Research Analysis

Qualitative Content Analysis: Defined

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 231-232).

The qualitative approach to content analysis traces its roots to the mid-20th Qualitative content analysis: Definedcentury when qualitative researchers began to modify the approaches that had been used by quantitative content analysis researchers. The purpose was to enrich what qualitative researchers believed was an overly sterile approach that focused preponderantly on manifest (surface) content and largely missed the richer latent content, consequently missing much of the meaning underlying the text or other form of content being studied. The “content” in qualitative content analysis often originates from other qualitative methods (e.g., transcripts from in-depth interviews, group discussions, and ethnographic field notes). With this point in mind, qualitative content analysis researchers devised and advocated for a methodical process similar to quantitative content analysis but with a greater emphasis on subjective interpretations of the meaning in qualitative content so as to identify relevant themes and patterns (Zhang & Wildemuth, 2009; Hsieh & Shannon, 2005).

There is no shortage of definitions associated with the content analysis method. In fact, there appear to be no two definitions that are identical. Two researchers, Berg and Lune (2017), draw on several sources to define content analysis as “a careful, detailed, systematic examination and interpretation of a particular body of material in an effort to identify patterns, themes, biases, and meanings” (p. 182). Similarly, Krippendorff (2019) states that “content analysis is a research technique for making replicable and valid inferences from text (or other meaningful matter) to the contexts of their use” (p. 24). Information researchers Zhang and Wildemuth (2009) take the latent aspect one step further in their discussion of qualitative content analysis with the assertion that the aim is “to understand social reality in a subjective but scientific manner” (p. 308).

Regardless of the definition, there are six essential components to the content analysis method in qualitative research. Qualitative content analysis:

  1. Encompasses all relevant qualitative data sources, including text, images, video, audio, graphics, and symbols.
  2. Is systematic, process-driven method.
  3. Draws meaningful interpretations or inferences from the data based on both manifest and latent content.
  4. Is contextual, that is, relies on the context within which the information is extracted to give meaning to the data.
  5. Reduces a unit of qualitative data to a manageable level while maintaining the critical content.
  6. Identifies patterns and themes in the data that support or refute existing hypotheses or reveal new hypotheses.

Looking at these elements of the content analysis method, Roller and Lavrakas (2015) derive the definition of qualitative content analysis as, the systematic reduction or “condensation” (Graneheim & Lundman, 2004) of content, analyzed with special attention to the context in which it was created, to identify themes and extract meaningful interpretations of the data. Qualitative content analysis can be used as a secondary or primary method.

Graneheim, U. H., & Lundman, B. (2004). Qualitative Content Analysis in Nursing Research: Concepts, Procedures and Measures to Achieve Trustworthiness. Nurse Education Today, 24(2), 105–112. https://doi.org/10.1016/j.nedt.2003.10.001

Hsieh, H.-F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687

Krippendorff, K. (2019). Content Analysis: An Introduction to Its Methodology (4th ed.). Thousand Oaks, CA: Sage Publications.

Lune, H., & Berg, B. L. (2017). Qualitative Research Methods for the Social Sciences. Pearson.

Roller, M. R., & Lavrakas, P. J. (2015). Applied Qualitative Research Design: A Total Quality Framework Approach. New York: Guilford Press.

Zhang, Y., & Wildemuth, B. M. (2009). Qualitative Analysis of Content. In B. M. Wildemuth (Ed.), Applications of Social Research Methods to Questions in Information and Library Science (pp. 308-319). Westport, CT: Libraries Unlimited.

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.