I want to understand the effect of IV on my DVs (each subject has multiple measurements). I realize that my default analysis approach is to use the subject id as the random variable and throw the IVs and DVs into the mixed-effects models. I think my default and simple approach makes total sense since my goal is to understand the effect of IV instead of other variables.

However, I am wondering when should we use the alternative approach, which is to quantitatively model individual differences by subjects' key characteristics (e.g., numerical variables like age, or categorical variables like gender). In addition, if we want to do that, do we still need to use mixed-effects models?

My default approach: DV ~ IV + (1|subject ID)

The alternative approach: DV ~ IV + subject's age + subject's gender

Any recommendations of textbooks/blogs about this topic are welcome! I need more systematic education about mixed-effects models.

Thank you so much!