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Recent research suggests that competitive work environments may influence individual's attitudes, behaviors, stress, and performance. Unfortunately, adequate measures of competitive environments are lacking. This article traces the development of a new multidimensional competitive work environment scale. An initial 59-item pool covering five dimensions of a competitive environment was generated and scrutinized. Then, using two independent data samples, the authors psychometrically analyzed, organized, and reduced the items using a combination of exploratory factor analysis, confirmatory factor analysis, and item response theory. The final scale consists of 20 items across five distinct dimensions: competition for tangible rewards, nontangible rewards, recognition, status, and competition influenced by coworkers. Results indicate that the final multidimensional scale maintains strong psychometric properties and provides preliminary evidence of convergent and discriminant validity. Implications and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Interest in end-of-year accountability exams has increased dramatically since the passing of the No Child Left Behind Act in 2001. With this increased interest comes a desire to use student data collected throughout the year to estimate student proficiency and predict how well they will perform on end-of-year exams. This article uses student performance on the Assistment System, an online mathematics tutor, to show that replacing percentage correct with an Item Response Theory estimate of student proficiency leads to better fitting prediction models. In addition, it uses other tutor performance metrics to further increase prediction accuracy. Prediction error bounds are also calculated to attain an absolute measure to which the models can be compared. [ABSTRACT FROM AUTHOR]

Standard-setting methods are widely used to determine cut scores on a test that examinees must meet for a certain performance standard. Because standard setting is a measurement procedure, it is important to evaluate variability of cut scores resulting from the standard-setting process. Generalizability theory is used in this study to estimate standard errors of cut scores resulting from two standard-setting methods: item rating (Angoff-based) and mapmark (bookmark-based) methods. In this study, two different generalizability (G) study designs and four different decision (D) study designs were examined, and the impact of varying different aspects of the study design and universes of generalization was examined. Results suggest that cut scores were generally consistent for both methods. The first round standard setting contributed the most to the overall variability for the mapmark method. Also, it is clear that there is no one standard error associated with a certain cut score. [ABSTRACT FROM AUTHOR]

Goal orientation research has developed two branches. One branch uses a two-factor dispositional model. The other branch uses a three-factor domain-specific model. The present multiwave study examines dispositional goal orientation constructs through both two- and three-factor models using repeated-measure confirmatory factor analysis. Results indicate dispositional performance goal orientation of the two-factor model can be better partitioned into the Prove and Avoid factors of the three-factor model. Further evidence is presented indicating the potential for more than three factors by further partitioning the Avoid factor into two separate constructs. Consistent with both branches of goal orientation research, learning goal orientation is largely independent of Performance, Prove, and Avoid factors in all tested models. [ABSTRACT FROM AUTHOR]

Examines the aspects of P.E. Spector's Work Locus of Control Scale in work settings. Factor structure; Criterion-related validity; Convergent and discriminant validity.

Examines two important parameters of the general utility function--SDy, the standard deviation of job performance, and predictor performance--in terms of their sensitivity to departures from underlying mathematical assumptions. Uses integral calculus to assess the effects of statistical violations of the assumptions underlying these parameters. Implications for the practical implementation of the utility function are discussed.