Analyzability

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

 

Research Integrity & a Total Quality Framework Approach to Qualitative Data Sharing

The September 2021 issue of Monitor on Psychology from the American Psychological Association includes an article “Leading the Charge to Address Research Misconduct” by Stephanie Pappas. The article discusses the various Qualitative data sharingcircumstances or “pressures” that may lead researchers towards weak research practices that result in anything from “honest” mistakes or errors (e.g., due to insufficient training or oversight) to deliberate “outright misconduct” (e.g., falsifying data, dropping outliers from the analysis and reporting). The article goes on to talk about what psychologists are doing to tackle the problem.

One of those psychologists is James DuBois, DSc, PhD at Washington University School of Medicine. Dr. DuBois and his colleague Alison Antes PhD direct the P.I. (professionalism and integrity in research) Program at Washington University. This program offers one-on-one coaching to researchers who are challenged by the demands of balancing scientific and compliance requirements, as well as researchers who have (or have staff who have) been investigated for noncompliance or misconduct. The P.I. Program also conducts an On the Road Workshop which is an onsite session for researchers “doing empirical research in funded research environments” covering such areas as decision-making strategies, effective communication, and professional growth goals.

Another approach to the problem of misconduct and the goal of research integrity is transparency by way of sharing data (and other elements of design), allowing other researchers the opportunity to examine research practices and substantiate the reported results. Dr. DuBois and his co-authors discuss this and other advantages to sharing qualitative data in their 2018 article “Is It Time to Share Qualitative Research Data?” The authors assert that allowing other researchers to assess supporting evidence and “comprehensiveness by examining our data may improve the quality of research by enabling correction and increasing attention to detail” (p. 384).

In response to DuBois et al., Roller and Lavrakas (2018) published a commentary expressing Read Full Text

Elevating Qualitative Design to Maximize Research Integrity

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 9-10).

All research that is aimed at understanding how people think and behave requires a principled approach to research design that is likely to maximize data quality and to instill users’ confidence in the research outcomes. This is no less so in qualitative than it is in quantitative research; and, in fact, the distinctive attributes and underlying complexities in qualitative research necessitate a quality approach to qualitative research design. This approach requires qualitative researchers to build certain principles into their research studies by way of incorporating and practicing fundamental research standards.

Total Quality FrameworkTo that end, the Total Quality Framework (TQF) was devised to provide a basis by which researchers can develop critical thinking skills necessary to the execution of qualitative designs that maximize the integrity of the research outcomes. This framework is not intended to prescribe a formula or specific procedure by which qualitative researchers should conduct qualitative inquiry. Rather, the TQF provides researchers with a flexible way to focus on quality issues, examine the sources of variability and possible bias in their qualitative methods, and incorporate features into their designs that mitigate these effects and maximize quality outcomes. Integral to the TQF is the idea that all qualitative research must be Credible, Analyzable, Transparent, and Useful. These four components are fundamental to the TQF and its ability to help researchers identify the strengths and limitations of their qualitative methods while also guiding them in the qualitative research design process.

By holding the quality of qualitative research design to a deep level of scrutiny when applied across the diverse, multidisciplinary fields utilizing qualitative methods — e.g., education; psychology; anthropology; sociology; nursing, public health, and medicine; communication; information management; business; geography and environmental science; and program evaluation — the discussion of qualitative research is significantly elevated and enables students, faculty, and practitioners to design and interpret qualitative research studies based on the quality standards that are the hallmark of the TQF.

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