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 (R[superscript 2]). 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 R[superscript 2] values (median = 0.32) is described to facilitate utilization and interpretation of regressions models. We also provide specific, empirically grounded recommendations for future research.