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Data Analytics for the Social Sciences

G. David Garson
€107.99
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A01=G. David Garson
ANOVA
applied statistical modelling in R
Author_G. David Garson
Category=GPS
Category=JMB
Category=UFM
Character Vector
data analysis
data analysis for the social sciences
data analysis with R
data analytics
Data Frame
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
FALSE FALSE FALSE FALSE FALSE
FE Model
Generalize Linear Model
Hausman Test
Interest Group Ratings
Linear Model
machine learning applications
MANCOVA
MANOVA
Model Performance Metrics
Mtry Values
multilevel modeling
network data visualisation
neural network modelling
neural networks
OOB Error
OOB Error Rate
OOB Estimate
Poisson Count Model
Polynomial Kernel
quantitative methods
quantitative research methods
R
R code
Random Forests
Regression Trees
Resampling Results
RF Model
Roc Curve
social science statistics
Splitting Variables
statistical analysis
statistical data analysis
statistics
SVM Model
Terminal Nodes
text analytics
text mining techniques
Variable Importance
Variable Importance Plots

Product details

  • ISBN 9780367624279
  • Weight: 1020g
  • Dimensions: 210 x 280mm
  • Publication Date: 30 Nov 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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 G. David Garson

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).

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