Berg, Nathan (2005): Non-response bias. Published in: In Kempf-Leonard, K. (ed.), Encyclopedia of Social Measure , Vol. 2, (2005): pp. 865-873.
Download (118kB) | Preview
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.
|Item Type:||MPRA Paper|
|Original Title:||Non-response bias|
|Keywords:||Sampling Error, Non-Representative Sample, Bias, Mis-reporting, Misreporting, Non-response, Nonresponse, Missing, Imputation, Weighting, Randomized Response|
|Subjects:||D - Microeconomics > D0 - General > D03 - Behavioral Economics; Underlying Principles|
|Depositing User:||Nathan Berg|
|Date Deposited:||04. Nov 2010 09:14|
|Last Modified:||15. Feb 2013 19:33|
Burkam, D. T., and Lee, V. E. (1998). Effects of Monotone and Nonmonotone Attrition on Parameter Estimates in Regression Models with Educational Data: Demographic Effects on Achievement, Aspirations, and Attitudes. Journal of Human Resources 33, 555-575.
Fitzgerald, J., Gottschalk, P. and Moffitt, R. (1998). An Analysis of Sample Attrition in Panel Data: the Michigan Panel Study of Income Dynamics. Journal of Human Resources 33, 25-74.
Fox, J. A., and Tracy, P. E. (1986). Randomized Response: A Method for Sensitive Surveys. Sage Publications, Beverly Hills.
Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica 47, 153-161.
Hausman, J. A., Abrevaya, J., and Scott-Morton F. M. (1998). Misclassification of the Dependent Variable in a Discrete-Response Setting. Journal of Econometrics 87, 239-269.
Hurd, M. D., McFadden, D., Chand, H., Gan, L., Merrill, A., and Roberts, M. (1998). Consumption and Savings Balances of the Elderly: Experimental Evidence on Survey Response Bias. In Frontiers in the Economics of Aging (J. P. Smith, ed.), pp. 387-91. University of Chicago Press, Chicago.
Keeter, S. (1995). Estimating Telephone Noncoverage BiasWith a Telephone Survey. Public Opinion Quarterly 59, 196-217.
Kupek, E. (1998). Determinants of Item Nonresponse in a Large National Sex Survey. Archives of Sexual Behavior 27, 581-589.
Lee, B. J. and Marsh, L. C. (2000). Sample Selection Bias Correction for Missing Response Observations. Oxford Bulletin of Economics and Statistics 62, 305-322.
Lien, D. and Rearden, D. (1988). Missing Measurements in Limited Dependent Variable Models. Economics Letters 26, 33-36.
Little, R. J. A. and Rubin, D. B. (1990). The Analysis of Social Science Data with Missing Values . In Modern Methods of Data Analysis (J. Fox and J. S. Long, eds.), pp. 374-409. Sage, Newbury Park.
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley and Sons, New York.
Turner, H. A. (1999). Participation Bias in AIDS-Related Telephone Surveys: Results from the National AIDS Behavioral Survey (NABS) Non- Response Study. The Journal of Sex Research 36, 52-66.
Whitehead, J. C., Groothuis, P. A., and Blomquist, G. C. (1993). Testing for Non-Response and Sample Selection Bias in Contingent Valuation: Analysis of a Combination Phone/Mail Survey. Economics Letters 41, 215-220.