Abstract The present article aims to introduce structural equation modeling, in particular measured variable path models, and discuss their great potential for corpus linguists. Compared to other techniques commonly employed in the field such as multiple regression, path models are highly flexible and enable testing a priori hypotheses about causal relations between multiple independent and dependent variables. In addition to increased methodological versatility, this technique encourages big-picture, model-based reasoning, thus allowing corpus linguists to move away from the, at times, somewhat overly simplified mindset brought about by the more narrow null-hypothesis significance testing paradigm. The article also includes commentary on corpus linguistics and its trajectory, arguing in favor of increased cumulative knowledge building.

中文翻译：

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结构方程建模对语料库语言学家的好处

摘要 本文旨在介绍结构方程建模，特别是测量变量路径模型，并讨论它们对语料库语言学家的巨大潜力。与该领域常用的其他技术（例如多元回归）相比，路径模型具有高度的灵活性，并且能够测试关于多个自变量和因变量之间因果关系的先验假设。除了增加方法论的多功能性外，这种技术还鼓励基于模型的大图推理，从而使语料库语言学家摆脱由更狭窄的零假设显着性检验范式带来的有时过于简化的思维方式。文章还包括对语料库语言学及其轨迹的评论，