Field Experiments Design, Analysis, and Interpretation

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  • Edition: 00
  • Format: Paperback
  • Copyright: 2012-05-29
  • Publisher: W. W. Norton & Company

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Supplemental Materials

What is included with this book?


Written by two leading experts on experimental methods, this concise text covers the major aspects of experiment design, analysis, and interpretation in clear language. Students learn how to design randomized experiments, analyze the data, and interpret the findings. Beyond the authoritative coverage of the basic methodology, the authors include numerous features to help students achieve a deeper understanding of field experimentation, including rich examples from the social science literature, problem sets and discussions, data sets, and further readings.

Author Biography

Alan S. Gerber is Professor of Political Science and Director of the Center for the Study of American Politics at Yale University, where he teaches courses on experimental methods, statistics, and American politics. His experimental research has appeared in numerous academic journals, including the leading journals in political science. Donald P. Green is Professor of Political Science at Columbia University and the former director of Yale University's Institution for Social and Policy Studies. He is the author of numerous articles and several scholarly books on voter turnout, party identification, and experimental methods, including Get Out the Vote! How to Increase Voter Turnout (with Alan S. Gerber).

Table of Contents

Prefacep. xv
Introductionp. 1
Drawing Inferences from Intuitions, Anecdotes, and Correlationsp. 2
Experiments as a Solution to the Problem of Unobserved Confoundersp. 5
Experiments as Fair Testsp. 7
Field Experimentsp. 8
Advantages and Disadvantages of Experimenting in Real-World Settingsp. 13
Naturally Occurring Experiments and Quasi-Experimentsp. 15
Plan of the Bookp. 17
Suggested Readingsp. 18
Exercisesp. 18
Causal Inference and Experimentationp. 21
Potential Outcomesp. 21
Average Treatment Effectsp. 23
Random Sampling and Expectationsp. 26
Random Assignment and Unbiased Inferencep. 30
The Mechanics of Random Assignmentp. 36
The Threat of Selection Bias When Random Assignment Is Not Usedp. 37
Two Core Assumptions about Potential Outcomesp. 39
Excludabilityp. 39
Non-interferencep. 43
Summaryp. 44
Suggested Readingsp. 46
Exercisesp. 46
Sampling Distributions, Statistical Inference, and Hypothesis Testingp. 51
Sampling Distributionsp. 52
The Standard Error as a Measure of Uncertaintyp. 54
Estimating Sampling Variability 59 3.4- Hypothesis Testingp. 61
Confidence Intervalsp. 66
Sampling Distributions for Experiments That Use Block or Cluster Random Assignmentp. 71
Block Random Assignmentp. 71
Matched Pair Designp. 77
Summary of the Advantages and Disadvantages of Blockingp. 79
Cluster Random Assignmentp. 80
Summaryp. 85
Suggested Readingsp. 86
Exercisesp. 86
Powerp. 93
Using Covariates in Experimental Design and Analysisp. 95
Using Covariates to Rescale Outcomesp. 96
Adjusting for Covariates Using Regressionp. 102
Covariate Imbalance and the Detection of Administrative Errorsp. 105
Blocked Randomization and Covariate Adjustmentp. 109
Analysis of Block Randomized Experiments with Treatment Probabilities That Vary by Blockp. 116
Summaryp. 121
Suggested Readingsp. 123
Exercisesp. 123
One-Sided Noncompliancep. 131
New Definitions and Assumptionsp. 134
Denning Causal Effects for the Case of One-Sided Noncompliance 13p. 137
The Non-interference Assumption for Experiments That Encounter Noncompliancep. 138
The Excludability Assumption for One-Sided Noncompliancep. 140
Average Treatment Effects, Intent-to-Treat Effects, and Complier Average Causal Effectsp. 141
Identification of the CACEp. 143
Estimationp. 149
Avoiding Common Mistakesp. 152
Evaluating the Assumptions Required to Identify the CACE 15p. 155
Non-interference Assumptionp. 155
Exclusion Restrictionp. 156
Statistical Inferencep. 157
Designing Experiments in Anticipation of Noncompliancep. 161
