Evaluation of Model Fit in Cognitive Diagnosis Models
Evaluation of Model Fit in Cognitive Diagnosis Models
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DOI：
10.1080/15305058.2015.1133627
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发表时间：
2016-01-01
影响因子：
1.7
通讯作者：
Chen, Yi-Hsin
中科院分区：
文献类型：
--
作者：
Hu, Jinxiang;Miller, M. David;Chen, Yi-Hsin
认知诊断模型(CDMS)利用潜在属性来估计学生的能力。需要确定适合数据的模型，以便确定来自CDM的推论是否有效。这项研究考察了一些流行的模型拟合统计量在检测CDM拟合方面的有效性，包括相对拟合指数(AIC、BIC和CAIC)和绝对拟合指数(RMSEA2、ABS(F Cor)和Max(chi(2)(jj‘)。在不同的清洁发展机制环境下，针对Q矩阵错误指定和清洁发展机制错误指定，对这些适合度指数进行了评估。结果表明，相对匹配指数在大多数情况下选择了正确的DINA模型，在大多数情况下选择了正确的G-DINA模型。如果Q矩阵以任何方式被错误指定，绝对拟合指数就会拒绝真正的DINA模型。当Q矩阵被低估时，绝对拟合指数拒绝了真正的G-DINA模型。当Q矩阵被过度指定时，RMSEA2可能被人为地降低。
Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC), and absolute fit indices (RMSEA2, ABS(f cor) and MAX(chi(2)(jj'))). These fit indices were assessed under different CDM settings with respect to Q-matrix misspecification and CDM misspecification. Results showed that relative fit indices selected the correct DINA model most of the times and selected the correct G-DINA model well across most conditions. Absolute fit indices rejected the true DINA model if the Q-matrix was misspecified in any way. Absolute fit indices rejected the true G-DINA model whenever the Q-matrix was under-specified. RMSEA2 could be artificially low when the Q-matrix was over-specified.