Structural equation models for finite mixtures: Simulation results and empirical applications
AbstractUnobserved heterogeneity is a serious but often neglected problem in structural equation modelling (SEM) challenging the validity of many empirical results. Recently, a finite mixture approach to SEM has been proposed to resolve this problem but until now only a few studies analyse the performance of the relevant software. The contribution of this paper is twofold: First, results from a Monte Carlo study into the properties of the program system MECOSA are presented. Second, an empirical application to data from a large-scale consumer survey in the fast moving consumer goods industry is described. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2002,33.
Date of creation: 2002
Date of revision:
Monte Carlo simulation; Structural equation modelling; Unobserved heterogeneity; Model-based clustering; Finite mixtures;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Asim Ansari & Kamel Jedidi & Sharan Jagpal, 2000. "A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models," Marketing Science, INFORMS, vol. 19(4), pages 328-347, August.
- Yiu-Fai Yung, 1997. "Finite mixtures in confirmatory factor-analysis models," Psychometrika, Springer, vol. 62(3), pages 297-330, September.
- Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
- Gerhard Arminger & Petra Stein & Jörg Wittenberg, 1999. "Mixtures of conditional mean- and covariance-structure models," Psychometrika, Springer, vol. 64(4), pages 475-494, December.
- Kamel Jedidi & Harsharanjeet S. Jagpal & Wayne S. DeSarbo, 1997. "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, INFORMS, vol. 16(1), pages 39-59.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.