6 QuantCrit

Stephanie D'Costa

Learning Objectives

By the end of this chapter, you will be able to

  1. Analyze the ways that research was used to further oppress individuals from marginalized backgrounds.
  2. List ways that their social location impacts the research process.
  3. Determine critical research practices for consuming and creating research.

What is QuantCrit?

tree drawing with different parts labeled for methods, methodology, epistemology, and ontology (image description available)
Figure 6.1. Research Methodology Tree. [Image Description]

We have discussed both how research is shaped by paradigms and ways of knowing. We have also acknowledged that no research is free from bias, and much of our historical research was used to perpetuate oppression and harm. It is also important that we infuse critical theories into the research process, as it allows us to engage in research as a form of resistance. QuantCrit is one such critical theory that uses the principles of critical race theory (for definition and overview, please see the previous chapter) and applies them to how we engage and conduct quantitative research. In the following sections, each of the main tenets of QuantCrit will be explored along with examples of how it’s applied. Additionally, some considerations for researchers will also be included.

Tenet 1: The Centrality of Racism

The first tenet of quantCrit acknowledges the centrality of racism. Racism is embedded in our societies and institutional structures, and without intentionality, quantitative methods serve to perpetuate existing racial hierarchies[1].

 

Example

In understanding college and career aspirations, Marrun and colleagues (2025) used a quantCrit lens to pair racially and ethnically minoritized (REM)[2] students’ aspirations with their access to resources that would support their success. Their study was framed by an exploration of the myth of meritocracy as a cultural narrative that leads to race-evasive policies[3] and practices in terms of accessing higher education. Post-secondary students were invited to complete surveys of their experiences and findings indicated that students across racial groups had high aspirations of going to college or a career. However, the researchers found limited exposure from college recruiters, limited conversations with current teachers about their college experience, and continued under-enrollment in advanced high school coursework. By connecting the centrality of racism to college and career success, the researchers demonstrated the structural gaps between aspirations and access[4].

Research Considerations

  1. Include a positionality statement (Please refer back to chapter 2) in your research and consider how your positionality impacts your research topic and process. As you review research, consider who the authors are and how they arrived at this research study. This could be found in the methods section, if authors identify their reflexivity. It could also be in the author biographies (if included).
  2. Include asset-based language in your research questions. Make sure your questions consider the ways that power structures impact the outcomes for marginalized

Tenet 2: Numbers Are Not Neutral

With our historical post positivist lens towards research (See chapter 4), there was an assumption that quantitative numbers obtained through statistical analyses were neutral and to be accepted at face value. QuantCrit challenges this underlying belief and argues that research has historically been used to uphold oppressive systems and to perpetuate dominant, elite values[5]. Many of the ways this happens in research are insidious, such as not considering race in a study or using deficit or blaming language.

Example

An example of numbers that are not neutral comes to light when creating outcome measures to understand individuals’ perspectives. Marroquín-Flores and colleagues (2024) wanted to better understand college students from minoritized backgrounds’ experience in entering the STEM field. While traditional studies that explore this topic focus on deficit language towards minoritized students, this study sought to center an asset-based approach called Community Cultural Wealth to demonstrate the ways individuals leverage their strengths in challenging circumstances. They also intentionally used a QuantCrit approach to guide the process for developing a 100-item survey. In reviewing their own experiences and interviews with college students, they found that data needed to be captured in the intersections between discrete cultural wealth areas. For example, they describe how a queer college student described challenges with familial strengths given her family’s religious background but was able to identify the ways her family supported her dreams and aspirations for college. The researchers decided to use a factor model with cross-loading to allow for space to interpret data from an intersectional lens. By centering the researchers’ lived experiences and minoritized college students’ perspectives, they were able to choose a statistical model to interpret the data in ways that aligned with the individual’s lived experiences.[6]. By centering the researchers’ lived experiences and minoritized college students’ perspectives, they were able to choose a statistical model to interpret the data in ways that aligned with individuals’ lived experiences.

Research Considerations

  1. Consider the type of statistical analysis you choose to answer the questions you have. Remember that variables don’t exist in a vacuum, and you should provide context for how you include them in your study. For example, “controlling” (or holding constant) for things like socio-economic status (SES) doesn’t take into account the constellation of things that are connected to that variable (e.g., higher likelihood of under-resourced schools with higher populations of students from low SES backgrounds) and may impact outcome measures.
  2. Consider who is in the sample of the study and whether the findings are truly generalizable. What limitations arise when cultural expectations are not accounted for?

Tenet 3: Categories Are Not Natural or Given

This tenet is explicit about the fact that race is a social construct. While research has consistently demonstrated that there are no biological differences between racial groups (in fact, we see greater levels of variability within groups than between groups), individuals’ experience of racism impacts their physical, psychological, and economic well-being. Many research studies include data about race without an analysis of what that category truly implies. This tenet also applies to categories like disability in which QuantCrit acknowledges that these categories are not stable across time and are connected to sociopolitical and historical contexts[7].

