Missing Values

The SURVEYREG Procedure

Missing Values

If an observation has a missing value or a nonpositive value for the WEIGHT variable, then PROC SURVEYREG excludes that observation from the analysis. An observation is also excluded if it has a missing value for any STRATA variable, CLUSTER variable, dependent variable, or any variable used in the independent effects. The analysis includes all observations in the data set that have nonmissing values for all these design and analysis variables.

If you have missing values in your survey data for any reason (such as nonresponse), this can compromise the quality of your survey results. If the respondents are different from the nonrespondents with regard to a survey effect or outcome, then survey estimates will be biased and will not accurately represent the survey population. There are a variety of techniques in sample design and survey operations that can reduce nonresponse. Once data collection is complete, you can use imputation to replace missing values with acceptable values, and you can use sampling weight adjustments to compensate for nonresponse. You should complete this data preparation and adjustment before you analyze your data with PROC SURVEYREG. Refer to Cochran (1977) for more details.

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