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In their 2005 "Harvard Business Review" article, Bennis and O'Toole described business schools as being "on the wrong track" as a result of their focus on so-called scientific research. Some commentators argue that business schools have slowly lost their relevance since the end of the 1950s when they undertook a major overhaul in response to the harsh criticism of the Ford and the Carnegie Foundations on the state of theory and research in business administration. Inspired by Khurana's (2007) book on the development of American business schools, this article describes the debate on the relevance of scientific business research that can be found in the popular business press and the academic literature, and suggests a number of structural and cultural changes to increase the relevance of business research and its impact on practice. (Contains 2 notes.)

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Statistical methods in general, and multiple regression analysis in particular, are being used increasingly to provide evidence in employment discrimination cases. While the technical issues involved in using statistical methods to detect discrimination are straightforward, the conceptual issues are much less clearly understood. This article provides a framework to help clarify the conditions under which an estimated effect can be properly attributed to discrimination. Several interrelated issues have caused particular confusion, including the distinction between disparate impact and disparate treatment, the definition of test bias, the use of “reverse regression,” proxy variables for true productivity, and measurement error. A simple mathematical model is developed to analyze the precise nature of these issues. It is concluded that although employment discrimination cases involve ali the usual problems involved in causal inference from observational data, certain aspects of the legal context may facilitate the valid application of statistical techniques. [ABSTRACT FROM AUTHOR]