AIDS and Behavior

, Volume 14, Issue 1, pp 162–172

Scaling Sexual Behavior or “Sexual Risk Propensity” Among Men at Risk for HIV in Kisumu, Kenya

  • C. L. Mattson
  • Richard T. Campbell
  • George Karabatsos
  • Kawango Agot
  • J. O. Ndinya-Achola
  • Stephen Moses
  • Robert C. Bailey
Original Paper


We present a scale to measure sexual risk behavior or “sexual risk propensity” to evaluate risk compensation among men engaged in a randomized clinical trial of male circumcision. This statistical approach can be used to represent each respondent’s level of sexual risk behavior as the sum of his responses on multiple dichotomous and rating scale (i.e. ordinal) items. This summary “score” can be used to summarize information on many sexual behaviors or to evaluate changes in sexual behavior with respect to an intervention. Our 18 item scale demonstrated very good reliability (Cronbach’s alpha of 0.87) and produced a logical, unidimensional continuum to represent sexual risk behavior. We found no evidence of differential item function at different time points (except for reporting a concurrent partners when comparing 6 and 12 month follow-up visits) or with respect to the language with which the instrument was administered. Further, we established criterion validity by demonstrating a statistically significant association between the risk scale and the acquisition of incident sexually transmitted infections (STIs) at the 6 month follow-up and HIV at the 12 month follow-up visits. This method has broad applicability to evaluate sexual risk behavior in the context of other HIV and STI prevention interventions (e.g. microbicide or vaccine trials), or in response to treatment provision (e.g., anti-retroviral therapy).


Non-parametric item response theory Male circumcision Risk compensation HIV/AIDS Africa 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • C. L. Mattson
    • 1
  • Richard T. Campbell
    • 1
  • George Karabatsos
    • 2
  • Kawango Agot
    • 3
  • J. O. Ndinya-Achola
    • 4
  • Stephen Moses
    • 5
  • Robert C. Bailey
    • 1
  1. 1.Division of Epidemiology and Biostatistics, School of Public HealthUniversity of Illinois at ChicagoChicagoUSA
  2. 2.College of EducationUniversity of Illinois at ChicagoChicagoUSA
  3. 3.UNIM ProjectKisumuKenya
  4. 4.Department of Medical MicrobiologyUniversity of NairobiNairobiKenya
  5. 5.Department of Medical MicrobiologyUniversity of ManitobaWinnipegCanada

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