This page shows an example of logistic regression with footnotes explaining the
output. These data were collected on 200 high schools students and are
scores on various tests, including science, math, reading and social studies (**socst**).
The variable **female** is a dichotomous variable coded 1 if the student was
female and 0 if male.

In the syntax below, the **get file** command is used to load the hsb2 data
into SPSS. In quotes, you need to specify where the data file is located
on your computer. Remember that you need to use the .sav extension and
that you need to end the command with a period. By default, SPSS does a
listwise deletion of missing values. This means that only cases with
non-missing values for the dependent as well as all independent variables will
be used in the analysis.

Because we do not have a suitable dichotomous
variable to use as our dependent variable, we will create one (which we will
call **honcomp**, for honors composition) based on the continuous variable **
write**. We do not advocate making dichotomous variables out of
continuous variables; rather, we do this here only for purposes of this
illustration.

Use the keyword **with** after the dependent variable to indicate all of the
variables (both continuous and categorical) that you want included in the model. If you
have a categorical variable with more than two levels, for example, a three-level ses variable (low, medium and high), you can use the
**categorical**
subcommand to tell SPSS to create the dummy variables necessary to include the
variable in the logistic regression, as shown below. You can use the
keyword **by** to create interaction terms. For example, the command **
logistic regression honcomp with read female read by female.** will create a
model with the main effects of **read** and **female**, as well as the
interaction of **read** by **female**.

We will start by showing the SPSS commands to open the data file, creating the dichotomous dependent variable, and then running the logistic regression. We will show the entire output, and then break up the output with explanation.

get file "c:\data\hsb2.sav". compute honcomp = (write ge 60). exe. logistic regression honcomp with read science ses /categorical ses.

**Logistic Regression**

**Block 0: Beginning Block**

**Block 1: Method = Enter**

This part of the output tells you about the
cases that were included and excluded from the analysis, the coding of the
dependent variable, and coding of any categorical variables listed on the **
categorical** subcommand. (Note: You will not get the third table
(“Categorical Variable Codings”) if you do specify the **categorical**
subcommand.)

**Logistic Regression**

b. **N** – This is the number of cases in each category (e.g.,
included in the analysis, missing, total).

c. **Percent** – This is the percent of cases in each category
(e.g., included in the analysis, missing, total).

d. **Included in Analysis** – This row gives the number and percent
of cases that were included in the analysis. Because we have no missing
data in our example data set, this also corresponds to the total number of
cases.

e. **Missing Cases** – This row give the number and percent of
missing cases. By default, SPSS **logistic regression** does a listwise
deletion of missing data. This means that if there is missing value for
any variable in the model, the entire case will be excluded from the analysis.

f. **Total** – This is the sum of the cases that were included in
the analysis and the missing cases. In our example, 200 + 0 = 200.

**Unselected Cases** – If the **select** subcommand is used and a logical condition is specified with a categorical variable in the dataset, then the number of unselected cases would be listed here. Using the **select** subcommand is different from using the **filter** command. When the **select** subcommand is used, diagnostic and residual values are computed for all cases in the data. If the **filter** command is used to select cases to be used in the analysis, residual and diagnostic values are not computed for unselected cases.

This part of the output describes a “null model”, which is model with no predictors and just the intercept. This is why you will see all of the variables that you put into the model in the table titled “Variables not in the Equation”.

**Block 0: Beginning Block**

c. **Step 0** – SPSS allows you to have different steps in your
logistic regression model. The difference between the steps is the
predictors that are included. This is similar to blocking variables into
groups and then entering them into the equation one group at a time. By
default,
SPSS logistic regression is run in two steps. The first step, called Step
0, includes no predictors and just the intercept. Often, this model is not
interesting to researchers.

d. **Observed** – This indicates the number of 0’s and 1’s that are
observed in the dependent variable.

e. **Predicted** – In this null model, SPSS has predicted that all
cases are 0 on the dependent variable.

f. **Overall Percentage** – This gives the percent of cases for
which the dependent variables was correctly predicted given the model. In
this part of the output, this is the null model. 73.5 = 147/200.

