Structural equation models (SEMs) describe relationships between variables. They are similar to combining multiple regression and factor analysis. SEMs also offer some important, additional benefits over these techniques including an effective way to deal with multicollinearity, and methods for taking into account the unreliability of consumer response data. This paper introduces you to SEMs as well as Amos, a software tool distributed by SPSS Inc. Amos stands for Analysis of Moment Structures. Amos has a unique graphical interface, and was specifically designed to make fitting SEMs easier. This paper will: n Review some SEM basics and compare SEMs to multiple regression and factor analysis models Cover sample size requirements and two important SEM issues: model identification and model equivalence Explain why using SEMs for multicollinearity and measurement reliability provides important advantages n Give two examples using Amos for structural equation models The first of the two examples compares the importance of satisfaction drivers across customer segments. The insight we get from Amos tells us the importance of the drivers probably shouldnt be compared directly, because the data aren't really measuring the same variables in the segments.
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