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Comparing Self-Reported Demographic and Sexual Behavioral Factors Among Men Who Have Sex with Men Recruited Through Mechanical Turk,Qualtrics, and a HIV/STI Clinic-Based Sample: Implications for Researchers and Providers
Authors:Matthew R. Beymer  Ian W. Holloway  Christian Grov
Affiliation:1.Division of Infectious Diseases, Geffen School of Medicine,University of California, Los Angeles,Los Angeles,USA;2.Los Angeles LGBT Center,Los Angeles,USA;3.Department of Social Welfare, Luskin School of Public Affairs,University of California, Los Angeles,Los Angeles,USA;4.City University of New York (CUNY) Graduate School of Public Health and Health Policy,New York City,USA
Abstract:Recruitment for HIV research among gay, bisexual, and other men who have sex with men (MSM) has increasingly moved to the online sphere. However, there are limited data comparing the characteristics of clinic-based respondents versus those recruited via online survey platforms. MSM were recruited from three sampling sites (STI clinic, MTurk, and Qualtrics) to participate in a survey from March 2015 to April 2016. Respondents were compared between each of the sampling sites on demographics, sexual history, substance use, and attention filter passage. Attention filter passage was high for the online sampling sites (MTurk = 93%; Qualtrics = 86%), but significantly lower for the clinic-based sampling site (72%). Clinic-based respondents were significantly more racially/ethnically diverse, reported lower income, and reported more unemployment than online respondents. Clinic-based respondents reported significantly more male sexual partners in the previous 3 months (M clinic-based = 6; MTurk = 3.6; Qualtrics = 4.5), a higher proportion of gonorrhea, chlamydia, and/or syphilis in the last year, and a greater proportion of methamphetamine use (clinic-based = 21%; MTurk = 5%), and inhaled nitrates use (clinic-based = 41%; MTurk = 11%). The clinic-based sample demonstrated more demographic diversity and a greater proportion of HIV risk behaviors when compared to the online samples, but also a relatively low attention filter passage rate. We recommend the use of attention filters across all modalities to assess response validity and urge caution with online survey engines as samples may differ demographically and behaviorally when compared to clinic-based respondents.
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