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On the Quality of Test Statistics in Covariance Structure Analysis: Caveat Emptor

  • Peter M. Bentler
Part of the Perspectives on Individual Differences book series (PIDF)

Abstract

Structural equation modeling, and its important special cases of covariance structure analysis and confirmatory factor analysis, has become an important tool for testing theories with nonexperimental data (see Bentler, 1986; Bollen, 1989a; Loehlin, 1987). Some of the useful properties of structural modeling methods in theory testing are: the requirement for explicitness of a theory, so that it can be represented in path diagram form; the emphasis on a distinction between the variables of true interest, typically constructs or latent variables, and particular operationalizations of these; the capability to distinguish between direct, indirect, and total effects of certain variables on others; the ability to provide a statistical evaluation of the adequacy of the theory as a whole; the availability of tests to compare competing, nested models for their relative adequacy; the possibility of isolating potential problems with a theory via tests on missing parameters; and so on. Clearly, structural modeling has provided a useful methodology for theory testing in situations where more traditional or alternative methods may not work.

Keywords

Normal Theory Elliptical Theory Covariance Structure Analysis Covariance Structure Model Maximum Likelihood Factor Analysis 
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 1994

Authors and Affiliations

  • Peter M. Bentler
    • 1
  1. 1.Department of PsychologyUniversity of California, Los AngelesLos AngelesUSA

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