The author derives D-optimal designs for main-effects, multinomial choice experiments using attribute levels as design parameters. The design solutions are similar to standard main-effects designs except that one attribute is used to manipulate response probabilities. The manipulator is key to implementing optimal designs in practice.
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References
1.
AbdelbasitK.M., and PlackettR.L. (1980), “Experimental Design for Binary Data,”Journal of the American Statistical Association, 87(381), 90–98.
2.
AignerD.J. (1979), “Sample Design for Electricity Pricing Experiments,”Journal of Econometrics, 11, 195–205.
3.
AlberiniA. (1995), “Optimal Designs for Discrete Choice Contingent Valuation Surveys: Single-Bound, Double-Bound and Bivariate Models,”Journal of Environmental Economics and Management, 28(3), 287–306.
4.
AlberiniA., and CarsonR.T. (1993), “Efficient Threshold Values for Binary Discrete Choice Contingent Valuation Surveys and Economic Experiments,” Discussion Paper No. QE93–14, Resources for the Future, Quality of the Environment Division, Washington, DC.
5.
AroraN., and HuberJ. (2001), “Improving Parameter Estimates and Model Prediction by Aggregate Customization,”Journal of Consumer Research, 28(September), 273–83.
6.
Ben-AkivaM., and LermanS. (1985), Discrete Choice Analysis.Cambridge, MA: MIT Press.
7.
BunchD.S., LouviereJ.J., and AndersonD. (1994), “A Comparison of Experimental Design Strategies for Multinomial Logit Models: The Case of Generic Attributes,” working paper, Graduate School of Management, University of California, Davis.
CopasJ.B. (1988), “Binary Regression Models for Contaminated Data,”Journal of the Royal Statistical Society, B, 50(2), 225–65.
10.
DellaertB.G.C., BrazellJ.D., and LouviereJ.J. (1999), “The Effect of Attribute Variation on Consumer Choice Consistency,”Marketing Letters, 10(2), 139–47.
11.
El-HelbawyA.T., AhmedE.A., and AlharbeyA.H. (1994), “Optimal Designs for Asymmetrical Factorial Paired Comparison Experiments,”Communications in Statistics, 23(3), 663–81.
12.
FedorovV. V. (1972), Theory of Optimal Designs.New York: Academic Press.
13.
HahnG.J., and ShapiroS.S. (1966), “A Catalog and Computer Program for the Design and Analysis of Orthogonal Symmetric and Asymmetric Fractional Factorial Experiments,”Technical Report 66-C 165.Schenectady, NY: General Electric Research and Development Center.
14.
HuberJoel, and ZwerinaKlaus (1996), “The Importance of Utility Balance in Efficient Choice Designs,”Journal of Marketing Research, 33(August), 307–17.
KuhfeldWarren F. (2000), “Multinomial Logit, Discrete Choice Modeling: An Introduction to Designing Choice Experiments, and Collecting, Processing, and Analyzing Choice Data with the SAS System,” technical report, SAS Institute Inc.
17.
KuhfeldWarren F., TobiasRandall D., and GarrattMark (1994), “Efficient Experimental Design with Marketing Research Applications,”Journal of Marketing Research, 21(November), 545–57.
18.
LazariAndreas G., and AndersonDonald A. (1994), “Designs of Discrete Choice Set Experiments for Estimating Both Attribute and Availability Cross Effects,”Journal of Marketing Research, 31(August), 375–83.
19.
LouviereJordan J. (1984), “Using Discrete Choice Experiments and Multinomial Logit Choice Models to Forecast Trial in a Competitive Retail Environment: A Fast Food Restaurant Illustration,”Journal of Retailing, 60(4), 81–105.
20.
LouviereJordan J. (1988), Analyzing Decision Making: Metric Conjoint Analysis.Newbury Park, CA: Sage Publications.
21.
LouviereJordan J., and WoodworthG. (1983), “Design and Analysis of Simulated Consumer Choice of Allocation Experiments: A Method Based on Aggregate Data,”Journal of Marketing Research, 20(August), 350–67.
22.
MagatW.A., ViscusiW.K., and HuberJ. (1988), “Paired Comparison and Contingent Valuation Approaches to Morbidity Risk Valuation,”Journal of Environmental Economics and Management, 15(December), 395–411.
23.
McFaddenD. (1974), “Conditional Logit Analysis of Qualitative Choice Behavior,” in Frontiers in Econometrics, ZarembkaP., ed. New York: Academic Press, 105–42.
24.
MinkinS. (1987), “Optimal Designs for Binary Data,”Journal of the American Statistical Association, 82(400), 1098–103.
25.
OffenW.W., and LittellR.C. (1987), “Design of Paired Comparison Experiments When Treatments Are Levels of a Single Quantitative Variable,”Journal of Statistical Planning and Inference, 15, 332–46.
26.
OpaluchJ.J., SwallowS.K., WeaverT., WesselsC.W., and WichelnsD. (1993), “Evaluating Impacts from Noxious Facilities: Including Public Preferences in Current Siting Mechanisms,”Journal of Environmental Economics and Management, 24(January), 41–59.
27.
SándorZsolt, and WedelMichel (2001), “Designing Conjoint Choice Experiments Using Managers' Prior Beliefs,”Journal of Marketing Research, 38(November), 430–44.
SteffensK., LupiF., KanninenB., and HoehnJ. (2001), “A Sequential Updating Approach to the Experimental Design of a Binary Choice Model,” working paper, Department of Economics, Northern Michigan University.
30.
SwaitJoffre, and LouviereJordan (1993), “The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models,”Journal of Marketing Research, 30(August), 305–14.
31.
van BerkumE.E.M. (1987), “Optimal Paired Comparison Designs for Factorial and Quadratic Models,”Journal of Statistical Planning and Inference, 15, 265–78.
32.
WinerB.J., BrownD.R., and MichelsK.M. (1991), Statistical Principles in Experimental Design, 3d ed. New York: McGraw-Hill.
33.
Wolfram Research Inc. (1999), Mathematica 4.0.Champaign, IL: Wolfram Research Inc.
34.
WuC.F.J. (1985), “Efficient Sequential Designs with Binary Data,”Journal of the American Statistical Association, 80(December), 974–84.
35.
WuC.F.J. (1988), “Optimal Design for Percentile Estimation of a Quantal Response Curve,” in Optimal Design and Analysis of Experiments, DodgeY., FedorovV., and WynnH., eds. Amsterdam, The Netherlands: Elsevier Science Publishers, 213–23.
