Essential Questions

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Identify your essential question(s) about promoting developmental progress in pre-K. Select from the framework’s essential questions or draft your own. Leading with essential questions can help you understand and prioritize what data you need to collect to answer those questions. The process of defining essential questions can also be a way to bring people together to agree on shared priorities.

For example, you want to understand “Are eligible children enrolled in quality, full-day pre-K programs?”

Indicators

Once you have identified your essential question(s), assess what data (indicators and metrics) you need to answer the question(s). What data can you already access? For which indicators would you need additional information?

For example, to answer the question above, you would need data on rates of enrollment in quality public pre-K, access to quality public pre-K, and access to full-day pre-K.

Disaggregates

Disaggregate data to understand trends and patterns. Breaking data down by different populations, system conditions, or experiences can help data users assess disparities, expose hidden trends, and make informed decisions that lead to more equitable outcomes.

For example, your organization focuses on mobilizing resources to support students experiencing poverty, so you disaggregate data on your chosen indicators by income level to understand how students experiencing poverty fare compared to students who are not experiencing poverty.

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Evidence-Based Practices

Once you have collected and analyzed data to understand trends and patterns, use this information to select evidence-based practices to improve supports or outcomes related to your essential question(s). Using data to guide your selection of practices can help you make choices that meet the unique needs and opportunities in your community.

For example, if the data reveal that students experiencing poverty in your community have lower rates of access to high-quality pre-K programs, you may advocate for or implement interventions that improve the quality of pre-K offerings in schools serving low-income populations, such as skill-based curricula or teacher coaching and professional development. 

Example equitable data practices  

• Hold listening sessions with pre-K community members—such as early childhood practitioners and parents—to understand what questions they consider most pressing for the community. Collaborators can help you prioritize essential questions that community members find relevant and understand what data are most important to answer those questions meaningfully (Data Equity Principle 1).
• Disaggregate both outcome and systems data at multiple levels to illuminate any disparities. For example, breaking down data by characteristics such as home language, parental education level, or migrant family household can provide educators with key insights for fostering strong and equitable relationships with families (Data Equity Principle 3).
• Acknowledge whom the analysis does not represent, whether due to insufficient data or another reason, to help you interpret data and improve future efforts. For example, publicly available state or district-level pre-K data may not include children who attend other early childhood programs, such as Head Start programs or private pre-K (Data Equity Principle 6).

Related Framework Content

To view other essential questions, indicators, disaggregates, and evidence-based practices that can help promote developmental progress in pre-K, visit any of the following website pages filtered by Sector: Pre-K. You can also generate a personalized, downloadable packet of related content using filters on any of the pages linked below. Apply filters and then select “Personalize Your Resource Packet.”

• Essential Questions
• Indicators
• Disaggregates
• Evidence-Based Practices

Looking for Data? 

To identify readily available data for promoting developmental progress in pre-K, explore the external data source suggested below—and don’t forget to seek out state and local data sources that are unique to your community.

• System Transformation for Equitable Preschools (STEP Forward with Data) Framework Implementation Guide – Child Trends. The STEP Forward with Data Framework supports preschool system leaders in using data to promote equity by helping them understand the experiences of children, families, and workforce members. The Appendix of the framework’s implementation guide offers a comprehensive list of external data sources related to preschool systems, such as the NIEER State of Preschool Yearbooks and the QRIS Compendium Datasets.

Essential Questions

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Identify your essential question(s) about K-12 outcomes. Select from the framework’s essential questions or draft your own. Leading with essential questions can help you understand and prioritize what data you need to collect to answer those questions. The process of defining essential questions can also be a way to bring people together to agree on shared priorities.

For example, you want to understand “Are students meeting reading and math benchmarks in grades 3 and 8?” 

Indicators

Once you have identified your essential question(s), assess what data (indicators and metrics) you need to answer the question(s). What data can you already access? For which indicators would you need additional information?

For example, to answer the question above, you would need data on math and reading proficiency in grade 3 and math and reading proficiency in grade 8. You are also interested in understanding whether students have access to effective teaching to help them achieve proficiency, so you decide to gather data on teacher credentials and teacher experience.