Example

Research has indicated that there has been disproportionate disproportionate representation of children of color receiving special education services. In order to qualify for these services, students need to be assessed and qualify for a disability that warrants additional educational services to support their learning. However, there is often variability in how disproportionality is calculated in the research[8]. For example, Sullivan and colleagues (2020) describe how Asian students are often missing from conversations about special education disproportionality, given that they are treated as a monolith. Asian students are often seen as being underrepresented in special education, which leaves them off the radar for needing additional educational supports. However, when data is disaggregated, the findings indicate that Pacific Islander students (who are often lumped into the Asian category) are qualifying for special education at higher rates[9]. By disaggregating data, we can identify which students need additional support and consider reasons for why schools are not meeting their needs. Statistical analyses have also been weaponized to resegregate Black & Latine students  in special education[10]. QuantCrit can be used to elucidate these patterns and invite researchers to consider anti-racist special education practices.

Research Considerations

  1. Consider disaggregating your data. Remember that racial categories are not a monolith, and by disaggregating data, you can see the ways that outcomes look different for subgroups.
  2. Create opportunities for participants to give input into the categories they identify with. Provide context for why these categories are used in the first place.

Tenet 4: Data Can’t Speak For Itself

This tenet speaks to the fact the research is shaped by the researcher and who the researcher is influences a) the types of questions they ask, b) the types of data they collect, c) the analysis procedure they choose, and d) the way they interpret the findings[11]. Especially in research that has implications for marginalized groups, QuantCrit argues that the interpretation needs to center those who are intimately impacted. This includes engaging marginalized voices throughout the research process.

Example

One example of the different ways researchers approach data can be demonstrated through the conceptualization of discipline data in schools. Time and time again, the research has indicated that Black youth experience higher rates of discipline and more egregious punishments than other groups of students. If researchers are not careful, this data can lead to further stereotyping of Black youth behavior without considerations of how school personnel are influenced by white supremacy ideologies and anti-Blackness[12]. Additional conversations are needed to identify the ways that schools replicate carceral in their desire to control Black bodies instead of educate them[13]. While data collected may be similar across studies, using critical frameworks for interpretation is vital in not replicating harm.

Research Considerations

  1. Be clear about how traditionally marginalized groups have been a part of the research process. This is through the use of critical theories, participant input, research team membership, and the methods you choose for analysis.
  2. Provide context for people to understand why your results are meaningful. Look for articles that help the reader demystify the results in ways that are applicable to them.

Tenet 5: Social Justice Orientation

This final tenet of QuantCrit is a strong reminder of the why we engage in the research process. Research is a powerful tool and medium to move towards justice or replicate oppression for marginalized groups. From a critical lens, there is no neutral middle ground when it comes to research. This tenet also reminds us that social justice is both a process and an end goal—therefore how we engage in research from the conceptualization to the findings, matters[14]. Engaging in quantitative research with intentionality can allow researchers and communities to use data for liberation.

Example

Vargas and colleagues (2024) used a QuantCrit approach when looking at the Equitable Housing Project (EHP). The EHP is a collaboration with an academic institution and a local non-profit that supports houseless community members in the Los Angeles area. The research team included a formerly houseless individual who is pivotal to the partnership with the non-profit and a member of the research design process. In conversations with the non-profit, a social justice question was raised around bias in the assessment process that supported prioritization of resource allocation. The research team reviewed the sociohistorical context, which indicates that racism and sexism play a big role in systemic access to affordable housing. The researchers intentionally used intersectionality theory to frame their research questions. In an educational process, the research team describes five different statistical procedures that they could have used to analyze the demographic data of service utilization for the EHP. Models that use intersectional labels for race and sex found that white men tend to benefit the most from service allocation, and women of color (specifically Black or Latine) are provided less access to resources. The researchers stress that a critical interpretation of the results is needed to promote social justice. When systems hold race-neutral approaches, they don’t consider the unique ways that people of color in general and women of color in particular experience houselessness[15].

Research Considerations

  1. Research implications matter. Don’t simply report your findings, but identify ways that your data contributes to the larger narrative. Integrate historical socio-political contexts into your discussion of findings.
  2. Consider how your research and data find their way back to support the communities from whom they were collected. What is the practical significance of your work, and how can oppressed groups leverage the data for justice?
  3. Make sure the measures you choose are salient to the population you are working with. If they are not, consider what information might be missed from this limitation.

Equity Activity: Consuming research from a justice lens

Consider the tenets of QuantCrit and how you could apply them in reading research articles related to your specific topic of interest. Together, let’s draft some guiding statements on how you will consider each tenet. If you get stuck at any point in this activity, feel free to review How to “QuantCrit:” Practices and questions for education data researchers and users as a starting framework.

