Course Purpose
The course aims to equip doctoral learners with advanced statistical knowledge, analytical skills, and professional attitudes for high-quality, data-driven educational research.
Course Learning Outcomes
By the end of this course, you should be able to;
CLO1: Demonstrate an understanding of various research paradigms and epistemological foundations
CLO2:Analyse large datasets using appropriate statistical software and interpret the results accurately
CLO3: Apply thematic analysis, grounded theory, and narrative analysis to interpret qualitative data effectively
CLO4: Recognise the importance of combining different methodologies to gain a comprehensive understanding of complex phenomena
Course Content
Introduction to Advanced Research Methodology
Key concepts and importance of research methodology in academic and practical contexts
Research Paradigms and Epistemological Foundations
Overview of major research paradigms (positivism, interpretivism, critical theory, etc.)
Epistemological assumptions and their implications for research
Critical Analysis of Research Designs
Comparative analysis of different research designs (experimental, quasi-experimental, non-experimental, etc.)
Strengths and limitations of various research designs
Case studies for practical understanding
Ethical Considerations in Advanced Research
Core principles of research ethics
Institutional Review Board (IRB) processes and protocols
Managing conflicts of interest and ethical dilemmas in research
Quantitative Research Methods
Overview of quantitative research designs
Advanced statistical techniques: multivariate analysis, structural equation modeling (SEM)
Practical application using statistical software
Qualitative Research Methods
Overview of qualitative research designs
Data collection techniques (interviews, focus groups, observations)
Data analysis methods (thematic analysis, grounded theory, content analysis)
Mixed Methods Research
Integration of quantitative and qualitative approaches
Triangulation and validation strategies
Designing and conducting mixed methods research
Sampling Techniques
Probability sampling methods (simple random sampling, stratified sampling, etc.)
Non-probability sampling techniques (purposive sampling, snowball sampling, etc.)
Determining sample size and ensuring representativeness
Research Instrument Development
Designing valid and reliable measurement tools
Psychometric properties and instrument validation
Adaptation and translation of instruments across cultures
Data Collection Methods
Technology-assisted data collection (online surveys, mobile data collection)
Ensuring data quality and integrity
Handling and storing data ethically and securely
Research Project Development
Formulating research questions and hypotheses
Developing research proposals and grant applications
Project management and timeline planning for research projects