Quantitative social science: an introduction in tidyverse
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
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Princeton ; Oxford
Princeton University Press
[2022]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xx, 458 Seiten, 8 ungezählte Seiten Tafeln Illustrationen, Diagramme, Karten |
| ISBN: | 9780691222271 9780691222288 |
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Contents 1 2 List of Tables xiii List of Figures xv Preface xvii Preface to the Original Book xix INTRODUCTION 1 1.1 Overview of the Book 1.2 How to Use This Book 7 1.3 Introduction to R and the tidyverse 1.3.1 Arithmetic Operations: R as a Calculator 8 9 3 1.3.2 R Scripts 1.3.3 Loading Packages 11 1.3.4 1.3.5 Objects Vectors 13 15 1.3.6 1.3.7 Functions Data Files: Loading and Subsetting 17 20 10 1.3.8 Adding Variables 1.3.9 Data Frames: Summarizing 1.3.10 Saving Objects 27 28 30 1.3.11 Loading Data in Other Formats 1.3.12 Programming and Learning Tips 31 32 1.4 Summary 33 1.5 Exercises 1.5.1 Bias in Self-Reported Turnout 1.5.2 Understanding World PopulationDynamics 34 34 35 CAUSALITY 2.1 Racial Discrimination in theLabor Market 2.2 Subsetting Data in R 2.2.1 Logical Values and Operators 38 38 45 46 2.2.2 Relational Operators 48 2.2.3 Subsetting 49
Contents viii 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 З 53 53 56 58 59 60 65 65 68 71 75 75 78 81 82 82 84 85 MEASUREMENT 88 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 88 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 93 96 97 100 103 105 106 107 111 114 116 116 120 124 128 128 130 131 136 137 137 139 141
Contents 4 PREDICTION 4.1 Predicting Election Outcomes 4.1.1 Loops in R General Conditional Statements in R 4.1.3 Poll Predictions 4.2 Linear Regression 4.1.2 4.2.1 174 181 188 188 191 197 203 Summary Exercises 209 209 Prediction Based on Betting Markets 209 211 214 216 Textual Data 216 5.1.1 The Disputed Authorship of The Federalist Papers 5.1.2 Document-Term Matrix 221 5.1.3 Topic Discovery 5.1.4 Authorship Prediction 223 232 5.1.5 Cross-Validation 235 Network Data 5.2.1 Marriage Network in Renaissance Florence 238 238 5.2.2 Undirected Graph and Centrality Measures 240 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 5.4 5.5 162 Randomized Experiments 4.4.1 Regression with Multiple Predictors 4.4.2 Heterogeneous Treatment Effects 4.4.3 Regression Discontinuity Design DISCOVERY 5.2 152 162 173 4.6.2 Election and Conditional Cash Transfer Program in Mexico 4.6.3 Government Transfer and Poverty Reduction in Brazil 5.1 148 4.2.4 Regression towards the Mean 4.6.1 5 145 165 166 4.2.5 Merging Data Sets in R 4.5 4.6 144 Facial Appearance and Election Outcomes 4.2.2 Correlation and Scatter Plots 4.2.3 Least Squares 4.2.6 Model Fit 4.3 Regression and Causation 4.4 144 216 245 247 255 256 5.3.2 Spatial Data in R 5.3.3 US Presidential Elections 5.3.4 Expansion of Walmart 258 264 268 5.3.5 Animation in R Summary Exercises 5.5.1 Analyzing the Preambles of Constitutions 270 272 273 273 5.5.2 International Trade Network 5.5.3 Mapping US Presidential Election Results over Time 275 277
x Contents 6 PROBABILITY 6.1 Probability 6.1.1 Frequentisi versus Bayesian 6.1.2 Definition and Axioms 6.1.3 6.1.4 Permutations Sampling with and without Replacement 6.1.5 Combinations Conditional Probability 6.2 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 6.3.2 6.3.3 6.3.4 6.3.5 6.3.6 Random Variables Bernoulli and Uniform Distributions Binomial Distribution Normal Distribution Expectation and Variance 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 7 279 Ճ/У O“/7Q ճ У not zol ΩΟ/1 Ճօ4 287 289 291 291 301 307 309 321 321 321 325 328 335 339 342 342 345 350 350 350 352 354 UNCERTAINTY 357 Estimation 357 7.1.1 7.1.2 7.1.3 358 366 371 7.1 7.2 Unbiasedness and Consistency Standard Error 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 /-Distribution 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 7.3 7.2.5 Pitfalls of Hypothesis Testing 7.2.6 Power Analysis Linear Regression Model with Uncertainty 7.3.1 7.3.2 Linear Regression as a Generative Model Unbiasedness of Estimated Coefficients 378 383 386
