# IES Blog

### Institute of Education Sciences

More than 50 years ago, Congress established Title I, Part A funding (generally just called Title I) to support school districts in educating the nation’s economically disadvantaged students. Today, billions of dollars in Title I funding are distributed to school districts across the country through four grants, using a complex set of formulas.

A new report from the National Center for Education Statistics (NCES) provides a look at how Title I funds are allocated and how the current formulas affect school districts of various sizes, socioeconomic status, and geographic locales, such as rural or urban. The Study of the Title I, Part A Grant Program Mathematical Formulas was conducted in response to a congressional mandate under the Every Student Succeeds Act (ESSA), which was passed in 2015.

In fiscal year 2015 (FY 15), the total Title I allocation per formula-eligible child in the United States was \$1,227.[1],[2] However, states varied in their total Title I final allocation per formula-eligible child, ranging from \$984 in Idaho to \$2,590 in Vermont, a difference of \$1,606. (NOTE: A child is "formula eligible" if he or she is ages 5–17 and living in a family below the national poverty level or one that is receiving Temporary Assistance for Needy Families [TANF], a neglected and delinquent child located in a locally funded institution, or a foster child.)

Total Title I allocations per formula-eligible child also differed by geographic locale, district poverty level, and district size:

• The locales with the highest total Title I final allocations were the most densely and least densely populated areas: large cities (\$1,466) and remote rural areas (\$1,313);
• The poorest districts (i.e., those in the highest poverty quarter) had the highest total Title I allocations (\$1,381), and the least-poor districts (i.e., those in the lowest poverty quarter) had the lowest total Title I allocations (\$1,023); and
• The smallest districts (those with a 5- to 17-year-old population of less than 300) had the highest total Title I final allocation (\$1,442) compared with districts of all other population sizes. The largest districts (those with a population of 25,000 or more) had the second-highest allocation (\$1,323). The allocation was lowest (\$1,107) for districts with a population of 5,000 to 9,999.

Because each of the federal allocation formulas use a series of provisions, there is not a direct link between the percentage of formula-eligible children in a district or state and the percentage of federal funds allocated to that district or state. It is also important to note that there is no direct link between the formula-eligible children upon whom the distribution of funds is based and the children who receive services from Title I. Today, 95 percent of children served by Title I receive services in schoolwide programs that serve all children in the school, regardless of whether they are formula eligible or not. Altogether, about 11.6 million children are counted as formula eligible in the United States, but more than twice that amount (about 25 million students) receive Title I services.

The 250-page report includes a number of other findings, including

• An overview of the Title I funding formula process;
• Detailed analyses for each of the grant programs (Basic, Concentration, Targeted, and Education Finance Incentive Grants);
• Alternative analyses that isolate components of each grant program;
• American Community Survey-Comparable Wage Index (CWI) adjusted allocations; and
• A table of Title I, Part A total allocations by grant type and school district.

To access the full report, please visit the NCES website at https://nces.ed.gov/pubs2019/titlei/.

By Tom Snyder and Rachel Dinkes

[1] The analytic metric used in the report is the amount of funding allocated for the designated Title I grant divided by the number of formula-eligible children used in the computation for that specific grant.

[2] Detailed information on the Title I formula grant process and the components of the mathematical formulas can be found in the report’s introduction.

Transfer students who attend full time complete a degree at higher rates than those attending part time. There were 2.1 million students who transferred into a 4-year institution during the 2009-10 academic year. At public institutions, which had the majority of transfer students (1.3 million) in 2009-10, 61 percent of full-time transfers completed their degree after 8 years of entering the institution, compared to 32 percent of part-time transfers (figure 1).

While NCES data users may be more familiar with the postsecondary transfer student data in the Beginning Postsecondary Study, NCES also collects data on this topic through the Integrated Postsecondary Education Data System (IPEDS) collection. IPEDS annually requires over 4,000 colleges and universities to report their transfer data starting from enrollment to completion. As defined by IPEDS, students who transfer into an institution with prior postsecondary experience–whether credit was earned or not–are considered transfer-in students. Students who leave an institution without completing their program of study and subsequently enrolled in another institution are defined as transfer-out students.

