Lecture note on research methodology for MBA students

Lecture note on research methodology for MBA students

 

Highlight 5 main ideas and 4 practice advices based on the 6 ideas on the topic of research methodology in the context of MBA dissertation projects.

Based on the standard six core elements of an MBA dissertation research methodology (research objectives, philosophy, approach, design, data collection/analysis, and sampling/validity), here are the 5 main ideas and 4 practical advices:

5 Main Ideas in MBA Research Methodology

#	Idea	Core Point
1	Coherent Chain of Reasoning	Methodology must present a logically connected argument: why your philosophy fits the question → how it shapes your approach → why your design/methods are appropriate → how you safeguard quality
2	Justify Choices, Don't Just List	For each element (philosophy, approach, design, methods, sampling), explain why it suits your topic and objectives rather than merely enumerating them
3	Research Philosophy Alignment	Explicitly name and justify your stance (positivism, interpretivism, or pragmatism) and link it to your data type and research questions
4	Mixed Methods for Hybrid Focus	For consulting-oriented or dual-purpose MBA dissertations, pragmatism justifies mixed methods (e.g., secondary data + interviews/focus groups) to address both theory gaps and client issues
5	Quality Safeguards Integrated	Validity, reliability/trustworthiness, limitations, and ethical considerations should be woven together as a combined subsection showing how you ensure credibility

4 Practical Advices for MBA Dissertation Projects

#		Advice	Actionable Tip
#	Advice			Actionable Tip
1	Choose a Workable, Dual-Purpose Topic			Select a topic rooted in a specific client's management issue that also targets a clear literature gap—this boosts career relevance while ensuring academic rigor
2	Match Methodology to Objectives Practically			Ensure your population size is large enough for data collection; if niche, use qualitative methodology. For quantitative studies, secure optimal response rates beforehand
3	Manage Research as a Project			Set specific milestones, outsource data collection/statistical work (ethically allowed), and get a statistician involved before data collection to assist with sample requirements
4	Secure Ethics and Collaboration Early			Obtain gatekeeper consent for client data access early, and involve managers in validating objectives and findings to mitigate insider bias while enhancing rigor

 

A collection of blog notes on using chatgpt for research purpose.

Lecture note on research approaches for MBA students

 Lecture note on research approaches for MBA students

 

 Highlight 6 main ideas and 6 practice advices based on the 6 ideas on each of the following 2 topics in the context of MBA dissertation projects: 1. Research approaches (quantitative and qualitative research approaches) 2. Research approaches (inductive reasoning approach and deductive reasoning approach)

Based on MBA dissertation best practices—especially aligned with your part-time MBA context and interest in coherent methodology thinking —here are 6 main ideas and 6 corresponding practice advices for each of your two topics.

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Topic 1: Research Approaches – Quantitative vs. Qualitative

6 Main Ideas

#	Idea	Key Explanation
1	Purpose differs	Quantitative interrogates relationships, cause-and-effect; qualitative seeks deep understanding of phenomena
2	Design flexibility	Quantitative designs are fixed before data collection; qualitative designs evolve as insights emerge
3	Theory role	Quantitative tests theory deductively; qualitative builds theory inductively
4	Researcher as instrument	Quantitative uses questionnaires/structured instruments; in qualitative, the researcher is the primary instrument
5	Sample size logic	Large samples support statistical inference; small carefully-selected samples support depth and meaning
6	Analysis alignment	Numbers require statistical analysis; narratives require interpretation

6 Practice Advices (based on the 6 ideas)

1.    Match purpose to question: If your MBA dissertation asks “What is the effect of X on Y?” use quantitative; if it asks “How do participants experience X?” use qualitative.

2.    Lock your quantitative design early: Pre-specify variables, survey instruments, and analysis plan before collecting data to avoid methodological drift.

3.    Clarify theory direction: Explicitly state whether you’re testing existing theory (quantitative/deductive) or developing new theory (qualitative/inductive) in your literature review.

4.    Train yourself as the qualitative instrument: In qualitative work, develop interview skills, coding discipline, and reflexivity; document your interpretive process.

5.    Justify sample size appropriately: Don’t chase large numbers in qualitative work—use 10–20 well-chosen interviews for depth; use 100+ for quantitative statistical power.

6.    Use matched analysis techniques: Apply SPSS/Excel regression for quantitative; use NVivo/thematic coding for qualitative—never mix analysis methods without justification.

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Topic 2: Research Approaches – Inductive vs. Deductive Reasoning

6 Main Ideas

#	Idea	Key Explanation
1	Direction of reasoning	Deductive: theory → hypothesis → data → confirmation; Inductive: data → patterns → theory → generalization
2	Theory relationship	Deductive tests/expands existing theory; Inductive generates new theory from empirical observations
3	Typical method pairing	Deductive usually pairs with quantitative; Inductive usually pairs with qualitative (but mixed methods exist)
4	Starting point	Deductive starts with literature/theory; Inductive starts with research problem/observations
5	Outcome certainty	Deductive aims for confirmation/rejection of hypotheses; Inductive aims for plausible theory with probabilistic generalization
6	Research flexibility	Deductive is rigid/structured; Inductive is flexible/adaptive as patterns emerge

6 Practice Advices (based on the 6 ideas)

1.    Align reasoning with your objectives: If your MBA dissertation aims to “test whether X affects Y among Singapore REITs,” use deductive; if it aims to “explore how managers perceive X,” use inductive.

2.    Start deductive with strong theory: Build a clear theoretical framework from literature before forming hypotheses—examiners expect this logic in your literature review.

3.    Start inductive with open questions: Begin qualitative inductive work with broad exploratory questions, not pre-formed hypotheses, to allow theory emergence.

4.    Document your reasoning chain: Explicitly write: “This dissertation adopts a coherent methodology where philosophy → approach (deductive/inductive) → design → methods”.

5.    Choose appropriate generalization: For deductive, use statistical generalization; for inductive, use theoretical generalization (findings apply to similar contexts, not populations).

6.    Remain flexible in inductive work: Allow your interview questions or coding framework to evolve as patterns emerge—don’t force pre-set categories onto qualitative data.

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Key Takeaway for Your MBA Dissertation

Examiners notice methodological misalignment immediately in Chapters 3–4. Ensure your research philosophy (e.g., pragmatism, positivism, interpretivism) logically leads to your approach (quantitative/ qualitative, deductive/ inductive), which then shapes your design and methods. Justify choices, not just list them.

 

A collection of blog notes on using chatgpt for research purpose.