  This study proposes a two-part model that includes components for reading
accuracy and reading speed. The speed component is a log-normal factor model,
for which speed data are measured by reading time for each sentence being
assessed. The accuracy component is a binomial-count factor model, where the
accuracy data are measured by the number of correctly read words in each
sentence. Both underlying latent components are assumed to be Gaussian in
nature. In this paper, the theoretical properties of the proposed model are
developed and an Monte Carlo EM algorithm for model fitting is outlined. The
predictive power of the model is illustrated in a real data application.
