IES Blog

The Integrated Postsecondary Education Data System (IPEDS) is a comprehensive federal data source that collects information on key features of higher education in the United States, including characteristics of postsecondary institutions, college student enrollment and academic outcomes, and institutions’ employees and finances, among other topics.

The National Center for Education Statistics (NCES) has created a new resource page, Student Cohorts and Subgroups in IPEDS, that provides data reporters and users an overview of how IPEDS collects information related to postsecondary students and staff. This blog post highlights key takeaways from the resource page.

IPEDS survey components collect counts of key student and staff subgroups of interest to the higher education community.

Data users—including researchers, policy analysts, and prospective college students—may be interested in particular demographic groups within U.S. higher education. IPEDS captures data on a range of student and staff subgroups, including race/ethnicity, gender, age categories, Federal Pell Grant recipient status, transfer-in status, and part-time enrollment status.

The Outcome Measures (OM) survey component stands out as an example of how IPEDS collects student subgroups that are of interest to the higher education community. Within this survey component, all entering degree/certificate-seeking undergraduates are divided into one of eight subgroups by entering status (i.e., first-time or non-first-time), attendance status (i.e., full-time or part-time), and Pell Grant recipient status.

Although IPEDS is not a student-level data system, many of its survey components collect counts of students and staff by subgroup.

Many IPEDS survey components—such as Admissions, Fall Enrollment, and Human Resources—collect data as counts of individuals (i.e., students or staff) by subgroup (e.g., race/ethnicity, gender) (exhibit 1). Other IPEDS survey components—such as Graduation Rates, Graduation Rates 200%, and Outcome Measures—also include selected student subgroups but monitor cohorts of entering degree/certificate-seeking students over time to document their long-term completion and enrollment outcomes. A cohort is a specific group of students established for tracking purposes. The cohort year is based on the year that a cohort of students begins attending college.

Exhibit 1. IPEDS survey components that collect counts of individuals by subgroup

IPEDS collects student and staff counts by combinations of interacting subgroups.

For survey components that collect student or staff counts, individuals are often reported in disaggregated demographic groups, which allows for more detailed understanding of specific subpopulations. For example, the Fall Enrollment (EF) and 12-month Enrollment (E12) survey components collect total undergraduate enrollment counts disaggregated by all possible combinations of students’ full- or part-time status, gender, degree/certificate-seeking status, and race/ethnicity. Exhibit 2 provides an excerpt of the EF survey component’s primary data collection screen (Part A), in which data reporters provide counts of students who fall within each demographic group indicated by the blank cells.

Exhibit 2. Excerpt of IPEDS Fall Enrollment (EF) survey component data collection screen for full-time undergraduate men: 2022­–23

[click image to enlarge]

NOTE: This exhibit reflects the primary data collection screen (Part A) for the 2022–23 Fall Enrollment (EF) survey component for full-time undergraduate men. This screen is duplicated three more times for undergraduate students, once each for part-time men, full-time women, and part-time women. For survey materials for all 12 IPEDS survey components, including complete data collection forms and detailed reporting instructions, visit the IPEDS Survey Materials website.

As IPEDS does not collect data at the individual student level, these combinations of interacting subgroups are the smallest unit of information available in IPEDS. However, data users may wish to aggregate these smaller subgroups to arrive at larger groups that reflect broader populations of interest.

For example, using the information presented in exhibit 2, a data user could sum all the values highlighted in the green column to arrive at the total enrollment count of full-time, first-time men. As another example, a data user could sum all the values highlighted in the blue row to determine the total enrollment count of full-time Hispanic/Latino men. Note, however, that many IPEDS data products provide precalculated aggregated values (e.g., total undergraduate enrollment), but data are collected at these smaller units of information (i.e., disaggregated subgroup categories).

Student enrollment counts and cohorts align across IPEDS survey components.

There are several instances when student enrollment or cohort counts reported in one survey component should match or very closely mirror those same counts reported in another survey component. For example, the number of first-time degree/certificate-seeking undergraduate students in a particular fall term should be consistently reported in the Admissions (ADM) and Fall Enrollment (EF) survey components within the same data collection year (see letter A in exhibit 3).

Exhibit 3. Alignment of enrollment counts and cohorts across IPEDS survey components

For a full explanation of the alignment of student counts and cohorts across IPEDS survey components (letters A to H in exhibit 3), visit the Student Cohorts and Subgroups in IPEDS resource page.

