Education Data Explorer

Education Data Explorer

Discovers available education data from the Urban Institute Education Data Portal: endpoints, variables, year coverage, and join keys for schools, districts, and colleges (CCD, IPEDS, CRDC, Scorecard, SAIPE, and more). Use during discovery and scoping phases when identifying what data exists, mapping research questions to endpoints, or resolving variable name discrepancies between documentation and actual field names. Load before education-data-query — this skill covers discovery; education-data-query handles the download.

Discover available education data from the Urban Institute Education Data Portal for research planning and query design.

What is the Education Data Portal?

• Comprehensive education data from Urban Institute - free and publicly available
• Three data levels: schools, school-districts, college-university
• Multiple data sources: CCD, IPEDS, CRDC, College Scorecard, EDFacts, SAIPE, FSA, MEPS, PSEO, etc.
• Coverage: 1980-2023 depending on source
• Access: Mirror downloads (parquet/CSV) via education-data-query skill
• Documentation: https://educationdata.urban.org/documentation/
• Curation layer, not a direct pass-through: The EDP standardizes data from original federal sources with lowercase variable names, integer-encoded categoricals, and uniform missing value codes (-1, -2, -3)
• Coverage varies by source: Some sources are fully mirrored; others are partially mirrored with only a subset of variables or datasets available. See individual education-data-source-* skills for source-specific coverage details

Skill Provenance Note: Each *-data-source-* skill includes provenance.skill_last_updated in its frontmatter. When exploring data sources during Stage 2, note the provenance dates of any skills you reference — if more than a few months old, flag this in your findings so the orchestrator can consider re-verifying with data-ingest.

Note: This workflow uses mirror-based file downloads, not paginated API calls. See education-data-query skill for fetch patterns and datasets-reference.md for file paths.

Reference File Structure

Decision Trees

What data level do I need?

What entity am I researching?
├─ Individual K-12 schools → schools level
│   └─ See ./references/schools-endpoints.md
├─ School districts / LEAs → school-districts level
│   └─ See ./references/districts-endpoints.md
├─ Colleges / Universities → college-university level
│   └─ See ./references/colleges-endpoints.md
└─ Not sure
    ├─ Need school-specific data (discipline, AP, demographics) → schools
    ├─ Need aggregate district data (finance, poverty) → school-districts
    └─ Need postsecondary data (enrollment, aid, outcomes) → college-university

What topic am I researching?

Research topic?
├─ Enrollment / Demographics
│   ├─ K-12 public schools → CCD enrollment endpoints
│   ├─ Civil rights indicators → CRDC enrollment
│   └─ Colleges → IPEDS enrollment
├─ School Finance
│   ├─ District revenue/expenditure → CCD finance
│   └─ College finance → IPEDS finance
├─ Student Outcomes
│   ├─ K-12 assessments → EDFacts
│   ├─ Graduation rates (K-12) → EDFacts
│   ├─ College completion → IPEDS completions
│   └─ Post-college earnings → College Scorecard
├─ Student Aid / Loans
│   ├─ College financial aid → IPEDS aid
│   ├─ Federal loans/grants → FSA
│   └─ Debt/repayment → College Scorecard
├─ Discipline / Civil Rights
│   └─ K-12 discipline, harassment, restraint → CRDC
├─ Poverty Estimates
│   └─ District-level → SAIPE
└─ Directory / Location
    ├─ K-12 schools → CCD directory
    ├─ Districts → CCD directory
    └─ Colleges → IPEDS directory

How do I find specific variables?

Finding variables?
├─ Know the endpoint → Check reference file for variable list
├─ Know the topic → Use topic index below
├─ Need to search programmatically → See ./references/metadata-api.md
└─ Need code definitions → See ./references/variable-codes.md

Quick Reference: Data Levels

Quick Reference: Data Sources

Quick Reference: Common Endpoints

Schools

Districts

Colleges

Exploration Workflow

Follow these steps to identify data for a research question:

1. Identify data level

   • Schools: individual K-12 school records
   • Districts: school district / LEA records
   • Colleges: postsecondary institution records

2. Identify relevant data source(s)

   • Use the data sources table above
   • Multiple sources may be needed (e.g., CCD + CRDC)