Below are some of the key data collected on student transfers through the different IPEDS survey components:

• Fall Enrollment (EF): Transfer-in data

Collected since 2006-07, institutions report the fall census count and specific characteristics—i.e., gender, race/ethnicity, and attendance status (full and part time)—of transfer-in students.

• Graduation Rates (GR): Transfer-out data

Collected since 1997-98, GR collects counts of students who are part of a specific first-time, full-time student cohort. Data users can calculate the transfer-out rates of first-time, full-time students by race/ethnicity and gender for each institution that reports transfer-out data. NCES requires the reporting of transfer-out data if the mission of the institution includes providing substantial preparation for students to enroll in another eligible institution without having completed a program. If it is not part of the institution’s mission, an institution has the option to report transfer-out data.

• Outcome Measures (OM): Transfer-in and transfer-out data

Collected since 2015-16, OM collects information on entering students who are first-time students as well as non-first-time students (i.e., transfer-in students). Institutions report on the completions of transfer-in students at three points in time: at 4, 6, and 8 years. Also, any entering student who does not earn an award (i.e., certificate, associate’s degree, or bachelor’s degree), leaves the institution, and subsequently enrolls in another institution is reported as a transfer-out student. Click to learn more about OM. All institutions reporting to OM must report their transfer-out students regardless of mission.

NCES has been collecting IPEDS for several decades, which allows for trend analysis. Check out the IPEDS Trend Generator’s quick analysis of transfer-in students' fall enrollment. Also, the National Postsecondary Education Cooperative commissioned a 2018 paper that provides a high-level examination of the most common issues regarding U.S. postsecondary transfer students and presents suggestions on how NCES could enhance its student transfer data collection. For example, one caveat to using IPEDS transfer data is that information on where students transfer from or to is not collected. This means IPEDS data cannot be used to describe the various pathways of transfer students, such as reverse, swirling, and lateral transferring.[1]. While these nuances are important in today’s transfer research, they are out of the scope of the IPEDS collection. However, IPEDS data do provide a valuable national look at transfers and at the institutions that serve them.

[1] A reverse transfer is defined as a student who transfers from a high-level institution to a low-level institution (e.g., transferring from a 4-year institution to a 2-year institution). Students who take a swirling pathway move back and forth between multiple institutions. A lateral transfer student is a student who transfers to another institution at a similar level (e.g., 4-year to 4-year or 2-year to 2-year).

By Gigi Jones

UPDATED Blog: New and Updated Modules Added

NCES provides a wealth of data online for users to access. However, the breadth and depth of the data can be overwhelming to first time users, and, sometimes, even for more experienced users. In order to help our users learn how to access, navigate, and use NCES datasets, we’ve developed a series of online training modules.

The Distance Learning Dataset Training  (DLDT) resource is an online, interactive tool that allows users to learn about NCES data across the education spectrum and evaluate it for suitability for specific  research purposes. The DLDT program at NCES has developed a growing number of online training modules for several NCES complex sample survey and administrative datasets.  The modules teach users about the intricacies of various datasets, including what the data represent, how the data are collected, the sample design, and considerations for analysis to help users in conducting successful analyses.

The DLDT is also a teaching tool that can be used by individuals both in and out of the classroom to learn about NCES complex sample survey and administrative data collections and appropriate analysis methods.