Disaggregates

Disaggregate data to understand trends and patterns. Breaking data down by different populations, system conditions, or experiences can help data users assess disparities, expose hidden trends, and make informed decisions that lead to more equitable outcomes.

For example, you are interested in the extent to which English learners are achieving these outcomes and milestones relative to native English speakers, so you disaggregate data on these indicators by English learner status. 

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Evidence-Based Practices

Once you have collected and analyzed data to understand trends and patterns, use this information to select evidence-based practices to improve supports or outcomes related to your essential question(s). Using data to guide your selection of practices can help you make choices that meet the unique needs and opportunities in your community.

For example, if the data show that native English speakers have higher rates of math and reading proficiency and greater access to credentialed, highly experienced teachers than do native English speakers, you may advocate for interventions that provide both individualized supports for English learners, such as small, personalized learning communities, as well as system supports for educators, such as teacher coaching and professional development. 

Example equitable data practices

• Hold listening sessions with K–12 community members—such as students, teachers, and administrators—to understand what essential questions are most pressing for the community. Collaborators can provide input on whether you have prioritized the right essential questions and what data you should collect to answer those questions (Data Equity Principle 1).
• Apply privacy-enhancing technologies, such as encryption or secure hashing, to protect student data while maintaining the data’s usefulness. To maintain confidentiality when reporting data, avoid reporting in a way that could lead to an individual being identified, such as reporting information on small groups of individuals in a specific district or school (Data Equity Principle 2). 
• Disaggregate both outcome and systems data to illuminate any disparities in how students are experiencing their K–12 journeys, not just how they are progressing on outcomes. Break down data by both individual characteristics (such as disability status or race and ethnicity) and system characteristics (such as K–12 school type) to empower communities to make more informed decisions that focus on improving systems, avoid inadvertently placing blame on particular groups, and ultimately achieve more equitable outcomes (Data Equity Principle 3).

Related Framework Content

To view other essential questions, indicators, disaggregates, and evidence-based practices that can help advance K-12 student outcomes, visit any of the following website pages filtered by Sector: K-12. You can also generate a personalized, downloadable packet of related content using filters on any of the pages linked below. Apply filters and then select “Personalize Your Resource Packet.”

• Essential Questions
• Indicators
• Disaggregates
• Evidence-Based Practices

Looking for Data?

To identify readily available data for advancing K–12 student outcomes, explore the external data sources suggested below—and don’t forget to seek out state and local data sources that are unique to your community.

• Condition of Education Dashboard – National Center for Education Statistics (NCES). The Condition of Education contains key indicators on all levels of education, labor force outcomes, and international comparisons. The indicators summarize important developments and trends using the latest statistics that are updated throughout the year. 
• National Assessment of Educational Progress (NAEP) – NCES. NAEP reports assessment results at the state level, most often in grades 4 and 8, in four subjects—mathematics, reading, science, and writing. Because NCES administers the same assessment in every state, NAEP provides a common measure for student achievement in public schools across the country.
• Education Data Explorer – Urban Institute. Developed by the Urban Institute, this tool allows you to build your own data set by narrowing your search by education level, geography, time frame, and indicator. The Data Explorer makes it easy to generate rigorous, accurate, and actionable insights to improve student outcomes.
• Stanford Education Data Archive (SEDA) – Stanford University. Developed by Stanford University’s Educational Opportunity Project, SEDA is a national database that includes applications and research articles on national academic performance from public-school test scores in grades 3–8 from 2008 to 2023. You can download and access SEDA and related data sets to start your own research or view interactive dashboards and export reports on measures of educational opportunity, COVID impact and recovery data, and more. 
• Civil Rights Data Collection (CRDC) – U.S. Department of Education. CRDC is a biennial survey required by the U.S. Department of Education's Office for Civil Rights. The CRDC includes data from a universe of all public local educational agencies and schools, including justice facilities, charter schools, and alternative schools. It focuses on civil rights indicators related to access and barriers to educational opportunities from early childhood through grade 12.

Essential Questions

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Identify your essential question(s) about students' educational experiences. Select from the framework’s essential questions or draft your own. Leading with essential questions can help you understand and prioritize what data you need to collect to answer those questions. The process of defining essential questions can also be a way to bring people together to agree on shared priorities.

