Also known as
- Hierarchical models (although this can have different connotations).
- Random effects models
MaxDiff mixture models
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.