After Karl Jöreskog's first presentation in 1970, Structural Equation Modelling or SEM has become a main statistical tool in many fields of science. It is the standard approach of factor analytic and causal modelling in such diverse fields as sociology, education, psychology, economics, management and medical sciences. In addition to an extension of its application area, Structural Equation Modelling also features a continual renewal and extension of its theoretical background. The sixteen contributions to this book, written by experts from many countries, present important new developments and interesting applications in Structural Equation Modelling. The book addresses methodologists and statisticians professionally dealing with Structural Equation Modelling to enhance their knowledge of the type of models covered and the technical problems involved in their formulation. In addition, the book offers applied researchers new ideas about the use of Structural Equation Modeling in solving their problems. Finally, methodologists, mathematicians and applied researchers alike are addressed, who simply want to update their knowledge of recent approaches in data analysis and mathematical modelling.

Presents important new developments and interesting applications

Inhalt
1: Theoretical Developments.- 1. Statistical Power in PATH Models for Small Sample Sizes.- 2. SEM State Space Modeling of Panel Data in Discrete and continuous Time and its Relationship to Traditional State Space Modeling.- 3. Thurstone's Case V Model: a Structural Equations Modeling Perspective.- 4. Evaluating Uncertainty of Model Acceptability in Empirical Applications: A Replacement Approach.- 5. Improved Analytic Interval Estimation of Scale Reliability.- 6. A Component Analysis Approach towards Multisubject Multivariate Longitudinal Data Analysis.- 7. Least Squares Optimal Scaling for Partially Observed Linear Systems.- 8. Multilevel Structural Equation Models: the Limited Information Approach and the Multivariate Multilevel Approach.- 9. Latent Differential Equation Modeling with Multivariate MultiOccasion Indicators.- 2: Applications.- 10. Varieties of Causal Modeling: How Optimal Research Design Varies by Explanatory Strategy.- 11. Is it Possible to Feel Good and Bad at the Same Time? New Evidence on the Bipolarity of Mood-State Dimensions.- 12. Development of a Short Form of the Eysenck Personality Profiler via Structural Equation Modeling.- 13. Methodological Issues in the Application of the Latent Growth Curve Model.- 14. Modeling Longitudinal Data of an Intervention Study on Travel Model Choice: Combining Latent Growth Curves and Autoregressive Models.- 15. Methods for Dynamic Change Hypotheses.- 16. Modeling Latent Trait-Change.