education-data-source-crdc

name	education-data-source-crdc
description	CRDC — OCR civil rights collection for U.S. public schools; Portal families have topic-specific coverage from 2011 through 2022. Discipline, course access, harassment, restraint/seclusion by race/sex/disability/EL. Use for civil rights and equity analysis. Official evidence identifies 2013-14 as a universe collection; 2020-21 is COVID-impacted.
metadata	{"audience":"any-agent","domain":"data-source","skill-authored":"2026-02-09","skill-last-updated":"2026-08-06"}

CRDC Data Source Reference

Civil Rights Data Collection (CRDC) — mandatory OCR collection measuring educational opportunity and civil rights compliance in U.S. public schools. Portal dataset families have topic-specific coverage from 2011 through 2022; use them for school discipline disparities, course access equity, harassment, restraint/seclusion, or chronic absenteeism by race, sex, disability, and English learner status. The 2013-14 collection was officially a universe collection covering 16,758 districts and 95,507 schools; this skill does not make an unverified coverage claim for 2011-12. The 2020-21 collection is COVID-impacted and not directly comparable to ordinary years.

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 OCR collection of 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: Collection-specific; official evidence establishes 2013-14 as a universe collection. Verify 2011-12 against its own authoritative documentation rather than applying a blanket early-year label.
• Cadence: Usually organized by school-year collection cycles, historically often biennial, but not governed by an odd/even parity rule; 2020-21 and 2021-22 are consecutive collections
• Disaggregation: Race/ethnicity, sex, disability status, English learner status
• History: Collected since 1968 (as Elementary and Secondary School Civil Rights Survey)
• Portal coverage (v2 mirror, build validated 2026-08-06): Topic-specific families spanning 2011 through 2022 (content-based year range confirmed in the v2 build); year availability differs by topic
• 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	Coverage status	Scale / Portal note	Key Notes
2011-12	Not re-verified in this correction cycle	Portal families include 2011 for some topics	Consult collection-specific authoritative documentation before generalizing
2013-14	Universe	16,758 districts; 95,507 schools (First Look figure; the revised target population was 95,958 schools)	Official first-look evidence explicitly calls this a universe collection
2015-16	Universe collection	~96,000 schools	Chronic absenteeism added
2017-18	Universe collection	~96,000 schools	Expanded variables
2020-21	Universe collection	~97,500 schools	COVID-impacted year
2021-22	Universe collection	~98,000 schools; selected Portal topics reach 2022	Consecutive collection after 2020-21
2023-24	No Portal data established	No Portal CRDC 2024 data as of the probes: original 2026-08-06 probe errored (HTTP 500; exact endpoint not recorded), and a 2026-08-07 re-probe of https://educationdata.urban.org/api/v1/schools/crdc/enrollment/2024/ returned HTTP 404 — either way zero rows	Do not infer 2024 data from the count of dataset families

Cadence: CRDC is organized by school-year collection cycles and is often described as biennial, but there is no valid odd/even-year rule. Portal year labels represent the terminal year of the school year; 2020 and 2021 are consecutive available collection labels. Re-probed 2026-08-06: chronic-absenteeism/2022/race/sex/ returned rows (HTTP 200), while a direct 2024 CRDC enrollment probe returned no rows (the original 2026-08-06 probe errored HTTP 500 with the exact endpoint not recorded; a 2026-08-07 re-probe of https://educationdata.urban.org/api/v1/schools/crdc/enrollment/2024/ returned HTTP 404).

Source verification (accessed 2026-07-21): Official 2013-14 first look, Portal endpoint catalog, and Portal bulk manifest.

