Automated specification search for composite-based structural equation modeling: A genetic approach
提出一种结合遗传算法与偏最小二乘路径建模的自动设定搜索方法（AGAS-PLS），通过蒙特卡洛模拟检验其在不同样本量和模型复杂度下识别真实结构模型的能力，并用实证数据展示其应用。
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.