Guanglei Hong
Ph.D., University of Michigan, 2004
Assistant Professor
Contact Information:
Department of Comparative Human Development
The University of Chicago
5736 S. Woodlawn Ave
Chicago, IL 60637, USA
Phone: 773-702-9481
Fax: 416-926-4713
Email: ghong@uchicago.edu
Research Interests:
- Quantitative research methodology
- Causal inference theories and methods
- Experimental and quasi-experimental designs
- Multi-level modeling
- Longitudinal data analysis
- Policy and program evaluation
- Mediation analysis
- Instructional effectiveness
I have focused my research on developing causal inference theories and methods for evaluating educational policies and instructional programs in multi-level, longitudinal settings. My work addresses issues including
(1) how to conceptualize and evaluate the causal effects of educational treatments when students’ responses to alternative treatments depend on various features of the organizational settings,
(2) how to adjust for selection bias in estimating the effects of concurrent multi-valued educational treatments,
(3) how to study instruction as time-varying treatments for students, and
(4) how to conceptualize and analyze the mediation mechanism in evaluating educational interventions.
Specifically, I argue that how much a child learns depends not only on the instructional program but also on who the classmates are, as the teacher may adapt curriculum and instruction to the class composition, and as students may contribute to the collective learning process. I view instruction as a multifaceted intervention program in which every single element needs to operate in concert with other parts of the program to produce a joint impact. Moreover, Schooling is a longitudinal process in which later learning experiences are built upon earlier learning results. Understanding the impact of sequences of instructional experiences is thus central to the study of teaching and learning. And finally, revealing the mediation mechansim is at the core of testing theories about causality. Because advancements in these quantitative research methods are best illustrated and utilized through empirical investigations of prominent educational issues, I communicate with a broad audience through applying the causal inference methods to studies of specific policies and instructional practices such as grade retention, within-class grouping, intensive math instruction, and educational services for English language learners.