Publication Date
1-1-2021
Document Type
Article
Publication Title
International Journal of Qualitative Methods
Volume
20
DOI
10.1177/16094069211019907
Abstract
In recent years, there has been a florescence of cross-cultural research using ethnographic and qualitative data. This cutting-edge work confronts a range of significant methodological challenges, but has not yet addressed how thematic analysis can be modified for use in cross-cultural ethnography. Thematic analysis is widely used in qualitative and mixed-methods research, yet is not currently well-adapted to cross-cultural ethnographic designs. We build on existing thematic analysis techniques to discuss a method to inductively identify metathemes (defined here as themes that occur across cultures). Identifying metathemes in cross-cultural research is important because metathemes enable researchers to use systematic comparisons to identify significant patterns in cross-cultural datasets and to describe those patterns in rich, contextually-specific ways. We demonstrate this method with data from a collaborative cross-cultural ethnographic research project (exploring weight-related stigma) that used the same sampling frame, interview protocol, and analytic process in four cross-cultural research sites in Samoa, Paraguay, Japan, and the United States. Detecting metathemes that transcend data collected in different languages, cultures, and sites, we discuss the benefits and challenges of qualitative metatheme analysis.
Funding Number
SBE-2017491
Funding Sponsor
National Science Foundation
Keywords
anthropology, coding, cross-cultural, ethnography, meta-code, meta-theme, metacode, qualitative, theme
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Department
Anthropology
Recommended Citation
Amber Wutich, Melissa Beresford, Cindi SturtzSreetharan, Alexandra Brewis, Sarah Trainer, and Jessica Hardin. "Metatheme Analysis: A Qualitative Method for Cross-Cultural Research" International Journal of Qualitative Methods (2021). https://doi.org/10.1177/16094069211019907