sjmisc (version 1.8)

reliab_test: Performs a reliability test on an item scale

Description

This function calculates the item discriminations (corrected item-total correlations for each item of x with the remaining items) and the Cronbach's alpha for each item, if it was deleted from the scale.

Usage

reliab_test(x, scale.items = FALSE, digits = 3)

Arguments

x

data.frame with items (from a scale).

scale.items

Logical, if TRUE, the data frame's vectors will be scaled. Recommended, when the variables have different measures / scales.

digits

Amount of digits for Cronbach's Alpha and correlation values in returned data frame.

Value

A data frame with the corrected item-total correlations (item discrimination) and Cronbach's alpha (if item deleted) for each item of the scale, or NULL if data frame had too less columns.

See Also

cronb

Examples

# Data from the EUROFAMCARE sample dataset
data(efc)

# retrieve variable and value labels
varlabs <- get_label(efc)

# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")

# create data frame with COPE-index scale
x <- data.frame(efc[, c(start:end)])
colnames(x) <- varlabs[c(start:end)]

## Not run: 
# library(sjPlot)
# sjt.df(reliab_test(x), describe = FALSE, showCommentRow = TRUE,
#        commentString = sprintf("Cronbach's α=%.2f", cronb(x)))## End(Not run)

# Compute PCA on Cope-Index, and perform a
# reliability check on each extracted factor.
## Not run: 
# factors <- sjt.pca(x)$factor.index
# findex <- sort(unique(factors))
# library(sjPlot)
# for (i in 1:length(findex)) {
#  rel.df <- subset(x, select = which(factors == findex[i]))
#  if (ncol(rel.df) >= 3) {
#    sjt.df(reliab_test(rel.df), describe = FALSE, showCommentRow = TRUE,
#           useViewer = FALSE, title = "Item-Total-Statistic",
#           commentString = sprintf("Scale's overall Cronbach's α=%.2f",
#                                   cronb(rel.df)))
#    }
#  }## End(Not run)