Abstract
Although originally built to support metrics for educational accountability under the No Child Left Behind Act of 2001 supporting elementary and secondary schools, state longitudinal data systems (SLDS) developed to incorporate state administrative records from pre-school all the way into the workforce (early learning, Kindergarten-12, higher education and workforce sectors). As it stands now, all 50 states have received funding from the federal government to build a K-12 SLDS and 43 states have received funding to build a P-20/Workforce SLDS (P20W). Washington has a comprehensive P20W data system because of its breadth of data sources and depth of data shared by state agencies. In addition, Washington has an office dedicated to P20W work. This is unique as most states’ P20W office is within the K-12 system where resources are split between K-12 reporting and P-20 work. Washington state and the Education Research and Data Center (ERDC) has been at the forefront of developing and utilizing their SLDS to follow cohorts both over time and across sectors. Examples for Washington State provided herein demonstrate the usefulness of the cohort approach as well as provide direction for future developments both in education-related research and applied demography.
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References
Alliance for Excellent Education, Civic Enterprises, & Data Quality Campaign. (2011). Education as a data-driven enterprise: A primer for leaders in business, philanthropy, and education. Retrieved December 10, 2015, from http://all4ed.org/reports-factsheets/education-as-a-data-driven-enterprise-a-primer-for-leaders-in-business-philanthropy-and-education/
America COMPETES Act of 2007. (2007). Retrieved December 9, 2015, from http://www.govtrack.us/congress/bills/110/hr2272/text
Culhane, D. P., Fantuzzo, J., Rouse, H. L., Tam, V., & Lukens, J. (2010). Connecting the dots: The promise of integrated data systems for policy analysis and systems reform. Intelligence for Social Policy, 1(3).
Cunningham, A. F., & Milam, J. (2005). Feasibility of a student unit record system within the Integrated Postsecondary Education Data System (NCES 2005–160). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office.
Data Quality Campaign. (2007). The right data to the right people at the right time: How interoperability helps America’s students succeed. Retrieved August 21, 2013, from, http://www.dataqualitycampaign.org/files/events/resources/Meetings-DQC_Quarterly_Issue_Brief_061307.pdf
Data Quality Campaign. (2011a). From compliance to service: Evolving the state role to support district data efforts to improve student achievement. Retrieved August 21, 2013, from, http://dataqualitycampaign.org/files/1455_From%20Compliance%20to%20Service.pdf
Data Quality Campaign. (2011b). Leveraging the power of state longitudinal data systems: Building capacity to turn data into useful information. Retrieved August 21, 2013, from, http://www.dataqualitycampaign.org/files/1303_DQCResearch% 20capacity%20May17.pdf
Data Quality Campaign. (2012). Investing in educator data literacy improves student achievement. Evidence of impact: The Oregon Data Project. Retrieved August 21, 2013, from, http://www.dataqualitycampaign.org/find-resources/investing-in-educator-dataliteracy-improves-student-achievement/
Education Research and Data Center. (2009). Washington ARRA SLDS grant application. Retrieved September 8, 2013, from http://www.erdc.wa.gov/arraslds2009/application/
Education Research and Data Center. (2012). P-20 reports on Washington Public High School graduates. Retrieved February 9, 2016, from, http://www.erdcdata.wa.gov/
Education Research and Data Center. (2014). Issue Trak Number 1025: Post-secondary attainment by students with disabilities, Washington State Student Achievement Council. Retrieved February 22, 2016.
Ewell, P. T. (2009). “Shovel-ready” data: The stimulus package and state longitudinal data systems. Assessment Update, 21(5), 11–12.
Gamson, D. (2007). Historical perspectives on democratic decision making in education: Paradigms, paradoxes, and promises. Yearbook of the National Society for the Study of Education, 106(1), 15–45.
Higher Education Opportunity Act of 2008. (2007). Retrieved December 9, 2015, from, http://www.govtrack.us/congress/bills/110/hr41
Honig, M. I., & Coburn, C. (2008). Evidence-based decision making in school district central offices: Toward a policy and research agenda. Educational Policy, 22, 578–608.
Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. Yearbook of the National Society for the Study of Education, 106(1), 105–131.
Improving America’s Schools Act. (1994). Retrieved December 9, 2015, from, http://www2.ed.gov/legislation/ESEA/index.html
Knapp, M. S., Copland, M. A., & Swinnerton, J. A. (2007). Understanding the promise and dynamics of data-informed leadership. Yearbook of the National Society for the Study of Education, 106(1), 74–104.
Land, K., & Hough, G. (1989). On the combination of prevalence rate and increment-decrement methods for tables of school life, with applications to the 1969–70, 974–75, and 1979–80 school years. Journal of the American Statistical Association, 84(March), 63–75.
Land, K., & McMillen, M. (1981). Demographic accounts and the study of social change, with applications to the post-world war II United States. In Social accounting systems: Essays on the state of the art (pp. 241–306). New York: Academic.
Lycan, D., Hough, G., Rynerson, C., & Proehl, R. (2007). A proposal for a metropolitan wide school enrollment forecasting system for the Portland, Oregon Metropolitan area. Portland: The Population Research Center, Portland State University.
Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71–85.
No Child Left Behind Act of 2001. (2001). Retrieved August 15, 2013, from, http://www2.ed.gov/policy/elsec/leg/esea02/index.html
Palaich, R., Good, D.G., & van der Ploeg, A. (2004). State education data systems that increase learning and improve accountability. Policy Issues, 16. Retrieved February 23, 2016, from, http://files.eric.ed.gov/fulltext/ED489522.pdf
Petrides, L. (2003). Turning data into decisions. Institute for the Study of Knowledge Management in Education, November, 2003, pp. 25–28. Retrieved February 23, 2016, from, http://www.issuelab.org/resource/turning_data_into_decisions
Phillips, D. C. (2007). Adding complexity: Philosophical perspectives on the relationship between evidence and policy. Yearbook of the National Society for the Study of Education, 106(1), 376–402.
Popoff, C., & Judson, D. (2004). Chapter 22: Some methods of estimation for statistically underdeveloped areas. In J. Siegel & D. Swanson (Eds.), The methods and material of demography (2nd ed.). San Diego: Elsevier Academic Press.
Poulain, M., & Herm, A., (2013). Le registre de population centralisé, source de statistiques démographiques en Europe , Population 2/2013 (Vol. 68), pp. 215–247, Translated to English by R. Depledge as Central population registers as a source of demographic statistics in Europe. www.cairn.info/revue-population-2013-2-page-215.htm
Revised Code of Washington 43.41.400. Office of Financial Management Education Data Center. Retrieved December 10, 2015, from, http://apps.leg.wa.gov/rcw/default.aspx?cite=43.41.400
Ryder, N. (1965). The cohort as a concept in the study of social change. American Sociological Review, 30, 843–861.
Sabel, J. (2013). Identity matching at ERDC. Presentation to the Research Coordination Committee September 6, 2013. Office of Financial Management, Education Research and Data Center. Retrieved December 29, 2015, from, http://qa-erdc.des.wa.gov/sites/default/files/publications/20130906_identity_matching.pdf
Schoen, R. (1988). Modeling multigroup populations. New York: Plenum Press.
Siegel, J., & Swanson, D. (2004). The methods and material of demography (2nd ed.). London: Elsevier Academic Press.
Snow-Renner, R., & Torrence, M. (2002). No child left behind policy brief: State information systems. Retrieved December 9, 2015, from, https://eric.ed.gov/?q=%22no+child+left+behind%22&ff1=subEducational+Policy&ff2=pubReports+−+Descriptive&ff3=autSnow-Renner%2c+Ravay&id=ED468187
Spillane, J. P., & Miele, D. B. (2007). Evidence in practice: A framing of the terrain. Yearbook of the National Society for the Study of Education, 106(1), 46–73.
Steffey, D., & Bradburn, M., (Eds.). (1994). Counting people in the information age. Panel to Evaluate Alternative Census Methods; Commission on Behavioral and Social Sciences and Education; Division of Behavioral and Social Sciences and Education; National Research Council.
Stockwell, E., & Nam, C. (1963). Illustrative tables of school life. Journal of the American Statistical Association, 58(304): 1113–1124.
U.S. Department of Education. (2005). Statewide longitudinal data system grants request for applications. Retrieved December 10, 2015, from, http://nces.ed.gov/Programs/SLDS/pdf/RFA_2005.pdf
U.S. Department of Education. (2008). Statewide longitudinal data system grants request for applications. Retrieved December 10, 2015, from, http://nces.ed.gov/Programs/SLDS/pdf/2009_84372A.pdf
U.S. Department of Education. (2011). Family educational rights and privacy final regulations. Retrieved December 10, 2015, from, http://www.gpo.gov/fdsys/pkg/FR-2011-12-02/pdf/2011-30683.pdf
U.S. Department of Labor. (2010). Workforce data quality initiative solicitation for grant applications. Retrieved August 12, 2013, from, http://www.doleta.gov/performance/workforcedatagrant09.cfm
Washington State House of Representatives. (1993). House bill report, 1209. Olympia: Washington State Legislature.
Wayman, J. C. (2007). Student data systems for school improvement: The state of the field. In Educational technology research symposium: Vol. 1 (p. 156–162). Lancaster, PA: ProActive.
Acknowledgements
The authors are grateful to Marc Baldwin and Jim Schmidt for the encouragement and support for all projects ERDC. We are also grateful to our ERDC colleagues for comments and suggestions, including Tim Norris, Carol Jenner, Karen Pyle, John Sabel, Vivien Chen, Thomas Aldrich, Gary Benson, Liz Coker, Lynn Cole, Teresa Greene, Kent Meneghin, Toby Paterson, Katie Weaver-Randall and Greg Weeks.
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Hough, G.C., Beard, M.M. (2017). State Longitudinal Data Systems: Applications to Applied Demography. In: Swanson, D. (eds) The Frontiers of Applied Demography. Applied Demography Series, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-43329-5_11
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