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.
Athena Title
Discrete Choice Conjoint Analy
Prerequisite
Permission of department
Semester Course Offered
Offered spring
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)
Syllabus