IES Blog

Institute of Education Sciences

Celebrating the ECLS-K:2024: Participating Children Are the Key to Increasing Our Knowledge of Children’s Education and Development Today

As we highlighted in our March blog post, NCES is excited to be in the field for the base-year data collection of our newest national early childhood study, the Early Childhood Longitudinal Study, Kindergarten Class of 2023–24 (ECLS-K:2024). Although the study collects much needed data from a variety of adult respondents (i.e., parents/guardians, teachers, and school administrators), the heart of the ECLS-K:2024 is our data collection with the participating children.

With the permission of their parent/guardian, the children take part in the ECLS-K:2024 child session activities, answering engaging, age-appropriate reading and math questions during one-on-one sessions with trained ECLS-K:2024 team members (watch an example of children participating in the child activities). These ECLS-K:2024 child sessions are planned for every currently expected round of data collection, starting with the fall and spring of the current school year (2023–24) when the children are in kindergarten.

Although the child sessions look pretty similar every year, there are some changes to the activities as the children age. For example, in kindergarten and first grade, we include memory-related items in the sessions; we then swap out these items for child surveys in the later rounds, when children are in higher grades. In prior ECLS kindergarten cohort studies, child surveys included items on topics such as children’s sense of school belonging; worry or stress about school; media usage; and peer relationships. Explore the items we asked in the child surveys in the ECLS-K:2024’s sister studies, the ECLS-K and ECLS-K:2011. Many of these items will likely be asked again of the children participating in ECLS-K:2024. Also, in past studies, children had their height and weight measured to provide information about early physical development; this study component returns to the ECLS-K:2024’s spring data collection in some schools.

Child-provided data from the earlier ECLS program studies have been used extensively. A recent analysis conducted by the ECLS program team found that more than 1,000 studies and reports published between 2000 and 2021 have analyzed the ECLS academic skills and school performance data, with more than 80 percent of those utilizing the child assessment data. For example, NCES published a report on reading achievement over children’s early elementary school years using the ECLS-K reading assessment data. Use NCES’s Bibliography Search Tool to explore these reports (select “ECLS” from the Data Source drop-down menu).

If you’re instead interested in exploring trend data, research has been conducted on the differences between children who were in kindergarten during the 1998–99 and 2010–11 school years (use the Bibliography Search Tool to find reports on this topic). Additional research comparing kindergartners in the 1998–99 and 2010–11 school years with kindergartners in the 2023–24 school year is expected after this year’s ECLS-K:2024 data collection. Once the ECLS-K:2024 collection concludes, NCES will produce data files—made available to the public with deidentified data—that will allow for direct comparisons between these three groups of children. Our understanding of how the abilities of today’s kindergartners vary from those of kindergartners in the late 1990s and early 2010s relies on the participation of children in the ECLS-K:2024.  

Of course, it’s not just the children’s reading and mathematics data that will provide answers to key questions about education and child development. All the data the ECLS-K:2024 children are providing now in the study’s base year—as well as the data they will provide as they advance through their elementary years—will help inform our understanding of what today’s children know and can do.

On behalf of the thousands of researchers, policymakers, educators, and parents who rely on the important data provided by the ECLS-K:2024’s youngest contributors—thank you to our ECLS-K:2024 children!

Want to learn more?


Next up in this blog series celebrating the ECLS-K:2024, we’ll highlight the study’s parents and families. Keep an eye out this summer!

 

By Jill McCarroll and Korrie Johnson, NCES

Leveraging Economic Data to Understand the Education Workforce

The Digest of Education Statistics recently debuted 13 new tables on K–12 employment and wages from a data source that is new to the Digest—the Occupational Employment Wage Statistics (OEWS) program of the Bureau of Labor Statistics (BLS). NCES’s Annual Reports and Information Staff conducted an extensive review of existing and emerging data sources and found that BLS’s OEWS program provides high-quality, detailed, and timely data that are suitable to inform policymaking in education and workforce development.1 In this blog post, we share why we added this new data source, how we evaluated and prepared these data, and our future plans to expand on these efforts.

 

Need for Education Workforce Data

NCES recognized that education stakeholders need more granular and timely data on the condition of the education workforce to inform decisionmaking. In the wake of the coronavirus pandemic, school districts are looking to address critical staffing needs. According to NCES’s School Pulse Panel, entering the 2023–24 school year (SY), just under half of U.S. public schools reported feeling understaffed and had a need for special education teachers, transportation staff, and mental health professionals.

