IDEAS home Printed from
   My bibliography  Save this article

Using the principal component analysis method as a tool in contractor pre-qualification


  • K. C. Lam
  • T. S. Hu
  • S. T. Ng


Contractor pre-qualification can be regarded as a complicated, two-group, non-linear classification problem. It involves a variety of subjective and uncertain information extracted from various parties such as contractors, pre-qualifiers and project teams. Non-linearity, uncertainty and subjectivity are the three predominant characteristics of the contractor pre-qualification process. This makes the process more of an art than a scientific evaluation. In addition to non-linearity, uncertainty and subjectivity, contractor pre-qualification is further complicated by the large number of contractor pre-qualification criteria (CPC) used in current practice and the multicollinearity existing between contractor attributes. An alternative empirical method using principal component analysis (PCA) is proposed for contractor pre-qualification in this study. The proposed method may alleviate the existing amount of multicollinearity and largely reduce the dimensionality of the pre-qualification data set. The applicability and potential of PCA for contractor pre-qualification has been examined by way of two data sets: (1) 73 pre-qualification cases (37 qualified and 36 disqualified) collected in England and (2) 85 (45 qualified and 40 disqualified) pre-qualification cases relating to 10 public sector projects in Hong Kong. The PCA-based results demonstrated that strong and positive inter-correlations existed between most of the qualifying variables, with the minimum correlation coefficient being 0.121 and the maximum being 0.899, and that qualified and disqualified contractors could be satisfactorily separated.

Suggested Citation

  • K. C. Lam & T. S. Hu & S. T. Ng, 2005. "Using the principal component analysis method as a tool in contractor pre-qualification," Construction Management and Economics, Taylor & Francis Journals, vol. 23(7), pages 673-684.
  • Handle: RePEc:taf:conmgt:v:23:y:2005:i:7:p:673-684
    DOI: 10.1080/01446190500041263

    Download full text from publisher

    File URL:
    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.

    References listed on IDEAS

    1. Patrick Sik-Wah Fong & Sonia Kit-Yung Choi, 2000. "Final contractor selection using the analytical hierarchy process," Construction Management and Economics, Taylor & Francis Journals, vol. 18(5), pages 547-557.
    2. Zedan Hatush & Martin Skitmore, 1997. "Evaluating contractor prequalification data: selection criteria and project success factors," Construction Management and Economics, Taylor & Francis Journals, vol. 15(2), pages 129-147.
    3. K. C. Lam & Tiesong Hu & S. Thomas Ng & Martin Skitmore & S. O. Cheung, 2001. "A fuzzy neural network approach for contractor prequalification," Construction Management and Economics, Taylor & Francis Journals, vol. 19(2), pages 175-188.
    4. Zedan Hatush & Martin Skitmore, 1997. "Assessment and evaluation of contractor data against client goals using PERT approach," Construction Management and Economics, Taylor & Francis Journals, vol. 15(4), pages 327-340.
    Full references (including those not matched with items on IDEAS)


    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:taf:conmgt:v:23:y:2005:i:7:p:673-684. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.