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Estimation of generalized structured component analysis models with alternating least squares

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  • Rainer Schlittgen

    (University of Hamburg)

Abstract

This paper presents a new algorithm estimating structural equation models by using the generalized structured component analysis (GSCA) formulation. In GSCA models, the latent variables are composites. The weights are estimated in conjunction with the other model parameters, optimizing a genuine least squares criterion. The new algorithm is designed for three subclasses often used in applications. Linear regressions are alternated for different sets of parameters according to the nonlinear manner in which the parameters are incorporated in the model. The new algorithm is compared with two existing ones by using different examples from the literature, as well as simulations. On the average the proposed algorithm produces better criterion values compared to the other two ones.

Suggested Citation

  • Rainer Schlittgen, 2018. "Estimation of generalized structured component analysis models with alternating least squares," Computational Statistics, Springer, vol. 33(1), pages 527-548, March.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0723-5
    DOI: 10.1007/s00180-017-0723-5
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    References listed on IDEAS

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    1. Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
    2. TENENHAUS, Michel, 2008. "Component-based structural equation modelling," HEC Research Papers Series 887, HEC Paris.
    3. Ringle, C.M. & Götz, O & Wetzels, M.G.M. & Wilson, B, 2009. "On the Use of Formative Measurement Specifications in Structural Equation Modelling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    4. Michel Tenenhaus, 2008. "Component-based Structural Equation Modelling," Working Papers hal-00580149, HAL.
    5. Heungsun Hwang & Yoshio Takane, 2004. "Generalized structured component analysis," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 81-99, March.
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    Cited by:

    1. Rainer Schlittgen & Marko Sarstedt & Christian M. Ringle, 2020. "Data generation for composite-based structural equation modeling methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 747-757, December.

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    Keywords

    Algorithm; Fit criterion; GSCA; PLS;
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