Qualitative Analysis: The Biggest Obstacle to Enriching Survey Outcomes

Analysis is probably the biggest obstacle to the broader utilization of qualitative research methods.  Other aspects of qualitative research – such as data collection (which is discussed at length throughout Research Design Review as it relates to applying quality standards) – may require a certain degree of resources and Obstacledeliberation but are not difficult to achieve.  Obtaining a representative list of potential participants, for example, or honing the necessary skills to mitigate interviewer bias and gain cooperation from participants demand concentrated efforts on the part of the qualitative researcher but there are fairly straightforward, well-documented procedures to accomplish these goals.

Analysis, however, is difficult and it is the reason why many survey researchers are loath to incorporate a qualitative component – open-ended questions in a survey questionnaire or a full-blown qualitative project – in their overall study designs. The idea that analysis presents a significant hurdle to potential users of qualitative research is revealed in comments made in a group discussion I conducted on schizophrenia with psychologists concerning their research with caregivers.  When asked why qualitative methods were so rarely (or never) conducted in conjunction with their survey research, these psychologists told me

“Open-ended questions are okay if there is someone with qualitative data analytic experience to analyze these data.”

“Open-ended questions provide interesting clinical data but there are real challenges in how best to code these qualitative data.”

Similarly, a researcher friend who explicitly advises against using open-ended questions in survey questionnaires, defends this bias asserting that

“[Survey researchers] don’t usually have the time, resources, or patience to actually analyze the qualitative data [and] you have to have a sophisticated researcher who understands that the conclusions drawn from qualitative data must be made carefully.”  

These comments point to the fundamental obstacle hampering the wider use and acceptance of qualitative research among survey researchers – that is, qualitative data are typically complex, multifaceted, and not easily herded into neat meaningful silos.  This makes qualitative analysis extremely “messy.”  A November 2010 RDR article, “The Messy Inconvenience of Qualitative Analysis,” discusses this messiness, stating in part

 “Unlike the structured borders we build into our quantitative designs that facilitate an orderly analytical process, qualitative research is built on the believe that there are real people beyond those quantitative borders and that rich learning comes from meaningful conversations…The course of conversation is not typically one complete coherent stream of thought followed by an equally well-thought-out rejoinder.  These conversations are not rehearsed to ensure consistent, logical feedback to our research questions; but instead are spontaneous discussions where both interviewee and interviewer are thinking out loud, continually modifying points of view or ideas as human beings do.”

By going “beyond those quantitative borders,” qualitative data add significant understanding of how people think, their motivations, and their lived experiences that help explain certain behavior or attitudes on a particular issue.  Yet, it is these same objectives that produce complex, rich qualitative data that complicates the analysis process and steers survey researchers away from qualitative research.   Qualitative analysis serves as an unfortunate roadblock to enriching survey outcomes by way of even the most modest gesture to qualitative, e.g., an open-ended question in a survey interview.

That is the problem but what is the solution.  Like qualitative data, there is no simple solution.  There are, however, ways our research designs could be made more inclusive.  To name the most obvious, survey researchers could

  • Begin a dialog with qualitative researchers – those working within the organization or outside consultants – by which everyone shares their knowledge and expertise, and gains an understanding of how survey and qualitative researchers can work together in the data collection as well as analysis and reporting phases of a study.
  • From this dialog, form a quantitative-qualitative collaboration. Create quant-qual teams whereby certain qualitative researchers work with particular survey researchers in a specific category or topic area.  The qualitative team members are responsible for designing the appropriate integration of qualitative in the overall study framework, overseeing data collection, conducting the qualitative data analysis, and working with the quantitative researchers in the interpretation and reporting phases.
  • Explore the possibility of using online quant-qual solutions – such as iModerate (utilizing their ThoughtPath approach), Knowledge Networks (via Quale Probe) – which may facilitate adding qualitative to a survey study, including an efficient option for analysis.
  • Become familiar with CAQDAS – computer-assisted qualitative data analysis software – and what the various software providers offer in the way of features that can “simplify” the analysis of qualitative data. To be clear, there is no simple solution to the analysis of qualitative data – the analysis requires a great deal of human time and brain power to absorb the material and consider contextual factors – however, CAQDAS does offer the researcher useful supporting functions, e.g., organizing the data and visualizing relationships.

Qualitative data analysis will never be “easy” but there are ways to make it less of an obstacle to survey research and ultimately produce more insightful outcomes.

 

 

Image captured from: http://www.business2community.com/content-marketing/5-common-content-marketing-obstacles-01348800

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