g. **B** – This is the coefficient for the constant (also called the
“intercept”) in the null model.

h. **S.E.** – This is the standard error around the coefficient for
the constant.

i. **Wald and Sig**. – This is the Wald chi-square test that tests
the null hypothesis that the constant equals 0. This hypothesis is
rejected because the p-value (listed in the column called “Sig.”) is smaller
than the critical p-value of .05 (or .01). Hence, we conclude that the
constant is not 0. Usually, this finding is not of interest to
researchers.

j. **df** – This is the degrees of freedom for the Wald chi-square
test. There is only one degree of freedom because there is only one
predictor in the model, namely the constant.

k. **Exp(B)** – This is the exponentiation of the B coefficient,
which is an odds ratio. This value is given by default because odds ratios
can be easier to interpret than the coefficient, which is in log-odds units.
This is the odds: 53/147 = .361.

l. **Score and Sig.** – This is a Score test that is used to predict
whether or not an independent variable would be significant in the model.
Looking at the p-values (located in the column labeled “Sig.”), we can see that
each of the predictors would be statistically significant except the first dummy
for **ses**.

m. **df** – This column lists the degrees of freedom for each
variable. Each variable to be entered into the model, e.g., **read**,
**science**, **ses(1)** and **ses(2)**, has one degree of freedom,
which leads to the total of four shown at the bottom of the column. The
variable **ses** is listed here only to show that if the dummy variables that
represent **ses** were tested simultaneously, the variable **ses** would
be statistically significant.

n. **Overall Statistics** – This shows the result of including all
of the predictors into the model.

The section contains what is frequently the most interesting part of the output: the overall test of the model (in the “Omnibus Tests of Model Coefficients” table) and the coefficients and odds ratios (in the “Variables in the Equation” table).

**Block 1: Method = Enter**

b. **Step 1** – This is the first step (or model) with predictors in
it. In this case, it is the full model that we specified in the **
logistic regression** command. You can have more steps if you do
stepwise or use blocking of variables.

c. **Chi-square** and **Sig.** – This is the chi-square statistic
and its significance level. In this example, the statistics for the Step,
Model and Block are the same because we have not used stepwise logistic
regression or blocking. The value given in the Sig. column is the
probability of obtaining the chi-square statistic given that the null hypothesis
is true. In other words, this is the probability of obtaining this
chi-square statistic (65.588) if there is in fact no effect of the independent
variables, taken together, on the dependent variable. This is, of course,
the p-value, which is compared to a critical value, perhaps .05 or .01 to
determine if the overall model is statistically significant. In this case,
the model is statistically significant because the p-value is less than .000.

d. **df** – This is the number of degrees of freedom for the model.
There is one degree of freedom for each predictor in the model. In this
example, we have four predictors: **read**, **write** and two
dummies for **ses** (because there are three levels of **ses**).

e. **-2 Log likelihood** – This is the -2 log likelihood for the
final model. By itself, this number is not very informative.
However, it can be used to compare nested (reduced) models.

f. **Cox & Snell R Square** and **Nagelkerke R Square **– These
are pseudo R-squares. Logistic
regression does not have an equivalent to the R-squared that is found in OLS
regression; however, many people have tried to come up with one. There are
a wide variety of pseudo-R-square statistics (these are only two of them). Because this statistic does
not mean what R-squared means in OLS regression (the proportion of variance
explained by the predictors), we suggest interpreting this
statistic with great caution.

g. **Observed** – This indicates the number of 0’s and 1’s that are
observed in the dependent variable.

h. **Predicted** – These are the predicted values of the dependent
variable based on the full logistic regression model. This table shows how
many cases are correctly predicted (132 cases are observed to be 0 and are
correctly predicted to be 0; 27 cases are observed to be 1 and are correctly
predicted to be 1), and how many cases are not correctly predicted (15 cases are
observed to be 0 but are predicted to be 1; 26 cases are observed to be 1 but
are predicted to be 0).

