Studies of Welfare Populations: Data Collection and Research Issues. Summary

06/01/2002

The occurrence of missing dataÑwhether for a unit or an item and whether due to nonresponse or noncoverage--creates the potential for bias. The potential for bias is particularly great in the presence of high nonresponse rates. In this paper, we provided brief descriptions of the methods most commonly used to adjust for unit nonresponse and noncoverage in general-purpose surveys. However, it is also very important to pay attention to nonresponse rates for each item in the questionnaire, and data analysts should consider using imputation procedures to compensate for missing items in the state surveys.

As discussed earlier, studies of the low-income population usually suffer from missing data. In studies that include only administrative data, noncoverage bias can result from using an incomplete administrative frame of eligible persons, and nonresponse occurs because of an inability to match the sample with the administrative file that includes the outcome data. Surveys are also subject to both underrepresentation due to nonresponse and frame noncoverage. Descriptions of nonresponse and frame noncoverage also are provided.

We also summarize the most commonly used procedures for nonresponse adjustments in multipurpose surveys. There are basically two types of adjustments, sample-based and population-based adjustments. The first group is based on procedures that use only sample information to reduce the nonresponse bias. The second approach uses external data to reduce the effects of both nonresponse and noncoverage. These adjustments are applied to respondents' records after the sample has been divided into a number of subgroups, called nonresponse adjustment classes. Adjustment methods for unit nonresponse involve deriving adjustment factors to be incorporated into sampling weights. A brief description of sample weighting is given in a previous section. When data are collected as part of a survey and sample weights are created, special procedures are needed to analyze the survey data. The previous section provides a brief review of the current procedures used to analyze weighted survey data.

Nonresponse adjustment methods can serve to reduce nonresponse bias. However, the total elimination of such bias generally is not possible, because within any weighting class the respondents ordinarily will not be fully representative of the nonrespondents. The impact of nonresponse bias is usually small in surveys with low nonresponse rates when nonresponse-adjusted weights are used along with the survey data. Although sample weighting cannot take all differences between respondents and nonrespondents into account, the weighting cells that are usually used appear, in general, to reduce the effect of any potential differences between respondents and nonrespondents.

The potential for bias is particularly great in the presence of high nonresponse rates. Thus, analysts are advised to take survey nonresponse rates and effects on the reliability of data into account when analyzing and reporting survey data. Analysis based on data from surveys with low nonresponse rates can be reported with a much higher level of confidence than those coming from surveys with high nonresponse rates.

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