Working with Administrative Data

The term administrative data refers to information about individual children, families, and/or providers of early care and education and other family benefits that is collected and maintained as part of the operation of government programs. The resources on this page were selected from a collection of resources about acquiring, using, managing, linking, and analyzing administrative data in early childhood or related fields. The current resources were selected because they:

  1. highlighted advantages and disadvantages to using administrative data;
  2. detailed steps or procedures about how to use administrative data;
  3. provided state examples about administrative data use; or
  4. were tools or toolkits that housed information about administrative data.

Linking and Integrating Administrative Data

Resources related to integrating data systems or combining two or more administrative datasets either within the same organization or between different organizations.

Resource
Resource
Type
Type
State Example?
State Example?
Description (author abstract if available)
Description (author abstract if available)
10 fundamentals of coordinated state early care and education data systems: Inaugural state analysis. Early Childhood Data Collaborative. (2011). Washington, DC: Early Childhood Data Collaborative. Fact sheets and briefs No
This brief presents highlights of an analysis of a survey of 48 states and the District of Columbia on state implementation of ten essential components of coordinated early care and education (ECE) data systems. Suggestions are offered in response to the finding that current systems, though they collect large amounts of data on children, providers, and program sites, are often inadequate for the needs of state ECE policymakers in assessing data and formulating policy.
2013 State of States' Early Childhood Data Systems (Publication No. 2014-06). Early Childhood Data Collaborative. (2014). Bethesda, MD: Early Childhood Data Collaborative. Reports and papers No
In July 2013, the ECDC surveyed 50 states and the District of Columbia to assess state early childhood data systems. The survey, completed by state education, health, and social services staff, focused on these three key aspects of state data systems, taken from ECDC's 10 Fundamentals: Do states have the ability to securely link child-level data across ECE programs and to other state data systems, including K-12, health, and social services? Do states collect developmental screening, assessment, and kindergarten entry data to examine children's developmental status and service needs? Do states have an ECE data governance structure designated to support the development and use of a coordinated longitudinal ECE data system? (author abstract)
Answering key questions with an early childhood data system. Cochenour, M., & Porowski, S. (2013). Washington, DC: National Center for Education Statistics. Fact sheets and briefs No This brief discussed how states are going about building early childhood data systems that are linked to other data systems.
AVANCE-Houston's partnership with the Houston Independent School District (Case Study #5, Publication # 2016-26). Abrams, J., Maxwell, K. L., & Epstein, D. (2016). Bethesda, MD: Child Trends. Report Yes This case study highlights AVANCE Houston, a Head Start program, that partnered with the Houston Independent School District to understand children's literacy and math skills in early elementary school and how they compared with other children from low-income families.
Building and using coordinated state early care and education data systems: A framework for state policymakers. Early Childhood Data Collaborative. (2010). Washington, DC: Early Childhood Data Collaborative. Tools No
In consultation with an early childhood data advisory group, and with feedback from early childhood stakeholder groups, the ECDC has developed a framework that: articulates principles for developing state ECE data systems that enable continuous improvement and answer states' critical policy questions; identifies the 10 ECE Fundamentals that provide the foundation for coordinated ECE data systems; and provides guidance to state policymakers to ensure appropriate data access and use while protecting privacy and keeping data secure. (author abstract)
Building data sharing infrastructures at the state level: Context, stakeholders, technology. Schroeder. A. D. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Presentation Yes
This presentation uses The Virginia Longitudinal Data System (VLDS) as an example to explain some best practices to implement a linked data system in a state.
CEDS in the Field: Alaska. CEDS in the Field: Alaska. Common Education Data Standards. (n.d.). Washington, DC: Common Education Data Standards. Fact sheets and briefs Yes
The "CEDS in the Field" series of briefs addresses various ways that different data stakeholders are using CEDS and its associated tools. CEDS is built in a way to support a variety of implementations that are all different; there is no one way to "use" CEDS. This brief focuses on how Alaska, while building its P-20W data system, used CEDS as a tool in the development process.
Challenges in linking state and federal data. Goerge, R. M. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Presentation No
This presentation reviews the benefits, challenges, and strategies when linking state and federal administrative data.
Connecting the dots: Using linked administrative data to improve public child welfare programs. Person, A., Stagner, M., Cancian, M., Lindert, B., & Noyes, J. (2015). Princeton, NJ: Mathematica Policy Research. Webinar summary No
This webinar summary focuses on how linked administrative data can be used to improve public child welfare programs. During the meeting presenters: 1) identify trends in the use of administrative data to improve child welfare programs; 2) address the challenges related to interoperability ; and 3) review best practices that will help agencies with program development and implementation.
Conquering the trials and tribulations of data sharing and linking. Nelson, R., Rodriguez, B., Goodman, L., & Franklin, S. (2014). Presentation at the Improving Data, Improving Outcomes Conference, New Orleans, LA. Webinars and presentations No
This presentation provides information on requirements and best practices for data sharing agreements. Panelists from two states discuss key decisions in sharing and linking data across early childhood programs, the policy considerations related to those decisions, and lessons learned from their efforts.
Developing coordinated longitudinal early childhood data systems: Trends and opportunities in Race to the Top Early Learning Challenge applications. Kipnis, F., Stebbins, H., & Szekely, A. (2012). Washington, DC: Early Childhood Data Collaborative. Fact sheets and briefs No
The Race to the Top Early Learning Challenge (ELC) encouraged states to demonstrate their commitment to integrating and aligning resources and policies across all of the state agencies that administer public funds related to early learning and development. Building or enhancing an early learning data system was an optional section of the application, and 30 of the 37 applicants addressed this priority. In 2011, nine states, six of which addressed the data priority, won competitive ELC grants. An additional five states are eligible to apply for smaller grants in 2012, and all but one of these states addressed the data priority in their initial applications. Some states that chose not to address this priority in their ELC applications indicated that they were already working on early learning data systems through other federal grants, including the State Longitudinal Data Systems (SLDS) Grant or grants that support State Advisory Councils on Early Childhood Development and Care. This issue brief analyzes the 30 state plans to build or enhance early learning data systems (section E2), with a more in-depth review of the states with the top 17 scores from this section. (author abstract)
Early care and education data landscape. Data Quality Campaign. (n.d.). Washington, DC: Data Quality Campaign. Fact sheets and briefs No
This fact sheet on early childhood data systems addresses the following questions: What child-level ECE data could be useful for matching with K–12 education data? How are student-level ECE data currently collected and maintained? What opportunities and guidance exist to inform state efforts to improve ECE data systems?
Early childhood integrated data systems toolkit. Cochenour, M., Chatis, C., Sellers, J., & Taylor, R. (2014). Washington, DC: National Center for Education Statistics. Planning guides No
The SLDS Early Childhood Integrated Data System Toolkit was designed for use by any state regardless of where it is in the process of developing an ECIDS. The Toolkit has seven components: (1) Purpose and Vision, (2) Planning and Management, (3) Stakeholder Engagement, (4) Data Governance, (5) System Design, (6) Data Use, and (7) Sustainability. Each component has a set of key indicators that describe the "what" is ideal for the specific component and each indicator has elements that discuss "how" to accomplish the "what" outlined in the indicator.
Establishing a standard data model for large-scale IDS. Wulczyn, F., Clinch, R., Coulton, C., Keller, S., Moore, J., Muschkin, C., et al. (2017). Philadelphia, PA: Actionable Intelligence for Social Policy. Report No
This paper provides both general and specific guidance to states and localities interested in building robust integrated data systems that take full advantage of all that these systems have to offer.
Forum guide to the teacher-student data link: A technical implementation resource (NFES 2013-802). National Forum on Education Statistics. (2013). Washington, DC: National Center for Education Statistics. Guides No
This publication is a practical guide for implementing a teacher-student data link (TSDL) that supports a range of uses at the local, regional, and state levels. The guide addresses the considerations for linking teacher and student data from multiple perspectives, including governance, policies, data components, business rules, system requirements, and practices. It provides references to promising practices for high quality data linkages, including TSDLspecific processes such as roster verification and the establishment of the Teacher of Record. The information and opinions published here are those of the Forum and do not necessarily represent the policy or views of the U.S. Department of Education or NCES.
Getting started: Incorporating Head Start data in an SLDS. National Center for Education Statistics. State Support Team. (2012).Washington, DC: National Center for Education Statistics. Fact sheets and briefs No
This resource addresses how to incorporate Head Start Data in an SLDS by discussing reasons for integrating Head Start data, who to involve when, and strategies for engagement.
IDEA Part C and Part B 619 state data systems: Current status and future priorities. Derrington, T., Spiker, D., Hebbeler, K., & Diefendorf, M. J. (2013). Menlo Park, CA: DaSy Center. Reports and papers No
