2026-09-19T12:25:49Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124626 2026-01-14 col_2142_5131 col_2142_11614 com_2142_5130 com_2142_11613 com_2142_8903

Zhang, Bo 2024-05 application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01 The student, Jinsoo Choi, accepted the attached license on 2024-02-15 at 14:41. The student, Jinsoo Choi, submitted this Thesis for approval on 2024-02-15 at 14:53. This Thesis was approved for publication on 2024-02-19 at 14:36. DSpace SAF Submission Ingestion Package generated from Vireo submission #20220 on 2024-09-16 at 00:48:50 A structural after measurement approach to bifactor predictive models Choi, Jinsoo 2024-02-19 Structural After Measurement Bifactor Model Augmentation 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. Text eng https://hdl.handle.net/2142/124626 Copyright 2024 Jinsoo Choi M.S. Thesis Psychology University of Illinois at Urbana-Champaign Psychology