{"status":"OK","data":{"id":47897,"identifier":"DVN/Q6CID7","persistentUrl":"https://doi.org/10.7910/DVN/Q6CID7","protocol":"doi","authority":"10.7910","separator":"/","publisher":"Harvard Dataverse","publicationDate":"2008-12-12","storageIdentifier":"s3://10.7910/DVN/Q6CID7","effectiveDatasetFileCountLimit":1000,"datasetFileUploadsAvailable":999,"datasetType":"dataset","locks":[],"latestVersion":{"id":44416,"datasetId":47897,"datasetPersistentId":"doi:10.7910/DVN/Q6CID7","datasetType":"dataset","storageIdentifier":"s3://10.7910/DVN/Q6CID7","versionNumber":1,"internalVersionNumber":1,"versionMinorNumber":0,"versionState":"RELEASED","latestVersionPublishingState":"RELEASED","deaccessionNote":"Initial version","deaccessionLink":"","distributionDate":"2008-09-26","productionDate":"2008-08-27","lastUpdateTime":"2008-12-13T03:15:14Z","releaseTime":"2008-12-12T00:00:00Z","createTime":"2008-12-12T16:19:07Z","alternativePersistentId":"hdl:1902.1/12174","publicationDate":"2008-12-12","citationDate":"2008-12-12","effectiveDatasetFileCountLimit":1000,"datasetFileUploadsAvailable":999,"license":{"name":"CC0 1.0","uri":"http://creativecommons.org/publicdomain/zero/1.0","iconUri":"https://licensebuttons.net/p/zero/1.0/88x31.png","rightsIdentifier":"CC0-1.0","rightsIdentifierScheme":"SPDX","schemeUri":"https://spdx.org/licenses/","languageCode":"en"},"fileAccessRequest":false,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Replication data for: Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign: The Compendium"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Jake Bowers"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Ben B. Hansen"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Mark Fredrickson"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Jake Bowers"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"jwbowers@illinois.edu"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"Early in the twentieth century, Fisher and Neyman demonstrated how to infer effects of agricultural interventions using only the very weakest of assumptions, by randomly varying which plots were to be manipulated. Although the methods permitted uncontrolled variation between experimental units, they required strict control over assignment of interventions; this hindered their application to field studies with human subjects, who could not ordinarily be compelled to comply with experimenters' instructions. In 1996, however, Angrist, Imbens and Rubin showed that inferences from randomized studies could accommodate non-compliance without significant strengthening of assumptions.\nPolitical scientists A. Gerber and D. Green responded quickly, fielding a randomized study of voter turnout campaigns in the November 1998 general election. Non-contacts and refusals were frequent, but Gerber and Green analyzed their data in the style of Angrist et al., avoiding having to model non-response. They did use models for other purposes: to address complexities of the randomization scheme; to permit heterogeneity among voters and campaigners; to account for deviations from experime\nntal protocol; and to take advantage of highly informative covariates. Although the added assumptions seemed straightforward and unassailable, a later analysis found them to be at odds with Gerber and Green's data. Using a different model, it reaches the very opposite of Gerber and Green's central conclusion about getting out the vote.\nThis paper shows that neither of the models are necessary, addressing all of the complications of Gerber and Green's study using methods in the tradition of Fisher and Neyman.  To do this, it merges recent developments in randomization-based inference for comparative studies with somewhat older developments in design-based analysis of sample surveys.  The method involves regression, but large-sample analysis and simulations demonstrate its lack of dependence on regression assumptions.  Its s\nubstantive results have consequences both for the design of campaigns to increase voter participation and for theories of political behavior more generally."}}]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"cluster randomization, group randomized trial, instrumental variable, model-assisted, randomization inference, voter turnout"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Hansen, B. B. and Bowers, J. (2009). Attributing effects to a cluster randomized get-out-the-vote campaign. Journal of the American Statistical Association, to appear."}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Hansen, B. B. and Bowers, J. (2009). Attributing effects to a cluster randomized get-out-the-vote campaign. Journal of the American Statistical Association, to appear."}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Hansen, B. B. and Bowers, J. (2008). Covariate balance in simple, stratified and clustered comparative studies. Statistical Science, 23(2):219–236."}}]},{"typeName":"producer","multiple":true,"typeClass":"compound","value":[{"producerName":{"typeName":"producerName","multiple":false,"typeClass":"primitive","value":"Jake Bowers"}}]},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2008-08-27"},{"typeName":"distributor","multiple":true,"typeClass":"compound","value":[{"distributorName":{"typeName":"distributorName","multiple":false,"typeClass":"primitive","value":"Jake Bowers"}}]},{"typeName":"distributionDate","multiple":false,"typeClass":"primitive","value":"2008-09-26"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2008-12-12"},{"typeName":"kindOfData","multiple":true,"typeClass":"primitive","value":["Reproduction archive."]}]}},"files":[{"description":"This compressed archive contains the code, data, and text necessary to reproduce all of the calculations and figured in our JASA article. 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