Multiple Regression in L2 Research: A Methodological Synthesis and Guide to Interpreting R2 Values

Luke Plonsky, Hessameddin Ghanbar

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

Multiple regression is a family of statistics used to investigate the relationship between a set of predictors and a criterion (dependent) variable. This procedure is applicable in a variety of research contexts and data structures. Consequently, and similar to quantitative traditions in sister-disciplines such as education and psychology (see Skidmore & Thompson, 2010), second language researchers have turned increasingly to multiple regression. The present study employs research synthetic techniques to describe and evaluate the use of this procedure in the field. Five hundred and forty-one regression analyses (K = 171) were coded for different models, variables, procedures, reporting practices, and overall variance explained (R2). Summary results reveal a number of inconsistencies (e.g., model types) as well as a lack of transparency (e.g., missing/unreported reliability estimates; see Larson–Hall & Plonsky, 2015). The distribution of R2 values (median =.32) is described to facilitate utilization and interpretation of regressions models. We also provide specific, empirically grounded recommendations for future research.

Original languageEnglish (US)
Pages (from-to)713-731
Number of pages19
JournalModern Language Journal
Volume102
Issue number4
DOIs
StatePublished - Dec 1 2018

Keywords

  • L2 research
  • multiple regression
  • quantitative research methods
  • research synthesis
  • statistics

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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