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I have to analyse data from a marketing study. I will use SPSS. The questionnaire will look like this:

Q: Imagine Situation X. Select 1-3 Criteria from the list that best describe your feeling.

  • C1
  • C2
  • C3
  • C4
  • C5
  • C6
  • C7
  • ...

I want to perform cluster analysis to find out which respondents have similar feelings and what are the most often selected feelings in each cluster. The output is basically a set of binary codes (present vs absent). The categories are asymmetric: In other words, a 0-0 match should not necessarily be considered similar.

From this incredibly helpful post, I understood, that hierarchical cluster analysis, using a dice measure should work in my case. However, I also understood that it is not suitable for a large number of samples due to performance issues. (My sample size is 1500.)

Questions:

  1. Is my way of thinking correct?
  2. Would you still recommend using hierarchical clustering?
  3. If no, what else would you recommend?
  4. If yes, how bad will the performance be / how long will it take SPSS to run this? (I don't have the dataset yet, so I can't try it out.)
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    $\begingroup$ You seem having partly misunderstood the links. Dice measure suggests itself when you have nominal data such as single-choice question (and so the binary variables from it will be the dummies). In your place, I see a multiple choice question leaving us a set of binary variables. They are of "ordinal" (selected vs not selected) sense. Measures such as Jaccard or Ochiai (cosine) will do. $\endgroup$
    – ttnphns
    Feb 3, 2016 at 20:35
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    $\begingroup$ You can do hierarchical clustering (for example complete or average linkage): 1500 objects is not too much (SPSS will process it in few seconds). But given that some greedy algos may become riskyly suboptimal with thousands of objects you might consider doing the analysis on halfs of the sample, and compare the results. $\endgroup$
    – ttnphns
    Feb 3, 2016 at 20:38
  • $\begingroup$ Thanks a lot for your explanations and remarks! Reading the replies to the other questions again, I see that i probably missed some points. Anyway, I am glad that the analysis should work. $\endgroup$
    – Lakai
    Feb 4, 2016 at 7:49

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