CHAPT. 6 – When products are grouped into homogeneous clusters

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“…data are usually aggregated into a matrix of distance between products. Unfortunately, in doing so, we lose the information related to the variability between subjects. This can easily be circumvent by directly analyzing the raw data with Multiple Correspondence Analysis (MCA). Moreover, similarly to what has been exposed in Chapter 2, MCA can be coupled with bootstrap techniques to get confidence ellipses around products. In practice, the analysis of sorting task data can be done by using the fast function of the SensoMineR package.


Download the database file(s)

 

  perfumes_sorting.csv
  cards_HierarSort.csv


Exercises – database file(s) and corrections

 

Chapter 6.zip


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