MaxDiff Mixture Models
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Revision as of 06:44, 24 July 2017 by Tim Bock (talk | contribs) (Tim Bock moved page Max-Diff Mixture Models to MaxDiff Mixture Models: Text replacement - "Max-Diff" to "MaxDiff")
The main mixture models used to analyze the MaxDiff experiments are:
- Latent class logit models, which assume that the population contains a number of segments (e.g., a segment wanting low priced phones with few features and another segment willing to pay a premium for more features) and identifies the segments automatically.
- Random parameters logit models, which assume that the distribution of the parameters in the population is described by a multivariate normal distribution. This model is sometimes referred to in market research as Hierarchical Bayes, although this is a misnomer. See Tricked Random Parameters Logit Model for an example.
- C-Factor models, which can be either latent class or random parameters logit models, but additionally allow for heterogeneity in scale factors.