Reliability Test for Items or Scales
Compute various measures of internal consistencies for tests or item-scales of questionnaires.
item_reliability(x, standardize = FALSE, digits = 3)
x |
A matrix or a data frame. |
standardize |
Logical, if |
digits |
Amount of digits for returned values. |
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.
The absolute value of the item discrimination indices should be
above 0.1. An index between 0.1 and 0.3 is considered as "fair",
while an index above 0.3 (or below -0.3) is "good". Items with
low discrimination indices are often ambiguously worded and
should be examined. Items with negative indices should be
examined to determine why a negative value was obtained (e.g.
reversed answer categories regarding positive and negative poles).
A data frame with the corrected item-total correlations (item
discrimination, column item_discrimination) and Cronbach's Alpha
(if item deleted, column alpha_if_deleted) for each item
of the scale, or NULL if data frame had too less columns.
data(mtcars)
x <- mtcars[, c("cyl", "gear", "carb", "hp")]
item_reliability(x)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.