Data analysis for social science: a friendly and practical introduction
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Princeton ; Oxford
Princeton University Press
[2023]
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xii, 238 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9780691199436 9780691199429 |
Staff View
MARC
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| 264 | 1 | |a Princeton ; Oxford |b Princeton University Press |c [2023] | |
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| 300 | |a xii, 238 Seiten |b Illustrationen, Diagramme, Karten | ||
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| adam_text |
DATA ANALYSIS FOR SOCIAL SCIENCE
A FRIENDLY AND PRACTICAL INTRODUCTION
ELENA LLAUDET AND KOSUKE IMAI
PRINCETON UNIVERSITY PRESS
Princeton and Oxford
CONTENTS
Preface
xt
1 Introduction 1
1 1 Book Overview 2 0 00202 e ee eee 3
1 2 Chapter Summaries 2 2 4
1 3 How to Use This Book 2 5
1 4 Why Learn to Analyze Data? 2 6
141 Learning to Code - ----- 2 ee 6
15 GettingReady - ---- 052s e eee 7
1 6 Introduction toR 22 0-0 ee ees 8
161 Doing Calculations inR -- 9
162 Creating ObjectsinR 2 0 10
16 3 Using FunetionsinR rc 12
1 7 Loading and Making Sense of Data - 14
171 Setting the Working Directory » 15
172 Loading the Dataset rer: 15
173 Understanding the Data - -- 16
174 Identifying the Types of Variables Included 19
175 Identifying the Number of Observations 20
1 8 Computing and Interpreting Means -- - 21
181 Accessing Variables inside Dataframes 21
182 Means 0 006 e ee eee tees 22
19 Summary 2 ee ts 24
1 10 Cheatsheets 2 0 000 ee eee ees 25
1 10 1 Concepts and Notation --- -- 25
1 10 2 R Symbols and Operators ------ 26
1 10 3 R Functions 6202 ees 26
Estimating Causal Effects with Randomized Experiments 27
2 1 Project STAR © 2-60 ee es 27
2 2 Treatment and Outcome Variables -- 28
221 Treatment Variables - 00: 29
22 2 DOutcome Variables :-- rer 29
2 3 Individual Causal Effects 22-0 r ern 29
24 Average Causal Effects cc e nenn 33
244 Randomized Experiments and the
Difference-in-Means Estimator 35
25 Do Small Classes Improve Student Performance? 39
viii
CONTENTS
251 Relational OperatorsinR , 39
252 Creating New Variables 2 2 2222 40
253 Subsetting Variables 2 222220 42
26 Summary none 46
2 7 Cheatsheets -,20, 47
274 Concepts and Notation , 47
272R Symbols and Operators , 50
2 73 RFunctions „2: Co m nn 50
Inferring Population Characteristics via Survey Research 51
3 1 The EU Referendum in the UK 2 22 22 51
3 2 Survey Research 2 2220 u on 52
321 Random Sampling 2 once 53
322 Potential Challenges 222 54
3 3 Measuring Support for Brexit 222 55
331 Predicting the Referendum Outcome 56
332 Frequency Tables 57
333 Tables of Proportions 57
3 4 Who Supported Brexit? 22 222222 58
341 Handling Missing Data 59
342 Two-Way Frequency Tables 62
343 Two-Way Tables of Proportions 64
344 Histograms 2000 000005 66
345 Density Histograms 68
346 Descriptive Statistics 00 0000 71
3 5 Relationship between Education and the Leave
Vote in the Entire UK 2 oo on 76
351 Scatter Plots oo o ee 78
35 2 Correlation oo on 82
36 Summary rn 88
3 7 Cheatsheets 200 90
371 Concepts and Notation 90
372R Symbols and Operators 96
37 3 RFunctions oo on 96
Predicting Outcomes Using Linear Regression 98
4 1 GDP and Night-Time Light Emissions 98
4 2 Predictors, Observed vs Predicted Outcomes, and
Prediction Errors 2 99
43 Summarizing the Relationship between Two
Variables witha Line 0 100
43 1 The Linear Regression Model 101
432 The Intercept Coefficient , 103
43 3 The Slope Coefficient 2 022202 104
44 bet The Least Squares Method 106
441 Re Using Prior GDP 107
442 Wi aonship between GDP and Prior GDP 109
FERN, ith Natural Logarithm Transformations 113
Predicting GDP C
t i :
Emissions rowth Using Night-Time Light
4 6 Measuring How Well the Model Fits the Data with
the Coefficient of Determination, | 120
461 How Well Do the Three Predictive Models
in This Chapter Fit the Data? 122
4 7 Summary 2200 00 ee ee es 123
4 8 Appendix: Interpretation of the Slope in the Log-
Log Linear Model 222 222er 124
4 9 Cheatsheets 22020e eee 126
491 Concepts and Notation - 126
49 2 RFunctions 0 0 ee eee 128
