The Bayesian Revolution in Second Language Research: An Applied Approach

Reza Norouzian, Michael de Miranda, Luke D Plonsky

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Frequentist methods have long dominated data analysis in quantitative second language (L2) research. Recently, however, several empirical fields have begun to embrace alternatives known as Bayesian methods. Using an open-source approach, we provide an applied, nontechnical rationale for Bayesian methods in L2 research. First, we compare the conceptual underpinning of Bayesian and frequentist methods. Second, using real as well as carefully simulated examples, we introduce and apply Bayesian methods to various research designs. Third, to promote the use of Bayesian methods in L2 research, we introduce a free Web-accessed point-and-click software package (https://rnorouzian.shinyapps.io/bayesian-t-tests) as well as a suite of flexible R functions developed by the first author. Additionally, we demonstrate Bayesian methods for conducting secondary analysis on previously published literature. Finally, we discuss practical and theoretical dimensions of a Bayesian revolution in L2 research. Open Practices: This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework at https://osf.io/jxd47, the IRIS Repository at https://www.iris-database.org, and GitHub at https://github.com/rnorouzian/i/blob/master/i.r. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

Original languageEnglish (US)
Pages (from-to)1032-1075
Number of pages44
JournalLanguage Learning
Volume68
Issue number4
DOIs
StatePublished - Dec 1 2018

    Fingerprint

Keywords

  • Bayesian methods
  • effect size
  • frequentist methods
  • research methods
  • second language research

ASJC Scopus subject areas

  • Education
  • Language and Linguistics
  • Linguistics and Language

Cite this