Zuchao "William" Shen

Areas of Expertise

  • Experimental Design
  • Statistics
  • Multilevel Modeling
  • Causal Inference


  • Artificial Intelligence
  • Latent Variable Modeling
  • School Psychology
  • Educational Policy



  •  Ph.D. in Quantitative and Mixed Methods, 2019
    University of Cincinnati
  •  M.A. in Economics of Education, 2005
    Peking University
  •  B.A. in Educational Administration, 2002
    Henan Normal University


 Aderhold Hall, Remote (2023-2024). Meet for 30 minutes? Please schedule at https://doodle.com/bp/zuchaoshen/30min

Research Summary

Zuchao “William” Shen’s research centers on experimental design and causal inference to develop frameworks and tools for study design and analysis (in multilevel settings). This line of research develops the optimal sampling strategies for experimental studies investigating whether interventions work through main effect analyses, their mechanisms through mediation analyses, and under what condition for whom intervention effects are most salient through moderation analyses. Zuchao “William” Shen’s research also includes statistical power, multilevel modeling, structural equation modeling, latent variable modeling, and their application in education, psychology, and health.


Optimal Design of Experimental Studies Investigating Moderation and Main Effects
8/1/2023– 7/31/2026
Experimental studies investigating moderation and main effects provide the source material for improving the quality of and equity in education by delineating the impact of an intervention, and the contexts, conditions, and sub-populations for which it is most effective. Despite sustained interest in experimental studies, the literature has not developed accessible optimal sampling strategies to help plan powerful and efficient designs to detect these effects. This project, sponsored by the Spencer Foundation, aims to develop a flexible optimal design framework so that we can design well-powered studies with minimal financial resources. Specifically, this project will (a) incorporate flexible costs structures of sampling, (b) cover single-level experiments, two-, and three-level cluster-randomized trials and multisite-randomized trials with moderators at different levels, (c) identify samples that are jointly optimal for main and moderation effects, and (d) implement the methods in the R package odr and a Shiny app. Preliminary results show that the proposed framework can identify much more efficient or powerful designs when contrasted with conventional design frameworks. This project has the potential for broad impacts because it facilitates a fundamental shift in the principles and strategies of study design.


Representative Publications

Intraclass correlations for evaluating the effects of teacher empowerment programs on student educational outcomes
  • Shen, Z., Curran, C., You, Y., Splett, J., & Zhang, H. (2023)
  • Educational Evaluation and Policy Analysis, 45(1), 134-156
Optimal sample allocation for three-level multisite cluster-randomized trials
  • Shen, Z., & Kelcey, B. (2022b)
  • Journal of Research on Educational Effectiveness, 15(1), 130-150
Optimal sample allocation in multisite randomized trials
  • Shen, Z., & Kelcey, B. (2022a)
  • The Journal of Experimental Education, 90(3), 693-711
Optimal sample allocation under unequal costs in cluster-randomized trials.
  • Shen, Z., & Kelcey, B. (2020).
  • Journal of Educational and Behavioral Statistics, 45(4), 446–474.

Awards and Accolades

Early Career Award

Society for Research on Educational Effectiveness (SREE), 2023

Early Career Award

American Educational Research Association (AERA), Division D, 2023

Outstanding Dissertation Award

American Educational Research Association (AERA), Division D, 2021

Researchers of Color Mentored Travel Award

Society for Research on Educational Effectiveness (SREE), 2021

NAEd/Spencer Dissertation Fellowship

National Academy of Education (NAEd), 2018