How to Make Sense of Statistics

Häftad, Engelska, 2021

AvStephen Gorard

839 kr

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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.

Produktinformation

Utgivningsdatum2021-02-24
Mått170 x 242 x 24 mm
Vikt510 g
FormatHäftad
SpråkEngelska
Antal sidor320
Upplaga1
FörlagSAGE Publications
ISBN9781526413826

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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 EdTech Testbed, and by the British Academy to look at the impact of schooling in India and Pakistan.

Part I: IntroductionChapter 1: Why we use numbers in researchChapter 2: What is a number?: Issues of measurementPart II: Basic analysesChapter 3: Working with one variableChapter 4: Working with tables of categorical variablesChapter 5: Examining differences between real numbersChapter 6: Significance tests: how to conduct them and what they do not meanChapter 7: Significance tests: why we should not report themPart III: Advanced issues for analysisChapter 8: The role of judgement in analysisChapter 9: Research designsChapter 10: Sampling and populationsChapter 11: What is randomness?Chapter 12: Handling missing data: The importance of what we don’t knowChapter 13: Handling missing data: more complex issuesPart IV: Modelling with dataChapter 14: Errors in measurementsChapter 15: Correlating two real numbersChapter 16: Predicting measurements using simple linear regressionChapter 17: Predicting measurements using multiple linear regressionChapter 18: Assumptions and limitations in regressionChapter 19: Predicting outcomes using logistic regressionChapter 20: Data reduction techniquesPart V: ConclusionChapter 21: Presenting data for your audience