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Keywords:

  • Classification;
  • diagnosis;
  • factor analysis;
  • latent classes;
  • substance abuse;
  • substance dependence

ABSTRACT

Aims  This paper discusses the representation of diagnostic criteria using categorical and dimensional statistical models. Conventional modeling using categorical or continuous latent variables in the form of latent class analysis and factor (IRT) analysis has limitations for the analysis of diagnostic criteria.

Methods  New hybrid models are discussed which provide both categorical and dimensional representations in the same model using mixture models. Conventional and new models are applied and compared using recent data for Diagnostic and Statistical Manual of Mental Disorders version IV (DSM-IV) alcohol dependence and abuse criteria from the National Epidemiologic Survey on Alcohol and Related Conditions. Classification results from hybrid models are compared to the DSM-IV approach of using the number of diagnostic criteria fulfilled.

Results  It is found that new hybrid mixture models are more suitable than latent class and factor (IRT) models.

Conclusion  Implications for DSM-V are discussed in terms of reporting results using both categories and dimensions.