1st Edition

Choice-Based Conjoint Analysis Models and Designs

    192 Pages 6 B/W Illustrations
    by Chapman & Hall

    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.

    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

    Biography

    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.

    It is a pleasure to review this book … For those already familiar with the subject, the text is well worth adding to their book collection …
    —Carl M. O’Brien, International Statistical Review, 2012

    this book is both educational and interesting to read and is suitable for anyone interested in developing a CA/DCE study. The book is unique, particularly in terms of the breadth and depth of information on experimental designs. The authors did an excellent job providing both contextual and technical details in a form that is both engaging and easy to read. The illustrations are easy to follow and relevant to the related content. … a nice addition to the CA/DCE literature and should be useful to researchers and graduate students alike.
    —Mayukh Dass, Journal of the American Statistical Association, December 2011