Daniel J. Bauer, Ph.D.
Dan Bauer is a Professor and the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina. He teaches graduate- and undergraduate-level courses in statistical methods for which he has won teaching awards from the University of North Carolina and from the American Psychological Association. Endeavoring to make advanced statistical techniques more accessible, Dan has spent the last 15 years developing and teaching workshops on a variety of topics in both the United States and abroad, including multilevel modeling, mixture modeling, longitudinal data analysis, structural equation modeling, latent curve analysis, missing data analysis, measurement, and integrative data analysis. His research interests lie at the intersection of quantitative and developmental psychology, particularly the development of problem and health-related behaviors over childhood and adolescence. He has published over 100 scientific papers, served as Associate Editor for Psychological Methods, currently serves on the editorial boards of several journals, and has reviewed grants for the National Science Foundation, National Institutes of Health, and the Institute of Educational Sciences. He received an early career award from the American Psychological Association in 2009. For more details, see his academic web page.
Niall Bolger, Ph.D.
Niall Bolger, is Professor of Psychology and former Chairperson of the Department of Psychology at Columbia University where he teaches a graduate statistics sequence on linear and mixed models as tools for psychological research. He also teaches an undergraduate research methods class and a graduate class on psychophysiological methods and analysis. His main research interests include the study of adjustment processes in close relationships using intensive longitudinal methods; laboratory-based studies of dyadic behavior, emotion and physiology; and personality processes as they are revealed in patterns of behavior, emotion, and physiology in daily life. He is also interested in statistical methods for analyzing longitudinal and multilevel data, and is co-author with J-P Laurenceau of Intensive Longitudinal Methods (Guilford, 2013). Niall is a Charter Member and Fellow of the Association for Psychological Science (APS), was elected to membership in the Society for Multivariate Experimental Psychology (SMEP), and is a Fellow of the Society of Experimental Social Psychology (SESP) and of the Society for Personality and Social Psychology (SPSP). He has served on National Institutes of Health and National Science Foundation grant review panels, and as Associate Editor of the Journal of Personality and Social Psychology: Interpersonal Relations and Group Processes. For further details, please see his academic web page.
S. Natasha Beretvas, Ph.D.
Tasha Beretvas is the senior vice provost of faculty affairs and the John L. and Elizabeth G. Hill Centennial professor of quantitative methods in the Educational Psychology department at the University of Texas at Austin. Tasha’s research focuses on the application and evaluation of statistical models in social, behavioral and health sciences research. Real world data (and especially meta-analytic data) are messy and Tasha enjoys finding ways to use statistical models to help herself, her students and fellow researchers make better sense of the mess. Since first becoming a professor 20 years ago, Tasha has taught graduate and undergraduate statistics courses as well as workshops at national conferences, and co-teaches an Institute of Education Sciences-funded national workshop focused on advanced meta-analytic techniques. Dr. Beretvas has received multiple teaching awards and is committed to making statistics and their use accessible to applied researchers. Tasha has published over 90 scientific papers and book chapters and co-authored a couple of books. She is a member of the Society for Research Synthesis Methodology and served as co-editor-in-chief of the Research Synthesis Methods journal and Associate Editor for the Journal of School Psychology and Journal of Educational Psychology. For more information, see her academic webpage.
Patrick J. Curran, Ph.D.
Patrick Curran is a Professor in the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. Patrick has dedicated much of his career to the teaching and dissemination of advanced quantitative methods and has won awards in recognition of teaching excellence from UNC and from the American Psychological Association. Over the past 20 years, Patrick has taught over 100 national and international workshops on structural equation modeling, multilevel modeling, latent curve analysis, longitudinal data analysis, and general linear modeling. Patrick’s program of research is primarily focused on the development and evaluation of statistical models of change over time, particularly as applied to studies of adolescent substance use. He thus draws on experiences from his own program of research on high-risk child development to guide and inform his quantitative teaching. He has published over 100 scientific papers and chapters and has co-authored a textbook on latent curve modeling with Ken Bollen. Patrick has served as Associate Editor for Psychological Methods and currently serves on the editorial boards of seven scientific journals. For more details, see his academic web page.
Craig K. Enders, Ph.D.
