Qualitative Research Module 5 Discussion Topic

Consider the readings for this module concerning the analysis of case study data. In your post, address the following:

1. What three key ideas were most significant from the readings;
2. Two analytic techniques that you would like to explore or discuss further; and
3. One element/issue/concept that you found difficult in your understanding or application of case study data analysis.

In your responses to other students, focus on questions 2 and 3.

This assignment is a discussion, so remember to join the conversation early in the module. Remember to cite sources—particularly in your initial post. Finally, respond to several of your classmates.

Qualitative research results

The following readings are required. Optional readings can be found at the end of each section and while not required, may help you understand the material better and be useful to you if you choose to conduct a case study research method for your doctoral study. All readings can be accessed in the Trident Online library, unless linked to another source.

The importance of records

We have presented in previous modules methods of qualitative data collection including interviews, focus groups, surveys, documentary analysis, and observations. Each of these methods produce results in the form of records. Such records include transcripts of recorded interviews or focus groups, open-ended survey data, and field notes of observations. In the case of documentary analysis—the records previously exist—but they are organized and cataloged for analysis by the researcher. Qualitative researchers typically file all records in electronic folders or databases. Analysis is then conducted using either common productivity software such as Microsoft Office (or similar open-source package such as Open Office or Google Docs), or software designed specifically for qualitative data analysis such as nVivo or Atlas Ti.

Getting started with analysis

The basic goal of qualitative data analysis is to be able to see patterns in the data that may not be immediately obvious from surface inspection. Getting to this level of insight requires the application of a systematic approach. Such an approach ensures that the data is analyzed at the appropriate level of depth and that the process may be repeated by other researchers. Suggested steps include the following:

1. Read: Thoroughly and carefully read each line of the transcript, the document, or field notes. It is important at this stage to “take in” and reflect on what is being read and avoid jumping to conclusions.
2. Code: After an initial in-depth reading of the transcript or document, you will now seek to find ideas, passages, or expressions that stand out in some way. For example, were they emphasized by the research subject in some way? Is there any passage that appears to repeat similar ideas in multiple ways throughout the document? Is there any passage that is somehow striking in its relevance to the topic or subject under study? Passages associated with these (or other relevant questions) are highlighted and identified by a code word or number for tracking purposes. This activity is referred to as “coding” the data (Gibbs & Taylor, 2010).
3. Themes: After a number of codes have been identified, it is now time to consider to what degree, if any, each of the codes are related to each other. For example, is some of the coded data similar? Is there a common idea or principle being articulated? Alternatively, some codes may deal with similar topics but in different ways. The important activity in this next step is to attempt to discover themes by grouping together the codes assigned to highlighted passages. What results from the grouping of codes is the next level of analysis—the underlying themes being expressed in the data (Ryan & Bernard, 2003b).
4. Conceptual framework: The highest level of analysis is the conceptual framework. It is at this point that we begin to see the big picture emerge from the underlying data. This step of the analysis is also rather “tricky”. For example, if the researcher asserts that one theme is related to another in some way, then some level of explanation or rationale for the observed relationships must be suggested. One technique for identifying related themes is to do a simple frequency analysis identifying how often a particular theme appears—and in how many sources. It is not uncommon for themes with the highest totals to relate to each other in some way.

Steps 1-4 bring to mind the analogy of the building of a brick wall. At the most fundamental analysis, a brick wall consists of bricks. Likewise, in qualitative data analysis, we have “codes”. When we put bricks together in a certain way—we may see a pattern in the brick. Likewise, we see patterns emerge from qualitative data in the form of themes. Finally, once all bricks are put together, we end up with a wall. In qualitative research, we arrive at a unique combination of themes, built from codes, with “mortar” (in the form of our rationale for expressing the relationships between themes) cementing the themes together in a resulting conceptual framework. In the same way that a brick wall—and the patterns made by the brick—are tangible and visible—researchers typically create a graphical depiction of the conceptual framework. This may be as simple as presenting several text boxes with themes and descriptions linked together using lines or arrows to indicate observed relationships between the themes.

Dedoose

Dedoose (http://www.dedoose.com) is an inexpensive subscription-based software package that provides support for qualitative and mixed-method research. You will use Dedoose in your DBA program. Visit the Dedoose site and sign up for a trial subscription in order to use it for the data analysis conducted in the Case Assignment for this module.

