This source did not publish a separate summary. Review SKILL.md before using the skill.
SKILL.md
CRDC Data Source Reference
Civil Rights Data Collection (CRDC) — mandatory biennial OCR survey of all U.S. public schools measuring educational opportunity and civil rights compliance (2011-2021). Use when analyzing school discipline disparities by race/disability, course access equity, harassment, restraint/seclusion, or chronic absenteeism. Data disaggregated by race, sex, disability, and English learner status. Note: 2020-21 is COVID-impacted and not directly comparable to other years; 2011-14 are sampled, not universe.
The Civil Rights Data Collection is a mandatory biennial survey of all U.S. public schools measuring educational opportunity and civil rights compliance. It is the only national source for school-level discipline disparities, course access equity, harassment, and restraint/seclusion data disaggregated by race, sex, disability, and English learner status.
CRITICAL: Value Encoding
The Education Data Portal uses integer codes, not the string codes shown in OCR documentation. Always filter using integers.
Variable
String Code (Raw)
Portal Integer
Race: White
WH
1
Race: Black
BL
2
Race: Hispanic
HI
3
Sex: Male
M
1
Sex: Female
F
2
See ./references/variable-definitions.md for complete encoding tables.
What is CRDC?
The Civil Rights Data Collection is a mandatory biennial survey of all public schools and districts that measures educational opportunity and civil rights compliance:
Collector: U.S. Department of Education, Office for Civil Rights (OCR)
Purpose: Enforce civil rights laws, identify discrimination, monitor equity
Coverage: All public LEAs and schools receiving federal financial assistance
Frequency: Biennial (every 2 school years)
Disaggregation: Race/ethnicity, sex, disability status, English learner status
History: Collected since 1968 (as Elementary and Secondary School Civil Rights Survey)
Available years: 2011, 2013, 2015, 2017, 2020, 2021 (biennial — no data for even-numbered school years)
Available through: Education Data Portal mirrors
Reference File Structure
File
Purpose
When to Read
civil-rights-context.md
Legal framework (Title VI, IX, Section 504, IDEA)
Understanding why data is collected
data-elements.md
All data categories and what's collected
Planning analysis, identifying variables
collection-methodology.md
Sampling, universe, timeline, reporting
Understanding coverage limitations
variable-definitions.md
Key variables, codes, disaggregation categories
Coding data, interpreting values
data-quality.md
Known issues, suppression, state variations
Addressing limitations in analysis
historical-changes.md
Evolution across collection years
Time series analysis, year comparison
Decision Trees
What CRDC data do I need?
Research topic?
├─ School discipline
│ ├─ Suspensions (ISS/OSS) → ./references/data-elements.md#discipline
│ ├─ Expulsions → ./references/data-elements.md#discipline
│ ├─ Referrals to law enforcement → ./references/data-elements.md#discipline
│ ├─ School-related arrests → ./references/data-elements.md#discipline
│ └─ Preschool suspensions → ./references/data-elements.md#discipline
├─ Restraint and seclusion
│ └─ Physical restraint, mechanical, seclusion → ./references/data-elements.md#restraint-seclusion
├─ Harassment and bullying
│ ├─ Allegations by type → ./references/data-elements.md#harassment
│ └─ Disciplined for harassment → ./references/data-elements.md#harassment
├─ Course access and enrollment
│ ├─ AP/IB courses → ./references/data-elements.md#advanced-courses
│ ├─ Gifted/talented → ./references/data-elements.md#gifted-talented
│ ├─ Math/science courses → ./references/data-elements.md#course-access
│ └─ Computer science → ./references/data-elements.md#course-access
├─ Chronic absenteeism
│ └─ Students missing 15+ days → ./references/data-elements.md#chronic-absenteeism
├─ Special populations
│ ├─ Students with disabilities (IDEA) → ./references/data-elements.md#students-with-disabilities
│ ├─ English learners → ./references/data-elements.md#english-learners
│ └─ Preschool enrollment → ./references/data-elements.md#preschool
├─ School staffing
│ ├─ Teacher experience/certification → ./references/data-elements.md#staffing
│ └─ Counselors, nurses, etc. → ./references/data-elements.md#staffing
└─ School safety
└─ Offenses, violence, weapons → ./references/data-elements.md#school-offenses
Understanding the legal context?