Craig Enders is a Professor and Area Chair of Quantitative Psychology in the Department of Psychology at the University of California, Los Angeles. His primary research focus is on analytic issues related to missing data analyses, and he leads the research team responsible for developing the Blimp statistical software application. Dr. Enders also conducts research in the areas of multilevel modeling and structural equation modeling, and is an active member of the Society of Multivariate Experimental Psychology, the American Psychological Association, and the American Educational Research Association. Over the past 20 years Craig has taught dozens of missing data workshops at major conferences, universities, and private organizations. He has published over 100 scientific papers and chapters and has authored a text book, Applied Missing Data Analysis, the second edition of which will be published in 2022.
Michael N. Hallquist, Ph.D.
Michael Hallquist is an Associate Professor in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. He is a core faculty member in both the Clinical Psychology program and the L. L. Thurstone Psychometric Laboratory. He directs the Developmental Personality Neuroscience Lab at UNC, focusing on decision processes and neurocomputational systems that underlie reward sensitivity, personality pathology, and suicidal behavior. His work is funded by grants from the National Institute of Mental and he has published over 80 peer-reviewed papers. Michael is a committed teacher who has taught graduate courses on data visualization and structural equation modeling; he has also co-organized ‘R bootcamp’ workshops for graduate students. He is committed to disseminating advanced quantitative methods and recently co-edited The Cambridge Handbook of Research Methods in Clinical Psychology. Prior to completing his graduate training in psychology, Michael worked as a computer programmer, focusing on relational database design and distributed component object models. These experiences provided the foundation for his passion for scientific programming, particularly in R. He has developed or contributed to multiple R packages, including MplusAutomation.
Gregory R. Hancock, Ph.D.
Greg Hancock is Professor, Distinguished Scholar-Teacher, and Director of the Measurement, Statistics, and Evaluation program in the Department of Human Development and Quantitative Methodology at the University of Maryland, College Park, and Director of the Center for Integrated Latent Variable Research (CILVR). His research interests include structural equation modeling and latent growth models, power, reliability, and the use of latent variables in (quasi)experimental design. His research has appeared in such journals as Psychometrika, Multivariate Behavioral Research, Structural Equation Modeling: A Multidisciplinary Journal, Psychological Methods, and British Journal of Mathematical and Statistical Psychology. He also co-edited the volumes Structural Equation Modeling: A Second Course (2006; 2013), The Reviewer's Guide to Quantitative Methods in the Social Sciences (2010; 2019), Advances in Latent Variable Mixture Models (2008), Advances in Longitudinal Methods in the Social and Behavioral Sciences (2012), and Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton (2019). He serves on the editorial board of a number of journals including Psychological Methods, Multivariate Behavioral Research, and Structural Equation Modeling: A Multidisciplinary Journal, and has taught over 200 methodological workshops in the United States, Canada, and abroad.
Emily Namey, M.A.
Emily Namey is a qualitative research methodologist with 20 years’ experience designing and implementing mixed methods research and evaluations. Her work has largely focused on health and development, both internationally and domestically, with research on issues including infectious disease (HIV, malaria, polio), maternal and reproductive health, bioethics, economic strengthening, and child protection. Across these areas, she has a specific interest in improving the evidence base for qualitative research methodology. She has conducted over 300 interviews and more than 100 focus groups and has lent her expertise to the design and implementation of capacity strengthening training courses in more than a dozen countries. Emily has also co-authored/edited several methods textbooks. See her LinkedIn profile for more details.
Doug Steinley, Ph.D.
Doug Steinley is a Professor in the Department of Psychology at the University of Missouri. His research and teaching focus on multivariate statistical methodology, with a primary interest in cluster analysis (both traditional procedures and more modern mixture modeling techniques) and social network analysis. Dedicated to dissemination, Doug has taught workshops on these and other advanced quantitative methods for many years, joining Curran-Bauer Analytics in 2016. He has also published over 100 peer-reviewed manuscripts, and held funding from the National Institutes of Health, the US Army Research Institute, and the Office of Naval Research. He currently serves as Editor of the flagship methods journal for the American Psychological Association, Psychological Methods. For more details, see his academic webpage.