Managing fear and anxiety in inductive analysis of qualitative data

There comes a time when qualitative researchers must begin working with the data that they have accumulated throughout a project, make sense of it, and present findings to others. Qualitative methodologists frequently recommend that the analytic process be pursued from the very beginning of a project – and implore researchers to begin data analysis while collecting data. Yet, even when researchers do this, sometimes feelings of anxiety and fear ensue. It’s even possible to lose the initial questions as one begins to follow ideas observable in one’s data set. What are strategies to manage the fear and anxiety that sometimes surround the process of makings sense of data?

Why does the analytic process in qualitative research sometimes produce researcher anxiety? Qualitative data analysis follows inductive logic. LeCompte and Preissle (1993, p. 42) comment that “purely inductive research begins with collection of data – empirical observations or measurements of some kind – and builds theoretical categories and propositions from relationships discovered among the data…That is, inductive research starts with examination of a phenomenon and then, from successive examinations of similar and dissimilar phenomena, develops a theory to explain what was studied.” Coffey and Atkinson (1996, p. 155) state that “inductivism is based on the presumption that laws or generalizations can be developed from the accumulation of observations and cases, that the close inspection of ever more data can be made to reveal regularities.” Put another way, Thomas Schwandt (2001, p. 125) writes that:

qualitative analysis often (but not always) seeks to construct hypotheses by mucking around for ideas and hunches in the data rather than by deriving those hypotheses in the first instance from established theory. Typically, however, qualitative analyses employ some combination of inductive and deductive analyses.

Here Schwandt is contrasting the analytic process used in qualitative research with the deductive logic used in research that tests hypotheses. Yet, as Schwandt points out above, qualitative analysis frequently relies on the application of theories and concepts from a discipline to interpret data, so in that sense purely inductive analysis is difficult to achieve. When researchers work back and forth from data to theory, this uses “abductive” reasoning. Coffey and Atkinson (1996, p. 156) explain the abductive process this way:

Abductive reasoning or inference implies that we start from the particular. We identify a particular phenomenon – a surprising or anomalous finding, perhaps. We then try to account for that phenomenon by relating it to broader concepts. We do so by inspecting our own experience our stock of knowledge of similar, comparable phenomena, and the equivalent stock of ideas that can be included from within our disciplines (including theories and frameworks).

For more information on inductive, abductive, and deductive reasoning, see Jo Reichertz’s chapter in Uwe Flick’s (2014) The Sage Handbook of Qualitative Data Analysis (chapter 9).

This is a rather long way to say that the reasoning processes involved in analyzing qualitative data mean that it not possible to know beforehand what we will find. Nor are there “right” answers to be found (although interpretations may be more or less convincing).  It takes systematic searching and thought, and going back and forward between literature in relevant fields (that provide concepts and theories to think with) and looking at the data collected to develop the findings in a project. Feeling comfortable with the process of doing analysis – whatever analytic approach is used (Flick’s (2014) handbook provides informative overviews of numerous analytic strategies to analyze different sorts of data) – involves ambiguity, and feelings of not knowing. For those who find the process of getting side-tracked and following leads that explore emergent questions more anxiety-producing than exciting, qualitative scholars have provided some tips.

Johnny Saldaña (2013, pp. 36-37) writes that in order to code data, qualitative researchers need particular abilities, including

  • Being organized
  • Exercising perseverance
  • Being able to deal with ambiguity
  • Exercising flexibility
  • Being creative
  • Being rigorously ethical
  • Having an extensive vocabulary.

I suspect that these qualities and abilities are useful for any researcher, whatever analytic methods are used, coding being but one approach.

I have found that a useful way to start the analytic process (particularly if I have been procrastinating!) is to begin with a data inventory. That is, I begin by organizing the data set in some way (whether in hard or digital copies), and making a detailed list or inventory of exactly what data has been generated and collected. When data are still being generated and collected in a study, this is an ongoing task that will likely not be completed until the final sentence of a report is written. Then of course, one must begin to make sense of the data. When I’m working on a data set, I typically experience a lack of surety about what I know from data collected, and I also find that the process always takes longer than I expect.

In their book on classroom inquiry, Hubbard and Power (2003, p. 198) use an insightful quote from Dorothy Allison’s memoir Two or three things I know for sure as a springboard to ask three questions in order to begin the process of making sense of data collected in classroom research.

“Lord, girl, there’s only two or three things I know for sure.” She put her head back, grinned, and made a small impatient noise. Her eyes glittered as bright as a sun reflecting off the scales of a cottonmouth’s back. She spat once and shrugged. “Only two or three things. That’s right,” she said. “Of course it’s never the same things, and I’m never as sure as I’d like to be.” (Allison, 1995, p. 5 cited in Hubbard & Power, 2003, p. 198).

If you are not sure where to begin, the questions suggested by Hubbard and Power may be helpful to any qualitative research project:

  • One finding I’m certain of in my research is….
  • What data led you to this conclusion?
  • What new data could make you change your mind?

Sociologists John and Lyn Lofland (1995, p. 185) write that

…as an inherently open-ended process, the situation of emergent induction can produce frustration and anxiety — as well as exhilaration. That is, the openness of the situation calls on the researcher to construct the social science order and, for some, that circumstance is fearsome. Success in forging such order in what at first can seem to be chaotic materials can seem impossible…

Writing in a later edition of the same book, Lofland and Lofland, writing with David Snow and Leon Anderson (2006, pp. 198-200) remind readers that “feelings of anxiety and difficulty in the face of open-ended tasks are commonplace,” and suggest a number of anxiety-reducing strategies:

  1. recognize and accept the fact that analyzing qualitative field data is neither a mechanical nor easy task, and therefore it is likely to generate anxiety.
  2. get started on analysis early in the data-collection phase of your project.
  3. work just as persistently and methodically at the task of analyzing your data as you did at collecting it.
  4. the sheer accumulation of information ensure that you will, at minimum, be able to say something, even if that something is not as analytic as you might like and is not known to you at the moment.
  5. develop a support group with others.

When faced with what seems like a mountain of qualitative data to work through and make meaning from, I am reminded of the saying: “The only way out is through.”

If you have tips for managing anxiety during the analytic process, please share those with others in the comment box below. And as always, best wishes with your research.

Kathy Roulston


Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data: Complementary research strategies. Thousand Oaks: Sage.

Flick, U. (Ed.) (2014). The SAGE handbook of qualitative data analysis. Los Angeles: Sage.

Hubbard, R. S., & Power, B. M. (2003). The art of classroom inquiry: A handbook for teacher-researchers. Portsmouth, NH: Heinemann.

LeCompte, M. D., & Preissle, J. (1993). Ethnography and qualitative design in educational research (2nd ed.). San Diego: Academic Press.

Lofland, J., & Lofland, L. (1995). Analyzing social settings: A guide to qualitative observation and analysis. Belmont, CA: Wadsworth Publishing Company.

Lofland, J., Snow, D., Anderson, L., & Lofland, L. H. (2006). Analyzing social settings: A guide to qualitative observation and analysis (4th ed.). Belmont, CA: Thomson, Wadsworth.

Saldaña, J. (2013). The coding manual for qualitative researchers (2nd ed.). Los Angeles: Sage.

Schwandt, T. A. (2001). Dictionary of qualitative inquiry (2nd ed.). Thousand Oaks, CA: Sage.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s