Silvia Cagnone

What is the public appetite for healthy eating policies? Evidence from a cross-European survey.

World Health Organization estimates that obesity accounts for 2-8% of health care costs in different parts of Europe, and highlights a key role for national policymaking in curbing the epidemic. A variety of healthy-eating policy instruments are available, ranging from more paternalistic policies to those less intrusive. Our aim is to measure and explain the level of public support for different types of healthy eating policy in Europe, based on data from a probabilistic sample of 3003 respondents in five European countries. We find that the main drivers of policy support are attitudinal factors, especially attribution of obesity to excessive availability of unhealthy foods, while socio-demographic characteristics and political preferences have little explanatory power. A high level of support for healthy eating policy does not translate into acceptance of higher taxes to fund them, however.

A factor mixture analysis model for multivariate binary data

The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional assumption of Gaussian distributed factors by a finite mixture of multivariate Gaussians. The aim of the proposed model is twofold: it allows to achieve dimension reduction when the data are dichotomous and, simultaneously, it performs model based clustering in the latent space. Model estimation is obtained by means of a maximum likelihood method via a generalized version of the EM algorithm. In order to evaluate the performance of the model a simulation study and two real applications are illustrated.

Latent variable models for multivariate longitudinal ordinal responses

The paper proposes a full information maximum likelihood estimation method for modelling multivariate longitudinal ordinal variables. Two latent variable models are proposed that account for dependencies among items within time and between time. One model fits item-specific random effects which account for the between time points correlations and the second model uses a common factor. The relationships between the time-dependent latent variables are modelled with a non-stationary autoregressive model. The proposed models are fitted to a real data set.

Analysing tourist satisfaction at a mature and multi-product destination

The paper investigates whether the relationship between destination attributes and overall visitor satisfaction, at a mature and multi-product destination, changes over time and with respect to tourist types. Multi-group multi-wave LISREL models are tested on data taken from the Tourist Satisfaction Survey conducted in Rimini from 2004 to 2006. The analysis shows that leisure service is the main destination attribute affecting overall satisfaction. In evaluating the destination, no differences over time and between tourist types are also detected. Empirical findings are used to propose management strategies supporting destination competitiveness.

A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses.

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate ordinal items. Time-dependent latent variables are linked with an autoregressive model. Simulation results have shown composite likelihood estimators to have a small amount of bias and mean square error and as such they are feasible alternatives to full maximum likelihood. Model selection criteria developed for composite likelihood estimation are used in the applications. Furthermore, lower-order residuals are used as measures-of-fit for the selected models.

Estimation of generalized linear latent variable models via fully exponential Laplace approximation

Latent variable models for ordinal data represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In such models, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. Numerical approximations, like the widely used Gauss Hermite (GH) quadrature, are generally applied to solve these problems. However, GH becomes unfeasible as the number of latent variables increases. Thus, alternative solutions have to be found. In this paper, we propose an extended version of the Laplace method for approximating the integrals, known as fully exponential Laplace approximation. It is computational feasible also in presence of many latent variables, and it is more accurate than the classical Laplace method.Latent variable models represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In such models, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. Numerical approximations, like the widely used Gauss-Hermite (GH) quadrature, are generally applied to solve these problems. However, GH becomes unfeasible as the number of latent variables increases. Thus, alternative solutions have to be found. In this paper, we propose an extended version of the Laplace method for approximating the integrals, known as fully exponential Laplace approximation. It is computational feasible also in presence of many latent variables, and it is more accurate than the classical Laplace approximation. The method is developed within the Generalized Linear Latent Variable Models (GLLVM) framework.

A General Multivariate Latent Growth Model With Applications to Student Achievement

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a cohort of students of the University of Bologna. The model proposed is innovative since it is composed by (a) multivariate growth models that allow the capture of different dynamics of student performance indicators over time and (b) a factor model that allows measurement of the general latent student capability. The flexibility of the model proposed allows its applications in several fields such as socioeconomic settings in which personal behaviors are studied using panel data.

Latent variable models for ordinal data

Latent variable models with observed ordinal variables are particularly useful for analyzing survey data. Typical ordinal variables express attitudinal statements with response alternatives like “strongly disagree”, “disagree”, “strongly agree” or “very dissatisfied”, “dissatisfied”, “satisfied” and “very satisfied”.

An Item Response Theory Model for Student Ability Evaluation Using Computer-Automated Test Results

The aim of this paper is to evaluate the student learning about Computer Science subjects. A questionnaire based on ordinal scored items has been submitted to the students through a computer automated system. The data collected have been analyzed by using a latent variable model for ordinal data within the Item Response Theory framework. The scores obtained from the model allow to classify the students according to the reached competence.

Multivariate Latent Growth Models for Mixed Data with Covariate Effects

The paper presents an extension of a new class of multivariate latent growth models (Bianconcini and Cagnone, 2012) to allow for covariate effects on manifest, latent variables and random effects. The new class of models combines: (i) multivariate latent curves that describe the temporal behavior of the responses, and (ii) a factor model that specifies the relationship between manifest and latent variables. Based on the Generalized Linear and Latent Variable Model framework (Bartholomew and Knott, 1999), the response variables are assumed to follow different distributions of the exponential family, with item-specific linear predictors depending on both latent variables and measurement errors. A full maximum likelihood method is used to estimate all the model parameters simultaneously. Data coming from the Data WareHouse of the University of Bologna are used to illustrate the methodology.