Optimal design of factorial paired comparison experiments in the presence of within-pair order effects

Goos, Peter; Großmann, Heiko

doi:10.1016/j.foodqual.2010.09.008

Cited by 13 publications

(5 citation statements)

References 48 publications

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“…continuing with the car analogy, if the attribute is ''colour'', its ''levels'' might be ''yellow'', ''orange'' and ''green'') and then the full factorial of the possible combinations of attributes and levels is reduced, with an experimental design. From among the various possible designs (Bush, Burgess, & Street, 2010;Crabbe & Vandebroek, 2012;Ferrini & Scarpa, 2007;Goos & Großmann, 2011;Vermeulen, Goos, & Vandebroek, 2008), fractional factorial orthogonal designs have been applied quite extensively in the literature, but other designs are possible and can be derived using open source 3 (R Development Core Team., 2011) or commercial software (SPSS Ò , Ngene Ò ).…”

Section: Choice Experiments Methodologymentioning

confidence: 99%

Consumer preferences regarding the introduction of new organic products. The case of the Mediterranean sea bass (Dicentrarchus labrax) in Italy

2013

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Section: Choice Experiments Methodologymentioning

confidence: 99%

Consumer preferences regarding the introduction of new organic products. The case of the Mediterranean sea bass (Dicentrarchus labrax) in Italy

2013

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“…Finally, Ruseckaite et al (2017) ignored a possible order effect. By including a within-pair order effect in the choice models introduced here, as described by Goos and Grossmann (2011), the analysis and optimal experimental design approach can be modified to account for possible order effects as well. Another useful extension of our work would be to combine mixture-process variable models with choice models.…”

Section: Discussionmentioning

confidence: 99%

Choice models with mixtures: An application to a cocktail experiment

Goos

Hamidouche

2019

Food Quality and Preference

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Data from choice experiments are analyzed using choice models. Proper modeling allows a good understanding of the consumer preferences, the identification of consumer segments and the optimization of products, and, eventually, offers opportunities for competitive advantage. However, in the analysis of choice data, products that are mixtures of ingredients have been largely overlooked. In this paper, we combine traditional choice models and traditional mixture models. We apply the resulting mixture choice models to data from a real-life experiment in which consumers made pair-wise comparisons involving seven cocktails. For the choice models, we first assume consumer homogeneity. Next, we allow for heterogeneity among individuals. Therefore, we discuss the multinomial logit model, the mixed logit model and the latent class model. For identifying segments, besides the latent class model, we explore various two-stage segmentation approaches in which forces, hierarchical Bayes estimates and Firth individual-level estimates are used as input for a cluster analysis. The results show that the mixed logit model and the latent class model describe the data better than the multinomial logit model. The optimal cocktails for the segments we identified using the various methods are not identical, but show many similarities.

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“…In Graßhoff and Schwabe (2008) a characterization of the locally optimal design in case of two factors design in a Bradley-Terry model is given, but for more complex situations it seems difficult to give general results. Goos and Grossmann (2011) consider also the problem when within-pair order effects are present. It seems that investigation of these issues in other models are not present in the literature.…”

Section: Discussionmentioning

confidence: 99%

Models for Paired Comparison Data: A Review with Emphasis on Dependent Data

Cattelan¹

2012

Statist. Sci.

137

105

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Thurstonian and Bradley-Terry models are the most commonly applied models in the analysis of paired comparison data. Since their introduction, numerous developments have been proposed in different areas. This paper provides an updated overview of these extensions, including how to account for object-and subject-specific covariates and how to deal with ordinal paired comparison data. Special emphasis is given to models for dependent comparisons. Although these models are more realistic, their use is complicated by numerical difficulties. We therefore concentrate on implementation issues. In particular, a pairwise likelihood approach is explored for models for dependent paired comparison data, and a simulation study is carried out to compare the performance of maximum pairwise likelihood with other limited information estimation methods. The methodology is illustrated throughout using a real data set about university paired comparisons performed by students.

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Optimal design of factorial paired comparison experiments in the presence of within-pair order effects

Cited by 13 publications

References 48 publications

Consumer preferences regarding the introduction of new organic products. The case of the Mediterranean sea bass (Dicentrarchus labrax) in Italy

Consumer preferences regarding the introduction of new organic products. The case of the Mediterranean sea bass (Dicentrarchus labrax) in Italy

Choice models with mixtures: An application to a cocktail experiment

Models for Paired Comparison Data: A Review with Emphasis on Dependent Data

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