The use of quotes or verbatims from participants is a typical and necessary component to any qualitative research report. It is by revealing participants’ exact language that the researcher helps the user of the research to understand the key takeaways by clarifying through illustration the essential points of the researcher’s interpretations. The idea is not to display an extensive list of what people said but rather provide quotes that have been carefully selected for being the most descriptive or explanatory of the researcher’s conceptual interpretation of the data. As Susan Morrow has written
“An overemphasis on the researcher’s interpretations at the cost of participant quotes will leave the reader in doubt as to just where the interpretations came from [however] an excess of quotes will cause the reader to become lost in the morass of stories.” (Morrow, 2005, p. 256)
By embedding carefully chosen extracts from participants’ words in the final document, the researcher uniquely gives participants a voice in the outcomes while contributing to the credibility – and transparency – of the research. In essence, the use of verbatims gives the users of the research a peek into the analyst’s codebook by Read Full Text
A February 2017 article posted in Research Design Review discusses qualitative data transcripts and, specifically, the potential pitfalls when depending only on transcripts in the qualitative analysis process. As stated in the article,
Although serving a utilitarian purpose, transcripts effectively convert the all-too-human research experience that defines qualitative inquiry to the relatively emotionless drab confines of black-on-white text. Gone is the profound mood swing that descended over the participant when the interviewer asked about his elderly mother. Yes, there is text in the transcript that conveys some aspect of this mood but only to the extent that the participant is able to articulate it. Gone is the tone of voice that fluctuated depending on what aspect of the participant’s hospital visit was being discussed. Yes, the transcriptionist noted a change in voice but it is the significance and predictability of these voice changes that the interviewer grew to know over time that is missing from the transcript. Gone is an understanding of the lopsided interaction in the focus group discussion among teenagers. Yes, the analyst can ascertain from the transcript that a few in the group talked more than others but what is missing is the near-indescribable sounds dominant participants made to stifle other participants and the choked atmosphere that pervaded the discussion along with the entire group environment.
Missing from this article is an explicit discussion of the central role audio and/or video recordings – that accompany verbal qualitative research modes, e.g., face-to-face and telephone group discussions and in-depth interviews (IDIs) – play in the analysis of qualitative data. Researchers who routinely utilize recordings during analysis are more likely to derive valid interpretations of the data while also staying connected to Read Full Text
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