It is not enough to simply continue to invent models in order to address substantive research problems. At some point, there is often a need for individual researchers to select among one of several competing models. This model selection process is partly holistic in that it balances theoretical concerns, evaluation of overall model fit and relative model fit, and concerns regarding overfitting or model complexity. The ultimate goal is to pick an interim model that we believe has good utility, yet is also parsimonious and may generalize to other datasets. My research in this area seeks to provide computationally efficient ways to perform model selection, winnowing down the number of available models, and help inform model selection choices based in part on parsimony.