IDEAS home Printed from https://ideas.repec.org/a/ucp/mresec/doi10.1086-679975.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1086/679975
    Download Restriction: Access to the online full text or PDF requires a subscription.
    File URL: http://dx.doi.org/10.1086/679975
    Download Restriction: Access to the online full text or PDF requires a subscription.
    File URL: https://libkey.io/10.1086/679975?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    2. Paul T. von Hippel, 2013. "Should a Normal Imputation Model be Modified to Impute Skewed Variables?," Sociological Methods & Research, , vol. 42(1), pages 105-138, February.
    3. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    4. Nancy, Jane Y. & Khanna, Nehemiah H. & Arputharaj, Kannan, 2017. "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 63-79.
    5. Jakubowski, Maciej & Pokropek, Artur, 2015. "Reading achievement progress across countries," International Journal of Educational Development, Elsevier, vol. 45(C), pages 77-88.
    6. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    7. Marcello D’Orazio, 2015. "Integration and imputation of survey data in R: the StatMatch package," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 57-68, June.
    8. Zhong, Hua & Hu, Wuyang, 2015. "Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196962, Southern Agricultural Economics Association.
    9. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    10. Thomas Masterson, 2012. "Simulations of Full-Time Employment and Household Work in the Levy Institute Measure of Time and Income Poverty (LIMTIP) for Argentina, Chile, and Mexico," Economics Working Paper Archive wp_727, Levy Economics Institute.
    11. Chiara Elena Dalla & Menon Martina & Perali Federico, 2019. "An Integrated Database to Measure Living Standards," Journal of Official Statistics, Sciendo, vol. 35(3), pages 531-576, September.
    12. Laura Zieger & John Jerrim & Jake Anders & Nikki Shure, 2020. "Conditioning: How background variables can influence PISA scores," CEPEO Working Paper Series 20-09, UCL Centre for Education Policy and Equalising Opportunities, revised Apr 2020.
    13. Nicklas Pettersson, 2013. "Bias reduction of finite population imputation by kernel methods," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(1), pages 139-160, March.
    14. Lisa Meehan & Gail Pacheco & Thomas Schober, 2023. "Basic Reading and Mathematics Skills and the Labour Market Outcomes of Young People: Evidence from PISA and Linked Administrative Data," The Economic Record, The Economic Society of Australia, vol. 99(327), pages 473-491, December.
    15. Daniel Araya & Guillermo Paraje, 2018. "The impact of prices on alcoholic beverage consumption in Chile," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
    16. Sullivan, Danielle & Andridge, Rebecca, 2015. "A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 173-185.
    17. Soyeon Ahn & John M. Abbamonte, 2020. "A new approach for handling missing correlation values for meta‐analytic structural equation modeling: Corboundary R package," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(1), March.
    18. Cheng, Xiaoyue & Cook, Dianne & Hofmann, Heike, 2015. "Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i06).
    19. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    20. Constantin Rudolf Salomo Bürgi, 2023. "How to deal with missing observations in surveys of professional forecasters," Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ucp:mresec:doi:10.1086/679975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journals Division (email available below). General contact details of provider: https://www.journals.uchicago.edu/MRE .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.