Robert Kapłon

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The goal of factor analysis is to reduce the redundancy among variables by using smaller number of factors that are treated as constructs or latent variables. Unfortunately, if we face with data heterogeneity, the estimates of a single set of means, factor loadings and specific variances may be misleading.One way of accounting for unobserved heterogeneity is to include another latent variable in a factor analysis model. As a consequence, the observations in a samples are assumed to arise from two or more subpopulations that are mixed in unknown proportions. Since putting some restrictions on parameters such as factor loadings and specific variancesone can get more parsimonious models. Therefore, the purpose of this paper is to present the eight factor analysis models. Methods of optimization to derive the maximum likelihood estimates based on EM algorithm as well as model selection procedure are considered. Proposed approach is illustrated by using a set of data referring to preferences.


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