This paper underscores the vital role of the chi-square test within political science research utilizing structural equation modeling (SEM). The ongoing debate regarding the inclusion of chi-square test statistics alongside fit indices in result presentations has sparked controversy. Despite the recognized limitations of relying solely on the chi-square test, its judicious application can enhance its effectiveness in evaluating model fit and specification. To exemplify this, we present three common scenarios pertinent to political science research where fit indices may inadequately address goodness-of-fit concerns, while the chi-square statistic can be effectively harnessed. Through Monte Carlo simulations, we examine strategies for enhancing chi-square tests within these scenarios, showcasing the potential of appropriately employed chi-square tests to provide a comprehensive model fit assessment. Our recommendation is to report both the chi-square test and fit indices, with a priority on precise model specification to ensure the trustworthiness of model fit indicators.

翻译：本文强调卡方检验在运用结构方程模型的政治学研究中的关键作用。关于在结果呈现中是否应同时包含卡方检验统计量与拟合指数的持续争论引发了广泛争议。尽管单独依赖卡方检验存在公认的局限性，但合理运用该检验可有效提升其在评估模型拟合与模型设定方面的效能。为阐明此观点，我们展示了政治学研究中三个常见场景：在这些场景中，拟合指数可能无法充分解决模型拟合优度问题，而卡方统计量可被有效利用。通过蒙特卡洛模拟，我们考察了在这些场景下优化卡方检验的策略，揭示了恰当运用卡方检验能够提供全面模型拟合评估的潜力。我们的建议是同时报告卡方检验与拟合指数，并优先确保模型设定的精确性，以保障模型拟合指标的可靠性。