IDEAS home Printed from https://ideas.repec.org/a/igg/rmj000/v10y1997i4p27-36.html
   My bibliography  Save this article

How Do Actuaries Use Data Containing Errors?: Models of Error Detection and Error Correction

Author

Listed:
  • Barbara D. Klein

    (University of Michigan-Dearborn, USA)

Abstract

Information systems provide data for business processes and decision making. There is strong evidence that data items stored in organizational databases have a significant rate of errors. If undetected in use, errors in data may significantly affect business outcomes. The question examined in this paper is the extent to which business professionals are able to evaluate the quality of data in the information systems they use and the impact of their evaluations on decision-making behavior. Models of error detection and error correction processes are developed. The validity of the models is then examined through an analysis of interviews with ten actuaries. The findings show that actuaries detect errors in data using three general methods and that actuaries consider feasibility and costs when deciding whether to correct data errors.

Suggested Citation

  • Barbara D. Klein, 1997. "How Do Actuaries Use Data Containing Errors?: Models of Error Detection and Error Correction," Information Resources Management Journal (IRMJ), IGI Global, vol. 10(4), pages 27-36, October.
    • Handle: RePEc:igg:rmj000:v:10:y:1997:i:4:p:27-36
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/irmj.1997100103
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
    2. Janvrin, Diane J. & Raschke, Robyn L. & Dilla, William N., 2014. "Making sense of complex data using interactive data visualization," Journal of Accounting Education, Elsevier, vol. 32(4), pages 31-48.
    3. Klein, Barbara D., 2001. "Detecting errors in data: clarification of the impact of base rate expectations and incentives," Omega, Elsevier, vol. 29(5), pages 391-404, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:rmj000:v:10:y:1997:i:4:p:27-36. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.