qualitative analysis

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.

 

 

 

 

 

A TQF Approach to Construct Validity

TQF approach to construct validity

Construct validity plays an important role in the design, implementation, analysis, and ultimate usefulness of qualitative research methods. The construct of validity itself in qualitative research is discussed in this article and cites qualitative researchers across disciplines who explore “unique dimensions” and other considerations  relating to validity in qualitative research.

The Total Quality Framework (TQF) relies heavily on construct validity in its quality approach to each phase of the qualitative research process. At each phase, the researcher must ask “Am I gaining real knowledge about the core concepts that are the focus of this research?” For example,

  • An important step when developing a research design is to identify the key constructs associated with the research objectives to investigate, and the particular attributes of each construct that the researcher wants to explore. So, for example, a researcher conducting a study on dietary behavior may have interest in “health consciousness,” including shopping behavior related to organic and fresh foods.
  • In the in-depth interview and focus group discussion methods, careful attention needs to be paid to guide development and the inclusion of questions relevant to the constructs of interest. When developing the guide, the researcher needs to ask “Is this [topic, question, technique] relevant to the construct we are investigating?”, and “Does this [topic, question, technique] provide us with knowledge about the aspect of the construct that we intended to explore in the interviews/discussions?”
  • In ethnography, the observation guide and observation grid are important tools. “The grid is similar to the guide in that it helps to remind the observer of the events and issues of most import; however, the observation grid is a spreadsheet or log of sorts that enables the observer to actually record and reflect on observable events in relationship to the research constructs of interest” (Roller & Lavrakas, 2015, p. 206).
  • The quality of qualitative data analysis hinges on the researcher’s ability to effectively identify, analyze, and develop valid interpretations of the data around the important constructs associated with the research objectives. To assist the researcher, a TQF approach to analysis recommends a codebook format and coding form (which is basically a reflexive journal for the coder[s] to record thoughts and justifications for their coding decisions) that highlights constructs of interest. For example,

TQF codebook and coding form

  • Construct validity also plays an important role in the transparency of the final research document. In the study report, the researcher can (and should) elaborate on the design, data gathering, and analysis decisions that were made pertaining to the key constructs, as well as the main themes that were derived from the data — i.e., the knowledge that was gained from the research — concerning these constructs.

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

 

Photo by Bon Vivant

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