Regression discontinuity designs (RDDs) are now a widely used tool for program evaluation in economics and many other fields. RDDs occur in situations where some treatment/program of interest is assigned on the basis of a numerical score (called the running variable), all units scoring above a certain threshold receiving treatment and all units scoring at or below the threshold having treatment withheld (or vice versa, with treatment assigned to units scoring below the threshold).
NOTE (2019-09-24): This post pertains to version 0.56 of the rdd package. The problems described in this post have been corrected in version 0.57 of the package, which was posted to CRAN on 2016-03-14.
I’ve recently been working with my colleague Beth Tipton on methods for cluster-robust variance estimation in the context of some common econometric models, focusing in particular on fixed effects models for panel data—or what statisticians would call “longitudinal data” or “repeated measures.
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.
UPDATED 10/2/2016 after posting the package to CRAN
Here are step-by-step instructions on how to download and install the scdhlm package for R. You’ll need to have a copy of R installed.
My article with Chris Runyon, titled “Alternating renewal process models for behavioral observation: Simulation methods, software , and validity illustrations” has been published in Behavioral Disorders. The abstract is below.
Version 1.0 of the ARPobservation package is now available on the Comprehensive R Archive Network. This makes it even easier to install. Here’s the package description:
ARPobservation: Tools for simulating different methods of observing behavior based on alternating renewal processes
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).