Student growth percentiles (SGPs, Betebenner, 2009) are used to locate a student's current score in a conditional distribution based on the student's past scores. Currently, following Betebenner (2009), quantile regression (QR) is most often used operationally to estimate the SGPs. Alternatively, multidimensional item response theory (MIRT) may also be used to estimate SGPs, as proposed by Lockwood and Castellano (2015). A benefit of using MIRT to estimate SGPs is that techniques and methods already developed for MIRT may readily be applied to the specific context of SGP estimation and inference. This research adopts a MIRT framework to explore the reliability of SGPs. More specifically, we propose a straightforward method for estimating SGP reliability. In addition, we use this measure to study how SGP reliability is affected by two key factors: the correlation between prior and current latent achievement scores, and the number of prior years included in the SGP analysis. These issues are primarily explored via simulated data. In addition, the QR and MIRT approaches are compared in an empirical application.