Effect sizes in single case research: How large is large?

Abstract

This study examined the problem of interpreting effect sizes in single case research. Nine single case analytic techniques were applied to a convenience sample of 77 published interrupted time series (AB) datasets, and the results were compared by technique across the datasets. Reanalysis of the published data helped answer questions about the nine analytic techniques: their effect sizes, autocorrelation, statistical power, and intercorrelations. The study's findings were that few effect sizes matched Cohen's (1988) guidelines, and that effect sizes varied greatly by analytic technique. Four techniques showed adequate power for typical published data series. Autocorrelation was a sizeable problem in most analyses. In general, individual techniques performed so differently that users need technique-specific information to guide both selection of an analytic technique and interpretation of its results.