Measurement invariance (MI) ensures that a given measure holds the same conceptual meaning for individuals from different groups and across multiple measurement occasions. Structural invariance (SI) is a logical extension of MI that examines whether relationships between latent constructs (e.g., structural paths within the model) hold equally across groups. To examine the status quo of MI and SI in second-language (L2) research, we systematically investigated the extent to which primary studies adhered to best practices in invariance testing and reporting. A total of 4,272 full-text records were screened, and 113 articles (116 independent samples; 147,856 participants) were included. The sample was fully double-coded to ensure accuracy and reliability. The results indicated alarming inconsistencies in how key invariance steps were implemented and reported. We offer empirically grounded recommendations for (a) improving methodological rigor of invariance assessments in the field and (b) contributing to more equitable and interpretable comparisons in multilingual settings.
