IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v20y1991i1p30-59.html
   My bibliography  Save this article

Estimating Nonresponse and Response Bias

Author

Listed:
  • NORA CATE SCHAEFFER

    (University of Wisconsin, Madison)

  • JUDITH A. SELTZER

    (University of Wisconsin, Madison)

  • MARIEKA KLAWITTER

    (University of Washington)

Abstract

Although researchers are aware that nonresponse and response bias may compromise the accuracy of estimates from survey data, it is difficult to obtain estimates of these biases. In this article, we estimate nonresponse and response bias for a particular case—child support awards and payments. The sample of divorced resident and nonresident parents was drawn from Wisconsin court records and subsequently interviewed by telephone in 1987. The court records provide the criterion to use in estimating nonresponse and response bias. The analysis shows that those not interviewed are less likely to have awards or to pay support than participants, and the average award and average amount paid are lower for them than for survey participants. Both resident mothers and nonresident fathers overreport the amount of support owed and paid, but the bias is larger for fathers. Nonparticipation bias is greater for measures of paying support than for measures of owing support. Response bias contributes more to total nonsampling bias than does nonparticipation bias for reports of amounts of support owed and paid (including direct payments). Only for resident mothers' reports of the amount paid (omitting direct payments) is the total nonsampling bias less than 3 times the standard error.

Suggested Citation

  • Nora Cate Schaeffer & Judith A. Seltzer & Marieka Klawitter, 1991. "Estimating Nonresponse and Response Bias," Sociological Methods & Research, , vol. 20(1), pages 30-59, August.
  • Handle: RePEc:sae:somere:v:20:y:1991:i:1:p:30-59
    DOI: 10.1177/0049124191020001002
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124191020001002
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0049124191020001002?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. Mathiowetz, Nancy A & Duncan, Greg J, 1988. "Out of Work, Out of Mind: Response Errors in Retrospective Reports of Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 221-229, April.
    2. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    3. Blair, Edward & Burton, Scot, 1987. "Cognitive Processes Used by Survey Respondents to Answer Behavioral Frequency Questions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(2), pages 280-288, September.
    4. Teresa Martin & Larry Bumpass, 1989. "Recent trends in marital disruption," Demography, Springer;Population Association of America (PAA), vol. 26(1), pages 37-51, February.
    5. Irwin Garfinkel & Donald Oellerich, 1989. "Noncustodial Fathers’ Ability to Pay Child Support," Demography, Springer;Population Association of America (PAA), vol. 26(2), pages 219-233, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vedran Lesic & Richard E. Hodgett & Alan Pearman & Amy Peace, 2019. "How to Improve Impact Reporting for Sustainability," Sustainability, MDPI, vol. 11(6), pages 1-21, March.

    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. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    2. Smith, J-P & Thomas, D, 1997. "Migration in Retrospect : Remembrances of Things Past," Papers 97-06, RAND - Labor and Population Program.
    3. Brownstone, David & Velletta, Robert G., 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," University of California Transportation Center, Working Papers qt2t08s22q, University of California Transportation Center.
    4. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 705-717, November.
    5. Jungmin Lee & Sokbae Lee, 2012. "Does it Matter WHO Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.
    6. Oyer, Paul, 2004. "Recall bias among displaced workers," Economics Letters, Elsevier, vol. 82(3), pages 397-402, March.
    7. Lynn, Peter & Jäckle, Annette & Sala, Emanuela & P. Jenkins, Stephen, 2004. "Validation of survey data on income and employment: the ISMIE experience," ISER Working Paper Series 2004-14, Institute for Social and Economic Research.
    8. John Bound & Charles Brown & Greg J. Duncan & Willard L. Rodgers, 1989. "Measurement Error In Cross-Sectional and Longitudinal Labor Market Surveys: Results From Two Validation Studies," NBER Working Papers 2884, National Bureau of Economic Research, Inc.
    9. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).
    10. James P. Smith & Duncan Thomas, 2003. "Remembrances of things past: test–retest reliability of retrospective migration histories," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 23-49, February.
    11. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    12. Anna Manzoni & Ruud Luijkx & Ruud Muffels, 2011. "Explaining differences in labour market transitions between panel and life-course data in West-Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(2), pages 241-261, February.
    13. Laisney, François & Pohlmeier, Winfried & Staat, Matthias, 1991. "Estimation of labour supply functions using panel data: a survey," ZEW Discussion Papers 91-05, ZEW - Leibniz Centre for European Economic Research.
    14. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    15. Adele Bergin, 2015. "Employer Changes and Wage Changes: Estimation with Measurement Error in a Binary Variable," LABOUR, CEIS, vol. 29(2), pages 194-223, June.
    16. Lin, Dajun & Lutter, Randall & Ruhm, Christopher J., 2018. "Cognitive performance and labour market outcomes," Labour Economics, Elsevier, vol. 51(C), pages 121-135.
    17. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    18. Katrin Drasch & Britta Matthes, 2013. "Improving retrospective life course data by combining modularized self-reports and event history calendars: experiences from a large scale survey," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 817-838, February.
    19. W. Michael Hanemann, 1994. "Valuing the Environment through Contingent Valuation," Journal of Economic Perspectives, American Economic Association, vol. 8(4), pages 19-43, Fall.
    20. Bonev, Petyo, 2020. "Nonparametric identification in nonseparable duration models with unobserved heterogeneity," Economics Working Paper Series 2005, University of St. Gallen, School of Economics and Political Science.

    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:sae:somere:v:20:y:1991:i:1:p:30-59. 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: SAGE Publications (email available below). General contact details of provider: .

    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.