What is going on? The conceptual framework

At the end of our qualitative data analysis, we can expect to examine how the themes come together, how they are related, and what the big picture looks like. In short, the resulting conceptual framework is our view, grounded in the data, of “What is going on here.” This is an essential step in theory building as conceptual frameworks may be refined into theory and then tested. For example, in quantitative research, we take a theory and test it. This is similar to stating, “This is what I think is going on here—and now I am going to test it.” Qualitative research—including case studies and action research—may benefit from a “pre” and “post” data analysis conceptual framework. For example, the researcher may state explicitly how the researcher views the problem or context under consideration prior to the data collection and analysis. The conceptual framework that results from the analysis may then be compared to the initial conceptual framework to clearly identify changes in understanding that have emerged from the qualitative data collection and analysis (Miles, Huberman, & Saldaña, 2014).

How do you know? A word on validity…

It could be argued—and often is argued—that deciding what to code, what to call a theme, and the building of a conceptual framework is a series of activities that are subjective in nature. What then should the researcher do in order to minimize subjectivity and to build validity? One answer is to use multiple sources of data. If multiple sources tend to align in a similar direction, this argues for validity. Also, one suggestion is to begin first with the most tangible data such as pre-existing written records or documents to ground the analysis in realism. Finally, it is always a good idea to use a focus group of stakeholders as a validation step to review the work that you have done in the thematic analysis and provide feedback and revision suggestions.

What does the end product look like?

Research based on qualitative data analysis not only presents findings in the form of a conceptual framework, but also walks the reader through the data itself. For example, the researcher should identify the most common themes, and discuss the thematic findings. Further, it is a good validation step to use one or more direct quotes from transcript analysis to give the reader a “taste” of the type of data found in the analysis. It is also a good idea, in addition to presenting the themes, to describe to the reader how relationships between themes were determined. For example, if a frequency analysis of themes and their appearance was performed, then present it in the final results. Finally, remember that qualitative data is characterized by rich description, graphical depiction, and in-depth discussion. The strength of the paper is not only in the data collection process, but how well the emergent themes and ideas are described and presented.

Gibbs, G. R., & Taylor, C. (2010, February 19). NEW. Retrieved December 03, 2016, from http://onlineqda.hud.ac.uk/Intro_QDA/how_what_to_c…

Yin, R.K. (2009). Analyzing case study evidence. In Case Study Research: Design and Methods, Fourth Ed. (pp. 126-163). Thousand Oaks, CA: Sage Inc.

Gagnon, Y. (2010). Stage 6: Analyzing data . In The Case Study As Research Method : A Practical Handbook (pp. 69-82). Québec [Que.]: Les Presses de l’Université du Québec (EBSCO ebook Collection)

Gagnon, Y. (2010). Stage 7: Interpreting data. In The Case Study As Research Method : A Practical

Handbook (pp.83-92). Québec [Que.]: Les Presses de l’Université du Québec (EBSCO ebook Collection)

Farquhar, J. D. (2012). Managing and analysing data. In Case study research for business (pp. 84-99). London : SAGE Publications Ltd (SAGE Research Methods Database)

Optional Reading

Beverland, M. & Lindgreen, A. (2010). What makes a good case study? A positivist review of qualitative case research published in Industrial Marketing Management, 1971-2006. Industrial Marketing Management, 39(1), 59-63.

Easton, G. (2005). Critical realism in case study research. Industrial marketing Management, 39(1), 118-128. (Science Direct DataBase)

Gillham, B. (2000). Case Study Research Methods. London: Continuum (EBSCO eBook Collection)

Johnson, P., Buehring, A., Cassell, C. & Symon, G. (2006). Evaluating qualitative management research: Toward a contingent criteriology. International Journal of Management Review, 8(3), 131-156. (EBSCO: Business Source Complete Database)

Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook. Thousand Oaks, Califorinia: SAGE Publications.

Ryan, G.W. and Bernard, H.R. (2003b) ‘Techniques to Identify Themes’, Field Methods, 15(1): 85-109.

Strauss, Anselm and Corbin, Juliet (1990) Basics of Qualitative Research. Grounded Theory Procedures and Techniques. Newbury Park, CA: Sage. (2nd Ed. 1998)