Users of SIPP data interested in assessing the influence of imputed data on their analyses should consider whether SIPP imputation procedures have properties that affect their specific analytical requirements. A general discussion of the treatment of missing data in sample surveys is given in Kalton and Kaspyrzyk (1986). Sedransk (1985), Little (1986), and Jinn and Sedransk (1987) discuss properties of commonly used imputation processes. An example of the impact of imputation procedures on the distributional characteristics of a low-income population is discussed in Doyle and Dalrymple (1987).
An evaluation of the effects of imputed data should include a review of rates of unit nonresponse and an assessment of the extent of item nonresponse. Unit nonresponse tends to increase over the life of a panel, as does the likelihood that nonresponse is not a random effect. And as the percentage of eligible sample members re-interviewed decreases, the pool from which donors3 are selected shrinks accordingly. This smaller pool of donors leads to an increased likelihood that individual donors will be used more than once, which in turn increases the variance of an estimate.
The effects of imputation will likely be small for items with low rates of missing data as long as rates of item nonresponse are not high among important subclasses. Lepkowski et al. (1987), using data from a large federal survey, provide a framework for evaluating the effect of imputed values on analyses. This framework can be readily adapted to SIPP analyses.Types of Missing Data
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