Using Clustering as a Tool: Mixed Methods in Qualitative Data Analysis

Authors

Laura Macia, University of PittsburghFollow

Abstract

In this article I discuss cluster analysis as an exploratory tool to support the identification of associations within qualitative data. While not appropriate for all qualitative projects, cluster analysis can be particularly helpful in identifying patterns where numerous cases are studied. I use as illustration a research project on Latino grievances to offer a detailed explanation of the main steps in cluster analysis, providing specific considerations for its use with qualitative data. I specifically describe the issues of data transformation, the choice of clustering methods and similarity measures, the identification of a cluster solution, and the interpretation of the data in a qualitative context.

Keywords

Cluster Analysis, Qualitative Analysis, Data Exploration, Mixed Methods

Author Bio(s)

I earned my PhD in Anthropology with a dissertation on the resources available to Latino immigrants in Southwestern Pennsylvania for managing conflicts and disputes. I am currently a Postdoctoral Associate at the Graduate School of Public Health, Behavioral and Community Health Sciences at the University of Pittsburgh. I have worked on research on Latino health in Southwestern Pennsylvania, an emerging immigrant community, for some years. My current research interest is intimate partner violence among Latinos in this area, and its relation to alcohol drinking. Correspondence regarding this article can be addressed directly to: Laura Macia at, 224C Parran Hall, 130 DeSoto Street, Pittsburgh, PA 15261; Phone: 412-383-8982; E-mail: lam60@pitt.edu.

Publication Date

7-20-2015

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

DOI

10.46743/2160-3715/2015.2201

Recommended APA Citation

Macia, L. (2015). Using Clustering as a Tool: Mixed Methods in Qualitative Data Analysis. The Qualitative Report, 20(7), 1083-1094. https://doi.org/10.46743/2160-3715/2015.2201