{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113281"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113281","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian estimation of restricted latent class models: Extending priors, link functions, and structural models","abstract":"Restricted latent class models (RLCMs) provide a pivotal framework for supporting diagnostic research that enhances human development and opportunities. In earlier research, the focus was on confirmatory methods that required a pre-specified expert-attribute mapping known as a Q matrix. Recent research directions have led to the creation of exploratory methodology that is able to infer the Q matrix without expert intervention. Within this thesis, we seek to extend and improve upon existing exploratory techniques and applications. We begin by developing novel Bayesian methodology that uses a less restrictive monotonicity condition when estimating the underlying latent structure and attributes. Under the formulation, we make further enhancements by extending the framework to the logit-link function through the Pólya-Gamma distribution. Moreover, we determine different regularization approaches that can be applied to the latent structure to induce sparsity. Next, we propose an extension that seeks to address the dependency structure found among attributes. The dependency structure is able to be described by using a higher-order structure for attributes. Estimating the higher-order structure is done by applying techniques from exploratory factor analysis (EFA). Moreover, the latent structure grows exponentially as the number of attributes increases and we provide an option to specify a subset of the latent structure. Another important consideration is there may be more than one strategy that can be used to achieve success on some tasks. We develop new methods for inferring multiple strategies in the presence of expert knowledge. Lastly, we discuss software implementations of the aforementioned methodological developments. Providing implementations lowers the barrier of entry to employing the methods within psychometric community.","abstract_html":"Restricted latent class models (RLCMs) provide a pivotal framework for supporting diagnostic research that enhances human development and opportunities. In earlier research, the focus was on confirmatory methods that required a pre-specified expert-attribute mapping known as a Q matrix. Recent research directions have led to the creation of exploratory methodology that is able to infer the Q matrix without expert intervention. Within this thesis, we seek to extend and improve upon existing exploratory techniques and applications. We begin by developing novel Bayesian methodology that uses a less restrictive monotonicity condition when estimating the underlying latent structure and attributes. Under the formulation, we make further enhancements by extending the framework to the logit-link function through the Pólya-Gamma distribution. Moreover, we determine different regularization approaches that can be applied to the latent structure to induce sparsity. Next, we propose an extension that seeks to address the dependency structure found among attributes. The dependency structure is able to be described by using a higher-order structure for attributes. Estimating the higher-order structure is done by applying techniques from exploratory factor analysis (EFA). Moreover, the latent structure grows exponentially as the number of attributes increases and we provide an option to specify a subset of the latent structure. Another important consideration is there may be more than one strategy that can be used to achieve success on some tasks. We develop new methods for inferring multiple strategies in the presence of expert knowledge. Lastly, we discuss software implementations of the aforementioned methodological developments. Providing implementations lowers the barrier of entry to employing the methods within psychometric community.","abstract_has_math":false,"creators":["Balamuta, James Joseph"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Informatics","degree_department":null,"school":null,"contributors":["Culpepper, Steven A","Douglas, Jeffrey A","Paquette, Luc","Zhang, Susu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:54:58Z","date_published":"2022-01-12T22:54:58Z","updated_at":"2026-07-22T22:24:53Z","subjects":["restricted latent class models, cognitive diagnosis, latent class, latent structure models, Bayesian, Pólya-gamma data augmentation"],"languages":["en"],"rights":["Copyright 2021 James Joseph Balamuta"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113281","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Culpepper, Steven A","Douglas, Jeffrey A","Paquette, Luc","Zhang, Susu"]},{"key":"dc:creator","label":"Author","values":["Balamuta, James Joseph"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:54:58Z","2024-01-12T22:56:20Z","2021-07-08","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Informatics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["restricted latent class models, cognitive diagnosis, latent class, latent structure models, Bayesian, Pólya-gamma data augmentation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 James Joseph Balamuta"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113281"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Restricted latent class models (RLCMs) provide a pivotal framework for supporting diagnostic research that enhances human development and opportunities. In earlier research, the focus was on confirmatory methods that required a pre-specified expert-attribute mapping known as a Q matrix. Recent research directions have led to the creation of exploratory methodology that is able to infer the Q matrix without expert intervention. Within this thesis, we seek to extend and improve upon existing exploratory techniques and applications. We begin by developing novel Bayesian methodology that uses a less restrictive monotonicity condition when estimating the underlying latent structure and attributes. Under the formulation, we make further enhancements by extending the framework to the logit-link function through the Pólya-Gamma distribution. Moreover, we determine different regularization approaches that can be applied to the latent structure to induce sparsity. Next, we propose an extension that seeks to address the dependency structure found among attributes. The dependency structure is able to be described by using a higher-order structure for attributes. Estimating the higher-order structure is done by applying techniques from exploratory factor analysis (EFA). Moreover, the latent structure grows exponentially as the number of attributes increases and we provide an option to specify a subset of the latent structure. Another important consideration is there may be more than one strategy that can be used to achieve success on some tasks. We develop new methods for inferring multiple strategies in the presence of expert knowledge. Lastly, we discuss software implementations of the aforementioned methodological developments. Providing implementations lowers the barrier of entry to employing the methods within psychometric community.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01","The student, James Balamuta, accepted the attached license on 2021-07-07 at 13:50.","The student, James Balamuta, submitted this Dissertation for approval on 2021-07-07 at 14:45.","This Dissertation was approved for publication on 2021-07-08 at 12:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16783 on 2022-01-12 at 13:04:02","Made available in DSpace on 2022-01-12T22:54:58Z (GMT). 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In earlier research, the focus was on confirmatory methods that required a pre-specified expert-attribute mapping known as a Q matrix. Recent research directions have led to the creation of exploratory methodology that is able to infer the Q matrix without expert intervention. Within this thesis, we seek to extend and improve upon existing exploratory techniques and applications. We begin by developing novel Bayesian methodology that uses a less restrictive monotonicity condition when estimating the underlying latent structure and attributes. Under the formulation, we make further enhancements by extending the framework to the logit-link function through the Pólya-Gamma distribution. Moreover, we determine different regularization approaches that can be applied to the latent structure to induce sparsity. Next, we propose an extension that seeks to address the dependency structure found among attributes. The dependency structure is able to be described by using a higher-order structure for attributes. Estimating the higher-order structure is done by applying techniques from exploratory factor analysis (EFA). Moreover, the latent structure grows exponentially as the number of attributes increases and we provide an option to specify a subset of the latent structure. Another important consideration is there may be more than one strategy that can be used to achieve success on some tasks. We develop new methods for inferring multiple strategies in the presence of expert knowledge. Lastly, we discuss software implementations of the aforementioned methodological developments. Providing implementations lowers the barrier of entry to employing the methods within psychometric community.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01","The student, James Balamuta, accepted the attached license on 2021-07-07 at 13:50.","The student, James Balamuta, submitted this Dissertation for approval on 2021-07-07 at 14:45.","This Dissertation was approved for publication on 2021-07-08 at 12:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16783 on 2022-01-12 at 13:04:02","Made available in DSpace on 2022-01-12T22:54:58Z (GMT). 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