Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial

By Hudson F. Golino, Dingjing Shi, Alexander P. Christensen, L. E. Garrido, M. D. Nieto, R. Sadana, J. Thiyagarajan, Agustín Martínez-Molina. Psychological methods, 2018

429 citations recorded by the source corpus.

Exploratory graph analysis (EGA) is a new 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. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are here addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented, one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with five widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale.

Subfields: Experimental and Cognitive Psychology, Social Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/470c4e8aeebd08699fe9092463540a1b24b7e2e8

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Citations in the corpus

A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires (2019, methodology): “…simulation studies have shown that EGA has comparable or better accuracy for identifying the number of dimensions than the most accurate factor analytic techniques (e.g. parallel analysis; Christensen, 2020; Golino and Demetriou, 2017; Golino and Epskamp, 2017; Golino et al., 2020).”
Looking up at the curious personality: individual differences in curiosity and openness to experience (2020, methodology): “The items of these questionnaires formed emergent clusters that were identified using a network dimension-reduction method [24], which in turn comprised the network of Openness to Experience (Figure 1).”