Data analytics for the social sciences: applications in R

By:

Garson, G. David

Material type: Text

TextPublication details: Routledge New York 2022Description: xviii, 686 pISBN:

9780367624279

Subject(s):

DDC classification:

300.15195 GAR

Summary: 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.

List(s) this item appears in: IT & Decision Sciences

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	300.15195 GAR (Browse shelf(Opens below))	1	Available		004124

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Previous	174.90063 VIE AI for the good:	174.9629892 VON Robotics, AI, and humanity: science, ethics, and policy	300.15195 FIE Discovering statistics using R	300.15195 GAR Data analytics for the social sciences:	300.2856312 FOS Big data and social science:	302.231 GIA How algorithms create and prevent fake news: exploring the impacts of social media, deepfakes, GPT-3, and more	302.30285 BAS Spatializing social media:	Next

Table of Contents
1. Using and Abusing Data Analytics in Social Science 2. Statistical Analytics with R, Part 1 3. Statistical Analytics with R, Part 2 4. Classification and Regression Trees in R 5. Random Forests 6. Modeling and Machine Learning 7. Neural Network Models and Deep Learning 8. Network Analysis 9. Text Analytics; Appendix 1. Introduction to R and R Studio Appendix 2. Data Used in this Book

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.

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