390 390 394 397 404 409 411 418 418 423
Contents 7.3.3 Standard Errors of EstimatedCoefficients 426 7.3.4 Inference about Coefficients Inference about Predictions 428 432 7.3.5 7.4 7.5 Summary Exercises 7.5.1 7.5.2 7 5.3 Sex Ratio and the Price of Agricultural Crops in China Filedrawer and Publication Bias inAcademic Research Analysis of the 1933 German Election during the Weimar Republic 8 NEXT 439 439 439 441 443 446 General Index 449 R Index 455 |
| adam_txt |
Contents 1 2 List of Tables xiii List of Figures xv Preface xvii Preface to the Original Book xix INTRODUCTION 1 1.1 Overview of the Book 1.2 How to Use This Book 7 1.3 Introduction to R and the tidyverse 1.3.1 Arithmetic Operations: R as a Calculator 8 9 3 1.3.2 R Scripts 1.3.3 Loading Packages 11 1.3.4 1.3.5 Objects Vectors 13 15 1.3.6 1.3.7 Functions Data Files: Loading and Subsetting 17 20 10 1.3.8 Adding Variables 1.3.9 Data Frames: Summarizing 1.3.10 Saving Objects 27 28 30 1.3.11 Loading Data in Other Formats 1.3.12 Programming and Learning Tips 31 32 1.4 Summary 33 1.5 Exercises 1.5.1 Bias in Self-Reported Turnout 1.5.2 Understanding World PopulationDynamics 34 34 35 CAUSALITY 2.1 Racial Discrimination in theLabor Market 2.2 Subsetting Data in R 2.2.1 Logical Values and Operators 38 38 45 46 2.2.2 Relational Operators 48 2.2.3 Subsetting 49
Contents viii 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 З 53 53 56 58 59 60 65 65 68 71 75 75 78 81 82 82 84 85 MEASUREMENT 88 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 88 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 93 96 97 100 103 105 106 107 111 114 116 116 120 124 128 128 130 131 136 137 137 139 141
Contents 4 PREDICTION 4.1 Predicting Election Outcomes 4.1.1 Loops in R General Conditional Statements in R 4.1.3 Poll Predictions 4.2 Linear Regression 4.1.2 4.2.1 174 181 188 188 191 197 203 Summary Exercises 209 209 Prediction Based on Betting Markets 209 211 214 216 Textual Data 216 5.1.1 The Disputed Authorship of The Federalist Papers 5.1.2 Document-Term Matrix 221 5.1.3 Topic Discovery 5.1.4 Authorship Prediction 223 232 5.1.5 Cross-Validation 235 Network Data 5.2.1 Marriage Network in Renaissance Florence 238 238 5.2.2 Undirected Graph and Centrality Measures 240 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 5.4 5.5 162 Randomized Experiments 4.4.1 Regression with Multiple Predictors 4.4.2 Heterogeneous Treatment Effects 4.4.3 Regression Discontinuity Design DISCOVERY 5.2 152 162 173 4.6.2 Election and Conditional Cash Transfer Program in Mexico 4.6.3 Government Transfer and Poverty Reduction in Brazil 5.1 148 4.2.4 Regression towards the Mean 4.6.1 5 145 165 166 4.2.5 Merging Data Sets in R 4.5 4.6 144 Facial Appearance and Election Outcomes 4.2.2 Correlation and Scatter Plots 4.2.3 Least Squares 4.2.6 Model Fit 4.3 Regression and Causation 4.4 144 216 245 247 255 256 5.3.2 Spatial Data in R 5.3.3 US Presidential Elections 5.3.4 Expansion of Walmart 258 264 268 5.3.5 Animation in R Summary Exercises 5.5.1 Analyzing the Preambles of Constitutions 270 272 273 273 5.5.2 International Trade Network 5.5.3 Mapping US Presidential Election Results over Time 275 277
