This paper offers an approach to enhancing trustworthiness of qualitative findings through data analysis triangulation using Leximancer, a text mining software that uses co-occurrence to conduct semantic and relational analyses of text corpuses to identify concepts, themes, and how they relate to one another. This study explores the usefulness of Leximancer for triangulation by examining 309 pages of previously analyzed interview data that resulted in a conceptual model. Findings show Leximancer to be an ideal tool for refining a priori conceptual models. The Leximancer analysis provided missing nuance from the a priori model, depicting the value of and connection between emergent themes. Dependability was also added to the findings by facilitating a better understanding of how participant quotes represent particular themes.


Leximancer, Qualitative Research, Triangulation, Trustworthiness, CAQDAS, Employee Engagement

Author Bio(s)

Laura L. Lemon, Ph.D. is an assistant professor of public relations at The University of Alabama. She received her Ph.D. in Communication and Information from the University of Tennessee. Her research interests include public relations, employee engagement, internal communication, social media, and mindfulness. She completed her M.A. in Communication at the University of Colorado Denver and a B.A. in Organizational Communication at Pepperdine University. Prior to pursuing her Ph.D., Dr. Lemon spent over seven years assisting non-profit organizations in Colorado with public relations initiatives. Correspondence regarding this article can be addressed directly to: lemon@apr.ua.edu.

Jameson Hayes is the Director of the Public Opinion Lab and an assistant professor in the Department of Advertising + Public Relations at The University of Alabama. Jameson’s research specialization is brand communication within emerging media specifically where brand and interpersonal relationships intersect. His research has been featured in a variety of advertising, marketing and communication journals including Journal of Advertising, International Journal of Advertising, Journal of Interactive Marketing, and Journal of Interactive Advertising (ORCID ID: 0000-0001-8571-3170).

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





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