This page shows an example of probit regression analysis with footnotes explaining the output in SPSS. The data in this example were gathered on undergraduates applying to graduate school and includes undergraduate GPAs, the reputation of the school of the undergraduate (a topnotch indicator), the students’ GRE score, and whether or not the student was admitted to graduate school. Using this dataset ( https://stats.idre.ucla.edu/wp-content/uploads/2016/02/probit.sav ), we can predict admission to graduate school using undergraduate GPA, GRE scores, and the reputation of the school of the undergraduate. Our outcome variable is binary, and we will use a probit model. Thus, our model will calculate a predicted probability of admission based on our predictors. The probit model does so using the cumulative distribution function of the standard normal.

First, let us examine the dataset and our response variable. Our binary
outcome variable must be coded with zeros and ones, so we will include a
frequency of our outcome variable **admit** to check this.

get file='C:\data\probit.sav'. descriptives variables=gre gpa.

frequencies variables=admit topnotch.

Next, we can specify our probit model using the **plum** command and indicating
probit as our link function.

plum admit with gre topnotch gpa /link = probit /print = parameter summary.

NOTE: It is also possible to run this probit regression in SPSS using **genlin**.
Please note that **distribution** and **link** are options on the **
/model** subcommand and are not separate subcommands (which is why there is no
/ in front of them).

genlin admit (reference=0) with gre gpa topnotch /model gre gpa topnotch distribution=binomial link=probit /print cps history fit solution.

## Case Processing Summary

a. **admit** – This is the response variable predicted by the model. Here,
we see that our outcome variable is binary and we are provided with frequency
counts. With our model, we predict the probability that **admit** is 1 for an
observation given the values of the predictors.

b. **Valid** – This is the number of observations in our dataset with
valid and non-missing data in the response and predictor variables specified in
our model.

c. **Missing** – This is the number of observations in our dataset with
missing data in the response or predictor variables specified in our model. Such observations will be excluded from the analysis.

d. **Total** – This is the total of the number of valid observations and
missing observations. It is equal to the number of observations in the
dataset.

## Model Fitting Information

e. **Model** – This indicates the parameters of the model for which the
model fit is calculated. “Intercept Only” describes a model that does not
control for any predictor variables and simply fits an intercept to predict the
outcome variable. “Final” describes a model that includes the specified
predictor variables and has been arrived at through an iterative process that
maximizes the log likelihood of the outcomes seen in the outcome variable. By
including the predictor variables and maximizing the log likelihood of the
outcomes seen in the data, the “Final” model should improve upon the “Intercept
Only” model. This can be seen in the differences in the **-2 Log Likelihood**
values associated with the models (see superscript f).

f.** -2 Log Likelihood **– This is the product of -2 and the log
likelihoods of the null model and fitted “final” model. The likelihood of the
model is used to test of whether all predictors’ regression coefficients in the
model are simultaneously zero and in tests of nested models.

g.** Chi-Square** – This is the Likelihood Ratio (LR) Chi-Square test that
at least one of the predictors’ regression coefficient is not equal to zero in
the model. The LR Chi-Square statistic can be calculated by -2*L(null model) –
(-2*L(fitted model)) = 479.887 – 457.797 = 22.090, where *L(null model)* is
from the log likelihood with just the response variable in the model (Iteration
0) and *L(fitted model)* is the log likelihood from the final iteration
(assuming the model converged) with all the parameters.

h. **df** – This indicates the degrees of freedom of the Chi-Square
distribution used to test the LR Chi-Sqare statistic and is defined by the
number of predictors in the model.

i.** Sig.** – This is the probability of getting a LR test statistic as
extreme as, or more so, than the observed under the null hypothesis; the null
hypothesis is that all of the regression coefficients in the model are equal to
zero. In other words, this is the probability of obtaining this chi-square
statistic (22.090) if there is in fact no effect of the predictor variables.
This p-value is compared to a specified alpha level, our willingness to accept a
type I error, which is typically set at 0.05 or 0.01. The small p-value from the
LR test, <0.0001, would lead us to conclude that at least one of the
regression coefficients in the model is not equal to zero. The parameter of the
Chi-Square distribution used to test the null hypothesis is defined by the
degrees of freedom in the prior column.

## Pseudo R-Square

j.** Psuedo R-Square **– These are several Pseudo R-Squareds. Probit
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. Because these statistics do not
mean what R-square means in OLS regression (the proportion of variance of the
response variable explained by the predictors), we suggest interpreting these
statistics with great caution. For more information on pseudo R-squareds, see
What are Pseudo R-Squareds?.

