How to write an excellent research thesis

Course Curriculum

Chapter 1: Preliminaries

Lecture 1: Introductory remarks

Lecture 2: Course outline

Lecture 3: Methods to remember the lessons in this course

Chapter 2: Exploring the fundamental questions in thesis writing

Lecture 1: Exploring the fundamental questions – Learning outcomes

Lecture 2: Why do we do research?

Lecture 3: Who are you writing for?

Lecture 4: What are the questions that your examiners would have?

Lecture 5: What are some of your examiners’ expectations?

Lecture 6: How to find suitable topics to work on?

Lecture 7: Using the RETAIL framework

Lecture 8: What is your word limit?

Lecture 9: How should your contents be distributed?

Lecture 10: How is a research proposal different from a research thesis?

Lecture 11: Possible outcomes of a thesis

Lecture 12: The key points of this section

Chapter 3: Crafting an engaging introduction chapter

Lecture 1: Learning outcomes

Lecture 2: What is the purpose of an introduction chapter?

Lecture 3: What are some characteristics of a great introduction chapter?

Lecture 4: How should you build your problem statements?

Lecture 5: How should you present your research purpose?

Lecture 6: How should you present your research questions?

Lecture 7: Dedicating time to formulating your research purpose and questions

Lecture 8: What about research hypotheses?

Lecture 9: What contextual details should you provide?

Lecture 10: What are some dos and don’ts in crafting an introduction chapter?

Lecture 11: How do novices present their introduction chapter?

Lecture 12: How do professionals present their introduction chapter?

Lecture 13: The key points of this section

Chapter 4: Exploring the key ideas in research methodology

Lecture 1: Learning outcomes

Lecture 2: Activity: What are some details that you find interesting?

Lecture 3: Activity: Debriefing

Lecture 4: What are variables?

Lecture 5: What values can variables carry? Steven's scheme

Lecture 6: Contemporary conceptions of variables

Lecture 7: Common taxonomies of variables

Lecture 8: Samples vs population

Lecture 9: Sampling methods

Lecture 10: Normal distribution and non-normal distribution curves

Lecture 11: Probabilistic sampling methods

Lecture 12: Non-probabilistic sampling methods

Lecture 13: [Addendum] Snowball sampling method

Lecture 14: A way to increase validity for non-probabilistic sampling methods

Lecture 15: Validity, reliability and trustworthiness

Lecture 16: Internal and external validity

Lecture 17: Some threats that can derail a research

Lecture 18: Quantitative and qualitative research

Lecture 19: Mixed methods according to programme

Lecture 20: The key points of this section

Chapter 5: Methods in reviewing literature

Lecture 1: Learning outcomes

Lecture 2: Activity: What do you see?

Lecture 3: Activity: Debriefing

Lecture 4: Purpose of a literature review

Lecture 5: Activity: Which is a better text?

Lecture 6: Activity: Debriefing

Lecture 7: How to develop your own voice?

Lecture 8: How to deliver a great written lecture?

Lecture 9: How to curate literature?

Lecture 10: Managing your repository of literature

Lecture 11: Some ways to cite literature

Lecture 12: How to do a systematic literature review

Lecture 13: How to present a literature review chapter?

Lecture 14: The key points of this section

Chapter 6: Laying out the methods and methodology chapter

Lecture 1: Learning outcomes

Lecture 2: What is the key difference between methods and methodology?

Lecture 3: What is the purpose of the methods and methodology chapter?

Lecture 4: The Triangle of Research Designs

Lecture 5: The traits of an engaging research methods and methodology chapter

Lecture 6: The dos of a research methods and methodology chapter

Lecture 7: An example of a table you can use to be organised

Lecture 8: The donts of a research methods and methodology chapter

Lecture 9: How to present your research methods and methodology chapter

Lecture 10: Example of a research methods and methodology chapter

Lecture 11: The key points of this section

Chapter 7: The principles of data analysis

Lecture 1: Learning outcomes

Lecture 2: The iceberg metaphor

Lecture 3: Activity: Analyse this image

Lecture 4: How to analyse data

Lecture 5: How to improve the quality of your analysis

Lecture 6: Developing phenomena awareness

Lecture 7: Developing knowledge and practical skills

Lecture 8: Developing intuition

Lecture 9: Being aware of logical connectors

Lecture 10: Being aware of how certain your statements are

Lecture 11: Activity: Did the prince publicly showed his middle finger to someone?

Lecture 12: Triangulation

Lecture 13: The dos and donts of data analysis

Lecture 14: How to structure a data analysis chapter