Wendy Chan

Assistant Professor

Human Development and Quantitative Methods Division

Graduate School of Education
University of Pennsylvania



Wendy Chan

Dr. Wendy Chan is an Assistant Professor of Education in the Human Development and Quantitative Methods Division at Penn GSE. She 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 Evaluation Review and Communications for Statistical Applications and Methods.

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 dissertation, Extensions to the Generalizability of Experiments in the Social Sciences, was about the intellectual and statistical challenges of generalizing the results of localized randomized trials to larger populations. Specifically, it focused on applications of partial identification and small area estimation methods to estimation of population average treatment effects in the absence of equal probability sampling. With partial identification, her work examines the role of alternative assumptions to strong ignorability of sample selection in making inferences on population parameters. With small area estimation, Dr. Chan’s work considers use of small area models in improving the precision of estimators when there is limited sample size.

She is currently working on a project that analyzes the impact of clustering on power and significance tests in longitudinal studies. She plans to extend her work in generalization to analyze the importance of covariates on improving external validity.


Tipton, E., Hallberg, K., Hedges, L. V., Chan, W. (2016). Implications of small samples for generalizations: Adjustments and rules of thumb. Evaluation Review (invited submission). doi:10.1177/0193841X16655665

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.