Large-scale educational assessments such as the National Assessment of Educational Progress (NAEP) sample examinees to whom an exam will be administered. In most situations the sampling design is not a simple random sample and must be accounted for in the estimating model. After reviewing the current operational estimation procedure for NAEP, this paper describes a Bayesian hierarchical model for the analysis of complex large-scale assessments. The model clusters students within schools and schools within primary sampling units. The paper discusses an estimation procedure that utilizes a Markov chain Monte Carlo algorithm to approximate the posterior distribution of the model parameters. Results from two Bayesian models, one treating item parameters as known and one treating them as unknown, are compared to results from the current operational method on a simulated data set and on a subset of data from the 1998 NAEP reading assessment. The point estimates from the Bayesian model and the operational method are quite similar in most cases, but there does seem to be systematic differences in measures of uncertainty (e.g., standard errors, confidence intervals). [Note: Figure 1 is missing from the ERIC version of this report.]