Analyzability

Contextual Analysis: A Fundamental Attribute of Qualitative Research

Contextual analysis-A unique attribute of qualitative research

One of the 10 unique or distinctive attributes of qualitative research is contextual, multilayered analysis. This is a fundamental aspect of qualitative research and, in fact, plays a central role in the unique attributes associated with data generation, i.e., the importance of context, the importance of meaning, the participant-researcher relationship, and researcher as instrument

“…the interconnections, inconsistencies, and sometimes seemingly illogical input reaped in qualitative research demand that researchers embrace the tangles of their data from many sources. There is no single source of analysis in qualitative research because any one research event consists of multiple variables that need consideration in the analysis phase. The analyzable data from an in-depth interview, for example, are more than just what was said in the interview; they also include a variety of other considerations, such as the context in which certain information was revealed and the interviewee–interviewer relationship.” (Roller & Lavrakas, pp. 7-8)

The ability — the opportunity — to contextually analyze qualitative data is also associated with basic components of research design, such as sample size and the risk of relying on saturation which “misguides the researcher towards prioritizing manifest content over the pursuit of contextual understanding derived from latent, less obvious data.” And the defining differentiator between a qualitative and quantitative approach, such as qualitative content analysis in which it is “the inductive strategy in search of latent content, the use of context, the back-and-forth flexibility throughout the analytical process, and the continual questioning of preliminary interpretations that set qualitative content analysis apart from the quantitative method.”

There are many ways that context is integrated into the qualitative data analysis process to ensure quality analytical outcomes and interpretations. Various articles in Research Design Review have discussed contextually grounded aspects of the process, such as the following (each header links to the corresponding RDR article).

Unit of Analysis

“Although there is no perfect prescription for every study, it is generally understood that researchers should strive for a unit of analysis that retains the context necessary to derive meaning from the data. For this reason, and if all other things are equal, the qualitative researcher should probably err on the side of using a broader, more contextually based unit of analysis rather than a narrowly focused level of analysis (e.g., sentences).”

Meaning of Words

“How we use our words provides the context that shapes what the receiver hears and the perceptions others associate with our words. Context pertains to apparent as well as unapparent influences that take the meaning of our words beyond their proximity to other words [or] their use in recognized terms or phrases…”

Categorical Buckets

“No one said that qualitative data analysis is simple or straightforward. A reason for this lies in the fact that an important ingredient to the process is maintaining participants’ context and potential multiple meanings of the data. By identifying and analyzing categorical buckets, the researcher respects this multi-faceted reality and ultimately reaps the reward of useful interpretations of the data.”

Use of Transcripts

“Although serving a utilitarian purpose, transcripts effectively convert the all-too-human research experience that defines qualitative inquiry to the relatively emotionless drab confines of black-on-white text. Gone is the profound mood swing that descended over the participant when the interviewer asked about his elderly mother. Yes, there is text in the transcript that conveys some aspect of this mood but only to the extent that the participant is able to articulate it.”

Use of Recordings

“Unlike the transcript, the recording reminds the researcher of how and when the atmosphere in the [focus] group environment shifted from being open and friendly to quiet and inhibited; and how the particular seating arrangement, coupled with incompatible personality types, inflamed the atmosphere and seriously colored participants’ words, engagement, and way of thinking.”

 

 

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

 

 

 

 

 

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