Compute various measures of internal consistencies for tests or item-scales of questionnaires.
item_intercor(x, method = c("pearson", "spearman", "kendall"))The mean inter-item-correlation value for x.
A matrix as returned by the cor()-function,
or a data frame with items (e.g. from a test or questionnaire).
Correlation computation method. May be one of
"spearman" (default), "pearson" or "kendall".
You may use initial letter only.
This function calculates a mean inter-item-correlation, i.e.
a correlation matrix of x will be computed (unless
x is already a matrix as returned by the cor()-function)
and the mean of the sum of all item's correlation values is returned.
Requires either a data frame or a computed cor()-object.
“Ideally, the average inter-item correlation for a set of
items should be between .20 and .40, suggesting that while the
items are reasonably homogeneous, they do contain sufficiently
unique variance so as to not be isomorphic with each other.
When values are lower than .20, then the items may not be
representative of the same content domain. If values are higher than
.40, the items may be only capturing a small bandwidth of the construct.”
(Piedmont 2014)
Piedmont RL. 2014. Inter-item Correlations. In: Michalos AC (eds) Encyclopedia of Quality of Life and Well-Being Research. Dordrecht: Springer, 3303-3304. tools:::Rd_expr_doi("10.1007/978-94-007-0753-5_1493")
data(mtcars)
x <- mtcars[, c("cyl", "gear", "carb", "hp")]
item_intercor(x)
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