The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction
Abstract
Clinical predictions made by mental health practitioners are compared with those using statistical approaches. Sixty-seven studies were identified from a comprehensive search of 56 years of research; 92 effect sizes were derived from these studies. The overall effect of clinical versus statistical prediction showed a somewhat greater accuracy for statistical methods. The most stringent sample of studies, from which 48 effect sizes were extracted, indicated a 13% increase in accuracy using statistical versus clinical methods. Several variables influenced this overall effect. Clinical and statistical prediction accuracy varied by type of prediction, the setting in which predictor data were gathered, the type of statistical formula used, and the amount of information available to the clinicians and the formulas. Recommendations are provided about when and under what conditions counseling psychologists might use statistical formulas as well as when they can rely on clinical methods. Implications for clinical judgment research and training are discussed.
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1. A complete description of the search process can be found in Spengler et al. (2005). To limit the retrieval of studies to a manageable, yet representative, sample, studies that appeared between 1970 and 1996 were included in the search. Electronic databases included PsychInfo, ERIC, Dissertation Abstracts, BRS, MEDLINE, and Social Science Index. Unavailable dissertations and journal articles were purchased, and authors were contacted to obtain material that was difficult to retrieve. After we identified likely clinical judgment studies, forward and backward cross-referencing was conducted until no new studies were obtained. By using this strategy, more than 35,000 articles were identified; 4,617 were coded and 1,135 met our inclusion criteria for the project. We chose this open-ended strategy to maximize the number of studies that would be reviewed. Because our search did not include studies published after 1996, we performed a file drawer analysis. In this analysis, we can project how many studies reporting significantly different effect sizes will be needed to change our overall results. This analysis indicated that to reduce our effect size to zero (within the 95% confidence interval boundaries), indicating no difference between clinical and statistical prediction, 99 additional studies are needed using cross-validated formulas and producing nonoutlying effect sizes.
2. Hit rates reported as percentage correct were directly transformed into d using the DSTAT program (Johnson, 1993). When hit rates were reported as a correlation of the prediction with the criterion (r) for clinical and statistical prediction, the r values were first converted to Z scores. Differences in Z scores were then transformed into a chi-square value. The chisquare value of Z score differences was transformed into d(Rosenthal, 1991).
3. These results are not reported but are available on request by contacting the first author.
4. Johnson (1993) and Hedges and Olkin (1985) recommend outlier analyses in metaanalyses when moderating variables fail to explain observed heterogeneity of studies. In this procedure, outliers are sequentially removed until the hypothesis of homogeneity cannot be rejected (i.e., the probability of Hedges's Q exceeds .05). In the current meta-analysis, the study design characteristics that we hypothesized would influence the overall effect size (e.g., prediction task) did not reduce the heterogeneity among the studies. This made the interpretation of the effect size difficult because the many studies differed from each other in terms of magnitude and direction of the effects. Therefore to make our findings more interpretable, we performed an outlier analysis.
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