Research Expertise: 
Missing Data | Data Visualization | Data Mining

Most statistical procedures assume (explicitly or implicitly) that samples were obtained using random selection. Rarely is this the case in Psychology, where convenience samples are frequently used. My research aims to discover how convenience sampling biases parameter estimates and how we can recover population parameters from biased samples. It turns out, if we consider non-random selection as a “missing data” problem, solutions are possible.

Aside from my main research area, I also spend a lot of time thinking and writing about, as well as programming algorithms for large-scale data mining operations. I have developed packages in R that aim to solve “small N, large p” type problems.

Honors and Awards:
Chuck Gettys Award for Outstanding Research in Graduate School, Department of Psychology at the University of Oklahoma, 2013 Best Paper Award, The Consortium for Student Retention Data Exchange (CSRDE), 2011

Member of:
Academic Educational Research Association