The Center for IDEA Early Childhood Data Systems, the DaSy Center, was funded by the Office of Special Education Programs (OSEP) to provide technical assistance (TA) to states to support them in developing or enhancing Part C and Part B Section 619 (Part B 619) data systems. TA also will assist Part C and Part B 619 state agencies in participating in the development of integrated early childhood data systems and longitudinal data systems in their states. To inform the DaSy Center's work, the Center collected information about the current status of Part C and Part B 619 state data systems, priorities for improvement, and areas where the states would like TA. State Part C and Part B 619 coordinators, their respective data managers, and other state staff completed an online survey over the summer of 2013. Responses were obtained from 94% of the 50 states, DC, and Puerto Rico for Part C and from 96% for Part B 619. This report summarizes what was learned about the current status of Part C and Part B 619 data systems and where states are in moving to improve their data systems. (author abstract)
Integrated data systems: An emerging tool to support services for low-income Hispanic families with young children (Publication #2015-34). Limlingan, M. C., Grindal, T., Lopez, M., Blocklin, M., & Bumgarner, E. (2015). Bethesda, MD: National Research Center on Hispanic Children & Families. Report Yes
This brief explores how integrated data systems (IDS) data may be an important and cost-efficient resource for better understanding public service use among low-income Hispanics in the United States.
The integration of early childhood data: State profiles and a report from the U.S. Department of Health and Human Services and the U.S. Department of Education. U.S. Department of Health and Human Services, & U.S. Department of Education. (2016). Washington, DC: U.S. Department of Health and Human Services. Report Yes
This report from the U.S. Departments of Health and Human Services (HHS) and Education (ED) helps states refine their capacity to use existing administrative data from early childhood programs to improve services for young children and families. The report includes key considerations, eight state examples, technical assistance, and other resources.
Investing in a data quality research program for administrative data linked to survey data for policy research purposes is essential. Davern, M., Roemer, R., &Thomas, W. (2009). Paper presented at the 2009 Federal Committee on Statistical Methodology Research Conference, Washington, DC. Reports and papers No
In this paper we set out a research agenda for improving linked data files for policy research considering research that needs to be conducted concerning coverage error, nonresponse error, sampling error, measurement error, editing/imputation, documentation of metadata and production of timely linked data files.
The Learning Center for Families' use of data to support children's healthy development (Case Study #2, Publication # 2016-29). King, C., Maxwell, K. L., Abrams, J. & Epstein, D. (2016). Bethesda, MD: Child Trends. Reports and papers Yes
This case study highlights how an early care and education program (The Learning Center) in Utah and Arizona linked health and early intervention data within their program and with other external programs to better serve children.
Linking data across agencies: States that are making it work. Carson R., Laird E., Gaines, E., & Ferber, T. (2010). Washington, DC: Data Quality Campaign. Reports and papers No
This issue brief captures the current status of states' ability to link data across agencies, the opportunities and challenges they face, and how leading states are breaking down silos to ensure data follow individual students over time to improve success. It also describes processes states can use in developing and implementing their cross-agency data sharing efforts.
Linking data to understand children's academic progress from early care and education through elementary school (Data Direction 5, Publication # 2016-35). Friese, S., Epstein, D., & Maxwell, K. L. (2016). Bethesda, MD: Child Trends. Reports and papers No
This report highlights a strategy for linking early care and education and early elementary data to track children's academic progress over time.
Linking education and social services data to improve child welfare. Smith S., Staub, D., Myslewicz, M., & Laird, E. (2007). Washington, DC: Data Quality Campaign. Fact sheets and briefs No
This brief discusses the next-generation issues for states to consider immediately; political and technical issues related to creating critical linkages within and across public agencies; issues of student confidentiality; three jurisdictions creating critical linkages between education and social services (Florida Department of Education, San Diego County Office of Education, Utah Department of Human Services); and further reports and resources on creating critical linkages to improve overall child welfare.
Linking Head Start data with state early care and education coordinated data systems (Publication No. 2015-11). Jordan, E., Schultz, T., & King, C. (2015). Bethesda, MD: Early Childhood Data Collaborative. Fact sheets and briefs No
At present, there is no requirement for local Head Start programs to link or share their data with other state data systems. However, several states are making advances toward linking and/or sharing data across their state's K-12 data system or other services' data systems. In this process, states have encountered some challenges and have had to tackle issues related to data privacy and security, among others. To better understand some of the challenges, successes, and strategies behind this work, the Early Childhood Data Collaborative (ECDC) contacted and interviewed a sample of Head Start and state early childhood leaders in a dozen states. Based on these interviews, this brief from the ECDC examines the actions some states have taken in linking Head Start data to other state systems. It describes the importance of including Head Start data in a coordinated early care and education data system, relays what we learned about current data linkage steps across states, and presents action steps for state and federal leaders. (author abstract)
A look at Maryland's early childhood data system. Stedron, J. (2010). Denver, CO: National Conference of State Legislatures. Case studies Yes
This report briefly discusses Maryland's Early Childhood Data System—covering governance, history, linkages, access, reporting, use, and next steps.
A look at Pennsylvania's early childhood data system. Stedron, J. (2010). Denver, CO: National Conference of State Legislatures. Case studies Yes
This report briefly discusses Pennsylvania's Early Childhood Data System—covering governance, history, privacy, linkages, access, reporting, use, and advice.
Maine Statewide Longitudinal Data System. Hurwitch, B. (2008, 2011).   Yes
Design, develop and implement a longitudinal data system that stakeholders can draw upon to make well-informed decisions about improving student achievement. Expand the SLDS to examine student progress from early childhood into career while protecting student privacy and confidentiality consistent with applicable privacy protection laws.
Many missing pieces: The difficult task of linking early childhood data and school-based data systems. Bornfreund, L., & Severns, M. (2010). Washington, DC: New America Foundation. Fact sheets and briefs No
The authors outline the main challenges states face in integrating early childhood data into their K-12 statewide longitudinal data systems. Proposals from recent grant winners are analyzed and data collection strategies are presented. Recommendations are made at both the federal and state levels to support the optimal use of early childhood data for all stakeholders.
Matching and cleaning administrative data. Goerge, R. M., & Lee, B. J. (2002). In M. Ver Ploeg, R. A. Moffitt, & C. F. Citro (Eds.), Studies of welfare populations: Data collection and research issues: Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs (pp. 197-219). Washington, DC: National Academy Press. Book chapters No
This paper addresses the cleaning and linking of individual-level administrative data for the purposes of social program research and evaluation.
Maximizing the impact of state early childhood home visitation programs. National Governors' Association. Center for Best Practices. (2011). Washington, DC: National Governors' Association, Center for Best Practices Fact sheets and briefs No
As new federal funds augment existing state investments in home visiting, governors have an opportunity to integrate home visiting into an effective and comprehensive early childhood system. Several strategies can ensure that states use new and existing resources wisely to ensure more consistently high-quality programs that are better targeted to families' needs with less duplication of effort. Governors should lead efforts to: Promote coordinated planning and shared accountability across the agencies that fund home visiting and other early childhood programs; Develop research-based quality standards and support ongoing program improvement; and Improve data linkages to track outcomes and better target services. (author abstract)
More than referral: Linkages between early intervention and child welfare data and improved child outcomes. Derrington, T., Sheppard, B., Ferguson, A., Scott, C., & Smith, K. (2013). Presentations at the Improving Data, Improving Outcomes Conference, Washington, DC. Webinar No
This presentation discusses implementation issues and their implications for state IDEA data systems. Panelists from three states describe their states' linkages and discuss how implementation issues have been addressed and how linkages have been (or could be) used to improve child outcomes. *Oregon Webinar includes screenshots of their EC data system
On track: Developing New Jersey's early childhood data system: A report to the New Jersey Council for Young Children. Squires, J. H., Carolan, M. E., & Barnett, W. (2011). Trenton: New Jersey Council for Young Children. Reports and papers Yes
An overview of early childhood data systems in New Jersey and other states, with recommendations for developing and implementing a comprehensive early childhood data system for New Jersey, based on a literature review and in-depth interviews with key informants
An overview of architectures and techniques for integrated data systems (IDS) implementation. Prashant, K. (2014). Unpublished paper. Reports and papers No
This paper begins with a discussion of potential applications of linked administrative data in policy-level and case-level decision-making. It presents primary data integration approaches and options that are available to health and human services enterprises based on today's technologies and know-how. The paper also addresses the data architecture options and business process implications of embarking on a data integration program.
A planning guide for linking data to support program improvement in early care and education (Report # 2016-39). Friese, S., Maxwell, K. L., Epstein, D., & Abrams, J. (2016). Bethesda, MD: Child Trend Reports and papers No
This planning tool is designed to help programs and technical assistance partners assess their capacity to engage in data linking efforts and identify next steps to accomplish data linking goals based on six areas of practice.
