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Module 1: Foundations of Research and Thesis Development
Upon completion of this module, residents will be able to:
• Understand the thesis requirements and academic standards in basic and paraclinical sciences.
• Identify feasible research topics aligned with departmental resources and expertise.
• Formulate clear, testable research questions using established frameworks.
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Module 2: Advanced Literature Review and Research Proposal Writing
This module builds the conceptual, regulatory, and methodological foundation required to design a sound postgraduate thesis- from understanding institutional requirements to translating ideas into structured research questions, hypotheses, and objectives.
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Module 3: Study Design, Methodology, and Data Management
This module builds the conceptual, regulatory, and methodological foundation required to design a sound postgraduate thesis- from understanding institutional requirements to translating ideas into structured research questions, hypotheses, and objectives.
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Module 4: Comprehensive Biostatistics for Thesis Research
To equip learners with the statistical thinking, analytical skills, and practical tools required to analyze thesis data correctly, interpret results responsibly, and present findings in a defensible, publication-ready format.
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Research Methodology and Biostatistics for Postgraduate Residents in Basic Sciences and Paraclinical Sciences
Learning Objectives:
By the end of this lesson, students will be able to:
- Apply range checks and logical consistency checks to screen data for errors
- Identify and interpret patterns of missing data
- Detect outliers and decide on appropriate management strategies
- Apply data transformation techniques to address departures from normality
- Prepare a clean, finalised dataset with transparent documentation of all cleaning decisions
Content:
- Data screening techniques
- Range checks and logical consistency checks
- Identifying missing data patterns
- Outlier detection and interpretation
- Data transformation for normality
- Preparing clean datasets for statistical analysis
- When not to “fix” data
- Transparency in data cleaning decisions