Awarding body: ESRC (Economic and Social Research Council) and EPSRC (Engineering and Physical Sciences Research Council)
Total value: £633,392
Grant holder: Professor Fiona Steele
Co-investigators: Dr Myrsini Katsikatsou, Dr Jouni Kuha, Professor Irini Moustaki, Professor Chris Skinner, Dr Tania Burchardt (Centre for Analysis of Social Exclusion (CASE), LSE), Dr Eleni Karagiannaki (CASE, LSE), Professor Emily Grundy (University of Essex)
Start/end date: 01/10/2017 - 30/9/2020
Summary: Data on pairs of subjects (dyads) are commonly collected in social research. In family research, for example, there is interest in how the strength of parent-child relationships depends on characteristics of parents and children. Dyadic data provide detailed information on interpersonal processes, but they are challenging to analyse because of their highly complex structure: they are often longitudinal because of interest in dependencies between individuals over time, dyads may be clustered into larger groups (e.g. in families or organisations), and variables of interest such as perceptions and attitudes may be measured by multiple indicators. This research will develop a general latent variable modelling framework for the analysis of clustered multivariate dyadic data, with applications to the study of exchanges of support between non-coresident family members. A particular feature of this framework will be to allow modelling of associations between an individual's exchanges over time, between help given and received (reciprocity), between exchanges of time and money, between respondent-child and respondent-parent exchanges, and between members of the same household. Sensitivity of results to measurement error and non-ignorable attrition will be considered.

Please find more information at this web page. 

Awarding body: National Centre for Research Methods
Total value: £106,800 (LSE: £14,524)
Grant holder: Dr Jose Pina-Sánchez (University of Leeds)
Co-investigators: (LSE Statistics): Dr Sara Geneletti, Dr John Paul Gosling (University of Leeds)
Start/end date: 01/10/2017 - 31/12/2018
Summary: Statistical models are widely used to investigate how criminal offenders are sentenced in Courts of Law. Through these types of models much has been learnt regarding the functioning and fairness of the processes taking place within courts. However, the validity and reliability of the findings obtained have been limited as a result of the important compromises that researchers dealing with sentencing data have had to make. There are a vast array of sentence outcomes available to judges with which to punish offenders. Importantly, these punishments vary in nature and cannot be measured in a straightforward manner. As a result, most model-based studies have focused on the analysis of simpler sentence outcomes such as the probability of prison and/or the duration of custodial sentences. Focussing on these two specific outcomes involves a tremendous loss of information that reduces the model’s capacity to grasp many of the nuances of the sentencing process while vastly limiting the generalisability of findings. For longer than four decades some of the best statisticians working on the field of Criminal Justice have sought to apply more sophisticated statistical models with which to expand the generalisability of findings based on custodial sentence lengths to the whole realm of sentencing. However, their efforts have been unsatisfactory. Although they manage to incorporate non-custodial outcomes into the model and with it improve the scope of their findings, they do so based on unrealistic assumptions – detailed in the case for support – that undermines the robustness of their approaches. We propose to use a new and more flexible statistical paradigm (based upon Bayesian inference) to develop a model where custodial and non-custodial outcomes could be integrated in a meaningful way. To do so we will rank sentence outcomes in terms of their relative severity, so custody being more severe than a suspended sentence, which in turns is more severe than a community order, and that more severe than a fine. To refine this scale of sentence outcomes’ severity, and given the deeply subjective nature of severity of punishments, the model will be further informed by personal views of Crown Court judges on the topic. The result of this work will be the elaboration of a new framework capable of obtaining more insightful and robust analyses of sentencing data. We will overcome a methodological conundrum that has affected the literature on the topic for far too long. Perhaps more importantly, the application of this new modelling strategy will allow academics and government researchers to provide higher quality information to policy-makers in the field of sentencing. A sector that is currently being reformed by government policy and through the application of sentencing guidelines both in England and Wales, and in Scotland.

