DOI: 10.1007/s11336-002-1047-z AN IRT MODEL WITH A PARAMETER-DRIVEN PROCESS FOR CHANGE

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative interindividual differences and autodependencies are accounted for by assuming within-subject variability with respect to the parameters of the IRT model. In particular, the parameters of the IRT model are governed by an unobserved or “hidden” ’ homogeneous Markov process. The model includes the mixture linear logistic test model (Mislevy & Verhelst, 1990), the mixture Rasch model (Rost, 1990), and the Saltus model (Wilson, 1989) as specific instances. The model is applied to a longitudinal experiment on discontinuity in conservation acquisition (van der Maas, 1993). Key words: latent markov model, item response theory, panel data. In longitudinal data, observations made on different subjects tend to be more heterogeneous than observations within the same subject. As a result, repeated observations of the same individ-uals are not statistically independent. Traditional latent class analysis (LCA) and item response theory (IRT) models are models for categorical longitudinal data that take the between-subject variability into account by incorporating latent variables. In LCA, the latent variables are categor-ical, each value representing a latent class. In IRT, the latent variables are continuous. LCA is bes