His quantitative research primarily focuses on using statistical simulations to evaluate novel methods for addressing missing data in longitudinal research. Missing data are a ubiquitous problem in a variety of applied research settings. While researchers often have clear a priori hypotheses concerning key variables in their substantive models, they rarely have well-developed a priori hypotheses concerning the factors that might lead to missing data. As a result, many existing methods of assessing the relationship between predictor variables and missing data are used by researchers in an inherently exploratory manner.