Paying Attention to Bias in Qualitative Research: A Message to Marketing Researchers (& Clients)

Researchers of all ilk care about bias and how it may creep into their research designs resulting in measurement error.  This is true among quantitative researchers as well as among qualitative researchers who routinely head-in-the-sand-2demonstrate their sensitivity to potential bias in their data by way of building interviewer training, careful recruitment screening, and appropriate modes into their research designs.  It is these types of measures that acknowledge qualitative researchers’ concerns about quality data; and yet, there are many other ways to mitigate bias in qualitative research that are often overlooked.

Marketing researchers (and marketing clients) in particular could benefit from thinking more deeply about bias and measurement error.  In the interest of “faster, cheaper, better” research solutions, marketing researchers often lose sight of quality design issues, not the least of which concern bias and measurement error in the data.  If marketing researchers care enough about mitigating bias to train interviewers/moderators, develop screening questions that effectively target the appropriate participant, and carefully select the suitable mode for the population segment, then it is sensible to adopt broader design standards that more fully embrace the collecting of quality data.

An example of a tool that serves to raise the design standard is the reflexive journal.  The reflexive journal has been the subject (in whole or in part) of many articles in Research Design Review, most notably Read Full Text

Lighting a Path to Guide Case-Centered Research Design: A Six-Step Approach

Elliot Mishler coined the term “case-centered research” to refer to the research approach that preserves the “unity and coherence” of research participants through the data collection and well-lit-pathanalysis process. Fundamental to case-centered research is its focus on complex social units (or “cases”) in their entirety as well as the emphasis on maintaining the cohesiveness of the social unit(s) throughout the research process. As discussed in Research Design Review back in 2013, two important examples of case-centered approaches are case study research and narrative research.

The complexity and need for cohesion in case-centered research present unique design challenges. Indeed, quality outcomes from case study and narrative research are the result of a well-defined process that guides the researcher from the initial conceptualization phase to data collecting in the field. Although the specifics within the process will vary from study to study, there exists an optimal design flow when implementing the case-centered research approach.

The appropriate path in case-centered designs, leading to data collection, involves the following six Read Full Text

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