As I’ve discussed in previous posts, meta-analyses in psychology, education, and other areas often include studies that contribute multiple, statistically dependent effect size estimates. I’m interested in methods for meta-analyzing and meta-regressing effect sizes from data structures like this, and studying this sort of thing often entails conducting Monte Carlo simulations. Monte Carlo simulations involve generating artificial data—in this case, a set of studies, each of which has one or more dependent effect size estimates—that follows a certain distributional model, applying different analytic methods to the artificial data, and then repeating the process a bunch of times.

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.

In today’s Quant Methods colloquium, I gave an introduction to the logic and purposes of Monte Carlo simulation studies, with examples written in R.
Here are the slides from my presentation. You can find the code that generates the slides here. Here is my presentation on the same topic from a couple of years ago. David Robinson’s blog has a much more in-depth discussion of beta-binomial regression. The data I used is from Lahman’s baseball database.

I have learned from Mr. Yaakoub El Khamra that he and the good folks at TACC have made some modifications to TACC’s custom MPI implementation and R build in order to correct bugs in Rmpi and snow that were causing crashes. My earlier post has been updated to reflect the modifications. The main changes are:
The version of MVAPICH2 has changed to 2.0b Changes to the Rmpi and snow packages necessitate using the latest version of R (Warm Puppy, 3.

UPDATE (4/8/2014): I have learned from Mr. Yaakoub El Khamra that he and the good folks at TACC have made some modifications to TACC’s custom MPI implementation and R build in order to correct bugs in Rmpi and snow that were causing crashes. This post has been updated to reflect the modifications.
I’ve started to use the Texas Advanced Computing Cluster to run statistical simulations in R. It takes a little bit of time to get up and running, but once you do it is an amazing tool.

Here are the slides from my presentation at this afternoon’s Quant. Methods brown bag. I gave a very quick introduction to using R for conducting simulation studies. I hope it was enough to get people intrigued about the possibilities of using R in their own work.
The second half of the presentation sketched out a quick-and-dirty simulation of the Behrens-Fisher problem, or more specifically the coverage rates of 95% confidence intervals using Welch’s degrees of freedom approximation, given independent samples with unequal variances.

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