item_reliability: Reliability Test for Items or Scales

In performance: Assessment of Regression Models Performance

Reliability Test for Items or Scales

Description

Compute various measures of internal consistencies for tests or item-scales of questionnaires.

Usage

item_reliability(x, standardize = FALSE, digits = 3, verbose = TRUE)

Arguments

x	A matrix or a data frame.
standardize	Logical, if TRUE, the data frame's vectors will be standardized. Recommended when the variables have different measures / scales.
digits	Amount of digits for returned values.
verbose	Toggle warnings and messages.

Details

This function calculates the item-total correlations, 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 0.2. An index between 0.2 and 0.4 is considered as "fair", while an index above 0.4 (or below -0.4) is "good". The range of satisfactory values is from 0.4 to 0.7. 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).

See check_itemscale() and item_discrimination() for more details on the interpretation of the results.

Value

A data frame with the item-total correlations (column Item_Total_Correlation), corrected item-total correlations (item discrimination, column 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.

Note

• Item difficulty should range between 0.2 and 0.8. Ideal value is p+(1-p)/2 (which mostly is between 0.5 and 0.8). See item_difficulty() for details.

• For item discrimination, also known as corrected item-total correlations, acceptable values are 0.20 or higher; the closer to 1.00 the better. See item_discrimination() for more details. If an item discrimination is negative, the corresponding item probably need to be reverse-coded (which can be done with datawizard::reverse()).

• In case the total Cronbach's alpha value is below the acceptable cut-off of 0.7 (mostly if an index has few items), the mean inter-item-correlation is an alternative measure to indicate acceptability. Satisfactory range lies between 0.2 and 0.4. See also item_intercor().

References

• Briggs SR, Cheek JM (1986) The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x

Examples

data(mtcars)
x <- mtcars[, c("cyl", "gear", "carb", "hp")]
item_reliability(x)

performance documentation built on Aug. 31, 2025, 1:07 a.m.