National Centre for Research Methods
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Narrative research, participation, and social transformation
No full textNarrative research is a dynamic and growing field in the social sciences. It frequently engages with issues of social justice and social change, particularly in the current context of global inequalities, conflict, mobility and precarity. How to address these issues in our methods, for instance through the involvement of research participants, and through relating research processes and analyses to social transformation, is a common and complex question. In November 2016, UK researchers will have the opportunity to develop their work in this field through a number of research and training events, in London and Edinburgh, with NCRM International Visitors, Professor Jill Bradbury, Witwatersrand University, and Professor Michelle Fine, City University of New York
Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates
Get PDFThis paper examines the secondary data requirements for multilevel small area synthetic estimation (ML-SASE). This research method uses secondary survey data sets as source data for statistical models. The parameters of these models are used to generate data for small areas. The paper assesses the impact of knowing the geographical location of survey respondents on the accuracy of estimates, moving beyond debating the generic merits of geocoded social survey datasets to examine quantitatively the hypothesis that knowing the approximate location of respondents can improve the accuracy of the resultant estimates. Four sets of synthetic estimates are generated to predict expected levels of limiting long term illnesses using different levels of knowledge about respondent location. The estimates were compared to comprehensive census data on limiting long term illness (LLTI). Estimates based on fully geocoded data were more accurate than estimates based on data that did not include geocodes
Statistical properties of simple random-effects models for genetic heritability
Get PDFAbstract: Random-effects models are a popular tool for analysing total narrow-sense her-itability for simple quantitative phenotypes on the basis of large-scale SNP data. Recently, there have been disputes over the validity of conclusions that may be drawn from such analysis. We derive some of the fundamental statistical properties of heritability estimates arising from these models, showing that the bias will generally be small. We show that that the score function may be manipulated into a form that facilitates intelligible interpretations of the results. We use this score function to explore the behavior of the model when certain key assumptions of the model are not satisfied — shared environment, measurement error, and genetic effects that are confined to a small subset of sites — as well as to elucidate the meaning of negative heritability estimates that may arise.
The variance and bias depend crucially on the variance of certain functionals of the singular values of the genotype matrix. A useful baseline is the singular value distribution associated with genotypes that are completely independent — that is, with no linkage and no relatedness— for a given number of individuals and sites. We calculate the corresponding variance and bias for this setting
An Introduction to Data Linkage
Get PDFThis guide is designed to give readers a practical introduction to data linkage and is aimed at researchers who would like to gain an understanding of data linkage techniques, either for the creation or analysis of linked data. It covers data preparation, deterministic and probabilistic linkage methods, and analysis of linked data, with examples relevant to health and other administrative data sources. This guide is relevant for academic researchers in the social and health sciences or those who work for government, survey agencies, official statistics, charities or the private sector
