Conducting critical quantitative research with international students: A scholar-practitioner’s perspective

Written by Keanen M. McKinley (William & Mary)

Critical quantitative research is gaining interest among researchers across fields, including researchers working with international students. Although quantitative research may have an air of objectivity—given its ties to experiments, statistics, mathematics, and the like—there is a growing awareness that quantitative researchers are implicated in the research process and current conventions must be challenged and improved. For me, this was made most evident when I began learning more about how the disreputable eugenics movement continues to influence research practices.

Currently, it appears few researchers who work with international students have explicitly pursued critical quantitative inquiry, though I strongly believe quantitative researchers in this field are striving to advance their methods and would find the critical quantitative research literature appealing. I recently had the opportunity to write a chapter on this topic (for more info, please see the Research with International Students book), so here I briefly outline some key points, then build upon the chapter and share how I strive to advance the principles of critical quantitative research in my work as a practitioner.

In my chapter, I drew upon the literature on Critical Race Theory and QuantCrit, a framework of five principles informed by Critical Race Theory. QuantCrit is particularly helpful because it clarifies how researchers might conduct critical quantitative research. Formal definitions and references are of course provided in the chapter, but to paraphrase the work of David Gillborn, Paul Warmington, Sean Demack and other leading QuantCrit scholars, as well as relate their work to research with international students, the five principles are:

1. Racism is a concept that cannot be easily quantified: Indicator variables, like international student status, oversimplify the social processes that lead students to study abroad.
2. Numbers can promote deficit analyses: Quantitative data and research serve certain interests. For example, categorizing international students by nationality risks portraying them as “exotic” that other students, especially home students, can learn from.
3. Categories are not natural nor given: Race is a social construct with diverse meanings across contexts (e.g., national and institutional) and cannot be parsed into mutually exclusive categories.
4. Data cannot “speak for itself”: Quantitative research can be interpreted differently, so it is important to feature the perspectives of marginalized groups.
5. Statistical analyses can contribute in the struggle for social justice: Quantitative research is not value-free, and researchers must be prepared to defend critical quantitative research.

These five principles underline key tenets of critical quantitative inquiry that I hope researchers working within international students will adopt and employ in their research. Practitioners, however, may find advancing the principles a bit more daunting: it might seem there are less opportunities to challenge “the system” while working within it. In my role as a practitioner, I primarily analyze quantitative data, and I have sometimes felt constrained by what data are available and the priorities of decision-makers, though I have rarely been dissuaded from promoting critical quantitative research. To help demonstrate the QuantCrit principles, I wish to share how I have worked to advance those principles as a practitioner:

1. Racism is a concept that cannot be easily quantified: I emphasize the limitations of my analyses and the extent that measures are imperfect. For instance, in my experience, it is unlikely practitioners readily have on hand data related to social processes or student identity development. I am upfront about what we might learn from the available data and, if feasible, work with campus partners to gather additional data.
2. Numbers can promote deficit analyses: I try to anticipate my audiences. While working on an analysis, and especially when writing a report, I try to consider my immediate audience, who that audience might share the data with, and how those less immediate audiences might use the data.
3. Categories are not natural nor given: I avoid collapsing categories. Sometimes this is not possible: for example, including every potential combination of race identities may cause the size of a table to increase from one page to several pages. However, beginning an analysis using all categories at least allows the possibility to uncover some findings that would have been overlooked had I initially collapsed categories.
4. Data cannot “speak for itself”: I have admittedly had very few opportunities to feature the perspectives of students in my work, and those opportunities were qualitative data analyses that required me to interpret student responses. If I were to offer a piece of advice, though, I would suggest practitioners be prepared. Having familiarity with qualitative and mixed methods research approaches helped make clear the importance of this principle.
5. Statistical analyses can contribute in the struggle for social justice: I keep a pulse on campus activities that align with the principles of critical quantitative research. Learning about campus priorities, like diversity, equity, and inclusion initiatives, can make it easier to advocate for advancing critical inquiry. Most important, I have been fortunate to work with colleagues who are generally open to critical quantitative research.

Researchers working with international students should continue exploring the critical quantitative research literature to learn how they might challenge convention and improve research practices. Critical Race Theory and QuantCrit provide well-researched insights, and I hope my own experiences as a practitioner help demonstrate the QuantCrit principles, as well as illustrate the need to include practitioners in these discussions.