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Automated specification search for composite-based structural equation modeling: A genetic approach

Laura Trinchera et al.

Computational Statistics and Data Analysis2026https://doi.org/10.1016/j.csda.2026.108348article
AJG 3ABDC A
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0.50

Abstract

Structural Equation Modeling (SEM) is primarily employed as a confirmatory approach for empirically testing theoretical models by assessing how well they fit collected data. In practice, researchers frequently take a more exploratory approach and manually assess alternative models. Although automated search techniques have been developed for factor-based SEM to identify the best-fitting model, automated specification search remains largely unexplored in composite-based SEM. To address this gap, a new method is introduced: Automated Genetic Algorithm Specification Search for Partial Least Squares Path Modeling (AGAS-PLS). The proposed algorithm combines partial least squares path modeling with a genetic algorithm to identify the ’best’ structural model. A Monte Carlo simulation was conducted to assess the ability of AGAS-PLS to accurately identify the structural model of the data-generating process under various conditions, including different sample sizes and levels of model complexity. The practical applicability of AGAS-PLS was further illustrated using empirical data.

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https://doi.org/https://doi.org/10.1016/j.csda.2026.108348

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@article{laura2026,
  title        = {{Automated specification search for composite-based structural equation modeling: A genetic approach}},
  author       = {Laura Trinchera et al.},
  journal      = {Computational Statistics and Data Analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.csda.2026.108348},
}

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