The Michigan Longitudinal Data System (MLDS), established in 2010 by the Michigan Advisory Council and the Center for Educational Performance and Information, is a P-20 longitudinal data systemA P-20 longitudinal data system (LDS) “integrates unit-level, high-quality student, staff, and program data that are linked across entities and over time” and spans sectors from multiple early childhood programs to higher education or beyond. Source: Institute of Education Sciences. (n.d.). that includes enrollment data on all statewide early childhood programs and services (e.g., data ranging from Head Start to Child Care Subsidies), early childhood impact on K-3 absenteeism, and continuity of service in special education. This federated system (i.e., a sharing system that does not consolidate all data in one warehouse) links data through sharing agreements across multiple agencies, including the Executive Office of Education, Children’s Trust, the Department of Early Education and Care, and the Department of Elementary and Secondary Education. The system provides demographic, program, and individual level data with unique identifier codes for agencies or approved data requests. A second system, MI School Data, uses MLDS information to provide demographic and program data for public use
MLDS was created under Executive Order No. 2010-15 and is supported by a federal American Recovery and Reinvestment Act (ARRA) grant. MI School Data has also been supported by a 2006 grant in partnership with Minnesota and Wisconsin ($3,000,000) and a Workforce Data Quality Initiative grant.
Talent2025. (2020). Longitudinal Data Systems in Michigan
MI School Data. (n.d.). Early Childhood Landing Page
Connections to Key Early Learning Study at Harvard (ELS@H) Findings:
Learn More about ELS@H Findings
Strong infrastructure and systems – including governance structures and data systems – are key aspects of high-quality early education and care. And research suggests there is a need for more accessible, affordable, and high-quality early education within a mixed-delivery system; strengthening infrastructure and systems is one important way states and cities can take action to address these needs and accomplish these goals.
Findings from the Early Learning Study at Harvard (ELS@H) that connect to the need for more robust infrastructure and systems, including data systems:
- Families rely on a range of formal (e.g., Head Start, center-based care, public pre-K) and more informal (e.g., home-based, relative care) early education settings; when choosing a setting for their child, families balance many logistical constraints and personal preferences.
- But for many families – and especially low- and middle-income families – early education choices remain tightly constrained due to issues of affordability and supply.
- No one early education setting type is inherently of higher quality than another; children develop and learn well in every setting type, and in the study, all setting types showed room to grow in quality.
- We have learned a great deal from this groundbreaking, large-scale study. Nevertheless, there is still much to learn about what children, families, and educators need, and about what “works” – for whom and under what circumstances – across all the diverse settings where young children learn and grow.