Certified Research Assistant (CRA) – 3 Months

• Basics of Research Methodology

This module introduces students to the foundational principles of scientific research. Participants will learn about the research process, formulation of research questions, hypothesis development, study design selection, ethical considerations, and the structure of a scientific paper. The goal is to develop a solid understanding of how to design and conduct methodologically sound research.

• Cross-sectional Studies & Review Articles

In this module, students will explore how to conduct cross-sectional studies, including sample selection, data collection methods, and identifying associations between variables. Additionally, learners will gain practical skills in writing review articles, including narrative and scoping reviews, focusing on literature search strategies, critical appraisal, and scientific writing techniques.

• Epidemiology & Biostatistics

This module covers essential concepts in epidemiology, including measures of disease frequency (incidence, prevalence), study designs, and risk assessment. Biostatistics training includes descriptive and inferential statistics, p-values, confidence intervals, and hypothesis testing. Students will learn how to apply these concepts in analyzing and interpreting real-world health data.

• Data Entry & Analysis (SPSS)

Students will gain hands-on experience with SPSS (Statistical Package for the Social Sciences), one of the most widely used tools for data analysis in medical research. The module includes instruction on data entry, variable coding, dataset cleaning, and running basic statistical tests (t-tests, chi-square, correlations). Emphasis is placed on interpreting SPSS output for research reporting.

• Introduction to Python Programming for Research

This module introduces students to Python, a powerful and versatile programming language increasingly used in data science and research. Students will learn the basics of Python syntax, data structures, and libraries like pandas and matplotlib, with a focus on data cleaning, manipulation, and visualization for research purposes.