Kyle is a postdoctoral research associate at the Texas Tech Institute for Measurement, Methodology, Analysis, and Policy. He earned his Ph.D. in quantitative psychology from the University of Kansas in 2015. Kyle’s research focuses on missing data analysis with a particular emphasis on developing and evaluating multiple imputation techniques for use with difficult missing data problems (e.g., imputing categorical data, imputation in large datasets, high-dimensional imputation models). He also has extensive experience applying cutting edge statistical methods such as those for testing mediation and moderation to substantive research questions in fields such as psychology, education, social work, and political science as both a statistical consultant and a collaborating researcher. Kyle has been involved in Stats Camp every year since 2009. He has provided general statistical consulting for all of the courses offered and given numerous guest lectures on topics such as mixture modeling, regularized regression modeling, and Bayesian structural equation modeling.