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Quantitative Social Science: An Introduction in tidyverse [Pehme köide]

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  • Formaat: Paperback / softback, 488 pages, kõrgus x laius: 254x178 mm, 43 color + 45 b/w illus. 51 tables.
  • Ilmumisaeg: 02-Aug-2022
  • Kirjastus: Princeton University Press
  • ISBN-10: 0691222282
  • ISBN-13: 9780691222288
Teised raamatud teemal:
Quantitative Social Science: An Introduction in tidyverseSuurem pilt
  • Formaat: Paperback / softback, 488 pages, kõrgus x laius: 254x178 mm, 43 color + 45 b/w illus. 51 tables.
  • Ilmumisaeg: 02-Aug-2022
  • Kirjastus: Princeton University Press
  • ISBN-10: 0691222282
  • ISBN-13: 9780691222288
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780691222288.html
Märksõnad:

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

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
xiii
List of Figures
xv
Preface xvii
Preface to the Original Book xix
1 Introduction
1(37)
1.1 Overview of the Book
3(4)
1.2 How to Use This Book
7(1)
1.3 Introduction to R and the tidyverse
8(25)
1.3.1 Arithmetic Operations: R as a Calculator
9(1)
1.3.2 R Scripts
10(1)
1.3.3 Loading Packages
11(2)
1.3.4 Objects
13(2)
1.3.5 Vectors
15(2)
1.3.6 Functions
17(3)
1.3.7 Data Files: Loading and Subsetting
20(7)
1.3.8 Adding Variables
27(1)
1.3.9 Data Frames: Summarizing
28(2)
1.3.10 Saving Objects
30(1)
1.3.11 Loading Data in Other Formats
31(1)
1.3.12 Programming and Learning Tips
32(1)
1.4 Summary
33(1)
1.5 Exercises
34(4)
1.5.1 Bias in Self-Reported Turnout
34(1)
1.5.2 Understanding World Population Dynamics
35(3)
2 Causality
38(50)
2.1 Racial Discrimination in the Labor Market
38(7)
2.2 Subsetting Data in R
45(11)
2.2.1 Logical Values and Operators
46(2)
2.2.2 Relational Operators
48(1)
2.2.3 Subsetting
49(4)
2.2.4 Simple Conditional Statements
53(1)
2.2.5 Factor Variables
53(3)
2.3 Causal Effects and the Counterfactual
56(2)
2.4 Randomized Controlled Trials
58(7)
2.4.1 The Role of Randomization
59(1)
2.4.2 Social Pressure and Voter Turnout
60(5)
2.5 Observational Studies
65(10)
2.5.1 Minimum Wage and Unemployment
65(3)
2.5.2 Confounding Bias
68(3)
2.5.3 Before-and-After and Difference-in-Differences Designs
71(4)
2.6 Descriptive Statistics for a Single Variable
75(6)
2.6.1 Quantiles
75(3)
2.6.2 Standard Deviation
78(3)
2.7 Summary
81(1)
2.8 Exercises
82(6)
2.8.1 Efficacy of Small Class Size in Early Education
82(2)
2.8.2 Changing Minds on Gay Marriage
84(1)
2.8.3 Success of Leader Assassination as a Natural Experiment
85(3)
3 Measurement
88(56)
3.1 Measuring Civilian Victimization during Wartime
88(5)
3.2 Handling Missing Data in R
93(3)
3.3 Visualizing the Univariate Distribution
96(10)
3.3.1 Bar Plot
97(3)
3.3.2 Histogram
100(3)
3.3.3 Box Plot
103(2)
3.3.4 Printing and Saving Graphs
105(1)
3.4 Survey Sampling
106(8)
3.4.1 The Role of Randomization
107(4)
3.4.2 Nonresponse and Other Sources of Bias
111(3)
3.5 Measuring Political Polarization
114(2)
3.6 Summarizing Bivariate Relationships
116(8)
3.6.1 Scatter Plot
116(4)
3.6.2 Correlation
120(4)
3.7 Quantile-Quantile Plot
124(4)
3.8 Clustering
128(8)
3.8.1 Matrix in R
128(2)
3.8.2 List in R
130(1)
3.8.3 The k-Means Algorithm
131(5)
3.9 Summary
136(1)
3.10 Exercises
137(7)
3.10.1 Changing Minds on Gay Marriage: Revisited
137(2)
3.10.2 Political Efficacy in China and Mexico
139(2)
3.10.3 Voting in the United Nations General Assembly
141(3)
4 Prediction
144(72)
4.1 Predicting Election Outcomes
144(18)
4.1.1 Loops in R
145(3)
4.1.2 General Conditional Statements in R
148(4)
4.1.3 Poll Predictions
152(10)
4.2 Linear Regression
162(26)
4.2.1 Facial Appearance and Election Outcomes
162(3)
4.2.2 Correlation and Scatter Plots
165(1)
4.2.3 Least Squares
166(7)