Awarding body: ESRC (Economic and Social Research Council)
Total value: £279,570 (Department of Statistics: £15,139)
Grant holder: Professor Jonathan Jackson (Department of Methodology)
Co-investigator (Department of Statistics): Dr Jouni Kuha
Start/end date: 01/07/2014 - 30/06/2016

Summary: This is a comparative, cross-national study into attitudes towards legal authorities, compliance with the law, co-operation with legal authorities and the policing of minority and majority groups. The proposal is to address questions of deterrence, legitimacy, co-operation and compliance using a powerful new dataset that we have generated from national probability sample surveys of 30 different countries. The goal is to mount an ambitious cross-national empirical test of deterrence theory and procedural justice theory. 

Awarding body: ESRC (Economic and Social Research Council)
Total value: £242,870
Grant holder: Dr Myrsini Katsikatsou (ESRC future research leaders fellowship)
Start/end date: 01/10/2014 - 30/09/2017

Summary: This project aims to contribute to methodological research and provide tools for latent variable modelling of social survey data. The methods will be applied to the analysis of data from the OECD Programme for the International Assessment of Adult Competencies (PIAAC) and from the European Social Survey (ESS). The goals of the methodological research are to evaluate, further develop and disseminate innovative statistical methods and practical tools for latent variable modelling of social data regardless of the model complexity and size, the data type and size, or the presence of item non-response (missingness in some survey questions).

The research will develop pairwise likelihood (PL) methods of estimation and testing for latent variable modelling (LVM), also known as structural equation modelling (SEM). SEM and LVM are standard well-established tools for modelling social survey data. PL is an emerging method for estimation and inference that has recently become popular in many disciplines because of its computational simplicity and statistical merits.

Awarding body: ERSC (Economic and Social Research Council)
Total value: £322,797 (LSE: £17,124)
Grant holder: Rory Fitzgerald (City University)
Co-investigator (LSE Statistics): Professor Chris Skinner
Start/end date: 01/08/2014 - 31/01/2016
Project website

Summary: This project will explore the extent to which the predictive power of various forms of Big Data can be harnessed to overcome the impact of poor response to surveys - one of the major challenges facing social research today. Social surveys are a key tool used by the media, policy makers and academics to understand more about public attitudes and behaviour. However, the value of surveys is put at risk by the fact that a large and growing number of those selected to take part in surveys do not respond.

As non-respondents may be very different from respondents, nonresponse can introduce significant bias into the conclusions drawn from survey data. There is a pressing need therefore to understand more about the extent and sources of nonresponse bias. This requires having information about both respondents and non-respondents. In the absence of interview data being available for non-respondents, this information must be obtained for other, external, sources.

Awarding body: MRC (Medical Research Council)
Total value: £398,544 (LSE: £24,078)
Grant holder: Dr Gianluca Baio (UCL)
Co-investigator (LSE Statistics): Dr Sara Geneletti
Start/end date: 02/09/2013 - 01/02/2016

Summary: A fundamental task in clinical practice is to determine whether a particular drug is being prescribed in the most effective way. While Randomised Clinical Trials (RCTs) are considered to be the best scientific method for evaluation of drug efficacy, these studies often have poor external validity. Prescription guidelines are not always evidence based and it typically falls to clinical experts to set them.

The regression discontinuity design (RDD) is an econometric quasi-experimental  design aimed at estimating the causal effects of a treatment by exploiting naturally occurring treatment rules. It was first introduced in the educational economics literature in the 1960s but it has not been widely used outside of this field until recently. This project has both substantive and methodological aims: the assessment of statin effectiveness in primary care and application and development of the RDD in epidemiology.

