Parametric and Non-Parametric Meta-Analyses of Single-Subject Research Designs with R:

According to the Council on Social Work Education (2022), social workers must have the skills to evaluate their practices, including how well clients respond to interventions. Often this is a grassroots effort, and evaluations frequently begin with assessing one client at a time. Single-subject research is one method for social workers to evaluate their practices; however, in many cases, it may be useful to aggregate the findings of multiple, related single-subject studies. This can occur in cases where multiple clients may have similar goals or when similar interventions are used with different clients. Single-case designs are among the most robust nonrandomized experimental techniques (Shadish, Cook & Campbell, 2002). Meta-analysis can be utilized to combine evaluations’ findings across studies. In the case of single-subject design, we can combine results from multiple clients to determine how effective an intervention is (Auerbach & Zeitlin, 2021). However, there has been an exclusion of single-subject techniques from the meta-analysis of interventions. Weaker designs such as non-equivalent single pre/post group designs are often included in a meta-analysis (Shadish, Rindskopf, & Hedges, 2008). This exclusion is not because of an unfamiliarity with experimental design, but rather a lack of statistical methods to perform a meta-analysis of single-subject findings (Shadish, Rindskopf, & Hedges, 2008). SSDforR is a comprehensive R package designed for analyzing single-subject data both visually and statistically. It has capabilities that may make it suitable for both practice evaluation and advanced, publishable research (Auerbach & Zeitlin, 2021). In its latest iteration, two functions have been added to conduct meta-analyses. The metareg() function allows users to calculate effect sizes and variances across any number of clients while the metaregi() function enables meta-analyses with the inclusion of a moderating variable. R functions to perform non-parametric meta-analysis will also be discussed. In the first part of this workshop, attendees learn how to freely obtain and utilize the basics of the package. This will include a demonstration of how to import single-subject data into SSDforR, conduct analyses based on characteristics of the data, and interpret findings that can guide practitioner decision-making. In the second part of the workshop, the presenters will focus on the utilization of the metareg() and metaregi() functions. An alternative to traditional effect sizes, the nonoverlap of all pairs (NAP) effect size will also be discussed (Parker & Vannest, 2009). A discussion of how these could improve published research will ensue with examples from previously published research studies. Participants will be provided with resources that will enable them to use all functions in SSDforR after the workshop including presentation materials, instructions for downloading R and SSDforR, scripts used during the presentation, and sample data.