Dr. Xu Qin is an Associate Professor of Research Methodology with tenure at the School of Education (primary) and an Associate Professor of Biostatistics at the School of Public Health (secondary). She holds a Ph.D. from the Department of Comparative Human Development at the University of Chicago and a B.S. and an M.S. in Statistics from the Renmin University of China.
Her research focuses on solving cutting-edge methodological problems in causal mediation analysis and multilevel modeling. She is also interested in using rigorous and innovative quantitative methods to evaluate the impacts of interventions and the underlying mechanisms. Methodologically, she has developed statistical methods and software for investigating the heterogeneity in causal mediation mechanisms in both multilevel and single-level settings, as well as sensitivity analysis and power analysis methods for causal mediation analysis. Substantively, she is interested in applying advanced statistical methods in developmental, educational, and health research.
Dr. Qin has served as the Principal Investigator or Co-Principal Investigator for grants funded by the Spencer Foundation, the National Science Foundation, and the U.S. Department of Education’s Institute of Education Sciences. She is a recipient of the 2024 NSF CAREER award, 2024 Society for Research on Educational Effectiveness Early Career Award, and 2022 National Academy of Education/Spencer Postdoctoral Fellowship.
I am accepting doctoral students. Motivated and interested students are welcome to send me a CV.
Statistics I
Applied Regression Analysis
Hierarchical Linear Modeling
Causal Moderation and Mediation Analysis
Causal moderation and mediation analysis method development
Sensitivity analysis and power analysis method development for causal mediation analysis
Intervention evaluation
Qin, X. (in press). An introduction to causal mediation analysis. Asia Pacific Education Review. (Invited paper for a special issue on causal research designs and analysis in education.)
Qin, X. (2024). Sample size and power calculations for causal mediation analysis: a tutorial and Shiny app. Behavior Research Methods, 56(3), 1738-1769.
Qin, X. & Wang, L. (2024). Causal moderated mediation analysis: methods and software. Behavior Research Methods, 56(3), 1314-1335.
Qin, X. & Yang, F. (2022). Simulation-based sensitivity analysis for causal mediation studies. Psychological Methods, 27(6), 1000–1013.
Qin, X., Wormington, S., Guzman-Alvarez, A., & Wang, M.-T. (2021). Why does a growth mindset intervention impact achievement differently across secondary schools? Unpacking the mediation mechanism from a national multisite randomized experiment. The Journal of Research on Educational Effectiveness, 14(3), 617-644.
Qin, X., Deutsch, J, & Hong, G. (2021). Unpacking complex mediation mechanisms and their heterogeneity between sites in a Job Corps evaluation. The 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. The Journal of the Royal Statistical Society, Series A (Statistics in Society), 182(4), 1343-1370.
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.
Principal Investigator (2024-2029), “CAREER: Complex Causal Moderated Mediation Analysis in Multisite Randomized Trials: Uncovering the Black Box Underlying the Impact of Educational Interventions on Math Performance”, National Science Foundation, $842,512
Principal Investigator (2022-2023), “New Tools for Causal Investigations of for Whom and Where an Educational Intervention is Effective and Why,” National Academy of Education/Spencer Postdoctoral Fellowship, Spencer Foundation, $70,000.
Principal Investigator (2022-2023), “Causal Moderation and Mediation Analyses in Multisite Randomized Trials”, Pitt Momentum Funds Priming Grants, $25,000.
Principal Investigator (2021-2022), “Statistical Power Analysis for Causal Mediation Studies in Single-Site and Multi-Site Randomized Trials,” Spencer Foundation, $50,000.
Co-Principal Investigator (2020-2023), “Causal Moderation and Mediation Analyses in Single-Site and Multisite Randomized Trials with Noncompliance,” the Institute of Education Sciences (IES) Statistical and Research Methodology in Education Grants. $899,920. (Principal Investigator: Guanglei Hong, University of Chicago)
Co-Principal Investigator (2019-2022). “Student Engagement in Mathematics: A Longitudinal Study of Classroom and Psychosocial Processes,” National Science Foundation, $1,500,000. (Principal Investigator: Ming-Te Wang, University of Pittsburgh)
Principal Investigator (2019-2020), “Sensitivity Analysis for Causal Mediation Analysis in the Presence of Unmeasured Pretreatment or Posttreatment Confounding,” Spencer Foundation, $49,898.
Awards and Honors:
- Early Career Award, Society for Research on Educational Effectiveness (2024)
- Faculty Early Career Development (CAREER) Award, National Science Foundation (2024)
- National Academy of Education/Spencer Postdoctoral Fellowship, Spencer Foundation (2022)
- Outstanding Dissertation Award, American Educational Research Association, Division D (2019)
- William E. Henry prize for the best PhD dissertation, Department of Comparative Human Development, University of Chicago (2019)
- National Study of Learning Mindsets Early Career Fellowship, Bezos Family Foundation (2018)
- National Academy of Education/Spencer Dissertation Fellowship, Spencer Foundation (2017)
- Joint Statistical Meetings Student Paper Award, American Statistical Association (2016)
Consulting Editor, British Journal of Mathematical and Statistical Psychology
Editorial Board, Journal of Research on Educational Effectiveness
Editorial Board, Sociological Methodology
Member, American Statistical Association
Member, American Educational Research Association
Member, Society for Research on Educational Effectiveness
Member, Society for Causal Inference
Causal Moderated Mediation Analysis for Investigating Heterogeneity of Mediation Mechanisms
