The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of the DIC for comparing latent variable models. In particular, the DIC calculated using the conditional likelihood (obtained by conditioning on the latent variables) is found to be inappropriate, whereas the DIC computed using the integrated likelihood (obtained by integrating out the latent variables) seems to perform well. In view of this, we propose fast algorithms for computing the DIC based on the integrated likelihood for a variety of highdimensional latent variable models. Through three empirical applications we show that the DICs based on the integrated likelihoods have much smaller numerical standard errors compared to the DICs based on the conditional likelihoods.