Detecting errors in data: clarification of the impact of base rate expectations and incentives
Organizational databases have a significant rate of data errors and detecting and correcting these errors can be problematic. This paper builds on a stream of research demonstrating that users of these databases can detect data errors under certain circumstances. A theory of error detection and research on the effect of base rate expectations in probabilistic judgement tasks are applied to the development of two propositions about error detection. It is argued that expectations about the base rate of errors in data affect error detection performance when they are developed through direct experience and that incentives affect error detection performance. The two research propositions are tested in a laboratory experiment. Experience-based expectations about the base rate of errors and incentives are found to affect error detection performance.
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Volume (Year): 29 (2001)
Issue (Month): 5 (October)
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- Donald P. Ballou & Harold L. Pazer, 1985. "Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems," Management Science, INFORMS, vol. 31(2), pages 150-162, February.
- David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, Oxford University Press, vol. 95(3), pages 537-557.
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- Donald P. Ballou & Harold L. Pazer, 1982. "The Impact of Inspector Fallibility on the Inspection Policy in Serial Production Systems," Management Science, INFORMS, vol. 28(4), pages 387-399, April.
- Anil Gaba & Robert L. Winkler, 1992. "Implications of Errors in Survey Data: A Bayesian Model," Management Science, INFORMS, vol. 38(7), pages 913-925, July.
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