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  • "A model that extends the Rasch model and the Partial Credit Model to account for subject-specific uncertainty when responding to items is proposed. It is demonstrated that ignoring the subject-specific uncertainty may yield biased estimates of model parameters. In the extended version of the model, uncertainty and the underlying trait are linked to explanatory variables. The parameterization allows to identify subgroups that differ in uncertainty and the underlying trait. The modeling approach is illustrated using data on the confidence of citizens in public institutions." Die ALLBUS-Daten aus dem Jahr 2012 dienen als Hauptdatensatz für die Analyse. (xsd:string)
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  • First published online: May 26, 2020, https://doi.org/10.1177/0146621620920932. (ALLBUS) (xsd:string)
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  • ALLBUS-Bibliography (xsd:string)
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  • Aufgenommen: 35. Fassung, Dezember 2020 (xsd:gyear)
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  • 2020 (xsd:gyear)
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  • 2020 (xsd:gyear)
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  • 10.1177/0146621620920932 ()
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  • 447 (xsd:string)
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  • english (xsd:string)
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?:issueNumber
  • 6 (xsd:string)
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?:name
  • Uncertainty in Latent Trait Models (xsd:string)
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  • article (xsd:string)
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  • Bibsonomy (xsd:string)
  • In Applied Psychological Measurement, 44(6), 447-464, 2020 (xsd:string)
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  • ALLBUS (xsd:string)
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  • 2020 (xsd:string)
  • ALLBUS (xsd:string)
  • ALLBUS2012 (xsd:string)
  • ALLBUS_input2020 (xsd:string)
  • ALLBUS_pro (xsd:string)
  • ALLBUS_version35 (xsd:string)
  • FDZ_ALLBUS (xsd:string)
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  • 464 (xsd:string)
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?:uploadDate
  • 18.12.2020 (xsd:gyear)
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  • 44 (xsd:string)