Calculating Conditional Reliability for Dynamic Measurement Model Capacity Estimates

Dynamic measurement modeling (DMM) is a recent framework for measuring developing constructs whose manifestation occurs after an assessment is administered (e.g., learning capacity). Empirical studies have suggested that DMM may improve consequential validity of test scores because DMM learning capacity estimates were shown to be much less related to demographic factors like examinees' socioeconomic status compared to traditional single-administration item response theory (IRT)-based estimates. Though promotion of DMM has hinged on improved validity, no methods for computing reliability (a prerequisite for validity) have been advanced and DMM is sufficiently different from classical test theory (CTT) and IRT that known methods cannot be directly imported. This article advances one method for computing conditional reliability for DMM so that precision of the estimates can be assessed.