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In Brief

• Traditional quantitative methodologies are rooted in studying and norming the experiences of W.E.I.R.D.
• Quantitative Criticalism is a transdisciplinary approach to resist traditional quantitative methodologies
• Quantitative Criticalists aim to produce socially just research informed by various critical theories (e.g., CRT, feminist, queer).

Race is frequently operationalized as an individual fixed trait that is used to explain individual differences in various outcomes (Zuberi, 2000). Family Scientists may use race to explain direct causal relationships (e.g., parenting styles) or as a control variable to account for the variation explained by race on a given outcome (e.g., communication styles; Zuberi & Bonilla-Silva, 2008). When using race as a variable, Family Scientists often unlink it from its sociocultural context. In effect, race is reduced to a phenotypic or genotypic marker for explaining research phenomena (Bonilla-Silva, 2009). This process is particularly pervasive in quantitative methods, which are frequently perceived as more empirically valuable than qualitative methods (Onwuegbuzie & Leech, 2005). Quantitative methods position the external world as independent of human perception and are subject to immutable scientific laws. Quantitative researchers utilize randomization, control, and manipulation to ensure that outside factors do not bias research findings. This emphasis on isolationism centers knowledge as objective. However, critical researchers argue that quantitative inquiry is no less socially constructed than any other form of research (Stage, 2007). For instance, sampling bias can greatly influence research findings and interpretations by privileging the lived experiences of certain groups of people (e.g., high number of studies conducted on individuals from Western, educated, industrial, rich, and democratic backgrounds; Nielsen et al., 2017) or presenting results that are nonrepresentative of the internal diversity that exists within marginalized groups (e.g., the plethora of comparison studies that consolidate members of the African American diaspora to a single racial category; Jackson & Cothran, 2003).

To challenge the assumptions that shape quantitative inquiry’s emphasis on neutrality and objectivity, critical-race-conscious scholars have embraced critical race theory (CRT) as a mechanism for addressing the replication of racial stereotypes and White supremacy in empirical research. By failing to account for how racism and White supremacy shape family scholarship, Family Scientists are inadvertently perpetuating the position that change and betterment are the sole responsibility of the individual rather than challenging the systemic “creators” of inequality (Walsdorf et al., 2020). While conceptualizations of CRT tenets are evolving, a common thread among the elements is a commitment to identify, deconstruct, and remedy the oppressive realities of people of color, their families, and their communities (Bridges, 2019). This commitment has recently been extended to the critical evaluation of quantitative research via the development of “Quantitative Criticalism” (QuantCrit), which provides a framework for applying the principles and insights of CRT to quantitative data whenever it is used in research or encountered in policy and practice (Gillborn et al., 2018). In this article, we briefly summarize the tenets of QuantCrit and its connections to the principles of CRT and provide a brief case example of how Family Scientists can use QuantCrit.

Quantitative Criticalism

QuantCrit is an analytic framework that utilizes the tenets of CRT to challenge normative assumptions embedded in quantitative methodology (Covarrubias & Vélez, 2013; Gillborn et al., 2018; Sullivan et al., 2010). Despite its origin, QuantCrit has been used to critique traditional approaches to investigating various racial disparities, including breast cancer and genomic uncertainty (Gerido, 2020), teaching evaluations (Campbell, 2020), Asian American experiences in higher education (Teranishi, 2007), teacher prioritization of student achievement (Quinn et al., 2019), and student learning outcomes (Young & Cunningham, 2021). While heavily utilized by education scholars, QuantCrit is a transdisciplinary framework that applies the principles of socially constructed inequality and inherent inequality found within CRT to quantitative inquiry (insofar as socially constructed and inherent inequalities function to create and maintain social, economic, and political inequalities between dominant and marginalized groups). According to Gillborn et al. (2018), QuantCrit is not “an off-shoot movement of CRT” but “a kind of toolkit that embodies the need to apply CRT understandings and insights whenever quantitative data is used in research and/or encountered in policy and practice” (p. 169). Several central tenets guide this framework.