For example, you want to understand “Do students have access to quality school environments, including quality curricula and instruction, experienced teachers, effective leaders, and adequate funding?” 

Indicators

Once you have identified your essential question(s), assess what data (indicators and metrics) you need to answer the question(s). What data can you already access? For which indicators would you need additional information?

For example, to answer the question above, you would need data on access to quality, culturally responsive curricula; teacher experience; effective program and school leadership; and expenditures per student.

Disaggregates

Disaggregate data to understand trends and patterns. Breaking data down by different populations, system conditions, or experiences can help data users assess disparities, expose hidden trends, and make informed decisions that lead to more equitable outcomes.

For example, if you are interested in the extent to which different types of schools offer quality environments, you might disaggregate data by K-12 school type. Or if you are interested in the extent to which students from low-income households have access to quality school environments, you might disaggregate data by income level. 

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Evidence-Based Practices

Once you have collected and analyzed data to understand trends and patterns, use this information to select evidence-based practices to improve supports or outcomes related to your essential question(s). Using data to guide your selection of practices can help you make choices that meet the unique needs and opportunities in your community.

For example, if the data show that students have access to experienced teachers and effective leaders, but not high-quality curricula, you could advocate for or implement an evidence-based curriculum.

Example equitable data practices

• Collaborate with in-school and school-adjacent community members—such as school social workers, parents, and district leaders—to select evidence-based practices to address disparities. Example evidence-based practices for improving educational experiences might include intensive, individualized support for students off track on early warning indicators or SEL curricula and programs (Data Equity Principle 4).
• Use qualitative data, such as interview or focus group data, to support your interpretation of quantitative data by capturing additional insights and ensuring students' voices are reflected in research findings (Data Equity Principle 5).
• Communicate findings to data providers in a format that is actionable and useful for improving educational practices. For example, share findings about school climate with educators, school social workers, and other support staff, using visualizations that can be easily interpreted and used to guide action (Data Equity Principle 6 and Data Equity Principle 7).

Related Framework Content

To view other indicators of students' environments and experiences, visit the Indicators page filtered by Indicator Type: E-W System Conditions. 

Looking for Data?

To identify readily available data or explore how to collect your own data on improving educational experiences, explore the external data sources suggested below—and don’t forget to seek out state and local data sources that are unique to your community.

• Civil Rights Data Collection (CRDC) – U.S. Department of Education. The CRDC is a biennial survey required by the U.S. Department of Education's Office for Civil Rights. The CRDC collects data from a universe of all public local educational agencies and schools, including justice facilities, charter schools, and alternative schools. It focuses on civil rights indicators related to access and barriers to educational opportunities from early childhood through grade 12.
• College Scorecard – U.S. Department of Education. This tool aims to help students of all ages, families, educators, counselors, and other college access professionals make data-informed decisions when choosing a college or university to attend. The College Scorecard offers increased transparency about the benefits and costs of higher education by publishing data on college costs, student debt, graduation rates, admissions test scores and acceptance rates, student body diversity, post-college earnings, and more.
• ED School Climate Surveys (EDSCLS) – National Center for Education Statistics (NCES). NCES has developed high-quality adaptable School Climate Surveys to help states, localities, and schools collect and report on reliable, nationally validated school climate data. Surveys can be administered through the U.S. Department of Education’s (ED’s) web-based platform to middle and high school students, families, and school staff. Educational entities store their own data locally, and ED does not have access to these data.
• Launch Guide for Collecting Curriculum Selection Data – Center for Education Market Dynamics. Developed by the Center for Education Market Dynamics, this guide is designed for leaders in state education agencies who are interested in collecting curriculum selection data from school districts. It includes strategy questions, conversation starters, and useful tools to kick-start data collection efforts and maximize effectiveness.

Essential Questions

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Identify your essential question(s) about postsecondary transitions and success. Select from the framework’s essential questions or draft your own. Leading with essential questions can help you understand and prioritize what data you need to collect to answer those questions. The process of defining essential questions can also be a way to bring people together to agree on shared priorities.

