Abstract
Discrete mixture distribution models (MDM) assume that observed data do not stem from a homogeneous population of individuals but are a mixture of data from two or more latent populations (Everitt and Hand, 1981; Titterington et al., 1985). Applied to item response data this means that a particular IRT model does not hold for the entire sample but that different sets of model parameters (item parameters, ability parameters, etc.) are valid for different subpopulations.
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Rost, J. (1997). Logistic Mixture Models. In: van der Linden, W.J., Hambleton, R.K. (eds) Handbook of Modern Item Response Theory. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2691-6_26
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DOI: https://doi.org/10.1007/978-1-4757-2691-6_26
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