Interview analysisTranscriptionQualitative researchUnlocking Deeper Insights: AI-Transcribed Interviews for Qualitative Interview Analysis
Sherlin Jannet

Sherlin Jannet

3 min read

Unlocking Deeper Insights: AI-Transcribed Interviews for Qualitative Interview Analysis

Artificial intelligence is used by researchers to engage in various activities associated with research. It has also been used by many in the scientific community, specifically for purposes of qualitative research. But how can it help with interview analysis? In this article, we discuss the importance of qualitative interview analysis and the process of analysing qualitative interview data for research study.

Qualitative interview analysis

Interview analysis is a widely used method of collecting data for qualitative research. Unlike quantitative data that is retrieved from crunching numbers, qualitative data involves careful analysis that can provide detailed and nuanced findings. This can be crucial to your research when dealing with less measurable data.

Why use interviews for qualitative data analysis?

Qualitative data can be best analysed from an interview. Interviews can help understand less measurable data such as the emotions and motives behind a participant’s decision, from which you can derive conclusions. Having an interview recording also provides a permanent verbatim record which can be reviewed or revisited when needed. Additionally, the researcher can quote the interviewee when necessary thus increasing the integrity of the report.

Process of analysing qualitative interview data for research study

Transcribing the interview

The first stage of analysing the interview is getting a transcribed verbatim of the interview. Traditionally, interview analysis for qualitative data was done through manual note-taking. But ever since audio recorders became mainstream transcribing interviews has become an easier process. The interview can be written down word-to-word by replaying the recording as sufficed. This task can, however, be extremely time-consuming, especially when there are more than a few interviews to transcribe. Not to mention the tedious effort and energy that goes into it. And so it is recommended to use a transcriber to save time.

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Reading the interview

Post interview, the researcher having a better overview of the interview can derive fresh insights from the transcript. With the help of the interview transcript the researcher can read and re-read interviews to understand the respondent’s experience and assess whether it is relevant to a particular research question. During this reading process the key phrases are to be underlined and thoughts or reflections brought on by a particular passage in the transcript are noted down as part of the coding process.

What is coding in qualitative research?

Coding is the process of analysing and categorising data collected from the interview to identify themes, patterns and other insights. Codes are assigned to segments of data and represent the content in the data. These codes are used to categorise data into concepts. The coding process is deliberately saved for after the interview.

The Narrative view

In the second stage, the understanding of the interviewee’s experiences is framed as a narrative report of the interview. The goal is to phrase or articulate the understanding to provide a general narrative view. By articulating the essence of the interviewee’s story in answer to the research questions, the essential characteristics of the interviewee’s story may contribute to a better insight in the research topic.

Narrative report to conceptual framework

At this stage, the narratives are converted to a conceptual level. This is done by filtering the most important data and grouping them into concepts. From this abstract, the concepts that grasp the essence of the interview in response to the research question should be selected to be written into the study report. In this way the qualitative data is segmented.

Aligning qualitative data with quantitative data

The qualitative concepts derived are now matched with the quantitative data in the study. You can now identify patterns supporting or answering your research questions and capture the broader perspective of your research study. This is how a simple automated process such as transcription can immensely improve the quality of your research work.

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