This page shows an example of a multinomial logistic regression analysis with
footnotes explaining the output. The data were collected on 200 high school
students and are scores on various tests, including a video game and a
puzzle. The outcome measure in this analysis is the student’s favorite flavor of
ice cream – vanilla, chocolate or strawberry- from which we are going to see
what relationships exists with video game scores (**video**), puzzle scores (**puzzle**)
and gender (**female**). The data set can be downloaded
here.

get file = 'D:mlogit.sav'.

Before running the regression, obtaining a frequency of the ice cream flavors in the data can inform the selection of a reference group. By default, SPSS uses the last category as the reference category.

frequencies /variables = ice_cream.

Vanilla is the most frequently preferred ice cream flavor and will be the
reference group in this example. In the data, vanilla is represented by the
number 2 (chocolate is 1, strawberry is 3). We will use the **nomreg**
command to run the multinomial logistic regression.

nomreg ice_cream (base = 2) with video puzzle female /print = paramter summary cps mfi.

In the above command, **base = 2 **indicates which level of the outcome
variable should be treated as the reference level. By default, SPSS sorts the
groups and chooses the last as the referent group.

## Case Processing Summary

b. **
N**
-N provides the number of observations fitting the description in the first
column. For example, the first three values give the number of observations for
which the subject’s preferred flavor of ice cream is chocolate, vanilla or
strawberry, respectively.

c.**
Marginal Percentage – **The marginal percentage lists the proportion of valid
observations found in each of the outcome variable’s groups. This can be
calculated by dividing the N for each group by the N for "Valid". Of the
200 subjects with valid data, 47 preferred chocolate ice cream to vanilla and
strawberry. Thus, the marginal percentage for this group is (47/200) * 100 =
23.5 %.

d.**
ice_cream** – **
**In this regression, the outcome variable is **ice_cream** which
contains a numeric code for the subject’s favorite flavor of ice cream. The data
includes three levels of **ice_cream** representing three different preferred
flavors: 1 = chocolate, 2 = vanilla and 3 = strawberry.

e**.
Valid – **This indicates the number of observations in the dataset where the
outcome variable and all predictor variables are non-missing.

f**.
Missing – **This indicates the number of observations in the dataset where data
are missing
from the outcome variable or any of the predictor variables.

g**. Total** – This indicates the total number of observations in the
dataset–the sum of the number of observations in which data are missing and the
number of observations with valid data.

h**.
Subpopulation – **This indicates the number of subpopulations
contained in the data. A subpopulation of the data consists of one
combination of the predictor variables specified for the model. For
example, all records where **female** = 0, **video** = 42 and **puzzle**
= 26 would be considered one subpopulation of the data. The footnote
SPSS provides indicates how many of these combinations of the predictor
variables consist of records that all have the same value in the outcome
variable. In this case, there are 143 combinations of **female**, **
video** and **puzzle** that appear in the data and 117 of these
combinations are composed of records with the same preferred flavor of ice cream.

## Model Fitting Information

i. **
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.

j.** -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.

k.** 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)) = 365.736 – 332.641 = 33.095, where *L(null model)* is
from the log likelihood with just the response variable in the model (Intercept
Only) and *L(fitted model)* is the log likelihood from the final iteration
(assuming the model converged) with all the parameters.

l.** 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 (three predictors in two models).

m.** Sig.** – This is the probability getting a LR test statistic being as
extreme as, or more so, than the observed statistic 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 (33.095), or one more extreme, 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, <0.00001, 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**

**Pseudo R-Square** – These are three pseudo R-squared values. 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-squared statistics which can give contradictory
conclusions. Because these statistics do not mean what R-squared means in OLS
regression (the proportion of variance of the response variable explained by the
predictors), we suggest interpreting them with great caution.

## Parameter Estimates

n. **B** – These are the estimated multinomial logistic regression
coefficients for the models. An important feature of the multinomial logit model
is that it estimates *k-1* models, where *k* is the number of levels
of the outcome variable. In this instance, SPSS is treating the vanilla as the
referent group and therefore estimated a model for chocolate relative to
vanilla and a model for strawberry relative to vanilla. Therefore, since the
parameter estimates are relative to the referent group, the standard
interpretation of the multinomial logit is that for a unit change in the
predictor variable, the logit of outcome *m* relative to the referent group
is expected to change by its respective parameter estimate (which is in log-odds
units) given the variables in the model are held constant.

