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MEASURING EVERYDAY RACIAL/ETHNIC DISCRIMINATION IN HEALTH SURVEYS: How Best to Ask the Questions, in One or Two Stages, Across Multiple Racial/Ethnic Groups?
Open accessAbstract
While it is clear that self-reported racial/ethnic discrimination is related to illness, there are challenges in measuring self-reported discrimination or unfair treatment. In the present study, we evaluate the psychometric properties of a self-reported instrument across racial/ethnic groups in a population-based sample, and we test and interpret findings from applying two different widely-used approaches to asking about discrimination and unfair treatment. Even though we found that the subset of items we tested tap into a single underlying concept, we also found that different groups are more likely to report on different aspects of discrimination. Whether race is mentioned in the survey question affects both frequency and mean scores of reports of racial/ethnic discrimination. Our findings suggest caution to researchers when comparing studies that have used different approaches to measure racial/ethnic discrimination and allow us to suggest practical empirical guidelines for measuring and analyzing racial/ethnic discrimination. No less important, we have developed a self-reported measure of recent racial/ethnic discrimination that functions well in a range of different racial/ethnic groups and makes it possible to compare how racial/ethnic discrimination is associated with health disparities among multiple racial/ethnic groups.
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“The MDM was developed using a sample representative of the US population (approximately 40% POC) and has shown good internal consistency (α=.81-.88) in a previous study [59].”
From App-Based Mindfulness Meditation for People of Color Who Experience Race-Related Stress: Protocol for a Randomized Controlled Trial (2022) · cites this paper as [59]
Experimental — accuracy not yet measuredIn the citing paper
“such measures are often deployed in limited ways comparing care or outcomes of care for specific groups of people defined by a single demographic characteristic (e.g., a particular ethnocultural or racialized group) [15, 16].”
From Using a health equity lens to measure patient experiences of care in diverse health care settings (2024) · cites this paper as [15]
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Metadata
- Authors
- Shariff-Marco, S., Breen, N., Landrine, H., Reeve, B. B., Krieger, N., Gee, G. C., Williams, D. R., Mays, V. M., Ponce, N. A., Alegría, M., Liu, B., Willis, G., & Johnson, T. P.
- Affiliations
- National Cancer Institute (UA)
- East Carolina University (US)
- Harvard University (US)
- University of California, Los Angeles (US)
- University of Illinois Chicago (US)
- ORCID
- Venue
- Du Bois Review: Social Science Research on Race
- Publisher
- Cambridge University Press (CUP)
- Published
- 2011
- Volume
- 8
- Issue
- 1
- Pages
- 159-177
- ISSN
- 1742-058X (ISSN-L)
- 1742-0598
- PMC
- PMC5771490
- OpenAlex
- W2185610270
- Cited by
- 155
- References
- 69
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 13 · 2023: 13 · 2022: 19 · 2021: 24 · 2020: 8 · 2019: 12 · 2018: 9 · 2017: 6 · 2016: 6 · 2015: 12 · 2014: 7 · 2013: 3 · 2012: 7
- FWCI
- 19.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Du Bois Review Social Science Research on Race (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 MD999999)
- Topics
- Social Sciences › Sociology and Political Science › Racial and Ethnic Identity Research
- Social Sciences › Health › Health disparities and outcomes
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- racial and ethnic discrimination, self-reported discrimination, unfair treatment, psychometric properties, discrimination measurement, health disparities, survey question wording, cross-group comparison, health survey
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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