Best of Both Worlds: Affordances of Mixing Machine Learning and Qualitative Content Analysis

• In Event: Tools for Data Collection and Analysis in Mixed-Methods Research

Abstract

We describe an equivalent status concurrent mixed methodological analytic design facilitating triangulation and complementarity (Creswell, Plano Clark, Gutmann, & Hanson, 2003; Greene, Caracelli, & Graham, 1989). Our primary focus in this proposal is to describe our efforts to integrate a quantitative artificial intelligence/machine learning approach to analyze text data with a qualitative content analysis approach to analyze that same data. Our review of the literature revealed that the combination of artificial intelligence/machine learning and qualitative content analysis is rare and possibly novel. We are most interested in the affordances of each approach and how they bring different aspects of the data into focus so that an integrated approach provides a view that would not be visible with either approach alone.

Authors

• Reagan Curtis, West Virginia University

• Abhik Roy

• Michelle Richards-Babb, West Virginia University

• Carinna F. Ferguson, West Virginia University