Critical Approaches for Nursing Research: Unpacking Critical Quantitative Inquiry (CritQuant)

Critical research is defined as “any research that challenges those conventional knowledge bases and methodologies—whether quantitative or qualitative—that make claims of scientific objectivity. Rather, critical social research attempts to reveal the socio-historical specificity of knowledge and to shed light on how particular knowledges reproduce structural relations of inequality and oppression” (Muncie, 2006, p. 51). Put simply, critical social research looks deeper than what seems obvious, to question the common ideas researchers have about society, phenomena, and individuals (Harvey, 2022).

Critical research remains limited in nursing and other health research. This editorial is the first in a series titled “Critical Approaches for Nursing Research.” This series aims to help researchers design and conduct critical research that goes beyond traditional methods, challenges existing power structures, and promotes equity in nursing. This first installment provides a brief overview of Critical Quantitative (CritQuant) inquiry and its key methodological principles and outlines implications for nursing research.

What Is CritQuant?

CritQuant is an approach to quantitative research that is fundamentally informed by the principles of Critical Theory and Social Justice (Baez, 2007; Stage, 2007). It is not a specific statistical technique like regression analysis or factor analysis, but rather a philosophical lens applied to quantitative methods. Stage (2007) stated that, like traditional quantitative research (i.e., informed by positivist or postpositivist paradigms), CritQuant inquiry's scope is broad, findings are generalizable, focus is on groups, data are aggregate, and results are context-independent. However, its motivation is different because it uses sociological and economic analysis to demonstrate that some models widely accepted as universal are actually inaccurate when applied to particular communities or populations.

The three key purposes of CritQuant research are:

To use large-scale data for identifying systemic inequities and understanding their origins and influences on society (Stage, 2007).

To critically evaluate the limitations and biases in commonly used quantitative methodologies and propose alternative, more equitable models and metrics that provide robust descriptions of underrepresented experiences (Stage, 2007).

To conduct culturally responsive research by using methodologies that fully account for the unique characteristics and context of the institutions and populations under investigation (Stage & Wells, 2014).

CritQuant seeks to reclaim the persuasive power of numbers, which often reinforces the status quo, and redirect it toward exposing structural inequality and oppression (Vargas & Peet, 2024). Unlike traditional postpositivist quantitative research, which focuses on model verification, CritQuant focuses on model modification and equity by recognizing historical and social context (Stage, 2007). Its core goal is quantification for emancipation and transformation, using statistical approaches to generate evidence that necessitates systemic change (Baez, 2007). Another alternative approach to CritQuan is quantitative critical race theory (QuantCrit), which is an approach used to study and measure concepts and ideas drawn from critical race theory using quantitative methods (Frisby, 2024). However, in this article, the focus is on CritQuant inquiry.

What Are the Key Principles and Strategies for CritQuant Inquiry?

Key Principles of CritQuant

Diemer et al. (2025) outlined five key principles of CritQuant inquiry: foundation, goals, parity, subjectivity, and self-reflexivity. CritQuant must be thoroughly grounded in critical theory (foundation) to advance both theory and quantitative methodologies toward social critique (goals). It must reject positivism, recognizing that research is inherently subjective and political, holding no value-free or objective truth (parity and subjectivity). CritQuant inquiry demands that quantitative methods are not granted inherent rigor or truth over qualitative approaches, and that they require structured attention to researcher positionality throughout the entire inquiry (self-reflexivity).

Methodological Features of CritQuant

Tabron and Thomas (2023) reviewed 63 CritQuant studies from the field of education and identified 11 key strategies that are used in such studies. These strategies are briefly described below. For a detailed overview, readers are encouraged to read Tabron and Thomas (2023).

For Us, by Us

CritQuant is often conducted by researchers from historically marginalized communities who reclaim quantitative methods to construct new, authentic narratives of their groups. Motivated by feeling misrepresented or excluded by traditional statistics, this work embodies the “for us, by us” approach, centering voices previously unheard and marginalized. For many scholars of color, engaging in quantitative criticality is an act of resistance against the historical ownership of these research tools by white scholars.

