Analyzability

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.

Finding Connections & Making Sense of Qualitative Data

The analysis of qualitative research data is no small thing. Because the very nature of qualitative research is complicated by the complexiconnectionsties inherent in being human, attempting to qualitatively measure and then make sense of behavior and attitudes is daunting. In fact, it is this overwhelming aspect of qualitative research that may lead researchers – who live in the real world of time and budget constraints – to succumb to a less-than-rigorous analytical process.

And yet, Analyzability* is a critical component in qualitative research design.

All of the data collection in the world – all the group discussions, IDIs, observations, storytelling, or in-the-moment research – amounts to a meaningless exercise unless and until a thorough processing and verification of the data is conducted. Without the thoughtful work required to achieve a quality research product, qualitative data simply sits as an inert compilation of discrete elements lacking import.

Finding the connections in the qualitative data that make sense of the phenomenon, concept, or construct under investigation may, for some, be difficult and worthy of shortcuts; but proper analysis is the only thing that separates an honest, professional qualitative study from a random amalgamation of conversations or online snapshots.

In April of 2014, Research Design Review discussed one facet of Analyzability, i.e., verification. Verification, however, only comes into play after the researcher has conducted the all-important processing phase that converts qualitative data – that amalgamation of discrete elements – into meaningful connections that give rise to interpretations and implications, and the ultimate usefulness, of the research.

A quality approach to qualitative research design necessitates a well-thought-out plan for finding connections and making sense of the data. Here are six recommended steps in that process*:

•  Select the unit of analysis – a subject matter, an activity, a complete narrative or interview.
•  Develop unique codes – an iterative process utilizing a codebook that pays particular attention to context to derive explicit, closely-defined code designations.
•  Code – a dynamic process that incorporates pretesting of codes, inter-coder checks, and coder retraining as necessary.
•  Identify categories – a group of codes that share an underlying construct.
•  Identify themes or patterns – by looking at the coding overall and the identified categories to reveal the essence of the outcomes. This may be made easier by way of visual displays via various programs such as PowerPoint and CAQDAS**.
Draw interpretations and implications – from scrutinizing the coded and categorized data as well as ancillary materials such as reflexive journals, coders’ coding forms (with their comments), and other supporting documents.

* Analyzability is one of four components of the Total Quality Framework. This framework and the six general steps in qualitative research analysis are discussed fully in Applied Qualitative Research Design: A Total Quality Framework Approach (Roller, M. R. & Lavrakas, P. J., 2015).

** Computer-assisted qualitative data analysis software, such as NVivo, Atlas.ti, MAXQDA.

Image captured from: http://www.breakthroughresults.co.uk/interim-management.php/