Rater behavior in essay grading can be viewed as a signal-detection task, in that raters attempt to discriminate between latent classes of essays, with the latent classes being defined by a scoring rubric. The present report examines basic aspects of an approach to constructed-response (CR) scoring via a latent-class signal-detection model. The model provides a psychological framework for CR scoring and includes rater parameters with a clear cognitive basis. Simulations are used to examine how well rater parameters and latent-class sizes are recovered as well as the accuracy of classification. The relation of rater parameters to agreement statistics and classification accuracy is examined. The effects of using a balanced, incomplete block design are compared to those for a fully crossed design. The model is applied to several ETS datasets.