Summary: "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."--Publisher's description.
Abstract:
Summary: Designed for students new to statistics and social data, author Stephen Gorard focuses on non-inferential statistics as a basis to provide readers with fundamental statistical literacy. Assuming no previous statistical knowledge, Gorard demystifies the subject in an engaging and approachable style.
Content Notes:
Part I: Introduction. Why we use numbers in research -- What is a number? Issues of measurement -- Part II: Basic analyses. Working with one variable -- Working with tables of categorical variables -- Examining differences between real numbers -- Significance tests: how to conduct them and what they do not mean -- Significance tests: why we should not report them -- Part III: Advanced issues for analysis. The role of judgement in analysis -- Research designs -- Sampling and populations -- What is randomness? -- Handling missing data: the importance of what we don't know -- Handling missing data: more complex issues -- Part IV: Modelling with data. Errors in measurements -- Correlating two real numbers -- Predicting measurements using simple linear regression -- Predicting measurements using multiple linear regression -- Assumptions and limitations in regression -- Predicting outcomes using logistic regression -- Data reduction techniques -- Part V: Conclusion. Presenting data for your audience.
Notes:
Includes bibliographical references (pages 278-286) and index.
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