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 \(w_i\).

Earlier this month, I taught at the Summer Research Training Institute on Single-Case Intervention Design and Analysis workshop, sponsored by the Institute of Education Sciences’ National Center for Special Education Research. While I was there, I shared a web-app for simulating data from a single-case design. This is a tool that I put together a couple of years ago as part of my ARPobservation R package, but haven’t ever really publicized or done anything formal with.

I’m very happy to share a new paper, co-authored with my student Danny Swan, “A gradual effects model for single-case designs,” which is now available online at Multivariate Behavioral Research. You can access the published version at the journal website (click here for free access while supplies last) or the pre-print on PsyArxiv (always free!). Here’s the abstract and the supplementary materials. Danny wrote R functions for fitting the model, (available as part of the SingleCaseES package) as well as a slick web interface, if you prefer to point-and-click.

Last night I attended a joint meetup between the Austin R User Group and R Ladies Austin, which was great fun. The evening featured several lightning talks on a range of topics, from breaking into data science to network visualization to starting your own blog. I gave a talk about sandwich standard errors and my clubSandwich R package. Here are links to some of the talks:
Caitlin Hudon: Getting Plugged into Data Science Claire McWhite: A quick intro to networks Nathaniel Woodward: Blogdown Demo!

Consider Pearson’s correlation coefficient, \(r\), calculated from two variables \(X\) and \(Y\) with population correlation \(\rho\). If one calculates \(r\) from a simple random sample of \(N\) observations, then its sampling variance will be approximately
\[ \text{Var}(r) \approx \frac{1}{N}\left(1 - \rho^2\right)^2. \]
But what if the observations are drawn from a multi-stage sample? If one uses the raw correlation between the observations (ignoring the multi-level structure), then the \(r\) will actually be a weighted average of within-cluster and between-cluster correlations (see Snijders & Bosker, 2012).

The delta method is surely one of the most useful techniques in classical statistical theory. It’s perhaps a bit odd to put it this way, but I would say that the delta method is something like the precursor to the bootstrap, in terms of its utility and broad range of applications—both are “first-line” tools for solving statistical problems. There are many good references on the delta-method, ranging from the Wikipedia page to a short introduction in The American Statistician (Oehlert, 1992).