pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
EADA — college athletics gender equity (~2,000+ institutions, 2002-2021). Participation, coaching, salaries, expenses, revenues, athletic aid by gender. Not Title IX compliance data. No sector column; join IPEDS on unitid for institution type.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-eada --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/education-data-source-eadaContext preview
The summary Claude sees to decide when to auto-load this skill.
EADA — college athletics gender equity (~2,000+ institutions, 2002-2021). Participation, coaching, salaries, expenses, revenues, athletic aid by gender. Not Title IX compliance data. No sector column; join IPEDS on unitid for institution type.
name: education-data-source-eada description: >- EADA — college athletics gender equity (~2,000+ institutions, 2002-2021). Participation, coaching, salaries, expenses, revenues, athletic aid by gender. Not Title IX compliance data. No sector column; join IPEDS on unitid for institution type. metadata: audience: any-agent domain: data-source skill-authored: "2026-02-09" skill-last-updated: "2026-02-09"
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.
| 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 |
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.mdVariable 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
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)| 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 |
| Metric | Calculation | Notes | |--------|-------------|-------| | Aid ratio | `ath_stuaid_women / (ath_stuaid_men + ath_stuaid_women)`
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
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