Rigorous Research Methodology for Social Scientists and Market Researchers
By Vinay Thakur
The Research Edge Series provides evidence-based guidance for researchers who want to move beyond casual data collection to rigorous research practice. Each installment focuses on fundamental methodological challenges and their solutions, helping you avoid common pitfalls that destroy research validity.
• Theoretical Rigor: Every recommendation is grounded in established research methodology
• Practical Application: Complex concepts explained through real-world examples
• Error Prevention: Focus on avoiding systematic biases that compound across studies
• Actionable Insights: Move from "interesting findings" to strategic decisions
Understanding Post-Hoc Rationalization in Consumer Research
Discover why consumers do not have conscious access to their decision-making processes and how post-hoc rationalization creates misleading research insights. Learn why direct "why" questions fail and how expert researchers use perception association methods to uncover real decision drivers.
Key Topics: • Post-hoc rationalization and System 1 vs System 2 thinking • Why direct questions about decision reasons fail • Perception association research techniques • Business impact of misunderstanding decision drivers
Available Formats: 📄 Article | 📁 PDF
Moving Beyond Casual Surveys to Rigorous Research Design
Learn why most research fails before data collection even begins. Discover the fundamental distinction between reflective and formative constructs, understand how measurement model misspecification can inflate your findings by 400%, and see how to fix the most common survey design errors.
Key Topics: • The consumer-as-researcher fallacy • Reflective vs. formative measurement models • Systematic bias in causal attribution • Step-by-step measurement design
Available Formats: 📄 Article | 📁 PDF
Why Statistical Precision Trumps Intuitive Mathematics
Contemporary business research suffers from a fundamental misunderstanding of statistical sampling theory, leading to systematic over-sampling and resource misallocation. Discover why population size bears minimal relationship to required sample sizes and how precision requirements should drive sampling decisions.
Key Topics: • The precision framework: Astronomical vs. engineering measurement standards • Mathematical reality vs. intuitive logic in population sampling • International research standards from WHO and UNICEF • Decision frameworks for optimal sample size determination
Available Formats: 📄 Article | 📁 PDF
Experimental Validation Using Real NYC Taxi Data
This supplementary article provides real-world experimental validation of the Sample Size Paradox using 1 million NYC taxi transactions. Demonstrates empirically how sample size affects estimate precision and validates the Central Limit Theorem through systematic sampling simulation.
Key Findings: • Dramatic accuracy improvements from n=30 to n=300 (6 percentage points precision gain) • Diminishing returns beyond n=500 samples • Statistical theory validated with 98% accuracy across all sample sizes • Visual demonstration of sampling variability through heatmap analysis
Supporting Materials: 🎯 Sampling Heatmap | 💻 Python Code | 📊 Data
Why Copying Successful Companies Usually Fails (And How Rigorous Research Fixes This)
Understand why most business advice misleads us by confusing correlation with causation. Learn the difference between anecdotal pattern-matching and rigorous causal analysis, including reverse causality, confounding variables, and how researchers use regression analysis and propensity score matching to establish probable cause.
Key Topics: • The halo effect and survivorship bias in business research • Three types of false causation: reverse causality, confounders, and pure correlation • Regression analysis and statistical controls explained • Propensity score matching: finding statistical twins • How to evaluate business advice critically
Available Formats: 📄 Article | 📁 PDF
research-edge-series/
├── articles/ # Series articles in markdown format
├── assets/ # PDFs, images, diagrams, and supporting materials
├── docs/ # Additional documentation and resources
└── README.md # This file
- Browse Articles: Each article is self-contained and addresses a specific methodological challenge
- Follow the Series: Articles build on each other but can be read independently
- Apply the Frameworks: Use the decision trees and checklists in your own research
- Share and Contribute: Reference these materials in your work and suggest improvements
When referencing articles from this series, please use:
Thakur, V. (2025). [Article Title]. Research Edge Series #[Number].
Retrieved from https://github.com/[username]/research-edge-series
For questions, feedback, or collaboration opportunities, connect with Vinay Thakur.
This content is designed for educational and professional development purposes. Please see individual articles for specific usage guidelines.
Research Edge Series - Transforming how we approach social & business research methodology.