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1 Cluster Analysis: A practical example

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2 Content  Introduction: the necessity to reduce the complexity  Recall: what cluster analysis does  An example : cluster analysis in consumer research on fair trade coffee  Discussion

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… “Where is the life we have lost in living Where is the wisdom we have lost in knowledge Where is the knowledge we have lost in information” … T. S. Elliot Choruses from the Rock 1888 – 1965 Intro

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… Where is the wisdom we have lost in knowledge Where is the knowledge we have lost in information … “Where is the information we have lost in data” Intro

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In order to go from data to information to knowledge and to wisdom we need to reduce the complexity of the data. Complexity can be reduced on - case level : cluster analysis - on variable level: factor analysis Intro

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Cluster analysis can get you from this: To this: What cluster analysis does a b d e f c

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Cluster analysis • generate groups which are similar • homogeneous within the group and as much as possible heterogeneous to other groups • data consists usually of objects or persons • segmentation based on more than two variables What cluster analysis does

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Cluster analysis • generates groups which are similar • the groups are homogeneous within themselves and as much as possible heterogeneous to other groups • data consists usually of objects or persons • segmentation is based on more than two variables What cluster analysis does

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Examples for datasets used for cluster analysis: • socio-economic criteria: income education profession age number of children size of city of residence .... • psychographic criteria: interest life style motivation values involvement • criteria linked to the buying behaviour: price range type of media used intensity of use choice of retail outlet fidelity buyer/non-buyer buying intensity What cluster analysis does

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Proximity Measures • Proximity measures are used to represent the nearness of two objects • relate objects with a high similarity to the same cluster and objects with low similarity to different clusters • differentiation of nominal-scaled and metric-scaled variables What cluster analysis does m dy i y s ∑ |y ij -y sj | r 1/r j1 y vector is different objects j the different characteristics r changes the weight of assigned distances the calculation of the distances measures is the basis of the cluster analysis.

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Two phases: 1. Forming of clusters by the chosen data set – resulting in a new variable that identifies cluster members among the cases 2. Description of clusters by re-crossing with the data What cluster analysis does

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Cluster Algorithm in agglomerative hierarchical clustering methods – seven steps to get clusters 1. each object is a independent cluster n 2. two clusters with the lowest distance are merged to one cluster. reduce the number of clusters by 1 n-1 3. calculate the the distance matrix between the new cluster and all remaining clusters 4. repeat step 2 and 3 n-1 times until all objects form one reminding cluster What cluster analysis does

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Finally… 1. decide upon the number of clusters you want to keep decision often based on the size of the clusters 2. description of the clusters by means of the cluster- forming variables 3. appellation of the clusters with catchy titles What cluster analysis does

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What cluster analysis does Cluster 5 Cluster 4 Cluster 3 Cluster 2 Cluster 1

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Practical Example Consumers and Fair Trade Coffee 1997  214 interviews of consumers of fair trade coffee personal and telephone interviews  Cluster analysis in order to identify consumer typologies  Identification of 6 clusters  Description of these clusters by further analysis: comparison of means crosstabs etc.

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Consumers and Fair Trade Coffee Description of clusters:  Cluster 1 116: “self-oriented fair trade buyer”  Cluster 2 136: “less ready to take personal constraints”  Cluster 3 182: ”less engaged about fair trade”  Cluster 4 322: “intensive buyer”  Cluster 5 187: “value-oriented”  Cluster 6 56: “does not like the taste of fair trade coffee”

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Consumers and Fair Trade Coffee Description of Cluster 1 116: “self-oriented fair trade buyer” :  Searches satisfaction by doing the good thing  Is not altruistic  Buys occasionally  Sticks to his conventional coffee brand  High level of formal education  Frequently religious catholic or protestant

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Consumers and Fair Trade Coffee Description of Cluster 2 136: “less ready to take personal constraints”  States that “fair trade coffee is hard to find”  Feels responsible for fare development issues  Believes that fair trade is efficient for developing countries  Is less ready to go to special fair trade outlets  Buys conventional coffee  Likes the taste of fair trade coffee

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Consumers and Fair Trade Coffee Description of clusters Cluster 3 182: ”less engaged about fair trade” :  Feels no personal responsibility with regard to development questions  Doesn’t see the efficiency of the consumption of fair trade goods  The only thing that can make him change is the influence of friends  Is older then the average fair trade buyer and has less formal education

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Consumers and Fair Trade Coffee Description of clusters: Cluster 4 322: “intensive buyer”  Has abandoned conventional coffee brands  Has started to buy fair trade quite a while ago 3 years  Shops frequently in fair trade stores and not in organic retail  Is ready to act for fair development and talks to friends about it  Relatively young with low incomes and high educational values

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Consumers and Fair Trade Coffee Description of clusters: Cluster 5 187: “value- oriented”  Together with cluster 4 highly aware of development issues  Ready to act and to constraint consumption habits  Buys for altruistic reasons  Highly involved in social / political action  Most frequently women highest household income among all clusters  Own security is the basis for solidary action

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Consumers and Fair Trade Coffee Description of clusters: Cluster 6 56: “does not like the taste of fair trade coffee”  Lowest purchase intensity of all clusters  Not willing to accept constraints in consumption habits or higher prices  Most members of these group are attached to a conventional coffee brand  Relatively high incomes age within the average of all groups lower level of formal education  Less religious than other groups.

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Conclusion / discussion Advantages • no special scales of measurement necessary • high persuasiveness and good assignment to realisable recommendations in practice Disadvantages • choice of cluster-forming variables often not based on theory but at random • determination of the right number of clusters often time- consuming – often decided upon arbitrarily • high influence on the interpretation of the scientist difficult to control good documentation is needed

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Conclusion / discussion

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Russell .L. Ackoff "From Data to Wisdom" Journal of Applied Systems Analysis 16 1989: 3-9. Milan Zeleny "Management Support Systems: Towards Integrated Knowledge Management" Human Systems Management 7 no 1 1987: 59-70. Tashakkori A. and Ch. Teddlie: Combining Qualitative and Quantitaive Approaches. Applied Social Research Methods Series Volume 46. Thousand Oaks London New Delhi 1998. xyxy Sources

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