research analysis

Actively Conducting an Analysis to Construct an Interpretation

It is not uncommon for researchers who are reporting the results of their quantitative studies to go beyond describing their numerical data and attempt to interpret the meaning associated with this data. For example, in a survey concerning services at a healthcare facility, the portion of respondents who selected the midpoint on a five-point scale to rate the improvement of these services from the year before might be interpreted as having a neutral opinion, i.e., these respondents believe the caliber of services has remained the same, neither better nor worse than a year earlier. And yet there are other interpretations of the midpoint response that may be equally viable. These respondents may not know whether the services have improved or not (e.g., they were not qualified to answer the question). Or, these respondents may believe that the services have gotten worse but are reluctant to give a negative opinion.

Survey researchers fall into this gray area of interpretation because they often lack the tools to build a knowledgeable understanding of vague data types, such as scale midpoints. Unless the study is a hybrid research design (i.e., a quantitative study that incorporates qualitative components), the researcher is left to guess respondents’ meaning.

In contrast, the unique attributes of qualitative research methods offer researchers the tools they need to construct informed interpretations of their data. By way of context, latent (coupled with manifest) meanings, the participant-researcher relationship, and other fundamentals associated with qualitative research, the trained researcher collects thick data from which to build an interpretation that addresses the research objectives in a profound and valuable manner for the users of the research.

Qualitative data analysis is a process by which the researcher is actively involved in the creation of themes from the data and the interpretation within and across themes to construct results that move the topic of investigation forward in some meaningful way. This active involvement is central to what it means to conduct qualitative research. Faithful to the principles that define qualitative research, researchers do not rest on manifest content, such as words alone, or on automated tools that exploit the obvious, such as word clouds.

This is another way of saying — as stated in this article on sample size and saturation — that “themes do not simply pop up…but rather are the result of actively conducting an analysis to construct an interpretation.” As Staller (2015) states, “In lieu of the language of ‘discovering’ things with its positivistic roots, the researcher is actually interpreting the evidence” (p. 147).

Braun and Clarke (2006, 2016, 2019, 2021) have written extensively about the idea that “themes do not passively emerge” (2019, p. 594, italics in original) from thematic analysis and that meaning

is not inherent or self-evident in data, that meaning resides at the intersection of the data and the researcher’s contextual and theoretically embedded interpretative practices – in short, that meaning requires interpretation. (2021, p. 210)

An article posted in 2018 in Research Design Review — “The Important Role of ‘Buckets’ in Qualitative Data Analysis” — illustrates this point. The article discusses the analytical step of creating categories (or “buckets”) of codes representing shared constructs prior to building themes. As an example, the discussion focuses on three categories that were developed from an in-depth interview study with financial managers — Technology, Partner, Communication. The researcher constructed themes by looking within and across categories, considering the meaning and context associated with each code. One such theme was “strong partnership,” as illustrated below.

Themes from buckets

The theme “strong partnership” did not simply emerge from the data, it was not lying in the data waiting to be discovered. Rather, the researcher utilized their analytical skills, in conjunction with their constructed understanding of each participant’s contribution to the data, to create contextually sound, meaningful themes such as “strong partnership.” Then, with the depth of definition associated with each theme, the researcher looked within and across themes to build an interpretation of the research data targeted at the research objectives, and provided the users of the research with a meaningful path forward.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Braun, V., & Clarke, V. (2016). (Mis)conceptualising themes, thematic analysis, and other problems with Fugard and Potts’ (2015) sample-size tool for thematic analysis. International Journal of Social Research Methodology, 19(6), 739–743. https://doi.org/10.1080/13645579.2016.1195588

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, Vol. 11, pp. 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216. https://doi.org/10.1080/2159676X.2019.1704846

Staller, K. M. (2015). Qualitative analysis: The art of building bridging relationships. Qualitative Social Work, 14(2), 145–153. https://doi.org/10.1177/1473325015571210

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