Quantitative social science : an introduction in Stata / Kosuke Imai, Lori D. Bougher.

Format:

Book

Author/Creator:

Imai, Kosuke, author.

Bougher, Lori D., 1979- author.

Language:

English

Subjects (All):

Social sciences--Methodology.

Social sciences.

Social sciences--Research.

Quantitative research--Data processing.

Quantitative research.

Stata.

Physical Description:

xx, 441 pages : illustrations (some color), maps (some color) ; 26 cm

Place of Publication:

Princeton : Princeton University Press, [2021]

Summary:

"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"-- Provided by publisher.

Contents:

Machine generated contents note: 1. Introduction

1.1. Overview of the Book

1.2. How to Use this Book

1.3. Introduction to Stata

1.3.1. Arithmetic Operations

1.3.2. Variables

1.3.3. Labels

1.3.4. Describing the Data

1.3.5. Data Files

1.3.6. Merging Data Sets in Stata

1.3.7. Packages

1.3.8. 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 the Data in Stata

2.2.1. Relational Operators

2.2.2. Logical Operators

2.2.3. Simple Conditional Statements and Variable Creation

2.2.4. Subsetting Using Conditions

2.2.5. Preserving and Transforming Data Sets

2.3. Causal Effects 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 Difference-in-Differences 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. Efficacy 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 Stata

3.2.1. Missings Package

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. Scatterplot

3.6.2. Correlation

3.6.3. Quantile

Quantile Plot

3.7. Clustering

3.7.1. The k-Means Algorithm

3.8. Summary

3.9. Exercises

3.9.1. Changing Minds on Gay Marriage: Revisited

3.9.2. Political Efficacy in China and Mexico

3.9.3. Voting in the United Nations General Assembly

4. Prediction

4.1. Predicting Election Outcomes

4.1.1. Macros

4.1.2. Loops

4.1.3. Poll Predictions

4.2. Linear Regression

4.2.1. Facial Appearance and Election Outcomes

4.2.2. Correlation and Scatterplots

4.2.3. Least Squares

4.2.4. Regression toward the Mean

4.2.5. Model Fit

4.3. Regression and Causation

4.3.1. Randomized Experiments

4.3.2. Regression with Multiple Predictors

4.3.3. Heterogeneous Treatment Effects

4.3.4. Regression Discontinuity Design

4.4. Summary

4.5. Exercises

4.5.1. Prediction Based on Betting Markets

4.5.2. Election and Conditional Cash Transfer Program in Mexico

4.5.3. Government Transfer and Poverty Reduction in Brazil

5. Probability

5.1. Probability

5.1.1. Frequentist versus Bayesian

5.1.2. Definition and Axioms

5.1.3. Permutations

5.1.4. Sampling with and without Replacement

5.1.5. Combinations

5.2. Conditional Probability

5.2.1. Conditional, Marginal, and Joint Probabilities

5.2.2. Independence

5.2.3. Bayes' Rule

5.2.4. Predicting Race Using Surname and Residence Location

5.3. Random Variables and Probability Distributions

5.3.1. Random Variables

5.3.2. Bernoulli and Uniform Distributions

5.3.3. Binomial Distribution

5.3.4. Normal Distribution

5.3.5. Expectation and Variance

5.3.6. Predicting Election Outcomes with Uncertainty

5.4. Large Sample Theorems

5.4.1. The Law of Large Numbers

5.4.2. The Central Limit Theorem

5.5. Summary

5.6. Exercises

5.6.1. The Mathematics of Enigma

5.6.2. A Probability Model for Betting Market Election Prediction

6. Uncertainty

6.1. Estimation

6.1.1. Unbiasedness and Consistency

6.1.2. Standard Error

6.1.3. Confidence Intervals

6.1.4. Margin of Error and Sample Size Calculation in Polls

6.1.5. Analysis of Randomized Controlled Trials

6.1.6. Analysis Based on Student's t-Distribution

6.2. Hypothesis Testing

6.2.1. Tea-Tasting Experiment

6.2.2. The General Framework

6.2.3. One-Sample Tests

6.2.4. Two-Sample Tests

6.2.5. Pitfalls of Hypothesis Testing

6.2.6. Power Analysis

6.3. Linear Regression Model with Uncertainty

6.3.1. Linear Regression as a Generative Model

6.3.2. Unbiasedness of Estimated Coefficients

6.3.3. Standard Errors of Estimated Coefficients

6.3.4. Inference about Coefficients

6.3.5. Inference about Predictions

6.4. Summary

6.5. Exercises

6.5.1. Sex Ratio and the Price of Agricultural Crops in China

6.5.2. Filedrawer and Publication Bias in Academic Research

6.5.3. The 1932 German Election in the Weimar Republic

7. Discovery

7.1. Network Data

7.1.1. Marriage Network in Renaissance Florence

7.1.2. Undirected Graph and Centrality Measures

7.1.3. Twitter Following Network

7.1.4. Directed Graph and Centrality

7.2. Spatial Data

7.2.1. The 1854 Cholera Outbreak in London

7.2.2. Spatial Data in Stata

7.2.3. United States Presidential Elections

7.2.4. Expansion of Walmart

7.2.5. Animation in Stata

7.3. Textual Data

7.3.1. The Disputed Authorship of The Federalist Papers

7.3.2. Topic Discovery

7.3.3. Document-Term Matrix and Clusters

7.3.4. Authorship Prediction

7.3.5. Cross Validation

7.4. Summary

7.5. Exercises

7.5.1. International Trade Network

7.5.2. Mapping US Presidential Election Results over Time

7.5.3. Analyzing the Preambles of Constitutions

8. Next.

Notes:

Includes bibliographical references and index.

Other Format:

Online version: Imai, Kosuke. Quantitative social science

ISBN:

9780691191096

0691191093

9780691191089

0691191085

OCLC:

1193559483

Publisher Number:

99991422290