EFA.dimensions-package {EFA.dimensions} | R Documentation |
EFA.dimensions
Description
This package provides exploratory factor analysis-related functions for
assessing dimensionality.
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), and an exploratory factor
analysis function (EFA) with 9 possible factor extraction methods.
There are 15 possible factor rotation methods that can be used with PCA and EFA.
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 focus on the factorability of a correlation matrix (FACTORABILITY),
the congruences between factors from different datasets (CONGRUENCE), the assessment
of local independence (LOCALDEP), the assessment of factor solution
complexity (COMPLEXITY), and internal consistency (INTERNAL.CONSISTENCY).
References
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.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R.
Los Angeles, CA: Sage. ISBN:978-1-4462-0045-2
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. https://doi.org/10.1037/pas0000623
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