The document discusses how partnerships between state agencies and higher education can use early childhood data for decision-making. It provides examples from Ohio of integrating data from the state to county and local levels, including the Ohio Education Research Center, the Cuyahoga County CHILD integrated data system, and projects analyzing third grade reading outcomes, homeless families, and child healthcare utilization. The examples show how integrated longitudinal data can inform policy and services to improve early childhood outcomes.

1. 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
“Improving Data, Improving Outcomes”
How Can Partnerships with Higher Education Help Your
State Agency Use Early Childhood Data
for Decision-Making?

2. Overview
2
• 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.
State
County
Local
OERC
CHILD
system
Projects
(examples)
Ohio
Cuyahoga
Cleveland
Area Project
• Education projects
• Collaboration with
partners
ImplementationLevel
• Database for children
• Geographic analyses
I. Health care
II. Homeless family
III. 3rd Grade reading*
*OERC project
Researcher
3

4. State: The OERC
4
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. Cleveland, OH
Ohio Education
Research Center
State: The OERC
5
Current Projects
Investigating the pathway
to proficiency from Birth
through 3rd grade
Standards /
Assess-
ments
State
Success
Factors
Teachers
&
Leaders
STEM
Education
Initiatives
Future-
Ready
Students
Early
Childhood
Education
Improve-
ment &
Innovation
Improving
with
Data
Cleveland, Ohio

6. County: CHILD system
• 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
The Need for Integrated Data.
6

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

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

9. Geographic Analyses
9
Indicators
County District 2
(2008)
County District 8
(2008)
Cuyahoga County
(2008)
Births 1,443 1,877 16,246
# Teen Births, mother’s age 10-14 (per 1,000) 2 (1) 12 (2) 42 (1)
# Teen Births, mother’s age 15-19 (per 1,000) 124 (39) 358 (79) 2,031 (41)
% Mothers without High School diploma 14% 32% 19%
% Low Birth Weight 9% 14% 10%
% Premature Low Weight Births 6% 9% 7%
% Mothers w/adequate prenatal care 52% 42% 53%
% Mothers w/out prenatal care 1% 2% 1%
% Healthy Births 53% 36% 49%
# Infant Death (per 1,000 births) 10 (7) 29 (15) 164 (10)

10. Cleveland Metropolitan School District Profile
10
Indicators
Kindergarten
2008-9
Cleveland
Cuyahoga
County
% Teen Births, mother’s age 10-14 <1 <1 <1
% Teen Births, mother’s age 15-19 22.4 16.7 9.8
% Mothers without High School diploma 41.7 30.2 15.9
% Low Birth Weight 12.6 11.6 9.4
% Premature Low Weight Births 8.7 8.2 6.7
% Mothers w/adequate prenatal care (Kessner Index) 63.1 69.4 81.3
% Mothers w/out prenatal care 1.9 1.9 .9
% Health Births 56.4 61.5 70.9
% Children with a substantiated or indicated report of abuse/neglect by age 4 12.1 9.6 5.1
% Children referred to ongoing services with Child & Family Services by age 4 19.8 14.7 7.6
% Children with any report of abuse/neglect by age 4, including substantiated
and unsubstantiated
35.2 26.7 14.7
% Children in households receiving Food Stamps in 2008 76.9 51.1 28.8
% Children in households receiving Cash Assistance in 2008 19.0 11.3 6.1

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

12. Local Example I: Child Health
• 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
o 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
Summary.
12

13. Local Example II: Homeless Families
• County undertaking social impact bond approach to social services
o 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
o 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
o 52% of these women had no children with them in shelter
o 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
Summary.
13

14. Example III: 3rd Grade Reading
14
Study Significance.
• Importance of early childhood exposures
o 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 3rd grade
• Districts can project how many of their students will be held back when the
policy is implemented
• What is less understood is
o What early childhood factors best predict the students who will be impacted by
this policy?
o What early childhood interventions appear to lessen the odds a child will not
attain third grade reading proficiency?

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

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

17. Example III: 3rd Grade Reading
• 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.
17
Current Process
Educational Information % Demographic / Welfare / Neighborhood %
Pass of 3rd grade readting test 55.7 Girl 49.7
KRA-L band 1 (Score 1-13) 38.1 Hispanic 10.6
KRA-L band 2 (Score 14-23) 44.6 African-American 69.3
KRA-L band 3 (Score 24-29) 17.3 Other race 4.3
White 15.8
Below 11% of attendance at Kindergarten 29.7
TANF + (Medicaid or SNAP) at Kindergarten 17.3
Reported disability before 3rd grade 14.5 Medicaid or SNAP at Kindergarten 67.4
No assistance at Kindergarten 15.3
Living a census tract with poverty rate above 30%
at Kindergarten
49.4
(Substantiated or indicated) maltreatment
before Kindergarten
17.5

19. Example III: 3rd Grade Reading
19
Implications.
• Collaboration with Cleveland Metropolitan School District
o Data Sharing
o 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
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
Data into Practice
20

21. Discussion
• Data inclusion decisions
o Relevance
o Continuity
o Correct geography
21
Ongoing Challenges for Integrated Data.
• Data usage issues
oData access
oData quality
oData 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
22

23. Discussion
• 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
Funding Prospects.
23

24. Thank you!
Q / A
24
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/
State
Local
County