The outcome of a qualitative in-depth interview (IDI) study, regardless of mode, is greatly affected by the interviewer’s conscious or unconscious influence within the context of the IDIs—that is, the absence or presence of interviewer bias. The interviewer’s demographic characteristics (e.g., age, race), physical appearance in face-to-face IDIs (e.g., manner of dress), voice in face-to-face and telephone IDIs (e.g., a regional accent), and personal values or presumptions are all potential triggers that may elicit false or inaccurate responses from interviewees. For example, imagine that an IDI study is being conducted with a group of public school teachers who are known to harbor negative feelings toward the district’s superintendent but who express ambivalent attitudes in the interviews as the result of the interviewers’ inappropriate interjection of their own personal positive opinions. In this way, the interviewers have caused the findings to be biased. In order to minimize this potential source of distortion in the data, the researcher can incorporate a number of quality enhancement measures into the IDI study design and interview protocol:
The IDI researcher should conduct a pretest phase during which each interviewer practices the interview and learns to anticipate what Sands and Krumer-Nevo (2006) call “master narratives” (i.e., the interviewer’s own predispositions) as well as “shocks” that may emerge from interviewees’ responses. Such an awareness of one’s own predispositions as an interviewer and possible responses from interviewees that might otherwise “jolt” the interviewer will more likely facilitate an uninterrupted interview that can smoothly diverge into other appropriate lines of questioning when the time presents itself. In this manner, the interviewer can build and maintain strong rapport with the interviewee as well as anticipate areas within the interview that might bias the outcome.
For example, Sands and Krumer-Nevo (2006) relate the story of a particular interview in a study among youth who, prior to the study, had been involved in drug use and other criminal behavior. Yami, the interviewer, approached one of the interviews with certain assumptions concerning the interviewee’s educational background and, specifically, the idea that a low-level education most likely contributed to the youth’s illicit activities. Because of these stereotypical expectations, Yami entered the interview with the goal of linking the interviewee’s “past school failures” to his current behavior and was not prepared for a line of questioning that was not aimed at making this connection. As a result Read Full Text
In qualitative research, the researcher – including the in-depth interviewer, focus group moderator, coder in content analysis, 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
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 demonstrate 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