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Tests for experiments with matched groups or repeated measures designs use error terms that involve the correlation between the measures as well as the variance of the data. The larger the correlation between the measures, the smaller the error and the larger the test statistic. If an effect size is computed from the test statistic without taking the correlation between the measures into account, effect size will be overestimated. Procedures for computing effect size appropriately from matched groups or repeated measures designs are discussed.

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Researchers of broad and narrow traits have debated whether narrow traits are important to consider in the prediction of job performance. Because personality-performance relationship meta-analyses have focused almost exclusively on the Big Five, the predictive power of narrow traits has not been adequately examined. In this study, the authors address this question by meta-analytically examining the degree to which the narrow traits of conscientiousness predict above and beyond global conscientiousness. Results suggest that narrow traits do incrementally predict performance above and beyond global conscientiousness, yet the degree to which they contribute depends on the particular performance criterion and occupation in question. Overall, the results of this study suggest that there are benefits to considering the narrow traits of conscientiousness in the prediction of performance.

The concomitant proliferation of causal modeling and hypotheses of multiplicative effects has brought about a tremendous need for procedures that allow the testing of moderated structural equation models (MSEMsAs the social sciences have developed, the complexity of hypothesized relationships has increased steadily (Cortina, 1993). Two of the more obvious indicators of this complexity are the increasing frequency of hypotheses involving multiplicative effects (e.g., linear interaction effects, nonlinear effects) and the popularity of structural equations modeling (SEM). In spite of the preponderance of both multiplicative effects and structural equations models, there is considerable confusion about the appropriate methods for combining the two. In other words, there is confusion with respect to the manner in which multiplicative effects should be incorporated into covariance structures models (Hayduk, 1987;Mathieu, Tannenbaum, & Salas, 1992;Ping, 1995).Strangely, this confusion is not due to a lack of methodology. There are a variety of techniques available for testing structural equations models with multiplicative terms (moderated structural equations models [MSEMs]), each with its own strengths and weaknesses. Nevertheless, most of these techniques are unknown outside mathemati-