Applied Statistics Using R
A Guide for the Social Sciences
- Mehmet Mehmetoglu - Norwegian University of Science & Technology, Norway
- Matthias Mittner - UiT The Arctic University of Norway, Norway
Quantitative/Statistical Research | Statistical Computing Environments
If you want to learn to use R for data analysis but aren’t sure how to get started, this practical book will help you find the right path through your data.
Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research.
It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers.
- Shows you how to use R packages and apply functions, adjusting them to suit different datasets.
- Gives you the tools to try new statistical techniques and empowers you to become confident using them.
- Encourages you to learn by doing when running and adapting the authors’ own code.
- Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect.
Instructor Resources (Log-in needed)
- PowerPoint slides featuring figures and tables from the book
- Case studies of applied statistics research with accompanying critical thinking questions
- A test bank of multiple-choice questions for each chapter
- Suggestions for further reading
Student Resources (Free to access)
- Downloadable R files with example code from the book
- Links to the datasets used in the book
- Weblinks to video lectures for more detail and support on topics
This book is the best I’ve seen for R, both in its clarity and coverage of topics. Practically oriented, with a profusion of examples and an engaging narrative, it is a must-have for all those studying applied social sciences.
A good, no-nonsense overview covering the most important aspects of social statistics in R (using tidyverse).
Out faculty prefers using other statistical tools than R.
At the same time, I still recommend to my students to use R when they are interested in getting to know different tools, and in this case I also recommend the manual.
For social sciences, the nature of applied statistics is essential in their appreciation of setting up independent research dissertations. Excellent approach for students with non mathematics background.