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Conjoint analysis (CA) and discrete choice experimentation (DCE) are tools used in marketing, economics, transportation, health, tourism, and other areas to develop and modify products, services, policies, and programs, specifically ones that can be described in terms of attributes. A specific combination of attributes is called a concept profile. Building on the authors' significant work in the field, Choice-Based Conjoint Analysis: Models and Designs explores the design of experiment (DOE) issues that occur when constructing concept profiles and shows how to modify commonly used designs for solving DCE and CA problems. The authors provide historical and statistical background and discuss the concepts and inference. The book covers designs appropriate for four classes of DOE problems: (1) attributes in CA and DCE studies are often ordered; (2) studies increasingly are computer-assisted; (3) choice is often influenced by competition; and (4) constraints may exist on attribute levels. Discussion begins with commonly used "generic" designs.
The text then presents designs that avoid "dominated" or "dominating" profiles that may occur with ordered attributes and explores the use of orthogonal polynomials to describe relationships between ordered attribute levels and preference. Computer administration entails limited "screen real estate" for presenting concept profiles. The book covers approaches for subsetting attributes and/or levels to "fit" profiles into available "screen real estate." It then discusses strategies for sequential experimentation. Choice also is influenced by the availability of competing alternatives. The book uses availability and cross-effects designs to illustrate the design and analysis of portfolios and shows the relationship between availability effects and interaction effects in analysis of variance models. The last chapter highlights approaches to experimental design in which constraints are imposed on the levels of attributes. These designs provide the means to untangle the pricing and formulation problems in CA and DCE.
Damaraju Raghavarao is the Laura H. Carnell professor of statistics and chair of the Department of Statistics at Temple University in Philadelphia, Pennsylvania. Dr. Raghavarao is a fellow of the Institute of Mathematical Statistics and the American Statistical Association as well as an elected member of the International Statistical Institute. He earned his Ph.D. from Bombay University. James B. Wiley is a senior Cochran research fellow in the Department of Marketing and Supply Chain Management and the Department of Statistics at Temple University in Philadelphia, Pennsylvania. Dr. Wiley is also a visiting scholar at the University of Western Sydney. He earned his Ph.D. from the University of Washington. Pallavi Chitturi is an associate professor of statistics at Temple University in Philadelphia, Pennsylvania. Dr. Chitturi's research encompasses the areas of design of experiments, quality control, and conjoint analysis. She earned her Ph.D. from the University of Texas at Austin.
Introduction Conjoint Analysis (CA) Discrete Choice Experimentation (DCE) Random Utility Models The Logistic Model Contributions of the Book Some Statistical Concepts Principles of Experimental Design Experimental versus Treatment Design Balanced Incomplete Block Designs and 3-Designs Factorial Experiments Fractional Factorial Experiments Hadamard Matrices and Orthogonal Arrays Foldover Designs Mixture Experiments Estimation Transformations of the Multinomial Distribution Testing Linear Hypotheses Generic Designs Introduction Four Linear Models Used in CA and DCE Brands-Only Designs Attribute-Only Designs Brands-Plus-Attributes Designs Brands, Attributes, and Interaction Design Estimation and Hypothesis Testing Appendix: Logit Analysis of Traditional Conjoint Rating Scale Data Designs with Ordered Attributes Introduction Linear, Quadratic, and Cubic Effects Interaction Components: Linear and Quadratic An Illustration Pareto Optimal Designs Inferences on Main Effects Inferences on Main Effects in 2m Experiments Inferences on Interactions Orthogonal Polynomials Substitution Rate of Attributes Reducing Choice Set Sizes Introduction Subsetting Choice Sets Subsetting Levels into Overlapping Sets Subsetting Attributes into Overlapping Sets Designs Generated from a BIBD Cyclic Construction: s Choice Sets of Size s Each for an ss Experiment Estimating a Subset of Interactions Availability (Cross-Effects) Designs Introduction Brands-Only Availability Designs Portfolio Designs Brand and One (or More) Attributes Brands and More Than One Attribute Sequential Methods Introduction Sequential Experiment to Estimate All Two- and Three-Attribute Interactions Sequential Methods to Estimate Main Effects and Interactions, Including a Common Attribute in 2m Experiments CA Testing Main Effects and a Two-Factor Interaction Sequentially Interim Analysis Some Sequential Plans for 3m Experiments Mixture Designs Introduction Mixture Designs: CA Example Mixture Designs: DCE Example Mixture-Amount Designs Other Mixture Designs Mixture Designs: Field Study Illustration References Index
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