COMPLEXITY {EFA.dimensions} R Documentation

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)

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.

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.

Value

A list with the following elements:

 comp_rows The complexity coefficient for each item percent The percent complexity in the factor solution

Author(s)

Brian P. O'Connor

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


# the Harman (1967) correlation matrix
PCAoutput <- PCA(data_Harman, Nfactors = 2, Ncases = 305, rotate='PROMAX', verbose=FALSE)
COMPLEXITY(loadings=PCAoutput$structure, verbose=TRUE) # Rosenberg Self-Esteem scale items PCAoutput <- PCA(data_RSE, Nfactors = 2, rotate='PROMAX', verbose=FALSE) COMPLEXITY(loadings=PCAoutput$structure, verbose=TRUE)

# NEO-PI-R scales
PCAoutput <- PCA(data_NEOPIR, Nfactors = 5, rotate='PROMAX', verbose=FALSE)