EADA Data Source Reference

Equity in Athletics Disclosure Act (EADA) data for college athletics gender equity analysis covering ~2,000+ institutions (2002-2021). Use when analyzing athletic participation, coaching staff, salaries, expenses, revenues, or athletic aid by gender at colleges/universities, or understanding Title IX context in athletics. EADA is NOT Title IX compliance data. Note: no sector column; join to IPEDS on unitid to filter by institution type.

The EADA provides the only standardized, publicly available dataset on college athletics participation, coaching, finances, and athletic aid by gender across ~2,000+ postsecondary institutions, enabling gender equity analysis in intercollegiate athletics.

CRITICAL: Value Encoding

EADA data from the Education Data Portal uses integer codes for categorical variables. Original EADA web tools use string labels; the Portal converts these to integers. Always verify codes against the codebook (see Truth Hierarchy below).

Context	ath_classification_code	Missing values
Portal (integers)	1 = NCAA DI FBS	-1, -2, -3
Original EADA	String labels	Blank / N/A

Note: There is no sector column in EADA Portal data. To filter by sector, join with IPEDS directory data on unitid.

See ./references/variable-definitions.md for complete encoding tables.

What is EADA?

• Collector: U.S. Department of Education (Office of Postsecondary Education)
• Coverage: ~2,000+ coeducational postsecondary institutions with intercollegiate athletics
• Mandate: Institutions participating in Title IV aid with athletic programs must report
• Frequency: Annual (data publicly available by October 15 each year)
• Available years: 2002–2021 (Portal mirror)
• Primary identifier: unitid (6-digit IPEDS institution ID)
• Content: Athletic participation, coaching staff, salaries, expenses, revenues, and athletic aid — all reported by gender
• Available through: Education Data Portal mirrors

Reference File Structure

File	Purpose	When to Read
title-ix-context.md	Legal framework, gender equity requirements	Understanding policy context
data-elements.md	Participation, coaches, salaries, expenses, revenues	Identifying available variables
sport-level-data.md	Data available by individual sport	Sport-specific analysis
variable-definitions.md	Key variables, codes, special values	Interpreting specific data elements
limitations.md	Data quality issues, comparability, self-reporting caveats	Assessing data reliability
fetch-patterns.md	Mirror URLs and fetch code patterns	Fetching data

Decision Trees

What analysis am I conducting?

Research question?
├─ Gender equity overview → Start with participation + aid ratios
│   └─ See ./references/data-elements.md
├─ Coaching disparities → Coach counts + salaries by gender
│   └─ See ./references/data-elements.md (Coaching section)
├─ Financial investment → Expenses + revenues by team gender
│   └─ See ./references/data-elements.md (Financial section)
├─ Sport-specific analysis → Individual sport data
│   └─ See ./references/sport-level-data.md
├─ Title IX compliance assessment → CAUTION: EADA ≠ compliance data
│   └─ See ./references/limitations.md (Critical)
└─ Trend analysis → Year-over-year comparisons
    └─ See ./references/fetch-patterns.md

What variables do I need?

Variable categories?
├─ Participation counts
│   ├─ Unduplicated by gender → `undup_athpartic_men`, `undup_athpartic_women`
│   ├─ Duplicated (sport-level sum) → `athpartic_men`, `athpartic_women`
│   ├─ Coed teams → `athpartic_coed_men`, `athpartic_coed_women`
│   └─ By sport → See ./references/sport-level-data.md
├─ Coaching
│   ├─ Head coaches → `men_fthdcoach_*`, `women_fthdcoach_*` variables
│   ├─ Assistant coaches → `men_ftascoach_*`, `women_ftascoach_*` variables
│   └─ Salaries → `hdcoach_salary_*`, `ascoach_salary_*` variables
├─ Financial
│   ├─ Expenses → `ath_exp_*` variables
│   ├─ Revenues → `ath_rev_*` variables
│   └─ Athletic aid → `ath_stuaid_*` variables
└─ Detailed definitions → See ./references/variable-definitions.md

How do I interpret the data?

