Effect size in single-case research: a review of nine nonoverlap techniques.

With rapid advances in the analysis of data from single-case research designs, the various behavior-change indices, that is, effect sizes, can be confusing. To reduce this confusion, nine effect-size indices are described and compared. Each of these indices examines data nonoverlap between phases. Similarities and differences, both conceptual and computational, are highlighted. Seven of the nine indices are applied to a sample of 200 published time series data sets, to examine their distributions. A generic meta-analytic method is presented for combining nonoverlap indices across multiple data series within complex designs.