An Evidence Base to Inform Rural Policy.:Department of Agriculture and Rural Development Northern Ireland.

Funded by ESRC Knowledge Research Fellowship Programme.

The role of previous experience and attitudes toward statistics in statistics assessment outcomes among undergraduate psychology students

Previous research has demonstrated that students’ cognitions about statistics are related to their performance in statistics assessments. The purpose of this research is to examine the nature of the relationships between undergraduate psychology students’ previous experiences of maths, statistics and computing; their attitudes toward statistics; and assessment on a statistics course. Of the variables examined, the strongest predictor of assessment outcome was students’ attitude about their intellectual knowledge and skills in relation to statistics at the end of the statistics curriculum. This attitude was related to students’ perceptions of their maths ability at the beginning of the statistics curriculum. Interventions could be designed to change such attitudes with the aim of improving students’ learning of statistics.

Using evidence in policy: The importance of mediating beliefs and practices

This article argues that to understand the use of evidence in policy, we need to examine how meanings and practices in the civil service shape what is accepted as knowledge, and how differences between the beliefs and values of the academy and the polity can impede the flow and transfer of knowledge. It considers the importance of social context and shared meanings in legitimating knowledge. Who counts as legitimate knowledge providers has expanded and here the role of stakeholder groups and experiential knowledge is of particular interest. How hierarchy, anonymity, and generalist knowledge within the civil service mediate the use of evidence in policy is examined. The difference in values and ideology of the civil service and the academy has implications for how academic research is interpreted and used to formulate policy and for its position in knowledge power struggles. There are particular issues about the social science nature of evidence to inform rural policy being mediated in a government department more used to dealing with natural science knowledge. This article is based on participant observation carried out in a UK Department of Agriculture and Rural Development. © 2013 The Author. Sociologia Ruralis © 2013 European Society for Rural Sociology.

An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study

This research aims to use the multivariate geochemical dataset, generated by the Tellus project, to investigate the appropriate use of transformation methods to maintain the integrity of geochemical data and inherent constrained behaviour in multivariate relationships. The widely used normal score transform is compared with the use of a stepwise conditional transform technique. The Tellus Project, managed by GSNI and funded by the Department of Enterprise Trade and Development and the EU’s Building Sustainable Prosperity Fund, involves the most comprehensive geological mapping project ever undertaken in Northern Ireland. Previous study has demonstrated spatial variability in the Tellus data but geostatistical analysis and interpretation of the datasets requires use of an appropriate methodology that reproduces the inherently complex multivariate relations. Previous investigation of the Tellus geochemical data has included use of Gaussian-based techniques. However, earth science variables are rarely Gaussian, hence transformation of data is integral to the approach. The multivariate geochemical dataset generated by the Tellus project provides an opportunity to investigate the appropriate use of transformation methods, as required for Gaussian-based geostatistical analysis. In particular, the stepwise conditional transform is investigated and developed for the geochemical datasets obtained as part of the Tellus project. The transform is applied to four variables in a bivariate nested fashion due to the limited availability of data. Simulation of these transformed variables is then carried out, along with a corresponding back transformation to original units. Results show that the stepwise transform is successful in reproducing both univariate statistics and the complex bivariate relations exhibited by the data. Greater fidelity to multivariate relationships will improve uncertainty models, which are required for consequent geological, environmental and economic inferences.