Race, ethnicity, and considerations for data collection and analysis in research studies

Abstract

Research studies involving human subjects require collection of and reporting on demographic data related to race and ethnicity. However, existing practices lack standardized guidelines, leading to misrepresentation and biased inferences and conclusions for underrepresented populations in research studies. For instance, sometimes there is a misconception that self-reported racial or ethnic identity may be treated as a biological variable with underlying genetic implications, overlooking its role as a social construct reflecting lived experiences of specific populations. In this manuscript, we use the We All Count data equity framework, which organizes data projects across seven stages: Funding, Motivation, Project Design, Data Collection, Analysis, Reporting, and Communication. Focusing on data collection and analysis, we use examples - both real and hypothetical - to review common practice and provide critiques and alternative recommendations. Through these examples and recommendations, we hope to provide the reader with some ideas and a starting point as they consider embedding a lens of justice, equity, diversity, and inclusivity from research conception to dissemination of findings.

Keywords: Race; analysis; data collection; ethnicity; generalizability.

Figure 1.

Examples of “Table 1” from several publications.

Figure 2.

Examples of data collection form segments to solicit race and ethnicity information from a study participant.

Figure 3.

Visual of hypothetical example involving controlling for ethnicity versus heterogeneity of treatment effect.