Present Strength Narratives of Historically Marginalized Communities

CritQuant's central strategy is to construct strengths-based counter-narratives using data to actively resist white supremacy and deficit thinking in quantitative research. The intention of this type of inquiry is to refute racialized statistics and disrupt deficit narratives and racial generalizations.

Ground Truthing of the Data

Recognizing numbers can highlight patterns and provide vital analysis, but these are hypothetical and prone to misinterpretation if they are decontextualized and ahistorical. Therefore, quantitative data must be validated and made meaningful by connecting statistical insights to experiential knowledge and the everyday lives of marginalized populations.

Critique Social Categorizations

CritQuant inquiry involves challenging the assumed cause-and-effect relationships established by surveys, especially by arguing that social categories (e.g., race or gender) should be analyzed as socially constructed outcomes rather than simple, fixed causal factors.

Reject Oversimplification of Aggregate Data

CritQuant requires researchers to use single within-group analysis to avoid homogenizing diverse racial, ethnic, or identity groups and masking crucial internal variations. This approach deliberately avoids “gap gazing,” which is the problematic practice of using White groups as the baseline standard. Single within-group analysis ensures that research focuses on challenging systemic policies rather than perpetuating deficit stereotypes. By focusing on lived experience rather than simple group differences, this approach offers a more accurate understanding of variation within marginalized communities.

Build Multidimensional Statistical Models

To replace conventional models that yield inaccurate results for marginalized groups, researchers should build multidimensional statistical models. These models use dynamic and more detailed analyses to better describe complex constructs, and reflect the actual experiences of diverse subpopulations, which traditional tests often fail to capture.

Critically Interrogate Validation of Measures

This strategy uses cultural and equity-minded approaches for creating surveys, entailing that instruments are not predominantly designed, tested, and/or validated solely among White male populations, thereby enhancing relevance and accuracy for diverse groups.

End the Checkbox Culture in Survey Design and Administration

CritQuan researchers track changes in data policies and actively challenge the checkbox culture of surveys that inaccurately characterize marginalized communities by forcing diverse identities (e.g., 2SLGBTQIA+, Black individuals, or Latinx immigrants) into oversimplified or monolithic categories.

Present a Numerically Broad Picture

CritQuant research emphasizes the need for methodological breadth, urging researchers to use diverse statistical analyses to create multiple lines of evidence. This is vital for accurately estimating and interpreting results for diverse populations because single measures often produce conflicting or unreliable outcomes for the same concept.

Advanced Heterogeneity of Variance

CritQuant researchers deliberately reject the traditional assumption of homogeneity (that groups should vary equally). Instead, they accept heterogeneity of variance as an important source of information, not just a methodological nuisance. Recognizing that diversity is inherently unequal, they design novel statistical models to capture the complex contextual, structural, and historical variability within samples. This approach ensures that comparisons are based on authentic differences, rather than forcing groups into a statistically uniform standard.

Model Specification and Estimation

CritQuant researchers should use detailed statistical tools, such as regression, to build models that break down how diverse identities and social stratifications, such as race, gender, and immigration status, work together to influence the outcomes of marginalized groups. This allows them to move beyond simple averages and accurately estimate the effects of multiple marginalized social categorizations.

What Can CritQuant Inquiry Do for Nursing?

CritQuant is invaluable for nursing research, as it allows nurse researchers to move beyond traditional methods to address structural inequities in health. Nursing scholars can utilize this approach in multiple ways.

Exposing Health Disparities

CritQuant can be used to reclaim and reframe existing large datasets (like electronic health records or national health surveys) to expose how systemic policies and structural determinants of health drive disparities in patient outcomes, quality of care, and access to services for marginalized communities.

Validating Equity-Minded Measures

Researchers can critically interrogate and redesign clinical measurement tools and patient-reported outcome measures, ensuring that they are culturally and linguistically validated across diverse populations and moving away from instruments normed solely on privileged groups.

Building Multi-Level Models

By applying advanced, multidimensional statistical models, nursing research can analyze the complex, intersecting effects of social categories (e.g., race, gender identity, and socioeconomic status) on specific health phenomena, such as pain management, maternal health outcomes, or chronic disease progression, thereby generating evidence that necessitates systemic practice and policy change.