Stephen Gorard 
How to Make Sense of Statistics [EPUB ebook] 

In a new textbook designed for students new to statistics and social data, Stephen Gorard focuses on non-inferential statistics as a basis to ensure students have basic statistical literacy. 



Understanding why we have to learn statistics and seeing the links between the numbers and real life is a crucial starting point. Using engaging, friendly, approachable language this book will demystify numbers from the outset, explaining exactly how they can be used as tools to understand the relationships between variables. 



This text assumes no previous mathematical or statistical knowledge, taking the reader through each basic technique with step-by-step advice, worked examples, and exercises. Using non-inferential techniques, students learn the foundations that underpin all statistical analysis and will learn from the ground up how to produce theoretically and empirically informed statistical results.

Table des matières

Part I: Introduction

Chapter 1: Why we use numbers in research

Chapter 2: What is a number?: Issues of measurement

Part II: Basic analyses

Chapter 3: Working with one variable

Chapter 4: Working with tables of categorical variables

Chapter 5: Examining differences between real numbers

Chapter 6: Significance tests: how to conduct them and what they do not mean

Chapter 7: Significance tests: why we should not report them

Part III: Advanced issues for analysis

Chapter 8: The role of judgement in analysis

Chapter 9: Research designs

Chapter 10: Sampling and populations

Chapter 11: What is randomness?

Chapter 12: Handling missing data: The importance of what we don’t know

Chapter 13: Handling missing data: more complex issues

Part IV: Modelling with data

Chapter 14: Errors in measurements

Chapter 15: Correlating two real numbers

Chapter 16: Predicting measurements using simple linear regression

Chapter 17: Predicting measurements using multiple linear regression

Chapter 18: Assumptions and limitations in regression

Chapter 19: Predicting outcomes using logistic regression

Chapter 20: Data reduction techniques

Part V: Conclusion

Chapter 21: Presenting data for your audience

A propos de l’auteur

Stephen Gorard is Professor of Education and Public Policy, and Director of the Evidence Centre for Education, at Durham University (https://www.dur.ac.uk/). He is a Fellow of the Academy of Social Sciences, member of the ESRC Commissioning Panel for the Research Methods Programme, the British Academy grants panel, and Lead Editor for BERA’s Review of Education. He is a member of the Cabinet Office Trials Advice Panel as part of the Prime Minister’s Implementation Unit. His work concerns the robust evaluation of education as a lifelong process, focused on issues of equity, especially regarding school intakes. He is author of around 30 books and over 1, 000 other publications. He is currently funded by Nesta to evaluate their Ed Tech Testbed, and by the British Academy to look at the impact of schooling in India and Pakistan.