Conjoint analysis (CA) and discrete choice experimentation (DCE) are tools used to facilitate development and modification of products, services, policies, and programs. Building on the authors’ significant work in the field, this book explores the experimental design issues that occur when constructing concept profiles for DCE and CA studies. It show how to modify commonly used designs and models as well as develop new types of designs for solving DCE and CA problems. Requiring minimal mathematical background, the text explains experimental design concepts through illustrative examples and provides detailed coding of design matrices for standard designs.
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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.
Conjoint analysis (CA) and discrete choice experimentation (DCE) are tools used to facilitate development and modification of products, services, policies, and programs. Building on the authors' significant work in the field, this book explores the experimental design issues that occur when constructing concept profiles for DCE and CA studies. It show how to modify commonly used designs and models as well as develop new types of designs for solving DCE and CA problems. Requiring minimal mathematical background, the text explains experimental design concepts through illustrative examples and provides detailed coding of design matrices for standard designs.
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