Equity and Justice in Teaching Quantitative Methods

Quantitative methods and the courses in which they are taught often present as if they are neutral, value-free and unbiased. However, the history of quantitative methods demonstrates an entanglement with eugenics, sexism, heterosexism, ableism and colonialism. Researchers have begun to grapple with those issues and propose ways forward in quantitative methods.

But what about the courses? How might quantitative methods courses and their instructors -- possibly unintentionally -- contribute to and reify oppressive ideologies? Too often, our taken-for-granted assumptions about the social world infiltrate the ways we teach quantitative methods. It is important for all of us who teach them to take seriously the idea of harm reduction in our teaching. We should do the work to understand how our classroom practices can reify and reinforce oppressive ideologies and narratives and look for opportunities to challenge and interrupt those ideologies and narratives. Continuing to use examples that perpetuate stereotypes about racial inferiority, that position women as subservient or objects of sexual desire, that treat trans and nonbinary individuals as disposable or imaginary, and that position ability differences as personal deficiencies inflicts ongoing harm on students, especially minoritized students.

For example, using gender as a pseudo-independent variable to illustrate methods of comparing two independent groups (like the independent samples t-test) can reinforce the false view that there are only two genders or that sex as assigned at birth and gender are interchangeable. There are also examples that compare outcomes like GRE scores by race, which can reinforce ideas of hierarchies of racial intelligence.

Quantitative methods instructors can take some simple steps to shift their teaching toward an equity orientation. They can review examples that are used in the class and replace any that:

• Treat gender as dichotomous (e.g., men versus women) or that conflate sex as assigned at birth and gender (e.g., that treat female as a gender, or “male” and “man” as being equivalent).
• Invoke misogynistic tropes (e.g., that have ratings of women’s attractiveness as an outcome variable).
• Treat race as a causal variable (because in virtually every instance, the causal factor is actually racism, discrimination, bias and the like).
• Treat social identities in general (ability, immigration status, nationality, sexual identity, gender identity, income level and so forth) as causal variables (again, because the causal factor would actually be ableism, nationalism, colorism, genderism, heterosexism, classism, etc.). In identity-based examples, instructors should direct students to think and write about systemic factors that drive group differences.

Whenever possible, faculty should also consider using examples from real published research, even if those examples have to be contrived (for example, to produce very small data sets suitable for practicing the formulas) to match up with published patterns of results. An instructor might create a sample data set, for instance, that matches up with the results of recent work on racialized patterns in educational programs, patterns of segregation and discipline in public schools, or the ways teacher beliefs in learning styles can disadvantage students. Instructors need not have access to the original data -- they can use the published pieces to explain a research scenario to students and present simplified data that essentially reproduce the published pattern so students can learn analytic processes. In so doing, instructors also can and should make use of work authored by scholars of color, women, queer and trans researchers, and researchers with (dis)abilities, among others.

Often, when instructors seek to create “fun” or “silly” examples, they also inadvertently introduce racist, sexist, heterosexist and/or ableist stereotypes into the example. Instructors should instead consider intentionally selecting examples that demonstrate equity scholarship. As an example, my co-author Mwarumba Mwavita and I recently published a set of ANOVA design case studies, Design and Analysis in Educational Research, that focus on race and racism in education and are available for free on our SPSS or jamovi book websites. Other resources also exist, such as work on critical race research methodologies, using quantitative methods to pursue social justice ends and research methods for justice in education, along with many others.

I’ve provided some basic moves that can make instruction more equitable. For instructors further along in their thinking about quantitative methods and the teaching of those methods with equity and justice in mind, I have additional suggestions:

• Consider beginning class by talking about the history of quantitative methods, perhaps assigning readings from the scholars who study that history.
• Center discussions of epistemology and ontology early in the class to help orient students to the traditions from which quantitative methods emerged and how they differ from modern critical perspectives.
• Return to conversations about the history of quantitative methods and the epistemological and ontological entanglements of the statistic models and their assumptions as students learn new statistical tests and research designs.
• Introduce students to readings and examples from traditions like QuantCrit (which combines critical race theory and quantitative methods), queer quantitative research, critical quantitative research, and others. It is entirely possible to teach the learning objectives of quantitative methods coursework while intentionally centering critical approaches, equity research and social justice orientations.

We who teach quantitative methods can also do more to understand their histories and then critically reflect on how we might engage, renovate and rectify those methods. For example, we should share work on the use of racial statistics, white logics/methods, the ways that science has sometimes pathologized LGBTQ+ people and more. We should examine the human activity of statistics and methodologies with more intention. For example, while we make use of theories that people like the 19th-century mathematician Francis Galton developed, do we also reflect on his deep and avid support for eugenics? When we teach the ideas of people like Raymond B. Cattell, Robert Yerkes and Lewis Terman, how do we deal with their simultaneous writing on the dangers of immigrants and people of color and the threat they posed to the intelligence level of the United States?

What might it mean to deeply examine those histories with the students in our classes? I argue that we must more seriously consider the philosophical and epistemological framing of various methodologies and work with students to identify how to more equitably engage them in ways that move toward justice.