The SURVEYLOGISTIC procedure is similar to the LOGISTIC procedure and other regression procedures in the SAS System. See Chapter 53, The LOGISTIC Procedure, for general information about how to perform logistic regression by using SAS. PROC SURVEYLOGISTIC is designed to handle sample survey data, and thus it incorporates the sample design information into the analysis.
The following example illustrates how to use PROC SURVEYLOGISTIC to perform logistic regression for sample survey data.
In the customer satisfaction survey example in the section Getting Started: SURVEYSELECT Procedure of Chapter 91, The SURVEYSELECT Procedure, an Internet service provider conducts a customer satisfaction survey. The survey population consists of the company’s current subscribers from four states: Alabama (AL), Florida (FL), Georgia (GA), and South Carolina (SC). The company plans to select a sample of customers from this population, interview the selected customers and ask their opinions on customer service, and then make inferences about the entire population of subscribers from the sample data. A stratified sample is selected by using the probability proportional to size (PPS) method. The sample design divides the customers into strata depending on their types ('Old' or 'New') and their states (AL, FL, GA, SC). There are eight strata in all. Within each stratum, customers are selected and interviewed by using the PPS with replacement method, where the size variable is Usage. The stratified PPS sample contains 192 customers. The data are stored in the SAS data set SampleStrata. Figure 87.1 displays the first 10 observations of this data set.
Customer Satisfaction Survey 
Stratified PPS Sampling 
(First 10 Observations) 
Obs  State  Type  CustomerID  Rating  Usage  SamplingWeight 

1  AL  New  2178037  Unsatisfied  23.53  14.7473 
2  AL  New  75375074  Unsatisfied  99.11  3.5012 
3  AL  New  116722913  Satisfied  31.11  11.1546 
4  AL  New  133059995  Neutral  52.70  19.7542 
5  AL  New  216784622  Satisfied  8.86  39.1613 
6  AL  New  225046040  Neutral  8.32  41.6960 
7  AL  New  238463776  Satisfied  4.63  74.9483 
8  AL  New  255918199  Unsatisfied  10.05  34.5405 
9  AL  New  395767821  Extremely Unsatisfied  33.14  10.4719 
10  AL  New  409095328  Satisfied  10.67  32.5295 
In the SAS data set SampleStrata, the variable CustomerID uniquely identifies each customer. The variable State contains the state of the customer’s address. The variable Type equals 'Old' if the customer has subscribed to the service for more than one year; otherwise, the variable Type equals 'New'. The variable Usage contains the customer’s average monthly service usage, in hours. The variable Rating contains the customer’s responses to the survey. The sample design uses an unequal probability sampling method, with the sampling weights stored in the variable SamplingWeight.
The following SAS statements fit a cumulative logistic model between the satisfaction levels and the Internet usage by using the stratified PPS sample:
title 'Customer Satisfaction Survey'; proc surveylogistic data=SampleStrata; strata state type/list; model Rating (order=internal) = Usage; weight SamplingWeight; run;
The PROC SURVEYLOGISTIC statement invokes the SURVEYLOGISTIC procedure. The STRATA statement specifies the stratification variables State and Type that are used in the sample design. The LIST option requests a summary of the stratification. In the MODEL statement, Rating is the response variable and Usage is the explanatory variable. The ORDER=internal is used for the response variable Rating to ask the procedure to order the response levels by using the internal numerical value (1–5) instead of the formatted character value. The WEIGHT statement specifies the variable SamplingWeight that contains the sampling weights.
The results of this analysis are shown in the following figures.
Customer Satisfaction Survey 
Model Information  

