Journal of Engineering Education

Background: There have been calls to shift how engineering education researchers investigate the experiences of engineering students from racially minoritized groups. These conversations have primarily involved qualitative researchers, but an echo of equal magnitude from quantitative inquiry has been largely absent. Purpose: This paper examines the data analysis practices used in quantitative engineering education research related to broadening participation. We highlight practical issues and promising practices focused on "racial difference" during analysis. Scope/Method: We conducted a systematic literature review of methods employed by quantitative studies related to Black students participating in engineering and computer science at the undergraduate level. Person-centered analyses and variable-centered analyses, coined by Jack Block, were used as our categorization framework, backdropped with the principles of QuantCrit. Results: Forty-nine studies qualified for review. Although each article involved some variable-centered analysis, we found strategies authors used that aligned and did not align with person-centered analyses, including forming groups based on participant attitudes and using race as a variable, respectively. We highlight person-centered approaches as a tangible step for authors to engage meaningfully with QuantCrit in their data analysis decision-making. Conclusions: Our findings highlight four areas of consideration for advancing quantitative data analysis in engineering education: operationalizing race and racism, sample sizes and data binning, claims with race as a variable, and promoting descriptive studies. We contend that engaging in deeper thought with these four areas in quantitative inquiry can help researchers engage with the difficult choices inherent to quantitative analyses. National Science Foundation [EEC-1647327, EEC-1926935] Published version ACKNOWLEDGMENTS This material is based on work supported by the National Science Foundation under Award Numbers EEC-1647327 and EEC-1926935. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors thank the editorial board and anonymous reviewers for their constructive comments that improved the structure and content of this article.