Multivariate logistic Regression for the Estimate of Response Functions in the Conjoint Analysis
Abstract
En
In the Conjoint Analysis (COA) model proposed here - an extension of traditional COA - the polytomous response variable (i.e. evaluation of the overall desirability of alternative product profiles) is described by a sequence of binary variables. To link the categories of overall evaluation to the factors levels, we adopt a multivariate logistic regression model at the aggregate level. The model provides several overall desirability functions (aggregated part-worths sets), as many as the overall ordered categories are, unlike the traditional metric e non metric COA, which gives only one response function. We provide an application of the model.
In the Conjoint Analysis (COA) model proposed here - an extension of traditional COA - the polytomous response variable (i.e. evaluation of the overall desirability of alternative product profiles) is described by a sequence of binary variables. To link the categories of overall evaluation to the factors levels, we adopt a multivariate logistic regression model at the aggregate level. The model provides several overall desirability functions (aggregated part-worths sets), as many as the overall ordered categories are, unlike the traditional metric e non metric COA, which gives only one response function. We provide an application of the model.
DOI Code:
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Keywords:
Aggregate Level Analysis; Conjoint analysis; Multivariate Logistic Regression
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