Rasch Analysis and Multilevel Models for the Evaluation of the Customer Satisfaction

The evaluation of attitudes, capabilities and individual satisfaction is one of the most important problem of experimental sciences. These qualities in fact, are not observable in direct way, but they are expressed with polycotomous measure scales. To effect the evaluation it’s necessary to substitute the qualitative modalities with some scores. These measures can be determined in different ways, but problems of quantification or relative to the conditions in which the survey is conduced can arise. For solving this problems one solution can be the Rasch Analysis. We used this technique to quantify the response to an hospital survey about the Customer Satisfaction. In a second step we try to verify if the patient satisfaction can be influenced by socio-economic factors and, for this reason, we use a Multilevel Model

Household’s Consumer Behaviour: Economic Recession and Quality of Institution. The case of Italy

The recent crisis saw the Italian household cutting consumption spending reshaping expenses behaviours. In this respect the role of macroeconomic fac- tors like institutions has received poor theoretical treatment and is scarcely proven. Based on the Istat Household Budgetary Survey, this paper focuses on the effects of crisis on selected consumption items (energy; healthcare; leisure; travels; eating out) controlling for micro and macro factors, such the Institutional-Quality-Index (IQI) and the regional GDP. IQI emerge as cru- cial in determining household healthcare expenses before the recession: where the local endowment of institutional quality is higher, the private expenses for medical/dental care, pharmaceuticals and diagnostic tests, significantly decrease. The higher the quality of institutional quality, and then of pub- lic health services, the lower the private expenditure. The recession resets the impact of IQI and increases the positive correlation with strictly microe- conomic variables such as income, wealth and the number of household’s earners

Sampling of alternatives using multidimensional analysis

Discrete choice models in general and random utility models in particular may be intractable when the number of alternatives is large. In the transportation context, it typically happens for route choice and destination choice models. In the specific case of the widely used multinomial logit model, it has been shown that the model could be estimated as if the choice was made among a subset of the alternatives. In this paper, we propose to design the sampling of alternatives based on a Principal Component Analysis and a Cluster Analysis of the actual data set, in order to increase the efficiency of the estimates. We present a case study of a destination choice model to empirically illustrate the added value of our approach

How do Calendar Anomalies Affect an Investment Choice? A Proposal of an Analytic Hierarchy Process Model

In recent years, financial markets changed for globalization. Today, a wide range of investment opportunities is available to investors. In this new scenario, markets are related, but each of them has specific characteristics with particular opportunities for investors. An investment choice can be influenced by numerous qualitative and quantitative factors that often conflict with one other. Thus, portfolio management choice is a multi-criteria decision problem today, so it requires flexible and analytic decision tools for investors. For this task, the Analytic Hierarchy Process (AHP) is suitable. We propose a theoretical model to analyse an investment choice problem taking into account different financial markets. Return of stock market, performance of the government bond and calendar effects in the financial markets considered are the evaluation criteria used in our model. The proposed model has strengths and weaknesses. First, through the AHP methodology the problem can be decomposed into a dominance hierarchy. Then, the subjective judgements expressed by means of pairwise comparisons are cheked in order to verify their consistency. Moreover, it is flexible and allows us to check if the ranking changes based on varying criteria weights. However, in that model we assume that criteria and alternatives are independent. Furthermore, our research lacks of a numerical application to test the model. Keywords: AHP, calendar anomalies, government bonds, stock market, investment choice. JEL Classifications: C13, C44, G11, G14, G40 DOI: https://doi.org/10.32479/ijefi.899

Cumulative chi-squared statistics for the service quality improvement: new properties and tools for the evaluation

In service quality evaluation, data are often categorical variables with ordered categories and collected in two way contingency table. The Taguchi’s statistic is a measure of the association between these variables as a simple alternative to Pearson’s test. An extension of this statistic for three way contingency tables handled in two way mode is introduced.We highlight its several properties, the approximated distribution, a decomposition according to orthogonal quantities reflecting the main effects and the interaction terms, and an extension of cumulative correspondence analysis based on it

Estimating multinomial logit model with multicollinear data

EnThe multinomial model is used to study the dependence relationship between a categorical response variable with more than two categories and a set of explicative variables. In presence of multicollinearity, the estimation of the multinomial model parameters becomes inaccurate. To solve this problem we develop an extension of Principal Component Logistic Regression (PCLR), model proposed by Aguilera et al. (2006). Finally a case study illustrates the advantages of the method

A New Scaling Proposal for Handling Ordinal Categorical Variables in Co-inertia (-PLS) Analysis

AbstractIn order to investigate the symmetrical relationships between several sets of variables, or regress one or more quantitative response variables on a set of variables of different nature, it is well known that it is necessary to transform non-quantitative variables in such a way that they can be analyzed together with the others measured on an interval scale.This paper suggests a proposal to cope with the problem of the treatment of ordinal qualitative variables in Co-Inertia(-PLS) Analysis. In the literature there are different proposals based on the application of known statistical techniques to quantify ordinal variables. The approach consists in quantifying each non-quantitative variable according to the empirical distributions of the variables involved in the analysis assuming the presence of a continuous underlying variable for each ordinal indicator

A model for assessing sea environmental quality

In this paper, we aim to study the key factors which affect the environmental quality of the sea. The Fp-ratio, utilized as an indicator of the trophic state of the coastal waters, is classified into three categories. Given the ordinal nature of the response variable, we believe ordinal logistic regression models are the most proper statistic methodology. As the regressors are strongly correlated, the parameters of ordinal logistic regression models cannot be estimated. For solving this problem and, in general, the consequences of multicollinearity, we develop an approach based on principal components. The new approach will be applied for estimating the Fp-ratio