Assessing Goodness of Fit in Item Response Theory With Nonparametric Models
Assessing Goodness of Fit in Item Response Theory With Nonparametric Models
复制标题
使用非参数模型评估项目响应理论的拟合优度
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
F. J. Abad
中科院分区：
文献类型：
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作者：
Manuel J. Sueiro;F. J. Abad
在项目反应理论中，非参数和参数项目特征曲线之间的距离以根积分平方误差指数的形式被提出作为拟合优度的指标。本文提出了使用后验分布的潜在特质的非参数模型，并比较了基于这种方法的指数的性能与另一种方法的基础上的核平滑模型。错误率和功效使用双参数logistic模型和三种类型的实际失配项进行评估。结果表明，对于拟合项目，参数和非参数项目特征曲线之间的距离随着样本量的增加而减小。核平滑根积分平方误差也随着测试长度的增加而减小。Bootstrap方法用于获得显著性检验。两种程序在I类错误率方面均表现良好。关于功效，后验概率方法更上级，特别是在小样本中，尽管在短期测试中，两种程序以类似的方式进行。
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index based on this method with another approach based on the kernel-smoothing model. Error rates and power are evaluated using the two-parameter logistic model and three types of realistic misfitted items. Results show that for fitting items, the distance between parametric and nonparametric item characteristic curves decreased as the sample size increased for both procedures. Kernel-smoothing root integrated squared error also decreased as test length increased. Bootstrap methods are used to obtain a significance test. Both procedures performed adequately in terms of Type I error rates. Regarding power, the posterior probabilities method was superior, especially in small samples, although in short tests both procedures performed in a similar way.