For example, you want to understand “Are students experiencing sufficient early momentum in postsecondary education to be on track for on-time completion?”

Indicators

Once you have identified your essential question(s), assess what data (indicators and metrics) you need to answer the question(s). What data can you already access? For which indicators would you need additional information?

For example, to answer the question above, you would need data on first-year credit accumulation, first-year program of study concentration, and postsecondary persistence. You also want to understand how well students are supported in their early postsecondary journey, so you aim to gather data on access to college and career advising.

Disaggregates

Disaggregate data to understand trends and patterns. Breaking data down by different populations, system conditions, or experiences can help data users assess disparities, expose hidden trends, and make informed decisions that lead to more equitable outcomes.

For example, if you are interested in the extent to which different types of schools offer quality environments, you might disaggregate data by postsecondary institution classification. Or if you are interested in the extent to which students in certain fields of study fare better or worse than their peers, you might disaggregate data by postsecondary major. 

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Evidence-Based Practices

Once you have collected and analyzed data to understand trends and patterns, use this information to select evidence-based practices to improve supports or outcomes related to your essential question(s). Using data to guide your selection of practices can help you make choices that meet the unique needs and opportunities in your community.

For example, if the data suggest that students could benefit from more robust advising supports early in their postsecondary experience, you could advocate for or implement a comprehensive, integrated advising program.

Example equitable data practices

• Establish a governance or review body with representation from multiple contributing groups, such as academic advisors and students themselves, to oversee data collection initiatives. Convene this body to agree on the goals of the project, identify risks and benefits, develop mitigation strategies, and inform decisions at each phase of the data cycle (Data Equity Principle 1).
• Disaggregate both outcome and systems data to illuminate any disparities and identify which student groups may need additional support. For example, break down data by characteristics such as credential-seeking status, which can help institutions support students seeking different types of credentials, or postsecondary institution classification, which can demonstrate the extent to which different types of institutions deliver positive outcomes for students (Data Equity Principle 3).
• Gather students’ input when selecting evidence-based practices to implement in postsecondary settings. Student-informed data should guide your decision making to ensure practices and programs, such as mentoring and coaching or digital learning, are tailored to the unique needs of your students (Data Equity Principle 4).

Related Framework Content

To view other essential questions, indicators, disaggregates, and evidence-based practices that can help support postsecondary transitions and success, visit any of the following website pages filtered by Sector: Postsecondary. You can also generate a personalized, downloadable packet of related content using filters on any of the pages linked below. Apply filters and then select “Personalize Your Resource Packet.”

• Essential Questions
• Indicators
• Disaggregates
• Evidence-Based Practices

Looking for Data?

To identify readily available data for preparing learners for postsecondary transitions and success, explore the external data sources suggested below—and don’t forget to seek out state and local data sources that are unique to your community.

• Integrated Postsecondary Education Data System (IPEDS) Data Explorer – National Center for Education Statistics (NCES). IPEDS is a system of 12 interrelated survey components conducted annually that gathers and reports data from every college, university, and technical and vocational institution that participates in federal student financial aid programs. It focuses on areas that include: institutional prices; admissions; enrollment; student financial aid; degrees and certificates conferred; student persistence and success; and institutional resources. NCES collects IPEDS data in fall, winter, and spring of each year.
• National Student Clearinghouse (NSC). The NSC is a leading provider of educational reporting, data exchange, verification, and research services. Its Research Center publishes data and reports to better inform practitioners and policymakers about student educational pathways and enable informed decision making. NSC also offers a variety of subscription-based services, such as StudentTracker® and the Postsecondary Data Partnership, to manage individual- and institution-level data.

Essential Questions

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Identify your essential question(s) about workforce transitions and success. Select from the framework’s essential questions or draft your own. Leading with essential questions can help you understand and prioritize what data you need to collect to answer those questions. The process of defining essential questions can also be a way to bring people together to agree on shared priorities.

For example, you want to understand “Are students gaining access to quality jobs that offer economic mobility and security after high school or postsecondary training and education?”

Indicators

Once you have identified your essential question(s), assess what data (indicators and metrics) you need to answer the question(s). What data can you already access? For which indicators would you need additional information?

