Should I use composite or latent variables? I am writing my master thesis about how the difference between majority's (Belgians) opinion about socially relevant topics and majority's perception of minority's (Turks and Moroccan's) opinion about socially relevant topics predicts intergroup hostility. For example, the data I am using has three questions about Belgians opinion about the role of women in society (e.g. "To what extent do you agree that men are more apt to lead than women"). I want to somehow compose those three questions to get an independent variable that would measure Belgians opinion about the role of women in society. I am confused about which approach would be more suitable for this; making latent variables or composite variables?

You say you have three questions about feelings toward women aimed at uncovering hostility, by description this is a latent variable analysis.
It is good way to uncover opinions, especially ones where people might have a reason to be elusive about how they actually feel.
There are very specific methods for doing this well and extracting meaningful inference. One of the more robust is using a Likert Analysis (see video here).
In a Likert analysis you have several questions aimed at discovering the level of a SINGLE latent variable. The creation of the scales & questions is non-trivial.
Although you say you have questions already, you may need to revisit and tune those questions to create meaningful variables with as little bias as possible.
The most important thing to understand is that all of the questions are reflective of a single variable and they must all be contributive in some way. But from your own description, this sounds like the way to go.
A good course (at your school or online) in the creation and evaluation of surveys might be a solid place to start!
