This study examines the use of cross-classified random effects models (CCrem) and cross-classified multiple membership random effects models (CCMMrem) to model rater bias and estimate teacher effectiveness. Effect estimates are compared using CTT versus item response theory (IRT) scaling methods and three models (i.e., conventional multilevel model, CCrem, CCMMrem). Results indicate that ignoring rater bias can lead to teachers being misclassified within an evaluation system. The best estimates of teacher effectiveness are produced using CCrems regardless of scaling method. Use of CCMMrems to model rater bias cannot be recommended based on the results of this study; combining the use of CCMMrems with an IRT scaling method produced especially unstable results.