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Abstract
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.
Highlights
Studies in corpus linguistics studies have seen a steady increase in the use of sophisticated statistical methods in recent years
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 an effort to expand our analytic repertoire, this article seeks to introduce structural equation modeling (SEM) and discuss its great potential for corpus linguistic analysis in a nontechnical manner; we focus on measured variable path models, a fundamental building block of SEM
Summary
Studies in corpus linguistics studies have seen a steady increase in the use of sophisticated statistical methods in recent years. Statistics and monofactorial methods to using techniques such as different forms of regression analyses and classification trees (Larsson et al under review) Despite these recent advancements, there are still questions pertaining to the complex nature of language that our current methods cannot address. SEM is a powerful analytical framework that encompasses a large array of statistical techniques (e.g., path analysis, confirmatory factor analysis). These techniques are commonly used in other social and behavioral sciences (including neighboring fields such as Second Language Acquisition) to investigate theories involving causal effects of one or more independent variables on one or more dependent variables, and even among those dependent variables themselves.
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