This module is intended to provide students with an introduction to, and grounding in, both qualitative and quantitative approaches to the analysis of talk and text. It is structured around, and takes students through, the process of conducting a research project. By the end of the module, students will be able to generate and collect qualitative and quantitative data from both mediums suitable for analysis, apply a range of analytical methods to such data, as well as consider the ethical issues surrounding the collection and analysis of speech and text-based datasets.

This module focuses on developing the statistical understanding and practical skills required to analyse quantitative data using the modern and powerful R statistical programming language and environment. During this module, our students will learn how to apply a range of analytic techniques that are standard in professional practice in psychological research, how to apply statistical tests across a variety of settings, as well as how to analyse both qualitative and quantitative data types.

Building on skills learnt in PSYC411, this module aims to equip students to conduct and present quantitative data analyses appropriately and effectively using advanced methods. Students will be introduced to popular, modern, data analysis techniques through a combination of lectures and practical workshops, learning especially how to utilise modelling techniques (such as linear and generalised linear mixed-effect models) across a wide range of psychological applications, as well as the importance of reproducibility within the psychological sciences.

In this module, students will embrace a fresh interdisciplinary approach that fuses psychology and computer science. Our curriculum introduces case studies that showcase how researchers have harnessed the potential of digital tools. Students will witness first-hand how psychology and new digital methods synthesise to enhance one another at every stage of the research process.

In a laboratory environment, students will master the art of analysing emerging forms of digital data and extract meaningful insights through hands-on experiences with cutting-edge tools (such as Integrated Development Environments like R Studio and state-of-the-art behavioural observation software like BORIS).

Students will be exposed to several digital data types (e.g., video, sensor data, smartphone data, and digital trace data) and will be taught several methods of how to create meaningful psychological knowledge from this data. They will learn a variety of data-driven and theoretically-driven analysis methods (e.g. data cleaning, predictive modelling, behavioural coding, data visualisation, and general data interrogation). By the end of this module, students will have enhanced their practical research and analytical skills through this interdisciplinary perspective on digital data, allowing them to seek solutions to real-world issues.

In this three-month dissertation project (undertaken over the summer), students will complete a substantial research project intended to tackle a current, real-world challenge, giving them the opportunity to consolidate, integrate and further develop the behaviour analytics skills gained during the taught course component.

Students may undertake a project with an associated 'external placement', facilitated by an industrial or research organisation working in partnership with the University. In these cases, the academic supervisor will ensure that the overall research-orientation of the project is preserved. For some students, however, an 'academic project' (i.e. without placement with an external partner) may better align with their career aspirations, such as for progression towards a PhD, or their skills level. In addition, if it is pedagogically or practically beneficial to both the student and/or company, the dissertation work could be undertaken at the University.

The aim of the dissertation is to enable students to synthesise, refine and extend their own scientific reflection and practice as well as apply and refine their technical skills, knowledge, and behaviour analytical skillset. By the end of the module, students will have completed a comprehensive literature review of the existing theory relating to their topic, formulate a testable research hypothesis, conduct a practical element in order to investigate their research question as well as appropriately analyse and interpret the data produced from their research.

This module aims to develop skills in critical reading, review, thinking and communication, alongside an awareness of ethical concerns in Psychology and Behavioural Analytics. Student will learn how to understand and accommodate for the moral and ethical implications of experimental and observational studies, complete literature reviews of scientific papers from across psychological domains, as well communicate experimental findings – in both written and oral mediums – in a format suitable for academic and non-academic settings.

This module is designed for students that are completely new to programming, and for experienced programmers, bringing them both to a high-skilled level to handle complex data science problems. Beginner students will learn the fundamentals of programming, while experienced students will have the opportunity to sharpen and further develop their programming skills. The students are going to learn data-processing techniques, including visualisation and statistical data analysis. For a broad formation, in order to handle the most complex data science tasks, we will also cover problem solving, and the development of graphical applications. Two open source programming languages will be used, R and Python.

This module is designed to explore meta-level issues that are important for work on psychology and behavioural analytics. Here, students learn a theoretical toolkit - and understand the implications for how they might use these concepts to explore or refine a psychologically-relevant question using digital data. To this end, the module discusses the current state of psychological science and the key tensions that exist as psychologists begin to embrace new forms of digital data. It introduces key psychological theories and explores which theories are best supported ‘outside of the lab’ by new forms of digital data. Beyond positioning psychology across this new digital plane, this module deals with issues of research ethics, morality, and scientific practice.