There are two types of NCES DLDT modules available: common modules and dataset-specific modules. The common modules help users broadly understand NCES data across the education spectrum, introduce complex survey methods, and explain how to acquire NCES micro-data. The dataset-specific modules introduce and educate users about particular datasets. The available modules are listed below and more information can be found on the DLDT website

AVAILABLE DLDT MODULES

Common Modules

• Introduction to the NCES Distance Learning Dataset Training System
• Introduction to the NCES Datasets
• Introduction to NCES Web Gateways: Accessing and Exploring NCES Data
• Analyzing NCES Complex Survey Data
• Statistical Analysis of NCES Datasets Employing a Complex Sample Design
• Acquiring Micro-level NCES Data
• DataLab Tools: QuickStats, PowerStats, and TrendStats

Dataset-Specific Modules

• Common Core of Data (CCD)
• Introduction to MapED
• Fast Response Survey System (FRSS)
• Early Childhood Longitudinal Study Birth Cohort (ECLS-B)
• Early Childhood Longitudinal Study Kindergarten Class of 1998-1999 (ECLS-K)
• Early Secondary Longitudinal Studies (1972 – 2000)
• National Longitudinal Study of 1972 (NLS-72)
• High School and Beyond (HS&B)
• National Education Longitudinal Study of 1988 (NELS:88)
• Educational Longitudinal Study of 2002 (ELS:2002)
• High School Longitudinal Study of 2009 (HSLS:09)
• Introduction to High School Transcript Studies
• Integrated Postsecondary Education Data System (IPEDS) – UPDATED!
• National Assessment of Educational Progress (NAEP)
• Main, State, and Long-Term Trend NAEP
• NAEP High School Transcript Study (HSTS)
• National Indian Education Study (NIES)
• National Household Education Survey Program (NHES)
• National Teacher and Principal Survey (NTPS) – NEW!
• Postsecondary Education Sample Survey Datasets
• National Postsecondary Student Aid Study (NPSAS)
• Beginning Postsecondary Student Longitudinal Study (BPS)
• Baccalaureate and Beyond Longitudinal Study (B&B)
• Postsecondary Education Quick Information System (PEQIS)
• Private School Universe Survey (PSS)
• Schools and Staffing Survey (SASS)
• Teacher Follow-up Survey (TFS)
• Principal Follow-up Survey (PFS)
• Beginning Teacher Longitudinal Study (BTLS)
• School Survey On Crime and Safety (SSOCS)
• International Activities Program Studies Datasets
• Progress in International Reading Literacy Study (PIRLS)
• Trends in International Mathematics and Science Study (TIMSS) – UPDATED!
• Program for International Student Assessment (PISA) – UPDATED!
• Program for the International Assessment of Adult Competencies (PIAAC)

Modules under Construction

• Accessing NCES Data via the Web
• Fast Response Survey System (FRSS)
• Introduction to the Annual Reports and Information Group
• NCES Longitudinal Studies
• NCES High School Transcript Collections
• Mapping Education Data (MapED)
• Postsecondary Education Quick Information System (PEQIS)

This blog was originally posted on July 12, 2016 and was updated on January 11, 2019.

By Andy White

By Molly Fenster, American Institutes for Research

Have you ever wondered how many public high school students graduate on time? Or wanted to know the types of safety and security measures schools use, or the latest trends in the cost of a college education? If so, the NCES Fast Facts website has the answers for you!

Launched on March 1, 1999, the Fast Facts site originally included 45 responses to the questions most frequently asked by callers to the NCES Help Line. Today, the more than 70 Fast Facts answer questions of interest to education stakeholders–such as a teacher, school administrator, or researcher–as well as college students, parents, and community members with a specific interest or data need. The facts feature text, tables, figures, and links from various published sources, primarily the Digest of Education Statistics and The Condition of Education, and they are updated periodically with new data from recently released publications and products.

For example, the screenshot below shows one of the most accessed Fast Facts on high school dropout rates:

Access the site for the full Fast Fact, as well as links to “Related Tables and Figures” and “Other Resources” on high school dropout rates.

The other facts on the site feature a diverse range of topics from child care, homeschooling, students with disabilities, teachers, and enrollment, to graduation rates, educational attainment, international education, finances, and more. The site is organized to provide concise, current information in the following areas:

• Assessments;
• Early Childhood;
• Elementary and Secondary;
• Library;
• Postsecondary and Beyond; and
• Resources.