3. Check available endpoints

   • Read the appropriate reference file
   • Note endpoint URL pattern and variables

4. Review variables and filters

   • Check variable lists in reference files
   • Note which variables can be used as filters

5. Check years available

   • Each endpoint has different year coverage
   • Use metadata API to get exact years

6. Understand source context (RECOMMENDED)

   • Load the appropriate education-data-source-* skill for deep context
   • Understand data collection methodology and limitations
   • Review variable definitions and coding schemes

7. Plan query

   • Load education-data-query skill for query construction
   • Or use metadata API to build query programmatically

URL Pattern Structure

All endpoints follow this pattern:

/api/v1/{level}/{source}/{topic}/{year}/[{disaggregation}/]

Examples:

• /api/v1/schools/ccd/directory/2022/
• /api/v1/schools/ccd/enrollment/2022/grade-5/
• /api/v1/schools/ccd/enrollment/2022/grade-5/race/
• /api/v1/school-districts/ccd/finance/2021/
• /api/v1/college-university/ipeds/fall-enrollment/2022/undergraduate/

Filtering

Query Parameters

Response Format

{
  "count": 12345,
  "next": "https://educationdata.urban.org/api/v1/...?page=2",
  "previous": null,
  "results": [
    {"ncessch": "...", "school_name": "...", ...},
    ...
  ]
}

Cross-Reference to Related Skills

Deep-Dive Data Source Skills

For comprehensive understanding of each data source beyond the portal documentation, load the appropriate source-specific skill:

When to load source skills:

• Need deeper understanding of data collection methodology
• Encountering unexpected values or patterns
• Planning analysis that requires understanding source limitations
• Working with less common data elements not covered in this skill

Topic Index

Example: Planning a Research Query

Research question: "What is the relationship between school poverty and AP course offerings in California high schools?"

1. Data level: Schools (individual school records)

2. Data sources needed:

   • CRDC for AP course data
   • MEPS or CCD for poverty measure

3. Endpoints:

   • /schools/crdc/ap-ib-enrollment/{year}/race/sex/ - AP enrollment (requires disaggregation)
   • /schools/meps/{year}/ - School poverty measure

4. Key variables:

   • ncessch - school identifier (for joining)
   • fips=6 - California filter
   • AP enrollment variables from CRDC
   • Poverty measure from MEPS

5. Years: Check overlap (CRDC: 2011-2021, MEPS: 2006-2019)

6. Next step: Load education-data-query skill to construct the actual API calls

Common Pitfalls

• Year coverage varies: Always check years available for each endpoint
• Different ID formats: ncessch (12-char), leaid (7-char), unitid (6-digit)
• Disaggregation in URL: Grade, race, sex are often URL path components, not query params
• Missing data codes: -1, -2, -3 have specific meanings (see variable-codes.md)

Pre-Query Validation

CRITICAL: Variable Name Discrepancies

The Education Data Portal API often uses different variable names than documentation suggests. Always fetch a sample first:

# Test query to verify actual column names
response = requests.get(
    "https://educationdata.urban.org/api/v1/college-university/ipeds/directory/2023/"
)
data = response.json()
print("Actual columns:", list(data['results'][0].keys()))

Known discrepancies:

See the relevant education-data-source-* skill for comprehensive variable mappings per source.

Metadata API Limitations

The metadata API has undocumented limitations:

• ?section=schools works to filter by data level
• ?source=ipeds does NOT work - filter client-side instead
• Response field names differ: source is actually class_name, source_name is actually label

Data Source Details

Quick summaries below. For comprehensive documentation including methodology, variable definitions, data quality issues, and historical changes, load the corresponding education-data-source-* skill.

CCD (Common Core of Data)

Coverage: All public elementary and secondary schools and districts in the U.S.

Key Variables:

• ncessch: 12-character NCES school ID
• leaid: 7-character NCES district ID
• enrollment: Total enrollment count
• free_or_reduced_price_lunch: FRPL-eligible students (poverty proxy)
• charter: Charter school indicator
• urban_centric_locale: Urban/suburban/town/rural classification

Deep dive: Load education-data-source-ccd for survey components, data collection process, variable coding, state variations, and historical changes (e.g., 2006 locale code revision, 2010 race category changes).

