Providing complete coverage of advanced research methods for undergraduates, Daniel H. Baker supports students in their mastery of more advanced research methods and their application in R.
Research Methods Using R brings together coverage of a variety of topics for readers with basic statistical knowledge. It begins with material on the fundamental tools – nonlinear curve fitting and function optimization, stochastic methods, and Fourier (frequency) analysis – before leading readers on to more specialist content – bivariate and multivariate statistics, Bayesian statistics, and machine learning methods. Several chapters also discuss methods that can be used to improve research practises, including power analysis, meta-analysis, and reproducible data analysis.
Written to build a student's confidence in using R in a step-by-step way, early chapters present the essentials, ensuring that the content is accessible to those that have never programmed before. By giving them a feel for how the software works in practice, students are gradually introduced to simple examples of techniques before building up to more detailed implementations demonstrated in worked examples.
Readers are also presented with opportunities to try analysis techniques for themselves. Practice questions are presented at the end of each chapter with answer guidance supplied in the book, while multiple-choice questions with instant feedback can be accessed online. The author also provides datasets online that students can use to practise their new skills.
2. Introduction to the R environment
3. Cleaning and preparing data for analysis
4. Statistical tests as linear models
5. Power analysis
7. Mixed-effects models
8. Stochastic methods
9. Non-linear curve fitting
10. Fourier analysis
11. Multivariate t-tests
12. Structural equation modelling
13. Multidimensional scaling and k-means clustering
14. Multivariate pattern analysis
15. Correcting for multiple comparisons
16. Signal detection theory
17. Bayesian statistics
18. Plotting graphs and data visualisation
19. Reproducible data analysis
Daniel H. Baker is a Senior Lecturer at the University of York. He has taught research methods for many years in the Department of Psychology, and also made contributions to the statistical literature on power analysis and multivariate methods. He studies human visual perception, with a particular emphasis on binocular vision, using a range of quantitative techniques including psychophysics, neuroimaging and computational modelling. In 2016 he was awarded the David Marr medal by the Applied Vision Association in recognition of his research contributions. He has a particular interest in making research more open, not only by sharing code and data but also by making analysis techniques more accessible and easy to use.
"Unique in surveying a number of advanced topics, this book is perfectly pitched for advanced undergraduates and above, providing the best introduction to fundamental skill sets in R."
– Paul Engelhardt, Associate Professor, School of Psychology, University of East Anglia
"Tricky ideas are grounded and explained well. A very good introduction to R and advanced statistics. [...] An extremely clear introduction to methodology in advanced research. The interplay between general explanations and particular illustrative examples is very well done."
– Stephen Hubbard, Honorary Professor of Ecology, School of Social Sciences, University of Dundee