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).
A common problem arising in many areas of meta-analysis is how to synthesize a set of effect sizes when the set includes multiple effect size estimates from the same study.
Several different approaches have been proposed for meta-analysis of correlation coefficients. One of the major differences between approaches is the choice of scale: whether effect sizes should be analyzed on the Pearson-r scale or first transformed to the Fisher-z scale.