The responses of non-effortful test-takers may have serious consequences as non-effortful responses can impair model calibration and latent trait inferences. This article introduces a mixture model, using both response accuracy and response time information, to help differentiating non-effortful and effortful individuals, and to improve item parameter estimation based on the effortful group. Two mixture approaches are compared with the traditional response time mixture model (TMM) method and the normative threshold 10 (NT10) method with response behavior effort criteria in four simulation scenarios with regard to item parameter recovery and classification accuracy. The results demonstrate that the mixture methods and the TMM method can reduce the bias of item parameter estimates caused by non-effortful individuals, with the mixture methods showing more advantages when the non-effort severity is high or the response times are not lognormally distributed. An illustrative example is also provided.