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
