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Missing Data In Complex Sample Surveys: Impact Of Deletion And Imputation Treatments On Point And Interval Parameter Estimates, Anh Pham Kellermann

USF Tampa Graduate Theses and Dissertations

The purpose of this simulation study was to evaluate the relative performance of five missing data treatments (MDTs) for handling missing data in complex sample surveys. The five missing data methods included in this study were listwise deletion (LW), single hot-deck imputation (HS), single regression imputation (RS), hot-deck-based multiple imputation (HM), and regression-based multiple imputation (RM). These MDTs were assessed in the context of regression weight estimates in multiple regression analysis in complex sample data with two data levels. In this study, the multiple regression equation had six regressors without missing data and two regressors with missing data. The four …