Unequal Opportunity

Leveraging Blended Data to Assess Disparities in Dress 

Code and Discipline Among K-12 Students

(Based on GAO Report: GAO-23-105348)

Frances Tirado

Senior Mathematical Statistician

TiradoF@gao.gov

Sonya Vartivarian

Assistant Director (Statistics)

VartivarianS@gao.gov

Applied Research and Methods

U.S. Government Accountability Office (GAO)

FCSM 2023 Research and Policy Conference
Agenda

• Issue Background

• Sources of Data

• Regression Modeling

• Sample Design and Analysis

• Recommendations
Issue Background

• Dress Codes

• School police
Issue Background

No standard dress code; variations across schools in:

 Guidelines 

 Enforcement

 Student discipline

Concerns raised by researchers, parents, students:

 Dress codes tend to restrict clothing typically worn by girls (e.g. leggings)

 Prohibition of hair styles and head coverings unfairly target Black students 

and students of certain religions and cultures.

 Negative impacts of removal from classroom due to dress code infractions

 Disproportionate impact of discipline for some students

Issue Background

Research Questions for ‘dress code’ report:

1. What are the characteristics of K-12 dress codes across 

school districts nationwide, and how Department of 

Education supports the design of equitable and safe dress 

codes.

2. What is known about the enforcement of dress codes, and 

how Education supports equitable dress code 

enforcement.
Issue Background

Research Questions for ‘dress code’ report:

1. What are the characteristics of K-12 dress codes across 

school districts nationwide, and how Department of 

Education supports the design of equitable and safe dress 

codes.

2. What is known about the enforcement of dress codes, and 

how Education supports equitable dress code 

enforcement.
Issue Background

Research Questions for ‘dress code’ report:

1. What are the characteristics of K-12 dress codes across 

school districts nationwide, and how Department of 

Education supports the design of equitable and safe dress 

codes.

2. What is known about the enforcement of dress codes, and 

how Education supports equitable dress code 

enforcement.
Issue Background

• Widespread presence of police in schools

• Concerns raised by researchers, parents, students:

 Overall disparities in arrests by police presence in schools

 Disparities in arrests of students, by demographic

 Questions about whether police presence makes schools safer
Focus on Student Discipline: Referrals to Law Enforcement and School-Related Arrests in U.S. Public Schools (Jan. 2023)

U.S. Department of Education, Office of Civil Rights

https://www2.ed.gov/about/offices/list/ocr/docs/referrals-and-arrests-part-5.pdf
Focus on Student Discipline: Referrals to Law Enforcement and School-Related Arrests in U.S. Public Schools (Jan. 2023)

U.S. Department of Education, Office of Civil Rights

https://www2.ed.gov/about/offices/list/ocr/docs/referrals-and-arrests-part-5.pdf
Issue Background

Research Question for ‘Police’ report:

1. Is the presence of police in K-12 schools associated with 

rates at which students are referred to law enforcement or 

arrested?
Sources of Data
Sources of Data

• We used Department of Education’s school-level data: 

 Common Core of Data 

(Annual admin. Census)

 School Survey on Crime and Safety 

(National survey)

 Civil Rights Data Collection 

(Mandatory biennial collection)

• We analyzed school years 2015-16, 2017-18

 Most recent available data at the time
Sources of Data

• Key variables are spread 

across data sources

• All 3 sources use common 

ID for linking

 97.4% of SOCCS records in 

CRDC & CCD

 92.3% match rate for CCD & 

CRDC– expected result 

because of differences in 

populations in file

Analysis 

Dataset

CRDC

- Student discipline

- Arrests

SSOCS

- Dress code 

enforcement

- Police Presence CCD

- School 

Characteristics

- Student 

Demographics
Regression Modeling

• Dress Codes

• School Policing
Regression Modeling

• Two separate analyses looking at:

 School dress codes 

 School police presence

• Used merged SSOCS-CCD-CRDC data for both analyses

 Limited records to merged SSOCS records for both years
Regression Modeling

First, check for potential bias due to having unmatched schools

 Conceptually treat ‘unmatched’ as a ‘nonresponse’

 Focused on SSOCS schools not matched to CRDC 

 Performed essentially a non-response bias analysis to assess if nonmatched schools are significantly different

 Modeled propensity of a school match

 Assessed variable distributions, estimates, and SE’s across response 

groups

 Also examined differences in SE’s under two assumptions
Regression Modeling

• Based on evidence of checks, we assume the missing 

data are likely missing at random within subclasses

• These subclasses were all factors controlled for in 

later analyses, thus implicitly adjusting for potential 

bias introduced due to non-matched SSOCS schools
Regression Modeling

• Mainly interested in two outcomes:

 Rates of discipline (by type)

 Control for school enforcement of strict dress code

 Rates of arrest

 Control for presence of police at school

• Modeling rates/ratios/proportions using OLS can be problematic 

 Usually efficient for unbounded continuous variables

 Not useful here since: 

 Data violate normality assumption

 Can predict values beyond the range of [0,1]
Regression Modeling

Use Negative Binomial 

regression to model the 

count of students 

 Evidence of overdispersion

detected using Poisson 

regression 

(i.e. variance ≠ mean)

 Exposure set to the total 

number of students at school

"File:Negative binomial.svg" by Facorread is licensed under CC BY-SA 4.0.
Regression Modeling

Limitations

• Data are by school, rather than by student. So can’t speak to 

associations for students, but rather associations with schools

• Omitted variable bias

• Not causal 

• Subject to sampling and non-sampling error

To account for this, we only reported direction of association, not 

magnitude
Findings

• Disparities in discipline of students in schools which 

enforce strict dress codes

• Dress code infractions remove students from the 

classroom

• Schools sometimes call student removals ‘informal 

removals’ – this data is not tracked by CRDC

• Department of Education lacked resources and guidance 

on how schools should address disparities in discipline

Regression Modeling

• For police report, we used the same data but took a 

different approach

• Goal: Reduce confounding due to using ‘observational’ 

data (e.g. not from randomized experimental design) 

