Modelos Estructurales. Septiembre 2013. Carlos Camacho Martínez Vara de Rey. [email protected]

Tema 1.- Modelos estructurales basados en la covarianza (MBC). El modelo LISREL. Introducción. Diagrama causal. Componentes del modelo: el modelo de medida y el modelo estructural. Ecuaciones estructurales. Tipos de matrices. Estimación de parámetros. Identificación de modelos. Significación de los parámetros. Validación del modelo. Tema 2.- Modelos basados en la varianza o Partial Least Squares (PLS). Introducción: especificidad de los modelos PLS frente a los modelos MBC. Indicadores reflectivos vs formativos. Interpretación de un modelo PLS. Aplicación mediante SmartPLS. Realización de un mismo modelo mediante LISREL y SmartPLS.

Bibliografía

Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: John Wiley & Sons. Bollen, K. A. y Long, J. S. (Ed.). (1993). Testing structural equation models. Newbury Park, CA: Sage. Chin, W.W. (1998): “The Partial Least Squares Approach to Structural Equation Modelling”, en Marcoulides, G. A. (ed.), Modern Methods for Business Research, Lawrence Erlbaum Associates Publisher, Mahwah, pp. 295-336. Du Toit, M. & du Toit, S. (2001). Interactive LISREL: User’s Guide. Lincolnwood, IL: Scientific Software International. Jöreskog, K. G. y Sörbom, D. /SPSS (1988). LISREL® 7: A guide to the Program and Applications. 2ª ed. Chicago, IL: SPSS. Jöreskog, K., Sörbom, D., du Toit, S. & du Toit, M. (2001). LISREL 8: New Statistical Features. Lincolnwood, IL: Scientific Software International. Helland I.S. (1990). Pls regression and statistical models. Scandivian Journal of Statistics, 17, 97–114. Henseler, J., Ringle, C. M., y Sinkovics, R. R. (2009). The use partial least squares path modelling in international marketing. Advances in International Marketing 20, 277-319. Hoyle, R. H. (1995). Structural equation modeling: Concepts, Issues, and Applications. Thousand Oaks, CA: Sage Publications, Inc. Höskuldson, A. (1988). Pls regression methods. Journal of Chemometrics, 2, 211-228. Hox, J.J. y Bechger, T.M.(1998). An introduction to structural equation modeling. Family Sciences Review, 11, 354-373. Geladi, P., & Kowlaski B. (1986). Partial least square regression: A tutorial. Analytica Chemica Acta, 35, 1–17. Kline, R.B. (1998). Principles and practice of structural equation modeling. NY: Guilford Press. Loehlin, J. C. (1998) Latent Variable Models: an introduction to factor, path, and structural analysis. Mahwah, N.J.: Lawrence Erlbaum Associates.

Pagès, J., Tenenhaus, M. (2001). Multiple factor analysis combined with PLS path modeling. Application to the analysis of relationships between physicochemical variables, sensory profiles and hedonic judgments. Chemometrics and Intelligent Laboratory Systems, 58, 261– 273. Saris, W. E. & Stronkhorst, L. H. (1984). Causal Modelling in Non-Experimental Research. Amsterdam: Sociometric Research. Schumacker, R. E. y Lomax, R. G. 2004. A Beginner´s guide to structural equation modeling. Hillsdale, NJ: LEA. Tenenhaus, M. (1998). La régression PLS. Paris: Technip. Thompson, R.; Barclay, D.W. y Higgins, C.A. (1995). The Partial Least Squares Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration. Technology Studies: Special Issue on Reseaech Methodology, vol. 2, 284-324. Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In P.R. Krishnaiaah (Ed.). Multivariate Analysis. (pp.391-420) New York: Academic Press.

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Partial least square regression: A tutorial. Analytica. Chemica Acta, 35 ... Causal Modeling: Personal Computer Adoption and Use as an Illustration. Technology.

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