Pre-kindergarten and child care coordination initiatives. Child Care State Systems Specialist Network. (2012). Fairfax, VA: Child Care State Systems Specialist Network. Fact sheets and briefs No
To support pre-kindergarten services in midst of budget cuts, States are coordinating services, funds, and efforts with other programs, such as child care, to support working families by offering comprehensive care for their children that is also high quality and supports children's development. The following tables provide examples of pre-kindergarten and child care coordination initiatives and highlights the income eligibility requirements, funding streams, and eligible provider requirements for both programs. (author abstract)
Putting administrative data to work: A toolkit for state agencies on advancing data integration and data sharing efforts to support sound policy and program development. Duran, F., Wilson, S. B., & Carroll, D. (2005). Farmington, CT: Child Health and Development Institute of Connecticut. Methods Yes
The ultimate goal of this toolkit is to help state agencies strengthen their data and research infrastructure. It provides an assessment tool to help agencies determine their enhancement needs as well as guidelines on how to approach implementation of several different infrastructure-enhancing strategies. The toolkit is not intended to function as a technical design manual, but rather seeks to help agencies identify necessary components for successful implementation of the strategy or strategies they choose to pursue. The guidance provided within the toolkit is based on best practice and lessons learned from those that have worked on similar efforts, both nationally and in Connecticut. (author abstract)
Researching the CCDF program by linking administrative data with data from the CCDF Policies Database: A how-to guide (OPRE Report 2016-24). Giannarelli, L., Minton, S., & Durham, C. (2016). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Brief No
This brief suggests answering questions about setting policies for the Child Care and Development Fund (CCDF) by linking case-level information on subsidized families and children with detailed policies from the federally funded CCDF Policies Database.
Roadmap for early childhood and K-12 data linkages: Key focus areas to ensure quality implementation. Data Quality Campaign, & Early Childhood Data Collaborative. (2016). Washington, DC: Data Quality Campaign. Brief Yes
This document describes how state policymakers can support the development of links between early childhood and K-12 data systems. State examples are provided.
Roadmap for foster care and K-12 data linkages: Key focus areas to ensure quality implementation. Data Quality Campaign, & Legal Center for Foster Care & Education. (2017). Washington, DC: Data Quality Campaign. Report Yes
This report explains ways child welfare and state education agencies can link data to improve outcomes for children in foster care.
Rochester Childfirst Network's use of linked data to support classroom teaching (Case Study #4, Publication # 2016-27). King, C., Maxwell, K. L., Abrams, J. & Epstein, D. (2016). Bethesda, MD: Child Trends. Report Yes
This case study highlights Rochester Childfirst Network, a Rochester, New York early care and education program, that partnered with The Children's Institute of the University of Rochester to use linked data to support classroom instruction and teacher professional development.
The role of administrative data in evidence-based policy and innovation. Stack, K. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Presentation No
This presentation provides a historical policy context for integrating data systems. It also reviews the benefits and challenges of integrating data with tips for how to integrate data, such as common data definitions, incentivizing states and localities, and providing education and technical assistance to integrate data.
SLDS Early Childhood Integrated Data System Self-Assessment. Cochenour, M., Chatis, C., Sellers, J., & Taylor, R. (2014). Washington, DC: National Center for Education Statistics. Brief No
This supplemental resource, known as the SLDS Early Childhood Integrated Data System Self-Assessment, was created to help states assess their needs as they integrate early childhood data into an early childhood data system and the P-20W+ (early childhood through workforce and beyond) statewide longitudinal data system (SLDS).
SLDS topical webinar summary: Head Start and SLDS: Getting to know you. Cochenour, M., Allen, B. L., Murphy, C., Shinn-Brown, K., & Thornburg, K. R. (2012). Washington, DC: National Center for Education Statistics. Webinar summaries No
This webinar summary is based on presentations from states on how Head Start fits into their statewide longitudinal data systems (SLDS). Missy Cochenour of the State Support Team (SST) explained the goals of SLDSs and how they align with Head Start. Ben Allen (Vermont) described Head Start and Early Head Start, while Kimberly Shinn-Brown (Missouri) discussed which data are collected, aggregated, and analyzed through Head Start. Colleen Murphy (Utah) presented on how Utah and Head Start work together on the state's SLDS, and Kathy Thornburg (Missouri) discussed why Head Start data are critical to answering school readiness questions.
SLDS topical webinar summary: Prioritizing early childhood data. Illgen, S., Laird, E., & Regenstein, E. M. (2011). Washington, DC: National Center for Education Statistics. Webinar summaries No
Early childhood data are a key component in developing robust P-20W data systems. This webinar summary focuses on how states can design early childhood data systems that a) address key issues, on the local, state, and national levels; b) are improvement-driven as opposed to compliance-driven; and c) can be coordinated with K-12 and other key program data. Elizabeth Laird spoke about the work of the Early Childhood Data Collaborative (ECDC), and Elliot Regenstein of EducationCounsel LLC and the Illinois Early Learning Council reviewed the process of developing early childhood (EC) data systems in three states.
Sources and linking strategies for employment data. McGrew, C. & Sellers, J. (2016). Washington, DC: National Center for Education Statistics. Tool Yes
As the demand to connect education and employment data grows, states are navigating the challenges of locating essential data across a number of sources as well as establishing agreements and technical processes. This brief describes common sources of workforce data and processes states use to link data across education and workforce programs. It is the first in a series of publications focused on the integration and use of workforce data for SLDS work.
Stacking the blocks: A look at integrated data strategies. Jordan, E., & King, C. (2015). In Rising to the Challenge: Building effective systems for young children and families: A BUILD e-book. Boston: Build Initiative. Book chapters Yes
Leaders from seven of the ten states (Illinois, Maryland, Minnesota, North Carolina, Oregon, Rhode Island, and Wisconsin) that had prioritized data systems development goals in their Race to the Top-Early Learning Challenge (ELC) applications and completed at least one year of the grant cycle were interviewed. The chapter identifies five "building blocks" or strategies that states have used to grapple with their early learning and development data and plan for improved integration.
State of the states. DaSy Center. (2016). Menlo Park, CA: DaSy Center. Tool Yes
This interactive map tool shows the national status of data systems for Part C and Part B 619, including linked data systems. They also offer a quick glance at 10 different facets of state early childhood data systems, plus detailed information for each state. These data are based on information collected by the DaSy Center and the Infant Toddler Coordination Association (ITCA) in fall 2015. If data were not provided in 2015, 2013 data were used if available.
State success stories. Early Childhood Data Collaborative. (n.d.). Washington, DC: Early Childhood Data Collaborative. State case studies Yes
States are beginning to make progress toward building and using coordinated state early care and education data systems. Although states have more work to do to ensure ECE data are collected and used for continuous improvement, promising state practices are beginning to emerge. This webpage provides links to a case study for each state.
Statewide Longitudinal Data Systems Grant Program resource database. National Center for Education Statistics. (n.d.). Washington, DC: National Center for Education Statistics. Tools No The Statewide Longitudinal Data Systems Grant Program offers a variety of support resources.
Strength in numbers: Supporting quality improvement in early care and education programs through linking administrative data (Publication # 2016-36). King, C., Epstein, D., Maxwell, K. L., Lin, V. K., Abrams, J., Hutchison, L., & Burgess, K. (2016). Bethesda, MD: Child Trends. Brief No This brief highlights opportunities and challenges in linking data and offers ideas for overcoming identified challenges.
Strengthening workforce data to support quality: State spotlight on Oregon. Early Childhood Data Collaborative. (2017). Bethesda, MD: Early Childhood Data Collaborative. Report Yes
This case study tells the story behind Oregon's recent workforce data developments and describes the different ways they are using their workforce data to strengthen their early care and education workforce.
Telamon North Carolina Corporation's collaboration with a county agency (Case Study #3, Publication # 2016-28). Epstein, D., & Maxwell, K. L. (2016). Bethesda, MD: Child Trends. Report Yes This case study highlights Telamon, a North Carolina Head Start program, that partnered with a TANF agency to improve enrollment in both programs and coordinate family services across programs.
Tulsa Community Action Project's commitment to data driven decision-making (Case Study #1, Publication # 2016-30). King, C., Maxwell, K. L., Abrams, J, & Epstein, D. (2016). Bethesda, MD: Child Trends Report Yes This case study highlights a Tulsa, OK early care and education program (CAP Tulsa) that linked data to public schools to understand transitions to kindergarten and support teacher effectiveness.
Using integrated data to improve communities: Lessons from a cross-site project. Hendley, L. (2016). Wasihngton, DC: Urban Institute. Brief Yes
Integrated data systems (IDS) periodically link individual-level records from multiple government agencies. These systems can be used for policy analysis, program planning, and evaluation. Six partners in the National Neighborhood Indicators Partnership (NNIP) worked to expand access to information from IDS in their own communities and strengthened the relationships between NNIP partners and agencies hosting IDS.
Workforce information: A critical component of coordinated state early care and education data systems. Kipnis, F., & Whitebook, M. (2011). Berkeley: University of California, Berkeley, Center for the Study of Child Care Employment. Fact sheets and briefs No
The brief describes the early care and education workforce data landscape in the states, focusing on the three main workforce data systems operating across multiple states. It also details the challenges to aligning these systems and current efforts to address these challenges. (author abstract)

