For example, you have conducted research on whether females and males, with and without jobs, are likely to be pet owners. The fact that these variables are dichotomous (each is simply a yes or no answer) makes it a little easier to check the odds of who is most likely to be a pet owner while using SPSS. When your variables have a wider range of possible responses, it gets a little more complex, but that’s something for another blog post. Let’s just start simply for now.
First, enter your data. Create three variables on the variable view tab called Pet Owner, Employed and Gender and fill in your responses on the Data View tab. Remember, you do not need to put ‘yes’ and ‘no’ responses into the Data View spreadsheet. By using the Values tab on the Variables View you can team numerical data with descriptions, so you could team ‘male’ with 1 and ‘female’ with 0, for example.
Now you need to perform a binary logistic regression analysis. Click on ‘Analyse’ in the toolbar and then ‘Regression’ and then ‘Binary Logistic’. Put ‘Pet Owners’ into the dependent variable space and then Employed and Gender into the Covariables space, then click ‘Ok’.
You will now be presented with a list of tables. The most important ones you need to look at are the Classification Table in Block 0, which will tell you how many people, unemployed and either female or male, are actually pet owners. Then look at the Classification Table in Block 1, which will show you what numbers the data predicted. If you have a good model to make predictions with, the numbers in Block 1 will be similar to or exactly those in Block 0, but if the predictions are way out, you may need to reconsider what data you use and how you deploy it.
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