Network Analysis for Rating Datasets in R: A Multi-Disciplinary Perspective
暫譯: R 中評分資料集的網路分析：跨學科視角

This is the first book to illustrate the use of Network Analysis for the analysis of rating datasets. It uses a multi-disciplinary approach to focus on the quantification and visualization of rater effects for exploration, diagnosis, reporting, decision-making, and planning.

Combining cutting-edge theories of rater effects with step-by-step practical examples using publicly available data and open-source code (R packages), this book provides an introduction to the concept of rating datasets. It uses simple language to discuss data requirements, model assumptions, and other important details, and it follows all theoretical sections with practical applications that analyze empirical data step-by-step. The book also demonstrates how the proposed methods can be integrated with more established approaches, such as Rasch models, to make the analysis of relevant data more efficient. With its accessible language and gentle introduction to graph theory and network analysis, this book is suitable for readers unfamiliar with the methodology. Throughout the book, readers will learn to plan a rater-effects study, format the dataset, conduct the analysis, and report the findings. The data and code are available as supplementary online material to encourage readers to replicate the analysis and reflect on the results.

This book will be of great interest to graduate students and researchers who conduct research on using ratings, and will also be an important reference for anyone studying measurement, research, and quantitative methods. Researchers and practitioners in the fields of testing services, awarding bodies, and social sciences in general will also find this book useful