User's Manual

Chapter

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

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 a re 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...

Select a plan ﬁ le. Optionally, select a custom joint probabilities ﬁle.

E Click Continue.