Session: Leveraging Education's Big Data in Prevention Science: Public- and Restricted-Use Data Sets from IES' National Center for Education Statistics (NCES) (Society for Prevention Research 27th Annual Meeting)

4-005 Leveraging Education's Big Data in Prevention Science: Public- and Restricted-Use Data Sets from IES' National Center for Education Statistics (NCES)

Schedule:
Friday, May 31, 2019: 8:30 AM-10:00 AM
Seacliff B (Hyatt Regency San Francisco)
Theme: Big Data Integration
Symposium Organizers:
Katherine Taylor and Emily J. Doolittle
The National Center for Education Statistics (NCES), housed within the U.S. Department of Education’s Institute of Education Sciences (IES), is mandated by Congress to collect and analyze data related to education in the United States. As a result of this work, NCES has public- and restricted-use datasets available to prevention scientists to integrate into their research efforts. This organized paper symposium will provide an overview of the “big data” education datasets available from NCES’ Sample Surveys Division and showcase examples of research using these NCES data funded by IES’ National Center for Education Research (NCER) and National Center for Special Education Research (NCSER).

This symposium illustrates the special conference theme related to integrating complex data sets to inform prevention science by highlighting two recent NCER-funded projects that integrated NCES datasets to understand how features of the school environment (visible security measures, instructional practices reflecting Native language and culture) relate to student behavior and academic outcomes. In the first example, researchers integrated data from multiple waves of cross-sectional data from two large national surveys – the School Crime Supplement (SCS) to the National Crime Victimization Survey and the School Survey on Crime & Safety (SSOCS) to determine whether school security activities are related to school safety and academic achievement. In the second example, researchers used two surveys from the National Indian Education Study (NIES) to explore instructional practices related to Native language and culture (NLC) and whether greater exposure to NLC is associated with better academic performance. These examples show how prevention researchers can use education data sets like those available through NCES to inform future development and testing of education interventions.

The symposium includes three papers. The first paper describes the longitudinal and cross-sectional data resources available from NCES. This paper will focus on those datasets most likely to be of interest to prevention scientists, how researchers may use NCES datasets to conduct several different types of “big data” linkages to facilitate complex analyses, and how researchers can access these NCES datasets. The second and third papers illustrate how such resources can be used to develop and test new theories related to prevention science using innovative statistical methods. Together, these three papers will introduce the SPR membership to the big education datasets available through NCES to address important questions in prevention and education research.


* noted as presenting author
535
Education’s Big Data: Longitudinal and Cross-Sectional Survey Studies at the National Center for Education Statistics
Emily J. Doolittle, PhD, National Center for Education Research, Institute of Education Sciences (IES), U.S. Department of Education; Jill Carlivati McCarroll, PhD, National Center for Education Statistics; Gail Mulligan, PhD, National Center for Education Statistics; Elise Christopher, PhD, National Center for Education Statistics; Carolyn Fidelman, PhD, National Center for Education Statistics; Katherine Taylor, PhD, Institute of Education Sciences/National Center for Special Education Research
537
Using Data from the National Indian Education Study (NIES) to Assess the Relationship between Native Language and Culture in the Classroom and Native American Students’ Educational Outcomes
Heather H. McClure, PhD, University of Oregon; Claudia Vincent, PhD, University of Oregon; Mark Van Ryzin, PhD, University of Oregon; Charles R. Martinez, PhD, University of Oregon