2026-09-21T11:55:12Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/127472 2026-02-03 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

This Dissertation was approved for publication on 2024-11-26 at 08:35. DSpace SAF Submission Ingestion Package generated from Vireo submission #21393 on 2025-03-28 at 14:55:15 application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01 The student, Jesse Bowers, accepted the attached license on 2024-11-25 at 22:03. The student, Jesse Bowers, submitted this Dissertation for approval on 2024-11-25 at 22:26. eng We discuss main contributions to Latent Class Modelings and Factor Analysis. 1) Domain Latent Class Models (DLCMs) extend latent class models to allow for related questions. 2) Equivalence Set Restricted Latent Class Models (ESRLCMs) allow for a much greater variety of restrictions compared with restricted latent trait models. 3) Kernel Exploratory Factor Analysis (KEFA) conduct factor analysis after controlling for covariates. 2024-12 Thesis https://hdl.handle.net/2142/127472 Copyright 2024 Jesse Bowers. Chapter 2 is a copy of a paper published in Bayesian Analysis (http://dx.doi.org/10.1214/24-BA1433) which is available by a creative commons license (https://creativecommons.org/licenses/by/4.0/) and reproduced here with only changes to formatting. Domain latent class models, equivalent set latent class models, and kernel factor analysis Bowers, Jesse Mark 2024-11-26 Culpepper, Steve Culpepper, Steve Douglas, Jeffrey Zhu, Ruoqing Park, Trevor Latent Class Modeling Clustering Factor Analysis Categorical Data Analysis Dimension Reduction Statistics Statistics University of Illinois at Urbana-Champaign Ph.D. Dissertation