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
\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \] 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.
Meta-analyses in education, psychology, and related fields rely heavily of Cohen’s $d$, or the standardized mean difference effect size, for quantitatively describing the magnitude and direction of intervention effects. In these fields, Cohen’s $d$ is so pervasive that its use is nearly automatic, and analysts rarely question its utility or consider alternatives (response ratios, anyone? POMP?). Despite this state of affairs, working with Cohen’s $d$ is theoretically challenging because the standardized mean difference metric does not have a singular definition. Rather, its definition depends on the choice of the standardizing variance used in the denominator.
In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares. We were recently alerted that Theorem 2 in the paper is incorrect as stated. It turns out, the conditions in the original version of the theorem are too general. A more limited version of the Theorem does actually hold, but only for models estimated using ordinary (unweighted) least squares, under a working model that assumes independent, homoskedastic errors. In this post, I’ll give the revised theorem, following the notation and setup of the previous post (so better read that first, or what follows won’t make much sense!).
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