用多维拉希模型测量个体差异的变化。

Journal of outcome measurement Pub Date : 1998-01-01
W C Wang, M Wilson, R J Adams
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引用次数: 0

摘要

项目反应模型已被开发用于探索变化测量,包括Fischer及其同事提出的模型(例如,Fischer & Pazer, 1991;Fischer & Ponocny, 1994), Andersen(1985)和Embretson(1991)。在本文中,我们提出了另一种多维Rasch模型,即多维随机系数多项logit (MRCML)模型(Adams, Wilson, & Wang, 1997)。对这些模型进行了简要的回顾和比较。MRCML不仅可以应用于多同构题,还可以用于题目难度变化的调查。根据不同场合和项目的难度差异,提出了五种模型。进行了仿真研究,考察了MRCML模型在各种测试情况下的参数恢复情况。所有参数都恢复得很好。分析了一个真实的数据集,以显示MRCML在测量个体差异变化方面的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring individual differences in change with multidimensional Rasch models.

Item response models have been developed to explore change measurement, including those proposed by Fischer and his colleagues (e.g., Fischer & Pazer, 1991; Fischer & Ponocny, 1994), Andersen (1985) and Embretson (1991). In this article, we propose another multidimensional Rasch model, the multidimensional random coefficient multinomial logit (MRCML) model (Adams, Wilson, & Wang, 1997). All these models are briefly reviewed and compared. The MRCML can be applied to not only polytomous items but also investigation of variations in item difficulties. Based on variations in difficulties across occasions and items, five kinds of models are proposed. Some simulation studies were conducted to examine parameter recovery of the MRCML model under various testing situations. All the parameters were recovered very well. A real data set was analyzed to show applications of the MRCML to measuring individual differences in change.

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