Transcripts of qualitative in-depth interviews and focus group discussions (as well as ethnographers’ field notes and recordings) are typically an important component in the data analysis process. It is by way of these transcribed accounts of the researcher-participant exchange that analysts hope to re-live each research event and draw meaningful interpretations from the data. Because of the critical role transcripts often play in the analytical process, researchers routinely take steps to ensure the quality of their transcripts. One such step is the selection of a transcriptionist; specifically, employing a transcriptionist whose top priorities are accuracy and thoroughness as well as someone who is knowledgeable about the subject category, sensitive to how people speak in conversation, comfortable with cultural and regional variations in the language, etc.*
Transcripts take a prominent role, of course, in the utilization of any text analytic or computer-assisted qualitative data analysis software (CAQDAS) program. These software solutions revolve around “data as text,” with any number of built-in features to help sort, count, search, diagram, connect, quote, give context to, and collaborate on the data. Analysts are often instructed to begin the analysis process by absorbing the content of each transcript (by way of multiple readings) followed by a line-by-line inspection of the transcript for relevant code-worthy text. From there, the analyst can work with the codes taking advantage of the various program features.
An important yet rarely discussed impediment to deriving meaningful interpretations from Read Full Text
Many 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:
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 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