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Compute various measures of internal consistencies for tests or item-scales of questionnaires.
item_difficulty(x)
Depending on the function, x
may be a matrix
as
returned by the cor
-function, or a data frame
with items (e.g. from a test or questionnaire).
A data frame with three columns: The name(s) of the item(s), the item difficulties for each item, and the ideal item difficulty.
This function calculates the item difficulty, which should
range between 0.2 and 0.8. Lower values are a signal for
more difficult items, while higher values close to one
are a sign for easier items. The ideal value for item difficulty
is p + (1 - p) / 2
, where p = 1 / max(x)
. In most
cases, the ideal item difficulty lies between 0.5 and 0.8.
# NOT RUN {
data(mtcars)
x <- mtcars[, c("cyl", "gear", "carb", "hp")]
item_difficulty(x)
# }
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