Version info: Code for this page was tested in SPSS 20.

Logistic regression, also called a logit model, is used to model dichotomous
outcome variables. In the logit model the log odds of the outcome is modeled as a linear
combination of the predictor variables.

Please note: The purpose of this page is to show how to use various data analysis commands.
It does not cover all aspects of the research process which researchers are expected to do. In
particular, it does not cover data cleaning and checking, verification of assumptions, model
diagnostics and potential follow-up analyses.

Examples

Example 1:  Suppose that we are interested in the factors

that influence whether a political candidate wins an election.  The

outcome (response) variable is binary (0/1);  win or lose.

The predictor variables of interest are the amount of money spent on the campaign, the

amount of time spent campaigning negatively and whether or not the candidate is an

incumbent.

Example 2:  A researcher is interested in how variables, such as GRE (Graduate Record Exam scores),

GPA (grade

point average) and prestige of the undergraduate institution, effect admission into graduate

school. The response variable, admit/don’t admit, is a binary variable.

Description of the data

For our data analysis below, we are going to expand on Example 2 about getting
into graduate school.  We have generated hypothetical data, which can be
obtained from our website by clicking on binary.sav.
You can store this anywhere you like, but the syntax below assumes it has been
stored in the directory c:data.
This dataset has a binary response (outcome, dependent) variable called admit,
which is equal to 1 if the individual was admitted to graduate school, and 0
otherwise. There are three
predictor variables: gre, gpa, and rank. We will treat the variables
gre and gpa as continuous.
The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest
prestige, while those with a rank of 4 have the lowest. We start out by opening
the dataset and looking at some descriptive statistics.

get file = "c:databinary.sav".

descriptives /variables=gre gpa.frequencies /variables = rank.crosstabs /tables = admit by rank.

Analysis methods you might consider

Below is a list of some analysis methods you may have encountered.
Some of the methods listed are quite reasonable while others have either
fallen out of favor or have limitations.

Logistic regression

Below we use the logistic regression command to run a model predicting the outcome variable
admit, using gre, gpa, and rank. The categorical option
specifies that rank is a categorical rather than continuous variable. The
output is shown in sections, each of which is discussed below.

logistic regression admit with gre gpa rank 
   /categorical = rank.

The first table above shows a breakdown of the number of cases used and not
used in the analysis. The second table above
gives the coding for the outcome variable, admit.

The table above shows how the values of the categorical variable
rank were handled, there are terms (essentially dummy variables) in the model for
rank=1,
rank=2, and rank=3; rank=4 is the omitted category.

Things to consider

See also

References