check_itemscale: Describe Properties of Item Scales

View source: R/check_itemscale.R

check_itemscaleR Documentation

Describe Properties of Item Scales

Description

Compute various measures of internal consistencies applied to (sub)scales, which items were extracted using parameters::principal_components().

Usage

check_itemscale(x)

Arguments

x

An object of class parameters_pca, as returned by parameters::principal_components().

Details

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.

Value

A list of data frames, with related measures of internal consistencies of each subscale.

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, 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().

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

  • Trochim WMK (2008) Types of Reliability. (web)

Examples

# 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)
}

performance documentation built on Nov. 25, 2022, 9:08 a.m.