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Hmong but not Asian, Sāmoan but not Pacific Islander: Tracing the ECLS-K Racial Data (Mis)Classification Journey

Wendy Castillo,
Daranee Taychachaiwongse Teng,
Kristine Jan Cruz Espinoza

Race is a socially and politically charged concept that remains contested in the United States. We examine racial data (mis)classification in the Early Childhood Longitudinal Studies (ECLS-K) dataset. Centering the racial data journey of Asian American, Native Hawaiian, and Pacific Islander (AA&NHPI) students, we find two types of racial data (mis)classification: (1) racial reformation related to the reconfiguration of parent/caregiver-reported racial data and (2) categorical friction when ethnicity was parent/caregiver-reported and race was not. Educational data practices and datasets like ECLS-K play a role in obscuring differentiated educational outcomes by operationalizing the myth that AA&NHPIs are a monolith. We offer recommendations for addressing racial data (mis)classification and engaging a critical race research praxis.

Keywords
Quant(Multi)Crit; race; racial categories; ECLS-K; racial reformation
Education level
K-12 Education
Topics
Methods
Tags
Early childhood education,
Equity,
Race, ethnicity, and education
Download 05/2026
Document Object Identifier (DOI)
10.26300/wk9y-sm74
EdWorkingPaper suggested citation:
Castillo, Wendy, Daranee Taychachaiwongse Teng, and Kristine Jan Cruz Espinoza. (). Hmong but not Asian, Sāmoan but not Pacific Islander: Tracing the ECLS-K Racial Data (Mis)Classification Journey. (EdWorkingPaper: -1486). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/wk9y-sm74

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