Data Analytics for the Social Sciences: Applications in R

Data Analytics for the Social Sciences is an introductory graduate-level text that covers R language applications in social science data analysis, including regression, classification trees, random forests, machine learning, neural network analysis, and text analysis. It emphasizes hands-on output interpretation and is suitable for advanced undergraduate and graduate students.

Format: Paperback / softback
Length: 686 pages
Publication date: 30 November 2021
Publisher: Taylor & Francis Ltd

Data Analytics for the Social Sciences is a comprehensive and introductory graduate-level treatment of data analytics specifically tailored for the social sciences. This book delves into the realm of data analysis, emphasizing its applications in the R language, which is widely recognized as one of the fastest-growing and most influential statistical tools among researchers.

The book begins with an ethical chapter that explores the uses and potential abuses of data analytics, shedding light on the responsible and ethical considerations involved in this field. This chapter serves as a foundational framework for the subsequent chapters, emphasizing the importance of transparency, accuracy, and fairness in data analysis.

Chapters 2 and 3 provide detailed implementations of a wide range of statistical procedures in R, a powerful programming language for statistical computing. These chapters cover essential topics such as descriptive statistics, hypothesis testing, and regression analysis, equipping readers with the skills to analyze and interpret social science data.

Chapters 4 and 5 delve into the realm of regression and classification trees, two powerful tools for modeling and predicting social phenomena. These chapters introduce readers to the concepts and techniques of these models, enabling them to build and evaluate prediction models for various social science applications.

Chapter 6 focuses on machine learning models, which have gained significant popularity in recent years due to their ability to analyze large datasets and make accurate predictions. The caret package, a widely used tool in R for machine learning, is introduced in this chapter, providing researchers with access to hundreds of different models. Readers will learn how to train and evaluate machine learning models, as well as how to apply them to real-world social science problems.

Chapter 7 explores neural network analysis, a powerful tool for modeling complex relationships and patterns in social science data. This chapter introduces readers to the concepts and techniques of neural networks, including their architecture, training, and applications. Readers will learn how to build and evaluate neural network models, as well as how to interpret their output.

Chapter 8 focuses on network analysis and visualization of network data, which is becoming increasingly important in understanding social networks and their dynamics. This chapter introduces readers to the concepts and techniques of network analysis, including network visualization, centrality measures, and network motifs. Readers will learn how to analyze and visualize network data, as well as how to identify key patterns and relationships within social networks.

A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. These techniques are essential for analyzing large volumes of text data and extracting valuable insights from social media, news articles, and other textual sources. Readers will learn how to apply these techniques to social science research, as well as how to interpret the results.

Throughout the book, each empirical chapter is accompanied by two Quick Start exercises designed to provide a hands-on introduction to the chapter's topics. These exercises are followed by In Depth coverage, which delves deeper into the concepts and techniques discussed. Additionally, the book includes an appendix that provides an extended tutorial on R and RStudio, a popular programming environment for statistical computing in R.

Almost 30 online supplements are available to complement the book, covering a wide range of topics within the social sciences. These supplements provide additional information, examples, and runnable R code, allowing readers to further explore the book's content and apply the techniques learned to their own research.

Data Analytics for the Social Sciences is designed to be a comprehensive and accessible resource for advanced-level undergraduate and graduate students learning statistical data analysis. It emphasizes hands-on obtaining of output for various social science models and how to interpret the output, rather than focusing solely on equations, derivations, and proofs. By emphasizing practical applications and real-world examples, this book equips readers with the skills and knowledge necessary to analyze and interpret social science data effectively, enabling them to make informed decisions and contribute to the field of social sciences.

Weight: 1706g
Dimension: 211 x 280 x 47 (mm)
ISBN-13: 9780367624279