MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables

We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.

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Publication Details

Journal
Multivariate Behavioral Research
Published
2024-12-27
DOI
https://doi.org/10.1080/00273171.2024.2436418
Primary Topic
Advanced Statistical Modeling Techniques
Type
article
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article

MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables

Kathleen M. Gates, Lan Luo, Kenneth A. Bollen
Multivariate Behavioral Research
Advanced Statistical Modeling Techniques
article

MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables

Kathleen M. Gates, Lan Luo, Kenneth A. Bollen
article en

Abstract

We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.

Multivariate Behavioral ResearchVol. 60(3)
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 72%
Advanced Statistical Modeling Techniques
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