Joint Quantitative Brownbag


Hudson Golino

Dr. Hudson Golino
Department of Psychology
University of Virginia

Dr. Hudson Golino’s research focuses on quantitative methods, psychometrics and machine learning applied in the fields of psychology, health and education. He is particularly interested in new ways to assess the number of dimensions (i.e. latent variables) underlying multivariate data using network psychometrics.

He has been developing a new set of quantitative techniques and metrics, integrated in a general approach - termed Exploratory Graph Analysis (EGA), that is part of the relatively new area of network psychometrics. Particularly, he combines network science, information and quantum information theory, as well as computational methods to address fundamental problems in psychometrics, with the following goals: (1) to improve the estimation of the number of latent factors in an automatic (or semi-automatic) way, (2) to develop innovative fit indices for structural analysis and dimensionality assessment/reduction, (3) to improve the estimation and the interpretability of latent factors in intensive longitudinal data, (4) to develop new techniques for item analysis from a network psychometrics perspective (including, for example, network loadings, item parameters and new metrics of reliability), and (5) to construct general representations of structure built from intraindividual variability, quantifying the homogeneity of individuals using new metrics of complexity.


On networks and online Russian trolls: How can the total entropy fit index be applied to optimize the number of embedded dimensions used in dynamic exploratory graph analysis, and why does it matter


The current presentation will show how a new fit index for dimensionality analysis termed total entropy fit index can be applied to tune the number of embedded dimensions used in the dynamic exploratory graph analysis (DynEGA) technique. DynEGA uses dynamical systems and network psychometrics to estimate the number of (dynamic) latent factors in multivariate time-series of continuous or categorical data. For each time series generalized local linear approximation (GLLA) is used to compute n-order derivatives for each individual. The stacked matrix of derivatives (combined row-wise) is then used to estimate a network structure in which communities represent dynamical factors. GLLA requires the user to set the number of embedded dimensions to transform each time series into a time delay embedding matrix. In a Monte-Carlo simulation, we show that the total entropy fit index can be used in a grid search to find the optimal number of embedded dimensions. In an applied example, we performed DynEGA with the TEFI optimization on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 U.S. presidential election. DynEGA revealed factors (in this case latent topics) that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the U.S. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.

Background Reading

  • Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53, 2563-1580.

  • Golino, H., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2022). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right wing and left-wing trolls in the 2016 US elections. Psychometrika, 87(1), 156-187.

  • Golino, H., Moulder, R., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020). Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research, 87(1), 874-902.