Assistant Professor of Quantitative Methods
Department of Psychology
University of Virginia
Exploratory graph analysis (EGA) is a highly accurate technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide –network plot– that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. In the current presentation EGA will be introduced and a summary of the previous simulation studies investigating its accuracy to correctly estimate the number of latent factors will be presented. Also, a new fit measure for dimensionality assessment termed Entropy Fit Indexwill be briefly presented together with a new estimation method that is able to capture non-linear relations between variables. The presentation will end with examples in the area of Psychology and text mining.
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. Golino is also interested in identifying stage-like cognitive development, and in the development and validation of assessment instruments (e.g. tests and questionnaires).
Golino completed his Ph.D. in March 2015 at the Universidade Federal de Minas Gerais (Brazil), where he studied applications of machine learning in Psychology, Education and Health.
Golino also holds an M.Sci. in Developmental Psychology (2012), an B.Sci. in Psychology (2011), all from Universidade Federal de Minas Gerais. At UVA, he will teach undergraduate and graduate courses on quantitative methods at the Department of Psychology. In the last couple of years, Golino has proposed a new approach, termed Exploratory Graph Analysis, that presents several advantages compared to traditional techniques used to verify the number of latent variables. At UVA, Golino continue his Exploratory Graph Analysis project, and extend it to deal with intensive longitudinal data.