A Two-Tier Full-Information Item Factor Analysis Model with Applications
A Two-Tier Full-Information Item Factor Analysis Model with Applications
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DOI：
10.1007/s11336-010-9178-0
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发表时间：
2010-12-01
期刊：
影响因子：
3
通讯作者：
Cai, Li
中科院分区：
文献类型：
--
作者：
Cai, Li
由Gibbons et al. 's（Appl. Psychol. Meas. 31：4-19，2007），以及Rijmen，Vansteelandt和De Boeck的（Psychometrika 73：167-182，2008）关于构建潜在变量模型的计算有效估计算法的工作，在本研究中开发了双层项目因子分析模型。模型框架包括标准的多维IRT模型，双因素IRT模型，和testlet反应理论模型的特殊情况。该模型的特点导致的潜在变量空间的维数减少，从而显着的计算节省。提出了一种全信息极大边缘似然估计的EM算法。仿真和真实的数据验证了所提方法的准确性和有效性。三个真实的数据集，从大规模的教育评估，纵向公共卫生调查，和一个规模的发展研究测量患者报告的生活质量的结果进行了分析，作为该模型的广泛适用性的说明。
Motivated by Gibbons et al.'s (Appl. Psychol. Meas. 31:4-19, 2007) full-information maximum marginal likelihood item bifactor analysis for polytomous data, and Rijmen, Vansteelandt, and De Boeck's (Psychometrika 73:167-182, 2008) work on constructing computationally efficient estimation algorithms for latent variable models, a two-tier item factor analysis model is developed in this research. The modeling framework subsumes standard multidimensional IRT models, bifactor IRT models, and testlet response theory models as special cases. Features of the model lead to a reduction in the dimensionality of the latent variable space, and consequently significant computational savings. An EM algorithm for full-information maximum marginal likelihood estimation is developed. Simulations and real data demonstrations confirm the accuracy and efficiency of the proposed methods. Three real data sets from a large-scale educational assessment, a longitudinal public health survey, and a scale development study measuring patient reported quality of life outcomes are analyzed as illustrations of the model's broad range of applicability.