Abstract
When employing factor analysis a major problem confronting researchers is the determination of how many components to retain for subsequent investigation. Many marketing scholars address this issue by automatically invoking the Kaiser-Guttman, or eigenvalue-greater-than-one, procedure. Yet this procedure is not without certain limitations. The authors therefore propose an alternative measure, a probability test for the significance of eigenvalues, based upon a random intercepts model. Preliminary results of the application of the model on marketing and psychology data are presented.
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Hubbard, R., Pandit, V. (2015). Determining Significant Principal Components: a Probability Test For Eigenvalues. In: Lindquist, J.D. (eds) Proceedings of the 1984 Academy of Marketing Science (AMS) Annual Conference. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-319-16973-6_98
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DOI: https://doi.org/10.1007/978-3-319-16973-6_98
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