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SKILL.md
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)
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
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)
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 is null for 2002.
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.
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
