Francis L. Huang

Professor, Co-Director (Methodology Branch)

University of Missouri

Missouri Prevention Science Institute

About me

I am a Professor in the Department of Educational, School, and Counseling Psychology (emphasis: Statistics, Measurement, & Evaluation in Education) Program in the College of Education and Human Development at the University of Missouri-Columbia. I am an applied quantitative methodologist and currently teach courses related to program (impact) evaluation, multilevel modeling, regression, and data management. My research interests focus on the use of applied quantitative methods for policy analysis (e.g., bullying, school violence), school climate, large scale data analysis, and the development and validation of empirically supported measures and scales. My methodological interest focus on the analysis of clustered data (or dealing with nonindependent data structures) and the design and analysis of experiments.

Updates

Disqus– which allowed comments to be posted in the blog section– has started forcing ads to appear so I have disabled the comments (2025.02.20)
~~We will be conducting our annual HLM workshop with large scale international datasets at AERA in April 2025. This time, we will be using R. (2024.11.14)~~ Due to federal contracts being cancelled, our collaborators at AIR will not be able to attend and we have cancelled the PD for this year. (2025.03.03)
Our new edited book on Dependent Data in Social Science Research is now out (2024.10.22)
For a list of quant/methods faculty postings (2024-2025), click here.
I am now the chair of the School Community, Climate, and Culture (S3) special interest group (SIG) of the American Educational Research Association (SG #104). If you have an interest in those areas, consider joining! (2023.07)
My book, Practical Multilevel Modeling Using R, is now out (2022.12.26)! You can read about it here.
Well, our university webpages are now use Googlesites and I don’t have time to figure out how to port my site over. For the most part, I have put datasets on Github. You can access these here: https://github.com/flh3/pubdata (2022.08.25)

Interests

Analysis of Clustered Data (Multilevel Modeling)
Measurement of School Climate
Evaluation and Causal Inference
Bullying and School Violence
School Discipline
Large-scale Assessments

Education

PhD in Research, Statistics, and Evaluation, 2009

University of Virginia
MA in Instructional Technology and Media, 1997

Teachers College, Columbia University

Selected Publications

U Atasever, FL Huang, L Rutkowski (2025). Reassessing weights in large-scale assessments and multilevel models. In LSA.

PDF Code

FL Huang, B Zhang (2025). Cluster-robust standard errors with three-level data. In CS.

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FL Huang, B Zhang, W Reinke, K Herman, J Sebastian (2024). Seasonality of school climate. School Psychology Review.

B Zhang, FL Huang (2024). Investigating the Use of Robust Standard Errors to Account for Two-Way Clustering in Cross-Classified Data Structures. In Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis, Second Edition.

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FL Huang (2024). Using plausible values when fitting multilevel models with large-scale assessment data using R. Large-scale Assessments in Education.

PDF Code

FL Huang, Bixi Zhang, Xintong Li (2023). Using robust standard errors for the analysis of binary outcomes with a small number of clusters. JREE.

PDF Code Dataset

FL Huang, W Wiedermann, B Zhang (2023). Accounting for heteroskedasticity resulting from between-group differences in multilevel models. MBR.

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FL Huang (2023). Alternatives to logistic regression models when analyzing cluster randomized trials with binary outcomes. Prev Sci.

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FL Huang, A Gregory, A Ward-Seidel (2023). The impact of restorative practices on the use of out-of-school suspensions: Results from a cluster randomized controlled trial. Prevention Science.

Preprint PDF

FL Huang (2023). Practical Multilevel Modeling Using R. Sage Publishing.

Preprint PDF

A Gregory, FL Huang, A Ward-Seidel (2022). Evaluation of the Whole School Restorative Practices Project: One-year impact on discipline incidents. JSP.

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FL Huang, Xintong Li (2022). Using cluster robust standard errors when analyzing cluster randomized trials with few clusters. BRM.

Preprint PDF Code Dataset

Francis L. Huang (2022). Alternatives to logistic regression models in experimental studies. JXE, 90(1).

PDF Code Dataset

FL Huang (2022). Analyzing cross-sectionally clustered data using generalized estimating equations. JEBS.

Preprint PDF Code Dataset

Recent Publications

See CV for complete list

More Publications

A Gregory, A Jimenez, L Norton, C Tieger, FL Huang (2025). Restorative Justice Conferences in Elementary and Secondary Schools. In JSV.

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D Cornell, J Maeng, S Winter, FL Huang, T Konold, J Kerere, K Afolabi, D Cowley (2025). Behavioral Threat Assessment and Equity in Exclusionary School Discipline. In SPR.

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D Cornell, J Kerere, T Konold, J Maeng, K Afolabi, FL Huang, D Cowley (2025). Referral Rates for School Threat Assessment. In PITS.

