If you’ve ever clicked through SPSS’s menus and chosen a regression type based on what sounded right, you’re not alone.
Linear regression? Sounds classic.
Logistic regression? Vaguely math-y, but let’s try it.
Stepwise? Ooh — automatic variable selection? Sexy.
You’re looking at a wall of coefficients, R-squares, p-values, and standardized betas like
“…Is this good? Did I do it right? Why does my advisor look worried?”
Here’s the uncomfortable truth:
Most people using SPSS are running the wrong kind of regression for their data , and they don’t even know it.
SPSS Makes It Too Easy to Get It Wrong.
SPSS is great. It’s user-friendly. Menu-driven. No code needed. But that’s also the trap.
You don’t need to understand regression to run regression.
Which means most people
- Don’t check the outcome variable’s scale.
- Don’t test assumptions.
- Don’t understand the implications of choosing linear vs. logistic.
- Use “Stepwise” like it’s magic when it’s statistical gambling.
And that leads to wildly misleading results — and worse, false confidence.
Linear Regression: Your outcome variable is continuous (e.g., income, GPA, age).
Logistic Regression: Your outcome is binary (e.g., yes/no, 0/1, win/lose).
Multinomial Logistic: Your outcome is categorical with 3+ options (e.g., low/medium/high).
Ordinal Regression: Your outcome has ordered categories (e.g., strongly disagree → strongly agree).
Stepwise Regression?: Only if you know what you’re doing and hate replicability.
Let’s Wreck a Study (and Then Fix It)
You’re studying whether hours of sleep predict exam success (pass/fail).
WRONG: You use linear regression because it’s familiar.
You plug in “Pass” = 1 and “Fail” = 0 and call it a day.
Problem: Linear regression assumes your outcome is continuous. Pass/fail is binary.
You’re violating assumptions left and right. Your p-values are invalid. Your predictions make no statistical sense.
RIGHT: Use binary logistic regression.
SPSS makes it easy: Analyze → Regression → Binary Logistic.
Now, your model treats the outcome correctly, your interpretation is meaningful, and your stats won’t be laughed out of peer review.
Stepwise Regression: The Most Abused Tool in SPSS
You know that moment when you click “Stepwise” and SPSS chooses variables for you?
Yeah. About that:
- It’s not replicable (tiny changes in data can flip results).
- It overfits like crazy.
- It hides important relationships just because they’re not “significant” enough.
Stepwise regression is like letting your GPS plan your road trip blindfolded — sure, you’ll get somewhere, but probably not where you meant to go.
A Human-Friendly Guide: Choosing the Right Regression in SPSS
Ask yourself two questions before clicking anything:
What kind of variable am I predicting?
- Continuous = Linear Regression
- Binary = Binary Logistic
- Ordinal = Ordinal Regression
- Multi-category = Multinomial Logistic
What assumptions does my model need to meet?
- Linearity? Normality? Independence?
- (SPSS won’t warn you — you need to check.)
Continuous (e.g., score): Linear Regression
Binary (e.g., yes/no): Binary Logistic
Regression Categorical (3+ values): Multinomial Logistic Regression
Ordinal (ordered scale): Ordinal Regression
Regression Isn’t Just a Menu Option
It’s a model of the world. And if you choose the wrong one, your whole analysis is built on sand. Regression isn’t supposed to be fast — it’s supposed to be right. You can still use SPSS. But stop guessing. Start asking, What is my outcome? What are my assumptions?
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