Bayesian continuous-time rasch models.

Continuous-time modeling offers a flexible approach to analyze longitudinal data from designs with unequally spaced measurement occasions. Measurement models are popular tools in psychological research to control for measurement error. The objective of the present article is to introduce the continuous-time Rasch model, a combination of the Rasch model and a continuous-time dynamic model. In a series of simulations we demonstrate the performance of the proposed model and that ignoring individual unequal time interval lengths, choosing a wrong measurement model, and selecting a wrong analysis strategy results in poor parameter estimates. The newly proposed continuous-time Rasch model overcomes these problems and offers a powerful new approach to longitudinal analysis with dichotomous items. A step-by-step tutorial on how to run a continuous-time Rasch model with the R package ctsem and an illustrative empirical example is given. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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