Be sure to follow NCES on Twitter, Facebook, LinkedIn, and YouTube, follow IPEDS on Twitter, and subscribe to the NCES News Flash to stay up-to-date on IPEDS data releases and resources.

By Katie Hyland and Roman Ruiz, AIR

Happy Get Ready for Kindergarten Month! 

For more than 20 years, the National Center for Education Statistics (NCES) has been collecting information about kindergartners’ knowledge and skills as part of the Early Childhood Longitudinal Studies (ECLS) program.

The first ECLS, the Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K), focused on children in kindergarten in the 1998–99 school year. At the time the ECLS-K began, no large national study focused on education had followed a cohort of children from kindergarten entry through the elementary school years. Some of today’s commonly known information about young children, such as the information about kindergartners’ early social and academic skills shown in the infographics below, comes out of the ECLS-K. For example, we all know that children arrive at kindergarten with varied knowledge and skills; the ECLS-K was the first study to show at a national level that this was the case and to provide the statistics to highlight the differences in children’s knowledge and skills by various background factors.

The second ECLS kindergarten cohort study, the Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), is the ECLS-K’s sister study. This study followed the students who were in kindergarten during the 2010–11 school year. The ECLS-K:2011, which began more than a decade after the inception of the ECLS-K, allows for comparisons of children in two nationally representative kindergarten classes experiencing different policy, educational, and demographic environments. For example, significant changes that occurred between the start of the ECLS-K and the start of the ECLS-K:2011 include the passage of No Child Left Behind legislation, a rise in school choice, and an increase in English language learners. 

From the parents of children in the ECLS-K:2011, we learned how much U.S. kindergartners like school, as shown in the following infographic.

The ECLS program studies also provide information on children’s home learning environments and experiences outside of school that may contribute to learning. For example, we learned from the ECLS-K:2011 what types of activities kindergartners were doing with their parents at least once a month (see the infographic below).

What’s next for ECLS data collections on kindergartners? NCES is excited to be getting ready to launch our next ECLS kindergarten cohort study, the Early Childhood Longitudinal Study, Kindergarten Class of 2023–24 (ECLS-K:2024). 

Before the ECLS-K:2024 national data collections can occur, the ECLS will conduct a field test—a trial run of the study to test the study instruments and procedures—in the fall of 2022.

If you, your child, or your school are selected for the ECLS-K:2024 field test or national study, please participate! While participation is voluntary, it is important so that the study can provide information that can be used at the local, state, and national levels to guide practice and policies that increase every child’s chance of doing well in school. The ECLS-K:2024 will be particularly meaningful, as it will provide important information about the experiences of children whose early lives were shaped by the COVID-19 pandemic.

Watch this video to learn more about participation in the ECLS-K:2024. For more information on the ECLS studies and the data available on our nation’s kindergartners, see the ECLS homepage, review our online training modules, or email the ECLS study team.

By Jill Carlivati McCarroll, NCES

For more than 3 decades, the Integrated Postsecondary Education Data System (IPEDS) has collected data from all postsecondary institutions participating in Title IV federal student aid programs, including universities, community colleges, and vocational and technical schools.

Since 2000, the 12 IPEDS survey components occurring in a given collection year have been organized into three seasonal collection periods: Fall, Winter, and Spring.

The timing of when data are collected (the “collection year”) is most important for the professionals who report their data to the National Center for Education Statistics (NCES). However, IPEDS data users are generally more interested in the year that is actually reflected in the data (the “data year”). As an example, a data user may ask, “What was happening with students, staff, and institutions in 2018–19?"

For data users, knowing the difference between the collection year and the data year is important for working with and understanding IPEDS data. Often, the collection year comes after the data year, as institutions need time to collect the required data and check to make sure they are reporting the data accurately. This lag between the time period reflected by the data and when the data are reported is typically one academic term or year, depending on the survey component. For example, fall 2021 enrollment data are not reported to NCES until spring 2022, and the data would not be publicly released until fall 2022.

After the data are collected by NCES, there is an additional time period before they are released publicly in which the data undergo various quality and validity checks. About 9 months after each seasonal collection period ends (i.e., Fall, Winter, Spring), there is a Provisional Data Release and IPEDS data products (e.g., web tools, data files) are updated with the newly released seasonal data. During this provisional release, institutions may revise their data if they believe it was inaccurately reported. A Revised/Final Data Release then happens the following year and includes any revisions that were made to the provisional data.

