The latent class reliability coefficient (LCRC) is improved by using the divisive latent class model instead of the unrestricted latent class model. This results in the divisive latent class reliability coefficient (DLCRC), which unlike LCRC avoids making subjective decisions about the best solution and thus avoids judgment error. A computational study using large numbers of items shows that DLCRC also is faster than LCRC and fast enough for practical purposes. Speed and objectivity render DLCRC superior to LCRC. A decisive feature of DLCRC is that it aims at closely approximating the multivariate distribution of item scores, which might render the method suited when test data are multidimensional. A simulation study focusing on multidimensionality shows that DLCRC in general has little bias relative to the true reliability and is relatively accurate compared to LCRC and classical lower bound methods coefficients α and λ2 and the greatest lower bound.