Sample Size Requirements for Applying Diagnostic Classification Models.

Sample Size Requirements for Applying Diagnostic Classification Models.
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DOI:
10.3389/fpsyg.2020.621251
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发表时间:
2020
影响因子:
3.8
通讯作者:
Cohen AS
Cohen AS
中科院分区:
心理学3区
文献类型:
--
作者:
Sen S;Cohen AS

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报告了一项综合模拟研究的结果,该研究调查了样本量、测试长度、属性数量和基本掌握率对四种 DCM(即 C-RUM、DINA、DINO 和 LCDMREDUCED)的项目参数恢复和分类准确性的影响。使用真实(即生成)参数和估计参数之间计算的偏差和 RMSE 来评估效果。还使用分类准确度百分比来评估模拟因素对属性分配的影响。通过更大的样本量和更长的测试长度,可以获得更精确的项目参数估计。随着属性数量从三个增加到五个,物品参数的恢复量下降,但基础掌握率对物品恢复量有不同的影响。 DINA 和 DINO 模型的项目参数和分类准确度较高。
Results of a comprehensive simulation study are reported investigating the effects of sample size, test length, number of attributes and base rate of mastery on item parameter recovery and classification accuracy of four DCMs (i.e., C-RUM, DINA, DINO, and LCDMREDUCED). Effects were evaluated using bias and RMSE computed between true (i.e., generating) parameters and estimated parameters. Effects of simulated factors on attribute assignment were also evaluated using the percentage of classification accuracy. More precise estimates of item parameters were obtained with larger sample size and longer test length. Recovery of item parameters decreased as the number of attributes increased from three to five but base rate of mastery had a varying effect on the item recovery. Item parameter and classification accuracy were higher for DINA and DINO models.
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