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El. knyga: Quantitative Social Science: An Introduction

4.35/5 (99 ratings by Goodreads)
  • Formatas: 432 pages
  • Išleidimo metai: 10-Sep-2024
  • Leidėjas: Princeton University Press
  • Kalba: eng
  • ISBN-13: 9780691270838
Kitos knygos pagal šią temą:
Quantitative Social Science: An IntroductionDidesnis paveikslėlis
  • Formatas: 432 pages
  • Išleidimo metai: 10-Sep-2024
  • Leidėjas: Princeton University Press
  • Kalba: eng
  • ISBN-13: 9780691270838
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An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it--or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. * Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science* Provides hands-on instruction using R programming, not paper-and-pencil statistics* Includes more than forty 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 interactive 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

Recenzijos

"The author has masterfully balanced careful explanations of the quantitative theory with the practical computer implementation of the methods applied to real world data sets. . . . That Quantitative Social Science: An Introduction is carefully written, detailed, and interactive makes it useful either as a textbook for a lecture course or for self-study. . . . I highly recommend the book to anyone looking for an introduction to data science."---Jason M. Graham, Mathematical Association of America Reviews

List of Tables xiii
List of Figures xv
Preface xvii
1 Introduction 1(31)
1.1 Overview of the Book
3(4)
1.2 How to Use this Book
7(3)
1.3 Introduction to R
10(17)
1.3.1 Arithmetic Operations
10(2)
1.3.2 Objects
12(2)
1.3.3 Vectors
14(2)
1.3.4 Functions
16(4)
1.3.5 Data Files
20(3)
1.3.6 Saving Objects
23(1)
1.3.7 Packages
24(1)
1.3.8 Programming and Learning Tips
25(2)
1.4 Summary
27(1)
1.5 Exercises
28(4)
1.5.1 Bias in Self-Reported Turnout
28(1)
1.5.2 Understanding World Population Dynamics
29(3)
2 Causality 32(43)
2.1 Racial Discrimination in the Labor Market
32(4)
2.2 Subsetting the Data in R
36(10)
2.2.1 Logical Values and Operators
37(2)
2.2.2 Relational Operators
39(1)
2.2.3 Subsetting
40(3)
2.2.4 Simple Conditional Statements
43(1)
2.2.5 Factor Variables
44(2)
2.3 Causal Effects and the Counterfactual
46(2)
2.4 Randomized Controlled Trials
48(6)
2.4.1 The Role of Randomization
49(2)
2.4.2 Social Pressure and Voter Turnout
51(3)
2.5 Observational Studies
54(9)
2.5.1 Minimum Wage and Unemployment
54(3)
2.5.2 Confounding Bias
57(3)
2.5.3 Before-and-After and Difference-in-Differences Designs
60(3)
2.6 Descriptive Statistics for a Single Variable
63(5)
2.6.1 Quantiles
63(3)
2.6.2 Standard Deviation
66(2)
2.7 Summary
68(1)
2.8 Exercises
69(6)
2.8.1 Efficacy of Small Class Size in Early Education
69(2)
2.8.2 Changing Minds on Gay Marriage
71(2)
2.8.3 Success of Leader Assassination as a Natural Experiment
73(2)
3 Measurement 75(48)
3.1 Measuring Civilian Victimization during Wartime
75(3)
3.2 Handling Missing Data in R
78(2)
3.3 Visualizing the Univariate Distribution
80(8)
3.3.1 Bar Plot
80(1)
3.3.2 Histogram
81(4)
3.3.3 Box Plot
85(2)
3.3.4 Printing and Saving Graphs
87(1)
3.4 Survey Sampling
88(8)
3.4.1 The Role of Randomization
89(4)
3.4.2 Nonresponse and Other Sources of Bias
93(3)
3.5 Measuring Political Polarization
96(1)
3.6 Summarizing Bivariate Relationships
97(11)
3.6.1 Scatter Plot
98(3)
3.6.2 Correlation
101(4)
3.6.3 Quantile-Quantile Plot
105(3)
3.7 Clustering
108(7)
3.7.1 Matrix in R
108(2)
3.7.2 List in R
110(1)
3.7.3 The k-Means Algorithm
111(4)
3.8 Summary
115(1)
3.9 Exercises
116(7)
3.9.1 Changing Minds on Gay Marriage: Revisited
116(2)
3.9.2 Political Efficacy in China and Mexico
118(2)
3.9.3 Voting in the United Nations General Assembly
120(3)
4 Prediction 123(66)
4.1 Predicting Election Outcomes
123(16)
4.1.1 Loops in R
124(3)
4.1.2 General Conditional Statements in R
127(3)
