Nonparametric meta-analysis for single-case research: Confidence intervals for combined effect sizes

Abstract

In this article we present a nonparametric technique for meta-analyzing randomized single-case experiments by using inverted randomization tests to calculate nonparametric confidence intervals for combined effect sizes (CICES). Over the years, several proposals for single-case meta-analysis have been made, but most of these proposals assume either specific population characteristics (e.g., heterogeneity of variances or normality) or independent observations. However, such assumptions are seldom plausible in single-case research. The CICES technique does not require such assumptions, but only assumes that the combined effect size of multiple randomized single-case experiments can be modeled as a constant difference in the phase means. CICES can be used to synthesize the results from various single-case alternation designs, single-case phase designs, or a combination of the two. Furthermore, the technique can be used with different standardized or unstandardized effect size measures. In this article, we explain the rationale behind the CICES technique and provide illustrations with empirical as well as hypothetical datasets. In addition, we discuss the strengths and weaknesses of this technique and offer some possibilities for future research. We have implemented the CICES technique for single-case meta-analysis in a freely available R function.

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