2022 Reports

Mapping 16 Equity Indicators to the New York City Schools Public Datasets: Access, Opportunity, and Outcomes

Bowers, Alex J.; Choi, Yeonsoo; Shi, Huijie; Sun, Feiyi; Sun, Haimei; Williams, Jonathan; Weeks, Sarah

Despite significant progress, educational inequities remain one of the most pressing issues in New York City schools. To address these inequities, comprehensive information across the currently available public datasets is needed to map the available data to possible questions and indicators of success and challenges in addressing inequities across NYC schools. A recent framework by the National Academies of Sciences, Engineering, and Medicine (2019, 2020) (NASEM) proposed 16 equity indicators for school districts including outcomes such as test scores and graduation rates, but just as importantly inputs such as student access to high quality instruction. The purpose of this report is to map the available public NYC school datasets to the NASEM 16 equity indicators to analyze to what extent the indicators may be addressed using available public data from the largest school district in the US. Our analysis focuses at the metadata level across more than 700 datasets to describe data resources at the local, state, and federal levels from years 2008-2021 (two mayoral administrations) including current data availability and online URL links, data formats, data interoperability through consistent use of ID codes, and assessments of variable availability across demographic groups as proposed by the NASEM framework, focusing on issues drawn from the FAIR data framework of Findable, Accessible, Interoperable, and Reusable. In this data mapping report, we describe and visualize our overall metadata findings across 14 of the 16 NASEM equity indicators, demonstrating that we were able to identify a wide range of public datasets available across the indicators at the local NYC school district level. We then provide summaries for all 16 equity indicators, which include brief descriptions of the (meta)datasets and challenges and considerations associated with using those datasets.