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

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

| School Year | Collection | Coverage | Key Notes | |-------------|------------|----------|-----------| | 2011-12 | Sample | ~7,000 districts | First modern CRDC; sampled | | 2013-14 | Expanded | ~16,000 districts | Larger sample | | 2015-16 | Near-universe | ~96,000 schools | First near-complete | | 2017-18 | Universe | ~96,000 schools | Full universe collection | | 2020-21 | Universe | ~97,500 schools | COVID-impacted year | | 2021-22 | Universe | ~98,000 schools | Post-pandemic baseline | | 2023-24 | Universe | In progress | Current collection |

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().

Key datasets (6 of 22 total):

| Dataset | Path | Type | Codebook | |---------|------|------|----------| | Discipline | crdc/schools_crdc_discipline_k12_{year} | Yearly | crdc/codebook_schools_crdc_discipline | | AP/IB Enrollment | crdc/schools_crdc_apib_enroll | Single | crdc/codebook_schools_crdc_ap-ib-enrollment | | Enrollment | crdc/schools_crdc_enrollment_k12_{year} | Yearly | crdc/codebook_schools_crdc_enrollment | | Chronic Absenteeism | crdc/schools_crdc_chronic_absenteeism_{year} | Yearly | crdc/codebook_schools_crdc_chronic-absenteeism | | Harassment/Bullying | crdc/schools_crdc_harass_bully_students_{year} | Yearly | crdc/codebook_schools_crdc_harrassment-bullying-students | | Restraint/Seclusion | crdc/schools_crdc_restraint_seclusion_students_{year} | Yearly | crdc/codebook_schools_crdc_restraint-seclusion-students |

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:

python
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

python
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

python
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 |

Topic Index

| Topic | Reference File | |-------|---------------| | Title VI (race) | ./references/civil-rights-context.md | | Title IX (sex) | ./references/civil-rights-context.md | | Section 504 (disability) | ./references/civil-rights-context.md | | IDEA | ./references/civil-rights-context.md | | OCR enforcement | ./references/civil-rights-context.md | | Discipline data | ./references/data-elements.md | | Restraint/seclusion | ./references/data-elements.md | | Harassment | ./references/data-elements.md | | Course access | ./references/data-elements.md | | AP/IB/Gifted | ./references/data-elements.md | | Chronic absenteeism | ./references/data-elements.md | | Staffing | ./references/data-elements.md | | Preschool | ./references/data-elements.md | | Sampling approach | ./references/collection-methodology.md | | Collection timeline | ./references/collection-methodology.md | | Variable codes | ./references/variable-definitions.md | | Suppression rules | ./references/data-quality.md | | COVID impact | ./references/data-quality.md | | Year changes | ./references/historical-changes.md |