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Nonlinear structural equation modeling: is partial least squares an alternative?

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  • Karin Schermelleh-Engel
  • Christina Werner
  • Andreas Klein
  • Helfried Moosbrugger

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  • Karin Schermelleh-Engel & Christina Werner & Andreas Klein & Helfried Moosbrugger, 2010. "Nonlinear structural equation modeling: is partial least squares an alternative?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 167-184, June.
    • Handle: RePEc:spr:alstar:v:94:y:2010:i:2:p:167-184
    DOI: 10.1007/s10182-010-0132-3
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    References listed on IDEAS

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    1. M. Barendse & F. Oort & G. Garst, 2010. "Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 117-127, June.
    2. Sik-Yum Lee & Xin-Yuan Song, 2003. "Model comparison of nonlinear structural equation models with fixed covariates," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 27-47, March.
    3. Kenneth Bollen, 1996. "An alternative two stage least squares (2SLS) estimator for latent variable equations," Psychometrika, Springer;The Psychometric Society, vol. 61(1), pages 109-121, March.
    4. Christian Geiser & Michael Eid & Fridtjof Nussbeck & Delphine Courvoisier & David Cole, 2010. "Multitrait-multimethod change modelling," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 185-201, June.
    5. Jörg Henseler, 2010. "On the convergence of the partial least squares path modeling algorithm," Computational Statistics, Springer, vol. 25(1), pages 107-120, March.
    6. Suzanne Jak & Frans Oort & Conor Dolan, 2010. "Measurement bias and multidimensionality; an illustration of bias detection in multidimensional measurement models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 129-137, June.
    7. TENENHAUS, Michel, 2008. "Component-based structural equation modelling," HEC Research Papers Series 887, HEC Paris.
    8. Michel Tenenhaus, 2008. "Component-based Structural Equation Modelling," Working Papers hal-00580149, HAL.
    9. Andreas Klein & Karin Schermelleh-Engel, 2010. "Introduction of a new measure for detecting poor fit due to omitted nonlinear terms in SEM," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 157-166, June.
    10. Andreas Klein & Helfried Moosbrugger, 2000. "Maximum likelihood estimation of latent interaction effects with the LMS method," Psychometrika, Springer;The Psychometric Society, vol. 65(4), pages 457-474, December.
    11. B. King-Kallimanis & F. Oort & G. Garst, 2010. "Using structural equation modelling to detect measurement bias and response shift in longitudinal data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 139-156, June.
    12. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    13. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
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    Cited by:

    1. M. Barendse & F. Oort & G. Garst, 2010. "Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 117-127, June.
    2. Theo Dijkstra & Karin Schermelleh-Engel, 2014. "Consistent Partial Least Squares for Nonlinear Structural Equation Models," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 585-604, October.
    3. Andreas Klein & Karin Schermelleh-Engel, 2010. "Introduction of a new measure for detecting poor fit due to omitted nonlinear terms in SEM," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 157-166, June.

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