Missing data occur in almost any research we conduct, but a more important point is whether and how we should deal with missing data. Most commonly, researchers simply ignore the missingness or use the classic methods of listwise deletion or mean substitution. However, in many cases neither of these produces good results. The two important steps in missing data management are to determine the type and amount of missing data. In particular, the type of the missingness allows us to determine how to handle the missing data. However, there are limited discipline-specific sources on handling missing data in the fields of SLA and AL. Hopefully, I am going to soon provide a picture of the current practices for missing data management in L2 research. Stay tuned for the details of this study!