Since staffing needs and labor markets vary from district to district and state to state, it is important that we create national- and state-level tabulations for specific occupations, including those of special interest since the pandemic, like bus drivers, social workers, and special education teachers. Similarly, we want to be able to provide annual data updates so stakeholders can make the most up-to-date decisions possible.

Annual Digest table updates, coupled with detailed occupational and state-level data, will provide relevant and timely information on employment and wage trends that will be valuable in current and future efforts to address teacher and staff retention and recruitment. See below for a list of the new Digest tables.

  • National-level employment and annual wages
  • Selected teaching occupations (211.70)
  • Selected noninstructional occupations (213.70)
  • State-level employment and annual wages
  • Preschool teachers (211.70a)
  • Kindergarten teachers (211.70b)
  • Elementary school teachers (211.70c)
  • Middle school teachers (211.70d)
  • Secondary school teachers (211.70e)
  • Kindergarten and elementary special education teachers (211.70f)
  • Middle school special education teachers (211.70g)
  • Secondary school special education teachers (211.70h)
  • Substitute teachers (211.70i)
  • Teaching assistants (211.70j)
  • All occupations in the Elementary and Secondary Education industry (213.75)

 

Strengths of OEWS

OEWS and the Digest tables are aligned with the Federal Committee on Statistical Methodology’s Data Quality Framework, specifically the principles of objectivity (standardization), utility (granularity and timeliness), and integrity (data quality).


Standardization

OEWS produces employment and wage estimates using standardized industry and occupational classifications. Using the North American Industry Classification System, establishments are grouped into categories—called industries—based on their primary business activities. Like industries, occupations are organized into groups or categories based on common job duties (using the Standard Occupational Classification). Occupations that are common to K–12 schools can also be found in other industries, and the OEWS provides both cross-industry estimates and industry-specific estimates for just Elementary and Secondary Education industry. To provide the most relevant and comparable data for education stakeholders, NCES chose to focus on distinct occupational estimates for the Elementary and Secondary Education industry, since all establishments (e.g., school boards, school districts) provide the same services: instruction or coursework for basic preparatory education (typically K–12).2     

Another advantage of the OEWS data is the ability to examine specific detailed occupations, like elementary school teachers, secondary school teachers, and education administrators. Digest tables include estimates for specific instructional and noninstructional occupations, which allows users to make comparisons among teachers and staff with similar job responsibilities, providing opportunities for more targeted decisionmaking.


Granularity

In addition to data on detailed occupations, OEWS data provide data at national and state and levels, allowing for comparisons across geographies. National-level Digest tables include estimates for public and private education employers.3 Publicly funded charter schools run by private establishments are included in private ownership estimates, as they can be managed by parents, community groups, or private organizations. Public ownership is limited to establishments that are run by federal, state, or local governments. State-level Digest tables provide more localized information covering labor markets for the 50 states, the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands.
   

Timeliness and Data Quality

OEWS data are updated annually from a sample of about 1.1 million establishments’ data collected over a 3-year period. The OEWS sample is drawn from an administrative list of public and private companies and organizations that is estimated to cover about 95 percent of jobs.4 When employers respond to OEWS, they report from payroll data that are maintained as a part of regular business operations and typically do not require any additional collections or calculations. Payroll data reflect wages paid by employers for a job, which has a commonly accepted definition across employers or industries. This allows for more accurate comparisons of annual wages for a particular job. In contrast, when wages are self-reported by a respondent in person-level or household surveys, the reported data may be difficult to accurately code to a specific industry or detailed occupation, and there is greater chance of recall error by the respondent. Additionally, OEWS provides specialized respondent instructions for elementary and secondary schools and postsecondary institutions that accommodate the uniqueness of what educators do and how they are paid. These instructions enable precise coding of the occupations commonly found in these industries and a more precise and consistent reporting of wages of workers with a variety of schedules (e.g., school year vs. annual, part time vs. full time).   

OEWS uses strict quality control and confidentiality measures and strong sampling and estimation methodologies.5 BLS also partners with state workforce agencies to facilitate the collection, coding, and quality review of OEWS data. States’ highly trained staff contribute local knowledge, establish strong respondent relationships, and provide detailed coding expertise to further ensure the quality of the data. 

