Quantitative social science: an introduction
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Princeton ; Oxford
Princeton University Press
[2017]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xix, 408 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9780691167039 9780691175461 |
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Datensatz im Suchindex
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| adam_text | Contents
List of Tables xiii
List of Figures XV
Preface xvii
Introduction 1
1.1 Overview of the Book 3
1.2 How to Use this Book 7
1.3 Introduction to R 10
1.3.1 Arithmetic Operations 10
1.3.2 Objects 12
1.3.3 Vectors 14
1 .3.4 Functions 16
1.3.5 Data Files 20
1.3.6 Saving Objects 23
1.3.7 Packages 24
1.3.8 Programming and Learning Tips 25
1.4 Summary 27
1.5 Exercises 28
1.5.1 Bias in Seif-Reported Turnout 28
1.5.2 Understanding World Population Dynamics 29
Causality 32
2.1 Racial Discrimination in the Labor Market 32
2.2 Subsetting the Data in R 36
2.2.1 Logical Values and Operators 37
2.2.2 Relational Operators 39
2.2.3 Subsetting 40
2.2.4 Simple Conditional Statements 43
2.2.5 Factor Variables 44
2.3 Causal Effects and the Counterfactual 46
Contents
2.4 Randomized Controlled Trials 48
2.4.1 The Role of Randomization 49
2.4.2 Social Pressure and Voter Turnout 51
2.5 Observational Studies 54
2.5.1 Minimum Wage and Unemployment 54
2.5.2 Confounding Bias 57
2.5.3 Before-and-After and Difference-in-Differences Designs 60
2.6 Descriptive Statistics for a Single Variable 63
2.6.1 Quantiles 53
2.6.2 Standard Deviation 66
2.7 Summary 68
2.8 Exercises 69
2.8.1 Efficacy of Small Class Size in Early Education 69
2.8.2 Changing Minds on Gay Marriage 71
2.8.3 Success of Leader Assassination as a Natural Experiment 73
Measurement 75
3.1 Measuring Civilian Victimization during Wartime 75
3.2 Handling Missing Data in R 78
3.3 Visualizing the Univariate Distribution 80
3.3.1 Bar Plot 80
3.3.2 Histogram 81
3.3.3 Box Plot 85
3.3.4 Printing and Saving Graphs 87
3.4 Survey Sampling 88
3.4.1 The Role of Randomization 89
3.4.2 Nonresponse and Other Sources of Bias 93
3.5 Measuring Political Polarization 96
3.6 Summarizing Bivariate Relationships 97
3.6.1 Scatter Plot 98
3.6.2 Correlation 101
3.6.3 Quantile-Quantile Plot 105
3.7 Clustering 108
3.7.1 Matrix in R 108
3.7.2 List in R 110
3.7.3 The/c-Means Algorithm 111
3.8 Summary 115
3.9 Exercises 116
3.9.1 Changing Minds on Gay Marriage: Revisited 116
3.9.2 Political Efficacy in China and Mexico 118
3.9.3 Voting in the United Nations General Assembly 120
Prediction 123
4.1 Predicting Election Outcomes 123
4.1.1 Loops in R 124
Contents
4.1.2 General Conditional Statements in R 127
4.1.3 Poll Predictions 130
4.2 Linear Regression 139
4.2.1 Facial Appearance and Election Outcomes 139
4.2.2 Correlation and Scatter Plots 141
4.2.3 Least Squares 143
4.2.4 Regression towards the Mean 148
4.2.5 Merging Data Sets in R 149
4.2.6 Model Fit 156
4.3 Regression and Causation 161
4.3.1 Randomized Experiments 162
4.3.2 Regression with Multiple Predictors 165
4.3.3 Heterogenous Treatment Effects 170
4.3.4 Regression Discontinuity Design 176
4.4 Summary 181
4.5 Exercises 182
4.5.1 Prediction Based on Betting Markets 182
4.5.2 Election and Conditional Cash Transfer
Program in Mexico 184
4.5.3 Government Transfer and Poverty Reduction in Brazil 187
5 Discovery 189
5.1 Textual Data 189
5.1.1 The Disputed Authorship of The Federalist Papers 189
5.1.2 Document-Term Matrix 194
5.1.3 Topic Discovery 195
5.1.4 Authorship Prediction 200
5.1.5 Cross Validation 202
5.2 Network Data 205
5.2.1 Marriage Network in Renaissance Florence 205
5.2.2 Undirected Graph and Centrality Measures 207
5.2.3 Twitter-Following Network 211
5.2.4 Directed Graph and Centrality 213
5.3 Spatial Data 220
5.3.1 The 1854 Cholera Outbreak in London 220
5.3.2 Spatial Data in R 223
5.3.3 Colors in R 226
5.3.4 US Presidential Elections 228
5.3.5 Expansion of Walmart 231
5.3.6 Animation in R 233
5.4 Summary 235
5.5 Exercises 236
5.5.1 Analyzing the Preambles of Constitutions 236
5.5.2 International Trade Network 238
5.5.3 Mapping US Presidential Election Results over Time 239
ЕА. ГЖ··՜· . ·· ! Contents
6 Probability 242
6.1 Probability 242
6.1.1 Frequentist versus Bayesian 242
6.1.2 Definition and Axioms 244
6.1.3 Permutations 247
6.1.4 Sampling with and without Replacement 250
