|
Size: 1889
Comment: R -> r
|
← Revision 8 as of 2026-02-06 20:21:41 ⇥
Size: 1893
Comment: R -> r
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| ## page was renamed from Statistics/NonResponseBias = Non-Response Bias = |
= Nonresponse Bias = |
| Line 4: | Line 3: |
| '''Non-response bias''' is bias introduced to estimates by non-response to a survey instrument. This is a critical component of [[Statistics/SurveyInference|survey error]]. | '''Nonresponse bias''' is bias introduced to estimates by [[Statistics/SurveyNonresponse|nonresponse]] to a survey instrument. This is a critical component of [[Statistics/SurveyInference|survey error]]. |
| Line 18: | Line 17: |
| == Addressing Non-Response == | == Addressing Nonresponse == |
| Line 24: | Line 23: |
| The simplest solution to non-response is to ignore incomplete responses. | The simplest solution to [[Statistics/SurveyNonresponse|nonresponse]] is to ignore incomplete responses. |
| Line 32: | Line 31: |
| Imputations solves non-response by creating a probable response value. | Imputations solves nonresponse by creating a probable response value. |
| Line 44: | Line 43: |
| == Addressing Non-Response Bias == | == Addressing Nonresponse Bias == |
| Line 46: | Line 45: |
| See [[Statistics/SurveyWeights#Non-Response_Adjustments|non-response adjustments]]. | See [[Statistics/SurveyWeights#Nonresponse_Adjustments|nonresponse adjustments]]. |
Nonresponse Bias
Nonresponse bias is bias introduced to estimates by nonresponse to a survey instrument. This is a critical component of survey error.
Contents
Description
Addressing Nonresponse
Case-wise Deletion
The simplest solution to nonresponse is to ignore incomplete responses.
This can introduce further bias into the response data, exacerbating systemic undercoverage.
Imputation
Imputations solves nonresponse by creating a probable response value.
One option for an imputed value is to reflect the collected responses. This would typically be one of the mean, median, or modal response. Alternatively, random response could be selected based on the observed distribution of responses. These approaches artificially lower the standard errors, however.
Another option is to randomly select a valid response from the set of similar respondent. The complete set of respondents would be filtered to just those similar to the non-respondent. One would be randomly selected from that subset, and their response would be 'borrowed'. This also leads to artificially low standard errors.
Multiple imputation by chained equations is an imputation method based on a repetition of a system of Bayesian equations. A primary value would be taken as given, and the system of equations would predict all others. This would be repeated to create a valid state. Analyses would be run multiple times in different states created from different given primary values. Standard errors will reflect uncertainty of the state.