We propose an integrative framework for understanding the relationship among 4 closely related issues in human resource (HR) selection: test validity, test bias, selection errors, and adverse impact. One byproduct of our integrative approach is the concept of a previously undocumented source of selection errors we call bias-based selection errors (i.e., errors that arise from using a biased test as if it were unbiased). Our integrative framework provides researchers and practitioners with a unique tool that generates numerical answers to questions such as the following: What are the anticipated consequences for bias-based selection errors of various degrees of test validity and test bias? What are the anticipated consequences for adverse impact of various degrees of test validity and test bias? From a theory point of view, our framework provides a more complete picture of the selection process by integrating 4 key concepts that have not been examined simultaneously thus far. From a practical point of view, our framework provides test developers, employers, and policy makers a broader perspective and new insights regarding practical consequences associated with various selection systems that vary on their degree of validity and bias. We present a computer program available online to perform all needed calculations.Human resource selection tests that are not supported by validity evidence are not useful in predicting job performance and other meaningful criteria. Tests that are biased are a legal liability and, in addition, using them can lead to unethical decision making. Consequently, test validity We thank Rich Arvey,

Faculty teaching in online environments are universally encouraged to incorporate a variety of student-to-student learning activities into their courses. Although there is a body of both theoretical and empirical work supporting this, adult professional students participating in an online MBA program at an urban business school reported being at best indifferent and often negative regarding these learning activities. A case study was performed to explore how pervasive this attitude was and the possible reasons for it. Through various sources of data and exploration, we discovered that common interactive modalities are not associated with either perceived learning or satisfaction. A content analysis of a data analysis course revealed that 64.5% of responses recalled student-to-student interactivities when responding to a "learned least from" query. We identified three possible reasons for these negative responses: time inefficiency, interaction dysfunction, and flexibility intrusion. We conclude that, although some working professional students probably do learn from student-to-student interactivity, the costs incurred may be too great. If working adult students present a different profile than those students typically represented in academic research and thus have different needs and expectations, we may need to rethink the design of online education delivered to them.

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This article discusses the development and delivery of online courses for the executive education audience. The goal is to introduce a new framework, the technical/strategic paradigm, that will help educators to identify the pedagogical needs of disparate executive groups and adjust their online course development plans accordingly. We describe how four key elements of online courses (course structure, content-based learning materials, assignments, and learning assessment) should be fashioned in a way that honors the technical or strategic focus of the learning environment. How the technical/strategic paradigm molds well with many different types of executive educational audiences and settings is illustrated. Course developers seeking advice on how to put these ideas into practice will find lists of resources and implementable recommendations. Ultimately, we argue that some of the pitfalls that faculty experience when transitioning from a traditional business school environment to an online executive education environment can be attributed to a misunderstanding of the degree to which students expect technical versus strategic content.

Since the demand for health services is the key driver for virtually all of a health care organisation's financial and operational activities, it is imperative that health care managers invest the time and effort to develop appropriate and accessible forecasting models for their facility's services. In this article, we analyse and forecast the demand for radiology services at a large, tertiary hospital in Florida. We demonstrate that a comprehensive and accurate forecasting model can be constructed using well-known statistical techniques. We then use our model to illustrate how to provide decision support for radiology managers with respect to department staffing. The methodology we present is not limited to radiology services and we advocate for more routine and widespread use of demand forecasting throughout the health care delivery system.