Making Community Punishments more Effective

量刑研究传统上受到数据可获得性的限制。研究人员不得不依赖司法部的汇总数据，或对法官的一次性调查，如现已过时的皇家法院量刑调查(数据收集于2014年停止)。ADR UK发布的新的链接行政数据库将允许研究人员探索更广泛和更深入的量刑问题。该项目将对有关有效量刑的文献作出重要贡献。具体地说，这些分析将探索附加于社区秩序的不同要求的相对有效性。法院有权对服务于社区秩序的罪犯施加一系列要求，但几乎不知道哪些条件或要求的组合在减少再次犯罪方面最有效。量刑人员依靠法官和治安法官的个人经验和直觉，有效地在黑暗中量刑。此外，这一数据集将使人们能够分析迄今未得到充分探索的量刑方面，这些方面可能会对社会不平等产生影响。特别是，该项目将审查要求的数量、性质和唯一性是否因犯罪者的种族而异。例如，如果对BAME被告的要求与对被判相同罪行的白人被告的要求不同，那么这反过来可以帮助我们了解再犯罪率的差异。鉴于中央政府希望使量刑更一致、更有效、更具成本效益和更公平，预计这项研究将对该领域的政策和实践产生重要影响。鉴于这一领域缺乏实证研究，仅从简单的描述性统计数据就可以得出有影响力的结果。然而，博士生将有机会接受两个领域的高级统计分析培训，数据可视化和纵向数据分析。创建直观的数据可视化工具将是能够以对未接受过统计培训的受众有意义的方式总结和传达大量发现的关键。同样，数据集的纵向维度和研究问题将要求学生接受高级统计技术方面的培训，如序列分析、增长曲线和自回归模型。其中许多技术目前在CDT数据分析与协会组织的数据分析理学硕士课程中涵盖。那些没有涵盖的技术将通过参加特定的短期课程来学习，例如由LIDA协会、消费者数据研究中心提供的课程，或由国家研究方法中心或皇家统计学会宣传的外部课程。

Sentencing research has been traditionally restricted by limitations on data availability. Researchers have had to rely on aggregate date from the Ministry of Justice, or one-off surveys of judges such as the Crown Court Sentencing Survey which is now outdated (data collection ceased in 2014). The new linked administrative databases released by ADR UK will permit researchers to explore a much wider range of sentencing questions and to greater depth. This project will make an important contribution to the literature on Effective Sentencing. Specifically, the analyses will explore the relative effectiveness of different requirements attached to a community order. Courts have the discretion to impose a range of requirements on offenders serving community orders, yet almost nothing is known about which conditions or combinations of requirements, are most effective in reducing re-offending. Sentencers are effectively sentencing in the dark, relying on the individual experiences and intuitions of judges and magistrates. Furthermore, this dataset will enable the analysis of hitherto under-explored aspects of sentencing that carry potential consequences for social inequalities. In particular, this project will examine whether the number, nature, and onerousness of requirements varies according to the ethnicity of the offender. If, for instance, the requirements imposed on BAME defendants differed from those imposed on White defendants convicted of the same offence, then this could, in turn, help us understand differences in re-offending rates. Given the great desire by central government to make sentencing more consistent, effective, cost-effective and fair, it is anticipated that this research will have an important impact on policy and practice in the field.Given the absence of empirical research on this area, there is scope to produce impactful findings just from simple descriptive statistics. However, the PhD student will have the opportunity to be trained in two areas of advanced statistical analysis, data visualisation and longitudinal data analysis. Creating intuitive data visualisation tools will be key to be able to summarise and convey large amounts of findings in a way that is meaningful to non-statistically trained audiences. Similarly, the longitudinal dimension of the dataset and the research questions, will require the student to be trained in advanced statistical techniques like sequence analysis and growth curve and autoregressive models. Many of these techniques are currently covered in the MSc in Data Analytics organised by the CDT Data Analytics & Society. Those techniques that are not covered there will be learn through participation in specific short courses such as those delivered by LIDA Societies, the Consumer Data Research Centre, or external courses such as those advertised by the National Centre for Research Methods or the Royal Statistical Society.