Data Set  WORK.SAMPLESTRATA  
Response Variable  Rating  
Number of Response Levels  5  
Stratum Variables  State  
Type  
Number of Strata  8  
Weight Variable  SamplingWeight  Sampling Weight 
Model  Cumulative Logit  
Optimization Technique  Fisher's Scoring  
Variance Adjustment  Degrees of Freedom (DF) 
PROC SURVEYLOGISTIC first lists the following model fitting information and sample design information in Figure 87.2:
The link function is the logit of the cumulative of the lower response categories.
The Fisher scoring optimization technique is used to obtain the maximum likelihood estimates for the regression coefficients.
The response variable is Rating, which has five response levels.
The stratification variables are State and Type.
There are eight strata in the sample.
The weight variable is SamplingWeight.
The variance adjustment method used for the regression coefficients is the default degrees of freedom adjustment.
Figure 87.3 lists the number of observations in the data set and the number of observations used in the analysis. Since there is no missing value in this example, observations in the entire data set are used in the analysis. The sums of weights are also reported in this table.
Number of Observations Read  192 

Number of Observations Used  192 
Sum of Weights Read  13262.74 
Sum of Weights Used  13262.74 
The "Response Profile" table in Figure 87.4 lists the five response levels, their ordered values, and their total frequencies and total weights for each category. Due to the ORDER=INTERNAL option for the response variable Rating, the category "Extremely Unsatisfied" has the Ordered Value 1, the category "Unsatisfied" has the Ordered Value 2, and so on.
Response Profile  

Ordered Value 
Rating  Total Frequency 
Total Weight 
1  Extremely Unsatisfied  52  2067.1092 
2  Unsatisfied  47  2148.7127 
3  Neutral  47  3649.4869 
4  Satisfied  38  2533.5379 
5  Extremely Satisfied  8  2863.8888 
Figure 87.5 displays the output of the stratification summary. There are a total of eight strata, and each stratum is defined by the customer types within each state. The table also shows the number of customers within each stratum.
Stratum Information  

Stratum Index 
State  Type  N Obs 
1  AL  New  22 
2  Old  24  
3  FL  New  25 
4  Old  22  
5  GA  New  25 
6  Old  25  
7  SC  New  24 
8  Old  25 
Figure 87.6 shows the chisquare test for testing the proportional odds assumption. The test is highly significant, which indicates that the cumulative logit model might not adequately fit the data.
Score Test for the Proportional Odds Assumption 


ChiSquare  DF  Pr > ChiSq 
911.1244  3  <.0001 
Figure 87.7 shows the iteration algorithm converged to obtain the MLE for this example. The "Model Fit Statistics" table contains the Akaike information criterion (AIC), the Schwarz criterion (SC), and the negative of twice the log likelihood () for the interceptonly model and the fitted model. AIC and SC can be used to compare different models, and the ones with smaller values are preferred.
Model Convergence Status 

Convergence criterion (GCONV=1E8) satisfied. 
Model Fit Statistics  

Criterion  Intercept Only 
Intercept and Covariates 
AIC  42099.954  41378.851 
SC  42112.984  41395.139 
2 Log L  42091.954  41368.851 
The table "Testing Global Null Hypothesis: BETA=0" in Figure 87.8 shows the likelihood ratio test, the efficient score test, and the Wald test for testing the significance of the explanatory variable (Usage). All tests are significant.
Testing Global Null Hypothesis: BETA=0  

Test  ChiSquare  DF  Pr > ChiSq 
Likelihood Ratio  723.1023  1  <.0001 
Score  465.4939  1  <.0001 
Wald  4.5212  1  0.0335 
Figure 87.9 shows the parameter estimates of the logistic regression and their standard errors.
Analysis of Maximum Likelihood Estimates  

Parameter  DF  Estimate  Standard Error 
Wald ChiSquare 
Pr > ChiSq  
Intercept  Extremely Unsatisfied  1  2.0168  0.3988  25.5769  <.0001 
Intercept  Unsatisfied  1  1.0527  0.3543  8.8292  0.0030 
Intercept  Neutral  1  0.1334  0.4189  0.1015  0.7501 
Intercept  Satisfied  1  1.0751  0.5794  3.4432  0.0635 
Usage  1  0.0377  0.0178  4.5212  0.0335 
Figure 87.10 displays the odds ratio estimate and its confidence limits.
Odds Ratio Estimates  

Effect  Point Estimate  95% Wald Confidence Limits 

Usage  1.038  1.003  1.075 