Among current state-of-art estimation methods for multilevel IRT models, the two-stage divide-and-conquer strategy has practical advantages, such as clearer definition of factors, convenience for secondary data analysis, convenience for model calibration and fit evaluation, and avoidance of improper solutions. However, various studies have shown that, under the two-stage framework, ignoring measurement error in the dependent variable in stage II leads to incorrect statistical inferences. To this end, we proposed a novel method to correct both measurement bias and measurement error of latent trait estimates from stage I in the stage II estimation. In this article, the HO-IRT model was considered as the measurement model, and a linear mixed effects model on overall (i.e., higher-order) abilities was considered as the structural model. The performance of the proposed correction method was illustrated and compared via a simulation study and a real data example using the National Educational Longitudinal Survey data (NELS 88). Results indicated that structural parameters could be recovered better after correcting measurement biases and errors. [This is the online version of article published in "British Journal of Mathematical and Statistical Psychology."]