7. METHODOLOGY
How do we identify and count charter schools?
The National Alliance for Public Charter Schools manages a database that serves as a repository for charter school data. The National Alliance collects data from a variety of sources including:
- State data files that contain enrollment, directory, and performance information.
- Federal datasets, such as Common Core of Data (CCD), the Civil Rights Data Collection (CRDC), and EDFacts.
- Charter support agencies, and other local partners and, in some cases, individual schools.
In addition to reviewing the quality of the data and verifying the charter status, our team processes and categorizes the data based on the type of record.
How do we categorize schools?
No exact definition of what constitutes a “school” exists. Because federal and standardization can be problematic when accounting for schools in data systems and in maintaining consistent data across time. For those interested in gaining a better understanding of the education landscape, these inconsistencies pose a problem, particularly in the counting of charter schools. There is no universally accepted definition of what constitutes a “school.” This lack of standardization presents challenges in data systems, particularly when maintaining consistent data over time. These inconsistencies are problematic for those seeking to understand the education landscape, especially in the context of charter schools. A single record of a school, typically represented as a row or unique identification number in a dataset, does not necessarily equate to a single school. For instance, a record might represent a program within a school or a campus associated with another school. These variations are common in both federal and state datasets and are frequently observed in charter school records. Consequently, comparing or combining federal and state datasets without properly understanding and addressing these discrepancies can result in inaccurate counts of charter school openings, closures, and other critical metrics. To mitigate this issue, the National Alliance has developed a “Modified Count” system.
Each database record is assigned a Modified Count code, which indicates whether the entity is a campus, charter holder, program, or school. Correcting these discrepancies is crucial for generating accurate counts of charter schools, openings, closures, and other key metrics. The National Alliance’s Modified Count code helps researchers consistently classify records across different data sources and school years. Some states are disproportionately affected by non-school records, such as campuses and holder records. Non-school records are administrative entries that typically include enrollment data for two or more schools. States with large charter school sectors, like Washington, D.C., Texas, and Florida, have some of the highest proportions of these non-school records. For more details, refer to our 2019 Modified Count Report. This coding method is particularly important when analyzing new and closed schools. A record might appear as a new holder, but if the same number of schools continue to operate, it does not count as a new school. Similarly, only new records that represent additional campuses or entirely new schools are counted as new schools.
| State | Campus | Holder | Non-School Record Share |
|---|---|---|---|
| AL | 2 | 0 | 11.11% |
| AR | 19 | 1 | 19.05% |
| AZ | 51 | 4 | 9.53% |
| CA | 67 | 10 | 5.82% |
| CO | 28 | 4 | 11.43% |
| CT | 0 | 1 | 4.17% |
| DC | 24 | 3 | 20.00% |
| FL | 88 | 2 | 12.28% |
| GA | 8 | 0 | 7.77% |
| ID | 1 | 0 | 1.22% |
| IL | 1 | 0 | 0.75% |
| IN | 9 | 1 | 7.75% |
| LA | 3 | 1 | 2.68% |
| MA | 1 | 2 | 3.61% |
| MD | 2 | 0 | 4.08% |
| MI | 26 | 0 | 7.16% |
| MN | 57 | 0 | 19.26% |
| MO | 14 | 0 | 17.28% |
| MS | 1 | 0 | 10.00% |
| NC | 0 | 1 | 0.47% |
| NH | 10 | 0 | 23.26% |
| NJ | 0 | 6 | 4.96% |
| NM | 1 | 0 | 0.98% |
| NV | 7 | 0 | 6.73% |
| NY | 9 | 5 | 3.84% |
| OH | 17 | 3 | 5.80% |
| OK | 14 | 0 | 21.21% |
| PA | 4 | 3 | 3.80% |
| RI | 5 | 0 | 12.20% |
| TN | 13 | 0 | 11.30% |
| TX | 156 | 2 | 15.09% |
| UT | 3 | 0 | 2.13% |
| WI | 7 | 0 | 2.98% |
| WY | 1 | 0 | 14.29% |
How do we account for changing school identification codes?
State and federal National Center for Education Statistics (NCES) school ID codes sometimes change from year to year. The National Alliance Data & Research Team developed an identification system to address this, where we merge data into our Database of Charter Schools. The system establishes a unique NAPCS ID that allows the team to identify schools across time, regardless of whether state or federal IDs have changed.
State and federal IDs are typically embedded with important details. For example, a code to identify a school’s district or county is part of a longer string of characters. If the underlying information relating to the code changes, such as when the school is redistricted, the ID will change as well. In this situation, the school remains operational, but the school’s original ID may be eliminated, making it hard to determine if the school is still open or to track the school properly over time. This means that the creation or elimination of a state or federal school ID is not necessarily connected to a school’s operational status. Accounting for these sorts of changes is important for accurately counting school openings and closings, especially for charter schools. Changes in IDs, at first glance, might incorrectly look like the closing of schools and opening of new schools, when that is not the case. We conducted an analysis and found that between the 2005-06 and 2019-20 school years, federal NCES IDs for charter schools changed more than 500 times. The reasons for changing IDs vary by state. For instance, an authorizer change can result in a new NCES ID. In Louisiana, many schools had their NCES IDs changed when their authorization shifted from the Recovery School District (RSD) to the Orleans Parish School Board. Other factors that can lead to an NCES ID change include conversions, mergers, and expansions.
