Bayesian estimation of item response theory models with power priors

In this paper, we propose the introduction of power priors in the Bayesian estimation of item response theory models. Within this approach, information coming from historical data can be used for the estimation of model parameters based on current data. In the literature, power priors have been discussed for generalized linear models. In this work, power priors are introduced as informative priors at item parameter and ability sampling steps within a Gibbs sampler scheme. By using data from the Hospital Anxiety and Depression Scale (HADS), the efficiency of this approach is demonstrated in terms of measurement precision with small samples.