Rosa Arboretti Giancristofaro

Consumer preferences in food packaging: CUB models and conjoint analysis

Purpose – Packaging features have been shown to be of great importance for the consumer final choice of fresh products (Silayoi and Speece, 2007). Packaging is an extrinsic attribute, which consumers tend to rely on, when relevant intrinsic attributes of the product are not available. In the current literature, studies on the influences of packaging features on consumer preferences are mainly related to classical preference evaluation methods like conjoint analysis (CA). The purpose of this paper is to apply both CA and the less known combination of uniform discrete and shifted binomial distributions (CUB) models to food packaging evaluations. Design/methodology/approach – Starting from a real case study in this field, along with CA, the author apply CUB models (Iannario and Piccolo, 2010) as a useful tool to evaluate preferences. CUB models can grasp some psychological characteristics of consumers related to the “feeling” toward packaging attributes and related to an inherently “uncertainty” that affects...

Advantages of the Closed Testing Method in Multiple Comparisons Procedures

This article deals with the use of the Closed Testing approach in Multiple Comparison Procedures (MCPs). MCPs occur when after rejection of a global hypothesis of no effect of a given treatment, a set of pairwise comparisons between levels of that treatment are performed in order to find out significant differences between levels. Given a set of partial hypotheses, such as the pairwise comparisons of an MCP, the Closed Testing approach concentrates on testing the family of all the non empty intersections of these partial hypotheses. The results of our simulation study highlight the advantages of closed testing methods and prove that they are more powerful than other classic MCPs controlling the FWER.

A New Conjoint Analysis Procedure with Application to Marketing Research

Abstract In this article we propose some extensions and applications of the nonparametric combination of dependent rankings (see Pesarin, F., Lago, A. (2000). Nonparametric combination of department rankings with applications to the quality assessment of industrial products. Metron LVIII (1–2):39–52.) This methodology is applied to Conjoint Analysis in order to aggregate (ex ante) preferences from a group of individuals. Furthermore, a new global association test (GAT) is introduced in order to test for the association of the global ranking with all attributes of interest. The GAT procedure allows the experimenter to have clear indications on significant attributes by considering the intensity of the optimal weights given by the procedure itself. This may help the experimenter in interpreting the usual analysis involving the normal plot for detecting active effects.

A Comparison among Combination-Based Permutation Statistics for Randomized Complete Block Design

It is well known that a best permutation test for all population distributions P does not generally exist, because the most powerful unbiased permutation test is a function of the population distribution P which is assumed to be unknown (Pesarin and Salmaso, 2010). In this work, we focus our attention on the randomized complete block design in case of an ordered categorical response variable, which is the typical reference setting in many psychometric studies. We compared via a Monte Carlo simulation study several combination-based permutation test statistics and we found out that the Multi-focus statistic (Finos and Salmaso, 2004) using the Fishers combining function appears to be the more powerful solution which we proved also to be better under non normal errors than traditional parametric and rank-based nonparametric counterparts.

Dependency and truncated forms of combinations in multivariate combination-based permutation tests and ordered categorical variables

ABSTRACT Quite an important problem usually occurs in several multi-dimensional hypotheses testing problems when variables are correlated. In this framework the non-parametric combination (NPC) of a finite number of dependent permutation tests is suitable to cover almost all real situations of practical interest since the dependence relations among partial tests are implicitly captured by the combining procedure itself without the need to specify them [Pesarin F, Salmaso L. Permutation tests for complex data: theory, applications and software. Chichester: Wiley; 2010a]. An open problem related to NPC-based tests is the impact of the dependency structure on combined tests, especially in the presence of categorical variables. This paper’s goal is firstly to investigate the impact of the dependency structure on the possible significance of combined tests in cases of ordered categorical responses using Monte Carlo simulations, then to propose some specific procedures aimed at improving the power of multivariate combination-based permutation tests. The results show that an increasing level of correlation/association among responses negatively affects the power of combination-based multivariate permutation tests. The application of special forms of combination functions based on the truncated product method [Zaykin DV, Zhivotovsky LA, Westfall PH, Weir BS. Truncated product method for combining p-values. Genet Epidemiol. 2002;22:170–185; Dudbridge F, Koeleman BPC. Rank truncated product of p-values, with application to genomewide association scans. Genet Epidemiol. 2003;25:360–366] or on Liptak combination allowed us, using Monte Carlo simulations, to demonstrate the possibility of mitigating the negative effect on power of combination-based multivariate permutation tests produced by an increasing level of correlation/association among responses.

