College of Arts and Sciences
Master of Science in Mathematical Sciences (MS)
Mathematics & Statistics
NACO controlled Corporate Body
University of North Florida. Department of Mathematics and Statistics
Dr. Donna Mohr
Dr. Ping Sa
Dr. Peter Wludyka
Dr. Richard Patterson
Dr. Barbara A. Hetrick
Missing data bias results if adjustments are not made accordingly. This thesis addresses this issue by exploring a scenario where data is missing at random depending on a covariate x. Four methods for comparing groups while adjusting for missingness are explored by conducting simulations: independent samples t-test with predicted mean stratification, independent samples t-test with response propensity stratification, independent samples t-test with response propensity weighting, and an analysis of covariance. Results show that independent samples t-test with response propensity weighting and analysis of covariance can appropriately adjust for bias. ANCOVA is the stronger method when the ANCOVA assumptions are met. When the ANCOVA assumptions are not met, a t-test with inverse response propensity score weighting is the superior method.
Stegmann, Gabriela M., "Comparing Group Means When Nonresponse Rates Differ" (2015). UNF Graduate Theses and Dissertations. 617.