Nianbo Dong
· Guy B. Phillips Professor in EducationVerifiedUniversity of North Carolina at Chapel Hill · Curriculum and Instruction
Active 2007–2026
About
Dr. Nianbo Dong is the Guy B. Phillips Professor in Education at the University of North Carolina at Chapel Hill. His research is centered on developing and applying rigorous quantitative methods to evaluate educational policies, programs, and practices. He specializes in quantitative methodologies such as cost-effectiveness analysis and power analysis of main, moderation, and mediation effects in multilevel experiments and causal inference. Dr. Dong's substantive research investigates the effectiveness of teacher and principal training programs as well as early childhood education initiatives. His work has been supported by the Institute of Education Sciences at the U.S. Department of Education and the National Science Foundation (NSF). In recognition of his contributions, he was awarded the NSF Faculty Early Career Award in 2017.
Research topics
- Computer Science
- Psychology
- Sociology
- Mathematics education
- Econometrics
- Statistics
- Developmental psychology
- Applied psychology
- Mathematics
- Medicine
- Social psychology
- Demography
Selected publications
American Journal of Evaluation · 2026-02-06
article1st authorCorrespondingThree-level multisite individual randomized trials (MIRTs), in which individuals are nested within teachers and schools and randomly assigned to treatment or control conditions, provide a robust framework for assessing overall intervention effects and moderation at multiple levels. This study develops a statistical framework for designing three-level MIRTs to evaluate moderated treatment effects and examines the impact of ignoring one level of nesting on Type I error rates and statistical power. We illustrate the framework with an example of an online tutoring program and derive formulas for statistical power and the minimum detectable effect size difference. These formulas are validated through Monte Carlo simulations, which also demonstrate the risks of ignoring nesting. Finally, we introduce a software tool to facilitate power analysis for moderation in three-level MIRTs and summarize key findings.
UNC Libraries · 2026-04-03
articleOpen accessOver the past 15 years, we have seen an increase in the use of cluster randomized trials (CRTs) to test the efficacy of educational interventions. These studies are often designed with the goal of determining whether a program works, or answering the what works question. Recently, the goals of these studies expanded to include for whom and under what conditions an intervention is effective. In this study, we examine the capacity of a set of CRTs to provide rigorous evidence about for whom and under what conditions an intervention is effective. The findings suggest that studies are more likely to be designed with the capacity to detect potentially meaningful individual-level moderator effects, for example, gender, than cluster-level moderator effects, for example, school type.
UNC Libraries · 2026-04-08
articleOpen accessMaximum likelihood estimation of multilevel structural equation model (MLSEM) parameters is a preferred approach to probe theories involving latent variables in multilevel settings. Although maximum likelihood has many desirable properties, a major limitation is that it often fails to converge and can incur significant bias when implemented in studies with a small to moderate multilevel sample (e.g., fewer than 100 organizations with 10 or less individuals/organization). To address similar limitations in single-level SEM, literature has developed Croon’s bias-corrected factor score path analysis estimator that converges more regularly than maximum likelihood and delivers less biased parameter estimates with small to moderate sample sizes. We derive extensions to this framework for MLSEMs and probe the degree to which the estimator retains these advantages with small to moderate multilevel samples. The estimator emerges as a useful alternative or complement to maximum likelihood because it often outperforms maximum likelihood in small to moderate multilevel samples in terms of convergence, bias, error variance, and power. The proposed estimator is implemented as a function in R using lavaan and is illustrated using a multilevel mediation example.
UNC Libraries · 2026-02-14
articleOpen access1st authorCorrespondingThree-level multisite individual randomized trials (MIRTs), in which individuals are nested within teachers and schools and randomly assigned to treatment or control conditions, provide a robust framework for assessing overall intervention effects and moderation at multiple levels. This study develops a statistical framework for designing three-level MIRTs to evaluate moderated treatment effects and examines the impact of ignoring one level of nesting on Type I error rates and statistical power. We illustrate the framework with an example of an online tutoring program and derive formulas for statistical power and the minimum detectable effect size difference. These formulas are validated through Monte Carlo simulations, which also demonstrate the risks of ignoring nesting. Finally, we introduce a software tool to facilitate power analysis for moderation in three-level MIRTs and summarize key findings.
2025-01-01
article1st authorCorrespondingJournal of School Psychology · 2025-07-25
article1st authorCorrespondingDesign and Analysis of Multisite Cluster-Randomized Trials Targeting (Conditional) Mediation Effects
The Journal of Experimental Education · 2025-07-01
articleSenior authorUNC Libraries · 2025-05-03
articleOpen accessSchool Psychology Review · 2025-10-01
article2025-01-01
article1st authorCorresponding
Recent grants
A Statistical Framework and Tools for Planning Multilevel Randomized Cost-Effectiveness Trials
NSF · $1.3M · 2020–2026
NSF · $37k · 2018–2019
NSF · $530k · 2018–2023
NSF · $244k · 2014–2019
Frequent coauthors
- 60 shared
Megan M. McClelland
- 29 shared
Jessaca Spybrook
Western Michigan University
- 24 shared
Christina Cameron
Université de Montréal
- 21 shared
Benjamin Kelcey
University of Cincinnati
- 18 shared
F. J. Morrison
- 16 shared
C. Blair
University Hospitals Bristol and Weston NHS Foundation Trust
- 16 shared
Shannon B. Wanless
- 14 shared
Alan C. Acock
Awards & honors
- NSF Faculty Early Career award (2017)
- Resume-aware match score
- Save to shortlist
- AI-drafted outreach
See your match with Nianbo Dong
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
- Free to start
- No credit card
- 30-second signup