The SURVEYLOGISTIC Procedure
- Overview
- Getting Started
-
Syntax
PROC SURVEYLOGISTIC Statement BY Statement CLASS Statement CLUSTER Statement CONTRAST Statement DOMAIN Statement EFFECT Statement ESTIMATE Statement FREQ Statement LSMEANS Statement LSMESTIMATE Statement MODEL Statement OUTPUT Statement REPWEIGHTS Statement SLICE Statement STORE Statement STRATA Statement TEST Statement UNITS Statement WEIGHT Statement -
Details
Missing Values Model Specification Model Fitting Survey Design Information Logistic Regression Models and Parameters Variance Estimation Domain Analysis Hypothesis Testing and Estimation Linear Predictor, Predicted Probability, and Confidence Limits Output Data Sets Displayed Output ODS Table Names ODS Graphics
-
Examples
- References
| Missing Values |
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 SURVEYLOGISTIC. 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 or FREQ variable, then that observation is excluded from the analysis.
An observation is also excluded if it has a missing value for any design (STRATA, CLUSTER, or DOMAIN) variable, unless you specify the MISSING option in the PROC SURVEYLOGISTIC 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 response, offset, or any explanatory variables 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 is not true sometimes. 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, FREQ, 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.