Course objectives: 1    To introduce the fundamental principles and essential elements that should be included in any research design aimed at investigating socio-economic issues in     the context of natural resources management.
2.    To provide an overview of the range of qualitative and quantitative research techniques that may be used in such a research exercise, and introduce the principles necessary for integrating these within a coherent methodological framework.
3.    To provide introductory material enabling the basic analysis and robust interpretation of both qualitative and quantitative data using appropriate software packages.
4.    To introduce the principle that different stakeholders with an interest in research results require different knowledge products.

Course contents: The topics covered in this module are: defining 'social research' and it's utility for, and application to different resource management contexts; participatory approaches to learning - what they are and when they are or are not useful; defining the research rationale, objectives, questions and hypotheses; developing the research design - frameworks, sampling and appropriate techniques; deciding between quantitative, qualitative and action research approaches matching objectives and questions to method; data collection techniques - complementarities between action focused, participatory and traditional extractive tools and techniques; data aggregation, analysis and display using EXCEL and SPSS.

Teaching and learning methods:  Lectures, group discussions, field survey

Examination:  Individual essay: Set question concerning design of social research exercise using mixed methods approaches. 100% Outcomes 1, 2, 3, 4 and 6

Competences acquired: design a piece of social research according to specific objectives and research questions; conduct social research using a robust methodological framework incorporating both qualitative and quantitative approaches; understand the procedures to follow for analysing quantitative and qualitative data using appropriate software