1,721,294 research outputs found
The Scaling Problems in Service Quality Evaluation
Full text linkIn service quality evaluation we have to treat data having different kinds of scales. In order to obtain a measure of the service quality level a conventional ordinal rating scale for each attribute of a service is used. Moreover additional information on the customers or on the objective characteristics of the service is available (interval, ordinal and or categorical scale). In the latter the importance or weight assigned to the different items must be also considered (compositional scale). To analyze these different kinds of data particular precaution should be used, a transformation of quality level perceived (expected) data in quantitative scale is carried out before a multidimensional data analysis. In literature more techniques are proposed for the quantification of ordinal data preserving the original characteristics. The aims of this paper are to analyze different ways to quantify ordinal data, and illustrate how the additional information on the customers or on the service could be used in the multidimensional analysis as external information
Discriminant partial least squares analysis on compositional data
No full textCompositional data are commonly present in many disciplines. Nevertheless, it is often improperly incorporated into statistical modelling and a misleading interpretation of the results is given. This paper explains how partial least squares for discrimination is an adequate technique for compositional data when a dimensional reduction of original variables is needed and difining the variables that more influence the discrimination between the observations is the goal
Quantification of customer satisfaction data: an approach based on the Spline Transformation
No full textPartial Least Squares for Compositional Data: an approach based on the splines
No full textThe constrained nature of compositional data gives many difficulties when one performs a multivariate data analysis technique. In literature, to respect the nature of compositional data Partial Least Squares (PLS) based on a Logcontrast PLS was suggesting by Hinkle and Rayen (1995). Moreover this approach presents two principal problems: a very strong assumption of strict positive and the curvature that generally the compositional data present. To resolve them, we present an alternative method based on a particular spline transformation of the compositional data and a constrained version of the PLS. Finally an application on Customer Satisfaction (CS) data is given
N-way partial least squares for compositional data
No full textPartial least squares (PLS) is a method for building regression models between independent and dependent variables. When a set of independent variables is measured on several occasions, the samples can subsequently be arranged in three-way arrays. In this case N-way partial least squares (N-PLS) can be used. N-PLS decomposes three-way array of independent variables and establishing a relation between the three-way array of independent variables and the array of dependent variables. Sometimes, the set of independent variables are parts of the same whole, thus each observation consists of vectors of positive values summing to a unit, or in general, to some fixed constant. When these data, known as compositional data (CoDa), are analyzed by N-PLS, it is necessary to take into account the specific relationships between the parts that compositions are made of. The problems that potentially occur when one performs a N-way partial least squares analysis on compositional data are examined. A strategy based on the log-ratio transformations is suggested
Measuring passenger satisfaction: a strategy based on Rasch Analysis and the ANOM
Full text linkMeasuring passenger satisfaction presents several difficulties since customer satisfaction in the public transport sector is subject to different conditions which are different than those that affect other sectors. In this work, a strategy based on Rasch analysis and the Analysis of Means (ANOM) is proposed. This study is based on the idea that the Rasch rating scale model gives ‘sufficient statistic’ for an underlying unidimensional latent trait such as the satisfaction generated by local transport operators. Furthermore, the ability of passengers, measured by the rating scale model, is studied by means of ANOM decision charts to verify if there are different levels of satisfaction between the different groups of passengers
Multidimensional Analysis of Customer Satisfaction Data: The Scaling Problems
No full textIn the analysis of Customer Satisfaction (CS) often we have to treat at the same time data having different kind of scale. In order to obtain a measure of the quality level perceived/expected a conventional ordinal rating scale for each attribute of a service is used in literature. Moreover additional information on the users or on the objective characteristics of the service is available (interval, ordinal and or categorical scale). In the latter the importance or weight assigned to the different items it must be also considered (compositional scale). To analyse these different kind of data particularly precaution should be used. A transformation of quality level perceived/expected data in quantitative scale is carried out before a multidimensional data analysis. In literature more techniques are proposed for the quantification of ordinal data preserving the original characteristics of this data. Aims of this paper are to analyse different ways to quantify ordinal data, and illustrate how the additional information on the customers or on the service could be used in the multidimensional analysis as external information
Tucker3 model for compositional data
No full textFor the exploratory analysis of three-way data, the Tucker3 is one of the most applied models to study three-way arrays when the data are approximately trilinear. When the data consist of vectors of positive values summing to a unit, as in the case of compositional data, this model should consider the special problems that compositional data analysis brings. The principal purpose of this paper is to describe how to do a Tucker3 analysis of compositional data, and to show the relationships between the loadings matrices when different preprocessing procedures are used
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