Evaluating Longitudinal Anchoring Methods for Rasch Models

Because modern, simultaneously estimated longitudinal Rasch models are unable to handle many timepoints, new methods of producing person and item estimates and evaluating test function are necessary. Longitudinal anchoring, in which a common scale of item parameters is used to estimate trait levels over multiple occasions, is a potential solution. With proper anchoring procedures, person and item estimates can be obtained without limiting the number of timepoints that can be analyzed. A simulation study examining the performance of six longitudinal anchoring methods (Floated, Racked, Time One, Mean, Random, and Stacked) was conducted. The Mean and the Stacked anchoring methods best recovered the population change over time, person and item estimates, and model fit. The Racked method could not produce reliable change estimates and should be avoided. Longitudinal anchoring is an easily implemented solution when analyzing large longitudinal datasets and shows promise as a low-computation method of producing latent trait estimates.