Mixture Models

Mixture models were created to decompose nonnormal univariate or multivariate distributions into distinct (i.e., a mixture of) normal distributions. In the social sciences, mixture models are typically referred to as person-centered analyses and used to identify distinct subpopulations of participants, referred to as profiles, differing from one another quantitatively and qualitatively on a series of indicators and/or relations among indicators. Quantitative research typically relies on statistics (e.g., correlations, regressions) obtained from a sample to infer what happens in the population from which the sample was extracted. Mixture models relax this assumption of population homogeneity (i.e., that the results can be generalized to all members of the population) to extract subpopulations (i.e., profiles) characterized by a distinct set of results. After briefly reviewing the history ...

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