Skip to main content

Measurement Error Evaluation of Self-reported Drug Use: a Latent Class Analysis of the US National Household Survey on Drug Abuse

Buy Article:

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

Latent class analysis (LCA) is a statistical tool for evaluating the error in categorical data when two or more repeated measurements of the same survey variable are available. This paper illustrates an application of LCA for evaluating the error in self-reports of drug use using data from the 1994, 1995 and 1996 implementations of the US National Household Survey on Drug Abuse. In our application, the LCA approach is used for estimating classification errors which in turn leads to identifying problems with the questionnaire and adjusting estimates of prevalence of drug use for classification error bias. Some problems in using LCA when the indicators of the use of a particular drug are embedded in a single survey questionnaire, as in the National Household Survey on Drug Abuse, are also discussed.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Classification error; Marijuana use; Non-sampling error; Survey error; Test-retest

Document Type: Research Article

Affiliations: Research Triangle Institute, Research Triangle Park, USA

Publication date: 2002-01-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more