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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:
- Use Microsoft Excel for data entry, cleaning, and worksheet management
- Apply key statistical functions (AVERAGE, MEDIAN, STDEV, COUNTIF) for descriptive analysis
- Conduct t-tests, ANOVA, and Chi-square tests using the Analysis ToolPak add-in
- Perform linear regression and create appropriately formatted charts in Excel
- Maintain data integrity through error prevention and version control practices
Content:
- Data entry, cleaning, and worksheet management
- Key statistical functions: AVERAGE, MEDIAN, STDEV, COUNTIF
- Using Analysis ToolPak add-in: One-sample and two-sample t-tests, F-tests, ANOVA, Chi-square tests
- Linear regression and binary logistic regression
- Non-parametric alternatives: Mann–Whitney, Kruskal–Wallis, Friedman test
- Creating and formatting charts: Bar charts, Histograms, Scatter plots
- Data integrity, error prevention, and version control