Expertise on the Team

Guanglei Hong is Professor in the Department of Comparative Human Development (https://humdev.uchicago.edu/) at the University of Chicago. She was the Inaugural Chair of the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences (https://voices.uchicago.edu/qrmeth/) and is Chair of the Committee on Education (https://voices.uchicago.edu/coed/). She attained 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 had been 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). Prof. Hong has focused her research on developing causal inference theories and methods for understanding the impacts of large-scale societal changes and 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 external interventions across individuals and contexts, and for rigorously testing theories about the mechanisms through which such exposures generate impacts. Her book “Causality in a social world: Moderation, mediation, and spill-over” was published by Wiley in 2015. She guest edited the Journal of Research on Educational Effectiveness special issue on the statistical approaches to studying mediator effects in education research in 2012. Additionally, through publishing in first-tier statistics, education, psychology, sociology, and public policy journals and disseminating new methods through workshops and training institutes, her research has generated a broad impact among quantitative methodologists as well as applied researchers. She has received research and training grants from the National Science Foundation, the U.S. Department of Education, the William T. Grant Foundation, the Spencer Foundation, and the Social Sciences and Humanities Research Council of Canada among other funding agencies. She was awarded a prestigious John Simon Guggenheim Memorial Foundation Fellowship in 2021. For more information, please visit her website: https://humdev.uchicago.edu/directory/guanglei-hong.

Stephen Raudenbush (Faculty Associate). is Lewis-Sebring Distinguished Service Professor in the Department of Sociology at the University of Chicago, Chair of the Committee on Education and a member of the National Academy of Sciences. He is best known for his work developing hierarchical linear models, with broad applications in the design and analysis of longitudinal and multilevel research. He is currently studying the development of math skills in early childhood, elementary school, and high school. The focus of this work is on tailoring instruction to individual differences in skills with the aim of reducing social inequality in math achievement. Raudenbush and colleagues have developed a new preschool intervention known as “Longitudinally Adaptive Instruction and Assessment in Pre-School Mathematics” (Carrazza, Dulaney, Levine, Sorokin, and Raudenbush, 2019). A randomized trial of this intervention produced a large and significant positive effect on children’s numerical learning. His co-authored book entitled “The Ambitious Elementary School” (McGhee-Hassrick, Raudenbush, and Rosen, 2017) applies a similar approach at the elementary school. A randomized lottery study showed large effects and lasting of this approach on math learning. At the high school level, Nomi and Raudenbush (2016; Nomi, Raudenbush, & Smith, 2021) evaluated the impact of “Double Dose Algebra” in Chicago’s public high schools using a regression discontinuity design, revealing that Double-Dose algebra in 9th grade significantly and substantially increased high-school and college graduation rates. All these studies used hierarchical linear models (developed by Raudenbush and Bryk, 2002). Together, they give evidence that tailoring instruction and expanding instructional time has substantial potential to improve math learning and reduce inequality, and these efforts pay at each epoch of the child’s school career.

Yanyan Sheng is Senior Instructional Professor in the Committee on Quantitative Methods in Social, Behavioral, and Health Sciences at the University of Chicago. She has more than eighteen years of experience in teaching graduate-level statistics and measurement courses in education, and researching in psychometrics and especially Bayesian item response theory modeling. Her work has focused on developing fully Bayesian IRT unidimensional and multidimensional models (e.g., Al Hakmani & Sheng, 2019; Kuo & Sheng, 2015; Sheng, 2015), empirically investigating test theory and models (e.g., Al Hakmani & Sheng, 2023; Ptukhin & Sheng, 2019; Sheng, 2017), and applying high-performance computing with Bayesian IRT models (e.g., Welling, Sheng, & Zhu, 2023; Sheng, Welling, & Zhu, 2014; 2015). In addition to theoretical work, she has also published research on serious game analytics and on psychometric properties of existing instruments. She is Associate Editor for Frontiers in Quantitative Psychology and Measurement and has led a psychometric team to work on evaluating and developing the Illinois Science Assessment for the Illinois State Board of Education from 2017 to 2023.

 Kenneth Frank received his Ph.D. in measurement, evaluation and statistical analysis from the School of Education at the University of Chicago in 1993.  He is a member of the National Academy of Education, a fellow of the American Educational Research Association and MSU Foundation Research Professor of Sociometrics in Counseling, Educational Psychology and Special Education; and adjunct (by courtesy) in Fisheries and Wildlife and Sociology at Michigan State University.  Dr. Frank’s current research projects include:

His work can be found at: https://sites.gogle.com/msu.edu/kenfrank

Della Cox is a PhD student in the Sociology department and an Institute of Education Sciences predoctoral fellow at the University of Chicago. She received her BA in Sociology and BS in Statistics at the University of Missouri in 2022 and received her MA in Sociology from the University of Chicago in 2024. She is interested in how stereotypes play a role in perceived contribution and competence of women and minorities in the college classroom, STEM student outcomes, and positive, strength-based approaches for student retention.

Aasha Francis is Business Administrator for the NSF SIARM team overseeing logistics, coordination, and communications for many aspects of summer institute planning. She’s been employed at the University of Chicago since 2018, for the Committee on Quantitative Methods in Education, Health, and Social Sciences (QMEHSS).

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