Develop your ability to apply advanced analysis methods to psychological data. In this module, you will build the skills needed to be effective at every stage of the modern data analysis workflow, from handling large-scale, rich and sometimes messy real-life datasets, to constructing insightful models, to presenting high-impact evidence-based visualisations. At every stage, we will develop your coding skills in R and gain experience working in a team. Throughout, you will develop the critical reflective thinking skills you need to independently make decisions in the workplace. You will be introduced to popular, modern data modelling techniques through a combination of lectures and practical workshops. Emphasis will be on developing your understanding and capacity for critical evaluation to enable you to choose and effectively apply cutting-edge techniques in a variety of contexts.
This introduction to both qualitative and quantitative approaches to the analysis of talk and text is structured around the process of conducting a research project. You will develop the practical skills involved in conducting insightful interviews and focus group-based research. You will demonstrate an understanding of the fundamental ideas and practices involved in:
- Natural language processing
- Thematic Analysis and Interpretative Phenomenological Analysis (IPA)
- Conversation analysis and discursive approaches
- Quantitative analysis through Linguistic Inquiry and Word Count (LIWC)
- Sentiment analysis and its applications
By the end of the module, you will be able to formulate a research question, engage with research ethics questions, and plan to sample and analyse talk and text.
Embrace a fresh interdisciplinary approach that fuses psychology and computer science. In a laboratory environment, you 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 R, Python, Java MakeCode, BORIS and Netlogo. You will be exposed to several digital data types (e.g., video, sensor data and digital trace data) and will explore how to create meaningful psychological knowledge from this data. As part of this, you will learn a variety of data-driven and theoretically driven analysis methods (e.g., data cleaning, predictive modelling, behavioural coding, data visualisation). By the end of this module, you will understand how psychology and new digital methods synthesise and will have enhanced your practical research and analytical skills, allowing you to seek solutions to real-world issues.
The module will teach the fundamentals of computing: historical development of computational machines, data storage, computational processing, and the development of computer science. This will be done using the R language for programming with focus on: simple calculations; structured programming with loops and conditions; data structures; writing functions. The students will be taught how to handle the R language for data file input/output; selection and filtering; exploratory graphics. Throughout the module, the students will also learn how to develop good computational research practice: project management for data and code; dynamic documents for reproducible research; source code management. In the second part of the module the students will learn the Python language basics: fundamentals for calculation and programming; writing functions and modules for code re-use; creating new classes and programming with objects; Python including command line and notebooks. The module will conclude with an introduction to numerical Python: using NumPy and Pandas for data analysis; plotting basic graphics.
Explore meta-level issues that are important for work on psychology and behavioural analytics. Here, you will learn a theoretical toolkit and understand the implications for how you might use these concepts to explore or refine psychologically relevant questions using digital data.
To this end, the module discusses the current state of psychological science and the key tensions that exist as psychologists embrace new forms of digital data.
You will be introduced to key psychological theories and explore 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.
Through a three-month dissertation project, you will complete a substantial research project intended to tackle a real-world challenge. This is an opportunity to consolidate, integrate and further develop the behaviour analytics skills gained throughout prior modules. You have the option to undertake a project through an external placement, facilitated by an industrial or research organisation working in partnership with the University, or carry out an academic project (a traditional dissertation without an external partner). The aim of the dissertation is to allow you to synthesise, refine and extend your own scientific reflection and practice and to apply and refine your technical skills, knowledge, and behavioural analytical skillset.
Through the project, you will:
- Complete a comprehensive literature review
- Formulate testable research hypotheses or define the parameters of an explorative analysis
- Conduct a piece of empirical work that interrogates your research question
- Appropriately analyse and interpret your data
Gain the understanding and skills needed to apply core analytic methods across a range of psychological research and practice settings. You will develop your knowledge and skills through a combination of lectures and practical workshops and build your capacity to code using R, a modern statistical programming language.
You will learn about the application of key statistical tests in a variety of real-life contexts that will enable you to confidently identify the appropriate technique for analysing data in diverse environments. By the end of this module, you will be able to:
- Employ data analysis and processing tools using R
- Identify the analysis approaches that can be applied to test predictions
- Apply statistical tests that are essential to current practices
- Describe findings effectively using text and visualisations
- Translate data analysis findings into psychological insights
