Review of “Questioning the Science: How Quantitative Methodologies Perpetuate Inequity in Higher Education”
Accepted research practices in higher education use established quantitative research methodologies based on a standard of the White ideology. The article Questioning the Science: How Quantitative Methodologies Perpetuate Inequity in Higher Education examines oppressive statistical practices deep-rooted in quantitative research. Arellano reveals inequity within quantitative research that impacts non-White communities and perpetuates subjectivity and bias. I chose this article because it uses the framework of QuantCrit to identify contributors to inequality in statistical data and interrogates quantitative research methodologies used in higher education.
Arellano uses QuantCrit as a framework for identifying contributors to inequality and for critiquing statistical practices used in quantitative research. Arellano reveals the deep anchoring of systematic oppressive research practices that represent the standard for quantitative research in higher education historically and present day. The article challenges the status quo in higher education that exemplifies Whiteness in quantitative research to change the oppressive underpinnings of established statistical practices. Increasing reliance on technology and the pervasiveness of artificial intelligence are some of the contemporary issues made aware of by Arellano. The article concludes with recommendations for current and future quantitative researchers in higher education. The first recommendation is the education of oneself as a faculty researcher that generates research using these methods. The second recommendation speaks to higher education curricula and instruction and their influence on the training of future faculty and researchers.
Identifying Contributors to Inequitable Statistical Practices
The article identifies multiple contributors to inequity in quantitative research and methodologies. Arellano indicates the framework of QuantCrit is used to illustrate the oppressive nature of quantitative methodology. The article outlines the emergence and conceptualization of QuantCrit. However, the framework of QuantCrit is limited to a listing of the tenets but is not explicitly utilized to support the identification of inequitable contributors from a QuantCrit framework.
Arellano uses the work of Zuberi (2001) to outline a historical understanding of White logic and White methods that are the standard research approach for statistical analysis alongside racial reasoning. I agree with Arellano about the interconnectedness of White logic and White methods, and one cannot be understood without the other (2002). The dynamic of White logic and White methods strengthens the inequality embedded within quantitative methodology by utilizing normative White perspectives, deficit-based research practices, and the imposition of White ideology frameworks on non-White community groups. Utilizing the QuantCrit tenet of the centrality of racism is ideal for framing White logic and White methods and understanding the barricade of oppression that quantitative methodologies construct.
The research skills taught to doctoral students as a part of higher education research curricula are scrutinized by Arellano leading to the continued status quo and impact of inequitable statistical practices on future faculty and researchers. The argument problematizes the shortcomings in the way quantitative methodology is taught in graduate education and fragments the research focused on the non-White experience (2020). My own experience as a novice researcher favors Arellano’s concerns that highlight the established canon of literature and methodological epistemologies graduate schools use within curricula and for training in higher education. Arellano suggests new models and frameworks not dominated by normative White perspectives require the experiences of non-White communities (2020).
Arellano makes a very insightful consideration of the intertwined relationship between the external review process and higher education research that creates a cycle of oppression in quantitative research. This is a consequential observation briefly presented in the article, yet it has significant implications for the viability of higher education research. The author positions the external review process in the introduction of the article and argues the external review process is another place where structural inequity, system oppression, and White supremacy are upheld (2020). I argue that the external review process acts as an intermediary in perpetuating inequity. I believe Arellano missed an opportunity to elaborate on the role of the external review process as an inequality contributor. Further elaboration can reveal how the external review process can be a change agent for equality in quantitative research.
Critiquing Inequitable Statistical Practices
The author positions the paper as using a QuantCrit framework to illustrate oppression, subjectivity, and bias in five statistical practices in quantitative research (2020). Arellano scrutinizes the statistical methods of comparing groups, eliminating outliers, addressing non-response bias, small sample sizes, and theory development. All five examples provide a concrete understanding of problematic and oppressive methodologies. However, an explicit connection to the QuantCrit framework is insufficiently applied to the five illustrative examples. The nuances of the QuantCrit tenets are woven throughout the article. I disagree that a QuantCrit framework is fully visible throughout the article.
