Description
This book provides a solid step-by-step practical guide to inter-rater reliability analyses using R software. The inter-rater reliability are statistical measures, which give the extent of agreement among two or more raters (i.e., "judges", "observers"). Other synonyms are: inter-rater agreement, inter-observer agreement or inter-rater concordance.
This book is designed to get you doing the analyses as quick as possible. It focuses on implementation and understanding of the methods, without having to struggle through pages of mathematical proofs.
You will be guided through the steps of basic explanations of the test formula and assumptions, performing the analysis in R, interpreting and reporting the results.
Key features
- Covers the most common statistical measures for the inter-rater reliability analyses, including cohen’s Kappa, weighted kappa, Light’s kappa , Fleiss kappa, intraclass correlation coefficient and agreement chart.
- Key assumptions are presented
- Short, self-contained chapters with practical examples.
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Books - Data Science
Our Books
- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)
Others
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet
Version: Français
Francisco Reyes-Vázquez (verified owner) –
Anonymous (verified owner) –
Charles Silber (verified owner) –
Never received the book I paid for. I would like a refund or the book.