Chapter 4: Handling Nonsampling Errors

Chapter 4: Handling Nonsampling Errors

In Chapter 4 of the textbook Practical Methods for Design and Analysis of Complex Surveys, handling nonsampling errors concentrates on techniques to adjust for unit nonresponse and item nonresponse. Unit nonresponse refers to the situation in which data are not available within the survey data set for a number of sampling units. Reweighting can then be used and applied to the observations from the respondents, with the auxiliary information available for both the respondents and the nonrespondents. Item nonresponse means that in the data set to be analysed some values are present for a sample element, but at least for one item a value is missing for that element. Imputation implies simply that a missing value of the study variable y for a sample element k in the data matrix is substituted by an imputed value.


In Training Key 114, the effect of unit nonresponse on the bias of an estimator is demonstrated by reproducing the results of Example 4.1


In Training Key 117, reweighting is demonstrated by reproducing the results of Example 4.2. A reweighted Horvitz-Thompson estimator, the response homogeneity group (RHG) method and ratio estimation are applied for a SRSWOR sample involving some degree of unit nonresponse.


In Training Key 123, single imputation and multiple imputation are demonstrated by reproducing the results of Example 4.3. Mean imputation, the nearest neighbor method and ratio estimation, providing single imputation methods, are applied for a SRSWOR sample involving some degree of item nonresponse. In addition, multiple imputation is demonstrated briefly.  



NOTE: Instructions for the use of Training Keys are given in the Instructions section.