Structural equation modeling, longitudinal data analysis, and principled treatment of missing data.
Back to ResearchThis line of work concerns latent variable models — structural equation models, latent class and mixture models, and growth models for longitudinal data — and the statistical challenges that arise in estimating them, such as label switching, class enumeration, and missing data.
A recurring theme is understanding when these models recover meaningful structure and how to quantify uncertainty in the conclusions they support.