EDFacts Data Source Reference

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.

What is EDFacts?

• Collector: U.S. Department of Education, via State Education Agencies (SEAs)
• Coverage: All public schools and districts in 50 states + DC
• Content: State assessment proficiency rates, ACGR graduation rates, participation rates, accountability indicators
• Frequency: Annual collection
• Available years: Assessments 2009-10 to present; Graduation rates 2010-11 to present
• Primary identifiers: ncessch (school ID, Int64), leaid (district ID, Int64), fips (state FIPS code, Int64)
• Key limitation: State assessment scores CANNOT be compared across states (different tests, different cut scores)
• Available through: Education Data Portal mirrors

Reference File Structure

| 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 |

Decision Trees

What type of analysis?

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.md

Is my comparison valid?

What 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

Quick Reference: EDFacts Data Elements

Assessment Data

| 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 |

Graduation Data

| 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 |

Key Identifiers

> 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) | |----|-------------|-------------------|-------|-----------------| | ncessch | Int64 | 12-char zero-padded | School | 10000500870 | | ncessch_num | Int64 | Same as ncessch | School | 10000500870 | | leaid | Int64 | 7-char zero-padded | District/LEA | 100005 | | leaid_num | Int64 | Same as leaid | District/LEA | 100005 | | fips | Int64 | 2-digit | State | 1 (Alabama) |

Data Levels

| Level | Identifier | Dataset Path Pattern | |-------|------------|---------------------| | School | ncessch (Int64) | edfacts/schools_edfacts_* | | District/LEA | leaid (Int64) | edfacts/districts_edfacts_* | | State | fips (Int64) | Aggregate from lower levels |

Subgroups Reported

> Note: Not all subgroup columns are present in every dataset. Grad rates data does NOT have sex, migrant, or military_connected columns.

| Subgroup | NCES Code | Portal Integer | Column | Available In | |----------|-----------|----------------|--------|--------------| | All students | ALL | 99 | race, sex, lep, disability | Assessments, Grad Rates | | Economically disadvantaged | ECODIS | 1 | econ_disadvantaged | Assessments, Grad Rates | | Students with disabilities | CWD | 1 | disability | Assessments, Grad Rates | | English learners | LEP | 1 | lep | Assessments, Grad Rates | | Homeless | HOM | 1 | homeless | Assessments, Grad Rates | | Foster care | FCS | 1 | foster_care | Assessments, Grad Rates | | Migrant | MIG | 1 | migrant | Assessments only | | Military connected | MIL | 1 | military_connected | Assessments only | | Race/ethnicity | Multiple | 1-7, 99 | race | Assessments, Grad Rates | | Sex | M/F | 1, 2, 99 | sex | Assessments only |

EDFacts Filter Column Pattern:

• Special population columns (lep, disability, homeless, etc.) use 1 = subgroup, 99 = total
• Race column uses integer codes (1=White, 2=Black, etc.)
• Sex column uses 1 = Male, 2 = Female, 99 = Total (assessments only)

Grade Codes (grade_edfacts)

| Code | Grade Level | |------|-------------| | 3-8 | Grades 3-8 (individual) | | 9 | Grades 9-12 combined | | 99 | Total (all grades) |

Race Codes

> Empirically verified from 2018 school assessment data. Only these values appear in the race column:

| Code | Category | |------|----------| | 1 | White | | 2 | Black | | 3 | Hispanic | | 4 | Asian | | 5 | American Indian/Alaska Native | | 7 | Two or More Races | | 99 | Total |

> Note: Code 6 (Native Hawaiian/Pacific Islander) is NOT observed in the data. Codes 8 (Nonresident alien), 9 (Unknown), 20 (Other), -1, -2, -3 are also not observed in the race column. These codes may exist in other Portal sources but are absent from EDFacts.

