View source: R/check_itemscale.R
check_itemscale | R Documentation |
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(0.9^(0:15))) set.seed(17) X <- matrix(rnorm(1600), 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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