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Despite its importance, measurement has received less attention in the management sciences than it deserves. Currently, there is an over-reliance on a narrow set of methods of measuring cognitive, affective, motivational, attitudinal and individual difference constructs that are often of interest in behavioural management research. The authors argue that there is a need to expand the scope of the measurement methods commonly employed by management researchers and that a greater diversity of measurement methods would benefit the field by contributing to theory development and the pursuit of new areas of research. The goals of this review are twofold: (1) to increase awareness among management researchers of the alternative measurement methods that can capture many of the cognitive, affective, motivational, attitudinal and individual difference constructs of interest; (2) to critically evaluate how these methods can and should be used, with a focus on both the strengths and limitations of each method. This review focuses on three classes of measures: physiological and biological measures; experience-sampling measures; and implicit measures. These measures have had a tremendous impact on the research and theories of other fields such as marketing and economics, despite still being in their infancy. The authors believe that these three classes of measures have the potential to impact the nature and scope of management research and theory as well. [ABSTRACT FROM AUTHOR]

Organizational researchers routinely use attitudinal surveys to track organizational development and identify areas for intervention. However, seemingly trivial changes to the survey instrument, such as question wording or question order, can introduce measurement artifacts leading to differences in observed responses that are not due to actual employee attitudinal change. Traditional methods for assessing the presence of artifacts because of survey changes require additional survey administration using multiple survey forms and random assignment. However, the item response theory method illustrated in this study eliminates the need for additional data collection, offers a more rigorous design, and requires fewer organizational resources. [ABSTRACT FROM AUTHOR]

The current study demonstrates the use of item response theory (IRT) to conduct measurement invariance analyses in careers research. A self-report survey was used to assess the importance 1,363 fourth-year medical students placed on opportunities to provide comprehensive patient care when choosing a career specialty. IRT analyses supported measurement invariance across gender. Additional analyses indicated that compared with men, women placed significantly greater importance on opportunities to provide comprehensive patient care. This in turn predicted career choice, with women being more likely than men to pursue primary care specialties. This study extends the careers literature both methodologically and substantively. Methodologically, this study exemplifies how and why to use IRT to assess measurement invariance prior to comparing groups on career-related attitudes. Substantively, this study is the first to demonstrate that the importance placed on comprehensive patient care mediates the effect of gender on intentions to pursue primary care specialties. [ABSTRACT FROM AUTHOR]

Three major reviews of the assessment center (AC) construct-validity literature have disagreed as to the most appropriate analytic model for AC postexercise dimension ratings. We report a Monte Carlo study addressing the following questions: (a) To what extent does the "true" model (i.e., the model that generated the data) actually appear to fit the data well? (b) To what extent can a model appear to fit the data well even though it is the wrong model? and (c) Is model fit actually a useful empirical criterion for judging which model is most likely the pop- ulation model? Results suggest that "true" models may not always appear as the best fitting models, whereas "false" models sometimes appear to offer better fit than the true models. Implications for the study of AC construct validity are discussed. [ABSTRACT FROM AUTHOR]

The article focuses on the new AlphaTest program that provides a user-friendly Windows interface for testing hypotheses to allow tests for equivalence among a user-defined set of coefficient alpha values. Further, a superior algorithm based on the incomplete gamma integral is used to evaluate the upper tail probability of the obtained chi-square value. Meanwhile, the program and documentation are available as a free self-installing executable file that works on any version of Windows with Microsoft .Net Framework 2.0 installed.