Dr. Bonifay’s research interests are in the area of psychological measurement, with particular focus in item response theory and model evaluation. He has published a number of quantitative research articles on psychometric topics such as dimensionality assessment, subscale analysis, and model complexity. He has also collaborated with substantive educational and psychological researchers, applying item response theory and structural equation models to better understand certain issues in school psychology, psychiatric treatment, and cognitive behavioral therapy.
Dr. Easter is an Educational Psychologist who teaches introductory applied statistics courses in the Educational, School, and Counseling Psychology department. His research interests include scale development, motivation, and online learning environments. He has worked on projects to develop online learning materials for the US Navy, US Department of Labor, and the University of Missouri.
Dr. Huang is an applied quantitative methodologist who focuses on both methodological (e.g., analysis of clustered data) and substantive (e.g., school climate, effectiveness of interventions, measurement of school bullying) areas of research. He is the recipient of a national research award given by the American Educational Research Association (AERA) and sits on the editorial boards of School Psychology Quarterly and AERA Open. His recent research has been funded by separate grants from the U.S. Department of Education and the Office of Juvenile Justice and Delinquency Prevention Program of the U.S. Department of Justice.
Dr. Kim’s research interests focus on causal inference in educational and psychological research that includes quasi-experimental designs (matching, propensity score methods, difference-in-differences, fixed effects models), causal mediational analysis, causal graphical models (directed acyclic graphs), and causal discovery. His research integrates conventional statistical methods and modern causal inference frameworks and he have developed intuitive causal graphical representations for popular statistical methods in nonexperimental studies such as regression discontinuity, instrumental variables, and gain scores methods.
Dr. Wang’s research integrates substantive theories and advanced quantitative methods to understand student learning processes and outcomes. Her research interests include statistical modeling using large-scale educational assessment data, measurement, scale development, and program evaluation. She has extensive experience in research design, applied statistical analysis, and statistical consulting. Dr. Wang has been on teams of projects funded by the National Science Foundation, the U.S. Department of Education, and the National Institutes of Health. She co-developed the Classroom Engagement Inventory (CEI) and won multiple awards for excellence in teaching and student advising.
Dr. Wiedermann’s primary research interests include the development of methods for causal inference, methods to determine the direction of effects in nonexperimental studies, and methods for intensive longitudinal data in the person-oriented research setting. He has edited volumes on advances in statistical methods for causal inference and new developments in statistical methods for dependent data analysis in the social and behavioral sciences. He serves as an associate editor of Behaviormetrika and the Journal of Person-Oriented Research.