Civil rights law question?
├─ Race/ethnicity discrimination → ./references/civil-rights-context.md#title-vi
├─ Sex/gender discrimination → ./references/civil-rights-context.md#title-ix
├─ Disability discrimination → ./references/civil-rights-context.md#section-504
├─ Special education services → ./references/civil-rights-context.md#idea
├─ Age discrimination → ./references/civil-rights-context.md#age-discrimination-act
└─ OCR enforcement process → ./references/civil-rights-context.md#ocr-enforcement
Critical: CRDC is biennial - no data for odd years (2012, 2014, 2016, 2018, 2019).
Data Categories
Category
Description
Disaggregation
Enrollment
Student counts by grade level
Race, sex, disability, LEP
Discipline
Suspensions, expulsions, arrests
Race, sex, disability, LEP
Restraint/Seclusion
Physical/mechanical restraint, seclusion
Race, sex, disability
Harassment
Allegations and discipline by type
Race, sex, disability
Course Access
AP, IB, math, science, CS offerings
School-level, enrollment by race/sex
Chronic Absenteeism
15+ days missed
Race, sex, disability, LEP
Staffing
Teachers, counselors, nurses, etc.
FTE counts, qualifications
Offenses
Violence, weapons, drugs at school
Type of offense
Retention
Students retained in grade
Race, sex, disability
Key Identifiers
ID
Format
Level
Example
Notes
crdc_id
12-digit string
School
010000201705
Primary CRDC identifier; always present
ncessch
12-digit string
School
010000201705
NCES school ID, joins to CCD; may be null for some entries
leaid
7-digit string
District
0100002
NCES district ID, joins to CCD; always present
Note: The OCR-internal combokey (e.g., AL-0010-00002) does NOT appear as a column in Portal data. Use crdc_id or ncessch for school-level identification.
WARNING: String Type Override Required. When reading CRDC data from CSV, ncessch, leaid, and crdc_id must be read as String (pl.Utf8) via schema_overrides. Polars infers these as Int64, silently destroying leading zeros for ~19% of rows (FIPS 01-09 states: AL, AK, AZ, AR, CA, CO, CT). Parquet files preserve types automatically.
Race/Ethnicity (Portal Integer Codes)
Code
Category
1
White
2
Black or African American
3
Hispanic/Latino of any race
4
Asian
5
American Indian or Alaska Native
6
Native Hawaiian or Other Pacific Islander
7
Two or more races
99
Total
Empirically observed values: Codes 1-7 and 99 appear in CRDC data. Additional codes (8 Nonresident alien, 9 Unknown, 20 Other) are defined in the codebook but are not observed in practice for K-12 CRDC datasets. See variable-definitions.md for the full codebook listing.
Sex (Portal Integer Codes)
Code
Category
1
Male
2
Female
3
Non-binary/other (newer collections; rows exist but mostly contain -1 or -2 values)
99
Total
Disability Status (Portal Integer Codes)
Code
Category
0
Students without disabilities
1
Students with disabilities (served under IDEA)
2
Students with Section 504 only
3
Students not served under IDEA (includes 504-only and non-disabled)
4
Students with disabilities (combined: IDEA + Section 504)
99
Total
Note: Not all disability codes appear in every dataset. Enrollment data typically has [1, 2, 99]; discipline data has [0, 1, 2, 4, 99]. Verify codes against the live codebook for your specific dataset.
English Learner Status (Portal Integer Codes)
Code
Category
1
English learner (EL/LEP)
99
All students
Missing Data Codes
Code
Meaning
When Used
-1
Missing
Data not reported by school/district
-2
Not applicable
Item doesn't apply to this entity
-3
Suppressed
Data suppressed for privacy (small cell sizes)
-9
Skip pattern
Question not asked in this collection year (rare; check codebook)
null
Not available
Value absent from dataset (e.g., ncessch is null for some schools)
Verify these codes against the live codebook for your specific dataset. Use get_codebook_url() from fetch-patterns.md.
Data Access
Datasets for CRDC are available via the Education Data Portal mirror system. See datasets-reference.md for canonical paths, mirrors.yaml for mirror configuration, and fetch-patterns.md for fetch code patterns including fetch_from_mirrors() and fetch_yearly_from_mirrors().
