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

Analyzability & a Qualitative Content Analysis Case Study

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 284-285).

Kuperberg and Stone (2008) present a case study where content analysis was used as the primary research method. Gender & SocietyIt is an example of how many of the Total Quality Framework (TQF) concepts can be applied — not only to the in-depth interview, focus group, observation, and case centered methods, discussed elsewhere in Research Design Review, but — to qualitative content analysis. The discussion below spotlights aspects of this study relevant to one of the four TQF components, Analyzability.

Purpose & Scope
The primary purpose of this content analysis study was to extend the existing literature on the portrayal of women’s roles in print media by examining the imagery and themes depicted of heterosexual college-educated women who leave the workforce to devote themselves to being stay-at-home mothers (a phenomenon referred to as “opting out”) across a wide, diverse range of print publications. More specifically, this research set out to investigate two areas of media coverage: the content (e.g., the women who are portrayed in the media and how they are described) and the context (e.g., the types of media and articles).

This study examined a 16-year period from 1988 to 2003. This 16-year period was chosen because 1988 was the earliest date on which the researchers had access to a searchable database for sampling, and 2003 was the year that the term “opting out” (referring to women leaving the workforce to become full-time mothers) became popular. The researchers identified 51 articles from 30 publications that represented a wide diversity of large-circulation print media. The researchers acknowledged that the sample “underrepresents articles appearing in small-town outlets” (p. 502).

Analyzability
There are two aspects of the TQF Analyzability component — processing and verification. In terms of processing, the content data obtained by Kuperberg and Stone from coding revealed three primary patterns or themes in the depiction of women who opt out: “family first, child-centric”; “the mommy elite”; and “making choices.” The researchers discuss these themes at some length and support their findings by way of research literature and other references. In some instances, they report that their findings were in contrast to the literature (which presented an opportunity for future research in this area). Their final interpretation of the data includes their overall assertion that print media depict “traditional images of heterosexual women” (p. 510).

Important to the integrity of the analysis process, the researchers absorbed themselves in the sampled articles and, in doing so, identified inconsistencies in the research outcomes. For example, a careful reading of the articles revealed that many of the women depicted as stay-at-home mothers were actually employed in some form of paid work from home. The researchers also enriched the discussion of their findings by giving the reader some context relevant to the publications and articles. For example, they revealed that 45 of the 51 articles were from general interest newspapers or magazines, a fact that supports their research objective of analyzing print media that reach large, diverse audiences.

In terms of verification, the researchers performed a version of deviant case analysis in which they investigated contrary evidence to the assertion made by many articles that there is a growing trend in the proportion of women opting out. Citing research studies from the literature as well as actual trend data, the researchers stated that the articles’ claim that women were increasingly opting out had weak support.

Kuperberg, A., & Stone, P. (2008). The media depiction of women who opt out. Gender & Society, 22(4), 497–517.

Qualitative Data Analysis: 16 Articles on Process & Method

“Qualitative Data Analysis: 16 Articles oQualitative Data Analysisn Process & Method” is a new compilation of selected articles appearing in Research Design Review from 2010 to December 2019 concerning various facets of qualitative data analysis. Although there are other RDR articles posted in this time period related to analysis — such as articles on transparency, e.g., “The Use of Quotes & Bringing Transparency to Qualitative Analysis” and those pertaining to quantitative-qualitative research topics, e.g., “Qualitative Analysis: The Biggest Obstacle to Enriching Survey Outcomes”  — the 16 articles in this compilation are narrowly focused on issues relevant to applying a quality approach to the analytical process — e.g., identifying the unit of analysis, coding, and use of “buckets” — and the qualitative content analysis method.

“Qualitative Data Analysis: 16 Articles on Process & Method” is available for download here.

Two other compilations are also available for download: “The Focus Group Method: 18 Articles on Design & Moderating is available for download here. And “The In-depth Interview Method: 12 Articles on Design & Implementation” is available for download here.