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 lavaan code for the book’s examples.

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Michael Eid, PhD, is Professor of Methods and Evaluation at the Free University of Berlin in Germany. His research focuses on measurement theory, in particular on the development of psychometric models for longitudinal and multimethod research. Since the early 2000s, he has been contributing to the development of structural equation models for analyzing multirater data for different types of raters and research designs. His more applied research contributions are in the areas of subjective well-being, mood regulation, and health psychology.

Christian Geiser, PhD, is a former professor of quantitative psychology. He currently works as an instructor and statistical consultant with QuantFish. His areas of expertise are in structural equation modeling, measurement, longitudinal data analysis, latent class modeling, and multitrait–multimethod analysis.

Tobias Koch, PhD, is Professor of Psychological Methods at the Friedrich-Schiller-Universit�t Jena in Germany. His research focuses on measurement theory and psychometrics, structural equation modeling, longitudinal data analysis, multilevel analysis, Bayesian analysis, and multitrait–multimethod analysis.

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