Our Work

Revising Federal Guidelines and Data Collection to Include Intersectionality

Primary Investigators:

Nancy López, The University of New Mexico

Yasmiyn Irizarry, The University of Texas at Austin

Edward D. Vargas, Arizona State University

This study consists of three interconnected programs: (1) a symposium and subsequent meetings with scholars, policymakers, and federal administrative bureaucrats/agency leaders in education, health, labor, and justice to revisit federal data collection in light of the insights of intersectionality; (2) qualitative case studies of the Office of Management and Budget (OMB) & the Census, including interviews with key stakeholders, participant observation at working group and advisory committee meetings, and content analysis of documents and public comment focused on ruling relations in Federal Agencies; and (3) quantitative intersectional analyses of leading education, health, justice, and workforce datasets using multiple measures of race (e.g., self-identified, ascribed, street race) and the creation of a summer institute for federal heads of statistical analysis and scholars to practice intersectionality as inquiry through quantitative methods.

This study is supported by the Robert Wood Johnson Foundation under Grant No. 79072. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Robert Wood Johnson Foundation.

June 2022 Convening, "Employing an Intersectionality Framework in Revising OMB Standards for Collecting Administrative Race and Ethnicity Data," hosted in Albuquerque, NM. Organizers: Nancy López, Yasmiyn Irizarry, and Edward D. Vargas. Sponsors: Robert Wood Johnson and The University of New Mexico.

Redefining Standards: A Call for Inclusive Race and Ethnicity Data Practices

AfroLatino Coalition. (2024). Concerns Regarding the 2030 Census Race and Ethnicity Data Collection and Tabulation Methodology. Submitted to Federal Register 2024-26827.

López, N., Irizarry, Y., & Vargas, E. D. (2024). Consensus memo responding to initial proposals for updating OMB’s race and ethnicity statistical standards (Federal Register 2023-01635). SocArXiv Preprint.

Irizarry, Y., Vargas, E., & López, N. (2023). Necessary research for revising OMB’s race and ethnicity standards. Submitted to Federal Register 2023-01635 and as public comment at May 2023 Census National Advisory Committee (NAC) meeting.

Employing Intersectionality for Student Success in Higher Education

Primary Investigators:

Yasmiyn Irizarry, The University of Texas at Austin

Nancy López, The University of New Mexico

Edward D. Vargas, Arizona State University

Removing barriers to postsecondary success for Latinx, Black, Indigenous, and other minoritized students requires illuminating intersectional inequities as a first step in advancing institutional transformation (Garcia & Cuella 2023; Casellas Connors, 2021; Giebel et al., 2023; Riegle-Crumb, King, Irizarry 2019). We propose phased mixed method activities: 1) Comprehensive literature review; 2) In-depth qualitative case studies of institutional data collection, document review, and reporting practices related to race, ethnicity, and intersectionality; and 3) Analysis of institutional data collection and reporting practices pre and post anti-DEI Executive Orders. We will focus our research on whether there are any institutions already employing promising practices as well as on missed opportunities.

Intersectionality Research Cooperative Reports

Read the report

Race, Immigration & School Safety

Primary Investigators:

Anthony Peguero, Arizona State University

Yasmiyn Irizarry, The University of Texas at Austin

In this project, we examine the effects of state-level population policies both on school-level safety and educational outcomes and on student-level perceptions of school-level safety and educational outcomes. Healthy and safe schools are likely crucial for academic progress, success, and attainment. As the United States becomes more demographically diverse, it becomes more critical to understand how states' approaches to population may affect school safety and education, and students' perceptions of school safety and education. This research will draw from multiple nationally representative datasets, including the School Survey on Crime and Safety, the High School Longitudinal Study of 2009, and Common Core Data, as well as data from the National Conference of State Legislatures.

This study is supported by the National Science Foundation under Grant No. 2214370. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

NSF collaborative team meeting hosted at Arizona State University in Tempe, AZ, January 5-7, 2025.

