Statistical methods for linguistic research: Foundational Ideas - Part II

TL;DR

This paper introduces Bayesian data analysis methods tailored for linguistics and cognitive science, emphasizing their interpretative advantages and flexible modeling, demonstrated through psycholinguistic data examples.

Contribution

It provides an accessible overview of Bayesian methods, including hypothesis testing and model selection, specifically applied to linguistic and psychological research.

Findings

Bayesian methods facilitate easier interpretation of research hypotheses.

Flexible model specification enhances analysis in linguistics and psychology.

Bayes factor and cross-validation are effective tools for hypothesis testing and model selection.

Abstract

We provide an introductory review of Bayesian data analytical methods, with a focus on applications for linguistics, psychology, psycholinguistics, and cognitive science. The empirically oriented researcher will benefit from making Bayesian methods part of their statistical toolkit due to the many advantages of this framework, among them easier interpretation of results relative to research hypotheses, and flexible model specification. We present an informal introduction to the foundational ideas behind Bayesian data analysis, using, as an example, a linear mixed models analysis of data from a typical psycholinguistics experiment. We discuss hypothesis testing using the Bayes factor, and model selection using cross-validation. We close with some examples illustrating the flexibility of model specification in the Bayesian framework. Suggestions for further reading are also provided.