Archives of Sexual Behavior

, Volume 29, Issue 1, pp 77–89

Can Self-Reported Drug Use Data Be Used to Assess Sex Risk Behavior in Adolescents?

  • Joseph S. Wislar
  • Michael Fendrich
Article

Abstract

To better understand and control the spread of sexually transmitted diseases among high-risk youth, we must first acquire reliable reports of sexual risk behavior. This study evaluates one potential method for validating such reports. We examined the association between marijuana and cocaine use reporting patterns and the number of reported recent sexual partners in a sample of juvenile arrestees/detainees. Using urinalysis to validate self-reported drug use, we categorized drug use reporting patterns into four groups: overreporters, underreporters, honest users, and honest nonusers. Analyses showed, in general, that overreporters reported more sexual partners than either underreporters or accurate reporters, suggesting that overreporters of drug use may also exaggerate sex partner reports. Findings suggest a new method for validating self-reported sexual behavior and provide a challenge to theories of juvenile delinquency.

sexual partners self-disclosure validity substance abuse juveniles arrestees 

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REFERENCES

  1. Aquilino, W. S., and LoSciuto, L. (1990). Effects of interview mode on self-reported drug use. Publ.Opin.Q.54: 362–395.Google Scholar
  2. Boekeloo, B. O., Schiavo, L., Rabin, D. L., Conlon, R. T., Jordan, C. S., and Mundt, D. J. (1994). Selfreports of HIV risk factors by patients at a sexually transmitted disease clinic: Audio vs written questionnaires. Am.J.Public Health84(5): 754–760.Google Scholar
  3. Catania, J. A., Gibson, D. R., Chitwood, D. D., and Coates, T. J. (1990a). Methodological problems in AIDS behavioral research: Influences on measurement error and participation bias in studies of sexual behavior. Psychol.Bull.108: 339–62.Google Scholar
  4. Catania, J. A., Binson, D., Canchola, J., Pollack, L. M., Hauck,W., and Coates, T. J. (1996). Effects of interviewer gender, interviewer choice, and item wording on responses to questions concerning sexual behavior. Publ.Opin.Q.60: 345–375.Google Scholar
  5. Catania, J. A., Gibson, D. R., Marin, B., Coates, T. J., and Greenblatt, R. M. (1990b). Response bias in assessing sexual behaviors relevant to HIV transmission. Eval.Prog.Plan.13: 19–29.Google Scholar
  6. Chaiken, J. M., and Chaiken, M. R. (1993). Understanding the Drug Use Forecasting (DUF) Sample of Adult Arrestees, National Institute of Justice, Lincoln, MA.Google Scholar
  7. Fendrich, M., and Xu, Y. (1994). The validity of drug use reports from juvenile arrestees. Int.J.Addict.29: 971–985.Google Scholar
  8. Gibson, D. R., and Young, M. (1994). Assessing the reliability and validity of self-reported risk behavior. In Battjes, R. J., Sloboda, Z., and Grace, W. C. (eds.), The Context of HIV Risk Among Drug Users and Their Sexual Partners, National Institute on Drug Abuse Research Monograph 143, NIH Publ. No. 94–3750, National Institute on Drug Abuse, Rockville, MD.Google Scholar
  9. Grella, C. E., Chaiken, S., and Anglin, M. D. (1995). A procedure for assessing the validity of selfreport data on high-risk sex behaviors from heroin addicts entering free methadone treatment. J.Drug Issues25(4): 723–733.Google Scholar
  10. Hedeker, D., and Gibbons, R. (1994).Arandom-effects ordinal regression model for multilevel analysis. Biometrics50: 933–944.Google Scholar
  11. Hedeker, D., and Gibbons, R. (1996). MIXOR: A computer program for mixed-effects ordinal regression analysis. Comput.Methods Programs Biomed.49: 157–176.Google Scholar
  12. Huber, P. J. (1967). The behavior of maximum liklihood estimates under non-standard conditions. Proceedings of the Fifth Berkeley Symposium in Mathematical Statistics and Probability, 1, University of California Press, Berkeley, CA.Google Scholar
