Mixed Tree and Spatial Representation of Dissimilarity Judgments

Whereas previous research has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, focussing on the comparative fit at the aggregate level, we investigate whether there is heterogeneity among subjects in the extent to which their dissimilarity judgments are better represented by ultrametric tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data, that is formulated in a stochastic context, and entails a representation and a measurement model component. The latter involves distributional assumptions on the measurement error, and enables estimation by maximum likelihood. The representation component allows dissimilarity judgments to be represented either by a tree structure or by a spatial configuration, or a mixture of both. In order to investigate the appropriateness of tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate tree and spatial models, as well as with mixtures of pure trees and mixtures of pure spaces, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments at the individual level.Multidimensional scaling;tree models;mixture models;dissimilarity judgments

Differentiated Bayesian Conjoint Choice Designs

Previous conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about the parameter values. A computational procedure is developed that enables fast and easy implementation in practice. Even though the number of such different designs in the optimal set is small, it is demonstrated through a Monte Carlo study that substantial gains in efficiency are achieved over aggregate designs.experiments;consumer preferences;multinomial logit;discrete choice;estimator efficiency

Adaptive Multidimensional Scaling: The Spatial Representation of Brand Consideration and Dissimilarity Judgments

We propose Adaptive Multidimensional Scaling (AMDS) for simultaneously deriving a brand map and market segments using consumer data on cognitive decision sets and brand dissimilarities.In AMDS, the judgment task is adapted to the individual respondent: dissimilarity judgments are collected only for those brands within a consumers' awareness set.Thus, respondent fatigue and subjects' unfamiliarity with any subset of the brands are circumvented; thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived.Estimation of the AMDS model results in a spatial map in which the brands and derived segments of consumers are jointly represented as points.The closer a brand is positioned to a segment's ideal brand, the higher the probability that the brand is considered and chosen.An assumption underlying this model representation is that brands within a consumers' consideration set are relatively similar.In an experiment with 200 subjects and 4 product categories, this assumption is validated.We illustrate adaptive multidimensional scaling on commercial data for 20 midsize car brands evaluated by 212 members of a consumer panel.Potential applications of the method and future research opportunities are discussed.scaling;brands;market segmentation

The influence of advertisement familiarity and originality on visual attention and brand memory.

Based on Mandler's theory of schema organization and previous visual attention research, we formulate and test hypotheses about the impact of ad familiarity and ad originality on attention and memory for print advertisements. To that end, one hundred and nineteen consumers browsed through two consumer magazines containing 68 print advertisements. Attention to the ads and their brand, picture and text components was assessed through infrared eye tracking. Trained judges rated the ads independently for familiarity and originality. In support of the hypotheses we find a sharp attention decline with ad familiarity, which is largely due to a reduction in attention to text. Originality of ad execution serves as a buffer against the negative influence of ad familiarity on attention, but only for the brand and picture components. The reduction of attention to the text is even larger for original than for unoriginal ads. Moreover, over and above their indirect influence through visual attention patterns, ad familiarity, ad originality and their interaction had a direct influence on brand memory.Advertising;

Mixture model analysis of complex samples

This paper investigates asymmetric effects of monetary policy over the business cycle. A two-state Markov Switching Model is employed to model both recessions and expansions. For the United States and Germany, strong evidence is found that monetary policy is more effective in a recession than during a boom. Also some evidence is found for asymmetry in the United Kingdom and Belgium. In the Netherlands, monetary policy is not very effective in either regime.

A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods

We compare three alternative Maximum Likelihood Multidimensional Scaling methods for pairwise dissimilarity ratings, namely MULTISCALE, MAXSCAL, and PROSCAL in a Monte Carlo study.The three MLMDS methods recover the true con gurations very well.The recovery of the true dimensionality depends on the test criterion (likelihood ratio test, AIC, or CAIC), as well as on the MLMDS method. The three MLMDS methods t the dissimilarity data equally well.The methods are relatively robust against violations of their distributional assumptions. MULTISCALE outperforms PROSCAL and MAXSCAL with respect to computation time.In a separate Monte Carlo study, it is shown that the MLMDS methods frequently converge to local optima, especially if a random start is used.Rational starts, however, turn out to provide a satisfactory solution for the local optima problem.Implications for researchers intending to apply MLMDS are provided

Searching the ideal inhaled vasodilator: From nitric oxide to prostacyclin

Today, the technique to directly administer vasodilators via the airway to treat pulmonary hypertension and to improve pulmonary gas exchange is widely accepted among clinicians. The flood of scientific work focussing on this new therapeutic concept had been initiated by a fundamental new observation by Pepke-Zaba {[}1] and Frostell in 1991 {[}2]: Both scientists reported, that inhalation of exogenous nitric oxide (NO) gas selectively dilates pulmonary vessels without a concomittant systemic vasodilation. No more than another decade ago NO was identified as an important endogenous vasodilator {[}3] while having merely been regarded an environmental pollutant before that time. Although inhaled NO proved to be efficacious, alternatives were sought-after due to NO's potential side-effects. In search for the ideal inhaled vasodilator another group of endogenous mediators - the prostanoids - came into the focus of interest. The evidence for safety and efficacy of inhaled prostanoids is - among a lot of other valuable work - based on a series of experimental and clinical investigations that have been performed or designed at the Institute for Surgical Research under the guidance and mentorship of Prof. Dr. med. Dr. h.c. mult. K. Messmer {[}4-19]. In the following, the current and newly emerging clinical applications of inhaled prostanoids and the experimental data which they are based on, will be reviewed. Copyright (C) 2002 S. Karger AG, Basel