{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110639"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110639","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Bayesian approach to crossed-random-effects mediation analysis for zero-inflated mediators and binary outcomes","abstract":"In crossed random effects designs, observations are nested in the combination of two random factors, e.g., subjects and stimuli. Such designs are popular in experimental research in social sciences. Crossed random effects models (CREM) can accommodate the random effects of both subjects and stimuli. Based on academic vocabulary research, the model of interest in the current study was a mediation model with crossed random effects, a zero-inflated mediator, and a binary outcome. With maximum likelihood estimation (MLE), the mediation model could not converge, which was consistent with the previous finding on analyzing CREM with MLE (Huang & Anderson, 2020). Therefore, the current study investigated whether Bayesian estimation can be a viable alternative as suggested by previous research. The simulation results indicated that Bayesian estimates were essentially unbiased and precise. There were only two out of 180 models did not converge; future studies can increase the iterations or use an informative prior to resolving the non-convergence issue. Moreover, a Bayesian method was developed to compute the mediation effect; the results of the Bayesian method closely aligned with the bootstrapping results. Lastly, the application of Bayesian estimation was demonstrated in an academic vocabulary study.","abstract_html":"In crossed random effects designs, observations are nested in the combination of two random factors, e.g., subjects and stimuli. Such designs are popular in experimental research in social sciences. Crossed random effects models (CREM) can accommodate the random effects of both subjects and stimuli. Based on academic vocabulary research, the model of interest in the current study was a mediation model with crossed random effects, a zero-inflated mediator, and a binary outcome. With maximum likelihood estimation (MLE), the mediation model could not converge, which was consistent with the previous finding on analyzing CREM with MLE (Huang & Anderson, 2020). Therefore, the current study investigated whether Bayesian estimation can be a viable alternative as suggested by previous research. The simulation results indicated that Bayesian estimates were essentially unbiased and precise. There were only two out of 180 models did not converge; future studies can increase the iterations or use an informative prior to resolving the non-convergence issue. Moreover, a Bayesian method was developed to compute the mediation effect; the results of the Bayesian method closely aligned with the bootstrapping results. Lastly, the application of Bayesian estimation was demonstrated in an academic vocabulary study.","abstract_has_math":false,"creators":["Pan, Pei-Yu (Marian)"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":["Anderson, Carolyn Jane","Anderson, Richard C","Jiang, Gabriella"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:19Z","date_published":"2021-09-17T02:34:19Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Bayesian estimation, mediation, crossed random effects, zero-inflated mediator, academic vocabulary"],"languages":["en"],"rights":["(Copyright 2021 Pei-Yu (Marian) Pan)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110639","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anderson, Carolyn Jane","Anderson, Richard C","Jiang, Gabriella"]},{"key":"dc:creator","label":"Author","values":["Pan, Pei-Yu (Marian)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:19Z","2023-09-17T02:34:57Z","2021-03-24","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educational Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Bayesian estimation, mediation, crossed random effects, zero-inflated mediator, academic vocabulary"]}]},{"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 Pei-Yu (Marian) Pan)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110639"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In crossed random effects designs, observations are nested in the combination of two random factors, e.g., subjects and stimuli. Such designs are popular in experimental research in social sciences. Crossed random effects models (CREM) can accommodate the random effects of both subjects and stimuli. Based on academic vocabulary research, the model of interest in the current study was a mediation model with crossed random effects, a zero-inflated mediator, and a binary outcome. With maximum likelihood estimation (MLE), the mediation model could not converge, which was consistent with the previous finding on analyzing CREM with MLE (Huang & Anderson, 2020). Therefore, the current study investigated whether Bayesian estimation can be a viable alternative as suggested by previous research. The simulation results indicated that Bayesian estimates were essentially unbiased and precise. There were only two out of 180 models did not converge; future studies can increase the iterations or use an informative prior to resolving the non-convergence issue. Moreover, a Bayesian method was developed to compute the mediation effect; the results of the Bayesian method closely aligned with the bootstrapping results. Lastly, the application of Bayesian estimation was demonstrated in an academic vocabulary study.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Pei-Yu (Marian) Pan, accepted the attached license on 2021-03-22 at 18:01.","The student, Pei-Yu (Marian) Pan, submitted this Thesis for approval on 2021-03-22 at 18:12.","This Thesis was approved for publication on 2021-03-24 at 13:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16206 on 2021-09-16 at 17:02:31","Made available in DSpace on 2021-09-17T02:34:19Z (GMT). No. of bitstreams: 2 PAN-THESIS-2021.pdf: 7074351 bytes, checksum: 7fce4617603b61942bb0d62c5546b390 (MD5) LICENSE.txt: 4207 bytes, checksum: 80cddd111cce490a15893b892c1dab16 (MD5) Previous issue date: 2021-03-24","Embargo set by: Seth Robbins for item 118482 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A Bayesian approach to crossed-random-effects mediation analysis for zero-inflated mediators and binary outcomes"]}]}],"canonical_facts":{"dc:contributor":["Anderson, Carolyn Jane","Anderson, Richard C","Jiang, Gabriella"],"dc:creator":["Pan, Pei-Yu (Marian)"],"dc:date":["2021-09-17T02:34:19Z","2023-09-17T02:34:57Z","2021-03-24","2021-05"],"dc:description":["In crossed random effects designs, observations are nested in the combination of two random factors, e.g., subjects and stimuli. Such designs are popular in experimental research in social sciences. Crossed random effects models (CREM) can accommodate the random effects of both subjects and stimuli. Based on academic vocabulary research, the model of interest in the current study was a mediation model with crossed random effects, a zero-inflated mediator, and a binary outcome. With maximum likelihood estimation (MLE), the mediation model could not converge, which was consistent with the previous finding on analyzing CREM with MLE (Huang & Anderson, 2020). Therefore, the current study investigated whether Bayesian estimation can be a viable alternative as suggested by previous research. The simulation results indicated that Bayesian estimates were essentially unbiased and precise. There were only two out of 180 models did not converge; future studies can increase the iterations or use an informative prior to resolving the non-convergence issue. Moreover, a Bayesian method was developed to compute the mediation effect; the results of the Bayesian method closely aligned with the bootstrapping results. Lastly, the application of Bayesian estimation was demonstrated in an academic vocabulary study.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Pei-Yu (Marian) Pan, accepted the attached license on 2021-03-22 at 18:01.","The student, Pei-Yu (Marian) Pan, submitted this Thesis for approval on 2021-03-22 at 18:12.","This Thesis was approved for publication on 2021-03-24 at 13:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16206 on 2021-09-16 at 17:02:31","Made available in DSpace on 2021-09-17T02:34:19Z (GMT). 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