 0.0021 0.041 0.39
## sp03 0.95 0.95 0.97 0.39 19 0.0021 0.042 0.38
## sp04 0.95 0.95 0.97 0.40 19 0.0020 0.043 0.40
## sp05 0.95 0.95 0.97 0.40 19 0.0020 0.042 0.39
## sp06 0.95 0.95 0.97 0.40 19 0.0021 0.042 0.39
## sp07 0.95 0.95 0.97 0.39 19 0.0021 0.041 0.39
## sp08 0.95 0.95 0.97 0.41 20 0.0019 0.040 0.41
## sp09 0.95 0.95 0.97 0.42 21 0.0019 0.036 0.42
## sp10 0.95 0.95 0.97 0.41 20 0.0019 0.040 0.41
## sp11 0.95 0.95 0.97 0.40 20 0.0020 0.043 0.42
## sp12 0.95 0.95 0.97 0.40 19 0.0020 0.042 0.41
## sp13 0.95 0.95 0.97 0.40 19 0.0020 0.042 0.41
## sp14 0.95 0.95 0.97 0.41 20 0.0020 0.042 0.41
## sp15 0.95 0.95 0.97 0.40 20 0.0020 0.041 0.41
## sp19 0.95 0.95 0.97 0.39 19 0.0020 0.042 0.39
## sp20 0.95 0.95 0.97 0.40 19 0.0020 0.043 0.38
## sp21 0.95 0.95 0.97 0.40 19 0.0020 0.043 0.40
## sp22 0.95 0.95 0.97 0.40 19 0.0020 0.042 0.40
## sp16 0.95 0.95 0.97 0.39 19 0.0021 0.040 0.39
## sp17 0.95 0.95 0.97 0.39 19 0.0021 0.041 0.39
## sp18 0.95 0.95 0.97 0.39 19 0.0021 0.040 0.39
## sp23.i 0.95 0.95 0.97 0.42 21 0.0019 0.036 0.42
## sp24.i 0.95 0.95 0.97 0.41 20 0.0019 0.038 0.42
## sp25 0.95 0.95 0.97 0.40 19 0.0020 0.042 0.38
## sp26 0.95 0.95 0.97 0.39 19 0.0021 0.041 0.38
## sp27 0.95 0.95 0.97 0.39 19 0.0021 0.041 0.40
## sp28 0.95 0.95 0.97 0.39 19 0.0021 0.041 0.40
## sp29 0.95 0.95 0.97 0.39 19 0.0021 0.040 0.39
## sp30 0.95 0.95 0.97 0.39 19 0.0021 0.040 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp01 1134 0.70 0.69 0.68 0.67 3.0 1.8
## sp02 1134 0.81 0.80 0.80 0.79 2.8 1.8
## sp03 1134 0.78 0.77 0.76 0.75 3.2 1.8
## sp04 1134 0.60 0.59 0.57 0.56 3.9 1.8
## sp05 1134 0.69 0.68 0.67 0.66 3.5 1.8
## sp06 1134 0.74 0.73 0.72 0.71 3.4 1.8
## sp07 1134 0.79 0.78 0.78 0.77 2.7 1.7
## sp08 1134 0.44 0.45 0.43 0.40 3.0 1.5
## sp09 1134 0.25 0.26 0.24 0.20 2.9 1.6
## sp10 1134 0.45 0.47 0.45 0.41 2.9 1.6
## sp11 1134 0.56 0.56 0.54 0.52 3.8 1.6
## sp12 1134 0.63 0.64 0.64 0.60 3.2 1.7
## sp13 1134 0.58 0.59 0.58 0.55 2.8 1.5
## sp14 1134 0.49 0.50 0.48 0.46 2.9 1.5
## sp15 1134 0.54 0.55 0.54 0.50 3.1 1.6
## sp19 1134 0.74 0.75 0.74 0.72 2.2 1.4
## sp20 1134 0.68 0.69 0.68 0.65 2.4 1.5
## sp21 1134 0.65 0.66 0.64 0.62 2.8 1.5
## sp22 1134 0.70 0.71 0.70 0.68 2.2 1.4
## sp16 1134 0.81 0.80 0.80 0.79 2.4 1.6
## sp17 1134 0.81 0.80 0.80 0.79 2.7 1.8
## sp18 1134 0.81 0.81 0.81 0.79 2.7 1.9
## sp23.i 1134 0.27 0.26 0.24 0.22 4.6 1.6
## sp24.i 1134 0.37 0.36 0.34 0.32 4.0 1.7
## sp25 1134 0.69 0.68 0.67 0.66 3.7 1.7
## sp26 1134 0.76 0.76 0.75 0.74 3.4 1.9
## sp27 1134 0.75 0.75 0.74 0.73 2.3 1.7
## sp28 1134 0.76 0.75 0.75 0.73 2.5 1.7
## sp29 1134 0.78 0.78 0.78 0.76 2.7 1.7
## sp30 1134 0.80 0.79 0.80 0.78 2.7 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp01 0.26 0.17 0.20 0.14 0.12 0.05 0.06 0
## sp02 0.33 0.18 0.20 0.11 0.08 0.04 0.06 0
## sp03 0.25 0.15 0.20 0.14 0.13 0.06 0.07 0
## sp04 0.14 0.12 0.16 0.16 0.24 0.09 0.09 0
## sp05 0.19 0.15 0.21 0.13 0.17 0.08 0.08 0
## sp06 0.20 0.13 0.22 0.17 0.15 0.06 0.07 0
## sp07 0.31 0.19 0.23 0.12 0.06 0.04 0.05 0
## sp08 0.22 0.16 0.18 0.30 0.11 0.02 0.01 0
## sp09 0.31 0.15 0.16 0.21 0.11 0.04 0.02 0
## sp10 0.30 0.14 0.19 0.23 0.10 0.03 0.02 0
## sp11 0.10 0.11 0.17 0.29 0.19 0.07 0.06 0
## sp12 0.24 0.12 0.17 0.25 0.13 0.05 0.04 0
## sp13 0.30 0.15 0.19 0.24 0.08 0.03 0.02 0
## sp14 0.26 0.19 0.20 0.21 0.10 0.03 0.02 0
## sp15 0.25 0.13 0.18 0.24 0.13 0.04 0.03 0
## sp19 0.47 0.19 0.17 0.10 0.03 0.02 0.02 0
## sp20 0.40 0.19 0.16 0.15 0.06 0.02 0.01 0
## sp21 0.28 0.19 0.19 0.21 0.08 0.03 0.02 0
## sp22 0.45 0.22 0.15 0.11 0.05 0.01 0.01 0
## sp16 0.41 0.21 0.14 0.11 0.06 0.02 0.04 0
## sp17 0.34 0.19 0.16 0.12 0.10 0.04 0.05 0
## sp18 0.38 0.18 0.14 0.10 0.08 0.05 0.07 0
## sp23.i 0.02 0.06 0.22 0.21 0.19 0.12 0.18 0
## sp24.i 0.05 0.11 0.28 0.19 0.15 0.10 0.12 0
## sp25 0.12 0.12 0.24 0.23 0.13 0.08 0.09 0
## sp26 0.21 0.16 0.20 0.16 0.12 0.07 0.09 0
## sp27 0.51 0.15 0.11 0.11 0.05 0.03 0.04 0
## sp28 0.44 0.19 0.11 0.12 0.06 0.04 0.04 0
## sp29 0.36 0.19 0.16 0.14 0.07 0.04 0.04 0
## sp30 0.37 0.19 0.14 0.14 0.07 0.04 0.05 06.1.2 Rationalization
alpha (data[c("sp01", "sp02", "sp03", "sp04", "sp05", "sp06", "sp07")])##
## Reliability analysis
## Call: alpha(x = data[c("sp01", "sp02", "sp03", "sp04", "sp05", "sp06",
## "sp07")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.92 0.63 12 0.0035 3.2 1.5 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.92 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp01 0.92 0.92 0.91 0.64 10.8 0.0039 0.0055 0.65
## sp02 0.91 0.91 0.89 0.62 9.7 0.0043 0.0045 0.64
## sp03 0.90 0.90 0.89 0.61 9.4 0.0045 0.0052 0.62
## sp04 0.92 0.92 0.91 0.66 11.8 0.0036 0.0026 0.66
## sp05 0.91 0.91 0.90 0.63 10.2 0.0041 0.0070 0.65
## sp06 0.91 0.91 0.90 0.63 10.0 0.0042 0.0070 0.64
## sp07 0.91 0.91 0.90 0.62 9.8 0.0043 0.0043 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp01 1134 0.79 0.79 0.74 0.71 3.0 1.8
## sp02 1134 0.86 0.86 0.84 0.80 2.8 1.8
## sp03 1134 0.88 0.88 0.86 0.83 3.2 1.8
## sp04 1134 0.74 0.74 0.67 0.65 3.9 1.8
## sp05 1134 0.83 0.83 0.79 0.76 3.5 1.8
## sp06 1134 0.84 0.84 0.80 0.77 3.4 1.8
## sp07 1134 0.85 0.85 0.83 0.79 2.7 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp01 0.26 0.17 0.20 0.14 0.12 0.05 0.06 0
## sp02 0.33 0.18 0.20 0.11 0.08 0.04 0.06 0
## sp03 0.25 0.15 0.20 0.14 0.13 0.06 0.07 0
## sp04 0.14 0.12 0.16 0.16 0.24 0.09 0.09 0
## sp05 0.19 0.15 0.21 0.13 0.17 0.08 0.08 0
## sp06 0.20 0.13 0.22 0.17 0.15 0.06 0.07 0
## sp07 0.31 0.19 0.23 0.12 0.06 0.04 0.05 06.1.3 Avoidance
alpha (data[c("sp08", "sp09", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15")])##
## Reliability analysis
## Call: alpha(x = data[c("sp08", "sp09", "sp10", "sp11", "sp12", "sp13",
## "sp14", "sp15")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.9 0.52 8.6 0.0046 3.1 1.2 0.53
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp08 0.88 0.88 0.88 0.51 7.4 0.0054 0.0152 0.50
## sp09 0.89 0.89 0.88 0.54 8.2 0.0049 0.0113 0.53
## sp10 0.88 0.88 0.88 0.51 7.3 0.0054 0.0141 0.50
## sp11 0.89 0.90 0.89 0.55 8.5 0.0048 0.0097 0.56
## sp12 0.87 0.87 0.87 0.50 6.9 0.0058 0.0110 0.48
## sp13 0.88 0.88 0.87 0.50 7.1 0.0056 0.0128 0.52
## sp14 0.89 0.89 0.89 0.55 8.4 0.0048 0.0130 0.56
## sp15 0.87 0.87 0.87 0.49 6.7 0.0059 0.0104 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp08 1134 0.77 0.77 0.73 0.70 3.0 1.5
## sp09 1134 0.69 0.69 0.64 0.59 2.9 1.6
## sp10 1134 0.78 0.78 0.75 0.71 2.9 1.6
## sp11 1134 0.66 0.66 0.58 0.55 3.8 1.6
## sp12 1134 0.84 0.84 0.83 0.78 3.2 1.7
## sp13 1134 0.81 0.82 0.79 0.75 2.8 1.5
## sp14 1134 0.66 0.67 0.59 0.56 2.9 1.5
## sp15 1134 0.86 0.86 0.85 0.81 3.1 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp08 0.22 0.16 0.18 0.30 0.11 0.02 0.01 0
## sp09 0.31 0.15 0.16 0.21 0.11 0.04 0.02 0
## sp10 0.30 0.14 0.19 0.23 0.10 0.03 0.02 0
## sp11 0.10 0.11 0.17 0.29 0.19 0.07 0.06 0
## sp12 0.24 0.12 0.17 0.25 0.13 0.05 0.04 0
## sp13 0.30 0.15 0.19 0.24 0.08 0.03 0.02 0
## sp14 0.26 0.19 0.20 0.21 0.10 0.03 0.02 0
## sp15 0.25 0.13 0.18 0.24 0.13 0.04 0.03 06.1.4 Denial of personal outcome severity
alpha (data[c("sp19", "sp20", "sp21", "sp22")])##
## Reliability analysis
## Call: alpha(x = data[c("sp19", "sp20", "sp21", "sp22")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.86 0.66 7.6 0.0057 2.4 1.3 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp19 0.83 0.84 0.77 0.63 5.1 0.0086 0.0022 0.60
## sp20 0.85 0.85 0.80 0.65 5.7 0.0080 0.0070 0.61
## sp21 0.88 0.88 0.83 0.71 7.3 0.0063 0.0014 0.69
## sp22 0.84 0.84 0.78 0.63 5.2 0.0084 0.0026 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp19 1134 0.88 0.89 0.85 0.79 2.2 1.4
## sp20 1134 0.86 0.86 0.80 0.75 2.4 1.5
## sp21 1134 0.82 0.82 0.71 0.67 2.8 1.5
## sp22 1134 0.88 0.88 0.84 0.78 2.2 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp19 0.47 0.19 0.17 0.10 0.03 0.02 0.02 0
## sp20 0.40 0.19 0.16 0.15 0.06 0.02 0.01 0
## sp21 0.28 0.19 0.19 0.21 0.08 0.03 0.02 0
## sp22 0.45 0.22 0.15 0.11 0.05 0.01 0.01 06.1.5 Denial of global outcome severity
alpha (data[c("sp16", "sp17", "sp18")])##
## Reliability analysis
## Call: alpha(x = data[c("sp16", "sp17", "sp18")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.89 0.79 11 0.0043 2.6 1.6 0.77
##
## lower alpha upper 95% confidence boundaries
## 0.91 0.92 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp16 0.87 0.87 0.77 0.77 6.6 0.0078 NA 0.77
## sp17 0.86 0.87 0.77 0.77 6.6 0.0079 NA 0.77
## sp18 0.91 0.91 0.83 0.83 10.1 0.0054 NA 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp16 1134 0.93 0.94 0.89 0.85 2.4 1.6
## sp17 1134 0.93 0.94 0.89 0.85 2.7 1.8
## sp18 1134 0.92 0.91 0.83 0.80 2.7 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp16 0.41 0.21 0.14 0.11 0.06 0.02 0.04 0
## sp17 0.34 0.19 0.16 0.12 0.10 0.04 0.05 0
## sp18 0.38 0.18 0.14 0.10 0.08 0.05 0.07 06.1.6 Denial of guilt
alpha (data[c("sp23.i", "sp24.i", "sp25", "sp26")])##
## Reliability analysis
## Call: alpha(x = data[c("sp23.i", "sp24.i", "sp25", "sp26")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.79 0.52 4.3 0.0092 3.9 1.4 0.5
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.81 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp23.i 0.80 0.80 0.74 0.57 3.9 0.010 0.0126 0.51
## sp24.i 0.76 0.76 0.71 0.51 3.1 0.012 0.0267 0.45
## sp25 0.74 0.74 0.67 0.49 2.9 0.014 0.0111 0.49
## sp26 0.76 0.76 0.69 0.52 3.2 0.012 0.0053 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp23.i 1134 0.75 0.76 0.64 0.56 4.6 1.6
## sp24.i 1134 0.80 0.81 0.72 0.64 4.0 1.7
## sp25 1134 0.84 0.83 0.77 0.69 3.7 1.7
## sp26 1134 0.82 0.80 0.73 0.64 3.4 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp23.i 0.02 0.06 0.22 0.21 0.19 0.12 0.18 0
## sp24.i 0.05 0.11 0.28 0.19 0.15 0.10 0.12 0
## sp25 0.12 0.12 0.24 0.23 0.13 0.08 0.09 0
## sp26 0.21 0.16 0.20 0.16 0.12 0.07 0.09 06.1.7 Literal denial
alpha (data[c("sp27", "sp28", "sp29", "sp30")])##
## Reliability analysis
## Call: alpha(x = data[c("sp27", "sp28", "sp29", "sp30")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.92 0.79 15 0.003 2.5 1.6 0.78
##
## lower alpha upper 95% confidence boundaries
## 0.93 0.94 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sp27 0.94 0.94 0.91 0.83 15 0.0033 0.0013 0.82
## sp28 0.92 0.92 0.89 0.79 11 0.0043 0.0051 0.76
## sp29 0.91 0.91 0.88 0.78 11 0.0044 0.0011 0.77
## sp30 0.91 0.91 0.87 0.77 10 0.0047 0.0010 0.77
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sp27 1134 0.89 0.89 0.82 0.80 2.3 1.7
## sp28 1134 0.92 0.92 0.89 0.86 2.5 1.7
## sp29 1134 0.93 0.93 0.90 0.87 2.7 1.7
## sp30 1134 0.94 0.94 0.92 0.89 2.7 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sp27 0.51 0.15 0.11 0.11 0.05 0.03 0.04 0
## sp28 0.44 0.19 0.11 0.12 0.06 0.04 0.04 0
## sp29 0.36 0.19 0.16 0.14 0.07 0.04 0.04 0
## sp30 0.37 0.19 0.14 0.14 0.07 0.04 0.05 06.2 Basic psychological needs
6.2.3 Autonomy satisfaction
alpha (data[c("gb01", "gb03", "gb02", "gb20")])##
## Reliability analysis
## Call: alpha(x = data[c("gb01", "gb03", "gb02", "gb20")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.78 0.51 4.1 0.0098 5.1 1.1 0.49
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## gb01 0.78 0.78 0.72 0.55 3.6 0.011 0.0097 0.51
## gb03 0.74 0.75 0.69 0.50 3.0 0.014 0.0199 0.43
## gb02 0.74 0.74 0.67 0.49 2.9 0.013 0.0066 0.51
## gb20 0.74 0.74 0.66 0.49 2.9 0.013 0.0049 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## gb01 1134 0.77 0.75 0.63 0.56 5.1 1.5
## gb03 1134 0.80 0.80 0.70 0.64 5.0 1.3
## gb02 1134 0.80 0.81 0.73 0.63 5.0 1.3
## gb20 1134 0.80 0.81 0.74 0.64 5.3 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## gb01 0.02 0.05 0.09 0.10 0.29 0.28 0.16 0
## gb03 0.01 0.03 0.09 0.13 0.34 0.26 0.13 0
## gb02 0.02 0.03 0.06 0.22 0.30 0.23 0.13 0
## gb20 0.01 0.02 0.05 0.14 0.33 0.28 0.17 06.2.4 Autonomy frustration
alpha (data[c("gb05", "gb04", "gb06")])##
## Reliability analysis
## Call: alpha(x = data[c("gb05", "gb04", "gb06")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.69 0.52 3.3 0.012 3.1 1.5 0.51
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## gb05 0.73 0.73 0.58 0.58 2.7 0.016 NA 0.58
## gb04 0.68 0.68 0.51 0.51 2.1 0.019 NA 0.51
## gb06 0.64 0.64 0.47 0.47 1.8 0.021 NA 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## gb05 1134 0.79 0.80 0.63 0.55 3.0 1.7
## gb04 1134 0.85 0.83 0.69 0.60 3.6 2.0
## gb06 1134 0.84 0.84 0.73 0.64 2.9 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## gb05 0.26 0.18 0.18 0.17 0.14 0.04 0.02 0
## gb04 0.22 0.16 0.11 0.14 0.20 0.10 0.08 0
## gb06 0.32 0.17 0.14 0.15 0.15 0.04 0.03 06.2.5 Competence satisfaction
alpha (data[c("gb15", "gb14", "gb16")])##
## Reliability analysis
## Call: alpha(x = data[c("gb15", "gb14", "gb16")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.72 0.56 3.8 0.011 4.4 1.3 0.59
##
## lower alpha upper 95% confidence boundaries
## 0.77 0.79 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## gb15 0.72 0.74 0.59 0.59 2.8 0.016 NA 0.59
## gb14 0.66 0.67 0.50 0.50 2.0 0.020 NA 0.50
## gb16 0.74 0.74 0.59 0.59 2.9 0.015 NA 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## gb15 1134 0.84 0.83 0.69 0.62 4.2 1.6
## gb14 1134 0.88 0.86 0.76 0.68 4.2 1.8
## gb16 1134 0.80 0.83 0.69 0.61 4.8 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## gb15 0.09 0.07 0.11 0.29 0.22 0.14 0.08 0
## gb14 0.13 0.06 0.09 0.27 0.22 0.13 0.10 0
## gb16 0.02 0.04 0.06 0.28 0.33 0.19 0.09 06.2.6 Competence frustration
alpha (data[c("gb17", "gb19","gb18")])##
## Reliability analysis
## Call: alpha(x = data[c("gb17", "gb19", "gb18")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.71 0.53 3.4 0.011 2.7 1.4 0.55
##
## lower alpha upper 95% confidence boundaries
## 0.75 0.77 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## gb17 0.60 0.60 0.43 0.43 1.5 0.024 NA 0.43
## gb19 0.70 0.71 0.55 0.55 2.4 0.017 NA 0.55
## gb18 0.76 0.76 0.61 0.61 3.2 0.014 NA 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## gb17 1134 0.89 0.87 0.79 0.69 3.0 1.8
## gb19 1134 0.82 0.82 0.69 0.60 3.0 1.6
## gb18 1134 0.78 0.80 0.62 0.55 2.1 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## gb17 0.31 0.17 0.14 0.15 0.13 0.05 0.05 0
## gb19 0.26 0.18 0.16 0.19 0.15 0.04 0.01 0
## gb18 0.52 0.17 0.12 0.10 0.06 0.02 0.01 06.3 Aspirations
alpha (data[c("asimp01", "asimp12", "asimp02", "asimp13",
"asimp10", "asimp21", "asimp07", "asimp18",
"asimp09", "asimp20", "asimp04", "asimp15",
"asimp06", "asimp17", "asimp08", "asimp19",
"asimp03", "asimp14", "asimp05", "asimp16",
"asimp11", "asimp22")])##
## Reliability analysis
## Call: alpha(x = data[c("asimp01", "asimp12", "asimp02", "asimp13",
## "asimp10", "asimp21", "asimp07", "asimp18", "asimp09", "asimp20",
## "asimp04", "asimp15", "asimp06", "asimp17", "asimp08", "asimp19",
## "asimp03", "asimp14", "asimp05", "asimp16", "asimp11", "asimp22")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.88 0.21 5.7 0.0065 4.5 0.8 0.2
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.85 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## asimp01 0.85 0.85 0.88 0.21 5.5 0.0066 0.016 0.21
## asimp12 0.84 0.84 0.87 0.20 5.3 0.0068 0.016 0.20
## asimp02 0.84 0.84 0.88 0.21 5.4 0.0068 0.016 0.20
## asimp13 0.85 0.85 0.88 0.21 5.5 0.0066 0.016 0.21
## asimp10 0.84 0.84 0.88 0.20 5.4 0.0067 0.016 0.20
## asimp21 0.85 0.85 0.88 0.21 5.5 0.0066 0.015 0.20
## asimp07 0.84 0.85 0.88 0.21 5.5 0.0067 0.015 0.20
## asimp18 0.84 0.85 0.88 0.21 5.5 0.0066 0.014 0.20
## asimp09 0.85 0.85 0.88 0.21 5.6 0.0066 0.015 0.21
## asimp20 0.85 0.85 0.88 0.21 5.5 0.0066 0.015 0.20
## asimp04 0.84 0.84 0.88 0.21 5.4 0.0068 0.016 0.20
## asimp15 0.84 0.84 0.88 0.21 5.4 0.0068 0.016 0.20
## asimp06 0.84 0.85 0.88 0.21 5.5 0.0067 0.014 0.20
## asimp17 0.84 0.84 0.87 0.20 5.4 0.0069 0.014 0.20
## asimp08 0.84 0.84 0.87 0.20 5.4 0.0069 0.014 0.20
## asimp19 0.84 0.84 0.88 0.21 5.4 0.0068 0.014 0.20
## asimp03 0.85 0.85 0.88 0.21 5.6 0.0066 0.016 0.21
## asimp14 0.84 0.84 0.88 0.20 5.4 0.0068 0.016 0.20
## asimp05 0.84 0.84 0.88 0.20 5.4 0.0069 0.016 0.20
## asimp16 0.84 0.84 0.87 0.20 5.3 0.0069 0.016 0.19
## asimp11 0.85 0.85 0.87 0.21 5.6 0.0066 0.014 0.21
## asimp22 0.84 0.85 0.87 0.21 5.5 0.0067 0.014 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## asimp01 1134 0.44 0.46 0.41 0.36 5.8 1.5
## asimp12 1134 0.55 0.57 0.55 0.48 5.4 1.5
## asimp02 1134 0.51 0.51 0.48 0.44 4.8 1.7
## asimp13 1134 0.46 0.46 0.42 0.38 4.9 1.7
## asimp10 1134 0.49 0.52 0.49 0.43 5.7 1.3
## asimp21 1134 0.42 0.47 0.43 0.36 5.9 1.3
## asimp07 1134 0.45 0.49 0.46 0.39 5.9 1.3
## asimp18 1134 0.44 0.49 0.47 0.38 6.2 1.2
## asimp09 1134 0.38 0.42 0.38 0.31 6.2 1.2
## asimp20 1134 0.42 0.47 0.44 0.36 6.0 1.3
## asimp04 1134 0.53 0.51 0.47 0.44 4.8 2.0
## asimp15 1134 0.52 0.52 0.48 0.44 4.6 1.8
## asimp06 1134 0.47 0.44 0.41 0.39 2.7 1.7
## asimp17 1134 0.56 0.53 0.51 0.49 2.7 1.7
## asimp08 1134 0.57 0.53 0.51 0.49 2.8 1.7
## asimp19 1134 0.55 0.52 0.49 0.47 2.7 1.7
## asimp03 1134 0.40 0.39 0.33 0.31 4.5 1.7
## asimp14 1134 0.54 0.53 0.50 0.46 4.4 1.7
## asimp05 1134 0.56 0.54 0.51 0.49 4.3 1.8
## asimp16 1134 0.59 0.58 0.56 0.52 4.3 1.7
## asimp11 1134 0.46 0.43 0.42 0.37 2.4 1.9
## asimp22 1134 0.49 0.45 0.45 0.40 2.3 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## asimp01 0.03 0.01 0.02 0.13 0.13 0.21 0.47 0
## asimp12 0.02 0.02 0.05 0.17 0.18 0.23 0.32 0
## asimp02 0.06 0.05 0.09 0.22 0.21 0.17 0.21 0
## asimp13 0.07 0.04 0.06 0.23 0.21 0.17 0.21 0
## asimp10 0.01 0.01 0.02 0.15 0.18 0.28 0.35 0
## asimp21 0.01 0.01 0.03 0.12 0.15 0.24 0.44 0
## asimp07 0.01 0.01 0.02 0.11 0.16 0.23 0.45 0
## asimp18 0.01 0.01 0.02 0.06 0.09 0.20 0.60 0
## asimp09 0.01 0.01 0.02 0.09 0.09 0.19 0.60 0
## asimp20 0.01 0.01 0.03 0.10 0.12 0.27 0.47 0
## asimp04 0.14 0.02 0.04 0.18 0.15 0.19 0.27 0
## asimp15 0.08 0.05 0.10 0.24 0.19 0.16 0.18 0
## asimp06 0.40 0.14 0.11 0.20 0.08 0.04 0.03 0
## asimp17 0.37 0.15 0.12 0.21 0.07 0.05 0.03 0
## asimp08 0.35 0.17 0.14 0.19 0.07 0.04 0.04 0
## asimp19 0.37 0.17 0.15 0.17 0.07 0.04 0.03 0
## asimp03 0.07 0.07 0.10 0.28 0.18 0.15 0.15 0
## asimp14 0.08 0.06 0.09 0.28 0.21 0.16 0.12 0
## asimp05 0.10 0.08 0.09 0.28 0.19 0.12 0.13 0
## asimp16 0.09 0.06 0.10 0.28 0.20 0.15 0.11 0
## asimp11 0.53 0.14 0.06 0.10 0.06 0.05 0.06 0
## asimp22 0.55 0.13 0.07 0.10 0.05 0.06 0.04 0alpha (data[c("aspro01", "aspro12", "aspro02", "aspro13",
"aspro10", "aspro21", "aspro07", "aspro18",
"aspro09", "aspro20", "aspro04", "aspro15",
"aspro06", "aspro17", "aspro08", "aspro19",
"aspro03", "aspro14", "aspro05", "aspro16",
"aspro11", "aspro22")])##
## Reliability analysis
## Call: alpha(x = data[c("aspro01", "aspro12", "aspro02", "aspro13",
## "aspro10", "aspro21", "aspro07", "aspro18", "aspro09", "aspro20",
## "aspro04", "aspro15", "aspro06", "aspro17", "aspro08", "aspro19",
## "aspro03", "aspro14", "aspro05", "aspro16", "aspro11", "aspro22")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.92 0.29 9 0.0044 4.1 0.99 0.29
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## aspro01 0.89 0.90 0.91 0.29 8.6 0.0045 0.013 0.29
## aspro12 0.89 0.89 0.91 0.29 8.4 0.0046 0.013 0.28
## aspro02 0.89 0.90 0.91 0.29 8.6 0.0045 0.013 0.29
## aspro13 0.90 0.90 0.92 0.30 8.8 0.0044 0.013 0.29
## aspro10 0.89 0.89 0.91 0.29 8.5 0.0045 0.013 0.29
## aspro21 0.89 0.89 0.91 0.29 8.4 0.0046 0.012 0.29
## aspro07 0.89 0.89 0.91 0.29 8.4 0.0046 0.012 0.28
## aspro18 0.89 0.89 0.91 0.29 8.4 0.0046 0.012 0.28
## aspro09 0.90 0.90 0.92 0.30 8.8 0.0044 0.012 0.29
## aspro20 0.89 0.90 0.91 0.29 8.5 0.0045 0.012 0.29
## aspro04 0.89 0.90 0.91 0.29 8.5 0.0045 0.013 0.28
## aspro15 0.89 0.89 0.91 0.29 8.4 0.0046 0.013 0.28
## aspro06 0.90 0.90 0.92 0.29 8.7 0.0045 0.013 0.29
## aspro17 0.89 0.89 0.91 0.29 8.4 0.0046 0.012 0.28
## aspro08 0.89 0.89 0.91 0.29 8.4 0.0046 0.012 0.28
## aspro19 0.89 0.89 0.91 0.29 8.5 0.0046 0.013 0.28
## aspro03 0.90 0.90 0.92 0.30 9.0 0.0044 0.012 0.29
## aspro14 0.89 0.89 0.91 0.29 8.4 0.0046 0.013 0.28
## aspro05 0.89 0.89 0.91 0.29 8.4 0.0046 0.013 0.28
## aspro16 0.89 0.89 0.91 0.28 8.2 0.0047 0.012 0.28
## aspro11 0.90 0.90 0.91 0.30 8.8 0.0044 0.011 0.29
## aspro22 0.90 0.90 0.91 0.29 8.7 0.0045 0.011 0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## aspro01 1134 0.55 0.56 0.53 0.49 5.1 1.7
## aspro12 1134 0.60 0.61 0.59 0.55 4.9 1.6
## aspro02 1134 0.54 0.54 0.51 0.48 4.2 1.8
## aspro13 1134 0.46 0.46 0.42 0.39 4.5 1.8
## aspro10 1134 0.56 0.58 0.55 0.51 5.1 1.4
## aspro21 1134 0.60 0.61 0.59 0.54 4.8 1.7
## aspro07 1134 0.62 0.63 0.62 0.57 4.6 1.6
## aspro18 1134 0.63 0.64 0.63 0.58 4.6 1.7
## aspro09 1134 0.43 0.45 0.41 0.37 5.9 1.4
## aspro20 1134 0.56 0.57 0.55 0.50 4.8 1.6
## aspro04 1134 0.58 0.57 0.54 0.51 4.0 2.1
## aspro15 1134 0.61 0.61 0.59 0.55 3.7 1.9
## aspro06 1134 0.52 0.51 0.47 0.45 3.2 1.9
## aspro17 1134 0.64 0.63 0.61 0.59 3.0 1.8
## aspro08 1134 0.65 0.64 0.62 0.60 3.0 1.7
## aspro19 1134 0.61 0.59 0.57 0.55 3.1 1.9
## aspro03 1134 0.38 0.38 0.33 0.31 4.7 1.7
## aspro14 1134 0.63 0.64 0.61 0.58 4.2 1.6
## aspro05 1134 0.61 0.61 0.58 0.56 4.2 1.8
## aspro16 1134 0.70 0.70 0.69 0.65 3.9 1.8
## aspro11 1134 0.46 0.45 0.43 0.39 2.5 2.0
## aspro22 1134 0.54 0.52 0.51 0.47 2.5 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## aspro01 0.05 0.03 0.05 0.19 0.20 0.21 0.26 0
## aspro12 0.04 0.05 0.07 0.22 0.21 0.19 0.21 0
## aspro02 0.11 0.08 0.10 0.27 0.20 0.12 0.11 0
## aspro13 0.08 0.08 0.10 0.25 0.19 0.14 0.16 0
## aspro10 0.03 0.03 0.06 0.22 0.26 0.22 0.18 0
## aspro21 0.05 0.06 0.08 0.21 0.22 0.19 0.19 0
## aspro07 0.07 0.06 0.09 0.23 0.23 0.20 0.12 0
## aspro18 0.09 0.06 0.08 0.22 0.21 0.22 0.13 0
## aspro09 0.01 0.02 0.02 0.13 0.13 0.19 0.48 0
## aspro20 0.05 0.05 0.08 0.21 0.22 0.24 0.15 0
## aspro04 0.22 0.06 0.08 0.18 0.18 0.14 0.14 0
## aspro15 0.21 0.10 0.12 0.24 0.14 0.10 0.10 0
## aspro06 0.30 0.11 0.11 0.24 0.11 0.06 0.08 0
## aspro17 0.31 0.14 0.13 0.23 0.10 0.04 0.05 0
## aspro08 0.29 0.13 0.13 0.25 0.10 0.04 0.05 0
## aspro19 0.30 0.15 0.13 0.19 0.09 0.06 0.07 0
## aspro03 0.06 0.05 0.07 0.26 0.19 0.19 0.17 0
## aspro14 0.09 0.08 0.08 0.33 0.19 0.14 0.09 0
## aspro05 0.11 0.08 0.11 0.27 0.16 0.13 0.13 0
## aspro16 0.13 0.12 0.11 0.27 0.19 0.10 0.09 0
## aspro11 0.52 0.10 0.07 0.13 0.06 0.04 0.07 0
## aspro22 0.53 0.11 0.07 0.11 0.07 0.05 0.06 06.3.1 Aspirations importance
alpha (data[c("asimp01", "asimp12",
"asimp02", "asimp13",
"asimp10", "asimp21",
"asimp04", "asimp15",
"asimp06", "asimp17",
"asimp08", "asimp19",
"asimp03", "asimp14",
"asimp07", "asimp18",
"asimp09", "asimp20")])##
## Reliability analysis
## Call: alpha(x = data[c("asimp01", "asimp12", "asimp02", "asimp13",
## "asimp10", "asimp21", "asimp04", "asimp15", "asimp06", "asimp17",
## "asimp08", "asimp19", "asimp03", "asimp14", "asimp07", "asimp18",
## "asimp09", "asimp20")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.83 0.85 0.21 4.7 0.0077 4.8 0.79 0.2
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## asimp01 0.81 0.82 0.85 0.21 4.5 0.0080 0.017 0.20
## asimp12 0.81 0.81 0.84 0.20 4.3 0.0083 0.016 0.19
## asimp02 0.81 0.82 0.84 0.21 4.5 0.0081 0.017 0.21
## asimp13 0.82 0.82 0.85 0.21 4.6 0.0079 0.016 0.20
## asimp10 0.81 0.81 0.84 0.21 4.4 0.0081 0.016 0.19
## asimp21 0.82 0.82 0.84 0.21 4.5 0.0080 0.015 0.20
## asimp04 0.81 0.82 0.84 0.21 4.4 0.0082 0.017 0.19
## asimp15 0.81 0.82 0.84 0.21 4.4 0.0082 0.017 0.19
## asimp06 0.82 0.82 0.85 0.21 4.6 0.0080 0.014 0.20
## asimp17 0.81 0.82 0.84 0.21 4.5 0.0082 0.014 0.20
## asimp08 0.81 0.82 0.84 0.21 4.5 0.0082 0.013 0.21
## asimp19 0.81 0.82 0.84 0.21 4.5 0.0082 0.014 0.20
## asimp03 0.82 0.82 0.85 0.21 4.7 0.0079 0.017 0.21
## asimp14 0.81 0.82 0.84 0.21 4.4 0.0083 0.017 0.19
## asimp07 0.81 0.82 0.84 0.21 4.4 0.0081 0.016 0.19
## asimp18 0.81 0.82 0.84 0.21 4.4 0.0081 0.015 0.19
## asimp09 0.82 0.82 0.85 0.21 4.5 0.0080 0.016 0.20
## asimp20 0.81 0.82 0.84 0.21 4.4 0.0081 0.015 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## asimp01 1134 0.47 0.48 0.43 0.38 5.8 1.5
## asimp12 1134 0.57 0.59 0.57 0.50 5.4 1.5
## asimp02 1134 0.50 0.50 0.45 0.40 4.8 1.7
## asimp13 1134 0.46 0.46 0.41 0.35 4.9 1.7
## asimp10 1134 0.51 0.55 0.51 0.44 5.7 1.3
## asimp21 1134 0.45 0.50 0.46 0.37 5.9 1.3
## asimp04 1134 0.55 0.52 0.48 0.44 4.8 2.0
## asimp15 1134 0.55 0.53 0.49 0.45 4.6 1.8
## asimp06 1134 0.46 0.42 0.37 0.36 2.7 1.7
## asimp17 1134 0.54 0.51 0.48 0.45 2.7 1.7
## asimp08 1134 0.54 0.50 0.48 0.45 2.8 1.7
## asimp19 1134 0.53 0.49 0.46 0.43 2.7 1.7
## asimp03 1134 0.42 0.41 0.34 0.32 4.5 1.7
## asimp14 1134 0.55 0.54 0.50 0.46 4.4 1.7
## asimp07 1134 0.49 0.53 0.50 0.41 5.9 1.3
## asimp18 1134 0.49 0.54 0.51 0.42 6.2 1.2
## asimp09 1134 0.42 0.47 0.42 0.35 6.2 1.2
## asimp20 1134 0.49 0.53 0.50 0.41 6.0 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## asimp01 0.03 0.01 0.02 0.13 0.13 0.21 0.47 0
## asimp12 0.02 0.02 0.05 0.17 0.18 0.23 0.32 0
## asimp02 0.06 0.05 0.09 0.22 0.21 0.17 0.21 0
## asimp13 0.07 0.04 0.06 0.23 0.21 0.17 0.21 0
## asimp10 0.01 0.01 0.02 0.15 0.18 0.28 0.35 0
## asimp21 0.01 0.01 0.03 0.12 0.15 0.24 0.44 0
## asimp04 0.14 0.02 0.04 0.18 0.15 0.19 0.27 0
## asimp15 0.08 0.05 0.10 0.24 0.19 0.16 0.18 0
## asimp06 0.40 0.14 0.11 0.20 0.08 0.04 0.03 0
## asimp17 0.37 0.15 0.12 0.21 0.07 0.05 0.03 0
## asimp08 0.35 0.17 0.14 0.19 0.07 0.04 0.04 0
## asimp19 0.37 0.17 0.15 0.17 0.07 0.04 0.03 0
## asimp03 0.07 0.07 0.10 0.28 0.18 0.15 0.15 0
## asimp14 0.08 0.06 0.09 0.28 0.21 0.16 0.12 0
## asimp07 0.01 0.01 0.02 0.11 0.16 0.23 0.45 0
## asimp18 0.01 0.01 0.02 0.06 0.09 0.20 0.60 0
## asimp09 0.01 0.01 0.02 0.09 0.09 0.19 0.60 0
## asimp20 0.01 0.01 0.03 0.10 0.12 0.27 0.47 06.3.2 Aspirations likelihood
alpha (data[c("aspro01", "aspro12",
