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Factor Analysis

Factor Analysis. Factor Analysis. A data reduction technique designed to represent a wide range of attributes on a smaller number of dimensions. Factor Analysis.

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Factor Analysis

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  1. Factor Analysis

  2. Factor Analysis • A data reduction technique designed to represent a wide range of attributes on a smaller number of dimensions.

  3. Factor Analysis • For example, suppose that a bank asked a large number of questions about a given branch. Consider how the following characteristics might be more parsimoniously represented by just a few constructs (factors).

  4. Factor Analysis

  5. Factor Analysis - Benefits include: (1) a more concise representation of the marketing situation and hence communication may be enhanced; (2) fewer questions may be required on future surveys; and, (3) perceptual maps become feasible. - Ideally, interval data (e.g., a rating on a 7 point scale), regarding the perceptions of consumers are required regarding a number of features, such as those noted above for the bank are gathered.

  6. Cumulative percent of variance explained. We are looking for an eigenvalue above 1.0.

  7. Expensive Exciting Luxury Distinctive Not Conservative Not Family Not Basic Appeals to Others Attractive Looking Trend Setting Reliable Latest Features Trust

  8. Expensive Exciting Luxury Distinctive Not Conservative Not Family Not Basic Appeals to Others Attractive Looking Trend Setting Reliable Latest Features Trust What shall these components be called?

  9. Expensive Exciting Luxury Distinctive Not Conservative Not Family Not Basic Appeals to Others Attractive Looking Trend Setting Reliable Latest Features Trust TRENDY EXCLUSIVE RELIABLE

  10. Calculate Component Scores EXCLUSIVE = (Expensive + Exciting + Luxury + Distinctive – Conservative – Family – Basic)/7 TRENDY = (Appeals to Others + Attractive Looking + Trend Setting)/3 RELIABLE = (Reliable + Latest Features + Trust)/3

  11. Not much differing on this dimension.

  12. Cluster Analysis

  13. Cluster Analysis • A mechanism for grouping objects, frequently used for segmentation.

  14. How would you group these faces?

  15. Vacation Anyone? Relaxation Distant Vacation Adventure Historical Local Vacation No Vacation

  16. Cluster Analysis • It would be possible to run factor analysis and then examine clusters after this. • Two fundamental types of clustering exist: (1) hierarchical; and, (2) k-means.

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