The precision of parameters estimation are determined by the sample size and the sampling design used in a study. Due to such practical constraints as the budget and manpower, most large-scale educational studies would not adopt the simple random sampling design. TIMSS 2007 used a two-stage stratified cluster sampling design. In the first stage, about 150 schools were selected according to some...

BACKGROUND Pragmatic cluster-randomised trials should seek to make unbiased estimates of effect and be reported according to CONSORT principles, and the study population should be representative of the target population. This is challenging when conducting trials amongst 'hidden' populations without a sample frame. We describe a pair-matched cluster-randomised trial of a combination HIV-prevent...

We propose a framework for an unsupervised analysis of electroencephalography (EEG) data based on possibilistic clustering, including a preliminary noise and artefact rejection. The proposed data flow identifies the existing similarities in a set of segments of EEG signals and their grouping according to relevant experimental conditions. The analysis is applied to a set of event-related potenti...

Objectives Many randomised trials use stratified permuted blocks or minimisation to balance key prognostic variables between treatment groups. It is widely argued in the statistical literature that any balancing variables should be adjusted for in the analysis, however a review of major medical journals shows that this is not commonly done. Our objective was to determine the effects of an unadj...

Microclustering refers to clustering models that produce small clusters or, equivalently, to models where the size of the clusters grows sublinearly with the number of samples. We formulate probabilistic microclustering models by assigning a prior distribution on the size of the clusters, and in particular consider microclustering models with explicit bounds on the size of the clusters. The com...

BACKGROUND Proper assessment of the magnitude of the problem is essential for devising adequate allocation of available resources and for developing future strategies to combat a disease. The cluster random sampling (CRS) technique is commonly used for rapid assessment of public health problems in developing countries. Our objective is to devise a nomogram that can instantly provide the number ...

This work explores the use of statistical techniques, namely stratified sampling and cluster analysis, as powerful tools for deriving traffic properties at the flow level. Our results show that the adequate selection of samples leads to significant improvements allowing further important statistical analysis. Although stratified sampling is a well-known technique, the way we classify the data p...