"aspro02", "aspro13",
"aspro10", "aspro21",
"aspro04", "aspro15",
"aspro06", "aspro17",
"aspro08", "aspro19",
"aspro03", "aspro14",
"aspro07", "aspro18",
"aspro09", "aspro20")])##
## Reliability analysis
## Call: alpha(x = data[c("aspro01", "aspro12", "aspro02", "aspro13",
## "aspro10", "aspro21", "aspro04", "aspro15", "aspro06", "aspro17",
## "aspro08", "aspro19", "aspro03", "aspro14", "aspro07", "aspro18",
## "aspro09", "aspro20")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.9 0.29 7.5 0.0051 4.3 0.99 0.29
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## aspro01 0.87 0.88 0.89 0.29 7.0 0.0054 0.012 0.29
## aspro12 0.87 0.87 0.89 0.29 6.9 0.0055 0.011 0.28
## aspro02 0.88 0.88 0.89 0.30 7.2 0.0053 0.012 0.29
## aspro13 0.88 0.88 0.89 0.30 7.4 0.0052 0.011 0.29
## aspro10 0.87 0.87 0.89 0.29 7.0 0.0054 0.012 0.29
## aspro21 0.87 0.87 0.89 0.29 6.9 0.0055 0.011 0.28
## aspro04 0.87 0.88 0.89 0.29 7.0 0.0054 0.012 0.28
## aspro15 0.87 0.87 0.89 0.29 6.9 0.0055 0.011 0.28
## aspro06 0.88 0.88 0.89 0.30 7.3 0.0052 0.011 0.29
## aspro17 0.87 0.88 0.89 0.29 7.0 0.0055 0.011 0.29
## aspro08 0.87 0.87 0.89 0.29 7.0 0.0055 0.011 0.29
## aspro19 0.87 0.88 0.89 0.29 7.1 0.0054 0.011 0.29
## aspro03 0.88 0.88 0.90 0.31 7.5 0.0051 0.010 0.30
## aspro14 0.87 0.87 0.89 0.29 6.9 0.0055 0.012 0.28
## aspro07 0.87 0.87 0.89 0.29 6.9 0.0055 0.010 0.28
## aspro18 0.87 0.87 0.88 0.29 6.8 0.0056 0.010 0.28
## aspro09 0.88 0.88 0.89 0.30 7.3 0.0053 0.011 0.29
## aspro20 0.87 0.87 0.89 0.29 7.0 0.0054 0.011 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## aspro01 1134 0.59 0.60 0.56 0.52 5.1 1.7
## aspro12 1134 0.63 0.64 0.61 0.57 4.9 1.6
## aspro02 1134 0.53 0.53 0.49 0.45 4.2 1.8
## aspro13 1134 0.45 0.45 0.40 0.37 4.5 1.8
## aspro10 1134 0.58 0.60 0.57 0.53 5.1 1.4
## aspro21 1134 0.63 0.64 0.61 0.56 4.8 1.7
## aspro04 1134 0.60 0.59 0.55 0.52 4.0 2.1
## aspro15 1134 0.63 0.62 0.60 0.56 3.7 1.9
## aspro06 1134 0.50 0.49 0.44 0.42 3.2 1.9
## aspro17 1134 0.61 0.60 0.57 0.54 3.0 1.8
## aspro08 1134 0.62 0.61 0.59 0.56 3.0 1.7
## aspro19 1134 0.59 0.57 0.54 0.51 3.1 1.9
## aspro03 1134 0.39 0.39 0.33 0.30 4.7 1.7
## aspro14 1134 0.65 0.65 0.62 0.59 4.2 1.6
## aspro07 1134 0.65 0.65 0.65 0.59 4.6 1.6
## aspro18 1134 0.67 0.67 0.66 0.61 4.6 1.7
## aspro09 1134 0.46 0.48 0.44 0.40 5.9 1.4
## aspro20 1134 0.60 0.61 0.58 0.53 4.8 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## aspro01 0.05 0.03 0.05 0.19 0.20 0.21 0.26 0
## aspro12 0.04 0.05 0.07 0.22 0.21 0.19 0.21 0
## aspro02 0.11 0.08 0.10 0.27 0.20 0.12 0.11 0
## aspro13 0.08 0.08 0.10 0.25 0.19 0.14 0.16 0
## aspro10 0.03 0.03 0.06 0.22 0.26 0.22 0.18 0
## aspro21 0.05 0.06 0.08 0.21 0.22 0.19 0.19 0
## aspro04 0.22 0.06 0.08 0.18 0.18 0.14 0.14 0
## aspro15 0.21 0.10 0.12 0.24 0.14 0.10 0.10 0
## aspro06 0.30 0.11 0.11 0.24 0.11 0.06 0.08 0
## aspro17 0.31 0.14 0.13 0.23 0.10 0.04 0.05 0
## aspro08 0.29 0.13 0.13 0.25 0.10 0.04 0.05 0
## aspro19 0.30 0.15 0.13 0.19 0.09 0.06 0.07 0
## aspro03 0.06 0.05 0.07 0.26 0.19 0.19 0.17 0
## aspro14 0.09 0.08 0.08 0.33 0.19 0.14 0.09 0
## aspro07 0.07 0.06 0.09 0.23 0.23 0.20 0.12 0
## aspro18 0.09 0.06 0.08 0.22 0.21 0.22 0.13 0
## aspro09 0.01 0.02 0.02 0.13 0.13 0.19 0.48 0
## aspro20 0.05 0.05 0.08 0.21 0.22 0.24 0.15 06.4 System justification
alpha (data[c("sj01", "sj02", "sj03.i", "sj04",
"sj05", "sj06", "sj07.i", "sj08")])##
## Reliability analysis
## Call: alpha(x = data[c("sj01", "sj02", "sj03.i", "sj04", "sj05", "sj06",
## "sj07.i", "sj08")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.84 0.38 5 0.0077 3.9 1.1 0.41
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.83 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sj01 0.79 0.79 0.80 0.35 3.8 0.0096 0.020 0.36
## sj02 0.79 0.79 0.79 0.35 3.8 0.0097 0.017 0.40
## sj03.i 0.82 0.82 0.83 0.40 4.7 0.0082 0.020 0.41
## sj04 0.82 0.82 0.83 0.40 4.6 0.0083 0.023 0.41
## sj05 0.79 0.80 0.80 0.36 3.9 0.0096 0.019 0.41
## sj06 0.83 0.83 0.83 0.42 5.0 0.0075 0.016 0.41
## sj07.i 0.82 0.82 0.82 0.40 4.7 0.0082 0.018 0.41
## sj08 0.81 0.82 0.82 0.39 4.4 0.0086 0.021 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sj01 1134 0.80 0.80 0.78 0.72 3.9 1.5
## sj02 1134 0.80 0.80 0.79 0.71 4.4 1.7
## sj03.i 1134 0.61 0.61 0.53 0.47 4.2 1.6
## sj04 1134 0.63 0.62 0.54 0.49 4.3 1.7
## sj05 1134 0.78 0.78 0.76 0.69 3.8 1.6
## sj06 1134 0.56 0.54 0.44 0.39 4.0 1.8
## sj07.i 1134 0.61 0.61 0.54 0.47 3.3 1.6
## sj08 1134 0.65 0.66 0.60 0.54 3.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sj01 0.07 0.12 0.18 0.23 0.25 0.11 0.03 0
## sj02 0.08 0.07 0.13 0.16 0.28 0.21 0.06 0
## sj03.i 0.06 0.08 0.17 0.26 0.18 0.13 0.11 0
## sj04 0.09 0.07 0.12 0.27 0.21 0.16 0.09 0
## sj05 0.12 0.12 0.18 0.19 0.24 0.11 0.04 0
## sj06 0.11 0.13 0.20 0.15 0.19 0.11 0.11 0
## sj07.i 0.16 0.15 0.24 0.22 0.10 0.08 0.04 0
## sj08 0.09 0.17 0.25 0.25 0.17 0.07 0.02 06.6 Human dominance over nature
alpha (data[c("nd01", "nd02", "nd03.i", "nd04", "nd05.i",
"nd06.i", "nd07", "nd08", "nd09.i", "nd10.i")])##
## Reliability analysis
## Call: alpha(x = data[c("nd01", "nd02", "nd03.i", "nd04", "nd05.i",
## "nd06.i", "nd07", "nd08", "nd09.i", "nd10.i")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.89 0.42 7.3 0.0054 2.5 1.1 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## nd01 0.85 0.86 0.86 0.40 6.0 0.0065 0.011 0.37
## nd02 0.86 0.87 0.87 0.42 6.4 0.0061 0.013 0.38
## nd03.i 0.87 0.87 0.87 0.42 6.5 0.0059 0.017 0.38
## nd04 0.86 0.86 0.87 0.41 6.3 0.0062 0.012 0.38
## nd05.i 0.88 0.88 0.88 0.44 7.2 0.0056 0.014 0.39
## nd06.i 0.87 0.87 0.88 0.43 6.8 0.0057 0.015 0.38
## nd07 0.86 0.86 0.87 0.41 6.3 0.0061 0.011 0.38
## nd08 0.86 0.87 0.87 0.42 6.5 0.0060 0.013 0.38
## nd09.i 0.87 0.87 0.88 0.43 6.8 0.0058 0.016 0.38
## nd10.i 0.87 0.87 0.88 0.43 6.9 0.0056 0.014 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## nd01 1134 0.81 0.81 0.80 0.75 2.3 1.6
## nd02 1134 0.73 0.72 0.69 0.64 2.9 1.8
## nd03.i 1134 0.68 0.70 0.65 0.61 2.0 1.4
## nd04 1134 0.75 0.74 0.73 0.67 2.7 1.7
## nd05.i 1134 0.55 0.57 0.50 0.46 1.9 1.3
## nd06.i 1134 0.66 0.65 0.60 0.56 2.7 1.8
## nd07 1134 0.74 0.73 0.72 0.66 2.7 1.7
## nd08 1134 0.71 0.71 0.67 0.63 2.7 1.6
## nd09.i 1134 0.66 0.65 0.59 0.56 2.6 1.7
## nd10.i 1134 0.64 0.63 0.57 0.52 2.9 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## nd01 0.48 0.15 0.14 0.13 0.06 0.03 0.02 0
## nd02 0.34 0.13 0.15 0.18 0.12 0.05 0.04 0
## nd03.i 0.51 0.19 0.16 0.08 0.03 0.01 0.01 0
## nd04 0.36 0.17 0.14 0.16 0.10 0.04 0.03 0
## nd05.i 0.52 0.20 0.15 0.07 0.03 0.01 0.01 0
## nd06.i 0.39 0.16 0.16 0.10 0.09 0.04 0.05 0
## nd07 0.37 0.16 0.15 0.16 0.10 0.03 0.03 0
## nd08 0.33 0.18 0.16 0.20 0.08 0.03 0.02 0
## nd09.i 0.37 0.18 0.17 0.14 0.06 0.03 0.05 0
## nd10.i 0.35 0.16 0.15 0.13 0.08 0.05 0.07 06.8 Climate anxiety
alpha (data[c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12", "cas13")])##
## Reliability analysis
## Call: alpha(x = data[c("cas01", "cas02", "cas03", "cas04", "cas05",
## "cas07", "cas08", "cas09", "cas10", "cas11", "cas12", "cas13")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.91 0.46 10 0.0041 1.9 0.91 0.45
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.91 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cas01 0.90 0.90 0.90 0.46 9.3 0.0046 0.0077 0.45
## cas02 0.89 0.90 0.90 0.45 9.1 0.0046 0.0071 0.45
## cas03 0.90 0.90 0.90 0.46 9.3 0.0045 0.0079 0.45
## cas04 0.90 0.91 0.91 0.47 9.6 0.0044 0.0076 0.45
## cas05 0.90 0.91 0.91 0.47 9.8 0.0043 0.0084 0.47
## cas07 0.90 0.91 0.91 0.47 9.9 0.0043 0.0075 0.46
## cas08 0.90 0.91 0.91 0.48 10.0 0.0043 0.0076 0.47
## cas09 0.89 0.90 0.90 0.45 9.1 0.0047 0.0077 0.45
## cas10 0.90 0.91 0.91 0.48 10.1 0.0042 0.0072 0.47
## cas11 0.89 0.90 0.90 0.45 9.0 0.0046 0.0074 0.45
## cas12 0.89 0.90 0.90 0.46 9.2 0.0046 0.0078 0.45
## cas13 0.90 0.91 0.91 0.47 9.6 0.0045 0.0091 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cas01 1134 0.75 0.75 0.73 0.69 1.9 1.3
## cas02 1134 0.77 0.77 0.76 0.71 1.8 1.3
## cas03 1134 0.72 0.74 0.72 0.67 1.5 1.0
## cas04 1134 0.67 0.70 0.67 0.62 1.4 1.0
## cas05 1134 0.69 0.66 0.62 0.60 2.5 1.6
## cas07 1134 0.62 0.65 0.60 0.56 1.4 1.0
## cas08 1134 0.66 0.63 0.59 0.57 2.4 1.5
## cas09 1134 0.78 0.78 0.77 0.73 1.9 1.3
## cas10 1134 0.64 0.61 0.55 0.54 2.4 1.6
## cas11 1134 0.78 0.79 0.77 0.73 1.6 1.1
## cas12 1134 0.76 0.76 0.74 0.70 1.8 1.2
## cas13 1134 0.70 0.70 0.66 0.63 1.8 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## cas01 0.60 0.15 0.11 0.10 0.03 0.00 0.01 0
## cas02 0.66 0.13 0.08 0.08 0.04 0.00 0.01 0
## cas03 0.77 0.10 0.05 0.05 0.02 0.00 0.01 0
## cas04 0.82 0.07 0.04 0.04 0.02 0.01 0.00 0
## cas05 0.45 0.12 0.13 0.16 0.10 0.02 0.01 0
## cas07 0.81 0.08 0.04 0.05 0.01 0.00 0.01 0
## cas08 0.44 0.14 0.11 0.21 0.07 0.01 0.01 0
## cas09 0.60 0.16 0.10 0.10 0.04 0.00 0.01 0
## cas10 0.46 0.16 0.12 0.12 0.10 0.03 0.02 0
## cas11 0.69 0.14 0.06 0.09 0.01 0.00 0.01 0
## cas12 0.62 0.15 0.09 0.11 0.02 0.01 0.01 0
## cas13 0.63 0.13 0.09 0.09 0.04 0.01 0.01 06.9 Anxiety and depressiveness
alpha (data[c("phq01", "phq02", "phq03", "phq04")])##
## Reliability analysis
## Call: alpha(x = data[c("phq01", "phq02", "phq03", "phq04")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.86 0.67 8 0.0054 1.7 0.74 0.68
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## phq01 0.87 0.87 0.82 0.69 6.8 0.0066 0.00091 0.69
## phq02 0.83 0.83 0.77 0.63 5.1 0.0085 0.00113 0.62
## phq03 0.86 0.86 0.81 0.68 6.3 0.0070 0.00306 0.69
## phq04 0.85 0.85 0.80 0.66 5.9 0.0075 0.00287 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## phq01 1134 0.84 0.84 0.76 0.71 1.7 0.85
## phq02 1134 0.90 0.90 0.86 0.81 1.7 0.85
## phq03 1134 0.86 0.85 0.78 0.74 1.7 0.87
## phq04 1134 0.87 0.87 0.81 0.76 1.6 0.85
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## phq01 0.48 0.37 0.09 0.06 0
## phq02 0.54 0.31 0.10 0.05 0
## phq03 0.50 0.36 0.08 0.06 0
## phq04 0.58 0.29 0.08 0.05 06.10 Policy support
corr.test (data [,c("ps01", "ps02")],
method = "spearman")## Call:corr.test(x = data[, c("ps01", "ps02")], method = "spearman")
## Correlation matrix
## ps01 ps02
## ps01 1.00 0.68
## ps02 0.68 1.00
## Sample Size
## ps01 ps02
## ps01 1088 1087
## ps02 1087 1091
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## ps01 ps02
## ps01 0 0
## ps02 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("ps01", "ps02")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## ps01 ps02
## ps01 1.00 0.74
## ps02 0.63 1.006.11 Intentions
alpha (data[c("int01", "int02", "int03")])##
## Reliability analysis
## Call: alpha(x = data[c("int01", "int02", "int03")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.73 0.7 0.47 2.7 0.013 3.5 1.3 0.36
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.74 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## int01 0.53 0.53 0.36 0.36 1.15 0.0276 NA 0.36
## int02 0.47 0.48 0.31 0.31 0.92 0.0309 NA 0.31
## int03 0.85 0.85 0.74 0.74 5.76 0.0088 NA 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## int01 1134 0.87 0.85 0.79 0.66 2.7 1.6
## int02 1134 0.89 0.87 0.83 0.70 2.8 1.7
## int03 1134 0.66 0.69 0.40 0.36 5.1 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## int01 0.35 0.16 0.14 0.20 0.09 0.04 0.02 0
## int02 0.35 0.15 0.15 0.20 0.09 0.04 0.02 0
## int03 0.04 0.02 0.04 0.15 0.34 0.23 0.18 07 Exploring the instrument
7.1 Diverging stacked barchart of answers
Load relevant packages
library(HH)## Loading required package: lattice## Loading required package: grid## Loading required package: latticeExtra## Loading required package: multcomp## Loading required package: mvtnorm## Loading required package: survival## Loading required package: TH.data##
## Attaching package: 'TH.data'## The following object is masked from 'package:MASS':
##
## geyser## Loading required package: gridExtra##
## Attaching package: 'HH'## The following objects are masked from 'package:car':
##
## logit, vif## The following object is masked from 'package:psych':
##
## logitlibrary(tidyverse)## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'## Also defined by 'Rmpfr'## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x ggplot2::%+%() masks psych::%+%()
## x ggplot2::alpha() masks psych::alpha()
## x dplyr::combine() masks gridExtra::combine()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x ggplot2::layer() masks latticeExtra::layer()
## x dplyr::recode() masks car::recode()
## x dplyr::select() masks MASS::select()
## x purrr::some() masks car::some()
## x purrr::transpose() masks HH::transpose()Create data frame with all CAS items
df = data.frame(data$cas01, data$cas02, data$cas03, data$cas04, data$cas05,
data$cas06, data$cas07, data$cas08, data$cas09, data$cas10,
data$cas11, data$cas12, data$cas13)Convert to long format
survey <- gather(df, measure, response, c(1:13))Convert to factors
survey$measure <- as.factor(survey$measure)
survey$response <- as.factor(survey$response)Create contingency table
survey_df <- table(survey$measure, survey$response) %>% as.data.frame.matrix()Rename rows and columns
colnames(survey_df) <- c("strongly disagree","disagree","somewhat disagree","neutral","somewhat agree","agree", "strongly agree")
rownames(survey_df)<-c("1: Thinking about climate change makes it difficult for me to concentrate.",
"2: Thinking about climate change makes it difficult for me to sleep.",
"3: I have nightmares about climate change",
"4: I find myself crying because of climate change.",
"5: I think, “why can’t I handle climate change better?”.",
"6: I go away by myself and think about why I feel this way about climate change.",
"7: I write down my thoughts about climate change and analyze them.",
"8: I think, “why do I react to climate change this way?”.",
"9: My concerns about climate change make it hard for me to have fun with my family or friends.",
"10: I have problems balancing my concerns about sustainability with the needs of my family.",
"11: My concerns about climate change interfere with my ability to get work or school assignments done.",
"12: My concerns about climate change undermine my ability to work to my potential.",
"13: My friends say I think about climate change too much.")
survey_df <- tibble::rownames_to_column(survey_df, var="Measure") Plotting the graph
likert(Measure ~ ., data=survey_df,
ylab=NULL,
scales = list(y = list(cex = 1.2)),
auto.key=list(cex = 1.2,just = 0.95),
ReferenceZero=4, as.percent=TRUE,
main = list(" ",x=unit(.55, "npc")),
xlim=c(-100, -80, -60, -40, -20, 0, 20, 40),
strip=FALSE,
par.strip.text=list(cex=.7))
7.2 Correlations
7.2.1 Socio-demographics and climate anxiety - individual items
corr.test (data [,c("cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13",
"age", "income", "pol.orient")],
method = "spearman")## Call:corr.test(x = data[, c("cas01", "cas02", "cas03", "cas04", "cas05",
## "cas06", "cas07", "cas08", "cas09", "cas10", "cas11", "cas12",
## "cas13", "age", "income", "pol.orient")], method = "spearman")
## Correlation matrix
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11
## cas01 1.00 0.60 0.50 0.45 0.45 0.30 0.41 0.46 0.58 0.44 0.58
## cas02 0.60 1.00 0.60 0.53 0.43 0.38 0.41 0.40 0.61 0.44 0.58
## cas03 0.50 0.60 1.00 0.54 0.40 0.37 0.49 0.36 0.50 0.36 0.54
## cas04 0.45 0.53 0.54 1.00 0.34 0.38 0.47 0.28 0.48 0.35 0.47
## cas05 0.45 0.43 0.40 0.34 1.00 0.36 0.35 0.56 0.47 0.46 0.47
## cas06 0.30 0.38 0.37 0.38 0.36 1.00 0.35 0.28 0.41 0.30 0.33
## cas07 0.41 0.41 0.49 0.47 0.35 0.35 1.00 0.34 0.39 0.29 0.48
## cas08 0.46 0.40 0.36 0.28 0.56 0.28 0.34 1.00 0.46 0.42 0.45
## cas09 0.58 0.61 0.50 0.48 0.47 0.41 0.39 0.46 1.00 0.51 0.60
## cas10 0.44 0.44 0.36 0.35 0.46 0.30 0.29 0.42 0.51 1.00 0.45
## cas11 0.58 0.58 0.54 0.47 0.47 0.33 0.48 0.45 0.60 0.45 1.00
## cas12 0.59 0.56 0.49 0.46 0.45 0.34 0.45 0.44 0.60 0.45 0.64
## cas13 0.49 0.50 0.45 0.42 0.45 0.41 0.44 0.40 0.52 0.43 0.51
## age 0.03 0.03 0.00 -0.02 0.01 0.06 0.00 0.03 0.00 -0.05 -0.01
## income -0.03 0.00 -0.02 0.01 0.01 -0.01 0.02 -0.03 -0.02 -0.02 -0.04
## pol.orient -0.09 -0.07 -0.09 -0.08 -0.08 -0.14 -0.07 -0.03 -0.10 -0.09 -0.07
## cas12 cas13 age income pol.orient
## cas01 0.59 0.49 0.03 -0.03 -0.09
## cas02 0.56 0.50 0.03 0.00 -0.07
## cas03 0.49 0.45 0.00 -0.02 -0.09
## cas04 0.46 0.42 -0.02 0.01 -0.08
## cas05 0.45 0.45 0.01 0.01 -0.08
## cas06 0.34 0.41 0.06 -0.01 -0.14
## cas07 0.45 0.44 0.00 0.02 -0.07
## cas08 0.44 0.40 0.03 -0.03 -0.03
## cas09 0.60 0.52 0.00 -0.02 -0.10
## cas10 0.45 0.43 -0.05 -0.02 -0.09
## cas11 0.64 0.51 -0.01 -0.04 -0.07
## cas12 1.00 0.50 0.02 -0.03 -0.05
## cas13 0.50 1.00 0.01 -0.01 -0.08
## age 0.02 0.01 1.00 0.11 0.05
## income -0.03 -0.01 0.11 1.00 0.11
## pol.orient -0.05 -0.08 0.05 0.11 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11
## cas01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## age 0.24 0.39 0.91 0.60 0.69 0.03 0.93 0.30 0.89 0.11 0.69
## income 0.36 0.89 0.59 0.83 0.80 0.66 0.53 0.31 0.53 0.44 0.21
## pol.orient 0.00 0.02 0.00 0.01 0.00 0.00 0.02 0.33 0.00 0.00 0.02
## cas12 cas13 age income pol.orient
## cas01 0.00 0.00 1.00 1.00 0.15
## cas02 0.00 0.00 1.00 1.00 0.73
## cas03 0.00 0.00 1.00 1.00 0.12
## cas04 0.00 0.00 1.00 1.00 0.32
## cas05 0.00 0.00 1.00 1.00 0.15
## cas06 0.00 0.00 0.87 1.00 0.00
## cas07 0.00 0.00 1.00 1.00 0.69
## cas08 0.00 0.00 1.00 1.00 1.00
## cas09 0.00 0.00 1.00 1.00 0.02
## cas10 0.00 0.00 1.00 1.00 0.11
## cas11 0.00 0.00 1.00 1.00 0.52
## cas12 0.00 0.00 1.00 1.00 1.00
## cas13 0.00 0.00 1.00 1.00 0.26
## age 0.57 0.62 0.00 0.01 1.00
## income 0.31 0.84 0.00 0.00 0.01
## pol.orient 0.08 0.01 0.07 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13",
"age", "income", "pol.orient")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## cs01 cs02 cs03 cs04 cs05 cs06 cs07 cs08 cs09 cs10 cs11 cs12
## cas01 1.00 0.67 0.56 0.54 0.47 0.33 0.46 0.48 0.63 0.46 0.66 0.65
## cas02 0.57 1.00 0.67 0.62 0.48 0.40 0.47 0.43 0.67 0.47 0.63 0.62
## cas03 0.42 0.54 1.00 0.67 0.47 0.39 0.61 0.42 0.60 0.41 0.62 0.54
## cas04 0.39 0.49 0.51 1.00 0.38 0.41 0.57 0.34 0.58 0.41 0.57 0.53
## cas05 0.34 0.35 0.35 0.26 1.00 0.40 0.41 0.60 0.50 0.49 0.52 0.49
## cas06 0.20 0.28 0.31 0.32 0.30 1.00 0.41 0.33 0.45 0.33 0.36 0.37
## cas07 0.31 0.33 0.44 0.39 0.29 0.29 1.00 0.41 0.47 0.30 0.57 0.52
## cas08 0.36 0.30 0.30 0.22 0.51 0.22 0.29 1.00 0.50 0.45 0.49 0.48
## cas09 0.51 0.55 0.45 0.44 0.39 0.33 0.32 0.39 1.00 0.52 0.66 0.65
## cas10 0.32 0.35 0.29 0.28 0.36 0.22 0.18 0.32 0.41 1.00 0.45 0.45
## cas11 0.54 0.50 0.47 0.42 0.41 0.26 0.42 0.39 0.54 0.33 1.00 0.69
## cas12 0.54 0.52 0.41 0.40 0.36 0.25 0.38 0.37 0.53 0.33 0.58 1.00
## cas13 0.39 0.42 0.38 0.39 0.39 0.36 0.34 0.32 0.46 0.34 0.43 0.40
## age -0.04 -0.03 0.05 0.00 0.05 0.00 0.04 -0.05 0.04 -0.01 0.04 -0.05
## income 0.01 0.04 0.05 -0.06 0.04 0.06 -0.05 0.03 0.02 0.04 0.00 0.02
## pol.orient 0.01 0.03 0.01 0.02 0.01 -0.08 0.06 0.05 -0.01 0.01 0.03 0.05
## cs13 age incm pl.r
## cas01 0.51 0.07 -0.10 -0.10
## cas02 0.53 0.07 -0.08 -0.09
## cas03 0.52 -0.07 -0.07 -0.13
## cas04 0.51 -0.10 0.07 -0.13
## cas05 0.51 -0.06 -0.05 -0.12
## cas06 0.47 0.12 -0.07 -0.20
## cas07 0.50 -0.07 0.07 -0.08
## cas08 0.44 0.07 -0.09 -0.06
## cas09 0.58 -0.06 -0.10 -0.13
## cas10 0.47 -0.12 -0.08 -0.12
## cas11 0.56 -0.08 -0.11 -0.08
## cas12 0.53 0.06 -0.10 -0.06
## cas13 1.00 0.06 -0.08 -0.10
## age -0.05 1.00 0.15 0.12
## income 0.05 0.03 1.00 0.14
## pol.orient 0.01 -0.01 0.03 1.007.3 Scatterplot age
group_pch <- data$gender # Create variable for symbols
group_pch[group_pch == 1] <- 16
group_pch[group_pch == 2] <- 8
group_col <- data$gender # Create variable for colors
group_col[group_col == 1] <- "black"
group_col[group_col == 2] <- "black"
plot(data$age, data$cas,
main = "CAS and Age Scatterplot",
xlab = "Age",
ylab = "CAS",
pch = group_pch,
col = group_col)
abline(lm(data$cas ~ data$age), col = "red") #regression line
lines(lowess(data$age, data$cas), col = "blue") #smooth fitting line
legend("topright", #add legend to scatterplot
legend = c("male", "female"),
col = c("black", "black"),
pch = c(16, 8))
7.4 CFA CAS - with all items
Increases the number of rows that can be printed
options(max.print = 10000000)7.4.1 Normality
Load package
library(MVN)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## sROC 0.1-2 loadedCreate data frame with all CAS items
data.cas.items = data.frame(data$cas01, data$cas02, data$cas03, data$cas04, data$cas05,
data$cas06, data$cas07, data$cas08, data$cas09, data$cas10,
data$cas11, data$cas12, data$cas13)Check for overall multivariate normality (Doornik-Hansen’s test)
result <- mvn(data = data.cas.items, mvnTest = "dh")
result$multivariateNormality## Test E df p value MVN
## 1 Doornik-Hansen 2320.646 26 0 NOInspect data visually
mvn(data = data.cas.items, mvnTest = "royston", univariatePlot = "histogram") #create histograms for all items## $multivariateNormality
## Test H p value MVN
## 1 Royston 2511.952 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk data.cas01 0.7051 <0.001 NO
## 2 Shapiro-Wilk data.cas02 0.6565 <0.001 NO
## 3 Shapiro-Wilk data.cas03 0.5165 <0.001 NO
## 4 Shapiro-Wilk data.cas04 0.4607 <0.001 NO
## 5 Shapiro-Wilk data.cas05 0.8254 <0.001 NO
## 6 Shapiro-Wilk data.cas06 0.8640 <0.001 NO
## 7 Shapiro-Wilk data.cas07 0.4699 <0.001 NO
## 8 Shapiro-Wilk data.cas08 0.8227 <0.001 NO
## 9 Shapiro-Wilk data.cas09 0.7093 <0.001 NO
## 10 Shapiro-Wilk data.cas10 0.8103 <0.001 NO
## 11 Shapiro-Wilk data.cas11 0.6230 <0.001 NO
## 12 Shapiro-Wilk data.cas12 0.6902 <0.001 NO
## 13 Shapiro-Wilk data.cas13 0.6832 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## data.cas01 1134 1.850088 1.279382 1 1 7 1 2 1.5091678 1.7194046
## data.cas02 1134 1.761023 1.273755 1 1 7 1 2 1.7308788 2.3783856
## data.cas03 1134 1.466490 1.042870 1 1 7 1 1 2.5823754 6.7536081
## data.cas04 1134 1.403880 1.017509 1 1 7 1 1 2.8729004 8.3327298
## data.cas05 1134 2.473545 1.623608 2 1 7 1 4 0.7321614 -0.5825074
## data.cas06 1134 2.911817 1.811306 3 1 7 1 4 0.3991987 -1.1260844
## data.cas07 1134 1.421517 1.039602 1 1 7 1 1 2.9263927 9.0596647
## data.cas08 1134 2.420635 1.549523 2 1 7 1 4 0.7114927 -0.5551500
## data.cas09 1134 1.865961 1.290069 1 1 7 1 2 1.4699642 1.4805367
## data.cas10 1134 2.393298 1.632251 2 1 7 1 4 0.9371281 -0.1800271
## data.cas11 1134 1.641975 1.143168 1 1 7 1 2 1.9091727 3.3080489
## data.cas12 1134 1.806878 1.245896 1 1 7 1 2 1.5640431 1.9406054
## data.cas13 1134 1.843915 1.335037 1 1 7 1 2 1.5583385 1.6126573mvn(data = data.cas.items, mvnTest = "royston", univariatePlot = "qqplot") #create Q-Q-plots for all items
## $multivariateNormality
## Test H p value MVN
## 1 Royston 2511.952 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk data.cas01 0.7051 <0.001 NO
## 2 Shapiro-Wilk data.cas02 0.6565 <0.001 NO
## 3 Shapiro-Wilk data.cas03 0.5165 <0.001 NO
## 4 Shapiro-Wilk data.cas04 0.4607 <0.001 NO
## 5 Shapiro-Wilk data.cas05 0.8254 <0.001 NO
## 6 Shapiro-Wilk data.cas06 0.8640 <0.001 NO
## 7 Shapiro-Wilk data.cas07 0.4699 <0.001 NO
## 8 Shapiro-Wilk data.cas08 0.8227 <0.001 NO
## 9 Shapiro-Wilk data.cas09 0.7093 <0.001 NO
## 10 Shapiro-Wilk data.cas10 0.8103 <0.001 NO
## 11 Shapiro-Wilk data.cas11 0.6230 <0.001 NO
## 12 Shapiro-Wilk data.cas12 0.6902 <0.001 NO
## 13 Shapiro-Wilk data.cas13 0.6832 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## data.cas01 1134 1.850088 1.279382 1 1 7 1 2 1.5091678 1.7194046
## data.cas02 1134 1.761023 1.273755 1 1 7 1 2 1.7308788 2.3783856
## data.cas03 1134 1.466490 1.042870 1 1 7 1 1 2.5823754 6.7536081
## data.cas04 1134 1.403880 1.017509 1 1 7 1 1 2.8729004 8.3327298
## data.cas05 1134 2.473545 1.623608 2 1 7 1 4 0.7321614 -0.5825074
## data.cas06 1134 2.911817 1.811306 3 1 7 1 4 0.3991987 -1.1260844
## data.cas07 1134 1.421517 1.039602 1 1 7 1 1 2.9263927 9.0596647
## data.cas08 1134 2.420635 1.549523 2 1 7 1 4 0.7114927 -0.5551500
## data.cas09 1134 1.865961 1.290069 1 1 7 1 2 1.4699642 1.4805367
## data.cas10 1134 2.393298 1.632251 2 1 7 1 4 0.9371281 -0.1800271
## data.cas11 1134 1.641975 1.143168 1 1 7 1 2 1.9091727 3.3080489
## data.cas12 1134 1.806878 1.245896 1 1 7 1 2 1.5640431 1.9406054
## data.cas13 1134 1.843915 1.335037 1 1 7 1 2 1.5583385 1.6126573
7.4.2 CFA - 2-factor solution
Load package
library(lavaan)## This is lavaan 0.6-7## lavaan is BETA software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covCFA
CFA.2 <- ' cas.ce =~ cas01 + cas02 + cas03 + cas04 + cas05 + cas06 + cas07 + cas08
cas.f =~ cas09 + cas10 + cas11 + cas12 + cas13'
fit.2 <- cfa(CFA.2, std.lv=TRUE, data=data, estimator = "MLM")
summary(fit.2, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)## lavaan 0.6-7 ended normally after 20 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 27
##
## Number of observations 1134
##
## Model Test User Model:
## Standard Robust
## Test Statistic 623.809 354.305
## Degrees of freedom 64 64
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.761
## Satorra-Bentler correction
##
## Model Test Baseline Model:
##
## Test statistic 6950.305 2767.895
## Degrees of freedom 78 78
## P-value 0.000 0.000
## Scaling correction factor 2.511
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.919 0.892
## Tucker-Lewis Index (TLI) 0.901 0.868
##
## Robust Comparative Fit Index (CFI) 0.924
## Robust Tucker-Lewis Index (TLI) 0.908
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -21706.398 -21706.398
## Loglikelihood unrestricted model (H1) -21394.494 -21394.494
##
## Akaike (AIC) 43466.796 43466.796
## Bayesian (BIC) 43602.700 43602.700
## Sample-size adjusted Bayesian (BIC) 43516.941 43516.941
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.088 0.063
