This analysis shows how mixture models assess ranked data over time, suggesting new insights into preferences.
This paper discusses the use of mixture models in the analysis of longitudinal partially ranked data, where respondents, for example, choose only the preferred and second preferred out of a set of items. To model such data we convert it to a set of paired comparisons. Covariates can be incorporated into the model. We use a nonparametric mixture to account for unmeasured variability in individuals over time. The resulting multivalued mass points can be interpreted as latent classes of the items. The work is illustrated by two questions on (post)
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Francis et al. (2014) studied this question.
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