Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- 1 History of Behavioural Data Science
- 2 Overview of Behavioural Data Science
- 3 Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
3 - Behavioural Data Science
Framework and Topology of Methods
from Part I - Introduction to Behavioural Data Science
Published online by Cambridge University Press: 29 May 2026
Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- 1 History of Behavioural Data Science
- 2 Overview of Behavioural Data Science
- 3 Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
Summary
This chapter presents a structured account of the Framework and Topology of Methods in Behavioural Data Science, expanding upon a four-stage methodological process – data collection, data processing, feature engineering and model development – and introducing four foundational conceptual pillars: Hypothesis Testing at Scale, Data Science Modelling for Behavioural Problems, Gap Detection and Hybrid Modelling. These pillars not only represent distinct methodological orientations but also correspond to divergent epistemological and ontological commitments within the field. The chapter provides a comprehensive overview of methods and tools used at each stage of the Behavioural Data Science workflow, illustrates how emerging technologies such as large language models (LLMs) reshape methodological choices and concludes with ethical considerations and future directions. In so doing, it advances a unified topology of Behavioural Data Science methods capable of accommodating both theory-driven and data-driven approaches, while responding to gaps in traditional behavioural science paradigms.
Keywords
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- Type
- Chapter
- Information
- The Cambridge Handbook of Behavioural Data Science , pp. 44 - 58Publisher: Cambridge University PressPrint publication year: 2026
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