Visualizing individual differences in pairwise comparison data

Böckenholt, Ulf

doi:10.1016/j.foodqual.2005.07.007

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…with elderly and hearing-impaired subjects). Paired comparisons are suitable in that case [6] because this method poses minimal constraints on the judge [7] [8] and allows for detection of subtle differences [7]. Therefore our assumption is that this method is better suited than for instance rating scales [9] [10].…”

Section: Introductionmentioning

confidence: 99%

Individual preference for the amount of noise reduction

Houben

Dijkstra²,

Dreschler

2010

3rd International Workshop on Perceptual Quality of Systems (PQS 2010)

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We measured individual preferences for pairs of audio streams differing in the amount of noise reduction (G min). To describe the paired comparison data a logistic probability model was used based on a quadratic preference utility function. This model allowed for the calculation of the G min that was optimal for each individual subject. For five out of ten subjects the optimized G min (ranging from 5.5 to 10dB) differed significantly from the optimum obtained for the grouped data (7.1dB). The predicted preference of the individually optimized G min over the group optimum was slightly higher than chance level (up to 60%).

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Section: Introductionmentioning

confidence: 99%

Individual preference for the amount of noise reduction

Houben

Dijkstra²,

Dreschler

2010

3rd International Workshop on Perceptual Quality of Systems (PQS 2010)

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“…The position of the health outcomes can be depicted graphically along these dimensions, which allows for an easy interpretation of the underlying similarity structure of the health outcomes. 6 Reason 6. It is easy to do.…”

Section: Reasonmentioning

confidence: 99%

Modeling Subjective Health Outcomes

Maydeu-Olivares¹,

Böckenholt²

2008

Medical Care

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T here is an increasing interest in measuring subjective health outcomes and in this issue of Medical Care, Krabbe 1 provides us with a much needed discussion of the potential usefulness of Thurstone's approach for quantifying subjective health outcomes using ordinal information, such as rankings. As pointed out eloquently by Krabbe, Thurstone's articles, 2,3 have had a tremendous influence on the development of methods for collecting and analyzing evaluative judgments in the form of preferences, attitudes, or more specifically, health outcomes. In fact, it is our view that Krabbe's discussion is rather modest. We feel more exuberant and present a list of top-10 reasons for using Thurstonian models. Although presented separately, these reasons should be viewed in conjunction because in this way they demonstrate the power and generality of this approach for studying and understanding evaluative judgments.For many years, it was not possible to apply Thurstone's model in its full generality because of computational limitations. In technical terms, given rankings of p health outcomes p-1 dimensional integration over a normal distribution is needed if the model is to be estimated by maximum likelihood. Before the computer revolution, this integration was unfeasible and when Thurstone proposed his model more than 80 years ago only univariate integration could be done. Having been trained as an engineer, Thurstone 2 proposed an effective 2-stage estimation procedure that has been the skeleton upon which most classic research on Thurstonian models have been built. 4 Krabbe discusses this method very nicely. Given the proportions by which one health outcome is preferred over another (the P matrix in Krabbe's article), one obtains the corresponding normal deviates (Z values) by univariate integration. Then, the parameters of the model are obtained from the Z values by minimum distance methods. If only univariate integration is to be performed, only highly restrictive versions of Thurstone's general model can be estimated (such as the so-called Case V model considered by Krabbe).Fortunately, these computational limitations are now overcome and Thurstone's model can be estimated in its full generality. As a result, the applicability of this approach has widened tremendously as our list of top-10 reasons illustrates. To put it bluntly, we are comparing a 1930s model car to the latest model when we look at Thurstone's classic Case V representation with its unrestricted counterpart. Still, the basic advantages of Thurstone's models should not be dismissed as illustrated by Krabbe, and our list of reasons highlights a number of them before we discuss the current capabilities of a Thurstonian analysis.Reason 1. It is easy for respondents. Asking individuals to rate all outcomes under investigation on a sufficiently fine scale is cognitively a complex task and casts much doubt as to the reliability of such ratings. On the other hand, comparing outcomes requires less cognitive effort on the side of the respondents. One of the simpl...

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“…The weights may be implicit or explicit. Böckenholt [6] defines weights in terms of 'attractiveness' of treatments.…”

Section: Introductionmentioning

confidence: 99%

Extending the Bradley–Terry model for paired comparisons to accommodate weights

Abbas¹,

Aslam

2011

Journal of Applied Statistics

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In the method of paired comparisons (PCs), treatments are compared on the basis of qualitative characteristics they possess, in the light of their sensory evaluations made by judges. However, there may emerge the situations where in addition to qualitative merits/worths, judges may assign quantitative weights to reflect/specify the relative importance of the treatments. In this study, an attempt is made to reconcile the qualitative and the quantitative PCs through assigning quantitative weights to treatments having qualitative merits using/extending the Bradley-Terry (BT) model. Behaviors of the existing BT model and the proposed weighted BT model are studied through the test of goodness-of-fit. Experimental and simulated data sets are used for illustration.

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Visualizing individual differences in pairwise comparison data

Cited by 4 publications

References 29 publications

Individual preference for the amount of noise reduction

Individual preference for the amount of noise reduction

Modeling Subjective Health Outcomes

Extending the Bradley–Terry model for paired comparisons to accommodate weights

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