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A Comparison of criteria to design efficient choice experiments

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The purpose of this article is to find designs that allow for precise predictions of consumers' choices rather than to search for designs that produce only an efficient estimation of the underlying statistical model. To this end, the authors employ two prediction-based criteria, the G- and V-optimality criteria, and compared them with the estimation-based D- and A-optimality criteria traditionally used in the marketing literature. The authors implement these criteria in a Bayesian manner to account for the fact that the parameters of the statistical model, the multinomial logit model, are unknown. As they expected, the results indicate that the Bayesian V-optimal designs and, to a lesser extent, the Bayesian G-optimal designs are best suited for predictive purposes. The D-optimal designs rank third in this aspect, but the differences in predictive ability compared with the V- and G-optimal designs are rather small. The results also show that the three-alternative optimal designs lead to better predictions than the two-alternative designs and to more accurate parameter estimates.Printed Journal

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Ketersediaan

Call Number	Location	Available
	PSB lt.dasar - Pascasarjana	1

Penerbit	American Marketing Association.,
Edisi	-
Subjek	Market research Product Choice studies Bayesian analysis Criteria Parameter optimization
ISBN/ISSN	222437
Klasifikasi	-
Deskripsi Fisik	-
Info Detail Spesifik	-
Other Version/Related	Tidak tersedia versi lain
Lampiran Berkas	Tidak Ada Data