Analyzing Administrative Data

Resources related to how administrative data have been or can be analyzed by organizations, best practices for analyzing administrative data, and guidance in interpreting results after analyzing administrative data.

Resource
Resource
Type
Type
State Example?
State Example?
Description (author abstract if available)
Description (author abstract if available)
2009 state of early childhood and school-age workforce registries. National Registry Alliance. (2009). Washington, DC: National Registry Alliance. Reports and papers No
A study of the characteristics of state and regional registries of early childhood and school-age practitioners and trainers, based on survey responses from 31 registries
Administrative data as children's well-being indicators: The South Carolina Data Bridge Project. Lavenda, O., Hunter, B., Noelle, M., Bolick, L., Haselden, C., Tester, D., Knopf, H., & Ha, Y. (2011). Child Indicators Research, 4(3), 439-451. Reports and papers Yes
To study the quality of care provided in the area and ultimately improving the well-being of children and families in South Carolina by linking different sources of child care administrative data to create analytic data cubes to allow examination of quality of care provided to children and factors contributing to it; Can use administrative data for potential impact on well-informed decision making and policy change to improve children and families' well-being
Administrative data for policy-relevant research: Assessment of current utility and recommendations for development. Hotz, V. J., Goerge, R., Balzekas, J., & Margolin, F. (n.d.). Chicago: Joint Center for Poverty Research. Reports and papers No
The report describes the key practical and political considerations of transforming the information in these programmatic records into research-ready databases; identifies the strengths and weaknesses of administrative data, relative to that gathered in national surveys, for use in descriptive and evaluative research and in accountability-based monitoring of program performance; describes examples of several states' efforts to develop an ongoing capacity to use administrative data for both programmatic and policy evaluations; and makes initial recommendations that will improve the quality and usefulness of administrative data for policymakers and program administrators.
Cheaper, faster, better: Are state administrative data the answer?: The Mother and Infant Home Visiting Program Evaluation-Strong Start second annual report (OPRE Report 2015-09). Lee, H., Warren, A., & Gill, L. (2015). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Reports and papers No
This report details MIHOPE-Strong Start's process of acquiring administrative vital records and Medicaid data from 20 states and more than 40 state agencies. The study relies on administrative data to measure infant and maternal health, health care use, and cost outcomes. Policymakers have increasingly encouraged greater access to and use of administrative data to produce timely, rigorous, and lower-cost evaluations of health and social programs, since these records may be less costly and more accurate than information collected directly from families. The MIHOPE-Strong Start experience sheds light on the process of acquiring permission to access such data.
Child care data tracker. United States. Office of Child Care. (n.d.). Washington, DC: U.S. Office of Child Care. Tools No
The Child Care Data Tracker (Tracker), available to tribes and territories, is a comprehensive case management tool designed to support the collection, management, and utilization of the case-level information needed for the generation of the required ACF-700 and ACF-801 reports.
Child care data viewer. United States. Office of Child Care. (n.d.). Washington, DC: U.S. Office of Child Care. Tools No
The Child Care Data Viewer, available to CCDF State and Territory grantees, is a software tool to help improve the quality of ACF-801 data and enhance its utilization.
Considerations in preparing to analyze administrative data to address child care and early education research questions (OPRE Research Brief # 2017-18). Lin, V., Maxwell, K., & Forry, N. (2017). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Report No
The resource can help researchers prepare for issues that may arise when using administrative data in a research project.
Data Activities Inventory: Leader's Level. National Center for Program Management and Fiscal Operations. (n.d.). Waltham, MA: National Center for Program Management and Fiscal Operations. Tools No
This is an inventory that leaders can use to determine their teams' proficiency levels with each of the four data activities: prepare, collect, aggregate and analyze, and use and share.
Data collection and use in early childhood education programs: Evidence from the Northeast Region (REL 2015-084). Zweig, J., Irwin, C. W., Fuccillo, J,. & Cox, J. (2015). Washington, DC: National Center for Education Evaluation and Regional Assistance. Reports and papers Yes
This study focuses on preschools' collection and use of administrative data on dosage, classroom quality, and early learning outcomes.
Data in Head Start and Early Head Start: Creating a culture that embraces data: Guide for users and trainers. National Center on Program Management and Fiscal Operations. (n.d.). Washington, DC: National Center on Program Management and Fiscal Operations. Guides No
An online series on using data in Head Start and Early Head Start. The module includes an introduction and five interactive activities. Each activity includes scenarios that are based on real-life examples from actual Head Start and Early Head Start programs.
Data in Head Start and Early Head Start: Tips for embracing data. National Center on Program Management and Fiscal Operations. (n.d.). Washington, DC: National Center on Program Management and Fiscal Operations. Fact sheets and briefs No This resource provides a list of tips for building a data culture in Head Start programs.
Data use for continuous quality improvement: What the Head Start field can learn from other disciplines: A literature review and conceptual framework (OPRE Report No. 2014-77). Derrick, T. M., Sandstrom, H., Pettijohn, S. L., Fyffe, S., & Koulish, J. (2014). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Reports and papers No
This report summarizes research on the processes, facilitators, and impediments to data use for continuous quality improvement; develops a conceptual framework representing the elements of data use for continuous quality improvement; provides linkages between the disciplines from which the literature was drawn and the Head Start field; and suggests areas for future research.
Demonstrating results: Minnesota Head Start's experience developing a child outcomes database. Minnesota Head Start Association. (2012). Duluth, MN: Minnesota Head Start Association. Reports and papers Yes
This report describes how the Minnesota Head Start community set out to describe the developmental progress of four-year-olds enrolled in Head Start across Minnesota using their research-based child assessment data. The exercise of aggregating data from diverse, locally-controlled Head Start programs offers many important lessons about building a state early childhood data system and establishing data variables that offer meaningful insights into the complex interactions that impact children's early developmental progress. It also contributes to the discussion about how children enrolled in Head Start make progress in essential developmental domains.
Determining the feasibility of using state early care and education administrative data (OPRE Research Brief # 2017-17). Lin, V., Maxwell, K., & Forry, N. (2017). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Report No
The resource can help researchers and their state partners determine whether analyzing administrative data is feasible and appropriate for addressing their child care and early education research questions.
Developing a robust research agenda that the SLDS can support. Gosa, K. & Howe, C. (2015). Washington, DC: National Center for Education Statistics. Brief No
This brief provides a step-by-step process for developing or improving a research agenda that the SLDS can support.
The feasibility of using electronic health records (EHRs) and other electronic health data for research on small populations. Devers, K., Gray, B., Ramos, C., Shah, A., Blavin, F., & Waidmann, T. (2013). Washington, DC: U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. Reports and papers No
This report explores the feasibility of using electronic health record (EHR) and other electronic health data for research on small populations.
IDEA Part C and Part B 619 state data systems: Current status and future priorities. Derrington, T., Spiker, D., Hebbeler, K., & Diefendorf, M. J. (2013). Menlo Park, CA: DaSy Center. Reports and papers No
The Center for IDEA Early Childhood Data Systems, the DaSy Center, was funded by the Office of Special Education Programs (OSEP) to provide technical assistance (TA) to states to support them in developing or enhancing Part C and Part B Section 619 (Part B 619) data systems. TA also will assist Part C and Part B 619 state agencies in participating in the development of integrated early childhood data systems and longitudinal data systems in their states. To inform the DaSy Center's work, the Center collected information about the current status of Part C and Part B 619 state data systems, priorities for improvement, and areas where the states would like TA. State Part C and Part B 619 coordinators, their respective data managers, and other state staff completed an online survey over the summer of 2013. Responses were obtained from 94% of the 50 states, DC, and Puerto Rico for Part C and from 96% for Part B 619. This report summarizes what was learned about the current status of Part C and Part B 619 data systems and where states are in moving to improve their data systems. (author abstract)