i. **Overall Percentage** – This gives the overall percent of cases
that are correctly predicted by the model (in this case, the full model that we
specified). As you can see, this percentage has increased from 73.5 for
the null model to 79.5 for the full model.

j. **B** – These are the values for the logistic regression equation
for predicting the dependent variable from the independent variable. They
are in log-odds units. Similar to OLS regression, the prediction equation is

**log(p/1-p) = b0 + b1*x1 + b2*x2 + b3*x3 + b3*x3+b4*x4**

where p is the probability of being in honors composition. Expressed in terms of the variables used in this example, the logistic regression equation is

**log(p/1-p)**** = –9.561 + 0.098*read + 0.066*science +
0.058*ses(1) – 1.013*ses(2)**

These estimates tell you about the relationship between the independent
variables and the dependent variable, where the dependent variable is on the
logit scale. These estimates tell the amount of
increase (or decrease, if the sign of the coefficient is negative) in the predicted log odds of honcomp = 1 that would be predicted by
a 1 unit increase (or decrease) in the predictor, holding all other predictors
constant. Note: For the independent variables which are not significant,
the coefficients are not significantly different from 0, which should be taken
into account when interpreting the coefficients. (See the columns labeled
Wald and Sig. regarding testing whether the coefficients are
statistically significant). Because these coefficients are in log-odds units, they are often
difficult to interpret, so they are often converted into odds ratios. You
can do this by hand by exponentiating the coefficient, or by looking at the
right-most column in the Variables in the Equation table labeled “Exp(B)”.
**read** – For every one-unit increase in reading score (so, for every
additional point on the reading test), we expect a 0.098 increase in the
log-odds of **honcomp**, holding all other independent variables
constant.
**science** – For every one-unit increase in science score, we expect
a 0.066 increase in the log-odds of **honcomp**, holding all other
independent variables constant.
**ses** – This tells you if the overall variable **ses** is
statistically significant. There is no coefficient listed, because **ses**
is not a variable in the model. Rather, dummy variables which code for **
ses** are in the equation, and those have coefficients. However, as you
can see in this example, the coefficient for one of the dummies is statistically
significant while the other one is not. The statistic given on this row
tells you if the dummies that represent **ses**, taken together, are
statistically significant. Because there are two dummies, this test has
two degrees of freedom. This is equivalent to using the **test**
statement in SAS or the **test** command is Stata.
**ses(1)** – The reference group is level 3 (see the Categorical
Variables Codings table above), so this coefficient represents the difference
between level 1 of **ses** and level 3. Note: The number in the
parentheses only indicate the number of the dummy variable; it does not tell you
anything about which levels of the categorical variable are being compared.
For example, if you changed the reference group from level 3 to level 1, the
labeling of the dummy variables in the output would not change.
**ses(2)** – The reference group is level 3 (see the Categorical
Variables Codings table above), so this coefficient represents the difference
between level 2 of **ses** and level 3. Note: The number in the
parentheses only indicate the number of the dummy variable; it does not tell you
anything about which levels of the categorical variable are being compared.
For example, if you changed the reference group from level 3 to level 1, the
labeling of the dummy variables in the output would not change.
**constant** – This is the expected value of the log-odds of **honcomp** when all of the predictor variables equal zero. In most cases,
this is not interesting. Also, oftentimes zero is not a realistic value
for a variable to take.

k. **S.E.** – These are the standard errors
associated with the coefficients. The standard error is used for testing
whether the parameter is significantly different from 0; by dividing the
parameter estimate by the standard error you obtain a t-value. The
standard errors can also be used to form a confidence interval for the
parameter.