** chocolate relative to vanilla**

** Intercept** – This is the multinomial logit estimate for chocolate**
**relative to vanilla when the predictor variables in the model are evaluated
at zero. For males (the variable **female** evaluated at zero) with zero **
video** and **puzzle** scores, the logit for preferring chocolate to vanilla is 1.912. Note that evaluating **video** and **puzzle**
at zero is out of the range of plausible scores, and if the scores were
mean-centered, the intercept would have a natural interpretation: log odds of
preferring chocolate to vanilla for a male with average **video**
and **puzzle** scores.

** video** – This is the multinomial logit estimate for a one unit
increase in **video** score for chocolate** **relative to vanilla given
the other variables in the model are held constant. If a subject were to
increase his **video** score by one point, the multinomial log-odds of
preferring chocolate to vanilla would be expected to decrease by 0.024 unit
while holding all other variables in the model constant.

** puzzle** – This is the multinomial logit estimate for a one unit
increase in **puzzle** score for chocolate** **relative to vanilla given
the other variables in the model are held constant. If a subject were to
increase his **puzzle** score by one point, the multinomial log-odds of
preferring chocolate** **to vanilla would be expected to decrease by 0.039 unit
while holding all other variables in the model constant.

** female** – This is the multinomial logit estimate comparing females
to males for chocolate** **relative to vanilla given the other variables in
the model are held constant. The multinomial logit for females relative to males
is 0.817 unit higher for preferring chocolate** **relative to vanilla given all
other predictor variables in the model are held constant. In other words,
females are more likely than males to prefer chocolate ice cream to vanilla ice
cream.

** strawberry relative to vanilla**

** Intercept** – This is the multinomial logit estimate for strawberry**
**relative to vanilla when the predictor variables in the model are evaluated
at zero. For males (the variable **female** evaluated at zero) with zero **
video** and **puzzle** scores, the logit for preferring strawberry** **to vanilla is -4.057.

** video** – This is the multinomial logit estimate for a one unit
increase in **video** score for strawberry** **relative to vanilla given
the other variables in the model are held constant. If a subject were to
increase his **video** score by one point, the multinomial log-odds for
preferring strawberry** **to vanilla would be expected to increase by 0.023
unit while holding all other variables in the model constant.

** puzzle** – This is the multinomial logit estimate for a one unit
increase in **puzzle** score for strawberry** **relative to vanilla given
the other variables in the model are held constant. If a subject were to
increase his **puzzle** score by one point, the multinomial log-odds for
preferring strawberry** **to vanilla would be expected to increase by 0.043
unit while holding all other variables in the model constant.

** female** – This is the multinomial logit estimate comparing females
to males for strawberry** **relative to vanilla given the other variables in
the model are held constant. The multinomial logit for females relative to males
is 0.033 unit lower for preferring strawberry** **to vanilla given all
other predictor variables in the model are held constant. In other words, males
are more likely than females to prefer strawberry ice cream to vanilla ice
cream.

o. **Std. Error** – These are the standard errors of the individual
regression coefficients for the two respective models estimated.

p. **Wald** – This is the Wald chi-square test that tests the null
hypothesis that the estimate equals 0.

q. **
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.

r. **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,
which can be calculated by dividing the square of the predictor’s estimate by
the square of its standard
error. 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. In multinomial logistic regression, the
interpretation of a parameter estimate’s significance is limited to the model in
which the parameter estimate was calculated. For example, the significance of a
parameter estimate in the chocolate relative to vanilla** **model cannot be
assumed to hold in the strawberry relative to vanilla** **model.

** chocolate relative to vanilla**

For chocolate** **relative to vanilla, the **Wald** test statistic
for the predictor **video** is 1.262 with an associated
p-value of 0.261. If we set our alpha level to 0.05, we would fail to reject the
null hypothesis and conclude that for chocolate** **relative to vanilla, the
regression coefficient for **video** has not been found to be statistically
different from zero given **puzzle** and **female** are in the model.

