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SEM of another flavour: two new applications of the supplemented EM algorithm.
British Journal of Mathematical and Statistical Psychology ( IF 2.6 ) Pub Date : 2007-11-01 , DOI: 10.1348/000711007x249603 Li Cai 1
British Journal of Mathematical and Statistical Psychology ( IF 2.6 ) Pub Date : 2007-11-01 , DOI: 10.1348/000711007x249603 Li Cai 1
Affiliation
The supplemented EM (SEM) algorithm is applied to address two goodness-of-fit testing problems in psychometrics. The first problem involves computing the information matrix for item parameters in item response theory models. This matrix is important for limited-information goodness-of-fit testing and it is also used to compute standard errors for the item parameter estimates. For the second problem, it is shown that the SEM algorithm provides a convenient computational procedure that leads to an asymptotically chi-squared goodness-of-fit statistic for the 'two-stage EM' procedure of fitting covariance structure models in the presence of missing data. Both simulated and real data are used to illustrate the proposed procedures.
中文翻译:
SEM的另一种特色:补充EM算法的两个新应用。
补充的EM(SEM)算法用于解决心理计量学中的两个拟合优度测试问题。第一个问题涉及在项目响应理论模型中计算项目参数的信息矩阵。该矩阵对于信息受限的拟合优度测试很重要,并且还用于计算项目参数估计值的标准误差。对于第二个问题,证明了SEM算法提供了一种方便的计算过程,从而导致在存在缺失的情况下拟合协方差结构模型的“两阶段EM”过程的渐近卡方拟合优度统计量数据。模拟数据和真实数据均用于说明所建议的过程。
更新日期:2019-11-01
中文翻译:
SEM的另一种特色:补充EM算法的两个新应用。
补充的EM(SEM)算法用于解决心理计量学中的两个拟合优度测试问题。第一个问题涉及在项目响应理论模型中计算项目参数的信息矩阵。该矩阵对于信息受限的拟合优度测试很重要,并且还用于计算项目参数估计值的标准误差。对于第二个问题,证明了SEM算法提供了一种方便的计算过程,从而导致在存在缺失的情况下拟合协方差结构模型的“两阶段EM”过程的渐近卡方拟合优度统计量数据。模拟数据和真实数据均用于说明所建议的过程。