## 90 Percent confidence interval - lower 0.082 0.058
## 90 Percent confidence interval - upper 0.094 0.068
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA 0.084
## 90 Percent confidence interval - lower 0.075
## 90 Percent confidence interval - upper 0.093
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048 0.048
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cas.ce =~
## cas01 0.942 0.041 22.784 0.000 0.942 0.737
## cas02 0.987 0.042 23.655 0.000 0.987 0.775
## cas03 0.758 0.053 14.270 0.000 0.758 0.727
## cas04 0.694 0.053 13.037 0.000 0.694 0.682
## cas05 0.971 0.040 23.970 0.000 0.971 0.598
## cas06 0.864 0.044 19.570 0.000 0.864 0.477
## cas07 0.630 0.056 11.234 0.000 0.630 0.607
## cas08 0.875 0.042 20.628 0.000 0.875 0.565
## cas.f =~
## cas09 1.009 0.042 24.154 0.000 1.009 0.782
## cas10 0.897 0.046 19.458 0.000 0.897 0.550
## cas11 0.898 0.044 20.283 0.000 0.898 0.786
## cas12 0.941 0.041 22.849 0.000 0.941 0.755
## cas13 0.880 0.045 19.523 0.000 0.880 0.659
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cas.ce ~~
## cas.f 0.974 0.012 79.126 0.000 0.974 0.974
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .cas01 0.747 0.061 12.186 0.000 0.747 0.457
## .cas02 0.647 0.058 11.204 0.000 0.647 0.399
## .cas03 0.513 0.048 10.644 0.000 0.513 0.472
## .cas04 0.553 0.053 10.506 0.000 0.553 0.534
## .cas05 1.692 0.087 19.444 0.000 1.692 0.642
## .cas06 2.531 0.103 24.516 0.000 2.531 0.772
## .cas07 0.683 0.072 9.544 0.000 0.683 0.632
## .cas08 1.633 0.079 20.650 0.000 1.633 0.681
## .cas09 0.646 0.051 12.712 0.000 0.646 0.388
## .cas10 1.858 0.107 17.373 0.000 1.858 0.698
## .cas11 0.500 0.044 11.418 0.000 0.500 0.383
## .cas12 0.666 0.060 11.108 0.000 0.666 0.430
## .cas13 1.006 0.073 13.710 0.000 1.006 0.565
## cas.ce 1.000 1.000 1.000
## cas.f 1.000 1.000 1.000
##
## R-Square:
## Estimate
## cas01 0.543
## cas02 0.601
## cas03 0.528
## cas04 0.466
## cas05 0.358
## cas06 0.228
## cas07 0.368
## cas08 0.319
## cas09 0.612
## cas10 0.302
## cas11 0.617
## cas12 0.570
## cas13 0.435parameterEstimates(fit.2, standardized=TRUE) #shows complete list of parameters in model 1## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 1 cas.ce =~ cas01 0.942 0.041 22.784 0 0.861 1.023 0.942 0.737
## 2 cas.ce =~ cas02 0.987 0.042 23.655 0 0.905 1.069 0.987 0.775
## 3 cas.ce =~ cas03 0.758 0.053 14.270 0 0.654 0.862 0.758 0.727
## 4 cas.ce =~ cas04 0.694 0.053 13.037 0 0.590 0.798 0.694 0.682
## 5 cas.ce =~ cas05 0.971 0.040 23.970 0 0.891 1.050 0.971 0.598
## 6 cas.ce =~ cas06 0.864 0.044 19.570 0 0.778 0.951 0.864 0.477
## 7 cas.ce =~ cas07 0.630 0.056 11.234 0 0.520 0.740 0.630 0.607
## 8 cas.ce =~ cas08 0.875 0.042 20.628 0 0.792 0.958 0.875 0.565
## 9 cas.f =~ cas09 1.009 0.042 24.154 0 0.927 1.090 1.009 0.782
## 10 cas.f =~ cas10 0.897 0.046 19.458 0 0.806 0.987 0.897 0.550
## 11 cas.f =~ cas11 0.898 0.044 20.283 0 0.811 0.984 0.898 0.786
## 12 cas.f =~ cas12 0.941 0.041 22.849 0 0.860 1.021 0.941 0.755
## 13 cas.f =~ cas13 0.880 0.045 19.523 0 0.792 0.968 0.880 0.659
## 14 cas01 ~~ cas01 0.747 0.061 12.186 0 0.627 0.868 0.747 0.457
## 15 cas02 ~~ cas02 0.647 0.058 11.204 0 0.534 0.760 0.647 0.399
## 16 cas03 ~~ cas03 0.513 0.048 10.644 0 0.418 0.607 0.513 0.472
## 17 cas04 ~~ cas04 0.553 0.053 10.506 0 0.450 0.656 0.553 0.534
## 18 cas05 ~~ cas05 1.692 0.087 19.444 0 1.521 1.862 1.692 0.642
## 19 cas06 ~~ cas06 2.531 0.103 24.516 0 2.329 2.734 2.531 0.772
## 20 cas07 ~~ cas07 0.683 0.072 9.544 0 0.542 0.823 0.683 0.632
## 21 cas08 ~~ cas08 1.633 0.079 20.650 0 1.478 1.789 1.633 0.681
## 22 cas09 ~~ cas09 0.646 0.051 12.712 0 0.546 0.745 0.646 0.388
## 23 cas10 ~~ cas10 1.858 0.107 17.373 0 1.648 2.068 1.858 0.698
## 24 cas11 ~~ cas11 0.500 0.044 11.418 0 0.414 0.586 0.500 0.383
## 25 cas12 ~~ cas12 0.666 0.060 11.108 0 0.549 0.784 0.666 0.430
## 26 cas13 ~~ cas13 1.006 0.073 13.710 0 0.862 1.150 1.006 0.565
## 27 cas.ce ~~ cas.ce 1.000 0.000 NA NA 1.000 1.000 1.000 1.000
## 28 cas.f ~~ cas.f 1.000 0.000 NA NA 1.000 1.000 1.000 1.000
## 29 cas.ce ~~ cas.f 0.974 0.012 79.126 0 0.949 0.998 0.974 0.974
## std.nox
## 1 0.737
## 2 0.775
## 3 0.727
## 4 0.682
## 5 0.598
## 6 0.477
## 7 0.607
## 8 0.565
## 9 0.782
## 10 0.550
## 11 0.786
## 12 0.755
## 13 0.659
## 14 0.457
## 15 0.399
## 16 0.472
## 17 0.534
## 18 0.642
## 19 0.772
## 20 0.632
## 21 0.681
## 22 0.388
## 23 0.698
## 24 0.383
## 25 0.430
## 26 0.565
## 27 1.000
## 28 1.000
## 29 0.974Factor loadings
library(dplyr)
library(tidyr)
library(knitr)
options(knitr.kable.NA = '')
parameterEstimates(fit.2, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'= pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")| Latent Factor | Indicator | B | SE | Z | p-value | Beta |
|---|---|---|---|---|---|---|
| cas.ce | cas01 | 0.942 | 0.041 | 22.784 | 0 | 0.737 |
| cas.ce | cas02 | 0.987 | 0.042 | 23.655 | 0 | 0.775 |
| cas.ce | cas03 | 0.758 | 0.053 | 14.270 | 0 | 0.727 |
| cas.ce | cas04 | 0.694 | 0.053 | 13.037 | 0 | 0.682 |
| cas.ce | cas05 | 0.971 | 0.040 | 23.970 | 0 | 0.598 |
| cas.ce | cas06 | 0.864 | 0.044 | 19.570 | 0 | 0.477 |
| cas.ce | cas07 | 0.630 | 0.056 | 11.234 | 0 | 0.607 |
| cas.ce | cas08 | 0.875 | 0.042 | 20.628 | 0 | 0.565 |
| cas.f | cas09 | 1.009 | 0.042 | 24.154 | 0 | 0.782 |
| cas.f | cas10 | 0.897 | 0.046 | 19.458 | 0 | 0.550 |
| cas.f | cas11 | 0.898 | 0.044 | 20.283 | 0 | 0.786 |
| cas.f | cas12 | 0.941 | 0.041 | 22.849 | 0 | 0.755 |
| cas.f | cas13 | 0.880 | 0.045 | 19.523 | 0 | 0.659 |
Modification indices
mod_ind <- modificationindices(fit.2)
head(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], 10) #Spotting the top 10## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 87 cas05 ~~ cas08 133.644 0.602 0.602 0.362 0.362
## 66 cas03 ~~ cas04 47.829 0.123 0.123 0.231 0.231
## 79 cas04 ~~ cas08 37.486 -0.186 -0.186 -0.196 -0.196
## 69 cas03 ~~ cas07 34.672 0.114 0.114 0.193 0.193
## 37 cas.f =~ cas03 31.888 -1.682 -1.682 -1.614 -1.614
## 89 cas05 ~~ cas10 30.643 0.306 0.306 0.173 0.173
## 99 cas06 ~~ cas13 30.409 0.276 0.276 0.173 0.173
## 47 cas01 ~~ cas06 28.533 -0.236 -0.236 -0.172 -0.172
## 76 cas04 ~~ cas05 28.139 -0.165 -0.165 -0.170 -0.170
## 35 cas.f =~ cas01 26.984 1.892 1.892 1.479 1.479subset(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], mi > 5) #bigger than 5## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 87 cas05 ~~ cas08 133.644 0.602 0.602 0.362 0.362
## 66 cas03 ~~ cas04 47.829 0.123 0.123 0.231 0.231
## 79 cas04 ~~ cas08 37.486 -0.186 -0.186 -0.196 -0.196
## 69 cas03 ~~ cas07 34.672 0.114 0.114 0.193 0.193
## 37 cas.f =~ cas03 31.888 -1.682 -1.682 -1.614 -1.614
## 89 cas05 ~~ cas10 30.643 0.306 0.306 0.173 0.173
## 99 cas06 ~~ cas13 30.409 0.276 0.276 0.173 0.173
## 47 cas01 ~~ cas06 28.533 -0.236 -0.236 -0.172 -0.172
## 76 cas04 ~~ cas05 28.139 -0.165 -0.165 -0.170 -0.170
## 35 cas.f =~ cas01 26.984 1.892 1.892 1.479 1.479
## 60 cas02 ~~ cas08 26.174 -0.175 -0.175 -0.170 -0.170
## 59 cas02 ~~ cas07 22.158 -0.105 -0.105 -0.158 -0.158
## 43 cas01 ~~ cas02 21.967 0.116 0.116 0.166 0.166
## 118 cas11 ~~ cas12 21.930 0.101 0.101 0.175 0.175
## 78 cas04 ~~ cas07 21.706 0.092 0.092 0.150 0.150
## 101 cas07 ~~ cas09 21.479 -0.103 -0.103 -0.155 -0.155
## 102 cas07 ~~ cas10 20.468 -0.159 -0.159 -0.141 -0.141
## 53 cas01 ~~ cas12 19.053 0.106 0.106 0.150 0.150
## 107 cas08 ~~ cas10 17.693 0.228 0.228 0.131 0.131
## 74 cas03 ~~ cas12 16.922 -0.082 -0.082 -0.141 -0.141
## 55 cas02 ~~ cas03 16.083 0.081 0.081 0.142 0.142
## 57 cas02 ~~ cas05 14.003 -0.131 -0.131 -0.125 -0.125
## 48 cas01 ~~ cas07 13.501 -0.086 -0.086 -0.121 -0.121
## 44 cas01 ~~ cas03 13.152 -0.077 -0.077 -0.124 -0.124
## 97 cas06 ~~ cas11 12.656 -0.132 -0.132 -0.117 -0.117
## 92 cas05 ~~ cas13 11.877 0.143 0.143 0.110 0.110
## 42 cas.f =~ cas08 11.243 1.596 1.596 1.031 1.031
## 52 cas01 ~~ cas11 10.502 0.070 0.070 0.114 0.114
## 85 cas05 ~~ cas06 10.123 0.204 0.204 0.099 0.099
## 70 cas03 ~~ cas08 10.006 -0.094 -0.094 -0.103 -0.103
## 38 cas.f =~ cas04 8.942 -0.886 -0.886 -0.871 -0.871
## 93 cas06 ~~ cas07 8.795 0.121 0.121 0.092 0.092
## 61 cas02 ~~ cas09 8.792 0.069 0.069 0.107 0.107
## 115 cas10 ~~ cas11 8.603 -0.095 -0.095 -0.099 -0.099
## 103 cas07 ~~ cas11 8.009 0.055 0.055 0.095 0.095
## 56 cas02 ~~ cas04 7.897 0.058 0.058 0.097 0.097
## 39 cas.f =~ cas05 7.528 1.349 1.349 0.831 0.831
## 111 cas09 ~~ cas10 7.182 0.099 0.099 0.090 0.090
## 72 cas03 ~~ cas10 5.879 -0.076 -0.076 -0.078 -0.078
## 120 cas12 ~~ cas13 5.575 -0.066 -0.066 -0.080 -0.080
## 98 cas06 ~~ cas12 5.518 -0.099 -0.099 -0.076 -0.076
## 46 cas01 ~~ cas05 5.257 -0.085 -0.085 -0.075 -0.075
## 117 cas10 ~~ cas13 5.180 0.099 0.099 0.072 0.072Variance-Covariance-Matrix
inspect(fit.2, "sampstat")$cov #empirisch## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.635
## cas02 1.011 1.621
## cas03 0.656 0.805 1.087
## cas04 0.617 0.728 0.623 1.034
## cas05 0.845 0.855 0.691 0.533 2.634
## cas06 0.613 0.795 0.665 0.670 1.026 3.278
## cas07 0.524 0.540 0.571 0.516 0.588 0.655 1.080
## cas08 0.836 0.724 0.584 0.447 1.390 0.768 0.564 2.399
## cas09 0.953 1.015 0.712 0.665 0.936 0.907 0.534 0.882 1.663
## cas10 0.823 0.851 0.589 0.577 1.132 0.812 0.404 0.981 0.981 2.662
## cas11 0.882 0.832 0.657 0.584 0.856 0.649 0.592 0.785 0.889 0.731 1.306
## cas12 0.948 0.904 0.622 0.594 0.862 0.707 0.590 0.829 0.949 0.796 0.908 1.551
## cas13 0.768 0.814 0.629 0.613 0.968 0.993 0.583 0.791 0.894 0.875 0.750 0.778
## cas13
## cas01
## cas02
## cas03
## cas04
## cas05
## cas06
## cas07
## cas08
## cas09
## cas10
## cas11
## cas12
## cas13 1.781fitted(fit.2)$cov #modellimpliziert## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.635
## cas02 0.930 1.621
## cas03 0.714 0.748 1.087
## cas04 0.654 0.685 0.526 1.034
## cas05 0.915 0.958 0.735 0.674 2.634
## cas06 0.814 0.853 0.655 0.600 0.839 3.278
## cas07 0.594 0.622 0.478 0.437 0.612 0.545 1.080
## cas08 0.824 0.863 0.663 0.607 0.849 0.756 0.551 2.399
## cas09 0.925 0.969 0.744 0.681 0.953 0.849 0.619 0.859 1.663
## cas10 0.823 0.861 0.661 0.606 0.847 0.754 0.550 0.764 0.904 2.662
## cas11 0.823 0.862 0.662 0.606 0.848 0.755 0.551 0.765 0.905 0.805 1.306
## cas12 0.863 0.904 0.694 0.635 0.889 0.791 0.577 0.801 0.949 0.843 0.844 1.551
## cas13 0.807 0.846 0.649 0.595 0.831 0.740 0.540 0.750 0.888 0.789 0.790 0.828
## cas13
## cas01
## cas02
## cas03
## cas04
## cas05
## cas06
## cas07
## cas08
## cas09
## cas10
## cas11
## cas12
## cas13 1.781Standardized residuals
cov_table <- resid(fit.2, type="standardized")$cov
cov_table[upper.tri(cov_table)] <- NA #erase the upper triangle
diag(cov_table) <- NA #erase the diagonal 0's
kable(cov_table, digits=2) #makes a nice table and rounds everyhing to 2 digits| cas01 | cas02 | cas03 | cas04 | cas05 | cas06 | cas07 | cas08 | cas09 | cas10 | cas11 | cas12 | cas13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cas01 | |||||||||||||
| cas02 | 2.05 | ||||||||||||
| cas03 | -2.93 | 2.74 | |||||||||||
| cas04 | -1.67 | 1.81 | 4.47 | ||||||||||
| cas05 | -1.74 | -3.05 | -1.39 | -4.81 | |||||||||
| cas06 | -5.08 | -1.70 | 0.34 | 1.79 | 2.84 | ||||||||
| cas07 | -2.98 | -3.59 | 3.69 | 3.21 | -0.64 | 2.38 | |||||||
| cas08 | 0.33 | -4.36 | -3.04 | -5.60 | 8.07 | 0.20 | 0.34 | ||||||
| cas09 | 0.90 | 1.62 | -1.55 | -0.75 | -0.50 | 1.48 | -3.93 | 0.71 | |||||
| cas10 | 0.00 | -0.30 | -2.26 | -0.74 | 4.54 | 0.83 | -4.17 | 3.53 | 2.18 | ||||
| cas11 | 1.93 | -1.42 | -0.25 | -1.15 | 0.24 | -3.10 | 1.82 | 0.68 | -0.97 | -2.76 | |||
| cas12 | 2.30 | 0.00 | -3.65 | -2.02 | -0.69 | -2.12 | 0.57 | 0.69 | 0.04 | -1.36 | 2.61 | ||
| cas13 | -1.20 | -1.11 | -0.64 | 0.57 | 2.74 | 4.51 | 1.34 | 1.04 | 0.25 | 1.69 | -1.61 | -1.69 |
7.4.3 Plot CFA - 2-factor solution
Load package
library(semPlot)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## Registered S3 methods overwritten by 'huge':
## method from
## plot.sim BDgraph
## print.sim BDgraphsemPaths(fit.2, rotation = 2, nodeLabels=1:13)
Create the vector for the node labels
nodeLabels <- c("1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11", "12", "13",
"cognitive-emotional impairment",
"functional impairment"
)Create a character vector for the order of latent variables
latents <- c("cas.ce", "cas.f")Plot
semPaths(fit.2,
style = "lisrel", #small arrows for residuals instead of half-circles
whatLabels = "std.all", #labels paths with standardized coefficients
nCharNodes = 0, #factor names not abbreviated
rotation = 2, #rotates the graph around, #s 1, 2, 3, 4
layout = "tree3",
curvePivot = TRUE, #makes curves straight with rounded edges
curvePivotShape = 2.1, #influences how round the curve around the pivot is
edge.label.cex = .5, #size of the edge labels
cardinal = TRUE, #all edge labels end in the middle/cardinal point of a node
sizeMan = 5, #size of manifest variables
sizeMan2 = 2, #height of manifest variables
sizeLat =10, #size of latent variables
sizeLat2 = 10, #height of latent variables
nodeLabels = nodeLabels,#vector that names the nodes
latents = latents #vector that reorders the nodes
)
7.4.4 CFA - 1-factor solution
CFA.1 <- ' cas.1 =~ cas01 + cas02 + cas03 + cas04 + cas05 + cas06 + cas07 + cas08 + cas09 + cas10 + cas11 + cas12 + cas13'
fit.1 <- cfa(CFA.1, std.lv=TRUE, data=data, estimator = "MLM")
summary(fit.1, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)## lavaan 0.6-7 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 26
##
## Number of observations 1134
##
## Model Test User Model:
## Standard Robust
## Test Statistic 634.969 359.675
## Degrees of freedom 65 65
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.765
## Satorra-Bentler correction
##
## Model Test Baseline Model:
##
## Test statistic 6950.305 2767.895
## Degrees of freedom 78 78
## P-value 0.000 0.000
## Scaling correction factor 2.511
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.917 0.890
## Tucker-Lewis Index (TLI) 0.900 0.869
##
## Robust Comparative Fit Index (CFI) 0.923
## Robust Tucker-Lewis Index (TLI) 0.908
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -21711.978 -21711.978
## Loglikelihood unrestricted model (H1) -21394.494 -21394.494
##
## Akaike (AIC) 43475.956 43475.956
## Bayesian (BIC) 43606.827 43606.827
## Sample-size adjusted Bayesian (BIC) 43524.244 43524.244
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.088 0.063
## 90 Percent confidence interval - lower 0.082 0.058
## 90 Percent confidence interval - upper 0.094 0.068
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA 0.084
## 90 Percent confidence interval - lower 0.076
## 90 Percent confidence interval - upper 0.093
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048 0.048
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cas.1 =~
## cas01 0.942 0.041 22.828 0.000 0.942 0.737
## cas02 0.980 0.042 23.337 0.000 0.980 0.769
## cas03 0.748 0.053 14.056 0.000 0.748 0.717
## cas04 0.687 0.053 12.903 0.000 0.687 0.675
## cas05 0.971 0.040 24.109 0.000 0.971 0.598
## cas06 0.861 0.044 19.505 0.000 0.861 0.475
## cas07 0.625 0.056 11.129 0.000 0.625 0.601
## cas08 0.877 0.042 20.796 0.000 0.877 0.566
## cas09 0.999 0.041 24.154 0.000 0.999 0.775
## cas10 0.891 0.046 19.449 0.000 0.891 0.546
## cas11 0.889 0.044 20.125 0.000 0.889 0.778
## cas12 0.930 0.041 22.620 0.000 0.930 0.747
## cas13 0.877 0.045 19.601 0.000 0.877 0.657
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .cas01 0.748 0.060 12.379 0.000 0.748 0.457
## .cas02 0.662 0.058 11.467 0.000 0.662 0.408
## .cas03 0.528 0.049 10.822 0.000 0.528 0.486
## .cas04 0.563 0.053 10.666 0.000 0.563 0.544
## .cas05 1.690 0.087 19.437 0.000 1.690 0.642
## .cas06 2.537 0.103 24.639 0.000 2.537 0.774
## .cas07 0.689 0.072 9.565 0.000 0.689 0.638
## .cas08 1.630 0.079 20.605 0.000 1.630 0.679
## .cas09 0.665 0.049 13.461 0.000 0.665 0.400
## .cas10 1.868 0.106 17.650 0.000 1.868 0.702
## .cas11 0.515 0.043 11.983 0.000 0.515 0.394
## .cas12 0.685 0.058 11.764 0.000 0.685 0.442
## .cas13 1.012 0.072 14.003 0.000 1.012 0.568
## cas.1 1.000 1.000 1.000
##
## R-Square:
## Estimate
## cas01 0.543
## cas02 0.592
## cas03 0.514
## cas04 0.456
## cas05 0.358
## cas06 0.226
## cas07 0.362
## cas08 0.321
## cas09 0.600
## cas10 0.298
## cas11 0.606
## cas12 0.558
## cas13 0.432parameterEstimates(fit.1, standardized=TRUE) #shows complete list of parameters in model 1## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 1 cas.1 =~ cas01 0.942 0.041 22.828 0 0.861 1.023 0.942 0.737
## 2 cas.1 =~ cas02 0.980 0.042 23.337 0 0.897 1.062 0.980 0.769
## 3 cas.1 =~ cas03 0.748 0.053 14.056 0 0.643 0.852 0.748 0.717
## 4 cas.1 =~ cas04 0.687 0.053 12.903 0 0.582 0.791 0.687 0.675
## 5 cas.1 =~ cas05 0.971 0.040 24.109 0 0.892 1.050 0.971 0.598
## 6 cas.1 =~ cas06 0.861 0.044 19.505 0 0.774 0.947 0.861 0.475
## 7 cas.1 =~ cas07 0.625 0.056 11.129 0 0.515 0.735 0.625 0.601
## 8 cas.1 =~ cas08 0.877 0.042 20.796 0 0.794 0.960 0.877 0.566
## 9 cas.1 =~ cas09 0.999 0.041 24.154 0 0.918 1.080 0.999 0.775
## 10 cas.1 =~ cas10 0.891 0.046 19.449 0 0.801 0.981 0.891 0.546
## 11 cas.1 =~ cas11 0.889 0.044 20.125 0 0.803 0.976 0.889 0.778
## 12 cas.1 =~ cas12 0.930 0.041 22.620 0 0.850 1.011 0.930 0.747
## 13 cas.1 =~ cas13 0.877 0.045 19.601 0 0.789 0.964 0.877 0.657
## 14 cas01 ~~ cas01 0.748 0.060 12.379 0 0.630 0.867 0.748 0.457
## 15 cas02 ~~ cas02 0.662 0.058 11.467 0 0.549 0.775 0.662 0.408
## 16 cas03 ~~ cas03 0.528 0.049 10.822 0 0.432 0.623 0.528 0.486
## 17 cas04 ~~ cas04 0.563 0.053 10.666 0 0.460 0.667 0.563 0.544
## 18 cas05 ~~ cas05 1.690 0.087 19.437 0 1.520 1.861 1.690 0.642
## 19 cas06 ~~ cas06 2.537 0.103 24.639 0 2.335 2.739 2.537 0.774
## 20 cas07 ~~ cas07 0.689 0.072 9.565 0 0.548 0.831 0.689 0.638
## 21 cas08 ~~ cas08 1.630 0.079 20.605 0 1.475 1.785 1.630 0.679
## 22 cas09 ~~ cas09 0.665 0.049 13.461 0 0.568 0.762 0.665 0.400
## 23 cas10 ~~ cas10 1.868 0.106 17.650 0 1.661 2.076 1.868 0.702
## 24 cas11 ~~ cas11 0.515 0.043 11.983 0 0.430 0.599 0.515 0.394
## 25 cas12 ~~ cas12 0.685 0.058 11.764 0 0.571 0.799 0.685 0.442
## 26 cas13 ~~ cas13 1.012 0.072 14.003 0 0.870 1.154 1.012 0.568
## 27 cas.1 ~~ cas.1 1.000 0.000 NA NA 1.000 1.000 1.000 1.000
## std.nox
## 1 0.737
## 2 0.769
## 3 0.717
## 4 0.675
## 5 0.598
## 6 0.475
## 7 0.601
## 8 0.566
## 9 0.775
## 10 0.546
## 11 0.778
## 12 0.747
## 13 0.657
## 14 0.457
## 15 0.408
## 16 0.486
## 17 0.544
## 18 0.642
## 19 0.774
## 20 0.638
## 21 0.679
## 22 0.400
## 23 0.702
## 24 0.394
## 25 0.442
## 26 0.568
## 27 1.000Factor loadings
options(knitr.kable.NA = '')
parameterEstimates(fit.1, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'= pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")| Latent Factor | Indicator | B | SE | Z | p-value | Beta |
|---|---|---|---|---|---|---|
| cas.1 | cas01 | 0.942 | 0.041 | 22.828 | 0 | 0.737 |
| cas.1 | cas02 | 0.980 | 0.042 | 23.337 | 0 | 0.769 |
| cas.1 | cas03 | 0.748 | 0.053 | 14.056 | 0 | 0.717 |
| cas.1 | cas04 | 0.687 | 0.053 | 12.903 | 0 | 0.675 |
| cas.1 | cas05 | 0.971 | 0.040 | 24.109 | 0 | 0.598 |
| cas.1 | cas06 | 0.861 | 0.044 | 19.505 | 0 | 0.475 |
| cas.1 | cas07 | 0.625 | 0.056 | 11.129 | 0 | 0.601 |
| cas.1 | cas08 | 0.877 | 0.042 | 20.796 | 0 | 0.566 |
| cas.1 | cas09 | 0.999 | 0.041 | 24.154 | 0 | 0.775 |
| cas.1 | cas10 | 0.891 | 0.046 | 19.449 | 0 | 0.546 |
| cas.1 | cas11 | 0.889 | 0.044 | 20.125 | 0 | 0.778 |
| cas.1 | cas12 | 0.930 | 0.041 | 22.620 | 0 | 0.747 |
| cas.1 | cas13 | 0.877 | 0.045 | 19.601 | 0 | 0.657 |
Modification indices
mod_ind <- modificationindices(fit.1)
head(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], 10) #Spotting the top 10## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 72 cas05 ~~ cas08 131.061 0.592 0.592 0.357 0.357
## 51 cas03 ~~ cas04 55.503 0.133 0.133 0.243 0.243
## 54 cas03 ~~ cas07 39.706 0.122 0.122 0.202 0.202
## 64 cas04 ~~ cas08 33.826 -0.176 -0.176 -0.184 -0.184
## 103 cas11 ~~ cas12 28.300 0.109 0.109 0.184 0.184
## 74 cas05 ~~ cas10 27.968 0.292 0.292 0.164 0.164
## 84 cas06 ~~ cas13 27.699 0.263 0.263 0.164 0.164
## 32 cas01 ~~ cas06 26.870 -0.227 -0.227 -0.165 -0.165
## 63 cas04 ~~ cas07 25.185 0.099 0.099 0.159 0.159
## 86 cas07 ~~ cas09 24.462 -0.110 -0.110 -0.163 -0.163subset(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], mi > 5) #bigger than 5## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 72 cas05 ~~ cas08 131.061 0.592 0.592 0.357 0.357
## 51 cas03 ~~ cas04 55.503 0.133 0.133 0.243 0.243
## 54 cas03 ~~ cas07 39.706 0.122 0.122 0.202 0.202
## 64 cas04 ~~ cas08 33.826 -0.176 -0.176 -0.184 -0.184
## 103 cas11 ~~ cas12 28.300 0.109 0.109 0.184 0.184
## 74 cas05 ~~ cas10 27.968 0.292 0.292 0.164 0.164
## 84 cas06 ~~ cas13 27.699 0.263 0.263 0.164 0.164
## 32 cas01 ~~ cas06 26.870 -0.227 -0.227 -0.165 -0.165
## 63 cas04 ~~ cas07 25.185 0.099 0.099 0.159 0.159
## 86 cas07 ~~ cas09 24.462 -0.110 -0.110 -0.163 -0.163
## 61 cas04 ~~ cas05 24.452 -0.153 -0.153 -0.157 -0.157
## 28 cas01 ~~ cas02 23.740 0.117 0.117 0.167 0.167
## 45 cas02 ~~ cas08 23.166 -0.163 -0.163 -0.157 -0.157
## 40 cas02 ~~ cas03 22.758 0.096 0.096 0.162 0.162
## 87 cas07 ~~ cas10 22.537 -0.167 -0.167 -0.148 -0.148
## 59 cas03 ~~ cas12 21.715 -0.094 -0.094 -0.156 -0.156
## 92 cas08 ~~ cas10 16.103 0.217 0.217 0.124 0.124
## 44 cas02 ~~ cas07 15.846 -0.088 -0.088 -0.131 -0.131
## 38 cas01 ~~ cas12 14.771 0.093 0.093 0.130 0.130
## 82 cas06 ~~ cas11 14.003 -0.139 -0.139 -0.121 -0.121
## 41 cas02 ~~ cas04 11.871 0.070 0.070 0.115 0.115
## 42 cas02 ~~ cas05 11.549 -0.118 -0.118 -0.112 -0.112
## 33 cas01 ~~ cas07 11.064 -0.077 -0.077 -0.108 -0.108
## 70 cas05 ~~ cas06 10.299 0.205 0.205 0.099 0.099
## 77 cas05 ~~ cas13 10.199 0.132 0.132 0.101 0.101
## 78 cas06 ~~ cas07 9.674 0.127 0.127 0.096 0.096
## 96 cas09 ~~ cas10 9.205 0.110 0.110 0.099 0.099
## 29 cas01 ~~ cas03 8.293 -0.060 -0.060 -0.096 -0.096
## 57 cas03 ~~ cas10 7.860 -0.089 -0.089 -0.089 -0.089
## 55 cas03 ~~ cas08 7.785 -0.083 -0.083 -0.089 -0.089
## 37 cas01 ~~ cas11 7.731 0.059 0.059 0.096 0.096
## 68 cas04 ~~ cas12 7.324 -0.056 -0.056 -0.089 -0.089
## 48 cas02 ~~ cas11 6.998 -0.054 -0.054 -0.093 -0.093
## 83 cas06 ~~ cas12 6.649 -0.109 -0.109 -0.082 -0.082
## 39 cas01 ~~ cas13 5.994 -0.070 -0.070 -0.080 -0.080
## 102 cas10 ~~ cas13 5.941 0.105 0.105 0.076 0.076
## 62 cas04 ~~ cas06 5.464 0.087 0.087 0.073 0.073
## 100 cas10 ~~ cas11 5.438 -0.075 -0.075 -0.076 -0.076
## 56 cas03 ~~ cas09 5.239 -0.046 -0.046 -0.078 -0.078
## 31 cas01 ~~ cas05 5.157 -0.083 -0.083 -0.073 -0.073
## 88 cas07 ~~ cas11 5.130 0.045 0.045 0.075 0.075Variance-Covariance-Matrix
inspect(fit.1, "sampstat")$cov #empirisch## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.635
## cas02 1.011 1.621
## cas03 0.656 0.805 1.087
## cas04 0.617 0.728 0.623 1.034
## cas05 0.845 0.855 0.691 0.533 2.634
## cas06 0.613 0.795 0.665 0.670 1.026 3.278
## cas07 0.524 0.540 0.571 0.516 0.588 0.655 1.080
## cas08 0.836 0.724 0.584 0.447 1.390 0.768 0.564 2.399
## cas09 0.953 1.015 0.712 0.665 0.936 0.907 0.534 0.882 1.663
## cas10 0.823 0.851 0.589 0.577 1.132 0.812 0.404 0.981 0.981 2.662
## cas11 0.882 0.832 0.657 0.584 0.856 0.649 0.592 0.785 0.889 0.731 1.306
## cas12 0.948 0.904 0.622 0.594 0.862 0.707 0.590 0.829 0.949 0.796 0.908 1.551
## cas13 0.768 0.814 0.629 0.613 0.968 0.993 0.583 0.791 0.894 0.875 0.750 0.778
## cas13
## cas01
## cas02
## cas03
## cas04
## cas05
## cas06
## cas07
## cas08
## cas09
## cas10
## cas11
## cas12
## cas13 1.781fitted(fit.1)$cov #modellimpliziert## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.635
## cas02 0.923 1.621
## cas03 0.704 0.732 1.087
## cas04 0.647 0.672 0.513 1.034
## cas05 0.915 0.951 0.726 0.667 2.634
## cas06 0.811 0.843 0.643 0.591 0.836 3.278
## cas07 0.589 0.612 0.467 0.429 0.607 0.538 1.080
## cas08 0.826 0.859 0.656 0.602 0.852 0.755 0.548 2.399
## cas09 0.941 0.978 0.747 0.686 0.970 0.860 0.624 0.876 1.663
## cas10 0.839 0.873 0.666 0.612 0.865 0.767 0.557 0.781 0.890 2.662
## cas11 0.838 0.871 0.665 0.611 0.864 0.766 0.556 0.780 0.888 0.792 1.306
## cas12 0.876 0.911 0.696 0.639 0.904 0.801 0.581 0.816 0.929 0.829 0.828 1.551
## cas13 0.826 0.859 0.655 0.602 0.852 0.755 0.548 0.769 0.876 0.781 0.780 0.816
## cas13
## cas01
## cas02
## cas03
## cas04
## cas05
## cas06
## cas07
## cas08
## cas09
## cas10
## cas11
## cas12
## cas13 1.781Standardized residuals
cov_table <- resid(fit.1, type="standardized")$cov
cov_table[upper.tri(cov_table)] <- NA #erase the upper triangle
diag(cov_table) <- NA #erase the diagonal 0's
kable(cov_table, digits=2) #makes a nice table and rounds everyhing to 2 digits| cas01 | cas02 | cas03 | cas04 | cas05 | cas06 | cas07 | cas08 | cas09 | cas10 | cas11 | cas12 | cas13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cas01 | |||||||||||||
| cas02 | 2.23 | ||||||||||||
| cas03 | -2.27 | 3.21 | |||||||||||
| cas04 | -1.29 | 2.18 | 4.80 | ||||||||||
| cas05 | -1.75 | -2.68 | -1.07 | -4.44 | |||||||||
| cas06 | -4.91 | -1.33 | 0.68 | 2.00 | 2.87 | ||||||||
| cas07 | -2.72 | -2.88 | 3.93 | 3.49 | -0.50 | 2.48 | |||||||
| cas08 | 0.29 | -4.03 | -2.74 | -5.40 | 8.00 | 0.21 | 0.42 | ||||||
| cas09 | 0.41 | 1.30 | -1.69 | -0.96 | -1.04 | 1.21 | -4.15 | 0.19 | |||||
| cas10 | -0.44 | -0.62 | -2.46 | -0.91 | 4.30 | 0.65 | -4.38 | 3.28 | 2.51 | ||||
| cas11 | 1.52 | -1.87 | -0.39 | -1.38 | -0.26 | -3.43 | 1.62 | 0.16 | 0.01 | -2.21 | |||
| cas12 | 1.99 | -0.21 | -3.73 | -2.22 | -1.10 | -2.39 | 0.39 | 0.33 | 0.85 | -0.88 | 2.89 | ||
| cas13 | -1.86 | -1.64 | -0.87 | 0.35 | 2.37 | 4.28 | 1.11 | 0.57 | 0.65 | 1.82 | -1.14 | -1.2 |
7.5 CFA CAS - without item cas06
cas06: “I go away by myself and think about why I feel this way about climate change.”
