I have 20 yes/no ratings (did this product help you). People were able to rate as many of the 20 products as they used (on average people rated 5 products).

The outcome we are looking at is % rated helpful for each product. But some products were used by older people than were others (and age correlates with perceptions of helpfulness).

So I want to adjust ratings for age but given that I have multiple outcomes am not sure what would work best. Also, we are just comparing ratings here and there are no other predictors.

I was thinking an ANOVA comparing all 20 products with age as a covariate and then getting adjusted means ... but I'm not sure this is the right path.

So, are you interested in comparing the helpfulness of products to eachother, or what groups/characteristics were identified/correlated with helpfulness of each product individually? In the latter case, a discrete choice model may be useful, since the possible outcome is Bernoulli distributed (0/1). I assume that "helpful" =1. not "helpful" =0.

Assume that we can represent the error logistically, then:

outcome (0/1) = a + b*X +e, where b is a vector of coefficients and X is your covariate matrix. You can show the probability expression analytically( but can probably skip this step) that you used in estimation. Estimate by maximum likelihood.

Be careful in interpretation: coefficients do not represent total effects like OLS, but instead give the direction of the effect on probability of the product being classified as "helpful". You can estimate total effects by calculating the marginal effects. See a stats textbook for this.

Good Luck!

PS: you have other regression options as well: probit, linear probability, etc.