I’m doing a thesis in educational sciences. As such, I’ve just collected the representations of around 150 students with spontaneous evocation questions (such as “name 3 words that spontaneously come to mind when you think of…”) and classification tasks (“from this list of words, choose the three most representative in your opinion of…”). The idea was to analyze co-occurrences and visualize the results graphically.
I was going to use Iramuteq but I don’t find it intuitive enough.
Could you tell me if this application would be suitable for my work?
Not an easy question!!
The main issue I see is how structured are the representations you are trying to catch, and if there is enough content to begin to see some underlying sub spaces using co-occurrences.
Basically, from 150 students, saying 3 words, if most of the words are distinct words, you won’t be able to see anything (which is also a result, btw).
And for the classification task, if the list proposed only 10 possible choices, you will be able to draw a network with at maximum 10 words, and most probably less.
I would say, the only way to really know, is to give a try!
Your data should be imported in cortext manager, in a zipped file, using robust cvs parser (utf8 charset, and tab separated).
I hope it helps,