Louisiana State University

Hierarchical Linear Modeling Against the "Gold Standard" of Visual Analysis in Single-Subject Design

Abstract
Visual analysis is the �Gold Standard� for single-subject data because of two assumptions: a low Type I error rate and consistency across raters. However, research has shown it is neither as reliable nor as accurate as desired. Autocorrelation, variability, trend, lack of obvious mean shift, and differences in the physical presentation of graphs contribute to inconsistencies and higher error rates. As a result, statistical analysis has been advocated as a judgmental aid to visual analysis, but an appropriate statistic has not been found. In the present study, the accuracy of Hierarchical Linear Modeling will be judged against rater�s visual analyses of previously published data using Receiver Operating Characteristic Curves and Contingency Probabilities in hopes of establishing a useful judgmental aid.
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QuestionPro will be used to create the visual analysis survey for the raters.