Accelerating Mixed Methods Research With Natural Language Processing of Big Text Data

• Published in: Journal of mixed methods research Vol. 15; no. 3; pp. 398 - 412
• Main Authors: Chang, Tammy, DeJonckheere, Melissa, Vydiswaran, V. G. Vinod, Li, Jiazhao, Buis, Lorraine R., Guetterman, Timothy C.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.07.2021; SAGE PUBLICATIONS, INC
• Subjects: Big Data; Change; COVID-19; Data; Health behavior; Health education; Health research; Interdisciplinary aspects; Language shift; Mixed Methods Research; Natural Language Processing; Research methodology; Social behavior; Social change; Social psychology; Technology; mixed methods research; natural language processing; content analysis; big data; qualitative analysis
• ISSN: 1558-6898; 1558-6901
• DOI: 10.1177/15586898211021196

Situations of catastrophic social change, such as COVID-19, raise complex, interdisciplinary research questions that intersect health, education, economics, psychology, and social behavior and require mixed methods research. The pandemic has been a quickly evolving phenomenon, which pressures the time necessary to perform mixed methods research. Natural language processing (NLP) is a promising solution that leverages computational approaches to analyze textual data in “natural language.” The aim of this article is to introduce NLP as an innovative technology to assist with the rapid mixed methods analysis of textual big data in times of catastrophic change. The contribution of this article is illustrating how NLP is a type of mixed methods analysis and making recommendations for its use in mixed methods research.