Module Directory

Evidence based practice is an essential component of responsive and responsible health policy, strategy, leadership, service provision and coordination of action for health. To scrutinise the knowledge that informs and guides practice, the practitioner needs to be research-literate. This module explores the different ways in which data can be collected, analysed and interpreted so that students become more confident in carrying out these activities themselves, as well as increasing their ability to critique the research carried out by others.

In particular, students will be able to reflect on the most suitable research methods for their particular research question, understanding that the data collected has implications for the analysis that can be carried out and the types of question that can be answered.

The aims of this module are:

• To provide students with a range of techniques for collecting, analysing and interpreting data. It combines a theoretical with a practical approach to enable students to fully understand the collection and analysis process so that they are able to make informed decisions when designing and carrying out research.

By the end of this module, students will be expected to be able to:

1. Debate the use of particular research methods in response to specific research questions.


2. Design and develop data collection instruments and critically reflect on their value and suitably.


3. Identify and debate the ethical implications of research.


4. Explain and carry out a range of data analysis using appropriate computer software.


5. Interpret the findings of statistical and qualitative analysis and relate this to their field.

This module introduces students to the differences between and similarities in quantitative and qualitative research, and how they complement each other. The module first focuses on quantitative methods, including research designs, the importance of research ethics, and the design of questionnaires. There will be a combination of lectures and workshops, during which a range of descriptive and inferential statistical analysis techniques are introduced using R, an open-source statistical software package that is free to use. It then moves on to approaches to qualitative data collection and analysis. There will be a lab session introducing NVivo, a commonly used software package for qualitative data analysis. The qualitative data analysis lab sessions will be held in parallel; students only need to attend one session of their choice on campus.

The module will be delivered via:

• Lectures
• Computer lab sessions

supported by Moodle. Moodle hosts extra readings, copies of slides, and other material. The assessment task includes piloting your own material which should be developed for formative assessment. Tutorials are scheduled to complement this process.