The Geography of Mathematical (Dis)Advantage: An Application of Multilevel Simultaneous Autoregressive (MSAR) Models to Public Data in Education Research

Author:

González Canché Manuel S.1ORCID

Affiliation:

1. The University of Pennsylvania

Abstract

Research has shown that mathematical proficiency gaps are related to students’ and schools’ indicators of poverty, with fewer studies on neighborhood effects on achievement gaps. Although this literature has accounted for students’ nesting within schools, so far, methodological constraints have not allowed researchers to formally account for multilevel and spatial effects. I contribute to this discussion by simultaneously considering test-takers’ own socioeconomic standing and the impact of their nesting schools and neighborhood structures. Multilevel simultaneous autoregressive (MSAR) models and population-level data of 2.09 million test-takers, whose standardized performances were measured at Grades 3–8 in New York State, revealed the presence of geography of mathematical (dis)advantage. Because mathematical performance is spatially dependent across schools and neighborhoods, moving forward, applied researchers should rely on MSAR to account for sources of spatially driven bias that cannot be handled with multilevel models alone. Full replication code and data are provided at https://cutt.ly/N4zRstL .

Funder

Spencer Foundation

National Academy of Education

Publisher

SAGE Publications

Subject

Social Sciences (miscellaneous),Developmental and Educational Psychology,Education

Reference67 articles.

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