Acategorical Intersectionality

Identity is a complex, multidimensional aspect of individuals’ lived experience. Intersectionality describes how systems of identity, discrimination, and disadvantage co-influence individuals, families, and communities (Collins, 2019). Intersectionality challenges the idea of a single social category as the primary dimension of inequity and asserts that complex social inequalities are firmly entrenched in all aspects of people’s lived experiences. The gendered, racialized, and economic factors that shape an individual’s lived experiences cannot be understood independently, as they are intertwined. For example, Suzuki et al. (2021) discuss this complexity by explaining how not including race in research may suggest that it is unimportant but addressing race only via the inclusion of racial categories without explicitly elaborating on how racism influenced the outcome may indicate that racial inequities are natural. As such, QuantCrit researchers refute the idea of categorization as natural or inherent, critically evaluating the categories they construct for analysis and provide a rationale regarding their use of categories.

Centrality of Counternarratives

QuantCrit places emphasis on reliably researching and centering individuals’ lived experience using counternarratives. Counternarratives represent the perspectives of minoritized groups that often contradict a culture’s dominant narrative. By centralizing minoritized voices and contextualizing privilege and power, QuantCrit researchers diversify research narratives. In doing so, QuantCrit researchers highlight the multidirectional effects of power, privilege, and oppression by disrupting narratives that frame minoritized group members as deficient. This disruption also includes critically evaluating and intervening in the oppressive systems that uphold the power and privilege of dominant groups.

Nonneutrality of Data

QuantCrit researchers heavily scrutinize the notion of objectivity and reject the idea that numbers “speak for themselves.” QuantCrit researchers acknowledge that all data and analytic methods have biases and strive to minimize and explicitly discuss these biases.

Bias in the Interpretation and Presentation of Research

Even when numbers are not explicitly used to advance oppressive notions, research findings are interpreted and presented through the cultural norms, values, and practices of the researcher (Gillborn et al., 2018). In presenting research results, QuantCrit researchers overtly discuss their positionality and how their lived experiences may have influenced their interpretation and presentation of their findings.

Social Justice Oriented

QuantCrit research is rooted in the goals of social justice; it rejects the notion that quantitative research is bias-free, identifying and acknowledging how prior and contemporary research is used as a tool of oppression, and disrupting systems of oppression by critically evaluating and changing oppressive aspects of the quantitative research process. In doing so, QuantCrit researchers commit themselves to capturing the nuances and depth of the lived experiences of marginalized groups while simultaneously challenging prevailing oppressive systems.

QuantCrit: A Brief Example

QuantCrit has far-reaching research implications for Family Science. Consider how Family Scientists construct variables to reflect aspects of social marginalization (e.g., neighborhood disadvantage). Neighborhood disadvantage refers to the “lack of economic and social resources that predisposes people to physical and social disorder” (Ross & Mirowsky, 2001, p. 258). These effects are often of interest to Family Scientists for their developmental and intergenerational consequences (e.g., social organization theory; Mancini & Bowen, 2013). However, this construct has issues regarding its operationalization and measurement. Prior studies have operationalized neighborhood disadvantage as an index of contextual elements of a participant’s environment, including, but not limited to, a) the proportion of households with children with single-parent mothers; b) the proportion of households living under poverty rate; c) unemployment rate; and d) proportion of African American households (Martin et al., 2019; Vazsonyi et al., 2006). These elements are frequently mathematically consolidated into a variable based on their high degree of relatability. An issue with this consolidation is the assumption that living near or around a higher proportion of African American households brings disadvantages. However, researchers rarely address how redlining has been used to systematically place African Americans in disenfranchised neighborhoods (Aaronson et al., 2021).

QuantCrit researchers would approach the assessment of neighborhood disadvantage much differently. They may use other factors such as access to resources (e.g., food, health care facilities, community resources) and physical signs of social disorder (e.g., graffiti, vandalism, abandoned buildings) as indicators of neighborhood disadvantage. In addition, QuantCrit researchers may collect data from residents on their perception of the neighborhood and how it has an impact on their lives. These factors respect the spirit of the unobserved concept without perpetuating harmful stereotypes about African Americans.

Conclusion

As Family Scientists seek to incorporate CRT into their praxis, there is a growing need to critically evaluate our approach to quantitative methodology and disrupt the perpetuation of racism and White supremacy within Family Science scholarship. QuantCrit provides researchers with fertile ground for such reflection as it challenges researchers to consider the historical, social, political, and economic power relations present within their research. While this article was a mere introduction to the QuantCrit framework, we hope that it inspires more Family Scientists to reflect upon its tenets and explore ways of dismantling racism and White supremacy within their quantitative research.

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