EFA.dimensions (version 0.1.8.1)

COMPLEXITY: Factor solution complexity

Description

Provides Hoffman's (1978) complexity coefficient for each item and (optionally) the percent complexity in the factor solution using the procedure and code provided by Pettersson and Turkheimer (2014).

Usage

COMPLEXITY(loadings, percent=TRUE, degree.change=100, averaging.value=100, verbose=TRUE)

Value

A list with the following elements:

comp_rows

The complexity coefficient for each item

percent

The percent complexity in the factor solution

Arguments

loadings

The factor loading matrix.

percent

(logical) Should the percent complexity be computed? The default = TRUE.

degree.change

If percent=TRUE, the number of incremental changes toward simple structure. The default = 100.

averaging.value

If percent=TRUE, the number of repeats per unit of degree change. The default = 100.

verbose

(logical) Should detailed results be displayed in console? The default = TRUE.

Author

Brian P. O'Connor

Details

This function provides Hoffman's (1978) complexity coefficient for each item and (optionally) the percent complexity in the factor solution using the procedure and code provided by Pettersson and Turkheimer (2014). For the percent complexity coefficient, values closer to zero indicate greater consistency with simple structure.

References

Hofmann, R. J. (1978). Complexity and simplicity as objective indices descriptive of factor solutions. Multivariate Behavioral Research, 13, 247-250.

Pettersson E, Turkheimer E. (2010) Item selection, evaluation, and simple structure in personality data. Journal of research in personality, 44(4), 407-420.

Pettersson, E., & Turkheimer, E. (2014). Self-reported personality pathology has complex structure and imposing simple structure degrades test information. Multivariate Behavioral Research, 49(4), 372-389.

Examples

# \donttest{
# the Harman (1967) correlation matrix
PCAoutput <- PCA(data_Harman, Nfactors = 2, Ncases = 305, rotation='promax', verbose=FALSE)
COMPLEXITY(loadings=PCAoutput$structure, verbose=TRUE)

# Rosenberg Self-Esteem scale items
PCAoutput <- PCA(data_RSE, Nfactors = 2, rotation='promax', verbose=FALSE)
COMPLEXITY(loadings=PCAoutput$structure, verbose=TRUE)

# NEO-PI-R scales
PCAoutput <- PCA(data_NEOPIR, Nfactors = 5, rotation='promax', verbose=FALSE)
COMPLEXITY(loadings=PCAoutput$structure, verbose=TRUE)
# }