I’m working with a really messy dataset. My dependent variables consist of Likert-scale responses that are judging a person’s speech (how annoying it sounds, how intelligent it sounds, etc). My independent variables mostly consist of the demographic/social data of the participants.
It has been suggested that a lot of the Likert-scale options that the participants responded to might be measuring a smaller set of latent variables. It’s been suggested that I use factor analysis to find these latent variables and thus reduce the number of dependent variables. My question is how to do this. I’ve been coached through the basics of FA using R, and I have the factor loadings for the different items. But I don’t know where to go from here.
For instance, my analysis seems to indicate that annoying, rude, being a know-it-all, and being unfriendly all load onto the same factor. This seems to make sense, thematically. But if I want to regress my (many) independent variables into this, how do I go about reducing the data set? Is there a way to combine those four dependent variables into a single dependent variable? Is there another way of reducing the dataset?
Am I totally off base here? I don’t want to continue with my analysis if this is a totally illogical way to go about handling this data. Apologies for how “layman" this is. My experience with stats is limited, but I’m trying to get my hands dirty.