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Full Description
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 resources
This 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.
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
