Research Methods Using R

Description

Providing complete coverage of advanced research methods and their implementation in R to increase students' confidence with programming techniques and their application to new situations and problems.

• Provides suitable coverage for students looking to develop a more advanced level of competency in analytical techniques and statistics using R
• Gives students a comprehensive picture by covering basic techniques such as nonlinear curve fitting and function optimization, more specialist topics including bivariate and multivariate statistics, Bayesian statistics, and machine learning methods, and content on methods that can be used to improve research practises, including power analysis and reproducible data analysis
• Carefully introduces the R programming language for those who have not encountered it before and builds up to demonstrate how techniques can be implemented using this software
• Includes worked examples of appropriate techniques using R so students can see coding used in context
• Practice questions with answer guidance are supplied in the book while multiple-choice-questions with instant feedback can be accessed online to give students an opportunity to test their understanding
• Also available as an e-book with functionality, navigation features, and links that offer extra learning support

About the Author(s)

Daniel H. Baker, Senior Lecturer (Associate Professor), University of York

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.

Table of Contents

1:Introduction
2:Introduction to the R environment
3:Cleaning and preparing data for analysis
4:Statistical tests as linear models
5:Power analysis
6:Meta-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

Reviews

"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." - Stephen Hubbard, Honorary Professor of Ecology, School of Social Sciences, University of Dundee

"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

Additional Resources

This title is available for students and institutions to purchase in a variety of formats and is supported by online resources.

- The e-book offers a mobile experience and convenient access along with functionality tools, navigation features and links that offer extra learning support: www.oxfordtextbooks.co.uk/ebooks
- This book is accompanied by online resources including multiple-choice-questions with instant feedback, example code, and data files allowing students to run examples independently.