Statistical methods for linguistic research: Foundational Ideas - Part II

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.

30 pages, 5 figures, 4 tables. Submitted to Language and Linguistics Compass. Comments and suggestions for improvement most welcome

References in corpus (10)

• Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
• Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
• Balancing Type I Error and Power in Linear Mixed Models
• A weakly informative default prior distribution for logistic and other regression models
• Parsimonious Mixed Models
• Comparison of Bayesian predictive methods for model selection
• A General Framework for the Parametrization of Hierarchical Models
• Pareto Smoothed Importance Sampling
• Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists
• Simulation-efficient shortest probability intervals

Cited by in corpus (6)

• Balancing Type I Error and Power in Linear Mixed Models
• Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists
• Mixed Effects Models are Sometimes Terrible
• Confronting Quasi-Separation in Logistic Mixed Effects for Linguistic Data: A Bayesian Approach
• What did we learn from forty years of research on semantic interference? A Bayesian metaanalysis
• Bayesian models are better than frequentist models in identifying differences in small datasets comprising phonetic data