Quantitative Social Science

Quantitative Social Science: An Introduction in tidyverse

Kosuke Imai and Nora Webb Williams

A tidyverse edition of the acclaimed textbook on data analysis and statistics for the social sciences and allied fields

Hardcover

Price:: $120.00/拢100.00
ISBN:
Published (US):: Aug 23, 2022
Published (UK):: Oct 18, 2022
Pages:: 488
Size:: 7 x 10 in.
Illus:: 43 color + 45 b/w illus. 51 tables.
Main_subject:: Sociology

Paperback

Price:: $120.00/拢100.00
ISBN:
Published (US):: Aug 23, 2022
Published (UK):: Oct 18, 2022
Pages:: 488
Size:: 7 x 10 in.
Illus:: 43 color + 45 b/w illus. 51 tables.
Main_subject:: Sociology

ebook

Price:: $120.00/拢100.00
ISBN:
Published (US):: Aug 23, 2022
Published (UK):: Oct 18, 2022
Pages:: 488
Size:: 7 x 10 in.
Illus:: 43 color + 45 b/w illus. 51 tables.
Main_subject:: Sociology

Buy This

Quantitative analysis is an essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it. Quantitative Social Science is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, including business, economics, education, political science, psychology, sociology, public policy, and data science. Proven in classrooms around the world, this one-of-a-kind textbook engages directly with empirical analysis, showing students how to analyze and interpret data using the tidyverse family of R packages. Data sets taken directly from leading quantitative social science research illustrate how to use data analysis to answer important questions about society and human behavior.

Emphasizes hands-on learning, not paper-and-pencil statistics
Includes data sets from actual research for students to test their skills on
Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools
Features a wealth of supplementary exercises, including additional data analysis exercises and programming exercises
Offers a solid foundation for further study
Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

