1,721,162 research outputs found
Consumer preferences in food packaging: cub models and conjoint analysis
Full text linkPurpose - 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 the consumers' choices. Both psychological characteristics "feeling" and "uncertainty" can be linked to relevant subject's information. At first we detect preferred packaging attributes of fresh food by means of CA, then we apply CUB models to some relevant attributes from the CA study.
Findings - Results show that attributes like packaging material and size/shape of packaging are the most important attributes and that biodegradable packaging, reclosable trays/bags and long "best by" date are also valuable features for consumers. The introduction of covariates showed that specific demographic characteristics are linked to both feeling and uncertainty.
Originality/value - The "data driven" segmentation results give to the integrated approach "CUB models and Conjoint Analysis" the most important added value
Statistical analysis of paired data: the multivariate paired permutation test.
No full textThis research reviews some statistica methods for the analysis of experiments with repeated measures. The considered data come from an epidemiological study on the quality of care of Beta-Thalassemia carried out from sexteen of the most representative specialized centers in Italy. Nonparametric methods are used and the multivariate paired permutation test is introduce
A permutation approach for multivariate repeated measures with application to a complex randomized clinical trial
No full textThis paper introduces an extension of the nonparametric combination methodology for multivariate repeated measures. The proposed solutions are implemented in MatLab and a complex real example from the biomedical field is fully analyzed using three possible ways for the analysi
Permutation Solutions for Multivariate Ranking and Testing with Applications
No full textIn 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 tourist's opinions about “Tre Cime” Park (District of Sesto Dolomites/Alta Pusteria, Italy)
METODI NON PARAMETRICI NEGLI STUDI OSSERVAZIONALI MULTIVARIATI IN PRESENZA DI FATTORI DI CONFONDIMENTO.
No full textIn this paper we propose a path of analysis for multivariate observational studies in presence of confounding factors. We start from a suitable post-stratification by using the propensity score method in order to provide hypotheses testing for coherent alternatives based on nonparametric techniques. Finally a comparative simulation study to compare the Rosenbaum test (Rosenbaum, 1995) and the multivariate and multistrata permutation test is also presented, even in the presence of missing abservations
Advances on inferential methods for heterogeneity comparisons
No full textThe purpose of the present work consists in the study of a non-parametric
procedure for a two sample test for heterogeneity comparisons in presence of categorical
variables. In particular a comparison between the approximated permutation solution
proposed by Arboretti et al. (2009) with a new proposal based on a bootstrap
resampling method is performed. Some results of the simulation study for analyzing the
performances of the compared methods are shown. An application related to a customer
satisfaction survey is also illustrated
Metodi Non Parametrici per l'Analisi di Performance con Applicazioni alla Valutazione del Sistema Universitario
No full textNonparametric directional tests in presence of confounding factors and categorical data
No full textIn modern socio-economic systems, often the aim of a performance analysis or quality evaluation is to compare different products, different manufacturing plants or service centres, different actions or distinct treatments. The question is, ‘‘Which is better?’’ This is complicated because the considered aspects are often measured through categorical data and the results can be affected by confounding factors. To solve this problem we discuss some directional permutation tests based on the nonparametric combination of dependent permutation tests (NPC) for two-sample comparisons in the presence of ordinal categorical variables and confounding factors. In particular we present a new permutation test based on the combination of a finite number of sample moments. To reduce the confounding effects we consider the joint application of stratification and the NPC method. We also show the results of Monte Carlo simulations in order to compare permutation solutions with other nonparametric tests and to evaluate the robustness of the test based on moments
Permutation tests for heterogeneity comparisons in presence of categorical variables with application to university evaluation
No full textIn social sciences researchers often meet the problem of determining if the distribution of a categorical variable is more concentrated in population X1 than in population X2. For example the effectiveness of two different PhD programs can be evaluated in terms of the heterogeneity of the set of job opportunities. The job opportunities are nominal categorical variables and populations X1 and X2 include all PhD holders for program 1 and program 2. We may define that a PhD program is better than another if it is able to offer a larger variety of job opportunities. Several other examples can be mentioned to highlight the importance of heterogeneity comparison problems in social sciences; moreover this problem occurs also very often in genetics, biology, medical studies and other sciences. The nonparametric solution of this problem has similarities to that of permutation testing for stochastic dominance on ordered categorical variables, i.e. testing under order restrictions. If ordering of probability parameters in H0 is unknown and it has to be estimated by sampling data, only approximate nonparametric solutions are possible within the permutation approach. Main properties of test solutions and some Monte Carlo simulations in order to evaluate the tests behaviour under H0 and H1, will be presented. A real problem concerned with University evaluation is also discussed
- …
