In statistics, non-sampling error is a catch-all term for the deviations of estimates from their true values that are not a function of the sample chosen, including various systematic errors and random errors that are not due to sampling. Non-sampling errors are much harder to quantify than sampling errors.
Non-sampling errors in survey estimates can arise from:
- Coverage errors, such as failure to accurately represent all population units in the sample, or the inability to obtain information about all sample cases;
- Response errors by respondents due for example to definitional differences, misunderstandings, or deliberate misreporting;
- Mistakes in recording the data or coding it to standard classifications;
- Pseudo-opinions given by respondents when they have no opinion, but do not wish to say so
- Other errors of collection, nonresponse, processing, or imputation of values for missing or inconsistent data.
An excellent discussion of issues pertaining to non-sampling error can be found in several sources such as Kalton (1983) and Salant and Dillman (1995),
See also
References
- Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9
- Fritz Scheuren (2005). "What is a Margin of Error?", Chapter 10, in "What is a Survey? Archived 2013-03-12 at the Wayback Machine", American Statistical Association, Washington, D.C. Accessed 2008-01-08.
- ^ U.S. Census Bureau. March 2012. Introduction. Quarterly Financial Report for Manufacturing, Mining, Trade, and Selected Service Industries. Fourth Quarter 2011. p. xxi
- Kalton, Graham. Introduction to survey sampling. Vol. 35. Sage, 1983.
- Salant, Priscilla, and Don A. Dillman. "How to Conduct your own Survey: Leading professional give you proven techniques for getting reliable results." (1995).
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