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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. Aloanofficer 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 R
2
statistics, classification
tables, and descriptive statistics for the dependent and independent variables are also available.
Data. The d ependent 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 C omplex Sam ple s Logistic Regression
From the menus choose:
Analyze > Complex Samples > Logistic Regression...
E
Select a plan file. Optionally, select a custom joint probabilities file.
E Click Continue.
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