Professional Certificate in Research Methodology

About Course

A practical journey from “What is research?” to “publish-ready reporting.”. Learn to frame problems, review literature, design studies, collect & analyze data (qual & quant), uphold ethics, and communicate results that influence decisions.

Course Curriculum

Orientation Session

Recording of the live session

47:04

Introduction to Research Methodology

Philosophical Foundations

Research Problem Identification

Literature Review & Writing

Assessment-01

Research Design & Sampling

Data Ethics & Research Integrity

Qualitative Data Collection

Quantitative Data Collection

Mixed Methods Research

APA References and Citation

Research Report Writing

Assessment-02

Student Ratings & Reviews

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A course by

Nazam Bhuiyan

PHD in Marketing (Malaysia), Award-Winning Researcher, AIB South Asia Conference 2025

What’s the course outcome?

You’ll complete a research blueprint—from problem statement and questions/hypotheses to a defensible design, ethics protocol, data plan, analysis approach, and report outline.

Do I need prior research experience?

No. We start from fundamentals and build up to advanced concepts like mixed methods and triangulation.

Is this academic or industry-focused?

Both. Examples span theses, policy studies, UX/product research, market research, and social-impact evaluations.

Will you cover tools?

Yes—foundational workflows for NVivo-style coding and survey tooling; guidance on spreadsheet/entry tools and referencing (APA). (Tool choice is flexible.)

Do you teach statistics?

We focus on research design, measurement, and validity/piloting. You’ll get essentials for survey construction and interpretation; deep stats are referenced, not the main goal.

What deliverables will I produce?

A polished problem statement, research questions/hypotheses, a structured literature-review framework, sampling & design plan, qualitative/quantitative instruments, and a report/presentation outline.

What deliverables will I produce?

A polished problem statement, research questions/hypotheses, a structured literature-review framework, sampling & design plan, qualitative/quantitative instruments, and a report/presentation outline.

What if my project needs both qual and quant?

You’ll learn convergent and sequential mixed-methods designs to integrate interviews, surveys, and observations into one coherent study.

Policy, authorship & disclosure — What exactly is allowed, what isn’t, and how do I disclose AI use properly?

AI can assist (brainstorming, language editing, code suggestions, transcription), but cannot be an author. Most institutions/journals allow AI with clear disclosure and human verification. You remain accountable.

Quality control & reproducibility — How do I keep AI from introducing errors or bias, and make my work auditable?

AI can speed coding, summaries, and starter analyses, but it also hallucinates, miscodes, and reflects training bias. Build guardrails: independent verification, inter-coder checks, prompt/version logs, and full reproducibility.

Data privacy & ethics — How can I use AI without violating consent, IRB, or data-protection rules?

Assume prompts may be logged. Do not paste identifiable/sensitive data into consumer tools. Use approved/enterprise instances, de-identify data, and reflect AI usage in consent/IRB documents.