[1]黎光明,张敏强,戴海琦.概化理论偏态分布数据方差分量标准误估计[J].江西师范大学学报(自然科学版),2012,(05):456-460.
 LI Guang-ming,ZHANG Min-qiang,DAI Hai-qi.The Estimating Standard Error of Variance Component for Skewed Distribution Data in Generalizability Theory[J].Journal of Jiangxi Normal University:Natural Science Edition,2012,(05):456-460.
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概化理论偏态分布数据方差分量标准误估计()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年05期
页码:
456-460
栏目:
出版日期:
2012-10-01

文章信息/Info

Title:
The Estimating Standard Error of Variance Component for Skewed Distribution Data in Generalizability Theory
作者:
黎光明;张敏强;戴海琦
广州大学教育学院心理学系, 广东 广州 510006;华南师范大学心理应用研究中心, 广东 广州 510631;江西师范大学心理学院, 江西 南昌 330027
Author(s):
LI Guang-ming ZHANG Min-qiang DAI Hai-qi
关键词:
概化理论偏态分布数据方差分量标准误估计Bootstrap方法Monte Carlo模拟
Keywords:
generalizability theory Skewed distribution data estimating standard error of variance component bootstrap method Monte Carlo simulation
分类号:
O626.4
文献标志码:
A
摘要:
利用 GH 分布性质,采用 Monte Carlo 数据模拟技术,模拟生成一定偏度的偏态分布数据,运用Traditional方法、Jackknife方法、Bootstrap方法和MCMC方法估计概化理论偏态分布数据的方差分量标准误,探讨了数据的不同偏度对概化理论方差分量标准误估计的影响.研究结果显示: Jackknife 方法估计偏态分布数据的方差分量标准误性能较差, Traditional和MCMC方法尚可, Bootstrap方法标准误偏差相对较小,且偏态分布数据的偏度对概化理论方差分量标准误估计有影响, Bootstrap方法对于偏态分布数据表现出良好的“适应性”,偏度对其影响较小.
Abstract:
To explore how skew has effect on estimating standard error of variance component for Generalizability Theory. Using nature of Generalized Hyperbolic distribution, the study adopts Monte Carlo data simulation technique to simulate skewed distribution data. Traditional method, bootstrap method, jackknife method and Markov Chain Monte Carlo (MCMC)method were used to compare estimating standard error of variance component for skewed distribution data in Generalizability Theory. Jackknife method is not good to estimate standard error of variance component for skewed distribution data. Traditional method and Markov Chain Monte Carlo (MCMC)method were not very suitable, but can be accepted and bootstrap method is better. Skew of skewed distribution data have a effect on estimating standard error of variance component. Bootstrap method is a good adaptability to estimate standard error of variance component for Generalizability Theory. Skew has less effect on Bootstrap method.

参考文献/References:

[1] 蔡艳, 陈抚良. 多元概化理论在教育评估信度分析中的应用研究[J]. 江西师范大学学报: 自然科学版, 2007, 31(3): 306-310.
[2] 漆书青, 戴海崎, 丁树良. 现代教育与心理测量学原理 [J]. 北京: 高等教育出版社, 2002: 42-78.
[3] 杨志明, 张雷. 测评的概化理论及其应用 [M]. 北京: 教育科学出版社, 2003.
[4] 戴海崎, 张锋, 陈雪枫. 心理与教育测量 [M]. 3版. 广州: 暨南大学出版社, 2011.
[5] Gao Xiaohong, Brennan R L. Variability of estimated variance components and related statistics in a performance assessment [J]. Applied Measurement in Education, 2001, 14(2): 191-203.
[6] Brennan R L. Generalizability theory [M]. New York: Springer- Verlag, 2001.
[7] 焦璨, 张敏强, 黄庆均, 等. 非正态分布测量数据对克伦巴赫信度α系数的影响 [J]. 应用心理学, 2008, 14(3): 276-281.
[8] Othman A R. Examining task sampling variability in science performance assessments. Unpublished doctoral dissertation [D]. Santa Barbara:University of California, 1995.
[9] Mena R H, Walker S G. On the stationary version of the generalized hyperbolic ARCH model [J]. AISM, 2007, 59: 325-348.
[10] 黎光明, 张敏强. 基于概化理论的方差分量变异量估计 [J]. 心理学报, 2009, 41(9): 889-901.

更新日期/Last Update: 1900-01-01