Ordinal Logistic Regression for the Estimate of the Response Functions in the Conjoint Analysis ()
Abstract
In the Conjoint Analysis (COA) model proposed here – a new approach to estimate more than one response function–an extension of the 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 factor levels, we adopt – at the aggregate level – an ordinal logistic regression, based on a main effects experimental design.The model provides several overall desirability functions (aggregated part-worths sets), as many as the overall ordered categories are, unlike the traditional metric and non metric COA, which gives only one response function. We provide an application of the model and an interpretation of the main effects.
Share and Cite:
A. Luca, "Ordinal Logistic Regression for the Estimate of the Response Functions in the Conjoint Analysis,"
iBusiness, Vol. 3 No. 4, 2011, pp. 383-389. doi:
10.4236/ib.2011.34051.
Conflicts of Interest
The authors declare no conflicts of interest.
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