Models and Optimal Designs for Conjoint Choice Experiments Including a No-Choice Option

In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life, the respondent does not always make a choice: often he/she does not prefer any of the options offered. Therefore, including a no-choice option in a choice set makes a conjoint choice experiment more realistic. In the literature, three different models are used to analyze the results of a conjoint choice experiment with a no-choice option: the no-choice multinomial logit model, the extended no-choice multinomial logit model, and the nested no-choice multinomial logit model. We develop optimal designs for the two most appealing of these models using the D-optimality criterion and the modified Fedorov algorithm and compare these optimal designs with a reference design, which is constructed while ignoring the no-choice option, in terms of estimation and prediction accuracy. We conclude that taking into account the no-choice option when designing a no-choice experiment only has a marginal effect on the estimation and prediction accuracy as long as the model used for estimation matches the data-generating model.

[1]  Peter Goos,et al.  Recommendations on the use of Bayesian optimal designs for choice experiments , 2008, Qual. Reliab. Eng. Int..

[2]  Peter Goos,et al.  A Comparison of Criteria to Design Efficient Choice Experiments , 2006 .

[3]  Deborah J. Street,et al.  Optimal designs for choice experiments with asymmetric attributes , 2005 .

[4]  Giorgos Zacharia,et al.  Generalized robust conjoint estimation , 2005 .

[5]  K. Ballman.  Discrete Choice Methods With Simulations , 2005 .

[6]  Deborah J. Street,et al.  Optimal stated preference choice experiments when all choice sets contain a specific option , 2004 .

[7]  Heinz Holling,et al.  Optimal designs for main effects in linear paired comparison models , 2004 .

[8]  John R. Hauser,et al.  Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis , 2004 .

[9]  R. Dhar,et al.  The Effect of Forced Choice on Choice , 2003 .

[10]  Deborah J. Street,et al.  Optimal Designs for 2 k Choice Experiments , 2003 .

[11]  Peter Goos,et al.  The optimal design of blocked and split-plot experiments , 2002 .

[12]  M. Wedel,et al.  Designing Conjoint Choice Experiments Using Managers' Prior Beliefs , 2001 .

[13]  Joel Huber,et al.  Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments , 2001 .

[14]  M. Wedel,et al.  The No—Choice Alternative in Conjoint Choice Experiments , 2001 .

[15]  R. Dhar.  Consumer Preference for a No-Choice Option , 1997 .

[16]  Joel Huber,et al.  The Importance of Utility Balance in Efficient Choice Designs , 1996 .

[17]  W. Kamakura,et al.  Modeling Preference and Structural Heterogeneity in Consumer Choice , 1996 .

[18]  Joel Huber,et al.  A General Method for Constructing Efficient Choice Designs , 1996 .

[19]  Michel Wedel,et al.  Assessing the effects of abstract attributes and brand familiarity in conjoint choice experiments , 1998 .

[20]  Michel Wedel,et al.  Concomitant Variable Latent Class Models for Conjoint Analysis , 1994 .

[21]  Mark J. Garratt,et al.  Efficient Experimental Design with Marketing Research Applications , 1994 .

[22]  Jonathan Baron,et al.  Reference Points and Omission Bias , 1994 .

[23]  Ruud H. Koning,et al.  On the compatibility of nested logit models with utility maximization , 1994 .

[24]  P. Laycock,et al.  Optimum Experimental Designs , 1995 .

[25]  R. D. Cook,et al.  A Comparison of Algorithms for Constructing Exact D-Optimal Designs , 1980 .