Bryan D. Edwards

Work stressors, role-based performance, and the moderating influence of organizational support.

As a test of the 2-dimensional model of work stressors, the present study proposed differential relationships between challenge stressors and hindrance stressors and role-based performance, which were expected to be moderated by organizational support. In a sample of 215 employees across 61 offices of a state agency, the authors obtained a positive relationship between challenge stressors and role-based performance and a negative relationship between hindrance stressors and role-based performance. In addition, organizational support moderated the relationship between challenge stressors and role-based performance but did not moderate the relationship between hindrance stressors and role-based performance. This suggests that organizations would benefit from increasing challenges in the workplace as long as they are supportive of employees and removing hindrances. Further implications for organizational theory and practice are discussed. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Taking a look behind the wheel: An investigation into the personality predictors of aggressive driving

The present study evaluated a theoretical model of the relationships among six aspects of driver personality (i.e., driving anger and the Big Five personality factors), aggressive driving, and two outcomes of aggressive driving: motor vehicle crashes and moving violations. Data from 308 drivers recruited from two vehicle licensing offices were analyzed using structural equation modeling. As expected, aggressive driving predicted crashes and moving violations. Based on the zero-order correlations, emotional stability, agreeableness, and conscientiousness were related to aggressive driving in the expected directions; however, the picture changed when the joint effects of all variables were examined via structural equation modeling. A model in which driver personality predicted aggressive driving, which in turn predicted crashes and moving violations was supported. Drivers who were high on driving anger and low on agreeableness reported driving more aggressively. Implications for traffic safety professionals and researchers are discussed.

A Longitudinal Examination of the Comparative Criterion-Related Validity of Additive and Referent-Shift Consensus Operationalizations of Team Efficacy

Using a longitudinal design, the authors examined the criterion-related validity of two operationalizations of task-specific team efficacy that differed in their approximation to the level of analysis of the criterion, team performance. Data were obtained from 85 highly interdependent dyadic teams trained over a 2-week period to perform a complex perceptual-motor skill task. Results indicated that, as expected, the operationalization with a team-level referent (referent-shift consensus) was superior to the operationalization with an individual-level referent (additive) across all three data collection periods. For the referent-shift consensus operationalization, within-team agreement and the criterion-related validity improved between the first and second data collection periods but not between the second and third. However, for both operationalizations, despite the increased strength of the team efficacy and team performance relationships, efficacy ratings collected later in the study protocol did not explain unique variance in subsequent team performance once the effect of previous performance was statistically controlled.

Examining the Consequences in the Tendency to Suppress and Reappraise Emotions on Task-Related Job Performance

This research tested the effects of individual differences in emotion regulation tendencies on task-related job performance and the mediating role of task focus. Emotion regulation has been divided into two broad classes, suppression and reappraisal, which may differentially relate to performance. By following self-regulation theories, it is believed that suppression requires more resources and will negatively relate to task performance via less task focus. Reappraisal requires fewer resources and should positively relate to performance via greater task focus. Results generally supported our expected relationships across both lab and field studies, and we discuss the theoretical and practical implications of our findings.

An examination of factors contributing to a reduction in subgroup differences on a constructed-response paper-and-pencil test of scholastic achievement.

The authors investigated subgroup differences on a multiple-choice and constructed-response test of scholastic achievement in a sample of 197 African American and 258 White test takers. Although both groups had lower mean scores on the constructed-response test, the results showed a 39% reduction in subgroup differences compared with the multiple-choice test. The results demonstrate that the lower subgroup differences were explained by more favorable test perceptions for African Americans on the constructed-response test. In addition, the two test formats displayed comparable levels of criterion-related validity. The results suggest that the constructed-response test format may be a viable alternative to the traditional multiple-choice test format in efforts to simultaneously use valid predictors of performance and minimize subgroup differences in high-stakes testing.

Change the Referent? A Meta-Analytic Investigation of Direct and Referent-Shift Consensus Models for Organizational Climate

Based on earlier taxonomies of group composition models, aggregating data from individual-level responses to operationalize group-level constructs is a common aspect of management research. The present study contributes to the literature on group composition models by quantitatively integrating the climate literature via meta-analysis to determine which of the two most common methods of aggregation, direct consensus and referent-shift consensus, is the stronger predictor of group-level outcomes. We found that referent-shift consensus was a stronger predictor of job performance and customer service performance than direct consensus. However, we found that direct consensus was a stronger predictor of job attitudes than referent-shift consensus. We also found that climate-performance relationships were moderated by aggregation method of the performance criterion. The implications of these findings for advancing multi-level theory and research are discussed.

