Statistical Comparison of Four Effect Sizes for Single-Subject Designs

Abstract

Controversy exists regarding appropriate methods for summarizing treatment outcomes for single-subject designs. Nonregression- and regression-based methods have been proposed to summarize the efficacy of single-subject interventions with proponents of both methods arguing for the superiority of their respective approaches. To compare findings for different single-subject effect sizes, 117 articles that targeted the reduction of problematic behaviors in 181 individuals diagnosed with autism were examined. Four effect sizes were calculated for each article: mean baseline reduction (MBLR), percentage of nonoverlapping data (PND), percentage of zero data (PZD), and one regression-based d statistic. Although each effect size indicated that behavioral treatment was effective, moderating variables were detected by the PZD effect size only. Pearson product-moment correlations indicated that effect sizes differed in statistical relationships to one another. In the present review, the regression-based d effect size did not improve the understanding of single-subject treatment outcomes when compared to nonregression effect sizes.