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Measurement error evaluation of self‐reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse

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  • Paul P. Biemer
  • Christopher Wiesen

Abstract

Summary. 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.

Suggested Citation

  • Paul P. Biemer & Christopher Wiesen, 2002. "Measurement error evaluation of self‐reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 97-119, February.
    • Handle: RePEc:bla:jorssa:v:165:y:2002:i:1:p:97-119
    DOI: 10.1111/1467-985X.00612
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    Cited by:

    1. Sunil Kumar, 2016. "Latent class analyisis for reliable measure of inflation expectation in the indian public," Papers 1603.01397, arXiv.org.
    2. Cinzia Di Novi, 2010. "The influence of traffic‐related pollution on individuals' life‐style: results from the BRFSS," Health Economics, John Wiley & Sons, Ltd., vol. 19(11), pages 1318-1344, November.
    3. Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    4. Javier Escobal & Sonia Laszlo, 2008. "Measurement Error in Access to Markets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(2), pages 209-243, April.
    5. Owen, Ann L. & Videras, Julio R., 2007. "Culture and public goods: The case of religion and the voluntary provision of environmental quality," Journal of Environmental Economics and Management, Elsevier, vol. 54(2), pages 162-180, September.
    6. Frauke Kreuter & Ting Yan & Roger Tourangeau, 2008. "Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 723-738, June.
    7. Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    8. O’Neill, Donal, 2015. "Measuring obesity in the absence of a gold standard," Economics & Human Biology, Elsevier, vol. 17(C), pages 116-128.
    9. Donal O'Neill & Olive Sweetman, 2013. "Estimating Obesity Rates in Europe in the Presence of Self-Reporting Errors," Economics Department Working Paper Series n236-13.pdf, Department of Economics, National University of Ireland - Maynooth.
    10. John M. Abowd & William R. Bell & J. David Brown & Michael B. Hawes & Misty L. Heggeness & Andrew D. Keller & Vincent T. Mule Jr. & Joseph L. Schafer & Matthew Spence & Lawrence Warren & Moises Yi, 2020. "Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report," Working Papers 20-33, Center for Economic Studies, U.S. Census Bureau.
    11. Adam Carle, 2010. "Interpreting the results of studies using latent variable models to assess data quality: an empirical example using confirmatory factor analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(3), pages 483-497, April.
    12. Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
    13. Kumar, Sunil & Husain, Zakir & Mukherjee, Diganta, 2017. "Assessing consistency of consumer confidence data using latent class analysis with time factor," Economic Analysis and Policy, Elsevier, vol. 55(C), pages 35-46.

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