I am a statistician and assistant professor in the College of Education at the University of Texas at Austin, where I teach in the Educational Psychology Department and the graduate program in Quantitative Methods. My research involves developing statistical methods for problems in education, psychology, and other areas of social science research, with a focus on methods related to research synthesis and meta-analysis.
PhD in Statistics, 2013
BA in Economics, 2003
If you’ve ever had class with me or attended one of my presentations, you’ve probably heard me grouse about how statisticians are mostly awful about naming things.1 A lot of the terminology in our field is pretty bad and ineloquent.
One common question about multivariate/multi-level meta-analysis is how such models assign weight to individual effect size estimates. When a version of the question came up recently on the R-sig-meta-analysis listserv, Dr.
About a year ago I added a code-folding feature to my site, following an approach developed by Sébastien Rochette. I recently updated my site to work with the latest version of the Academic theme for Hugo, and it turns out that this broke my code-folding implementation.
As I’ve discussed in previous posts, meta-analyses in psychology, education, and other areas often include studies that contribute multiple, statistically dependent effect size estimates. I’m interested in methods for meta-analyzing and meta-regressing effect sizes from data structures like this, and studying this sort of thing often entails conducting Monte Carlo simulations.
In meta-analyses of psychology, education, and other social science research, it is very common that some of the included studies report more than one relevant effect size. For example, in a meta-analysis of intervention effects on reading outcomes, some studies may have used multiple measures of reading outcomes (each of which meets inclusion criteria), or may have measured outcomes at multiple follow-up times; some studies might have also investigated more than one version of an intervention, and it might be of interest to include effect sizes comparing each version to the no-intervention control condition; and it’s even possible that some studies may have all of these features, potentially contributing lots of effect size estimates.