Don't Blame the Software: Using Qualitative Data Analysis Software Successfully in Doctoral Research


  • Michelle Salmona Institute for Mixed Methods Research
  • Dan Kaczynski Central Michigan University



doctoral education, qualitative methodology, dissertation research, qualitative data analysis software, QDAS, technology acceptance model, TAM, action research


In this article, we explore the learning experiences of doctoral candidates as they use qualitative data analysis software (QDAS). Of particular interest is the process of adopting technology during the development of research methodology. Using an action research approach, data was gathered over five years from advanced doctoral research candidates and supervisors. The technology acceptance model (TAM) was then applied as a theoretical analytic lens for better understanding how students interact with new technology.

Findings relate to two significant barriers which doctoral students confront: 1. aligning perceptions of ease of use and usefulness is essential in overcoming resistance to technological change; 2. transparency into the research process through technology promotes insights into methodological challenges. Transitioning through both barriers requires a competent foundation in qualitative research. The study acknowledges the importance of higher degree research, curriculum reform and doctoral supervision in post-graduate research training together with their interconnected relationships in support of high-quality inquiry.



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Author Biographies

Michelle Salmona, Institute for Mixed Methods Research

Michelle SALMONA is President of the Institute for Mixed Methods Research (IMMR) and an adjunct associate professor at the University of Canberra. With a background as a project management professional and a senior fellow of the Higher Education Academy, UK, she is a specialist in research design and methods. Michelle is a co-founder of IMMR, building global collaborations for grant development, and customized training and consultancy services for individuals and groups engaged in mixed-methods and qualitative analysis. Michelle works as an international consultant in: program evaluation; research design; and mixed-methods and qualitative data analysis using software. Her research focus is to better understand how to support doctoral success and strengthen the research process; and build data-driven decision making capacity in the corporate world. Michelle's particular interests relate to the relationship between technology and doctoral success. Recent research includes exploring the changing practices of qualitative research during the dissertation phase of doctoral studies, and investigates how we bring learning into the use of technology during the research process. Michelle is currently working on different projects with researchers from education, information systems, business communication, leadership, and finance.

Dan Kaczynski, Central Michigan University

Dan KACZYNSKI is a professor of educational leadership at Central Michigan University and an adjunct professor at the University of Canberra, Australia. Dan is a senior research fellow at the Institute for Mixed Methods Research. His publications and presentations promote technological innovations in qualitative data analysis, professional development for doctoral supervision, and is actively engaged in applied research and program evaluation.




How to Cite

Salmona, M., & Kaczynski, D. (2016). Don’t Blame the Software: Using Qualitative Data Analysis Software Successfully in Doctoral Research. Forum Qualitative Sozialforschung Forum: Qualitative Social Research, 17(3).



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