On the benefits of structural equation modeling for corpus linguists

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.