After assessing the strengths of the OEWS data, the Digest team focused on the comparability of the data over time to ensure that the data would be best suited for stakeholder needs and have the most utility. First, we checked for changes to the industrial and occupational classifications. Although there were no industrial changes, the occupational classifications of some staff occupations—like librarians, school bus drivers, and school psychologists—did change. In those cases, we only included comparable estimates in the tables.

Second, all new Digest tables include nonoverlapping data years to account for the 3-year collection period. While users cannot compare wages in 2020 with 2021 and 2022, they can explore data from 2016, 2019, and 2022. Third, the Digest tables present estimates for earlier data years to ensure the same estimation method was used to produce estimates over time.6 Finally, we did not identify any geographical, scope, reference period, or wage estimation methodology changes that would impact the information presented in tables. These checks ensured we presented the most reliable and accurate data comparisons.

 

Next Steps  

The use of OEWS data in the Digest is a first step in harnessing the strength of BLS data to provide more relevant and timely data, leading to a more comprehensive understanding of the education workforce. NCES is investigating ways we can partner with BLS to further expand these granular and timely economic data, meeting a National Academies of Science, Engineering, and Medicine recommendation to collaborate with other federal agencies and incorporate data from new sources to provide policy-relevant information. We plan to explore the relationship between BLS data and NCES data, such as the Common Core of Data, and increase opportunities for more detailed workforce analyses.

NCES is committed to exploring new data sources that can fill important knowledge gaps and expand the breadth of quality information available to education stakeholders. As we integrate new data sources and develop new tabulations, we will be transparent about our evaluation processes and the advantages and limitations of sources. We will provide specific examples of how information can be used to support evidence-based policymaking. Additionally, NCES will continue to investigate new data sources that inform economic issues related to education. For example, we plan to explore Post-Secondary Employment Outcomes to better understand education-to-employment pathways. We are investigating sources for building and land use data to assess the condition and utilization of school facilities. We are also looking for opportunities to integrate diverse data sources to expand to new areas of the education landscape and to support timelier and more locally informed decisionmaking.
 

How will you use the new Digest tables? Do you have suggestions for new data sources? Let us know at ARIS.NCES@ed.gov.

 

By Josue DeLaRosa, Kristi Donaldson, and Marie Marcum, NCES


[1] See these frequently asked questions for a description of current uses, including economic development planning and to project future labor market needs.

[2] Although most of the K–12 instructional occupations are in the Elementary and Secondary Education industry, both instructional and noninstructional occupations can be found in others (e.g., Colleges, Universities, and Professional Schools; Child Care Services). See Educational Instruction and Library Occupations for more details. For example, preschool teachers differ from some of the other occupations presented in the Digest tables, where most of the employment is in the Child Care Services industry. Preschool teachers included in Digest tables reflect the employment and average annual wage of those who are employed in the Elementary and Secondary Education industry, not all preschool teachers.

[3] Note that estimates do not consider differences that might exist between public and private employers, such as age and experience of workers, work schedules, or cost of living.

[4] This includes a database of businesses reporting to state unemployment insurance (UI) programs. For more information, see Quarterly Census of Employment and Wages.

[5] See Occupational Employment and Wage Statistics for more details on specific methods.

[6] Research estimates are used for years prior to 2021, and Digest tables will not present estimates prior to 2015, the first year of revised research estimates. See OEWS Research Estimates by State and Industry for more information.

Celebrating the ECLS-K:2024: Providing Key National Data on Our Country’s Youngest Learners

It’s time to celebrate!

This spring, the Early Childhood Longitudinal Study, Kindergarten Class of 2023–24 (ECLS-K:2024) is wrapping up its first school year of data collection with tens of thousands of children in hundreds of schools across the nation. You may not know this, but NCES is congressionally mandated to collect data on early childhood. We meet that charge by conducting ECLS program studies like the ECLS-K:2024 that follow children through the early elementary grades. Earlier studies looked at children in the kindergarten classes of 1998–99 and 2010–11. We also conducted a study, the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), that followed children from birth through kindergarten entry.

As the newest ECLS program study, the ECLS-K:2024 will collect data from both students and adults in these students’ lives (e.g., parents, teachers, school administrators) to help us better understand how different factors at home and at school relate to children’s development and learning. In fact, the ECLS-K:2024 allows us to provide data not only on the children in the cohort but also on kindergarten teachers and the schools that educate kindergartners.

