Psychological Assessments in Legal Contexts: Are Courts Keeping “Junk Science” Out of the Courtroom?

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Abstract

In this article, we report the results of a two-part investigation of psychological assessments by psychologists in legal contexts. The first part involves a systematic review of the 364 psychological assessment tools psychologists report having used in legal cases across 22 surveys of experienced forensic mental health practitioners, focusing on legal standards and scientific and psychometric theory. The second part is a legal analysis of admissibility challenges with regard to psychological assessments. Results from the first part reveal that, consistent with their roots in psychological science, nearly all of the assessment tools used by psychologists and offered as expert evidence in legal settings have been subjected to empirical testing (90%). However, we were able to clearly identify only about 67% as generally accepted in the field and only about 40% have generally favorable reviews of their psychometric and technical properties in authorities such as the Mental Measurements Yearbook. Furthermore, there is a weak relationship between general acceptance and favorability of tools’ psychometric properties. Results from the second part show that legal challenges to the admission of this evidence are infrequent: Legal challenges to the assessment evidence for any reason occurred in only 5.1% of cases in the sample (a little more than half of these involved challenges to validity). When challenges were raised, they succeeded only about a third of the time. Challenges to the most scientifically suspect tools are almost nonexistent. Attorneys rarely challenge psychological expert assessment evidence, and when they do, judges often fail to exercise the scrutiny required by law.

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Clinical versus mechanical prediction: a meta-analysis.

William M. Grove, David H. Zald, Boyd S. Lebow … (2000)

The process of making judgments and decisions requires a method for combining data. To compare the accuracy of clinical and mechanical (formal, statistical) data-combination techniques, we performed a meta-analysis on studies of human health and behavior. On average, mechanical-prediction techniques were about 10% more accurate than clinical predictions. Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33%-47% of studies examined. Although clinical predictions were often as accurate as mechanical predictions, in only a few studies (6%-16%) were they substantially more accurate. Superiority for mechanical-prediction techniques was consistent, regardless of the judgment task, type of judges, judges' amounts of experience, or the types of data being combined. Clinical predictions performed relatively less well when predictors included clinical interview data. These data indicate that mechanical predictions of human behaviors are equal or superior to clinical prediction methods for a wide range of circumstances.