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Abstract

Authors and researchers have many alternatives to consider when seeking reporting guidance or assessing the thoroughness of reporting in published qualitative research articles.  However, widely used reporting guidance and checklist processes such as CASP, COREQ, SRQR, and the American Psychological Association JARS-Qual have few prompts related to researcher use of Qualitative Data Analysis Software (QDAS) for data preparation, processing, or analysis. The purpose of this paper is to describe the development of the Qualitative Software Article Data Analysis Checklist (QS-ADAC), a tool developed to provide authors with reporting guidance and to provide students with a consistent process for examining details provided in published research with respect to software-facilitated data analysis. The QS-ADAC also includes sections to guide users to describe processes and outcomes of the use of data analysis software, regardless of the qualitative approach and the nature of the data.  In this paper, I compare QS-ADAC with existing checklists, describe the published resources used to inform components of the QS-ADAC, and identify strengths and limitations of the QS-ADAC. The QS-ADAC, now in its second version, is provided as an Appendix for interested readers to use in their research or research instruction.

Keywords

qualitative research, qualitative data analysis software, reporting guidance, artificial intelligence, research instruction

Author Bio(s)

Dr. Sheryl Chatfield is an Associate Professor in the College of Public Health at Kent State University and Co-Coordinator of the Kent State University Graduate Certificate in Qualitative Research. She welcomes feedback and comments regarding this article and the QS-ADAC checklist. Contact information: schatfi1@kent.edu, Kent State University, College of Public Health, Moulton Hall, 800 Hilltop Drive, Kent, OH, USA 44242.  

Acknowledgements

A portion of this work was previously presented at TQR: The Qualitative Report 16th Annual Conference at Nova Southeastern University, FL, USA, in March of 2025.

Publication Date

5-19-2026

Creative Commons License

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

ORCID ID

0000-0002-0893-7469

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