Survey Item Response Rates by Survey Modality, Language, and Sociodemographic Factors in a Large U.S. Cohort - Rittase, Melissa; Kirkland, Elizabeth; Dudas, Daniela M; Patel, Alpa V (Cancer epidemiology, biomarkers & prevention, 2020)
Large-scale prospective cohorts traditionally use English, paper-based, mailed surveys, but Web-based surveys can lower costs and increase data quality, and multi-language surveys may aid in capturing diverse populations. Little evidence exists examining item response for multiple survey modalities or languages in epidemiologic cohorts. A total of 254,475 men and women completed a comprehensive lifestyle and medical survey at enrollment (2006-2013) for the Cancer Prevention Study-3, a U.S.-based prospective cohort. Web-based (English only) or paper (Spanish or English) surveys were offered. Using generalized linear models, differences in item response rates overall and by topical areas (e.g., reproductive history) by modality and language were examined. We further examined whether differences in response quality by sociodemographic characteristics within each survey modality existed. Overall, English Web-based surveys had the highest average item response rate (97.6%), followed by English paper (95.5%) and Spanish paper (83.1%). Lower item response rates were seen among nonwhite, lower income, or less-educated participants. When examining individual survey sections by topic, results varied the most for residential history, with the lowest item response rate among Spanish language respondents (women, 62.7% and men, 64.3%) and the highest in English language Web-based, followed by paper respondents (women, 94.6% and men, 95.3%; and women, 92.8% and men, 92.1%, respectively). This study supports that utilizing multimodal survey approaches in epidemiologic studies does not differentially affect data quality. However, for some topic areas, further analysis should be considered for assessing data quality differences in Spanish language surveys. Multimodal survey administration is effective in nondifferentially capturing high-quality data.