Data Science with R for Psychologists and Healthcare Professionals

Christian Ryan

• Publisher: CRC Press
• Year: 2021
• ISBN:
  9781000530582
• Edition: 1
• Topic: Economics
• Subtopic: Statistics for Business & Economics
• Index: Economics

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

• Cover Page
• Title Page
• Copyright Page
• Preface
• Acknowledgements
• Table of Contents
• 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
• References
• Index