A structural after measurement approach to bifactor predictive models
Choi, Jinsoo
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https://hdl.handle.net/2142/124626
Description
Title
A structural after measurement approach to bifactor predictive models
Author(s)
Choi, Jinsoo
Issue Date
2024-02-19
Director of Research (if dissertation) or Advisor (if thesis)
Zhang, Bo
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Structural After Measurement
Bifactor Model
Augmentation
Language
eng
Abstract
The bifactor model has regained popularity due to its conceptual appeal. However, the bifactor predictive model, which extends a bifactor model to a criterion variable, often encounters empirical under-identification due to approximate linear dependency. This limitation leads to various statistical issues (e.g., non-convergence, estimation bias, inaccurate standard errors), hindering the use of the bifactor model for predictive purposes. To address this limitation, we introduced the recently developed Structural After Measurement (SAM; Rosseel & Loh, 2022) approach to the bifactor predictive model and examined its robustness with a series of Monte Carlo simulations. Our simulation results indicated that the SAM approach effectively enhances the statistical performance of bifactor predictive models compared to the SEM approach in terms of model convergence, stability of point estimates, accuracy of standard error estimates, coverage rates, and Type I error rates, at the cost of slight bias. Our empirical illustration also supported the simulation findings, further illustrating the effectiveness of the SAM approach.
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