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Harden"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"University of North Carolina at Chapel Hill"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Jeffrey J. Harden"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"University of North Carolina at Chapel Hill"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"jjharden@unc.edu"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"State politics researchers often analyze data with observations grouped into clusters. This structure commonly produces unmodeled correlation within clusters, leading to downward bias in the standard errors of regression coefficients. Estimating robust cluster standard errors (RCSE) is a common approach to correcting this bias. However, despite their frequent use, recent work indicates that RCSE can also be biased downward. Here I show evidence of that bias and offer a potential solution. Through Monte Carlo simulation of an Ordinary Least Squares (OLS) regression model, I compare conventional standard error (OLS-SE) and RCSE performance to that of a bootstrap method that resamples clusters of observations (BCSE). I show that both OLS-SE and RCSE are biased downward, with OLS-SE being the most biased. In contrast, BCSE are not biased and consistently outperform the other two methods. I conclude with three replications from recent work and offer recommendations to researchers."}}]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Clustered Data; Standard Errors; Monte Carlo Simulation"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Harden, Jeffrey J. 2011. \"A Bootstrap Method for Conducting Statistical Inference with Clustered Data.\" Forthcoming, State Politics and Policy Quarterly."}}]},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2010-05-23"}]}},"files":[{"description":"Online appendix, R simulation code, and full results.","label":"Harden (2011) Online Supplement.zip","restricted":false,"version":1,"datasetVersionId":54579,"dataFile":{"id":2138063,"persistentId":"doi:10.7910/DVN/LDXPUU/UNRRUT","pidURL":"https://doi.org/10.7910/DVN/LDXPUU/UNRRUT","filename":"Harden (2011) Online Supplement.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":168578,"description":"Online appendix, R simulation code, and full results.","storageIdentifier":"s3://dvn-cloud:62492","rootDataFileId":-1,"md5":"595a180dc4645b2152f987633c8b7676","checksum":{"type":"MD5","value":"595a180dc4645b2152f987633c8b7676"},"tabularData":false,"creationDate":"2010-12-09","publicationDate":"2010-12-08","lastUpdateTime":"2010-12-09T21:06:47Z","fileAccessRequest":false}}]}}}