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.
In panel data models and other regressions with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) to account for heteroscedasticity and un-modeled dependence among the errors. Although …
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.
Methods for meta-analyzing single-case designs (SCDs) are needed to inform evidence-based practice in clinical and school settings and to draw broader and more defensible generalizations in areas where SCDs comprise a large part of the research base. …
In many systematic reviews, it is common for eligible studies to contribute effect size estimates from not just one, but multiple relevant outcome measures, for a common sample of participants.
Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces …
I’ve recently been working on small-sample correction methods for hypothesis tests in linear regression models with cluster-robust variance estimation. My colleague (and grad-schoolmate) Beth Tipton has developed small-sample adjustments for t-tests (of single regression coefficients) in the context of meta-regression models with robust variance estimation, and together we have developed methods for multiple-contrast hypothesis tests.
In an earlier post about sandwich standard errors for multi-variate meta-analysis, I mentioned that Beth Tipton has recently proposed small-sample corrections for the covariance estimators and t-tests, based on the bias-reduced linearization approach of McCaffrey, Bell, and Botts (2001).
In a previous post, I provided some code to do robust variance estimation with metafor and sandwich. Here’s another example, replicating some more of the calculations from Tanner-Smith & Tipton (2013).