For chocolate relative to vanilla, the **Wald** test statistic for
the predictor **puzzle** is 3.978 with an associated p-value
of 0.046. If we again set our alpha level to 0.05, we would reject the null
hypothesis and conclude that the regression coefficient for **puzzle** has
been found to be statistically different from zero for chocolate** **relative
to vanilla given that **video** and **female** are in the model.

For chocolate relative to vanilla, the **Wald** test statistic for
the predictor **female** 4.362 with an associated p-value of
0.037. If we again set our alpha level to 0.05, we would reject the null
hypothesis and conclude that the difference between males and females has been
found to be statistically different for chocolate** **relative to vanilla
given that **video** and **female** are in the model.

For chocolate relative to vanilla, the **Wald** test statistic for
the intercept, **Intercept** is 2.878 with an associated p-value
of 0.090. With an alpha level of 0.05, we would fail to reject the null
hypothesis and conclude, a) that the multinomial logit for males (the variable
**female** evaluated at zero) and with zero **video** and **puzzle**
scores in chocolate** **relative to vanilla are found not to be statistically
different from zero; or b) for males with zero **video** and **puzzle**
scores, you are statistically uncertain whether they are more likely to be
classified as chocolate** **or vanilla. We can make the second interpretation
when we view the **Intercept** as a specific covariate profile (males with
zero **video** and **puzzle** scores). Based on the direction and
significance of the coefficient, the **Intercept** indicates whether
the profile would have a greater propensity to be classified in one level of the
outcome variable than the other level.

** strawberry relative to vanilla**

For strawberry** **relative to vanilla, the **Wald** test statistic
for the predictor **video** is 1.206 with an associated p-value
of 0.272. If we set our alpha level to 0.05, we would fail to reject the null
hypothesis and conclude that for strawberry** **relative to vanilla, the
regression coefficient for **video** has not been found to be statistically
different from zero given **puzzle** and **female** are in the model.

For strawberry** **relative to vanilla, the **Wald** test statistic for
the predictor **puzzle** is 4.675 with an associated p-value of
0.031. If we again set our alpha level to 0.05, we would reject the null
hypothesis and conclude that the regression coefficient for **puzzle** has
been found to be statistically different from zero for strawberry** **
relative to vanilla given that **video** and **female** are in the model.

For strawberry** **relative to vanilla, the **Wald** test statistic for
the predictor **female** is 0.009 with an associated p-value
of 0.925. If we again set our alpha level to 0.05, we would fail to reject the
null hypothesis and conclude that for strawberry** **relative to vanilla, the
regression coefficient for **female** has not been found to be statistically
different from zero given **puzzle** and **video** are in the model.

For strawberry** **relative to vanilla, the **Wald** test statistic for
the intercept, **Intercept** is 11.007 with an associated
p-value of 0.001. With an alpha level of 0.05, we would reject the null
hypothesis and conclude that a) that the multinomial logit for males (the
variable **female** evaluated at zero) and with zero **video** and **
puzzle** scores in strawberry** **relative to vanilla are statistically
different from zero; or b) for males with zero **video** and **puzzle**
scores, there is a statistically significant difference between the likelihood
of being classified as strawberry** **or vanilla. We can make the second
interpretation when we view the **Intercept** as a specific covariate
profile (males with zero **video** and **puzzle** scores). Based on the
direction and significance of the coefficient, the **Intercept** indicates
whether the profile would have a greater propensity to be classified in one
level of the outcome variable than the other level.

s. **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
**ice_cream** because **ice_cream **(as a variable with 2 degrees of
freedom) was not entered into the logistic regression equation. The odds ratio
of a coefficient indicates how the risk of the outcome falling in the comparison
group compared to the risk of the outcome falling in the referent group changes
with the variable in question. An odds ratio > 1 indicates that the risk of the
outcome falling in the comparison group relative to the risk of the outcome
falling in the referent group increases as the variable increases. In
other words, the comparison outcome is more likely. An odds ratio < 1
indicates that the risk of the outcome falling in the comparison group relative
to the risk of the outcome falling in the referent group decreases as the
variable increases. See the interpretations of the relative risk ratios below
for examples. In general, if the odds ratio < 1, the outcome is more likely to be
in the referent group. For more information on interpreting odds ratios, please see
How do I interpret
odds ratios in logistic regression? and
Understanding RR ratios in multinomial logistic regression .

