Another line of research in our lab examines methods and practices within the young field of network psychometrics, in which network models –typically undirected graphical models (a.k.a., Markov random fields) –are fit to data from a set of observed variables as a way to understand the structure of a psychological construct. These models have become popular as an alternative to latent variable models, because they have the potential to foster a finer-grained understanding of how particular observed indicators of a construct relate to each other. Being a relatively new set of models for psychological data, though, there are many open methodological questions. We work on some of those questions, such as: which estimation methods produce network estimates with the best statistical properties? What factors lead networks to be more or less likely to replicate in new data?