Therapists × AI & DX
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Part Three

Working with Data

— Question, collect, analyse, communicate — the loop that moves research and practice —

Speed up the whole journey of data with AI at your side. Standing on epidemiology and causal inference, so that fast and correct can hold together. 15 chapters in all.

Start from the beginning

CONTENTS

Contents

Introduction

  1. 00 Before you begin — about this guide — Question, collect, analyse, communicate: speeding up the loop that moves research and practice —

Part 1 | Foundations — Why a therapist becomes someone who works with data

  1. 01 Chapter 1. From instinct to data, and from data to meaning Research and business are both, at bottom, a loop of decisions based on objective data
  2. 02 Chapter 2. Where the line sits on research ethics and security [most important] The security principles from the earlier parts carry the most weight here, where data is involved
  3. 03 Chapter 3. The whole map — question, collect, analyse, communicate Think of data work as a journey through four steps that loop

Part 2 | Questions — Designing the question before the analysis

  1. 04 Chapter 4. Building hypotheses — running induction, deduction and abduction with AI The quality of an analysis is decided by the question and hypothesis, not the method
  2. 05 Chapter 5. Study design — deciding what to look at through the lens of epidemiology Study designs divide broadly into descriptive and analytic
  3. 06 Chapter 6. Designing variables and outcomes — choosing your ruler Understanding the type of your data makes the later analysis precise

Part 3 | Collecting — Gathering data

  1. 07 Chapter 7. Quantity and quality — two kinds of attention Data comes in two kinds: quantitative (numbers) and qualitative (accounts)
  2. 08 Chapter 8. Designing surveys with AI — questions that carry less bias A survey's design decides the quality of everything it returns
  3. 09 Chapter 9. Collecting qualitative data — accounts, observation and discourse, with AI The main qualitative methods are interviews, participant observation and discourse analysis

Part 4 | Analysis — Reading it with AI

  1. 10 Chapter 10. Before analysis — handing cleaning to AI Data preparation, which blocks the way before analysis, is where AI takes the most labour off you
  2. 11 Chapter 11. Looking at relationships — from univariate to multivariate, with AI Analysis climbs: look at one variable → look at two together → look at many at once
  3. 12 Chapter 12. Never mistake correlation for causation — thinking about causal inference "There is a correlation" does not mean "this is the cause". This is the biggest pitfall of all

Part 5 | Communicating — Towards being understood

  1. 13 Chapter 13. Turning numbers into meaning — figures, reports, presentations, practice An analysis becomes valuable only when it lands and moves someone

Closing Chapter | Facing what cannot be measured — the therapist stands outside the data

  1. ★ Closing Chapter | Facing what cannot be measured — the therapist stands outside the data We have walked the journey of questioning, collecting, analysing and communicating. One last pause.

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