PDG B-5 SMART Grant: Resource Guide

How to Use This Guide

1. The first section focuses on foundational resources that will support your application.
2. The second section of this guide focuses on the proposed investments section of the the application, which awards up to eight points for how well the investments address four cross-system expectations. An investment need not hit all four, but the strongest applications will connect to as many as honestly fit.
3. The final section covers the 15-point Project Performance Evaluation Plan andcontinuous quality improvement (CQI). By understanding these federal resources, you can build stronger, more confident proposals.

Section 1: Foundational Resources

These build or strengthen the data infrastructure, governance, and data-sharing agreements that every cross-system connection relies on. They also directly support the Readiness and Proposed Investment write-ups.

• Child Trends: Five Ways for States to Maximize the PDG B-5 SMART Grant — a short, practical guide (July 2026) written specifically for this grant. It offers five recommendations: set policy priorities before choosing technology, assess existing infrastructure before building new systems, prioritize actionable analytics and data visualization, focus on improving family experiences, and plan for sustainability beyond the one-year grant.
• SLDS Early Childhood Integrated Data Systems (ECIDS) Toolkit — the canonical federal how-to (US Dept. of Education / IES SLDS State Support Team), organized around seven components from planning through implementation and continuous improvement; usable at any stage.
• SLDS “Which ECIDS System Model Is Best for Our State?” — short brief comparing centralized, federated, and hybrid models and their implications — helps a state describe and justify its design choice.
• AISP: Introduction to Data Sharing and Integration and Finding a Way Forward (legal framework) — University of Pennsylvania’s plain-language primer plus the legal-framework guide, which includes MOU and Data Use License templates in its appendices — the go-to for data-sharing agreements.
• Early Childhood Data Collaborative (ECDC), Child Trends — tools and the “10 Fundamentals of Coordinated State ECE Data Systems” framework for building and using coordinated ECE data.
• New America: Count What Matters (2026) — current, readable overview of dashboards and integrated / longitudinal data systems, with state examples.
• HHS/ED: The Integration of Early Childhood Data — joint report profiling eight states’ ECIDS work with practical considerations for linking EC data.

Section 2: Proposed Investments

The four cross-system scoring areas

1. Head Start / Early Head Start integration
2. Program integrity and fraud prevention
3. Unduplicated counts of children served across ECE programs
4. Child-welfare / foster-care coordination

Area 1

Head Start / EHS Integration

What this scores: Deepening integration of Head Start and Early Head Start with child care, state ECE programs, pre-K, home visiting, early intervention, and family support.

• PTAC: Case Study #2 — Head Start Program — explains how a state education agency shares data with a Head Start program under FERPA’s audit-and-evaluation exception — the mechanism most states rely on. From the US Dept. of Education’s Privacy Technical Assistance Center.
• PTAC checklists (Data Sharing Agreement; Reasonable Methods; Data Governance) — practical checklists for building compliant data-sharing agreements and governance.
• ACF Confidentiality Toolkit (ACF Interoperability Initiative) — helps state and local programs navigate the intersecting confidentiality laws around ACF-funded programs so human-services data can be shared.
• Office of Head Start — Program Information Report (PIR) — the source for Head Start / EHS enrollment and program-characteristics data that states integrate.

Area 2

Program Integrity and Fraud Prevention

What this scores: Strengthening the state’s ability to prevent, detect, monitor, and respond to risks to program integrity and support effective administration of ECE funds.

• OCC: CCDF Program Integrity and Accountability — the Office of Child Care hub for integrity resources — and it hosts a Sample Inter-Agency Data Sharing MOU, bridging integrity and data sharing.
• CCDF Fraud Toolkit (NCSIA) — five evaluation tools — an overall fraud-risk assessment plus prevention, detection, enforcement/recovery, and monitoring — with internal-control resources.
• ACF: How Texas Achieves Strong Program Integrity in CCDF — current state example that frames data and analytics as a core integrity practice.
• HHS OIG — CCDF Program Integrity report series — federal oversight findings on state program-integrity efforts; useful context for framing the need.

Area 3

Unduplicated Counts of Children Served

What this scores: Identifying the number of unique children served across ECE programs and understanding how children and families move through the mixed-delivery system.

• ECDC (Child Trends) — unduplicated-count guidance — frames why states without a central database must link across datasets to produce an unduplicated count, and the data-quality issues that raises.
• SLDS webinar — Processes for Handling Multiple IDs to Ensure Data Quality — Kentucky, Washington, and Michigan on resolving multiple identifiers — the person-matching required for unduplication. On the SLDS site.
• Nebraska — “The Importance of Having a Distinct Count” white paper — concrete state example of producing a distinct count across programs, including Head Start / EHS.
• Common Education Data Standards (CEDS) — shared data definitions and dictionaries that make cross-program matching possible.

Area 4

Child-Welfare / Foster-Care Coordination

What this scores: Improving the state’s ability to identify, reach, support, and coordinate services for young children and families at risk of, involved in, or in foster care.

• Chapin Hall — Aligning Systems to Improve ECE Access for Children Involved with Child Welfare — report squarely on this requirement: how to improve early care and education access for child-welfare-involved children. Find it on chapinhall.org by searching the report title.
• Chapin Hall — Center for State Child Welfare Data — the deeper resource on record linkage and unique identifiers across systems, including recent work on assigning and maintaining unique identifiers (FFPSA).
• (See also) PTAC checklists and AISP legal framework — for the DCFS / child-welfare data-sharing agreements this coordination depends on — listed under Area 1 and Foundational Resources.

One addition

Early Intervention and Preschool Special Ed

• DaSy Center — Center for IDEA Early Childhood Data Systems — the federal framework for Part C / 619 (early-intervention and preschool special-education) data — for states integrating EI into the ECE data picture.

Impact — 15 points

Section 3: Project Performance Evaluation Plan and CQI

What this scores: A monitoring approach (inputs, key activities, expected outcomes, and how each is measured); specific performance measures with targets; feasible data-collection and data-quality systems; and how performance information will drive continuous quality improvement. For Option 2 states, these same methods are the learning design the NOFO asks for — rapid-cycle testing, CQI, pre/post.

• OPRE — Learn, Innovate, Improve (LI2) — ACF’s own three-phase framework for guiding program change and building evidence through CQI — the CQI language application reviewers will most readily recognize.
• OPRE — Rapid Learning Methods Portfolio — hub for the Understanding Rapid Learning Methods FAQ, Continuous Quality Improvement Best Practices, and Tips for Using CQI — distinguishes rapid-cycle evaluation, CQI, and Plan-Do-Study-Act cycles and helps a state describe its learning design.
• OPRE — Culture of Continuous Learning / Breakthrough Series Collaborative — a CQI methodology tested specifically in child care and Head Start settings — the closest ECE-native example.
• CDC — Program Evaluation Framework and Logic Model guidance — step-by-step guidance for building the inputs → activities → outputs → outcomes chain and a one-page logic model — directly serves the monitoring-approach sub-criterion.
• W.K. Kellogg Foundation — Logic Model Development Guide — the classic guide (with templates and checklists) for a results chain linking activities to short- and long-term outcomes; pairs well with the CDC framework.

For the data-quality sub-criterion, the SLDS Data Governance materials and AISP resources listed under Foundational Resources apply here too.