Single-case experimental designs are often used to examine the efficacy and effectiveness of interventions that are designed to change behaviors so that they are less problematic and more similar to the behavior of typically developing peers. By defining a goal level for the behavior, we were able to develop an effect size based on the percent of goal obtained (PoGO). We discuss methods for determining the goal and methods of estimating PoGO, including those for when there are and are not trends. Applicability is demonstrated using data from single-case experimental design studies of speech production, early literacy, and social skill interventions in the early childhood literature and by comparing PoGO to graphic displays and other summary measures, including Tau-U, NAP, the standardized mean difference, and the log-response ratio. We consider how PoGO could be used in systematic reviews, provide recommendations, and discuss areas in need of research.

Forty years ago, Eugene Edgington developed a single-case AB intervention design-and-analysis procedure based on a random determination of the point at which the B phase would start. In the present simulation studies encompassing a variety of AB-type contexts, it is demonstrated that by also randomizing the order in which the A and B phases are administered, a researcher can markedly increase the procedure’s statistical power.

Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed.