Data analytics for the social sciences : applications in R / G. David Garson.

Format:

Book

Author/Creator:

Garson, G. David, author.

Language:

English

Subjects (All):

R (Computer program language).

Social sciences--Statistical methods.

Social sciences.

Physical Description:

1 online resource (705 pages)

Edition:

1st ed.

Place of Publication:

Abingdon, Oxon, England ; New York, New York : Routledge, [2022]

Biography/History:

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).

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 analysis.

Contents:

Cover

Half Title

Title Page

Copyright Page

Dedication

Contents

Acknowledgments

Preface

1. Using and abusing data analytics in social science

1.1. Introduction

1.2. The promise of data analytics for social science

1.2.1. Data analytics in public affairs and public policy

1.2.2. Data analytics in the social sciences

1.2.3. Data analytics in the humanities

1.3. Research design issues in data analytics

1.3.1. Beware the true believer

1.3.2. Pseudo-objectivity in data analytics

1.3.3. The bias of scholarship based on algorithms using big data

1.3.4. The subjectivity of algorithms

1.3.5. Big data and big noise

1.3.6. Limitations of the leading data science dissemination models

1.4. Social and ethical issues in data analytics

1.4.1. Types of ethical issues in data analytics

1.4.2. Bias toward the privileged

1.4.3. Discrimination

1.4.4. Diversity and data analytics

1.4.5. Distortion of democratic processes

1.4.6. Undermining of professional ethics

1.4.7. Privacy, profiling, and surveillance issues

1.4.8. The transparency issue

1.5. Summary: Technology and power

Endnotes

2. Statistical analytics with R, Part 1

PART I: OVERVIEW OF STATISTICAL ANALYSIS WITH R

2.1. Introduction

2.2. Data and packages used in this chapter

2.2.1. Example data

2.2.2. R packages used

PART II: QUICK START ON STATISTICAL ANALYSIS WITH R

2.3. Descriptive statistics

2.4. Linear multiple regression

PART III: STATISTICAL ANALYSIS WITH R IN DETAIL

2.5. Hypothesis testing

2.5.1. One-sample test of means

2.5.2. Means test for two independent samples

2.5.3. Means test for two dependent samples

2.6. Crosstabulation, significance, and association

2.7. Loglinear analysis for categorical variables

2.8. Correlation, correlograms, and scatterplots.

2.9. Factor analysis (exploratory)

2.10. Multidimensional scaling

2.11. Reliability analysis

2.11.1. Cronbach's alpha and Guttman's lower bounds

2.11.2. Guttman's lower bounds and Cronbach's alpha

2.11.3. Krippendorff's alpha and Cohen's kappa

2.12. Cluster analysis

2.12.1. Hierarchical cluster analysis

2.12.2. K-means clustering

2.12.3. Nearest neighbor analysis

2.13. Analysis of variance

2.13.1. Data and packages used

2.13.2. GLM univariate: ANOVA

2.13.3. GLM univariate: ANCOVA

2.13.4. GLM multivariate: MANOVA

2.13.5. GLM multivariate: MANCOVA

2.14. Logistic regression

2.14.1. ROC and AUC analysis

2.14.2. Confusion table and accuracy

2.15. Mediation and moderation

2.16. Chapter 2 command summary

3. Statistical analytics with R, Part 2

PART I: OVERVIEW OF STATISTICAL ANALYTICS WITH R

3.1. Introduction

3.2. Data and packages used in this chapter

3.2.1. Example data

3.2.2. R Packages used

PART II: QUICK START ON STATISTICAL ANALYSIS PART 2

3.3. Quick start: Linear regression as a generalized linear modeling (GZLM)

3.3.1. Background to GZLM

3.3.2. The linear model in glm()

3.3.3. GZLM output

3.3.4. Fitted value, residuals, and plots

3.3.5. Noncanonical custom links

3.3.6. Multiple comparison tests

3.3.7. Estimated marginal means (EMM)

3.4. Quick start: Testing if multilevel modeling is needed

PART III: STATISTICAL ANALYSIS, PART 2, IN DETAIL

3.5. Generalized linear models (GZLM)

3.5.1. Introduction

3.5.2. Setup for GZLM models in R

3.5.3. Binary logistic regression example

3.5.4. Gamma regression model

3.5.5. Poisson regression model

3.5.6. Negative binomial regression

3.6. Multilevel modeling (MLM)

3.6.1. Introduction

3.6.2. Setup and data

3.6.3. The random coefficients model.

3.6.4. Likelihood ratio test

3.7. Panel data regression (PDR)

3.7.1. Introduction

3.7.2. Types of PDR model

3.7.3. The Hausman test

3.7.4. Setup and data

3.7.5. PDR with the plm package

3.7.6. PDR with the panelr package

3.8. Structural equation modeling (SEM)

3.9. Missing data analysis and data imputation

3.10. Chapter 3 command summary

4. Classification and regression trees in R

PART I: OVERVIEW OF CLASSIFICATION AND REGRESSION TREES WITH R

4.1. Introduction

4.2. Advantages of decision tree analysis

4.3. Limitations of decision tree analysis

4.4. Decision tree terminology

4.5. Steps in decision tree analysis

4.6. Decision tree algorithms