Sex Codes

| Code | Category | |------|----------| | 1 | Male | | 2 | Female | | 9 | Unknown | | 99 | Total |

Disability Codes

> Empirically verified from 2018 school assessment and 2019 grad rate data. Only 1 and 99 are observed in the disability column. The expanded codes (0-4) documented in other Portal sources are NOT present in EDFacts datasets.

| Code | Category | |------|----------| | 1 | Students with disabilities (IDEA-eligible) | | 99 | Total (all students) |

LEP Codes

| Code | Category | |------|----------| | 1 | Students who are limited English proficient | | 99 | All students (total) |

Special Population Columns

For homeless, migrant, econ_disadvantaged, foster_care, military_connected:

| Code | Category | |------|----------| | 1 | Yes (in subgroup) | | 99 | Total (all students) |

Missing Data Codes

| Code | Meaning | When Used | |------|---------|-----------| | -1 | Missing/not applicable | Data not reported | | -2 | Not reported | Item doesn't apply to this entity | | -3 | Suppressed for privacy | Data suppressed for small N-size | | -9 | Rounds to zero | Value rounds to zero | | Range values | Exact value suppressed | Range provided instead of exact value | | _midpt suffix | Calculated midpoint of suppressed range | Use for analysis when exact values are suppressed |

Always use _midpt variables for analysis when exact values are suppressed.

Data Access

All EDFacts data is fetched via the Education Data Portal mirror system. There is no API access.

Key references:

• mirrors.yaml -- Mirror definitions, URL templates, read strategies
• datasets-reference.md -- Canonical dataset paths (one path works for all mirrors)
• fetch-patterns.md -- fetch_from_mirrors() and fetch_yearly_from_mirrors() patterns

> Truth Hierarchy: When interpreting variable values, apply this priority: > 1. Actual data file (what you observe in the parquet/CSV) — this IS the truth > 2. Live codebook (.xls in mirror) — authoritative documentation, may lag > 3. This skill documentation — convenient summary, may drift from codebook > > If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate.

Key Datasets

| Dataset | Path | Type | Columns | |---------|------|------|---------| | School Assessments | edfacts/schools_edfacts_assessments_{year} | Yearly (2009-2018, 2020) | 26 cols | | School Grad Rates | edfacts/schools_edfacts_grad_rates_{year} | Yearly (2010-2019) | 18 cols | | District Assessments | edfacts/districts_edfacts_assessments_{year} | Yearly (2009-2018, 2020) | 23 cols | | District Grad Rates | edfacts/districts_edfacts_grad_rates_{year} | Yearly (2010-2019) | 15 cols |

> Note: 2019 assessment data is NOT available (at any level) due to COVID testing waivers.

Codebooks

Codebook .xls files are available for both assessment and graduation rate datasets. Use get_codebook_url() from fetch-patterns.md:

python
# Assessment codebooks:
url = get_codebook_url("edfacts/codebook_schools_edfacts_assessments")
url = get_codebook_url("edfacts/codebook_districts_edfacts_assessments")

# Graduation rate codebooks:
url = get_codebook_url("edfacts/codebook_schools_edfacts_graduation")
url = get_codebook_url("edfacts/codebook_districts_edfacts_graduation")

> Codebook naming note: Graduation rate codebooks use _graduation (not _grad_rates), while the data files use _grad_rates. This follows the same pattern as other Portal sources where codebook names differ from data file names. See datasets-reference.md for the authoritative path mapping.

Dataset Column Differences

Assessment and graduation rate datasets have different column sets:

| Column | Assessments | Grad Rates | |--------|-------------|------------| | sex | Yes (1, 2, 99) | No | | migrant | Yes (1, 99) | No | | military_connected | Yes (1, 99) | No | | grade_edfacts | Yes (3-9, 99) | No | | read_test_* / math_test_* | Yes | No | | grad_rate_* | No | Yes | | cohort_num | No | Yes | | school_name / lea_name | Yes | Yes |

Filtering

python
# Grade filtering: grade_edfacts uses integer codes
df = df.filter(pl.col("grade_edfacts") == 4)  # Grade 4
df = df.filter(pl.col("grade_edfacts") == 99)  # All grades combined

# Subgroup filtering: special population columns use 1/99 pattern
df_total = df.filter(pl.col("sex") == 99)  # All students (total)
df_econ = df.filter(pl.col("econ_disadvantaged") == 1)  # Economically disadvantaged only

