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?

Ultimately it is up to you. Statisticians and psychometricians would likely suggest you at least consider a latent variable approach because such an approach (among other things) would allow you to (1) investigate the properties of the items (e.g., at what areas of the latent trait does each item provide the most statistical information) and (2) statistically adjust regression estimates for attenuation. Investigation of item properties (i.e., 1) can be done using item response theory (IRT), and correction for attenuation (i.e., 2) can be done using structural equation modeling (SEM). Given your interest is to treat the latent variable as an independent variable, SEM may be the latent variable model most useful to your application. Though, as @bethanyP points out, the use of latent variable models is "non-trivial".
Because this is for a master's thesis, I would think your advisor would be ok with the composite variable approach (especially because, without prior experience, learning how to use and interpret latent variable methods such as SEM could take weeks). Just keep in mind that the use of composite variables may attenuate (i.e., artificially shrink) regression parameters involving latent variables.