The Validity of Narcissism and Driving Anger in Predicting Aggressive Driving in a Sample of Young Drivers

The present study examined narcissism and driving anger as explanations of aggressive driving behavior in young adults. Using a sample of 362 young adult drivers, we found that narcissism and driving anger were significant predictors of aggressive driving. Although driving anger was a stronger predictor of aggressive driving, narcissism was uniquely related to aggressive driving above and beyond driving anger. In addition, aggressive driving was a significant predictor of crashes and moving violations. A simultaneous test of all relationships demonstrated that aggressive driving mediated the relationships between narcissism and driving anger and our outcomes of crashes and moving violations. Consistent with the theory of threatened egotism, our results suggests that younger drivers with high but unstable self-esteem reported reacting more aggressively when provoked while driving, which in turn led to negative driving performance outcomes.

Multistage selection strategies: simulating the effects on adverse impact and expected performance for various predictor combinations.

Examination of the trade-off between mean performance and adverse impact has received empirical attention for single-stage selection strategies; however, research for multistage selection strategies is almost nonexistent. The authors used Monte Carlo simulation to explore the trade-off between expected mean performance and minority hiring in multistage selection strategies and to identify those strategies most effective in balancing the trade-off. In total, 43 different multistage selection strategies were modeled; they reflected combinations of predictors with a wide range of validity, subgroup differences, and predictor intercorrelations. These selection models were examined across a variety of net and stage-specific selection ratios. Though it was still the case that an increase in minority hiring was associated with a decrease in predicted performance for many scenarios, the current results revealed that certain multistage strategies are much more effective than others for managing the performance and adverse impact trade-offs. The current study identified several multistage strategies that are clearly more desirable than those strategies previously suggested in the literature for practitioners who seek a practical selection system that will yield a high-performing and highly representative workforce.

Relating Ability and Personality to the Efficacy and Performance of Dyadic Teams

We examined the extent to which member ability and personality relate to differences in team performance and team efficacy in a task setting that simulated the high degree of role interdependence and human-technology interaction found in many military contexts. 168 male participants were assigned to dyadic teams and trained for two weeks to learn and perform a complex computer task that simulated the demands of a dynamic aviation environment. Participants also completed measures of general mental ability, psychomotor ability, and the Big Five personality traits (extraversion, openness, conscientiousness, agreeableness, and emotional stability). Team performance and team efficacy were assessed multiple times throughout training. Results indicated that ability was a critical determinant of both performance and efficacy, and personality traits yielded an incremental contribution to both performance and efficacy. In particular, psychomotor ability and conscientiousness were the strongest and most consistent factors associated with team effectiveness.

Book Review: Timothy A. Brown. (2006). Confirmatory factor analysis for applied research. New York: Guilford:

Confirmatory Factor Analysis: For Applied Research is a well-written, timely, and accessible description of confirmatory factor analysis (CFA) and the many uses of this statistical method. The intended audiences of this book are applied researchers and graduate students in the social and behavioral sciences. Several aspects of this book deserve comment. First, most books in this area focus on either structural equation modeling (SEM) or exploratory factor analysis (EFA), with little attention paid to CFA. Such emphasis often results in inadequate coverage of CFA in most statistics textbooks. This has been an unfortunate circumstance because researchers have had to rely on multiple sources (books, book chapters, journal articles, technical reports) for a complete understanding of CFA. Second, it is my opinion that CFA is underutilized, particularly among applied researchers who may not have access to all of the aforementioned resources. Third, there is often some confusion about how and under which circumstances CFA should be used, and this book does an excellent job comparing CFA and EFA and explaining how CFA fits into various research strategies such as scale development, construct validity, SEM, and measurement equivalence. Brown’s book fills a very important gap in this regard by integrating all technical literature and recent advances surrounding the purpose, use, and interpretation of CFA into one source. As such, I was pleased to see a book devoted to CFA, and it was with great enthusiasm that I accepted the invitation to review it. One of the primary strengths of this book is that Brown strikes a good balance between providing the technical specifics of conducting CFA with theoretical and practical explanations making it more than just a ‘‘how-to’’ guide. By way of example, tracing rules are presented through an inclusion of exactly how output values are generated in model-fitting programs and what they mean, which serves as the delicate balance that I think applied researchers will find attractive. In fact, there is ample guidance on developing, testing, and interpreting CFA models (and computer-generated output) for all potential consumers of CFA. In addition, Brown does a good job of pointing out the many ‘‘pitfalls’’ that one could encounter in CFA and, where relevant, provides alternative suggestions. This is one of the few statistical books that provide good integration of research methods and statistics. Indeed, Brown does an admirable job describing how CFA is used within several different research contexts such as scale development, assessing measurement equivalence, testing group differences, and analyzing multitrait–multimethod matrices to establish convergent and discriminant validity. Another strength of the present book is the careful integration and comprehensive coverage of syntax programming and output interpretation of common model-fitting programs including LISREL, PROC CALIS, Mplus, EQS, and Amos. Brown provides simple, wellorganized, and understandable syntax for each of the programs (provided in tables) and carefully explains the purpose of each syntax command in the text and what output was generated by the syntax. Another helpful feature of the text is related to explaining and translating the different labels used by model-fitting programs (e.g., the squared factor Book Review