EFA.dimensions (version 0.1.7.3)

LOCALDEP: Local dependence

Description

Provides the residual correlations after partialling the first component out of a correlation matrix as a method of assessing local dependence (independence) between variables.

Usage

LOCALDEP(data, corkind, Ncases, verbose)

Arguments

data

An all-numeric dataframe where the rows are cases & the columns are the variables, or a correlation matrix with ones on the diagonal. The function internally determines whether the data are a correlation matrix.

corkind

The kind of correlation matrix to be used if data is not a correlation matrix. The options are 'pearson', 'kendall', 'spearman', 'gamma', and 'polychoric'. Required only if the entered data is not a correlation matrix.

Ncases

The number of cases. Required only if data is a correlation matrix.

verbose

Should detailed results be displayed in console? TRUE (default) or FALSE

Value

A list with the following elements:

correlations

The correlation matrix

residcor

The residualized correlation matrix

Details

Item response theory models are based on the assumption that the items display local independence. The latent trait is presumed to be responsible for the associations between the items. Once the latent trait is partialled out, the residual correlations between pairs of items should be negligible. Local dependence exists when there is additional systematic covariance among the items. It can occur when pairs of items have highly similar content or between sequentially presented items in a test. Local dependence distorts IRT parameter estimates, it can artificially increase scale information, and it distorts the latent trait, which becomes too heavily defined by the locally dependent items. The LOCALDEP function partials out the first component (not the IRT latent trait) from a correlation matrix. Examining the residual correlations is a preliminary, exploratory method of determining whether local dependence exists. The function also displays the number of residual correlations that are >= a range of values.

Examples

# NOT RUN {
# the Harman (1967) correlation matrix
LOCALDEP(data_Harman, Ncases = 305, verbose=TRUE)

# Rosenberg Self-Esteem scale items
LOCALDEP(data_RSE, corkind = 'polychoric', verbose=TRUE)

# NEO-PI-R scales
LOCALDEP(data_NEOPIR, verbose=TRUE)
# }