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Logistic Regression in SPSS
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.
Example Scenario
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)
2=Yes
Our independent variable is smoking behavior, and it has 3 categories:
1=Never smoked (reference category)
2=Ex-smoker
3=Current smoker
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?
Step 1
Analyze-> Regression-> Binary Logistic
Step 2
Select the dependent variable (depression) and move it into the Dependent box. Move the independent variable
(smoke3) into the Covariates box.
Step 3
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.
Step 4
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.

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Logistic regression in spss

  • 1. Logistic Regression in SPSS 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. Example Scenario 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) 2=Yes Our independent variable is smoking behavior, and it has 3 categories: 1=Never smoked (reference category) 2=Ex-smoker 3=Current smoker 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? Step 1 Analyze-> Regression-> Binary Logistic
  • 2. Step 2 Select the dependent variable (depression) and move it into the Dependent box. Move the independent variable (smoke3) into the Covariates box.
  • 3. Step 3 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.
  • 4. Step 4 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.