Qualitative Analysis

Qualitative Research: A Collection of Articles from 2016

qr-2016-collection-headerMany of the articles published in Research Design Review in 2016 were dedicated to qualitative research for the simple reason that qualitative researchers are faced with myriad issues when attempting to achieve quality outcomes, and yet there is relatively little discussion about the quality standards by which to guide their research.  RDR attempts to fill this void by focusing on the unique attributes of qualitative research and how they serve to define the optimal approaches to conducting qualitative research that is credible, analyzable, transparent, and useful.

Qualitative Research: A Collection of Articles from Research Design Review Published in 2016 is a compilation of the 17 RDR articles that were published in 2016 devoted to qualitative research.  These 17 articles include articles on:

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 research renders data that is, not absolute “truth” but, useful knowledge that is the matter of the researcher’s own 10 Unique Attributes of Qualitative Researchsubjective 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

Chaos & Problem Solving in Qualitative Analysis

In Conceptual Blockbusting: A Guide to Better Ideas, James Adams offers readers a varied and ingenious collection of approaches to overcoming the barriers to effective problem solving.  Specifically, Adams emphasizes the idea that to solve complex problems, it is necessary to identify the barriers chaotic-lifeand then learn to think differently.  As far as barriers, he discusses four “blocks” that interfere with conceptual thinking – perceptual, emotional, cultural and environmental, and intellectual and expressive – as well as ways to modify thinking to overcome these blocks – e.g., a questioning attitude, looking for the core problem, list-making, and soliciting ideas from other people.

Adams’ chapter on emotional blocks discusses ways that the thinking process builds barriers to problem solving.  One of these is the inability or unwillingness to think through “chaotic situations.”  Adams contends that a path to complex problem solving is bringing order to chaos yet some people have “an excessive fondness for order in all things” leaving them with an “inability to tolerate ambiguity.”  In other words, they have “no appetite for chaos.”  Adams puts it this way –

The solution of a complex problem is a messy process.  Rigorous and logical techniques are often necessary, but not sufficient.  You must usually wallow in misleading and ill-fitting data, hazy and difficult-to-test concepts, opinions, values, and other such untidy quantities.  In a sense, problem-solving is bringing order to chaos. (p. 48)

Problem solving is a “messy process” and no less so when carrying out an analysis of qualitative data.  There are several articles in Research Design Review that Read Full Text