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Weighting and Imputation for Missing Data in a Cost and Earnings Fishery Survey

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  • Daniel K. Lew
  • Amber Himes-Cornell
  • Jean Lee

Abstract

Surveys of fishery participants are often voluntary and, as a result, commonly have missing data associated with them. The two primary causes of missing data that generate concern are unit non-response and item non-response. Unit non-response occurs when a potential respondent does not complete and return a survey, resulting in a missing respondent. Item non-response occurs in returned surveys when an individual question is unanswered. Both may lead to issues with extrapolating results to the population. We explain how to adjust data to estimate population parameters from surveys using two of the principal approaches available for addressing missing data, weighting and data imputation, and illustrate the effects they have on estimates of costs and earnings in the Alaska charter boat sector using data from a recent survey. The results suggest that ignoring missing data will lead to markedly different results than those estimated when controlling for the missing data.

Suggested Citation

  • Daniel K. Lew & Amber Himes-Cornell & Jean Lee, 2015. "Weighting and Imputation for Missing Data in a Cost and Earnings Fishery Survey," Marine Resource Economics, University of Chicago Press, vol. 30(2), pages 219-230.
    • Handle: RePEc:ucp:mresec:doi:10.1086/679975
    DOI: 10.1086/679975
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    References listed on IDEAS

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    1. John Micklewright & Sylke V. Schnepf & Chris Skinner, 2012. "Non-response biases in surveys of schoolchildren: the case of the English Programme for International Student Assessment (PISA) samples," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 915-938, October.
    2. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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