IPEDS Data Source Reference

IPEDS (Integrated Postsecondary Education Data System) — the primary federal data system for ~6,500 U.S. postsecondary institutions, comprising 12+ annual survey components: enrollment, completions, graduation rates, finance, financial aid, admissions, human resources, and institutional characteristics (1979-present in the mirror — the IPEDS program dates from 1980; varies by component). Use when analyzing postsecondary enrollment, degree completions by CIP code, institutional finances, or admissions data. Graduation rates track first-time full-time students only (150% cohort). Cross-sector finance comparisons require care due to GASB vs. FASB accounting.

Comprehensive guide to understanding and using IPEDS data correctly. IPEDS is the most widely used source for postsecondary education data but has significant complexities — including sector-specific accounting standards, cohort-limited graduation rates, and integer-encoded categorical variables — that users must understand.

CRITICAL: Value Encoding

This document describes Education Data Portal integer encodings, which differ from NCES raw file string codes. The Portal converts categorical variables to integers for consistency across sources.

Context	Race White	Race Black	Sex Male	Sector Public 4-yr
Portal (integers)	1	2	1	1
NCES raw files	EFFY_WHITE	EFFY_BKAA	M	varies

Always verify codes against Portal codebooks (available alongside each dataset in the Portal mirrors).

What is IPEDS?

IPEDS (Integrated Postsecondary Education Data System) is a system of 12+ interrelated survey components:

• Administered by: National Center for Education Statistics (NCES)
• Coverage: ~6,500 Title IV-participating postsecondary institutions
• Frequency: Annual collection in three periods (Fall, Winter, Spring)
• Mandate: Required for Title IV federal student aid participation
• Available years: content spans 1979-2024 in the v2 mirror (build validated 2026-08-06), varies by component. Note the content minimum is 1979 (not 1980); most series now reach 2023-24, while Finance lags (see Data Availability & Lag Times).
• Primary identifier: UNITID (6-digit institution ID)
• Available through: Education Data Portal mirrors (32 datasets covering most survey components; some variables not mirrored — see Data Access section)

Reference File Structure

File	Purpose	When to Read
survey-components.md	All 12+ IPEDS surveys with collection periods	Understanding data structure
graduation-rates.md	CRITICAL GRS limitations and who is tracked	Any graduation rate analysis
enrollment-data.md	Fall vs 12-month, FTE calculations	Enrollment comparisons
finance-data.md	GASB vs FASB accounting standards	Cross-sector finance analysis
financial-aid.md	Net price, aid types, populations	Aid and cost analysis
institution-identifiers.md	UNITID, OPEID, mergers, closures	Data linking and longitudinal work
completions-data.md	Degrees awarded, CIP codes	Completions and outcomes
data-quality.md	Known issues, sector comparisons	Quality assurance

Decision Trees

What data am I working with?

Working with IPEDS data?
├─ Graduation rates → ./references/graduation-rates.md (READ FIRST!)
├─ Enrollment counts → ./references/enrollment-data.md
├─ Finance/revenue/expenses → ./references/finance-data.md
├─ Financial aid/net price → ./references/financial-aid.md
├─ Degrees/completions → ./references/completions-data.md
├─ Institutional info → ./references/survey-components.md (IC section)
├─ Human resources/salaries → ./references/survey-components.md (HR section)
└─ Linking to other data → ./references/institution-identifiers.md

Is my analysis valid?

Cross-sector comparison?
├─ Comparing grad rates across sectors
│   └─ CAUTION: Different populations → ./references/graduation-rates.md
├─ Comparing finances across sectors
│   └─ CAUTION: GASB vs FASB → ./references/finance-data.md
├─ Comparing net price across sectors
│   └─ CAUTION: Aid populations differ → ./references/financial-aid.md
└─ Time series analysis
    └─ Check for institutional changes → ./references/institution-identifiers.md

Finding specific variables?

