I’m working on a research project in which I need to analyse 20 qualitative interviews (6000+ words). The main topic is “community”. The sub-topics are “community development”, “community investment”, “community banking” and “community groups”.
The aim is to identify – based on aforementioned topics – the most important/problematic/potential areas are to improve the “sense of community”.
What type of analysis is most appropriate for this?
That’s an interesting topic!
In general it really depends on the questionnaires that you are analyzing and whether there are specific questions related to how to develop or improve the sense of community.
I guess you could approach your analysis as follows (I let other users in the community to contribute on this).
a) anonymization of the questionnaires
b) considering the number of the questionnaires, you could analyse the questionnaires individually, to identify which sentences are important, and which ones are not. This would help you to identify what are the important/problematic/potential areas to improve the sense of community
c) you could try to find out what are the terms that develop a sense of community. For instance, what people think about or what are their feelings. I guess this should be based on what they like, dislike, what are they beliefs, interests, or personality types. All those terms could be used to tag the text that you are analyzing.
d) maybe a topic detection might help you to identify the topics and which are the words that are most important to improve the sense of community? To implement that, the workflow available at this link might be useful: https://www.knime.com/blog/topic-extraction-optimizing-the-number-of-topics-with-the-elbow-method
Hope that helps!