## EFA.dimensions: Exploratory Factor Analysis Functions for Assessing
Dimensionality

Functions for eleven procedures for determining the number of
factors, including functions for parallel analysis and the minimum average partial
test. There are also 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, gamma 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, the assessment of local
independence, the assessment of factor solution complexity, and internal consistency.
Auerswald & Moshagen (2019, <doi:10.1037/met0000200>);
Fabrigar & Wegener (2012, ISBN:978-0-19-973417-7);
Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2);
O'Connor (2000, <doi:10.3758/bf03200807>);
O'Connor (2001, <doi:10.1177/01466216010251011>).

Version: |
0.1.7.6 |

Imports: |
stats, psych, polycor, EFAtools, utils |

Suggests: |
lattice |

Published: |
2023-01-11 |

Author: |
Brian P. O'Connor [aut, cre] |

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 |

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