Predicting addiction severity index (asi) interviewer severity ratings for a computer-administered asi

Stephen F. Butler, John S. Cacciola, Simon H. Budman, Sabrina Ford, David Gastfriend, Ihsan M. Salloum, Frederick L. Newman, Arlene Frank, A. Thomas McLellan, Jack Blaine, Karla Moras, Jacques P. Barber

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients' answers to preselected interview items. The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, standardized vignettes, with 'gold standard,' expert-generated ISRs. Reliabilities compared well with those of intensively trained interviewers. The PSRs could provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and make possible a computer-administered version of the ASI.

Original languageEnglish (US)
Pages (from-to)399-407
Number of pages9
JournalPsychological Assessment
Issue number4
StatePublished - Dec 1 1998
Externally publishedYes

ASJC Scopus subject areas

  • Clinical Psychology
  • Psychiatry and Mental health


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