Data analytics for the social sciences: applications in R /
Material type: TextPublication details: London: Routledge, 2022Description: 1 online resource : color illustrationsISBN: 9780367624279Subject(s): Social sciences | R (Computer program language) | Sciences sociales | Psychology | Social sciencesDDC classification: 300.15195 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 andhow to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysisItem type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Stack | Stack | 300.15195 GAR/D (Browse shelf (Opens below)) | Available | 59897 |
Browsing Kannur University Central Library shelves, Shelving location: Stack, Collection: Stack Close shelf browser (Hides shelf browser)
300.1 HIR/S Social evolution and sociological categories | 300.14 FAI/A Analysing discourse : textual analysis for social research | 300.15118 STR Structural equation modeling : Concepts, issues and applications | 300.15195 GAR/D Data analytics for the social sciences: applications in R / | 300.285 555 WAG/U Using IBM SPSS statistics for research methods and social science statistics / | 300.2851 VAN/N Network analysis | 300.2/854678 HEW/I Internet research methods / |
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 andhow to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis
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