Visual analysis is the primary method to detect functional relations in single-case experimental design (SCED) research. Meta-Visual-Analysis (MVA) is a novel approach used to synthesize SCED data where the estimated effect size measures are principally anchored to primary aspects of visual analysis: change in the magnitude of level, trend, variability, and trend-adjusted level of projected trends. For each of these aspects, percentage point differences between baseline and intervention conditions are estimated and quantified for every participant across studies. MVA effect sizes are standardized, and their aggregates are graphically displayed in a manner similar to individual SCED graphs. MVA graphs are compared and visually analyzed with the aim of better understanding the effectiveness and generality of interventions across SCED studies. In this discussion paper we provide general steps to conduct an MVA and describe MVA's utility in reviewing, organizing, and directing future SCED research syntheses.
