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Angrist"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Massachusetts Institute of Technology"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"angrist@mit.edu"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"Quantitative criminology focuses on straightforward causal questions that are ideally addressed with randomized experiments. In practice, however, traditional randomized trials are difficult to implement in the untidy world of criminal justice. Even when randomized trials are implemented, not everyone is treated as intended and some control subjects may obtain experimental services. Treatments may also be more complicated than a simple yes/no coding can capture. This paper argues that the instrumental variables methods (IV) used by economists to solve omitted variables bias problems in observational studies also solve the major statistical problems that arise in imperfect criminological experiments. In general, IV methods estimate causal effects on subjects who comply with a randomly assigned treatment. The use of IV in criminology is illustrated through a re-analysis of the Minneapolis domestic violence experiment. The results point to substantial selection bias in estimates using treatment delivered as the causal variable, and IV estimation generates deterrent effects of arrest that are about one-third larger than the corresponding intention-to-treat effects."}}]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"causal effects"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"domestic violence"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"local average treatment effects"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"non-compliance"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"two-stage least squares"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Joshua D. Angrist. 2006. \"Instrumental Variables Methods in Experimental Criminological Research: What, Why and How.\" Journal of Experimental Criminology, 2(1), 23-44. <a href=\"http://www.springerlink.com/content/u4k241g167075786/\" target= \"_new\">article available here</a>"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"Subject: STANDARD DEPOSIT TERMS 1.0  Type: DATAPASS:TERMS:STANDARD:1.0  Notes: This study was deposited under the of the Data-PASS standard deposit terms. A copy of the usage agreement is included in the file section of this study.;"},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2006"},{"typeName":"distributor","multiple":true,"typeClass":"compound","value":[{"distributorName":{"typeName":"distributorName","multiple":false,"typeClass":"primitive","value":"Joshua D. 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