Students Mobility: Assessing the Determinants of Attractiveness Across Competing Territorial Areas

A central question for education authorities has become “which factors make a territory attractive for tertiary students?” Tertiary education is recognised as one of the most important assets for the development of a territory, thus students’ mobility becomes a brain drain issue whenever there are prevalent areas that attract students from other territories. In this paper, we try to identify the most important factors that could affect student mobility in Italy. In doing that we analyse students’ flows across competing territorial areas which supply tertiary education programs. We will consider a wide range of determinants related to the socio-economic characteristics of the areas as well as resources of the universities in the territories in terms of variety and quantity of the degree programs there available, financial endowments provided by Central Government, and services available to students. The Bradley–Terry modelling approach based on pair comparisons has been adopted to define the attractiveness of competing territories and assess how much the detected divergences can be attributed to factors directly related to the considered characteristics of the universities in the territory and how much is ascribable to inherent characteristics of the areas where the universities are located such as the labour market conditions. Furthermore, the adopted approach allows us to consider uncertainty in defining territorial attractiveness and making comparisons. In this way, we would like to provide some evidences to assess if the rules currently used by the Central Government to finance public universities on the basis of their capabilities to attract students really reward the efforts made by the university system in the area to improve their standard of quality or, on the contrary, reward the territorial features.     

Comparing degree programs from students’ assessments: A LCRA-based adjusted composite indicator

Taking into account the students’ evaluation of the quality of degree programs this paper presents a proposal for building up an adjusted performance indicator based on Latent Class Regression Analysis. The method enables us (i) to summarize in a single indicator statement multiple facets evaluated by students through a survey questionnaire and (ii) to control the variability in the evaluations that is mainly attributable to the characteristics (often referred as the Potential Confounding Factors) of the evaluators (students) rather than to real differences in the performances of the degree programs under evaluation. A simulation study is implemented in order to test the method and assess its potential when the composition of the degree programs as regards to students’ characteristics is sensibly different between one another. Results suggest that when the evaluations are strongly affected by the students’ covariates, the assessment based on the value of an unadjusted indicator can lead to bias and unreliable conclusions about the differences in performance. An application to real data is also provided.     

On the Use of Student Evaluation of Teaching: A Longitudinal Analysis Combining Measurement Issues and Implications of the Exercise

Social Indicators Research - Multi item questionnaires are widely used to collect students’ evaluation of teaching at university. This article makes an attempt to analyse students’...     

On a nonlinear Birnbaum–Saunders model based on a bivariate construction and its characteristics

Abstract

The Birnbaum-Saunders (BS) distribution is an asymmetric probability model that is receiving considerable attention. In this article, we propose a methodology based on a new class of BS models generated from the Student-t distribution. We obtain a recurrence relationship for a BS distribution based on a nonlinear skew–t distribution. Model parameters estimators are obtained by means of the maximum likelihood method, which are evaluated by Monte Carlo simulations. We illustrate the obtained results by analyzing two real data sets. These data analyses allow the adequacy of the proposed model to be shown and discussed by applying model selection tools.     

Recent advances to model anisotropic space–time data

Building new and flexible classes of nonseparable spatio-temporal covariances and variograms has resulted a key point of research in the last years. The goal of this paper is to present an up-to-date overview of recent spatio-temporal covariance models taking into account the problem of spatial anisotropy. The resulting structures are proved to have certain interesting mathematical properties, together with a considerable applicability. In particular, we focus on the problem of modelling anisotropy through isotropy within components. We present the Bernstein class, and a generalisation of Gneiting’s approach (2002a) to obtain new classes of space–time covariance functions which are spatially anisotropic. We also discuss some methods for building covariance functions that attain negative values. We finally present several differentiation and integration operators acting on particular space–time covariance classes.      

Weighted composite likelihood-based tests for space-time separability of covariance functions

Testing for separability of space-time covariance functions is of great interest in the analysis of space-time data. In this paper we work in a parametric framework and consider the case when the parameter identifying the case of separability of the associated space-time covariance lies on the boundary of the parametric space. This situation is frequently encountered in space-time geostatistics. It is known that classical methods such as likelihood ratio test may fail in this case.     

Asymmetric matrix-valued covariances for multivariate random fields on spheres

ABSTRACT

Matrix-valued covariance functions are crucial to geostatistical modelling of multivariate spatial data. The classical assumption of symmetry of a multivariate covariance function is overly restrictive and has been considered as unrealistic for most of the real data applications. Despite of that, the literature on asymmetric covariance functions has been very sparse. In particular, there is some work related to asymmetric covariances on Euclidean spaces, depending on the Euclidean distance. However, for data collected over large portions of planet Earth, the most natural spatial domain is a sphere, with the corresponding geodesic distance being the natural metric. In this work, we propose a strategy based on spatial rotations to generate asymmetric covariances for multivariate random fields on the d-dimensional unit sphere. We illustrate through simulations as well as real data analysis that our proposal allows to achieve improvements in the predictive performance in comparison to the symmetric counterpart.     

Estimation of slowly time-varying trend function in long memory regression models

ABSTRACT

We study the asymptotic properties of the least-squares estimator for the trend function of a particular class of locally stationary models, which are defined by considering a smooth variation of the trend function. Additionally, errors are assumed to be realizations from a long-range dependent stationary Gaussian process. Our findings are then illustrated through Monte Carlo simulations.     

Geostatistical Analysis Through Spectral Techniques: Some Words of Caution

Tapering is a technique proposed in order to avoid the so-called edge-effects in spectral estimation. Recent literature has emphasised the importance of tapering for spectral methods applied to the analysis of spatial dependence. In this work we show, through applications and an extensive simulation study, that tapering can be very dangerous if it is not used with caution. An interesting aspect of spectral estimation arises in the presence of a nugget effect in the spatial structure.     

Covariance tapering for multivariate Gaussian random fields estimation

In recent literature there has been a growing interest in the construction of covariance models for multivariate Gaussian random fields. However, effective estimation methods for these models are somehow unexplored. The maximum likelihood method has attractive features, but when we deal with large data sets this solution becomes impractical, so computationally efficient solutions have to be devised. In this paper we explore the use of the covariance tapering method for the estimation of multivariate covariance models. In particular, through a simulation study, we compare the use of simple separable tapers with more flexible multivariate tapers recently proposed in the literature and we discuss the asymptotic properties of the method under increasing domain asymptotics.