Typically a longitudinal growth modeling based on item response theory (IRT) requires repeated measures data from a single group with the same test design. If operational or item exposure problems are present, the same test may not be employed to collect data for longitudinal analyses and tests at multiple time points are constructed with unique item sets, as well as a set of common items (i.e., anchor test) for a study of examinee growth. In this study, three IRT approaches to examinee growth modeling were applied to a single-group anchor test design and their examinee growth estimates were compared. In terms of tracking individual growth, growth patterns in the examinee population distribution, and the overall model–data fit, results show the importance of modeling the serial correlation over multiple time points and other additional dependence coming from the use of the unique item sets, as well as the anchor test.