The SWDTP has the following steers from the ESRC
- 3 studentships to be awarded for Data Skills (applicants are eligible from any of our disciplinary or interdisciplinary pathways, except for AQM)
- 3 studentships to be awarded to AQM (applicants must have applied to the AQM pathway)
Steer 1 – Data Skills
The focus of this steer is developing researchers with the skills to fully exploit the increasing volumes of large and complex data for social research purposes. The steer includes survey data and ‘big data’ (social media, administrative, transactional and geospatial data). These studentships can use qualitative and/or quantitative approaches and will provide opportunity for students to develop advanced data skills throughout their studentship. Applications are encouraged that access new forms of data previously not routinely available, such as administrative data and unstructured commercial data (such as loyalty card databases), as well as data created through digital interactions between people, people and organisations, and interactions with urban environments (such as transport footfall data). Projects in this steer are likely to demonstrate the ability to engineer data together from a range of sources, understand caveats such as potential biases (particularly where data are linked), check and monitor data integrity, as well as curate data and write efficient statistical programme code to ensure that research outcomes can be robustly peer reviewed, and, with a focus on reproducibility, consider how these data pipelines can be shared again in the future. This steer is towards data science and/or digital methods but should not be regard as simply quantitative. Applications from qualitative domains are welcomed and encouraged – the data might include imagery, textual or aural data, for example. Applicants that wish to be considered for the Data Skills steer may fit within our Sociotechnical Futures and Digital Methods pathway but are not restricted to being so. Applications are welcome from any of our disciplinary or interdisciplinary pathways at any of our eight partner institutions, except for Advanced Quantitative Methods (AQM).
Steer 2 – Advanced Quantitative Methods (AQM) and its distinction from Data Skills
Advanced Quantitative Methods (AQM) focuses on methodological development for quantitative social science, including original applications of cutting-edge (i.e. advanced) methods to topics of relevance to social science. Whereas the Data Skills steer focuses on using and making usable new and/or unconventional sources of digital data (either quantitative or qualitative), thereby showcasing the utility of those data for social scientific research, AQM’s focus is less on operationalising or drawing knowledge from specific data pipelines but, instead, on developing/applying advanced quantitative methods that have the potential for broader utility across the social sciences. That development can involve directly advancing or refining the method itself (with a clear social scientific purpose in mind) or tackling how to apply and therefore demonstrate its value to a particular research topic / area of research. As such, the methods should not be conceived as limited to only particular data sets, data linkages or specific instances of quantitative information (although their development, testing and/or application may be) but as ‘portable’ – methods that will be useful to and can operate across a range of data sets and disciplinary domains. In broad terms, the Data Skills steer will typically focus on social scientific applications of data and data science – the data are core, within a social science setting. For AQM, it is the method that is central, typically drawing on statistical, mathematical, econometric and/or computational modelling (including process modelling, as well as machine learning and AI) to develop and/or apply more general methods and techniques relevant to the social sciences, although this distinction is neither absolute nor exhaustive. For example, A project that uses consumer data to visualise consumer behaviours using an established mapping technique might be regarded as Data Skills. A project that develops or applies a new method for handling and visualising multi-dimensional social data, as AQM. Critically, AQM is an interdisciplinary pathway so applications should demonstrate the interdisciplinary nature or potential of the proposed methods and research, as well as demonstrating either methodological advancement or a novel application of a method that is genuinely advanced for the disciplinary context. It is not sufficient for the method just to be quantitative: more routine, standard and disciplinary-focused applications of quantitative methods should be directed towards one of our various disciplinary pathways and not AQM. Applicants that wish to be considered for the AQM steer must apply to the Advanced Quantitative Methods pathway at one of the Universities of Bath, Bristol, Exeter and Plymouth. Applicants who apply to the AQM pathway are not eligible to be considered for the Data Skills steer.