Identifying Latent Socioeconomic Student Groups in High-Poverty and Racially Segregated Student Populations With Administrative Data

• In Event: Moscone Center West Roundtable Session 8
  In Roundtable Session: Interdisciplinary Perspectives on Inequity in Educational Access and Success

Abstract

While administrative data offer rich information on students’ academic histories and offer opportunities for longitudinal research, socioeconomic status (SES) data is limited to blunt proxies. Through a critical quantitative framework, this paper examines the how researchers can account for hard-to-observe differences when studying racially segregated, high-poverty populations. This study combines student-level administrative data with block group-level neighborhood characteristics to identify latent socioeconomic classes of students in Detroit, and tests relationships between class membership and academic outcomes. The results suggest that using more complex measures of SES may reveal even greater stratification in educational outcomes based on student SES, or even greater inequity produced by different educational policies or practices, than is currently reflected in quantitative educational research.

Author

• Jeremy Lee Singer, Wayne State University