Missing Values

The SURVEYMEANS Procedure

Missing Values

If an observation has a missing value or a nonpositive value for the WEIGHT variable, then PROC SURVEYMEANS excludes that observation from the analysis. An observation is also excluded if it has a missing value for any STRATA or CLUSTER variable, unless the MISSING option is used.

When computing statistics for an analysis variable, PROC SURVEYMEANS omits observations with missing values for that variable. The procedure bases statistics for each variable only on observations that have nonmissing values for that variable. If you specify the MISSING option in the PROC SURVEYMEANS statement, the procedure treats missing values of a categorical variable as a valid category.

The procedure performs univariate analysis and analyzes each VAR variable separately. Thus, the number of missing observations may be different for different variables. You can specify the keyword NMISS in the PROC SURVEYMEANS statement to display the number of missing values for each analysis variable in the "Statistics" table.

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 SURVEYMEANS. Refer to Cochran (1977) for more details.

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