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Trait emotional intelligence (trait EI) covers a wide range of self-perceived skills and personality dispositions such as motivation, confidence, optimism, peer relations and coping with stress. In the last few years, there has been a growing awareness that social and emotional factors play an important part in students' academic success and it has been claimed that those with high scores on a trait EI measure perform better. This research investigated whether scores on a questionnaire measure of trait EI were related to school performance in a sample of British pupils. Results showed that high performing students had higher trait EI scores than low performing students and that some aspects of trait EI (motivation and low impulsivity) as well as total trait EI were significant predictors of academic achievement after controlling for prior attainment at school. Therefore, initiatives to develop the emotional and social abilities of schoolchildren might be worthwhile and more effective than concentrating solely on teaching and curriculum activities. (Contains 5 tables and 2 notes.)

General Certificates of Secondary Education, known as GCSEs, are organised into modules which can be assessed at the end of the course (linear route) or at different points throughout the course (modular route). This research investigates students' performance in English and mathematics in each assessment route and the effects of certification session and re-sits on examination outcomes using data from one English awarding body. In mathematics, modular students performed better than linear students once prior attainment was accounted for. In contrast, modular assessment in English did not always lead to better performance. In the modular routes of English, students certificating early in the course were at a disadvantage compared to those certificating at the end. However, modular mathematics students obtained significantly better results in early sessions than at the end of the course. The benefits of re-sitting seemed clear, as most students performed better on their second attempt at a module than on their first. However, this did not often lead to an improvement on the module grade. (Contains 7 tables, 2 figures, and 5 notes.)

Background: The BioMedical Admissions Test (BMAT) forms part of the undergraduate medical admission process at the University of Cambridge. The fairness of admissions tests is an important issue. Aims: Aims were to investigate the relationships between applicants' background variables and BMAT scores, whether they were offered a place or rejected and, for those admitted, performance on the first year course examinations. Method: Multilevel regression models were employed with data from three combined applicant cohorts. Admission rates for different groups were investigated with and without controlling for BMAT performance. The fairness of the BMAT was investigated by determining, for those admitted, whether scores predicted examination performance equitably. Results: Despite some differences in applicants' BMAT performance (e.g. by school type and gender), BMAT scores predicted mean examination marks equitably for all background variables considered. The probability of achieving a 1st class examination result, however, was slightly under-predicted for those admitted from schools and colleges entering relatively few applicants. Not all differences in admission rates were accounted for by BMAT performance. However, the test constitutes only one part of a compensatory admission system in which other factors, such as interview performance, are important considerations. Conclusion: Results are in support of the equity of the BMAT. [ABSTRACT FROM AUTHOR]

Online surveillance of disease has become an important issue in public health. In particular, the space-time monitoring of disease plays an important part in any syndromic system. However, methodology for these systems is generally lacking. One approach to space-time monitoring of health data is to consider the space-time model parameters as the focus and to monitor their changes as multivariate time series (Lawson AB. Some considerations in spatial-temporal analysis of public health surveillance data. In Brookmeyer R, Stroup DF eds. Monitoring the Health of Populations. Oxford University Press, 2004; Vidal Rodeiro CL, Lawson AB. Monitoring changes in spatio-temporal maps of disease. Biometrical Journal 2006; to appear). However with complex space-time models, this becomes very time consuming. Some simplifications may be necessary and these can be made in a number of ways. In this article, the focus is on particle filters that can be used to resample the history of the process and thereby reduce computation time. This article describes a particular case of particle filters, the resample-move algorithm, proposed by Gilks and Berzuini (Gilks WR, Berzuini C. Following a moving target--Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society, Series B 2001; 63: 127-46), in the context of disease map surveillance. This is followed by an application to a real data set in which a comparison between the use of Markov chain Monte Carlo methods and the resample-move algorithm is carried out. [ABSTRACT FROM AUTHOR]