Chapter
10
Complex Samples Logistic Regression
The Complex Samples Logistic Regression procedure performs logistic regression analysis on a binary or multinomial dependent variable for samples drawn by complex sampling methods. Optionally, you can request analyses for a subpopulation.
Example. A loan officer has collected past records of customers given loans at several different branches, according to a complex design. While incorporating the sample design, the officer wants to see if the probability with which a customer defaults is related to age, employment history, and amount of credit debt.
Statistics. The procedure produces estimates, exponentiated estimates, standard errors, confidence intervals, t tests, design effects, and square roots of design effects for model parameters, as well as the correlations and covariances between parameter estimates. Pseudo R2 statistics, classification tables, and descriptive statistics for the dependent and independent variables are also available.
Data. The dependent variable is categorical. Factors are categorical. Covariates are quantitative variables that are related to the dependent variable. Subpopulation variables can be string or numeric but should be categorical.
Assumptions. The cases in the data file represent a sample from a complex design that should be analyzed according to the specifications in the file selected in the Complex Samples Plan dialog box.
Obtaining Complex Samples Logistic Regression
From the menus choose:
Analyze > Complex Samples > Logistic Regression...
ESelect a plan file. Optionally, select a custom joint probabilities file.
EClick Continue.
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