Five recently released Fast Facts on ACT scores; science, technology, engineering, and mathematics (STEM) education; public school students eligible for free or reduced-price lunch; postsecondary student debt; and Historically Black Colleges and Universities offer the latest data on these policy-relevant and interesting education topics.

Join our growing base of users and visit the Fast Facts site today!

By Joel McFarland

NCES and the Department of Education have released national and state-level Average Cohort Graduation Rates for the 2015-16 school year. You can see the data on the NCES website (as well as data from 2010-11 through 2015-16).

In recent years, NCES has released two widely-used annual measures of high school completion: the Adjusted Cohort Graduation Rate (ACGR) and the Averaged Freshman Graduation Rate (AFGR). Both measure the percent of public school students who attain a regular high school diploma within 4 years of starting 9th grade. However, they also differ in important ways. This post provides an overview of how each measure is calculated and why they may result in different rates.

The ACGR was first collected for 2010-11 and is a newer graduation rate measure. To calculate the ACGR, states identify the “cohort” of first-time 9th graders in a particular school year, and adjust this number by adding any students who transfer into the cohort after 9th grade and subtracting any students who transfer out, emigrate to another country, or pass away. The ACGR is the percentage of the students in this cohort who graduate within four years. States calculate the ACGR for individual schools and districts and for the state as a whole using detailed data that track each student over time. In many states, these student-level records have become available at a state level only in recent years. As an example, the ACGR formula for 2012-13 was calculated like this:

What is the Averaged Freshman Graduation Rate (AFGR)?

The AFGR uses aggregate student enrollment data to estimate the size of an incoming freshman class, which is compared to the number of high school diplomas awarded 4 years later. The incoming freshman class size is estimated by summing 8th grade enrollment in year one, 9th grade enrollment for the next year, and 10th grade enrollment for the year after, and then dividing by three. The averaging of the enrollment counts helps to smooth out the enrollment bump typically seen in 9th grade. The AFGR estimate is less accurate than the ACGR, but it can be estimated as far back as the 1960s since it requires only aggregate annual counts of enrollment and graduate data. As an example, the AFGR formula for 2012-13 was:

Why do they produce different rates?

There are several reasons the AFGR and ACGR do not match exactly.

• The AFGR’s estimate of the incoming freshman class is fixed, and is not adjusted to account for students entering or exiting the cohort during high school. As a result it is very sensitive to migration trends. If there is net out-migration after the initial cohort size is estimated, the AFGR will understate the graduation rate relative to the ACGR. If there is net in-migration, the AFGR will overstate the graduation rate;
• The diploma count used in the AFGR includes any students who graduate with a regular high school diploma in a given school year, which may include students who took more or less than four years to graduate. The ACGR includes only those students who graduate within four years of starting ninth grade. This can cause the AFGR to be inflated relative to the ACGR; and
• The AFGR’s averaged enrollment base is sensitive to the presence of 8th and 9th grade dropouts. Students who drop out in the 8th grade in one year are not eligible to be first-time freshmen the next year, but are included in the calculation of the AFGR enrollment base. At the same time, 9th grade dropouts should be counted as first-time 9th graders, but are excluded from the 10th grade enrollment counts used in the AFGR enrollment base. Since more students typically drop out in 9th grade than in 8th grade, the overall impact is likely to underestimate the AFGR enrollment base relative to the true ACGR cohort.

At the national level, these factors largely balance out, and the AFGR closely tracks the ACGR. For instance, in 2012-13, there was less than one percentage point difference between the AFGR (81.9%) and the ACGR (81.4%). At the state level, especially for small population subgroups, there is often more variation between the two measures.

On the NCES website you can access the most recently available data for each measure, including 2016-17 adjusted cohort graduation rates and 2012-13 averaged freshman graduation rates. You can find more data on high school graduation and dropout rates in the annual report Trends in High School Dropout and Completion Rates in the United States.

This blog was originally posted on July 15, 2015 and was updated on February 2, 2016, December 4, 2017, and January 24, 2019.