Research Design Analysis: A Practical Methodological Toolkit

From Research Design to Data Analysis

What you'll learn

Real-world case studies from IITs, DRDO, and leading Indian companies

Hands-on software training (R, Python, SPSS, NVivo, LaTeX)

Industry-relevant applications beyond academic research

Contemporary examples including AI, blockchain, and startup ecosystems

Requirements

No programming experience needed.

Description

Expanded course descriptionThis course equips you with the end-to-end skills needed to design, conduct, analyze, and present rigorous, reproducible research across disciplines. You’ll begin by sharpening the craft of asking excellent questions—translating curiosities into precise problem statements, operationalizing constructs into measurable variables, refining hypotheses, and anchoring everything in an appropriate theoretical or conceptual framework. Along the way, you’ll learn how to scope a literature review strategically, map gaps, and build a defensible conceptual model that guides method choice and analysis plans.From there, we dive deep into methodology with equal emphasis on practicality and rigor. On the quantitative side, you’ll learn survey construction (question wording, scaling, pilot testing, bias reduction), experimental and quasi-experimental design (controls, randomization, power and sample size considerations), sampling strategies, and data quality safeguards to ensure validity and reliability. On the qualitative side, you’ll gain hands-on proficiency with interviews, focus groups, and participant observation, including recruitment, protocol design, field notes, reflexivity, ethics, and rich data capture. Mixed-methods integration is addressed throughout, so you can triangulate insights and align methods to your research goals rather than forcing a one-size-fits-all approach.Analysis and interpretation are taught as a disciplined conversation with your data. You’ll practice descriptive summaries and visualizations that reveal structure, then progress to inferential techniques (assumption checks, effect sizes, reporting standards) and model-based reasoning that answers “so what?” with clarity. For qualitative data, you’ll apply systematic coding, memoing, and theme development, strengthen credibility through audit trails and inter-coder agreement, and synthesize findings into coherent narratives. You’ll also learn to pre-empt common pitfalls—p-hacking, overfitting, confirmation bias—by building transparent analysis plans and ethical guardrails.Finally, you’ll translate results into compelling outputs that stand up to scrutiny: literature reviews and conceptual diagrams, IRB/ethics-ready protocols, data-collection instruments, analysis notebooks, and polished deliverables such as manuscripts, conference presentations, and policy briefs. Throughout, real-world case studies, templates, and checklists make each step concrete, so you can move from idea to impact with confidence and a repeatable, professional workflow.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Research Philosophy & Strategies

Lecture 3 Quantitative Research

Lecture 4 Data Gathering

Lecture 5 Questions

Section 2: Qualitative Research

Lecture 6 Introduction

Lecture 7 Qualitative Research Design

Lecture 8 Qualitative Research Techniques

Lecture 9 Non probabilistic Sampling

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