Exploratory Graph Analysis in context

Abstract

This paper presents the network psychometric framework for dimensionality and item analysis termed exploratory graph analysis (EGA). It starts by briefly contextualizing the field of network psychometrics and the early work from the 1950s and 1960s. Then, it provides a brief overview of EGA and other recent developments, such as the network loadings (a metric akin to factor loadings), total entropy fit index (verification of dimensionality fit), dynamic EGA, bootstrap EGA for dimensionality and item stability, random intercepts EGA (handling wording effects), and hierarchical EGA to estimate high-order structures (e.g., generalized bifactor models). The goal of the paper is to present the reader with a list of contextualized references.

The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment

“…The EBICglasso approach operates by minimizing a penalized log-likelihood function and selecting the best model fit (i.e., the optimum level of sparsity in a network) using the extended Bayesian information criterion (EBIC; Chen & Chen, 2008 ). As Golino et al ( 2022 ) argue, the use of weighted network models in psychology opened the doors for network science methods developed in other areas of science to psychological problems such as dimensionality (e.g., factor analysis).…”

“…Golino et al ( 2022 ) summarized the advantages of the EGA framework over more traditional methods (Golino, Shi, et al, 2020b ): (1) unlike exploratory factor analysis (EFA) methods, EGA does not require a rotation method to interpret the estimated first-order factors (although rotations are rarely discussed in the validation literature, they have significant consequences for validation, e.g., estimation of factor loadings; Sass & Schmitt, 2010 ); (2) EGA automatically places items into factors without the researcher’s direction, which contrasts with exploratory factor analysis, where researchers must decipher a factor loading matrix (such a placement opens the door for dimension and item stability methods, which is presented next); and (3) the network representation depicts how items relate within and between dimensions.…”

“…Item stability indicates how often an item replicates in their designated EGA dimension, with values lower than .75 (i.e., that are estimated in their original dimensions in 75% of the bootstrapped samples) indicating problematic (or unstable) items. Structural consistency, by its turn, indicates how often an EGA dimension exactly replicates and can be used to verify configural (or structural) invariance and determine poor-functioning items (Golino et al, 2022 ). A complementary approach, called unique variable analysis , was developed to identify redundant items and can be used to identify the reason why some items function poorly (Christensen, Garrido, & Golino, 2020a ).…”

The role of the body in the experience of installation art: a case study of visitors' bodily, emotional, and transformative experiences in Tomás Saraceno's “in orbit”

“…We used Exploratory Graph Analysis (EGA; Golino and Demetriou, 2017 ; Golino and Epskamp, 2017 ) to determine the number of dimensions in the data (i.e., identification of communities of related items) (Golino and Epskamp, 2017 ), using the used R packages EGAnet (version 1.1.0; Golino et al, 2022 ) and qgraph (Epskamp et al, 2012 ). Like principal component or factor analysis, this approach offers data reduction abilities while, in addition providing information about centrality, interconnection, and relative importance of items, allowing for the visualization of the entire network.…”

Measurement matters: A commentary on the state of the science on patient reported outcome measures (PROMs) in autism research

“…There is a great need to perform more psychometric analyses on PROM data from multiple timepoints (e.g., test–retest reliability, sensitivity to change, and temporal measurement invariance), particularly in the context of secondary data analyses of large‐scale longitudinal cohorts. Given the evolving landscape of psychometric methods (Jebb et al, 2021; Stover et al, 2019), researchers should also leverage recent statistical advances (e.g., exploratory graph analysis; Golino et al, 2022) to test psychometric properties. Ideally, this psychometric work should be conducted by research groups who did not create the measure(s) being evaluated to limit vested interests (e.g., financial, academic/reputational) in the PROM's validity or utility.…”