This paper discusses the need for multiple comparison and cross-validation in evaluating structural equation models from the perspectives of philosophy of science, statistics, and methodology. After the issues associated with a single model test are examined, a comparison of competing models is suggested in its place. Theoretical arguments are also made for the use of cross-validation in comparing alternative models.
Over the last decade, structural equation models have been used by many marketing researchers (e.g., Fornell 1987). Nevertheless, relatively few studies have addressed the question of how structural equation models should be evaluated (cf. Bagozzi 1981; Fornell and Larcker 1981). Furthermore, many of these studies have focused mainly on the statistical problems with current modeling procedures (Bagozzi and Yi 1988). The purpose of this paper is to investigate the issues in evaluating structural equation models from a different perspective based on the principles of scientific inferences.
- Structural Equation Model
- Model Adequacy
- Validation Sample
- Comparative Approach
- Rival Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
Unable to display preview. Download preview PDF.
Anderson, James C. and David W. Gerbing (1988),“Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,” Psychological Bulletin, 103 (3), 411–423.
Bagozzi, Richard P. (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Errors,” Journal of Consumer Research, 18 (August), 375–381.
—and Youjae Yi (1988), “On the Evaluation of Structural Equation Models,” Journal of the Academy of Marketing Science, 16 (Spring), 74–94.
Bentler, Peter (1980), “Multivariate Analysis with Latent Variables: Causal Modeling,” Annual Review of Psychology, 31, 419–456.
Cassirer, E. (1972), An Essay on Man, New Haven: Yale University Press.
Cliff, Norman (1983), “Some Cautions Concerning the Application of Causal Modeling Methods,” Multivariate Behavioral Research, 18 (January), 115–126.
Cooil, Bruce, Russel Winer, and David Rados (1987), “Cross-Validation for Prediction,” Journal of Marketing Research, 24 (August), 271–279.
Copi, I. M. (1953), Introduction to Logic, New York: MacMilhan.
Cudeck, R. and M. Browne (1983), “Cross-validation of Covariance Structures,” Multivariate Behavioral Research, 18 (April), 147–167.
Diaconis, P. (1983), “Theories of Data Analysis: From Magical Thinking through Classical Statistics,” Unpublished working paper, Stanford University.
Edwards, Ward (1965), “Tactical Note on the Relation Between Scientific and Statistical Hypotheses,” Psychological Bulletin, 63, 400–402.
Fornell, Claes (1987), “A Second Generation of Multivariate Analysis: Classification of Methods and Implications for Marketing Research,” in Review of Marketing 1987, Michael Houston, ed. Chicago, IL: American Marketing Association, 407–450.
—and David Larcker (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Errors,” Journal of Marketing Research, 18 (February), 39–50.
—and Youjae Yi (1992), “Assumptions of the Two- Step Approach to Latent Variable Modeling,” Sociological Methods & Research, 20 (February), 291–320.
Henkel, Ramon and D. Morrison (1970), The Significance Test Controversy, Chicago, IL: Aldine.
Homburg, Christian (1991), "Cross-Validation and Information Criteria in Causal Modeling," Journal of Marketing Research, 28 (May), 137–144.
Jöreskog, Karl and Dag Sörbom (1984), LISREL VI: Analysis of Linear Structural Relationships by the Method of Maximum Likelihood, Mooresville, IN: Scientific Software, Inc.
Mackenzie, Scott B., Richard J. Lutz, and George E. Belch (1986), “The Role of Attitude toward the Ad as a Mediator of Advertising Effectiveness: A Test of Competing Explanations,” Journal of Marketing Research, 23 (May), 130–143.
Editors and Affiliations
© 2015 Academy of Marketing Science
About this paper
Cite this paper
Yi, Y., Nassen, K. (2015). Multiple Comparison and Cross-Validation in Evaluating Structural Equation Models. In: Crittenden, V.L. (eds) Proceedings of the 1992 Academy of Marketing Science (AMS) Annual Conference. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-319-13248-8_83
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13247-1
Online ISBN: 978-3-319-13248-8