Research methods using R : advanced data analysis in the behavioural and biological sciences

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.

This brand new title 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, reproducible data analysis. Written to build a student's confidence with 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 which students can use to practise their new skills.

Digital formats and resourcesThis book 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, navigation features, and links that offer extra learning support.

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.