Title: LEMMA 3: Longitudinal Effects, Multilevel Modelling and Applications
Funder: Economic and Social Research Council

Abstract: Social science is all about understanding complex social processes. For example, the processes by which people from families with differing socio-economic status obtain markedly different life outcomes are complex and develop over time. It has long been recognised that understanding such processes requires longitudinal data comprising repeated measurements of the key factors over time. There has been a substantial investment of resources in the collection of longitudinal data such as the major new ESRC study, Understanding Society, and the British birth cohorts. However, analysing these data requires models that reflect the time-dimension, which are necessarily more complex and difficult to use than for analysing 'snapshots' of these processes available from cross-sectional data. At present, expensively collected longitudinal data are not being fully exploited within quantitative social research because of a deficit in the skills required to use the appropriate statistical methods. To address this problem, we propose to set up an NCRM Node with the following aims: (i) Develop and adapt new methodology for longitudinal data analysis that addresses important problems in social research today. Part of our research here will seek to overcome one of the barriers preventing social researchers from learning this methodology, namely, texts on longitudinal methods are often written for advanced statistical users, and use discipline-specific terminology. Novel methodological developments will also be made: models that allow the analysis of subjects whose outcomes are highly variable (e.g. people with volatile stress levels); and 'causal' methods that can be used to answer policy-relevant problems (e.g. estimating the effect of changing tax credit allowance on childbearing). Finally, we will also use recently developed methods to handle the ubiquitous problem of missing longitudinal data, and in particular how these methods can be used to link longitudinal studies with information available from other studies. (ii) Implement the methodological research of (i) in the user-friendly 'e-STAT' software environment. e-STAT has been developed to overcome one of the biggest barriers facing social researchers, namely, learning to use statistical software packages. Our research here has three aspects: first is to extend e-STAT to fitting complex models for longitudinal data analysis; second, is to develop user-friendly e-STAT 'templates' through which users from social research can specify a wide variety of longitudinal models and prepare their data; and third is to allow e-STAT to become a gateway to other software packages by developing translators that convert e-STAT templates into code usable by both popular and more specialist software packages to take advantage of any features these possess. (iii) Apply the methods of (i) to three substantive research projects in collaboration with experts from medical sociology, health psychology, economics, education and developmental psychology. These questions are not only of genuine scientific interest, but each involves distinct challenges and uses data from a variety of sources. These applications will also act as exemplars for the use of the methodological and software work above to aid our training and capacity building activities (below). (iv) Conduct an extensive programme of training and capacity building activities for academic and non-academic users. These activities include workshops on the methods of (i) and software of (ii) for users of varying competence. Additionally, we will identify key users and train them to become trainers in the use of longitudinal methods. These activities will be augmented by developing online training materials and continuing to make available free software to UK-based academic users.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE