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Hypothesis Testing with Scanner Data: The Advantage of Bayesian Methods

Greg M. Allenby
Journal of Marketing Research
Vol. 27, No. 4 (Nov., 1990), pp. 379-389
DOI: 10.2307/3172624
Stable URL: http://www.jstor.org/stable/3172624
Page Count: 11
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Hypothesis Testing with Scanner Data: The Advantage of Bayesian Methods
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Abstract

The author presents a Bayesian method of testing possibly non-nested restrictions in a multivariate linear model and, using store-level scanner data, compares it with classical methods. The Bayesian tests are shown to be either equal or superior to classical tests in terms of objectivity, ease of use, and ease of interpretation. Classical tests lack a natural metric for comparing non-nested models and often employ super models in which the entertained hypotheses are special cases (i.e., nested). Nested classical tests are almost always significant when used with scanner data, making their interpretation problematic. In contrast, large samples cause Bayesian methods to become less dependent on subjective aspects of the prior distribution and therefore more objective.

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