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Error in Variables: Analysis of Covariance Structure – Structural Equation Models

  • Hubert Gatignon
Chapter
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Abstract

In this chapter, we bring together the notions of measurement error discussed in Chaps. 3 and 4 with the structural modeling of simultaneous relationships presented in Chap. 6. We demonstrate that a bias is introduced when estimating the relationship between two variables measured with error if that measurement error is ignored. We then present a methodology for estimating the parameters of structural relationships between variables that are not observed directly: analysis of covariance structures. We focus on the role of the measurement model as discussed in Chap. 4 with the confirmatory factor analytic model.

Keywords

Confirmatory Factor Analysis Measurement Model Covariance Structure Canonical Correlation Analysis Canonical Variate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Hubert Gatignon
    • 1
  1. 1.INSEADFontainebleau CedexFrance

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