Natural experiments in the social sciences: a design-based approach
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2012
|
| Ausgabe: | 1. publ. |
| Schriftenreihe: | Strategies for social inquiry
|
| Schlagwörter: | |
| Links: | http://www.loc.gov/catdir/enhancements/fy1210/2012009061-b.html http://www.loc.gov/catdir/enhancements/fy1210/2012009061-d.html http://www.loc.gov/catdir/enhancements/fy1210/2012009061-t.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025317545&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Umfang: | XIX, 358 S. graph. Darst. |
| ISBN: | 9781107017665 9781107698000 |
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Datensatz im Suchindex
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| adam_text | Titel: Natural experiments in the social sciences
Autor: Dunning, Thad
Jahr: 2012
Detailed table of contents
Preface and acknowledgements page xvii
Introduction: why natural experiments? l
1.1 The problem of confounders 5
1.1.1 The role of randomization 6
1.2 Natural experiments on military conscription and land titles 8
1.3 Varieties of natural experiments 15
1.3.1 Contrast with quasi-experiments and matching 18
1.4 Natural experiments as design-based research 21
1.5 An evaluative framework for natural experiments 27
1.5.1 The plausibility of as-if random 27
1.5.2 The credibility of models 28
1.5.3 The relevance of the intervention 29
1.6 Critiques and limitations of natural experiments 32
1.7 Avoiding conceptual stretching 34
1.8 Plan for the book, and how to use it 35
1.8.1 Some notes on coverage 37
Part I Discovering natural experiments 39
2 Standard natural experiments 41
2.1 Standard natural experiments in the social sciences 43
2.2 Standard natural experiments with true randomization 48
2.2.1 Lottery studies 49
2.3 Standard natural experiments with as-if randomization 53
2.3.1 Jurisdictional borders 57
2.3.2 Redistricting and jurisdiction shopping 59
2.4 Conclusion 60
Exercises 61
Regression-discontinuity designs 63
3.1 The basis of regression-discontinuity analysis 63
3.2 Regression-discontinuity designs in the social sciences 68
3.2.1 Population-and size-based thresholds 72
3.2.2 Near-winners and near-losers of close elections 77
3.2.3 Age as a regression discontinuity 79
3.2.4 Indices 80
3.3 Variations on regression-discontinuity designs 81
3.3.1 Sharp versus fuzzy regression discontinuities 81
3.3.2 Randomized regression-discontinuity designs 82
3.3.3 Multiple thresholds 83
3.4 Conclusion 84
Exercises 85
Instrumental-variables designs 87
4.1 Instrumental-variables designs: true experiments 91
4.2 Instrumental-variables designs: natural experiments 92
4.2.1 Lotteries 94
4.2.2 Weather shocks 95
4.2.3 Historical or institutional variation induced by deaths 97
4.3 Conclusion 101
Exercises 102
Part II Analyzing natural experiments 103
Simplicity and transparency: keys to quantitative analysis 105
5.1 The Neyman model 107
5.1.1 The average causal effect 109
5.1.2 Estimating the average causal effect 112
5.1.3 An example: land titling in Argentina 115
5.1.4 Key assumptions of the Neyman model 118
5.1.5 Analyzing standard natural experiments 121
5.2 Analyzing regression-discontinuity designs 121
5.2.1 Two examples: Certificates of Merit and digital democratization 123
5.2.2 Defining the study group: the question of bandwidth 127
5.2.3 Is the difference-of-means estimator biased in
regression-discontinuity designs? 128
5.2.4 Modeling functional form 133
5.2.5 Fuzzy regression discontinuities 134
5.3 Analyzing instrumental-variables designs 135
5.3.1 Natural experiments with noncompliance 136
5.3.2 An example: the effect of military service 143
5.3.3 The no-Defiers assumption 148
5.3.4 Fuzzy regression-discontinuities as instrumental-variables
designs 149
5.3.5 From the Complier average effect to linear regression 150
5.4 Conclusion 153
Appendix 5.1 Instrumental-variables estimation of the Complier average
