Robert L. Fischer, Ph.D., Claudia J. Coulton , Ph.D., & Seok-Joo Kim, Ph.D.

1. “Improving Data, Improving Outcomes”How Can Partnerships with Higher Education Help Your State Agency Use Early Childhood Data for Decision-Making? Robert L. Fischer, Ph.D., Claudia J. Coulton, Ph.D., & Seok-Joo Kim, Ph.D. Center on Urban Poverty & Community Development Jack, Joseph and Morton Mandel School of Applied Social Sciences Case Western Reserve University Cleveland, Ohio September 16, 2013; Washington, DC

2. Overview • State-wide resource in Ohio (Ohio Educational Research Center) • Local data system in Cuyahoga County (Cleveland) • Leveraging existing data to answer new questions • Recommendations for pursuing this kind of work

3. Overview Educational Data Projects from State to Local. Implementation Level Area Project Researcher Ohio State OERC • Education projects • Collaboration with partners Cuyahoga CHILD system • Database for children • Geographic analyses County Cleveland I. Health care II. Homeless family III. 3rd Grade reading* *OERC project Local Projects (examples)

4. State: The OERC The Ohio Education Research Center (OERC), is a network of Ohio-based researchers and research institutions, that develops and implements a statewide, preschool-through-workforce research agenda to address critical issues of education practice and policy. • Provide timely and high quality evaluation & research products • Maintain a research data base • Bridge needs, research, practice & policy • Bring together resources to improve access to knowledge

5. State: The OERC Current Projects Teachers & Leaders Standards / Assess- ments Ohio Education Research Center Future-Ready Students STEM Education Initiatives Improve- ment & Innovation Improving with Data Investigating the pathway to proficiency from Birth through 3rd grade Cleveland, OH State Success Factors Early Childhood Education Cleveland, Ohio

6. County: CHILD system The Need for Integrated Data. • Data helps inform our understanding of the early childhood system • Individuals and families interact with multiple systems and services, so integrated data offers a more complete view of reality [“Big Data”] • Understanding of how systems work and how to better meet existing needs can be informed by integrated data • Service models emphasize long term and collective impact, so data needed across services and over time

7. County: CHILD system Concept. Birth Cert. Child Medical Data • Teen births • Low weight birth • Infant mortality • Elevated Blood Lead ID1 ID2 Public School Public Assists • ChildHood Integrated • Longitudinal Data • (CHILD) System ID3 ID6 • Common • ID • Attendance • KRA-L • Proficiency test • Graduation test • Disability • Medicaid • Food Stamp • TANF • Child care voucher ID4 ID5 Child Maltreat ment Services • Home visiting • Special needs child care • Early childhood mental health • Universal pre-k • Abuse/neglect reports • Involvement with ongoing services

8. County: CHILD system Structure. Data files-Births, Home Visiting, DCFS, UPK, KRA-L, Medicaid, etc. REPORTS Time Trends e.g. Total Children Served by birth cohort Geocode & Standardize Geographic E.g. % LBW births receiving ongoing home visits by neighborhood Longitudinal Master Files for Each Data Source Profiles E.g. Birth characteristics & service use for children entering kindergarten IDS Register-includes ID#’s, names, addresses, DOB, etc. Updated IDS Register-includes ID#’s, names, addresses, DOB, etc. Match New Records to IDS Register Outcomes E.g. Kindergarten Readiness Scores among children in UPK program

9. Geographic Analyses

10. Cleveland Metropolitan School District Profile

11. Data Influence Examples • More children have access to health care via public insurance, but are they using it? • How are homeless families involved with child welfare services? • What children will be most impacted by the State’s 3rd Grade reading Guarantee?

