Matilde Bini

Statistical methods for the evaluation of educational services and quality of products

Latent variable models for ordinal data.- Issues on item response theory modelling.- Nonlinearity in the analysis of longitudinal data.- Multilevel models for the evaluation of educational institutions: a review.- Multilevel mixture factor models for the evaluation of educational programs#x2019 effectiveness.- A class of statistical models for evaluating services and performances.- Choices and conjoint analysis: critical aspects and recent developments.- Robust diagnostics in university performance studies.- A novel global performance score with an application to the evaluation of new detergents.- Nonparametric tests for the randomized complete block design with ordered categorical variables.- A permutation test for umbrella alternatives.- Nonparametric methods for measuring concordance between rankings: a case study on the evaluation of professional profiles of municipal directors.

Quality Assessment of the University Educational Process: an Application of the ECSI Model

In a university, students represent the final users as well as the principal actors of the formative services. A measure of their perceived quality is essential for planning changes that would increase the level of the quality of these services. This perceived quality is analysed in this paper with the ECSI (European Customer Satisfaction Index) methodology. The ECSI, which implements a structural equation model, is aimed to represent the satisfaction of the students with some latent variables gauged through a set of observable indicators. We extend the ECSI to the data obtained from graduates of the University of Florence employed one year after graduation.

Robust diagnostics in university performance studies

The presence of anomalous observations (outliers) in a set of data is one of the greatest problems in methodological statistics, one that scientists were already aware of many years ago, as can be seen in the comments made by the American astronomer Peirce1 over 150 years ago.

Evaluating the University Educational Process. A Robust Approach to the Drop-out Problem

The use of robust procedures in regression model estimation identifies outlier data that can inform on specific subpopulations. The aim of this study is to analyse the problem of first year dropouts at the University of Florence. A set of administrative data, collected at the moment of enrolment, combined with the information gathered through a specific survey of the students enrolled in the 2001–2002 academic year at the same athenaeum, was used for the purpose. In order to identify the most important variables affecting the students’ dropout, the data were first fitted with generalized linear models estimated with classical methods. The same models were then estimated with robust methods that allowed the detection of groups of outliers. These in turn were analysed to determine the personal or contextual characteristics. These results may be relevant for the implementation of academic policy changes.

Robust Transformation of Proportions Using the Forward Search

The aim of this work is to detect the best transformation parameters to normality when data are proportions. To this purpose we extend the forward search algorithm introduced by Atkinson and Riani (2000), and Atkinson et al. (2004) to the transformation proposed by Aranda-Ordaz (1981). The procedure, implemented by authors with R package, is applied to the analysis of a particular characteristic of Tuscany industries. The data used derive from the Italian industrial census conducted in the year 2001 by the Italian National Statistical Institute (ISTAT).

Evaluating the External Effectiveness of the University Education in Italy

This paper aims at checking the possibility to measure the external effectiveness of course programs groups of all Italian universities, taking account of both characteristics of individuals and context factors that differently affect the Italian regions. We perform the analysis using a multilevel logistic model on data set from survey on job opportunities of the Italian graduates in 2004, conducted in 2007 by the Italian National Institute of Statistics

Italian Firms’ Geographical Location in High-tech Industries: A Robust Analysis

Recent debates in economic-statistical research concern the relationship between firms’ performance and their capabilities to develop new technologies and products. Several studies argue that economic performance and geographical proximity strongly affect firms’ level of technology. The aim of the paper is twofold. Firstly, we propose to generalize this approach and to develop a model to identify the relationship between the firm’s technology level and some firm’s characteristics. Secondly, we use an outlier detection method to identify units that affect the analysis results and the estimates stability. This analysis is implemented using a generalized regression model with a diagnostic robust approach based on forward search. The method we use reveals how the fitted regression model depends on individual observations and the results show how the firms’ technology level is influenced by their geographical proximity.

Robust Fuzzy Classification

One of the most important problems among the methodological issues discussed in cluster analysis is the identification of the correct number of clusters and the correct allocation of units to their natural clusters. The most widely used index to determine the optimal number of groups is the Calinski Harabasz index. As shown in this paper, the presence of atypical observations has a strong effect on this index and may lead to the determination of a wrong number of groups. Furthermore, in order to study the degree of belonging of each unit to each group it is standard practice to apply a fuzzy k-means algorithm. In this paper we tackle this problem using a robust and efficient approach based on a forward search algorithm. The method is applied on a data set containing performance evaluation indicators of Italian universities.

Robust Multivariate Methods for the Analysis of the University Performance

One of the most important problems among the methodological issues discussed in cluster analysis is the identification of the correct number of clusters and the correct allocation of units to their natural clusters. In this paper we use the forward search algorithm, recently proposed by Atkinson, Riani and Cerioli (2004) to scrutinize in a robust and efficient way the output of k-means clustering algorithm. The method is applied to a data set containing efficiency and effectiveness indicators, collected by the National University Evaluation Committee (NUEC), used to evaluate the performance of Italian universities.