causal effect 154
Appendix 5.2 Is the difference-of-means estimator biased in
regression-discontinuity designs (further details)? 158
Exercises 160
Sampling processes and standard errors 165
6.1 Standard errors under the Neyman urn model 166
6.1.1 Standard errors in regression-discontinuity and
instrumental-variables designs 173
6.2 Handling clustered randomization 175
6.2.1 Analysis by cluster mean: a design-based approach 179
6.3 Randomization inference: Fisher s exact test 186
6.4 Conclusion 191
Appendix 6.1 Conservative standard errors under the Neyman model 192
Appendix 6.2 Analysis by cluster mean 195
Exercises 201
The central role of qualitative evidence 208
7.1 Causal-process observations in natural experiments 210
7.1.1 Validating as-if random: treatment-assignment CPOs 212
7.1.2 Verifying treatments: independent-variable CPOs 219
7.1.3 Explaining effects: mechanism CPOs 222
7.1.4 Interpreting effects: auxiliary-outcome CPOs 224
7.1.5 Bolstering credibility: model-validation CPOs 225
7.2 Conclusion 228
Exercises 230
Part III Evaluating natural experiments 233
8 How plausible is as-if random? 235
8.1 Assessing as-if random 236
8.1.1 The role of balance tests 239
8.1.2 Qualitative diagnostics 243
8.2 Evaluating as-if random in regression-discontinuity and
instrumental-variables designs 244
8.2.1 Sorting at the regression-discontinuity threshold: conditional
density tests 245
8.2.2 Placebo tests in regression-discontinuity designs 246
8.2.3 Treatment-assignment CPOs in regression-discontinuity designs 248
8.2.4 Diagnostics in instrumental-variables designs 248
8.3 A continuum of plausibility 249
8.4 Conclusion 252
Exercises 254
9 How credible is the model? 256
9.1 The credibility of causal and statistical models 258
9.1.1 Strengths and limitations of the Neyman model 259
9.1.2 Linear regression models 263
9.2 Model specification in instrumental-variables regression 269
9.2.1 Control variables in instrumental-variables regression 277
9.3 A continuum of credibility 278
9.4 Conclusion: how important is the model? 283
Appendix 9.1 Homogeneous partial effects with multiple treatments and
instruments 285
Exercises 287
10 How relevant is the intervention? 289
10.1 Threats to substantive relevance 293
10.1.1 Lack of external validity 293
10.1.2 Idiosyncrasy of interventions 297
10.1.3 Bundling of treatments 300
10.2 A continuum of relevance 303
10.3 Conclusion 306
Exercises 309
Part IV Conclusion 311
11 Building strong designs through multi-method research 313
11.1 The virtues and limitations of natural experiments 315
11.2 A framework for strong research designs 318
11.2.1 Conventional observational studies and true experiments 319
11.2.2 Locating natural experiments 321
11.2.3 Relationship between dimensions 324
11.3 Achieving strong design: the importance of mixed methods 326
11.4 A checklist for natural-experimental research 328
References 338
Index 353
|
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| isbn | 9781107017665 9781107698000 |
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| spellingShingle | Dunning, Thad 1973- Natural experiments in the social sciences a design-based approach Sozialwissenschaften Social sciences Experiments Social sciences Research Experimental design Sozialwissenschaften (DE-588)4055916-6 gnd Experiment (DE-588)4015999-1 gnd |
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| title | Natural experiments in the social sciences a design-based approach |
| title_auth | Natural experiments in the social sciences a design-based approach |
| title_exact_search | Natural experiments in the social sciences a design-based approach |
| title_full | Natural experiments in the social sciences a design-based approach Thad Dunning |
| title_fullStr | Natural experiments in the social sciences a design-based approach Thad Dunning |
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