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Structural Equation Modeling of Multiple Rater Data

Om Structural Equation Modeling of Multiple Rater Data

The use of multiple raters can improve the validity of conclusions made on self- (and other) reports of emotions, attitudes, goals, and self-perceptions of personality. Yet analyzing these ratings requires special psychometric models that take into account the specific nature of these data. From leading authorities, this book offers the first comprehensive introduction to structural equation modeling (SEM) of multiple rater data. Rather than taking a one-size-fits-all approach, the book shows how the choice of a model should be guided by measurement design and purpose. Practical recommendations are provided for selecting suitable measurement designs, raters, and psychometric models. Models for different combinations of rater types and for cross-sectional as well as longitudinal research designs are described step by step, with a strong emphasis on the substantive meaning of the latent variables in the models. User-friendly features include equation boxes, application boxes, and a companion website with Mplus and R lavaan code for the book's examples.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9781462555710
  • Bindende:
  • Hardback
  • Sider:
  • 358
  • Utgitt:
  • 11. desember 2024
  • Dimensjoner:
  • 178x254x0 mm.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: Kan forhåndsbestilles

Beskrivelse av Structural Equation Modeling of Multiple Rater Data

The use of multiple raters can improve the validity of conclusions made on self- (and other) reports of emotions, attitudes, goals, and self-perceptions of personality. Yet analyzing these ratings requires special psychometric models that take into account the specific nature of these data. From leading authorities, this book offers the first comprehensive introduction to structural equation modeling (SEM) of multiple rater data. Rather than taking a one-size-fits-all approach, the book shows how the choice of a model should be guided by measurement design and purpose. Practical recommendations are provided for selecting suitable measurement designs, raters, and psychometric models. Models for different combinations of rater types and for cross-sectional as well as longitudinal research designs are described step by step, with a strong emphasis on the substantive meaning of the latent variables in the models. User-friendly features include equation boxes, application boxes, and a companion website with Mplus and R lavaan code for the book's examples.

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