Kosuke Imai: Quantitative Social Science

Cover: 9780691222288 | Quantitative Social Science | An Introduction in tidyverse | Buch

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Beschreibung

"This "translation" of Imai's Quantiative Social Science makes the book adoptable in courses that use tidyverse. Tidyverse is a "dialect" of R--that is, it is a set of R packages that make data science work more user-friendly. Many instructors find tidyverse much easier to teach and are increasingly using this in the classroom setting"--

Über den Autor

Kosuke Imai and Nora Webb Williams

Inhaltsverzeichnis

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 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 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–Quantile 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 Eﬃcacy 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 Eﬀects
        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 Deﬁnition 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’ 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 Conﬁdence 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’s 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 Coeﬃcients
        7.3.3 Standard Errors of Estimated Coeﬃcients
        7.3.4 Inference about Coeﬃcients
        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

Details

Erscheinungsjahr:	2022
Fachbereich:	Kommunikationswissenschaften
Genre:	Importe , Medienwissenschaften
Rubrik:	Wissenschaften
Medium:	Taschenbuch
Inhalt:	Einband - flex.(Paperback)
ISBN-13:	9780691222288
ISBN-10:	0691222282
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Imai, Kosuke Williams, Nora Webb
Hersteller:	Princeton University Press
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	173 x 250 x 29 mm
Von/Mit:	Kosuke Imai (u. a.)
Erscheinungsdatum:	20.09.2022
Gewicht:	0,866 kg