Abstract
High-point coding refers to the popular practice of classifying Minnesota Multiphasic Personality Inventory (Hathaway & McKinley, 1983) profiles based on which clinical scales are the most elevated. A previous review of high-point code studies (McGrath & Ingersoll, 1999a) noted marked discrepancies across studies in the rules used to define high-point codes. This study was conducted to evaluate the costs and benefits of different strategies for high-point coding. The impact of 4 rules for high-point coding on effect sizes and group sizes was evaluated. The 4 rules included requiring a minimum elevation, excluding potentially invalid protocols, restricting coding to well-defined codes, and replacing the lower scale in infrequently occurring codes with the next most elevated scale. The evidence supported the clinical utility of requiring a minimum elevation for code scales. The results were more equivocal concerning the value of well-defined coding and for not replacing the lower scale in infrequent codes. Results were surprisingly negative concerning the utility of excluding potentially invalid protocols, suggesting that guidelines developed in situations in which there is a clear motivation to distort results may not generalize to other settings.
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