  13. Jessor, R., and Jessor, S. L. (1977). Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth, Academic Press, New York.Google Scholar
  14. Johnson, T. P., Fendrich, M., Sudman, S., Wislar, J. S., and Severns, E. (1999). An experiment to improve drug use reports during survey interviews. 1998 Proceedings of the Section on Survey Research Methods, American Statistical Association, Alexandria, VA, pp. 888–893.Google Scholar
  15. Johnston, L. D, O'Malley, P. M., and Bachman, J. G. (1998). Monitoring the Future 1998 Data Tables/Figures[online]. Available: Http://www.isr.umich.edu/src/mtf.Google Scholar
  16. Kirk, J., and Miller, M. L. (1986). Reliability and Validity in Qualitative Research, Sage University Paper Series on Qualitative Research Methods, Vol. 1, Sage, Beverly Hills, CA.Google Scholar
  17. Lowry, R., Holtzman, D., Truman, B. I., Kann, L., Collins, J. L., and Kolbe, L. J. (1994). Substance use and HIV-related sexual behaviors among US high school students: Are they related? Am.J.Public Health84: 1116–1120.Google Scholar
  18. Measham, F., Parker, H., and Aldridge, J. (1998). The teenage transition: From adolescent recreational drug use to the young adult dance culture in Britain in the mid-1990's. J.Drug Issues28(1): 9–32.Google Scholar
  19. Metzler, C. W., Noell, J., and Biglan, A. (1992). The validation of a construct of high-risk sexual behavior in heterosexual adolescents. J.Adolesc.Res.7(2): 223–249.Google Scholar
  20. National Institute of Justice (1996). 1995 Drug Use Forecasting Annual Report on Adult and Juvenile Arresstees, National Institute of Justice, Washington, DC.Google Scholar
  21. Nunnally, J. C., and Bernstein, I. H. (1967). Psychometric Theory, McGraw-Hill, New York.Google Scholar
  22. Romer, D., Hornik, R., Stanton, B., Black, M., Li, X., Ricardo, I., and Feigelman, S. (1997). “Talking” computers: A reliable and private method to conduct interviews on sensitive topics with children. J.Sex Res. 34(1): 3–9.Google Scholar
  23. Schopper, D., Doussantousse, S., and Orav, J. (1993). Sexual behaviors relevant to HIV transmission in a rural African population. Soc.Sci.Med.37(3): 401–412.Google Scholar
  24. Seidman, S. N., and Rieder, R. O. (1994). A review of sexual behavior in the United States. Am.J.Psychiatry151(3): 330–341.Google Scholar
  25. StatCorp (1997). Stata Statistical Software: Release 5.0, Stata Corporation, College Station, TX.Google Scholar
  26. Turner, C. F., Ku, L, Rogers, S. M., Lindberg, L. D., Pleck, J. H., and Sonenstein, F. L. (1998). Adolescent sexual behavior, drug use, and violence: Increased reporting with computer survey technology. Science280(5): 867–873.Google Scholar
  27. Udry, J., and Morris, N. (1967). A method for validation of reported sexual data. J.Marriage Family29(3): 422–446.Google Scholar
  28. Visher, C. (1991). A Comparison of Urinalysis Technologies for Drug Testing in Criminal Justice, National Institute of Justice and the Bureau of Justice Assistance, Washington, DC.Google Scholar
  29. Weinhardt, L. S., Forsyth, A. D., Carey, M. P., Jaworski, B. C., and Durant, L. E. (1998). Reliability and validity of self-report measures of HIV-related sexual behavior: Progress since 1990 and recommendations for research and practice. Arch.Sex.Behav.27(2): 155–180.Google Scholar
  30. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica48: 817–830.Google Scholar
  31. White, H. (1982). Maximum liklihood estimation of misspecified models. Econometrica50: 1–25.Google Scholar

Copyright information

© Plenum Publishing Corporation 2000

Authors and Affiliations

  • Joseph S. Wislar
    • 1
  • Michael Fendrich
    • 1
    • 2
  1. 1.Department of Psychiatry, Institute for Juvenile ResearchUniversity of Illinois at ChicagoChicago
  2. 2.Institute for Juvenile ResearchUniversity of Illinois at ChicagoChicago

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