Evidence-based research methodology for social scientists and market researchers — so that rigorous methods become increasingly accessible, practical, and open to everyone who works with data.
About the series ↓| Date | Category | Title |
|---|---|---|
| May 2026 | Survey Design | A Research Survey Is an Instrument, Not a Form |
| May 2026 | Synthetic Research | Build Your Own Synthetic Research Studio |
| Apr 2026 | AI & Quantitative | A Working Framework for Using GenAI on Quantitative Survey Data |
| Apr 2026 | AI & Qualitative | AI and the Qualitative Analysis Problem |
| Nov 2025 | Causation | The Trait Trap: Why Copying Successful Companies Usually Fails |
| Sep 2025 | Experiment | Can 300 Taxi Rides Represent a Million Rides? |
| Sep 2025 | Sampling | The Sample Size Paradox: Why Statistical Precision Trumps Intuitive Mathematics |
| Aug 2025 | Measurement | The Science of Proper Measurement |
| Aug 2025 | Consumer Behavior | Why Consumers Unconsciously Mislead Us |
Open-source skills, specifications, and browser tools that go with the series — built so the methods are runnable, not just describable. Click any tool to see full details, install steps, and the article it pairs with.
Four artefacts — two Claude Code skills for prep, a delivery spec for data processing teams, and an analysis skill that picks up after — designed to compose end-to-end. The LLM interprets, Python computes, every number traces back to a cell. The companion article explains the framework; the toolkit page has the full details on each piece.
A structured qualitative analysis pipeline for Claude. Grounded theory coding, thematic analysis, and six-lens pattern detection on transcripts, focus groups, and open-ended survey data — with full source traceability instead of summaries.
View details →Turn any grid of numbers into a perceptual map. See what a thousand data points say in one picture — runs entirely in the browser, no upload, no sign-in. Export publication-ready PNG and SVG.
View details →Every article is grounded in established research methodology — not opinion, not convention, not what worked once for one company. We address measurement validity, sampling theory, causal inference, and qualitative discipline with the same standards applied in peer-reviewed research.
Rigorous methods shouldn't live behind paywalls. Every article, tool, dataset, and code sample in this series is freely available on GitHub. Download it, fork it, adapt it for your own research context. The knowledge belongs to the community.
This isn't academic theory for its own sake. Each piece is written for working researchers, analysts, and strategists who need methods that survive real-world conditions — tight timelines, imperfect data, stakeholders who need to trust the findings.
On a mission to uncover extraordinary insights hidden in the most ordinary human behaviours, and turn them into something profitable for business. Through consumer research, insights, and analytics, I turn meaningful data into stories, and stories into decisions.
My path through varied roles across Asia Pacific and CEMEA has given me a global lens and a knack for spotting patterns beyond the numbers. I explore everything from subtle shifts in consumer behaviour, through surveys and interviews, to enterprise-level data — connecting what people do with what we could do next.
Personally, I am carving out more time to build with AI: hands-on data projects, real-world solutions, and pushing creativity through code art and experimental forms — where logic meets imagination and dashboards flirt with design.
Everything here is open source: articles, tools, datasets, workflows. Contributors, co-editors, and experts welcome — if you have a challenge, a tool, or a learning to share, reach out. This is your platform too.
This project is created by Vinay Thakur (vpst18@gmail.com) in a personal capacity for educational and knowledge-sharing purposes only. It does not represent, reflect, or endorse the views of any employer, organisation, or affiliated entity. The content is not intended for commercial use. All materials are provided as-is under the MIT License. You are free to use, adapt, and reference this work at your own discretion — please assess its suitability for your context independently. If you use or reference this work, please cite as:
Thakur, V. (2026). Research Edge Series. https://github.com/vtmade/research-edge-series