l. **Wald** and **Sig.** – These columns provide the Wald
chi-square value and 2-tailed p-value used in testing the null hypothesis that
the coefficient (parameter) is 0. If you use a 2-tailed test, then
you would compare each p-value to your preselected value of alpha. Coefficients having p-values
less than alpha are statistically significant. For example, if you chose alpha
to be 0.05, coefficients having a p-value of 0.05 or less would be statistically
significant (i.e., you can reject the null hypothesis and say that the
coefficient is significantly different from 0). If you use a 1-tailed test
(i.e., you predict that the parameter will go in a particular direction), then
you can divide the p-value by 2 before comparing it to your preselected alpha
level.
For the variable **read**, the p-value is .000, so the null hypothesis
that the coefficient equals 0 would be rejected.
For the variable **science**, the p-value is .015, so the null
hypothesis that the coefficient equals 0 would be rejected.
For the variable **ses**, the p-value is .035, so the null hypothesis
that the coefficient equals 0 would be rejected. Because the test of the
overall variable is statistically significant, you can look at the one degree of
freedom tests for the dummies ses(1) and ses(2). The dummy ses(1) is not
statistically significantly different from the dummy ses(3) (which is the
omitted, or reference, category), but the dummy ses(2) is statistically
significantly different from the dummy ses(3) with a p-value of .022.

m. **df** – This column lists the degrees of freedom for each of the
tests of the coefficients.

n. **Exp(B)** – These are the odds ratios for the predictors.
They are the exponentiation of the coefficients. There is no odds ratio
for the variable **ses** because **ses** (as a variable with 2 degrees of
freedom) was not entered into the logistic regression equation.

## Odds Ratios

In this next example, we will illustrate the interpretation of odds ratios. In this example, we will simplify our model so that
we have only one predictor, the binary variable **female**. Before we
run the logistic regression, we will use the **crosstabs** command to obtain a
crosstab of the two variables.

crosstabs female by honcomp.

If we divide the number of males who are in honors composition, 18, by the number of males who are not in honors composition, 73, we get the odds of being in honors composition for males, 18/73 = .246. If we do the same thing for females, we get 35/74 = .472. To get the odds ratio, which is the ratio of the two odds that we have just calculated, we get .472/.246 = 1.918. As we can see in the output below, this is exactly the odds ratio we obtain from the logistic regression. The thing to remember here is that you want the group coded as 1 over the group coded as 0, so honcomp=1/honcomp=0 for both males and females, and then the odds for females/odds for males, because the females are coded as 1.

You can get the odds ratio from the **crosstabs** command by using the **
/statistics risk** subcommand, as shown below.

crosstabs female by honcomp /statistics risk.

As you can see in the output below, we get the same odds ratio when we run
the logistic regression. (NOTE: Although it is equivalent to the odds ratio estimated from the logistic regression, the odds ratio in the “Risk Estimate” table is calculated as the ratio of the odds of honcomp=0 for males over the odds of honcomp=0 for females, which explains the confusing row heading “Odds Ratio for female (.00/1.00)”). If we calculated a 95% confidence interval, we
would not want this to include
the value of 1. When we were considering the coefficients, we did not want
the confidence interval to include 0. If we exponentiate 0, we get 1
(exp(0) = 1). Hence, this is two ways of saying the same thing. As
you can see, the 95% confidence interval includes 1; hence, the odds ratio is
not statistically significant. Because the lower bound of the 95%
confidence interval is so close to 1, the p-value is very close to .05. We
can use the **/print = ic(95)** subcommand to get the 95% confidence
intervals included in our output.

There are a few other things to note about the output below. The first is that although we have only one predictor variable, the test for the odds ratio does not match with the overall test of the model. This is because the test of the coefficient is a Wald chi-square test, while the test of the overall model is a likelihood ratio chi-square test. While these two types of chi-square tests are asymptotically equivalent, in small samples they can differ, as they do here. Also, we have the unfortunate situation in which the results of the two tests give different conclusions. This does not happen very often. In a situation like this, it is difficult to know what to conclude. One might consider the power, or one might decide if an odds ratio of this magnitude is important from a clinical or practical standpoint.

logistic regression honcomp with female /print = ci(95).

For more information on interpreting odds ratios, please see How do I interpret odds ratios in logistic regression? . Although this FAQ uses Stata for purposes of illustration, the concepts and explanations are useful.