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 (1980-present, 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: 1980-present (varies by component)
• 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

survey-components.md

graduation-rates.md

enrollment-data.md

finance-data.md

financial-aid.md

institution-identifiers.md

completions-data.md

data-quality.md

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

Key Identifiers

unitid

100654

opeid

00100200

Institution Type Codes

inst_control

institution_level

sector

hbcu

tribal_college

degree_granting

Note: There is no code 3 for

institution_level

inst_size

Categories

Note:

inst_size

Race/Ethnicity Codes (Portal Integer Encoding)

1

2

3

4

5

6

7

8

9

20

99

-1

-2

-3

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)

1

2

3

4

9

99

-1

-2

-3

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

-1

-2

-3

null

Year Field Meanings

Data Access

Datasets for IPEDS are available via the mirror system. See

datasets-reference.md

mirrors.yaml

fetch-patterns.md

Key datasets:

ipeds/colleges_ipeds_directory

ipeds/codebook_colleges_ipeds_directory

ipeds/colleges_ipeds_admissions-enrollment

ipeds/codebook_colleges_ipeds_admissions-enrollment

ipeds/colleges_ipeds_enrollment-fte

ipeds/codebook_colleges_ipeds_enrollment-fte

ipeds/colleges_ipeds_grad-rates

ipeds/codebook_colleges_ipeds_grad-rates

ipeds/colleges_ipeds_finance

ipeds/codebook_colleges_ipeds_finance

32 IPEDS datasets exist in the mirror (5 shown above). See

datasets-reference.md

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

get_codebook_url()

fetch-patterns.md

url = get_codebook_url("ipeds/codebook_colleges_ipeds_directory")

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)
)

Data Availability & Lag Times

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

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:

INSTNM

inst_name

STABBR

state_abbr

CONTROL

inst_control

ICLEVEL

institution_level

DEGGRANT

degree_granting

CYACTIVE

currently_active_ipeds

DEATHYR

year_deleted

APPLCN

number_applied

ADMSSN

number_admitted

EFTOTLT

enrollment_fall

GR*

completion_rate_150pct

completers_150pct

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:

fall-enrollment-race

enrollment_fall

race

sex

level_of_study

ftpt

class_level

degree_seeking

fall-enrollment-age

enrollment-fte

est_fte

rep_fte

enrollment-headcount

fall-retention

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

./references/finance-data.md

sex == 99

institution_level

currently_active_ipeds

year_deleted

./references/institution-identifiers.md

inst_size

inst_size

efdeexc

efdesom

efdenom

unitid

cohort_rev

subcohort == 2

completion_rate_150pct

unique(subset=["unitid"], keep="first")

open_public

open_public

openpubl

open_public=1

OPENADMP

open_public

OPENADMP

type_of_aid=9

type_of_aid=9

sfa_grants_and_net_price

type_of_aid=9

Critical Limitations

Graduation Rates (GRS)

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

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

See

./references/graduation-rates.md

Finance Data

CRITICAL: Public and private institutions use different accounting standards.

See

./references/finance-data.md

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

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

Related Data Sources

education-data-source-scorecard

education-data-source-fsa

education-data-source-nccs

education-data-source-pseo

education-data-source-eada

education-data-source-nacubo

education-data-source-campus-safety

education-data-explorer

education-data-query

Topic Index

./references/survey-components.md

./references/graduation-rates.md

./references/graduation-rates.md

./references/graduation-rates.md

./references/graduation-rates.md

./references/graduation-rates.md

./references/enrollment-data.md

./references/enrollment-data.md

./references/enrollment-data.md

./references/enrollment-data.md

./references/finance-data.md

./references/finance-data.md

./references/finance-data.md

./references/finance-data.md

./references/financial-aid.md

./references/financial-aid.md

./references/financial-aid.md

./references/institution-identifiers.md

./references/institution-identifiers.md

./references/institution-identifiers.md

./references/institution-identifiers.md

./references/completions-data.md

./references/completions-data.md

./references/completions-data.md

./references/data-quality.md

./references/data-quality.md

./references/data-quality.md

./references/graduation-rates.md

./references/graduation-rates.md

open_public

type_of_aid

./references/financial-aid.md