Golino develops new quantitative techniques and metrics, integrated into a general approach – termed Exploratory Graph Analysis (EGA) – which is part of the relatively new area of network psychometrics. This work combines network science, information, and quantum information theory, as well as computational methods to address fundamental problems in psychometrics, with the following goals:
- Improve the estimation of the number of latent factors in an automatic (or semi-automatic) way
- Develop innovative fit indices for structural analysis and dimensionality assessment/reduction
- Improve the estimation and the interpretability of latent factors in intensive longitudinal data
- Develop new techniques for item analysis from a network psychometrics perspective (including, for example, network loadings, item parameters, and new metrics of reliability)
- Improve text mining (via new techniques to detect emotions in text and to estimate latent topics)
- Construct general representations of structure built from intraindividual variability, quantifying the homogeneity of individuals using new metrics of complexity.
Golino collaborates with applied researchers from the U.S. and abroad in conducting research in intelligence, cognition, aging, and other topics. He also has an active line of research in text mining and machine learning.
The EGAnet package for R, developed by Golino, in partnership with Alexander Christensen and other collaborators, is one of the leading packages in the area of network psychometrics, with more than 3,000 downloads per month.
Golino has published in many high-level methodological journals, including Psychological Methods, Psychometrika, Multivariate Behavior Research, Behavioral Research Methods, Journal of Behavioral Data Science, and Assessment, as well as applied journals, including Nature Scientific Reports, Intelligence, Psychological Test Development and Adaptation, Journal of Intelligence, and European Journal of Personality.
You can visit his personal webpage here.
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