Describe Properties of Item Scales
Compute various measures of internal consistencies
applied to (sub)scales, which items were extracted using
parameters::principal_components()
.
check_itemscale(x)
x |
An object of class |
check_itemscale()
calculates various measures of internal
consistencies, such as Cronbach's alpha, item difficulty or discrimination etc.
on subscales which were built from several items. Subscales are retrieved from
the results of parameters::principal_components()
, i.e. based on
how many components were extracted from the PCA, check_itemscale()
retrieves those variables that belong to a component and calculates the above
mentioned measures.
A list of data frames, with related measures of internal consistencies of each subscale.
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, acceptable values are 0.20 or higher; the closer to 1.00 the better. See item_reliability
for more details.
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
.
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
Trochim WMK (2008) Types of Reliability. (web)
# data generation from '?prcomp', slightly modified C <- chol(S <- toeplitz(.9^(0:15))) set.seed(17) X <- matrix(rnorm(16000), 100, 16) Z <- X %*% C if (require("parameters") && require("psych")) { pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3) pca check_itemscale(pca) }
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