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 SURVEYREG. 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.

By default, if an observation contains missing values for the dependent variable or for any variable used in the independent effects, the observation is excluded from the analysis. This treatment is based on the assumption that the missing values are missing completely at random (MCAR). However, this assumption sometimes is 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 include these observations with missing values in the dependent variable and the independent variables in the variance estimation.

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 variables in the regression model as two different domains, and it performs a domain analysis in the domain of nonmissing observations.

If you use a REPWEIGHTS statement, all REPWEIGHTS variables must contain nonmissing values.