Choice-Based Conjoint Analysis: Models and Designs / Edition 1

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Overview

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.

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Editorial Reviews

From the Publisher
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

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Product Details

  • ISBN-13: 9781420099966
  • Publisher: Taylor & Francis
  • Publication date: 8/11/2010
  • Edition number: 1
  • Pages: 192
  • Product dimensions: 6.40 (w) x 9.40 (h) x 0.70 (d)

Meet the Author

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.

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Table of Contents

Preface

About the Authors

1 Introduction 1

1.1 Conjoint Analysis 3

1.2 Discrete Choice Experimentation 7

1.3 Random Utility Models 10

1.4 The Logistic Model 11

1.5 Contributions of the Book 13

2 Some Statistical Concepts 15

2.1 Principles of Experimental Design 15

2.2 Experimental versus Treatment Design 17

2.3 Balanced Incomplete Block Designs and 3-Designs 19

2.4 Factorial Experiments 21

2.5 Fractional Factorial Experiments 25

2.6 Hadamard Matrices and Orthogonal Arrays 28

2.7 Foldover Designs 31

2.8 Mixture Experiments 32

2.9 Estimation 35

2.9.1 Probability Models 35

2.9.2 Linear Estimation: Least Squares and Weighted Least Squares 36

2.9.3 Maximum Likelihood Estimation 38

2.10 Transformations of the Multinomial Distribution 40

2.11 Testing Linear Hypotheses 43

3 Generic Designs 45

3.1 Introduction 45

3.2 Four Linear Models Used in CA and DCE 45

3.2.1 Brands-Only Models 45

3.2.2 Attributes-Only Models 46

3.2.3 Brand-Plus-Attributes Designs 46

3.2.4 Brand-Plus-Attributes and Selected Two-Way Interactions Models 47

3.3 Brands-Only Designs 47

3.4 Attributes-Only Designs 52

3.5 Brands-Plus-Attributes Designs 53

3.6 Brands, Attributes, and Interaction Design 56

3.7 Estimation and Hypothesis Testing 72

Appendix 3A: Logit Analysis of Traditional Conjoint Rating Scale Data 77

4 Designs with Ordered Attributes 83

4.1 Introduction 83

4.2 Linear, Quadratic, and Cubic Effects 85

4.2.1 Attributes at Two Levels 87

4.2.2 Attributes at Three Levels 87

4.2.3 Attributes at Four Levels 87

4.2.4 Attributes at Five Levels 88

4.3 Interaction Components: Linear and Quadratic 89

4.4 An Illustration 90

4.5 Pareto Optimal Designs 94

4.6 Inferences on Main Effects 98

4.7 Inferences on Main Effects in 2m Experiments 99

4.8 Inferences on Interactions 100

4.9 Orthogonal Polynomials 101

4.10 Substitution Rate of Attributes 103

5 Reducing Choice Set Sizes 107

5.1 Introduction 107

5.2 Subsetting Choice Sets 108

5.3 Subsetting Levels into Overlapping Sets 108

5.4 Subsetting Attributes into Overlapping Sets 110

5.5 Designs Generated from a BIBD 111

5.6 Cyclic Construction: s Choice Sets of Size s Each for an ss Experiment 112

5.7 Estimating a Subset of Interactions 113

6 Availability (Cross-Effects) Designs 117

6.1 Introduction 117

6.2 Brands-Only Availability Designs 119

6.2.1 Relationships between Availability Effects and Interactions 123

6.2.2 Generating the Design Matrix for an Availability Design 127

6.3 Portfolio Designs 128

6.3.1 Random Utility Model for Portfolio Choice 129

6.4 Brand and One (or More) Attributes 135

6.4.1 Brands with One Attribute 136

6.4.2 One Attribute at Three or More Levels 140

6.5 Brands and More than One Attribute 141

6.5.1 General Results for Two or More Attributes with Two or More Levels 142

7 Sequential Methods 145

7.1 Introduction 145

7.2 Sequential Experiment to Estimate All Two- and Three-Attribute Interactions 146

7.3 Sequential Methods to Estimate Main Effects and Interactions, Including a Common Attribute in 2m Experiments 151

7.4 CA Testing Main Effects and a Two-Factor Interaction Sequentially 152

7.5 Interim Analysis 153

7.6 Some Sequential Plans for 3m Experiments 153

8 Mixture Designs 155

8.1 Introduction 155

8.2 Mixture Designs: CA Example 155

8.3 Mixture Designs: DCE Example 158

8.4 Mixture-Amount Designs 162

8.5 Other Mixture Designs 166

8.6 Mixture Designs: Field Study illustration 167

References 171

Index 177

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