What we at NCES think is worthy of celebrating is that the ECLS-K:2024—like other ECLS program studies,

  • provides the statistics policymakers need to make data-driven decisions to improve education for all;
  • contributes data that researchers need to answer today’s most pressing questions related to early childhood and early childhood education; and
  • allows us to produce resources for parents, families, teachers, and schools to better inform the public at large about children’s education and development.

Although smaller-scale studies can answer numerous questions about education and development, the ECLS-K:2024 allows us to provide answers at a national level. For example, you may know that children arrive to kindergarten with different skills and abilities, but have you ever wondered how those skills and abilities vary for children who come from different areas of the country? How they vary for children who attended prekindergarten programs versus those who did not? How they vary for children who come from families of different income levels? The national data from the ECLS-K:2024 allow us to dive into these—and other—issues.

The ECLS-K:2024 is unique in that it’s the first of our early childhood studies to provide data on a cohort of students who experienced the coronavirus pandemic. How did the pandemic affect these children’s early development and how did it change the schooling they receive? By comparing the experiences of the ECLS-K:2024 cohort to those of children who were in kindergarten nearly 15 and 25 years ago, we’ll be able to answer these questions.

What’s more, the ECLS-K:2024 will provide information on a variety of topics not fully examined in previous national early childhood studies. The study is including new items on families’ kindergarten selection and choice; availability and use of home computers and other digital devices; parent-teacher association/organization contributions to classrooms; equitable school practices; and a myriad of other constructs.

Earlier ECLS program studies have had a huge impact on our understanding of child development and early education, with hundreds of research publications produced using their data (on topics such as academic skills and school performance; family activities that promote learning; and children’s socioemotional development, physical health, and well-being). ECLS data have also been referenced in media outlets and in federal and state congressional reports. With the launch of the ECLS-K:2024, we cannot wait to see the impact of research using the new data.

Want to learn more? 

Plus, be on the lookout late this spring for the next ECLS blog post celebrating the ECLS-K:2024, which will highlight children in the study. Future blog posts will focus on parents and families and on teachers and schools. Stay tuned!

 

By Jill McCarroll and Korrie Johnson, NCES

Using IPEDS Data: Available Tools and Considerations for Use

The Integrated Postsecondary Education Data System (IPEDS) contains comprehensive data on postsecondary institutions. IPEDS gathers information from every college, university, and technical and vocational institution that participates in federal student financial aid programs. The Higher Education Act of 1965, as amended, requires institutions that participate in federal student aid programs to report data on enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid.

These data are made available to the public in a variety of ways via the IPEDS Use the Data webpage. This blog post provides a description of available IPEDS data tools as well as considerations for determining the appropriate tool to use.


Available Data Tools

College Navigator

College Navigator is a free consumer information tool designed to help students, parents, high school counselors, and others access information about postsecondary institutions.

Note that this tool can be found on the Find Your College webpage (under "Search for College"), along with various other resources to help users plan for college.

IPEDS provides data tools for a variety of users that are organized into three general categories: (1) Search Existing Data, (2) Create Custom Data Analyses, and (3) Download IPEDS Data.

Search Existing Data

Users can search for aggregate tables, charts, publications, or other products related to postsecondary education using the Data Explorer or access IPEDS data via NCES publications like the Digest of Education Statistics or the Condition of Education.

Create Custom Data Analyses

Several data tools allow users to create their own custom analyses with frequently used and derived variables (Data Trends) or all available data collected within IPEDS (Statistical Tables). Users can also customize tables for select subgroups of institutions (Summary Tables). Each of these options allows users to generate analyses within the limitations of the tool itself.

For example, there are three report types available under the Data Feedback Report (DFR) tool. User can

  1. select data from the most recent collection year across frequently used and derived variables to create a Custom DFR;
     
  2. create a Statistical Analysis Report using the variables available for the Custom DFR; and
     
  3. access the NCES developed DFR for any institution.

Download IPEDS Data

Other data tools provide access to raw data through a direct download (Complete Data Files) or through user selections in the IPEDS Custom Data Files tool. In addition, IPEDS data can be downloaded for an entire collection year for all survey components via the Access Database.

IPEDS Data Tools Help

The IPEDS Data Tools User Manual is designed to help guide users through the various functions, processes, and abundant capabilities of IPEDS data tools. The manual contains a wealth of information, hints, tips, and insights for using the tools.

