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Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions

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  • Frauke Kreuter
  • Ting Yan
  • Roger Tourangeau

Abstract

Summary. Latent class analysis has been used to model measurement error, to identify flawed survey questions and to estimate mode effects. Using data from a survey of University of Maryland alumni together with alumni records, we evaluate this technique to determine its usefulness for detecting bad questions in the survey context. Two sets of latent class analysis models are applied in this evaluation: latent class models with three indicators and latent class models with two indicators under different assumptions about prevalence and error rates. Our results indicated that the latent class analysis approach produced good qualitative results for the latent class models—the item that the model deemed the worst was the worst according to the true scores. However, the approach yielded weaker quantitative estimates of the error rates for a given item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:171:y:2008:i:3:p:723-738
    DOI: 10.1111/j.1467-985X.2007.00530.x
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    References listed on IDEAS

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    1. 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.
    2. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    3. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
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    1. Martin Lukac & Nadja Doerflinger & Valeria Pulignano, 2019. "Developing a Cross-National Comparative Framework for Studying Labour Market Segmentation: Measurement Equivalence with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(1), pages 233-255, August.
    2. Chakrabarty, Subhajit & Nag, Biswajit, 2013. "Empirical study to segment firms and capture dynamic business context using LCA," MPRA Paper 51622, University Library of Munich, Germany.
    3. 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.
    4. Bruce D. Spencer, 2012. "When Do Latent Class Models Overstate Accuracy for Diagnostic and Other Classifiers in the Absence of a Gold Standard?," Biometrics, The International Biometric Society, vol. 68(2), pages 559-566, June.

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