IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Development and validation of classification models to identify hidden child molesters applying to child service organizations

Listed author(s):
  • Abel, Gene G.
  • Wiegel, Markus
  • Jordan, Alan
  • Harlow, Nora
  • Hsu, Yu-Sheng
  • Martinez, Marc
Registered author(s):

    Organizations caring for children sometimes unknowingly accept hidden child molesters as staff or volunteers because they have no reliable way to identify these individuals. The goal of the present study was to develop and validate a screening methodology to identify individuals who have a higher likelihood of having sexually touched minors (17years of age or younger) in the past. Long term studies of untreated adults who have sexually abused children in the past have found them to have a high rate of continuing to abuse children, ranging from 17% to 37%. Currently, the most common method of screening for child sexual abusers is to use criminal background checks. However, studies have shown that criminal background checks identify less than 1% of candidates as having sexual offense histories against either adults or children. This new classification methodology was designed to improve on this by accurately identifying a larger percentage of applicants who may present a risk to the children served by these organizations. In developing these models, one major challenge was creating models with a high specificity to correctly identify over 90% of adults in the population who do not molest, while retaining high sensitivity to identify child sexual abusers who conceal to gain access to children. To develop and validate a child sexual abuse prevention screen to identify child sexual abusers hidden among applicants seeking jobs or volunteer positions working with children, the present study used classification models derived from linear regression analyses to discriminate between samples of concealing child sexual abusers and general population volunteers who had never been accused of sexual misconduct. Researchers developed separate models for men and women. For the male classification model, the specificity was 90.3% and the bootstrapped sensitivity was 51.0%. For the female classification model, the specificity was 90.0% and the sensitivity was 37.1%. Thus, this classification model was able to correctly identify approximately 50% of men and 40% of women who have sexually abused a child in the past. Compared to the less than 1% identified by criminal background checks, this classification methodology has the potential of substantially increasing a child service organization's ability to identify individuals who have the highest probability of having sexually abused children in the past and are concealing from the organization they are attempting to join.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Elsevier in its journal Children and Youth Services Review.

    Volume (Year): 34 (2012)
    Issue (Month): 7 ()
    Pages: 1378-1389

    in new window

    Handle: RePEc:eee:cysrev:v:34:y:2012:i:7:p:1378-1389
    DOI: 10.1016/j.childyouth.2012.03.017
    Contact details of provider: Web page:

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:eee:cysrev:v:34:y:2012:i:7:p:1378-1389. 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: (Dana Niculescu)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.