In a previous post, I talked about complex survey designs and why analysis of such survey data requires the use of SAS survey procedures. PROC SURVEYREG and PROC SURVEYLOGISTIC have some of the same options available for output/diagnostics as do their non-survey counterparts, PROC REG and PROC LOGISTIC. Default output includes fit statistics (R squared, AIC, and Schwartz’s criterion), chi-squared tests of the global null hypothesis, degrees of freedom, and coefficient estimates for each parameter along with standard error of coefficient estimates and p-values. PROC SURVEYLOGISTIC also includes odds ratio point estimates and 95% Wald confidence intervals for each input parameter, as does PROC LOGISTIC.

The survey procedures are more limited in some ways, though. For example, PROC LOGISTIC can use an option such as stepwise selection to restrict the output to only predictors with significance above a certain level; there is also an option to rank those predictors. These options do not work with PROC SURVEYLOGISTIC, which makes the output more unwieldy with a large number of predictors. Most notably in terms of differences, PROC LOGISTIC automatically outputs a chi-squared test of the residuals for each input variable; however, any analysis of residuals is irrelevant for the survey procedures since assumptions of normality and equal variance are not applicable due to survey design. Tabled residuals are not output at all for the survey procedures, although covariance matrices are available for both as a non-default option. Similarly, influential observations/outliers are also not analyzed due to the use of person weights. As long as we use person weights, we would get the same coefficients with a regular PROC REG as we would with PROC SURVEYREG, but standard error estimates would be different and predictor significance could also vary.