 

Data Tool Considerations

Users may consider several factors—related to both data selection and data extraction—when determining the right tool for a particular question or query.

Data Selection

  1. Quick access – Accessing data in a few steps may be helpful for users who want to find data quickly. Several data tools provide data quickly but may be limited in their selection options or customizable output.

  2. Data release – IPEDS data are released to the public in two phases: Provisional and Final. Provisional data have undergone quality control procedures and imputation for missing data but have not been updated based on changes within the Prior Year Revision System. Final data reflect changes made within the Prior Year Revision System and additional quality control procedures and will not change. Some tools allow users to access only final data. Table 1 summarizes how provisional and final data are used by various data tools. The IPEDS resource page “Timing of IPEDS Data Collection, Coverage, and Release Cycle” provides more information on data releases.


    Table 1. How provisional and final data are used in various data tools

  1. Select institutions – Users may want to select specific institutions for their analyses. Several tools allow users to limit the output for a selected list of institutions while others include all institutions in the output.
     
  2. Multiple years – While some tools provide a single year of data, many tools provide access to multiple years of data in a single output.
     
  3. Raw data – Some data tools provide access to the raw data as submitted to IPEDS. For example, Look Up an Institution allows users access to survey forms submitted by an institution.
     
  4. Institution-level data – Many data tools provide data at the institution level, since this is the unit of analysis within the IPEDS system.
     
  5. All data available – Many data tools provide access to frequently used and derived variables, but others provide access to the entirety of variables collected within the IPEDS system.

Data Extraction

  1. Save/upload institutions – Several data tools allow a user to create and download a list of institutions, which can be uploaded in a future session.

  2. Save/upload variables – Two data tools allow a user to save the variables selected and upload in a future session.
     
  3. Export data – Many data tools allow a user to download data into a spreadsheet, while others provide information within a PDF. Note that several tools have limitations on the number of variables that can be downloaded in a session (e.g., Compare Institutions has a limit of 250 variables).
     
  4. Produce visuals – Several data tools produce charts, graphs, or other visualizations. For example, Data Trends provides users with the opportunity to generate a bar or line chart and text table.


Below is a graphic that summarizes these considerations for each IPEDS data tool (click the image to enlarge it). 

 

To find training opportunities—including video tutorials, workshops, and keyholder courses—check out the IPEDS Training Center. Plus, access the IPEDS Distance Learning Dataset Training modules for more guidance on how to use IPEDS data. For additional questions, call the IPEDS Data Use Help Desk at (866) 558-0658 or e-mail ipedstools@rti.org.

 

By Tara B. Lawley, NCES, and Eric S. Atchison, Arkansas State University System and Association for Institutional Research IPEDS Educator

Using Federal Education Data to Inform Policymaking: Part 2–Challenges and Opportunities

In part 1 of this blog series, we highlight the benefits and advantages of using federal education data for policymaking at the federal, state, and district levels. In part 2, we will explore the challenges of and opportunities afforded by using these data.

States, districts, and schools are inundated with requests for data. To manage the volume of requests and avoid overwhelming educators, many districts have established processes to vet and limit the number of surveys allowed in their schools and administrative offices. District clearance processes are also understandably meant to make sure everyone is in compliance with data privacy laws. NCES data collections are sometimes not cleared by district offices, which then means NCES is not allowed to contact schools or educators to learn from their perspectives or experiences.

Since many state or district policymakers prioritize local survey collections, federal surveys are occasionally rejected by district offices that are striving to keep from overburdening their educators with too many survey requests. Without district permission, NCES surveys won’t include the educators in those schools, meaning that those districts’ voices will be missing from the table when decisions are made. This is problematic for the entire education system for a few reasons.

  1. Local data rarely reach federal policymakers. We know state and district decisionmakers derive a lot of value from state and/or district survey collections—since they’re designed to provide detailed local data—and do not always see value in federal data collection and reporting efforts. More localized data are critical to their day-to-day decisions. However, the presence of numerous state-run surveys—in addition to the myriad individualized district surveys that can exist within a single state—has begun to create a data silo where information remains frozen within a state or district system. Since NCES (and therefore the Department of Education and Congress) rarely receives data from state or local collections, these data sources cannot readily be used to generate national policies, which greatly limits opportunities for state and district systems to learn from each other. These data silos can, for example, impact the focus or breadth of federal grants or funding available for schools.

