{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121404"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121404","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The performance of propensity score matching and weighting methods on samples of various properties","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_has_math":false,"creators":["Man, Qidi"],"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","Kern, Justin","Jiang, Ge"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:24:57Z","subjects":["Propensity Score","Matching","Weighting","Observational Study"],"languages":["en","eng"],"rights":["Copyright 2023 Qidi Man"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121404","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anderson, Carolyn","Kern, Justin","Jiang, Ge"]},{"key":"dc:creator","label":"Author","values":["Man, Qidi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-06-01"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Propensity Score","Matching","Weighting","Observational Study"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Qidi Man"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121404"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Qidi Man, accepted the attached license on 2023-05-27 at 21:11.","The student, Qidi Man, submitted this Thesis for approval on 2023-05-27 at 21:39.","This Thesis was approved for publication on 2023-06-01 at 08:31.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19394 on 2023-12-04 at 16:59:49","Observational studies tend to have samples with selection bias and therefore confound the treatment effect by variables that can influence both treatment and outcome, and propensity score analysis has become more widely used to process data in these conditions. Various types of propensity score matching or weighting methods can be used to balance covariate distributions between treatment and control groups and simulate randomized experiments, including matching methods like nearest neighbor matching without replacement, matching with replacement, optimal matching, variable ratio matching, and weighting methods like inverse probability of weighting and overlap weighting. This study compares effect estimation performance in terms of bias and mean square error (MSE) after balancing covariates of simulated observational data using matching or weighting methods in different propensity score distribution conditions. The study also examines the effect of using different variable selection strategies and various estimators for effect estimation. The results suggest that the propensity score method choice and variable selection strategies can minimize the MSE of causal effect estimation in different scenarios. This study complements previous studies by providing guidance for the practical use of these propensity score-based methods. Limitations and future directions are also discussed."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The performance of propensity score matching and weighting methods on samples of various properties"]}]}],"canonical_facts":{"dc:contributor":["Anderson, Carolyn","Kern, Justin","Jiang, Ge"],"dc:creator":["Man, Qidi"],"dc:date":["2023-08","2023-06-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Qidi Man, accepted the attached license on 2023-05-27 at 21:11.","The student, Qidi Man, submitted this Thesis for approval on 2023-05-27 at 21:39.","This Thesis was approved for publication on 2023-06-01 at 08:31.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19394 on 2023-12-04 at 16:59:49","Observational studies tend to have samples with selection bias and therefore confound the treatment effect by variables that can influence both treatment and outcome, and propensity score analysis has become more widely used to process data in these conditions. Various types of propensity score matching or weighting methods can be used to balance covariate distributions between treatment and control groups and simulate randomized experiments, including matching methods like nearest neighbor matching without replacement, matching with replacement, optimal matching, variable ratio matching, and weighting methods like inverse probability of weighting and overlap weighting. This study compares effect estimation performance in terms of bias and mean square error (MSE) after balancing covariates of simulated observational data using matching or weighting methods in different propensity score distribution conditions. The study also examines the effect of using different variable selection strategies and various estimators for effect estimation. The results suggest that the propensity score method choice and variable selection strategies can minimize the MSE of causal effect estimation in different scenarios. This study complements previous studies by providing guidance for the practical use of these propensity score-based methods. Limitations and future directions are also discussed."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121404"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Qidi Man"],"dc:subject":["Propensity Score","Matching","Weighting","Observational Study"],"dc:title":["The performance of propensity score matching and weighting methods on samples of various properties"],"dc:type":["text"],"thesis:degree_discipline":["Educational Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}