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Estimator Conditioning Diagnostics for Covariance Structure Models

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  • DAVID KAPLAN

    (University of Delaware)

Abstract

This article studies the utility of a general set of diagnostics for assessing conditioning problems in the covariance structure modeling framework. The diagnostics are based on extensions of the condition index and variance decomposition proportions advanced by Belsley and are based on using the covariance matrix of the estimates. A series of simulations with a variety of covariance structure models as well as a real data example show that these diagnostics are useful for gauging the sensitivity of parameter estimates to conditioning problems arising from collinearity in the raw data. The relationship between ill-conditioning and local identification as it pertains to the proposed diagnostics is also discussed. It is suggested that these diagnostics be implemented in existing covariance structure modeling software.

Suggested Citation

  • David Kaplan, 1994. "Estimator Conditioning Diagnostics for Covariance Structure Models," Sociological Methods & Research, , vol. 23(2), pages 200-229, November.
    • Handle: RePEc:sae:somere:v:23:y:1994:i:2:p:200-229
    DOI: 10.1177/0049124194023002003
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    References listed on IDEAS

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    1. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    2. Roderick McDonald, 1982. "A note on the investigation of local and global identifiability," Psychometrika, Springer;The Psychometric Society, vol. 47(1), pages 101-103, March.
    3. P. Bentler & David Weeks, 1980. "Linear structural equations with latent variables," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 289-308, September.
    4. Bengt Muthén & David Kaplan & Michael Hollis, 1987. "On structural equation modeling with data that are not missing completely at random," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 431-462, September.
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    Cited by:

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    3. Rajdeep Grewal & Joseph A. Cote & Hans Baumgartner, 2004. "Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing," Marketing Science, INFORMS, vol. 23(4), pages 519-529, June.

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