Consumer Research • Choice Models
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Choice Models
Fit Models for Choice Experiments
The Choice platform is designed for use in market research experiments, where the ultimate goal is to discover the preference structure of consumers. Then, this information is used to design products or services that have the attributes most desired by consumers.
Features provided in the Choice platform include:
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Ability to use information about consumer traits as well as product attributes.
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Integration of data from one, two, or three sources.
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Ability to use the integrated profiler to understand, visualize, and optimize the response (utility) surface.
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Provides subject-level scores for segmenting or clustering your data.
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Uses a special default bias-corrected maximum likelihood estimator described by Firth (1993). This method has been shown to produce better estimates and tests than MLEs without bias correction. In addition, bias-corrected MLEs ameliorate separation problems that tend to occur in logistic-type models. Refer to Heinze and Schemper (2002) for a discussion of the separation problem in logistic regression.
The Choice platform is not appropriate to use for fitting models that involve:
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Ranking or scoring.
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Nested hierarchical choices. (PROC MDC in SAS/ETS can be used for such analysis.)
Choice Platform Example
Contents 
Choice Modeling Platform Overview
Example of the Choice Platform
Launch the Choice Platform
Choice Model Output
Choice Platform Options
Valuing Trade-offs
Example of Valuing Trade-offs
One-Table Analysis
Example of One-Table Analysis
Segmentation
Example of Segmentation
Special Data Rules
Default Choice Set
Subject Data with Response Data
Logistic Regression
Transforming Data
Example of Transforming Data to Two Analysis Tables
Example of Transforming Data to One Analysis Table
Additional Examples of Logistic Regression Using the Choice Platform
Example of Logistic Regression Using the Choice Platform
Example of Logistic Regression for Matched Case-Control Studies
Statistical Details