Need variable definitions?
├─ Survey component overview → ./references/survey-components.md
├─ Graduation cohort definitions → ./references/graduation-rates.md
├─ Enrollment level/status → ./references/enrollment-data.md
├─ Revenue/expense categories → ./references/finance-data.md
├─ Aid types and populations → ./references/financial-aid.md
└─ CIP codes for programs → ./references/completions-data.md

Quick Reference: Survey Components

Component	Abbrev	Collection	Key Content
Institutional Characteristics	IC	Fall	Directory, tuition, mission
12-Month Enrollment	E12	Fall	Unduplicated headcount, FTE
Completions	C	Fall	Degrees by CIP, demographics
Cost	CST	Fall/Winter	Cost of attendance, net price
Admissions	ADM	Winter	Applications, admits, enrollees
Student Financial Aid	SFA	Winter	Aid counts and amounts
Graduation Rates	GR	Winter	150% completion rates
Graduation Rates 200%	GR200	Winter	200% completion rates
Outcome Measures	OM	Winter	Part-time and transfer outcomes
Fall Enrollment	EF	Spring	Point-in-time enrollment
Finance	F	Spring	Revenue, expenses, assets
Human Resources	HR	Spring	Employees, salaries
Academic Libraries	AL	Spring	Library resources (biennial)

Key Identifiers

ID	Format	Level	Example	Notes
unitid	6-digit integer	Institution	100654	Unique, persistent across years; changes on merger
opeid	8-digit string	Institution (Title IV)	00100200	Links to FSA/NSLDS; shared across branches

Institution Type Codes

Variable	Values	Meaning
inst_control	1	Public
	2	Private nonprofit
	3	Private for-profit
	-1	Missing/not reported
institution_level	1	Less than 2-year
	2	2-year (at least 2 but less than 4)
	4	4-year or above
	-1	Missing/not reported
sector	0	Administrative unit
	1	Public, 4-year or above
	2	Private not-for-profit, 4-year or above
	3	Private for-profit, 4-year or above
	4	Public, 2-year
	5	Private not-for-profit, 2-year
	6	Private for-profit, 2-year
	7	Public, less-than 2-year
	8	Private not-for-profit, less-than 2-year
	9	Private for-profit, less-than 2-year
	-1	Sector unknown (not active)
hbcu	1	Historically Black College/University
	0	Not HBCU
	-1	Missing/not reported
tribal_college	1	Tribal College
	0	Not Tribal College
	-1	Missing/not reported
degree_granting	1	Degree-granting
	0	Non-degree-granting

Note: There is no code 3 for institution_level. The Portal uses codes 1, 2, 4 (not 1, 2, 3).

inst_size Categories

Code	Meaning
1	Under 1,000
2	1,000 - 4,999
3	5,000 - 9,999
4	10,000 - 19,999
5	20,000 and above

Note: inst_size is a category code (1-5), not an actual enrollment count.

Race/Ethnicity Codes (Portal Integer Encoding)

Code	Category	Notes
1	White	Single race, non-Hispanic
2	Black	Single race, non-Hispanic
3	Hispanic	Any race
4	Asian	Single race, non-Hispanic
5	American Indian/Alaska Native	Single race, non-Hispanic
6	Native Hawaiian/Pacific Islander	Single race, non-Hispanic
7	Two or more races	Multiple races selected, non-Hispanic
8	Nonresident alien	International students
9	Unknown	Race/ethnicity unknown
20	Other	Other race/ethnicity
99	Total	All races combined
-1	Missing/not reported
-2	Not applicable
-3	Suppressed	Privacy protection

Historical note: Prior to 2010, Asian included Pacific Islanders (code 6 did not exist), and "Two or more races" (code 7) was not collected.

Sex Codes (Portal Integer Encoding)

Code	Category
1	Male
2	Female
3	Nonbinary/Another gender
4	Unknown/Prefer not to say
9	Unknown
99	Total
-1	Missing/not reported
-2	Not applicable
-3	Suppressed

Note: Codes 3 and 4 are recent additions for non-binary gender reporting. Historical data may only have codes 1, 2, and 99. The exact meaning of codes 3 vs 4 may vary by endpoint — check the specific codebook.

Missing Data Codes

Code	Meaning	When Used
-1	Missing/not reported	Data not submitted by institution
-2	Not applicable	Item doesn't apply to this institution type
-3	Suppressed	Data suppressed for privacy
null	Not available	Field not collected for this survey year

Year Field Meanings

Data Type	Year Field Meaning
Institutional characteristics	As of fall of indicated year
Fall enrollment	As of fall census date
12-month enrollment	July 1 to June 30 academic year
Completions	Awarded during academic year
Graduation rates	Cohort entered in indicated year
Finance	Fiscal year ending in indicated year
Student financial aid	For indicated academic year

Data Access

Datasets for IPEDS are available via the mirror system. See datasets-reference.md for canonical paths, mirrors.yaml for mirror configuration, and fetch-patterns.md for fetch code patterns.