For example, to answer the question above, you would need data on rates of employment in a quality job, economic mobility, and economic security. You also want to understand labor market conditions affecting this question, so you aim to gather data on whether job seekers have access to jobs paying a living wage.

Disaggregates

Disaggregate data to understand trends and patterns. Breaking data down by different populations, system conditions, or experiences can help data users assess disparities, expose hidden trends, and make informed decisions that lead to more equitable outcomes.

For example, if you are interested in the extent to which individuals in different occupations experience economic mobility and security, you may disaggregate data by occupation category.

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Evidence-Based Practices

Once you have collected and analyzed data to understand trends and patterns, use this information to select evidence-based practices to improve supports or outcomes related to your essential question(s). Using data to guide your selection of practices can help you make choices that meet the unique needs and opportunities in your community.

For example, if the data show strong prospects for job growth and economic mobility in a certain sector, you might promote the expansion of sector-oriented job training programs in that field.

Example equitable data practices

• Work with community members who support workforce transitions to determine which characteristics to measure during data collection. Disaggregate both outcome and systems data at multiple levels to illuminate any disparities. Example disaggregates for the workforce sector might include occupation category or age group, which can help identify whether individuals aligned to certain occupation types or within certain age groups are experiencing barriers to advancement (Data Equity Principle 3).
• Use qualitative data, such as interview or focus group data, to help you interpret quantitative data by capturing additional insights and ensuring workers’ voices are reflected in research findings (Data Equity Principle 5).
• Communicate findings to data providers in a format that is actionable and useful for improving workforce supports. For example, use visualizations to share findings about worker well-being with employers so they can easily interpret and use the data to guide action (Data Equity Principle 6 and Data Equity Principle 7).

Related Framework Content

To view other essential questions, indicators, disaggregates, and evidence-based practices that can help support workforce transitions and success, visit any of the following website pages filtered by Sector: Workforce. You can also generate a personalized, downloadable packet of related content using filters on any of the pages linked below. Apply filters and then select “Personalize Your Resource Packet.”

• Essential Questions
• Indicators
• Disaggregates
• Evidence-Based Practices

Looking for Data?

To identify readily available data for supporting workforce transitions and success, explore the external data sources suggested below—and don’t forget to seek out state and local data sources that are unique to your community.

• Perkins State Plans and Data Explorer – U.S. Department of Education. The Perkins State Plans and Data Explorer is designed to provide career and technical education (CTE) practitioners, researchers, and other interest holders with access to information and data submitted by states in their Perkins V State plans. This information and data can help interested parties understand each state’s vision, goals, and priorities for CTE as well as explore the outcomes for students who concentrate in CTE programs.
• Interactive Data Analysis Tool – Workforce Innovation and Opportunity Act (WIOA). This tool contains data compiled from the WIOA Individual Performance Records Full Use Data files, which collect data submitted by states on a quarterly and yearly basis about who is served under WIOA and their outcomes.

The E-W Framework can be used to help establish priorities, identify what data to collect, and understand how to strengthen data practices to guide action. You might choose to:

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• Strengthen data systems by tracking and reporting well-established indicators. Refer to our Indicator Readiness Brief for more information on which indicators and metrics are most well-established and ready for adoption in existing state and local data systems. 
• Map existing data to the framework’s recommended indicators and metrics. Download our E-W Framework indicators and metrics Excel spreadsheet tool and use it as a checklist to identify which indicators and metrics your organization is already collecting. 

Explore our case studies on how other organizations have taken an equity-centered approach to developing new or improved data systems:

• The District of Columbia’s Office of Education Through Employment Pathways used the E-W Framework as a resource to initiate and guide discussions about what types of data are possible to collect, thinking beyond common accountability-focused outcome indicators to more expansive indicators of neighborhood context and system supports. This endeavor found the framework’s overall structure—anchoring data collection efforts in essential questions to determine what measures matter most—to be a helpful, easy-to-understand frame for communicating and sharing with both internal and external audiences.
• California’s Cradle-to-Career Data System exemplifies an equity-centered approach to designing and developing a new E-W data system. More than 200 people from 15 state agencies and several educational institutions, research and policy organizations, and community groups worked together to design the blueprint for the Cradle-to-Career Data System. California has also implemented a transparent, inclusive decision-making and governance structure for the new system to ensure students, families, educators, researchers, and policymakers alike can use the data effectively. 

