Data Analytics for the Social Sciences : Applications in R

G. David Garson

ISBN 10: 036762429X / ISBN 13: 9780367624293

Published by Taylor & Francis Ltd Nov 2021, 2021

About this Item

Neuware - This book presents a complete exploration of statistical data analysis in R for a wide variety of social science disciplines and quantitative methods courses.

Seller Inventory # 9780367624293

Bibliographic Details

• Title: Data Analytics for the Social Sciences : Applications in R
• Publisher: Taylor & Francis Ltd Nov 2021
• Publication Date: 2021
• Language: English
• ISBN 10: 036762429X
• ISBN 13: 9780367624293
• Binding: Buch
• Condition: Neu
• Dimensions: 280W x 210H x 0D millimeters 280 millimeters width by 210 millimeters height by 0 millimeters depth
• Weight: 2100 grams

About this title

Synopsis

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers.

The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling.

Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

About the Author

G. David Garson teaches advanced research methodology in the School of Public and International Affairs, North Carolina State University, USA. Founder and longtime editor emeritus of the Social Science Computer Review, he is president of Statistical Associates Publishing, which provides free digital texts worldwide. His degrees are from Princeton University (BA, 1965) and Harvard University (PhD, 1969).