License & Attribution

Research consideration examples were expanded on from Castillo, W., & Gillborn, D. (2022). How to “QuantCrit:” Practices and questions for education data researchers and users. (EdWorkingPaper: 22-546). Retrieved from Annenberg Institute at Brown University. https://doi.org/10.26300/v5kh-dd65

“QuantCrit” by Stephanie D’Costa is licensed under CC BY-NC-SA 4.0.

Image Descriptions

Figure 6.1. A poster titled “Research Methodology Tree” depicts a leafy tree with arrows to four parts: at the top-left branch “Methods (details of exactly how we collect data)”; top-right branch “Methodology (how we should best collect data)”; on the trunk “Epistemology (how we should investigate the world)”; and on the roots “Ontology (how we view the world).” The design visualizes how ontology and epistemology underpin methodology and methods. [Return to Figure 6.1]

Media Attributions

  • research tree
  1. Crawford, C. E., Demack, S., Gillborn, D., & Warmington, P. (2018). Quants and crits: Using numbers for social justice (or, how not to be lied to with statistics) In Understanding critical race research methods and methodologies (pp. 125–137). Routledge. ↵
  2. The term racially and ethnically minoritized refers to individuals who self-identify as African American/ Black, Hispanic/Latinx, Asian American/Pacific Islander, or Native American. This term intentionally recognizes that we are not describing these communities by any biological differences but instead recognizing the way systemic racism impacts their existence. We acknowledge the limitation of this term as this aggregation can obfuscate how racism impacts these communities differently given the historical ways oppression has shown up within the U.S. ↵
  3. policies that ignore the consideration of race ↵
  4. Marrun, N. A., Mireles, D., Beck, J. S., & Clark, C. (2025). The (non) numeric truths behind college and career aspirations: a QuantCrit analysis of the precarious pathways for secondary students of color. Race Ethnicity and Education, 1-23. ↵
  5. Zuberi, T., & Bonilla-Silva, E. (Eds.). (2008). White logic, white methods: Racism and methodology. Rowman & Littlefield Publishers. ↵
  6. Marroquín-Flores, R. A., Tijerina, R. M., Tedeschi, M., Banjara, S., Warmsley, R., McFather, L., ... & Limeri, L. B. (2024). Using a QuantCrit Approach to Develop and Collect Evidence of Validity for a Measure of Community Cultural Wealth. CBE—Life Sciences Education, 23(4), ar55. ↵
  7. Cruz, R. A., Kulkarni, S. S., & Firestone, A. R. (2021). A QuantCrit analysis of context, discipline, special education, and disproportionality. AERA Open, 7, 23328584211041354. ↵
  8. D’Costa, S., Grant, S., Kulkarni, T., Crossing, A., Zahn, M., & Tanaka, M. L. (2024). A call for QuantCrit methodologies: Unpacking the need for a critical lens in school psychology research. School psychology international, 45(3), 254-279. ↵
  9. Sullivan, A. L., Kulkarni, T., & Chhuon, V. (2020). Making visible the invisible: Multistudy investigation of disproportionate special education identification of US Asian American and Pacific Islander students. Exceptional children, 86(4), 449-467. ↵
  10. Bell, N. S., Collier, Z., Vélez, V. N., & Ford, D. Y. (2025). CritSEM: Advancing QuantCrit to examine racialized resegregation in special education. Journal of Research on Educational Effectiveness, 18(2), 390-422. ↵
  11. Castillo, W., & Gillborn, D. (2022). How to “QuantCrit:” Practices and questions for education data researchers and users. (EdWorkingPaper: 22-546). Retrieved from Annenberg Institute at Brown University. https://doi.org/10.26300/v5kh-dd65 ↵
  12. Castillo, W., & Strunk, K. K. (2025). How to QuantCrit: Applying critical race theory to quantitative data in education (p. 134). Taylor & Francis. ↵
  13. Shedd, C. (2015). Unequal city: Race, schools, and perceptions of injustice. Russell Sage Foundation. ↵
  14. D’Costa, S., Grant, S., Kulkarni, T., Crossing, A., Zahn, M., & Tanaka, M. L. (2024). A call for QuantCrit methodologies: Unpacking the need for a critical lens in school psychology research. School psychology international, 45(3), 254-279. ↵
  15. Vargas, J. H., & Peet, J. Z. (2024). The first primer for the QuantCrit‐curious critical race theorist or psychologist: On intersectionality theory, interaction effects, and AN (C) OVA/regression models. Journal of Social Issues, 80(1), 168-217. ↵
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Critical Research Methods in Psychology Copyright © 2025 by Stephanie D'Costa; Mireille Ukeye; Makenzie O'Neil; and Rebecca Anguiano is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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