A collection of R functions for eleven factor number determination procedures, including parallel analysis and the minimum average partial test. The package, authored by Brian P. O’Connor, provides functions for multiple factor analysis methods and correlation types, with references to methodological literature from 2000 to 2019. It supports various rotations and includes functions for assessing factorability, congruence, local independence, and internal consistency.
Use Cases
- Determining the number of factors in a dataset based on parallel analysis and minimum average partial test functions.
- Conducting principal components or maximum likelihood factor analysis using raw data or correlation matrices.
- Applying varimax or promax rotation to factor solutions for interpretability.
- Assessing the factorability of a correlation matrix or the congruences between factors from different datasets.
- Evaluating factor solution complexity and internal consistency of measurement scales.
Strengths
- Implements eleven distinct procedures for determining the number of factors.
- Supports five correlation types: Pearson, Kendall, Spearman, gamma, and polychoric.
- Includes functions for five factor analysis methods: principal components, principal axis, maximum likelihood, image, and extension.
- Methodology is grounded in cited academic literature spanning from 2000 to 2019.
Limitations
- Row count and dataset size are unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Brian P. O’Connor
- Collection Method
- null
- Time Range
- null
- Freshness
- null
- Geography
- null