Introducing a Bayesian Approach to Determining Degree of Fit With Existing Rorschach Norms

This article offers a new methodological approach to investigate the degree of fit between an independent sample and 2 existing sets of norms. Specifically, with a new adaptation of a Bayesian method, we developed a user-friendly procedure to compare the mean values of a given sample to those of 2 different sets of Rorschach norms. To illustrate our technique, we used a small, U.S. community sample of 80 adults and tested whether it resembled more closely the standard Comprehensive System norms (CS 600; Exner, 2003), or a recently introduced, internationally based set of Rorschach norms (Meyer, Erdberg, & Shaffer, 2007 Meyer, G. J., Erdberg, P., & Shaffer, T. W. (2007). Toward international normative reference data for the Comprehensive System. Journal of Personality Assessment, 89(Suppl. 1), S201–S216.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Strengths and limitations of this new statistical technique are discussed.     

The Effect of CS Administration or an R-Optimized Alternative on R–PAS Variables: A Meta-Analysis of Findings From Six Studies

Controlling the number of Rorschach responses (R) as a method to reduce variability in the length of records has stimulated controversy among researchers for many years. Recently, the Rorschach Performance Assessment System (R–PAS; Meyer, Viglione, Mihura, Erard, &; Erdberg, 2011 Meyer, G. J., Viglione, D. J., Mihura, J. L., Erard, R. E., &; Erdberg, P. (2011). Rorschach Performance Assessment System: Administration, coding, interpretation, and technical manual. Toledo, OH: Rorschach Performance Assessment System. [Google Scholar]) introduced an R-Optimized method to reduce variability in R. Using 4 published and 2 previously unpublished studies (N = 713), we examine the extent to which 51 Comprehensive System–based scores on the R–PAS profile pages are affected as a result of receiving Comprehensive System (CS; Exner, 2003 Exner, J. E. (2003). The Rorschach: A Comprehensive System. Vol. 1. Basic foundations (4th ed.). Hoboken, NJ: Wiley. [Google Scholar]) administration versus a version of R-Optimized administration. As hypothesized, R—the intended target of R-Optimized administration—showed reliable weighted average differences across each method of administration. As expected, its mean modestly increased and its standard deviation notably decreased. Also as hypothesized, the next largest effects were decreases in the variability (SD) of 2 variables directly related to R, R8910% and Complexity. No other reliable differences were observed. Therefore, because R-Optimized administration does not notably modify the existing CS-based normative values for other profiled R–PAS variables, the data do not support concerns that R-Optimized administration notably modifies the Rorschach task or that existing CS research data would not generalize to R–PAS. However, because R-Optimized administration reduces variability in R, it allows a single set of norms to apply readily to more people.