Convergence and Stability of Alternative Estimators in Misspecified Covariance Structures

UIf Henning Olsson; Roy D. Howell; Sigurd Villads Troye

doi:10.1007/978-3-319-13144-3_72

Convergence and Stability of Alternative Estimators in Misspecified Covariance Structures

UIf Henning Olsson, Roy D. Howell, Sigurd Villads Troye

Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

This paper presents results on how Maximum Likelihood (ML) and Generalized Least Squares (GLS) estimators in covariance structure models converge to a constant difference, and how they behave differently when the degree of misspecification increases. Model fit is assessed using the RMSEA fit index. The results indicate that the ML estimator is more stable across sample sizes than the GLS estimator, is more powerful in detecting model misspecification, and produces less biased parameter estimates with lower MSE’s in substantially misspecified models. Researchers are advised to use the ML estimates in most practical modeling situations.

Original language	English
Title of host publication	Developments in Marketing Science
Subtitle of host publication	Proceedings of the Academy of Marketing Science
Publisher	Springer Nature
Pages	230
Number of pages	1
DOIs	https://doi.org/10.1007/978-3-319-13144-3_72
State	Published - 2015

Publication series

Name	Developments in Marketing Science: Proceedings of the Academy of Marketing Science
ISSN (Print)	2363-6165
ISSN (Electronic)	2363-6173

Keywords

Covariance Structure
Generalize Little Square
Model Misspecification
Modeling Situation
Practical Modeling

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10.1007/978-3-319-13144-3_72

Cite this

Olsson, UI. H., Howell, R. D., & Troye, S. V. (2015). Convergence and Stability of Alternative Estimators in Misspecified Covariance Structures. In Developments in Marketing Science: Proceedings of the Academy of Marketing Science (pp. 230). (Developments in Marketing Science: Proceedings of the Academy of Marketing Science). Springer Nature. https://doi.org/10.1007/978-3-319-13144-3_72

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abstract = "This paper presents results on how Maximum Likelihood (ML) and Generalized Least Squares (GLS) estimators in covariance structure models converge to a constant difference, and how they behave differently when the degree of misspecification increases. Model fit is assessed using the RMSEA fit index. The results indicate that the ML estimator is more stable across sample sizes than the GLS estimator, is more powerful in detecting model misspecification, and produces less biased parameter estimates with lower MSE{\textquoteright}s in substantially misspecified models. Researchers are advised to use the ML estimates in most practical modeling situations.",

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Olsson, UIH, Howell, RD & Troye, SV 2015, Convergence and Stability of Alternative Estimators in Misspecified Covariance Structures. in Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Developments in Marketing Science: Proceedings of the Academy of Marketing Science, Springer Nature, pp. 230. https://doi.org/10.1007/978-3-319-13144-3_72

Convergence and Stability of Alternative Estimators in Misspecified Covariance Structures. / Olsson, UIf Henning; Howell, Roy D.; Troye, Sigurd Villads.
Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer Nature, 2015. p. 230 (Developments in Marketing Science: Proceedings of the Academy of Marketing Science).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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KW - Model Misspecification

KW - Modeling Situation

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Convergence and Stability of Alternative Estimators in Misspecified Covariance Structures

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