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Quantity (Conjoint Analysis (Determine Criteria (Attribute Combination
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Quantity
Conjoint Analysis
Attribute levels (often) are assumed to only have additive contributions (main effects only, no interactions, cf. analysis of variance) L4-6
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Note
- The importance of one attribute depends on the range of the attribute used to structure the stimuli, eg. price levels of 28,38,48 and levels of 28,32,36 (paper 4A)
- a relationship between the number of levels and the inferred importance of the attribute to the respondent: more levels, more importance
Determine Criteria
Select Attributes
- actionable
- important to individuals; actually affect consumer choice
Attribute Levels
- relatively large number of stimuli versus number of parameters
- a relationship between the number of levels and the inferred importance of the attribute to the respondent: more levels, more importance
Attribute Combination
- maximum 32 profiles
- In terms of predictive validity: is it really necessary/useful to estimate (two and higherorder) interaction effects, or is estimation of only main effects (additive model) good enough?
- interaction effects tend to have low predictive validity
- Fractional factorial designs also exist for unconfounded estimation of all main effects and some interaction effects
How many profiles?
- Lower bound 1: (total number of attribute levels) - (total number of attributes) + 1
– rationale: we need more data than to-be-estimated parameters
– lower bound for fractional factorial (42x23) (maineffects) design?
- Lower bound 2: (number of levels of attribute with most levels) x (number of levels of attribute with one butmost levels)
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Cluster
Hierarchical clustering procedures
Cluster distance
- single linkage
- complete linkage
- average linkage
- ward's method
- centroid method
How many homogeneous groups?
- A priori idea
- Absolute value of agglomeration coefficient
- Screetest criterion based on agglomeration coefficient
- Measures of tradeoff between withincluster homogeneity and number of clusters
-Variance between clusters versus variance within clusters, choose the greatest one
– Pseudo F-value does not follow theoretical F-distribution
- Interpretability/usefulness of clusters
Disadvantage of hierarchical cluster analysis
- joins and divisions are irreversible → successive solutions may depend on minor details the data
- with equal dissimilarities (ties) in the data, successive solutions even depend on (arbitrary) order of respondents in the dataset!
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Validity
Internal validity
- convergent validity: compare results from different (reasonable) dissimilarity measures
- convergent validity: compare results from different clustering procedures
- cross-validation: compare results from one half of the sample to results from another half of the sample)
- simulation using random data: do we get more homogenous clusters with our actual data?
External validity
- compare results with expected clustering (theoretical classification ⇒ taxonomy)
- check expected relations (= ?? validity)
- does the clustering yield useful market segments? (criteria?)
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