Multilevel Rasch Models

  • Akihito Kamata
  • Yuk Fai Cheong
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)


Over the past few years, several studies have investigated and demonstrated the relationships between generalized linear mixed models (GLIMM) and item response modeling. Some benefits associated with this GLIMM-based modeling framework include the modeling of nested structure of data, such as examinees nested within schools (Kamata, 2001), of multidimensional measures (Cheong & Raudenbush, 2000), and of wider class of item response models, such as 2PL item response model (Rijmen et al., 2003).


Latent Trait Unconditional Model Bayesian Information Criterion Item Response Model Student Disadvantage 
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Copyright information

© Springer Science + Business Media, LLC 2007

Authors and Affiliations

  • Akihito Kamata
    • 1
  • Yuk Fai Cheong
    • 2
  1. 1.Florida State UniversityTallahasseeUSA
  2. 2.Emory UniversityAtlantaUSA

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