Example equitable data practices

• As appropriate, securely share data with partners to reduce the burden of duplicate data collection (Data Equity Principle 1).
• Communicate data privacy and security processes when collecting data. Seek informed consent even if not required. Only collect data that are necessary and have been approved (Data Equity Principle 2).
• Work with community members to determine which characteristics to measure during data collection or to link into the data (if available), and how to label these characteristics in data collection and reporting (for example, Hispanic, Latino/a, Latinx) (Data Equity Principle 3).

External resources

Deciding what to measure and how to measure it is only part of the puzzle, and there are many partners in the field who offer additional tools to help you with data modernization efforts. Explore the resources below for guidance on how to develop infrastructure (such as state longitudinal data systems) or understand legal requirements (such as data sharing agreements). 

• Data Integration Support Center. Developed by WestEd, this collection of resources provides a variety of quick reads, videos, and in-depth resources to support education-to-workforce (also known as P20W+) data integration efforts.
• State Information Request: Establishing a Statewide Longitudinal Data System. Developed by the Education Commission of the States, this resource contains an overview of how states establish their statewide longitudinal data systems and examples of how these systems can be effectively used.
• Getting the Facts Straight About Statewide Longitudinal Data Systems (SLDS). Developed by the Data Quality Campaign (DQC), this resource explores common myths about linking data within SLDSs. Explore DQC’s Resources page for additional tools—such as fact sheets, research reports, and infographics—for advancing the effective use of education data.
• Statewide Longitudinal Data Systems (SLDS) Grant Program Publications. The National Center for Education Statistics’ SLDS team produces various types of products to capture best practices from the field and meet the evolving needs of the community. This page includes best practices, guides, issue briefs, state spotlights, and target team publications. States can also request customized technical assistance from the SLDS State Support Team.
• Community of Innovation (COI) Map. Developed by the P20W+ COI, this map seeks to support anyone stewarding and advancing critical data systems from early childhood through workforce. Resources are organized by role and by type of work (such as planning, governance, infrastructure, privacy, and data use) to help you along your path.

The E-W Framework’s five components can be used to guide evidence-informed decisions. For example:

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Funding and research

• Download our Indicator Readiness Brief to view the E-W Framework indicators categorized into three levels of readiness for adoption: well-established (high readiness), evolving (medium readiness), and emerging (low readiness). Funders and researchers might be particularly interested in advancing the evidence base for indicators classified as “evolving” or “emerging.”
• Explore Disaggregates In Depth for guidance on how to identify background or contextual characteristics of individuals and systems by which data should be examined to analyze disparities, monitor progress, and guide action.

Example equitable data practice

Visit Data Equity Principles In Depth for more detailed information on how to apply all seven principles throughout research projects. Our Data Equity Principles At-a-Glance resource provides an overview of the principles and helpful reflection questions. 

Programmatic

• Visit Evidence-Based Practices In Depth to learn more about what evidence-based practices are and how to select them. The E-W Framework highlights 26 example practices that research shows can move the needle on key student outcomes and milestones and related system conditions. You can filter the framework’s 26 evidence-based practices by sector, as well as generate a personalized, downloadable packet of related by selecting “Personalize Your Resource Packet" once you have selected a filter.

Example equitable data practice

Collaborate with communities to select, implement, and make any necessary adaptations to evidence-based practices that can help improve outcomes and fit the needs of the community (Data Equity Principle 4).

Advocacy

• Use our Essential Questions Guide for step-by-step guidance on identifying essential questions that matter most to your community. Advocacy groups can use this guide as a tool for bringing people together to agree on shared priorities. You can filter the framework’s 20 essential questions by sector, as well as generate a personalized, downloadable packet of related by selecting “Personalize Your Resource Packet" once you have selected a filter.

Example equitable data practice 

Recruit community members to participate in initiative teams or advisory councils tasked with determining the highest-priority essential questions. Use facilitation methods that promote equitable participation (Data Equity Principle 7).