Summary
Contents
Subject index
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.
Factor Analysis
Factor Analysis
As mentioned in the previous chapter, we ideally require that a scale (summated or not, weighted or not) measures one and only one concept - that the scale is unidimensional. Another problem you faced was that of finding the optimal item weights when using parcelling or other summated scales.
In this chapter you will therefore learn how to employ two classical and easy-to-use techniques for mapping the dimensionality of a data set and for calculating item weights.
The first technique is principal components analysis whereby a set of manifest variables (e.g. items) are transformed into new and fewer uncorrelated variables called principal components, each representing a dimension in the data.
The other is exploratory factor analysis where the roles, so to speak, are inverted. Instead ...
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