x Contents 6 PROBABILITY 6.1 Probability 6.1.1 Frequentisi versus Bayesian 6.1.2 Definition and Axioms 6.1.3 6.1.4 Permutations Sampling with and without Replacement 6.1.5 Combinations Conditional Probability 6.2 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 6.3.2 6.3.3 6.3.4 6.3.5 6.3.6 Random Variables Bernoulli and Uniform Distributions Binomial Distribution Normal Distribution Expectation and Variance 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 7 279 Ճ/У O“/7Q ճ У not zol ΩΟ/1 Ճօ4 287 289 291 291 301 307 309 321 321 321 325 328 335 339 342 342 345 350 350 350 352 354 UNCERTAINTY 357 Estimation 357 7.1.1 7.1.2 7.1.3 358 366 371 7.1 7.2 Unbiasedness and Consistency Standard Error 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 /-Distribution 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 7.3 7.2.5 Pitfalls of Hypothesis Testing 7.2.6 Power Analysis Linear Regression Model with Uncertainty 7.3.1 7.3.2 Linear Regression as a Generative Model Unbiasedness of Estimated Coefficients 378 383 386
390 390 394 397 404 409 411 418 418 423
Contents 7.3.3 Standard Errors of EstimatedCoefficients 426 7.3.4 Inference about Coefficients Inference about Predictions 428 432 7.3.5 7.4 7.5 Summary Exercises 7.5.1 7.5.2 7 5.3 Sex Ratio and the Price of Agricultural Crops in China Filedrawer and Publication Bias inAcademic Research Analysis of the 1933 German Election during the Weimar Republic 8 NEXT 439 439 439 441 443 446 General Index 449 R Index 455 |
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| spelling | Imai, Kosuke Verfasser (DE-588)1129905446 aut Quantitative social science an introduction in tidyverse Kosuke Imai, Nora Webb Williams Princeton ; Oxford Princeton University Press [2022] © 2022 xx, 458 Seiten, 8 ungezählte Seiten Tafeln Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Quantitative Methode (DE-588)4232139-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Sozialwissenschaften (DE-588)4055916-6 s Quantitative Methode (DE-588)4232139-6 s R Programm (DE-588)4705956-4 s DE-604 Webb Williams, Nora ca. 20./21. Jh. Verfasser (DE-588)1216063001 aut Erscheint auch als Online-Ausgabe 978-0-691-22229-5 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033684460&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Imai, Kosuke Webb Williams, Nora ca. 20./21. Jh Quantitative social science an introduction in tidyverse Quantitative Methode (DE-588)4232139-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4232139-6 (DE-588)4055916-6 (DE-588)4705956-4 (DE-588)4123623-3 |
| title | Quantitative social science an introduction in tidyverse |
| title_auth | Quantitative social science an introduction in tidyverse |
| title_exact_search | Quantitative social science an introduction in tidyverse |
| title_exact_search_txtP | Quantitative social science an introduction in tidyverse |
| title_full | Quantitative social science an introduction in tidyverse Kosuke Imai, Nora Webb Williams |
| title_fullStr | Quantitative social science an introduction in tidyverse Kosuke Imai, Nora Webb Williams |
| title_full_unstemmed | Quantitative social science an introduction in tidyverse Kosuke Imai, Nora Webb Williams |
| title_short | Quantitative social science |
| title_sort | quantitative social science an introduction in tidyverse |
| title_sub | an introduction in tidyverse |
| topic | Quantitative Methode (DE-588)4232139-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Quantitative Methode Sozialwissenschaften R Programm Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033684460&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT imaikosuke quantitativesocialscienceanintroductionintidyverse AT webbwilliamsnora quantitativesocialscienceanintroductionintidyverse |