title: Modeling local dependence in longitudinal IRT models.
abstract: Measuring change in a latent variable over time is often done using the same instrument at several time points. This can lead to dependence between responses across time points for the same person yielding within person correlations that are stronger than what can be attributed to the latent variable. Ignoring this can lead to biased estimates of changes in the latent variable. In this paper we propose a method for modeling local dependence in the longitudinal 2PL model. It is based on the concept of item splitting, and makes it possible to correctly estimate change in the latent variable.
authors.affiliation: Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
authors.forename: Maja
authors.initials: M
authors.lastname: Olsbjerg
authors.affiliation: Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark. kach@sund.ku.dk.
authors.forename: Karl Bang
authors.initials: KB
authors.lastname: Christensen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D017116:Low Back Pain / Q000175:diagnosis; D008960:Models, Psychological; D015233:Models, Statistical
keywords: IRT model; Local dependence; Longitudinal Rasch model; Rasch model; longitudinal IRT model