Still Using Multiple Regression for Everything? When You Should Be Using Multivariate

Photo by Markus Winkler on Unsplash

If you’ve been using multiple regression for all your predictive modeling, even when you have multiple dependent variables, you’re not alone. You’re probably running the wrong analysis. And I don’t mean a small “wrong.”

I mean, your whole model may be invalid, your p-values misleading, and your conclusions off the mark.

Multiple ≠ Multivariate (Yes, Really)

Just because you have multiple predictors doesn’t mean you’re doing multivariate analysis.

That’s multiple regression:

• 1 dependent variable
• 2+ independent variables
• Fine for most classic use cases

But what about when your study involves more than one outcome (like test scores and satisfaction scores or depression and anxiety measures)?

That’s a multivariate problem. And using multiple regression in that case is like trying to fly a plane using a bicycle manual.

Why This Mix-Up Happens

This confusion shows up everywhere — student theses, published articles, and even dashboards built by data pros — because tools like SPSS, R, and Python make it stupidly easy to run multiple regressions, even when it’s statistically inappropriate.

Y1 ~ X1 + X2
Y2 ~ X1 + X2

But the second you split those outcomes into separate models, you ignore something huge:

The potential correlation between your dependent variables.

And that’s not just a technicality. It can massively inflate Type I error, distort your interpretation, and lead to conclusions that don’t hold up.

Here’s the Rule No One Teaches You

If your developers are conceptually related or likely correlated, you should be using multivariate regression, not multiple regression.

Bad Approach (Multiple Regression):

• Run a separate model for math score, reading score, and writing score.
• Treat them like three different problems.

Better Approach (Multivariate Regression):

• Treat them as joint outcomes.
• Acknowledge that students who do well in math often do well in reading, too.
• Model those correlations directly.

By doing this, your analysis

• Controls for DV correlations
• Reduces false positives
• Gives a clearer picture of which predictors affect all outcomes — and how.

3 Signs You Should Switch to Multivariate Analysis

• You Have Multiple Dependent Variables That Feel “Connected.”: Like physical health and mental health scores?
• Your Dependent Variables Are Correlated: Check with a correlation matrix — if r > .3, start thinking multivariate.
• You Want to Know How Predictors Affect Outcomes Together: Sometimes you care if a predictor changes the whole pattern, not just one result

Tools That Support Multivariate Analysis the Right Way

• SPSS → Use GLM > Multivariate (not the default linear regression!)
• R → Use manova() or mlm models.
• Python (Statsmodels) → Use MANOVA or dig deeper with multivariate stats packages.
• Jamovi → Has a friendly MANOVA interface

Why Most People Don’t Use It

• It’s buried under menus in SPSS.
• It sounds more “advanced.”
• People don’t feel the difference unless they’ve seen a model break in real life.
• Most tutorials oversimplify regression and skip this nuance.

That’s why you’ll see hundreds of academic papers running multiple regressions for multivariate outcomes, and reviewers never call it out.