Implementing a research agenda. Gosa, K., & Howe, C. (2016). Washington, DC: National Center for Education Statistics. Brief No
This brief describes key steps and considerations for communicating and carrying out a research agenda that a state longitudinal data system can support. The brief also discusses how to ensure that the research agenda is sustainable over time. The guidance in this brief is intended for states that have already developed a research agenda but have not taken steps to implement it.
INQUIRE data toolkit (OPRE Report No. 2013-58). Friese, S., King, C., & Tout, K. (2013). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Methods No
The INQUIRE Data Toolkit was designed to provide tools to support effective data collection and the use of data to answer important policy and reporting questions through the use of common data elements.
Making effective use of postsecondary data in K-12 education settings. Heinkel, L., Hallgren, K., Gill, B., & Shoji, M. (2015) Princeton, NJ: Mathematica Policy Research. Fact sheets and briefs No
This issue brief (1) summarizes the uses and limitations of postsecondary data in raw form, (2) describes three sophisticated ways that K-12 officials can use these data to inform K-12 policy and practice, and (3) discusses the importance of dissemination.
Measuring employment and income for low-income populations with administrative and survey data. Hotz, V. J., & Scholz, J. K. (2002). In M. Ver Ploeg, R. A. Moffitt, & C. F. Citro (Eds.), Studies of welfare populations: Data collection and research issues: Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs (pp. 275-315). Washington, DC: National Academy Press. Book chapters No
In this paper we assess the strengths and weaknesses of using survey or administrative data to measure the employment and income of low-income populations. We review a number of studies, most of which have been conducted in the past 10-15 years,(3) that assess the comparability of income and employment measures derived from surveys and administrative records.
The nuts and bolts of working with administrative data: Linking data across multiple states and multiple data sources. Henly, J. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Presentation Yes
This presentation uses the Illinois-New York child care research partnership as an example to explain how to use administrative data to answer policy-relevant research questions through linking administrative data across states.
PFS + ECE: Using data to inform decisionmaking: Pay for Success Early Childhood Education toolkit report #2. Sandstrom, H. & Greenberg, E. (2016). Washington, DC: Urban Institute. Report No
Data play an integral role in pay for success (PFS) projects. Knowing the kinds of data needed for a successful project, how to collect them, and how they should be used may seem straightforward. But the complexity of early childhood outcome measures and data systems can create challenges.
Planning, conducting, and documenting data analysis for program improvement. DaSy Center, & Early Childhood Technical Assistance Center. (2015). Menlo Park, CA: DaSy Center. Tools No
This document was developed to help technical assistance (TA) providers and state staff define and limit the scope of data analysis for program improvement efforts, including the State Systemic Improvement Plan (SSIP); develop a plan for data analysis; document alternative hypotheses and additional analyses as they are generated; and summarize findings and document results.
Putting administrative data to work: A toolkit for state agencies on advancing data integration and data sharing efforts to support sound policy and program development. Duran, F., Wilson, S. B., & Carroll, D. (2005). Farmington, CT: Child Health and Development Institute of Connecticut. Methods Yes
The ultimate goal of this toolkit is to help state agencies strengthen their data and research infrastructure. It provides an assessment tool to help agencies determine their enhancement needs as well as guidelines on how to approach implementation of several different infrastructure-enhancing strategies. The toolkit is not intended to function as a technical design manual, but rather seeks to help agencies identify necessary components for successful implementation of the strategy or strategies they choose to pursue. The guidance provided within the toolkit is based on best practice and lessons learned from those that have worked on similar efforts, both nationally and in Connecticut. (author abstract)
Putting the pieces together: New York early learning program data systems. Kreader, J., & Schneider, W. (2011). Rensselaer, NY: New York State Council on Children and Families, Early Childhood Advisory Council. Case studies Yes
An inventory of the elements and accessibility of early education and care data systems maintained by New York State and New York City agencies in the areas of: (1) program/provider supply; (2) enrollment, participant demographics, and demand; (3) early childhood workforce; (4) program quality; (5) outcomes for children and families; and (6) costs and financing; with recommendations for steps toward the development of integrated comprehensive early childhood data systems
The role of administrative data within economic evaluation. Crowley, M. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Presentation Yes
This presentation details possible administrative datasets that can be used to perform a cost analysis, cost-effectiveness, cost-savings analysis, or benefit-cost analysis with an example.
The role of the policy context in using and understanding administrative data. Davis, E. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Report Yes
This case study highlights AVANCE Houston, a Head Start program, that partnered with the Houston Independent School District to understand children's literacy and math skills in early elementary school and how they compared with other children from low-income families.
Statewide Longitudinal Data Systems Grant Program resource database. National Center for Education Statistics. (n.d.). Washington, DC: National Center for Education Statistics. Tools No The Statewide Longitudinal Data Systems Grant Program offers a variety of support resources.
Understanding data use for continuous quality improvement in Head Start: Preliminary findings (OPRE Report #2015-33). Derrick, T. M. (2015). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Reports and papers No This brief summarizes common themes across two studies of local Head Start programs and a multidisciplinary literature review. Preliminary evidence shows that Head Start programs experience similar challenges and facilitators to data use for continuous quality improvement as those experienced in other fields.
Using administrative data for research: A companion guide to A descriptive analysis of the principals workforce in Florida schools (REL 2015-049). Folsom, J. S., Osborne-Lampkin, L., & Herrington, C. D. (2014). Washington, DC: National Center for Education Evaluation and Regional Assistance. Reports and papers Yes This companion guide describes the methods used to extract information from the Florida Department of Education database for data on school leaders and transform it into an understandable structure for use. The report focuses on methods of data cleaning, merging, and analysis in order to help those interested in analyzing similar databases in other states or contexts.
Using aggregate administrative data in social policy research (OPRE Report #2016-91). Jacob, R. (2016). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Report No This report provides considerations for using aggregate administrative data to analyze policy-relevant questions when it may be too difficult or costly to obtain individual-level administrative data.
Using linked survey and administrative data to better measure income: Implications for poverty, program effectiveness and holes in the safety net (AEI Economic Policy Working Paper 2015-10). Meyer, B. D., & Mittag, N. (2015). Washington, DC: American Enterprise Institute for Public Policy Research. Reports and papers No The report examines the consequences of underreporting transfer programs in household survey data for low-income populations. The report also focuses on the Current Population Survey (CPS), the source of official poverty and inequality statistics. Administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State are linked to the CPS at the individual level.
Wisconsin Child Care Data Sharing Project. Sweet, A. (2002). Wisconsin, Department of Workforce Development. ACF/OPRE projects Yes
A project to develop a statewide research infrastructure to improve Wisconsin's ability to address child care policy questions at the state and local level. The infrastructure is designed to be housed within the Department of Workforce Development Data Warehouse, to draw upon multiple data sources, and to coordinate data across several programs and agencies, including resource and referral, the State's professional development system, and child care licensing. An initial product of this project is the Statewide Child Care Provider File--a resource for use by parents, the State's professional development system, and the Bureau of Regulation and Licensing.

