A tacit but fundamental assumption of the Theory of Signal Detection (TSD) is that criterion placement is a noise-free process. This paper challenges that assumption on theoretical and empirical grounds and presents the Noisy Decision Theory of Signal Detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of discrimination that incorporate criterion variability are derived, and the model's relationship with extant models of decision-making in discrimination tasks is examined. An experiment that evaluates recognition memory for ensembles of word stimuli reveals that criterion noise is not trivial in magnitude and contributes substantially to variance in the slope of the isosensitivity function. We discuss how ND-TSD can help explain a number of current and historical puzzles in recognition memory, including the inconsistent relationship between manipulations of learning and the slope of the isosensitivity function, the lack of invariance of the slope with manipulations of bias or payoffs, the effects of aging on the decision-making process in recognition, and the nature of responding in Remember/Know decision tasks. ND-TSD poses novel and theoretically meaningful constraints on theories of recognition and decision-making more generally, and provides a mechanism for rapprochement between theories of decision-making that employ deterministic response rules and those that postulate probabilistic response rules.

Extensive research has examined the effectiveness of admissions tests for use in higher education. What has gone unexamined is the extent to which tests are similarly effective for predicting performance at both the master's and doctoral levels. This study empirically synthesizes previous studies to investigate whether or not the Graduate Record Examination (GRE) predicts the performance of students in master's programs as well as the performance of doctoral students. Across nearly 100 studies and 10,000 students, this study found that GRE scores predict first year grade point average (GPA), graduate GPA, and faculty ratings well for both master's and doctoral students, with differences that ranged from small to zero.

To reduce adverse impact potential and improve diversity outcomes from personnel selection, one promising technique is De Corte, Lievens, and Sackett's (2007) Pareto-optimal weighting strategy. De Corte et al.'s strategy has been demonstrated on (a) a composite of cognitive and noncognitive (e.g., personality) tests (De Corte, Lievens, & Sackett, 2008) and (b) a composite of specific cognitive ability subtests (Wee, Newman, & Joseph, 2014). Both studies illustrated how Pareto-weighting (in contrast to unit weighting) could lead to substantial improvement in diversity outcomes (i.e., diversity improvement), sometimes more than doubling the number of job offers for minority applicants. The current work addresses a key limitation of the technique-the possibility of shrinkage, especially diversity shrinkage, in the Pareto-optimal solutions. Using Monte Carlo simulations, sample size and predictor combinations were varied and cross-validated Pareto-optimal solutions were obtained. Although diversity shrinkage was sizable for a composite of cognitive and noncognitive predictors when sample size was at or below 500, diversity shrinkage was typically negligible for a composite of specific cognitive subtest predictors when sample size was at least 100. Diversity shrinkage was larger when the Pareto-optimal solution suggested substantial diversity improvement. When sample size was at least 100, cross-validated Pareto-optimal weights typically outperformed unit weights-suggesting that diversity improvement is often possible, despite diversity shrinkage. Implications for Pareto-optimal weighting, adverse impact, sample size of validation studies, and optimizing the diversity-job performance tradeoff are discussed. (PsycINFO Database Record

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