Full Citation
Title: Methodology of the Discrimination in the United States survey
Citation Type: Journal Article
Publication Year: 2019
ISBN:
ISSN:
DOI: 10.1111/1475-6773.13226
NSFID:
PMCID:
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Abstract: Objective: To describe survey methods used to examine reported experiences of dis- crimination against African Americans, Latinos, Asian Americans, Native Americans, women, and LGBTQ (lesbian, gay, bisexual, transgender, and queer) adults. Data Source and Study Design: Data came from a nationally representative, prob- ability-based telephone survey of 3453 US adults, conducted January-April 2017. Methods: We examined the survey instrument, sampling design, and weighting of the survey, and present selected survey findings. Principal Findings: Examining reported discrimination experienced by multiple groups in a telephone survey requires attention to details of sampling and weighting. In health care settings, 32 percent of African Americans reported discrimination, as did 23 percent of Native Americans, 20 percent of Latinos, 18 percent of women, 16 percent of LGBTQ adults, and 13 percent of Asian Americans. Also, 51 percent of LGBTQ adults, 42 percent of African Americans, and 38 percent of Native Americans reported identity-based violence against themselves or family members; 57 percent of African Americans and 41 percent of women reported discrimination in pay or pro- motions; 50 percent of African Americans, 29 percent of Native Americans, and 27 percent of Latinos reported being discriminated against in interactions with police. Conclusions: Even the small selection of results presented in this article as examples of survey measures show a pattern of substantial reported discrimination against all six groups studied.
Url: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1475-6773.13226
User Submitted?: No
Authors: Benson, John, M; Ben-Porath, Eran, N; Casey, Logan, S
Periodical (Full): Health Services Research
Issue:
Volume: 54
Pages: 1389-1398
Data Collections: IPUMS CPS
Topics: Health, Methodology and Data Collection, Population Data Science, Race and Ethnicity
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