{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/5097"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/5097","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Estimates of school productivity and implications for policy","abstract":"School productivity was not perfectly estimated because of the sampling error and the measurement error. The traditional Ordinary Least Square (OLS) leaves the estimation of school productivity questionable. Moreover, Hierarchical Linear Model (HLM) encounters a large proportion of the variance unexplained in the level-1 equation. In the paper, I will first introduce the Kalman Filter (KF) algorithm together with the Bayesian random draw mechanism to simulate the accurate school effects, and then compare the simulated results with the estimates generated from OLS and HLM. The comparison of the school effects will conclude that the Kalman Filter is more reliable and accurate for the educators and school administrators to supervise the allocation of the school resources for school improvement.","abstract_html":"School productivity was not perfectly estimated because of the sampling error and the measurement error. The traditional Ordinary Least Square (OLS) leaves the estimation of school productivity questionable. Moreover, Hierarchical Linear Model (HLM) encounters a large proportion of the variance unexplained in the level-1 equation. In the paper, I will first introduce the Kalman Filter (KF) algorithm together with the Bayesian random draw mechanism to simulate the accurate school effects, and then compare the simulated results with the estimates generated from OLS and HLM. The comparison of the school effects will conclude that the Kalman Filter is more reliable and accurate for the educators and school administrators to supervise the allocation of the school resources for school improvement.","abstract_has_math":false,"creators":["Peng, Xiao"],"institution":"University of Missouri--Columbia","degree_name":"M.A.","degree_level":"Masters","degree_discipline":"Economics (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Podgursky, Michael John","Sun, Jianguo, 1961-"],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-24T03:09:16Z","subjects":[],"languages":["eng","English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/5097"],"render_values":[{"text":"https://doi.org/10.32469/10355/5097","href":"https://doi.org/10.32469/10355/5097","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/5097","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Podgursky, Michael John","Sun, Jianguo, 1961-"]},{"key":"dc:creator","label":"Author","values":["Peng, Xiao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2010-01-12T19:09:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2010-01-12T19:09:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2007"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.A."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Columbia"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/5097"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/5097"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.","Title from title screen of research.pdf file (viewed on January 8, 2008)","Includes bibliographical references.","Student awarded a Master of Arts in Economics and a Master of Arts in Statistics.","Thesis (M.A.) University of Missouri-Columbia 2007.","Dissertations, Academic -- University of Missouri--Columbia -- Economics.","Dissertations, Academic -- University of Missouri--Columbia -- Statistics."]},{"key":"dc:description.abstract","label":"Abstract","values":["School productivity was not perfectly estimated because of the sampling error and the measurement error. The traditional Ordinary Least Square (OLS) leaves the estimation of school productivity questionable. Moreover, Hierarchical Linear Model (HLM) encounters a large proportion of the variance unexplained in the level-1 equation. In the paper, I will first introduce the Kalman Filter (KF) algorithm together with the Bayesian random draw mechanism to simulate the accurate school effects, and then compare the simulated results with the estimates generated from OLS and HLM. The comparison of the school effects will conclude that the Kalman Filter is more reliable and accurate for the educators and school administrators to supervise the allocation of the school resources for school improvement."]},{"key":"dc:title","label":"Title","values":["Estimates of school productivity and implications for policy"]}]}],"canonical_facts":{"dc:contributor.advisor":["Podgursky, Michael John","Sun, Jianguo, 1961-"],"dc:creator":["Peng, Xiao"],"dc:date.accessioned":["2010-01-12T19:09:57Z"],"dc:date.available":["2010-01-12T19:09:57Z"],"dc:date.issued":["2007"],"dc:description":["The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.","Title from title screen of research.pdf file (viewed on January 8, 2008)","Includes bibliographical references.","Student awarded a Master of Arts in Economics and a Master of Arts in Statistics.","Thesis (M.A.) University of Missouri-Columbia 2007.","Dissertations, Academic -- University of Missouri--Columbia -- Economics.","Dissertations, Academic -- University of Missouri--Columbia -- Statistics."],"dc:description.abstract":["School productivity was not perfectly estimated because of the sampling error and the measurement error. The traditional Ordinary Least Square (OLS) leaves the estimation of school productivity questionable. Moreover, Hierarchical Linear Model (HLM) encounters a large proportion of the variance unexplained in the level-1 equation. In the paper, I will first introduce the Kalman Filter (KF) algorithm together with the Bayesian random draw mechanism to simulate the accurate school effects, and then compare the simulated results with the estimates generated from OLS and HLM. 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