Analysis of the social consequences and value implications of the Everyday Discrimination Scale (EDS): implications for measurement of discrimination in health research. Issue 3 (2nd September 2022)

• Record Type: Journal Article
• Authors: Slemon, Allie; Susan Dahinten, V.; Stones, Cheyanne; Bungay, Vicky; Varcoe, Colleen
• Is Part Of: Health sociology review. Volume 31:Issue 3(2022)
• Page Start: 247
• Page End: 261
• Publication Date: 2022-09-02
• Subjects: Everyday Discrimination Scale (EDS) -- discrimination -- health research -- psychometrics -- intersectionality; Public health -- Periodicals; Social medicine -- Periodicals; Sociology, Medical -- Periodicals; Public health; Social medicine; Periodicals; 362.1042
• DOI: 10.1080/14461242.2021.1969980
• Languages: English
• ISSNs: 1446-1242

Abstract

ABSTRACT: The Everyday Discrimination Scale (EDS) is one of the most widely used measures of discrimination in health research, and has been useful for capturing the impact of discrimination on health. However, psychometric analysis of this measure has been predominantly among Black Americans, with limited examination of its effectiveness in capturing discrimination against other social groups. This paper explores the theoretical and historical foundations of the EDS, and draws on the analytic framework of Messick's theory of unified validity to examine the effectiveness of the EDS in capturing diverse experiences of discrimination. Encompassing both social consequences and value implications, Messick's unified validity contends that psychometric evaluation alone is insufficient to justify instrument use or ensure social resonance of findings. We argue that despite the robust psychometric properties and utility in addressing anti-Black race-related discrimination, the theoretical foundations and research use of the EDS have yet to respond to current discrimination theory, particularly intersectionality. This paper concludes with guidance for researchers in using the EDS in health research across diverse populations, including in data collection, analysis, and presentation of findings.