Interpretation question?
├─ What counts as "participation"?
│   └─ See ./references/variable-definitions.md
├─ Why don't participation ratios match enrollment?
│   └─ See ./references/limitations.md
├─ Is this institution Title IX compliant?
│   └─ CANNOT determine from EADA data alone
│       └─ See ./references/limitations.md (Critical)
├─ Why are some values missing or zero?
│   └─ See ./references/limitations.md
└─ How do I compare across institutions?
    └─ See ./references/limitations.md (Comparability section)

Quick Reference: Key Metrics

Participation Equity Indicators

Metric	Calculation	Interpretation
Female participation ratio	undup_athpartic_women / (undup_athpartic_men + undup_athpartic_women)	Compare to female enrollment ratio
Participation gap	Female enrollment % - Female participation %	Positive = underrepresentation
Opportunities per student	undup_athpartic_total / enrollment_total	Athletic opportunity rate

Financial Equity Indicators

Metric	Calculation	Notes
Aid ratio	ath_stuaid_women / (ath_stuaid_men + ath_stuaid_women)	Should approximate participation ratio
Per-participant expense	ath_opexp_perpart_men, ath_opexp_perpart_women	Pre-calculated per-participant operating expense
Recruiting investment	recruitexp_men, recruitexp_women	Indicator of program investment

Coaching Equity Indicators

Metric	Focus	Variables
Female coaches of women's teams	% female	women_fthdcoach_fem, women_pthdcoach_fem
Salary equity	Avg salary comparison	hdcoach_salary_men, hdcoach_salary_women

Key Identifiers

ID	Format	Level	Example	Notes
unitid	6-digit integer	Institution	110635	Same as IPEDS; primary join key
opeid	String	Institution	"00123400"	OPE ID (may be null for early years)
year	4-digit integer	Reporting year	2021	Fiscal year ending
fips	Integer	State	6 (California)	Federal FIPS code
inst_name	String	Institution	"University of..."	Institution name

Common Filters

Filter	Variable	Example Values
Institution	unitid	6-digit IPEDS ID
Year	year	2002–2021
State	fips	Integer FIPS code (e.g., 6 = California)
Athletic Division	ath_classification_code	Integer codes 1–20 (see below)

Note: There is no sector column in the EADA Portal data. To filter by institutional sector, join with IPEDS directory data on unitid.

Athletic Classification Codes

Code	Division	Code	Division
1	NCAA Division I FBS	12	NJCAA Division I
2	NCAA Division I FCS	13	NJCAA Division II
3	NCAA Division I (no football)	14	NJCAA Division III
4	NCAA Division II (with football)	15	NCCAA Division I
5	NCAA Division II (no football)	16	NCCAA Division II
6	NCAA Division III (with football)	17	CCCAA
7	NCAA Division III (no football)	18	Independent
8	Other (check ath_classification_other)	19	NWAC
9	NAIA Division I	20	USCAA
10	NAIA Division II
11	NAIA Division III

Note: Code 1 was historically labeled "NCAA Division I-A" and code 2 "NCAA Division I-AA" in earlier years. The ath_classification_name string column reflects the label used at the time of reporting.

Missing Data Codes

Code	Meaning	When Used
-1	Missing/not reported	Data not submitted by institution
-2	Not applicable	Item doesn't apply (e.g., no men's team)
-3	Suppressed	Data suppressed for privacy

Data Availability

Topic	Years Available	Update Frequency
Institution-level	2002–2021	Annual
Sport-level	2002–2021	Annual
Coaching details	2002–2021	Annual
Financial data	2002–2021	Annual

Note: Some columns (e.g., num_sports, aggregated totals with _all suffix) are null for earlier years (2002) and were added in later reporting cycles. The opeid column has substantial nulls in early years (2002) but is not all-null there (live-verified 2026-08-07) — do not rely on it as a complete key for early years.

Example Research Questions

Question	Key Variables	Reference
Are women underrepresented in athletics?	undup_athpartic_*, enrollment_*	data-elements.md
How much do institutions invest in women's sports?	ath_exp_*, ath_rev_*	data-elements.md
Are coaches of women's teams paid fairly?	hdcoach_salary_*	variable-definitions.md
Which sports have most female participants?	Sport-level data	sport-level-data.md
Has participation equity improved over time?	Multi-year trend	fetch-patterns.md

Data Access

Datasets for EADA are available via the Education Data Portal mirror system. All data fetching uses fetch_from_mirrors() from fetch-patterns.md, with mirrors defined in mirrors.yaml and canonical paths in datasets-reference.md.

Key datasets:

Dataset	Path	Type	Codebook
Institutional Characteristics	eada/colleges_eada_inst_characteristics	Single	eada/codebook_colleges_eada_inst-characteristics

EADA naming note: The data path uses inst_characteristics (underscores) while the codebook path uses inst-characteristics (hyphens). Always use the exact paths from datasets-reference.md.