22 CRDC datasets exist total (6 yearly, 16 single-file). See datasets-reference.md for the complete list with all paths and codebook references.
CRDC naming note: Some data file paths use concatenated names (e.g., disciplineinstances, mathandscience) while their codebook counterparts use underscored names (e.g., discipline_instances, math_and_science). Always use the exact paths from datasets-reference.md.
Codebooks are .xls files co-located with data in all mirrors. Use get_codebook_url() from fetch-patterns.md to construct download URLs:
from fetch_patterns import get_codebook_url
url = get_codebook_url("crdc/codebook_schools_crdc_discipline")
Truth Hierarchy: When interpreting variable values, apply this priority:
Actual data file (what you observe in the parquet/CSV) -- this IS the truth
Live codebook (.xls in mirror) -- authoritative documentation, may lag
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.
Filtering
import polars as pl
# Filter to a single state (California) and disaggregated race groups
df = df.filter(
(pl.col("fips") == 6) & # California
(pl.col("race") < 99) # Exclude totals row
)
# Filter to specific demographic intersection
df = df.filter(
(pl.col("race") == 2) & # Black students
(pl.col("sex") == 99) & # Both sexes (total)
(pl.col("disability") == 99) # All disability statuses
)
Common Pitfalls
Pitfall
Issue
Solution
Using string codes
Portal uses integers, not strings
race == 2 not race == "BL"
Raw counts
Different enrollment sizes
Use rates per 100/1000 students
Missing years
Assuming annual data
Remember biennial schedule
COVID year
2020-21 not comparable
Flag or exclude from trends
Suppression
Small cell suppression
Check suppression rates first
Sample years
Early years sampled
Use 2015+ for national estimates
Definition drift
Variables change over time
Check codebooks for each year
Forgetting code 99
Including totals in calculations
Filter race < 99 for disaggregated analysis
CSV type inference
Polars infers ncessch/leaid/crdc_id as Int64
Use schema_overrides={"ncessch": pl.Utf8, "leaid": pl.Utf8, "crdc_id": pl.Utf8}
Equity Analysis Framework
CRDC data is designed for civil rights analysis. Key analytical approaches:
Disparity Ratios
import polars as pl
# Calculate discipline disparity using Portal integer codes
def discipline_disparity(df, discipline_var, group_a, group_b):
"""
Calculate risk ratio between two groups.
Value > 1 indicates group_a has higher rate.
Args:
df: DataFrame with CRDC data
discipline_var: Column with discipline counts
group_a: Integer race code (e.g., 2 for Black)
group_b: Integer race code (e.g., 1 for White)
Example:
# Black vs White OSS disparity
disparity = discipline_disparity(df, 'students_susp_out_sch_single', 2, 1)
"""
# Filter to each group (using integer codes)
df_a = df.filter(pl.col('race') == group_a)
df_b = df.filter(pl.col('race') == group_b)
# Calculate rates
rate_a = df_a.select(pl.col(discipline_var).sum()).item() / \
df_a.select(pl.col('enrollment_crdc').sum()).item()
rate_b = df_b.select(pl.col(discipline_var).sum()).item() / \
df_b.select(pl.col('enrollment_crdc').sum()).item()
return rate_a / rate_b
# Example: Black (race=2) vs White (race=1) disparity
# disparity = discipline_disparity(df, 'students_susp_out_sch_single', 2, 1)
Composition vs. Representation
Composition: What share of suspended students are Black?
Representation: Are Black students suspended at higher rates than enrollment share?
Risk Ratios
Compare discipline/outcome rates across groups
Adjust for school-level factors when appropriate
Related Data Sources
Source
Relationship
When to Use
education-data-source-ccd
School/district characteristics
Linking CRDC to school demographics, locale, Title I status (join on ncessch or leaid)
education-data-source-edfacts
Assessment outcomes
Comparing discipline patterns to academic outcomes
education-data-explorer
Parent discovery skill
Finding available CRDC endpoints and variables
education-data-query
Data fetching
Downloading CRDC parquet/CSV files from mirrors
education-data-context
General interpretation
Education data interpretation and citation generation
