I am a statistician and associate professor in the School of Education at the University of Wisconsin-Madison, 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
Northwestern University
BA in Economics, 2003
Boston College
A while back, I posted the outline of a problem about the number of significant effect size estimates in a study that reports multiple outcomes. This problem interests me because it connects to the issue of selective reporting of study results, which creates problems for meta-analysis.
\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\cor{{\text{cor}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \] In Tipton and Pustejovsky (2015), we examined several different small-sample approximations for cluster-robust Wald test statistics, which are like \(F\) statistics but based on cluster-robust variance estimators.
\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \] For a project I am working on, we are using Stan to fit generalized random effects location-scale models to a bunch of count data.
\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \] For a project I am working on, we are using Stan to fit generalized random effects location-scale models to a bunch of count data.
In this post, we will sketch out what we think is a promising and pragmatic method for examining selective reporting while also accounting for effect size dependency. The method is to use a cluster-level bootstrap, which involves re-sampling clusters of observations to approximate the sampling distribution of an estimator. To illustrate this technique, we will demonstrate how to bootstrap a Vevea-Hedges selection model.
Information Matrices for ‘lmeStruct’ and ‘glsStruct’ Objects
Helper package to assist in running simulation studies
Simulate systematic direct observation data
Cluster-robust variance estimation
Between-case SMD for single-case designs
Single-case design effect size calculator