Using Ssd for R to Calculate Effect Sizes in Single-Subject Research

Sunday, January 18, 2015: 10:00 AM-11:45 AM
Iberville, Fourth Floor (New Orleans Marriott)
Cluster: Research Design and Measurement
Charles Auerbach, PhD, Yeshiva University, Wendy Zeitlin, PhD, Yeshiva University and Susan Mason, PhD, Yeshiva University
Rigorous single-subject research designs are increasingly recognized as foundational to building evidence to the efficacy of interventions implemented in practice settings (Byiers, Reichle, & Symons, 2012; Jindani & Newman, 2006; Kratochwill et al., 2013; Perdices & Tate, 2009).  Additionally, they enable practitioners to become involved in the research process at the client-level and add to the knowledge base.

 In order to add to the relevance of single-subject research studies, it is important to consider two related issues:  1) the practical/clinical relevance of these studies, since small sample sizes are not likely to yield statistically significant results even for effective interventions, and 2) the ability to aggregate these studies into meta-analyses.  A major requirement for addressing both of these is the ability to calculate meaningful effect sizes (Morgan, 2008; Parker, 2006; Schlosser & Wendt, 2008; Scruggs & Mastropieri, 2013).

 A number of effect size methods have emerged as useful in single-subject research.  These include those that are typically used with group research designs, such as Cohen’s d and Hedge’s g, but there is a also a class of effect sizes that has been designed specifically for single-subject research.  Examples of these include non-parametric, non-overlapping methods such as the Percentage of All Non-Overlapping Data (PAND) and the Percentage of Data Exceeding the Median (PEM). 

 In this demonstration, the presenters will use an R package that they developed, SSD for R, to illustrate how and when to use a host of effect size calculations based upon characteristics of the data.  Many of these effect sizes have accompanying graphs, and a demonstration of how to produce, annotate, and export these graphs will be demonstrated.

 R is a robust, free, open-source statistical programming language (The R Project for Statistical Computing, n.d.).  It can be used in lieu of proprietary statistical software packages such as SPSS, SAS, and Stata.  R can be installed on almost any computing platform, making it easy for researcher-practitioners to use regardless of the type of computing environment they are working in.  Another benefit to using Ris that, because it is freely available, it can easily be introduced into agency settings, increasing the research capacity within agencies by reducing identified barriers.

In this session, participants will be given instructions for freely downloading all required software, including R and RStudio, a free graphical user interface that is compatible with R.  Participants will also be provided access to datasets used in the demonstration.

During the demonstration, the presenters will explain how to generate and interpret a variety of effect sizes, including some that have only been calculated by hand in the past, such as the Improvement Rate Difference.   The advantage of using SSD for R is that, in some cases, effect sizes for larger datasets have been cumbersome to manually calculate accurately despite the fact that these effect sizes are useful and appropriate for use in single-subject research.

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