This student orientated guide to structural equation modeling promotes theoretical understanding and inspires students with the confidence to successfully apply SEM. Assuming no previous experience, and a minimum of mathematical knowledge, this is an invaluable companion for students taking introductory SEM courses in any discipline. Niels Blunch shines a light on each step of the structural equation modeling process, providing a detailed introduction to SPSS and EQS with a focus on EQS’ excellent graphical interface. He also sets out best practice for data entry and programming, and uses real life data to show how SEM is applied in research. 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. • A wide variety of examples from multiple disciplines and real world contexts. • Exercises for each chapter on an accompanying companion website. • A detailed glossary. Clear, engaging and built around key software, this is an ideal introduction for anyone new to SEM.
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 parceling 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) is 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. ...