Load Packages
haven
Import and export SPSS, STATA, and SAS files
if(!require(haven)){
install.packages("haven", dependencies = TRUE)
library(haven)
}
psych
Provides various procedures for psychological, psychometric, and
personality research
if(!require(psych)){
install.packages("psych", dependencies = TRUE)
library(psych)
}
Import Data
dataset <- read_sav("https://osf.io/kd4ej/download")
Scoring Variable
#create dataframe with only relevant variables to work with
Extraversion <- data.frame (dataset$FFM_1, dataset$FFM_6, dataset$FFM_11, dataset$FFM_16, dataset$FFM_21, dataset$FFM_26, dataset$FFM_31, dataset$FFM_36)
#create list of 'keys'. The numbers just refer to the order of the question in the data.frame() you just made. The most important thing is to mark the questions that should be reversed scored with a '-'.
Extraversion.keys <- make.keys(Extraversion, list(Extraversion=c(1,-2,3,4,-5,6,-7,8)))
#score the scale
Extraversion.scales <- scoreItems (Extraversion.keys, Extraversion)
#save the scores
Extraversion.scores <- Extraversion.scales$scores
#save the scores back in 'dataset'
dataset$Extraversion <- Extraversion.scores[,]
#print the cronbach alpha
Extraversion.scales$alpha
Extraversion
alpha 0.8141661
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