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Non-response bias

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  • Berg, Nathan

Abstract

Non-response bias refers to the mistake one expects to make in estimating a population characteristic based on a sample of survey data in which, due to non-response, certain types of survey respondents are under-represented. Social scientists often attempt to make inferences about a population by drawing a random sample and studying relationships among the measurements contained in the sample. When individuals from a special subset of the population are systematically omitted from a particular sample, however, the sample cannot be said to be “random,” in the sense that every member of the population is equally likely to be included in the sample. It is important to acknowledge that any patterns uncovered in analyzing a non-random sample do not provide valid grounds for generalizing about a population in the same way that patterns present in a random sample do. The mismatch between the average characteristics of respondents in a non-random sample and the average characteristics of the population can lead to serious problems in understanding the causes of social phenomena and may lead to misdirected policy action. Therefore, considerable attention has been given to the problem of non-response bias, both at the stages of data collection and data analysis.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 26373.

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Date of creation: 2005
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Handle: RePEc:pra:mprapa:26373

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Related research

Keywords: Sampling Error; Non-Representative Sample; Bias; Mis-reporting; Misreporting; Non-response; Nonresponse; Missing; Imputation; Weighting; Randomized Response;

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References

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  1. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1997. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Boston College Working Papers in Economics 394, Boston College Department of Economics.
  2. Michael D. Hurd & Daniel McFadden & Harish Chand & Li Gan & Angela Menill & Michael Roberts, 1998. "Consumption and Savings Balances of the Elderly: Experimental Evidence on Survey Response Bias," NBER Chapters, in: Frontiers in the Economics of Aging, pages 353-392 National Bureau of Economic Research, Inc.
  3. Lee, Byung-Joo & Marsh, Lawrence C, 2000. " Sample Selection Bias Correction for Missing Response Observations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(2), pages 305-22, May.
  4. Whitehead, John C. & Groothuis, Peter A. & Blomquist, Glenn C., 1993. "Testing for non-response and sample selection bias in contingent valuation : Analysis of a combination phone/mail survey," Economics Letters, Elsevier, vol. 41(2), pages 215-220.
  5. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
  6. Lien, Donald & Rearden, David, 1988. "Missing measurements in limited dependent variable models," Economics Letters, Elsevier, vol. 26(1), pages 33-36.
  7. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
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Citations

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Cited by:
  1. Nathan Berg & Donald Lien, 2009. "Sexual orientation and self-reported lying," Review of Economics of the Household, Springer, vol. 7(1), pages 83-104, March.
  2. Nathan Berg & Todd Gabel, 2013. "Effects of New Welfare Reform Strategies on Welfare Participation: Microdata Estimates from Canada," Working Papers 1304, University of Otago, Department of Economics, revised Feb 2013.

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