Synopsis
Structural equation models are used by biologists, educational and medical researchers, psychologists, social scientists and others who traditionally deal with nonexperimental and quasi-experimental data. Perhaps the most important and influential statistical revolution to have recently occurred in the scientific arena, the development of structural equation models has provided researchers with a comprehensive method for the quantification and testing of theories. Accepted today as a major component of applied multivariate analysis, structural equation modelling includes latent variables, measurement errors in both dependent and independent variables, multiple indicators, reciprocal causation, simultaneity and interdependence. As implemented in most commerical computer packages - Amos, EQS, LISREL, LISCOMP, Mx, SAS PROC-CALIS, STATISTICA-SEPATH - the method includes as special cases such procedures as confirmatory factor analysis, multiple regression, path analysis, models for time-dependent data, recursive and non-recursive models for cross-sectional and longitudinal data, and covariance structure analysis.
This volume introduces the latest issues and developments in structural equation modelling techniques. By focusing primarily on the application of structural equation modelling techniques in example cases and situations, it provides an understanding and working knowledge of advanced structural equation modelling techniques with a minimum of mathematical derivations. This didactic approach allows readers to better understand the underlying logic of advanced structural equation modelling techniques and thereby, to effectively assess the suitability of the method for their own research. The volume was written for a broad audience crossing many disciplines, and makes the assumption that readers have mastered the equivalent of graduate level multivariate statistics courses that include coverage of introductory structural equation modelling techniques.
