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Constrained multilevel latent class models for the analysis of three-way three-mode binary data

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Author Info

  • Meulders, Michel

    ()
    (HUBrussel, KU Leuven)

  • Tuerlinckx, Francis

    ()
    (KU Leuven)

  • Vanpaemel, Wolf

    ()
    (KU Leuven)

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    Abstract

    Probabilistic feature models (PFMs) can be used to explain binary rater judgements about the associations between two types of elements (e.g., objects and attributes) on the basis of binary latent features. In particular, to explain observed object-attribute associations PFMs assume that respondents classify both objects and attributes with re- spect to a, usually small, number of binary latent features, and that the observed object- attribute association is derived as a specific mapping of these classifications. Standard PFMs assume that the object-attribute association probability is the same according to all respondents, and that all observations are statistically independent. As both assump- tions may be unrealistic, a multilevel latent class extension of PFMs is proposed which allows objects and/or attribute parameters to be different across latent rater classes, and which allows to model dependencies between associations with a common object (attribute) by assuming that the link between features and objects (attributes) is fixed across judgements. Formal relationships with existing multilevel latent class models for binary three-way data are described. As an illustration, the models are used to study rater differences in product perception and to investigate individual differences in the situational determinants of anger-related behavior.

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    Bibliographic Info

    Paper provided by Hogeschool-Universiteit Brussel, Faculteit Economie en Management in its series Working Papers with number 2012/38.

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    Length: 52 page
    Date of creation: Oct 2012
    Date of revision:
    Handle: RePEc:hub:wpecon:201238

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    Web page: http://research.hubrussel.be
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    Related research

    Keywords: multilevel latent class model; latent feature; three-way data;

    This paper has been announced in the following NEP Reports:

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