To help you with the Thesis Writing process, GMBA offers a thesis Writing course.

This course evolves out of the recognition that GMBA students have a growing need for guidance on practical Thesis Writing.

This course focuses on developing core academic writing skills through analysing examples and course lectures. Specifically, students are introduced to the structure of thesis writing and the needed academic writing tools. In addition, students are introduced to a sect of qualitative and quantitative data collection tools and are shown how to interpret the findings.

In short, students are walked through the process of Thesis Writing and armed with a core set of skills for doing so.

Course Objectives:

After completing this course, students will be able:

• Collect and Introduce Literature in a critical discourse
• Understand relevant tools to be used for secondary and primary data analysis
• Read and Write about Data in a Results section
• Improve their Academic Writing Skills

These goals are achieved through lectures and applied activities. 


This class has 5 Sessions:

 

Session 1: Starting the Thesis Journey

1. Types of Theses and Expectations
2. Structure and Formatting of a Thesis
3. Topic Identification
4. Identifying a Research Gap
5. Introduction to Reference Management Tools and the Office Template

In Class: Example Analysis & Identifying a Topic

Session 2: Secondary Research Methods

1. Identifying a Unit of Analysis and Population
2. Literature Review
3. Theoretical Framework
4. Business Environmental Analysis Methods: PESTLE; PORTERs
5. Developing and Presenting Research Questions

In Class: Example Analysis and Using Tools for Accessing Secondary Data Sources

Session 3: Analytical model, Research design

1. Research approach:
   • Theoretical to Analytical Model
   • Research questions to hypotheses
   • Mediation/ Moderation
2. Research design and rationale
   • From hypothesis to variables and from variables to study design
   • Types of quantitative studies: Experimental, survey-based, and secondary-data-based studies
   • Validity and Reliability
3. Data collection
   • Survey design  
   • Measurement theory
   • Sampling methods

In Class: Case on experiment or survey design; application of class learning on research approach and design for individual study

Session 4: Data Analysis Techniques

1. Data Processing
   • Quantitative data coding, editing
   • Multi-item constructs and reliability
2. Examples of research techniques:  output requirements and interpretation
   • Regression
   • Factor analysis
   • Mediation/ Moderation
3. Linking results to implications
4. Research limitations and suggestions for future research

In Class: Applying class learning to understand techniques for data analysis and interpretation

Session 5: Lectures on Data Analysis Reporting and Formatting

1. Data Analysis Continues
2. Data Reporting
3. Academic Formatting