LSE Statistics - ADR UK PhD Studentship 2023

Assessing inequality in the Criminal Justice System using novel causal inference methods and Bayesian spatial models

Supervisors: Dr Sara Geneletti and Professor Fiona Steele

About the project: The Lammy Review showed evidence of discrimination in the Criminal Justice System. The aim of this project is to use causal inference methods to corroborate these findings by quantifying the impact of discrimination in sentencing using Data First: Criminal Justice System Linked Datasets.

The proposed aims of this studentship are to understand:

• whether and how early life course events such as education and being in care are impacted by systematic discrimination;
• how early experiences impact involvement with the Criminal Justice system later in life and whether discrimination continues to play a part; 
• whether there are regional differences (e.g. London vs the rest of England).

The following methods could be used:

• longitudinal mediation analysis to determine the impact of being in care and educational attainment on later outcomes such as violence and arrest and how discrimination confounds these relationships;
• proximal causal inference to determine the impact of discrimination on the pathway between life events in relation to violence and involvement with the Criminal Justice System;
• Bayesian Spatial analysis to address spatial/regional differences in discrimination. 

We are looking for someone who: 

• has some experience in data analysis, and is interested in analysing 'big data'
• is interested in understanding how life events can lead to individuals being involved in the criminal justice system and what part discrimination plays in this process
• is interested in causal inference broadly and can see themselves developing methods in statistical causality 

The studentship includes training provided by ADR UK on how to use administrative data for social and health research as well as training on how to be a safe researcher provided by the ONS.

To complement this, the Department of Statistics at the LSE is able to provide in-house training that will enhance the quantitative skills of the student and enable them to extract the maximum insight from the data.

The chosen candidate will be able to attend MSc level courses in advanced quantitative methods necessary for the research including Spatial Statistics, Bayesian Inference, Causal Inference and Longitudinal and Multilevel methods. Training will also be provided in programming including R, python and JAGs (a Bayesian analysis software).

The supervisors of this studentship, Dr Geneletti and Prof. Steele, have been involved in research driven by and using administrative data, large panel survey or cohort datasets.

Dr. Geneletti is currently involved in a related ESRC grant, “Exploring the Nature of Ethnic Disparities in Sentencing through Causal Inference” (PI: Jose Pina-Sanchez (Leeds)), which uses the Data First: Criminal Justice Systems Linked Database.

Prof. Steele is a leading researcher in methods for longitudinal data with applications in educational attainment, family psychology and health.

The ADR Studentship is tenable for three years and will cover full fees at the UK home rate (for international students, LSE will cover the difference between UK Home rate and overseas fees) and provide an annual stipend (which for 2022 entry was £19,668) for three years, commencing in 2023.

A fourth-year stipend will also be provided by LSE which will match the UKRI rate at that time. 

Applicants should have completed (or be on track to complete) a taught MSc in Statistics, Mathematics or a related discipline (e.g. economics, etc.) or equivalent, plus an undergraduate degree of good standing.

There is also an English language requirement for all applicants for whom English is not their first language. 

International students may consult the graduate prospectus for details of equivalent qualifications. 

Applications must be made via the Graduate Admissions Office.

The application deadline for this studentship is Thursday 27 July  - all supporting documents and references should be received by this date. 

Applicants of UK nationality from BAME (Black, Asian and Minority Ethnic) backgrounds who opt-in to the ACE PGR Initiative will benefit from an application fee waiver and other support during the application process. 

Research Proposal

When applying, you should focus on an aspect of causal inference and/or longitudinal data analysis that you are interested in and is relevant to this project. Possible topics could be (but are not limited to) experiments vs observational data, confounding, mediation, time-varying data, missing data etc. Most applicants will have minimal experience of research and therefore we do not expect a fully-developed research proposal. We are assessing the potential of the applicant for research and the chosen topic. The following is a guideline of what to emphasise in the proposal: 

• Demonstrate your understanding of the area and the need for further research
• Be detailed in your explanation of your topic of interest. 
• Be specific about the training and skills you have to undertake the proposed research (do not simply list courses attended: this information is already available in the CV and transcripts)

Your proposal should be at most 1,000​ words in length. MPhil/PhD applications that are received without a research proposal that addresses these questions will not be considered.  

Personal statement

In addition, you should submit a personal statement of between 1,000 and 1,500 words, describing your academic interests and your purpose and objectives in undertaking a doctoral research degree. Your personal statement should also explain why you have chosen LSE.

Contacts

You are welcome to contact Dr Geneletti at s.geneletti@lse.ac.uk to discuss your application.

For information on the application process, you are welcome to contact Penny Montague at p.montague@lse.ac.uk.