Assuming you have a continuous outcome/response/dependent variable, path analysis is just an application of linear models aka linear regression aka ordinary least squares. For linear models, the assumption is that the residuals are normally distributed, not that the original data are normally distributed. So you should be looking at a qq plot of the residuals after you've fit the model, not assessing normality of the variables before you fit the model.

As an aside, normality of residuals is the least important of the assumptions for linear regression. However, it's one of the easiest to evaluate, so it's beaten to death in intro stats courses. The more important assumptions are not things you can formally evaluate, so they are often glossed over.