Challenging Data Aggregation Practices: A MAIHDA Study of Asian Student Outcomes in Introductory Physics

Abstract

Aggregation of Asian student data can reinforce the model minority myth by obscuring educational disparities among Asian student subgroups. This study investigated variation in conceptual physics knowledge across Asian racial and ethnic subgroups using data from the LASSO platform, analyzing responses from 16,810 students enrolled in 493 introductory calculus-based physics courses across 64 U.S. institutions. We applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy to examine predicted pre- and posttest performance on the Force Concept Inventory and Force and Motion Conceptual Evaluation. The findings revealed performance differences among 19 Asian subgroups that the pan-Asian strata (the single aggregated Asian group) concealed. Subgroup predicted means spanned 15.8 percentage points on the pretest and 15.4 percentage points on the posttest. The lowest-performing subgroup's posttest mean was roughly equal to the highest-performing subgroup's pretest mean, indicating a performance gap of about a full semester of instruction. Mean absolute error between the pan-Asian strata and the 19-subgroup estimates was 3.9 percentage points at pretest and 4.0 percentage points at posttest, equivalent to approximately 4-5 weeks of learning in a 16-week course. These findings demonstrate that fine-grained identity data collection can support identifying disparities that common aggregation practices conceal.