概化理论多侧面设计缺失数据方差分量及其变异量估计

项目名称： 概化理论多侧面设计缺失数据方差分量及其变异量估计

项目编号： No.31470050

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 医药、卫生

项目作者： 黎光明

作者单位： 华南师范大学

项目金额： 30万元

中文摘要： 概化理论广泛应用于心理测评实践中。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。多数学者仅关注概化理论完备数据的方差分量及其变异量估计，却对缺失数据视而不见。但各种心理调查与心理实验中，数据的缺失随处可见。人们通常采用删除缺失记录或对缺失值进行插补处理，但弊端是不能充分利用现有数据进行方差分量及其变异量估计，可供分析的数据大量减少。为了解决概化理论缺失数据方差分量及其变异量估计的问题，本研究拟从以下三个方面进行创新：第一，将单侧面p×i设计发展到双侧面p×i×r和p×(i:r)设计。第二，探讨不需要删除记录或对缺失值进行插补的方法，并比较这些方法的性能优劣。对于方差分量估计，包括公式法、拆分法、REML法和MCMC法，对于方差分量变异量估计，包括传统法、Jackknife法、Bootstrap法和MCMC法。第三，将数据模拟技术与实际数据验证相结合。

中文关键词： 概化理论；多侧面设计；缺失数据；方差分量估计；方差分量变异量估计

英文摘要： Generalizability theory is widely applied in psychological measurement and evaluation. 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. Most researchers only focused on full data and often ignored missing data. But missing observation are common in psychological surveys and mental experiments. Because these assessments are time-consuming to administer and score, examinees seldom respond to all test items and raters seldom evaluate all examinee responses. As a result, a common problem encountered by those using generalizability theory with large-scale performance assessments is working with missing data. The data from such examinations compose a missing data matrix. Researchers have to be usually concerned about how to make good use of limited missing data. As for these missing data, researchers usually delete them or make an interpolation for missing records, but some problems may be caused in following aspects. First, it is not very effective if doing it and can not carry out some statistical analysis. Second, there are many different rules of interpolation, but people do not know which rules are unbiased. Because of missing dat

英文关键词： Generalizability theory；Multifaceted design；Missing data；Estimating variance components；Estimating the variability of variance components