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 might be biased and cannot accurately represent the survey population. There are a variety of techniques in sample design and survey operations that can reduce nonresponse. After data collection is complete, you can use imputation to replace missing values with acceptable values, and/or 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. See Cochran (1977); Kalton and Kaspyzyk (1986); and Brick and Kalton (1996) for more information.

If an observation has a missing value or a nonpositive value for the WEIGHT variable, then that observation is excluded from the analysis.

An observation is also excluded if it has a missing value for design variables such as STRATA variables, CLUSTER variables, and DOMAIN variables, unless missing values are regarded as a legitimate categorical level for these variables, as specified by the MISSING option.

If you specify the MISSING option in the PROC SURVEYMEANS statement, the procedure treats missing values of a categorical variable as a valid category.

By default, when computing statistics for an analysis variable, PROC SURVEYMEANS omits observations with missing values for that variable. The procedure computes statistics for each variable based only on observations that have nonmissing values for that variable. This treatment is based on the assumption that the missing values are missing completely at random (MCAR). However, this assumption is sometimes not true. For example, evidence from other surveys might suggest that observations with missing values are systematically different from observations without missing values. If you believe that missing values are not missing completely at random, then you can specify the NOMCAR option to let variance estimation include these observations with missing values in the analysis variables.

Whether or not the NOMCAR option is used, observations with missing or invalid values for WEIGHT, STRATA, CLUSTER, or DOMAIN variables are always excluded, unless the MISSING option is also specified.

When you specify the NOMCAR option, the procedure treats observations with and without missing values for analysis variables as two different domains, and it performs a domain analysis in the domain of nonmissing observations.

The procedure performs univariate analysis and analyzes each VAR variable separately. Thus, the number of missing observations might 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 use a REPWEIGHTS statement, all REPWEIGHTS variables must contain nonmissing values.