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
My article “Measurement-comparable effect sizes for single-case studies of free-operant behavior” has been accepted at Psychological Methods. Postprint and supporting materials are available. Here’s the abstract:
Single-case research comprises a set of designs and methods for evaluating the effects of interventions, practices, or programs on individual cases, through comparison of outcomes measured at different points in time.
It is well known that the partial interval recording procedure produces an over-estimate of the prevalence of a behavior. Here I will demonstrate how to use the ARPobservation package to study the extent of this bias.
The ARPobservation package provides a set of tools for simulating data generated by different procedures for direct observation of behavior. This is accomplished in two steps. The first step is to simulate a “behavior stream” itself, which is assumed to follow some type of alternating renewal process.
UPDATED 5/29/2014 after posting the package to CRAN
Here are step-by-step instructions on how to download and install ARPobservation. For the time being, ARPobservation is available as a pre-compiled binary for Windows.
Interested in working with me? See below for descriptions of several potential projects. If you have interest and abilities that line up with one of these, feel free to contact me.
This thesis studies quantitative methods for summarizing and synthesizing single-case studies, a class of research designs for evaluating the effects of interventions through repeated measurement of individuals. Despite long-standing interest in meta-analytic synthesis of single-case research, there remains a lack of consensus about appropriate methods, even about the most basic question of what effect size metrics are useful and appropriate.