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 |
|---|---|---|
| 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 |
A structured qualitative analysis pipeline for Claude. Analyzes interview transcripts, focus groups, survey responses, and other unstructured text using rigorous methods — grounded theory coding, thematic analysis, and six-lens pattern detection. Produces traceable, auditable output instead of summaries.
qual-analysis.zip file aboveTurn any grid of numbers into a perceptual map. See what a thousand data points say in one picture — which columns cluster with which rows, where differentiation lives, and where the white space is.
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