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Conducting Tetrad Tests of Model Fit and Contrasts of Tetrad-Nested Models: A New SAS Macro.

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Title: Conducting Tetrad Tests of Model Fit and Contrasts of Tetrad-Nested Models: A New SAS Macro.
Authors: Hipp, John R.1 johnhipp@email.unc.edu, Bauer, Daniel J.2, Bollen, Kenneth A.1
Source: Structural Equation Modeling. 2005, Vol. 12 Issue 1, p76-93. 18p.
Subjects: Macro instructions (Electronic computers), Macroprogramming, Mathematical models, Mathematics, Simulation methods & models, Computer simulation
Abstract: This article describes a SAS macro to assess model fit of structural equation models by employing a test of the model-implied vanishing tends. Use of this test has been limited in the past, in pan due to the lack of software that fully automates the test in a user-friendly way. The current SM macro provides a straightforward method for researchers to use the vanishing tetrads implied by models to assess the fit of (a) structural equation models containing continuous endogenous variables; (b) structural equation models containing continuous endogenous variables nested for vanishing tends: and (c) structural equation models containing dichotomous, ordinal, or censored endogenous variables. Resides providing an alternative assessment of model fit to the usual likelihood-ratio test (LRT), the vanishing tetrads test occasionally provides a statistical assessment of competing models nested for vanishing tetrads but not nested for the LRT. The macro permits formal comparisons between tetrad-nested structural equation models containing dichotomous, ordinal, or censored endogenous variables. [ABSTRACT FROM AUTHOR]
Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Conducting Tetrad Tests of Model Fit and Contrasts of Tetrad-Nested Models: A New SAS Macro.
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  Data: This article describes a SAS macro to assess model fit of structural equation models by employing a test of the model-implied vanishing tends. Use of this test has been limited in the past, in pan due to the lack of software that fully automates the test in a user-friendly way. The current SM macro provides a straightforward method for researchers to use the vanishing tetrads implied by models to assess the fit of (a) structural equation models containing continuous endogenous variables; (b) structural equation models containing continuous endogenous variables nested for vanishing tends: and (c) structural equation models containing dichotomous, ordinal, or censored endogenous variables. Resides providing an alternative assessment of model fit to the usual likelihood-ratio test (LRT), the vanishing tetrads test occasionally provides a statistical assessment of competing models nested for vanishing tetrads but not nested for the LRT. The macro permits formal comparisons between tetrad-nested structural equation models containing dichotomous, ordinal, or censored endogenous variables. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Macroprogramming
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              Text: 2005
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