Metric invariance in exploratory graph analysis via permutation testing

Establishing measurement invariance (MI) is crucial for the validity and comparability of psychological measurements across different groups. If MI is violated, mean differences among groups could be due to the measurement rather than differences in the latent variable. Recent research has highlighted the prevalence of inaccurate MI models in studies, often influenced by the software used. Additionally, unequal group sample sizes, noninvariant referent indicators, and reliance on data-driven methods reduce the power of traditional SEM methods. Network psychometrics lacks methods comparing network structures conceptually similar to MI. We propose a more conceptually consistent method within the Exploratory Graph Analysis (EGA) framework using network loadings, analogous to factor loadings. Our simulation study demonstrates that this method offers comparable or improved power, especially in scenarios with smaller or unequal sample sizes and lower noninvariance effect sizes, compared to SEM MI testing. peerReviewed publishedVersion