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Robust confirmatory factor analysis based on the forward search algorithm

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  • Aleš Toman

Abstract

A key concept of the forward search algorithm in confirmatory factor analysis is ordering of the data on the basis of observational residuals. These residuals are computed under the proposed model and measure the discrepancy between the observed and predicted response for each unit of the sample. Regression-type factor scores are used to estimate model predictions. Informative forward plots are created for indexing influential observations and to show the dynamics of the estimates throughout the search. The detailed influence of each observation on the model parameters and fit indices is analyzed and a robust model inference is achieved. Real and simulated data sets with known contamination schemes are used to demonstrate the performance of the forward search algorithm. Copyright Springer-Verlag Berlin Heidelberg 2014

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  • Aleš Toman, 2014. "Robust confirmatory factor analysis based on the forward search algorithm," Statistical Papers, Springer, vol. 55(1), pages 233-252, February.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:1:p:233-252
    DOI: 10.1007/s00362-013-0525-y
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    References listed on IDEAS

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    1. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
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    3. Pison, Greet & Rousseeuw, Peter J. & Filzmoser, Peter & Croux, Christophe, 2003. "Robust factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 145-172, January.
    4. Ke-Hai Yuan & Peter Bentler, 2000. "Robust mean and covariance structure analysis through iteratively reweighted least squares," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 43-58, March.
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    Cited by:

    1. Valdério Anselmo Reisen & Céline Lévy-Leduc & Edson Zambon Monte & Pascal Bondon, 2024. "A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method," Statistical Papers, Springer, vol. 65(5), pages 2865-2886, July.

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