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Recent & Upcoming Talks

More Talks

Replication studies using secondary and nonexperimental datasets

May 13, 2024 10:00 AM

Talk at the Education University of Hong Kong

Slides

Multilevel Modeling with Large-Scale International Databases Using HLM (Philadelphia)

Apr 10, 2024 9:00 AM

American Educational Research Association

Using cluster robust standard errors to analyze nested data with a few clusters (Korea)

Oct 12, 2023 2:00 PM

23rd International Conference on Education Research

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Using Cluster Robust Standard Errors to Analyze Cross-Classified Data with a Small Number of Clusters

Sep 29, 2023 2:00 PM

Society for Research on Educational Effectiveness

PDF

Multilevel Modeling with Large-Scale International Databases Using HLM (Chicago)

Apr 12, 2023 9:00 AM

American Educational Research Association

Alternatives to logistic regression models in the analysis of cluster randomized trials with binary outcomes

Jun 4, 2021 10:00 AM

Society for Prevention Research Annual Meeting

PDF Slides

Multilevel Modeling with Large-Scale International Databases Using HLM

Apr 15, 2021 1:00 PM

American Educational Research Association

Multilevel Modeling with Large-Scale International Databases Using HLM

Apr 16, 2020 9:00 AM

American Educational Research Association

Alternatives to logistic regression models in experimental studies

Feb 21, 2020 10:00 AM

PDF Slides

Instrumental Variables for Educational Researchers

Sep 20, 2019 9:00 AM

PDF Slides

Recent Posts

Applied Example Using Weights in Multilevel Models

This is an applied example regarding the use of weights in multilevel models when using large scale assessments. This is using the Germany TIMSS dataset. This accompanies the article: Atasever, U., Huang, F., & Rutkowski, L. (2025). Reassessing weights in large-scale assessments and multilevel models. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00245-y 1. Read in the data and load the required libraries Download the data from the TIMSS website.

Last updated on Sep 7, 2024 16 min read large scale datasets, weights

Selecting the proper weights in LSAs with multilevel models

Which weights to use with multilevel models? A common question with the use of large-scale assessments (LSAs) is related to the use of weights. Another issue is how to specify these weights properly. Software such as SAS and Mplus, when specifying weights at two levels, require the use of conditional weights at level 1 if the level-2 weight is specified (or you can just use the level-2 weights alone; see Mang et al.

Last updated on Jan 31, 2025 3 min read jobs5

Job postings (AY 2024-25)

List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of 2025-01-31. Postings for (2024-2025): As of 2025.01 Post-doctoral Research Associate- Educational Measurement and Statistics- University of Tennessee, Knoxville, Knoxville, TN Learning Sciences Faculty Cluster Hires (Open-Rank and Tenure-Track; Educational Data Science and Analytics, AI and Education, and Advanced Quantitative Research Methods, Measurement and/or Evaluation; 5 positions) Clemson University in Clemson, SC Assistant Director for Education Research, The University of Illinois at Chicago in Chicago, IL Assistant/Associate Professor of Research Design, Methods, and Analysis, William & Mary in Williamsburg, VA As of 2024.

Last updated on Jun 8, 2024 3 min read wemix, plausible values

Using Plausible Values with Multilevel Models Using R (update)

This is an update to: Huang, F. (2024). Using plausible values when fitting multilevel models with large-scale assessment data using R. Large-scale Assessments in Education. This is an update to mixPV, load it using this function: source("https://raw.githubusercontent.com/flh3/pubdata/main/mixPV/mixPVv2.R") The function has been updated to be able to use parallel processing or multiple cores of your computer (to make computation faster). Load in the dataset. data(pisa2012, package = 'MLMusingR') The usual mixPV function can be used as normal.

Last updated on Mar 31, 2024 8 min read large scale datasets, multiple imputation

Analyzing large scale assessments using R (using weights, imputations, and plausible values) in 2024

Years ago I had written a post on using multiple imputation, weights, and accounting for clustering using R. However, the process was actually quite cumbersome and now in 2024, there are more straightforward ways of handling this. 1. Load in the required packges library(dplyr) #for basic data management library(tidyr) #converting wide to tall library(estimatr) #estimating models with robust SEs library(mitml) #for imputation and analyzing MI datasets library(MLMusingR) #contains the sample dataset library(mice) #for carrying out the analysis with MI data library(modelsummary) #outputting the results nicely library(survey) #alternative (classic) way 2.

Projects

Missouri Prison Transformation Project

Randomized controlled trial with the support of Arnold Ventures.

Ongoing Grant Funded Work

Various ongoing grants funded by the National Institute of Justice, Department of Education (i3), and Institute of Education Sciences.

Missouri Prevention Science Institute

The Missouri Prevention Science Institute (MPSI) brings community members and researchers together to help schools and families apply techniques that promote social and academic success. Through community outreach, the institute’s staff provides parent training and teacher consultation services.

Youth Violence Project @ UVA

Our team of faculty and graduate students conducts research on effective methods and policies for youth violence prevention and school safety.

Teaching

I teach the following courses at the University of Missouri:

Multilevel Modeling

The goal of the course is to provide students with the necessary skills needed to review/critique, analyze, interpret, and write-up studies involving nested (clustered) data using multilevel modeling (MLM). Clustered data (e.g., students within schools, patients within clinics) occur quite naturally in the social sciences and being able to understand and conduct their own analyses using nested data is an important skill. Alternatives are discussed as well.

Applied Multivariate Statistics

This course is designed to provide students with both a theoretical and applied understanding of useful multivariate statistical procedures (e.g., factor analysis, principal components analysis, discriminant function analysis, cluster analysis, MANOVA) in education sciences.