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E-raamat: Quantitative Social Science: An Introduction in Stata

  • Formaat: 472 pages
  • Ilmumisaeg: 10-Sep-2024
  • Kirjastus: Princeton University Press
  • Keel: eng
  • ISBN-13: 9780691270852
  • Formaat - EPUB+DRM
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  • Formaat: 472 pages
  • Ilmumisaeg: 10-Sep-2024
  • Kirjastus: Princeton University Press
  • Keel: eng
  • ISBN-13: 9780691270852

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"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

The Stata edition of the groundbreaking textbook on data analysis and statistics for 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 business, economics, education, political science, psychology, sociology, public policy, and data science.

Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the Stata statistical software and interpret the results—it emphasizes hands-on learning, not paper-and-pencil statistics. More than fifty 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 classrooms around the world, this one-of-a-kind textbook features numerous additional data analysis exercises, and also comes with supplementary teaching materials for instructors.

  • Written especially for students in the social sciences and allied fields, including business, economics, education, psychology, political science, sociology, public policy, and data science
  • Provides hands-on instruction using Stata, not paper-and-pencil statistics
  • Includes more than fifty 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
List of Tables
xiii
List of Figures
xv
Preface xvii
Preface to the Original Book xix
1 Introduction
1(31)
1.1 Overview of the Book
3(4)
1.2 How to Use this Book
7(1)
1.3 Introduction to Stata
8(18)
1.3.1 Arithmetic Operations
9(1)
1.3.2 Variables
10(6)
1.3.3 Labels
16(2)
1.3.4 Describing the Data
18(2)
1.3.5 Data Files
20(1)
1.3.6 Merging Data Sets in Stata
21(2)
1.3.7 Packages
23(2)
1.3.8 Programming and Learning Tips
25(1)
1.4 Summary
26(1)
1.5 Exercises
27(5)
1.5.1 Bias in Self-Reported Turnout
27(1)
1.5.2 Understanding World Population Dynamics
28(4)
2 Causality
32(49)
2.1 Racial Discrimination in the Labor Market
32(6)
2.2 Subsetting the Data in Stata
38(9)
2.2.1 Relational Operators
38(1)
2.2.2 Logical Operators
39(1)
2.2.3 Simple Conditional Statements and Variable Creation
40(3)
2.2.4 Subsetting Using Conditions
43(1)
2.2.5 Preserving and Transforming Data Sets
44(3)
2.3 Causal Effects and the Counterfactual
47(2)
2.4 Randomized Controlled Trials
49(7)
2.4.1 The Role of Randomization
50(1)
2.4.2 Social Pressure and Voter Turnout
51(5)
2.5 Observational Studies
56(11)
2.5.1 Minimum Wage and Unemployment
56(4)
2.5.2 Confounding Bias
60(3)
2.5.3 Before-and-After and Difference-in-Differences Designs
63(4)
2.6 Descriptive Statistics for a Single Variable
67(8)
2.6.1 Quantiles
67(5)
2.6.2 Standard Deviation
72(3)
2.7 Summary
75(1)
2.8 Exercises
75(6)
2.8.1 Efficacy of Small Class Size in Early Education
75(2)
2.8.2 Changing Minds on Gay Marriage
77(2)
2.8.3 Success of Leader Assassination as a Natural Experiment
79(2)
3 Measurement
81(47)
3.1 Measuring Civilian Victimization during Wartime
81(3)
3.2 Handling Missing Data in Stata
84(2)
3.2.1 Missings Package
85(1)
3.3 Visualizing the Univariate Distribution
86(10)
3.3.1 Bar Plot
87(2)
3.3.2 Histogram
89(3)
3.3.3 Box Plot
92(2)
3.3.4 Printing and Saving Graphs
94(2)
3.4 Survey Sampling
96(9)
3.4.1 The Role of Randomization
96(5)
3.4.2 Nonresponse and Other Sources of Bias
101(4)
3.5 Measuring Political Polarization
105(1)
3.6 Summarizing Bivariate Relationships
106(11)
3.6.1 Scatterplot
106(3)
3.6.2 Correlation
109(5)
3.6.3 Quantile--Quantile Plot
114(3)
3.7 Clustering
117(4)
3.7.1 The k-Means Algorithm
117(4)
3.8 Summary
121(1)
3.9 Exercises
122(6)
3.9.1 Changing Minds on Gay Marriage: Revisited
122(1)
3.9.2 Political Efficacy in China and Mexico
123(2)
3.9.3 Voting in the United Nations General Assembly
125(3)
4 Prediction
128(69)
