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Rmarkdown documents now have a very nifty code folding option, which allows the reader of a compiled html document to toggle whether to view or hide code chunks. However, the feature is not supported in blogdown, the popular Rmarkdown-based website/blog creation package. I recently ran across an implementation of codefolding for blogdown, developed by Sébastien Rochette. I have been putzing around, trying to get it to work with my blog, which uses the Hugo Academic theme—alas, to no avail.


At AERA this past weekend, one of the recurring themes was how software availability (and its usability and default features) influences how people conduct meta-analyses. That got me thinking about the R packages that I’ve developed, how to understand the extent to which people are using them, how they’re being used, and so on. I’ve had badges on my github repos for a while now: clubSandwich: ARPobservation: scdhlm: SingleCaseES: These statistics come from the METACRAN site, which makes available data on daily downloads of all packages on CRAN (one of the main repositories for sharing R packages).


This year, Dr. Laura Dunne and I are serving as program co-chairs for the AERA special interest group on Systematic Reviews and Meta-Analysis, which is a great group of scholars interested in the methodology and application of research synthesis to questions in education and the broader social sciences. We had a strong batch of submissions to the SIG and (since we’re new and still a fairly small group) only a few sessions to fill with them.


I’m just back from the Society for Research on Educational Effectiveness meetings, where I presented work on small-sample corrections for cluster-robust variance estimators in two-stage least squares models, which I’ve implemented in the clubSandwich R package. Here’s my presentation. So I had “clubSandwich” estimators on the brain when a colleague asked me about whether the methods were implemented in SAS. The short answer is “no.” The moderately longer answer is “not unless we can find funding to pay someone who knows how to program properly in SAS.


In settings with independent observations, sample size is one way to quickly characterize the precision of an estimate. But what if your estimate is based on weighted data, where each observation doesn’t necessarily contribute to equally to the estimate? Here, one useful way to gauge the precision of an estimate is the effective sample size or ESS. Suppose that we have \(N\) independent observations \(Y_1,...,Y_N\) drawn from a population with standard deviation \(\sigma\), and that observation \(i\) receives weight \(w_i\).


Recent Publications

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(2019). Current practices in meta-regression in psychology, education, and medicine. Research Synthesis Methods, forthcoming.

Preprint Dataset Journal

(2018). A history of meta-regression: Technical, conceptual, and practical developments between 1974 and 2018. Research Synthesis Methods, forthcoming.

Preprint Journal

(2018). Testing for funnel plot asymmetry of standardized mean differences. Research Synthesis Methods, 10(1), 57-71.

Preprint Code Journal

(2018). Small sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models. Journal of Business and Economic Statistics, 36(4), 672–683.

Preprint Journal R package

(2018). Between-case standardized effect size analysis of single case design: Examination of the two methods. Research in Developmental Disabilities, 79, 88-96.


Recent Presentations

More Talks

Evaluating meta-analytic methods to detect outcome reporting bias in the presence of dependent effect sizes
The impact of response-guided designs on count outcomes in single-case design baselines
An examination of measurement procedures and baseline behavioral outcomes in single-case research
Small-sample cluster-robust variance estimators for two-stage least squares models
Combining robust variance estimation with models for dependent effect sizes



Simulate systematic direct observation data.


Single-case design effect size calculator.


cluster-robust variance estimation.


Between-case SMD for single-case designs.