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Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis

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  • Golino, Hudson F.
  • Demetriou, Andreas

Abstract

This study compared various exploratory and confirmatory factor methods for recovering factors of cognitive test-like data. We first note the problems encountered by several widely used methods, such as parallel analysis, minimum average partial procedure, and confirmatory factor analysis, in estimating the number of dimensions underlying performance on test batteries. We then argue that a new method, Exploratory Graph Analysis (EGA), can more accurately uncover underlying dimensions or factors and demonstrate how this method outperforms the other methods. We use several published data sets to demonstrate the advantages of EGA. We conclude that a combination of EGA and confirmatory factor analysis or structural equation modeling may be the ideal in precisely specifying latent factors and their relations.

Suggested Citation

  • Golino, Hudson F. & Demetriou, Andreas, 2017. "Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis," Intelligence, Elsevier, vol. 62(C), pages 54-70.
    • Handle: RePEc:eee:intell:v:62:y:2017:i:c:p:54-70
    DOI: 10.1016/j.intell.2017.02.007
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    References listed on IDEAS

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    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. John Horn, 1965. "A rationale and test for the number of factors in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 30(2), pages 179-185, June.
    3. Louis Guttman, 1954. "Some necessary conditions for common-factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 19(2), pages 149-161, June.
    4. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    5. Wayne Velicer, 1976. "Determining the number of components from the matrix of partial correlations," Psychometrika, Springer;The Psychometric Society, vol. 41(3), pages 321-327, September.
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    Cited by:

    1. Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    2. Yuki Nozaki & Alicia Puente-Martínez & Moïra Mikolajczak, 2019. "Evaluating the higher-order structure of the Profile of Emotional Competence (PEC): Confirmatory factor analysis and Bayesian structural equation modeling," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-17, November.
    3. Boris Forthmann & Mark A. Runco, 2020. "An Empirical Test of the Inter-Relationships between Various Bibliometric Creative Scholarship Indicators," Publications, MDPI, vol. 8(2), pages 1-16, June.
    4. Colom, Roberto & García, Luis F. & Shih, Pei Chun & Abad, Francisco J., 2023. "Generational intelligence tests score changes in Spain: Are we asking the right question?," Intelligence, Elsevier, vol. 99(C).
    5. Wafa Mohammed Aldighrir, 2024. "Development and validation of the educational leadership scale for mental health providers: A network analysis approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    6. Hudson Golino & Alexander P. Christensen & Robert Moulder & Seohyun Kim & Steven M. Boker, 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, Springer;The Psychometric Society, vol. 87(1), pages 156-187, March.

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