In item response theory (IRT) models, assessing model-data fit is an essential step in IRT calibration. While no general agreement has ever been reached on the best methods or approaches to use for detecting misfit, perhaps the more important comment based upon the research findings is that rarely does the research evaluate IRT misfit by focusing on the practical consequences of misfit. The study investigated the practical consequences of IRT model misfit in examining the equating performance and the classification of examinees into performance categories in a simulation study that mimics a typical large-scale statewide assessment program with mixed-format test data. The simulation study was implemented by varying three factors, including choice of IRT model, amount of growth/change of examinees' abilities between two adjacent administration years, and choice of IRT scaling methods. Findings indicated that the extent of significant consequences of model misfit varied over the choice of model and IRT scaling methods. In comparison with mean/sigma (MS) and Stocking and Lord characteristic curve (SL) methods, separate calibration with linking and fixed common item parameter (FCIP) procedure was more sensitive to model misfit and more robust against various amounts of ability shifts between two adjacent administrations regardless of model fit. SL was generally the least sensitive to model misfit in recovering equating conversion and MS was the least robust against ability shifts in recovering the equating conversion when a substantial degree of misfit was present. The key messages from the study are that practical ways are available to study model fit, and, model fit or misfit can have consequences that should be considered when choosing an IRT model. Not only does the study address the consequences of IRT model misfit, but also it is our hope to help researchers and practitioners find practical ways to study model fit and to investigate the validity of particular IRT models for achieving a specified purpose, to assure that the successful use of the IRT models are realized, and to improve the applications of IRT models with educational and psychological test data.