Awarding body: NCRM (National Centre for Research Methods); ESRC (Economic and Social Research Council)
Total value: £192,247
Grant holder (LSE Statistics): Dr Jouni Kuha
Co-investigators (LSE Statistics): Professor Irini Moustaki; Professor Chris Skinner
Start/end date: 01/04/2013 - 30/09/2014

Summary: Cross-national surveys are one of the key resources of social sciences. The complexity of the surveys raises methodological challenges, which need to be met in order to make the best use of the data. Two of these are problems of data quality: measurement error where the answers by survey respondents are in some way erroneous, and nonresponse where some questions are not answered at all.

The goal of this project is to develop and evaluate research methods for these problems. The project has three main strands, which concern latent variable modelling of non-ignorable item nonresponse, effects of interviewer prompting, and the use of item count questions on sensitive topics, all of these particularly in the context of cross-national survey data. The work is carried out in collaboration with investigators from the Core Scientific Team of the European Social Survey.

Awarding body: EPSRC (Engineering and Physical Sciences Research Council
Total value: £129,460
Grant holder (LSE Statistics): Dr Kostas Kalogeropoulos
Start/end date: 01/02/2013 - 31/01/2015

Summary: A substantial amount of publicly available datasets represent educated predictions on the evaluation of stochastic processes. These include financial derivative instruments, such as option prices, that can be formulated as expectations of the underlying price process.

This project consider models with latent diffusion processes that can be linked to direct observations, but also to such conditional expectations. The goal is to utilise advanced computational methods to estimate that data generating mechanism from both datasets; moreover, to develop a general inferential framework to handle parameter and model uncertainty.

Awarding body: ESRC (Economic and Social Research Council)
Total value: £520,435 (LSE: £42,632)
Grant holder: Dr Erzsébet Bukodi (University of Oxford)
Co-investigator (LSE Statistics): Dr Jouni Kuha
Start/end date: 01/03/2012 - 28/02/2015

Summary: The main objectives of this project are to examine the importance of education, relative to that of other factors, in determining individuals’ chances of social mobility in Britain over recent decades and to investigate the social processes through which education actually impacts on mobility chances. The project is primarily based on three British birth cohort studies covering individuals born in 1946, 1958 and 1970. The research uses advanced techniques of statistical analysis, such as path analysis with categorical variables, multiple imputation of missing data in the cohort studies, and simultaneous hazard modelling.

Awarding body: NCRM (National Centre for Research Methods); ESRC (Economic and Social Research Council)
Total value: £1.4 million
Grant holder (LSE Statistics and Bristol): Professor Fiona Steele
Start/end date: 01/10/2011 - 30/09/2014

Summary: LEMMA 3 is a node in the second phase of the ESRC-funded National Centre for Research Methods (NCRM). The mission of NCRM is to provide a strategic focal point for the identification, development and delivery of an integrated national research, training and capacity-building programme. The project builds on the work of LEMMA 1 and LEMMA 2. Social science is all about understanding complex social processes that develop over time. For example, the processes through which people from families with differing socio-economic backgrounds end up with markedly different life outcomes. It has long been recognised that understanding such processes requires longitudinal data comprising repeated measurements of the key factors over time, and there has been substantial investment in the collection of such data.

The overarching objective of LEMMA 3 is to build capacity in the analysis of longitudinal data. LEMMA 3 aims to:

• Review and synthesise important developments in longitudinal data analysis.
• Develop and adapt new methodology that addresses important problems in social research today.
• Apply the newly developed methods to substantive research projects in collaboration with experts from medical sociology, health psychology, economics, education and developmental psychology.
• Implement the methodological research in the 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.

More information can be found on the website of the Centre for Multilevel Modelling, University of Bristol.

Awarding body: MRC (Medical Research Council)
Total value: £434,603 (LSE: £3,672)
Grant holder: Professor Kate Tilling (University of Bristol)
Co-investigator (LSE Statistics): Professor Fiona Steele
Start/end date: 01/04/2011 - 31/03/2014

Summary: There is increasing emphasis in medical research on foetal and childhood antecedents of disease, and how these interact with other exposures throughout the lifecourse to influence later-life conditions. Answering questions about the relative importance of aspects and timing of growth, behaviour and health status for longer term outcomes requires longitudinal data.