Increases the number of rows that can be printed
options(max.print = 10000000)7.5.1 Normality
Load package
library(MVN)Create data frame with all CAS items
data.cas.items = data.frame(data$cas01, data$cas02, data$cas03, data$cas04, data$cas05,
data$cas07, data$cas08, data$cas09, data$cas10,
data$cas11, data$cas12, data$cas13)Check for overall multivariate normality (Doornik-Hansen’s test)
result <- mvn(data = data.cas.items, mvnTest = "dh")
result$multivariateNormality## Test E df p value MVN
## 1 Doornik-Hansen 5040.957 24 0 NOInspect data visually
mvn(data = data.cas.items, mvnTest = "royston", univariatePlot = "histogram") #create histograms for all items
## $multivariateNormality
## Test H p value MVN
## 1 Royston 2373.022 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk data.cas01 0.7051 <0.001 NO
## 2 Shapiro-Wilk data.cas02 0.6565 <0.001 NO
## 3 Shapiro-Wilk data.cas03 0.5165 <0.001 NO
## 4 Shapiro-Wilk data.cas04 0.4607 <0.001 NO
## 5 Shapiro-Wilk data.cas05 0.8254 <0.001 NO
## 6 Shapiro-Wilk data.cas07 0.4699 <0.001 NO
## 7 Shapiro-Wilk data.cas08 0.8227 <0.001 NO
## 8 Shapiro-Wilk data.cas09 0.7093 <0.001 NO
## 9 Shapiro-Wilk data.cas10 0.8103 <0.001 NO
## 10 Shapiro-Wilk data.cas11 0.6230 <0.001 NO
## 11 Shapiro-Wilk data.cas12 0.6902 <0.001 NO
## 12 Shapiro-Wilk data.cas13 0.6832 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## data.cas01 1134 1.850088 1.279382 1 1 7 1 2 1.5091678 1.7194046
## data.cas02 1134 1.761023 1.273755 1 1 7 1 2 1.7308788 2.3783856
## data.cas03 1134 1.466490 1.042870 1 1 7 1 1 2.5823754 6.7536081
## data.cas04 1134 1.403880 1.017509 1 1 7 1 1 2.8729004 8.3327298
## data.cas05 1134 2.473545 1.623608 2 1 7 1 4 0.7321614 -0.5825074
## data.cas07 1134 1.421517 1.039602 1 1 7 1 1 2.9263927 9.0596647
## data.cas08 1134 2.420635 1.549523 2 1 7 1 4 0.7114927 -0.5551500
## data.cas09 1134 1.865961 1.290069 1 1 7 1 2 1.4699642 1.4805367
## data.cas10 1134 2.393298 1.632251 2 1 7 1 4 0.9371281 -0.1800271
## data.cas11 1134 1.641975 1.143168 1 1 7 1 2 1.9091727 3.3080489
## data.cas12 1134 1.806878 1.245896 1 1 7 1 2 1.5640431 1.9406054
## data.cas13 1134 1.843915 1.335037 1 1 7 1 2 1.5583385 1.6126573mvn(data = data.cas.items, mvnTest = "royston", univariatePlot = "qqplot") #create Q-Q-plots for all items
## $multivariateNormality
## Test H p value MVN
## 1 Royston 2373.022 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk data.cas01 0.7051 <0.001 NO
## 2 Shapiro-Wilk data.cas02 0.6565 <0.001 NO
## 3 Shapiro-Wilk data.cas03 0.5165 <0.001 NO
## 4 Shapiro-Wilk data.cas04 0.4607 <0.001 NO
## 5 Shapiro-Wilk data.cas05 0.8254 <0.001 NO
## 6 Shapiro-Wilk data.cas07 0.4699 <0.001 NO
## 7 Shapiro-Wilk data.cas08 0.8227 <0.001 NO
## 8 Shapiro-Wilk data.cas09 0.7093 <0.001 NO
## 9 Shapiro-Wilk data.cas10 0.8103 <0.001 NO
## 10 Shapiro-Wilk data.cas11 0.6230 <0.001 NO
## 11 Shapiro-Wilk data.cas12 0.6902 <0.001 NO
## 12 Shapiro-Wilk data.cas13 0.6832 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## data.cas01 1134 1.850088 1.279382 1 1 7 1 2 1.5091678 1.7194046
## data.cas02 1134 1.761023 1.273755 1 1 7 1 2 1.7308788 2.3783856
## data.cas03 1134 1.466490 1.042870 1 1 7 1 1 2.5823754 6.7536081
## data.cas04 1134 1.403880 1.017509 1 1 7 1 1 2.8729004 8.3327298
## data.cas05 1134 2.473545 1.623608 2 1 7 1 4 0.7321614 -0.5825074
## data.cas07 1134 1.421517 1.039602 1 1 7 1 1 2.9263927 9.0596647
## data.cas08 1134 2.420635 1.549523 2 1 7 1 4 0.7114927 -0.5551500
## data.cas09 1134 1.865961 1.290069 1 1 7 1 2 1.4699642 1.4805367
## data.cas10 1134 2.393298 1.632251 2 1 7 1 4 0.9371281 -0.1800271
## data.cas11 1134 1.641975 1.143168 1 1 7 1 2 1.9091727 3.3080489
## data.cas12 1134 1.806878 1.245896 1 1 7 1 2 1.5640431 1.9406054
## data.cas13 1134 1.843915 1.335037 1 1 7 1 2 1.5583385 1.61265737.5.2 CFA - 2 factor solution
CFA.4 <- ' cas.ce =~ cas01 + cas02 + cas03 + cas04 + cas05 + cas07 + cas08
cas.f =~ cas09 + cas10 + cas11 + cas12 + cas13'
fit.4 <- cfa(CFA.4, std.lv=TRUE, data=data, estimator = "MLM")
summary(fit.4, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)## lavaan 0.6-7 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 25
##
## Number of observations 1134
##
## Model Test User Model:
## Standard Robust
## Test Statistic 543.203 289.957
## Degrees of freedom 53 53
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.873
## Satorra-Bentler correction
##
## Model Test Baseline Model:
##
## Test statistic 6606.087 2419.177
## Degrees of freedom 66 66
## P-value 0.000 0.000
## Scaling correction factor 2.731
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.925 0.899
## Tucker-Lewis Index (TLI) 0.907 0.875
##
## Robust Comparative Fit Index (CFI) 0.931
## Robust Tucker-Lewis Index (TLI) 0.914
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -19555.977 -19555.977
## Loglikelihood unrestricted model (H1) -19284.375 -19284.375
##
## Akaike (AIC) 39161.954 39161.954
## Bayesian (BIC) 39287.791 39287.791
## Sample-size adjusted Bayesian (BIC) 39208.384 39208.384
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.090 0.063
## 90 Percent confidence interval - lower 0.084 0.058
## 90 Percent confidence interval - upper 0.097 0.068
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA 0.086
## 90 Percent confidence interval - lower 0.076
## 90 Percent confidence interval - upper 0.096
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047 0.047
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cas.ce =~
## cas01 0.952 0.041 23.076 0.000 0.952 0.744
## cas02 0.990 0.042 23.693 0.000 0.990 0.778
## cas03 0.757 0.053 14.174 0.000 0.757 0.726
## cas04 0.691 0.054 12.911 0.000 0.691 0.679
## cas05 0.963 0.041 23.576 0.000 0.963 0.593
## cas07 0.626 0.056 11.104 0.000 0.626 0.602
## cas08 0.874 0.043 20.542 0.000 0.874 0.564
## cas.f =~
## cas09 1.007 0.042 24.016 0.000 1.007 0.781
## cas10 0.894 0.046 19.308 0.000 0.894 0.548
## cas11 0.902 0.044 20.385 0.000 0.902 0.789
## cas12 0.944 0.041 22.992 0.000 0.944 0.758
## cas13 0.872 0.045 19.224 0.000 0.872 0.654
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cas.ce ~~
## cas.f 0.973 0.012 78.579 0.000 0.973 0.973
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .cas01 0.730 0.061 12.045 0.000 0.730 0.446
## .cas02 0.641 0.057 11.236 0.000 0.641 0.395
## .cas03 0.514 0.049 10.474 0.000 0.514 0.473
## .cas04 0.557 0.054 10.353 0.000 0.557 0.538
## .cas05 1.707 0.088 19.393 0.000 1.707 0.648
## .cas07 0.688 0.073 9.471 0.000 0.688 0.637
## .cas08 1.636 0.080 20.564 0.000 1.636 0.682
## .cas09 0.649 0.051 12.665 0.000 0.649 0.391
## .cas10 1.862 0.108 17.317 0.000 1.862 0.700
## .cas11 0.492 0.044 11.273 0.000 0.492 0.377
## .cas12 0.660 0.059 11.148 0.000 0.660 0.425
## .cas13 1.020 0.075 13.630 0.000 1.020 0.573
## cas.ce 1.000 1.000 1.000
## cas.f 1.000 1.000 1.000
##
## R-Square:
## Estimate
## cas01 0.554
## cas02 0.605
## cas03 0.527
## cas04 0.462
## cas05 0.352
## cas07 0.363
## cas08 0.318
## cas09 0.609
## cas10 0.300
## cas11 0.623
## cas12 0.575
## cas13 0.427parameterEstimates(fit.4, standardized=TRUE) #shows complete list of parameters in model 1## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 1 cas.ce =~ cas01 0.952 0.041 23.076 0 0.871 1.032 0.952 0.744
## 2 cas.ce =~ cas02 0.990 0.042 23.693 0 0.908 1.072 0.990 0.778
## 3 cas.ce =~ cas03 0.757 0.053 14.174 0 0.652 0.861 0.757 0.726
## 4 cas.ce =~ cas04 0.691 0.054 12.911 0 0.586 0.796 0.691 0.679
## 5 cas.ce =~ cas05 0.963 0.041 23.576 0 0.883 1.043 0.963 0.593
## 6 cas.ce =~ cas07 0.626 0.056 11.104 0 0.515 0.736 0.626 0.602
## 7 cas.ce =~ cas08 0.874 0.043 20.542 0 0.790 0.957 0.874 0.564
## 8 cas.f =~ cas09 1.007 0.042 24.016 0 0.925 1.089 1.007 0.781
## 9 cas.f =~ cas10 0.894 0.046 19.308 0 0.804 0.985 0.894 0.548
## 10 cas.f =~ cas11 0.902 0.044 20.385 0 0.815 0.989 0.902 0.789
## 11 cas.f =~ cas12 0.944 0.041 22.992 0 0.864 1.025 0.944 0.758
## 12 cas.f =~ cas13 0.872 0.045 19.224 0 0.783 0.961 0.872 0.654
## 13 cas01 ~~ cas01 0.730 0.061 12.045 0 0.611 0.849 0.730 0.446
## 14 cas02 ~~ cas02 0.641 0.057 11.236 0 0.529 0.753 0.641 0.395
## 15 cas03 ~~ cas03 0.514 0.049 10.474 0 0.418 0.610 0.514 0.473
## 16 cas04 ~~ cas04 0.557 0.054 10.353 0 0.451 0.662 0.557 0.538
## 17 cas05 ~~ cas05 1.707 0.088 19.393 0 1.534 1.879 1.707 0.648
## 18 cas07 ~~ cas07 0.688 0.073 9.471 0 0.546 0.831 0.688 0.637
## 19 cas08 ~~ cas08 1.636 0.080 20.564 0 1.480 1.791 1.636 0.682
## 20 cas09 ~~ cas09 0.649 0.051 12.665 0 0.549 0.750 0.649 0.391
## 21 cas10 ~~ cas10 1.862 0.108 17.317 0 1.651 2.073 1.862 0.700
## 22 cas11 ~~ cas11 0.492 0.044 11.273 0 0.407 0.578 0.492 0.377
## 23 cas12 ~~ cas12 0.660 0.059 11.148 0 0.544 0.776 0.660 0.425
## 24 cas13 ~~ cas13 1.020 0.075 13.630 0 0.874 1.167 1.020 0.573
## 25 cas.ce ~~ cas.ce 1.000 0.000 NA NA 1.000 1.000 1.000 1.000
## 26 cas.f ~~ cas.f 1.000 0.000 NA NA 1.000 1.000 1.000 1.000
## 27 cas.ce ~~ cas.f 0.973 0.012 78.579 0 0.949 0.997 0.973 0.973
## std.nox
## 1 0.744
## 2 0.778
## 3 0.726
## 4 0.679
## 5 0.593
## 6 0.602
## 7 0.564
## 8 0.781
## 9 0.548
## 10 0.789
## 11 0.758
## 12 0.654
## 13 0.446
## 14 0.395
## 15 0.473
## 16 0.538
## 17 0.648
## 18 0.637
## 19 0.682
## 20 0.391
## 21 0.700
## 22 0.377
## 23 0.425
## 24 0.573
## 25 1.000
## 26 1.000
## 27 0.973Factor loadings
options(knitr.kable.NA = '')
parameterEstimates(fit.4, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'= pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")| Latent Factor | Indicator | B | SE | Z | p-value | Beta |
|---|---|---|---|---|---|---|
| cas.ce | cas01 | 0.952 | 0.041 | 23.076 | 0 | 0.744 |
| cas.ce | cas02 | 0.990 | 0.042 | 23.693 | 0 | 0.778 |
| cas.ce | cas03 | 0.757 | 0.053 | 14.174 | 0 | 0.726 |
| cas.ce | cas04 | 0.691 | 0.054 | 12.911 | 0 | 0.679 |
| cas.ce | cas05 | 0.963 | 0.041 | 23.576 | 0 | 0.593 |
| cas.ce | cas07 | 0.626 | 0.056 | 11.104 | 0 | 0.602 |
| cas.ce | cas08 | 0.874 | 0.043 | 20.542 | 0 | 0.564 |
| cas.f | cas09 | 1.007 | 0.042 | 24.016 | 0 | 0.781 |
| cas.f | cas10 | 0.894 | 0.046 | 19.308 | 0 | 0.548 |
| cas.f | cas11 | 0.902 | 0.044 | 20.385 | 0 | 0.789 |
| cas.f | cas12 | 0.944 | 0.041 | 22.992 | 0 | 0.758 |
| cas.f | cas13 | 0.872 | 0.045 | 19.224 | 0 | 0.654 |
Modification indices
mod_ind <- modificationindices(fit.4)
head(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], 10) #Spotting the top 10## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 79 cas05 ~~ cas08 136.412 0.612 0.612 0.366 0.366
## 61 cas03 ~~ cas04 50.510 0.128 0.128 0.238 0.238
## 63 cas03 ~~ cas07 37.441 0.119 0.119 0.201 0.201
## 72 cas04 ~~ cas08 35.692 -0.182 -0.182 -0.191 -0.191
## 81 cas05 ~~ cas10 32.087 0.315 0.315 0.177 0.177
## 35 cas.f =~ cas03 30.888 -1.648 -1.648 -1.581 -1.581
## 55 cas02 ~~ cas08 27.281 -0.179 -0.179 -0.175 -0.175
## 70 cas04 ~~ cas05 24.609 -0.156 -0.156 -0.160 -0.160
## 71 cas04 ~~ cas07 24.238 0.098 0.098 0.159 0.159
## 54 cas02 ~~ cas07 21.053 -0.103 -0.103 -0.155 -0.155subset(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], mi > 5) #bigger than 5## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 79 cas05 ~~ cas08 136.412 0.612 0.612 0.366 0.366
## 61 cas03 ~~ cas04 50.510 0.128 0.128 0.238 0.238
## 63 cas03 ~~ cas07 37.441 0.119 0.119 0.201 0.201
## 72 cas04 ~~ cas08 35.692 -0.182 -0.182 -0.191 -0.191
## 81 cas05 ~~ cas10 32.087 0.315 0.315 0.177 0.177
## 35 cas.f =~ cas03 30.888 -1.648 -1.648 -1.581 -1.581
## 55 cas02 ~~ cas08 27.281 -0.179 -0.179 -0.175 -0.175
## 70 cas04 ~~ cas05 24.609 -0.156 -0.156 -0.160 -0.160
## 71 cas04 ~~ cas07 24.238 0.098 0.098 0.159 0.159
## 54 cas02 ~~ cas07 21.053 -0.103 -0.103 -0.155 -0.155
## 33 cas.f =~ cas01 18.947 1.576 1.576 1.232 1.232
## 87 cas07 ~~ cas10 18.923 -0.154 -0.154 -0.136 -0.136
## 86 cas07 ~~ cas09 18.671 -0.097 -0.097 -0.145 -0.145
## 92 cas08 ~~ cas10 18.211 0.231 0.231 0.133 0.133
## 68 cas03 ~~ cas12 18.182 -0.085 -0.085 -0.147 -0.147
## 103 cas11 ~~ cas12 18.087 0.092 0.092 0.161 0.161
## 40 cas01 ~~ cas02 17.006 0.102 0.102 0.149 0.149
## 41 cas01 ~~ cas03 16.780 -0.087 -0.087 -0.142 -0.142
## 51 cas02 ~~ cas03 15.679 0.081 0.081 0.141 0.141
## 49 cas01 ~~ cas12 15.342 0.094 0.094 0.136 0.136
## 44 cas01 ~~ cas07 14.469 -0.089 -0.089 -0.126 -0.126
## 84 cas05 ~~ cas13 14.451 0.160 0.160 0.121 0.121
## 53 cas02 ~~ cas05 12.933 -0.127 -0.127 -0.121 -0.121
## 39 cas.f =~ cas08 12.125 1.637 1.637 1.057 1.057
## 37 cas.f =~ cas05 10.852 1.603 1.603 0.988 0.988
## 64 cas03 ~~ cas08 9.595 -0.092 -0.092 -0.101 -0.101
## 100 cas10 ~~ cas11 9.217 -0.098 -0.098 -0.103 -0.103
## 56 cas02 ~~ cas09 9.050 0.070 0.070 0.109 0.109
## 52 cas02 ~~ cas04 8.327 0.060 0.060 0.100 0.100
## 88 cas07 ~~ cas11 8.308 0.057 0.057 0.097 0.097
## 96 cas09 ~~ cas10 7.885 0.104 0.104 0.094 0.094
## 48 cas01 ~~ cas11 6.990 0.056 0.056 0.094 0.094
## 58 cas02 ~~ cas11 6.950 -0.054 -0.054 -0.096 -0.096
## 36 cas.f =~ cas04 6.784 -0.766 -0.766 -0.753 -0.753
## 102 cas10 ~~ cas13 6.218 0.109 0.109 0.079 0.079
## 42 cas01 ~~ cas04 5.975 -0.053 -0.053 -0.083 -0.083
## 43 cas01 ~~ cas05 5.661 -0.088 -0.088 -0.079 -0.079
## 66 cas03 ~~ cas10 5.343 -0.073 -0.073 -0.075 -0.075
## 90 cas07 ~~ cas13 5.028 0.060 0.060 0.071 0.071Variance-Covariance-Matrix
inspect(fit.4, "sampstat")$cov #empirisch## cas01 cas02 cas03 cas04 cas05 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## cas01 1.635
## cas02 1.011 1.621
## cas03 0.656 0.805 1.087
## cas04 0.617 0.728 0.623 1.034
## cas05 0.845 0.855 0.691 0.533 2.634
## cas07 0.524 0.540 0.571 0.516 0.588 1.080
## cas08 0.836 0.724 0.584 0.447 1.390 0.564 2.399
## cas09 0.953 1.015 0.712 0.665 0.936 0.534 0.882 1.663
## cas10 0.823 0.851 0.589 0.577 1.132 0.404 0.981 0.981 2.662
## cas11 0.882 0.832 0.657 0.584 0.856 0.592 0.785 0.889 0.731 1.306
## cas12 0.948 0.904 0.622 0.594 0.862 0.590 0.829 0.949 0.796 0.908 1.551
## cas13 0.768 0.814 0.629 0.613 0.968 0.583 0.791 0.894 0.875 0.750 0.778 1.781fitted(fit.4)$cov #modellimpliziert## cas01 cas02 cas03 cas04 cas05 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## cas01 1.635
## cas02 0.942 1.621
## cas03 0.720 0.749 1.087
## cas04 0.658 0.684 0.523 1.034
## cas05 0.916 0.953 0.729 0.665 2.634
## cas07 0.595 0.619 0.474 0.432 0.602 1.080
## cas08 0.831 0.865 0.661 0.604 0.841 0.547 2.399
## cas09 0.932 0.970 0.741 0.677 0.943 0.613 0.856 1.663
## cas10 0.828 0.861 0.658 0.601 0.838 0.544 0.760 0.900 2.662
## cas11 0.835 0.869 0.664 0.606 0.845 0.549 0.766 0.908 0.806 1.306
## cas12 0.874 0.909 0.695 0.635 0.884 0.575 0.802 0.950 0.844 0.851 1.551
## cas13 0.807 0.840 0.642 0.586 0.817 0.531 0.741 0.878 0.780 0.786 0.823 1.781Standardized residuals
cov_table <- resid(fit.4, type="standardized")$cov
cov_table[upper.tri(cov_table)] <- NA #erase the upper triangle
diag(cov_table) <- NA #erase the diagonal 0's
kable(cov_table, digits=2) #makes a nice table and rounds everyhing to 2 digits| cas01 | cas02 | cas03 | cas04 | cas05 | cas07 | cas08 | cas09 | cas10 | cas11 | cas12 | cas13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cas01 | ||||||||||||
| cas02 | 1.83 | |||||||||||
| cas03 | -3.33 | 2.65 | ||||||||||
| cas04 | -1.85 | 1.84 | 4.55 | |||||||||
| cas05 | -1.81 | -2.90 | -1.16 | -4.48 | ||||||||
| cas07 | -3.10 | -3.41 | 3.86 | 3.39 | -0.39 | |||||||
| cas08 | 0.14 | -4.41 | -2.96 | -5.45 | 8.13 | 0.45 | ||||||
| cas09 | 0.69 | 1.62 | -1.41 | -0.53 | -0.19 | -3.62 | 0.81 | |||||
| cas10 | -0.13 | -0.29 | -2.17 | -0.61 | 4.66 | -4.04 | 3.60 | 2.29 | ||||
| cas11 | 1.61 | -1.73 | -0.35 | -1.16 | 0.35 | 1.89 | 0.61 | -1.12 | -2.85 | |||
| cas12 | 2.07 | -0.16 | -3.74 | -2.00 | -0.58 | 0.68 | 0.66 | -0.05 | -1.39 | 2.32 | ||
| cas13 | -1.21 | -0.91 | -0.41 | 0.80 | 2.94 | 1.61 | 1.23 | 0.60 | 1.83 | -1.47 | -1.53 |
7.5.3 Plot CFA - 2 factor solution
Load package
library(semPlot)semPaths(fit.4, rotation = 2, nodeLabels=1:13)
Create the vector for the node labels
nodeLabels <- c("1", "2", "3", "4", "5", "7", "8",
"9", "10", "11", "12", "13",
"cognitive-emotional impairment",
"functional impairment"
)Create a character vector for the order of latent variables
latents <- c("cas.ce", "cas.f")Plot
semPaths(fit.4,
style = "lisrel", #small arrows for residuals instead of half-circles
whatLabels = "std.all", #labels paths with standardized coefficients
nCharNodes = 0, #factor names not abbreviated
rotation = 2, #rotates the graph around, #s 1, 2, 3, 4
layout = "tree3",
curvePivot = TRUE, #makes curves straight with rounded edges
curvePivotShape = 2.1, #influences how round the curve around the pivot is
edge.label.cex = .5, #size of the edge labels
cardinal = TRUE, #all edge labels end in the middle/cardinal point of a node
sizeMan = 5, #size of manifest variables
sizeMan2 = 2, #height of manifest variables
sizeLat =10, #size of latent variables
sizeLat2 = 10, #height of latent variables
nodeLabels = nodeLabels,#vector that names the nodes
latents = latents #vector that reorders the nodes
)
7.5.4 CFA - 1 factor solution
CFA.3 <- ' cas.3 =~ cas01 + cas02 + cas03 + cas04 + cas05 + cas07 + cas08 + cas09 + cas10 + cas11 + cas12 + cas13'
fit.3 <- cfa(CFA.3, std.lv=TRUE, data=data, estimator = "MLM")
summary(fit.3, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)## lavaan 0.6-7 ended normally after 16 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 24
##
## Number of observations 1134
##
## Model Test User Model:
## Standard Robust
## Test Statistic 554.563 295.655
## Degrees of freedom 54 54
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.876
## Satorra-Bentler correction
##
## Model Test Baseline Model:
##
## Test statistic 6606.087 2419.177
## Degrees of freedom 66 66
## P-value 0.000 0.000
## Scaling correction factor 2.731
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.923 0.897
## Tucker-Lewis Index (TLI) 0.906 0.874
##
## Robust Comparative Fit Index (CFI) 0.929
## Robust Tucker-Lewis Index (TLI) 0.914
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -19561.657 -19561.657
## Loglikelihood unrestricted model (H1) -19284.375 -19284.375
##
## Akaike (AIC) 39171.314 39171.314
## Bayesian (BIC) 39292.118 39292.118
## Sample-size adjusted Bayesian (BIC) 39215.887 39215.887
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.090 0.063
## 90 Percent confidence interval - lower 0.084 0.058
## 90 Percent confidence interval - upper 0.097 0.068
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA 0.086
## 90 Percent confidence interval - lower 0.077
## 90 Percent confidence interval - upper 0.096
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047 0.047
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cas.3 =~
## cas01 0.950 0.041 23.078 0.000 0.950 0.743
## cas02 0.981 0.042 23.323 0.000 0.981 0.771
## cas03 0.746 0.053 13.962 0.000 0.746 0.716
## cas04 0.683 0.053 12.786 0.000 0.683 0.672
## cas05 0.964 0.041 23.762 0.000 0.964 0.594
## cas07 0.621 0.056 11.012 0.000 0.621 0.597
## cas08 0.876 0.042 20.729 0.000 0.876 0.566
## cas09 0.997 0.042 24.018 0.000 0.997 0.773
## cas10 0.889 0.046 19.275 0.000 0.889 0.545
## cas11 0.894 0.044 20.238 0.000 0.894 0.783
## cas12 0.935 0.041 22.770 0.000 0.935 0.750
## cas13 0.868 0.045 19.256 0.000 0.868 0.650
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .cas01 0.733 0.060 12.279 0.000 0.733 0.448
## .cas02 0.658 0.057 11.504 0.000 0.658 0.406
## .cas03 0.530 0.050 10.693 0.000 0.530 0.488
## .cas04 0.567 0.054 10.541 0.000 0.567 0.549
## .cas05 1.704 0.088 19.396 0.000 1.704 0.647
## .cas07 0.695 0.073 9.504 0.000 0.695 0.643
## .cas08 1.631 0.079 20.532 0.000 1.631 0.680
## .cas09 0.669 0.050 13.343 0.000 0.669 0.402
## .cas10 1.872 0.107 17.570 0.000 1.872 0.703
## .cas11 0.506 0.043 11.772 0.000 0.506 0.388
## .cas12 0.678 0.057 11.790 0.000 0.678 0.437
## .cas13 1.027 0.074 13.875 0.000 1.027 0.577
## cas.3 1.000 1.000 1.000
##
## R-Square:
## Estimate
## cas01 0.552
## cas02 0.594
## cas03 0.512
## cas04 0.451
## cas05 0.353
## cas07 0.357
## cas08 0.320
## cas09 0.598
## cas10 0.297
## cas11 0.612
## cas12 0.563
## cas13 0.423parameterEstimates(fit.3, standardized=TRUE) #shows complete list of parameters in model 1## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 1 cas.3 =~ cas01 0.950 0.041 23.078 0 0.869 1.031 0.950 0.743
## 2 cas.3 =~ cas02 0.981 0.042 23.323 0 0.899 1.064 0.981 0.771
## 3 cas.3 =~ cas03 0.746 0.053 13.962 0 0.641 0.851 0.746 0.716
## 4 cas.3 =~ cas04 0.683 0.053 12.786 0 0.579 0.788 0.683 0.672
## 5 cas.3 =~ cas05 0.964 0.041 23.762 0 0.885 1.044 0.964 0.594
## 6 cas.3 =~ cas07 0.621 0.056 11.012 0 0.510 0.731 0.621 0.597
## 7 cas.3 =~ cas08 0.876 0.042 20.729 0 0.793 0.959 0.876 0.566
## 8 cas.3 =~ cas09 0.997 0.042 24.018 0 0.916 1.078 0.997 0.773
## 9 cas.3 =~ cas10 0.889 0.046 19.275 0 0.798 0.979 0.889 0.545
## 10 cas.3 =~ cas11 0.894 0.044 20.238 0 0.808 0.981 0.894 0.783
## 11 cas.3 =~ cas12 0.935 0.041 22.770 0 0.854 1.015 0.935 0.750
## 12 cas.3 =~ cas13 0.868 0.045 19.256 0 0.780 0.956 0.868 0.650
## 13 cas01 ~~ cas01 0.733 0.060 12.279 0 0.616 0.850 0.733 0.448
## 14 cas02 ~~ cas02 0.658 0.057 11.504 0 0.546 0.770 0.658 0.406
## 15 cas03 ~~ cas03 0.530 0.050 10.693 0 0.433 0.627 0.530 0.488
## 16 cas04 ~~ cas04 0.567 0.054 10.541 0 0.462 0.673 0.567 0.549
## 17 cas05 ~~ cas05 1.704 0.088 19.396 0 1.532 1.876 1.704 0.647
## 18 cas07 ~~ cas07 0.695 0.073 9.504 0 0.551 0.838 0.695 0.643
## 19 cas08 ~~ cas08 1.631 0.079 20.532 0 1.476 1.787 1.631 0.680
## 20 cas09 ~~ cas09 0.669 0.050 13.343 0 0.571 0.767 0.669 0.402
## 21 cas10 ~~ cas10 1.872 0.107 17.570 0 1.663 2.081 1.872 0.703
## 22 cas11 ~~ cas11 0.506 0.043 11.772 0 0.422 0.590 0.506 0.388
## 23 cas12 ~~ cas12 0.678 0.057 11.790 0 0.565 0.790 0.678 0.437
## 24 cas13 ~~ cas13 1.027 0.074 13.875 0 0.882 1.172 1.027 0.577
## 25 cas.3 ~~ cas.3 1.000 0.000 NA NA 1.000 1.000 1.000 1.000
## std.nox
## 1 0.743
## 2 0.771
## 3 0.716
## 4 0.672
## 5 0.594
## 6 0.597
## 7 0.566
## 8 0.773
## 9 0.545
## 10 0.783
## 11 0.750
## 12 0.650
## 13 0.448
## 14 0.406
## 15 0.488
## 16 0.549
## 17 0.647
## 18 0.643
## 19 0.680
## 20 0.402
## 21 0.703
## 22 0.388
## 23 0.437
## 24 0.577
## 25 1.000Factor loadings
options(knitr.kable.NA = '')
parameterEstimates(fit.3, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'= pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")| Latent Factor | Indicator | B | SE | Z | p-value | Beta |
|---|---|---|---|---|---|---|
| cas.3 | cas01 | 0.950 | 0.041 | 23.078 | 0 | 0.743 |
| cas.3 | cas02 | 0.981 | 0.042 | 23.323 | 0 | 0.771 |
| cas.3 | cas03 | 0.746 | 0.053 | 13.962 | 0 | 0.716 |
| cas.3 | cas04 | 0.683 | 0.053 | 12.786 | 0 | 0.672 |
| cas.3 | cas05 | 0.964 | 0.041 | 23.762 | 0 | 0.594 |
| cas.3 | cas07 | 0.621 | 0.056 | 11.012 | 0 | 0.597 |
| cas.3 | cas08 | 0.876 | 0.042 | 20.729 | 0 | 0.566 |
| cas.3 | cas09 | 0.997 | 0.042 | 24.018 | 0 | 0.773 |
| cas.3 | cas10 | 0.889 | 0.046 | 19.275 | 0 | 0.545 |
| cas.3 | cas11 | 0.894 | 0.044 | 20.238 | 0 | 0.783 |
| cas.3 | cas12 | 0.935 | 0.041 | 22.770 | 0 | 0.750 |
| cas.3 | cas13 | 0.868 | 0.045 | 19.256 | 0 | 0.650 |
Modification indices
mod_ind <- modificationindices(fit.3)
head(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], 10) #Spotting the top 10## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 65 cas05 ~~ cas08 133.393 0.600 0.600 0.360 0.360
## 47 cas03 ~~ cas04 58.366 0.137 0.137 0.250 0.250
## 49 cas03 ~~ cas07 42.409 0.127 0.127 0.209 0.209
## 58 cas04 ~~ cas08 32.131 -0.172 -0.172 -0.179 -0.179
## 67 cas05 ~~ cas10 29.441 0.301 0.301 0.169 0.169
## 57 cas04 ~~ cas07 27.663 0.105 0.105 0.167 0.167
## 89 cas11 ~~ cas12 23.933 0.100 0.100 0.171 0.171
## 41 cas02 ~~ cas08 23.690 -0.165 -0.165 -0.159 -0.159
## 54 cas03 ~~ cas12 23.149 -0.097 -0.097 -0.162 -0.162
## 37 cas02 ~~ cas03 22.954 0.096 0.096 0.163 0.163subset(mod_ind[order(mod_ind$mi, decreasing=TRUE), ], mi > 5) #bigger than 5## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 65 cas05 ~~ cas08 133.393 0.600 0.600 0.360 0.360
## 47 cas03 ~~ cas04 58.366 0.137 0.137 0.250 0.250
## 49 cas03 ~~ cas07 42.409 0.127 0.127 0.209 0.209
## 58 cas04 ~~ cas08 32.131 -0.172 -0.172 -0.179 -0.179
## 67 cas05 ~~ cas10 29.441 0.301 0.301 0.169 0.169
## 57 cas04 ~~ cas07 27.663 0.105 0.105 0.167 0.167
## 89 cas11 ~~ cas12 23.933 0.100 0.100 0.171 0.171
## 41 cas02 ~~ cas08 23.690 -0.165 -0.165 -0.159 -0.159
## 54 cas03 ~~ cas12 23.149 -0.097 -0.097 -0.162 -0.162
## 37 cas02 ~~ cas03 22.954 0.096 0.096 0.163 0.163
## 72 cas07 ~~ cas09 21.414 -0.104 -0.104 -0.153 -0.153
## 56 cas04 ~~ cas05 21.389 -0.144 -0.144 -0.147 -0.147
## 73 cas07 ~~ cas10 20.869 -0.162 -0.162 -0.142 -0.142
## 26 cas01 ~~ cas02 19.697 0.106 0.106 0.153 0.153
## 78 cas08 ~~ cas10 16.523 0.220 0.220 0.126 0.126
## 40 cas02 ~~ cas07 14.639 -0.085 -0.085 -0.126 -0.126
## 38 cas02 ~~ cas04 12.694 0.073 0.073 0.119 0.119
## 70 cas05 ~~ cas13 12.586 0.148 0.148 0.112 0.112
## 30 cas01 ~~ cas07 11.621 -0.079 -0.079 -0.111 -0.111
## 35 cas01 ~~ cas12 11.017 0.080 0.080 0.113 0.113
## 39 cas02 ~~ cas05 10.451 -0.113 -0.113 -0.106 -0.106
## 27 cas01 ~~ cas03 10.228 -0.067 -0.067 -0.107 -0.107
## 82 cas09 ~~ cas10 9.937 0.115 0.115 0.103 0.103
## 44 cas02 ~~ cas11 9.931 -0.064 -0.064 -0.112 -0.112
## 62 cas04 ~~ cas12 7.371 -0.056 -0.056 -0.090 -0.090
## 50 cas03 ~~ cas08 7.351 -0.081 -0.081 -0.087 -0.087
## 52 cas03 ~~ cas10 7.255 -0.086 -0.086 -0.086 -0.086
## 88 cas10 ~~ cas13 7.106 0.116 0.116 0.084 0.084
## 86 cas10 ~~ cas11 6.007 -0.078 -0.078 -0.081 -0.081
## 36 cas01 ~~ cas13 5.771 -0.069 -0.069 -0.079 -0.079
## 74 cas07 ~~ cas11 5.442 0.046 0.046 0.077 0.077
## 29 cas01 ~~ cas05 5.439 -0.085 -0.085 -0.076 -0.076Variance-Covariance-Matrix
inspect(fit.3, "sampstat")$cov #empirisch## cas01 cas02 cas03 cas04 cas05 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## cas01 1.635
## cas02 1.011 1.621
## cas03 0.656 0.805 1.087
## cas04 0.617 0.728 0.623 1.034
## cas05 0.845 0.855 0.691 0.533 2.634
## cas07 0.524 0.540 0.571 0.516 0.588 1.080
## cas08 0.836 0.724 0.584 0.447 1.390 0.564 2.399
## cas09 0.953 1.015 0.712 0.665 0.936 0.534 0.882 1.663
## cas10 0.823 0.851 0.589 0.577 1.132 0.404 0.981 0.981 2.662
## cas11 0.882 0.832 0.657 0.584 0.856 0.592 0.785 0.889 0.731 1.306
## cas12 0.948 0.904 0.622 0.594 0.862 0.590 0.829 0.949 0.796 0.908 1.551
## cas13 0.768 0.814 0.629 0.613 0.968 0.583 0.791 0.894 0.875 0.750 0.778 1.781fitted(fit.3)$cov #modellimpliziert## cas01 cas02 cas03 cas04 cas05 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## cas01 1.635
## cas02 0.932 1.621
## cas03 0.709 0.732 1.087
## cas04 0.649 0.671 0.510 1.034
## cas05 0.916 0.946 0.720 0.659 2.634
## cas07 0.590 0.609 0.463 0.424 0.599 1.080
## cas08 0.832 0.860 0.654 0.599 0.845 0.544 2.399
## cas09 0.947 0.978 0.744 0.681 0.962 0.619 0.873 1.663
## cas10 0.844 0.872 0.663 0.607 0.857 0.552 0.779 0.886 2.662
## cas11 0.849 0.878 0.667 0.611 0.862 0.555 0.783 0.892 0.795 1.306
## cas12 0.888 0.917 0.697 0.639 0.901 0.580 0.819 0.932 0.831 0.836 1.551
## cas13 0.824 0.852 0.648 0.593 0.837 0.539 0.760 0.865 0.771 0.776 0.811 1.781Standardized residuals
cov_table <- resid(fit.3, type="standardized")$cov
cov_table[upper.tri(cov_table)] <- NA #erase the upper triangle
diag(cov_table) <- NA #erase the diagonal 0's
kable(cov_table, digits=2) #makes a nice table and rounds everyhing to 2 digits| cas01 | cas02 | cas03 | cas04 | cas05 | cas07 | cas08 | cas09 | cas10 | cas11 | cas12 | cas13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cas01 | ||||||||||||
| cas02 | 2.06 | |||||||||||
| cas03 | -2.52 | 3.18 | ||||||||||
| cas04 | -1.40 | 2.24 | 4.89 | |||||||||
| cas05 | -1.81 | -2.53 | -0.86 | -4.14 | ||||||||
| cas07 | -2.80 | -2.71 | 4.08 | 3.65 | -0.28 | |||||||
| cas08 | 0.12 | -4.04 | -2.65 | -5.27 | 8.06 | 0.52 | ||||||
| cas09 | 0.20 | 1.31 | -1.56 | -0.75 | -0.77 | -3.89 | 0.27 | |||||
| cas10 | -0.58 | -0.61 | -2.37 | -0.78 | 4.41 | -4.27 | 3.33 | 2.62 | ||||
| cas11 | 1.16 | -2.22 | -0.52 | -1.43 | -0.22 | 1.65 | 0.04 | -0.15 | -2.33 | |||
| cas12 | 1.74 | -0.38 | -3.83 | -2.23 | -1.04 | 0.44 | 0.26 | 0.75 | -0.93 | 2.60 | ||
| cas13 | -1.82 | -1.38 | -0.60 | 0.61 | 2.59 | 1.38 | 0.78 | 1.00 | 1.96 | -1.01 | -1.05 |
7.6 Comparing CFA’s
- fit.1 - 1-factor solution with all items
- fit.2 - 2-factor solution with all items (proposed by Clayton & Karazsia, 2020)
- fit.3 - 1-factor solution without item cas06
- fit.4 - 2-factor solution without item cas06
7.6.1 compare CFA-2 factors with all items vs. CFA-1 factor with all items
anova(fit.2, fit.1, test="Chisq")## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.2 64 43467 43603 623.81
## fit.1 65 43476 43607 634.97 5.3942 1 0.0202 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.6.2 compare CFA-2 factors with all items vs. CFA-1 factor without item cas06
anova(fit.2, fit.3, test="Chisq")## Warning in lavTestLRT(object = new("lavaan", version = "0.6.7", call =
## lavaan::lavaan(model = CFA.2, : lavaan WARNING: some models are based on a
## different set of observed variables## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.3 54 39171 39292 554.56
## fit.2 64 43467 43603 623.81 60.775 10 2.584e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.6.3 various other comparisons
anova(fit.4, fit.3, test="Chisq") #compare 1-factor solution without item cas06 and 2-factor solution without item cas06## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.4 53 39162 39288 543.20
## fit.3 54 39171 39292 554.56 5.684 1 0.01712 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit.2, fit.4, test="Chisq") #compare 2-factor solution with all items and 2-factor solution without item cas06## Warning in lavTestLRT(object = new("lavaan", version = "0.6.7", call =
## lavaan::lavaan(model = CFA.2, : lavaan WARNING: some models are based on a
## different set of observed variables## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.4 53 39162 39288 543.20
## fit.2 64 43467 43603 623.81 66.207 11 6.385e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.7 EFA CAS - with all items
Correlations between items to inspect for high item-inter-correlations
corr.test (data [,c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas06", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12",
"cas13")],
method = "spearman")## Call:corr.test(x = data[, c("cas01", "cas02", "cas03", "cas04", "cas05",
## "cas06", "cas07", "cas08", "cas09", "cas10", "cas11", "cas12",
## "cas13")], method = "spearman")
## Correlation matrix
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.00 0.60 0.50 0.45 0.45 0.30 0.41 0.46 0.58 0.44 0.58 0.59
## cas02 0.60 1.00 0.60 0.53 0.43 0.38 0.41 0.40 0.61 0.44 0.58 0.56
## cas03 0.50 0.60 1.00 0.54 0.40 0.37 0.49 0.36 0.50 0.36 0.54 0.49
## cas04 0.45 0.53 0.54 1.00 0.34 0.38 0.47 0.28 0.48 0.35 0.47 0.46
## cas05 0.45 0.43 0.40 0.34 1.00 0.36 0.35 0.56 0.47 0.46 0.47 0.45
## cas06 0.30 0.38 0.37 0.38 0.36 1.00 0.35 0.28 0.41 0.30 0.33 0.34
## cas07 0.41 0.41 0.49 0.47 0.35 0.35 1.00 0.34 0.39 0.29 0.48 0.45
## cas08 0.46 0.40 0.36 0.28 0.56 0.28 0.34 1.00 0.46 0.42 0.45 0.44
## cas09 0.58 0.61 0.50 0.48 0.47 0.41 0.39 0.46 1.00 0.51 0.60 0.60
## cas10 0.44 0.44 0.36 0.35 0.46 0.30 0.29 0.42 0.51 1.00 0.45 0.45
## cas11 0.58 0.58 0.54 0.47 0.47 0.33 0.48 0.45 0.60 0.45 1.00 0.64
## cas12 0.59 0.56 0.49 0.46 0.45 0.34 0.45 0.44 0.60 0.45 0.64 1.00
## cas13 0.49 0.50 0.45 0.42 0.45 0.41 0.44 0.40 0.52 0.43 0.51 0.50
## cas13
## cas01 0.49
## cas02 0.50
## cas03 0.45
## cas04 0.42
## cas05 0.45
## cas06 0.41
## cas07 0.44
## cas08 0.40
## cas09 0.52
## cas10 0.43
## cas11 0.51
## cas12 0.50
## cas13 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 0 0 0 0 0 0 0 0 0 0 0 0
## cas02 0 0 0 0 0 0 0 0 0 0 0 0
## cas03 0 0 0 0 0 0 0 0 0 0 0 0
## cas04 0 0 0 0 0 0 0 0 0 0 0 0
## cas05 0 0 0 0 0 0 0 0 0 0 0 0
## cas06 0 0 0 0 0 0 0 0 0 0 0 0
## cas07 0 0 0 0 0 0 0 0 0 0 0 0
## cas08 0 0 0 0 0 0 0 0 0 0 0 0
## cas09 0 0 0 0 0 0 0 0 0 0 0 0
## cas10 0 0 0 0 0 0 0 0 0 0 0 0
## cas11 0 0 0 0 0 0 0 0 0 0 0 0
## cas12 0 0 0 0 0 0 0 0 0 0 0 0
## cas13 0 0 0 0 0 0 0 0 0 0 0 0
## cas13
## cas01 0
## cas02 0
## cas03 0
## cas04 0
## cas05 0
## cas06 0
## cas07 0
## cas08 0
## cas09 0
## cas10 0
## cas11 0
## cas12 0
## cas13 0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionSubset with CAS-items
subset.cas <- data[c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas06", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12",
"cas13")]
View(subset.cas)7.7.1 Kaiser-Maier-Olkin coefficient
KMO(subset.cas)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = subset.cas)
## Overall MSA = 0.94
## MSA for each item =
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## 0.95 0.94 0.94 0.94 0.92 0.94 0.93 0.92 0.96 0.95 0.95 0.95 0.96options(max.print=1000000)7.7.2 Extracting factors and parallel analysis
Scree Plot
VSS.scree (subset.cas)
Parallel analysis (Horn)
parallel.cas <- fa.parallel (subset.cas, fm="pa", fa = "fa")
## Parallel analysis suggests that the number of factors = 4 and the number of components = NAEigenvalues of factors
parallel.cas$fa.values## [1] 5.87239919 0.40783298 0.28728318 0.15169673 0.05423509 -0.03139294
## [7] -0.05466853 -0.06596327 -0.09120038 -0.11452880 -0.15293368 -0.16864509
## [13] -0.221878887.7.3 Factor analysis with fixed number of factors = 4
as suggested by parallel analysis
fa.pa.promax.cas <- fa(subset.cas, 4, fm = "pa", rotate = "Promax")
print (fa.pa.promax.cas, digits = 2, cut = .3, sort = TRUE) ## Factor Analysis using method = pa
## Call: fa(r = subset.cas, nfactors = 4, rotate = "Promax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item PA1 PA2 PA4 PA3 h2 u2 com
## cas01 1 0.76 0.62 0.38 1.0
## cas12 12 0.63 0.59 0.41 1.2
## cas02 2 0.61 0.41 0.66 0.34 1.9
## cas11 11 0.59 0.64 0.36 1.3
## cas09 9 0.49 0.32 0.62 0.38 2.0
## cas05 5 0.75 0.58 0.42 1.0
## cas08 8 0.73 0.52 0.48 1.1
## cas10 10 0.35 0.38 0.62 3.0
## cas04 4 0.59 0.57 0.43 1.9
## cas06 6 0.52 0.34 0.66 1.6
## cas03 3 0.45 0.57 0.43 2.2
## cas13 13 0.41 0.47 0.53 1.8
## cas07 7 0.67 0.65 0.35 1.2
##
## PA1 PA2 PA4 PA3
## SS loadings 2.78 1.65 1.98 0.81
## Proportion Var 0.21 0.13 0.15 0.06
## Cumulative Var 0.21 0.34 0.49 0.56
## Proportion Explained 0.38 0.23 0.27 0.11
## Cumulative Proportion 0.38 0.61 0.89 1.00
##
## With factor correlations of
## PA1 PA2 PA4 PA3
## PA1 1.00 0.66 0.69 0.52
## PA2 0.66 1.00 0.66 0.37
## PA4 0.69 0.66 1.00 0.50
## PA3 0.52 0.37 0.50 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 6.13 with Chi Square of 6912.51
## The degrees of freedom for the model are 32 and the objective function was 0.08
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1134 with the empirical chi square 35.19 with prob < 0.32
## The total number of observations was 1134 with Likelihood Chi Square = 87.07 with prob < 5.4e-07
##
## Tucker Lewis Index of factoring reliability = 0.98
## RMSEA index = 0.039 and the 90 % confidence intervals are 0.029 0.049
## BIC = -138
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA4 PA3
## Correlation of (regression) scores with factors 0.93 0.90 0.90 0.82
## Multiple R square of scores with factors 0.87 0.80 0.80 0.68
## Minimum correlation of possible factor scores 0.75 0.61 0.61 0.357.7.4 Factor analysis with fixed number of factors = 2
as suggested by interpretation of scree plot
fa.pa.promax.cas <- fa(subset.cas, 2, fm = "pa", rotate = "Promax")
print (fa.pa.promax.cas, digits = 2, cut = .4, sort = TRUE) ## Factor Analysis using method = pa
## Call: fa(r = subset.cas, nfactors = 2, rotate = "Promax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item PA1 PA2 h2 u2 com
## cas04 4 0.89 0.55 0.45 1.1
## cas03 3 0.80 0.56 0.44 1.0
## cas02 2 0.78 0.61 0.39 1.0
## cas11 11 0.60 0.60 0.40 1.2
## cas07 7 0.60 0.38 0.62 1.0
## cas01 1 0.58 0.52 0.48 1.2
## cas12 12 0.55 0.54 0.46 1.3
## cas09 9 0.55 0.59 0.41 1.4
## cas13 13 0.40 0.44 0.56 1.9
## cas06 6 0.23 0.77 1.7
## cas05 5 0.83 0.56 0.44 1.0
## cas08 8 0.80 0.50 0.50 1.0
## cas10 10 0.47 0.34 0.66 1.2
##
## PA1 PA2
## SS loadings 4.26 2.18
## Proportion Var 0.33 0.17
## Cumulative Var 0.33 0.50
## Proportion Explained 0.66 0.34
## Cumulative Proportion 0.66 1.00
##
## With factor correlations of
## PA1 PA2
## PA1 1.00 0.77
## PA2 0.77 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 6.13 with Chi Square of 6912.51
## The degrees of freedom for the model are 53 and the objective function was 0.33
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 1134 with the empirical chi square 220.2 with prob < 2.9e-22
## The total number of observations was 1134 with Likelihood Chi Square = 373.9 with prob < 8e-50
##
## Tucker Lewis Index of factoring reliability = 0.931
## RMSEA index = 0.073 and the 90 % confidence intervals are 0.066 0.08
## BIC = 1.13
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## PA1 PA2
## Correlation of (regression) scores with factors 0.95 0.92
## Multiple R square of scores with factors 0.91 0.84
## Minimum correlation of possible factor scores 0.82 0.68–> other factors than in Clayton & Karazsia, 2020 here: F1 = cas01:cas04, cas07, cas09, cas11, cas12:cas13, cas06; F2 = cas08, cas05, cas10 Clayton & Karazsia, 2020: F1 (cognitive-emotional impairment) = cas01:cas08; F2 (functional impairment) = cas09:cas13
7.7.5 Visualizing results
fa.diagram(fa.pa.promax.cas, simple=TRUE, cut=.4, digits=2)
fa.diagram(fa.pa.promax.cas, simple=FALSE, cut=.4, digits=2)
7.8 EFA CAS - without item cas06
cas06: “I go away by myself and think about why I feel this way about climate change.”
Subset with CAS-items
subset.cas <- data[c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12",
"cas13")]
View(subset.cas)7.8.1 Kaiser-Maier-Olkin coefficient
KMO(subset.cas)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = subset.cas)
## Overall MSA = 0.94
## MSA for each item =
## cas01 cas02 cas03 cas04 cas05 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## 0.95 0.94 0.93 0.94 0.92 0.93 0.92 0.95 0.95 0.95 0.95 0.97options(max.print=1000000)7.8.2 Extracting factors and parallel analysis
Scree Plot
VSS.scree (subset.cas)
Parallel analysis (Horn)
parallel.cas <- fa.parallel (subset.cas, fm="pa", fa = "fa")
## Parallel analysis suggests that the number of factors = 4 and the number of components = NAEigenvalues of factors
parallel.cas$fa.values## [1] 5.63819704 0.40431474 0.21395012 0.13875411 0.02648145 -0.03174959
## [7] -0.06044693 -0.07149824 -0.08643973 -0.14316111 -0.17188052 -0.218976417.8.3 Factor analysis with fixed number of factors = 4
as suggested by parallel analysis
fa.pa.promax.cas <- fa(subset.cas, 4, fm = "pa", rotate = "Promax")
print (fa.pa.promax.cas, digits = 2, cut = .3, sort = TRUE) ## Factor Analysis using method = pa
## Call: fa(r = subset.cas, nfactors = 4, rotate = "Promax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item PA1 PA4 PA2 PA3 h2 u2 com
## cas12 11 0.77 0.64 0.36 1.0
## cas01 1 0.65 0.59 0.41 1.2
## cas11 10 0.61 0.64 0.36 1.2
## cas09 8 0.41 0.36 0.62 0.38 2.3
## cas04 4 0.72 0.58 0.42 1.2
## cas03 3 0.64 0.61 0.39 1.3
## cas02 2 0.33 0.63 0.66 0.34 1.6
## cas05 5 0.86 0.60 0.40 1.0
## cas08 7 0.73 0.51 0.49 1.2
## cas10 9 0.44 0.38 0.62 2.0
## cas13 12 0.44 0.56 2.3
## cas07 6 0.64 0.62 0.38 1.1
##
## PA1 PA4 PA2 PA3
## SS loadings 2.21 2.19 1.69 0.81
## Proportion Var 0.18 0.18 0.14 0.07
## Cumulative Var 0.18 0.37 0.51 0.58
## Proportion Explained 0.32 0.32 0.24 0.12
## Cumulative Proportion 0.32 0.64 0.88 1.00
##
## With factor correlations of
## PA1 PA4 PA2 PA3
## PA1 1.00 0.78 0.75 0.47
## PA4 0.78 1.00 0.69 0.51
## PA2 0.75 0.69 1.00 0.41
## PA3 0.47 0.51 0.41 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 66 and the objective function was 5.83 with Chi Square of 6572.1
## The degrees of freedom for the model are 24 and the objective function was 0.05
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1134 with the empirical chi square 19.35 with prob < 0.73
## The total number of observations was 1134 with Likelihood Chi Square = 50.83 with prob < 0.0011
##
## Tucker Lewis Index of factoring reliability = 0.989
## RMSEA index = 0.031 and the 90 % confidence intervals are 0.019 0.043
## BIC = -117.98
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA4 PA2 PA3
## Correlation of (regression) scores with factors 0.93 0.93 0.91 0.81
## Multiple R square of scores with factors 0.87 0.86 0.82 0.66
## Minimum correlation of possible factor scores 0.74 0.72 0.65 0.327.8.4 Factor analysis with fixed number of factors = 2
as suggested by interpretation of scree plot
fa.pa.promax.cas <- fa(subset.cas, 2, fm = "pa", rotate = "Promax")
print (fa.pa.promax.cas, digits = 2, cut = .4, sort = TRUE) ## Factor Analysis using method = pa
## Call: fa(r = subset.cas, nfactors = 2, rotate = "Promax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item PA1 PA2 h2 u2 com
## cas04 4 0.87 0.54 0.46 1.1
## cas03 3 0.79 0.56 0.44 1.0
## cas02 2 0.77 0.61 0.39 1.0
## cas11 10 0.60 0.61 0.39 1.3
## cas07 6 0.59 0.37 0.63 1.0
## cas01 1 0.57 0.54 0.46 1.2
## cas12 11 0.55 0.55 0.45 1.4
## cas09 8 0.55 0.59 0.41 1.5
## cas13 12 0.41 0.43 0.57 1.8
## cas08 7 0.80 0.51 0.49 1.0
## cas05 5 0.79 0.54 0.46 1.0
## cas10 9 0.47 0.34 0.66 1.2
##
## PA1 PA2
## SS loadings 4.11 2.10
## Proportion Var 0.34 0.17
## Cumulative Var 0.34 0.52
## Proportion Explained 0.66 0.34
## Cumulative Proportion 0.66 1.00
##
## With factor correlations of
## PA1 PA2
## PA1 1.00 0.75
## PA2 0.75 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 66 and the objective function was 5.83 with Chi Square of 6572.1
## The degrees of freedom for the model are 43 and the objective function was 0.26
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 1134 with the empirical chi square 154.43 with prob < 1.8e-14
## The total number of observations was 1134 with Likelihood Chi Square = 298.59 with prob < 5.6e-40
##
## Tucker Lewis Index of factoring reliability = 0.94
## RMSEA index = 0.072 and the 90 % confidence intervals are 0.065 0.08
## BIC = -3.85
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2
## Correlation of (regression) scores with factors 0.95 0.91
## Multiple R square of scores with factors 0.91 0.83
## Minimum correlation of possible factor scores 0.81 0.66–> other factors than in Clayton & Karazsia, 2020 here: F1 = cas01:cas04, cas07, cas09, cas11, cas12:cas13, cas06; F2 = cas08, cas05, cas10 Clayton & Karazsia, 2020: F1 (cognitive-emotional impairment) = cas01:cas08; F2 (functional impairment) = cas09:cas13
7.8.5 Visualizing results
fa.diagram(fa.pa.promax.cas, simple=TRUE, cut=.4, digits=2)
fa.diagram(fa.pa.promax.cas, simple=FALSE, cut=.4, digits=2)
7.9 t-Test gender
Load relevant package
library(lsr)
library(gplots)## Registered S3 method overwritten by 'gplots':
## method from
## reorder.factor DescTools##
## Attaching package: 'gplots'## The following object is masked from 'package:HH':
##
## residplot## The following object is masked from 'package:stats':
##
## lowessAssumption check
by(data$cas, data$gender.d, describe)## data$gender.d: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 564 1.79 0.89 1.5 1.63 0.74 1 7 6 1.74 3.96 0.04
## ------------------------------------------------------------
## data$gender.d: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 570 1.93 0.93 1.67 1.8 0.86 1 7 6 1.52 3.56 0.04leveneTest(data$cas, data$gender.d, mean)## Warning in leveneTest.default(data$cas, data$gender.d, mean): data$gender.d
## coerced to factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 1 1.7875 0.1815
## 1132by(data$cas, data$gender.d, shapiro.test)## data$gender.d: 0
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.81738, p-value < 2.2e-16
##
## ------------------------------------------------------------
## data$gender.d: 1
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.86306, p-value < 2.2e-16t-Test and Cohen’s d
t.test(data$cas ~ data$gender.d)##
## Welch Two Sample t-test
##
## data: data$cas by data$gender.d
## t = -2.6698, df = 1131.1, p-value = 0.007699
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.25088879 -0.03833294
## sample estimates:
## mean in group 0 mean in group 1
## 1.789746 1.934357cohensD(cas ~ gender.d, data=data, method="pooled")## [1] 0.15853Plotting results
par(mfrow = c(1,2))
boxplot(data$cas ~ data$gender.d)
plotmeans(data$cas ~ data$gender.d)
Descriptive comparisons
describeBy(data$cas, data$gender.d, mat = TRUE)## item group1 vars n mean sd median trimmed mad min max
## X11 1 0 1 564 1.789746 0.8942831 1.500000 1.631453 0.74130 1 7
## X12 2 1 1 570 1.934357 0.9295857 1.666667 1.799342 0.86485 1 7
## range skew kurtosis se
## X11 6 1.742123 3.959978 0.03765611
## X12 6 1.519489 3.560711 0.038936067.10 Kruskal-Wallis test education
Descriptive comparisons
describeBy(data$cas, data$edu, mat = TRUE)## item group1 vars n mean sd median trimmed mad min
## X11 1 1 1 5 2.466667 1.4500958 1.833333 2.466667 0.864850 1.25
## X12 2 2 1 3 1.583333 0.8036376 1.250000 1.583333 0.370650 1.00
## X13 3 3 1 60 2.201389 1.4908140 1.708333 1.901042 1.050175 1.00
## X14 4 4 1 177 1.922316 0.9095624 1.666667 1.793706 0.864850 1.00
## X15 5 5 1 263 1.813371 0.8093873 1.583333 1.697077 0.864850 1.00
## X16 6 6 1 286 1.834790 0.8868889 1.583333 1.689493 0.741300 1.00
## X17 7 7 1 338 1.818047 0.8626952 1.583333 1.677390 0.741300 1.00
## X18 8 8 1 2 3.208333 0.2946278 3.208333 3.208333 0.308875 3.00
## max range skew kurtosis se
## X11 4.416667 3.1666667 3.247542e-01 -2.0701617 0.64850255
## X12 2.500000 1.5000000 3.434206e-01 -2.3333333 0.46398036
## X13 7.000000 6.0000000 1.937480e+00 3.4853853 0.19246326
## X14 6.083333 5.0833333 1.274556e+00 1.8662234 0.06836690
## X15 4.416667 3.4166667 1.067979e+00 0.5898619 0.04990896
## X16 5.500000 4.5000000 1.503281e+00 2.4108649 0.05244284
## X17 5.416667 4.4166667 1.345956e+00 1.5124749 0.04692443
## X18 3.416667 0.4166667 1.000000e-15 -2.7500000 0.20833333Kruskal-Wallis test
kruskal.test(cas ~ edu, data = data)##
## Kruskal-Wallis rank sum test
##
## data: cas by edu
## Kruskal-Wallis chi-squared = 8.6797, df = 7, p-value = 0.27658 Hypotheses
8.1 H1: Climate anxiety correlates positively with depressiveness and anxiety
8.1.1 depressiveness and anxiety and climate anxiety
corr.test (data [,c("phq", "phq.d", "phq.a",
"cas")],
method = "spearman")## Call:corr.test(x = data[, c("phq", "phq.d", "phq.a", "cas")], method = "spearman")
## Correlation matrix
## phq phq.d phq.a cas
## phq 1.00 0.94 0.92 0.25
## phq.d 0.94 1.00 0.74 0.22
## phq.a 0.92 0.74 1.00 0.25
## cas 0.25 0.22 0.25 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## phq phq.d phq.a cas
## phq 0 0 0 0
## phq.d 0 0 0 0
## phq.a 0 0 0 0
## cas 0 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals
data.ci <- cor.ci(data [,c("phq", "phq.d", "phq.a",
"cas")])
#to show the upper and lower confidence intervals
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form
ci ##
## High and low confidence intervals
## phq phq.d phq.a cas
## phq 1.00 0.95 0.95 0.32
## phq.d 0.93 1.00 0.81 0.29
## phq.a 0.93 0.75 1.00 0.31
## cas 0.18 0.16 0.17 1.008.1.2 Individual items
corr.test (data [,c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas06", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12",
"cas13",
"cas",
"phq01", "phq02", "phq03", "phq04",
"phq", "phq.d", "phq.a")],
method = "spearman")## Call:corr.test(x = data[, c("cas01", "cas02", "cas03", "cas04", "cas05",
## "cas06", "cas07", "cas08", "cas09", "cas10", "cas11", "cas12",
## "cas13", "cas", "phq01", "phq02", "phq03", "phq04", "phq",
## "phq.d", "phq.a")], method = "spearman")
## Correlation matrix
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.00 0.60 0.50 0.45 0.45 0.30 0.41 0.46 0.58 0.44 0.58 0.59
## cas02 0.60 1.00 0.60 0.53 0.43 0.38 0.41 0.40 0.61 0.44 0.58 0.56
## cas03 0.50 0.60 1.00 0.54 0.40 0.37 0.49 0.36 0.50 0.36 0.54 0.49
## cas04 0.45 0.53 0.54 1.00 0.34 0.38 0.47 0.28 0.48 0.35 0.47 0.46
## cas05 0.45 0.43 0.40 0.34 1.00 0.36 0.35 0.56 0.47 0.46 0.47 0.45
## cas06 0.30 0.38 0.37 0.38 0.36 1.00 0.35 0.28 0.41 0.30 0.33 0.34
## cas07 0.41 0.41 0.49 0.47 0.35 0.35 1.00 0.34 0.39 0.29 0.48 0.45
## cas08 0.46 0.40 0.36 0.28 0.56 0.28 0.34 1.00 0.46 0.42 0.45 0.44
## cas09 0.58 0.61 0.50 0.48 0.47 0.41 0.39 0.46 1.00 0.51 0.60 0.60
## cas10 0.44 0.44 0.36 0.35 0.46 0.30 0.29 0.42 0.51 1.00 0.45 0.45
## cas11 0.58 0.58 0.54 0.47 0.47 0.33 0.48 0.45 0.60 0.45 1.00 0.64
## cas12 0.59 0.56 0.49 0.46 0.45 0.34 0.45 0.44 0.60 0.45 0.64 1.00
## cas13 0.49 0.50 0.45 0.42 0.45 0.41 0.44 0.40 0.52 0.43 0.51 0.50
## cas 0.72 0.71 0.59 0.54 0.74 0.47 0.52 0.71 0.73 0.71 0.70 0.72
## phq01 0.16 0.15 0.12 0.10 0.10 0.04 0.01 0.13 0.17 0.20 0.12 0.13
## phq02 0.18 0.20 0.15 0.14 0.14 0.08 0.06 0.16 0.17 0.18 0.13 0.17
## phq03 0.17 0.21 0.19 0.15 0.11 0.13 0.04 0.15 0.18 0.19 0.13 0.14
## phq04 0.20 0.22 0.21 0.18 0.13 0.10 0.05 0.16 0.20 0.21 0.13 0.16
## phq 0.20 0.22 0.19 0.16 0.15 0.11 0.04 0.17 0.21 0.23 0.14 0.17
## phq.d 0.19 0.18 0.14 0.13 0.13 0.06 0.03 0.15 0.18 0.20 0.13 0.16
## phq.a 0.20 0.23 0.22 0.17 0.14 0.13 0.05 0.17 0.21 0.23 0.14 0.17
## cas13 cas phq01 phq02 phq03 phq04 phq phq.d phq.a
## cas01 0.49 0.72 0.16 0.18 0.17 0.20 0.20 0.19 0.20
## cas02 0.50 0.71 0.15 0.20 0.21 0.22 0.22 0.18 0.23
## cas03 0.45 0.59 0.12 0.15 0.19 0.21 0.19 0.14 0.22
## cas04 0.42 0.54 0.10 0.14 0.15 0.18 0.16 0.13 0.17
## cas05 0.45 0.74 0.10 0.14 0.11 0.13 0.15 0.13 0.14
## cas06 0.41 0.47 0.04 0.08 0.13 0.10 0.11 0.06 0.13
## cas07 0.44 0.52 0.01 0.06 0.04 0.05 0.04 0.03 0.05
## cas08 0.40 0.71 0.13 0.16 0.15 0.16 0.17 0.15 0.17
## cas09 0.52 0.73 0.17 0.17 0.18 0.20 0.21 0.18 0.21
## cas10 0.43 0.71 0.20 0.18 0.19 0.21 0.23 0.20 0.23
## cas11 0.51 0.70 0.12 0.13 0.13 0.13 0.14 0.13 0.14
## cas12 0.50 0.72 0.13 0.17 0.14 0.16 0.17 0.16 0.17
## cas13 1.00 0.66 0.09 0.09 0.07 0.11 0.10 0.09 0.09
## cas 0.66 1.00 0.19 0.22 0.21 0.24 0.25 0.22 0.25
## phq01 0.09 0.19 1.00 0.63 0.56 0.57 0.82 0.91 0.62
## phq02 0.09 0.22 0.63 1.00 0.64 0.68 0.86 0.88 0.73
## phq03 0.07 0.21 0.56 0.64 1.00 0.61 0.82 0.65 0.90
## phq04 0.11 0.24 0.57 0.68 0.61 1.00 0.82 0.68 0.87
## phq 0.10 0.25 0.82 0.86 0.82 0.82 1.00 0.94 0.92
## phq.d 0.09 0.22 0.91 0.88 0.65 0.68 0.94 1.00 0.74
## phq.a 0.09 0.25 0.62 0.73 0.90 0.87 0.92 0.74 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas02 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas03 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas04 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas05 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas06 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas07 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas08 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas09 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas10 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas11 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas12 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas13 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## cas 0 0 0 0 0 0.00 0.00 0 0 0 0 0
## phq01 0 0 0 0 0 0.17 0.66 0 0 0 0 0
## phq02 0 0 0 0 0 0.01 0.05 0 0 0 0 0
## phq03 0 0 0 0 0 0.00 0.18 0 0 0 0 0
## phq04 0 0 0 0 0 0.00 0.10 0 0 0 0 0
## phq 0 0 0 0 0 0.00 0.16 0 0 0 0 0
## phq.d 0 0 0 0 0 0.03 0.25 0 0 0 0 0
## phq.a 0 0 0 0 0 0.00 0.10 0 0 0 0 0
## cas13 cas phq01 phq02 phq03 phq04 phq phq.d phq.a
## cas01 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas03 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas04 0.00 0 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## cas05 0.00 0 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## cas06 0.00 0 0.81 0.06 0.00 0.01 0.01 0.31 0.00
## cas07 0.00 0 0.81 0.42 0.81 0.68 0.81 0.81 0.68
## cas08 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas09 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas10 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas11 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas12 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas13 0.00 0 0.05 0.04 0.22 0.01 0.02 0.04 0.03
## cas 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq01 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq02 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq03 0.02 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq04 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq.d 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## phq.a 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals
data.ci <- cor.ci(data [,c("cas01", "cas02", "cas03", "cas04",
"cas05", "cas06", "cas07", "cas08",
"cas09", "cas10", "cas11", "cas12",
"cas13",
"cas",
"phq01", "phq02", "phq03", "phq04",
"phq", "phq.d", "phq.a")])
#to show the upper and lower confidence intervals
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form
ci ##
## High and low confidence intervals
## cs01 cs02 cs03 cs04 cs05 cs06 cs07 cs08 cs09 cs10 cs11 cs12 cs13 cas
## cas01 1.00 0.67 0.56 0.54 0.47 0.32 0.47 0.48 0.63 0.45 0.66 0.65 0.51 0.78
## cas02 0.56 1.00 0.67 0.62 0.47 0.39 0.48 0.43 0.67 0.47 0.63 0.63 0.54 0.80
## cas03 0.41 0.54 1.00 0.67 0.46 0.39 0.61 0.42 0.59 0.40 0.62 0.54 0.52 0.76
## cas04 0.39 0.50 0.49 1.00 0.38 0.41 0.58 0.35 0.57 0.40 0.58 0.55 0.53 0.73
## cas05 0.34 0.35 0.34 0.26 1.00 0.40 0.40 0.60 0.50 0.49 0.51 0.48 0.50 0.72
## cas06 0.21 0.29 0.30 0.31 0.30 1.00 0.40 0.33 0.43 0.33 0.36 0.37 0.46 0.51
## cas07 0.31 0.33 0.44 0.39 0.28 0.29 1.00 0.41 0.48 0.30 0.58 0.53 0.50 0.68
## cas08 0.36 0.30 0.29 0.21 0.50 0.22 0.28 1.00 0.50 0.45 0.50 0.49 0.45 0.69
## cas09 0.52 0.56 0.46 0.43 0.39 0.34 0.32 0.39 1.00 0.52 0.66 0.64 0.58 0.81
## cas10 0.33 0.35 0.29 0.29 0.38 0.22 0.18 0.33 0.41 1.00 0.44 0.45 0.46 0.67
## cas11 0.54 0.51 0.46 0.42 0.41 0.26 0.41 0.39 0.54 0.34 1.00 0.69 0.55 0.81
## cas12 0.54 0.51 0.41 0.39 0.37 0.26 0.38 0.37 0.54 0.34 0.59 1.00 0.53 0.79
## cas13 0.38 0.41 0.38 0.38 0.40 0.36 0.35 0.32 0.45 0.35 0.42 0.41 1.00 0.74
## cas 0.71 0.73 0.66 0.61 0.66 0.43 0.56 0.63 0.75 0.60 0.74 0.72 0.66 1.00
## phq01 0.10 0.09 0.06 0.04 0.05 -0.01 -0.04 0.09 0.11 0.18 0.06 0.07 0.04 0.14
## phq02 0.12 0.13 0.09 0.06 0.08 0.01 -0.01 0.10 0.13 0.16 0.07 0.10 0.05 0.16
## phq03 0.10 0.14 0.12 0.09 0.04 0.05 -0.03 0.09 0.12 0.15 0.06 0.07 0.02 0.14
## phq04 0.13 0.15 0.13 0.12 0.06 0.04 0.00 0.10 0.14 0.18 0.07 0.08 0.05 0.17
## phq 0.14 0.16 0.12 0.10 0.08 0.04 -0.02 0.12 0.16 0.20 0.08 0.10 0.06 0.18
## phq.d 0.13 0.12 0.08 0.06 0.08 0.01 -0.02 0.11 0.14 0.19 0.08 0.10 0.06 0.16
## phq.a 0.13 0.16 0.14 0.12 0.06 0.05 -0.01 0.11 0.15 0.19 0.07 0.09 0.04 0.18
## ph01 ph02 ph03 ph04 phq phq.d phq.a
## cas01 0.22 0.23 0.23 0.24 0.26 0.24 0.25
## cas02 0.22 0.25 0.28 0.27 0.28 0.25 0.30
## cas03 0.20 0.22 0.25 0.26 0.26 0.22 0.27
## cas04 0.18 0.19 0.23 0.25 0.23 0.19 0.25
## cas05 0.16 0.20 0.17 0.18 0.19 0.19 0.19
## cas06 0.10 0.12 0.16 0.13 0.14 0.11 0.15
## cas07 0.10 0.12 0.10 0.12 0.12 0.11 0.12
## cas08 0.21 0.22 0.22 0.23 0.24 0.23 0.24
## cas09 0.24 0.25 0.25 0.25 0.28 0.26 0.27
## cas10 0.29 0.27 0.27 0.29 0.31 0.30 0.30
## cas11 0.18 0.19 0.18 0.19 0.21 0.20 0.20
## cas12 0.20 0.23 0.20 0.21 0.23 0.23 0.22
## cas13 0.18 0.17 0.15 0.18 0.19 0.19 0.17
## cas 0.26 0.28 0.27 0.29 0.31 0.29 0.30
## phq01 1.00 0.73 0.65 0.66 0.86 0.93 0.71
## phq02 0.65 1.00 0.73 0.76 0.91 0.93 0.80
## phq03 0.54 0.65 1.00 0.71 0.88 0.74 0.93
## phq04 0.57 0.69 0.62 1.00 0.89 0.76 0.92
## phq 0.81 0.88 0.83 0.85 1.00 0.95 0.95
## phq.d 0.91 0.91 0.66 0.69 0.94 1.00 0.81
## phq.a 0.62 0.75 0.90 0.90 0.93 0.75 1.008.2 H2: Climate anxiety correlates negatively with climate denial
8.2.1 Correlations
corr.test (data [,c("sp", "sp.1", "sp.2", "sp.3", "sp.4", "sp.5", "sp.6",
"cas")],
method = "spearman")## Call:corr.test(x = data[, c("sp", "sp.1", "sp.2", "sp.3", "sp.4",
## "sp.5", "sp.6", "cas")], method = "spearman")
## Correlation matrix
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 cas
## sp 1.00 0.87 0.67 0.79 0.85 0.64 0.81 -0.09
## sp.1 0.87 1.00 0.46 0.61 0.65 0.55 0.61 -0.13
## sp.2 0.67 0.46 1.00 0.47 0.44 0.12 0.39 0.20
## sp.3 0.79 0.61 0.47 1.00 0.73 0.46 0.65 -0.01
## sp.4 0.85 0.65 0.44 0.73 1.00 0.57 0.83 -0.12
## sp.5 0.64 0.55 0.12 0.46 0.57 1.00 0.53 -0.37
## sp.6 0.81 0.61 0.39 0.65 0.83 0.53 1.00 -0.09
## cas -0.09 -0.13 0.20 -0.01 -0.12 -0.37 -0.09 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 cas
## sp 0 0 0 0.00 0 0 0 0.01
## sp.1 0 0 0 0.00 0 0 0 0.00
## sp.2 0 0 0 0.00 0 0 0 0.00
## sp.3 0 0 0 0.00 0 0 0 0.75
## sp.4 0 0 0 0.00 0 0 0 0.00
## sp.5 0 0 0 0.00 0 0 0 0.00
## sp.6 0 0 0 0.00 0 0 0 0.01
## cas 0 0 0 0.75 0 0 0 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("sp", "sp.1", "sp.2", "sp.3", "sp.4", "sp.5", "sp.6",
"cas")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 cas
## sp 1.00 0.89 0.68 0.83 0.89 0.70 0.86 -0.09
## sp.1 0.87 1.00 0.49 0.66 0.73 0.63 0.71 -0.14
## sp.2 0.61 0.39 1.00 0.50 0.45 0.17 0.40 0.30
## sp.3 0.78 0.59 0.39 1.00 0.77 0.51 0.71 0.14
## sp.4 0.86 0.66 0.33 0.69 1.00 0.66 0.89 -0.15
## sp.5 0.64 0.56 0.05 0.43 0.58 1.00 0.62 -0.36
## sp.6 0.82 0.64 0.29 0.62 0.85 0.54 1.00 -0.12
## cas 0.06 0.00 0.18 -0.02 -0.01 -0.27 0.03 1.008.2.2 Correlations - individual items
corr.test (data [,c("sp", "sp.1", "sp.2", "sp.3", "sp.4", "sp.5", "sp.6",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")],
method = "spearman")## Call:corr.test(x = data[, c("sp", "sp.1", "sp.2", "sp.3", "sp.4",
## "sp.5", "sp.6", "cas01", "cas02", "cas03", "cas04", "cas05",
## "cas06", "cas07", "cas08", "cas09", "cas10", "cas11", "cas12",
## "cas13")], method = "spearman")
## Correlation matrix
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 cas01 cas02 cas03 cas04 cas05
## sp 1.00 0.87 0.67 0.79 0.85 0.64 0.81 0.06 -0.04 -0.05 -0.05 -0.09
## sp.1 0.87 1.00 0.46 0.61 0.65 0.55 0.61 -0.02 -0.08 -0.06 -0.09 -0.11
## sp.2 0.67 0.46 1.00 0.47 0.44 0.12 0.39 0.27 0.16 0.06 0.07 0.16
## sp.3 0.79 0.61 0.47 1.00 0.73 0.46 0.65 0.07 -0.01 0.02 0.02 -0.01
## sp.4 0.85 0.65 0.44 0.73 1.00 0.57 0.83 0.02 -0.07 -0.08 -0.04 -0.10
## sp.5 0.64 0.55 0.12 0.46 0.57 1.00 0.53 -0.18 -0.22 -0.19 -0.16 -0.36
## sp.6 0.81 0.61 0.39 0.65 0.83 0.53 1.00 0.03 -0.03 -0.03 -0.01 -0.10
## cas01 0.06 -0.02 0.27 0.07 0.02 -0.18 0.03 1.00 0.60 0.50 0.45 0.45
## cas02 -0.04 -0.08 0.16 -0.01 -0.07 -0.22 -0.03 0.60 1.00 0.60 0.53 0.43
## cas03 -0.05 -0.06 0.06 0.02 -0.08 -0.19 -0.03 0.50 0.60 1.00 0.54 0.40
## cas04 -0.05 -0.09 0.07 0.02 -0.04 -0.16 -0.01 0.45 0.53 0.54 1.00 0.34
## cas05 -0.09 -0.11 0.16 -0.01 -0.10 -0.36 -0.10 0.45 0.43 0.40 0.34 1.00
## cas06 -0.29 -0.26 -0.15 -0.19 -0.24 -0.28 -0.23 0.30 0.38 0.37 0.38 0.36
## cas07 -0.02 -0.04 0.02 0.08 0.02 -0.10 0.03 0.41 0.41 0.49 0.47 0.35
## cas08 0.02 -0.01 0.21 0.06 -0.01 -0.23 0.00 0.46 0.40 0.36 0.28 0.56
## cas09 -0.06 -0.10 0.14 0.01 -0.09 -0.24 -0.04 0.58 0.61 0.50 0.48 0.47
## cas10 -0.10 -0.11 0.14 -0.01 -0.11 -0.36 -0.10 0.44 0.44 0.36 0.35 0.46
## cas11 0.01 -0.04 0.18 0.07 0.00 -0.19 0.03 0.58 0.58 0.54 0.47 0.47
## cas12 0.04 -0.02 0.19 0.11 0.02 -0.17 0.05 0.59 0.56 0.49 0.46 0.45
## cas13 -0.04 -0.07 0.08 0.03 -0.07 -0.17 -0.02 0.49 0.50 0.45 0.42 0.45
## cas06 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## sp -0.29 -0.02 0.02 -0.06 -0.10 0.01 0.04 -0.04
## sp.1 -0.26 -0.04 -0.01 -0.10 -0.11 -0.04 -0.02 -0.07
## sp.2 -0.15 0.02 0.21 0.14 0.14 0.18 0.19 0.08
## sp.3 -0.19 0.08 0.06 0.01 -0.01 0.07 0.11 0.03
## sp.4 -0.24 0.02 -0.01 -0.09 -0.11 0.00 0.02 -0.07
## sp.5 -0.28 -0.10 -0.23 -0.24 -0.36 -0.19 -0.17 -0.17
## sp.6 -0.23 0.03 0.00 -0.04 -0.10 0.03 0.05 -0.02
## cas01 0.30 0.41 0.46 0.58 0.44 0.58 0.59 0.49
## cas02 0.38 0.41 0.40 0.61 0.44 0.58 0.56 0.50
## cas03 0.37 0.49 0.36 0.50 0.36 0.54 0.49 0.45
## cas04 0.38 0.47 0.28 0.48 0.35 0.47 0.46 0.42
## cas05 0.36 0.35 0.56 0.47 0.46 0.47 0.45 0.45
## cas06 1.00 0.35 0.28 0.41 0.30 0.33 0.34 0.41
## cas07 0.35 1.00 0.34 0.39 0.29 0.48 0.45 0.44
## cas08 0.28 0.34 1.00 0.46 0.42 0.45 0.44 0.40
## cas09 0.41 0.39 0.46 1.00 0.51 0.60 0.60 0.52
## cas10 0.30 0.29 0.42 0.51 1.00 0.45 0.45 0.43
## cas11 0.33 0.48 0.45 0.60 0.45 1.00 0.64 0.51
## cas12 0.34 0.45 0.44 0.60 0.45 0.64 1.00 0.50
## cas13 0.41 0.44 0.40 0.52 0.43 0.51 0.50 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 cas01 cas02 cas03 cas04 cas05 cas06
## sp 0.00 0.00 0.00 0.00 0.00 0 0.00 1 1.0 1.00 1.0 0.09 0
## sp.1 0.00 0.00 0.00 0.00 0.00 0 0.00 1 0.4 1.00 0.1 0.01 0
## sp.2 0.00 0.00 0.00 0.00 0.00 0 0.00 0 0.0 1.00 1.0 0.00 0
## sp.3 0.00 0.00 0.00 0.00 0.00 0 0.00 1 1.0 1.00 1.0 1.00 0
## sp.4 0.00 0.00 0.00 0.00 0.00 0 0.00 1 1.0 0.56 1.0 0.03 0
## sp.5 0.00 0.00 0.00 0.00 0.00 0 0.00 0 0.0 0.00 0.0 0.00 0
## sp.6 0.00 0.00 0.00 0.00 0.00 0 0.00 1 1.0 1.00 1.0 0.07 0
## cas01 0.06 0.50 0.00 0.03 0.50 0 0.37 0 0.0 0.00 0.0 0.00 0
## cas02 0.18 0.01 0.00 0.67 0.02 0 0.34 0 0.0 0.00 0.0 0.00 0
## cas03 0.07 0.05 0.04 0.48 0.01 0 0.33 0 0.0 0.00 0.0 0.00 0
## cas04 0.08 0.00 0.03 0.42 0.14 0 0.74 0 0.0 0.00 0.0 0.00 0
## cas05 0.00 0.00 0.00 0.71 0.00 0 0.00 0 0.0 0.00 0.0 0.00 0
## cas06 0.00 0.00 0.00 0.00 0.00 0 0.00 0 0.0 0.00 0.0 0.00 0
## cas07 0.57 0.13 0.47 0.01 0.55 0 0.27 0 0.0 0.00 0.0 0.00 0
## cas08 0.44 0.80 0.00 0.06 0.83 0 0.90 0 0.0 0.00 0.0 0.00 0
## cas09 0.05 0.00 0.00 0.68 0.00 0 0.16 0 0.0 0.00 0.0 0.00 0
## cas10 0.00 0.00 0.00 0.77 0.00 0 0.00 0 0.0 0.00 0.0 0.00 0
## cas11 0.68 0.15 0.00 0.02 0.99 0 0.39 0 0.0 0.00 0.0 0.00 0
## cas12 0.21 0.40 0.00 0.00 0.45 0 0.06 0 0.0 0.00 0.0 0.00 0
## cas13 0.14 0.02 0.01 0.29 0.02 0 0.44 0 0.0 0.00 0.0 0.00 0
## cas07 cas08 cas09 cas10 cas11 cas12 cas13
## sp 1.00 1 1.00 0.03 1.00 1.00 1.00
## sp.1 1.00 1 0.07 0.01 1.00 1.00 1.00
## sp.2 1.00 0 0.00 0.00 0.00 0.00 0.41
## sp.3 0.32 1 1.00 1.00 0.86 0.01 1.00
## sp.4 1.00 1 0.18 0.01 1.00 1.00 1.00
## sp.5 0.07 0 0.00 0.00 0.00 0.00 0.00
## sp.6 1.00 1 1.00 0.07 1.00 1.00 1.00
## cas01 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas03 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas04 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas05 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas06 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas07 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas08 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas09 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas10 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas11 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas12 0.00 0 0.00 0.00 0.00 0.00 0.00
## cas13 0.00 0 0.00 0.00 0.00 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("sp", "sp.1", "sp.2", "sp.3", "sp.4", "sp.5", "sp.6",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## sp sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 cs01 cs02 cs03 cs04 cs05
## sp 1.00 0.89 0.69 0.83 0.89 0.70 0.86 0.14 0.06 -0.09 -0.10 -0.16
## sp.1 0.87 1.00 0.50 0.67 0.73 0.63 0.72 0.08 -0.11 -0.09 -0.14 -0.18
## sp.2 0.60 0.39 1.00 0.51 0.45 0.17 0.40 0.36 0.25 0.17 0.16 0.24
## sp.3 0.78 0.59 0.38 1.00 0.77 0.51 0.71 0.16 0.09 0.16 0.13 -0.07
## sp.4 0.86 0.67 0.33 0.70 1.00 0.66 0.89 0.08 -0.12 -0.11 -0.10 -0.19
## sp.5 0.62 0.55 0.03 0.41 0.57 1.00 0.63 -0.22 -0.24 -0.23 -0.19 -0.41
## sp.6 0.82 0.64 0.29 0.62 0.85 0.54 1.00 0.08 -0.09 -0.07 -0.07 -0.17
## cas01 0.03 -0.05 0.25 0.01 -0.05 -0.11 -0.04 1.00 0.68 0.56 0.54 0.47
## cas02 -0.07 0.03 0.12 -0.06 -0.01 -0.13 0.02 0.56 1.00 0.68 0.62 0.49
## cas03 0.07 0.04 0.02 -0.03 0.03 -0.14 0.07 0.43 0.54 1.00 0.66 0.47
## cas04 0.05 0.00 0.01 -0.03 0.03 -0.11 0.06 0.41 0.50 0.52 1.00 0.39
## cas05 -0.03 -0.05 0.10 0.07 -0.08 -0.31 -0.06 0.35 0.35 0.35 0.26 1.00
## cas06 -0.21 -0.19 -0.08 -0.10 -0.19 -0.21 -0.15 0.21 0.29 0.30 0.31 0.29
## cas07 -0.02 -0.05 0.00 0.06 -0.01 -0.05 0.00 0.32 0.32 0.44 0.40 0.28
## cas08 -0.04 0.05 0.17 -0.01 0.01 -0.20 0.03 0.36 0.30 0.30 0.22 0.50
## cas09 0.05 0.01 0.13 -0.04 -0.03 -0.18 0.01 0.52 0.55 0.46 0.45 0.39
## cas10 -0.02 -0.05 0.09 0.06 -0.08 -0.30 -0.06 0.33 0.34 0.28 0.28 0.37
## cas11 -0.02 0.04 0.16 0.03 0.06 -0.15 -0.03 0.55 0.51 0.48 0.44 0.41
## cas12 0.00 -0.06 0.16 0.05 -0.06 -0.11 -0.03 0.54 0.50 0.41 0.40 0.38
## cas13 0.02 0.00 0.01 -0.03 -0.02 -0.14 0.02 0.39 0.41 0.39 0.39 0.39
## cs06 cs07 cs08 cs09 cs10 cs11 cs12 cs13
## sp -0.33 0.13 0.08 -0.08 -0.16 0.11 0.14 -0.10
## sp.1 -0.31 0.08 -0.07 -0.13 -0.17 -0.08 0.07 -0.12
## sp.2 -0.21 0.15 0.29 0.26 0.22 0.28 0.29 0.15
## sp.3 -0.23 0.22 0.12 0.11 -0.08 0.19 0.20 0.11
## sp.4 -0.29 0.11 -0.10 -0.15 -0.20 -0.06 0.08 -0.13
## sp.5 -0.32 -0.14 -0.30 -0.27 -0.40 -0.24 -0.22 -0.24
## sp.6 -0.26 0.14 -0.08 -0.11 -0.18 0.09 0.11 -0.09
## cas01 0.32 0.47 0.49 0.64 0.46 0.66 0.65 0.51
## cas02 0.40 0.49 0.43 0.68 0.48 0.64 0.63 0.55
## cas03 0.40 0.60 0.42 0.59 0.41 0.62 0.54 0.53
## cas04 0.41 0.57 0.35 0.57 0.41 0.56 0.54 0.52
## cas05 0.40 0.42 0.61 0.51 0.49 0.52 0.48 0.51
## cas06 1.00 0.40 0.34 0.45 0.33 0.37 0.37 0.46
## cas07 0.29 1.00 0.42 0.47 0.30 0.58 0.53 0.49
## cas08 0.21 0.27 1.00 0.50 0.45 0.50 0.49 0.45
## cas09 0.33 0.32 0.38 1.00 0.53 0.66 0.65 0.58
## cas10 0.21 0.17 0.34 0.40 1.00 0.45 0.46 0.46
## cas11 0.26 0.41 0.39 0.54 0.34 1.00 0.70 0.56
## cas12 0.25 0.38 0.38 0.53 0.33 0.57 1.00 0.53
## cas13 0.36 0.35 0.32 0.45 0.34 0.43 0.41 1.008.2.3 Scatterplot self-protection overall
plot(data$sp, data$cas,
main = "CAS and Self-Protection Scatterplot",
xlab = "Self-Protection overall",
ylab = "CAS")
abline(lm(data$cas ~ data$sp), col = "red") #regression line
lines(lowess(data$sp, data$cas), col = "blue") #smooth fitting line
8.2.4 Scatterplot rationalization
plot(data$sp.1, data$cas,
main = "CAS and Rationalization Scatterplot",
xlab = "Rationalization",
ylab = "CAS")
abline(lm(data$cas ~ data$sp.1), col = "red") #regression line
lines(lowess(data$sp.1, data$cas), col = "blue") #smooth fitting line
8.2.5 Scatterplot avoidance
plot(data$sp.2, data$cas,
main = "CAS and Avoidance Scatterplot",
xlab = "Avoidance",
ylab = "CAS")
abline(lm(data$cas ~ data$sp.2), col = "red") #regression line
lines(lowess(data$sp.2, data$cas), col = "blue") #smooth fitting line
8.2.6 Scatterplot denial personal outcome severity
plot(data$sp.3, data$cas,
main = "CAS and Denial of personal outcome severity",
xlab = "Denial of personal outcome severity",
ylab = "CAS")
abline(lm(data$cas ~ data$sp.3), col = "red") #regression line
lines(lowess(data$sp.3, data$cas), col = "blue") #smooth fitting line
8.2.7 Scatterplot denial global outcome severity
plot(data$sp.4, data$cas,
main = "CAS and Denial of global outcome severity",
xlab = "Denial of global outcome severity",
ylab = "CAS")
abline(lm(data$cas ~ data$sp.4), col = "red") #regression line
lines(lowess(data$sp.4, data$cas), col = "blue") #smooth fitting line
8.2.8 Scatterplot denial of guilt
plot(data$sp.5, data$cas,
main = "CAS and Denial of guilt",
xlab = "Denial of guilt",
ylab = "CAS")
abline(lm(data$cas ~ data$sp.5), col = "red") #regression line
lines(lowess(data$sp.5, data$cas), col = "blue") #smooth fitting line
8.2.9 Scatterplot literal climate denial
plot(data$sp.6, data$cas,
main = "CAS and literal climate denial",
xlab = "Literal climate denial",
ylab = "CAS")
abline(lm(data$cas ~ data$sp.6), col = "red") #regression line
lines(lowess(data$sp.6, data$cas), col = "blue") #smooth fitting line
8.3 H3: Climate anxiety correlates negatively with ideological beliefs
8.3.1 Correlations
corr.test (data [,c("sj", "sdo", "nd", "rwa", "pol.orient",
"cas")],
method = "spearman")## Call:corr.test(x = data[, c("sj", "sdo", "nd", "rwa", "pol.orient",
