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.
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.
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.
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.
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