Selected Publications

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

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(2023). Using robust standard errors for the analysis of binary outcomes with a small number of clusters. JREE.

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(2023). Accounting for heteroskedasticity resulting from between-group differences in multilevel models. MBR.

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

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(2023). Seasonality of school climate. School Psychology Review.

(2023). The impact of restorative practices on the use of out-of-school suspensions: Results from a cluster randomized controlled trial. Prevention Science.

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

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(2023). Advanced categorical data analysis in prevention science. Prevention Science.

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(2022). Evaluation of the Whole School Restorative Practices Project: One-year impact on discipline incidents. JSP.

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(2022). Using cluster robust standard errors when analyzing cluster randomized trials with few clusters. BRM.

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

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

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Recent Publications

See CV for complete list

More Publications

(2024). Adolescent Exposure to Restorative Practices and Their Perceptions of Support, Structure, and Bullying in the School Climate. AERA Open.

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(2024). Advancing Diversity, Equity, and Inclusion in School Psychology Science and Scholarship: Changing Training and Practice in the Field of School Psychology. Canadian Journal of School Psychology.

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(2024). Replication Studies Using Secondary or Nonexperimental Datasets. School Psychology Review.

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Recent & Upcoming Talks

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Replication studies using secondary and nonexperimental datasets
May 13, 2024 10:00 AM
Talk at the Education University of Hong Kong
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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
PDF
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
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Recent Posts

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Last updated on 16 min read large scale datasets, weights

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.

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Last updated on 2 min read jobs5

List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of 2024-10-13. Postings for (2024-2025): As of 2024.10 Assistant Professor of Quantitative Methods and Assessment in Education, University of Northern Iowa, Cedar Falls, IA Assistant Professor-Educational Research, Measurement, and Analysis, Auburn University, Auburn, AL Assistant Professor - Psychology, University of Arkansas at Little Rock, AR School Of Education-Tenure-track Assistant Professor- Educational Psychology And Research; Department Human Studies, University of Alabama at Birmingham, Birmingham, AL As of 2024.

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Last updated on 3 min read wemix, plausible values

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.

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

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.

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Last updated on 12 min read wemix, plausible values

Syntax to accompany the article: Huang, F. (2024). Using plausible values when fitting multilevel models with large-scale assessment data using R. Large-scale Assessments in Education. When fitting multilevel models using large scale assessments such as PISA or TIMSS, it is important to account for: the use of weights at different levels and the presence of multiple plausible values. I am often asked how do you run this analysis in R.

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Projects

All School climate Youth violence project Grants

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.

Program (Impact) Evaluation

Evaluating the quantifiable impact of social programs is a key task that policy makers, governments, and program funders perform. In education and the social sciences, a fundamental question asked is ‘How do we know our policy or program works?’

Quantitative Foundations in Educational Research

This course is designed to provide students the fundamental and necessary quantitative methods in educational research.

Data Management (using R)

Good data management is a prerequisite for successful research, needed for reproducibility of results, and essential when collaborating with others.

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