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A contribution on the nature and treatment of missing data in large market surveys

Author

Listed:
  • Gary Madden
  • María Rosalía Vicente
  • Paul Rappoport
  • Andy Banerjee

Abstract

Nonresponse (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this article is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed missing completely at random, missing at random and not missing at random processes, with data removed.

Suggested Citation

  • Gary Madden & María Rosalía Vicente & Paul Rappoport & Andy Banerjee, 2017. "A contribution on the nature and treatment of missing data in large market surveys," Applied Economics, Taylor & Francis Journals, vol. 49(22), pages 2179-2187, May.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:22:p:2179-2187
    DOI: 10.1080/00036846.2016.1234699
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    References listed on IDEAS

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    1. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    2. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    3. Annie Qu, 2002. "Testing ignorable missingness in estimating equation approaches for longitudinal data," Biometrika, Biometrika Trust, vol. 89(4), pages 841-850, December.
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