qualitative data

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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Qualitative Data: Achieving Accuracy in the Absence of “Truth”

One of the 10 unique attributes of qualitative research is the “absence of truth.” This refers to the idea that the highly contextual and social constructionist nature of qualitative Characteristics of qualitative researchresearch renders data that is, not absolute “truth” but, useful knowledge that is the matter of the researcher’s own subjective interpretation. For all these reasons – contextuality, social constructionism, and subjectivity – qualitative researchers continually question their data, scrutinize outliers (negative cases), and implement other steps towards verification.

Qualitative researchers also conduct their research in such a way as to maximize the accuracy of the data. Accuracy should not be confused with “truth.” Accuracy in the data refers to gaining information that comes as close as possible to what the research participant is thinking or experiencing at any moment in time. This information may be the product of any number of contextual (situational) and co-constructed factors – i.e., the absence of “truth” – yet an accurate account of a participant’s stance on a given issue or topic.

It is accuracy that qualitative researchers strive for when they craft their research designs to mitigate bias and inconsistency. For example, focus group moderators are trained to give equal attention to their group participants – allowing everyone an opportunity to communicate their thoughts – rather than bias the data – i.e., leading to inaccurate information – by favoring more attention on some participants than on others. A trained moderator is also skilled at listening for inconsistencies or contradictions throughout a discussion in order to follow up on each participant’s comments, asking Read Full Text

Exploring the True Colors in Qualitative Data

Reliability, in the sense of being able to obtain identical findings from repeated executions of a qualitative research design, is debatable.  Validity, however, is another matter.  Validity, in the sense of whether the qualitative researcher is collecting the information (data) he or she claims to be gathering (i.e., ttrue colorshe accuracy of the data), is a topic worthy of much more discussion in the research community, or at the least a greater emphasis in our qualitative research designs.  While qualitative researchers may not be able to replicate their studies, they surely have the means to consider the authenticity of the data.

There was a Research Design Review post back in 2010 that discussed the importance and appropriateness of validity in qualitative research, including the idea that there are ready-made techniques for looking at validity in qualitative research and that, in some ways, validity is already built into our research methods.  To illustrate how qualitative researchers typically incorporate validity Read Full Text