Eyewitness accuracy and confidence: within- versus between-subjects correlations

Previous researchers using between-subjects comparisons have found eyewitness confidence and accuracy to be only negligibly correlated. In this study, we examined the predictive power of confidence in within-subject terms. Ninety-six subjects answered, and made confidence ratings for, a series of questions about a crime they witnessed. The average between-subjects and within-subject accuracy-confidence correlations were comparably low: r = .14 (p less than .001) and r = .17 (p less than .001), respectively. Confidence is neither a useful predictor of the accuracy of a particular witness nor of the accuracy of particular statements made by the same witness. Another possible predictor of accuracy, response latency, correlated only negligibly with accuracy (r = -.09 within subjects), but more strongly with confidence (r = -.27 within subjects). This pattern was obtained for both between-subjects and within-subject comparisons. The theoretical and practical implications of these results are discussed.     

Cognitive mechanisms mediating the biasing effects of leader sex on ratings of leader behavior

The present study examined the effects of the sex of the leader, the sex of the rater, and the temporal position of leader performance information on ratings of leader behavior. Male and female subjects listened to an audiotape of a male or female leader and then rated the leader's initiating structure and consideration behaviors. The position of the leader performance information was manipulated by describing the leader as successful before or after raters listened to the audiotape or not presenting raters with the performance information. Analyses indicated that the sex of the leader and/or sex of the rater biased ratings of initiating structure when the performance information was presented after the audiotape and when no leader performance information was presented. Consideration ratings were affected only by the position of the leader performance information. The results are interpreted within a cognitive/implicit sex theory framework. Implications for leadership research are also discussed.Portions of the data analyzed in this study were collected by the second author as part of a master's thesis directed by Joseph A. Sgro.     

Pursuing and distancing: the construct and its measurement

Although the concept of interpersonal pursuing and distancing has been introduced and used clinically, the lack of a reliable and valid measure has deterred its more formal investigation and relevance to personality theory. The Pursuing-Distancing (P-D) Scale, a 92-item measure of the behavioral expression of these characteristics, was developed. Evidence for internal consistency as well as internal support of construct validity is presented and discussed. An 80-item revised scale is available from the authors and is currently being evaluated in a variety of external validation settings.     

Evaluation of the BSRI masculine and feminine items using desirability and stereotype ratings

This study empirically tested Bem's (1974) assumption that the BSRI Masculinity and Femininity scales measure sex-typed standards of desirable behavior for men and women in American society. The adequacy of items in the two scales was evaluated by Bem's (1974) criteria using two types of desirability and stereotype ratings. Results obtained in all the experimental conditions except one involving Bem's desirability instructions and Bem's rating scale did not support the tested assumption. Implications of these results for revising the BSRI scales were discussed.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.