Normality Assumption - how to check you have not violated it?

Question

I am rleatively new to statistics and am stuggling with the normality assumption.

I understand that parametric tests are underpinned by the assumption that the data is normally distributed, but there seems to be lots of papers and articles providing conflicting information.

Some articles say that independant variables need to be normally disrbiuted and this may require a transformation (log, SQRT etc.). Others says that in linear modelling there are no assumptions about any linear the distribution of the independent variables.

I am trying to create a multiple regression model to predict highest pain scores on hospital admissions:

DV: numeric pain scores (0-no pain -> 5 intense pain)(discrete- dependant variable).

IVs: age (continuous), weight (continuous), sex (nominal), depreviation status (ordinal), race (nominal).

Can someone help clear up the following for me?

1. Before fitting a model, do I need to check the whether my independant variables are normally distributed? If so, why? Does this only apply to continuous variables (e.g. age and weight in my model)?

2. If age is positively skewed, would a transformation (e.g. log, SQRT) be appropriate and why? Is it best to do this before or after fitting a model? I assume I am trying to get close to a linear relationship between my DV and IV.

3. As part of the SPSS outputs it provides plots of the standardised residuals against predicted values and also normal P-P plots of standardised residuals. Are these tests all that is needed to check the normality assumption after fitting a model?

Many Thanks in advance!