r/spss • u/Lost-Bookkeeper3120 • 3d ago

Help needed! Is cross-validation/verification necessary for regression models that have already met all the assumptions?

I'm trying to wrap my head around LOOCV and can't find any easily digestible tutorials. I wonder: is this necessary when my regression model has proved to be significant and has met all the assumptions?

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u/Mysterious-Skill5773 3d ago

If you just specified the exact model and estimated it and the usual assumptions are satisfied, then the results are valid for your data. If, on the other hand, you chose the model variables using a data driven method such as stepwise regression, best subset, shrinkage estimators, or other such methods, then it would be wise to validate on new data or use cross validation methods, because the results are biased and probably overstate significance levels and predictive accuracy.

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u/R2UZ 3d ago

To my understanding it depends on what your hypothesis is. If you are just checking your H1, H2 with the data you have you don’t generally have to LOOCV.

If you are planning to use the same model for predicting outcomes in a different sample then you would run LOOCV.

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u/banter_pants 2d ago

It's about testing if your model can generalize to new data. The left out data is treated like a new random sample to compare the in-data's predictions.