Posts

Sometimes, aggregating effect sizes is fine

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.

Code folding with blogdown + Academic theme

2020-05-03 This post describes an implementation of code folding for an older version of the Academic Theme. It does not work with Academic 4.+. See my updated instructions to get it working with newer versions of Academic.

CRAN downloads of my packages

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.

Systematic Reviews and Meta-analysis SIG at AERA 2019

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.

A handmade clubSandwich for multi-site trials

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.

Effective sample size aggregation

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?

Easily simulate thousands of single-case designs

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.

New paper: A gradual effects model for single-case designs

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.

clubSandwich at the Austin R User Group Meetup

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.

Sampling variance of Pearson r in a two-level design

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