A Mixed Methods Study of Public Perception of Social Distancing: Integrating Qualitative and Computational Analyses for Text Data. (July 2021)

• Record Type: Journal Article
• Authors: Ho, Pauline; Chen, Kaiping; Shao, Anqi; Bao, Luye; Ai, Angela; Tarfa, Adati; Brossard, Dominique; Brown, Lori; Brauer, Markus
• Is Part Of: Journal of mixed methods research. Volume 15:Number 3(2021)
• Page Start: 374
• Page End: 397
• Publication Date: 2021-07
• Subjects: computational social science -- mixed methods research -- public health research -- qualitative method -- science communication; Mixed methods research -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Research -- Evaluation -- Periodicals; 507.21
• DOI: 10.1177/15586898211020862
• Languages: English
• ISSNs: 1558-6898

Abstract

In a rapidly changing public health crisis such as COVID-19, researchers need innovative approaches that can effectively link qualitative approaches and computational methods. In this article, computational and qualitative methods are used to analyze survey data collected in March 2020 ( n = 2, 270) to explore the content of persuasive messages and their relationship with self-reported health behavior—that is, social distancing. Results suggest that persuasive messages, based on participants' perspectives, vary by gender and race and are associated with self-reported health behavior. This article illustrates how qualitative analysis and structural topic modeling can be used in synergy in a public health study to understand the public's perception and behavior related to science issues. Implications for health communication and future research are discussed.