## "cas")], method = "spearman")
## Correlation matrix
## sj sdo nd rwa pol.orient cas
## sj 1.00 0.06 0.09 -0.17 -0.05 0.06
## sdo 0.06 1.00 0.37 0.45 0.40 0.01
## nd 0.09 0.37 1.00 0.30 0.23 0.10
## rwa -0.17 0.45 0.30 1.00 0.44 0.01
## pol.orient -0.05 0.40 0.23 0.44 1.00 -0.12
## cas 0.06 0.01 0.10 0.01 -0.12 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## sj sdo nd rwa pol.orient cas
## sj 0.00 0.15 0.01 0.0 0.24 0.15
## sdo 0.03 0.00 0.00 0.0 0.00 1.00
## nd 0.00 0.00 0.00 0.0 0.00 0.01
## rwa 0.00 0.00 0.00 0.0 0.00 1.00
## pol.orient 0.08 0.00 0.00 0.0 0.00 0.00
## cas 0.03 0.76 0.00 0.7 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("sj", "sdo", "nd", "rwa", "pol.orient",
"cas")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## sj sdo nd rwa pl.r cas
## sj 1.00 0.10 0.14 -0.22 -0.11 0.13
## sdo -0.03 1.00 0.40 0.53 0.46 0.09
## nd 0.02 0.29 1.00 0.37 0.30 0.17
## rwa -0.10 0.42 0.25 1.00 0.49 0.09
## pol.orient 0.02 0.34 0.18 0.41 1.00 -0.12
## cas 0.02 -0.04 0.04 -0.01 0.01 1.008.3.2 Correlations - individual items
corr.test (data [,c("sj", "sdo", "nd", "rwa", "pol.orient",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")],
method = "spearman")## Call:corr.test(x = data[, c("sj", "sdo", "nd", "rwa", "pol.orient",
## "cas01", "cas02", "cas03", "cas04", "cas05", "cas06", "cas07",
## "cas08", "cas09", "cas10", "cas11", "cas12", "cas13")], method = "spearman")
## Correlation matrix
## sj sdo nd rwa pol.orient cas01 cas02 cas03 cas04 cas05
## sj 1.00 0.06 0.09 -0.17 -0.05 0.02 0.08 0.05 0.03 0.09
## sdo 0.06 1.00 0.37 0.45 0.40 0.08 0.06 0.02 0.04 0.00
## nd 0.09 0.37 1.00 0.30 0.23 0.14 0.07 0.07 0.04 0.07
## rwa -0.17 0.45 0.30 1.00 0.44 0.06 0.01 -0.01 0.01 0.02
## pol.orient -0.05 0.40 0.23 0.44 1.00 -0.09 -0.07 -0.09 -0.08 -0.08
## cas01 0.02 0.08 0.14 0.06 -0.09 1.00 0.60 0.50 0.45 0.45
## cas02 0.08 0.06 0.07 0.01 -0.07 0.60 1.00 0.60 0.53 0.43
## cas03 0.05 0.02 0.07 -0.01 -0.09 0.50 0.60 1.00 0.54 0.40
## cas04 0.03 0.04 0.04 0.01 -0.08 0.45 0.53 0.54 1.00 0.34
## cas05 0.09 0.00 0.07 0.02 -0.08 0.45 0.43 0.40 0.34 1.00
## cas06 0.06 -0.11 -0.09 -0.08 -0.14 0.30 0.38 0.37 0.38 0.36
## cas07 0.04 0.04 0.12 0.07 -0.07 0.41 0.41 0.49 0.47 0.35
## cas08 0.02 0.00 0.13 0.08 -0.03 0.46 0.40 0.36 0.28 0.56
## cas09 0.06 0.04 0.10 0.01 -0.10 0.58 0.61 0.50 0.48 0.47
## cas10 0.07 0.00 0.05 -0.11 -0.09 0.44 0.44 0.36 0.35 0.46
## cas11 0.04 0.07 0.13 0.07 -0.07 0.58 0.58 0.54 0.47 0.47
## cas12 0.06 0.09 0.12 0.09 -0.05 0.59 0.56 0.49 0.46 0.45
## cas13 0.06 0.03 0.10 0.03 -0.08 0.49 0.50 0.45 0.42 0.45
## cas06 cas07 cas08 cas09 cas10 cas11 cas12 cas13
## sj 0.06 0.04 0.02 0.06 0.07 0.04 0.06 0.06
## sdo -0.11 0.04 0.00 0.04 0.00 0.07 0.09 0.03
## nd -0.09 0.12 0.13 0.10 0.05 0.13 0.12 0.10
## rwa -0.08 0.07 0.08 0.01 -0.11 0.07 0.09 0.03
## pol.orient -0.14 -0.07 -0.03 -0.10 -0.09 -0.07 -0.05 -0.08
## cas01 0.30 0.41 0.46 0.58 0.44 0.58 0.59 0.49
## cas02 0.38 0.41 0.40 0.61 0.44 0.58 0.56 0.50
## cas03 0.37 0.49 0.36 0.50 0.36 0.54 0.49 0.45
## cas04 0.38 0.47 0.28 0.48 0.35 0.47 0.46 0.42
## cas05 0.36 0.35 0.56 0.47 0.46 0.47 0.45 0.45
## cas06 1.00 0.35 0.28 0.41 0.30 0.33 0.34 0.41
## cas07 0.35 1.00 0.34 0.39 0.29 0.48 0.45 0.44
## cas08 0.28 0.34 1.00 0.46 0.42 0.45 0.44 0.40
## cas09 0.41 0.39 0.46 1.00 0.51 0.60 0.60 0.52
## cas10 0.30 0.29 0.42 0.51 1.00 0.45 0.45 0.43
## cas11 0.33 0.48 0.45 0.60 0.45 1.00 0.64 0.51
## cas12 0.34 0.45 0.44 0.60 0.45 0.64 1.00 0.50
## cas13 0.41 0.44 0.40 0.52 0.43 0.51 0.50 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## sj sdo nd rwa pol.orient cas01 cas02 cas03 cas04 cas05 cas06
## sj 0.00 1.00 0.08 0.00 1.00 1.00 0.22 1.00 1.00 0.11 1.00
## sdo 0.03 0.00 0.00 0.00 0.00 0.27 1.00 1.00 1.00 1.00 0.01
## nd 0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.77 1.00 0.76 0.08
## rwa 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 0.29
## pol.orient 0.08 0.00 0.00 0.00 0.00 0.20 0.83 0.16 0.42 0.21 0.00
## cas01 0.43 0.01 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0.06 0.02 0.74 0.02 0.00 0.00 0.00 0.00 0.00 0.00
## cas03 0.13 0.47 0.02 0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas04 0.28 0.21 0.19 0.68 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## cas05 0.00 0.98 0.02 0.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas06 0.05 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 0.19 0.15 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00
## cas08 0.58 0.88 0.00 0.01 0.33 0.00 0.00 0.00 0.00 0.00 0.00
## cas09 0.06 0.21 0.00 0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas10 0.02 0.88 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas11 0.13 0.02 0.00 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00
## cas12 0.05 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00
## cas13 0.04 0.33 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 cas08 cas09 cas10 cas11 cas12 cas13
## sj 1.00 1.00 1.00 0.76 1.00 1.00 1.00
## sdo 1.00 1.00 1.00 1.00 0.65 0.11 1.00
## nd 0.00 0.00 0.04 1.00 0.00 0.00 0.03
## rwa 0.71 0.49 1.00 0.02 0.92 0.15 1.00
## pol.orient 0.78 1.00 0.04 0.16 0.65 1.00 0.34
## cas01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas03 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas04 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas05 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas06 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas08 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas09 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas10 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("sj", "sdo", "nd", "rwa", "pol.orient",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## sj sdo nd rwa pl.r cs01 cs02 cs03 cs04 cs05 cs06
## sj 1.00 0.11 0.15 -0.22 -0.12 0.07 0.13 0.13 0.10 0.16 0.12
## sdo -0.04 1.00 0.41 0.53 0.46 0.13 0.11 0.08 0.09 -0.08 -0.18
## nd 0.02 0.28 1.00 0.36 0.30 0.19 0.13 0.11 0.10 0.10 -0.17
## rwa -0.09 0.43 0.25 1.00 0.50 0.13 0.10 -0.05 -0.08 0.08 -0.14
## pol.orient 0.01 0.35 0.18 0.41 1.00 -0.10 -0.09 -0.13 -0.12 -0.11 -0.20
## cas01 -0.04 0.00 0.07 0.01 0.01 1.00 0.67 0.56 0.55 0.46 0.32
## cas02 0.01 -0.02 0.01 -0.02 0.03 0.56 1.00 0.67 0.63 0.47 0.40
## cas03 0.00 -0.05 -0.01 0.05 0.01 0.41 0.54 1.00 0.66 0.46 0.40
## cas04 -0.01 -0.03 -0.03 0.05 0.01 0.40 0.50 0.50 1.00 0.38 0.41
## cas05 0.05 0.04 -0.02 -0.03 0.00 0.34 0.34 0.34 0.26 1.00 0.39
## cas06 -0.01 -0.06 -0.05 -0.04 -0.07 0.20 0.28 0.30 0.32 0.29 1.00
## cas07 0.01 0.00 0.07 0.04 0.06 0.31 0.32 0.44 0.40 0.28 0.29
## cas08 -0.03 0.03 0.03 0.03 0.06 0.36 0.30 0.30 0.21 0.50 0.21
## cas09 -0.01 -0.03 0.03 -0.04 -0.01 0.51 0.55 0.46 0.44 0.38 0.34
## cas10 0.00 0.03 -0.05 -0.06 0.00 0.33 0.35 0.28 0.29 0.36 0.22
## cas11 -0.01 0.00 0.06 0.02 0.04 0.54 0.51 0.47 0.42 0.40 0.25
## cas12 0.01 0.03 0.06 0.05 0.05 0.54 0.51 0.40 0.38 0.36 0.25
## cas13 0.01 -0.04 0.02 -0.02 0.02 0.37 0.40 0.37 0.38 0.38 0.36
## cs07 cs08 cs09 cs10 cs11 cs12 cs13
## sj 0.11 0.09 0.11 0.12 0.11 0.12 0.13
## sdo 0.11 -0.07 0.10 -0.09 0.13 0.15 0.07
## nd 0.18 0.17 0.15 0.07 0.18 0.18 0.13
## rwa 0.14 0.14 0.07 -0.18 0.12 0.15 0.07
## pol.orient -0.08 -0.05 -0.12 -0.11 -0.08 -0.06 -0.10
## cas01 0.47 0.47 0.64 0.45 0.66 0.65 0.52
## cas02 0.48 0.42 0.67 0.46 0.63 0.62 0.55
## cas03 0.60 0.41 0.59 0.41 0.61 0.54 0.53
## cas04 0.57 0.35 0.57 0.40 0.57 0.54 0.53
## cas05 0.40 0.59 0.50 0.48 0.51 0.48 0.50
## cas06 0.41 0.32 0.44 0.32 0.36 0.37 0.46
## cas07 1.00 0.41 0.47 0.30 0.56 0.53 0.49
## cas08 0.28 1.00 0.49 0.45 0.50 0.48 0.44
## cas09 0.32 0.38 1.00 0.52 0.66 0.65 0.59
## cas10 0.17 0.31 0.40 1.00 0.44 0.44 0.45
## cas11 0.42 0.38 0.54 0.34 1.00 0.70 0.56
## cas12 0.38 0.36 0.52 0.33 0.57 1.00 0.54
## cas13 0.36 0.32 0.45 0.35 0.42 0.39 1.008.3.3 Scatterplot system justification
plot(data$sj, data$cas,
main = "CAS and System Justification Scatterplot",
xlab = "System Justification",
ylab = "CAS")
abline(lm(data$cas ~ data$sj), col = "red") #regression line
lines(lowess(data$sj, data$cas), col = "blue") #smooth fitting line
8.3.4 Scatterplot social dominance orientation
plot(data$sdo, data$cas,
main = "CAS and Social Dominance Orientation Scatterplot",
xlab = "Social Dominance Orientation",
ylab = "CAS")
abline(lm(data$cas ~ data$sdo), col = "red") #regression line
lines(lowess(data$sdo, data$cas), col = "blue") #smooth fitting line
8.3.5 Scatterplot Human dominance over nature
plot(data$nd, data$cas,
main = "CAS and Human dominance over nature Scatterplot",
xlab = "Human dominance over nature",
ylab = "CAS")
abline(lm(data$cas ~ data$nd), col = "red") #regression line
lines(lowess(data$nd, data$cas), col = "blue") #smooth fitting line
8.3.6 Scatterplot right-wing authoritarianism
plot(data$rwa, data$cas,
main = "CAS and Right-Wing Authoritarianism Scatterplot",
xlab = "Right-Wing Authoritarianism",
ylab = "CAS")
abline(lm(data$cas ~ data$rwa), col = "red") #regression line
lines(lowess(data$rwa, data$cas), col = "blue") #smooth fitting line
8.3.7 Scatterplot political orientation
plot(data$pol.orient, data$cas,
main = "CAS and political orientation",
xlab = "Political orientation",
ylab = "CAS")
abline(lm(data$cas ~ data$pol.orient), col = "red") #regression line
lines(lowess(data$pol.orient, data$cas), col = "blue") #smooth fitting line
8.4 H4: Climate anxiety correlates positively with basic psychological need satisfaction
8.4.1 Correlations basic psychological needs and climate anxiety
corr.test (data [,c("gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af", "gb.cf",
"cas")],
method = "spearman")## Call:corr.test(x = data[, c("gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af",
## "gb.cf", "cas")], method = "spearman")
## Correlation matrix
## gb.rs gb.as gb.cs gb.rf gb.af gb.cf cas
## gb.rs 1.00 0.48 0.31 -0.38 -0.27 -0.32 -0.12
## gb.as 0.48 1.00 0.31 -0.33 -0.47 -0.39 -0.14
## gb.cs 0.31 0.31 1.00 -0.01 0.04 -0.10 0.02
## gb.rf -0.38 -0.33 -0.01 1.00 0.63 0.66 0.29
## gb.af -0.27 -0.47 0.04 0.63 1.00 0.62 0.21
## gb.cf -0.32 -0.39 -0.10 0.66 0.62 1.00 0.31
## cas -0.12 -0.14 0.02 0.29 0.21 0.31 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## gb.rs gb.as gb.cs gb.rf gb.af gb.cf cas
## gb.rs 0 0 0.00 0 0.00 0 0
## gb.as 0 0 0.00 0 0.00 0 0
## gb.cs 0 0 0.00 1 0.63 0 1
## gb.rf 0 0 0.63 0 0.00 0 0
## gb.af 0 0 0.21 0 0.00 0 0
## gb.cf 0 0 0.00 0 0.00 0 0
## cas 0 0 0.58 0 0.00 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af", "gb.cf",
"cas")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## gb.rs gb.s gb.cs gb.rf gb.f gb.cf cas
## gb.rs 1.00 0.52 0.37 -0.40 -0.30 -0.34 -0.12
## gb.as 0.41 1.00 0.38 -0.36 -0.51 -0.42 -0.17
## gb.cs 0.25 0.26 1.00 0.07 0.12 -0.17 0.12
## gb.rf -0.30 -0.25 -0.06 1.00 0.67 0.68 0.36
## gb.af -0.18 -0.41 -0.02 0.58 1.00 0.65 0.29
## gb.cf -0.23 -0.31 -0.04 0.60 0.57 1.00 0.37
## cas -0.01 -0.04 -0.01 0.24 0.18 0.25 1.008.4.2 Correlations basic psychological needs and climate anxiety - individual items
corr.test (data [,c("gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af", "gb.cf",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")],
method = "spearman")## Call:corr.test(x = data[, c("gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af",
## "gb.cf", "cas01", "cas02", "cas03", "cas04", "cas05", "cas06",
## "cas07", "cas08", "cas09", "cas10", "cas11", "cas12", "cas13")],
## method = "spearman")
## Correlation matrix
## gb.rs gb.as gb.cs gb.rf gb.af gb.cf cas01 cas02 cas03 cas04 cas05 cas06
## gb.rs 1.00 0.48 0.31 -0.38 -0.27 -0.32 -0.13 -0.11 -0.09 -0.07 -0.02 0.09
## gb.as 0.48 1.00 0.31 -0.33 -0.47 -0.39 -0.11 -0.09 -0.09 -0.04 -0.07 0.07
## gb.cs 0.31 0.31 1.00 -0.01 0.04 -0.10 0.00 -0.01 0.02 0.04 0.04 0.10
## gb.rf -0.38 -0.33 -0.01 1.00 0.63 0.66 0.23 0.23 0.22 0.21 0.16 0.12
## gb.af -0.27 -0.47 0.04 0.63 1.00 0.62 0.18 0.16 0.15 0.13 0.13 0.07
## gb.cf -0.32 -0.39 -0.10 0.66 0.62 1.00 0.25 0.22 0.23 0.20 0.20 0.10
## cas01 -0.13 -0.11 0.00 0.23 0.18 0.25 1.00 0.60 0.50 0.45 0.45 0.30
## cas02 -0.11 -0.09 -0.01 0.23 0.16 0.22 0.60 1.00 0.60 0.53 0.43 0.38
## cas03 -0.09 -0.09 0.02 0.22 0.15 0.23 0.50 0.60 1.00 0.54 0.40 0.37
## cas04 -0.07 -0.04 0.04 0.21 0.13 0.20 0.45 0.53 0.54 1.00 0.34 0.38
## cas05 -0.02 -0.07 0.04 0.16 0.13 0.20 0.45 0.43 0.40 0.34 1.00 0.36
## cas06 0.09 0.07 0.10 0.12 0.07 0.10 0.30 0.38 0.37 0.38 0.36 1.00
## cas07 -0.06 -0.01 0.09 0.14 0.08 0.12 0.41 0.41 0.49 0.47 0.35 0.35
## cas08 -0.09 -0.09 0.01 0.23 0.18 0.25 0.46 0.40 0.36 0.28 0.56 0.28
## cas09 -0.09 -0.10 0.02 0.25 0.19 0.26 0.58 0.61 0.50 0.48 0.47 0.41
## cas10 -0.13 -0.18 -0.04 0.23 0.19 0.26 0.44 0.44 0.36 0.35 0.46 0.30
## cas11 -0.14 -0.12 0.01 0.23 0.17 0.24 0.58 0.58 0.54 0.47 0.47 0.33
## cas12 -0.10 -0.14 0.03 0.24 0.20 0.25 0.59 0.56 0.49 0.46 0.45 0.34
## cas13 -0.09 -0.09 0.03 0.18 0.15 0.19 0.49 0.50 0.45 0.42 0.45 0.41
## cas07 cas08 cas09 cas10 cas11 cas12 cas13
## gb.rs -0.06 -0.09 -0.09 -0.13 -0.14 -0.10 -0.09
## gb.as -0.01 -0.09 -0.10 -0.18 -0.12 -0.14 -0.09
## gb.cs 0.09 0.01 0.02 -0.04 0.01 0.03 0.03
## gb.rf 0.14 0.23 0.25 0.23 0.23 0.24 0.18
## gb.af 0.08 0.18 0.19 0.19 0.17 0.20 0.15
## gb.cf 0.12 0.25 0.26 0.26 0.24 0.25 0.19
## cas01 0.41 0.46 0.58 0.44 0.58 0.59 0.49
## cas02 0.41 0.40 0.61 0.44 0.58 0.56 0.50
## cas03 0.49 0.36 0.50 0.36 0.54 0.49 0.45
## cas04 0.47 0.28 0.48 0.35 0.47 0.46 0.42
## cas05 0.35 0.56 0.47 0.46 0.47 0.45 0.45
## cas06 0.35 0.28 0.41 0.30 0.33 0.34 0.41
## cas07 1.00 0.34 0.39 0.29 0.48 0.45 0.44
## cas08 0.34 1.00 0.46 0.42 0.45 0.44 0.40
## cas09 0.39 0.46 1.00 0.51 0.60 0.60 0.52
## cas10 0.29 0.42 0.51 1.00 0.45 0.45 0.43
## cas11 0.48 0.45 0.60 0.45 1.00 0.64 0.51
## cas12 0.45 0.44 0.60 0.45 0.64 1.00 0.50
## cas13 0.44 0.40 0.52 0.43 0.51 0.50 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## gb.rs gb.as gb.cs gb.rf gb.af gb.cf cas01 cas02 cas03 cas04 cas05 cas06
## gb.rs 0.00 0.00 0.00 0 0.00 0.00 0.00 0.01 0.08 0.34 1.00 0.06
## gb.as 0.00 0.00 0.00 0 0.00 0.00 0.01 0.06 0.06 1.00 0.34 0.34
## gb.cs 0.00 0.00 0.00 1 1.00 0.02 1.00 1.00 1.00 1.00 1.00 0.04
## gb.rf 0.00 0.00 0.63 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## gb.af 0.00 0.00 0.21 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40
## gb.cf 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04
## cas01 0.00 0.00 0.91 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0.00 0.67 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas03 0.00 0.00 0.43 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas04 0.02 0.23 0.13 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas05 0.49 0.02 0.14 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas06 0.00 0.02 0.00 0 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 0.06 0.78 0.00 0 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas08 0.00 0.00 0.66 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas09 0.00 0.00 0.57 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas10 0.00 0.00 0.18 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas11 0.00 0.00 0.81 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas12 0.00 0.00 0.33 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas13 0.00 0.00 0.24 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 cas08 cas09 cas10 cas11 cas12 cas13
## gb.rs 1.00 0.09 0.05 0 0 0.02 0.06
## gb.as 1.00 0.06 0.02 0 0 0.00 0.06
## gb.cs 0.05 1.00 1.00 1 1 1.00 1.00
## gb.rf 0.00 0.00 0.00 0 0 0.00 0.00
## gb.af 0.13 0.00 0.00 0 0 0.00 0.00
## gb.cf 0.00 0.00 0.00 0 0 0.00 0.00
## cas01 0.00 0.00 0.00 0 0 0.00 0.00
## cas02 0.00 0.00 0.00 0 0 0.00 0.00
## cas03 0.00 0.00 0.00 0 0 0.00 0.00
## cas04 0.00 0.00 0.00 0 0 0.00 0.00
## cas05 0.00 0.00 0.00 0 0 0.00 0.00
## cas06 0.00 0.00 0.00 0 0 0.00 0.00
## cas07 0.00 0.00 0.00 0 0 0.00 0.00
## cas08 0.00 0.00 0.00 0 0 0.00 0.00
## cas09 0.00 0.00 0.00 0 0 0.00 0.00
## cas10 0.00 0.00 0.00 0 0 0.00 0.00
## cas11 0.00 0.00 0.00 0 0 0.00 0.00
## cas12 0.00 0.00 0.00 0 0 0.00 0.00
## cas13 0.00 0.00 0.00 0 0 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("gb.rs", "gb.as", "gb.cs", "gb.rf", "gb.af", "gb.cf",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## gb.rs gb.s gb.cs gb.rf gb.f gb.cf cs01 cs02 cs03 cs04 cs05 cs06
## gb.rs 1.00 0.51 0.37 -0.40 -0.29 -0.34 -0.14 -0.12 -0.09 -0.09 0.09 0.16
## gb.as 0.40 1.00 0.39 -0.36 -0.51 -0.42 -0.16 -0.16 -0.14 -0.08 -0.12 0.14
## gb.cs 0.24 0.26 1.00 0.07 0.12 -0.16 0.08 0.08 0.12 0.12 0.12 0.16
## gb.rf -0.29 -0.23 -0.05 1.00 0.66 0.68 0.28 0.30 0.28 0.27 0.22 0.18
## gb.af -0.18 -0.41 -0.01 0.58 1.00 0.65 0.24 0.25 0.23 0.21 0.19 0.13
## gb.cf -0.22 -0.31 -0.04 0.60 0.57 1.00 0.30 0.29 0.29 0.27 0.25 0.16
## cas01 -0.01 -0.01 -0.05 0.17 0.13 0.18 1.00 0.68 0.56 0.55 0.46 0.32
## cas02 0.00 -0.01 -0.05 0.16 0.11 0.15 0.56 1.00 0.67 0.62 0.47 0.39
## cas03 0.03 0.02 -0.01 0.16 0.10 0.17 0.43 0.54 1.00 0.66 0.47 0.40
## cas04 0.03 0.05 0.01 0.15 0.08 0.14 0.40 0.50 0.50 1.00 0.38 0.41
## cas05 -0.04 0.01 0.00 0.11 0.07 0.14 0.35 0.35 0.35 0.26 1.00 0.41
## cas06 0.03 0.01 0.04 0.06 0.00 0.04 0.20 0.29 0.31 0.31 0.29 1.00
## cas07 0.05 -0.03 0.06 0.09 0.05 0.08 0.32 0.33 0.45 0.41 0.28 0.29
## cas08 0.02 -0.02 -0.04 0.17 0.13 0.19 0.36 0.30 0.30 0.21 0.50 0.22
## cas09 0.00 -0.01 -0.04 0.21 0.16 0.21 0.52 0.56 0.46 0.45 0.39 0.33
## cas10 -0.03 -0.11 0.02 0.18 0.17 0.22 0.34 0.36 0.29 0.29 0.39 0.22
## cas11 -0.01 0.00 -0.03 0.17 0.11 0.18 0.55 0.51 0.48 0.42 0.41 0.26
## cas12 0.01 -0.03 0.00 0.16 0.14 0.18 0.54 0.50 0.41 0.40 0.37 0.25
## cas13 0.00 0.01 -0.01 0.12 0.08 0.12 0.39 0.42 0.39 0.38 0.39 0.36
## cs07 cs08 cs09 cs10 cs11 cs12 cs13
## gb.rs -0.07 -0.09 -0.13 -0.16 -0.14 -0.11 -0.11
## gb.as 0.10 -0.15 -0.15 -0.25 -0.14 -0.17 -0.14
## gb.cs 0.18 0.09 0.09 -0.11 0.10 0.11 0.10
## gb.rf 0.22 0.29 0.33 0.29 0.29 0.28 0.23
## gb.af 0.17 0.25 0.27 0.27 0.24 0.25 0.20
## gb.cf 0.22 0.30 0.32 0.34 0.30 0.30 0.24
## cas01 0.48 0.48 0.64 0.46 0.66 0.66 0.52
## cas02 0.49 0.43 0.67 0.47 0.63 0.63 0.54
## cas03 0.61 0.42 0.59 0.41 0.63 0.54 0.51
## cas04 0.57 0.35 0.57 0.41 0.58 0.54 0.52
## cas05 0.42 0.60 0.50 0.47 0.51 0.49 0.50
## cas06 0.40 0.33 0.44 0.33 0.37 0.37 0.46
## cas07 1.00 0.42 0.48 0.31 0.58 0.53 0.49
## cas08 0.28 1.00 0.50 0.45 0.51 0.49 0.44
## cas09 0.33 0.38 1.00 0.52 0.66 0.65 0.58
## cas10 0.18 0.33 0.42 1.00 0.45 0.45 0.46
## cas11 0.42 0.38 0.54 0.33 1.00 0.70 0.56
## cas12 0.39 0.37 0.53 0.34 0.58 1.00 0.53
## cas13 0.36 0.32 0.46 0.35 0.43 0.40 1.008.4.3 Scatterplot relatedness satisfaction
plot(data$gb.rs, data$cas,
main = "CAS and relatedness satisfaction",
xlab = "Relatedness satisfaction",
ylab = "CAS")
abline(lm(data$cas ~ data$gb.rs), col = "red") #regression line
lines(lowess(data$gb.rs, data$cas), col = "blue") #smooth fitting line
8.4.4 Scatterplot relatedness frustration
plot(data$gb.rf, data$cas,
main = "CAS and relatedness frustration",
xlab = "Relatedness frustration",
ylab = "CAS")
abline(lm(data$cas ~ data$gb.rf), col = "red") #regression line
lines(lowess(data$gb.rf, data$cas), col = "blue") #smooth fitting line
8.4.5 Scatterplot autonomy satisfaction
plot(data$gb.as, data$cas,
main = "CAS and autonomy satisfaction",
xlab = "Autonomy satisfaction",
ylab = "CAS")
abline(lm(data$cas ~ data$gb.as), col = "red") #regression line
lines(lowess(data$gb.as, data$cas), col = "blue") #smooth fitting line
8.4.6 Scatterplot autonomy frustration
plot(data$gb.af, data$cas,
main = "CAS and autonomy frustration",
xlab = "Autonomy frustration",
ylab = "CAS")
abline(lm(data$cas ~ data$gb.af), col = "red") #regression line
lines(lowess(data$gb.af, data$cas), col = "blue") #smooth fitting line
8.4.7 Scatterplot competence satisfaction
plot(data$gb.cs, data$cas,
main = "CAS and competence satisfaction",
xlab = "Competence satisfaction",
ylab = "CAS")
abline(lm(data$cas ~ data$gb.cs), col = "red") #regression line
lines(lowess(data$gb.cs, data$cas), col = "blue") #smooth fitting line
8.4.8 Scatterplot competence frustration
plot(data$gb.cf, data$cas,
main = "CAS and competence frustration",
xlab = "Competence frustration",
ylab = "CAS")
abline(lm(data$cas ~ data$gb.cf), col = "red") #regression line
lines(lowess(data$gb.cf, data$cas), col = "blue") #smooth fitting line
8.5 H5: Climate anxiety correlates negatively with extrinsic aspirations
8.5.1 Correlations
corr.test (data [,c("asimp.all", "aspro.all",
"cas")],
method = "spearman")## Call:corr.test(x = data[, c("asimp.all", "aspro.all", "cas")], method = "spearman")
## Correlation matrix
## asimp.all aspro.all cas
## asimp.all 1.00 0.61 0.18
## aspro.all 0.61 1.00 0.14
## cas 0.18 0.14 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## asimp.all aspro.all cas
## asimp.all 0 0 0
## aspro.all 0 0 0
## cas 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("asimp.all", "aspro.all",
"cas")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## asm. asp. cas
## asimp.all 1.00 0.65 0.29
## aspro.all 0.57 1.00 0.21
## cas 0.16 0.12 1.008.5.2 Correlations - individual items
corr.test (data [,c("asimp.all", "aspro.all",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")],
method = "spearman")## Call:corr.test(x = data[, c("asimp.all", "aspro.all", "cas01", "cas02",
## "cas03", "cas04", "cas05", "cas06", "cas07", "cas08", "cas09",
## "cas10", "cas11", "cas12", "cas13")], method = "spearman")
## Correlation matrix
## asimp.all aspro.all cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08
## asimp.all 1.00 0.61 0.20 0.14 0.15 0.13 0.14 -0.05 0.15 0.16
## aspro.all 0.61 1.00 0.13 0.11 0.15 0.08 0.07 -0.04 0.10 0.16
## cas01 0.20 0.13 1.00 0.60 0.50 0.45 0.45 0.30 0.41 0.46
## cas02 0.14 0.11 0.60 1.00 0.60 0.53 0.43 0.38 0.41 0.40
## cas03 0.15 0.15 0.50 0.60 1.00 0.54 0.40 0.37 0.49 0.36
## cas04 0.13 0.08 0.45 0.53 0.54 1.00 0.34 0.38 0.47 0.28
## cas05 0.14 0.07 0.45 0.43 0.40 0.34 1.00 0.36 0.35 0.56
## cas06 -0.05 -0.04 0.30 0.38 0.37 0.38 0.36 1.00 0.35 0.28
## cas07 0.15 0.10 0.41 0.41 0.49 0.47 0.35 0.35 1.00 0.34
## cas08 0.16 0.16 0.46 0.40 0.36 0.28 0.56 0.28 0.34 1.00
## cas09 0.15 0.12 0.58 0.61 0.50 0.48 0.47 0.41 0.39 0.46
## cas10 0.11 0.11 0.44 0.44 0.36 0.35 0.46 0.30 0.29 0.42
## cas11 0.20 0.14 0.58 0.58 0.54 0.47 0.47 0.33 0.48 0.45
## cas12 0.21 0.16 0.59 0.56 0.49 0.46 0.45 0.34 0.45 0.44
## cas13 0.12 0.07 0.49 0.50 0.45 0.42 0.45 0.41 0.44 0.40
## cas09 cas10 cas11 cas12 cas13
## asimp.all 0.15 0.11 0.20 0.21 0.12
## aspro.all 0.12 0.11 0.14 0.16 0.07
## cas01 0.58 0.44 0.58 0.59 0.49
## cas02 0.61 0.44 0.58 0.56 0.50
## cas03 0.50 0.36 0.54 0.49 0.45
## cas04 0.48 0.35 0.47 0.46 0.42
## cas05 0.47 0.46 0.47 0.45 0.45
## cas06 0.41 0.30 0.33 0.34 0.41
## cas07 0.39 0.29 0.48 0.45 0.44
## cas08 0.46 0.42 0.45 0.44 0.40
## cas09 1.00 0.51 0.60 0.60 0.52
## cas10 0.51 1.00 0.45 0.45 0.43
## cas11 0.60 0.45 1.00 0.64 0.51
## cas12 0.60 0.45 0.64 1.00 0.50
## cas13 0.52 0.43 0.51 0.50 1.00
## Sample Size
## [1] 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## asimp.all aspro.all cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08
## asimp.all 0.00 0.00 0 0 0 0.00 0.00 0.17 0 0
## aspro.all 0.00 0.00 0 0 0 0.03 0.05 0.23 0 0
## cas01 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas02 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas03 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas04 0.00 0.01 0 0 0 0.00 0.00 0.00 0 0
## cas05 0.00 0.01 0 0 0 0.00 0.00 0.00 0 0
## cas06 0.09 0.23 0 0 0 0.00 0.00 0.00 0 0
## cas07 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas08 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas09 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas10 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas11 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas12 0.00 0.00 0 0 0 0.00 0.00 0.00 0 0
## cas13 0.00 0.01 0 0 0 0.00 0.00 0.00 0 0
## cas09 cas10 cas11 cas12 cas13
## asimp.all 0 0 0 0 0.00
## aspro.all 0 0 0 0 0.05
## cas01 0 0 0 0 0.00
## cas02 0 0 0 0 0.00
## cas03 0 0 0 0 0.00
## cas04 0 0 0 0 0.00
## cas05 0 0 0 0 0.00
## cas06 0 0 0 0 0.00
## cas07 0 0 0 0 0.00
## cas08 0 0 0 0 0.00
## cas09 0 0 0 0 0.00
## cas10 0 0 0 0 0.00
## cas11 0 0 0 0 0.00
## cas12 0 0 0 0 0.00
## cas13 0 0 0 0 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("asimp.all", "aspro.all",
"cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## asm. asp. cs01 cs02 cs03 cs04 cs05 cs06 cs07 cs08 cs09 cs10 cs11
## asimp.all 1.00 0.65 0.27 0.21 0.22 0.19 0.20 -0.10 0.26 0.21 0.22 0.14 0.26
## aspro.all 0.57 1.00 0.19 0.17 0.18 0.13 0.12 -0.10 0.18 0.20 0.19 0.16 0.18
## cas01 0.16 0.08 1.00 0.68 0.55 0.54 0.47 0.32 0.47 0.49 0.64 0.46 0.66
## cas02 0.09 0.07 0.57 1.00 0.66 0.62 0.47 0.40 0.49 0.43 0.67 0.47 0.64
## cas03 0.08 0.07 0.43 0.55 1.00 0.66 0.47 0.40 0.59 0.42 0.60 0.41 0.62
## cas04 0.06 0.03 0.41 0.50 0.51 1.00 0.38 0.41 0.56 0.35 0.58 0.41 0.57
## cas05 0.09 0.02 0.35 0.36 0.36 0.26 1.00 0.40 0.42 0.60 0.50 0.48 0.52
## cas06 0.01 0.01 0.21 0.29 0.31 0.31 0.29 1.00 0.41 0.33 0.45 0.33 0.37
## cas07 0.13 0.07 0.33 0.33 0.46 0.41 0.28 0.28 1.00 0.42 0.47 0.30 0.58
## cas08 0.09 0.09 0.36 0.31 0.30 0.21 0.50 0.21 0.28 1.00 0.50 0.44 0.50
## cas09 0.11 0.08 0.52 0.56 0.46 0.44 0.40 0.33 0.32 0.38 1.00 0.52 0.67
## cas10 0.02 0.04 0.33 0.35 0.28 0.29 0.36 0.22 0.18 0.33 0.41 1.00 0.44
## cas11 0.15 0.08 0.54 0.50 0.47 0.43 0.41 0.26 0.42 0.39 0.54 0.34 1.00
## cas12 0.17 0.10 0.54 0.51 0.42 0.41 0.37 0.25 0.39 0.37 0.54 0.33 0.57
## cas13 0.07 0.01 0.39 0.41 0.39 0.38 0.39 0.36 0.35 0.32 0.46 0.35 0.43
## cs12 cs13
## asimp.all 0.27 0.18
## aspro.all 0.20 0.12
## cas01 0.66 0.52
## cas02 0.63 0.55
## cas03 0.54 0.52
## cas04 0.54 0.52
## cas05 0.49 0.50
## cas06 0.37 0.46
## cas07 0.53 0.49
## cas08 0.50 0.44
## cas09 0.65 0.58
## cas10 0.46 0.46
## cas11 0.70 0.56
## cas12 1.00 0.53
## cas13 0.41 1.008.5.3 Scatterplot importance aspirations
plot(data$asimp.all, data$cas,
main = "CAS and Aspirations Scatterplot",
xlab = "Aspirations",
ylab = "CAS")
abline(lm(data$cas ~ data$asimp.all), col = "red") #regression line
lines(lowess(data$asimp.all, data$cas), col = "blue") #smooth fitting line
8.6 H6: Climate anxiety is uncorrelated with policy support and intentions
8.6.1 Correlations
corr.test (data [,c("cas",
"ps", "ps01", "ps02", "int", "int01", "int02", "int03")],
method = "spearman")## Call:corr.test(x = data[, c("cas", "ps", "ps01", "ps02", "int", "int01",
## "int02", "int03")], method = "spearman")
## Correlation matrix
## cas ps ps01 ps02 int int01 int02 int03
## cas 1.00 0.15 0.17 0.08 0.45 0.42 0.49 0.15
## ps 0.15 1.00 0.93 0.89 0.37 0.22 0.30 0.43
## ps01 0.17 0.93 1.00 0.68 0.40 0.25 0.33 0.41
## ps02 0.08 0.89 0.68 1.00 0.29 0.15 0.22 0.36
## int 0.45 0.37 0.40 0.29 1.00 0.87 0.89 0.61
## int01 0.42 0.22 0.25 0.15 0.87 1.00 0.74 0.28
## int02 0.49 0.30 0.33 0.22 0.89 0.74 1.00 0.33
## int03 0.15 0.43 0.41 0.36 0.61 0.28 0.33 1.00
## Sample Size
## cas ps ps01 ps02 int int01 int02 int03
## cas 1134 1087 1088 1091 1134 1134 1134 1134
## ps 1087 1087 1087 1087 1087 1087 1087 1087
## ps01 1088 1087 1088 1087 1088 1088 1088 1088
## ps02 1091 1087 1087 1091 1091 1091 1091 1091
## int 1134 1087 1088 1091 1134 1134 1134 1134
## int01 1134 1087 1088 1091 1134 1134 1134 1134
## int02 1134 1087 1088 1091 1134 1134 1134 1134
## int03 1134 1087 1088 1091 1134 1134 1134 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## cas ps ps01 ps02 int int01 int02 int03
## cas 0 0 0 0 0 0 0 0
## ps 0 0 0 0 0 0 0 0
## ps01 0 0 0 0 0 0 0 0
## ps02 0 0 0 0 0 0 0 0
## int 0 0 0 0 0 0 0 0
## int01 0 0 0 0 0 0 0 0
## int02 0 0 0 0 0 0 0 0
## int03 0 0 0 0 0 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("cas",
"ps", "ps01", "ps02", "int", "int01", "int02", "int03")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## cas ps ps01 ps02 int in01 in02 in03
## cas 1.00 0.18 0.22 0.12 0.49 0.46 0.51 0.22
## ps 0.06 1.00 0.94 0.93 0.47 0.29 0.36 0.53
## ps01 0.10 0.91 1.00 0.74 0.48 0.32 0.38 0.51
## ps02 -0.01 0.90 0.63 1.00 0.38 0.22 0.28 0.48
## int 0.38 0.36 0.37 0.26 1.00 0.88 0.90 0.69
## int01 0.35 0.18 0.20 0.11 0.85 1.00 0.78 0.36
## int02 0.41 0.26 0.28 0.17 0.87 0.70 1.00 0.41
## int03 0.10 0.42 0.40 0.36 0.63 0.26 0.32 1.008.6.2 Correlations individual items
corr.test (data [,c("cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13",
"ps", "ps01", "ps02", "int", "int01", "int02", "int03")],
method = "spearman")## Call:corr.test(x = data[, c("cas01", "cas02", "cas03", "cas04", "cas05",
## "cas06", "cas07", "cas08", "cas09", "cas10", "cas11", "cas12",
## "cas13", "ps", "ps01", "ps02", "int", "int01", "int02", "int03")],
## method = "spearman")
## Correlation matrix
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1.00 0.60 0.50 0.45 0.45 0.30 0.41 0.46 0.58 0.44 0.58 0.59
## cas02 0.60 1.00 0.60 0.53 0.43 0.38 0.41 0.40 0.61 0.44 0.58 0.56
## cas03 0.50 0.60 1.00 0.54 0.40 0.37 0.49 0.36 0.50 0.36 0.54 0.49
## cas04 0.45 0.53 0.54 1.00 0.34 0.38 0.47 0.28 0.48 0.35 0.47 0.46
## cas05 0.45 0.43 0.40 0.34 1.00 0.36 0.35 0.56 0.47 0.46 0.47 0.45
## cas06 0.30 0.38 0.37 0.38 0.36 1.00 0.35 0.28 0.41 0.30 0.33 0.34
## cas07 0.41 0.41 0.49 0.47 0.35 0.35 1.00 0.34 0.39 0.29 0.48 0.45
## cas08 0.46 0.40 0.36 0.28 0.56 0.28 0.34 1.00 0.46 0.42 0.45 0.44
## cas09 0.58 0.61 0.50 0.48 0.47 0.41 0.39 0.46 1.00 0.51 0.60 0.60
## cas10 0.44 0.44 0.36 0.35 0.46 0.30 0.29 0.42 0.51 1.00 0.45 0.45
## cas11 0.58 0.58 0.54 0.47 0.47 0.33 0.48 0.45 0.60 0.45 1.00 0.64
## cas12 0.59 0.56 0.49 0.46 0.45 0.34 0.45 0.44 0.60 0.45 0.64 1.00
## cas13 0.49 0.50 0.45 0.42 0.45 0.41 0.44 0.40 0.52 0.43 0.51 0.50
## ps 0.05 0.10 0.11 0.15 0.12 0.24 0.05 0.03 0.09 0.11 0.04 0.02
## ps01 0.08 0.13 0.13 0.18 0.13 0.26 0.09 0.04 0.13 0.11 0.07 0.06
## ps02 0.01 0.04 0.08 0.09 0.07 0.17 0.01 -0.01 0.03 0.07 -0.01 -0.04
## int 0.27 0.32 0.35 0.33 0.34 0.45 0.35 0.25 0.31 0.29 0.30 0.30
## int01 0.25 0.31 0.34 0.31 0.32 0.35 0.36 0.24 0.30 0.27 0.29 0.28
## int02 0.32 0.35 0.38 0.34 0.36 0.40 0.36 0.29 0.35 0.30 0.36 0.35
## int03 0.04 0.10 0.10 0.13 0.10 0.34 0.09 0.04 0.08 0.11 0.04 0.06
## cas13 ps ps01 ps02 int int01 int02 int03
## cas01 0.49 0.05 0.08 0.01 0.27 0.25 0.32 0.04
## cas02 0.50 0.10 0.13 0.04 0.32 0.31 0.35 0.10
## cas03 0.45 0.11 0.13 0.08 0.35 0.34 0.38 0.10
## cas04 0.42 0.15 0.18 0.09 0.33 0.31 0.34 0.13
## cas05 0.45 0.12 0.13 0.07 0.34 0.32 0.36 0.10
## cas06 0.41 0.24 0.26 0.17 0.45 0.35 0.40 0.34
## cas07 0.44 0.05 0.09 0.01 0.35 0.36 0.36 0.09
## cas08 0.40 0.03 0.04 -0.01 0.25 0.24 0.29 0.04
## cas09 0.52 0.09 0.13 0.03 0.31 0.30 0.35 0.08
## cas10 0.43 0.11 0.11 0.07 0.29 0.27 0.30 0.11
## cas11 0.51 0.04 0.07 -0.01 0.30 0.29 0.36 0.04
## cas12 0.50 0.02 0.06 -0.04 0.30 0.28 0.35 0.06
## cas13 1.00 0.09 0.11 0.05 0.39 0.38 0.41 0.11
## ps 0.09 1.00 0.93 0.89 0.37 0.22 0.30 0.43
## ps01 0.11 0.93 1.00 0.68 0.40 0.25 0.33 0.41
## ps02 0.05 0.89 0.68 1.00 0.29 0.15 0.22 0.36
## int 0.39 0.37 0.40 0.29 1.00 0.87 0.89 0.61
## int01 0.38 0.22 0.25 0.15 0.87 1.00 0.74 0.28
## int02 0.41 0.30 0.33 0.22 0.89 0.74 1.00 0.33
## int03 0.11 0.43 0.41 0.36 0.61 0.28 0.33 1.00
## Sample Size
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas02 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas03 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas04 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas05 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas06 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas07 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas08 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas09 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas10 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas11 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas12 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas13 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## ps 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087 1087
