Rotating Factors to Simplify Their Structural Paths

Guangjian Zhang (), Minami Hattori and Lauren A. Trichtinger
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Guangjian Zhang: University of Notre Dame
Minami Hattori: Soka University
Lauren A. Trichtinger: Simmons University

Psychometrika, 2023, vol. 88, issue 3, No 7, 865-887

Abstract: Abstract Applications of structural equation modeling (SEM) may encounter issues like inadmissible parameter estimates, nonconvergence, or unsatisfactory model fit. We propose a new factor rotation method that reparameterizes the factor correlation matrix in exploratory factor analysis (EFA) such that factors can be either exogenous or endogenous. The proposed method is an oblique rotation method for EFA, but it allows directional structural paths among factors. We thus referred it to as FSP (factor structural paths) rotation. In particular, we can use FSP rotation to “translate” an SEM model to incorporate theoretical expectations on both factor loadings and structural parameters. We illustrate FSP rotation with an empirical example and explore its statistical properties with simulated data. The results include that (1) EFA with FSP rotation tends to fit data better and encounters fewer Heywood cases than SEM does when there are cross-loadings and many small nonzero loadings, (2) FSP rotated parameter estimates are satisfactory for small models, and (3) FSP rotated parameter estimates are more satisfactory for large models when the structural parameter matrices are sparse.

Keywords: structural equation modeling; SEM; factor analysis; EFA; factor rotation; oblique rotation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-022-09877-3

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