When we perform a multinomial regression in Stata, the table looks like this:
In this example, yvar is a nominal variable with three categories, whereas xvar1 is a binary (0/1) variable and xvar2 is a continuous variable ranging between 100 and 500.
The upper part of the table shows a model summary. This is what the different rows mean:
Log likelihood
This value does not mean anything in itself, but can be used if we would like compare nested models.
Number of obs
The number of observations included in the model.
LR chi2(x)
The likelihood ratio (LR) chi-square test. The number within the brackets shows the degrees of freedom (one per variable).
Prob >chi2
Shows the probability of obtaining the chi-square statistic given that there is no statistical effect of the x-variables on y. If the p-value is below 0.05, we can conclude that the overall model is statistically significant.
Pseudo R2
A type of R-squared value. Seldom used.
The lower part of the table presents the parameter estimates from the analysis.
The first column lists the y-variable on top, followed by our x-variable(s). We get one set of x-variables per level of the y-variable (always in comparison to the base outcome).
RRR
These are the relative risk ratios.
Std. Err.
The standard errors associated with the coefficient.
Z
Z-value (coefficient divided by the standard error of the coefficient).
P>|z|
P-value.
[95% Conf. Interval]
95% confidence intervals (lower limit and upper limit).
The analytical sample used for the examples
In the subsequent sections, we will use the following variables:
Dataset
StataData1.dta
Variable name
marstat40
Variable label
Marital status (Age 40, Year 2010)
Value labels
1=Married 2=Unmarried 3=Divorced 4=Widowed
Variable name
gpa
Variable label
Grade point average (Age 15, Year 1985)
Value labels
N/A
Variable name
sex
Variable label
Sex
Value labels
0=Man 1=Woman
Variable name
educ
Variable label
Educational level (Age 40, Year 2010)
Value labels
1=Compulsory 2=Upper secondary 3=University
sum marstat40 gpa sex educ
We define our analytical sample through the following command:
gen pop_multinom=1 if marstat40!=. & gpa!=. & sex!=. & educ!=.
This means that new the variable pop_multinom gets the value 1 if the four variables do not have missing information. In this case, we have 8,409 individuals that are included in our analytical sample.
