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Hasan Jamil
Hasan Jamil

Reputation: 37

What to do if a significant factor in univariate linear regression become insignificant in multivariate analysis?

I am studying the impact of several factors "sleeping time, studying time, anxiety degree, depression degree ..." on students final exam mark.

when I did the univaraite linear regression analysis all models where significant (as final exam mark is a dependent variable) despite some have a small R^2.

then I tried to put all predictor factors in one multiple linear regression model, the result was most of the predictors are insignificant with exception to study time which was significant and has a big R^2 in uni and multi variate analysis.

How should I explain this in my paper? is it okay to have this result? or should I search for another model?

Upvotes: 1

Views: 1057

Answers (1)

Alison Weir
Alison Weir

Reputation: 1

I sound like you have highly correlated predictors. This gives you a very unstable model, one where small changes in a few observations could produce large changes in regression coefficients.

You should try various models that use subsets of your predictors, and select a final model that has a significant overall F statistic, and significant t stats for your included predictors.

In your paper, you could explain that anxiety score and depression score were too highly correlated to allow them both into the model and you’ve selected the best model that doesn’t contain both of these scores.

Upvotes: 0

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