This comprehensive Second Edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM). Updated to include extensive analysis of AMOS' graphical interface, a new chapter on latent curve models and detailed explanations of the structural equation modeling process, this Second Edition is the ideal guide for those new to the field. The book includes: •Learning objectives, key concepts and questions for further discussion in each chapter •Helpful diagrams and screenshots to expand on concepts covered in the texts •Real life examples from a variety of disciplines to show hoe SEM is applied in real research contexts •Exercises for each chapter on an accompanying companion website •A new glossary at the end of book Assuming no previous experience of the subject, and a minimum of mathematical knowledge, this is the ideal guide for those new to SEM and an invaluable companion for students taking introductory SEM courses in any discipline.

Incomplete and Non-Normal Data

Missing data are more the rule than the exception in empirical research, and several solutions to the problem have been suggested. As will be argued in the opening paragraphs of this chapter, the most widespread ones are not wholly satisfactory.

Then you will learn about the two more satisfying methods implemented in AMOS, the first of which is a special maximum likelihood procedure that makes it possible to estimate a model using all the data at hand even if some observations are missing. However, it turns out that this technique is not without its drawbacks, the most serious being that you can easily run out of degrees of freedom, because it is necessary to estimate means and intercepts.

Unfortunately this is also ...

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