CRDC (Civil Rights Data Collection)

Coverage: Biennial survey of public schools (2011, 2013, 2015, 2017, 2020, 2021)

Topics:

• Discipline (suspensions, expulsions, arrests)
• Chronic absenteeism
• Harassment and bullying
• Restraint and seclusion
• Advanced courses (AP, IB, gifted)
• Course offerings
• Teacher qualifications
• Retention
• COVID impacts (2020 only)

Key Feature: Disaggregation by race, sex, disability, and LEP status

Deep dive: Load education-data-source-crdc for civil rights legal context (Title VI, IX, Section 504), collection methodology, underreporting issues, and year-to-year changes.

EDFacts

Coverage: State assessment and accountability data

Topics:

• Assessment proficiency rates (reading, math)
• Graduation rates (4-year adjusted cohort)

Key Feature: Data available by special populations (disability, economically disadvantaged, LEP, homeless, migrant, foster care)

CRITICAL: State assessment scores CANNOT be compared across states (different tests, cut scores).

Deep dive: Load education-data-source-edfacts for ESSA/NCLB accountability context, why cross-state comparison is invalid, ACGR methodology, and subgroup reporting rules.

IPEDS (Integrated Postsecondary Education Data System)

Coverage: All Title IV-eligible postsecondary institutions

Topics:

• Institutional characteristics and directory
• Admissions and enrollment
• Student charges (tuition, fees, room, board)
• Financial aid
• Finance (revenue, expenditure, assets)
• Graduation rates
• Completions (degrees awarded by CIP code)
• Human resources (salaries, faculty)

Key Variables:

• unitid: 6-digit IPEDS institution ID
• inst_control: 1=Public, 2=Private nonprofit, 3=Private for-profit
• institution_level: 1=Less-than-2-year, 2=2-year, 4=4-year (no code 3)
• hbcu: Historically Black college indicator

Deep dive: Load education-data-source-ipeds for critical graduation rate limitations (first-time full-time only), GASB vs FASB finance accounting, survey components, and identifier changes.

College Scorecard

Coverage: Title IV institutions with outcome data

Topics:

• Post-college earnings (6 and 10 years after entry)
• Student debt and repayment
• Default rates
• Completion rates by income level

Key Feature: Links education to labor market outcomes

CRITICAL: Only covers Title IV aid recipients (selection bias toward lower-income students).

Deep dive: Load education-data-source-scorecard for earnings methodology (IRS data), population coverage limitations, suppression rules, and field-of-study data.

SAIPE (Small Area Income and Poverty Estimates)

Coverage: Census Bureau poverty estimates for school districts

Key Variables:

• population_5_17_poverty: Children 5-17 in poverty
• population_5_17_poverty_pct: Percent in poverty
• median_household_income: District median income

Deep dive: Load education-data-source-saipe for model-based estimation methodology, confidence intervals (not available at district level), and comparison to other poverty measures.

FSA (Federal Student Aid)

Coverage: Title IV institutions receiving federal aid

Topics:

• Pell grants
• Direct loans (subsidized, unsubsidized, PLUS)
• Campus-based aid (Perkins, work-study)
• Financial responsibility scores
• 90/10 revenue (for-profit institutions)

Deep dive: Load education-data-source-fsa for Title IV program details, financial responsibility composite scores, and 90/10 rule compliance.

Additional Data Sources

Joining Data Across Sources

School-Level Joins

Join school data across sources using ncessch:

Note: Match on year when joining (years may not align perfectly)

District-Level Joins

Join district data using leaid:

College-Level Joins

Join college data using unitid:

Disaggregation Patterns

URL Path Disaggregation

Some disaggregations are part of the URL path:

/schools/ccd/enrollment/{year}/{grade}/           # By grade
/schools/ccd/enrollment/{year}/{grade}/race/      # By grade and race
/schools/ccd/enrollment/{year}/{grade}/race/sex/  # By grade, race, and sex

Query Parameter Disaggregation

Other filters are query parameters:

?fips=6                    # California only
?charter=1                 # Charter schools only
?school_level=3            # High schools only
?urban_centric_locale=11   # Large cities only

Available Disaggregations by Source

Year Coverage Quick Reference

Example Research Scenarios