## ps01 1088 1088 1088 1088 1088 1088 1088 1088 1088 1088 1088 1088
## ps02 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091
## int 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## int01 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## int02 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## int03 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134 1134
## cas13 ps ps01 ps02 int int01 int02 int03
## cas01 1134 1087 1088 1091 1134 1134 1134 1134
## cas02 1134 1087 1088 1091 1134 1134 1134 1134
## cas03 1134 1087 1088 1091 1134 1134 1134 1134
## cas04 1134 1087 1088 1091 1134 1134 1134 1134
## cas05 1134 1087 1088 1091 1134 1134 1134 1134
## cas06 1134 1087 1088 1091 1134 1134 1134 1134
## cas07 1134 1087 1088 1091 1134 1134 1134 1134
## cas08 1134 1087 1088 1091 1134 1134 1134 1134
## cas09 1134 1087 1088 1091 1134 1134 1134 1134
## cas10 1134 1087 1088 1091 1134 1134 1134 1134
## cas11 1134 1087 1088 1091 1134 1134 1134 1134
## cas12 1134 1087 1088 1091 1134 1134 1134 1134
## cas13 1134 1087 1088 1091 1134 1134 1134 1134
## ps 1087 1087 1087 1087 1087 1087 1087 1087
## ps01 1088 1087 1088 1087 1088 1088 1088 1088
## ps02 1091 1087 1087 1091 1091 1091 1091 1091
## int 1134 1087 1088 1091 1134 1134 1134 1134
## int01 1134 1087 1088 1091 1134 1134 1134 1134
## int02 1134 1087 1088 1091 1134 1134 1134 1134
## int03 1134 1087 1088 1091 1134 1134 1134 1134
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## cas01 cas02 cas03 cas04 cas05 cas06 cas07 cas08 cas09 cas10 cas11 cas12
## cas01 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas02 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas03 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas04 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas05 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas06 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas07 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas08 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas09 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas10 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas11 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas12 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## cas13 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## ps 0.09 0.00 0.00 0 0.00 0 0.10 0.36 0.00 0.00 0.19 0.50
## ps01 0.01 0.00 0.00 0 0.00 0 0.00 0.15 0.00 0.00 0.01 0.04
## ps02 0.70 0.23 0.01 0 0.02 0 0.73 0.80 0.32 0.02 0.70 0.23
## int 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## int01 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## int02 0.00 0.00 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00
## int03 0.19 0.00 0.00 0 0.00 0 0.00 0.18 0.01 0.00 0.15 0.06
## cas13 ps ps01 ps02 int int01 int02 int03
## cas01 0.00 1.00 0.28 1.00 0 0 0 1.00
## cas02 0.00 0.04 0.00 1.00 0 0 0 0.02
## cas03 0.00 0.01 0.00 0.29 0 0 0 0.02
## cas04 0.00 0.00 0.00 0.11 0 0 0 0.00
## cas05 0.00 0.01 0.00 0.48 0 0 0 0.01
## cas06 0.00 0.00 0.00 0.00 0 0 0 0.00
## cas07 0.00 1.00 0.13 1.00 0 0 0 0.11
## cas08 0.00 1.00 1.00 1.00 0 0 0 1.00
## cas09 0.00 0.06 0.00 1.00 0 0 0 0.16
## cas10 0.00 0.01 0.01 0.50 0 0 0 0.01
## cas11 0.00 1.00 0.30 1.00 0 0 0 1.00
## cas12 0.00 1.00 0.74 1.00 0 0 0 1.00
## cas13 0.00 0.11 0.01 1.00 0 0 0 0.01
## ps 0.00 0.00 0.00 0.00 0 0 0 0.00
## ps01 0.00 0.00 0.00 0.00 0 0 0 0.00
## ps02 0.12 0.00 0.00 0.00 0 0 0 0.00
## int 0.00 0.00 0.00 0.00 0 0 0 0.00
## int01 0.00 0.00 0.00 0.00 0 0 0 0.00
## int02 0.00 0.00 0.00 0.00 0 0 0 0.00
## int03 0.00 0.00 0.00 0.00 0 0 0 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#bootstrapped confidence intervals#
data.ci <- cor.ci(data [,c("cas01", "cas02", "cas03", "cas04", "cas05",
"cas06", "cas07", "cas08", "cas09", "cas10",
"cas11", "cas12", "cas13",
"ps", "ps01", "ps02", "int", "int01", "int02", "int03")])
#to show the upper and lower confidence intervals#
ci <- cor.plot.upperLowerCi(data.ci) 
#print the confidence intervals in matrix form#
ci ##
## High and low confidence intervals
## cs01 cs02 cs03 cs04 cs05 cs06 cs07 cs08 cs09 cs10 cs11 cs12 cs13
## cas01 1.00 0.68 0.56 0.54 0.47 0.32 0.47 0.47 0.63 0.46 0.67 0.65 0.51
## cas02 0.57 1.00 0.66 0.62 0.47 0.40 0.49 0.43 0.67 0.47 0.63 0.63 0.54
## cas03 0.42 0.54 1.00 0.66 0.47 0.40 0.61 0.42 0.59 0.40 0.62 0.54 0.53
## cas04 0.41 0.49 0.51 1.00 0.39 0.41 0.58 0.35 0.57 0.41 0.57 0.53 0.51
## cas05 0.35 0.35 0.34 0.26 1.00 0.41 0.42 0.60 0.50 0.48 0.51 0.49 0.50
## cas06 0.21 0.28 0.30 0.31 0.29 1.00 0.40 0.34 0.44 0.34 0.36 0.37 0.46
## cas07 0.33 0.34 0.45 0.41 0.28 0.29 1.00 0.41 0.48 0.31 0.57 0.53 0.50
## cas08 0.37 0.31 0.30 0.22 0.50 0.21 0.28 1.00 0.50 0.44 0.50 0.49 0.44
## cas09 0.53 0.55 0.46 0.43 0.39 0.34 0.32 0.38 1.00 0.52 0.65 0.65 0.58
## cas10 0.33 0.35 0.28 0.27 0.37 0.21 0.17 0.33 0.41 1.00 0.45 0.45 0.46
## cas11 0.55 0.51 0.48 0.43 0.40 0.26 0.43 0.39 0.55 0.33 1.00 0.69 0.56
## cas12 0.55 0.51 0.41 0.40 0.36 0.25 0.38 0.36 0.54 0.33 0.58 1.00 0.54
## cas13 0.39 0.42 0.38 0.39 0.39 0.37 0.34 0.32 0.46 0.35 0.42 0.40 1.00
## ps -0.01 0.03 0.05 0.09 0.06 0.17 -0.03 -0.02 0.04 0.07 -0.01 -0.04 0.06
## ps01 0.01 0.07 0.08 0.14 0.07 0.19 -0.01 0.00 0.08 0.07 0.02 0.01 0.07
## ps02 -0.06 -0.03 -0.02 0.02 0.02 0.11 0.03 -0.05 -0.02 0.04 -0.06 0.02 0.02
## int 0.18 0.25 0.32 0.28 0.28 0.41 0.30 0.18 0.25 0.20 0.23 0.21 0.34
## int01 0.16 0.22 0.31 0.26 0.25 0.30 0.30 0.16 0.23 0.16 0.21 0.18 0.31
## int02 0.22 0.27 0.35 0.29 0.28 0.35 0.31 0.21 0.28 0.19 0.27 0.26 0.35
## int03 -0.01 0.06 0.08 0.09 0.10 0.30 0.06 0.02 0.04 0.08 0.03 0.02 0.10
## ps ps01 ps02 int in01 in02 in03
## cas01 0.09 0.12 0.07 0.29 0.29 0.34 0.11
## cas02 0.15 0.18 0.10 0.36 0.34 0.38 0.17
## cas03 0.15 0.18 0.10 0.42 0.40 0.44 0.17
## cas04 0.19 0.23 0.12 0.38 0.37 0.39 0.18
## cas05 0.18 0.20 0.14 0.40 0.38 0.40 0.21
## cas06 0.30 0.32 0.23 0.50 0.40 0.45 0.39
## cas07 0.08 0.12 -0.07 0.41 0.42 0.43 0.15
## cas08 0.10 0.12 0.07 0.30 0.27 0.32 0.15
## cas09 0.14 0.18 0.09 0.36 0.34 0.39 0.15
## cas10 0.18 0.19 0.15 0.32 0.29 0.32 0.19
## cas11 0.10 0.13 0.05 0.35 0.33 0.39 0.13
## cas12 0.08 0.12 -0.10 0.33 0.31 0.37 0.13
## cas13 0.15 0.17 0.11 0.45 0.43 0.47 0.19
## ps 1.00 0.93 0.93 0.46 0.29 0.35 0.54
## ps01 0.91 1.00 0.73 0.48 0.32 0.38 0.51
## ps02 0.90 0.63 1.00 0.37 0.22 0.27 0.48
## int 0.35 0.37 0.27 1.00 0.88 0.90 0.69
## int01 0.17 0.20 0.10 0.85 1.00 0.78 0.35
## int02 0.25 0.27 0.17 0.87 0.70 1.00 0.40
## int03 0.42 0.39 0.36 0.63 0.26 0.32 1.008.6.3 Scatterplot policy support
plot(data$ps, data$cas,
main = "CAS and policy support",
xlab = "Policy support",
ylab = "CAS")
abline(lm(data$cas ~ data$ps), col = "red") #regression line
8.6.4 Scatterplot intentions overall
plot(data$int, data$cas,
main = "CAS and pro-environmental intentions",
xlab = "Pro-environmental intentions",
ylab = "CAS")
abline(lm(data$cas ~ data$int), col = "red") #regression line
lines(lowess(data$int, data$cas), col = "blue") #smooth fitting line
8.6.5 Scatterplot intentions - political engagement
plot(data$int01, data$cas,
main = "CAS and intentions for political engagement",
xlab = "Intentions for political engagement",
ylab = "CAS")
abline(lm(data$cas ~ data$int01), col = "red") #regression line
lines(lowess(data$int01, data$cas), col = "blue") #smooth fitting line
8.6.6 Scatterplot intentions - activist engagement
plot(data$int02, data$cas,
main = "CAS and intentions for activist engagement",
xlab = "Intentions for activist engagement",
ylab = "CAS")
abline(lm(data$cas ~ data$int02), col = "red") #regression line
lines(lowess(data$int02, data$cas), col = "blue") #smooth fitting line
8.6.7 Scatterplot intentions - everyday engagement
plot(data$int03, data$cas,
main = "CAS and intentions for everyday engagement",
xlab = "Intentions for everyday engagement",
ylab = "CAS")
abline(lm(data$cas ~ data$int03), col = "red") #regression line
lines(lowess(data$int03, data$cas), col = "blue") #smooth fitting line
8.7 old interaction hypothesis
8.7.1 Predicting policy support
Load relevant package
library(lm.beta)8.7.1.1 original data
8.7.1.1.1 Step 1: Predicting policy support from controls
reg.1 <- lm(ps ~ age + gender.d + income, data = data)
summary(reg.1)##
## Call:
## lm(formula = ps ~ age + gender.d + income, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.036 -12.627 1.755 15.807 34.744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.94665 2.37405 27.357 < 2e-16 ***
## age 0.13535 0.04583 2.953 0.00321 **
## gender.d 4.17673 1.27761 3.269 0.00111 **
## income -0.30221 0.22402 -1.349 0.17760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.88 on 1083 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.02005, Adjusted R-squared: 0.01734
## F-statistic: 7.386 on 3 and 1083 DF, p-value: 6.695e-05lm.beta(reg.1) ##
## Call:
## lm(formula = ps ~ age + gender.d + income, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income
## 0.00000000 0.08932565 0.09920522 -0.04114884confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -4.6582587347 4.6582587
## age -0.0006093248 0.1792606
## gender.d -2.4076676858 2.6060781
## income -0.4807097067 0.3984120res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.85 1.75 1.66 21.16 -72.04 34.74 106.78 -0.81 0.7
## se
## X1 0.63qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.95396, p-value < 2.2e-16par(mfrow = c(2, 2))
plot(reg.1) 
par(mfrow = c(1, 1))
vif(reg.1) ## age gender.d income
## 1.011218 1.017697 1.0282108.7.1.1.2 Step 2: Predicting policy support from controls and CAS
reg <- lm(ps ~ age + gender.d + income +
cas, data = data)
summary(reg)##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.964 -12.387 2.352 15.710 36.823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.88963 2.69825 22.196 < 2e-16 ***
## age 0.13980 0.04556 3.069 0.00220 **
## gender.d 3.76776 1.27390 2.958 0.00317 **
## income -0.28854 0.22262 -1.296 0.19522
## cas 2.69903 0.69913 3.861 0.00012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.74 on 1082 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.03337, Adjusted R-squared: 0.02979
## F-statistic: 9.337 on 4 and 1082 DF, p-value: 2.027e-07lm.beta(reg) ##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas
## 0.00000000 0.09225973 0.08949148 -0.03928687 0.11587238confint(lm.beta(reg), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -5.294392194 5.2943922
## age 0.002867918 0.1816515
## gender.d -2.410102689 2.5890856
## income -0.476108726 0.3975350
## cas -1.255926951 1.4876717res.lm <- residuals(reg)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.71 2.35 1.6 20.83 -72.96 36.82 109.79 -0.77 0.59
## se
## X1 0.63qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.95833, p-value < 2.2e-16par(mfrow = c(2, 2))
plot(reg) 
par(mfrow = c(1, 1))
vif(reg)## age gender.d income cas
## 1.011864 1.024783 1.028471 1.0083728.7.1.1.3 Step 3: Predicting policy support from controls and CAS and needs
reg <- lm(ps ~ age + gender.d + income +
cas +
gb.rs + gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
summary(reg)##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas + gb.rs + gb.rf +
## gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.690 -12.339 2.004 15.303 40.838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.58561 5.55463 10.007 < 2e-16 ***
## age 0.12531 0.04866 2.575 0.01015 *
## gender.d 3.86292 1.29617 2.980 0.00294 **
## income -0.26219 0.22583 -1.161 0.24591
## cas 2.95062 0.74480 3.962 7.93e-05 ***
## gb.rs 0.44573 0.64054 0.696 0.48666
## gb.rf -1.05928 0.64391 -1.645 0.10025
## gb.as 1.01497 0.73558 1.380 0.16792
## gb.af -0.12665 0.64757 -0.196 0.84497
## gb.cs -0.64485 0.54617 -1.181 0.23799
## gb.cf 1.04927 0.66787 1.571 0.11646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.7 on 1076 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.04258, Adjusted R-squared: 0.03368
## F-statistic: 4.786 on 10 and 1076 DF, p-value: 9.462e-07lm.beta(reg) ##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas + gb.rs + gb.rf +
## gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas gb.rs
## 0.000000000 0.082696205 0.091751758 -0.035698613 0.126673290 0.025317969
## gb.rf gb.as gb.af gb.cs gb.cf
## -0.073208994 0.054564823 -0.008789671 -0.040520344 0.068738339confint(lm.beta(reg), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -10.89913713 10.8991371
## age -0.01277756 0.1781700
## gender.d -2.45155637 2.6350599
## income -0.47882129 0.4074241
## cas -1.33475466 1.5881012
## gb.rs -1.23153350 1.2821694
## gb.rf -1.33666727 1.1902493
## gb.as -1.38876119 1.4978908
## gb.af -1.27942906 1.2618497
## gb.cs -1.11220379 1.0311631
## gb.cf -1.24173440 1.3792111res.lm <- residuals(reg)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.61 2 1.58 20.71 -73.69 40.84 114.53 -0.78 0.65
## se
## X1 0.63qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.95975, p-value < 2.2e-16par(mfrow = c(2, 2))
plot(reg) 
par(mfrow = c(1, 1))
vif(reg)## age gender.d income cas gb.rs gb.rf gb.as gb.af
## 1.158871 1.065199 1.062603 1.149039 1.487714 2.225692 1.757452 2.269789
## gb.cs gb.cf
## 1.323707 2.1513658.7.1.1.4 Step 4: Predicting policy support from controls, CAS, needs, and interaction
reg <- lm(ps ~ age + gender.d + income +
gb*cas, data = data)
summary(reg)##
## Call:
## lm(formula = ps ~ age + gender.d + income + gb * cas, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.856 -12.409 2.203 15.407 37.516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.19642 8.59547 8.516 < 2e-16 ***
## age 0.12405 0.04632 2.678 0.00751 **
## gender.d 3.78947 1.26994 2.984 0.00291 **
## income -0.30143 0.22278 -1.353 0.17632
## gb -2.73126 1.67981 -1.626 0.10426
## cas -7.02682 3.96314 -1.773 0.07650 .
## gb:cas 2.10269 0.81991 2.565 0.01047 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.68 on 1080 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.04123, Adjusted R-squared: 0.0359
## F-statistic: 7.74 on 6 and 1080 DF, p-value: 3.637e-08lm.beta(reg) ##
## Call:
## lm(formula = ps ~ age + gender.d + income + gb * cas, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income gb cas
## 0.00000000 0.08186810 0.09000713 -0.04104261 -0.11536000 -0.30166861
## gb:cas
## 0.42603780confint(lm.beta(reg), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -16.865704478 16.8657045
## age -0.009009976 0.1727462
## gender.d -2.401830084 2.5818443
## income -0.478174788 0.3960896
## gb -3.411422594 3.1807026
## cas -8.078002947 7.4746657
## gb:cas -1.182753809 2.0348294res.lm <- residuals(reg)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.62 2.2 1.56 20.46 -76.86 37.52 114.37 -0.77 0.64
## se
## X1 0.63qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96026, p-value < 2.2e-16par(mfrow = c(2, 2))
plot(reg) 
par(mfrow = c(1, 1))
vif(reg) ## age gender.d income gb cas gb:cas
## 1.052411 1.024879 1.036452 5.670369 32.608431 31.0873628.7.1.2 transformed data
8.7.1.2.1 Step 1: Predicting policy support from controls
reg.1 <- lm(ps ~ age + gender.d + income, data = data)
summary(reg.1)##
## Call:
## lm(formula = ps ~ age + gender.d + income, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.036 -12.627 1.755 15.807 34.744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.94665 2.37405 27.357 < 2e-16 ***
## age 0.13535 0.04583 2.953 0.00321 **
## gender.d 4.17673 1.27761 3.269 0.00111 **
## income -0.30221 0.22402 -1.349 0.17760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.88 on 1083 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.02005, Adjusted R-squared: 0.01734
## F-statistic: 7.386 on 3 and 1083 DF, p-value: 6.695e-05lm.beta(reg.1) ##
## Call:
## lm(formula = ps ~ age + gender.d + income, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income
## 0.00000000 0.08932565 0.09920522 -0.04114884confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -4.6582587347 4.6582587
## age -0.0006093248 0.1792606
## gender.d -2.4076676858 2.6060781
## income -0.4807097067 0.3984120res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.85 1.75 1.66 21.16 -72.04 34.74 106.78 -0.81 0.7
## se
## X1 0.63qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.95396, p-value < 2.2e-16par(mfrow = c(2, 2))
plot(reg.1)
par(mfrow = c(1, 1))
vif(reg.1) ## age gender.d income
## 1.011218 1.017697 1.0282108.7.1.2.2 Step 2.tt: Predicting policy support from controls and CAS.tt
reg.2.tt <- lm(ps ~ age + gender.d + income +
cas.tt, data = data)
summary(reg.2.tt)##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas.tt, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.898 -12.572 2.209 15.713 38.164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.27226 2.46176 24.890 < 2e-16 ***
## age 0.13724 0.04534 3.027 0.00253 **
## gender.d 3.60537 1.26902 2.841 0.00458 **
## income -0.28276 0.22164 -1.276 0.20230
## cas.tt 7.29711 1.46631 4.977 7.53e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.65 on 1082 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.04198, Adjusted R-squared: 0.03844
## F-statistic: 11.85 on 4 and 1082 DF, p-value: 1.99e-09lm.beta(reg.2.tt) ##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas.tt, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas.tt
## 0.00000000 0.09057012 0.08563438 -0.03850068 0.14876223confint(lm.beta(reg.2.tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -4.830357115 4.8303571
## age 0.001602783 0.1795375
## gender.d -2.404391459 2.5756602
## income -0.473384639 0.3963833
## cas.tt -2.728370400 3.0258949res.lm <- residuals(reg.2.tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.61 2.21 1.53 20.81 -73.9 38.16 112.06 -0.75 0.55
## se
## X1 0.63qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96148, p-value = 2.411e-16par(mfrow = c(2, 2))
plot(reg.2.tt) 
par(mfrow = c(1, 1))
vif(reg.2.tt) ## age gender.d income cas.tt
## 1.011288 1.026095 1.028530 1.0092238.7.1.2.3 Step 3.tt: Predicting policy support from controls and CAS.tt and needs
reg.3.tt <- lm(ps ~ age + gender.d + income +
cas.tt +
gb.rs + gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
summary(reg.3.tt)##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas.tt + gb.rs +
## gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.933 -12.768 2.145 15.240 42.409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.41114 5.51502 10.410 < 2e-16 ***
## age 0.11539 0.04851 2.379 0.01755 *
## gender.d 3.71556 1.29037 2.879 0.00406 **
## income -0.25720 0.22471 -1.145 0.25263
## cas.tt 8.04767 1.55937 5.161 2.93e-07 ***
## gb.rs 0.43714 0.63731 0.686 0.49292
## gb.rf -1.08794 0.63991 -1.700 0.08939 .
## gb.as 1.14699 0.73246 1.566 0.11766
## gb.af -0.06834 0.64427 -0.106 0.91555
## gb.cs -0.70257 0.54359 -1.292 0.19647
## gb.cf 0.85671 0.66635 1.286 0.19883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.6 on 1076 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.05208, Adjusted R-squared: 0.04327
## F-statistic: 5.912 on 10 and 1076 DF, p-value: 9.578e-09lm.beta(reg.3.tt) ##
## Call:
## lm(formula = ps ~ age + gender.d + income + cas.tt + gb.rs +
## gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas.tt gb.rs
## 0.000000000 0.076149218 0.088251613 -0.035019999 0.164063489 0.024829821
## gb.rf gb.as gb.af gb.cs gb.cf
## -0.075189432 0.061662273 -0.004742443 -0.044147214 0.056123616confint(lm.beta(reg.3.tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -10.82142214 10.8214221
## age -0.01903199 0.1713304
## gender.d -2.44367137 2.6201746
## income -0.47593817 0.4058982
## cas.tt -2.89569175 3.2238187
## gb.rs -1.22568609 1.2753457
## gb.rf -1.33080674 1.1804279
## gb.as -1.37555700 1.4988816
## gb.af -1.26890817 1.2594233
## gb.cs -1.11076558 1.0224711
## gb.cf -1.25136322 1.3636105res.lm <- residuals(reg.3.tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.5 2.14 1.51 20.67 -74.93 42.41 117.34 -0.75 0.62
## se
## X1 0.62qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96297, p-value = 5.387e-16par(mfrow = c(2, 2))
plot(reg.3.tt) 
par(mfrow = c(1, 1))
vif(reg.3.tt)## age gender.d income cas.tt gb.rs gb.rf gb.as gb.af
## 1.163321 1.066263 1.062600 1.147161 1.487511 2.220180 1.760074 2.269234
## gb.cs gb.cf
## 1.324364 2.1630338.7.1.2.4 Step 4.tt: Predicting policy support from controls, CAS.tt, needs, and interaction
reg.4.tt <- lm(ps ~ age + gender.d + income +
gb*cas.tt, data = data)
summary(reg.4.tt)##
## Call:
## lm(formula = ps ~ age + gender.d + income + gb * cas.tt, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.094 -12.386 1.976 15.424 38.432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 63.36072 6.19291 10.231 < 2e-16 ***
## age 0.11896 0.04618 2.576 0.01012 *
## gender.d 3.65874 1.26630 2.889 0.00394 **
## income -0.30208 0.22204 -1.361 0.17395
## gb -0.29922 1.15884 -0.258 0.79630
## cas.tt -7.53380 8.36667 -0.900 0.36808
## gb:cas.tt 3.16730 1.67642 1.889 0.05912 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.6 on 1080 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.04812, Adjusted R-squared: 0.04283
## F-statistic: 9.1 on 6 and 1080 DF, p-value: 9.916e-10lm.beta(reg.4.tt) ##
## Call:
## lm(formula = ps ~ age + gender.d + income + gb * cas.tt, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income gb cas.tt
## 0.00000000 0.07851019 0.08690194 -0.04113059 -0.01263806 -0.15358753
## gb:cas.tt
## 0.31163066confint(lm.beta(reg.4.tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -12.15150588 12.1515059
## age -0.01209383 0.1691142
## gender.d -2.39778818 2.5715921
## income -0.47680048 0.3945393
## gb -2.28647206 2.2611959
## cas.tt -16.57035589 16.2631808
## gb:cas.tt -2.97777091 3.6010322res.lm <- residuals(reg.4.tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1087 0 20.55 1.98 1.5 20.64 -78.09 38.43 116.53 -0.74 0.6
## se
## X1 0.62qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96293, p-value = 5.248e-16par(mfrow = c(2, 2))
plot(reg.4.tt) 
par(mfrow = c(1, 1))
vif(reg.4.tt) ## age gender.d income gb cas.tt gb:cas.tt
## 1.053653 1.026392 1.036989 2.718151 33.008951 30.8679858.7.1.2.5 Comparing different models
Step 1 vs. step 2
anova(reg.1, reg.2.tt, test="Chisq")## Analysis of Variance Table
##
## Model 1: ps ~ age + gender.d + income
## Model 2: ps ~ age + gender.d + income + cas.tt
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 1083 472028
## 2 1082 461466 1 10562 6.474e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Step 2 vs. step 3
anova(reg.2.tt, reg.3.tt, test="Chisq")## Analysis of Variance Table
##
## Model 1: ps ~ age + gender.d + income + cas.tt
## Model 2: ps ~ age + gender.d + income + cas.tt + gb.rs + gb.rf + gb.as +
## gb.af + gb.cs + gb.cf
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 1082 461466
## 2 1076 456600 6 4866 0.07497 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Step 3 vs. step 4
anova(reg.3.tt, reg.4.tt, test="Chisq")## Analysis of Variance Table
##
## Model 1: ps ~ age + gender.d + income + cas.tt + gb.rs + gb.rf + gb.as +
## gb.af + gb.cs + gb.cf
## Model 2: ps ~ age + gender.d + income + gb * cas.tt
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 1076 456600
## 2 1080 458506 -4 -1906.5 0.34348.7.2 Predicting pro-environmental intentions
Load relevant package
library(lm.beta)8.7.2.1 original data
8.7.2.1.1 Step 1: Predicting pro-environmental intentions from controls
reg.1 <- lm(int ~ age + gender.d + income, data = data)
summary(reg.1)##
## Call:
## lm(formula = int ~ age + gender.d + income, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8411 -0.9374 -0.0898 0.8571 3.6891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.827412 0.142174 26.921 < 2e-16 ***
## age -0.009190 0.002720 -3.379 0.000753 ***
## gender.d 0.264796 0.076199 3.475 0.000530 ***
## income -0.004053 0.013446 -0.301 0.763132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.272 on 1130 degrees of freedom
## Multiple R-squared: 0.02075, Adjusted R-squared: 0.01815
## F-statistic: 7.982 on 3 and 1130 DF, p-value: 2.877e-05lm.beta(reg.1)##
## Call:
## lm(formula = int ~ age + gender.d + income, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income
## 0.000000000 -0.099963821 0.103216563 -0.008992639confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.27895403 0.27895403
## age -0.10530064 -0.09462700
## gender.d -0.04628992 0.25272305
## income -0.03537461 0.01738933res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.27 -0.09 -0.03 1.32 -2.84 3.69 6.53 0.28 -0.16 0.04qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.98995, p-value = 5.243e-07par(mfrow = c(2, 2))
plot(reg.1) 
par(mfrow = c(1, 1))
vif(reg.1) ## age gender.d income
## 1.010123 1.018019 1.0269468.7.2.1.2 Step 2: Predicting pro-environmental intentions from controls and CAS
reg <- lm(int ~ age + gender.d + income +
cas, data = data)
summary(reg)##
## Call:
## lm(formula = int ~ age + gender.d + income + cas, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4571 -0.8059 -0.0835 0.7671 4.0868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7107704 0.1466645 18.483 < 2e-16 ***
## age -0.0085119 0.0024615 -3.458 0.000564 ***
## gender.d 0.1817050 0.0691440 2.628 0.008707 **
## income 0.0003424 0.0121692 0.028 0.977557
## cas 0.5945028 0.0375042 15.852 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.151 on 1129 degrees of freedom
## Multiple R-squared: 0.199, Adjusted R-squared: 0.1962
## F-statistic: 70.13 on 4 and 1129 DF, p-value: < 2.2e-16lm.beta(reg) ##
## Call:
## lm(formula = int ~ age + gender.d + income + cas, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas
## 0.0000000000 -0.0925868915 0.0708281052 0.0007597088 0.4237378953confint(lm.beta(reg), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.28776567 0.28776567
## age -0.09741642 -0.08775736
## gender.d -0.06483712 0.20649333
## income -0.02311714 0.02463655
## cas 0.35015211 0.49732368res.lm <- residuals(reg)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.15 -0.08 -0.04 1.15 -3.46 4.09 7.54 0.38 0.27 0.03qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99043, p-value = 9.703e-07par(mfrow = c(2, 2))
plot(reg) 
par(mfrow = c(1, 1))
vif(reg) ## age gender.d income cas
## 1.010428 1.023904 1.027480 1.0072088.7.2.1.3 Step 3: Predicting pro-environmental intentions from controls and CAS and needs
reg <- lm(int ~ age + gender.d + income +
cas +
gb.rs + gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
summary(reg)##
## Call:
## lm(formula = int ~ age + gender.d + income + cas + gb.rs + gb.rf +
## gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3157 -0.7929 -0.0661 0.7476 4.1973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.719858 0.295575 5.819 7.73e-09 ***
## age -0.007223 0.002591 -2.788 0.00539 **
## gender.d 0.176387 0.069514 2.537 0.01130 *
## income -0.005467 0.012198 -0.448 0.65409
## cas 0.586044 0.039526 14.827 < 2e-16 ***
## gb.rs 0.053888 0.034464 1.564 0.11819
## gb.rf -0.003895 0.034143 -0.114 0.90920
## gb.as 0.039571 0.039439 1.003 0.31591
## gb.af -0.067465 0.034167 -1.975 0.04856 *
## gb.cs 0.115229 0.029360 3.925 9.21e-05 ***
## gb.cf 0.078071 0.035912 2.174 0.02992 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.134 on 1123 degrees of freedom
## Multiple R-squared: 0.2266, Adjusted R-squared: 0.2197
## F-statistic: 32.9 on 10 and 1123 DF, p-value: < 2.2e-16lm.beta(reg) ##
## Call:
## lm(formula = int ~ age + gender.d + income + cas + gb.rs + gb.rf +
## gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas gb.rs
## 0.000000000 -0.078568878 0.068755155 -0.012129998 0.417708776 0.050141348
## gb.rf gb.as gb.af gb.cs gb.cf
## -0.004432139 0.034876599 -0.077452285 0.118602455 0.083911912confint(lm.beta(reg), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.57994108 0.57994108
## age -0.08365173 -0.07348603
## gender.d -0.06763687 0.20514718
## income -0.03606382 0.01180382
## cas 0.34015509 0.49526246
## gb.rs -0.01748013 0.11776282
## gb.rf -0.07142325 0.06255898
## gb.as -0.04250572 0.11225892
## gb.af -0.14449043 -0.01041414
## gb.cs 0.06099568 0.17620923
## gb.cf 0.01344933 0.15437450res.lm <- residuals(reg)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.13 -0.07 -0.03 1.13 -3.32 4.2 7.51 0.34 0.2 0.03qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99265, p-value = 2.027e-05par(mfrow = c(2, 2))
plot(reg) 
par(mfrow = c(1, 1))
vif(reg) ## age gender.d income cas gb.rs gb.rf gb.as gb.af
## 1.152908 1.066068 1.063477 1.152447 1.493146 2.191999 1.754384 2.234022
## gb.cs gb.cf
## 1.325999 2.1633028.7.2.1.4 Step 4: Predicting pro-environmental intentions from controls, CAS, needs, and interaction
reg <- lm(int ~ age + gender.d + income +
gb*cas, data = data)
summary(reg)##
## Call:
## lm(formula = int ~ age + gender.d + income + gb * cas, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2885 -0.7880 -0.0817 0.7428 4.1966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.329536 0.463636 5.024 5.87e-07 ***
