Year of Publication

2015

Season of Publication

Fall

Paper Type

Master's Thesis

College

College of Arts and Sciences

Degree Name

Master of Science in Mathematical Sciences (MS)

Department

Mathematics & Statistics

NACO controlled Corporate Body

University of North Florida. Department of Mathematics and Statistics

First Advisor

Dr. Donna Mohr

Second Advisor

Dr. Ping Sa

Third Advisor

Dr. Peter Wludyka

Department Chair

Dr. Richard Patterson

College Dean

Dr. Barbara A. Hetrick

Abstract

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.

Share

COinS