This could be due to a suppressor effect. A suppressor effect can occur when predictor variables are highly correlated with one another. In that case, one predictor can "suppress" variance in the other predictor and thereby enhance the predictive value of that other predictor. It is even possible that a predictor that is not significantly correlated with the DV turns out to be a "significant" predictor in a multiple regression due to suppression.

If you do a literature search for "suppression" or "suppressor effect" in regression, you should be able to find a number of relevant resources.

Dear Charlenne Ordonez

I think Christian Geiser has explained it already.

If you have run the necessary tests before run the regression, then it's fine. Otherwise, run the necessary tests. You may get help from the following book:

https://economics.ut.ac.ir/documents/3030266/14100645/Jeffrey_M._Wooldridge_Introductory_Econometrics_A_Modern_Approach__2012.pdf

Hello Charlenne Ordonez

When you are working with SPSS software, choose the following options

• Part and partial correlations
• Collinearity diagnostics
• Partial Regression Plots

In the next step, after getting the results of the software, search about any of the options above. You will find good results.

If the processes are correct, always if you are working with parametric data and there is normality, if so, you just have to explain

http://hummedia.manchester.ac.uk/institutes/cmist/archive-publications/working-papers/2020/multiple-linear-regression.pdf