Wendy Chan

Professional Biography

Wendy Chan is an expert on educational statistics. She seeks to improve statistical models and analysis so research can better inform policy and practice. Her work tries to unpack the intellectual and statistical challenges of generalizing the results of localized randomized trials to larger populations. She began her career in education as a member of Teach for America, where she taught sixth- and eighth-grade mathematics in a large middle school in New York City.

Wendy Chan is an assistant professor of education in the Human Development and Quantitative Methods Division at Penn GSE. Dr. Chan’s research focuses on applied statistical methods to improve generalizations from small studies in education.

Dr. Chan received her Ph.D. in statistics from Northwestern University, where she was a graduate research assistant for the Institute for Policy Research. She began her career in education as a member of Teach for America, where she taught sixth- and eighth-grade mathematics in a large middle school in New York City. Her work has appeared in the Journal of Research on Educational Effectiveness, the Journal of Educational and Behavioral Statistics, Psychological Methods, and Evaluation Review.

Ph.D. (Statistics) Northwestern University, 2016
M.A. (Mathematics Education) Lehman College, City University of New York, 2010
B.A. (Economics and Mathematics) Barnard College, Columbia University, 2008

Statistical methods to improve generalizations in education
Partial identification methods with applications to the social sciences
Multilevel modeling and experimental design
Propensity score methods for generalization
Small area estimation

Dr. Chan's website

Statistics, Measurement, Assessment, and Research Technology, M.S.Ed.

Quantitative Methods, M.Phil.Ed.

Quantitative Methods, Ph.D.

Research Interests and Current Projects

Dr. Chan specializes in applied educational statistics, and her research projects and interests are at the leading edge of work on statistics methods in field contexts, including scaling up interventions. Her current projects assess the extent to which generalizations can be made from small studies and they consider the advantages and tradeoffs of different estimation approaches in the presence of limited data. In another project, Dr. Chan considers the impact of violations in model assumptions on the validity of estimates in generalization studies. In this work, she assesses the effectiveness of robust estimation approaches in improving the precision and bias of estimates when core model assumptions are violated. In addition to her work in generalization, Dr. Chan has also worked on research in experimental design and examined corrections to test statistics in the presence of clustering and treatment effect heterogeneity.

Chan, W., & Oh, J. (2023). Robust small area estimation for generalization. Journal of Experimental Education, 91(3), 539–556.

Chan, W., & Hedges, L. V. (2022). Pooling interactions into error terms in multisite experiments. Journal of Educational and Behavioral Statistics, 47(6), 639–665.

Chan, W., Oh, J., & Luo, P. (2021). The implications of population changes on generalization and study design. Journal of Research on Educational Effectiveness, 14(2), 471–500.

Chan, W. (forthcoming, 2021). Analysis of variance. In R. Coe, M. Waring, L. V. Hedges, & L. D. Ashley (Eds.), Research methods & methodologies in education (3rd ed.). SAGE Publishing.

Chan, W. (2022). The role of distributional overlap on the precision gain of bounds for generalization. American Journal of Evaluation, 43(1), 90–107.

Chan, W., Hedges, L. V., & Hedberg, E. C. (2020). The impact of downstream clustering in longitudinal studies. Journal of Experimental Education.

Chan, W. (2019). An evaluation of bounding approaches for generalization. Journal of Experimental Education. Advanced online publication. https://doi.org/10.1080/00220973.2019.1697191

Chan, W. (2019). The relation among design parameters for statistical power between continuous and binomial outcomes in cluster randomized trials. Psychological Methods, 24(2), 179-195.

Chan, W. (2018). Applications of small area estimation to generalization with subclassification by propensity scores. Journal of Educational and Behavioral Statistics, 43(2), 182–224.

Chan, W. (2017). Partially identified treatment effects for generalizability. Journal of Research on Educational Effectiveness, 10(3), 646–669.

Tipton, E., Hallberg, K., Hedges, L. V., and Chan, W. (2016). Implications of small samples for generalization: Adjustments and rules of thumb. Evaluation Review (Special Issue: External Validity 2), 41(5), 472–505.

Kang, J., Chan, W., Kim, M. O., & Steiner, P. M. (2016). Practice of causal inference with the propensity of being zero or one: Assessing the effect of arbitrary cutoffs of propensity scores. Communications for Statistical Applications and Methods, 23(1), 1–20.

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