qualitative analysis

Qualitative Data Processing: Minding the Knowledge Gaps

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 34-37).

Once all the data for a qualitative study have been created and gathered, they are rarely ready to be analyzed without further analytic work of some nature being done. At this stage the researcher is working with preliminary data from a collective datasetKnowledge gap that most often must be processed in any number of ways before “sense making” can begin.

For example, it may happen that after the data collection stage has been completed in a qualitative research study, the researcher finds that some of the information that was to be gathered from one or more participants is missing. In a focus group study, for instance, the moderator may have forgotten to ask participants in one group discussion to address a particular construct of importance—such as, the feeling of isolation among newly diagnosed cancer patients. Or, in a content analysis, a coder may have failed to code an attribute in an element of the content that should have been coded.

In these cases, and following from a Total Quality Framework (TQF) perspective, the researcher has the responsibility to actively decide whether or not to go back and fill in the gap in the data when that is possible. Regardless of what decision the researcher makes about these potential problems that are discovered during the data processing stage, the researcher working from the TQF perspective should keep these issues in mind when the analyses and interpretations of the findings are conducted and when the findings and recommendations are disseminated.

It should also be noted that the researcher has the opportunity to mind these gaps during the data collection process itself by continually monitoring interviews or group discussions. As discussed in this Research Design Review article, the researcher should continually review the quality of completions by addressing such questions as Did every interview cover every question or issue important to the research? and Did all interviewees provide clear, unambiguous answers to key questions or issues? In doing so, the researcher has mitigated the potential problem of knowledge gaps in the final data.



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The Important Role of “Buckets” in Qualitative Data Analysis

An earlier article in Research Design Review“Finding Connections & Making Sense of Qualitative Data” – discusses the idea that a quality approach to a qualitative research design incorporates a carefully considered plan for analyzing, and making sense of, the data in order to produce outcomes that are ultimately useful to the users of the research. Specifically, this article touches on the six recommended steps in the analysis process.* These steps might be thought of as a variation of the classic Braun & Clarke (2006) thematic analysis scheme in that the researcher begins by selecting a unit of analysis (and thus becoming familiar with the data) which is then followed by a coding process.

Unique to the six-step process outlined in the earlier RDR article is the step that comes after coding. Rather than immediately digging into the codes searching for themes, it is recommended that the researcher look through the codes to identify categories. These categories basically represent buckets of codes that are deemed to share a certain underlying construct or meaning. In the end, the researcher is left with any number of buckets filled with a few or many codes from which the researcher can identify patterns or themes in the data overall. Importantly, any of the codes within a category or bucket can (and probably will) be used to define more than one theme.

As an example, consider an in-depth interview study with financial managers of a large non-profit organization concerning their key considerations when selecting financial service providers. After the completion of 35 interviews, the researcher absorbs the content, selects the unit of analysis (the entire interview), and develops 75-100 descriptive codes. In the next phase of the process the researcher combs through the codes looking for participants’ thoughts/comments that convey similar broad meaning related to the research question(s). In doing so, Read Full Text

Secondary & Primary Qualitative Content Analysis: Distinguishing Between the Two Methods

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 241-244).

The definition and use of the content analysis method in qualitative research varies depending on the particular type of qualitative content analysis (QCA) being conducted. The most common QCA method is utilized when it plays a supportive analytical role in combination with other qualitative methods, such as in-depth interviews (IDIs) and focus group discussions, i.e., when content analysis is being used as a secondary method. The other less common QCA method is used when the source of content is an existing, naturally occurring repository of information (such as historical documents, media content, and diaries), i.e., when content analysis is being used as a primary method.

Secondary Method

A systematic application of QCA* as a secondary method has been conducted across a variety of disciplines.  Health care researchers in particular have used content analysis in conjunction with other qualitative methods to investigate a broad range of topics.  For example, Söderberg and Lundman (2001) applied the content analysis method to analyze the results from 25 unstructured IDIs conducted with women inflicted with fibromyalgia, from which they isolated five areas in these women’s lives impacted by the onset of this condition. In a similar approach, Berg and Hansson (2000) examined the lived experiences of 13 nurses working in dementia care at a psychogeriatric clinic who received clinical group supervision and individually planned nursing care. Berg and Hansson conducted unstructured, open-ended IDIs with each nurse and executed a content analysis that revealed two principal and five subordinate themes indicating supportive needs at the personal and professional level. Kyngäs (2004) studied the support network among 40 teenagers suffering from a chronic disease, such as asthma or epilepsy, by way of semi-structured IDIs.  Content analysis in this instance showed six distinct social network Read Full Text