Academic journal article The Qualitative Report

Preparing a Qualitative Research-Based Dissertation: Lessons Learned

Academic journal article The Qualitative Report

Preparing a Qualitative Research-Based Dissertation: Lessons Learned

Article excerpt

In this article, a newly minted Ph.D. shares seven lessons learned during the process of preparing a dissertation based on qualitative research methods. While most of the lessons may be applicable to any kind of research, the writer focuses on the special challenges of employing a qualitative methodology. The lessons are: (1) Read, read, read; (2) Consult the experts; (3) Adhere to university regulations; (4) Pay attention to rigor and trustworthiness; (5) Give details of the methodology; (6) Don't be afraid to include numerical data; and (7) Prepare to publish. Key Words: Confirmability, Credibility, Dependability, Grounded Theory, Inductive Analysis, Transferability, and Trustworthiness


As the new millennium dawned, I made a decision: I would reach for something seemingly beyond my grasp. That special something turned out to be a doctoral degree. However, even after I had fully embarked on that upward journey of discovery, I had no inkling of the methodological challenges that would mark many milestones on that journey.

A social research neophyte, I have spent my adult life honing my skills and developing expertise as a journalist and public relations practitioner. Over the years, I have researched and written numerous news stories, feature articles, general-interest columns, and special reports for newspapers, magazines, and radio.

Researching and writing a dissertation--particularly one based on qualitative research methods--demanded a different set of skills and offered some special challenges because of its nature and scope. In reflecting on that experience, I can identify various lessons learned along the way.

During coursework, I learned all the quantitative stuff: descriptive statistics, t-test procedures, univariate and multivariate analyses of variance, chi-square test, regression analysis, factor analysis, structural equation modeling, and the like. What's more, I developed facility in Statistical Package for the Social Sciences (SPSS). Coursework also focused on basic matters like having a well-written research question; stating the purpose of the study (exploratory, descriptive, explanatory, or evaluative, or some combination); reviewing the literature thoroughly; and presenting a conceptual or theoretical framework for the study.

Guiding me on the early part of my journey, my statistics professor emphasized the "power" of numbers and the precision of measures characterizing quantitative studies. Like so many numbers-crunching researchers, my stats professor viewed qualitative research with suspicion. It seemed the good professor considered a methodology in which the generation of hypotheses often replaces the testing thereof, explanation replaces measurement, and understanding replaces the making of generalizations as "airy fairy" (not "real") research (Labuschagne, 2003).

Consequently, I became somewhat skeptical of this kind of research. In the end, though, I let the topic and goals of my research dictate the methodology. Fortunately, all four members of my dissertation committee (including the outgoing coordinator of the doctoral program) were enlightened enough to appreciate and support my choice of research methodology. Indeed, they emphasized the need for me to gather data reflecting the interactions and experiences of individuals and communities in relation to the research problem that I had identified. It was important to know quantitative research methodology and its assumptions, as well, so I could defend my choice of research design and methods. It is like preparing for a debate. To be effective, the debater had better know both (or all) sides of the issue. Before too long, it became clear to me that quantitative and qualitative research have distinct and complementary strengths. The main strength of qualitative research is that it yields data that provide depth and detail to create understanding of phenomena and lived experiences. …

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