When do we use a logistic regression?
When we want to produce odds ratios to see if our independent variables (e.g. smoking: never smoker, ex-smoker, current smoker) predicts higher odds of the dependent variable (e.g. depression: yes or no). The outcome variable must have 2 categories.
Computing the odds ratio of having depression based on people's smoking behavior.
In this scenario, our dependent variable is depression, and it has 2 categories:
1=No (reference category)
Our independent variable is smoking behavior, and it has 3 categories:
1=Never smoked (reference category)
Our research question is:
Compared to those who never smoked, do those who are ex-smokers and/or those who are current smokers have higher odds of having depression?
Analyze-> Regression-> Binary Logistic
Select the dependent variable (depression) and move it into the Dependent box. Move the independent variable (smoke3) into the Covariates box.
Click the Categorical... box. Move smoke3 into the categorical covariates box because smoke3 is a categorical variable (do not need this step if your independent variable is a continuous variable). Select First as the Reference Category and click change, because we want the first group (never smoked) to be the reference category.
Click the Options... box. Tick CI for exp(B): 95% -> this will give you 95% confidence intervals for your odds ratios
As the 95%CI do not overlap, we can conclude that compared to those who have never smoked, ex-smokers have 1.14 times higher odds (95%CI=1.05 to 1.24), and current smokers have 1.79 times higher odds (95%CI=1.64 to 1.95) to be depressed.