Selected Publications

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

(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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Recent Publications

See CV for complete list

More Publications

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

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(2024). Steadfast restorative and supportive approaches to student behavior during the COVID-19 pandemic. JEPC.

(2024). Positive student-teacher relationships and exclusionary discipline practices. Journal of School Psychology.

Recent & Upcoming Talks

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Replication studies using secondary and nonexperimental datasets
May 13, 2024 10:00 AM
Multilevel Modeling with Large-Scale International Databases Using HLM (Philadelphia)
Apr 10, 2024 9:00 AM
Using cluster robust standard errors to analyze nested data with a few clusters (Korea)
Oct 12, 2023 2:00 PM
Using Cluster Robust Standard Errors to Analyze Cross-Classified Data with a Small Number of Clusters
Sep 29, 2023 2:00 PM
Multilevel Modeling with Large-Scale International Databases Using HLM (Chicago)
Apr 12, 2023 9:00 AM
Multilevel Modeling with Large-Scale International Databases Using HLM
Apr 15, 2021 1:00 PM
Multilevel Modeling with Large-Scale International Databases Using HLM
Apr 16, 2020 9:00 AM

Recent Posts

More Posts

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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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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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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List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of 2024-04-05. Postings for (2023-2024): As of 2024.04 Visiting Assistant Professor- Educational Research, University of Southern Mississippi, The Hattiesburg, MS As of 2024.03 Assistant Professor in Evaluation (tenure track). Louisville, KY. University of Louisville. Postdoc, Research and Evaluation Methodology. Gainesville, FL. University of Florida. As of 2023.12 Associate/Full Professor of Quantitative Research Methods, University of Texas at Arlington in Arlington, TX As of 2023.

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Random notes. Regression based techniques often involve finding a maximum (e.g., the maximum likelihood) or a minimum (e.g., least squares or mean square error) value. Gradient descent is an iterative optimization algorithm used to find the minimum of a function (or gradient ascent to find the maximum). The algorithm for solving for \(\theta_j\) looks like: \[\theta_j = \theta_j - \alpha\frac{\partial{}}{\partial{\theta_j} }J(\theta)\] \(\alpha\) is the learning rate (smaller step size takes more iterations)

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Projects

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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