• Doubly robust method:

 Create a ‘matched’ dataset of similar schools using propensity 

score matching 

 Perform a negative binomial regression using matched dataset
Regression Modeling

Propensity score matching:

 ‘Treatment’ is presence of police at a school

 ‘Control’ is a school without police

 Interested in the average treatment effect in the treated (ATT)

 To create propensity score:

 Logistic regression with police presence as the outcome 

 Controlled for school characteristics and policies associated 

with police presence

 Used survey weights
Regression Modeling

Propensity score matching:

 ‘Treatment’ group larger than 

‘Control’ group

 One-to-One matching would discard 

records, reducing precision

 N-to-One matching increases 

precision, but introduces bias

 Considerations given to weighting 

adjustment

 Good covariate balance between 

groups after matching

School 

1

“Similar”

School 

without

Police

School 

2

School 

3

Schools with Police Presence
Regression Modeling

• We performed a similar regression analysis using this 

matched data

 Used the ‘new’ weight instead of the original sampling weight

 Considerations given to variance estimation and interpretation of 

variable coefficients

• Results are ongoing, stay tuned!
Sample Design and 

Analysis

• Dress Codes
Goal

 Select a sample of Local Education Agencies (LEAs) 

– school districts – in order to examine dress code 

policies through a pre-defined set of questions

Generalizable estimates to the population of all 

public LEAs in the 50 states, the District of 

Columbia, and territories...plus other properties I 

will describe
Sample Frame

• Common Core Data (CCD) for the 2020-2021 School Year

• 17,600 public K-12 school districts

• Excluded LEAs: 

 Under Bureau of Indian Education, Department of Defense. 

 Closed, not operational, supervisory unions, or federally 

operated agencies. 

 Without schools or students.

• Combined the 33 NYC LEAs into one unit with aggregated 

school and student sizes.

• Charter schools are generally their own LEAs
Sample Design Considerations

• Dress code review would be a manually intensive process

• Features that GAO considered important in the design:

 LEA size (number of students)

 Urbanity

 Racial/Ethnic make-up of the LEAs Key reporting group

 Charter status 

• 90% expected response rate based on GAO pre-testing and 

previous GAO work
Sample Design

 Certainty strata: 10 largest LEAs (student size) 

 Within Majority White and Majority Non-White LEAs, proportionately 

allocate sample across Urbanity (Urban/Suburban/Town Rural) and 

Charter/Non-Charter for a total of 13 strata

 Sample size = 236, with an expected 213 completes

 95% Margins of Errors for percentage estimates are within:

 +/-9.8 percentage points in White Majority and Non-White Majority 

LEAs

 +/-7 percentage points for overall estimates 

 Other groups: between +/- 9 (non-charter LEAs) and +/- 14.4 (urban 

LEAs)
Non-Response Adjustment

• 92% response rate with an overall correction for non-response

• Within strata non-response adjustment factor 

• Generally, surveys of schools and districts based on the Common 

Core Data sampling frame have variables available for a 

nonresponse bias analysis and weighting adjustments

 Variables that form the stratification

 Size of schools and districts

 Type of school or district

 Grades offered
Estimation

• Used SUDAAN to produce estimates that incorporate the sample design

• Flagged instances where the margin of error was large

Findings

Department of Education lacked resources and guidance on 

how schools should design equitable and safe dress codes

• While school districts often cite safety as the reason for 

having a dress code, many dress codes include elements that 

may make the school environment less equitable and safe for 

students. 

• For example, an estimated 60 percent of dress codes have 

rules involving measuring students’ bodies and clothing—

which may involve adults touching students.
Findings continued

• Districts more frequently restrict items typically worn by 

girls—such as skirts, tank tops, and leggings—than 

those typically worn by boys—such as muscle shirts.
Findings continued

• Most dress codes contain rules about students’ hair, hair styles, and head 

coverings, which may disproportionately impact Black students and those of 

certain religions and cultures. Few specify religious, cultural, or medial exemptions.
Findings continued

• More than three-quarters of dress codes contain non-exclusionary disciplinary 

options for enforcing dress codes. 

• An estimated 44 percent of dress codes outlined “informal” removal policies, 

such as removing a student from class without documenting it as a suspension.
Impact

Dress Codes
Statistical Impact

• Surveys designed with purpose can have impact 

• By combining methodologies and using blended data, this 

report resulted in a comprehensive view of disparities in 

dress code and discipline among K-12 students

Recommendations

The Secretary of Education should:

1. Provide resources to help districts and schools design equitable 

dress codes

2. Include dress code information in existing resources on safe and 

supportive schools (e.g. body autonomy)

3. Provide resources for states, school districts, and schools on 

equitable enforcement of dress codes and the use of discipline

4. Collect information on the prevalence and effect of removing 

students from the classroom (and other discipline)
Recommendations

Department of Education released guidance

addressing safe inclusive climates in school
Acknowledgements to the Dress Code team

In addition to the presenters: 

Jacqueline M. (Director)

Nagla’a El-Hodiri (Assistant Director)

Alison Grantham (Analyst-in-Charge)

Abby Marcus, Jessica L. Yutzy, James Bennett, Elizabeth Calderon, 

Tonnyé ConnerWhite, Sherri Doughty, Randi Hall, Abigail Loxton, 

Abinash Mohanty, John Mingus, Jessica Orr, David Reed, Monica 

Savoy, Almeta Spencer, Manuel Valverde
For more information please 

see: 

https://www.gao.gov/products

/gao-23-105348
Thank you!