The answer to your question is YES you can apply ordinal logistic regression to predict DVs using categorical IVs. I am making a BIG ASSUMPTION here your DV have 2 interval something like LOW and HIGH.
I used it once to predict methods used for contraception methods. I assumed proportional odds assumption in our model. The trick is not just fitting all IVs in your model but to identify the IVs which influence DVs.
If you use R language then polr can help you. But in order to identify relevant IVs I used linear regression to find p-value for each IVs and remove IVs which had greater p-value. Ideally the rule I applied was eliminate IV one at a time with high p-value and then run the linear regression again till you find all IVs below p < 0.1.
Once I identified IVs I use polr
Suppose you have identified IVs : IV1 and IV2 and your Response variable is DV
o_reg <- polr(DV ~ IV1+ IV2, data = mydata, Hess=TRUE)
summary(o_reg) would give you the output in units of ordered logits, or ordered log odds.
Its slightly tricky to interpret polr output as it makes some assumption on the relationship between each pair of outcome groups. You need to read through the literature to find out.
Then comes the prediction part. Suppose you had test data as well ( we had testdata ) we used predict function in R to predict the probability of each DV value in your case it could be P(LOW) and P(HIGH).
predict(o_reg, testdata, type = "probs")
We prediction came out to be very accurate.
Again this was our experience this may or may not help.