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

civil-rights-context.md

data-elements.md

collection-methodology.md

variable-definitions.md

data-quality.md

historical-changes.md

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

Data quality concerns?

Data quality issue?
├─ Missing or suppressed data → ./references/data-quality.md#suppression
├─ Definition inconsistencies → ./references/data-quality.md#definition-variation
├─ Year-to-year comparability → ./references/historical-changes.md
├─ COVID-19 impact (2020-21) → ./references/data-quality.md#covid-impact
├─ Underreporting concerns → ./references/data-quality.md#underreporting
└─ State-level variations → ./references/data-quality.md#state-variations

Quick Reference: CRDC Data Categories

Collection Years

Critical: CRDC is biennial - no data for odd years (2012, 2014, 2016, 2018, 2019).

Data Categories

Key Identifiers

crdc_id

010000201705

ncessch

010000201705

leaid

0100002

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)

1

2

3

4

5

6

7

99

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)

1

2

3

99

Disability Status (Portal Integer Codes)

0

1

2

3

4

99

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)

1

99

Missing Data Codes

-1

-2

-3

-9

null

ncessch

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

mirrors.yaml

fetch-patterns.md

fetch_from_mirrors()

fetch_yearly_from_mirrors()

Key datasets (6 of 22 total):

crdc/schools_crdc_discipline_k12_{year}

crdc/codebook_schools_crdc_discipline

crdc/schools_crdc_apib_enroll

crdc/codebook_schools_crdc_ap-ib-enrollment

crdc/schools_crdc_enrollment_k12_{year}

crdc/codebook_schools_crdc_enrollment

crdc/schools_crdc_chronic_absenteeism_{year}

crdc/codebook_schools_crdc_chronic-absenteeism

crdc/schools_crdc_harass_bully_students_{year}

crdc/codebook_schools_crdc_harrassment-bullying-students

crdc/schools_crdc_restraint_seclusion_students_{year}

crdc/codebook_schools_crdc_restraint-seclusion-students

22 CRDC datasets exist total (6 yearly, 16 single-file). See

datasets-reference.md

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

get_codebook_url()

fetch-patterns.md

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:

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.

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

race == 2

race == "BL"

race < 99

ncessch

leaid

crdc_id

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

education-data-source-ccd

ncessch

leaid

education-data-source-edfacts

education-data-explorer

education-data-query

education-data-context

Topic Index

./references/civil-rights-context.md

./references/civil-rights-context.md

./references/civil-rights-context.md

./references/civil-rights-context.md

./references/civil-rights-context.md

./references/data-elements.md

./references/data-elements.md

./references/data-elements.md

./references/data-elements.md

./references/data-elements.md

./references/data-elements.md

./references/data-elements.md

./references/data-elements.md

./references/collection-methodology.md

./references/collection-methodology.md

./references/variable-definitions.md

./references/data-quality.md

./references/data-quality.md

./references/historical-changes.md