4.2.4 Regression towards the Mean
173(1)
4.2.5 Merging Data Sets in R
174(7)
4.2.6 Model Fit
181(7)
4.3 Regression and Causation
188(1)
4.4 Randomized Experiments
188(21)
4.4.1 Regression with Multiple Predictors
191(6)
4.4.2 Heterogeneous Treatment Effects
197(6)
4.4.3 Regression Discontinuity Design
203(6)
4.5 Summary
209(1)
4.6 Exercises
209(7)
4.6.1 Prediction Based on Betting Markets
209(2)
4.6.2 Election and Conditional Cash Transfer Program in Mexico
211(3)
4.6.3 Government Transfer and Poverty Reduction in Brazil
214(2)
5 Discovery
216(63)
5.1 Textual Data
216(22)
5.1.1 The Disputed Authorship of The Federalist Papers
216(5)
5.1.2 Document-Term Matrix
221(2)
5.1.3 Topic Discovery
223(9)
5.1.4 Authorship Prediction
232(3)
5.1.5 Cross-Validation
235(3)
5.2 Network Data
238(17)
5.2.1 Marriage Network in Renaissance Florence
238(2)
5.2.2 Undirected Graph and Centrality Measures
240(5)
5.2.3 Twitter-Following Network
245(2)
5.2.4 Directed Graph and Centrality
247(8)
5.3 Spatial Data
255(17)
5.3.1 The 1854 Cholera Outbreak in London
256(2)
5.3.2 Spatial Data in R
258(6)
5.3.3 US Presidential Elections
264(4)
5.3.4 Expansion of Walmart
268(2)
5.3.5 Animation in R
270(2)
5.4 Summary
272(1)
5.5 Exercises
273(6)
5.5.1 Analyzing the Preambles of Constitutions
273(2)
5.5.2 International Trade Network
275(2)
5.5.3 Mapping US Presidential Election Results over Time
277(2)
6 Probability
279(78)
6.1 Probability
279(12)
6.1.1 Frequentist versus Bayesian
279(2)
6.1.2 Definition and Axioms
281(3)
6.1.3 Permutations
284(3)
6.1.4 Sampling with and without Replacement
287(2)
6.1.5 Combinations
289(2)
6.2 Conditional Probability
291(30)
6.2.1 Conditional, Marginal, and Joint Probabilities
291(10)
6.2.2 Independence
301(6)
6.2.3 Bayes' Rule
307(2)
6.2.4 Predicting Race Using Surname and Residence Location
309(12)
6.3 Random Variables and Probability Distributions
321(21)
6.3.1 Random Variables
321(1)
6.3.2 Bernoulli and Uniform Distributions
321(4)
6.3.3 Binomial Distribution
325(3)
6.3.4 Normal Distribution
328(7)
6.3.5 Expectation and Variance
335(4)
6.3.6 Predicting Election Outcomes with Uncertainty
339(3)
6.4 Large Sample Theorems
342(8)
6.4.1 The Law of Large Numbers
342(3)
6.4.2 The Central Limit Theorem
345(5)
6.5 Summary
350(1)
6.6 Exercises
350(7)
6.6.1 The Mathematics of Enigma
350(2)
6.6.2 A Probability Model for Betting Market Election Prediction
352(2)
6.6.3 Election Fraud in Russia
354(3)
7 Uncertainty
357(89)
7.1 Estimation
357(33)
7.1.1 Unbiasedness and Consistency
358(8)
7.1.2 Standard Error
366(5)
7.1.3 Confidence Interval
371(7)
7.1.4 Margin of Error and Sample Size Calculation in Polls
378(5)
7.1.5 Analysis of Randomized Controlled Trials
383(3)
7.1.6 Analysis Based on Student's t-Distribution
386(4)
7.2 Hypothesis Testing
390(28)
7.2.1 Tea-Tasting Experiment
390(4)
7.2.2 The General Framework
394(3)
7.2.3 One-Sample Tests
397(7)
7.2.4 Two-Sample Tests
404(5)
7.2.5 Pitfalls of Hypothesis Testing
409(2)
7.2.6 Power Analysis
411(7)
7.3 Linear Regression Model with Uncertainty
418(21)
7.3.1 Linear Regression as a Generative Model
418(5)
7.3.2 Unbiasedness of Estimated Coefficients
423(3)
7.3.3 Standard Errors of Estimated Coefficients
426(2)
7.3.4 Inference about Coefficients
428(4)
7.3.5 Inference about Predictions
432(7)
7.4 Summary
439(1)
7.5 Exercises
439(7)
7.5.1 Sex Ratio and the Price of Agricultural Crops in China
439(2)
7.5.2 Filedrawer and Publication Bias in Academic Research
441(2)
7.5.3 Analysis of the 1933 German Election during the Weimar Republic
443(3)
8 Next
446(3)
General Index 449(6)
R Index 455
Kosuke Imai is Professor of Government and of Statistics at Harvard University. Nora Webb Williams is Assistant Professor of Political Science at the University of Illinois, Urbana-Champaign.