Ssd for R: A Comprehensive Statistical Package to Analyze Single-Subject Research Data
Analysis of single-subject research typically occurs on two levels: visually and statistically (Mattaini, 2010; Rubin & Babbie, 2011). Visual analysis is the most commonly used approach (Kazdin, 2011; Orme & Combs-Orme, 2012); however, there are situations in which visual analysis alone may not be sufficient. Statistical analysis may be valuable if observed effects are small; variation is large; effects of the intervention do not appear immediately as phases change; there is no clear trend in the data within phases; or when there may be an issue of autocorrelation, which is nearly impossible to detect through visual inspection alone (Kazdin, 2011; Thyer & Myers, 2011).
The need for statistical analysis in single-subject designs, however, presents a challenge as analytical methods that are applied to group comparison studies are not appropriate in single-subject research. This is because single-subject research typically compares measured outcome(s) over time under varying conditions, but across the same client unit. Therefore, it is often appropriate to apply different statistical procedures and analysis in single-subject designs than in group comparisons (Kratochwill et al., 2010). These consist of both parametric and non-parametric methods including analysis of autocorrelation, effect size, regression methods, standard-deviation band analysis, conservative dual criteria analysis, and others (Bloom, Fischer, & Orme, 2009; Fisher, Kelley, & Lomas, 2003; Kratochwill et al., 2010).
Until now, there has been little in the way of software to help those interested in conducting single-subject research collect, analyze, and interpret their data. SINGWIN was the first software package developed specifically for the analysis of single-system data (Auerbach, Schnall, & Heft-LaPorte, 2009). SINGWIN, however, has some limitations: data entry is restricted to a single behavior, group data cannot be analyzed, it works only in a Windows environment, and most procedures are limited to A-B designs.
Because of these limitations, a new software application has been developed to analyze single-subject data in the social sciences. This new application, named SSD for R, has capabilities that may make it more suitable for advanced, publishable research. SSD for Ris a comprehensive package of statistical tests that produces accompanying graphs for almost every function. These include, among others, tests of autocorrelation, t-tests, effect sizes, chi-square analyses, conservative-dual criteria analysis and SPC charts.
In this workshop, participants will be shown where and how to download the software and functions free-of-charge and will be given the opportunity to walk through the use of these functions by tracking a hypothetical client through a practice evaluation study. Participants will be given resources to enable them to use this package with their students and in their own research.