Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs

Recently, there has been an increased interest in developing statistical methodologies for analyzing single case experimental design (SCED) data to supplement visual analysis. Some of these are simulation-driven such as Bayesian methods because can compensate small sample sizes, which is a main challenge SCEDs. Two approaches: unknown change-point model (BUCP) and simulation modeling analysis (SMA) were compared the present study three real datasets that exhibit “clear” immediacy, “unclear” delayed effects. Although SMA estimates be used answer some aspects functional relationship between independent outcome variables, they cannot address immediacy or provide effect size estimate considers autocorrelation required by What Works Clearinghouse (WWC) Standards. BUCP overcomes drawbacks SMA. In final analysis, it recommended both analyses conducted thorough