Structural equation modeling (SEM) and meta-analysis (MA) are both powerful techniques employed frequently throughout the social and behavioral sciences, including applied linguistics. Although meta-analytic data are typically analyzed by calculating weighted means or correlation coefficients, other statistical models such as SEM can also be applied (Schoemann, 2016). SEM models gauge conceptualized models vis-à-vis empirical data across a given domain. Despite a considerable expansion of the analytical repertoire in applied linguistics in recent years (Gass, Loewen, & Plonsky, 2021), this particular technique has yet to be formally introduced or applied. The present methods tutorial, therefore, aims to introduce MASEM to applied linguistics. In doing so, we provide a conceptual rationale for MASEM, an outline of major stages involved, and a worked example of how MASEM might be utilized in the field, along with the data and code necessary for re-running all analyses.
- L2 research
- Quantitative research methods
- Structural equation modeling (SEM)
ASJC Scopus subject areas
- Social Sciences (miscellaneous)
- Linguistics and Language