Assessment of school performance through a multilevel latent Markov Rasch model

An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, e.g. students clustered in classes. For each subject, the latent process is used to represent the characteristic of interest (e.g. ability) conditional on the effect of the cluster to which he/she belongs. The latter effect is modeled by a discrete latent variable associated with each cluster. For the maximum likelihood estimation of the model parameters we outline an EM algorithm. We show how the proposed model may be used for assessing the development of cognitive Math achievement. This approach is applied to the analysis of a dataset collected in the Lombardy Region (Italy) and based on test scores over three years of middle-school students attending public and private schools

The 2005 European e-Business Readiness Index

Assessment of the eEurope 2005 Action Plan Benchmarking Index “E-Business Readiness Composite Indicator” using data collected by National Statistical Institutes and harmonised by Eurostat, using surveys “ICT usage of enterprises”, with reference years 2003 and 2004. This report contains data from 26 countries as collected in 2004 and as reported by Eurostat in June 2005. Performed analyses include obustness analysis, uncertainty and sensitivity analysis for two categories of ICT (Adoption and Use), univariate analysis of basic indicators; principal component analysis and finally assessment of resulted country rankings and methodological notes.ICT, e-business, adoption, composite, indicator, eEurope, e-Europe, EU, multiple imputation imputation

The 2005 European e-Business Readiness Index

Assessment of the eEurope 2005 Action Plan Benchmarking Index “E-Business Readiness Composite Indicator” using data collected by National Statistical Institutes and harmonised by Eurostat, using surveys “ICT usage of enterprises”, with reference years 2003 and 2004. This report contains data from 26 countries as collected in 2004 and as reported by Eurostat in June 2005. Performed analyses include robustness analysis, uncertainty and sensitivity analysis for two categories of ICT (Adoption and Use), univariate analysis of basic indicators; principal component analysis and finally assessment of resulted country rankings and methodological notes. Report includes 21 Tables and 20 Figures. 47 pp.JRC.G.9-Econometrics and statistical support to antifrau

The influence of parental divorce, parental temporary separation and parental relationship quality on children’s school readiness

We use the first three waves of the Millennium Cohort Study (MCS), a longitudinal and representative UK survey, to explore the interrelationship between parental divorce, parental temporary separation and parental relationship quality on cognitive abilities and psychological dimensions of the children at age five. By using an appropriate imputation method, we apply the augmented inverse propensity weighted estimator to test the hypothesis that parental divorce may be a positive experience for children with parents in high-distress unions, while the dissolution of low-distress unions may have a negative effect. Overcoming some of the limitations of previous research, we find that that the dissolution of high-quality parental unions has the most harmful effects on children, especially concerning conduct problems. We also find that children who experienced parental temporary separation - which has been absent in most previous research - have more conduct and hyperactivity problems than children from stable or divorced families

Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison

We propose a short review between two alternative ways of modeling stability and change of longitudinal data when time-fixed and time-varying covariates referred to the observed individuals are available. They both build on the foundation of the finite mixture models and are commonly applied in many fields. They look at the data by a different perspective and in the literature they have not been compared when the ordinal nature of the response variable is of interest. The latent Markov model is based on time-varying latent variables to explain the observable behavior of the individuals. The model is proposed in a semi-parametric formulation as the latent Markov process has a discrete distribution and it is characterized by a Markov structure. The growth mixture model is based on a latent categorical variable that accounts for the unobserved heterogeneity in the observed trajectories and on a mixture of normally distributed random variable to account for the variability of growth rates. To illustrate the main differences among them we refer to a real data example on the self reported health status