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Summer Research Training Institute: Design and Analysis of Practical Quasi-Experiments for use in Education: Faculty Biographies

Thomas D. Cook is a professor of sociology, psychology, education and social policy at Northwestern University where he is also the Joan and Sarepta Harrison Chair in Ethics and Justice and an Institute for Policy Research faculty fellow. He is best known for his work on the theory and practice of the design and analysis of various forms of quasi-experiment. He has published heavily on threats to validity, and enumerating threats to internal validity and external validity in particular, on regression discontinuity studies, on interrupted time series work and on various forms of individual and group-level matching. He has authored or co-authored ten books and about one hundred articles on these topics, including Cook & Campbell, Quasi-Experimentation: Design and Analysis Issues for Field Settings (1979) and Shadish, Cook & Campbell, Experimental and Quasi-Experimental Designs for Generalized Causal Inference (2002). His current work is on testing hypotheses about the kinds of quasi-experimental practice that often lead to causal results that are similar to those that a randomized experiment achieves. It is from this literature done with all of the other instructors, as well as from statistical theory, that grow the ideas identifying the better quasi-experimental practices worth pursuing in research in education.

William R. Shadish is Distinguished Professor and Founding Faculty, University of California, Merced. He received his bachelor's degree in sociology from Santa Clara University in 1972, and his M.S. (1975) and Ph.D. (1978) degrees from Purdue University in clinical psychology, with minor areas in statistics and measurement. He completed a postdoctoral fellowship in methodology and program evaluation at Northwestern University from 1978-1981. His current research interests include experimental and quasi-experimental design, the empirical study of methodological issues, and the methodology and practice of meta-analysis. He is author (with T.D. Cook & D.T. Campbell, 2002) of Experimental and Quasi-Experimental Designs for Generalized Causal Inference, (with T.D. Cook & L.C. Leviton, 1991) of Foundations of Program Evaluation, (with L. Robinson & C. Lu, 1997) of ES: A Computer Program and Manual for Effect Size Calculation, co-editor of five other volumes, and the author of over 165 articles and chapters.  He was the founding Secretary-Treasurer of the Society for Research Synthesis Methodology (2005-2010) and was its 2013 President. He is currently President of the Society for Multivariate Experimental Psychology. He was 1997 President of the American Evaluation Association, winner of the 1994 Paul F. Lazarsfeld Award for Evaluation Theory from the American Evaluation Association, the 2000 Robert Ingle Award for service to the American Evaluation Association, the 1994 and 1996 Outstanding Research Publication Awards from the American Association for Marriage and Family Therapy, the 2002 Donald T. Campbell Award for Innovations in Methodology from the Policy Studies Organization, the 2009 Frederick Mosteller Award for Lifetime Contributions to Systematic Reviews from the Campbell Collaboration, and the 2011 Ingram Olkin Award for Lifetime Contributions to Systematic Reviews from the Society for Research Synthesis Methodology. He is a Fellow of the American Psychological Association, Associate Editor of American Psychologist, past Associate Editor of Multivariate Behavioral Research, and past editor of New Directions for Evaluation.

Peter M. Steiner is an Assistant Professor in the Department of Educational Psychology at the University of Wisconsin-Madison. He is affiliated with the Interdisciplinary Training Program in the Education Sciences and the Center for Demography and Ecology at the UW-Madison. He holds a master's and doctorate degree in statistics from the University of Vienna, and a master's degree in economics from the Vienna University of Economics and Business Administration. His research program focuses on methodological questions about causal inference. He is particularly interested in experimental and quasi-experimental designs, including PS matching designs, interrupted time series designs, and regression discontinuity designs. He regularly applies these designs and corresponding analyses to educational data, either in the context of methodological within-study comparisons or in collaboration with substantive researchers evaluating educational interventions. His work is published in well-known journals (e.g., Journal of the American Statistical Association, Psychological Methods, Journal of Educational and Behavioral Statistics, and Educational Evaluation and Policy Analysis) and presented at national and international conferences. He also teaches courses on the Design and Analysis of Quasi-Experiments and Graphical Models for Causal Inference.

Coady Wing is assistant professor in the School of Public Policy at University of Indiana, Bloomington, where he does research and teaches on econometric methods, particularly as they pertain to health policy issues. He is interested in regression-discontinuity designs that include a comparison regression function without treatment and in the ways that instrumental variables can be used in education, both when random assignment or regression discontinuity provides the instrument and also in the usually more problematic situations where some other variable does.

Vivian C. Wong is a research methodologist in the field of Education. Currently, Dr. Wong is an Assistant Professor in Research, Statistics, and Evaluation in the Curry School of Education at the University of Virginia. Her research focuses on evaluating interventions in early childhood and K-12 systems. As a methodologist, her expertise is in improving the design, implementation and analysis of randomized experiments, regression-discontinuity, interrupted time series, and matching designs in field settings. Dr. Wong is the lead author or co-author of numerous articles and book chapters on research methodology. Along with colleagues, she published a paper on regression-discontinuity designs when multiple assignment variables and cutoffs are available, as well as a paper that uses a regression-discontinuity design to evaluate five state pre-kindergarten programs. She is currently examining within-study comparison methods for evaluating non-experimental methods in field settings, as well as developing innovative methods for evaluating No Child Left Behind. Dr. Wong's work has appeared in the Journal of Educational and Behavioral Statistics, Journal of Policy Analysis and Management, and Psychological Methods. Dr. Wong participated in the Institute for Education Sciences (IES) Predoctoral Training Program at Northwestern University, and received the Outstanding IES Predoctoral Fellow Award in 2010 for her dissertation work on "Addressing Theoretical and Practical Challenges in the Regression-Discontinuity Design."  She is a panel reviewer for the Institute for Education Sciences.

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