The paper demonstrates how vignettes can be very useful research tools yielding valuable data when studying people's attitudes, perceptions and beliefs in social and nursing research.

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The article describes the background and methods employed in developing an index of child well-being at small area level for England, the first of its kind. The index uses mainly administrative data on children covering seven domains of well-being: income, health, education, housing, environment, crime and children in need. Indicators are combined at "lower layer super output area" (LSOA) level. There are 32,482 of these geographical areas with populations of around 1,500. The indicators in the domains are combined using shrinkage estimation, and factor analysis is then used to combine indicators, where appropriate, with suitable weights. To combine domains into an overall index of child well-being, domain scores are ranked and the ranks transformed into an exponential distribution, then domains are combined with equal weights. The relationships between the domains are explored using correlation coefficients. An overall index is created at both LSOA and local or unitary authority levels. The results of the index are explored using mapping software and case studies. The strengths and weaknesses of the index are discussed and suggestions are made for improvements in further iterations.

At a global level, increasing emphasis on sustainable development highlights the importance of maintaining and enhancing biodiversity. Within the European Union, agricultural reforms will have a considerable influence on the links between biodiversity, agriculture and rural communities over the next 10-20 years. An understanding of the associations between biodiversity and the rural economy is important for the development of policies to promote environmentally sustainable economic and social well-being. However, investigations of this type are frequently hindered by the different ways in which data in the natural and social sciences are collected. In this paper, we develop an innovative approach to combine data using a common spatial unit. We illustrate the application of this approach by examining associations between bird species richness, environmental and socio-economic variables. Our results show how the statistical relationship between biodiversity and environmental factors is strengthened and contextualised by prevailing socio-economic conditions. This example highlights the additional insights that can be gained from such an integrative approach, especially in terms of the questions it raises about the inter-relationships between the social, physical and ecological dynamics of rural environments. Copyright 2006 Blackwell Publishing Ltd.

The sustainability of rural development depends on the distribution of the social and environmental resources needed to maintain and improve the vitality of rural areas. Here we examine the complexity of measuring patterns of distribution using examples of socioeconomic data on rural poverty and affluence as well as data on environmental quality and species richness. We demonstrate how changes in the base spatial units used for analysis have different effects on different measures of inequality. The effects of such changes in spatial resolution also depend on the underlying processes that generate the data. The results of our investigations into the effects of scale on the assessment of inequality suggest that, where data come from both the social and natural science sources, the most appropriate level for analysis is that of the finest common resolution. This may result in redundancy of effort for some types of data but any such disadvantage is offset by the benefits of identifying inequalities that are masked at coarser resolutions.

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