List of Tables
List of Figures
Preface
Preface to the Original Book
1 Introduction
- 1.1 Overview of the Book
- 1.2 How to Use This Book
- 1.3 Introduction to R and the tidyverse
  - 1.3.1 Arithmetic Operations: R as a Calculator
  - 1.3.2 R Scripts
  - 1.3.3 Loading Packages
  - 1.3.4 Objects
  - 1.3.5 Vectors
  - 1.3.6 Functions
  - 1.3.7 Data Files: Loading and Subsetting
  - 1.3.8 Adding Variables
  - 1.3.9 Data Frames: Summarizing
  - 1.3.10 Saving Objects
  - 1.3.11 Loading Data in Other Formats
  - 1.3.12 Programming and Learning Tips
- 1.4 Summary
- 1.5 Exercises
  - 1.5.1 Bias in Self-Reported Turnout
  - 1.5.2 Understanding World Population Dynamics
2 Causality
- 2.1 Racial Discrimination in the Labor Market
- 2.2 Subsetting Data in R
  - 2.2.1 Logical Values and Operators
  - 2.2.2 Relational Operators
  - 2.2.3 Subsetting
  - 2.2.4 Simple Conditional Statements
  - 2.2.5 Factor Variables
- 2.3 Causal E铿€ects and the Counterfactual
- 2.4 Randomized Controlled Trials
  - 2.4.1 The Role of Randomization
  - 2.4.2 Social Pressure and Voter Turnout
- 2.5 Observational Studies
  - 2.5.1 Minimum Wage and Unemployment
  - 2.5.2 Confounding Bias
  - 2.5.3 Before-and-After and Di铿€erence-in-Di铿€erences Designs
- 2.6 Descriptive Statistics for a Single Variable
  - 2.6.1 Quantiles
  - 2.6.2 Standard Deviation
- 2.7 Summary
- 2.8 Exercises
  - 2.8.1 E铿僣acy of Small Class Size in Early Education
  - 2.8.2 Changing Minds on Gay Marriage
  - 2.8.3 Success of Leader Assassination as a Natural Experiment
3 Measurement
- 3.1 Measuring Civilian Victimization during Wartime
- 3.2 Handling Missing Data in R
- 3.3 Visualizing the Univariate Distribution
  - 3.3.1 Bar Plot
  - 3.3.2 Histogram
  - 3.3.3 Box Plot
  - 3.3.4 Printing and Saving Graphs
- 3.4 Survey Sampling
  - 3.4.1 The Role of Randomization
  - 3.4.2 Nonresponse and Other Sources of Bias
- 3.5 Measuring Political Polarization
- 3.6 Summarizing Bivariate Relationships
  - 3.6.1 Scatter Plot
  - 3.6.2 Correlation
- 3.7 Quantile鈥換uantile Plot
- 3.8 Clustering
  - 3.8.1 Matrix in R
  - 3.8.2 List in R
  - 3.8.3 The k-Means Algorithm
- 3.9 Summary
- 3.10 Exercises
  - 3.10.1 Changing Minds on Gay Marriage: Revisited
  - 3.10.2 Political E铿僣acy in China and Mexico
  - 3.10.3 Voting in the United Nations General Assembly
4 Prediction
- 4.1 Predicting Election Outcomes
  - 4.1.1 Loops in R
  - 4.1.2 General Conditional Statements in R
  - 4.1.3 Poll Predictions
- 4.2 Linear Regression
  - 4.2.1 Facial Appearance and Election Outcomes
  - 4.2.2 Correlation and Scatter Plots
  - 4.2.3 Least Squares
  - 4.2.4 Regression towards the Mean
  - 4.2.5 Merging Data Sets in R
  - 4.2.6 Model Fit
- 4.3 Regression and Causation
- 4.4 Randomized Experiments
  - 4.4.1 Regression with Multiple Predictors
  - 4.4.2 Heterogeneous Treatment E铿€ects
  - 4.4.3 Regression Discontinuity Design
- 4.5 Summary
- 4.6 Exercises
  - 4.6.1 Prediction Based on Betting Markets
  - 4.6.2 Election and Conditional Cash Transfer Program in Mexico
  - 4.6.3 Government Transfer and Poverty Reduction in Brazil
5 Discovery
- 5.1 Textual Data
  - 5.1.1 The Disputed Authorship of The Federalist Papers
  - 5.1.2 Document-Term Matrix
  - 5.1.3 Topic Discovery
  - 5.1.4 Authorship Prediction
  - 5.1.5 Cross-Validation
- 5.2 Network Data
  - 5.2.1 Marriage Network in Renaissance Florence
  - 5.2.2 Undirected Graph and Centrality Measures
  - 5.2.3 Twitter-Following Network
  - 5.2.4 Directed Graph and Centrality
- 5.3 Spatial Data
  - 5.3.1 The 1854 Cholera Outbreak in London
  - 5.3.2 Spatial Data in R
  - 5.3.3 US Presidential Elections
  - 5.3.4 Expansion of Walmart
  - 5.3.5 Animation in R
- 5.4 Summary
- 5.5 Exercises
  - 5.5.1 Analyzing the Preambles of Constitutions
  - 5.5.2 International Trade Network
  - 5.5.3 Mapping US Presidential Election Results over Time
6 Probability
- 6.1 Probability
  - 6.1.1 Frequentist versus Bayesian
  - 6.1.2 De铿乶ition and Axioms
  - 6.1.3 Permutations
  - 6.1.4 Sampling with and without Replacement
  - 6.1.5 Combinations
- 6.2 Conditional Probability
  - 6.2.1 Conditional, Marginal, and Joint Probabilities
  - 6.2.2 Independence
  - 6.2.3 Bayes鈥� Rule
  - 6.2.4 Predicting Race Using Surname and Residence Location
- 6.3 Random Variables and Probability Distributions
  - 6.3.1 Random Variables
  - 6.3.2 Bernoulli and Uniform Distributions
  - 6.3.3 Binomial Distribution
  - 6.3.4 Normal Distribution
  - 6.3.5 Expectation and Variance
  - 6.3.6 Predicting Election Outcomes with Uncertainty
- 6.4 Large Sample Theorems
  - 6.4.1 The Law of Large Numbers
  - 6.4.2 The Central Limit Theorem
- 6.5 Summary
- 6.6 Exercises
  - 6.6.1 The Mathematics of Enigma
  - 6.6.2 A Probability Model for Betting Market Election Prediction
  - 6.6.3 Election Fraud in Russia
7 Uncertainty
- 7.1 Estimation
  - 7.1.1 Unbiasedness and Consistency
  - 7.1.2 Standard Error
  - 7.1.3 Con铿乨ence Interval
  - 7.1.4 Margin of Error and Sample Size Calculation in Polls
  - 7.1.5 Analysis of Randomized Controlled Trials
  - 7.1.6 Analysis Based on Student鈥檚 t-Distribution
- 7.2 Hypothesis Testing
  - 7.2.1 Tea-Tasting Experiment
  - 7.2.2 The General Framework
  - 7.2.3 One-Sample Tests
  - 7.2.4 Two-Sample Tests
  - 7.2.5 Pitfalls of Hypothesis Testing
  - 7.2.6 Power Analysis
- 7.3 Linear Regression Model with Uncertainty
  - 7.3.1 Linear Regression as a Generative Model
  - 7.3.2 Unbiasedness of Estimated Coe铿僣ients
  - 7.3.3 Standard Errors of Estimated Coe铿僣ients
  - 7.3.4 Inference about Coe铿僣ients
  - 7.3.5 Inference about Predictions
- 7.4 Summary
- 7.5 Exercises
  - 7.5.1 Sex Ratio and the Price of Agricultural Crops in China
  - 7.5.2 Filedrawer and Publication Bias in Academic Research
  - 7.5.3 Analysis of the 1933 German Election during the Weimar Republic
8 Next
General Index
R Index

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Quantitative Social Science

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