# Race filtering: integer codes
df_black = df.filter(pl.col("race") == 2)  # Black students

Common Pitfalls

| Pitfall | Issue | Solution | |---------|-------|----------| | Ranking states by proficiency | Different tests, different cut scores make comparisons meaningless | Use NAEP for cross-state comparisons | | Comparing 2019-20 to other years | COVID testing waivers created data gaps | Note data gap, exclude year | | Ignoring suppression | Results biased toward larger schools/subgroups | Document suppression rates, use _midpt variables | | Assuming proficiency = same thing | State definitions of "proficient" vary widely | Clarify each state's definition | | Pre/post ESSA comparison | Different accountability systems (NCLB vs ESSA) | Note policy change at 2015 boundary | | Using string codes for filtering | Portal uses integer encoding, not NCES strings | Always check actual data values; see encoding tables above |

Key Policy Context

| Law | Years | Key Features | |-----|-------|--------------| | NCLB | 2002-2015 | AYP, 100% proficiency goal, HQT | | ESSA | 2015-present | State flexibility, multiple indicators |

• AYP (Adequate Yearly Progress): NCLB requirement eliminated by ESSA
• ESSA Accountability: States design own systems with federal guardrails
• N-size: Minimum students required for reporting (varies by state, typically 10-30)

CRITICAL WARNING: Cross-State Comparisons

State assessment proficiency rates CANNOT be compared across states.

| Factor | Why It Varies | |--------|---------------| | Assessment content | Each state creates its own tests | | Proficiency cut scores | Each state sets own thresholds | | Standards alignment | State academic standards differ | | Test difficulty | Not calibrated nationally |

A student "proficient" in one state may score "below basic" in another state taking a harder test with higher cut scores. Rankings of states by proficiency rates are meaningless.

Use NAEP (National Assessment of Educational Progress) for valid cross-state comparisons.

Valid vs. Invalid Analysis Examples

Valid Analysis:

python
# Within-state trend analysis
state_df = df.filter(pl.col("fips") == 6)  # California only
trend = state_df.group_by("year").agg(
    pl.col("read_test_pct_prof_midpt").mean()
)
# Valid: Same state, same test system

INVALID Analysis:

python
# DO NOT DO THIS - Cross-state comparison
# This comparison is MEANINGLESS
state_comparison = df.group_by("fips").agg(
    pl.col("read_test_pct_prof_midpt").mean()
).sort("read_test_pct_prof_midpt", descending=True)
# INVALID: Different tests, different standards

Related Data Sources

| Source | Relationship | When to Use | |--------|--------------|-------------| | education-data-source-ccd | CCD provides school/district demographics | Combining outcome data with school characteristics | | education-data-source-crdc | CRDC has discipline, AP, school climate data | Analyzing school equity alongside achievement | | education-data-source-saipe | SAIPE provides district poverty estimates | Linking poverty to achievement | | education-data-source-meps | MEPS provides school poverty estimates | School-level poverty and assessment analysis | | education-data-explorer | Parent discovery skill | Finding available endpoints | | education-data-query | Data fetching | Downloading via mirrors |

Topic Index

| Topic | Reference File | |-------|---------------| | NCLB to ESSA transition | ./references/accountability-context.md | | State accountability systems | ./references/accountability-context.md | | Federal reporting requirements | ./references/accountability-context.md | | Proficiency levels | ./references/assessment-data.md | | Why states can't be compared | ./references/assessment-data.md | | NAEP comparison | ./references/assessment-data.md | | Assessment system changes | ./references/assessment-data.md | | ACGR calculation | ./references/graduation-rates.md | | Cohort adjustments | ./references/graduation-rates.md | | Extended graduation rates | ./references/graduation-rates.md | | Diploma types | ./references/graduation-rates.md | | Suppression codes | ./references/variable-definitions.md | | Missing data values | ./references/variable-definitions.md | | Range/midpoint variables | ./references/variable-definitions.md | | Participation rates | ./references/variable-definitions.md | | COVID-19 data gaps | ./references/data-quality.md | | State reporting variations | ./references/data-quality.md | | Known data issues | ./references/data-quality.md | | Time series breaks | ./references/data-quality.md | | Students with disabilities | ./references/subgroup-reporting.md | | English learners | ./references/subgroup-reporting.md | | Economically disadvantaged | ./references/subgroup-reporting.md | | Race/ethnicity reporting | ./references/subgroup-reporting.md | | Homeless/foster/migrant | ./references/subgroup-reporting.md | | N-size requirements | ./references/subgroup-reporting.md |