The study looked at how accurately different models can analyze complex data with multiple levels of membership. By simulating various scenarios, the researchers found that ignoring certain aspects of the data structure led to incorrect estimates of parameters and model fit. Specifically, not accounting for multiple memberships at the intermediate level resulted in underestimating some factors and overestimating others. Overall, using a specific type of model that correctly incorporates all levels of membership provided the most accurate results.