State Immigration Legislation Data Archive

The State Immigration Legislation Data Archive (SILDA) is a comprehensive, systematically coded resource cataloging every state immigration law enacted in the United States between 2008 and 2017. SILDA includes approximately 3,000 laws from all 50 states, each coded across 80 distinct variables. These variables capture legislative metadata (such as year, state, chamber, and bill type), political and procedural context (including party affiliation, sponsors, voting data, and committee involvement), and substantive content (such as main topics, targeted populations, and provisions related to civil rights, fines, or incarceration). The coding framework also assesses legislative sentiment, civil rights implications, and the specific populations affected.

Racialized Learning Environments in Mathematics Education

Primary Investigators:

Yasmiyn Irizarry, The University of Texas at Austin

Tia C. Madkins, The University of Texas at Austin

In racialized learning environments, minoritized learners experience exclusionary classroom environments with differential opportunities to learn rigorous mathematics. Understanding how teachers’ beliefs influence their work with minoritized learners is a key component of fostering inclusive mathematics classrooms to promote equity and student success. Our research team examines the interconnectedness between mathematics teachers’ racialized beliefs (e.g., Black learners often struggle in mathematics courses) and the how and what of mathematics teaching. Using both qualitative and quantitative analyses, we describe learners’ experiences within racialized mathematics learning environments and how teachers foster inclusive classroom environments. We also developed the first large-scale teacher survey to examine secondary mathematics teachers’ beliefs and practices with explicit attention to race.

Presentation of "An Examination of Secondary Mathematics Teachers' Racialized Beliefs. In Sociopolitical Perspectives on Math and Science Teaching and Teacher Education" at American Educational Research Association 2023 Annual Meeting in Chicago, IL.

National Survey of High School Math Teachers

The National Survey of High School Math Teachers, piloted in the summer of 2021, is the first large-scale survey focusing on mathematics teachers’ 1) racial beliefs, 2) views about minoritized learners and their families, and 3) inclusive teaching practices. This survey was developed using qual-to-quant translation, an iterative approach to survey design rooted in the principles of QuantCrit. The survey includes questions from existing surveys, as well as original questions informed by existing literature, focus group interviews with mathematics teachers, and regular working group meetings. Teachers for the national pilot were recruited using a split sample design. Over 220 teachers participated in the survey: 58% were part of a national multistage, stratified random sample and 42% were from a national convenience sample recruited through professional associations listservs and social media.

Black epiSTEMologies: Examining Blackness in Postsecondary STEM Education

Primary Investigators (Texas Site):

Tia C. Madkins, The University of Texas at Austin

Yasmiyn Irizarry, The University of Texas at Austin

Black epiSTEMologies is a multi-institutional collaborative research project seeking to develop theories, research methods and tools (e.g., qualitative protocols, quantitative instruments), and forms of knowledge that expand the field of STEM education’s conceptual understandings of and implications for racial equity in STEM for Black students.

The University of Texas at Austin

Tennessee State University

North Carolina A&T State University

American University

University of Illinois Chicago (The Hub)

Georgia State University

Learn more about Black epiSTEMologies →

Centering Black Women in Research

Primary Investigators:

Shannon Malone Gonzalez, University of North Carolina, Chapel Hill

Yasmiyn Irizarry, The University of Texas at Austin

The In Her Place Survey of Black Women and Policing was developed as a part of a larger mixed-methods project on police violence against black women and girls. The survey, administered in October 2020 by Qualtrics, includes a nationally representative sample of Black women aged 18 and above, stratified by age and geographic region. Over 1,600 black women participated in the study.

Survey questions covered a range of subjects related to policing, including but not limited to black women's experiences of police violence (childhood and adulthood) and the circumstances surrounding these experiences; how black women prepare for the possibility of police encounters; their opinions and views of police, police violence, and protests, as well as what we should tell black youth regarding police encounters, and social media and activism regarding police violence. The survey also includes detailed information on respondents' socioeconomic and other background characteristics, physical features, health and wellbeing, gender, sexual, and ethnic identity (self and partner), home and neighborhood characteristics, and day-to-day experiences of discrimination.

