Open-ended responses are widely used to explore and understand participants’ experiences and perspectives in a variety of fields. As one of the most powerful computer-assisted qualitative data analysis software, NVivo allows researchers to analyze open-ended responses to survey and/or interview questions, as well as other text data like reflective writing, image, and videos. The purpose of this paper is to describe and demonstrate how the NVivo word frequency, text search, and matrix coding features can be used to analyze qualitative data from a longitudinal evaluation project. The authors show how the matrix coding feature maximizes NVivo utilities in an analysis of open-ended responses and highlights differences across and within participants’ groups. The authors explain this approach by presenting a step by step overview: data cleaning and case coding; data import; word frequency analysis; text coding and reference extracting; and matrix coding and inductive analysis. Using this approach, the Clinical Translational Science Institute (CTSI) evaluation team acquired deeper insight into the participants’ experiences and perspectives about CTSI programs and received insights that may lead to improvement. From a methodological perspective, this approach capitalizes on NVivo’s features to mine qualitative data. The methodology described in this paper is applicable to other educational or program evaluations. Also, it is appropriate for analyzing large samples or longitudinal qualitative data in marketing and management.


NVivo, Matrix Coding, Qualitative Method, Open-Ended Responses, Large Sample Size Data

Author Bio(s)

Xiaoying Feng, Ph.D. is a graduate of University of Florida; Senior Analyst, Avar Consulting, Inc.; Contract Researcher, American Institutes for Research. Correspondence regarding this article can be addressed directly to: fengxy@ufl.edu.

Linda S. Behar-Horenstein, Ph.D. is Professor Emeritus, University of Florida. Correspondence regarding this article can also be addressed directly to: lsbhoren@ufl.edu.


Research reported in this publication was supported by the University of Florida Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing Translational Sciences under award number UL1TR001427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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