research analysis

Focus Group Data Analysis: Accounting for Participant Interaction

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 153-154).

The complexity of the substantive data resulting from the focus group discussion method is no small matter. For one thing, more and richer data sources typically stem from focus group research compared to the in-depth interview (IDI) method. Video recording, for instance, is more Focus group interaction analysiscommon in the in-person focus group method and requires special attention because it may include important nonverbal information beyond the substance of the words that were spoken. For example, the participants’ facial expressions may provide valuable insights in addition to what is manifest by the spoken words themselves.

A more profound contributor to the complexity of processing group discussion research is not a data source but a component that is the essence of the method: that is, the interactivity of the group participants. It is participant interaction that sets this method apart from the one-on-one IDI approach. From the perspective of the Total Quality Framework, complete and accurate analyses and interpretations of group discussions are achieved by expending the necessary time and effort to consider the group members’ interactions with each other and with the moderator.

Whether it is by way of video or transcriptions of the discussions, the dynamic interaction fostered by the group environment has the potential of offering the analyst views of the research outcomes that go beyond what is learned from the process of developing codes and identifying themes. Grønkjær et al. (2011) talk about analyzing “sequences of interactions” (e.g., “adjacency pairs,” a comment
from one participant followed by a response from another participant), stating that the analysis “revealed a variety of events that impacted on content” (p. 27). Other suggested means of studying group interaction include the template from Lehoux et al. (2006), discussed in “Accounting for Interactions in Focus Group Research”; asking relevant questions during the analysis, such as, “How did the group resolve disagreements?” (Stevens, 1996, p. 172); and, as espoused by Duggleby (2005) and complementing the work of Morrison-Beedy, Côté-Arsenault, and Feinstein (2001), the integration of participants’ interactions into the written transcripts, for example, incorporating both verbal and nonverbal behavior that more fully explains how participants reacted to each other’s and the moderator’s comments.

Whereas online discussions produce their own transcripts (i.e., the text is captured by way of the online platform), the in-person and telephone modes require one or more transcriptionists to commit the discussions to text. Roller and Lavrakas (2015, p. 35) discuss the necessary qualities of transcriptionists and the importance of embracing them as members of the research team. In addition to the six required characteristics outlined by Roller & Lavrakas, the transcriptionist in the group discussion method must be particularly attentive to the dynamics and interactivity of the discussion. To accomplish this complete task, the requirements of the transcriptionist need to go beyond their knowledge of the subject matter and extend to their know-how of the focus group method. Ideally, the person transcribing the discussions will be someone who has at least some experience as a moderator and can readily isolate interaction among participants and communicate, by way of the transcripts, what the interaction is and how it may have shifted the conversation. For example, a qualified transcriptionist would include any audible (or visual, if working from a video recording) cues from the group participants (e.g., sighs of exasperation or expressions of acceptance or agreement) that would provide the researcher with a clearer understanding of the dynamic environment than simply the words that were spoken.

Duggleby, W. (2005). What about focus group interaction data? Qualitative Health Research, 15(6), 832–840.

Grønkjær, M., Curtis, T., de Crespigny, C., & Delmar, C. (2011). Analysing group interaction in focus group research: Impact on content and the role of the moderator. Qualitative Studies, 2(1), 16–30.

Lehoux, P., Poland, B., & Daudelin, G. (2006). Focus group research and “the patient’s view.” Social Science & Medicine, 63(8), 2091–2104. https://doi.org/10.1016/j.socscimed.2006.05.016

Morrison-Beedy, D., Côté-Arsenault, D., & Feinstein, N. F. (2001). Maximizing results with focus groups: Moderator and analysis issues. Applied Nursing Research, 14(1), 48–53. https://doi.org/10.1053/apnr.2001.21081

Roller, M. R., & Lavrakas, P. J. (2015). Applied qualitative research design: A total quality framework approach. New York: Guilford Press.

Stevens, P. E. (1996). Focus groups: Collecting aggregate-level data to understand community health phenomena. Public Health Nursing, 13(3), 170–176. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8677232

 

Qualitative Analysis: A Reflexive Exercise for Category Development

The second component of the Total Quality Framework (TQF) is Analyzability. This component provides researchers with critical thinking considerations relevant to the completeness and accuracy of their analyses and interpretations of the data. Analyzability consists of two fundamental elements — processing and verification — the first of which involves coding followed by deriving categories and themes from the data.

From a TQF perspective, a useful exercise for category development — particularly when the study entails multiple researchers and a large amount of data — is by way of the reflexive template. Although similar in spirit to the writing function in computer-assisted qualitative data analysis software programs, the primary purpose of this reflexive template is to encourage researchers to actively reflect as they go about developing categories or buckets from the underlying constructs gained from the data. By way of the template, the analyst can document the relationship they perceive between the category and the construct as well as provide an example or further input to support their thinking.

For instance, a researcher conducting a qualitative content analysis study of diaries written by women confined to prison concerning their activities and experiences during confinement, may have derived the category “educational opportunity” (EDUOPPTY) from the coded data defined in part (i.e., along with other relevant constructs) by the underlying construct “well-being.” Within the well-being construct, the researcher also identified three key subconstructs — physical well-being, mental well-being, and financial well-being — that play a central role in understanding the meaning of the well-being construct as well as deepening the definition of the EDUOPPTY category. In this example, the reflexive exercise (by way of the template, see below) has facilitated the researcher’s ability to record the connections between the category and key constructs — highlighting instances of the relationship between EDUOPPTY (e.g., how to use the exercise equipment and art classes) and physical well-being, mental well-being, as well as financial well-being — while aiding collaboration with the research team and adding transparency to the analysis process.

Reflexive template for category development

The Qualitative Analysis Trap (or, Coding Until Blue in the Face)

There is a trap that is easy to fall into when conducting a thematic-style analysis of qualitative data. The trap revolves around coding and, specifically, the idea that after a general familiarization with the in-depth interview or focus group discussion content the researcher pores over the data scrupulously looking for anything deemed worthy of a code. If you think this process is daunting for the seasoned analyst who has categorized and themed many qualitative data sets, consider the newly initiated graduate student who is learning the process for the first time.

Recent dialog on social media suggests that graduate students, in particular, are susceptible to falling into the qualitative analysis trap, i.e., the belief that a well done analysis hinges on developing lots of codes and coding, coding, coding until…well, until the analyst is blue in the face. This is evident by overheard comments such as “I thought I finished coding but every day I am finding new content to code” and “My head is buzzing with all the possible directions for themes.”

Coding of course misses the point. The point of qualitative analysis is not to deconstruct the interview or discussion data into bits and pieces, i.e., codes, but rather to define the research question from participants’ perspectives Read Full Text