I have five ordinal variables with a 7-point likert scale attached to them, where all 7 possible answers have a "meaning" (for example: 1 = totally disagree, 7 = totally agree). These five variables together are presumed to measure "Feelings of Autonomy", this has been confirmed by several analyses. Now I have constructed this "Feelings of Autonomy" scale by having SPSS calculate the mean of all five ordinal variables. Hence these 5 variables together form a scale, in which the values can contain decimals. Before conducting a regression analysis, I have to check normality of the scale variables. My scale variable is constructed by using five ORDINAL variables (it's not exactly the same as a scale variable representing seconds, height etc.), and therefore my question: Do I need to check for normality?
Before conducting a regression analysis, I have to check normality of the scale variables.
This notion is wrong. There is no assumption of normality of either the DV nor the IVs in regression. The usual hypothesis tests and confidence intervals and prediction intervals make use of an assumption of normality, of the error term in the regression model (equivalently, the conditional distribution of the DV is assumed to be normal) but you can't assess that by looking at the DV itself. The DV might be very far from normally distributed (e.g. it might be skewed or bimodal) without any problem for the assumptions of your inference. [It shouldn't matter what the distribution of the IVs is at all. There are things you might worry about but that isn't one of them.]
You know that the conditional distribution of the DV cannot actually be normal (so it's pointless to use hypotheses testing for it), but that's not really the relevant question, which is whether the extent to which it's non-normal will badly affect your properties of your inference. (i.e. its about how much effect your non-normality has, not whether it is normal; you already know it's not)
(In any case, there are alternative possible tests - and indeed estimators if that were felt necessary - one could use without assuming normality)
My scale variable is constructed by using five ORDINAL variables (it's not exactly the same as a scale variable representing seconds, height etc.),
Note that in order to add the components, you already assumed at the moment you added them that the components were interval -- as soon as you say a "5" plus a "2" is the same as a "3" plus a "4" or a "6" plus a "1" (and for that matter, a "5" on the second scale plus a "2" on the first scale) -- calling them all $7$. When you do that (along with all the other things that are treated as equal by the process of addition), you incorporate the assumption that "5" - "4" = "3" - "2" (and so forth), which is explicitly assuming you have interval data. You no longer have ordinal scales you already treated them all as interval.
So whether or not it was okay to add them (i.e. treat them as interval), the choice about whether they could be treated that way was already made back at the point you added the components. This is not something you can figure out from whether or not the sum looks normal (that's not relevant to any aspect of your question).
Do I need to check for normality?
If you need to assume something is normal it would be good to have some reason to think it's not so far from normal that it would badly affect your results -- or to avoid the assumption if you don't have a suitable reason to think so (but again, normality assumptions could be avoided in your inference, whether you use ordinary least squares linear regression or some other form of linear model). But if you are going to assess the assumption beware of looking at the wrong thing (per the earlier comments).