CRDC Data Source Reference

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)

Reference File Structure

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

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)

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)

Disability Status (Portal Integer Codes)

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)

Missing Data Codes

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 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().

Key datasets (6 of 22 total):

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:

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

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

Topic Index