Managing Administrative Data

Resources related to how to store, organize, prepare, and maintain the quality of administrative data.

Resource
Resource
Type
Type
State Example?
State Example?
Description (author abstract if available)
Description (author abstract if available)
The ABCs of data dictionaries. Gould, T., Nicholas, A., Blandford, W., Ruggiero, T., Peters, M., & Thayer, S. K. (2014). Menlo Park, CA: DaSy Center. Reports and papers No
This overview of the basic components of a data dictionary is designed to educate and inform IDEA Part C and Part B 619 state staff about the purpose and benefits of having up-to-date data dictionaries for their data systems.
Avoid the pitfalls: Benefits of formal Part C data system governance. Mauzy, D., Bull, B., & Gould, T. (2016). Menlo Park, CA: DaSy Center. Brief No
This brief promotes formal data governance for Part C data by describing lessons learned from other industries; outlining the risks of informal data governance and benefits of formal data governance; and providing action steps to support state Part C systems in establishing or improving formal data governance.
Best practices in data governance and management for early care and education: Supporting effective quality rating and improvement systems (OPRE Research Brief No. 2014-35). Weber, R. & Iruka, I. (2014). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Reports and papers No
As a centerpiece of state early care and education (ECE) activities, Quality Rating and Improvement Systems (QRIS) serve as an example of the how an effective ECE data system can support planning, operations, service delivery, monitoring and evaluation. Intentional and rigorous data management practices are essential for data gathered exclusively for the QRIS (such as program observation scores), as well as for external data accessed by the QRIS (such as workforce registry data). Implementing strong ECE data governance and management practices will ensure the quality of QRIS data and thus the integrity of the QRIS itself. Incomplete, inaccurate, or unreliable data can introduce errors in the ratings that can threaten the credibility of the QRIS and have negative consequences for ECE and school-age care (ECE-SAC) programs through skewed reimbursement rates and inaccurate marketing tied to incorrect ratings. The purpose of this brief is to illustrate the need for and benefits of building strong ECE data governance structures and implementing system-wide data management policies and practices, using the example of QRIS. (author abstract)
Best practices in ensuring data quality in quality rating and improvement systems (QRIS) (OPRE Research Brief No. 2014-47). Friese, S., Tout, K. & Kirby, G. (2014). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Fact sheets and briefs No
Collecting and using data are core activities in a well-functioning Quality Rating and Improvement System (QRIS). Yet, data used in a QRIS are frequently housed in different systems, using different data management techniques. Ensuring a high level of QRIS data quality involves implementing a number of best practices drawn from established practices used in other fields. The purpose of this brief is to describe the specific strategies QRIS data stakeholders can use to improve upon the collection, management, and dissemination of QRIS data. The audience for this brief includes QRIS program administrators, technical assistance providers, data managers, and researchers. This brief is structured around the five stages of the Data Lifecycle: planning, collection, processing, management and distribution. Best practices are recommended for each stage of the Lifecycle. (author abstract)
Building a data sharing partnership with other organizations (Data Direction 4, Publication # 2016-34). Epstein, D., Maxwell, K. L., & Lin, V. K. (2016). Bethesda, MD: Child Trends. Report No
This report provides practical steps for how to establish a data-sharing partnership between two programs or organizations.
Child care data tracker. United States. Office of Child Care. (n.d.). Washington, DC: U.S. Office of Child Care. Tools No
The Child Care Data Tracker (Tracker), available to tribes and territories, is a comprehensive case management tool designed to support the collection, management, and utilization of the case-level information needed for the generation of the required ACF-700 and ACF-801 reports.
Core data elements for early childhood and school-age registries. National Registry Alliance. (2013). Washington, DC: National Registry Alliance Fact sheets and briefs No
The 2013 Core Data Elements for Early Childhood and School-Age Registries builds on and synthesizes the prior work and captures current trends in registry data collection processes and advancements in data systems planning.
Data Activities Inventory: Leader's Level. National Center for Program Management and Fiscal Operations. (n.d.). Waltham, MA: National Center for Program Management and Fiscal Operations. Tools No
This is an inventory that leaders can use to determine their teams' proficiency levels with each of the four data activities: prepare, collect, aggregate and analyze, and use and share.
Data governance and stewardship. Privacy Technical Assistance Center. (2011). Washington, DC: Privacy Technical Assistance Center. Fact sheets and briefs No
This brief provides guidance on how to successfully manage complex data systems by establishing a comprehensive data governance approach. Data governance principles discussed in this paper apply to a large number of audiences and can be used to improve data management of systems spanning pre-school through postsecondary education and into the workforce.
Data Governance Checklist. Privacy Technical Assistance Center. (2011). Washington, DC: Privacy Technical Assistance Center. Checklists No
This checklist is designed to assist stakeholder organizations with establishing and maintaining a successful data governance program by summarizing the key data privacy and security components of such a program and listing specific best practice action items.
Data stewardship: Managing personally identifiable information in student education records (SLDS Technical Brief 2, NCES 2011-602). National Center for Education Statistics. (2010). Washington, DC: National Center for Education Statistics. Fact sheets and briefs No
This Statewide Longitudinal Data Systems (SLDS) Technical Brief focuses on data stewardship, which involves each organization's commitment to ensuring that privacy, confidentiality, security, and the appropriate use of data are respected when personally identifiable information is collected. Data stewardship involves all aspects of data collection, from planning, collection and maintenance to use and dissemination. The Brief also discusses internal control procedures that should be implemented to protect personally identifiable information, including the use of unique student identifiers and linking codes, workforce security, authorization for access, role based access to student record data, permitted uses, and the handling of data breaches. This Brief concludes with a discussion of accountability and auditing, including an overview of the types of audit activities that can be implemented to ensure that all stages of data stewardship have been successfully implemented.
Developing collaborative partnerships with state agencies to strengthen research using early care and education administrative data (OPRE Research Brief# 2017-16). Maxwell, K. & Lin, V. (2017). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Report No
The resource provides considerations for building strong partnerships between researchers who want to analyze administrative data and state partners who oversee the administrative data.
Early childhood data governance in action!: An introduction. Cochenour, M., Chatis, C., & Irvine, S. (n.d.). Washington, DC: National Center for Education Statistics. Fact sheets and briefs No
Recognizing that social and academic success is shaped long before students enter the elementary school classroom, states are working to increase access to high-quality early childhood programs. Supporting that work, Early Childhood Integrated Data Systems (ECIDS) allow agencies and programs to efficiently collect and use robust information about state and local early childhood programs, workforce, and child outcomes. As states work to integrate information across these programs--including state preschool programs, Head Start and Early Head Start, Early Intervention, Preschool Special Education, and private programs--data governance is critical to success. Data governance provides a means to establish a common vision for early childhood data use in the state, with key policy and program decisions supporting that vision. Further, when data governance is effectively established, the quality and security of data collected, reported, and used by early childhood programs and agencies is enhanced; staff burden is reduced; and communication, collaboration, and relationships across the various agencies, programs, and information technology (IT) staff is improved. This introduction defines data governance for an ECIDS, identifies who should be involved in it, and describes their roles and responsibilities. It is intended to support states in beginning the process of developing their early childhood data governance. (author abstract)
Early childhood data governance in action!: Initial steps to establish data governance. Chatis, C., Cochenour, M., & Irvine, S. (n.d.). Washington, DC: National Center for Education Statistics. Fact sheets and briefs No
