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Field Experiments Design, Analysis, and Interpretation

by: Gerber, Alan S.; Green, Donald P.

Field Experiments Design, Analysis, and Interpretation

by: Gerber, Alan S.; Green, Donald P.

ISBN 13:
9780393979954
ISBN 10:
0393979954
Edition: 00
Format: Paperback
Copyright: 05/29/2012
Publisher: W. W. Norton & Company

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Summary

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.

Preface	p. xv
Introduction	p. 1
Drawing Inferences from Intuitions, Anecdotes, and Correlations	p. 2
Experiments as a Solution to the Problem of Unobserved Confounders	p. 5
Experiments as Fair Tests	p. 7
Field Experiments	p. 8
Advantages and Disadvantages of Experimenting in Real-World Settings	p. 13
Naturally Occurring Experiments and Quasi-Experiments	p. 15
Plan of the Book	p. 17
Suggested Readings	p. 18
Exercises	p. 18
Causal Inference and Experimentation	p. 21
Potential Outcomes	p. 21
Average Treatment Effects	p. 23
Random Sampling and Expectations	p. 26
Random Assignment and Unbiased Inference	p. 30
The Mechanics of Random Assignment	p. 36
The Threat of Selection Bias When Random Assignment Is Not Used	p. 37
Two Core Assumptions about Potential Outcomes	p. 39
Excludability	p. 39
Non-interference	p. 43
Summary	p. 44
Suggested Readings	p. 46
Exercises	p. 46
Sampling Distributions, Statistical Inference, and Hypothesis Testing	p. 51
Sampling Distributions	p. 52
The Standard Error as a Measure of Uncertainty	p. 54
Estimating Sampling Variability 59 3.4- Hypothesis Testing	p. 61
Confidence Intervals	p. 66
Sampling Distributions for Experiments That Use Block or Cluster Random Assignment	p. 71
Block Random Assignment	p. 71
Matched Pair Design	p. 77
Summary of the Advantages and Disadvantages of Blocking	p. 79
Cluster Random Assignment	p. 80
Summary	p. 85
Suggested Readings	p. 86
Exercises	p. 86
Power	p. 93
Using Covariates in Experimental Design and Analysis	p. 95
Using Covariates to Rescale Outcomes	p. 96
Adjusting for Covariates Using Regression	p. 102
Covariate Imbalance and the Detection of Administrative Errors	p. 105
Blocked Randomization and Covariate Adjustment	p. 109
Analysis of Block Randomized Experiments with Treatment Probabilities That Vary by Block	p. 116
Summary	p. 121
Suggested Readings	p. 123
Exercises	p. 123
One-Sided Noncompliance	p. 131
New Definitions and Assumptions	p. 134
Denning Causal Effects for the Case of One-Sided Noncompliance 13	p. 137
The Non-interference Assumption for Experiments That Encounter Noncompliance	p. 138
The Excludability Assumption for One-Sided Noncompliance	p. 140
Average Treatment Effects, Intent-to-Treat Effects, and Complier Average Causal Effects	p. 141
Identification of the CACE	p. 143
Estimation	p. 149
Avoiding Common Mistakes	p. 152
Evaluating the Assumptions Required to Identify the CACE 15	p. 155
Non-interference Assumption	p. 155
Exclusion Restriction	p. 156
Statistical Inference	p. 157
Designing Experiments in Anticipation of Noncompliance	p. 161
Estimating Treatment Effects When Some Subjects Receive "Partial Treatment"	p. 164
Summary	p. 165
Suggested Readings	p. 167
Exercises	p. 168
Two-Sided Noncompliance	p. 173
Two-Sided Noncompliance: New Definitions and Assumptions	p. 175
ITT, ITT_D, and CACE under Two-Sided Noncompliance	p. 179
A Numerical Illustration of the Role of Monotonicity	p. 181
Estimation of the CACE: An Example	p. 185
Discussion of Assumptions	p. 189
Monotonicity	p. 190
Exclusion Restriction	p. 191
Random Assignment	p. 192
Design Suggestions	p. 192
Downstream Experimentation	p. 193
Summary	p. 204
Suggested Readings	p. 206
Exercises	p. 206
Attrition	p. 211
Conditions Under Which Attrition Leads to Bias	p. 215
Special Forms of Attrition	p. 219
