Researcher Bias

Ethnography: Mitigating Observer Bias

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 207-212).

In qualitative research, the researcher – including the in-depth interviewer, focus group moderator, coder in content Observationanalysis, and observer – is the instrument, meaning that the qualitative researcher wields substantial control in the design content, the gathering of data, the outcomes, and interpretation of the research.  Ethnography is no different in that the observer – albeit not controlling participants’ natural environment – plays a central role in creating the data for the study by way of recording observations.  In this respect, the credibility of an ethnographic study essentially rests on the observer’s ability to identify and record the relevant observations.

The necessary observer skills have been discussed elsewhere in Research Design Review – for example, “The Importance of Analytical Sensibilities to Observation in Ethnography.” Without these skills, an observer has the potential for biasing the data which in turn will negatively impact the analysis, interpretation, transferability, and ultimate usefulness of an ethnographic study.  The potential for bias exists regardless of observer role. An offsite, non-participant observer may knowingly or not impose subjective values on an observed event – e.g., ignoring certain comments the observer finds personally offensive in a study of an online forum discussing alcohol use – while an onsite observer, operating either overtly or covertly, may bias results by way of Read Full Text

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

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