Data governance provides a means to establish a common vision for early childhood data use in the state, with key policy and program decisions supporting that vision. Further, when data governance is effectively established, the quality and security of data collected, reported, and used by early childhood programs and agencies is enhanced; staff burden is reduced; and communication, collaboration, and relationships across the various agencies and programs and information technology (IT) staff are improved. This document describes the initial, concrete steps necessary to establish data governance for an early childhood integrated data system (ECIDS). It is intended to support states beginning the process of developing their early childhood data governance program.
Early childhood integrated data systems toolkit. Cochenour, M., Chatis, C., Sellers, J., & Taylor, R. (2014). Washington, DC: National Center for Education Statistics. Planning guides No
The SLDS Early Childhood Integrated Data System Toolkit was designed for use by any state regardless of where it is in the process of developing an ECIDS. The Toolkit has seven components: (1) Purpose and Vision, (2) Planning and Management, (3) Stakeholder Engagement, (4) Data Governance, (5) System Design, (6) Data Use, and (7) Sustainability. Each component has a set of key indicators that describe the "what" is ideal for the specific component and each indicator has elements that discuss "how" to accomplish the "what" outlined in the indicator.
Engaging an external research partner to help use linked data (Data Direction 3, Publication # 2016-33). Lin, V. K., Epstein, D., & Maxwell, K. L. (2016). Bethesda, MD: Child Trends. Report No
This report details steps for identifying and negotiating a partnership with an external research partner to help local early care and education programs use linked data.
From compliance to service: Evolving the state role to support district data efforts to improve student achievement. Data Quality Campaign. (2011). Washington, DC: Data Quality Campaign. Reports and papers No
This framework presents guiding principles for states on how to support districts' data efforts to ensure that data are not only collected but also used to improve student achievement.
IDEA Part C and Part B 619 state data systems: Current status and future priorities. Derrington, T., Spiker, D., Hebbeler, K., & Diefendorf, M. J. (2013). Menlo Park, CA: DaSy Center. Reports and papers No
The Center for IDEA Early Childhood Data Systems, the DaSy Center, was funded by the Office of Special Education Programs (OSEP) to provide technical assistance (TA) to states to support them in developing or enhancing Part C and Part B Section 619 (Part B 619) data systems. TA also will assist Part C and Part B 619 state agencies in participating in the development of integrated early childhood data systems and longitudinal data systems in their states. To inform the DaSy Center's work, the Center collected information about the current status of Part C and Part B 619 state data systems, priorities for improvement, and areas where the states would like TA. State Part C and Part B 619 coordinators, their respective data managers, and other state staff completed an online survey over the summer of 2013. Responses were obtained from 94% of the 50 states, DC, and Puerto Rico for Part C and from 96% for Part B 619. This report summarizes what was learned about the current status of Part C and Part B 619 data systems and where states are in moving to improve their data systems. (author abstract)
Issues in accessing and using administrative data (OPRE Report #2017-24). Maxwell, K. (2017). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Report No
This report details issues in accessing and using administrative data for social policy research, including access, relationship-building, confidentiality, and capacity-building.
Mapping data flows: Checklist (PTAC-CL-7). Privacy Technical Assistance Center, & DaSy Center. (2015). Washington, DC: Privacy Technical Assistance Center. Fact sheets and briefs No
The purpose of the checklist is to provide steps to understand data flows, sources, and elements in a data system helps to determine which laws apply to which types of elements. Mapping also helps to understand the data and communicate about data-related issues more effectively, both internally and externally.
Maryland: Establishing partnerships to build data use capacity . Ruggiero, T., Gupta, S., Nicholas, A., & Mauzy, D. (2016). Menlo Park, CA: DaSy Center. Report Yes
This report describes the partnership between the Maryland State Department of Education (MSDE) and the Johns Hopkins University Center for Technology in Education to build a statewide longitudinal data system with linkages across early intervention, early childhood special education, school-age special education, and general education. The brief also offers guiding questions for other state agencies to use when pursuing partnerships with institutions of higher education.
SLDS topical webinar summary: Identity management approaches: Protecting access while serving multiple stakeholders. Rodriguez, B., Gibson, N., & Swiggum, R. (2012). Washington, DC: National Center for Education Statistics. Webinar summaries No
This webinar summary is based on a webinar that addressed innovative use of local early childhood data. Dr. Cindy Decker, Director of Assessment and Accountability for the Tulsa Community Action Project (CAP), presented CAP's efforts to gather classroom quality data, child outcomes, and health and workforce data, as well as CAP's efforts to link these data to area public schools' data systems. Dr. Jason Sachs, Early Childhood Director for Boston Public Schools (BPS), presented Boston's efforts to track school readiness assessment data, classroom quality data, workforce data, and child outcomes from pre-K to the K12 system, as well as how the district has used these data to influence policy and program development.
State spotlight: Data sharing: Alaska: Improving referrals of victims of maltreatment to the IDEA Part C program. Derrington, T., Peters, M. L., Mauzy, D., & Ruggiero, R. (2015). Menlo Park, CA: DaSy Center. Fact sheets and briefs yes
This spotlight describes how Alaska Part C improved the referral of children from Child Welfare to the Individuals with Disabilities Education Act (IDEA) Part C Program by an automated transfer of data from Child Welfare to Part C for substantiated cases of child maltreatment (i.e., child abuse and/or neglect).
State data policies for data collection. National Association for the Education of Young Children. (2009). Washington, DC: National Association for the Education of Young Children. State profiles No
An online resource that provides links to state's policies regarding data collection.
Statewide Longitudinal Data Systems Grant Program resource database. National Center for Education Statistics. (n.d.). Washington, DC: National Center for Education Statistics. Tools No
The Statewide Longitudinal Data Systems Grant Program offers a variety of support resources.
The status of state data dictionaries. Common Education Data Standards. (n.d.). Washington, DC: Common Education Data Standards. Fact sheets and briefs Yes
This paper looks at the status of state data dictionaries in order to highlight states' experiences, common challenges, and guidance for other states. The purpose of this paper is to provide a perspective on the development and implementation of state data dictionaries and to offer guidance to states. In addition, the paper considers how tools such as data dictionaries factor into larger data initiatives (e.g.,CEDS, EDFacts, and SLDS).
Strategies for handling 'opt out'. Gosa, K., Popp, J., & Rodriguez, B. (2016). Washington, DC: National center for Education Statistics. Fact sheets and briefs Yes
This brief provides an overview of the data collections and data uses that are often part of opt-out measures. It also examines strategies that states are developing to handle opt-out preferences in their SLDSs and ensure that data are properly managed.
Toolkit for communities using health data: How to collect, use, protect, and share data responsibly. National Committee on Vital and Health Statistics. (2015). Hyattsville, MD: National Center for Health Statistics. Fact sheets and briefs No
The Toolkit briefly introduces each important principle of data stewardship for communities using health data. It provides both broad background information and tips for data users. Descriptions of stewardship principles are provided, along with checklists for each principle.
Unique identifiers: Beyond K12. Cochenour, M., Duarte, S., Irvine, S., & Sellers, J. (2014). Washington, DC: National Center for Education Statistics. Fact sheets and briefs No
The purpose of this brief is to support early childhood integrated data systems leaders in understanding unique identification numbers (UID); communicating the benefit of assigning UIDs in early childhood; understanding possible approaches to assigning UIDs; and identifying key considerations in carrying out the work.
Using administrative data in social policy research (OPRE Report #2016-62). U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. (2016). Washington, DC: U.S. Administration for Children and Families, Office of Planning, Research and Evaluation. Fact sheets and briefs No
This brief summarizes an Innovative Methods Meeting that was organized by OPRE in the fall of 2015 that considered the potential benefits and pitfalls of using administrative data for research purposes. Topics included: Promises and challenges; Balancing access to data with maintaining confidentiality; Innovative applications; Working with administrative data; Federal efforts and future directions.

