H
Hudson Golino
Researcher at University of Virginia
Publications - 63
Citations - 1413
Hudson Golino is an academic researcher from University of Virginia. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 12, co-authored 54 publications receiving 682 citations. Previous affiliations of Hudson Golino include State University of Feira de Santana & Universidade Federal de Minas Gerais.
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Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research.
Hudson Golino,Sacha Epskamp +1 more
TL;DR: In this article, exploratory graph analysis (EGA) was used to estimate the number of dimensions in the four-factor structure when the correlation between factors was.7, showing an accuracy of 100% for a sample size of 5,000 observations.
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Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial.
Hudson Golino,Dingjing Shi,Alexander P. Christensen,Luis Eduardo Garrido,Maria Dolores Nieto,Ritu Sadana,Jotheeswaran Amuthavalli Thiyagarajan,Agustín Martínez-Molina +7 more
TL;DR: A straightforward R tutorial is presented on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale, and both EGA techniques are compared with 5 widely used factor analytic techniques.
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A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires
TL;DR: In this paper, the causal implications of latent variable and psychometric network models for the validation of personality trait questionnaires are discussed, and the models imply different data generating models for different data sets.
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Predicting Increased Blood Pressure Using Machine Learning
Hudson Golino,Liliany Souza de Brito Amaral,Stenio Fernando Pimentel Duarte,Cristiano Mauro Assis Gomes,Telma de Jesus Soares,Luciana Araújo dos Reis,Joselito Santos +6 more
TL;DR: The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance, and the former outperformed the latter in terms of predictive power.
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Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis
Hudson Golino,Andreas Demetriou +1 more
TL;DR: This study compared various exploratory and confirmatory factor methods for recovering factors of cognitive test-like data and concludes that a new method, Exploratory Graph Analysis (EGA), can more accurately uncover underlying dimensions or factors.