4.1 Predicting Election Outcomes
128(16)
4.1.1 Macros
129(2)
4.1.2 Loops
131(2)
4.1.3 Poll Predictions
133(11)
4.2 Linear Regression
144(23)
4.2.1 Facial Appearance and Election Outcomes
144(2)
4.2.2 Correlation and Scatterplots
146(2)
4.2.3 Least Squares
148(6)
4.2.4 Regression toward the Mean
154(6)
4.2.5 Model Fit
160(7)
4.3 Regression and Causation
167(23)
4.3.1 Randomized Experiments
167(4)
4.3.2 Regression with Multiple Predictors
171(6)
4.3.3 Heterogeneous Treatment Effects
177(7)
4.3.4 Regression Discontinuity Design
184(6)
4.4 Summary
190(1)
4.5 Exercises
190(7)
4.5.1 Prediction Based on Betting Markets
190(3)
4.5.2 Election and Conditional Cash Transfer Program in Mexico
193(2)
4.5.3 Government Transfer and Poverty Reduction in Brazil
195(2)
5 Probability
197(79)
5.1 Probability
197(13)
5.1.1 Frequentist versus Bayesian
197(2)
5.1.2 Definition and Axioms
199(3)
5.1.3 Permutations
202(3)
5.1.4 Sampling with and without Replacement
205(3)
5.1.5 Combinations
208(2)
5.2 Conditional Probability
210(31)
5.2.1 Conditional, Marginal, and Joint Probabilities
210(8)
5.2.2 Independence
218(8)
5.2.3 Bayes' Rule
226(2)
5.2.4 Predicting Race Using Surname and Residence Location
228(13)
5.3 Random Variables and Probability Distributions
241(23)
5.3.1 Random Variables
241(1)
5.3.2 Bernoulli and Uniform Distributions
241(5)
5.3.3 Binomial Distribution
246(3)
5.3.4 Normal Distribution
249(7)
5.3.5 Expectation and Variance
256(4)
5.3.6 Predicting Election Outcomes with Uncertainty
260(4)
5.4 Large Sample Theorems
264(7)
5.4.1 The Law of Large Numbers
264(2)
5.4.2 The Central Limit Theorem
266(5)
5.5 Summary
271(1)
5.6 Exercises
272(4)
5.6.1 The Mathematics of Enigma
272(2)
5.6.2 A Probability Model for Betting Market Election Prediction
274(2)
6 Uncertainty
276(88)
6.1 Estimation
276(31)
6.1.1 Unbiasedness and Consistency
277(8)
6.1.2 Standard Error
285(5)
6.1.3 Confidence Intervals
290(6)
6.1.4 Margin of Error and Sample Size Calculation in Polls
296(5)
6.1.5 Analysis of Randomized Controlled Trials
301(3)
6.1.6 Analysis Based on Student's t-Distribution
304(3)
6.2 Hypothesis Testing
307(29)
6.2.1 Tea-Tasting Experiment
307(6)
6.2.2 The General Framework
313(3)
6.2.3 One-Sample Tests
316(6)
6.2.4 Two-Sample Tests
322(5)
6.2.5 Pitfalls of Hypothesis Testing
327(2)
6.2.6 Power Analysis
329(7)
6.3 Linear Regression Model with Uncertainty
336(20)
6.3.1 Linear Regression as a Generative Model
337(6)
6.3.2 Unbiasedness of Estimated Coefficients
343(2)
6.3.3 Standard Errors of Estimated Coefficients
345(3)
6.3.4 Inference about Coefficients
348(2)
6.3.5 Inference about Predictions
350(6)
6.4 Summary
356(1)
6.5 Exercises
357(7)
6.5.1 Sex Ratio and the Price of Agricultural Crops in China
357(2)
6.5.2 Filedrawer and Publication Bias in Academic Research
359(2)
6.5.3 The 1932 German Election in the Weimar Republic
361(3)
7 Discovery
364(65)
7.1 Network Data
364(22)
7.1.1 Marriage Network in Renaissance Florence
365(2)
7.1.2 Undirected Graph and Centrality Measures
367(8)
7.1.3 Twitter Following Network
375(1)
7.1.4 Directed Graph and Centrality
376(10)
7.2 Spatial Data
386(14)
7.2.1 The 1854 Cholera Outbreak in London
386(3)
7.2.2 Spatial Data in Stata
389(4)
7.2.3 United States Presidential Elections
393(2)
7.2.4 Expansion of Walmart
395(2)
7.2.5 Animation in Stata
397(3)
7.3 Textual Data
400(20)
7.3.1 The Disputed Authorship of The Federalist Papers
400(4)
7.3.2 Topic Discovery
404(7)
7.3.3 Document-Term Matrix and Clusters
411(2)
7.3.4 Authorship Prediction
413(4)
7.3.5 Cross Validation
417(3)
7.4 Summary
420(1)
7.5 Exercises
420(9)
7.5.1 International Trade Network
420(2)
7.5.2 Mapping US Presidential Election Results over Time
422(2)
7.5.3 Analyzing the Preambles of Constitutions
424(5)
8 Next
429(4)
General Index 433(6)
Stata Index 439(4)
Stata Command Abbreviation List 443
Kosuke Imai is Professor of Government and of Statistics at Harvard University. Lori D. Bougher is a senior research specialist at the Data-Driven Social Science Initiative at Princeton University.