Malone Gonzalez, S., & Irizarry, Y. (2024). In Her Place Survey on Black Women and Policing (Version V1) [dataset]. Harvard Dataverse. https://doi.org/doi:10.7910/DVN/UXTOVN

Survey Innovations

Irizarry Hair Texture Scale

The Irizarry Hair Texture Scale (IHTS) is a novel framework that captures both the physical and socio-cultural dimensions of hair as key aspects of phenotype. Developed through interdisciplinary research and informed by critiques of racialized beauty standards, the IHTS comprises two complementary measures that provide a scientifically grounded approach to studying hair as a marker of identity and a lens for examining systemic inequities.

Irizarry, Y. (2025). The Irizarry hair texture scale. SocArXiv Preprint.

Question #1 spans the full spectrum of natural hair textures.

Using the image below as a guide, what is the primary texture of your hair as it grows from your head? That is - without using any product or tools that would change your natural hair.

Category descriptions incorporate factors like curl pattern and density.

Question #2 ranks the diverse ways individuals style, protect, and modify their natural hair.

On a typical day, what is the style that you wear your hair? Indicate 1st for the way you most often wear your hair, 2nd for sometimes, and 3rd for occasionally.

How do you typically wear you hair?

In a cut or style (with or without product) where my natural hair texture is visible
Let my hair grow out a bit
Very short, clean shaven, or bald
In braids, twists, or locs, with or without added hair or extensions
Pressed or blow dried straight using only tool and heat
Relaxed (i.e., chemically treated to "relax" or straighten natural curls)
Permed (i.e., chemically treated to create permanent curls or waves)
Concealed by a scarf wrap, or other head covering.

You deserve to show up as your full self, rocking your crown with your head held high.

— Congresswoman Ayanna Pressley, Remarks on the CROWN Act

What's Your Street Race?

The original "Street Race" measure asks individuals to identify how they believe strangers perceive their race when they are walking down the street, based solely on their appearance. This approach captures the social reality of racial classification as experienced in everyday life, rather than relying only on self-identified or official racial categories. Building on this foundation, our revised multidimensional measure offers a more nuanced and comprehensive assessment of street race by allowing respondents to indicate multiple perceived racial categories and to rank them in order of likelihood.

Publications

Substantive Changes

Provides examples like skin color, facial features, and hair to help people think about what others might notice.
Allows respondents to rank up to three racial categories in order of how likely others are to see them as each race, rather than selecting just one.
Includes more detailed racial categories, splitting broad groups like "Asian American" into "East Asian," "South Asian," and "Southeast Asian," with examples for each.
Updates Middle Eastern/Arab to Middle Eastern or North African with examples for clarity.
Uses more inclusive phrasing “out in public” to describe the setting.

Original Street Race Measure

If you were walking down the street, what race do you think other Americans who do not know you personally would assume you were based on what you look like?

White, not Hispanic
Black
Latino or Hispanic
Asian American
Middle Eastern/Arab
American Indian or Alaskan Native
Native Hawaiian
Pacific Islander (not Hawaiian)
Other: SPECIFY

Revised Street Race Measure

This question is about how others see your race, not how you identify yourself. If you were out in public, what race do you think other people who do not know you personally would assume you were based on what you look like (for example, your skin color, facial features, and hair)?

Using the list below, indicate what race other people are most likely to think you are. Rank your answers where 1st means most likely, 2nd means next most likely, and so forth. If people almost always see you as one race, just indicate 1st for one category.

American Indian or Alaska Native
Black or African American
East Asian (such as Chinese, Japanese, or Korean)
South Asian (such as Indian or Pakistani)
Southeast Asian (such as Filipino or Vietnamese)
Hispanic or Latino
Middle Eastern or North African (such as Lebanese or Egyptian)
Native Hawaiian or other Pacific Islander
White
Some other race (please specify)

Through everyday critical reflection about street race, we create bridges of understanding, empathy, unity and solidarity.

— Dr. Nancy López, What is 'Street Race'?

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