Choice-Based Conjoint Analysis : Models and Designs book cover
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Choice-Based Conjoint Analysis
Models and Designs




ISBN 9781420099966
Published August 3, 2010 by Chapman and Hall/CRC
192 Pages 6 B/W Illustrations

 
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Book Description

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.

Table of Contents

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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Author(s)

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.

Reviews

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