@article{Nussbeck2015Multi-43793,
  year={2015},
  doi={10.3389/fpsyg.2015.01332},
  title={Multimethod latent class analysis},
  volume={6},
  journal={Frontiers in Psychology},
  author={Nussbeck, Fridtjof W. and Eid, Michael},
  note={Article Number: 1332}
}

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    <dc:creator>Nussbeck, Fridtjof W.</dc:creator>
    <dc:contributor>Nussbeck, Fridtjof W.</dc:contributor>
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    <dcterms:title>Multimethod latent class analysis</dcterms:title>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-11-13T09:30:39Z</dc:date>
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    <dc:language>eng</dc:language>
    <dc:contributor>Eid, Michael</dc:contributor>
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    <dcterms:abstract xml:lang="eng">Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model.</dcterms:abstract>
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    <dc:creator>Eid, Michael</dc:creator>
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