Visualizing Racism: Towards a QuantCrit Epistemic Network Analysis

• In Event: Overcoming Master Narratives: Unforgetting, Reimagining, and Reconstructing Through Quantitative Ethnography

Abstract

Objectives
This paper introduces QuantCrit Epistemic Network Analysis (QENA), an innovative methodological framework that synthesizes Quantitative Critical Race Theory (QuantCrit), Quantitative Ethnography (QE), and Epistemic Network Analysis (ENA). The objective is to interrogate and visualize how race and ethnicity inform meaning-making in educational discourse, centering epistemologies from Communities of Color while challenging dominant statistical paradigms (Gillborn et al., 2018; Garcia et al., 2018). Inspired by W.E.B. Du Bois’s groundbreaking use of data visualizations to counter racial pseudoscience (Battle-Baptiste & Rusert, 2018; Morris, 2015), this work explores how computational tools can expose racialized knowledge structures and advance anti-racist research practices.
Theoretical Framework(s)
QENA is grounded in three intersecting frameworks. Critical Race Theory (CRT) highlights the centrality of race and racism, critiques dominant ideologies such as colorblindness and meritocracy, and promotes social justice and equity in education (Delgado & Stefancic, 2012; Ladson-Billings & Tate, 1995). QuantCrit extends CRT into quantitative research, interrogating how statistical practices historically reinforced white supremacy and advocating for data practices that align with anti-racist commitments (Gillborn et al., 2018; Garcia et al., 2018). Quantitative Ethnography (QE) and Epistemic Network Analysis (ENA) provide methodological tools to model and visualize meaning-making processes, allowing researchers to map complex relationships among coded concepts within discourse and reveal racialized epistemic structures (Shaffer, 2017; Shaffer et al., 2016).
Methods
This study employs QENA to analyze discourse data from Puerto Rican and Chicanx undergraduate students reflecting on disaster preparedness during the COVID-19 pandemic. ENA was used to model and visualize co-occurrences among concepts such as community, trauma, and degree aspiration. Nodes represented racialized lived experiences (e.g., Support, Trauma), structural inequities (e.g., Disaster Unpreparedness), and cultural knowledge (e.g., Community), while edges illustrated how participants constructed meaning through their experiences with race, resilience, and resistance in educational spaces (Tan et al., 2022; Shaffer et al., 2016).
Findings
The Puerto Rican ENA model revealed collective resilience, with strong connections among Disaster Unpreparedness, Support, and Community, suggesting systemic critique and community-based responses (Figure a). The Chicanx ENA model highlighted aspirational discourse informed by familial cultural capital and perseverance in higher education (Figure b). These findings underscore intraracial heterogeneity and challenge reductive racial categories in conventional quantitative research (Covarrubias & Vélez, 2013; Solórzano & Yosso, 2001).
Significance
QENA advances anti-racist quantitative inquiry by visualizing racialized knowledge structures and centering marginalized epistemologies. This approach critiques the assumed neutrality of quantitative techniques and offers computational tools for amplifying counter-narratives and fostering epistemic resistance in educational research (Bonilla-Silva, 2006). Building on Du Bois’s legacy, QENA positions data visualization as a site of resistance and liberation, encouraging methodological pluralism and community accountability (Morris, 2015; Battle-Baptiste & Rusert, 2018).

Author

• Nichole M. Garcia, Rutgers University