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). In R, readr::read_csv() has the identical failure mode — apply the same guard with col_types = cols(ncessch = col_character(), leaid = col_character(), crdc_id = col_character()). Parquet files preserve whatever dtype the file was written with — and that dtype is not uniformly String across CRDC files. A 2026-08-07 per-file audit found id typing is heterogeneous even within the 2020 vintage (e.g. school_characteristics all String, but enrollment_k12_2020 crdc_id and harass_bully_students_2020 all three ids are Int64). Do not assume a parquet read yields String ids — inspect the schema and normalize/cast on read. See references/data-quality.md § Identifier Typing for the per-file evidence.

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().

Portal bulk family inventory observed 2026-07-21 (24 families: 10 year-sharded, 14 multi-year/single-file):

Dataset family	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
Algebra	crdc/schools_crdc_algebra_{year}	Yearly	crdc/codebook_schools_crdc_algebra-1
AP Exams	crdc/schools_crdc_ap_exams_{year}	Yearly	crdc/codebook_schools_crdc_ap-exams
Retention	crdc/schools_crdc_retention_{year}	Yearly	crdc/codebook_schools_crdc_retention
SAT/ACT Participation	crdc/schools_crdc_sat_and_act_participation_{year}	Yearly	crdc/codebook_schools_crdc_sat-act-participation
COVID Indicators	crdc/schools_crdc_covid_indicators	Single	crdc/codebook_schools_crdc_covid_indicators
Credit Recovery	crdc/schools_crdc_credit_recovery	Single

The 24 count is the deduplicated Urban bulk family inventory, not a year, row count, or API endpoint count. The live Portal catalog separately exposed 50 CRDC endpoint templates because topic/disaggregation routes split more finely than bulk files. Family-specific year coverage varies; neither number implies CRDC 2024 data.

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")

# get_codebook_url() is a Python helper; in R, construct the codebook URL from the
# mirror root in mirrors.yaml (codebooks are .xls files co-located with the data).
# Mirror failover: see `education-data-query/references/fetch-patterns.md` (R pattern).
config <- yaml::read_yaml("mirrors.yaml")
mirror <- config$mirrors[[1]]
url <- paste0(mirror$root_url, "/", "crdc/codebook_schools_crdc_discipline", ".xls")

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
)

library(dplyr)

# Filter to a single state (California) and disaggregated race groups
df <- df |> filter(
    (fips == 6) &       # California
    (race < 99)          # Exclude totals row
)

# Filter to specific demographic intersection
df <- df |> filter(
    (race == 2) &        # Black students
    (sex == 99) &         # Both sexes (total)
    (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
Collection cadence	Assuming annual data or applying an odd/even parity rule	Check topic-specific collection labels; 2020 and 2021 are consecutive
COVID year	2020-21 not comparable	Flag or exclude from trends
Suppression	Small cell suppression	Check suppression rates first
Coverage history	Applying one sampling label to both early collections	2013-14 is an official universe collection; verify 2011-12 separately
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 and readr infer ncessch/leaid/crdc_id as integer, destroying leading zeros	Python: schema_overrides={"ncessch": pl.Utf8, "leaid": pl.Utf8, "crdc_id": pl.Utf8}; R: readr::read_csv(..., col_types = cols(ncessch = col_character(), leaid = col_character(), crdc_id = col_character()))

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)

library(dplyr)

# Calculate discipline disparity using Portal integer codes.
# Risk ratio between two groups; value > 1 indicates group_a has a higher rate.
# Example: Black (race=2) vs White (race=1) OSS disparity
discipline_var <- "students_susp_out_sch_single"
group_a <- 2  # Black
group_b <- 1  # White

# Filter to each group (using integer codes)
df_a <- df |> filter(race == group_a)
df_b <- df |> filter(race == group_b)

# Calculate rates
rate_a <- sum(df_a[[discipline_var]], na.rm = TRUE) /
  sum(df_a$enrollment_crdc, na.rm = TRUE)
rate_b <- sum(df_b[[discipline_var]], na.rm = TRUE) /
  df_benrollment_crdc na.rm  

disparity  rate_a  rate_b

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	Routing questions to mirror CRDC dataset files 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