The ongoing COVID-19 pandemic has greatly impacted the ability to conduct research in the Education Sciences. For example, access to students was severely limited by factors such as the move to online learning, building entrance restrictions, and waves of student and teacher absences resulting in noisy, incomplete, or missing data. In this interactive discussion, we highlight the use of various methods to make valid and generalizable inferences from the types of sparse and often non-representative data generated during COVID-19; highlighting key approaches used in their own research on student learning and affect in a Civic Education context.
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