A Students Guide Stats Using R 1E

1. Auflage 2026
ISBN: 978-1-5296-0200-5
Verlag: Sage Publications - IPS UK

Buch, Englisch, 336 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 343 g

Reihe: SAGE 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.

Autoren/Hrsg.

Andrews, Mark

Justice, Lucy

Inhaltsverzeichnis

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

Über Autor(innen)

Justice, Lucy
Lucy Justice is a Principal Lecturer in the School of Social Sciences at Nottingham Trent University. She completed her PhD in quantitative psychology at the University of Leeds in 2012, followed by four years working as a statistician in the private sector. After returning to academia, she worked as a Senior Lecturer in NTU Psychology, focusing on statistical modelling of psychological phenomena and implementing best practices for teaching statistics. In 2022, she moved to a Principal Lecturer position in the School of Social Sciences, where she works to improve student outcomes using computational statistics and machine learning.

Lucy’s research interests include quantitative methods in psychology and social science, with particular attention to the application of statistical modelling techniques to psychological and social phenomena. Her work spans both methodological research in statistical analysis and substantive research on memory and cognition. She has co-authored book chapters on statistical methods in forensic psychology and has published research on memory perspective, flashbulb memories, and short-term memory development. She has also contributed to applied research using statistical methods to analyze profiles for deaths under probation supervision in England and Wales.

Andrews, Mark
Mark Andrews is an Associate Professor of Statistical Methods in the Department of Psychology at Nottingham Trent University. He teaches statistics to undergraduate and postgraduate students and is the course leader for the MSc in Behavioural Data Science. He also teaches advanced training courses on statistical methods, data science, and machine learning using R and Python.

Mark has a PhD and MSc in Cognitive Science from Cornell University and was previously a postdoctoral research fellow at University College London, working first in the Gatsby Computational Neuroscience Unit and later in the Division of Psychology and Language Sciences. His research interests include statistical methods in the social and behavioural sciences, computational cognitive science and neuroscience, and the application of mathematical and statistical models to understanding human cognition.

Mark was Chair of the British Psychological Society’s Mathematical, Statistical, and Computing Psychology section and is currently deputy chair of the BPS Statistics and Research Methods Advisory Panel. He is also a committee member of the Royal Statistical Society’s section on teaching statistics. He is the author of “Doing Data Science in R: An Introduction for Social Scientists” (SAGE, 2021).