Jennings Jeremy Kyle, Engelhard George
Kyle Jennings, 159 East Cloverhurst Ave., Apt. 4, Athens, GA 30605, USA,
J Appl Meas. 2017;18(1):54-66.
This study describes an approach for examining model-data fit for the dichotomous Rasch model using Tukey-Hann item response functions (TH-IRFs). The procedure proposed in this paper is based on an iterative version of a smoothing technique proposed by Tukey (1977) for estimating nonparametric item response functions (IRFs). A root integrated squared error (RISE) statistic (Douglas and Cohen, 2001) is used to compare the TH-IRFs to the Rasch IRFs. Data from undergraduate students at a large university are used to demonstrate this iterative smoothing technique. The RISE statistic is used for comparing the item response functions to assess model-data fit. A comparison between the residual based Infit and Outfit statistics and RISE statistics are also examined. The results suggest that the RISE statistic and TH-IRFs provide a useful analytical and graphical approach for evaluating item fit. Implications for research, theory and practice related to model-data fit are discussed.
本研究描述了一种使用Tukey-Hann项目反应函数（TH-IRF）来检验二分Rasch模型的模型-数据拟合度的方法。本文提出的程序基于Tukey（1977）提出的用于估计非参数项目反应函数（IRF）的平滑技术的迭代版本。使用根积分平方误差（RISE）统计量（Douglas和Cohen，2001）将TH-IRF与Rasch IRF进行比较。来自一所大型大学的本科生数据用于演示这种迭代平滑技术。RISE统计量用于比较项目反应函数以评估模型-数据拟合度。还检验了基于残差的Infit和Outfit统计量与RISE统计量之间的比较。结果表明，RISE统计量和TH-IRF为评估项目拟合提供了一种有用的分析和图形方法。讨论了与模型-数据拟合相关的研究、理论和实践意义。
