Schmidt Receives Mosteller Award for Distinctive Contributions
Frank Schmidt, professor emeritus of management and organizations, has received the 2013 Frederick Mosteller Award for Distinctive Contributions to Methods for Systematic Reviewing of Research Findings from the Campbell Collaboration. He received the award at the Campbell Colloquium on May 22 in Chicago.
The Mosteller Award is given each year to the individual who has made an important contribution to the theory, method, or practice of systematic reviewing in criminal justice, education, social welfare, or other areas within the scope of the Campbell Collaboration. Schmidt specifically received the award for his contributions to the development of quantitative methods for conducting systematic reviews of research findings.
Starting in the 1970s and continuing to the present, Schmidt has been the co-developer of two related data analysis methods that changed the way people thought about standardized tests, particularly as they related to businesses hiring and developing employees and also changed the way research findings are interpreted in a wide variety of different areas.
Schmidt's first major contribution is validity generalization (VG), which uses data analysis methods to test whether conflicting employment test findings are real or simply the result of statistical and measurement artifacts. For years, employers used tests to determine whom to hire, promote, or assign tasks, but the results of validity studies varied so widely they were seen as specific to particular situations, jobs, or employers. Schmidt, however, discovered that if certain variables were accounted for, the test results did not vary much and were quite consistent.
Over the years, tests that drew conflicting conclusions were reinterpreted using Schmidt's VG technique and found to be accurate, changing the way human resources professionals think about selection methods. This has led to changes in employment selection practices in many industries, corporations and government agencies. Through validity generalization, Schmidt and his colleague, the late John Hunter of Michigan State University, have shown that, no matter what the job, general intelligence is the single best predictor of both job performance and occupational level attained.
Schmidt's second major contribution is his development of meta-analytic methods, which clarify and reveal the meaning of research results on any topic that appear, at first, to conflict. These methods have been applied to most major human resource research areas. Examples include the relationship between measures of employee attitudes and financial outcomes, as well as a variety of relationships such as employee job satisfaction and job performance, determinants of organizational citizenship behavior and the relationship between work-family conflict and job satisfaction. Schmidt is now completing the third edition of his book on these methods. It will be published early next year.
During his academic career, Schmidt has received many awards, including the Thomas A. Mahoney Mentoring Award from the Academy of Management in 2011 and the UI Graduate College Outstanding Mentor Award in Social Science (2008); the 2010 Distinguished Interviewee from the International Society for Intelligence Research; and the Career Achievement Award for Scientific Contributions from the Association of Test Publishers and the James McKeen Cattell Award for Scientific Contributions to Applied Psychology from the Association for Psychological Science in 2007. In addition, he received the Michael R. Losey Human Resource Research Award from the Society for Human Resource Management in 2005, and he was a co-recipient of the Moskowitz Price in 2004 for the best quantitative research in the field of social investment.
The Mosteller Award is named in honor of the late Frederick Mosteller, who was the Roger I. Lee Professor of Mathematical Statistics at Harvard University. Many of his works in theoretical and applied statistics are considered classic texts and are widely used and cited.
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