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首页> 外文期刊>Journal of applied measurement >Evaluating Model-Data Fit by Comparing Parametric and Nonparametric Item Response Functions: Application of a Tukey-Hann Procedure
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Evaluating Model-Data Fit by Comparing Parametric and Nonparametric Item Response Functions: Application of a Tukey-Hann Procedure

机译：通过比较参数和非参数项目响应函数评估模型数据拟合：Tukey-Hann过程的应用

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摘要

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.
机译：这项研究描述了一种使用Tukey-Hann项目响应函数（TH-IRF）检查二分Rasch模型的模型数据拟合的方法。本文提出的程序基于Tukey（1977）提出的用于估计非参数项响应函数（IRF）的平滑技术的迭代版本。使用根综合平方误差（RISE）统计量（Douglas和Cohen，2001）将TH-IRF与Rasch IRF进行比较。大型大学的本科生数据被用来证明这种迭代平滑技术。 RISE统计信息用于比较项目响应函数以评估模型数据的拟合度。还检查了基于残差的Infit和Outfit统计量与RISE统计量之间的比较。结果表明，RISE统计数据和TH-IRF提供了一种有用的分析和图形化方法来评估项目的契合度。讨论了与模型数据拟合相关的研究，理论和实践的含义。

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