EFA.dimensions: COMPLEXITY – R documentation

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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)

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)
COMPLEXITY(loadings=PCAoutput$structure, verbose=TRUE)

EFA.dimensions

Exploratory Factor Analysis Functions for Assessing Dimensionality

v0.1.7.2

GPL (>= 2)

Authors