EFA.dimensions (version 0.1.6)
Exploratory Factor Analysis Functions for Assessing
Dimensionality
Description
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, );
O'Connor (2001, );
Fabrigar & Wegener (2012, ISBN:978-0-19-973417-7);
Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2).