Sound confusing? The data collection and release cycle can be a technical and complex process, and it varies slightly for each of the 12 IPEDS survey components. Luckily, NCES has created a comprehensive resource page that provides information about the IPEDS data collection and release cycles for each survey component as well as key details for data users and data reporters, such as how to account for summer enrollment in the different IPEDS survey components.

Table 1 provides a summary of the IPEDS 2021–22 data collection and release schedule information that can be found on the resource page. Information on the data year and other details about each survey component can also be found on the resource page.

Table 1. IPEDS 2021–22 Data Collection and Release Schedule

Here are a few examples of how to distinguish the data year from the collection year in different IPEDS data products.

Example 1: IPEDS Trend Generator

Suppose that a data user is interested in how national graduation rates have changed over time. One tool they might use is the IPEDS Trend Generator. The Trend Generator is a ready-made web tool that allows users to view trends over time on the most frequently asked subject areas in postsecondary education. The Graduation Rate chart below displays data year (shown in green) in the headline and on the x-axis. The “Modify Years” option also allows users to filter by data year. Information about the collection year (shown in gold) can be found in the source notes below the chart.

Example 2: IPEDS Complete Data Files

Imagine that a data user was interested enough in 6-year Graduation Rates that they wanted to run more complex analyses in a statistical program. IPEDS Complete Data Files include all variables for all reporting institutions by survey component and can be downloaded by these users to create their own analytic datasets.

Data users should keep in mind that IPEDS Complete Data Files are organized and released by collection year (shown in gold) rather than data year. Because of this, even though files might share the same collection year, the data years reflected within the files will vary across survey components.

The examples listed above are just a few of many scenarios in which this distinction between collection year and data year is important for analysis and understanding. Knowing about the IPEDS reporting cycle can be extremely useful when it comes to figuring out how to work with IPEDS data. For more examples and additional details on the IPEDS data collection and release cycles for each survey component, please visit the Timing of IPEDS Data Collection, Coverage, and Release Cycle resource page.

Be sure to follow NCES on Twitter, Facebook, LinkedIn, and YouTube, follow IPEDS on Twitter, and subscribe to the NCES News Flash to stay up-to-date on all IPEDS data releases.

By Katie Hyland and Roman Ruiz, American Institutes for Research

Dear reader,

You’ve been asked to participate in a study.

. . . I know what you’re thinking. Oh, great. Another request for my time. I am already so busy.

Hmm, if I participate, what is my information going to be used for? Well, the letter says that collecting data from me will help researchers study education, and it says something else about how the information I provide would “inform education policy . . .”

But what does that mean?

If you’re a parent, student, teacher, school administrator, or district leader, you may have gotten a request like this from me or a colleague at the National Center for Education Statistics (NCES). NCES is one of 13 federal agencies that conducts survey and assessment research in order to help federal, state, and local policymakers better understand public needs and challenges. It is the U.S. Department of Education’s (ED’s) statistical agency and fulfills a congressional mandate to collect, collate, analyze, and report statistics on the condition of American education. The law also directs NCES to do the same about education across the globe.

But how does my participation in a study actually support the role Congress has given NCES?

Good question. When NCES conducts a study, participants are asked to provide information about themselves, their students or child/children, teachers, households, classrooms, schools, colleges, or other education providers. What exactly you will be asked about is based on many considerations, including previous research or policy needs. For example, maybe a current policy might be based on results from an earlier study, and we need to see if the results are still relevant. Maybe the topic has not been studied before and data are needed to determine policy options. In some cases, Congress has charged NCES with collecting data for them to better understand education in general.

Data collected from participants like you are combined so that research can be conducted at the group level. Individual information is not the focus of the research. Instead, NCES is interested in the experiences of groups of people or groups of institutions—like schools—based on the collected data. To protect respondents, personally identifiable information like your name (and other information that could identify you personally) is removed before data are analyzed and is never provided to others. This means that people who participate in NCES studies are grouped in different ways, such as by age or type of school attended, and their information is studied to identify patterns of experiences that people in these different groups may have had.

