Missing data cause a number of problems: analyses of data sets with missing data are more problematic than analyses of complete data sets; there is a lack of consistency among analyses because analysts compensate for missing data in different ways and their analyses may be based on different subsets of data; and, in the presence of nonresponse that is unlikely to be completely random, estimates of population parameters are biased.
Because missing data are always present to some degree, analyses of survey data must be based on assumptions about patterns of missing data. When missing data are not imputed or otherwise accounted for in the model being estimated, the implicit assumption is that data are missing at random after controlling for other variables in the model. The imputation procedures used for SIPP are based on the assumption that data are missing at random within subgroups of the population (as defined by the cells of the imputation matrices described later in this section). The statistical goal of imputation is to reduce the bias of survey estimates. This goal is achieved to the extent that systematic patterns of item nonresponse are correctly identified and modeled. In SIPP, the statistical goals of imputation are general, rather than specific. Instead of addressing the estimation of specific parameters, SIPP procedures are designed to provide reasonable estimates for a variety of analytical purposes.
Data editing is generally preferred over statistical imputation, and it is used whenever a missing item can be logically inferred from other data that have been provided. When information exists on the same record from which missing information can logically be inferred, that information is used to replace the missing information. The advantage of data editing is that it avoids the increase in variance that occurs when missing items on one record are imputed with nonmissing responses from other records.Types of Missing Data
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