In this line of research, we examine the implications and consequences of one’s choice of measurement model. Latent variable models (e.g., structural equation models) have become firmly established as the gold standard in psychological measurement, because they are able to account for measurement error and disentangle that error from reliable sources of variation. As a result, it is increasingly common to see these models used in all areas of psychological research, to “deal with” measurement error. But these models can create new problems (e.g., biased estimates) when the model assumptions are not met, potentially leading to incorrect scientific inferences (e.g., Rhemtulla, van Bork, & Borsboom, 2020). Our ongoing research examines alternative measurement models and explores ways to diagnose these problems in empirical research.