BIAS: Bayesian methods for combining multiple Individual and Aggregate data Sources in observational studies ( Imperial College)
The aim of BIAS is to develop a set of statistical frameworks for combining data from multiple sources to improve the capacity of social science methods to handle the intricacies of observational data. Bayesian hierarchical models offer a natural tool for linking together many different sub-models and data sources and will be used as the basic building blocks for these developments.
Visit the project at http://www.bias-project.org.uk/ .
LEMMA: Learning environment for multilevel methodology and applications (University of Bristol)
This interdisciplinary Node focuses on the quantitative multilevel analysis of data with complex structure that mirrors substantive research questions. Such complex structure includes household and family data, contextual, neighbourhood and area effects, spatial analytical models, longitudinal data structures, event-duration models, and mover-stayer models. The aim is to develop existing multilevel modelling techniques, apply them to substantive research questions, and to disseminate good practice through capacity building, training workshops and a virtual learning environment.
Developing Statistical Modelling in the Social Sciences (Lancaster University & University of Warwick)
The aim of this node is to develop and extend statistical methodology and models concentrating on substantive problems in the social sciences related to social and developmental change. Specific methodological areas will include the development of pseudo-likelihood methods for mixed-effects statistical models, local likelihood methods for the analysis of event-history data, new models and methods for longitudinal ranked-comparison data, and joint modelling of repeated-measurement and time-to-event data. The methodological programmel involves the development of new algorithms and their implementation as packages, and the group organises joint meetings where engagement with both the statistics and the social science communities can occur.
Methods for Research Synthesis Programme (Institute of Education, University of London)
Before undertaking any new policy, practice or research or making personal decisions in our lives it can be useful to find out what others already know about the issue. Research synthesis can assist such processes by providing a method for identifying and synthesising the findings of primary research. Methods for research synthesis provide rigorous, explicit, transparent and accountable methods to determine what we know, how we know it, what more we need to know and how we might know it. Research synthesis needs to be question-led, as the questions we ask will determine the answers we find. Methods for Research Synthesis can also inform methods for synthesising primary data other than from research (Methods for Information Synthesis).
Qualitative Research Methods in the Social Sciences: Innovation, Integration and Impact (QUALITI) (Cardiff University)
The Cardiff-based QUALITI focuses on the innovation, integration and impact of qualitative research methods, paying particular attention to the social contexts in which research methods and methodologies are situated. The methodological aims include:
• Exploring the opportunities and challenges for integrating different qualitative research approaches, modes of data collection, data types and analytical strategies;
• Exploiting new opportunities for the recording, display and communication of qualitative data;
• Developing innovative and participatory methods of qualitative inquiry; and
• Enhancing the role, impact and understanding of qualitative inquiry in the public domain.
Real Life Methods (Universities of Manchester & Leeds)
The Real Life Methods node aims to pioneer research methods that can grasp the multi-dimensionality of everyday real life. The approach is qualitatively-driven, whilst spanning and transcending the qualitative/quantitative divide. The Node's team and programme of work is interdisciplinary and involves the creative blending of methods, and the development of context sensitive or cross-contextual forms of explanation.
See website for more information: http://www.reallifemethods.ac.uk/