A network-based framework for estimating the number of dimensions in multivariate psychological data, using community detection on regularized partial correlation networks.

A resampling framework that evaluates the structural stability and generalizability of EGA-estimated dimensions, quantifying confidence in the number and content of communities.

Loading analogs derived directly from network topology, offering insights into how much nodes contribute to the emergence of coherent dimension.

An information-theoretic fit index evaluating how well a proposed dimensional structure accounts for the number and content of dimensions in the observed data, applicable across SEM, factor, and network frameworks.

An extension of EGA to time-series and intensive longitudinal data, recovering idiographic psychological structures that evolve dynamically within individual people over time.

Leveraging large language model embeddings and network psychomerics to automatically generate, evaluate, and validate psychological scale items at scale.