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. For more information, see Cochran (1977); Kalton and Kasprzyk (1986); Brick and Kalton (1996).
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 from the analysis if it has a missing value for any design (STRATA , CLUSTER , or DOMAIN ) variable, unless you specify the MISSING option in the PROC SURVEYREG statement. If you specify the MISSING option, the procedure treats missing values as a valid (nonmissing) category for all categorical variables.
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 you specify the NOMCAR option, the procedure always excludes observations with missing or invalid values for the WEIGHT, STRATA, CLUSTER, and DOMAIN variables, unless you specify the MISSING option.
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.