One example in which Arellano provides an illuminating and simple understanding for novice researchers is Theory Development in which conceptual and theoretical frameworks are explained. As a doctoral student and novice researcher, I have struggled to distinguish between each of these frameworks. As a part of this illustrative example, the author points out the influence of White supremacy on theory development. Arellano contrasts the conceptual and theoretical frameworks in a manner that produces clear definitions. A conceptual framework is birthed out of new ideas within research and is connected to a tested population, and is often a White normative group. If a concept, or new idea, is tested outside of the original testing population then the results may not be the same. Theory development is further explained as the multiple successful replications of results by various scholars that can lead to the concept being considered as a theory (2020).
Arellano makes an effort to bring in the Critical Race Theory (CRT) principle of essentialism and apply it to the example of Non-Response Bias. Instruments to collect data account for non-response bias within a sample by applying weights to groups with lower responses to ensure the sample is representative of the national population (Arellano, 2020). Voices of a few are amplified representatives of their entire group. Essentialism is briefly mentioned and I consider this to be another missed opportunity by Arellano to elaborate on an important insight of CRT and to frame this example within QuantCrit.
Arellano seeks to problematize the enduring inequalities in quantitative methodologies in higher education. The article uses elements of the QuantCrit framework to confront the five illustrative examples of statistical practices perpetuating inequity in research and higher education. I believe an expanded integration of the QuantCrit tenets is necessary. Despite the limited positioning of the QuantCrit framework in the article, Arellano provides a thorough understanding of the historical context for the emergence and conceptualization of QuantCrit. Additionally, the author provides robust suggestions for moving forward in the work of securing equity in quantitative methodologies in higher education. For example, Arellano provides a starting point for engaging with scholarly work such as Zuberi and Bonilla-Silva (2001) as well as the research of Garcia, Lopez, and Velez (2018). In consideration of contemporary influences, Arellano notes the impact of research decisions not being made by humans and the influence of artificial intelligence using mathematical models and algorithms as a part of important decision-making (2020).
I agree with Arellano’s broadcast of concern for questioning the inequitable traditions, standards, and practices of quantitative research in higher education. Future research questioning the science of quantitative methodology should focus on the external review process for peer-reviewed journals. The author raises a concern about the role of the external review process in the perpetuation of inequitable statistical practices in higher education. The QuantCrit framework can bolster the work of quantitative research scholars with the use of insights from CRT (Castillo & Gillborn, 2022). I am hopeful about the recommendations the article suggests as a response to the question posed by Arellano. One recommendation suggests individual researchers look within and self-educate on how to conduct research critically. I definitely believe this is effective but does not have the urgent stance of the second recommendation. Arellano’s second recommendation suggests a systematic approach to reframing the way future researchers are taught in higher education by addressing research-based curriculum and instruction.
- Castillo, W., & Gillborn, D. (2022). How to “QuantCrit:” Practices and questions for education data researchers and users (No. 22-546). Working Paper.
- Garcia, N. M., López, N., & Vélez, V. N. (2018). QuantCrit: Rectifying quantitative methods through critical race theory. Race Ethnicity and Education, 21(2), 149-157.
- Mittelmeier, J., Edwards, R. L., Davis, S. K., Nguyen, Q., Murphy, V. L., Brummer, L., & Rienties, B. (2018). ‘A double-edged sword. This is powerful but it could be used destructively’: Perspectives of early career researchers on learning analytics. Frontline Learning Research, 6(2), 20–38. https://doi.org/10.14786/flr.v6i2.348
- Wofford, A. M., & Winkler, C. E. (2022). Publication Patterns of Higher Education Research Using Quantitative Criticalism and QuantCrit Perspectives. Innovative Higher Education, 1-22.
- Zuberi, T., & Bonilla-Silva, E. (Eds.). (2008). White logic, white methods: Racism and methodology. Rowman & Littlefield Publishers.
- How can the tenets of QuantCrit become more commonplace in quantitative research?
- How can an understanding of racism and inequitable statistical practices be incorporated into higher education curricula and instruction?
- What changes can the external review process for peer-reviewed journals make to influence researchers to identify and critique statistical practices that perpetuate racism?