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SKILL.md
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
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
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.
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
# 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:
# 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:
# 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
