EFA.dimensions: Exploratory Factor Analysis Functions for Assessing Dimensionality

Functions for seven different procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, and for assessing local independence. O'Connor (2000, <doi:10.3758/bf03200807>); O'Connor (2001, <doi:10.1177/01466216010251011>); Fabrigar & Wegener (2012, ISBN:978-0-19-973417-7); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2).

Version: 0.1.6
Imports: stats, psych, polycor
Suggests: lattice
Published: 2020-07-20
Author: Brian P. O'Connor
Maintainer: Brian P. O'Connor <brian.oconnor at ubc.ca>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: EFA.dimensions results


Reference manual: EFA.dimensions.pdf
Package source: EFA.dimensions_0.1.6.tar.gz
Windows binaries: r-devel: EFA.dimensions_0.1.6.zip, r-release: EFA.dimensions_0.1.6.zip, r-oldrel: EFA.dimensions_0.1.6.zip
macOS binaries: r-release: EFA.dimensions_0.1.6.tgz, r-oldrel: EFA.dimensions_0.1.6.tgz


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