THE UNIVERSITY OF CHICAGO
Guanglei Hong is Professor with tenure in the Comparative Human Development Department at the University of Chicago. She was the inaugural Chair of the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences from 2018 to 2021 and is currently Chair of the Committee on Education. She obtained a master’s degree in Applied Statistics in 2002 and a Ph.D. in Education in 2004 from the University of Michigan. Before joining the University of Chicago faculty in July 2009, she was an Assistant Professor in the Human Development and Applied Psychology Department in the Ontario Institute for Studies in Education of the University of Toronto (OISE/UT).
Hong develops and applies causal inference theories and methods for understanding the impacts of large-scale societal changes and for evaluating the effects of social and educational policies and programs on child and youth development. She has contributed original concepts and developed multiple methods for drawing valid inferences about causal relationships, for investigating heterogeneity in responses to interventions across individuals and contexts, and for rigorously testing theories about the mechanisms through which such exposures generate impacts. Her research monograph “Causality in a social world: Moderation, mediation, and spill-over” was published by John Wiley & Sons in July 2015. She was Guest Editor for the Journal of Research on Educational Effectiveness 2012 special issue on the statistical approaches to studying mediator effects in education research. Her other publications have appeared in leading journals in statistics, education, psychology, sociology, public policy, and evaluation. She leads an NSF Summer Institute in Advanced Quantitative Methods for Science, Technology, Engineering, and Mathematics Education Research (SIARM for STEM) from 2020-2024 and again from 2024-2027.
She has received research funding from the National Science Foundation (NSF), the Institute of Education Sciences (IES) of the U.S. Department of Education, the William T Grant Foundation, the Spencer Foundation, and the Social Sciences and Humanities Research Council (SSHRC) of Canada. She received a 2009-2014 William T. Grant Foundation Scholars Award and a 2021-2022 John Simon Guggenheim Memorial Foundation Fellowship. For more information, please visit her website: https://humdev.uchicago.edu/directory/guanglei-hong.
Recent Research / Recent Publications
Hong, G., Qin, X., Xu, Z., & Yang, F., (In Press). Two-Phase Treatment with Noncompliance: Identifying the Cumulative ATE via Multisite IV. Journal of the Royal Statistical Society, Series A: Statistics in Society.
Hong, G., Deutsch, J., Kress, P., Trinidad, J. E., & Xu, Z. (2026). Evaluating organizational effectiveness: A new strategy to leverage multisite randomized trials for valid assessment. American Journal of Evaluation, 47(3), 392-420.
Hong, G., & Chung, H. J. (2024). Assessing the Impact of the Great Recession on the Transition to Adulthood. Sociological Methods & Research, 53(3), 1453-1490.
Hong, G., Yang, F., & Qin, X. (2023). Posttreatment confounding in causal mediation studies: A cutting-edge problem and a novel solution via sensitivity analysis. Biometrics, 79, 1042-1056.
Hong, G., Yang, F., & Qin, X. (2021). Did you conduct a sensitivity analysis? A new weighting-based approach for evaluations of the average treatment effect for the treated. Journal of the Royal Statistical Society, Series A: Statistics in Society, 184(1), 227-254.
Qin, X., Deutsch, J., Hong, G. (2021). Revealing heterogeneity in complex mediation mechanisms: Two concurrent mediators. Journal of Policy Analysis and Management, 40(1), 158-190.
Qin, X., Hong, G., Deutsch, J., & Bein, E. (2019). Multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse. Journal of the Royal Statistical Society, Series A: Statistics in Society, Vol. 182, Part 4, 1343-1370.
Hong, G., Qin, X., & Yang, F. (2018). Weighting-based sensitivity analysis in causal mediation studies. Journal of Educational and Behavioral Statistics, 43(1), 32-56.
Bein, E., Deutsch, J., Hong, G., Porter, K., Qin, X., & Yang, C. (2018). Two-step estimation in RMPW analysis. Statistics in Medicine, 37(8), 1304-1324.
Qin, X., & Hong, G. (2017). A weighting method for assessing between-site heterogeneity in causal mediation mechanism. Journal of Educational and Behavioral Statistics, 42(3), 308-
340.
Garrett, R., & Hong, G. (2016). Impacts of grouping and time on the math learning of language minority kindergartners. Educational Evaluation and Policy Analysis, 38(2), 222-
244.
Hong, G., Deutsch, J., & Hill, H. D. (2015). Ratio-of-mediator-probability weighting for causal mediation analysis in the presence of treatment-by-mediator interaction. Journal of Educational and Behavioral Statistics, 40(3), 307-340
Hong, G., & Nomi, T. (2012). Weighting methods for assessing policy effects mediated by peer change. Journal of Research on Educational Effectiveness special issue on the statistical approaches to studying mediator effects in education research, 5(3), 261-289.
Hong, G. (2012). Marginal mean weighting through stratification: A generalized method for evaluating multi-valued and multiple treatments with non-experimental data. Psychological Methods, 17(1), 44-60.
Hong, G., Corter, C., Hong, Y., & Pelletier, J. (2012). Differential effects of literacy instruction time and homogeneous grouping in kindergarten: Who will benefit? Who will suffer? Educational Evaluation and Policy Analysis. 34(1), 69-88.
Hong, G. (2010). Marginal mean weighting through stratification: Adjustment for selection bias in multilevel data. Journal of Educational and Behavioral Statistics, 35(5), 499-531.
Hong, G., & Raudenbush, S. W. (2008) Causal inference for time-varying instructional treatments. Journal of Educational and Behavioral Statistics, 33(3), 333-362.
Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multi-level observational data. Journal of the American Statistical Association, 101(475), 901-910.
Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children’s cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27(3), 205-224.