12. Local Example I: Child Health Summary. • Dramatic increase in health insurance coverage for children ages 0-6 in the county: Hooray! • But only 43% of children get all the recommended well-child visits in the first year of life: Oh no! • Data show that 49% of these families were involved with supportive services close to birth, so we can use that connection to reach families: Hooray! • But wait, due to data lags and coordination issues, outreach would happen too late to have an effect: Oh, no! • A preventive approach could be adopted by having dedicated staff at clinics reach out to families… • Result • Medical Home Pilot launched at two health clinics; 86% of families completed scheduled well-child visits, double the rate for children born on Medicaid in Cuyahoga County; one clinic has integrated the model into care with 9 patient advocates serving the needs of families with infants

13. Local Example II: Homeless Families Summary. • County undertaking social impact bond approach to social services • Fund preventive services that pay for themselves through lower use of later high-cost services • Focus on homeless families who are also involved with child welfare services • High-costs associated with of out-of-home placements and shelter stays • Found that 30% of women in shelter had children involved with welfare agency • 52% of these women had no children with them in shelter • 25% of their children were in a foster care placement • County developing strategies to intervene with mothers before they become homeless and to intervene when mothers enter shelters

14. Example III: 3rd Grade Reading Study Significance. • Importance of early childhood exposures • Early exposure to stressful circumstances, environmental hazards, and less than optimal early learning environments negatively and persistently affect early development. • Usefulness of longitudinal data • State adopted ‘3rd Grade reading Guarantee’ to ensure that students pass reading proficiency test before advancing beyond 3rdgrade • Districts can project how many of their students will be held back when the policy is implemented • What is less understood is • What early childhood factors best predict the students who will be impacted by this policy? • What early childhood interventions appear to lessen the odds a child will not attain third grade reading proficiency?

15. Example III: 3rd Grade Reading Cohort Design. Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 B K 3rd Cohort 1 Collected Cohort 2 B K 3rd Cohort 3 B K 3rd Recently collected Will be collected Cohort 4 B K 3rd

16. Example III: 3rd Grade Reading Conceptual model • Abuse/Neglect • Out-of-home placement • Access to • well-child care • Newborn home visit • Help Me Grow • Mom’s First • Out-of-home • child care Child Welfare Medical Home Visits Child Care K-3 Outcomes Birth K 3rd 1st • Birth weight • Maternal risk • Housing distress • KRA-L • STAR • STAR Early Literacy • NWEA MAP • OAA • Benchmark Assessments Family Economic Nhood / Residence Pre-K • Cash assist/ Poverty • Food insecurity • Public preschool • Universal Pre-K Pilot • Nhood condition • Housing distress • Residential instability

17. Example III: 3rd Grade Reading Current Process • Sample (N=3,679): Children who took KRA-L in 2007 & 2008 and 3rd grade proficiency test in 2010 & 2011 in Cleveland Metropolitan School District, OH. • Sample and variables will be updated.

19. Example III: 3rd Grade Reading Implications. • Collaboration with Cleveland Metropolitan School District • Data Sharing • Uses • Building profiles • Community collaborative planning • Risk factor reduction • Helpful to establish educational planning; especially schools with large numbers of disadvantaged students • Understand challenges for 3rd grade guarantee

20. Discussion Data into Practice Observations… • Data don’t make policy… People with data make policy • Policy shapes research • Everyone wants outcomes… few want to pay for them (or pay very much) • Great divides need to be bridged in terms of institutional practice and philosophy

21. Discussion Ongoing Challenges for Integrated Data. • Data inclusion decisions • Relevance • Continuity • Correct geography • Data usage issues • Data access • Data quality • Data linkage

22. Discussion Recommendations. • Identify what data exist and in what form it exists; consider partnering with universities in this work • Become familiar with relevant federal and state laws and policies regarding data sharing/use • Convene interested parties – data holders and data users – to discuss the opportunities to learn from integrated data • Pilot data matching procedures to demonstrate how specific questions can be answered

23. Discussion Funding Prospects. • Institute of Education Sciences has funding work to integrate data related to young children • US Department of Education Race to the Top funds can be used for longitudinal data systems using integrated data • Various federal funding opportunities exist for studies that could develop and draw on integrated data systems • MacArthur Foundation very interested in use of integrated data

24. State Thank you! Q / A County Local Contact Information: Robert Fischer, Ph.D. (fischer@case.edu) Resources Ohio Education Research Center: http://oerc.osu.edu/ Center on Urban Poverty & Community Development: http://povertycenter.case.edu/ NEO CANDO: http://neocando.case.edu/