Redefining the Estimand When Attrition Is Not a Function of Treatment Assignment	p. 224
Placing Bounds on the Average Treatment Effect	p. 226
Addressing Attrition: An Empirical Example	p. 230
Addressing Attrition with Additional Data Collection	p. 236
Two Frequently Asked Questions	p. 241
Summary	p. 243
Suggested Readings	p. 244
Exercises	p. 244
Optimal Sample Allocation for Second-Round Sampling	p. 248
Interference between Experimental Units	p. 253
Identifying Causal Effects in the Presence of Localized Spillover	p. 256
Spatial Spillover	p. 260
Using Nonexperimental Units to Investigate Spillovers	p. 264
An Example of Spatial Spillovers in Two Dimensions	p. 264
Within-Subjects Design and Time-Series Experiments	p. 273
Waitlist Designs (Also Known as Stepped-Wedge Designs)	p. 276
Summary	p. 281
Suggested Readings	p. 283
Exercises	p. 283
Heterogeneous Treatment Effects	p. 289
Limits to What Experimental Data Tell Us about Treatment Effect Heterogeneity	p. 291
Bounding Var (¿) and Testing for Heterogeneity	p. 292
Two Approaches to the Exploration of Heterogeneity: Covariates and Design	p. 296
Assessmg Treatment-by-Covariate Interactions	p. 296
Caution Is Required When Interpreting Treatment-by-Covariate Interactions	p. 299
Assessing Treatment-by-Treatment Interactions	p. 303
Using Regression to Model Treatment Effect Heterogeneity	p. 305
Automating the Search for Interactions	p. 310
Summary	p. 310
Suggested Readings	p. 312
Exercises	p. 313
Mediation	p. 319
Regression-Based Approaches to Mediation	p. 322
Mediation Analysis from a Potential Outcomes Perspective	p. 325
Why Experimental Analysis of Mediators Is Challenging	p. 328
Ruling Out Mediators?	p. 330
What about Experiments That Manipulate the Mediator?	p. 331
Implicit Mediation Analysis	p. 333
Summary	p. 336
Suggested Readings	p. 338
Exercises	p. 338
Treatment Postcards Mailed to Michigan Households	p. 343
Integration of Research Findings	p. 347
Estimation of Population Average Treatment Effects	p. 350
A Bayesian Framework for Interpreting Research Findings	p. 353
Replication and Integration of Experimental Findings: An Example	p. 358
Treatments That Vary in Intensity: Extrapolation and Statistical Modeling	p. 366
Summary	p. 377
Suggested Readings	p. 378
Exercises	p. 379
Instructive Examples of Experimental Design	p. 383
Using Experimental Design to Distinguish between Competing Theories	p. 384
Oversampling Subjects Based on Their Anticipated Response to Treatment	p. 387
Comprehensive Measurement of Outcomes	p. 393
Factorial Design and Special Cases of Non-interference	p. 395
Design and Analysis of Experiments In Which Treatments Vary with Subjects' Characteristics	p. 400
Design and Analysis of Experiments In Which Failure to Receive Treatment Has a Causal Effect	p. 406
Addressing Complications Posed by Missing Data	p. 410
Summary	p. 414
Suggested Readings	p. 415
Exercises	p. 416
Writing a Proposal, Research Report, and Journal Article	p. 425
Writing the Proposal	p. 426
Writing the Research Report	p. 435
Writing the Journal Article	p. 440
Archiving Data	p. 442
Summary	p. 444
Suggested Readings	p. 445
Exercises	p. 445
Protection of Human Subjects	p. 447
Regulatory Guidelines	p. 447
Guidelines for Keeping Field Experiments within Regulatory Boundaries	p. 449
Suggested Field Experiments for Class Projects	p. 453
Crafting Your Own Experiment	p. 453
Suggested Experimental Topics for Practicum Exercises	p. 455
References	p. 461
Index	p. 479
Table of Contents provided by Ingram. All Rights Reserved.

Amazon no longer offers textbook rentals. We do!

Field Experiments Design, Analysis, and Interpretation

Field Experiments Design, Analysis, and Interpretation

9780393979954

0393979954

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