Latent Class Technical Paper (2019)

Latent Class MNL is a utility estimation approach that finds groups (segments) of respondents who have similar preferences as captured via CBC or MaxDiff experiments. Latent Class MNL simultaneously estimates utilities for each segment and the probability that each respondent belongs to each segment. Respondents can be assigned to the group they have the highest probability of belonging to, thus creating a new segmentation variable that you may use in subsequent analyses including cross-tabulations.

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