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

Autor/inn/en	Chang, Tammy; DeJonckheere, Melissa; Vydiswaran, V. G. Vinod; Li, Jiazhao; Buis, Lorraine R.; Guetterman, Timothy C.
Titel	Accelerating Mixed Methods Research with Natural Language Processing of Big Text Data
Quelle	In: Journal of Mixed Methods Research, 15 (2021) 3, S.398-412 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (DeJonckheere, Melissa) ORCID (Vydiswaran, V. G. Vinod) ORCID (Li, Jiazhao) ORCID (Guetterman, Timothy C.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1558-6898
DOI	10.1177/15586898211021196
Schlagwörter	Mixed Methods Research; Natural Language Processing; Data; Change + Suchen Sie Ihr Suchwort? Natürliche Sprache; Daten; Wandel
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01