The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log-Linear Diagnostic Classification Models
The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log-Linear Diagnostic Classification Models
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DOI：
10.1111/j.1745-3984.2011.00160.x
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发表时间：
2012-03-01
影响因子：
1.3
通讯作者：
Wilhelm, Oliver
中科院分区：
文献类型：
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作者：
Kunina-Habenicht, Olga;Rupp, Andre A.;Wilhelm, Oliver
通过复杂的模拟研究，我们研究了对数线性建模框架内正确和错误诊断分类模型的参数恢复、分类准确性和两个项目拟合统计的性能。基本的操纵测试设计因素包括被调查者的数量（1,000对10,000）、属性（3对5）和项目（25对50）以及不同属性的相关性（25对50）。50对。80)和边际属性困难（相等vs.不同）。研究了正确q矩阵规范下相互作用效应参数的错配和两种类型的q矩阵错配。交互效应的不规范对分类精度影响不大，而无效的q矩阵规范导致分类精度显著下降。两项拟合指标对q矩阵项的规格过高比对规格不足更敏感。信息化拟合指标AIC和BIC对规格过高和规格不足都很敏感。
Using a complex simulation study we investigated parameter recovery, classification accuracy, and performance of two item-fit statistics for correct and misspecified diagnostic classification models within a log-linear modeling framework. The basic manipulated test design factors included the number of respondents (1,000 vs. 10,000), attributes (3 vs. 5), and items (25 vs. 50) as well as different attribute correlations (.50 vs. .80) and marginal attribute difficulties (equal vs. different). We investigated misspecifications of interaction effect parameters under correct Q-matrix specification and two types of Q-matrix misspecification. While the misspecification of interaction effects had little impact on classification accuracy, invalid Q-matrix specifications led to notably decreased classification accuracy. Two proposed item-fit indexes were more strongly sensitive to overspecification of Q-matrix entries for items than to underspecification. Information-based fit indexes AIC and BIC were sensitive to both over- and underspecification.