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.

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.

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.

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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 we 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: (1) multivariate growth models that allow to capture the different dynamics of student performance indicators over time and (2) a factor model that allows to measure the general latent student capability. The flexibility of the model proposed allows its applications in several fields such as socio-economic settings in which personal behaviours are studied by using panel data.