Estimating Treatment Effects When Some Subjects Receive "Partial Treatment"p. 164
Summaryp. 165
Suggested Readingsp. 167
Exercisesp. 168
Two-Sided Noncompliancep. 173
Two-Sided Noncompliance: New Definitions and Assumptionsp. 175
ITT, ITTD, and CACE under Two-Sided Noncompliancep. 179
A Numerical Illustration of the Role of Monotonicityp. 181
Estimation of the CACE: An Examplep. 185
Discussion of Assumptionsp. 189
Monotonicityp. 190
Exclusion Restrictionp. 191
Random Assignmentp. 192
Design Suggestionsp. 192
Downstream Experimentationp. 193
Summaryp. 204
Suggested Readingsp. 206
Exercisesp. 206
Attritionp. 211
Conditions Under Which Attrition Leads to Biasp. 215
Special Forms of Attritionp. 219
Redefining the Estimand When Attrition Is Not a Function of Treatment Assignmentp. 224
Placing Bounds on the Average Treatment Effectp. 226
Addressing Attrition: An Empirical Examplep. 230
Addressing Attrition with Additional Data Collectionp. 236
Two Frequently Asked Questionsp. 241
Summaryp. 243
Suggested Readingsp. 244
Exercisesp. 244
Optimal Sample Allocation for Second-Round Samplingp. 248
Interference between Experimental Unitsp. 253
Identifying Causal Effects in the Presence of Localized Spilloverp. 256
Spatial Spilloverp. 260
Using Nonexperimental Units to Investigate Spilloversp. 264
An Example of Spatial Spillovers in Two Dimensionsp. 264
Within-Subjects Design and Time-Series Experimentsp. 273
Waitlist Designs (Also Known as Stepped-Wedge Designs)p. 276
Summaryp. 281
Suggested Readingsp. 283
Exercisesp. 283
Heterogeneous Treatment Effectsp. 289
Limits to What Experimental Data Tell Us about Treatment Effect Heterogeneityp. 291
Bounding Var (¿) and Testing for Heterogeneityp. 292
Two Approaches to the Exploration of Heterogeneity: Covariates and Designp. 296
Assessmg Treatment-by-Covariate Interactionsp. 296
Caution Is Required When Interpreting Treatment-by-Covariate Interactionsp. 299
Assessing Treatment-by-Treatment Interactionsp. 303
Using Regression to Model Treatment Effect Heterogeneityp. 305
Automating the Search for Interactionsp. 310
Summaryp. 310
Suggested Readingsp. 312
Exercisesp. 313
Mediationp. 319
Regression-Based Approaches to Mediationp. 322
Mediation Analysis from a Potential Outcomes Perspectivep. 325
Why Experimental Analysis of Mediators Is Challengingp. 328
Ruling Out Mediators?p. 330
What about Experiments That Manipulate the Mediator?p. 331
Implicit Mediation Analysisp. 333
Summaryp. 336
Suggested Readingsp. 338
Exercisesp. 338
Treatment Postcards Mailed to Michigan Householdsp. 343
Integration of Research Findingsp. 347
Estimation of Population Average Treatment Effectsp. 350
A Bayesian Framework for Interpreting Research Findingsp. 353
Replication and Integration of Experimental Findings: An Examplep. 358
Treatments That Vary in Intensity: Extrapolation and Statistical Modelingp. 366
Summaryp. 377
Suggested Readingsp. 378
Exercisesp. 379
Instructive Examples of Experimental Designp. 383
Using Experimental Design to Distinguish between Competing Theoriesp. 384
Oversampling Subjects Based on Their Anticipated Response to Treatmentp. 387
Comprehensive Measurement of Outcomesp. 393
Factorial Design and Special Cases of Non-interferencep. 395
Design and Analysis of Experiments In Which Treatments Vary with Subjects' Characteristicsp. 400
Design and Analysis of Experiments In Which Failure to Receive Treatment Has a Causal Effectp. 406
Addressing Complications Posed by Missing Datap. 410
Summaryp. 414
Suggested Readingsp. 415
Exercisesp. 416
Writing a Proposal, Research Report, and Journal Articlep. 425
Writing the Proposalp. 426
Writing the Research Reportp. 435
Writing the Journal Articlep. 440
Archiving Datap. 442
Summaryp. 444
Suggested Readingsp. 445
Exercisesp. 445
Protection of Human Subjectsp. 447
Regulatory Guidelinesp. 447
Guidelines for Keeping Field Experiments within Regulatory Boundariesp. 449
Suggested Field Experiments for Class Projectsp. 453
Crafting Your Own Experimentp. 453
Suggested Experimental Topics for Practicum Exercisesp. 455
Referencesp. 461
Indexp. 479
Table of Contents provided by Ingram. All Rights Reserved.

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