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.
|Date of creation:||2005|
|Date of revision:|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Heckman, James, 2013.
"Sample selection bias as a specification error,"
Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
- John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998.
"An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics,"
Journal of Human Resources,
University of Wisconsin Press, vol. 33(2), pages 251-299.
- John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," NBER Technical Working Papers 0220, National Bureau of Economic Research, Inc.
- J. Fitzgerald & P. Gottschalk & R. Moffitt, . "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Institute for Research on Poverty Discussion Papers 1156-98, University of Wisconsin Institute for Research on Poverty.
- John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of income Dynamics," Economics Working Paper Archive 379, The Johns Hopkins University,Department of Economics.
- 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.
- 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.
- 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.
- 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.
- Lien, Donald & Rearden, David, 1988. "Missing measurements in limited dependent variable models," Economics Letters, Elsevier, vol. 26(1), pages 33-36.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:26373. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.