## Parameter Estimates

k. **Estimate** – These are the regression coefficients. The predicted
probability of admission can be calculated using these coefficients (the first
number in the column, the coefficient for “Threshold” is the constant term in
the model). For a
given record, the predicted probability of admission is

where *F* is the cumulative distribution function of the
standard normal. However, interpretation of the coefficients in probit
regression is not as straightforward as the interpretations of coefficients in
linear regression or logit regression. The increase in probability
attributed to a one-unit increase in a given predictor is dependent both on the
values of the other predictors and the starting value of the given predictors.
For example, if we hold **gre** and **topnotch** constant at zero, the one
unit increase in **gpa** from 2 to 3 has a different effect than the one unit
increase from 3 to 4 (note that the probabilities do not change by a common
difference or common factor):

and the effects of these one unit increases are different if we
hold **gre** and **topnotch** constant at their respective means instead
of zero:

However, there are limited ways in which we can interpret the individual regression coefficients. A positive coefficient mean that an increase in the predictor leads to an increase in the predicted probability. A negative coefficient means that an increase in the predictor leads to a decrease in the predicted probability.

**gre** – The coefficient of **gre** is 0.002.
This means that an increase in GRE score increases the predicted probability of
admission.

**topnotch** – The coefficient of **topnotch** is
0.273. This means attending a top notch institution as an undergraduate
increases the predicted probability of admission.

**gpa** – The coefficient of **gpa** is 0.401.
This means that an increase in GPA increases the predicted probability of
admission.

**Threshold [admit= .00]**– This is the constant term
in the model. The constant term is -2.798. This
means that if all of the predictors (**gre**, **topnotch** and **gpa**) are evaluated at
zero, the predicted probability of admission is F(-2.798) = .00257101. So,
as expected, the predicted probability of a student with a GRE score of zero and
a GPA of zero from a non-topnotch school has an extremely low predicted
probability of admission.

l. **Std. Error** – These are the standard errors of the individual
regression coefficients. They are used both in the calculation of the **Wald** test statistic, superscript
m, and the confidence interval of the
regression coefficient, superscript m.

m. **Wald** – These are the test statistics for the individual regression
coefficients. The test statistic is the squared ratio of the regression
coefficient** Estimate** to the **Std. Error** of the respective predictor. The
test statistic follows a Chi-Square distribution which is used to test against a
two-sided alternative hypothesis that the **Estimate** is not equal to zero.

n. **df **– This column lists the degrees of freedom for each of the
variables included in the model. For each of these variables, the degree of
freedom is 1.

o. **Sig.** – These are the p-values of the coefficients or the
probability that, within a given model, the null hypothesis that a particular
predictor’s regression coefficient is zero given that the rest of the predictors
are in the model. They are based on the **Wald** test statistics
of the predictors. The probability that a particular **Wald** test statistic
is as extreme as, or more so, than what has been observed under the null
hypothesis is defined by the p-value and presented here. By looking at the
estimates of the standard errors to a greater degree of precision, we can
calculate the test statistics and see that they match those produced in SPSS. To
view the estimates with more decimal places displayed, click on the Parameter
Estimates table in your SPSS output, then double-click on the number of
interest.

The **Wald **test statistic for the **Threshold **is** **18.664 with
an associated p-value <.0001. If we set our alpha level to 0.05, we would
reject the null hypothesis and conclude that the model intercept has been found
to be statistically different from zero given **gre, gpa** and **
topnotch** are in the model.

The **Wald** test statistic for the predictor **gre **is**
**5.667 with an associated p-value of 0.017. If we set our alpha level to
0.05, we would reject the null hypothesis and conclude that the regression
coefficient for **gre** has been found to be statistically different from
zero in estimating **gre **given **topnotch** and **gpa** are in the
model.

The **Wald** test
statistic for the predictor **topnotch **is** **2.292 with an associated
p-value of 0.130. If we set our alpha level to 0.05, we would fail to reject the
null hypothesis and conclude that the regression coefficient for **topnotch **
has not been found to be statistically different from zero in estimating **
topnotch **given **gre** and **gpa** are in the model.

The **Wald** test statistic for the predictor **gpa **is**
**4.237 with an associated p-value of 0.040. If we set our alpha level to
0.05, we would reject the null hypothesis and conclude that the regression
coefficient for **gpa **has been found to be statistically different from
zero in estimating **gpa **given **topnotch** and **gre** are in the
model.

p. **95% Wald Confidence Interval** – This is the confidence interval (CI)
of an individual poisson regression coefficient, given the other predictors are
in the model. For a given predictor variable with a level of 95% confidence,
we’d say that we are 95% confident that upon repeated trials 95% of the CI’s
would include the “true” population poisson regression coefficient. It is
calculated as **B** (z_{α/2})*(**Std.Error**), where z_{α/2}
is a critical value on the standard normal distribution. The CI is equivalent to
the z test statistic: if the CI includes zero, we’d fail to reject the null
hypothesis that a particular regression coefficient is zero, given the other
predictors are in the model. An advantage of a CI is that it is illustrative; it
provides information on where the “true” parameter may lie and the precision of
the point estimate.