Key datasets:

Dataset	Type	Path	Codebook
Directory	Single	ipeds/colleges_ipeds_directory	ipeds/codebook_colleges_ipeds_directory
Admissions	Single	ipeds/colleges_ipeds_admissions-enrollment	ipeds/codebook_colleges_ipeds_admissions-enrollment
Enrollment FTE	Single	ipeds/colleges_ipeds_enrollment-fte	ipeds/codebook_colleges_ipeds_enrollment-fte
Graduation Rates	Single	ipeds/colleges_ipeds_grad-rates	ipeds/codebook_colleges_ipeds_grad-rates
Finance	Single	ipeds/colleges_ipeds_finance	ipeds/codebook_colleges_ipeds_finance

32 IPEDS datasets exist in the mirror (5 shown above). See datasets-reference.md for the complete list with all paths and codebook paths.

Known Portal gaps:

• Distance education enrollment variables (efdeexc, efdesom, efdenom) are not in Portal mirror datasets. Use the NCES IPEDS Data Center for these.
• Open-admissions policy variable (OPENADMP) is not in Portal mirror datasets. Note: open_public is NOT the same thing — see Common Pitfalls below.
• Finance data may have a year lag relative to NCES releases (last verified through 2017 in some datasets).

For data not available through Portal mirrors, access NCES directly at https://nces.ed.gov/ipeds/.

Codebooks are .xls files co-located with data in all mirrors. Use get_codebook_url() from fetch-patterns.md to construct download URLs:

url = get_codebook_url("ipeds/codebook_colleges_ipeds_directory")

# get_codebook_url() is a Python helper; in R, build the URL from the mirror root.
# Mirror failover: see `education-data-query/references/fetch-patterns.md` (R pattern)
mirror <- yaml::read_yaml("mirrors.yaml")$mirrors[[1]]
url <- paste0(mirror$root_url, "/", "ipeds/codebook_colleges_ipeds_directory", ".xls")

Truth Hierarchy: When interpreting variable values, apply this priority:

1. Actual data file (what you observe in the parquet/CSV) — this IS the truth
2. Live codebook (.xls in mirror) — authoritative documentation, may lag
3. This skill documentation — convenient summary, may drift from codebook

If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate.

Filtering

import polars as pl

# Admissions totals: filter to sex=99 for institution-level totals
# WRONG - includes duplicates (~26K rows with multiple sex values per institution)
df = pl.read_parquet("data/raw/admissions.parquet")
# CORRECT - one row per institution-year (~8K rows)
df_totals = df.filter(pl.col("sex") == 99)

# Calculate admission rate (not provided directly)
df = df.with_columns(
    (pl.col("number_admitted") / pl.col("number_applied") * 100).alias("admit_rate")
)

# Filter to active, degree-granting, 4-year public institutions
df = df.filter(
    (pl.col("sector") == 1) &
    (pl.col("degree_granting") == 1)
)

library(arrow)
library(dplyr)

# Admissions totals: filter to sex=99 for institution-level totals
# WRONG - includes duplicates (~26K rows with multiple sex values per institution)
df <- read_parquet("data/raw/admissions.parquet")
# CORRECT - one row per institution-year (~8K rows)
df_totals <- df |> filter(sex == 99)

# Calculate admission rate (not provided directly)
df <- df |> mutate(admit_rate = number_admitted / number_applied * 100)

# Filter to active, degree-granting, 4-year public institutions
df <- df |> filter(sector == 1, degree_granting == 1)

Data Availability & Lag Times

IPEDS data becomes available with significant lag. Always verify year availability before committing to a year range.