    Federal, state, and district education agencies serve different roles in the education sector but have mutually beneficial responsibilities that should complement and support one another. The solution isn’t to supplant federal data collections with local ones, or vice versa, but instead to supplement local collections with federal collections like the National Teacher and Principal Survey (NTPS) so education decisionmakers at all levels have access to necessary information to make good decisions for our schools.
     
  2. Benchmarking and comparability are limited. Without federal data collections, it can be difficult or impossible for states, districts, and local policymakers to compare their schools and educators with those in other areas because of the lack of common focus and definitions across data collections. Even if the topics being collected are similar, individualized district or state surveys can differ widely in both content and wording.

    National data collections—like the NTPS—are excellent tools local policymakers should use when setting priorities on behalf of the students and staff in their state or district. Since the data from the NTPS are collected from educators in the same way across the entire country, they can be used to establish benchmarks against which local collections can measure themselves.
     
  3. Lack of participation decreases the representativeness of storytelling. If districts do not approve NCES’s survey research applications, we are unable to reach educators in certain schools, which can limit the kinds of perspectives that are included in the data. To paint a true picture of the education landscape, our survey teams select districts, schools, and/or educators that are as representative of the education field as possible.

    Teachers and principals who participate in NCES studies are grouped in different ways—such as by age, race/ethnicity, or the type of school at which they work—and their information is studied to identify patterns of experiences that people in these different groups may have had. This is what makes our datasets representative, or similar enough to the demographics of the population to able to accurately reflect the characteristics of everyone (even those who aren’t sampled to participate).

    For example, the NTPS is designed to support analysis of a variety of subgroups, such as those by
  • school level (i.e., elementary, middle, high, and combined);
     
  • school community type (i.e., urban, suburban, town, and rural);
     
  • teachers’ and principals’ years of experience in the profession; and
     
  • race/ethnicity of teachers and principals (figure 1).

These diverse subgroups are critically important for both federal and local policymakers who want to make decisions using information that truly represents everyone in the field.


Figure 1. Percentage of K–12 public and private school teachers who reported that they have any control over various areas of planning and teaching in their classrooms, by school type and selected school characteristic: 2020–21 

 

NOTE: Data are weighted estimates of the population. Response options included “no control,” “minor control,” “moderate control,” and “a great deal of control.” Teachers who reported “minor control,” “moderate control,” or “a great deal of control” were considered to have reported having “any control.”
SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), “Public and Private School Teacher Data File,” 2020–21.


Larger datasets allow for more nuanced comparisons by school, principal, or teacher characteristics that aren’t possible in smaller datasets, allowing for more equity in national and local estimates and more distinct answers to key policy questions. But we need district-level support to provide these nuanced data.

Although the NTPS has a fairly large sample to support state representation, it still only includes a small percentage of all schools and educators in the country. Sampling is used to avoid collecting data from all systems, staff, and students, thus helping to limit overall respondent burden on our education system. For this reason, it’s important that all sampled schools and educators participate if selected. Since some districts also have formal review processes—through which a survey must be approved before any schools or educators can be contacted—it is also important that districts with sampled schools grant NCES surveys permission to collect data from their schools.

Both levels of participation will help us collect data that accurately describe a state or population. Otherwise, the story we are telling in the data is only augmenting some voices—and these are the experiences that will be reflected in federal policy and funding.

 

As the education sector strives to understand the needs of students and staff on the tails of the coronavirus pandemic, trustworthy data are only becoming more critical to the decisionmaking process. NCES datasets like the NTPS are critical resources that federal, state, and district policymakers can and have used for benchmarking strategic goals or conducting analyses on how a topic has (or hasn’t) changed over time.

The catch being, of course, that all data on the NTPS—and many other NCES surveys—come directly from schools, principals, and teachers themselves. These analyses and reports are not possible without district and educator participation. While it may seem counterintuitive that any one person could make such a large difference in federal education policy, the concept doesn’t differ from civic duties such as voting in federal or local elections.

Below is a visual summary of this blog post that can be used in your own professional discussions about the importance of participating in federal education surveys.



NCES would like to thank every district, school, administrator, teacher, parent, and student who has previously approved or participated in an NCES survey. We wouldn’t be able to produce our reports and data products without your participation.

We are currently conducting the 2023–24 NTPS to learn more about school and educator experiences following the pandemic. Find more information about the NTPS, including findings and details from prior collections.

 

By Maura Spiegelman and Julia Merlin, NCES