Abstract #301565

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JSM 2003 Abstract #301565
Activity Number: 29
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301565
Title: Product Comparison Through Paired Logistic Regression
Author(s): Pradipta Sarkar*+ and Charles H. Taylor and Jorge G. Morel
Companies: Procter & Gamble Company and Procter & Gamble Company and Procter & Gamble Company
Address: Ivorydale Technical Center, Cincinnati, OH, 45217-1025,
Keywords: logistic regression ; parametric bootstrap ; random logit
Abstract:

Suppose the performance of a product is measured in the laboratory with respect to two characteristics (X, W). Also assume that the likelihood of a typical consumer liking the product, denoted by the binary variable {Y=1, if consumer likes & Y=0, otherwise}, is solely dependent on these two characteristics. The probability that {Y=1} is modeled as a function of X and W via the logistic link. The model is fitted based on a reasonably large sample size covering a range of products with varying degrees of product acceptance. To compare expected consumer acceptance of a new product relative to an established benchmark, bivariate measurements (X, W) are collected on n pairs of experimental units and denoted by {(X1, W1)_i, (X2, W2)_i; i=1, .,n}. Here the vector of observations (X1, W1)_i is highly positively correlated with the vector of observations (X2, W2)_i. We capitalize on the relationships between (X, W) and Y along with the correlation between observations collected on paired sampling units to compare consumer acceptance of a new product with that of the benchmark product, based on laboratory testing. A two-step parametric bootstrap based methodology is proposed.

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