Survey Component	Typical Lag	Latest Available (v2 mirror, 2026-08-06)
Directory	~1 year	2024 (also +7 cc_*_2025 Carnegie classification columns)
Admissions-Enrollment	~2 years	2024
Fall Enrollment	~2-3 years	2024 (race and age files extended through 2024)
Completions	~2 years	2023 (2-digit and 6-digit CIP completions; the completers file itself drops 2022, reaching 2021)
Finance	~4+ years	2017 (not re-verified against the v2 mirror; see warning below)
Graduation Rates	~2-3 years	2023 (grad-rates-pell reaches 2023; the 150% grad-rates file reaches 2023 — see the dedup+revaluation note in education-data-query/references/vintage-drift.md)

New in the v2 mirror (2026-08-06): the directory gained the 2023-24 year and seven Carnegie-2025 classification columns (cc_*_2025); fall-enrollment-race and fall-enrollment-age each gained 2023 and 2024 files; 2-digit/6-digit CIP completions gained 2023. The 150% graduation-rates file was de-duplicated and wholesale-revalued (10.69M → 6.14M rows) — any pre-2026q3 analysis of it must be fully re-run. See the vintage-drift reference for the complete change map.

CRITICAL: IPEDS Finance Data Cutoff. As of January 2026, IPEDS Finance data is only available through 2017 in the Portal mirrors. This affects endowment values (endowment_end), revenue/expense data, and any financial ratios. Options: (1) limit analysis to available years, (2) use NCCS 990 data for private institutions as an alternative, or (3) forward-fill with a documented caveat and indicator column.

Variable Name Mappings

The Portal uses different names than NCES raw file documentation. The table below lists commonly confused mappings:

NCES Raw File Name	Actual Portal Name	Notes
INSTNM	inst_name	Institution name
STABBR	state_abbr	State abbreviation
CONTROL	inst_control	Institutional control
ICLEVEL	institution_level	Level of institution
DEGGRANT	degree_granting	Degree-granting status
CYACTIVE	currently_active_ipeds	Currently active flag
DEATHYR	year_deleted	Year institution closed
APPLCN	number_applied	Total applicants
ADMSSN	number_admitted	Total admitted
EFTOTLT	enrollment_fall	Fall enrollment (in fall-enrollment-race dataset)
various GR*	completion_rate_150pct, completers_150pct, etc.	Grad rate variables

Note: Portal variable names are always lowercase with underscores. NCES documentation often uses UPPERCASE or CamelCase. When in doubt, fetch a sample of the actual data and inspect its column names.

Enrollment Dataset Clarification

IPEDS has multiple enrollment-related datasets in the Portal:

Dataset	Key Columns	Best For
fall-enrollment-race (yearly)	enrollment_fall, race, sex, level_of_study, ftpt, class_level, degree_seeking	Detailed demographic breakdowns
fall-enrollment-age (yearly)	Enrollment by age group	Age distribution analysis
enrollment-fte (single)	est_fte, rep_fte	FTE-based comparisons
enrollment-headcount (single)	Headcount data	Headcount-based analysis
fall-retention (single)	Retention rates	Retention analysis

Note: The fall-enrollment-race yearly dataset provides the most granular enrollment data, disaggregated by multiple dimensions. For institution-level totals, filter to race == 99, sex == 99, ftpt == 99, level_of_study == 99.

Common Pitfalls

Pitfall	Issue	Solution
Using string codes	Portal uses integer encodings, not NCES string codes	Always verify against Portal codebooks; see encoding table above
Grad rates as sole quality metric	IPEDS tracks only first-time, full-time, fall-entering students; excludes ~40% transfers, ~40% part-time	Use Outcome Measures (OM) for part-time/transfer data; note limitations
Cross-sector finance comparison	Public (GASB) and private (FASB) use different accounting standards	Compare within sector only; see ./references/finance-data.md for crosswalk
Net price for all students	Net price covers only first-time, full-time students who received Title IV aid	Document population limitation; excludes full-pay students
Admissions without sex filter	Admissions data disaggregated by sex — unfiltered data has duplicates	Filter to sex == 99 for institution totals
No institution_level 3	Codes are 1, 2, 4 — not sequential 1, 2, 3	Use exact codes: 1=less-than-2yr, 2=2yr, 4=4yr+
Ignoring mergers/closures	Institutions merge, close, or change sector over time	Check currently_active_ipeds and year_deleted; track UNITID changes; see ./references/institution-identifiers.md
inst_size as enrollment	inst_size is a 1-5 category code, not an enrollment count	Use enrollment endpoints for actual counts
Distance education variables missing	efdeexc, efdesom, efdenom are not in Portal mirror datasets	Use the NCES IPEDS Data Center directly for distance education enrollment
GRS duplicate rows per institution	Graduation rates data has multiple rows per unitid within the same subcohort/year, differing in cohort_rev and count columns	Filter to target subcohort first (e.g., subcohort == 2 for bachelor's-seeking at 4-yr), then deduplicate: sort by completion_rate_150pct descending (nulls last), then unique(subset=["unitid"], keep="first")
open_public is not open admissions	open_public (from openpubl) means "open to the general public" (i.e., a currently operating institution) — Harvard has open_public=1. The actual open-admissions policy variable (OPENADMP) is not available in the Portal mirror	Do not use open_public to identify open-admissions institutions. Use admissions data (admit rate near 100%) as a proxy, or access OPENADMP via the IPEDS API directly
SFA type_of_aid=9 is all grants, not Pell	type_of_aid=9 in sfa_grants_and_net_price captures ALL grant/scholarship recipients (Pell + institutional + state/local). The median ratio to total students is ~0.98 — nearly universal. This dramatically overestimates "Pell share" if used as a Pell proxy	For Pell-specific data, use FSA (pre-2020) or College Scorecard bulk download. SFA type_of_aid=9 is appropriate for total grant aid analysis but not for Pell isolation