** chocolate relative to vanilla**

** video** – This is the odds or "relative risk" ratio for a one unit
increase in **video** score for chocolate** **relative to vanilla level
given that the other variables in the model are held constant. If a subject
were to increase her **video** score by one unit, the relative risk for
preferring chocolate** **
to vanilla would be expected to decrease by a factor of 0.977 given the other variables in the model are held constant. So, given a
one unit increase in **video**, the relative risk of being in the chocolate**
**group would be 0.977 times more likely when the other variables in the model
are held constant. More generally, we can say that if a subject were to increase
her **video**
score, we would expect her to be more likely to prefer vanilla ice cream over
chocolate ice cream.

** puzzle** – This is the relative risk ratio for a one unit increase
in **puzzle** score for chocolate** **relative to vanilla level given that
the other variables in the model are held constant. If a subject were to
increase her **puzzle** score by one unit, the relative risk for preferring
chocolate** **
to vanilla would be expected to decrease by a factor of 0.962 given
the other variables in the model are held constant. More generally, we can say
that if two subjects have identical **video** scores and are both female (or both male),
the subject with the higher **puzzle** score is more likely to prefer vanilla
ice cream over chocolate ice cream than the subject with the lower **puzzle**
score.

** female** – This is the relative risk ratio comparing females to
males for chocolate** **relative to vanilla level given that the other
variables in the model are held constant. For females relative to males, the
relative risk for preferring chocolate relative to vanilla would be expected to
increase by a factor of 2.263 given the other variables in the model are held
constant. In other words, females are more likely than males to prefer chocolate
ice cream over vanilla ice cream.

** strawberry relative to vanilla**

** video** – This is the relative risk ratio for a one unit increase in
**video** score for strawberry** ** relative to vanilla** **level given
that the other variables in the model are held constant. If a subject were to
increase her **video** score by one unit, the relative risk for strawberry** **
relative to vanilla would be expected to increase by a factor of 1.023
given the other variables in the model are held constant. More generally, we can
say that if a subject were to increase her **video** score, we would expect
her to be more likely to prefer strawberry ice cream over vanilla ice cream.

** puzzle** – This is the relative risk ratio for a one unit increase
in **puzzle** score for strawberry** **relative to vanilla level given
that the other variables in the model are held constant. If a subject were to
increase her **puzzle** score by one unit, the relative risk for strawberry** **
relative to vanilla would be expected to increase by a factor of 1.044 given
the other variables in the model are held constant. More generally, we can say
that if two subjects have identical **video** scores and are both female (or both
male), the subject with the higher **puzzle** score is more likely to prefer
strawberry ice cream to vanilla ice cream than the subject with the lower **
puzzle** score.

** female** – This is the relative risk ratio comparing females to
males for strawberry** **relative to vanilla given that the other
variables in the model are held constant. For females relative to males, the
relative risk for preferring strawberry to vanilla would be expected to decrease
by a factor of 0.968 given the other variables in the model are held
constant. In other words, females are less likely than males to prefer
strawberry ice cream to vanilla ice cream.

t. **95% Confidence Interval for Exp(B)** – This is the Confidence
Interval (CI) for an individual multinomial odds ratio given the other
predictors are in the model for outcome *m* relative to the referent group.
For a given predictor with a level of 95% confidence, we’d say that we are 95%
confident that the "true" population multinomial odds ratio lies between
the lower and upper limit of the interval for outcome *m* relative to the
referent group. It is calculated as the Exp**(B** (z_{α/2})*(**Std.Error**)),
where z_{α/2} is a critical value on the standard normal distribution.
This CI is equivalent to the **z** test statistic: if the CI includes one,
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 a range where the "true" odds ratio may
lie.