Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research

By Hudson F. Golino, Sacha Epskamp. PLoS ONE, 2016

868 citations recorded by the source corpus.

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman’s eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA), since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal, .20, .50 and .70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were .7, showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study.

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

View Paper: https://www.semanticscholar.org/paper/f44110bff4345eb228b27de8a0b8aec235edd478

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Citations in the corpus

A Tutorial on Regularized Partial Correlation Networks (2016, background): “As a result, if a latent variable causes covariation between two or more variables in the network, it is expected that all these variables will be connected in the network, forming a cluster (Golino and Epskamp, 2017).”
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, background): “We can readily apply the LASSO regularization meth-ods described earlier for this purpose: the methodology outlined by Abegaz and Wit (2013) to estimate temporal and contemporaneous networks and the methodology outlined by Foygel & Drton (2010) to estimate the between-subjects GGM.”
A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires (2019, methodology): “Therefore, factors of a latent variable model and communities of a network model are statistically equivalent (Golino and Epskamp, 2017).”
Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial (2018, methodology): “A key part of our tutorial will showcase the new EGA algorithm by demonstrating how it can be used to first estimate dimensionality and then verify the unidimensionality of the dimensions in the SDS. Golino and Epskamp (2017) proposed EGA as a new method to estimate the number of latent variables un”