How Big Is Big? Interpreting Effect Sizes in L2 Research

The calculation and use of effect sizessuch as d for mean differences and r for correlationshas 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.