`/education-data-source-ipeds`¶

Source guide to IPEDS, the main federal postsecondary dataset, across its survey components (enrollment, completions, graduation rates, finance, aid, admissions, staff), with caveats on first-time full-time graduation rates and GASB versus FASB finance data.

Pack: DAAF — Data Analyst Augmentation Framework (Brian Heseung Kim)

Category: data-handling

Field: social-sciences

License:

LGPL-3.0-or-later (as declared in the README, badge and CITATION.cff; the repo ships the GPL-3.0 text as LICENSE plus COPYING.LESSER)

Updated: 2026-08-09

Stages: data-acquisition

↗ view SKILL.md on source · GitHub stars

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?¶

Text Only

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?¶

Text Only

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?¶

Text Only

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:

Python

url = get_codebook_url("ipeds/codebook_colleges_ipeds_directory")

S

## 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¶

Python

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)
)

S

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¶

Python

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

S

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")

Source	Relationship	When to Use
`education-data-source-scorecard`	Non-traditional student outcomes	Post-college earnings, broader student population
`education-data-source-fsa`	Detailed loan/grant data	Federal student aid analysis (link on OPEID)
`education-data-source-nccs`	Private institution 990 data	Financial data beyond IPEDS cutoff year
`education-data-source-pseo`	Post-college employment	State-level employment outcomes
`education-data-source-eada`	College athletics	Athletics equity and finance
`education-data-source-nacubo`	Endowment data	Endowment analysis beyond IPEDS
`education-data-source-campus-safety`	Campus crime statistics	Safety and compliance
`education-data-explorer`	Parent discovery skill	Routing questions to mirror dataset files
`education-data-query`	Data fetching	Downloading parquet/CSV files

Topic Index¶

Topic	Reference File
Survey components overview	`./references/survey-components.md`
Graduation rate cohort definition	`./references/graduation-rates.md`
First-time full-time limitation	`./references/graduation-rates.md`
Transfer-out rates	`./references/graduation-rates.md`
Outcome Measures survey	`./references/graduation-rates.md`
150% vs 200% time	`./references/graduation-rates.md`
Fall enrollment	`./references/enrollment-data.md`
12-month enrollment	`./references/enrollment-data.md`
FTE calculations	`./references/enrollment-data.md`
Enrollment by level	`./references/enrollment-data.md`
GASB accounting	`./references/finance-data.md`
FASB accounting	`./references/finance-data.md`
Revenue categories	`./references/finance-data.md`
Expense categories	`./references/finance-data.md`
Net price definition	`./references/financial-aid.md`
Pell grant data	`./references/financial-aid.md`
Aid by income level	`./references/financial-aid.md`
UNITID	`./references/institution-identifiers.md`
OPEID	`./references/institution-identifiers.md`
Institutional mergers	`./references/institution-identifiers.md`
Sector changes	`./references/institution-identifiers.md`
CIP codes	`./references/completions-data.md`
Award levels	`./references/completions-data.md`
Completers vs completions	`./references/completions-data.md`
Data quality issues	`./references/data-quality.md`
Missing data codes	`./references/data-quality.md`
Sector comparisons	`./references/data-quality.md`
Subcohort codes (GRS)	`./references/graduation-rates.md`
GRS deduplication	`./references/graduation-rates.md`
`open_public` vs open admissions	Common Pitfalls (this file)
SFA `type_of_aid` codes	`./references/financial-aid.md`

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

/education-data-source-ipeds¶

IPEDS Data Source Reference¶

What is IPEDS?¶

Reference File Structure¶

Decision Trees¶

What data am I working with?¶

Is my analysis valid?¶

Finding specific variables?¶

Quick Reference: Survey Components¶

Key Identifiers¶

Institution Type Codes¶

inst_size Categories¶

Race/Ethnicity Codes (Portal Integer Encoding)¶

Sex Codes (Portal Integer Encoding)¶

Missing Data Codes¶

Year Field Meanings¶

Data Access¶

Filtering¶

Data Availability & Lag Times¶

Variable Name Mappings¶

Enrollment Dataset Clarification¶

Common Pitfalls¶

Critical Limitations¶

Graduation Rates (GRS)¶

Finance Data¶

Net Price¶

Data Quality Checklist¶

Related Data Sources¶

Topic Index¶

`/education-data-source-ipeds`¶

`inst_size` Categories¶