Hi all!

I have  been doing some readings for my Advanced Qualitative Methods Class and run into some interesting remarks about the challenges of  qualitative data analysis. I though I would share this with you. If you are still to dive into data analysis for your projects, I think these are good references to have as they offered many strategies to cope with the challenges of analyzing qualitative data.

The readings brought forth the idea that the steps and rationale of qualitative data analysis is often obscured in research reports.  There is no widespread understanding in the field as to how qualitative analysis is to be done. Can there ever be such an understanding? Given the very nature of qualitative analysis, no single cookbook is possible, but some strategies have been proposed by various researchers and have been proven helpful in aiding analysis of data.

Bulmer (1979) discusses concept generation, referring to previous work from other researchers who attempted to address the “categorization paradox” and the problem of validating concepts defined/used in qualitative analysis. The “sensitizing” concepts of Blumer, the “analytical induction” of Znaniecki, and the “grounded theory” of Glaser and Strauss are all, within their limitations, sources of insight for thinking about concept validation, as they bring forth the importance of conceptualizing in a way that is faithful to the data collected. I believe this was important to the development of inductive research in more rigid ways that allowed for appropriate generalizations.

Since then, other publications have emphasized the practice of qualitative data analysis and strategies to consider along the way (e.g. Emerson et al. 1995; Lofland et al. 1984; Weiss 1995).  Developments have been made in discussing concerns about data faithfulness and its interplay with the subjectivity of the researcher. I particularly like the Lofland et al.  (1984) definition of analysis as a transformative process, turning raw data into “findings/results”. Here the researcher is a central agent in the inductive analysis process, which is highly interactive, labor intensive and time consuming, and therefore requires a systematic approach to analyzing data in order to account for the interplay between the data and the researcher-produced theoretical constructs.  The authors suggest a few strategies to use while analyzing data, two of which I would like to elaborate on here: normalizing and managing anxiety and memoing.

I have read many qualitative methods materials and they all discuss the need for the qualitative researcher to recognize and be aware of his/her subjectivity in the course of preparing for, conducting and writing about a research problem.  Lofland et al. (1984) touches further on a point that I now believe to be key to subjective interference in data analysis, the issue of researcher anxiety.  At first it seemed to be an overstatement, but the more I read the more I found substance in the issue. Understanding a social situation is no easy task and requires an open-ended approach that can cause much anxiety as the researcher is confronted with the challenge of finding significance in the materials. Ethical and emotional issues come into play in the midst of making sense and organizing a rapidly growing body of data and they can negatively affect the research experience if not dealt with properly.

The authors emphasized five anxiety-management principles for researchers to think about: 1) recognition and acceptance of anxiety; 2) Start analysis during data collection; 3) be persistent and methodical; 4) accumulation of information, at minimum, will ensure some content to talk about; 5) Discuss with others in same situation.   These strategies really addressed my worries regarding the process of data analysis. High emotions, fears, and wanting to quit are all part of anxiety reactions I have been feeling myself.  I believe starting early and being methodological and persistent are key strategies to deal with anxiety issues because it can assure you have time to address the challenges, make changes and not be so frustrated in the course of doing so.

If starting early, initial coding can be done in advance of starting focused coding, giving the researcher time away from the data that may needed to reduce anxiety. Early coding assures the possibility for early memos, which can help clarify connections along the way and assure persistence will prevail due to observable progress. I believe memos are the start of the  “transformative process’’ that Lofland et al. (1984) were referring to while defining data analysis. It is the bridge between the data and the researcher’s meanings, a first draft of a completed analysis where the interplay between data and theoretical constructs take place. Consequently, writing memos become necessary rather than optional.

Both Lofland et al. (1984) and Emerson et al. (2011) extensively discuss the memoing process. Operational memos are notes to self about research procedures and strategies. Code memos clarify assumptions underlying written codes. Theoretical memos record the researcher’s ideas about the codes and relationships. These are the memos that can take place even before coding starts, and that provide the basis for the “integrative” memoing that Emerson et al. (2011) refer to as they talk about identifying, developing, and modifying broader analytic themes and arguments into narrower focused core themes. Furthermore, while Lofland et al. (1984) explores the art of writing memos, Emerson et al. (2011) emphasizes the “reading” of memos, and the importance of reading notes as a whole and in the order they were written as beneficial to this integrative process of making meaning. This aspect added a fourth layer of subjectivity in addition to the layers of observing, deciding and writing about a phenomenon – the layer of reading and making sense of them.

In the course of doing so, the researcher’s assumptions, interests and theoretical commitments influence analytical decisions. In this sense, data analysis is not just a matter of “discovering” but a matter of giving priority to certain incidents and events from data materials in order to understand them in a given case or in relationship to other events.  This idea is interesting to me as I used to think of theoretical constructs emerging from the data in a process of discovery, and now I see it as a process of immersion. The researcher not only can immerse him/herself in the phenomenon being studied during data collection, be he/she is also immersed during data analysis as these inseparable subjective decisions shape the theoretical constructs. While I still think there is an aspect of discovery, it is somewhat created rather than naturally occurring.

In sum, there are several methodological attempts to clarify the logic of qualitative data analysis. However, the use of such guidelines and strategies are not very transparent in research reports and one may be left wondering about how the data analysis was actually done, how exactly the concepts came to be in a given study. Nevertheless, such methodological strategies highly emphasize the interplay between concept use and empirical data observation. Although a logical process does take place in analysis and it is indeed crucial to the systematization of ideas and formation of concepts, it seems to me this process is as logical as the researcher makes it within his/her sociological orientation, the study of substantive framework and the nature of the phenomenon in study. In this sense, nothing is really created but transformed through a logical theorizing process that is unique to the research in question.  Nothing is discovered by chance, qualitative analysis is rather an “analytical” discovery.



Bulmer, M. (1979). Concepts in the analysis of qualitative data. Sociological Review, 27(4), 651-677.

Emerson, R. M.; Fretz, R. I.;  & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. University of Chicago Press, Chicago, IL.

Glaser, B. G. & Strauss, A. L. (1967). The discovery of grounded theory: strategies for qualitative research. Aldine de Gruyter.

Lofland, J.; Snow, D.; Anderson, L. & Lofland, L. (2011). Analyzing Social Settings: a guide to qualitative observation and analysis. Wadsworth.

Weiss, R. S. (1995). Learning from strangers: The art and method of qualitative interview studies. Simon and Schuster Inc. New York.


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