Hierarchical Linear Modeling Meta-Analysis of Single-Subject Design Research

Abstract

The identification of evidence-based practices continues to provoke issues of disagreement across multiple fields. One area of contention is the role of single-subject design (SSD) research in providing scientific evidence. The debate about SSD’s utility centers on three issues: sample size, effect size, and serial dependence. One potential method for addressing all three issues is hierarchical linear modeling (HLM) meta-analysis. This study explored the utility of HLM meta-analysis of SSD. A total of 206 functional behavior assessment–based intervention outcome graphs were aggregated to assess whether HLM meta-analysis could identify (a) an overall effect size and statistical significance for mean shift, slope, and variability; (b) how the results mapped to two additional effect size calculations; and (c) whether the procedure met SSD synthesis criteria outlined by Wolery, Busick, Reichow, and Barton.

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