Cronbach's $\alpha$ is only designed for measures that are essentially $\tau$-equivalent, which essentially means they contribute equally to the underlying construct. One way to test this is to see if they have the same factor loadings in a factor model. If your measures are not $\tau$-equivalent, then $\alpha$ will underestimate reliability, regardless if the data are continuous or dichotomous.
There are many indices of internal-consistency reliability. $\omega$ (omega) as identified by McDonald (1999) is one of the most flexible for unidimensional constructs, and it can be easily extended to a multidimensional construct. Here is a procedure I recommend taking to identify which measure of reliability to use:
1) First assess dimensionality. Do you have 1 construct or many? If there are many, then no measure of unidimensional reliability will be accurate. Do this with factor analysis, ideally confirmatory factor analysis (CFA), but if you don't have the knowledge or the software you can use exploratory factor analysis (EFA). If you have more than 1 factor that is substantive, then you have a multidimensional construct. If that's the case, look for a measure of multidimensional reliability (these exist for both $\alpha$ and $\omega$. See here. Alternatively, identify the items that don't fit your desired construct and remove them (though take caution here, there are a lot of other psychometric tests you should do as well).
2) Assess $\tau$-equivalence. Again doing this in a factor model may be easiest. Basically, you test to see if the loadings are all equal - in a CFA, you can constrain the loadings and test fit, in an EFA you just have to ballpark the loadings to see if they are reasonably close. If you have $\tau$ equivalence, go ahead and use $\alpha$. If not, use $\omega$.
From what I can tell, SPSS does not calculate $\omega$ (see here). In my view, R is one of the best packages out there for psychometrics because it has the flexibility to do all of this. If you don't know R and don't have the time/energy to learn it (it's a big leap from SPSS) then you can probably safely go with $\alpha$ if you construct is unidimensional, just keep in mind reliability will be higher than what $\alpha$ gives you.
Reference:
McDonald, R. P. (1999). Test Theory: A Unified Treatment. New York: Psychology Press.
You should not be using Cronbach's alpha in your case. I don't think you should be using any measure of internal reliability because your variables are not intended to form a scale.
And, if your professor really said that the first thing you do, in all cases, is Cronbach's alpha, then I would drop the class. That is like a cooking class where the teacher says "First, turn on your oven" even if the recipe involves no baking or broiling.
Best Answer
KR 20 is an algebraic simplification of alpha for dichotomous data. If your data are dichotomous then you can use either one -- they will give the same result.
KR 21 is an approximation that is of historical interest only. Don't use it.