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Using FIML in R with Multilevel Data (Part 3) A recurring question that I get asked is how to use full information maximum likelihood (FIML) when performing a multiple regression analysis BUT this time, accounting for nesting or clustered data structure. For this example, I use the the leadership dataset in the mitml package (Grund et al., 2021). We’ll also use lavaan (Roseel, 2012) to estimate the two-level model. The chapter of Grund et al.


In a recent article in Multivariate Behavioral Research, we (Huang, Wiedermann, and Zhang; HWZ; doi: 10.1080/00273171.2022.2077290) discuss a robust standard error that can be used with mixed models that accounts for violations of homogeneity. Note that these robust standard errors have been around for years though are not always provided in statistical software. These can also be computed using the CR2 package or the clubSandwich package. This page shows how to compute the traditional Liang and Zeger (1986) robust standard errors (CR0) and the CR2 estimator- see Bell and McCaffrey (2002) as well as McCaffrey, Bell, and Botts (2001) (BM and MBB).


List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of 2022-08-16. I know, I know… there are other places that keep this list but I find this interesting to track… Postings for (2022-2023) - 2022.08.04. Assistant Professor, Quantitative Methods. Princeton University. - Assistant Professor of Education with Expertise in Quantitative Methodologies in Service of Latina/o/x Communities. UC San Diego. - 2022.06.21. Visiting Assistant Professor (in Education Policy and Equity, with a focus on teaching quantitative methods and applied research).


In an earlier post, I had shown this using iteratively reweighted least squares (IRLS). This is just an alternative method using Newton Raphson and the Fisher scoring algorithm. For further details, you can look here as well. library(MLMusingR) data(suspend) m1 <- glm(sus ~ male + gpa * frpl + fight + frmp.c * pminor.c, data = suspend, family = binomial) ### extracting raw components dat <- model.frame(m1) fml <- formula(m1) X <- model.


List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of 2022-06-21. I thought 21-22 postings were done but a few more have popped up (some start in Aug 2022): 2022.06.21. Visiting Assistant Professor (in Education Policy and Equity, with a focus on teaching quantitative methods and applied research). Saint Louis University. Visiting Assistant Teaching Professor. Research, Evaluation, Statistics and Assessment. University of Southern Mississippi. 2022.05.28. Assistant, Associate or Full Professor - Ph.



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.


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.