Design of Experiments for Eliciting Customer Preferences



In this chapter, the survey methods needed to elicit the customer preference data to estimate a DCA or OL model, are introduced. The survey methods are based upon established Design of Experiments methodologies, but adapted for the specific needs of stated preference experiments.


Choice Experiment Design Alternative Fisher Information Matrix Prediction Variance Order Logit Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Customer-desired product attributes


Pairwise correlation coefficient for multinomial covariance matrix


Choice model coefficient in customer’s utility function


Number of configurations given to a single respondent


Derivative of π i


Matrix determinant


Engineering Attributes


Random unobservable part of the utility of configuration i for respondent n


Extended experiment design point, including intercept/cutpoints, interaction, and higher ordered terms than x


PDF of the logistic distribution


CDF of the logistic distribution


Extended design matrix


Candidate set of design points


Generalized least squares (Regression)


A configuration


Matrix inverse


Ordered logit cutpoints


Number of configurations in a complete experimental design


Fisher information matrix of an experimental design


A block or respondent


Ordinary least squares (Regression)


Number of ordered ratings categories


Working correlation matrix

\( \pi_{inp} \)

Probability of rating p for respondent n and configuration i




Pairwise ratings correlation coefficient


Model fit statistic for ordered logit/probit model


Variance at the respondent level


Variance at the observation level


Human attributes


Number of tries conducted in the algorithm


Utility of configuration i for respondent n in the ordered logit/probit equation


Asymptotic variance-covariance matrix


Design point for product and human factors. A sub-set of f(x)


Extended design matrix, composed of f(x)


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Wei Chen
    • 1
  • Christopher Hoyle
    • 2
  • Henk Jan Wassenaar
    • 3
  1. 1.Department of Mechanical EngineeringNorthwestern UniversityEvanstonUSA
  2. 2.Mechanical, Industrial & Manufacturing EngineeringOregon State UniversityCorvallisUSA
  3. 3.Zilliant Inc.AustinUSA

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