Course introduction:

The course 《Research Methods and Statistical Software》provides the foundational framework for conducting systematic inquiry, enabling researchers to collect, analyze, and interpret data accurately. They guide the formulation of research questions, selection of appropriate designs (qualitative, quantitative, or mixed methods), and validation of results. In parallel, statistical software such as SPSS, R, Stata, or Python enhances the efficiency and accuracy of data analysis. These tools allow for complex computations, visualization of data, and application of statistical tests, which are critical for evidence-based conclusions. Together, research methods and statistical software equip students and professionals with the skills to solve real-world problems, support decision-making, and contribute to knowledge advancement in fields such as health sciences, engineering, education, social sciences, and business. The course aims to build competence in both theoretical understanding and practical application, fostering a scientific mindset and analytical proficiency.

Teaching Objectives

The aim of this course is to equip students with the knowledge and skills needed to design, conduct, and analyze research effectively across various disciplines. It focuses on developing a solid understanding of research methodologies and enhancing practical competence in using statistical software for data analysis. By the end of the course, students should be able to apply appropriate research techniques, interpret statistical results accurately, and use data-driven insights to inform decisions and solve problems in their respective fields.

Course Objective 1: To enable students to comprehend various research designs and methodologies and apply them appropriately to real-world research problems.

Course Objective 2: To train students in the use of statistical software for effective data entry, analysis, visualization, and interpretation.

Course Objective 3: To cultivate the ability to critically analyze research findings, interpret statistical results, and draw evidence-based conclusions that support academic, professional, or policy-related decision-making.

Teaching Arrangement:

Chapter 1: Overview of research methods and their importance

1.1 Introduction to basic research methods

1.2 Introduction to some software

Lecturer: J.P. Namahoro

Chapter 2: Data and sources

2.1 Types of data and specific methods for analysis

2.2 Open data sources available

Lecturer: J.P. Namahoro

Chapter 3: Regression analysis and estimation techniques

3.1 Linear and non-linear regression analysis

3.2 Least Square Estimations

Lecturer: J.P. Namahoro

Chapter 4: Advanced estimation techniques

4.1 PMG, ARDL, and others

4.2 Practical examples

Lecturer: J.P. Namahoro

Chapter 5: Results interpretation

5.1 Comparative results interpretation

5.2 Open discusion

Lecturer: J.P. Namahoro and participants

Chapter 6: Software

6.1 SPSS, STATA, and others

6.2 Practical examples

Lecturer: J.P. Namahoro

Practical work: Data analysis and interpretation

Practice1: Software Installation

Practice 2: Data cleaning

Practice 3: Running some codes

Practice 4: Interpretation of some results