This package provides exploratory factor analysis-related functions
for an assortment of factor analysis-related procedures.
There are 11 functions for determining the number of factors (DIMTESTS, EMPKC,
HULL, MAP, NEVALSGT1, PARALLEL, RAWPAR, ROOTFIT, SALIENT, SCREE_PLOT, SESCREE, and SMT).
There is a principal components analysis function (PCA), an exploratory factor
analysis function (EFA), a bifactor analysis function (BIFACTOR), and an
extension factor analysis function (EXTENSION_FA),
all with 9 possible factor extraction methods and 12 possible factor rotation
methods.
Most of the analyses can be conducted using raw data or correlation matrices as input.
The analyses can be conducted using Pearson correlations, Kendall correlations,
Spearman correlations, Goodman-Kruskal gamma correlations (Thompson, 2006),
or polychoric correlations (using the psych and polychor packages).
Additional functions include:
COMPLEXITY, for the assessment of factor solution complexity
CONGRUENCE, for the congruences between factors from different datasets
CORRECTED_CORRELS, correcting Pearson correlation coefficients for attenuation due to unreliability
ESEM, for exploratory structural equation measurement models
FACTORABILITY, for the factorability of a correlation matrix
Factorial_Invariance, for factorial invariance
LOCALDEP, for the assessment of local independence
INTERNAL_CONSISTENCY, for internal consistency statistics
Auerswald, M., & Moshagen, M. (2019). How to determine the number of factors to
retain in exploratory factor analysis: A comparison of extraction methods under
realistic conditions. Psychological Methods, 24(4), 468-491.
Mulaik, S. A. (2010). Foundations of factor analysis (2nd ed.). Boca Raton, FL: Chapman
and Hall/CRC Press, Taylor & Francis Group.
O'Connor, B. P. (2000). SPSS and SAS programs for determining
the number of components using parallel analysis and Velicer's
MAP test. Behavior Research Methods, Instrumentation, and
Computers, 32, 396-402.
O'Connor, B. P. (2000). SPSS and SAS programs for determining
the number of components using parallel analysis and Velicer's
MAP test. Behavior Research Methods, Instrumentation, and
Computers, 32, 396-402.
Sellbom, M., & Tellegen, A. (2019). Factor analysis in psychological assessment research:
Common pitfalls and recommendations.
Psychological Assessment, 31(12), 1428-1441.
Watts, A. L., Greene, A. L., Ringwald, W., Forbes, M. K., Brandes, C. M., Levin-Aspenson,
H. F., & Delawalla, C. (2023). Factor analysis in personality disorders research: Modern issues
and illustrations of practical recommendations.
Personality Disorders: Theory, Research, and Treatment, 14(1), 105-117.
https://doi.org/10.1037/per0000581