How is demographic data collected?
Demographic data for student racial/ethnic backgrounds are collected using a combination of CCD and state-level datasets. We aggregated demographic data for “Other/Two or More Races,” “Native American/Alaskan Native,” and “Pacific Islander/Hawaiian Native” classifications into a single category labeled “Other,” along with any missing demographic enrollment, for the purposes of this report. As individual groups, these populations constituted very low percentages of the overall population in both charter and district schools with a relatively steady trend. However, “Other/Two or More Races” alone reached greater than 4% of the population for the first time in 2010-11.
Demographic data for students with disabilities comes from the U.S. Department of Education’s Civil Rights Data Collection. We calculated free and reduced-price lunch (FRPL) data using only schools that reported data. Schools with no FRPL data were removed from the calculations. In 1998-99, the Free Lunch Eligible Status shifted to Free and Reduced Priced Lunch, or the more standard FRPL Eligible.
How are missing data addressed?
The National Alliance collects and verifies the most up-to-date and thorough data on charter schools. However, some gaps in data inevitably occur. We calculate all statistics throughout the digest using the best available data and exclude any records for which no data were available.
How do we distinguish school districts?
Because charter schools often operate as their own districts, the National Alliance assigns each charter school to a geographic school district in order to calculate charter school enrollment share. Addresses for all public charter schools and district schools are obtained from the Common Core of Data (CCD) and supplemented with the Alliance’s own data collection. Using Geocodio, we generate latitude and longitude coordinates for each school, then map those locations onto EDGE school district shapefiles in ArcGIS. This process identifies the geographic school district in which each charter school is physically located. Locale data is also obtained from NCES.
The National Alliance excludes known virtual schools* from geospatial analyses. While most virtual schools are flagged by CCD, the Alliance conducts additional manual checks to identify cyber and hybrid schools. The team also reviews schools with unusually large student populations relative to their location, as this often signals virtual enrollment.
*Note: Data in the 2025 Charter School Data Digest includes data from the 1992-93 through 2023-24 school years. The term “virtual school” in this report does not apply to schools that have adopted virtual, remote, hybrid, or other social distancing practices in response to the COVID-19 pandemic unless this change became permanent.
Measuring school finances
The primary data source the National Alliance uses to analyze and report summary data about annual differences in school revenue and expenditures across the charter and non-charter sectors is the NCES Local Education Agency (School District) Finance Survey, commonly referred to as the F-33 survey. Published annually by the National Center for Education Statistics, the F-33 provides comprehensive data on school district finances across all fifty states and the District of Columbia. For this analysis, the National Alliance used F-33 data from FY2018 through FY2022, the most recent years available at the district (LEA) level. All dollar amounts were adjusted for inflation using the Bureau of Labor Statistics Consumer Price Index (CPI), with September of the initial school year as the reference point.
To examine differences in per-pupil revenues and per-pupil expenditures between sectors, the National Alliance relied on several key F-33 variables. Specifically, V33 was used for total district or charter LEA enrollment, TOTALREV was used for total public revenues, TOTALEXP was used to capture LEAs’ total expenditure, and AGCHRT denotes whether the school district included only charter schools (1), no charter schools (3), or a combination of both (2). Districts coded as mixed (AGCHRT = 2) were excluded, as it was not possible to distinguish between charter and non-charter finances within those entities. Records with suppressed values for enrollment, revenues, or expenditures were also removed.
Because this study focuses on typical K-12 school district funding, the analysis excluded LEAs with fundamentally different structures or missions. Specifically, districts coded in SCHLEV as vocational or special education school systems (05), nonoperating school systems (06), education service agencies (07), or “not applicable/could not be determined” (N) were removed. Only systems coded as 01 (elementary only), 02 (secondary only), or 03 (elementary/secondary combined) were retained for sector comparisons.
The National Alliance did not automatically exclude apparent outliers, since unusual cases can reflect real-world financial variation. However, states were included in state-level analysis only if at least 50% of both charter and district enrollment was captured in the cleaned F-33 data. States with 50-79% coverage are noted with an asterisk in tables; those below 50% coverage were excluded entirely. This threshold ensures that state-level findings are drawn from sufficiently representative samples, improving the reliability and comparability of the results.
Citation Styles for “The Data Dashboard” or “Data Digest”
APA (6th ed.)
- The National Alliance for Public Charter Schools (Washington, D.C.). (2025). “The Data Dashboard”. Washington, D.C: The National Alliance for Public Charter Schools.
Chicago (Author-Date, 15th ed.)
- The National Alliance for Public Charter Schools (Washington, D.C.). 2025. “The Data Dashboard”. Washington, D.C.: The National Alliance for Public Charter Schools.
Harvard (18th ed.)
- THE NATIONAL ALLIANCE FOR PUBLIC CHARTER SCHOOLS (WASHINGTON, D.C.). (2025). “The Data Dashboard”. Washington, D.C., The National Alliance for Public Charter Schools.
MLA (7th ed.)
- “The Data Dashboard”. Washington, D.C: The National Alliance for Public Charter Schools, 2025. Web.
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