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Greetings from CARMA!
We would like to remind you of our upcoming live webcast Friday, November 21st. This event will feature a live webcast from Dr.Stephen G. West, Arizona State University and Jason A. Colquitt, University of Georgia. The live webcast will begin at 11:30AM with Dr. West presenting "Causal Inference in Quasi-Experimental Studies" followed by Dr. Colquitt at 1:00PM presenting "Lab vs. Field OB: Do Findings Converge?".
****Please note the time change for this Webcast****
As you may or may not know, for this Live Webcast, Dr. Larry Williams will be hosting it from the University of Georgia. For your convenience we have set up two viewing rooms for the webcast. One is in the Corwin-Larimore building, room 140 and the second viewing room is in the School of Medicine and Health Sciences room 5520. The viewing will begin at 11:30AM. We hope you can join us for some good CARMA!
The webcast abstract and presenter biography can be found below and on our website. Also, references and PowerPoint slides for the presentation will be available early next week. You can view them by logging in to the CARMA Website User Area using your registered CARMA Website User email address and password at:
We hope to see you connect for a great CARMA Webcast event!
Dr. Larry J. Williams
Consortium for the Advancement of Research Methods and Analysis
Professor of Psychology
University of North Dakota
Biography for Dr. Stephen West
Stephen G. West is currently professor of psychology at Arizona State University and (in Summer) Gastprofessor in the at Freie Universität Berlin, Germany. He received his B.A. from Cornell University and his Ph.D. from the University of Texas, Austin. He has held previous faculty positions at University of Wisconsin, Florida State University, University of Texas, Duke University and UCLA in the United States and Universität Kiel, Universität Heidelberg, and Freie Universität Berlin in Germany. He is co-author of 13 books and edited volumes including Multiple regression: Testing and interpreting interactions (1991) and Applied multiple regression/correlation analysis for the behavioral sciences (2003). He is the author of over 30 chapters and over 140 articles in referred journals. He has served as the editor of Psychological Methods and the Journal of Personality. Currently he is associate editor of Multivariate Behavioral Research and serves on the editorial boards of six journals. His methodological work has focused on developing and improving randomized and non-randomized (quasi-experimental) designs), causal inference, methods for understanding the effects of intervention components, mediation analysis, multiple regression, structural equation models, multilevel data analysis, and longitudinal data analysis. His substantive research is in the areas of personality research and prevention-related issues in health, mental health, and education. He is a winner of the Henry Murray award for lifetime contributions to the study of lives from Division 8 (personality and social psychology) of the American Psychological Association, the Saul B. Sells award for distinguished lifetime contributions to multivariate research from the Society for Multivariate Experimental Psychology, and the Forschungspreis from the Alexander von Humboldt Stiftung for his lifetime contributions to methodology. He has won the Outstanding Graduate Mentor award from the Graduate College at Arizona State University and the Jacob Cohen Award for Outstanding Teaching and Mentoring from Division 5 (Evaluation, Statistics, and Measurement) of the American Psychological Association. He is most proud of the many graduate students with whom he has worked who have gone on to careers at major research universities in the US, Canada, and Germany, some of whom have received awards for their early career contributions.
Abstract: "Causal Inference in Quasi-Experimental Studies"
Randomized experiments provide the strongest warrant for causal inference. However, in many randomized experiments conducted in the field key assumptions may fail (e.g., treatment non-adherence; attrition) leading to a "broken" randomized experiment. In other cases, randomization may not be possible for ethical or practical reasons so weaker quasi-experimental designs must be utilized. This talk uses ideas from the perspectives of Donald Campbell in psychology and Donald Rubin in statistics to outline approaches that strengthen causal inference in field research. Special attention is given to strengthening causal inference when there is treatment non-adherence in randomized experiments, participants are assigned to treatment conditions on the basis of need or merit (regression discontinuity designs), or the mechanism through which participants are assigned to treatment conditions is unknown (observational studies). Strengths and limitations of these approaches are discussed.
Biography for Dr. Jason Colquitt
Jason A. Colquitt is the William Harry Willson Distinguished Chair and Professor in the University of Georgia's Terry College of Business, Department of Management. He received his Ph.D. from Michigan State University's Eli Broad Graduate School of Management, and earned his B.S. in Psychology from Indiana University. His research interests include justice, trust, and personality influences on task and learning performance. He has published more than thirty articles on these and other topics in Academy of Management Journal, Academy of Management Review, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, and Personnel Psychology. He recently completed a three-year term as the Editor-in-Chief for Academy of Management Journal, after previously serving as an Associate Editor. Professor Colquitt is currently serving on the editorial boards of Academy of Management Journal, Administrative Science Quarterly, Journal of Applied Psychology, and Personnel Psychology. He is a recipient of the Society for Industrial and Organizational Psychology's Distinguished Early Career Contributions Award and the Cummings Scholar Award for early to mid-career achievement, sponsored by the Organizational Behavior division of the Academy of Management. He also authors one of the top-selling organizational behavior textbooks, now in its fourth edition.
Abstract: "Lab vs. Field OB: Do Findings Converge?"
Laboratory studies have become a less common design choice in top journals in organizational behavior and industrial/organizational psychology. One potential reason for that trend could be the assumption-on the part of reviewers or editors-that findings from laboratory studies do not generalize to findings from field studies. Our study utilizes laboratory vs. field effect size breakdowns in published meta-analyses to examine this assumption. We coded 65 meta-analyses, published between 1985 and 2012, yielding a total of 228 relationships. Our results showed a high degree of convergence between effect sizes in laboratory studies and effect sizes in field studies. We also examine facets of relationships that could predict effect size convergence from laboratory to field.