Diagnostic classification models (DCM) are restricted latent class models with a set of cross-class equality constraints and additional monotonicity constraints on their item parameters, both of which are needed to ensure the meaning of classes and model parameters. In this paper, we develop an efficient, Gibbs sampling-based Bayesian Markov chain Monte Carlo estimation method for general DCMs with monotonicity constraints. A simulation study was conducted to evaluate parameter recovery of the algorithm which showed accurate estimation of model parameters. An analysis of the 2000 Programme for International Student Assessment reading assessment data using this algorithm was also conducted.
A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models