## age -0.010468 0.002490 -4.204 2.83e-05 ***
## gender.d 0.183025 0.068659 2.666 0.00779 **
## income -0.004075 0.012141 -0.336 0.73723
## gb 0.079099 0.090796 0.871 0.38385
## cas 0.409769 0.212539 1.928 0.05411 .
## gb:cas 0.047136 0.043924 1.073 0.28344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.142 on 1127 degrees of freedom
## Multiple R-squared: 0.2116, Adjusted R-squared: 0.2074
## F-statistic: 50.42 on 6 and 1127 DF, p-value: < 2.2e-16lm.beta(reg) ##
## Call:
## lm(formula = int ~ age + gender.d + income + gb * cas, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income gb cas
## 0.000000000 -0.113867239 0.071342650 -0.009040577 0.054752453 0.292067346
## gb:cas
## 0.158581775confint(lm.beta(reg), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.90968725 0.90968725
## age -0.11875312 -0.10898136
## gender.d -0.06337174 0.20605704
## income -0.03286297 0.01478182
## gb -0.12339651 0.23290141
## cas -0.12494980 0.70908450
## gb:cas 0.07240053 0.24476302res.lm <- residuals(reg)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.14 -0.08 -0.04 1.11 -3.29 4.2 7.49 0.36 0.21 0.03qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99153, p-value = 4.183e-06par(mfrow = c(2, 2))
plot(reg) 
par(mfrow = c(1, 1))
vif(reg) ## age gender.d income gb cas gb:cas
## 1.048821 1.023931 1.037316 5.646747 32.806504 31.2172178.7.2.2 transformed data
8.7.2.2.1 Step 1: Predicting pro-environmental intentions from controls
reg.1 <- lm(int ~ age + gender.d + income, data = data)
summary(reg.1)##
## Call:
## lm(formula = int ~ age + gender.d + income, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8411 -0.9374 -0.0898 0.8571 3.6891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.827412 0.142174 26.921 < 2e-16 ***
## age -0.009190 0.002720 -3.379 0.000753 ***
## gender.d 0.264796 0.076199 3.475 0.000530 ***
## income -0.004053 0.013446 -0.301 0.763132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.272 on 1130 degrees of freedom
## Multiple R-squared: 0.02075, Adjusted R-squared: 0.01815
## F-statistic: 7.982 on 3 and 1130 DF, p-value: 2.877e-05lm.beta(reg.1) ##
## Call:
## lm(formula = int ~ age + gender.d + income, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income
## 0.000000000 -0.099963821 0.103216563 -0.008992639confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.27895403 0.27895403
## age -0.10530064 -0.09462700
## gender.d -0.04628992 0.25272305
## income -0.03537461 0.01738933res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.27 -0.09 -0.03 1.32 -2.84 3.69 6.53 0.28 -0.16 0.04qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.98995, p-value = 5.243e-07par(mfrow = c(2, 2))
plot(reg.1)
par(mfrow = c(1, 1))
vif(reg.1) ## age gender.d income
## 1.010123 1.018019 1.0269468.7.2.2.2 Step 2.tt: Predicting pro-environmental intentions from controls and CAS.tt
reg.2.tt <- lm(int ~ age + gender.d + income +
cas.tt, data = data)
summary(reg.2.tt)##
## Call:
## lm(formula = int ~ age + gender.d + income + cas.tt, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4226 -0.7699 -0.0878 0.7248 4.1992
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1675952 0.1334738 23.732 < 2e-16 ***
## age -0.0090963 0.0024384 -3.730 0.000201 ***
## gender.d 0.1658463 0.0685669 2.419 0.015731 *
## income 0.0004869 0.0120569 0.040 0.967792
## cas.tt 1.3088349 0.0786248 16.647 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.14 on 1129 degrees of freedom
## Multiple R-squared: 0.2137, Adjusted R-squared: 0.211
## F-statistic: 76.73 on 4 and 1129 DF, p-value: < 2.2e-16lm.beta(reg.2.tt) ##
## Call:
## lm(formula = int ~ age + gender.d + income + cas.tt, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas.tt
## 0.000000000 -0.098944493 0.064646417 0.001080355 0.441221828confint(lm.beta(reg.2.tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.26188451 0.26188451
## age -0.10372874 -0.09416024
## gender.d -0.06988640 0.19917923
## income -0.02257603 0.02473674
## cas.tt 0.28695472 0.59548893res.lm <- residuals(reg.2.tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.14 -0.09 -0.04 1.09 -3.42 4.2 7.62 0.4 0.35 0.03qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99038, p-value = 9.048e-07par(mfrow = c(2, 2))
plot(reg.2.tt) 
par(mfrow = c(1, 1))
vif(reg.2.tt) ## age gender.d income cas.tt
## 1.010128 1.025728 1.027472 1.0087638.7.2.2.3 Step 3.tt: Predicting pro-environmental intentions from controls and CAS.tt and needs
reg.3.tt <- lm(int ~ age + gender.d + income +
cas.tt +
gb.rs + gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
summary(reg.3.tt)##
## Call:
## lm(formula = int ~ age + gender.d + income + cas.tt + gb.rs +
## gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3685 -0.7545 -0.0808 0.7240 4.4042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.048479 0.291600 7.025 3.7e-12 ***
## age -0.008136 0.002566 -3.171 0.001561 **
## gender.d 0.161415 0.068775 2.347 0.019098 *
## income -0.004929 0.012060 -0.409 0.682848
## cas.tt 1.306661 0.082525 15.833 < 2e-16 ***
## gb.rs 0.055317 0.034071 1.624 0.104748
## gb.rf 0.001295 0.033701 0.038 0.969361
## gb.as 0.062265 0.039032 1.595 0.110947
## gb.af -0.055346 0.033786 -1.638 0.101678
## gb.cs 0.112530 0.029032 3.876 0.000112 ***
## gb.cf 0.064620 0.035602 1.815 0.069781 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.121 on 1123 degrees of freedom
## Multiple R-squared: 0.244, Adjusted R-squared: 0.2372
## F-statistic: 36.24 on 10 and 1123 DF, p-value: < 2.2e-16lm.beta(reg.3.tt)##
## Call:
## lm(formula = int ~ age + gender.d + income + cas.tt + gb.rs +
## gb.rf + gb.as + gb.af + gb.cs + gb.cf, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income cas.tt gb.rs
## 0.000000000 -0.088499885 0.062919166 -0.010935515 0.440488890 0.051471046
## gb.rf gb.as gb.af gb.cs gb.cf
## 0.001473393 0.054878177 -0.063539464 0.115824346 0.069454893confint(lm.beta(reg.3.tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.5721427767 0.572142777
## age -0.0935342759 -0.083465494
## gender.d -0.0720224057 0.197860737
## income -0.0345990060 0.012727976
## cas.tt 0.2785675254 0.602410255
## gb.rs -0.0153797508 0.118321842
## gb.rf -0.0646503039 0.067597090
## gb.as -0.0217065593 0.131462914
## gb.af -0.1298311500 0.002752223
## gb.cs 0.0588606659 0.172788027
## gb.cf -0.0003993541 0.139309139res.lm <- residuals(reg.3.tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.12 -0.08 -0.04 1.07 -3.37 4.4 7.77 0.36 0.31 0.03qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.9923, p-value = 1.23e-05par(mfrow = c(2, 2))
plot(reg.3.tt) 
par(mfrow = c(1, 1))
vif(reg.3.tt)## age gender.d income cas.tt gb.rs gb.rf gb.as gb.af
## 1.157028 1.067501 1.063486 1.149613 1.492849 2.184692 1.757905 2.234763
## gb.cs gb.cf
## 1.326362 2.1749818.7.2.2.4 Step 4.tt: Predicting pro-environmental intentions from controls, CAS.tt, needs, and interaction
reg.4.tt <- lm(int ~ age + gender.d + income +
gb*cas.tt, data = data)
summary(reg.4.tt)##
## Call:
## lm(formula = int ~ age + gender.d + income + gb * cas.tt, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2717 -0.7746 -0.0873 0.7173 4.4772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.352253 0.333368 7.056 2.99e-12 ***
## age -0.011261 0.002467 -4.565 5.54e-06 ***
## gender.d 0.166357 0.068028 2.445 0.01462 *
## income -0.004585 0.012021 -0.381 0.70296
## gb 0.175658 0.062612 2.806 0.00511 **
## cas.tt 1.359403 0.447542 3.037 0.00244 **
## gb:cas.tt 0.008883 0.089580 0.099 0.92103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.131 on 1127 degrees of freedom
## Multiple R-squared: 0.2276, Adjusted R-squared: 0.2235
## F-statistic: 55.34 on 6 and 1127 DF, p-value: < 2.2e-16lm.beta(reg.4.tt) ##
## Call:
## lm(formula = int ~ age + gender.d + income + gb * cas.tt, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income gb cas.tt
## 0.00000000 -0.12248761 0.06484568 -0.01017265 0.12159097 0.45826872
## gb:cas.tt
## 0.01445770confint(lm.beta(reg.4.tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.654092023 0.65409202
## age -0.127327240 -0.11764799
## gender.d -0.068630574 0.19832193
## income -0.033758317 0.01341302
## gb -0.001257964 0.24443990
## cas.tt -0.419840831 1.33637827
## gb:cas.tt -0.161303833 0.19021923res.lm <- residuals(reg.4.tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1134 0 1.13 -0.09 -0.04 1.09 -3.27 4.48 7.75 0.38 0.32 0.03qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99122, p-value = 2.733e-06par(mfrow = c(2, 2))
plot(reg.4.tt) 
par(mfrow = c(1, 1))
vif(reg.4.tt)## age gender.d income gb cas.tt gb:cas.tt
## 1.050287 1.025934 1.037779 2.740583 33.210502 31.0142648.7.2.2.5 Comparing different models
Step 1 vs. step 2
anova(reg.1, reg.2.tt, test="Chisq")## Analysis of Variance Table
##
## Model 1: int ~ age + gender.d + income
## Model 2: int ~ age + gender.d + income + cas.tt
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 1130 1827.1
## 2 1129 1467.0 1 360.07 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Step 2 vs. step 3
anova(reg.2.tt, reg.3.tt, test="Chisq")## Analysis of Variance Table
##
## Model 1: int ~ age + gender.d + income + cas.tt
## Model 2: int ~ age + gender.d + income + cas.tt + gb.rs + gb.rf + gb.as +
## gb.af + gb.cs + gb.cf
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 1129 1467.0
## 2 1123 1410.6 6 56.38 4.934e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Step 3 vs. step 4
anova(reg.3.tt, reg.4.tt, test="Chisq")## Analysis of Variance Table
##
## Model 1: int ~ age + gender.d + income + cas.tt + gb.rs + gb.rf + gb.as +
## gb.af + gb.cs + gb.cf
## Model 2: int ~ age + gender.d + income + gb * cas.tt
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 1123 1410.6
## 2 1127 1441.2 -4 -30.576 6.822e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 19 Predicting climate anxiety from all assessed correlates
9.1 original data without sociodemographics
reg.1 <- lm(cas ~
phq +
sp.1 + sp.2 + sp.3 + sp.4 + sp.5 + sp.6 +
sj + sdo + rwa + nd + pol.orient +
gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
asimp.all + aspro.all +
ps + int, data = data)
summary(reg.1)##
## Call:
## lm(formula = cas ~ phq + sp.1 + sp.2 + sp.3 + sp.4 + sp.5 + sp.6 +
## sj + sdo + rwa + nd + pol.orient + gb.rs + gb.rf + gb.cs +
## gb.cf + gb.as + gb.af + asimp.all + aspro.all + ps + int,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.97197 -0.44270 -0.08112 0.34954 3.01376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.509307 0.277406 -1.836 0.066642 .
## phq 0.144013 0.039312 3.663 0.000261 ***
## sp.1 0.020787 0.023714 0.877 0.380906
## sp.2 0.152444 0.021939 6.949 6.42e-12 ***
## sp.3 0.087608 0.027518 3.184 0.001497 **
## sp.4 -0.054804 0.030883 -1.775 0.076258 .
## sp.5 -0.159005 0.022547 -7.052 3.16e-12 ***
## sp.6 0.061890 0.028575 2.166 0.030546 *
## sj 0.016127 0.021888 0.737 0.461420
## sdo 0.035095 0.026138 1.343 0.179664
## rwa 0.040826 0.027496 1.485 0.137892
## nd 0.072180 0.022406 3.221 0.001314 **
## pol.orient 0.001700 0.001344 1.264 0.206401
## gb.rs 0.001216 0.021887 0.056 0.955705
## gb.rf 0.030392 0.022118 1.374 0.169711
## gb.cs 0.006043 0.019171 0.315 0.752667
## gb.cf 0.018685 0.023775 0.786 0.432099
## gb.as 0.011334 0.024931 0.455 0.649479
## gb.af 0.028668 0.022785 1.258 0.208599
## asimp.all 0.048946 0.025934 1.887 0.059388 .
## aspro.all -0.001127 0.028597 -0.039 0.968565
## ps 0.002164 0.001361 1.591 0.112016
## int 0.298209 0.020859 14.296 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7026 on 1064 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.4084, Adjusted R-squared: 0.3962
## F-statistic: 33.39 on 22 and 1064 DF, p-value: < 2.2e-16lm.beta(reg.1) ##
## Call:
## lm(formula = cas ~ phq + sp.1 + sp.2 + sp.3 + sp.4 + sp.5 + sp.6 +
## sj + sdo + rwa + nd + pol.orient + gb.rs + gb.rf + gb.cs +
## gb.cf + gb.as + gb.af + asimp.all + aspro.all + ps + int,
## data = data)
##
## Standardized Coefficients::
## (Intercept) phq sp.1 sp.2 sp.3 sp.4
## 0.000000000 0.117306482 0.033898436 0.204051202 0.121849046 -0.098529947
## sp.5 sp.6 sj sdo rwa nd
## -0.242428266 0.108143885 0.019630331 0.039888996 0.046047920 0.090986190
## pol.orient gb.rs gb.rf gb.cs gb.cf gb.as
## 0.036354484 0.001608838 0.048925668 0.008844659 0.028512380 0.014192685
## gb.af asimp.all aspro.all ps int
## 0.046342036 0.062046046 -0.001216712 0.050406227 0.419883871confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.544324441 0.54432444
## phq 0.040168127 0.19444484
## sp.1 -0.012632939 0.08042981
## sp.2 0.161002998 0.24709941
## sp.3 0.067852640 0.17584545
## sp.4 -0.159129390 -0.03793050
## sp.5 -0.286668965 -0.19818757
## sp.6 0.052073314 0.16421446
## sj -0.023318218 0.06257888
## sdo -0.011398970 0.09117696
## rwa -0.007904452 0.10000029
## nd 0.047021378 0.13495100
## pol.orient 0.033716598 0.03899237
## gb.rs -0.041338154 0.04455583
## gb.rf 0.005525629 0.09232571
## gb.cs -0.028772717 0.04646204
## gb.cf -0.018139351 0.07516411
## gb.as -0.034726180 0.06311155
## gb.af 0.001633459 0.09105061
## asimp.all 0.011158520 0.11293357
## aspro.all -0.057330274 0.05489685
## ps 0.047736538 0.05307592
## int 0.378954615 0.46081313res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 0 0.7 -0.08 -0.05 0.58 -1.97 3.01 4.99 0.77 1.06 0.02qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96699, p-value = 5.275e-15par(mfrow = c(2, 2))
plot(reg.1) 
par(mfrow = c(1, 1))
vif(reg.1) ## phq sp.1 sp.2 sp.3 sp.4 sp.5 sp.6
## 1.844222 2.689545 1.550950 2.634617 5.544656 2.125290 4.484030
## sj sdo rwa nd pol.orient gb.rs gb.rf
## 1.276723 1.587374 1.729805 1.434703 1.487077 1.508227 2.280202
## gb.cs gb.cf gb.as gb.af asimp.all aspro.all ps
## 1.416073 2.367246 1.752900 2.439902 1.943776 1.713593 1.806375
## int
## 1.5513639.2 transformed data without sociodemographics
reg.2tt <- lm(cas.tt ~
phq.tt +
sp.1 + sp.2 + sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt +
sj + sdo + rwa + nd + pol.orient +
gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
asimp.all + aspro.all +
ps + int, data = data)
summary(reg.2tt)##
## Call:
## lm(formula = cas.tt ~ phq.tt + sp.1 + sp.2 + sp.3.tt + sp.4.tt +
## sp.5.t + sp.6.tt + sj + sdo + rwa + nd + pol.orient + gb.rs +
## gb.rf + gb.cs + gb.cf + gb.as + gb.af + asimp.all + aspro.all +
## ps + int, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.04328 -0.22512 -0.00994 0.21120 0.97113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0571279 0.1431661 -0.399 0.689949
## phq.tt 0.1022968 0.0344436 2.970 0.003045 **
## sp.1 0.0045202 0.0107347 0.421 0.673783
## sp.2 0.0823437 0.0104339 7.892 7.36e-15 ***
## sp.3.tt 0.0705169 0.0305041 2.312 0.020984 *
## sp.4.tt -0.0468928 0.0359813 -1.303 0.192770
## sp.5.t -0.3204497 0.0404819 -7.916 6.14e-15 ***
## sp.6.tt 0.0422004 0.0323182 1.306 0.191910
## sj 0.0029154 0.0102558 0.284 0.776259
## sdo 0.0134933 0.0123118 1.096 0.273342
## rwa 0.0260586 0.0129235 2.016 0.044012 *
## nd 0.0403751 0.0105584 3.824 0.000139 ***
## pol.orient 0.0004748 0.0006324 0.751 0.452934
## gb.rs -0.0043430 0.0102906 -0.422 0.673088
## gb.rf 0.0105575 0.0103915 1.016 0.309872
## gb.cs 0.0035716 0.0089916 0.397 0.691286
## gb.cf 0.0133350 0.0111678 1.194 0.232719
## gb.as -0.0091521 0.0117277 -0.780 0.435340
## gb.af 0.0065861 0.0107284 0.614 0.539419
## asimp.all 0.0177356 0.0121880 1.455 0.145916
## aspro.all 0.0002348 0.0134684 0.017 0.986097
## ps 0.0011490 0.0006294 1.825 0.068215 .
## int 0.1412707 0.0097857 14.436 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3305 on 1064 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.4194, Adjusted R-squared: 0.4074
## F-statistic: 34.94 on 22 and 1064 DF, p-value: < 2.2e-16lm.beta(reg.2tt) ##
## Call:
## lm(formula = cas.tt ~ phq.tt + sp.1 + sp.2 + sp.3.tt + sp.4.tt +
## sp.5.t + sp.6.tt + sj + sdo + rwa + nd + pol.orient + gb.rs +
## gb.rf + gb.cs + gb.cf + gb.as + gb.af + asimp.all + aspro.all +
## ps + int, data = data)
##
## Standardized Coefficients::
## (Intercept) phq.tt sp.1 sp.2 sp.3.tt
## 0.0000000000 0.0938633292 0.0155225184 0.2321074070 0.0868005877
## sp.4.tt sp.5.t sp.6.tt sj sdo
## -0.0668926923 -0.2636272682 0.0596558357 0.0074733234 0.0322966005
## rwa nd pol.orient gb.rs gb.rf
## 0.0618947981 0.1071776314 0.0213853938 -0.0121004384 0.0357907667
## gb.cs gb.cf gb.as gb.af asimp.all
## 0.0110086604 0.0428512331 -0.0241345245 0.0224200429 0.0473447950
## aspro.all ps int
## 0.0005336073 0.0563603274 0.4188803564confint(lm.beta(reg.2tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.280919917 0.280919917
## phq.tt 0.026278238 0.161448420
## sp.1 -0.005541121 0.036586158
## sp.2 0.211633957 0.252580857
## sp.3.tt 0.026945491 0.146655685
## sp.4.tt -0.137495148 0.003709764
## sp.5.t -0.343060648 -0.184193888
## sp.6.tt -0.003758878 0.123070549
## sj -0.012650497 0.027597144
## sdo 0.008138448 0.056454753
## rwa 0.036536277 0.087253320
## nd 0.086460072 0.127895190
## pol.orient 0.020144584 0.022626203
## gb.rs -0.032292691 0.008091814
## gb.rf 0.015400683 0.056180850
## gb.cs -0.006634601 0.028651921
## gb.cf 0.020937846 0.064764620
## gb.as -0.047146669 -0.001122380
## gb.af 0.001368796 0.043471290
## asimp.all 0.023429629 0.071259961
## aspro.all -0.025894080 0.026961294
## ps 0.055125262 0.057595393
## int 0.399678842 0.438081871res.lm <- residuals(reg.2tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 0 0.33 -0.01 -0.01 0.33 -1.04 0.97 2.01 0.16 -0.17 0.01qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99686, p-value = 0.02963par(mfrow = c(2, 2))
plot(reg.2tt) 
par(mfrow = c(1, 1))
vif(reg.2tt) ## phq.tt sp.1 sp.2 sp.3.tt sp.4.tt sp.5.t sp.6.tt
## 1.830459 2.490430 1.585223 2.583756 4.828084 2.032627 3.825099
## sj sdo rwa nd pol.orient gb.rs gb.rf
## 1.266605 1.591462 1.726809 1.439624 1.486798 1.506577 2.274320
## gb.cs gb.cf gb.as gb.af asimp.all aspro.all ps
## 1.407625 2.360193 1.752827 2.444369 1.939952 1.717556 1.746976
## int
## 1.5428989.3 original data
reg.1 <- lm(cas ~ age + gender.d + income +
phq +
sp.1 + sp.2 + sp.3 + sp.4 + sp.5 + sp.6 +
sj + sdo + rwa + nd + pol.orient +
gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
asimp.all + aspro.all +
ps + int, data = data)
summary(reg.1)##
## Call:
## lm(formula = cas ~ age + gender.d + income + phq + sp.1 + sp.2 +
## sp.3 + sp.4 + sp.5 + sp.6 + sj + sdo + rwa + nd + pol.orient +
## gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af + asimp.all +
## aspro.all + ps + int, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.01058 -0.45946 -0.07653 0.33907 3.05179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.668917 0.281456 -2.377 0.017648 *
## age 0.007955 0.001774 4.484 8.12e-06 ***
## gender.d 0.070194 0.045259 1.551 0.121214
## income -0.001554 0.007717 -0.201 0.840416
## phq 0.136983 0.039074 3.506 0.000474 ***
## sp.1 0.025007 0.023737 1.054 0.292349
## sp.2 0.141974 0.021872 6.491 1.30e-10 ***
## sp.3 0.085995 0.027267 3.154 0.001656 **
## sp.4 -0.050787 0.030622 -1.659 0.097512 .
## sp.5 -0.163697 0.022426 -7.299 5.66e-13 ***
## sp.6 0.058717 0.028362 2.070 0.038672 *
## sj 0.014073 0.021692 0.649 0.516618
## sdo 0.024650 0.026080 0.945 0.344790
## rwa 0.016312 0.027838 0.586 0.558031
## nd 0.071713 0.022288 3.218 0.001332 **
## pol.orient 0.002000 0.001337 1.496 0.135003
## gb.rs 0.002157 0.021956 0.098 0.921763
## gb.rf 0.026267 0.022058 1.191 0.233992
## gb.cs 0.017087 0.019224 0.889 0.374296
## gb.cf 0.038055 0.023914 1.591 0.111823
## gb.as 0.005706 0.024978 0.228 0.819353
## gb.af 0.030379 0.022614 1.343 0.179444
## asimp.all 0.074007 0.026232 2.821 0.004874 **
## aspro.all 0.013089 0.028495 0.459 0.646079
## ps 0.001289 0.001364 0.945 0.344824
## int 0.300784 0.020674 14.549 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6961 on 1061 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.421, Adjusted R-squared: 0.4073
## F-statistic: 30.86 on 25 and 1061 DF, p-value: < 2.2e-16lm.beta(reg.1) ##
## Call:
## lm(formula = cas ~ age + gender.d + income + phq + sp.1 + sp.2 +
## sp.3 + sp.4 + sp.5 + sp.6 + sj + sdo + rwa + nd + pol.orient +
## gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af + asimp.all +
## aspro.all + ps + int, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income phq sp.1
## 0.000000000 0.122284253 0.038835128 -0.004929607 0.111580727 0.040779297
## sp.2 sp.3 sp.4 sp.5 sp.6 sj
## 0.190037277 0.119606091 -0.091307331 -0.249581643 0.102600210 0.017131072
## sdo rwa nd pol.orient gb.rs gb.rf
## 0.028017304 0.018398040 0.090398051 0.042770803 0.002853739 0.042286331
## gb.cs gb.cf gb.as gb.af asimp.all aspro.all
## 0.025009423 0.058070358 0.007145177 0.049108331 0.093813936 0.014128079
## ps int
## 0.030025842 0.423508995confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.5522732001 0.55227320
## age 0.1188033069 0.12576520
## gender.d -0.0499717506 0.12764201
## income -0.0200722896 0.01021308
## phq 0.0349105659 0.18825089
## sp.1 -0.0057973170 0.08735591
## sp.2 0.1471206288 0.23295393
## sp.3 0.0661035388 0.17310864
## sp.4 -0.1513941933 -0.03122047
## sp.5 -0.2935861648 -0.20557712
## sp.6 0.0469473614 0.15825306
## sj -0.0254329092 0.05969505
## sdo -0.0231571435 0.07919175
## rwa -0.0362252751 0.07302135
## nd 0.0466651729 0.13413093
## pol.orient 0.0401476609 0.04539395
## gb.rs -0.0402283152 0.04593579
## gb.rf -0.0009966893 0.08556935
## gb.cs -0.0127121176 0.06273096
## gb.cf 0.0111472264 0.10499349
## gb.as -0.0418675442 0.05615790
## gb.af 0.0047344289 0.09348223
## asimp.all 0.0423405979 0.14528727
## aspro.all -0.0417851220 0.07004128
## ps 0.0273495502 0.03270213
## int 0.3829423055 0.46407568res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 0 0.69 -0.08 -0.05 0.59 -2.01 3.05 5.06 0.79 1.24 0.02qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96497, p-value = 1.634e-15par(mfrow = c(2, 2))
plot(reg.1) 
par(mfrow = c(1, 1))
vif(reg.1) ## age gender.d income phq sp.1 sp.2 sp.3
## 1.362736 1.148885 1.097689 1.856204 2.745512 1.570507 2.635343
## sp.4 sp.5 sp.6 sj sdo rwa nd
## 5.553885 2.142246 4.500635 1.277570 1.610108 1.806477 1.446332
## pol.orient gb.rs gb.rf gb.cs gb.cf gb.as gb.af
## 1.498185 1.546303 2.310620 1.450734 2.439958 1.792761 2.448760
## asimp.all aspro.all ps int
## 2.026230 1.733410 1.849498 1.5526929.4 transformed data
reg.2tt <- lm(cas.tt ~ age + gender.d + income +
phq.tt +
sp.1 + sp.2 + sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt +
sj + sdo + rwa + nd + pol.orient +
gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
asimp.all + aspro.all +
ps + int, data = data)
summary(reg.2tt)##
## Call:
## lm(formula = cas.tt ~ age + gender.d + income + phq.tt + sp.1 +
## sp.2 + sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt + sj + sdo +
## rwa + nd + pol.orient + gb.rs + gb.rf + gb.cs + gb.cf + gb.as +
## gb.af + asimp.all + aspro.all + ps + int, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1278 -0.2295 -0.0078 0.2046 0.9949
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1321089 0.1443736 -0.915 0.360374
## age 0.0044761 0.0008311 5.385 8.90e-08 ***
## gender.d 0.0303525 0.0212172 1.431 0.152850
## income -0.0015299 0.0036149 -0.423 0.672212
## phq.tt 0.0980469 0.0340942 2.876 0.004111 **
## sp.1 0.0062385 0.0107080 0.583 0.560288
## sp.2 0.0767096 0.0103575 7.406 2.64e-13 ***
## sp.3.tt 0.0685782 0.0301048 2.278 0.022926 *
## sp.4.tt -0.0435846 0.0355212 -1.227 0.220094
## sp.5.t -0.3316210 0.0401081 -8.268 4.03e-16 ***
## sp.6.tt 0.0403490 0.0319533 1.263 0.206956
## sj 0.0019008 0.0101242 0.188 0.851111
## sdo 0.0082276 0.0122338 0.673 0.501394
## rwa 0.0120024 0.0130403 0.920 0.357569
## nd 0.0398381 0.0104622 3.808 0.000148 ***
## pol.orient 0.0006347 0.0006263 1.013 0.311104
## gb.rs -0.0031137 0.0102842 -0.303 0.762130
## gb.rf 0.0085717 0.0103246 0.830 0.406600
## gb.cs 0.0099834 0.0089847 1.111 0.266755
## gb.cf 0.0238141 0.0111820 2.130 0.033428 *
## gb.as -0.0128182 0.0117009 -1.095 0.273552
## gb.af 0.0071290 0.0106061 0.672 0.501626
## asimp.all 0.0316792 0.0122845 2.579 0.010048 *
## aspro.all 0.0080240 0.0133662 0.600 0.548424
## ps 0.0006469 0.0006288 1.029 0.303779
## int 0.1426145 0.0096607 14.762 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3262 on 1061 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.4362, Adjusted R-squared: 0.4229
## F-statistic: 32.84 on 25 and 1061 DF, p-value: < 2.2e-16lm.beta(reg.2tt) ##
## Call:
## lm(formula = cas.tt ~ age + gender.d + income + phq.tt + sp.1 +
## sp.2 + sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt + sj + sdo +
## rwa + nd + pol.orient + gb.rs + gb.rf + gb.cs + gb.cf + gb.as +
## gb.af + asimp.all + aspro.all + ps + int, data = data)
##
## Standardized Coefficients::
## (Intercept) age gender.d income phq.tt sp.1
## 0.000000000 0.144901164 0.035363159 -0.010218202 0.089963799 0.021423325
## sp.2 sp.3.tt sp.4.tt sp.5.t sp.6.tt sj
## 0.216226291 0.084414164 -0.062173529 -0.272817621 0.057038632 0.004872460
## sdo rwa nd pol.orient gb.rs gb.rf
## 0.019693038 0.028508344 0.105752123 0.028586530 -0.008675395 0.029058883
## gb.cs gb.cf gb.as gb.af asimp.all aspro.all
## 0.030771449 0.076524917 -0.033802048 0.024268366 0.084566969 0.018238532
## ps int
## 0.031731927 0.422864730confint(lm.beta(reg.2tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.2832901704 0.283290170
## age 0.1432702855 0.146532043
## gender.d -0.0062692484 0.076995567
## income -0.0173113389 -0.003125066
## phq.tt 0.0230640735 0.156863525
## sp.1 0.0004119926 0.042434657
## sp.2 0.1959028594 0.236549722
## sp.3.tt 0.0253423763 0.143485953
## sp.4.tt -0.1318733610 0.007526304
## sp.5.t -0.3515178950 -0.194117348
## sp.6.tt -0.0056601950 0.119737459
## sj -0.0149932627 0.024738183
## sdo -0.0043122478 0.043698324
## rwa 0.0029205722 0.054096116
## nd 0.0852231573 0.126281089
## pol.orient 0.0273576711 0.029815388
## gb.rs -0.0288551419 0.011504352
## gb.rf 0.0087999346 0.049317832
## gb.cs 0.0131415804 0.048401318
## gb.cf 0.0545834717 0.098466362
## gb.as -0.0567615419 -0.010842553
## gb.af 0.0034570396 0.045079693
## asimp.all 0.0604623184 0.108671620
## aspro.all -0.0079887494 0.044465814
## ps 0.0304981838 0.032965671
## int 0.4039085161 0.441820944res.lm <- residuals(reg.2tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 0 0.32 -0.01 -0.01 0.33 -1.13 0.99 2.12 0.16 -0.05 0.01qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99709, p-value = 0.04461par(mfrow = c(2, 2))
plot(reg.2tt) 
par(mfrow = c(1, 1))
vif(reg.2tt) ## age gender.d income phq.tt sp.1 sp.2 sp.3.tt
## 1.362411 1.149990 1.096978 1.841767 2.544734 1.604095 2.584265
## sp.4.tt sp.5.t sp.6.tt sj sdo rwa nd
## 4.831993 2.048952 3.839804 1.267525 1.613653 1.805465 1.451556
## pol.orient gb.rs gb.rf gb.cs gb.cf gb.as gb.af
## 1.497523 1.545187 2.305553 1.443295 2.429889 1.791749 2.453230
## asimp.all aspro.all ps int
## 2.023827 1.737108 1.790129 1.5441779.5 original data without age
reg.1 <- lm(cas ~ gender.d + income +
phq +
sp.1 + sp.2 + sp.3 + sp.4 + sp.5 + sp.6 +
sj + sdo + rwa + nd + pol.orient +
gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
asimp.all + aspro.all +
ps + int, data = data)
summary(reg.1)##
## Call:
## lm(formula = cas ~ gender.d + income + phq + sp.1 + sp.2 + sp.3 +
## sp.4 + sp.5 + sp.6 + sj + sdo + rwa + nd + pol.orient + gb.rs +
## gb.rf + gb.cs + gb.cf + gb.as + gb.af + asimp.all + aspro.all +
## ps + int, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.96062 -0.43975 -0.07639 0.36147 2.97812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5615010 0.2829460 -1.984 0.047459 *
## gender.d 0.0768914 0.0456392 1.685 0.092328 .
## income 0.0031523 0.0077139 0.409 0.682881
## phq 0.1398474 0.0394182 3.548 0.000405 ***
## sp.1 0.0265363 0.0239470 1.108 0.268059
## sp.2 0.1505709 0.0219826 6.850 1.25e-11 ***
## sp.3 0.0869935 0.0275099 3.162 0.001610 **
## sp.4 -0.0542182 0.0308867 -1.755 0.079482 .
## sp.5 -0.1572020 0.0225797 -6.962 5.86e-12 ***
## sp.6 0.0596648 0.0286157 2.085 0.037304 *
## sj 0.0153197 0.0218844 0.700 0.484063
## sdo 0.0318603 0.0262636 1.213 0.225362
## rwa 0.0415270 0.0275080 1.510 0.131435
## nd 0.0752356 0.0224733 3.348 0.000843 ***
## pol.orient 0.0017775 0.0013479 1.319 0.187537
## gb.rs -0.0041567 0.0221070 -0.188 0.850893
## gb.rf 0.0264972 0.0222559 1.191 0.234087
## gb.cs 0.0060523 0.0192367 0.315 0.753109
## gb.cf 0.0202003 0.0237908 0.849 0.396028
## gb.as 0.0153771 0.0251080 0.612 0.540380
## gb.af 0.0308950 0.0228166 1.354 0.176005
## asimp.all 0.0514996 0.0259783 1.982 0.047691 *
## aspro.all 0.0002819 0.0286055 0.010 0.992139
## ps 0.0022134 0.0013603 1.627 0.104015
## int 0.2985423 0.0208531 14.316 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7023 on 1062 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.41, Adjusted R-squared: 0.3967
## F-statistic: 30.75 on 24 and 1062 DF, p-value: < 2.2e-16lm.beta(reg.1) ##
## Call:
## lm(formula = cas ~ gender.d + income + phq + sp.1 + sp.2 + sp.3 +
## sp.4 + sp.5 + sp.6 + sj + sdo + rwa + nd + pol.orient + gb.rs +
## gb.rf + gb.cs + gb.cf + gb.as + gb.af + asimp.all + aspro.all +
## ps + int, data = data)
##
## Standardized Coefficients::
## (Intercept) gender.d income phq sp.1
## 0.0000000000 0.0425406797 0.0099976300 0.1139137083 0.0432731503
## sp.2 sp.3 sp.4 sp.5 sp.6
## 0.2015441082 0.1209945548 -0.0974762530 -0.2396792451 0.1042563974
## sj sdo rwa nd pol.orient
## 0.0186481323 0.0362126064 0.0468385487 0.0948383968 0.0380197884
## gb.rs gb.rf gb.cs gb.cf gb.as
## -0.0054996043 0.0426562192 0.0088585176 0.0308245807 0.0192557661
## gb.af asimp.all aspro.all ps int
## 0.0499423409 0.0652831273 0.0003042626 0.0515565911 0.4203528365confint(lm.beta(reg.1), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.555196692 0.55519669
## gender.d -0.047012617 0.13209398
## income -0.005138672 0.02513393
## phq 0.036567335 0.19126008
## sp.1 -0.003715686 0.09026199
## sp.2 0.158409825 0.24467839
## sp.3 0.067014710 0.17497440
## sp.4 -0.158082236 -0.03687027
## sp.5 -0.283985109 -0.19537338
## sp.6 0.048106761 0.16040603
## sj -0.024293466 0.06158973
## sdo -0.015321854 0.08774707
## rwa -0.007137753 0.10081485
## nd 0.050741327 0.13893547
## pol.orient 0.035374974 0.04066460
## gb.rs -0.048877969 0.03787876
## gb.rf -0.001014264 0.08632670
## gb.cs -0.028887693 0.04660473
## gb.cf -0.015857795 0.07750696
## gb.as -0.030011154 0.06852269
## gb.af 0.005171669 0.09471301
## asimp.all 0.014308411 0.11625784
## aspro.all -0.055825514 0.05643404
## ps 0.048887352 0.05422583
## int 0.379434857 0.46127082res.lm <- residuals(reg.1)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 0 0.69 -0.08 -0.05 0.59 -1.96 2.98 4.94 0.76 1.05 0.02qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.96742, p-value = 6.8e-15par(mfrow = c(2, 2))
plot(reg.1) 
par(mfrow = c(1, 1))
vif(reg.1) ## gender.d income phq sp.1 sp.2 sp.3 sp.4
## 1.147633 1.077382 1.855708 2.744945 1.558440 2.635167 5.550417
## sp.5 sp.6 sj sdo rwa nd pol.orient
## 2.133310 4.500385 1.277360 1.603987 1.732764 1.444535 1.496128
## gb.rs gb.rf gb.cs gb.cf gb.as gb.af asimp.all
## 1.539944 2.310608 1.426963 2.372308 1.779395 2.448697 1.952048
## aspro.all ps int
## 1.715995 1.807252 1.5517859.6 transformed data without age
reg.2tt <- lm(cas.tt ~ gender.d + income +
phq.tt +
sp.1 + sp.2 + sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt +
sj + sdo + rwa + nd + pol.orient +
gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
asimp.all + aspro.all +
ps + int, data = data)
summary(reg.2tt)##
## Call:
## lm(formula = cas.tt ~ gender.d + income + phq.tt + sp.1 + sp.2 +
## sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt + sj + sdo + rwa + nd +
## pol.orient + gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
## asimp.all + aspro.all + ps + int, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.05536 -0.22186 -0.00679 0.21398 0.95476
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0829899 0.1459725 -0.569 0.56979
## gender.d 0.0342704 0.0214825 1.595 0.11095
## income 0.0011082 0.0036285 0.305 0.76010
## phq.tt 0.0987558 0.0345405 2.859 0.00433 **
## sp.1 0.0070426 0.0108472 0.649 0.51631
## sp.2 0.0815142 0.0104541 7.797 1.50e-14 ***
## sp.3.tt 0.0701006 0.0304978 2.299 0.02172 *
## sp.4.tt -0.0461249 0.0359833 -1.282 0.20018
## sp.5.t -0.3175993 0.0405478 -7.833 1.15e-14 ***
## sp.6.tt 0.0391568 0.0323710 1.210 0.22669
## sj 0.0025403 0.0102561 0.248 0.80442
## sdo 0.0121726 0.0123719 0.984 0.32539
## rwa 0.0263359 0.0129330 2.036 0.04197 *
## nd 0.0417706 0.0105930 3.943 8.57e-05 ***
## pol.orient 0.0005131 0.0006341 0.809 0.41855
## gb.rs -0.0067265 0.0103967 -0.647 0.51779
## gb.rf 0.0087712 0.0104598 0.839 0.40190
## gb.cs 0.0036700 0.0090246 0.407 0.68434
## gb.cf 0.0139881 0.0111767 1.252 0.21101
## gb.as -0.0074618 0.0118112 -0.632 0.52768
## gb.af 0.0075489 0.0107447 0.703 0.48248
## asimp.all 0.0188869 0.0122105 1.547 0.12222
## aspro.all 0.0008988 0.0134748 0.067 0.94683
## ps 0.0011685 0.0006294 1.857 0.06364 .
## int 0.1413856 0.0097845 14.450 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3304 on 1062 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.4208, Adjusted R-squared: 0.4077
## F-statistic: 32.15 on 24 and 1062 DF, p-value: < 2.2e-16lm.beta(reg.2tt) ##
## Call:
## lm(formula = cas.tt ~ gender.d + income + phq.tt + sp.1 + sp.2 +
## sp.3.tt + sp.4.tt + sp.5.t + sp.6.tt + sj + sdo + rwa + nd +
## pol.orient + gb.rs + gb.rf + gb.cs + gb.cf + gb.as + gb.af +
## asimp.all + aspro.all + ps + int, data = data)
##
## Standardized Coefficients::
## (Intercept) gender.d income phq.tt sp.1 sp.2
## 0.000000000 0.039927822 0.007401798 0.090614240 0.024184770 0.229769402
## sp.3.tt sp.4.tt sp.5.t sp.6.tt sj sdo
## 0.086288133 -0.065797229 -0.261282270 0.055353234 0.006511826 0.029135589
## rwa nd pol.orient gb.rs gb.rf gb.cs
## 0.062553441 0.110882008 0.023112153 -0.018741450 0.029735156 0.011311838
## gb.cf gb.as gb.af asimp.all aspro.all ps
## 0.044949659 -0.019677014 0.025697802 0.050418099 0.002042999 0.057318519
## int
## 0.419220944confint(lm.beta(reg.2tt), level = 0.95)## 2.5 % 97.5 %
## (Intercept) -0.2864271902 0.286427190
## gender.d -0.0022250512 0.082080696
## income 0.0002820515 0.014521545
## phq.tt 0.0228388223 0.158389658
## sp.1 0.0029003189 0.045469222
## sp.2 0.2092563147 0.250282488
## sp.3.tt 0.0264453102 0.146130957
## sp.4.tt -0.1364037063 0.004809248
## sp.5.t -0.3408451244 -0.181719416
## sp.6.tt -0.0081652454 0.118871714
## sj -0.0136126968 0.026636349
## sdo 0.0048595385 0.053411640
## rwa 0.0371762159 0.087930667
## nd 0.0900964072 0.131667608
## pol.orient 0.0218680085 0.024356298
## gb.rs -0.0391419529 0.001659054
## gb.rf 0.0092110042 0.050259307
## gb.cs -0.0063962431 0.029019919
## gb.cf 0.0230187483 0.066880570
## gb.as -0.0428530273 0.003499000
## gb.af 0.0046144755 0.046781128
## asimp.all 0.0264586202 0.074377577
## aspro.all -0.0243972987 0.028483296
## ps 0.0560835361 0.058553501
## int 0.4000218157 0.438420073res.lm <- residuals(reg.2tt)
describe (res.lm)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1087 0 0.33 -0.01 -0.01 0.32 -1.06 0.95 2.01 0.15 -0.17 0.01qqnorm(res.lm)
qqline(res.lm,col="red")
shapiro.test(res.lm)##
## Shapiro-Wilk normality test
##
## data: res.lm
## W = 0.99698, p-value = 0.03681par(mfrow = c(2, 2))
plot(reg.2tt) 
par(mfrow = c(1, 1))
vif(reg.2tt) ## gender.d income phq.tt sp.1 sp.2 sp.3.tt sp.4.tt
## 1.148638 1.076832 1.841739 2.544239 1.592193 2.584038 4.831141
## sp.5.t sp.6.tt sj sdo rwa nd pol.orient
## 2.040318 3.839619 1.267351 1.607868 1.730255 1.449848 1.495578
## gb.rs gb.rf gb.cs gb.cf gb.as gb.af asimp.all
## 1.538612 2.305523 1.418723 2.365196 1.778803 2.453098 1.948158
## aspro.all ps int
## 1.720088 1.747649 1.543316
2.5 Social dominance orientation