Discrepancies between biological assays and self-report of illicit drug use could undermine epidemiological research findings. Two objectives of the present study are to examine the degree of agreement between self-reported illicit drug use and hair analysis in a community sample of middle-aged men, and to identify factors that may predict discrepancies between self-report and hair testing. Male participants followed since 1972 were interviewed about substance use, and hair samples were analyzed for marijuana, cocaine, opiates, phencyclidine (PCP) and methamphetamine using radioimmunoassay and gas chromatography-mass spectrometry (GC-MS) techniques. Self-report and hair testing generally met good, but not excellent, agreement. Apparent underreporting of recent cocaine use was associated with inpatient hospitalization for the participant's most recent quit attempt, younger age, identifying as African American or other, and not having a diagnosis of antisocial personality disorder. The overestimate of marijuana use relative to hair test was associated with frequent use since 1972 and providing an inadequate hair sample. Additional research is needed to identify factors that differentially affect the validity of both hair drug testing and self-report.

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Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed.In the area of psychological assessment, a computer-based algorithm approach has relied heavily on the expert knowledge of clinicians. In a more empirically based approach-used when insufficient knowledge is available, existing knowledge is debated, or a special population is investigated-linear models such as factor analysis or regression models are frequently applied to empirical data to derive meaningful constructs and appropriate measures. In this approach, statistical inferences are needed to assess the generality of constructs and rules because the gold standard of expert knowledge may not be applied. mortality. The details of ANN methodology in these contexts include ANN parameter specification, software, cross-validation, and the receiver operating characteristic (ROC) evaluation. The performance of the ANN is compared with the performance of the linear statistical analyses frequently used in the area of psychological assessment: linear and quadratic discriminant analyses and logistic regression analyses. We examine the performance of ANN models with varying numbers of hidden neurons to show when complex network architecture is needed to achieve a better level of prediction than that achieved in linear models. We then discuss issues for research practitioners to consider in order to make optimal use of ANNs. Future directions of ANN applications and current salient issues in psychological assessment are discussed. Artificial Neural Networks for Psychological AssessmentBackground

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