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Data Analytics for the Social Sciences - Applications in R (Paperback)
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Data Analytics for the Social Sciences - Applications in R (Paperback)
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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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