Analysis of such data must account for dependencies between repeated observations on the same person, including serial autocorrelation (greater correlation among measurements closer together in time). Where there are repeated measures of exposures related to a later-life outcome, standard regression models may be affected by multicollinearity. Measurement error may vary over time, and there will usually be dropout over time (due to death, illness, etc.).

The proposed research will focus on developing methods for more robust analysis of lifecourse data, by tackling these problems. We will develop guidelines for deciding when simpler methods are likely to be useful and unbiased, and when more statistically sophisticated methods would be more appropriate.

Awarding body: ESRC (Economic and Social Research Council)
Total value: £488.000
Grant holder (LSE Statistics and Bristol): Professor Fiona Steele
Start/end date: 01/10/2010 - 31/03/2014

Summary: Housing transitions - such as changes in housing tenure, residential mobility - are the outcomes of a complex history of other life course events such as union formation and dissolution, births of children, and changes in employment. The principal aim of this project is to examine the extent to which housing transitions and residential location choice are influenced by fertility outcomes such as the birth of a(nother) child or a child reaching primary or secondary school age, allowing for the effects of other social processes such as union formation and dissolution and employment changes.

Another aim of the project is to explore spatial variation in fertility by residential context, distinguishing rural areas and different size urban areas, and the effects of area characteristics on mobility and location choice. In addition, we examine housing market effects on residential mobility and fertility. The project also investigates a number of important methodological issues in the analysis of household panel data.

Methodological research considers different approaches to the analysis of household-level decisions using longitudinal individual-level data when household composition changes over time, adjustment for unmeasured individual characteristics that affect both changes in housing and changes in fertility, non-ignorable attrition when drop-out is directly influenced by moving home, and estimation of push and pull effects of area characteristics in residential location choice.

More information can be found on the website of the Centre for Multilevel Modelling, University of Bristol.

Awarding body: MRC (Medical Research Council)
Total value: £348,381 (LSE: £13,387)
Grant holder: Dr Artemis Koukounari (Imperial College)
Co-investigator (LSE Statistics): Professor Irini Moustaki
Start/end date: 10/01/2011 - 31/08/2013

Dr Artemis Koukounari is now BRS Lecturer in Statistics at King's College London

Summary: To use advanced biostatistical analysis to further understanding of the effect upon the prevalence and intensity of schistosomiasis and of the ocular bacteria causing trachoma, and the likelihood of their elimination, of interventions based on Mass Drug Administration (MDA), as well as to evaluate the performance of the diagnostic tools currently used for the Monitoring & Evaluation (M&E) of these two infections.

Awarding body: ESRC (Economic and Social Research Council)
Total value: £396.000 (LSE: £256,091)
Grant holder (LSE Statistics): Professor Chris Skinner
Start/end date: 01/02/2010 - 31/01/2013 (transferred to LSE from 01/10/2011)

Summary: The quality of data collected in surveys is subject to a wide range of threats in the modern world, including the public's declining willingness to take part at all. Yet sources of information about this quality are increasing, in particular as a by-product of the evolving technologies used in survey data collection.

This fellowship investigates new ways of using this information to address a range of data quality issues which face social science researchers when analysing survey data. The research addresses methodological questions such as: is it possible to improve analyses by giving greater emphasis to parts of the data which are of higher relative quality and if so how?

Awarding body: ESRC (Economic and Social Research Council)
Total value: £215,000
Grant holder (LSE Statistics): Dr Jouni Kuha
Co-investigator (LSE Statistics): Professor Irini Moustaki
Start/end date: 01/04/2010 - 30/09/2012

Summary: To develop and encourage the use of particular statistical tools that will lead to better utilization of data collection of cross-national social surveys, more valid conclusions, and more relevant input into social science and public policy making.

Research project website