Structural Equation Modeling (SEM) or Covariance Structure Analysis (CSA) is a versatile and powerful method in the social and behavioral sciences, providing a framework for modeling complex relationships, testing mediation, accounting for measurement error, and analyzing latent constructs. However, SEM remains underutilized in in political science; its application is often marred by misunderstandings, misinterpretations, and methodological pitfalls that can compromise the validity and interpretability of findings. This article examines key challenges in SEM applications within political science, including test statistics and fit indices, model specification, estimator selection, and causal inference. It offers practical recommendations for enhancing methodological rigor and introduces recent advancements in causal inference.

翻译：结构方程模型（SEM）或协方差结构分析（CSA）是社会科学与行为科学中一种多功能且强大的方法，它为建模复杂关系、检验中介效应、处理测量误差以及分析潜变量提供了框架。然而，SEM在政治学领域仍未得到充分利用；其应用常常因误解、误读和方法论陷阱而受损，这些都可能损害研究结果的有效性和可解释性。本文探讨了政治学中SEM应用面临的主要挑战，包括检验统计量与拟合指数、模型设定、估计量选择以及因果推断。文章为提高方法论严谨性提供了实用建议，并介绍了因果推断领域的最新进展。