Using Visual Analysis to Evaluate and Refine Multilevel Models of Single-Case Studies

Abstract

In special education, multilevel models of single-case research have been used as a method of estimating treatment effects over time and across individuals. Although multilevel models can accurately summarize the effect, it is known that if the model is misspecified, inferences about the effects can be biased. Concern with the potential for model misspecification motivates our method for evaluating multilevel models of single-case data. This method is based on the visual analysis of graphs that have the model-implied individual trajectories superimposed on plots of the raw data. Through the reanalysis of a published study, we show how this visual analysis approach can identify model misspecifications and motivate the consideration of alternative model specifications that lead to better fit.