000			02724nam a22002177a 4500
999			_c3891 _d3891
005			20230104124649.0
008			230104b ||||| |||| 00| 0 eng d
020			_a9780367624279
082			_a300.15195 _bGAR
100			_aGarson, G. David _99112
245			_aData analytics for the social sciences: _bapplications in R
260			_bRoutledge _aNew York _c2022
300			_axviii, 686 p.
365			_aGBP _b74.99
504			_aTable 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
520			_aData 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.
650			_aR (Computer program language) _91512
650			_aSocial sciences--Statistical methods _91897
650			_aSocial sciences--Statistical methods--Data processing _911235
942			_2ddc _cBK