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Effect of Missing Data on Classification Error in Panel Surveys

Author

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  • Edwards Susan L.

    (RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709, United States of America)

  • Berzofsky Marcus E.

    (RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709, United States of America.)

  • Biemer Paul P.

    (RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709, United States of America.)

Abstract

Sensitive outcomes of surveys are plagued by wave nonresponse and measurement error (classification error for categorical outcomes). These types of error can lead to biased estimates and erroneous conclusions if they are not understood and addressed. The National Crime Victimization Survey (NCVS) is a nationally representative rotating panel survey with seven waves measuring property and violent crime victimization. Because not all crime is reported to the police, there is no gold standard measure of whether a respondent was victimized. For panel data, Markov Latent Class Analysis (MLCA) is a model-based approach that uses response patterns across interview waves to estimate false positive and false negative classification probabilities typically applied to complete data.

Suggested Citation

  • Edwards Susan L. & Berzofsky Marcus E. & Biemer Paul P., 2017. "Effect of Missing Data on Classification Error in Panel Surveys," Journal of Official Statistics, Sciendo, vol. 33(2), pages 551-570, June.
  • Handle: RePEc:vrs:offsta:v:33:y:2017:i:2:p:551-570:n:12
    DOI: 10.1515/jos-2017-0026
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