Estimating Causal Effects with Observational Data 129
5 1 Russian State-Controlled TV Coverage of 2014
Ukrainian Affairs 2 2 ee 129
5 2 Challenges of Estimating Causal Effects with
Observational Data 2 0 0- 0 0- 0050- 130
521 Confounding Variables 130
522 Why Are Confounders a Problem? 131
523 Confounders in Randomized Experiments 133
5 3 The Effect of Russian TV on Ukrainians’ Voting
Behavior 0 ee 135
531 Using the Simple Linear Model to Compute
the Difference-in-Means Estimator 136
532 Controlling for Confounders Using a
Multiple Linear Regression Madel 142
5 4 The Effect of Russian TV on Ukrainian Electoral
Outcomes 2 0 ee es 147
541 Using the Simple Linear Model to Compute
the Difference-in-Means Estimator 149
542 Controlling for Confounders Using a
Multipte Linear Regression Model 151
55 Internal and External Validity 2000: 153
551 Randomized Experiments vs
Observational Studies - - 153
55 2 The Role of Randomization 154
553 How Good Are the Two Causal Analyses
in This Chapter? --020-- 155
554 How Good Was the Causal Analysis in
Chapter 2? 200 eee ee ees 156
555 The Coefficient of Determination, R? 157
5 6 Summary 2 es 157
5 7 Cheatsheets 0 000 e eee eee 159
571 Concepts and Notation 2 2 159
572 RFunctions 6 222 ee ee ee 161
Probability 162
6 1 What Is Probability? 2 0 2-2 0-056: 162
6 2 Axioms of Probability - - -- --- 163
6 3 Events, Random Variables, and Probability
Distributions © 0-0 cee ee 165
CONTENTS
ix
x
CONTENTS
6 4 Probability Distributions 2 oc oo 22 166
641 The Bernoulli Distribution 166
642 The Normal Distribution 169
643 The Standard Normal Distribution 173
6 44 Reaper 179
6 5 Population Parameters vs Sample Statistics 179
651 The Law of Large Numbers 180
652 The Central Limit Theorem , 183
653 Sampling Distribution of the Sample Mean 188
6 6 Summary 22202 00000 en 189
6 7 Appendix: For Loops , 190
6 8 Cheatsheets 00, 192
681 Concepts and Notation 192
682R Symbols and Operators 194
683R Functions 2 2 oo con 195
Quantifying Uncertainty 196
71 Estimators and Their Sampling Distributions 196
7 2 Confidence Intervals 22 2 con 202
721 For the Sample Mean 203
722 For the Difference-in-Means Estimator 206
723 For Predicted Outcomes 209
7 3 Hypothesis Testing 2 2 222 onen 211
73 1 With the Difference-in-Means Estimator 218
73 2 With Estimated Regression Coefficients 220
7 4 Statistical vs Scientific Significance 224
I Summary non 225
76 Cheatsheets 0 00 00 0000000 226
761 Concepts and Notation 226
762R Symbols and Operators 2220 229
763 RFunctions oo 222 oo 229
Index of Concepts 231
Index of Mathematical Notation 235
Index of R and RStudio |
| adam_txt |
DATA ANALYSIS FOR SOCIAL SCIENCE
A FRIENDLY AND PRACTICAL INTRODUCTION
ELENA LLAUDET AND KOSUKE IMAI
PRINCETON UNIVERSITY PRESS
Princeton and Oxford
CONTENTS
Preface
xt
1 Introduction 1
1 1 Book Overview 2 0 00202 e ee eee 3
1 2 Chapter Summaries 2 2 4
1 3 How to Use This Book 2 5
1 4 Why Learn to Analyze Data? 2 6
141 Learning to Code - ----- 2 ee 6
15 GettingReady - ---- 052s e eee 7
1 6 Introduction toR 22 0-0 ee ees 8
161 Doing Calculations inR -- 9
162 Creating ObjectsinR 2 0 10
16 3 Using FunetionsinR rc 12
1 7 Loading and Making Sense of Data - 14
171 Setting the Working Directory » 15
172 Loading the Dataset rer: 15
173 Understanding the Data - -- 16
174 Identifying the Types of Variables Included 19
175 Identifying the Number of Observations 20
1 8 Computing and Interpreting Means -- - 21
181 Accessing Variables inside Dataframes 21
182 Means 0 006 e ee eee tees 22
19 Summary 2 ee ts 24
1 10 Cheatsheets 2 0 000 ee eee ees 25
1 10 1 Concepts and Notation --- -- 25
1 10 2 R Symbols and Operators ------ 26
1 10 3 R Functions 6202 ees 26
Estimating Causal Effects with Randomized Experiments 27
2 1 Project STAR © 2-60 ee es 27
2 2 Treatment and Outcome Variables -- 28
221 Treatment Variables - 00: 29
22 2 DOutcome Variables :-- rer 29
2 3 Individual Causal Effects 22-0 r ern 29
24 Average Causal Effects cc e nenn 33
244 Randomized Experiments and the
Difference-in-Means Estimator 35
25 Do Small Classes Improve Student Performance? 39
viii
CONTENTS
251 Relational OperatorsinR , 39
252 Creating New Variables 2 2 2222 40
253 Subsetting Variables 2 222220 42
