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How Big Is “Big”? Interpreting Effect Sizes in L2 Research

2014/10/15 by Luke Plonsky, Frederick L. Oswald · 1,904 citations
Decision Sciences · Mathematics · Psychology · #Cognitive psychology #Field (mathematics) #Linguistics #Mathematics #Psychology #Psychometric Methodologies and Testing #Statistics

paper · doi:10.1111/lang.12079

published in Language Learning 64(4), 878-912 (Wiley)

openalex publication_date 2014/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The calculation and use of effect sizes—such as d for mean differences and r for correlations—has increased dramatically in second language (L2) research in the last decade. Interpretations of these effects, however, have been rare and, when present, have largely defaulted to Cohen's levels of small ( d = .2, r = .1), medium (.5, .3), and large (.8, .5), which were never intended as prescriptions but rather as a general guide. As Cohen himself and many others have argued, effect sizes are best understood when interpreted within a particular discipline or domain. This article seeks to promote more informed and field‐specific interpretations of d and r by presenting a description of L2 effects from 346 primary studies and 91 meta‐analyses ( N > 604,000). Results reveal that Cohen's benchmarks generally underestimate the effects obtained in L2 research. Based on our analysis, we propose a field‐specific scale for interpreting effect sizes, and we outline eight key considerations for gauging relative magnitude and practical significance in primary and secondary studies, such as theoretical maturity in the domain, the degree of experimental manipulation, and the presence of publication bias.

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