4.7. Random forests and ensemble methods

4.8. Software

4.8.1. R language

4.8.2. Stata

4.8.3. SAS

4.8.4. SPSS

4.8.5. Python language

4.9. Data and packages used in this chapter

4.9.1. Example data

4.9.2. R packages used

PART II: QUICK START - CLASSIFICATION AND REGRESSION TREES

4.10. Classification tree example: Survival on the Titanic

4.11. Regression tree example: Correlates of murder

PART III: CLASSIFICATION AND REGRESSION TREES, IN DETAIL

4.12. Overview

4.13. The rpart() program

4.13.1. Introduction

4.13.2. Training and validation datasets

4.13.3. Setup for rpart() trees

4.14. Classification trees with the rpart package

4.14.1. The basic rpart classification tree

4.14.2. Printing tree rules

4.14.3. Visualization with prp() and draw.tree()

4.14.4. Visualization with fancyRpartPlot()

4.14.5. Interpreting tree summaries

4.14.6. Listing nodes by country and countries by node

4.14.7. Node distribution plots

4.14.8. Saving predictions and residuals

4.14.9. Cross-validation and pruning

4.14.10. The confusion matrix and model performance metrics.

4.14.11. The ROC curve and AUC

4.14.12. Lift plots

4.14.13. Gains plots

4.14.14. Precision vs. recall plot

4.15. Regression trees with the rpart package

4.15.1. Setup

4.15.2. Creating an rpart regression tree

4.15.3. Printing tree rules

4.15.4. Visualization with prp() and fancyRpartPlot()

4.15.5. Interpreting tree summaries

4.15.6. The CP table

4.15.7. Listing nodes by country and countries by node

4.15.8. Saving predictions and residuals

4.15.9. Plotting residuals

4.15.10. Cross-validation and pruning

4.15.11. R-squared for regression trees

4.15.12. MSE for regression trees

4.15.13. The confusion matrix

4.15.14. The ROC curve and AUC

4.15.15. Gains plots

4.15.16. Gains plot with OLS comparison

4.16. The tree package

4.17. The ctree() program for conditional decision trees

4.18. More decision trees programs for R

4.19. Chapter 4 command summary

5. Random forests

PART I: OVERVIEW OF RANDOM FORESTS IN R

5.1. Introduction

5.1.1. Social science examples of random forest models

5.1.2. Advantages of random forests

5.1.3. Limitations of random forests

5.1.4. Data and packages

PART II: QUICK START - RANDOM FORESTS

5.2. Classification forest example: Searching for the causes of happiness

5.3. Regression forest example: Why so much crime in my town?

PART III: RANDOM FORESTS, IN DETAIL

5.4. Classification forests with randomForest()

5.4.1. Setup

5.4.2. A basic classification model

5.4.3. Output components of randomForest() objects for classification models

5.4.4. Graphing a randomForest tree?

5.4.5. Comparing randomForest() and rpart() performance

5.4.6. Tuning the random forest model

5.4.7. MDS cluster analysis of the RF classification model

5.5. Regression forests with randomForest()

5.5.1. Introduction.

5.5.2. Setup

5.5.3. A basic regression model

5.5.4. Output components for regression forest models

5.5.5. Graphing a randomForest tree?

5.5.6. MDS plots

5.5.7. Quartile plots

5.5.8. Comparing randomForest() and rpart() regression models

5.5.9. Tuning the randomForest() regression model

5.5.10. Outliers: Identifying and removing

5.6. The randomForestExplainer package

5.6.1. Setup for the randomForestExplainer package

5.6.2. Minimal depth plots

5.6.3. Multiway variable importance plots

5.6.4. Multiway ranking of variable importance

5.6.5. Comparing randomForest and OLS rankings of predictors

5.6.6. Which importance criteria?

5.6.7. Interaction analysis

5.6.8. The explain _ forest() function

5.7. Summary

5.8. Conditional inference forests

5.9. MDS plots for random forests

5.10. More random forest programs for R

5.11. Command summary

6. Modeling and machine learning

PART I: OVERVIEW OF MODELING AND MACHINE LEARNING

6.1. Introduction

6.1.1. Social science examples of modeling and machine learning in R

6.1.2. Advantages of modeling and machine learning in R

6.1.3. Limitations of modeling and machine learning in R

6.1.4. Data, packages, and default directory

PART II: QUICK START - MODELING AND MACHINE LEARNING

6.2. Example 1: Bayesian modeling of county-level poverty

6.2.1. Introduction

6.2.2. Setup

6.2.3. Correlation plot

6.2.4. The Bayes generalized linear model

6.3. Example 2: Predicting diabetes among Pima Indians with mlr3

6.3.1. Introduction

6.3.2. Setup

6.3.3. How mlr3 works

6.3.4. The Pima Indian data

PART III: MODELING AND MACHINE LEARNING IN DETAIL

6.4. Illustrating modeling and machine learning with SVM in caret

6.4.1. How SVM works

6.4.2. SVM algorithms compared to logistic and OLS regression.

6.4.3. SVM kernels, types, and parameters.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-00-310939-X

1-000-46716-3

1-000-46708-2

1-003-10939-X

9781003109396

OCLC:

1276854635