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Quantitative Methods and Data Science

The quantitative psychology faculty lend their expertise in quantitative and statistical modeling, big data processing and machine learning to almost all research programs in the department, as well as hold ongoing collaborations at the Data Science Institute, School of Medicine, and Curry School of Education. Current research foci include linear and non-linear structural equation modeling, dynamic systems modeling, optimal research design, and quantitative analysis of neuroimages. Topics of special interest include longitudinal latent variable models, growth curve models, applications of structural equation models to behavior genetics, analysis of individual and group differences in family studies of ability, and the development of numerical and graphical estimation techniques for multivariate models.

Software Packages Developed by Our Faculty

Several software packages that are developed by our faculty are available for use:

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Hudson Golino | EGAnet: Exploratory Graph Analysis

EGAnet: Exploratory Graph Analysis: A Framework for Estimating the Number of Dimensions in Multivariate Data Using Network Psychometrics

An implementation of the Exploratory Graph Analysis (EGA) framework for dimensionality assessment. EGA is part of a new area called network psychometrics that focuses on the estimation of undirected network models in psychological datasets. EGA estimates the number of dimensions or factors using graphical lasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community analysis. A bootstrap method for verifying the stability of the estimation is also available. The fit of the structure suggested by EGA can be verified using confirmatory factor analysis and a direct way to convert the EGA structure to a confirmatory factor model is also implemented. Documentation and examples are available.

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Steve Boker | OpenMx Project

The OpenMx Project. Funded by NIH, OpenMx is a software development project for an open source Structural Equation Modeling (SEM) package that is free of charge and tied into the R statistical system. The OpenMx project involves collaborators at the University of Virginia, Medical College of Virginia, University of Chicago, University of Houston, McMaster University and University of Edinburgh. The project was officially released in the fall of 2010 and has been downloaded more than 70,000 times. Statistical researchers, quantitative psychologists, and software developers who may be thinking about writing extensions to SEM are encouraged to visit the OpenMx Wiki where they can learn about the project and possibly either contribute to the project or find software and open source code that may help them in their own work.


Hudson Golino

Golino Lab

Steve Boker

Human Dynamics Lab

Teague Henry  Functional Connectivity, Network Psychometrics and Social Networks, and Language Networks

Teague Henry

The Brain as a Data Science

The Brain as a Data Science