Truth Hierarchy

When interpreting EADA variable definitions and coded values, apply this priority:

Priority	Source	Rationale
1 (highest)	Actual data file (parquet)	What you observe IS the truth
2	Live codebook (.xls via get_codebook_url())	Authoritative documentation; may lag
3 (lowest)	This skill's reference docs	Summarized; convenient but may drift

Use get_codebook_url("eada/codebook_colleges_eada_inst-characteristics") from fetch-patterns.md to construct the codebook download URL.

Filtering

import polars as pl

# Filter by athletic division (NCAA Division I FBS only)
df_d1_fbs = df.filter(pl.col("ath_classification_code") == 1)

# Exclude coded missing values before calculations
df_clean = df.filter(
    (pl.col("undup_athpartic_men") >= 0) &
    (pl.col("undup_athpartic_women") >= 0)
)

# Note: No `sector` column in EADA data. To filter by sector,
# join with IPEDS directory data on unitid first.

library(dplyr)

# Filter by athletic division (NCAA Division I FBS only)
df_d1_fbs <- df |> filter(ath_classification_code == 1)

# Exclude coded missing values before calculations
df_clean <- df |> filter(
    (undup_athpartic_men >= 0) &
    (undup_athpartic_women >= 0)
)

# Note: No `sector` column in EADA data. To filter by sector,
# join with IPEDS directory data on unitid first.

Common Pitfalls

Pitfall	Issue	Solution
Including coded missing values	-1, -2, -3 treated as real numbers skew totals and ratios	Filter >= 0 on all numeric columns before aggregation
Assuming Title IX compliance	EADA data cannot determine Title IX compliance — it is a disclosure tool, not an enforcement mechanism	Read ./references/limitations.md; use EADA for descriptive analysis only
Comparing across institutions naively	Different reporting practices, program sizes, and classification levels make raw comparisons misleading	Normalize by enrollment, filter to same classification, and note caveats
Using wrong variable names	Portal variable names differ from EADA source documentation (e.g., undup_athpartic_men not partic_men)	Always verify column names against actual data or codebook; see ./references/variable-definitions.md
Self-reported data accuracy	Institutions self-report without independent verification; errors and inconsistencies exist	Cross-check outliers against institution websites or IPEDS data
Ignoring zero values	Zero may mean "no team" or "not reported" depending on context	Distinguish between true zeros and missing data using -1/-2 codes
Assuming sector column exists	EADA data has no sector column	Join with IPEDS directory on unitid to get sector

EADA vs. Title IX Compliance

EADA Data                          Title IX Compliance
──────────────────────────────────────────────────────────
Self-reported                      OCR investigation
Snapshot (Oct 15)                  Continuous obligation
Participation counts only          Participation + interest + ability
No "laundry list" items           13+ treatment areas
Public disclosure                  Enforcement mechanism

Always read: ./references/limitations.md before drawing compliance conclusions.

Key Limitations Summary

• Self-reported: No independent verification
• Counting methods: Differ from Title IX counting
• Not comprehensive: Misses many equity factors
• Comparability issues: Different reporting practices across institutions

Related Data Sources

Source	Relationship	When to Use
education-data-source-ipeds	Complementary institution data	Joining enrollment, demographics, finances via unitid
education-data-explorer	Parent discovery skill	Routing questions to mirror dataset files across all sources
education-data-query	Data fetching	Downloading parquet/CSV files from mirrors

Topic Index

Topic	Reference File
Title IX law	./references/title-ix-context.md
Gender equity requirements	./references/title-ix-context.md
Three-prong test	./references/title-ix-context.md
Participation variables	./references/data-elements.md
Coaching variables	./references/data-elements.md
Salary variables	./references/data-elements.md
Expense variables	./references/data-elements.md
Revenue variables	./references/data-elements.md
Athletic aid	./references/data-elements.md
Sport-specific data	./references/sport-level-data.md
Variable definitions	./references/variable-definitions.md
Integer encoding tables	./references/variable-definitions.md
Data limitations	./references/limitations.md
Self-reporting issues	./references/limitations.md
EADA vs Title IX	./references/limitations.md
Fetch patterns	./references/fetch-patterns.md
Mirror URLs	./references/fetch-patterns.md