4.1.3 Poll Predictions
130(9)
4.2 Linear Regression
139(22)
4.2.1 Facial Appearance and Election Outcomes
139(2)
4.2.2 Correlation and Scatter Plots
141(2)
4.2.3 Least Squares
143(5)
4.2.4 Regression towards the Mean
148(1)
4.2.5 Merging Data Sets in R
149(7)
4.2.6 Model Fit
156(5)
4.3 Regression and Causation
161(20)
4.3.1 Randomized Experiments
162(3)
4.3.2 Regression with Multiple Predictors
165(5)
4.3.3 Heterogenous Treatment Effects
170(6)
4.3.4 Regression Discontinuity Design
176(5)
4.4 Summary
181(1)
4.5 Exercises
182(7)
4.5.1 Prediction Based on Betting Markets
182(2)
4.5.2 Election and Conditional Cash Transfer Program in Mexico
184(3)
4.5.3 Government Transfer and Poverty Reduction in Brazil
187(2)
5 Discovery 189(53)
5.1 Textual Data
189(16)
5.1.1 The Disputed Authorship of The Federalist Papers
189(5)
5.1.2 Document-Term Matrix
194(1)
5.1.3 Topic Discovery
195(5)
5.1.4 Authorship Prediction
200(2)
5.1.5 Cross Validation
202(3)
5.2 Network Data
205(15)
5.2.1 Marriage Network in Renaissance Florence
205(2)
5.2.2 Undirected Graph and Centrality Measures
207(4)
5.2.3 Twitter-Following Network
211(2)
5.2.4 Directed Graph and Centrality
213(7)
5.3 Spatial Data
220(15)
5.3.1 The 1854 Cholera Outbreak in London
220(3)
5.3.2 Spatial Data in R
223(3)
5.3.3 Colors in R
226(2)
5.3.4 US Presidential Elections
228(3)
5.3.5 Expansion of Walmart
231(2)
5.3.6 Animation in R
233(2)
5.4 Summary
235(1)
5.5 Exercises
236(6)
5.5.1 Analyzing the Preambles of Constitutions
236(2)
5.5.2 International Trade Network
238(1)
5.5.3 Mapping US Presidential Election Results over Time
239(3)
6 Probability 242(72)
6.1 Probability
242(12)
6.1.1 Frequentist versus Bayesian
242(2)
6.1.2 Definition and Axioms
244(3)
6.1.3 Permutations
247(3)
6.1.4 Sampling with and without Replacement
250(2)
6.1.5 Combinations
252(2)
6.2 Conditional Probability
254(23)
6.2.1 Conditional, Marginal, and Joint Probabilities
254(7)
6.2.2 Independence
261(5)
6.2.3 Bayes' Rule
266(2)
6.2.4 Predicting Race Using Surname and Residence Location
268(9)
6.3 Random Variables and Probability Distributions
277(23)
6.3.1 Random Variables
278(1)
6.3.2 Bernoulli and Uniform Distributions
278(4)
6.3.3 Binomial Distribution
282(4)
6.3.4 Normal Distribution
286(6)
6.3.5 Expectation and Variance
292(4)
6.3.6 Predicting Election Outcomes with Uncertainty
296(4)
6.4 Large Sample Theorems
300(6)
6.4.1 The Law of Large Numbers
300(2)
6.4.2 The Central Limit Theorem
302(4)
6.5 Summary
306(1)
6.6 Exercises
307(7)
6.6.1 The Mathematics of Enigma
307(2)
6.6.2 A Probability Model for Betting Market Election Prediction
309(1)
6.6.3 Election Fraud in Russia
310(4)
7 Uncertainty 314(83)
7.1 Estimation
314(28)
7.1.1 Unbiasedness and Consistency
315(7)
7.1.2 Standard Error
322(4)
7.1.3 Confidence Intervals
326(6)
7.1.4 Margin of Error and Sample Size Calculation in Polls
332(4)
7.1.5 Analysis of Randomized Controlled Trials
336(3)
7.1.6 Analysis Based on Student's t-Distribution
339(3)
7.2 Hypothesis Testing
342(28)
7.2.1 Tea-Tasting Experiment
342(4)
7.2.2 The General Framework
346(4)
7.2.3 One-Sample Tests
350(6)
7.2.4 Two-Sample Tests
356(5)
7.2.5 Pitfalls of Hypothesis Testing
361(2)
7.2.6 Power Analysis
363(7)
7.3 Linear Regression Model with Uncertainty
370(19)
7.3.1 Linear Regression as a Generative Model
370(5)
7.3.2 Unbiasedness of Estimated Coefficients
375(3)
7.3.3 Standard Errors of Estimated Coefficients
378(2)
7.3.4 Inference about Coefficients
380(4)
7.3.5 Inference about Predictions
384(5)
7.4 Summary
389(1)
7.5 Exercises
390(7)
7.5.1 Sex Ratio and the Price of Agricultural Crops in China
390(2)
7.5.2 File Drawer and Publication Bias in Academic Research
392(2)
7.5.3 The 1932 German Election in the Weimar Republic
394(3)
8 Next 397(4)
General Index 401(5)
R Index 406
Kosuke Imai is professor of politics and founding director of the Program in Statistics and Machine Learning at Princeton University.
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