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Experiences of discrimination: Validity and reliability of a self-report measure for population health research on racism and health
Open accessAbstract
Population health research on racial discrimination is hampered by a paucity of psychometrically validated instruments that can be feasibly used in large-scale studies. We therefore sought to investigate the validity and reliability of a short self-report instrument, the "Experiences of Discrimination" (EOD) measure, based on a prior instrument used in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Study participants were drawn from a cohort of working class adults, age 25-64, based in the Greater Boston area, Massachusetts (USA). The main study analytic sample included 159 black, 249 Latino, and 208 white participants; the validation study included 98 African American and 110 Latino participants who completed a re-test survey two to four weeks after the initial survey. The main and validation survey instruments included the EOD and several single-item discrimination questions; the validation survey also included the Williams Major and Everyday discrimination measures. Key findings indicated the EOD can be validly and reliably employed. Scale reliability was high, as demonstrated by confirmatory factor analysis, Cronbach's alpha (0.74 or greater), and test-re-test reliability coefficients (0.70). Structural equation modeling demonstrated the EOD had the highest correlation (r=0.79) with an underlying discrimination construct compared to other self-report discrimination measures employed. It was significantly associated with psychological distress and tended to be associated with cigarette smoking among blacks and Latinos, and it was not associated with social desirability in either group. By contrast, single-item measures were notably less reliable and had low correlations with the multi-item measures. These results underscore the need for using validated, multi-item measures of experiences of racial discrimination and suggest the EOD may be one such measure that can be validly employed with working class African Americans and Latino Americans.
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Metadata
- Authors
- Krieger, N., Smith, K., Naishadham, D., Hartman, C., & Barbeau, E. M.
- Affiliations
- Harvard University (US)
- New England Research Institutes (US)
- Dana-Farber Cancer Institute (US)
- Corresponding author
- Krieger, Nancy
- Venue
- Social Science & Medicine
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 61
- Issue
- 7
- Pages
- 1576-1596
- ISSN
- 0277-9536 (ISSN-L)
- OpenAlex
- W2029880396
- Cited by
- 1,704
- References
- 49
- Citations by year
- 2026: 97 · 2025: 132 · 2024: 156 · 2023: 111 · 2022: 148 · 2021: 141 · 2020: 118 · 2019: 104 · 2018: 71 · 2017: 75 · 2016: 75 · 2015: 68 · 2014: 58 · 2013: 52 · 2012: 72
- FWCI
- 118.60
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Social Science & Medicine (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- University Libraries (University of Maryland) (submittedVersion)
- Digital Repository at the University of Maryland (University of Maryland College Park)
- Funders
- Chongqing University of Arts and Sciences
- National Institute for Occupational Safety and Health (1R01 OHO7366-01)
- 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 discrimination, racism and health, psychometric validation, scale reliability, scale validity, psychological distress, cigarette smoking, African Americans, Hispanic Americans, social desirability
- MeSH terms
- Adult, Boston, Employment, Female, Health Status Indicators, Humans, Male, Middle Aged, Population Surveillance, Prejudice, Surveys and Questionnaires, Self-Assessment, Cohort Studies
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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