The Multivariate Randomized Complete Block Design: A Novel Permutation Solution in Case of Ordered Categorical Variables

In this article, multivariate extensions of the combination-based test statistics for the comparison of several treatments in the multivariate Randomized Complete Block designs are introduced in case of categorical response variables. Several tests for the multivariate Randomized Complete Block designs, including MANOVA procedure, are compared with the method proposed via a Monte Carlo simulation study. The method has also been applied to a real case study in the field of sensorial testing studies. Results suggest that in each experimental situation where normality of the supposed underlying continuous model is hard to justify and especially when errors have heavy-tailed distributions, the proposed nonparametric procedure can be considered as a valid solution.

Association between proxy-reported sleep bruxism and quality of life aspects in Colombian children of different social layers

ObjectiveTo describe and explore the association between proxy-reported sleep bruxism (SB) and quality of life (QoL) in a population of Colombian children belonging to different social layers.MethodsThe parents of 1556, 6–13-year-old school children, were administered the Pediatric Inventory of Quality of Life (PedsQL4.0™) and an evaluation of their sociodemographic and socioeconomic conditions. Associations between such proxy-reported, viz., “possible” SB and QoL features were assessed by means of the linear-by-linear association test on the overall sample and for distinct socioeconomic groups.ResultsNo significant associations were shown between proxy-reported sleep bruxism and the total and domain PedsQL scores, with the exception of a weak correlation with the School Functioning Score. As for the specific QoL items, only two variables of the Emotional Functioning Scale of the PedsQL4.0 (i.e., “feeling afraid or scared” and “trouble sleeping”) and a feature of the School Functioning Scale (i.e., “forgetting things”) were weakly correlated with SB, with correlation coefficients ranging from 0.092 to 0.119. Considering the different socioeconomic groups, no associations were found in the low layer. In the medium socioeconomic group, a significant association was pointed out with one emotional functioning aspect, while in the high layer an association was found with two emotional features and three school functioning variables.ConclusionThe results pointed out only a few associations between proxy-reported SB and the emotional and school functioning aspects of children’s quality of life, both in the total sample as well as in children belonging to medium and high socioeconomic status, while no associations were found with physical health and social functioning domains.Clinical relevanceThis article gives information to help clinicians evaluating the QoL, sociodemographic, and socioeconomic characteristics in children with possible sleep bruxism.

Permutation solutions for multivariate ranking and testing with applications.

In this article, we consider permutation methods for multivariate testing on ordered categorical variables based on the nonparametric combination of permutation dependent tests (NPC; Pesarin and Salmaso, 2010). Furthermore, an extension of the nonparametric combination of dependent rankings (Arboretti et al., 2007) is proposed in order to construct a synthesis of composite indicators. The methodological approaches are applied to a study of risk factors for skin cancer in a cohort of adult patients with heart transplants followed for a minimum of three years after transplantation (Belloni et al, 2004) and to a survey on tourists opinions about “Tre Cime” Park (District of Sesto Dolomites/Alta Pusteria, Italy).

Before-and-After Field Investigation of the Effects on Pollutant Emissions of Replacing a Signal-Controlled Road Intersection with a Roundabout

The purpose of this study is to assess the effects on air pollution that may derive from replacing a signal-controlled intersection with a roundabout, using a before-and-after approach. Based on field data collected with a test car instrumented with a Portable Emission Measurement System, the two intersection configurations were compared in terms of emissions of CO2, CO, and . The existence of significant differences in emissions between the two types of control was assessed by means of a statistical technique known as two-sample biaspect permutation test. In addition, focusing on trips carried out in peak traffic conditions, binary logistic regression models were developed to identify the factors that significantly affect vehicular emissions and to quantify their effect. The findings of our analyses show that emissions of CO2 and CO are generally lower for the roundabout than for the signal-controlled intersection, while an opposite result arises for emissions. As far as other influential factors are concerned, trip direction (reflecting site-specific conditions) and driver behavior have a considerable impact on the emissions of all three pollutants.

The Influence of the Dependency Structure in Combination-Based Multivariate Permutation Tests in Case of Ordered Categorical Responses

A quite important problem usually occurs in several multidimensional hypothesis testing problems when variables are correlated and their associated regression forms are different (linear, quadratic, exponential, general monotonic, etc.). In this framework the NonParametric Combination (NPC) of a finite number of dependent permutation tests is suitable to cover almost all real situations of practical interest since the dependence relations among partial tests are implicitly captured by the combining procedure itself without the need to specify them (Pesarin and Salmaso, Permutation Tests for Complex Data: Theory, Applications and Software. Wiley, Chichester, 2010). The goal of this paper is to investigate via Monte Carlo simulations the impact of the dependency structure on the possible significance of combined tests in case of ordered categorical responses. The results showed that an increasing level of correlation/association among responses negatively affects the power of combination-based multivariate permutation tests.