Dr. Kylie Anglin
Dr. Kylie Anglin is an assistant professor in the Research Methods, Measurement, and Evaluation program. Her research develops methods for efficiently monitoring program implementation in impact evaluations using natural language processing techniques, as well as methods for improving the causal validity and replicability of impact estimates. Her work has appeared in journals such as the Journal of Research on Educational Effectiveness, Prevention Science, AERA Open, and Evaluation Review. She received her Ph.D. from the University of Virginia where she participated in the Institute for Education Sciences (IES) Pre-doctoral Training Program and received an NAEd/Spencer dissertation fellowship.
Research Interests: Program Implementation & Evaluation, Natural Language Processing Techniques, Causal Validity, Replicability
Dr. Eric Loken
Dr. Eric Loken is an associate professor in the Research Methods, Measurement, and Evaluation program. He received his Ph.D. from Harvard University and studies advanced statistical modeling with applications to large scale educational testing.
Research Interests: Latent Variable Models, Bayesian Inference, Methods for Reproducible Science
Dr. D. Betsy McCoach
Dr. D. Betsy McCoach teaches courses in Structural Equation Modeling, Advanced Latent Variable Modeling, Instrument Design, and Quantitative Research Methods. Betsy has extensive experience in latent variable modeling, longitudinal data analysis, multilevel modeling, factor analysis, and instrument design. She has authored or co-authored over 100 journal articles and 25 book chapters. She has also published several books, including Introduction to Modern Modeling Methods (2021, Sage), Multilevel Modeling of Educational Data (2008, co-edited with Ann O’Connell) Instrument Development in the Affective Domain (2013, co-authored with Bob Gable). Betsy is the founder and conference chair of the Modern Modeling Methods (M3) conference. Betsy serves as a co-principal investigator and lead research methodologist on several federally funded research grants, including the National Center for Research on Gifted Education.
Research Interests: Latent Variable Modeling, Longitudinal Analysis, Multilevel Modeling, Instrument Design, Assessing/Measuring School Effectiveness, Gifted Education, Underachievement
Dr. Bianca Montrosse-Moorhead
Dr. Montrosse-Moorhead teaches graduate courses in research methods, assessment, and evaluation. She previously served as an assistant professor of educational research at Western Carolina University, as a research and evaluation specialist at the Southeast Regional Educational Laboratory at the University of North Carolina at Greensboro, and as a doctoral fellow at the University of North Carolina at Chapel Hill. Dr. Montrosse-Moorhead currently conducts research on evaluation as a a means to develop stronger evidence-based program evaluation practices, models, and theories. Additionally, her scholarship explores the practical application of evaluation and research methods, both in order to better understand the impact of K-12 policies, practices and programs, and to provide credible, relevant, and useful evidence to the policy community. She received her Ph.D. in psychology with an emphasis in evaluation and applied research methods from Claremont Graduate University in 2009, where she worked with and studied under Drs. Tina Christie and Michael Scriven.
Research Interests: Program and Policy Evaluation, Research on Evaluation, Evaluation Specific Methodology, Educational Equity
Dr. Christopher Rhoads
Dr. Christopher Rhoads teaches courses in statistics and research design. His research interests focus on methods for improving causal inference in educational research, particularly in the areas of experimental design and the analysis of multi-level data structures. He has published in journals such as Journal of Educational and Behavioral Statistics, British Journal of Mathematical and Statistical Psychology and Statistics, Politics and Policy. Rhoads’ current work involves exploring the implications of “contamination” of experimental interventions for the design and analysis of experiments with clustering; using prior information about the correlation structure to improve power and precision in experiments with clustering; determining optimal experimental designs for regression discontinuity studies; generalizing the results of RCT to other populations in multi-level settings and methods for integrating implementation fidelity variables into the analysis of education RCTs.
Research Interests: Multilevel Modeling, Design of Field Experiments in Education Research, Non-experimental Designs for Causal Inference, External Validity of RCT Studies