Introductory description

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Principles of mixed methods analysis, research design and analysis, data transformation (qual to quant and quant to qual), the meta-matrix, PIP method, barriers and challenges to analysis and integration, computer assisted analysis, planning a mixed methods analysis.

Learning outcomes

By the end of the module, students should be able to:

• 1. Discriminate between the two underpinning approaches to mixed methods analysis: merging and connecting.
• 2. Identify commonly used analysis techniques within these broad approaches (e.g. meta-matrices/PIP/data transformation).
• 3. Identify, and construct means of addressing, common barriers to data analysis integration (conflicting datasets, sample sizes discrepancies, loss of meaning or precision during data transformation etc.)
• 4. To justify the relationship between analysis and research design.
• 5. Formulate an analysis plan for use in their own mixed methods project.

Indicative reading list

Andrew, S. and E. Halcomb (eds). 2009. Mixed Methods Research for Nursing and the Health Sciences Oxford: Wiley Blackwell.
Creswell, J. 2009. Research Design: Qualitative, Quantitative and Mixed Methods Approaches (3rd Edition) London: Sage.
Plano Clark, V. and J. Creswell (eds). 2008. The Mixed Methods Reader London: Sage.
Teddlie, C. and A. Tashakkori. 2009. Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioural Sciences London: Sage.

Subject specific skills

Sound understanding of subject
Critically evaluate
Reflection

Transferable skills

Numeracy
Thinking and problem solving
written communication
oral communication
Teamwork
Organisation & time management
Use of tools and technology
Commercial awareness
Independence and initiative
Adaptability/Flexibility