Description
| Product ID: | 9781032259635 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | GB |
| Title: | Bayesian Multilevel Models for Repeated Measures Data |
| Subtitle: | A Conceptual and Practical Introduction in R |
| Authors: | Author: Noah Silbert, Santiago Barreda |
| Page Count: | 460 |
| Subjects: | Research methods: general, Research methods: general, Psychological theory, systems, schools and viewpoints, Psychological methodology, Econometrics and economic statistics, Econometrics and economic statistics, Probability and statistics, Psychological theory & schools of thought, Psychological methodology, Econometrics, Economic statistics, Probability & statistics |
| Description: | Select Guide Rating This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text. |
| Imprint Name: | Routledge |
| Publisher Name: | Taylor & Francis Ltd |
| Country of Publication: | GB |
| Publishing Date: | 2023-05-18 |
