Abstract
Quantitative Critical Race Theory (QuantCrit) calls researchers to reconsider not only what quantitative analyses report, but also how analytic choices reproduce or disrupt racialized ways of knowing. This methods-focused article develops confidence interval analysis as a practical statistical strategy within a QuantCrit methodological framework. Confidence intervals are not inherently critical or anti-racist. Their value for QuantCrit emerges from how researchers use estimation, uncertainty, comparison, visualization, and interpretation. We demonstrate this approach with National Assessment of Educational Progress (NAEP) Long-Term Trend mathematics data for Black students ages 9, 13, and 17 from 1978 to 2012. Rather than treating White performance as the normative benchmark, the demonstration centers within-group estimates across time makes sampling uncertainty visible and separates statistical description from causal explanation. We describe a five-part application: define the population and comparison logic, report point estimates with interval estimates, interpret precision before significance, visualize change without constructing a racial hierarchy, and contextualize patterns through QuantCrit tenets. The NAEP example functions as a methodological demonstration rather than as the substantive endpoint of the article. We conclude by identifying the affordances, limitations, and reporting requirements of confidence intervals as part of a broader QuantCrit analytic praxis.
Recommended Citation
Young, Jamaal; Young, Jemimah; and Edosomwan, Kristian
(2026)
"Confidence Intervals as a QuantCrit Method: Reframing Longitudinal Educational Data Through Estimation, Uncertainty, and Within-Group Analysis,"
Journal of Research Initiatives: Vol. 9:
Iss.
3, Article 7.
Available at:
https://digitalcommons.uncfsu.edu/jri/vol9/iss3/7
Included in
Bilingual, Multilingual, and Multicultural Education Commons, Educational Assessment, Evaluation, and Research Commons, Science and Mathematics Education Commons