# Why is my multiple regression only able to explain a small amount of variance?

I have run multiple regression models with factor scores as the DV with several predictor variables (all dichotomous patient characteristics e.g. gender, age). Whilst some predictors and the models are significant, it can only explain less than 2% of the variance at best. We have a large sample size of over 800 participants.

Could this be because... - all the predictors variables are dichotomous - the predictors are not highly correlated with the DV (e.g. ~0.1) - there is not enough variance in the DV

My main questions are why is there such little variance explained, and would it be an issue to report the significant predictors within a paper yet with the overall model only explaining about 2% of the variance?

Thank you

• It would certainly be an issue if the overall model F-test is not significant--which it very well might not be with such a low $R^2$ (depending on how many variables you have considered).
– whuber
Apr 14, 2020 at 16:09
• Thank you for your comment. The overall model was always significant and the predictors also often highly significant. Apr 15, 2020 at 8:43