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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.
0/4
Module 2: Advanced Literature Review and Research Proposal Writing
Upon completion of this module, residents will be able to:
• Conduct systematic literature searches using biomedical databases.
• Write comprehensive research proposals meeting institutional requirements.
• Develop theoretical frameworks for thesis research.
• Navigate ethical requirements for thesis research.
0/5
Module 3: Study Design and Methodology
Upon completion of this module, residents will be able to:
• Select appropriate study designs for basic sciences and paraclinical sciences research questions.
• Design experimental study with proper controls.
• Design observational study.
• Address validity threats and confounding variables.
• Sample size calculation
0/5
Module 4: Data Management and Analysis for Thesis
Upon completion of this module, residents will be able to:
• Design efficient data collection systems for thesis research
• Manage and prepare data for statistical analysis.
• Conduct comprehensive statistical analyses appropriate for thesis.
0/2
Module 5: Comprehensive Biostatistics for Thesis Research
Upon completion of this module, residents will be able to:
• Apply appropriate statistical methods.
• Perform sample size calculations for thesis projects.
• Conduct statistical analyses and interpret results correctly.
• Manage and analyze data using Microsoft Excel for biostatistical analysis of thesis research data
• Create clear, publication-quality tables and visualizations for thesis and presentations
0/6
Research Methodology and Biostatistics for Postgraduate Residents in Basic Sciences and Paraclinical Sciences
Learning Objectives:
- Identify and correct data entry errors.
- Handle missing data appropriately.
- Detect and manage outliers.
Content:
- Realtime data collection and data entry.
- Data screening and error detection techniques.
- Range checks and logical consistency checks.
- Missing data patterns and handling strategies.
- Outlier detection: statistical methods and graphical methods.
- Data transformation for normality.
- Creating clean datasets for analysis.