Webcast Lecture Series
Best Practices for Using Matched Sample Designs to Reduce Selection Effects
Dr. Don Bergh
University of Denver
January 31, Noon EST
Lecture Abstract
A major threat to empirical findings for studies that have nonrandom samples is selection bias. Currently, most solutions to this threat have concentrated on analytical modeling and little guidance exists in organizational research for how study designs can reduce selection bias prior to the analysis stage. This webcast will address how sample selection bias can be reduced through matched sample research designs. It will describe what matched sample research designs are, the dominant approaches for creating them, and when a particular matching methodology provides the most optimal solution. Best practices for creating, using, evaluating, and reporting matched samples are provided.
Meet the Presenter
Donald D. Bergh (PhD, University of Colorado Boulder) is the Louis D. Beaumont Chair of Business Administration and professor of management at the Daniels College of Business, University of Denver. He also serves as a Visiting Professor at Erasmus University Rotterdam.
His research interests lie primarily in corporate strategy and research methodology. He has served as an associate editor of AMJ, ORM, JMS, guest co-editor of four ORM special issues, and was the inaugural chair of the Scientific Integrity and Rigor Task Force of JOM. Along with David Ketchen, Jr., he co-edited the Emerald series, Research Methodology in Strategy and Management. He is currently editor-in-chief of the Oxford Research Encyclopedia of Business and Management.
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