IPEDS Data Source Reference

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)

Reference File Structure

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

Institution Type Codes

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

inst_size Categories

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

Race/Ethnicity Codes (Portal Integer Encoding)

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)

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

Year Field Meanings

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:

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

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")

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:

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:

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

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 for complete details.

Finance Data

CRITICAL: Public and private institutions use different accounting standards.

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 are 0-100
    if "completion_rate_150pct" in df.columns:
        bad = df.filter(
            (pl.col("completion_rate_150pct") > 100) |
            (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

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