Hudson Golino

Assistant Professor of Psychology


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).

Hudson Golino is the leading author of the first book written in Portuguese about the Rasch models (published by Pearson in Brazil in 2015). In 2012 he was awarded with the International Test Commission Young Scholar Scholarship and in 2015 he received the Sanofi Innovation in Medical Services award for developing a system to improve the prediction accuracy of outcomes in intensive care units using machine learning models.

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. He expects to offer courses on applied machine learning for Psychologists and on the construction and validation of assessment instruments.

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 will continue his Exploratory Graph Analysis project, and extend it to deal with intensive longitudinal data, which may contribute, for example, to the understanding of (1) human development, (2) the dynamics of symptoms in psychopathology, and (3) the performance of students in educational tests over time.



1)         Exploratory graph analysis: a new approach for estimating the number of dimensions in psychological research

2)         Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis
3)        Developmental Differentiation and Binding of Mental Processes with g through the Life-Span
4)         Psychometric properties of the Epistemological Development in Teaching Learning Questionnaire (EDTLQ): An inventory to measure higher order epistemological development

5)         Random forest as an imputation method for education and psychology research: its impact on item fit and difficulty of the Rasch model

6)      c-Fos expression predicts long-term social memory retrieval in mice

7)      Stage of pricing strategy predicts earnings: A study of informal economics.

8)      The validity of the Cattel-Horn-Carroll model on the intraindividual approach.
9)       Predicting Academic Achievement of High-School Students Using Machine Learning
10)      Predicting school achievement rather than intelligence: does metacognition matter?
11)      Visualizing Random Forest’s Prediction Results
12)      The validity of the Cattel-Horn-Carroll model on the intraindividual approach.
13)       The construction and validation of a developmental test for stage identification: Two exploratory studies.
14)        Psychology data from the “BAFACALO project: The Brazilian Intelligence Battery based on two state-of-the-art models–Carroll’s Model and the CHC model”

15)        Four Machine Learning methods to predict academic achievement of college students: a comparison study

16)        Predicting Increased Blood Pressure Using Machine Learning


18)        Self-reports on students' learning processes are academic metacognitive knowledge

19)        Mining concepts of health responsibility using text mining and exploratory graph analysis