In regression, and many methods, it is assumed that some variables are predicting/influencing other variables. These are the variables on the right side of the regression equation. There are many names for these, but I think the most prefered these days are predictor variables (independent variables is what they were called when I first learned statistics and many still use this). They influence what is on the left side of the equation the response variable (many call this the dependent variable).

I think by ratio you mean odds ratio. This is a calculation in logistic regression that shows the odds of being in one state of the response variable for a change in a predictor variable compared to the odds of being in the other state. So if you have an odds ratio of 4 and the state of the response variable you are maximising is attractive, that means for a one unit increase in the predictor variable (controlling for all others) it is four times more likely that a person would consider someone attractive than not attractive.

This is a form of regression called logistic regression (where the response variable has only two levels). You can also use it if the response variable has a few levels not just two but they are ordered (ordinal) - which is common for a likert scale variables. I am not sure that is the best method for someone brand new to statistics, it has taken me a while to learn it and I am still learning it.

A much simpler alternative might be doing something like a chi square test - although the results will not usually be as interesting as regression and you can only test two variables at a time not many predictors as in regression. Factor analysis is entirely different. It is a way of taking large data sets, large sets of variables measured by questions, and reducing them to a smaller set of factors. I was suggesting that you might look for common factors behind the questions you are using to predict the response variable with this. It does not show how one variable influences another variable, it just looks for common relationships behind what you measure.

If you are new to statistics you should get some introductory material. While not supper complex these methods are not simple in my opinion.