基于概化理论的方差分量变异量估计  

Estimating the Variability of Estimated Variance Components for Generalizability Theory

作　　者：黎光明[1] 张敏强[1] 

机构地区：[1]华南师范大学心理应用研究中心,广州510631

出　　处：《心理学报》2009年第9期889-901,共13页Acta Psychologica Sinica

基　　金：广东省普通高校人文社会科学"十五"规划研究项目(06JDXMXLX002)

摘　　要：概化理论广泛应用于心理与教育测量实践中,方差分量估计是进行概化理论分析的关键。方差分量估计受限于抽样,需要对其变异量进行探讨。采用蒙特卡洛(Monte Carlo)数据模拟技术,在正态分布下讨论不同方法对基于概化理论的方差分量变异量估计的影响。结果表明:Jackknife方法在方差分量变异量估计上不足取;不采取Bootstrap方法的"分而治之"策略,从总体上看,Traditional方法和有先验信息的MCMC方法在标准误及置信区间这两个变异量估计上优势明显。Generalizability theory is widely applied in psychological and educational measurement.The variability of estimated variance component, which is constrained by sampling, is the ＂Achilles heel＂ of generalizability theory. Therefore, estimating the variability of estimated variance components needs to be further explored. In pre- vious literature, some problems remain to be settled： first, the previous studies failed to compare the variability of estimated variance components among different methods simultaneously： traditional, bootstrap, jackknife and Markov Chain Monte Carlo （MCMC）; second, some studies only focused on such variability of estimated variance components as the standard error, while neglected other variability such as confidence interval; last but not least, MCMC method which can be used in generalizability theory hasn＇t gained sufficient exploration.

关 键 词：概化理论 方差分量 方差分量变异量 MCMC方法 蒙特卡洛模拟 

分 类 号：B841.7[哲学宗教—基础心理学]