sjmisc (version 1.0.0)

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 df with the remaining items) and the Cronbach's alpha for each item, if it was deleted from the scale.

Usage

reliab_test(df, scaleItems = FALSE, digits = 3)

Arguments

df

A data frame with items (from a scale)

scaleItems

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_var_labels(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
df <- as.data.frame(efc[, c(start:end)])
colnames(df) <- varlabs[c(start:end)]

sjt.df(reliab_test(df),
       describe = FALSE,
       showCommentRow = TRUE,
       commentString = sprintf("Cronbach's α=%.2f",
                               cronb(df)))

# ---------------------------------------
# Compute PCA on Cope-Index, and perform a
# reliability check on each extracted factor.
# ---------------------------------------
factors <- sjt.pca(df)$factor.index
findex <- sort(unique(factors))
for (i in 1:length(findex)) {
 rel.df <- subset(df, 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)))
   }
 }