Sensitivity Analysis of Composite Indicators through Mixed Model Anova

The paper proposes a new approach for analysing the stability of Composite Indicators. Starting from the consideration that different subjective choices occur in their construction, the paper emphasizes the importance of investigating the possible alternatives in order to have a clear and objective picture of the phenomenon under investigation. Methods dealing with Composite Indicator stability are known in literature as Sensitivity Analysis. In such a framework, the paper presents a new approach based on a combination of explorative and confirmative analysis aiming to investigate the impact of the different subjective choices on the Composite Indicator variability and the related individual differences among the statistical units as well.sensitivity analysis,composite indicators,analysis of variance,principal component analysis

On the use of quantile regression to deal with heterogeneity: the case of multi-block data

AbstractThe aim of the paper is to propose a quantile regression based strategy to assess heterogeneity in a multi-block type data structure. Specifically, the paper deals with a particular data structure where several blocks of variables are observed on the same units and a structure of relations is assumed between the different blocks. The idea is that quantile regression complements the results of the least squares regression by evaluating the impact of regressors on the entire distribution of the dependent variable, and not only exclusively on the expected value. By taking advantage of this, the proposed approach analyses the relationship among a dependent variable block and a set of regressors blocks but highlighting possible similarities among the statistical units. An empirical analysis is provided in the consumer analysis framework with the aim to cluster groups of consumers according to the similarities in the dependence structure among their overall liking and the liking for different drivers

A quantile regression perspective on consumer heterogeneity

The main objective of the consumer analysis is to analyze the heterogeneity of preferences with respect to a predefined set of products. In some cases, consumer preferences are also related to some specific drivers in order to obtain preference models to be used in planning marketing strategies. The aim of this work is to present a strategy that allows to estimate preference models taking into account the individual differences of consumers in the liking pattern. The proposed strategy consists in using quantile regression to obtain preference models for homogeneous groups of consumers with respect to the quantile that best represents them. The strategy will be tested on data deriving from a case study on consumer’s preferences for muscadine grape juices

MODELLING DRIVERS OF CONSUMER LIKING HANDLING CONSUMER AND PRODUCT EFFECTS

The aim of the present paper is to approach the analysis of the relationship between overall liking and specific likings for eleven types of white corn tortilla chips. The main objective is to estimate a model for predicting the overall liking that also considers the heterogeneity in consumer liking. A further objective is to evaluate the adequacy of a single model for the different products. The first objective is achieved by using quantile regression, providing an estimate of the dependency relationship between overall and specific likings with respect to predefined quantiles, each corresponding to a specific segment of consumers. The second objective is achieved by using a specific strategy aimed at finding specific models for each product or group of similar products. The results show that the overall liking mostly depends on one specific liking, and its impact varies significantly for different segments of consumers. Furthermore, three different models are identified for three groups of products that differ in the same most important driver of the global model

The use of quantile regression in consumer studies

The main objective of this paper is to describe and discuss the use of quantile regression in consumer studies. The situation analyzed is one of relating segments of consumers obtained based on their acceptance pattern to additional consumer characteristics, including attitudes, habits and demographics variables. The paper shows how the conditional quantiles of a distribution can provide additional insight that is not provided by standard regression approaches. This type of information can be important for understanding how for instance a consumer characteristic may influence disagreement in liking, which can be equally important as the predicted average liking. The advantages of the proposed methodology will be illustrated by data from a consumer test on iced coffee