Developing an e-infrastructure for social science

We outline the aims and progress to date of the National Centre for e-Social Science e-Infrastructure project. We examine the challenges faced by the project, namely in ensuring outputs are appropriate to social scientists, managing the transition from research projects to service and embedding software and data within a wider infrastructural framework. We also provide pointers to related work where issues which have ramifications for this and similar initiatives are being addressed

The effects of test scores and truancy on youth unemployment and inactivity: a simultaneous equations approach

AbstractWe analyse the relationships between test scores, truancy and labour market outcomes for youths. Our econometric approach enables us to disentangle the observable direct and indirect effects of truancy and test scores on the risk of unemployment or ‘Not in Education, Employment or Training’ (NEET) from their unobserved effects. Using data for England and Wales, we show that models of youth unemployment and NEET that ignore the correlation between the unobservable determinants of test scores and truancy will lead to misleading inference about the strength of their effects. Truancy has an indirect observed effect on labour market outcomes via its effect on test scores, and a weak direct effect. The unobserved component of truancy has a direct effect on labour market outcomes. Test scores have a direct effect on those outcomes, but also mitigate the detrimental effects of truancy. Our analysis raises important implications for education policy.</jats:p

e-Collaboration Workshop: Access Grid, Portals and other VREs for the Social Sciences

Rob Allan, Rob Crouchley and Michael Daw cover a one-day workshop reporting on the latest developments in e-Collaboration technology and applications

Workshop on e-Research, Digital Repositories and Portals

Rob Allan, Rob Crouchley and Caroline Ingram report on a two-day workshop held at University of Lancaster over 6-7 September 200

A comparison of frailty models for multivariate survival data.

This paper reviews some of the main approaches to the analysis of multivariate censored survival data. Such data typically have correlated failure times. The correlation can be a consequence of the observational design, for example with clustered sampling and matching, or it can be a focus of interest as in genetic studies, longitudinal studies of recurrent events and other studies involving multiple measurements. We assume that the correlation between the failure or survival times can be accounted for by fixed or random frailty effects. We then compare the performance of conditional and mixture likelihood approaches to estimating models with these frailty effects in censored bivariate survival data. We find that the mixture methods are surprisingly robust to misspecification of the frailty distribution. The paper also contains an illustrative example on the times to onset of chest pain brought on by three endurance exercise tests during a drug treatment trial of heart patients