The Bayesian Revolution in Second Language Research: An Applied Approach
We would like to sincerely thank Dr. Atsushi Mizumoto for his assistance with the Web application. The “Bayesian Revolution” project has been part of the doctoral dissertation of the first author focused on methodological reform in quantitative second language research at Texas A&M University.
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.
Number of times cited: 1
- LUKE PLONSKY and HESSAMEDDIN GHANBAR, Multiple Regression in L2 Research: A Methodological Synthesis and Guide to Interpreting R2 Values , The Modern Language Journal, 102, 4, (713-731), (2018).
