pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
EDFacts — K-12 outcomes: assessment proficiency, ACGR graduation rates, ESSA accountability at school/district level (2009-2020). Within-state trends and subgroup gaps. Complements CCD with outcome data. Cannot compare across states — use NAEP.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-edfacts --agent claude-codeHow it fires
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EDFacts — K-12 outcomes: assessment proficiency, ACGR graduation rates, ESSA accountability at school/district level (2009-2020). Within-state trends and subgroup gaps. Complements CCD with outcome data. Cannot compare across states — use NAEP.
name: education-data-source-edfacts description: >- EDFacts — K-12 outcomes: assessment proficiency, ACGR graduation rates, ESSA accountability at school/district level (2009-2020). Within-state trends and subgroup gaps. Complements CCD with outcome data. Cannot compare across states — use NAEP. metadata: audience: any-agent domain: data-source skill-authored: "2026-02-09" skill-last-updated: "2026-02-09"
EDFacts — federal K-12 outcome data from State Education Agencies, covering state assessment proficiency rates, ACGR graduation rates, and ESSA accountability indicators at school and district level (assessments 2009-2020, graduation rates 2010-2019). Use when analyzing within-state achievement trends, subgroup proficiency gaps, or adjusted cohort graduation rates. Complements CCD (school characteristics) with outcome data. State assessment scores CANNOT be compared across states; use NAEP for cross-state comparisons.
EDFacts is the U.S. Department of Education's centralized data collection system for pre-K through grade 12 education data from State Education Agencies (SEAs). It provides state assessment proficiency rates, graduation rates, and accountability indicators — the authoritative federal source for state-level K-12 outcome data.
> **CRITICAL: Value Encoding** > > The Urban Institute Education Data Portal converts NCES string codes (e.g., `ALL`, `CWD`, `LEP`) to **integer codes**. Always verify actual data values before filtering — do not rely on documentation labels alone. > > | Context | Subgroup "All" | English Learner | Sex "Male" | > |---------|----------------|-----------------|------------| > | **Portal integer** | `99` | `1` | `1` | > | NCES string | `ALL` | `LEP` | `M` | > > See `./references/variable-definitions.md` for complete encoding tables.
| File | Purpose | When to Read | |------|---------|--------------| | `accountability-context.md` | ESSA, NCLB history, accountability systems | Understanding policy context | | `assessment-data.md` | Proficiency levels, test scores, limitations | Working with assessment data | | `graduation-rates.md` | ACGR methodology, cohort definitions | Analyzing graduation data | | `variable-definitions.md` | Key variables, suppression codes, special values | Interpreting specific variables | | `data-quality.md` | Known issues, state variations, COVID impacts | Data cleaning, limitations | | `subgroup-reporting.md` | Special populations, disaggregation | Analyzing by student groups |
What EDFacts data do you need?
├─ Assessment/proficiency data
│ ├─ Within-state trends → Valid analysis
│ ├─ Cross-state comparison → INVALID - use NAEP instead
│ └─ Subgroup gaps → See ./references/subgroup-reporting.md
├─ Graduation rates (ACGR)
│ ├─ Understand methodology → See ./references/graduation-rates.md
│ ├─ Extended rates (5-year, 6-year) → See ./references/graduation-rates.md
│ └─ Subgroup rates → See ./references/subgroup-reporting.md
├─ Understanding variables
│ ├─ Missing/suppressed values → See ./references/variable-definitions.md
│ ├─ Range vs. exact values → See ./references/variable-definitions.md
│ └─ Subgroup codes → See ./references/subgroup-reporting.md
└─ Data quality concerns
├─ COVID-19 impacts (2019-20) → See ./references/data-quality.md
├─ State reporting changes → See ./references/data-quality.md
└─ Suppression rates → See ./references/data-quality.mdWhat are you comparing? ├─ Same state, different years │ ├─ Same assessment system? → Valid │ └─ Different tests? → Break in time series ├─ Schools within same state → Valid ├─ Districts within same state → Valid ├─ Subgroups within same school → Valid (check suppression) ├─ Different states │ ├─ Proficiency rates → INVALID │ ├─ Graduation rates (ACGR) → More comparable │ └─ Use NAEP instead → Valid └─ National ranking by proficiency → INVALID
| Data Element | Description | Available Years | |--------------|-------------|-----------------| | Proficiency rates | % meeting state standards in reading/math | 2009-10 to present | | Participation rates | % of students assessed | 2012-13 to present | | Achievement levels | Below Basic, Basic, Proficient, Advanced | Varies by state | | Grade levels | Grades 3-8, high school (varies) | 2009-10 to present |
| Data Element | Description | Available Years | |--------------|-------------|-----------------| | 4-year ACGR | Adjusted Cohort Graduation Rate | 2010-11 to present | | 5-year ACGR | Extended graduation rate | 2011-12 to present | | 6-year ACGR | Further extended rate | 2012-13 to present | | Diploma types | Regular diploma only in ACGR | All years |
> **Portal Data Types:** All identifiers are **Int64** in the Portal parquet files. The NCES source format (zero-padded strings) is shown for reference only. When joining with other Portal datasets, join on the integer columns directly.
| ID | Portal Type | NCES Source Format | Level | Example (Int64) | |----|-------------|-------------------|-------|-----------------| |
📌 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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