IDEAS home Printed from https://ideas.repec.org/p/ags/unasrr/327249.html
   My bibliography  Save this paper

The Farm Producer Survey: Unit and Item Nonresponse

Author

Listed:
  • Young, Linda J
  • Rater, Barbara R

Abstract

No abstract is available for this item.

Suggested Citation

  • Young, Linda J & Rater, Barbara R, 2021. "The Farm Producer Survey: Unit and Item Nonresponse," NASS Research Reports 327249, United States Department of Agriculture, National Agricultural Statistics Service.
    • Handle: RePEc:ags:unasrr:327249
    DOI: 10.22004/ag.econ.327249
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/327249/files/Farm_Producer_Survey_Initial_Resultsnew_07_22_2022.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.327249?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
    ---><---

    References listed on IDEAS

    as
    1. Miratrix, Luke W. & Sekhon, Jasjeet S. & Theodoridis, Alexander G. & Campos, Luis F., 2018. "Worth Weighting? How to Think About and Use Weights in Survey Experiments," Political Analysis, Cambridge University Press, vol. 26(3), pages 275-291, July.
    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. Chris Hanretty & Benjamin E. Lauderdale & Nick Vivyan, 2020. "A Choice‐Based Measure of Issue Importance in the Electorate," American Journal of Political Science, John Wiley & Sons, vol. 64(3), pages 519-535, July.
    2. Ranjbar, Setareh & Salvati, Nicola & Pacini, Barbara, 2023. "Estimating heterogeneous causal effects in observational studies using small area predictors," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    3. Hege H. Bye & Hui Yu & Jennie Sofia Portice & Charles A. Ogunbode, 2023. "Interactions between migrant race and social status in predicting acceptance of climate migrants in Norway," Climatic Change, Springer, vol. 176(4), pages 1-16, April.
    4. Robert Johns & Ann‐Kristin Kölln, 2020. "Moderation and Competence: How a Party's Ideological Position Shapes Its Valence Reputation," American Journal of Political Science, John Wiley & Sons, vol. 64(3), pages 649-663, July.
    5. Shannan K. Sweet & Jonathon P. Schuldt & Johannes Lehmann & Deborah A. Bossio & Dominic Woolf, 2021. "Perceptions of naturalness predict US public support for Soil Carbon Storage as a climate solution," Climatic Change, Springer, vol. 166(1), pages 1-15, May.
    6. Nicole E. Pashley & Luke W. Miratrix, 2021. "Insights on Variance Estimation for Blocked and Matched Pairs Designs," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 271-296, June.
    7. Melody Y Huang & Sarah E Robertson & Harsh Parikh, 2024. "Towards Generalizing Inferences from Trials to Target Populations," Papers 2402.17042, arXiv.org.
    8. Jens Eger & Sebastian H. Schneider & Martin Bruder & Solveig H. Gleser, 2023. "Does Evidence Matter? The Impact of Evidence Regarding Aid Effectiveness on Attitudes Towards Aid," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 35(5), pages 1149-1172, October.
    9. Di Shu & Jessica G. Young & Sengwee Toh & Rui Wang, 2021. "Variance estimation in inverse probability weighted Cox models," Biometrics, The International Biometric Society, vol. 77(3), pages 1101-1117, September.
    10. Nicole E. Pashley & Luke W. Miratrix, 2022. "Block What You Can, Except When You Shouldn’t," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 69-100, February.
    11. Benjamin Lu & Jia Wan & Derek Ouyang & Jacob Goldin & Daniel E. Ho, 2024. "Quantifying the Uncertainty of Imputed Demographic Disparity Estimates: The Dual Bootstrap," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
    12. Zach Branson & Tirthankar Dasgupta, 2020. "Sampling‐based Randomised Designs for Causal Inference under the Potential Outcomes Framework," International Statistical Review, International Statistical Institute, vol. 88(1), pages 101-121, April.
    13. Naoki Egami & Erin Hartman, 2021. "Covariate selection for generalizing experimental results: Application to a large‐scale development program in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1524-1548, October.

    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:ags:unasrr:327249. 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: AgEcon Search (email available below). General contact details of provider: https://www.nass.usda.gov/ .

    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.