research design

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

Ethnography: A Multi-method Approach

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 182-184).

Ethnography

There are several key strengths associated with ethnography. A critical differentiator of ethnography from other qualitative methods, that contributes greatly to the credibility of the data, is the in situ approach which allows the researcher to observe people’s actual experience. Another strength of ethnography is the process of immersion, especially if the observer assumes the role of complete participant, which enables the researcher to gain a sensibility and depth of understanding of the contextual, emotional, and social factors that define meaning within a group or for an individual.

Complementing the immersion process is the fact that ethnography is not an observation-only approach. Although observation typically represents the key component to an ethnographic study, true immersion and absorption in the study environment is derived from gaining participants’ input on many levels. Researchers often use observation as a starting point in the field from which they form an idea of where they need clarification or follow-up. This often leads to in-depth interviews or group discussions with participants and, in some instances, influential others (e.g., parents of the children participating in the Christensen et al. [2011] study). Unlike the multi-method approach discussed in this article, the utilization of multiple data sources in ethnography is squarely focused on augmenting the researcher’s observations, with the observations serving as the primary data. For example, an overt observer’s targeted questions may allow participants the opportunity to contribute their thoughts of what is going on in the study environment, help to clarify observed events for the observer, and enhance the observer’s ability to ultimately find patterns or themes in the study activities along with the meanings that participants associate with their actions. For a covert participant observer, this same process of augmenting observational data has to play out much more subtlety and with continued subterfuge, since the observer must avoid “blowing cover” while, at the same time, probing for information to help identify the patterns or themes without appearing to be doing so.

Other ancillary methods such as the review of relevant documents can also enrich observations and strengthen an ethnographic study overall. Russell et al. (2012), for example, were better able to understand their observations of team interaction among clinical and administrative staff in primary care offices by analyzing the internal communications and minutes from office meetings.

 

Christensen, P., Mikkelsen, M. R., Nielsen, T. A. S., & Harder, H. (2011). Children, mobility, and space: Using GPS and mobile phone technologies in ethnographic research. Journal of Mixed Methods Research, 5(3), 227–246. https://doi.org/10.1177/1558689811406121

Russell, G., Advocat, J., Geneau, R., Farrell, B., Thille, P., Ward, N., & Evans, S. (2012). Examining organizational change in primary care practices: Experiences from using ethnographic methods. Family Practice, 29(4), 455–461. https://doi.org/10.1093/fampra/cmr117

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.