26 Summary none 46
2 7 Cheatsheets -,20, 47
274 Concepts and Notation , 47
272R Symbols and Operators , 50
2 73 RFunctions „2: Co m nn 50
Inferring Population Characteristics via Survey Research 51
3 1 The EU Referendum in the UK 2 22 22 51
3 2 Survey Research 2 2220 u on 52
321 Random Sampling 2 once 53
322 Potential Challenges 222 54
3 3 Measuring Support for Brexit 222 55
331 Predicting the Referendum Outcome 56
332 Frequency Tables 57
333 Tables of Proportions 57
3 4 Who Supported Brexit? 22 222222 58
341 Handling Missing Data 59
342 Two-Way Frequency Tables 62
343 Two-Way Tables of Proportions 64
344 Histograms 2000 000005 66
345 Density Histograms 68
346 Descriptive Statistics 00 0000 71
3 5 Relationship between Education and the Leave
Vote in the Entire UK 2 oo on 76
351 Scatter Plots oo o ee 78
35 2 Correlation oo on 82
36 Summary rn 88
3 7 Cheatsheets 200 90
371 Concepts and Notation 90
372R Symbols and Operators 96
37 3 RFunctions oo on 96
Predicting Outcomes Using Linear Regression 98
4 1 GDP and Night-Time Light Emissions 98
4 2 Predictors, Observed vs Predicted Outcomes, and
Prediction Errors 2 99
43 Summarizing the Relationship between Two
Variables witha Line 0 100
43 1 The Linear Regression Model 101
432 The Intercept Coefficient , 103
43 3 The Slope Coefficient 2 022202 104
44 bet The Least Squares Method 106
441 Re Using Prior GDP 107
442 Wi aonship between GDP and Prior GDP 109
FERN, ith Natural Logarithm Transformations 113
Predicting GDP C
t i :
Emissions rowth Using Night-Time Light
4 6 Measuring How Well the Model Fits the Data with
the Coefficient of Determination, | 120
461 How Well Do the Three Predictive Models
in This Chapter Fit the Data? 122
4 7 Summary 2200 00 ee ee es 123
4 8 Appendix: Interpretation of the Slope in the Log-
Log Linear Model 222 222er 124
4 9 Cheatsheets 22020e eee 126
491 Concepts and Notation - 126
49 2 RFunctions 0 0 ee eee 128
Estimating Causal Effects with Observational Data 129
5 1 Russian State-Controlled TV Coverage of 2014
Ukrainian Affairs 2 2 ee 129
5 2 Challenges of Estimating Causal Effects with
Observational Data 2 0 0- 0 0- 0050- 130
521 Confounding Variables 130
522 Why Are Confounders a Problem? 131
523 Confounders in Randomized Experiments 133
5 3 The Effect of Russian TV on Ukrainians’ Voting
Behavior 0 ee 135
531 Using the Simple Linear Model to Compute
the Difference-in-Means Estimator 136
532 Controlling for Confounders Using a
Multiple Linear Regression Madel 142
5 4 The Effect of Russian TV on Ukrainian Electoral
Outcomes 2 0 ee es 147
541 Using the Simple Linear Model to Compute
the Difference-in-Means Estimator 149
542 Controlling for Confounders Using a
Multipte Linear Regression Model 151
55 Internal and External Validity 2000: 153
551 Randomized Experiments vs
Observational Studies - - 153
55 2 The Role of Randomization 154
553 How Good Are the Two Causal Analyses
in This Chapter? --020-- 155
554 How Good Was the Causal Analysis in
Chapter 2? 200 eee ee ees 156
555 The Coefficient of Determination, R? 157
5 6 Summary 2 es 157
5 7 Cheatsheets 0 000 e eee eee 159
571 Concepts and Notation 2 2 159
572 RFunctions 6 222 ee ee ee 161
Probability 162
6 1 What Is Probability? 2 0 2-2 0-056: 162
6 2 Axioms of Probability - - -- --- 163
6 3 Events, Random Variables, and Probability
Distributions © 0-0 cee ee 165
CONTENTS
ix
x
CONTENTS
6 4 Probability Distributions 2 oc oo 22 166
641 The Bernoulli Distribution 166
642 The Normal Distribution 169
643 The Standard Normal Distribution 173
6 44 Reaper 179
6 5 Population Parameters vs Sample Statistics 179
651 The Law of Large Numbers 180
652 The Central Limit Theorem , 183
653 Sampling Distribution of the Sample Mean 188
6 6 Summary 22202 00000 en 189
6 7 Appendix: For Loops , 190
6 8 Cheatsheets 00, 192
681 Concepts and Notation 192
682R Symbols and Operators 194
683R Functions 2 2 oo con 195
Quantifying Uncertainty 196
71 Estimators and Their Sampling Distributions 196
7 2 Confidence Intervals 22 2 con 202
721 For the Sample Mean 203
722 For the Difference-in-Means Estimator 206
723 For Predicted Outcomes 209
7 3 Hypothesis Testing 2 2 222 onen 211
73 1 With the Difference-in-Means Estimator 218