Critical Limitations

Graduation Rates (GRS)

CRITICAL: IPEDS graduation rates track ONLY first-time, full-time, fall-entering students.

Excluded Population	Approximate % of Undergrads
Transfer students	~40%
Part-time students	~40%
Spring/summer starts	Varies
Students who transfer OUT	Counted as non-completers

At community colleges, IPEDS grad rates may represent <25% of students.

See ./references/graduation-rates.md for complete details.

Finance Data

CRITICAL: Public and private institutions use different accounting standards.

Standard	Institution Type	Comparison
GASB	Public	Compare within sector only
FASB	Private nonprofit	Different from GASB
FASB	Private for-profit	Different revenue treatment

See ./references/finance-data.md for crosswalk guidance.

Net Price

Net price is calculated ONLY for:

• First-time, full-time students
• Who received Title IV aid
• Excludes full-pay students

See ./references/financial-aid.md for details.

Data Quality Checklist

import polars as pl

def ipeds_quality_check(df):
    """Basic IPEDS data quality checks using Portal variable names."""
    issues = []

    # Check graduation rates — Portal stores as 0-1 proportions (not 0-100)
    # See education-data-context skill > Rate and Proportion Normalization
    if "completion_rate_150pct" in df.columns:
        bad = df.filter(
            (pl.col("completion_rate_150pct") > 1.0) |
            (pl.col("completion_rate_150pct") < 0)
        )
        if bad.height > 0:
            issues.append(f"Invalid grad rates: {bad.height} rows")

    # Check for non-active institutions (directory dataset)
    if "currently_active_ipeds" in df.columns:
        inactive = df.filter(pl.col("currently_active_ipeds") != 1)
        if inactive.height > 0:
            issues.append(f"Non-active institutions: {inactive.height}")

    # Check sector consistency
    if "inst_control" in df.columns:
        invalid = df.filter(
            ~pl.col("inst_control").is_in([1, 2, 3, -1])
        )
        if invalid.height > 0:
            issues.append(f"Invalid control codes: {invalid.height}")

    return issues

library(dplyr)

# Basic IPEDS data quality checks using Portal variable names
issues <- character(0)

# Check graduation rates — Portal stores as 0-1 proportions (not 0-100)
# See education-data-context skill > Rate and Proportion Normalization
if ("completion_rate_150pct" %in% names(df)) {
  bad <- df |> filter(completion_rate_150pct > 1.0 | completion_rate_150pct < 0)
  if (nrow(bad) > 0) {
    issues <- c(issues, paste0("Invalid grad rates: ", nrow(bad), " rows"))
  }
}

# Check for non-active institutions (directory dataset)
if ("currently_active_ipeds" %in% names(df)) {
  inactive <- df |> filter(currently_active_ipeds != 1)
  if (nrow(inactive) > 0) {
    issues <- c(issues, paste0("Non-active institutions: ", nrow(inactive)))
  }
}

# Check sector consistency
if ("inst_control" %in% names(df)) {
  invalid <- df |> filter(!inst_control %in% c(1, 2, 3, -1))
  if (nrow(invalid) > 0) {
    issues <- c(issues, paste0("Invalid control codes: ", nrow(invalid)))
  }
}

cat("Issues found:", length(issues), "\n")
if (length(issues) > 0) cat(paste(issues, collapse = "\n"), "\n")

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