{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/47968"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/47968","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Improving Survey Methodology Through Matrix Sampling Design, Integrating Statistical Review Into Data Collection, and Synthetic Estimation Evaluation","abstract":"The research presented in this dissertation touches on all aspects of survey methodology, from questionnaire design to final estimation. We first approach the questionnaire development stage by proposing a method of developing matrix sampling designs, a design where a subset of questions are administered to a respondent in such a way that the administered questions are predictive of the omitted questions. 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Previous methodology aggregates estimates for stability, while our proposed methodology allows area-specific estimates. We applied the proposed mean squared error estimation methodology and methods found during literature review to simulated data and estimates from 2010 Census Coverage Measurement (CCM). We found that our proposed mean squared error estimation methodology compares favorably to the previous methods, while allowing for area-specific estimates.","abstract_html":"The research presented in this dissertation touches on all aspects of survey methodology, from questionnaire design to final estimation. We first approach the questionnaire development stage by proposing a method of developing matrix sampling designs, a design where a subset of questions are administered to a respondent in such a way that the administered questions are predictive of the omitted questions. 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Previous methodology aggregates estimates for stability, while our proposed methodology allows area-specific estimates. We applied the proposed mean squared error estimation methodology and methods found during literature review to simulated data and estimates from 2010 Census Coverage Measurement (CCM). We found that our proposed mean squared error estimation methodology compares favorably to the previous methods, while allowing for area-specific estimates.","abstract_has_math":false,"creators":["Seiss, Mark Thomas"],"institution":"Virginia Tech","degree_name":"Ph. 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