In this review, we give a general overview of latent variable models. We introduce the general model and discuss various inferential approaches. Afterward, we present several commonly applied special cases, including mixture or latent class models, as well as mixed models. We apply many of these models to a single data set with simple structure, allowing for easy comparison of the results. This allows us to discuss advantages and disadvantages of the various approaches, but also to illustrate several problems inherently linked to models incorporating latent structures. Finally, we touch on model extensions and applications and highlight several issues often ignored when applying latent variable models.


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