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A Student’s Guide to Statistics Using R
- Mark Andrews - Nottingham Trent University
- Lucy Justice - Nottingham Trent University
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January 2027 | 336 pages | SAGE Publications Ltd
Statistics doesn’t have to feel confusing or intimidating.
Written by authors who genuinely love statistics, A Student’s Guide to Statistics Using R offers a clear, confidence-building introduction to quantitative analysis for the social sciences. It introduces core statistical methods through a coherent model-building approach, helping students understand how statistics works, why it matters for social research, and how different techniques fit together.
Covering all the key methods students encounter in social science degrees—including t-tests, regression, ANOVA, confidence intervals, hypothesis testing, and Bayesian approaches—the book shows how statistical models make sense of messy, real-world data. R is introduced step by step, with clear explanations of code and output so students understand the analysis and can work with confidence, not guesswork.
By focusing on underlying principles rather than rules, this book builds lasting understanding and prepares students for more advanced statistical challenges.
For undergraduate and postgraduate social science students learning statistics and data analysis using R.
Written by authors who genuinely love statistics, A Student’s Guide to Statistics Using R offers a clear, confidence-building introduction to quantitative analysis for the social sciences. It introduces core statistical methods through a coherent model-building approach, helping students understand how statistics works, why it matters for social research, and how different techniques fit together.
Covering all the key methods students encounter in social science degrees—including t-tests, regression, ANOVA, confidence intervals, hypothesis testing, and Bayesian approaches—the book shows how statistical models make sense of messy, real-world data. R is introduced step by step, with clear explanations of code and output so students understand the analysis and can work with confidence, not guesswork.
By focusing on underlying principles rather than rules, this book builds lasting understanding and prepares students for more advanced statistical challenges.
For undergraduate and postgraduate social science students learning statistics and data analysis using R.
Part I: Foundations
Chapter 1: Introducing statistics
Chapter 2: Introducing R & RStudio
Chapter 3: Exploratory data analysis
Chapter 4: Introducing inference
Chapter 5: Data wrangling
Part II: Linear models and friends
Chapter 6: Normal models
Chapter 7: Simple linear regression
Chapter 8: Multiple linear regression
Chapter 9: ANOVA and general linear models
Chapter 10: Repeated measures analysis
Chapter 11: Multilevel and mixed effects models
Chapter 12: Logistic regression
Chapter 13: Models for count data
Part III: Bayesian methods
Chapter 14: Bayesian data analysis