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

Principal Investigator(s):  View help for Principal Investigator(s) Manuel S. Gonzalez Canche, University of Pennsylvania

Version:  View help for Version V1

Version Title:  View help for Version Title V0

Name File Type Size Last Modified
Full_Replication_Codes_and_Data_Access.zip application/zip 22.6 KB 08/04/2023 08:10:PM
Geography_Math_DisOpportunity.csv text/csv 11.1 MB 07/29/2023 11:54:PM

Project Citation: 

Gonzalez Canche, Manuel S. . Replication Materials for “The Geography of Mathematical (Dis)Advantage: An Application of Multilevel Simultaneous Autoregressive (MSAR) Models to Public Data in Education Research.” Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-08-05. https://doi.org/10.3886/E193127V1

Project Description

Summary:  View help for Summary 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 both multilevel and spatial effects. We contribute to this discussion by simultaneously considering test-takers own socioeconomic standing and the impact of their nesting school 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 to 8 in New York State, revealed the presence of geography of mathematical (dis)advantage. Since 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 is provided https://cutt.ly/N4zRstL.

Scope of Project

Geographic Coverage:  View help for Geographic Coverage New York State
Time Period(s):  View help for Time Period(s) 2017 – 2019 (Two academic years)
Collection Date(s):  View help for Collection Date(s) 2017 – 2019 (Administrative records released by the NYSED)
Universe:  View help for Universe All students 3 to 8 grade enrolled in public institutions in the state of New York
Data Type(s):  View help for Data Type(s) administrative records data; geographic information system (GIS) data; survey data
Collection Notes:  View help for Collection Notes Multiple data sources including ZCTA, County, School, levels and standardized testing in Mathematical performance.

Methodology

Response Rate:  View help for Response Rate Not applicable, universe of students attending public schools in NY state.
Sampling:  View help for Sampling
Not applicable, universe of students attending public schools in NY state.

Data Source:  View help for Data Source New York State Education Department
Collection Mode(s):  View help for Collection Mode(s) other; paper and pencil interview (PAPI)
Scales:  View help for Scales Standardized tests
Weights:  View help for Weights
Not applicable, universe of students attending public schools in NY state.

Unit(s) of Observation:  View help for Unit(s) of Observation Grades 3 to 8 in each school
Geographic Unit:  View help for Geographic Unit ZCTA

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Published Versions

V1 [2023-08-05]

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