Measurement Error in Criminal Justice Data



While accurate data are critical in understanding crime and assessing criminal justice policy, data on crime and illicit activities are invariably measured with error. In this chapter, we illustrate and evaluate several examples of measurement error in criminal justice data. Errors are evidently pervasive, systematic, frequently related to behaviors and policies of interest, and unlikely to conform to convenient textbook assumptions. Using both convolution and mixing models of the measurement error generating process, we demonstrate the effects of data error on identification and statistical inference. Even small amounts of data error can have considerable consequences. Throughout this chapter, we emphasize the value of auxiliary data and reasonable assumptions in achieving informative inferences, but caution against reliance on strong and untenable assumptions about the error generating process.


Ordinary Little Square Illicit Drug Crime Rate Uniform Crime Report Ordinary Little Square Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Stephen Bruestle, Alex Piquero, and David Weisburd for their helpful comments. Pepper’s research was supported, in part, by the Bankard Fund for Political Economy.


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of VirginiaCharlottesvilleUSA
  2. 2.Committee on Law and JusticeNational Research CouncilWashingtonUSA

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