check_itemscale {performance}

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, factor_index = NULL)

Arguments

x

An object of class parameters_pca, as returned by parameters::principal_components(), or a data frame.

factor_index

If x is a data frame, factor_index must be specified. It must be a numeric vector of same length as number of columns in x, where each element is the index of the factor to which the respective column in x.

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

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

pca <- parameters::principal_components(
  as.data.frame(Z),
  rotation = "varimax",
  n = 3
)
pca
check_itemscale(pca)

# as data frame
check_itemscale(
  as.data.frame(Z),
  factor_index = parameters::closest_component(pca)
)