Data Confidentiality/Security

Resources related to best practices for implementing data security agreements and addressing confidentiality/privacy issues when using and reporting administrative data.

Resource
Resource
Type
Type
State Example?
State Example?
Description (author abstract if available)
Description (author abstract if available)
Access and confidentiality issues with administrative data. Brady, H. E., Grand, S. A., Powell, M. A., & Schink, W. (2002). In M. Ver Ploeg, R. A. Moffitt, & C. F. Citro (Eds.), Studies of welfare populations: Data collection and research issues: Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs (pp. 220-274). Washington, DC: National Academy Press. Book chapters No
In this paper we consider ways to facilitate researchers' access to administrative data collected about individuals and their families in the course of providing public benefits. In most cases, applicants to social welfare programs are required to disclose private information deemed essential to determining eligibility for those programs. Individuals who are otherwise eligible for services but who refuse to provide information may be denied those services. Most people forgo privacy in these circumstances; that is, they decide to provide personal information in order to obtain public benefits. They believe that they have little choice but to provide the requested information. Consequently, it is widely agreed that the uses of this information should be limited through confidentiality restrictions to avoid unwanted disclosures about the lives of those who receive government services.
Barriers to using administrative data for evidence-building. U.S. Office of Management and Budget. (2016). Washington, DC: U.S. Office of Management and Budget. Fact sheets and briefs No
This white paper details many of the barriers to using administrative data for evidence-building and how resource and capacity concerns can constrain the functional access and use of data even when legal and policy issues are resolved. The paper also provides a case study on how these barriers interact with access to various sources of wage data for evidence-building purposes.
Best practices for data destruction (PTAC-IB-5). Privacy Technical Assistance Center. (2014). Washington, DC: Privacy Technical Assistance Center. Fact sheets and briefs No
This document provides an overview of various methods for disposing of electronic data, and discusses how these methods relate to legal requirements and established best practices for protecting student information.
Case study #5: Minimizing access to PII: Best practices for access controls and disclosure avoidance techniques (PTAC-CS-5). Privacy Technical Assistance Center. (2015). Washington, DC: Privacy Technical Assistance Center. Fact sheets and briefs No
The purpose of this document is to provide a case study to illustrate best practices for minimizing access to sensitive information with education data maintained in a statewide longitudinal data system.
Challenges in using administrative data: Legal, technical and political. Petrila, J. (2015). Paper presented at the Office of Planning, Research, and Evaluation meeting, The Promises and Challenges of Administrative Data in Social Policy Research, Washington, DC. Presentation No
This presentation acknowledges the various legal, technical, and political barriers faced when trying to access and use administrative data.
Creating and managing a data governance entity (Data Direction 1, Publication # 2016-31). Lin, V. K., Epstein, D., & Maxwell, K. L. (2016). Bethesda, MD: Child Trends. Report No
This report provides practical steps for how to establish a data governance structure.
Confidentiality issues: Addressing questions about sharing data among organizations. Thornburg, K., & Rodriguez, B. (2014). Washington, DC: Early Learning Challenge Technical Assistance Program. Fact sheets and briefs No
This brief summarizes of a webinar on confidentiality issues faced by early childhood education programs that manage, view, and share data on children and/or interface with integrated and longitudinal data systems. It addresses select restrictions in place under Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act (HIPPA), and includes a list of commonly-asked questions and answers on the topic as well as a record of those asked and answered during the webinar.
Confidentiality toolkit: A resource tool from the ACF Interoperability Initiative. ACF Interoperability Initiative. (2014). Washington, DC: U.S. Administration for Children and Families. Tools No
The Administration for Children and Families (ACF) developed this Confidentiality Toolkit to help jurisdictions successfully navigate the delicate balance between privacy and security with the delivery of efficient and effective services. The Confidentiality Toolkit analyzes, explains and aids states and local jurisdictions in the navigation of a number of federal laws that impact the implementation of human services. Embedded throughout are success stories and sample documents from across the country from which jurisdictions using the Toolkit can borrow freely. This Toolkit has been developed for leaders in the human service field, to support their best efforts to share information across silos. (author abstract)
Data de-identification: An overview of basic terms. Privacy Technical Assistance Center, & DaSy Center. (2014). Washington, DC: Privacy Technical Assistance Center. Fact sheets and briefs No
This document is intended to assist early childhood stakeholders in maintaining compliance with privacy and confidentiality requirements under IDEA and FERPA. It reviews the terminology used to describe data de-identification as well as related concepts and approaches; provides general best practice de-identification strategies and statistical techniques to protect children against data disclosures; and identifies additional resources on applicable IDEA and FERPA requirements.
Data Governance Checklist. Privacy Technical Assistance Center. (2011). Washington, DC: Privacy Technical Assistance Center. Checklists No
This checklist is designed to assist stakeholder organizations with establishing and maintaining a successful data governance program by summarizing the key data privacy and security components of such a program and listing specific best practice action items.
Data Security Checklist. Privacy Technical Assistance Center. (2011). Washington, DC: Privacy Technical Assistance Center. Checklists No
This checklist is designed to assist stakeholder organizations with developing and maintaining a successful data security program by listing essential components that should be considered when building such a program, with focus on solutions and procedures relevant for supporting data security operations of educational agencies.
Data sharing agreement checklist for IDEA Part C and Part B 619 agencies and programs. Privacy Technical Assistance Center, & DaSy Center (2014). Menlo Park, CA: DaSy Center. Report No
This 2014 document is an adaptation of the 2012 release of "Data Sharing Agreement Checklist" intended for K-12 audiences. Presented as a checklist, the document summarizes the requirements for the written agreements under the audit or evaluation exception that is specified in FERPA and that also applies to the IDEA for Part C early intervention and Part B 619 preschool special education.
Policies for users of student data: A checklist (PTAC-CL-8). Privacy Technical Assistance Center. (2015). Washington, DC: Privacy Technical Assistance Center. Fact sheets and briefs No
The purpose of this checklist is to define a data use policy and provide best practices for using student data.
Privacy and confidentiality in the use of administrative and survey data. U.S. Office of Management and Budget. (2016). Washington, DC: U.S. Office of Management and Budget. Report No
This white paper reviews some of the most relevant U.S. privacy laws and discusses how they provide a strong legal framework that governs Federal agencies, using the Census Bureau's Data Stewardship program as a case study. The paper then considers a range of protocols used by different agencies to provide researcher access to restricted data, such as the National Center for Education Statistics data licensing program.
Statewide Longitudinal Data Systems Grant Program resource database. National Center for Education Statistics. (n.d.). Washington, DC: National Center for Education Statistics. Tools No The Statewide Longitudinal Data Systems Grant Program offers a variety of support resources.
Toolkit for communities using health data: How to collect, use, protect, and share data responsibly. National Committee on Vital and Health Statistics. (2015). Hyattsville, MD: National Center for Health Statistics. Reports and papers No The Toolkit briefly introduces each important principle of data stewardship for communities using health data. It provides both broad background information and tips for data users. Descriptions of stewardship principles are provided, along with checklists for each principle.
Toolkit for districts working with external researchers. Regional Educational Laboratory Northeast & Islands. (n.d.). Washington, DC: Regional Educational Laboratory Northeast & Islands. Tool Yes This toolkit is made up of a set of procedures and materials for districts and states to better help them manage data requests from external researchers and encourage them to support district or state research agendas in meaningful ways. The results of this work are organized into five modules.
Statewide Longitudinal Data Systems Grant Program resource database. National Center for Education Statistics. (n.d.). Washington, DC: National Center for Education Statistics. Fact sheets and briefs No This document is intended to assist elementary and secondary schools and local educational agencies (LEAs or "districts") in achieving greater transparency with respect to their data practices.

Websites as Resources

The following websites offer resources for linking, analyzing, managing, and protecting administrative data. Visit these websites to learn about additional tools, conferences, trainings, and technical assistance that may be available.

The resources on this page were compiled by the Child Care Administrative Data Analysis Center (CCADAC), a project carried out by Child Trends and funded by the Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. CCADAC was created to support the use of child care and early childhood administrative data to address policy-relevant research questions.