| Course ID: | MARK 7700. 3 hours. |
| Course Title: | Discrete Choice and Conjoint Analysis |
Course
Description: | A practitioner-oriented introduction to conjoint and discrete
choice analysis. Topics includes self-explicated approaches,
full profile ratings/rankings conjoint, hybrid methods, choice-
based conjoint, MaxDiff, latent class and hierarchical Bayes
methodologies. Students receive hands-on real-world experience
applying the different methods using relevant software.
Applications include pricing, optimization, segmentation, and
new product development. |
| Oasis Title: | Discrete Choice Conjoint Analy |
| Prerequisite: | Permission of department |
Semester Course
Offered: | Offered spring semester every year. |
| Grading System: | A-F (Traditional) |
|
| Course Objectives: | Upon completion of this course, students should be able to:
• Define and explain the following: Conjoint analysis, self-
explicated approaches, hybrid methods, discrete choice analysis,
MaxDiff scaling, latent class methods, hierarchical Bayes
methods.
• Perform conjoint/choice/MaxDiff for marketing research
applications. Tasks include: formulation of the problem,
questionnaire construction, creation of the appropriate design,
model estimation and analysis, validation, prediction, and
reporting results.
• Evaluate, interpret, and utilize conjoint/choice/MaxDiff
studies performed by others.
• Recall recent advances in conjoint/choice/MaxDiff that
appear in the practitioner-oriented literature. |
| Topical Outline: | • Conjoint Analysis Fundamentals and Self-Explicated
Methods
• Reporting Conjoint Results: Descriptives and Conjoint
Simulators
• Experimental Design for Conjoint
• Conjoint Modeling and Estimation
• Discrete Choice Fundamentals
• Experimental Design for Discrete Choice Analysis
• Discrete Choice Modeling and Estimation
• Reporting Discrete Choice Results: Descriptives and
Discrete Choice Simulators
• Latent Class Methods
• Hierarchical Bayes Methods
• MaxDiff Scaling (Design, Estimation, Reporting) |