A Bayesian Rate Ratio effect size to quantify intervention effects for count data in single case experimental research

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A Bayesian Rate Ratio effect size to quantify intervention effects for count data in single case experimental research

Single case experimental design (SCED) is an indispensable methodology when evaluating intervention efficacy. Despite longstanding success with using visual analyses to evaluate SCED data, this method has limited utility for conducting meta-analyses. This is critical because meta-analyses should drive practice and policy in behavioral disorders, more than evidence derived from individual SCEDs. Even when analyzing data from individual studies, there is merit to using multiple analytic methods since statistical analyses in SCED can be challenging given small sample sizes and autocorrelated data. These complexities are exacerbated when using count data, which are common in SCEDs. Bayesian methods can be used to develop new statistical procedures that may address these challenges. I will focus on the formulation of a within-subject Bayesian rate ratio effect size (BRR) for autocorrelated count data which obviates the need for small sample corrections and is scale-free. I will illustrate this within-subject effect size using real data for an ABAB design.

Event Details

Monday 1 February 2021
16:00 - 17:15
Zoom webinar, registration required
Public

Event Speakers

Dr. Prathiba Natesan, Brunel University