AIDS and Behavior

, Volume 10, Issue 5, pp 541–552 | Cite as

Accuracy of Audio Computer-Assisted Self-Interviewing (ACASI) and Self-Administered Questionnaires for the Assessment of Sexual Behavior

Original Paper


This study examined the accuracy of two retrospective methods and assessment intervals for recall of sexual behavior and assessed predictors of recall accuracy. Using a 2 [mode: audio-computer assisted self-interview (ACASI) vs. self-administered questionnaire (SAQ)] by 2 (frequency: monthly vs. quarterly) design, young women (N =102) were randomly assigned to one of four conditions. Participants completed baseline measures, monitored their behavior with a daily diary, and returned monthly (or quarterly) for assessments. A mixed pattern of accuracy between the four assessment methods was identified. Monthly assessments yielded more accurate recall for protected and unprotected vaginal sex but quarterly assessments yielded more accurate recall for unprotected oral sex. Mode differences were not strong, and hypothesized predictors of accuracy tended not to be associated with recall accuracy. Choice of assessment mode and frequency should be based upon the research question(s), population, resources, and context in which data collection will occur.


Audio-computer assisted self interview (ACASI) Self-administered questionnaire (SAQ) Sexual behavior Women Survey methods 



This research was funded by the National Institutes of Health (R01 NR008194 to DMB) as well as the National Institute of Mental Health (K02 MH01582 to MPC). The authors would like to thank the participants of the study for their important contribution to HIV-prevention science.


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Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  • Dianne Morrison-Beedy
    • 1
  • Michael P. Carey
    • 2
  • Xin Tu
    • 3
  1. 1.RN, School of NursingUniversity of Rochester, 601 Elmwood Avenue, Box SONRochesterUSA
  2. 2.Center for Health and BehaviorSyracuse UniversitySyracuseUSA
  3. 3.Biostatistics Computational BiologyUniversity of RochesterRochesterUSA

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