Description
This introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines.
Table of Contents
1. Introduction 2. The R Environment 3. The Basics 4. Working Practices 5. Dataset Excel 6. Dataset csv 7. Dataset SPSS 8. Coding New Variables and Scale Reliability 9. Normality 10. Outliers 11. Descriptive Statistics 12. Graphs with ggplot2 13. Correlation—Bivariate 14. Correlation—Partial 15. One-Way ANOVA—Model Data 16. One-Way ANOVA—Real Data 17. Factorial ANOVA 18. ANCOVA 19. Repeated Measures ANOVA 20. Regression 21. Non-parametric Tests 22. Categorical Data Analysis 23. What Else can R Do? 24. Functions
