Model-robust choice experiments: Discussion and case study

Quality and Reliability Engineering International

Volumn 28, Issue 1, 2012, Pages 115-122

  Lawson, John S ^a   Henderson, Sheldon ^b   Peterson, Jenifer ^b  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

^b Dionex Corp   (United States)

Author keywords

Bayesian optimal design; interactions; locally optimal design; projection properties; utility neutral design

Indexed keywords

BAYESIAN OPTIMAL DESIGNS; CHOICE EXPERIMENTS; CUSTOMER PREFERENCES; INDUSTRIAL EXPERIMENTATION; MAIN EFFECT; MARKETING RESEARCH; OBJECTIVE INFORMATION; OPTIMAL DESIGN; PRODUCT ATTRIBUTES; PROJECTION PROPERTY; QUALITY ENGINEERING; VOICE OF THE CUSTOMER;

BEAM PLASMA INTERACTIONS; CUSTOMER SATISFACTION; DESIGN; OPTIMAL SYSTEMS; OPTIMIZATION; QUALITY FUNCTION DEPLOYMENT; SALES;

EXPERIMENTS;

EID: 84856079272     PISSN: 07488017     EISSN: 10991638     Source Type: Journal    
DOI: 10.1002/qre.1225     Document Type: Article

References (33)

1. Hauser JR, Clausing D,. The house of quality. Harvard Business Review 1988; 66: 63-73.
2. Lai X, Tan K, Xie M,. Optimizing product design using quality function deployment: a case study. Quality and Reliability Engineering International 2007; 23: 45-57. (Pubitemid 46278026)
3. Barone S, Lombardo A, Tarantino,. A weighted logistic regression for conjoint analysis and Kansei engineering. Quality and Reliability Engineering International 2007; 23: 689-706. (Pubitemid 350202600)
4. Raharjo H, Brombacher AC, Goh TN, Bergman B,. On integrating Kano's model dynamics into QFD for multiple product design. Quality and Reliability Engineering International 2010; 26: 351-363.
5. Green PE, Wind Y,. A new way to measure consumers' judgments. Harvard Business Review 1975; 53: 107-117.
6. Louviere JJ, Woodworth G,. Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. Journal of Marketing Research 1983; 20: 350-367.
7. Ragavarao D, Wiley JB, Chitturi P,. Choice-based Conjoint Analysis: Models and Designs. Chapman & Hall/CRC: Boca Raton, 2011.
8. McFadden D,. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, Zarembka P, (ed.). Academic Press: New York, 1974.
9. McFadden D, Train K,. Mixed MNL models of discrete response. Journal of Applied Econometrics 2000; 15: 447-470.
10. Sándor Z, Wedel M,. Profile construction in experimental choice designs for mixed logit models. Marketing Science 2002; 21: 455-475.
11. Yu J, Goos P, Vandebroek M,. Efficient conjoint choice designs in the presence of respondent heterogeneity. Marketing Science 2009; 28: 122-135.
12. Huber J, Zwerina K,. The importance of utility balance in efficient choice designs. Journal of Marketing Research 1996; 33: 307-317.
13. Kuhfeld WF,. Marketing research methods in SAS-report mr2009. Technical Report, SAS Institute, Cary NC, 2009. Available at: [Accessed 15 March 2010 ].
14. Sándor Z, Wedel M,. Designing conjoint choice experiments using managers prior beliefs. Journal of Marketing Research 2001; 38: 430-444.
15. Information about JMP Software is available at.
16. Kessels R, Jones B, Goos P, Vandebroek M,. Recommendations on the use of Bayesian optimal designs for choice experiments. Quality and Reliability Engineering International 2008; 24: 737-744.
17. Yu J, Goos P, Vandebroek M,. Model-robust design of conjoint choice experiments. Communications in Statistics-Simulation and Computation 2008; 37: 1603-1621.
18. Street DJ, Burgess L,. The Construction of Optimal Stated Choice Experiments. Wiley: New York, 2007.
19. Kuhfeld WF, Tobias RD, Garratt M,. Efficient experimental design with marketing research applications. Journal of Marketing Research 1994; 31: 545-547.
20. k choice experiments. Communications in Statistics 2003; 32: 2185-2206.
21. Box GEP, Hunter WG, Hunter JS,. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Wiley: New York, 1978.
22. Lawson J,. Design and Analysis of Experiments with SAS. Chapman & Hall/CRC: Boca Raton, 2010.
23. Lawson J,. One step screening and process optimization experiments. The American Statistician 2003; 57: 15-20. (Pubitemid 36260038)
24. Jones B, Montgomery DC,. Alternatives to resolution IV screening designs in 16 runs. International Journal of Experimental Design and Process Optimization 2010; 1: 285-295.
25. Wang JC, Wu CFJ,. A hidden projection property of Plackett-Burman and related designs. Statistica Sinica 1995; 5: 235-250.
26. Loeppky JL, Sitter RR, Tang B,. Nonregular designs with desirable projection properties. Technometrics 2007; 49: 456-467.
27. Plackett RL, Burman JP,. The design of optimum multifactorial experiments. Biometrika 1946; 31: 305-325.
28. Hall M,. Hadamard matrix of order 16. Jet Propulsion Laboratory Research Summary 1961; 1: 21-26.
29. Lin D,. Spotlight interaction effects in main effects plans: A supersaturated design approach. Quality Engineering 1999; 11: 133-139.
30. Hamada M, Wu CFJ,. Analysis of designed experiments with complex aliasing. Journal of Quality Technology 1992; 24: 130-137.
31. Lawson J,. Regression analysis of experiments with complex confounding patterns guided by the alias matrix. Computational Statistics and Data Analysis 2002; 39: 227-241. (Pubitemid 34468299)
32. Chipman H, Hamada M, Wu CFJ,. A Bayesian variable selection approach for analyzing designed experiments with complex aliasing. Technometrics 1997; 39: 372-381. (Pubitemid 127642258)
33. Cook RD, Nachtsheim CJ,. Computer aided blocking of factorial and response surface designs. Technometrics 1989; 31: 339-346.