6.1.5 Combinations 252
6.2 Conditional Probability 254
6.2.1 Conditional, Marginal, and Joint Probabilities 254
6.2.2 Independence 261
6.2.3 Bayes Rule 266
6.2.4 Predicting Race Using Surname and Residence Location 268
6.3 Random Variables and Probability Distributions 277
6.3.1 Random Variables 278
6.3.2 Bernoulli and Uniform Distributions 278
6.3.3 Binomial Distribution 282
6.3.4 Normal Distribution 286
6.3.5 Expectation and Variance 292
6.3.6 Predicting Election Outcomes with Uncertainty 296
6.4 Large Sample Theorems 300
6.4.1 The Law of Large Numbers 300
6.4.2 The Central Limit Theorem 302
6.5 Summary 306
6.6 Exercises 307
6.6.1 The Mathematics of Enigma 307
6.6.2 A Probability Model for Betting Market Election Prediction 309
6.6.3 Election Fraud in Russia 310
7 Uncertainty 314
7.1 Estimation 314
7.1.1 Unbiasedness and Consistency 315
7.1.2 Standard Error 322
7.1.3 Confidence Intervals 326
7.1.4 Margin of Error and Sample Size Calculation in Polls 332
7.1.5 Analysis of Randomized Controlled Trials 336
7.1.6 Analysis Based on Student s ¿-Distribution 339
7.2 Flypothesis Testing 342
7.2.1 Tea-Tasting Experiment 342
7.2.2 The General Framework 346
7.2.3 One-Sample Tests 350
7.2.4 Two-Sample Tests 356
7.2.5 Pitfalls of Flypothesis Testing 361
7.2.6 Power Analysis 363
7.3 Linear Regression Model with Uncertainty 370
7.3.1 Linear Regression as a Generative Model 370
7.3.2 Unbiasedness of Estimated Coefficients 375
Contents
7.3.3 Standard Errors of Estimated Coefficients 378
7.3.4 Inference about Coefficients 380
7.3.5 Inference about Predictions 384
7.4 Summary 389
7.5 Exercises 390
7.5.1 Sex Ratio and the Price of Agricultural Crops in China 390
7.5.2 File Drawer and Publication Bias in Academic Research 392
7.5.3 The 1932 German Election in the Weimar Republic 394
8 Next 397
Generalindex 401
R Index
406
Quantitative Social Science 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. The book
engages directly with empirical analysis, showing students how to analyze data using the R program-
ming language and to interpret the results-encouraging hands-on learning, not paper-and-pencil
statistics. Examples 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 analysis and program-
ming exercises, comes with supplementary teaching materials for instructors, and offers a solid
foundation for further study.
|
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| discipline | Soziologie Politologie Sozial-/Kulturanthropologie / Empirische Kulturwissenschaft |
| format | Book |
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| spelling | Imai, Kosuke Verfasser (DE-588)1129905446 aut Quantitative social science an introduction Kosuke Imai Princeton ; Oxford Princeton University Press [2017] © 2017 xix, 408 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Quantitative Methode (DE-588)4232139-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Sozialwissenschaften (DE-588)4055916-6 s Quantitative Methode (DE-588)4232139-6 s Statistik (DE-588)4056995-0 s R Programm (DE-588)4705956-4 s DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029654489&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029654489&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Imai, Kosuke Quantitative social science an introduction Sozialwissenschaften (DE-588)4055916-6 gnd Quantitative Methode (DE-588)4232139-6 gnd Statistik (DE-588)4056995-0 gnd R Programm (DE-588)4705956-4 gnd |
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| title | Quantitative social science an introduction |
| title_auth | Quantitative social science an introduction |
| title_exact_search | Quantitative social science an introduction |
| title_full | Quantitative social science an introduction Kosuke Imai |
| title_fullStr | Quantitative social science an introduction Kosuke Imai |
| title_full_unstemmed | Quantitative social science an introduction Kosuke Imai |
| title_short | Quantitative social science |
| title_sort | quantitative social science an introduction |
| title_sub | an introduction |
| topic | Sozialwissenschaften (DE-588)4055916-6 gnd Quantitative Methode (DE-588)4232139-6 gnd Statistik (DE-588)4056995-0 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Sozialwissenschaften Quantitative Methode Statistik R Programm Einführung |
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