73 2 With Estimated Regression Coefficients 220
7 4 Statistical vs Scientific Significance 224
I Summary non 225
76 Cheatsheets 0 00 00 0000000 226
761 Concepts and Notation 226
762R Symbols and Operators 2220 229
763 RFunctions oo 222 oo 229
Index of Concepts 231
Index of Mathematical Notation 235
Index of R and RStudio |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Llaudet, Elena Imai, Kosuke |
| author_GND | (DE-588)1279131217 (DE-588)1129905446 |
| author_facet | Llaudet, Elena Imai, Kosuke |
| author_role | aut aut |
| author_sort | Llaudet, Elena |
| author_variant | e l el k i ki |
| building | Verbundindex |
| bvnumber | BV048469266 |
| classification_rvk | QH 231 MR 2000 MR 2200 MR 2100 |
| classification_tum | DAT 815 TEC 006 |
| ctrlnum | (OCoLC)1368170563 (DE-599)KXP1813911576 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Technik Informatik Soziologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Soziologie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4151278-9 Einführung gnd-content |
| genre_facet | Einführung |
| id | DE-604.BV048469266 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:36:27Z |
| indexdate | 2024-08-27T00:10:33Z |
| institution | BVB |
| isbn | 9780691199436 9780691199429 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033847102 |
| oclc_num | 1368170563 |
| open_access_boolean | |
| owner | DE-188 DE-703 DE-19 DE-BY-UBM DE-11 DE-29 DE-521 DE-N2 DE-355 DE-BY-UBR DE-91 DE-BY-TUM |
| owner_facet | DE-188 DE-703 DE-19 DE-BY-UBM DE-11 DE-29 DE-521 DE-N2 DE-355 DE-BY-UBR DE-91 DE-BY-TUM |
| physical | xii, 238 Seiten Illustrationen, Diagramme, Karten |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Princeton University Press |
| record_format | marc |
| spelling | Llaudet, Elena Verfasser (DE-588)1279131217 aut Data analysis for social science a friendly and practical introduction Elena Llaudet and Kosuke Imai Princeton ; Oxford Princeton University Press [2023] © 2023 xii, 238 Seiten 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 Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Social sciences / Statistical methods Social sciences / Data processing Social sciences / Methodology SOCIAL SCIENCE / Statistics COMPUTERS / Data Science / Data Analytics (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 Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s DE-604 Imai, Kosuke Verfasser (DE-588)1129905446 aut Erscheint auch als Online-Ausgabe 978-0-691-22934-8 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033847102&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Llaudet, Elena Imai, Kosuke Data analysis for social science a friendly and practical introduction Quantitative Methode (DE-588)4232139-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4232139-6 (DE-588)4055916-6 (DE-588)4705956-4 (DE-588)4123037-1 (DE-588)4056995-0 (DE-588)4151278-9 |
| title | Data analysis for social science a friendly and practical introduction |
| title_auth | Data analysis for social science a friendly and practical introduction |
| title_exact_search | Data analysis for social science a friendly and practical introduction |
| title_exact_search_txtP | Data analysis for social science a friendly and practical introduction |
| title_full | Data analysis for social science a friendly and practical introduction Elena Llaudet and Kosuke Imai |
| title_fullStr | Data analysis for social science a friendly and practical introduction Elena Llaudet and Kosuke Imai |
| title_full_unstemmed | Data analysis for social science a friendly and practical introduction Elena Llaudet and Kosuke Imai |
| title_short | Data analysis for social science |
| title_sort | data analysis for social science a friendly and practical introduction |
| title_sub | a friendly and practical introduction |
| topic | Quantitative Methode (DE-588)4232139-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Quantitative Methode Sozialwissenschaften R Programm Datenanalyse Statistik Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033847102&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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