Let’s take a look at specific examples that show how data from NCES studies provide valuable information for policy decisions.

When policymakers are considering how data can inform policy—either in general or for a specific law under consideration—data from NCES studies play an important role. For example, policymakers concerned that students in their state/district/city often struggle to pay for college may be interested in this question:

“What can education data tell me about how to make college more affordable?”

Or policymakers further along in the law development process might have more specific ideas about how to help low-income students access college. They may have come across research linking programs such as dual enrollment—when high school students take college courses—to college access for underrepresented college students. An example of this research is provided in the What Works Clearinghouse (WWC) dual-enrollment report produced by ED’s Institute for Education Sciences (IES), which shows that dual-enrollment programs are effective at increasing students’ access to and enrollment in college and attainment of degrees. This was found to be the case especially for students typically underrepresented in higher education.   

Then, these policymakers might need more specific questions answered about these programs, such as:

“What is the benefit of high school students from low-income households also taking college courses?”

Thanks to people who participate in NCES studies, we have the data to address such policy questions. Rigorous research using data from large datasets, compiled from many participants, can be used to identify differences in outcomes between groups. In the case of dual-enrollment programs, college outcomes for dual-enrollment participants from low-income households can be compared with those of dual-enrollment participants from higher-income households, and possible causes of those differences can be investigated.

The results of these investigations may then inform enactment of laws or creation of programs to support students. In the case of dual enrollment, grant programs might be set up at the state level for districts and schools to increase students’ local access to dual-enrollment credit earning.

This was very close to what happened in 2012, when I was asked by analysts in ED’s Office of Planning, Evaluation, and Policy Development to produce statistical tables with data on students’ access to career and technical education (CTE) programs. Research, as reviewed in the WWC dual-enrollment report, was already demonstrating the benefits of dual enrollment for high school students. Around 2012, ED was considering a policy that would fund the expansion of dual enrollment specifically for CTE. The reason I was asked to provide tables on the topic was my understanding of two important NCES studies, the Education Longitudinal Study of 2002 (ELS:2002) and the High School Longitudinal Study of 2009 (HSLS:09). Data provided by participants in those studies were ideal for studying the question. The tables were used to evaluate policy options. Based on the results, ED, through the President, made a budget request to Congress to support dual-enrollment policies. Ultimately, dual-enrollment programs were included in the Strengthening Career and Technical Education for the 21st Century Act (Perkins V).  

The infographic below shows that this scenario—in which NCES data provided by participants like you were used to provide information about policy—has happened on different scales for different policies many times over the past few decades. The examples included are just some of those from the NCES high school longitudinal studies. NCES data have been used countless times in its 154-year history to improve education for American students. Check out the full infographic (PDF) with other examples.

However, it’s not always the case that a direct line can be drawn between data from NCES studies and any one policy. Research often informs policy indirectly by educating policymakers and the public they serve on critical topics. Sometimes, as in the dual-enrollment and CTE programs research question I investigated, it can take time before policy gets enacted or a new program rolls out. This does not lessen the importance of the research, nor the vital importance of the data participants provide that underpin it.

The examples in the infographic represent experiences of actual individuals who took the time to tell NCES about themselves by participating in a study.  

If you are asked to participate in an NCES study, please consider doing so. People like you, schools like yours, and households in your town do matter—and by participating, you are helping to inform decisions and improve education across the country.

By Elise Christopher, NCES

NCES is excited to announce the release of a resource hub that focuses on data by geographic locale—Education Across America: Cities, Suburbs, Towns, and Rural Areas—using a three-phased approach. Released today, Phase I of this new resource hub involves the consolidation of locale-focused data across NCES surveys and programs and makes updates to the latest data available. The result of this work is 140 tables with data disaggregated by all four locales (i.e., cities, suburbs, towns, and rural areas). These tables cover a wide range of topics grouped into broad themes: family characteristics, educational experiences, school resources and staffing, and educational outcomes. Phases II and III will focus on rural areas and involve summarizing findings in text.

To make these data more relevant and useful, NCES adopted a pyramid approach¹ to attend to various user segments with tiered products (exhibit 1). Source tables containing data disaggregated by locale form the base of the pyramid. These tables, which contain the most detailed statistical information about education in each locale, target data-savvy users such as researchers.

Exhibit 1. Tiered Approach to Products in Education Across America Resource Hub