IDEAS home Printed from https://ideas.repec.org/a/ids/ijpqma/v8y2011i3p313-332.html
   My bibliography  Save this article

Assessing the prediction of employee productivity: a comparison of OLS vs. CART

Author

Listed:
  • Ina S. Markham

Abstract

This research compares the results of utilising an ordinary least squares (OLS) approach vs. a classification and regression tree (CART) approach for identifying employees with a high likelihood of being productive. Relevant performance data were collected from 378 employees of a large garment manufacturer. Past research (Markham et al., 2006) has shown that a combined genetic algorithm with an artificial neural network substantially outperformed (R² = 0.30) an equivalent OLS solution (R² = 0.14) when predicting individual level productivity. The current research compares the use of CART to OLS using the same data set. With an R² of 0.43, the CART results were even more powerful in identifying and classifying high performance employees. The implications of this finding for the field of productivity research and employee selection are discussed.

Suggested Citation

  • Ina S. Markham, 2011. "Assessing the prediction of employee productivity: a comparison of OLS vs. CART," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 8(3), pages 313-332.
    • Handle: RePEc:ids:ijpqma:v:8:y:2011:i:3:p:313-332
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=42511
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijpqma:v:8:y:2011:i:3:p:313-332. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=177 .

    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.