概化理论方差分量估计的跨分布分析

方差分量估计是进行概化理论分析的关键。采用MonteCarlo模拟技术,探讨心理与教育测量数据分布对概化理论各种方法估计方差分量的影响。数据分布包括正态、二项和多项分布,估计方法包括Traditional、Jackknife、Bootstrap和MCMC方法。结果表明:(1)Traditional方法估计正态分布和多项分布数据的方差分量相对较好,估计二项分布数据需要校正,Jackknife方法准确地估计了三种分布数据的方差分量,校正的Bootstrap方法和有先验信息的MCMC方法(MCMCinf)估计三种分布数据的方差分量结果较好;(2)心理与教育测量数据分布对四种方法估计概化理论方差分量有影响,数据分布制约着各种方差分量估计方法性能的发挥,需要加以区分地使用。     

涉疫人员隐私保护的现实困境与法治路径

涉疫人员的隐私权利能够体现其独立人格与个体尊严,蕴藏人文价值,对于防疫工作促进和公共健康保护具有积极意义。新型冠状病毒肺炎疫情与大数据发展的双重背景之下,涉疫人员隐私保护存在知情同意困难、信息泄露和持续监控的新型难点。因此,分析其背后公民知情权与个人隐私权之冲突原因,在立法完善、执法优化、司法救济和守法普及四个维度为优化涉疫人员个人隐私保护的法律规范提供建议,帮助缓和权利冲突,为后续规范疫情常态化防控提供依据。

     

社会网络分析在心理研究中的应用

个体不是孤立存在的, 其心理与行为的变化常依赖于与他人存在的关系。社会网络分析正是考虑到了这种依赖性, 发展出相应的处理关系变量的方法。在介绍社会网络理论的中心思想、社会网络分析的基本概念、数据表示方法和研究方法以及在个体水平和关系水平上的静态统计模型后, 使用UCINET软件展现了处理关系数据的基本步骤, 并简述社会网络分析在人格心理学、发展与教育心理学和管理心理学中的应用。     

支持死后医疗数据捐赠：呼吁对个人健康数据的伦理利用

死后医疗数据捐赠对于科学研究的意义重大。但目前不存在有针对性的准则用于指导捐赠过程。如果死后医疗数据的使用缺乏监管可能会引发多种风险,一是给他人利益造成损害;二是造成数据的不规范使用。因此,应当类比现有捐献制度,允许并鼓励捐献死后医疗数据用于科学研究,并起草专门的捐赠准则。该准则的制定应当建立在遵循五大基本原则的前提下,主张以自愿参与的方式使用个人医疗数据,反对未经书面同意将数据用于以病例档案为基础的医学研究。

     

A general approach to categorical data analysis with missing data,using generalized linear models with composite links

A general approach for analyzing categorical data when there are missing data is described and illustrated. The method is based on generalized linear models with composite links. The approach can be used (among other applications) to fill in contingency tables with supplementary margins, fit loglinear models when data are missing, fit latent class models (without or with missing data on observed variables), fit models with fused cells (including many models from genetics), and to fill in tables or fit models to data when variables are more finely categorized for some cases than others. Both Newton-like and EM methods are easy to implement for parameter estimation.The author thanks the editor, the reviewers, Laurie Hopp Rindskopf, and Clifford Clogg for comments and suggestions that substantially improved the paper.     

Empirical bayes estimation of coefficients in the general linear model from data of deficient rank

Empirical Bayes methods are shown to provide a practical alternative to standard least squares methods in fitting high dimensional models to sparse data. An example concerning prediction bias in educational testing is presented as an illustration.The authors would like to thank the referees for several useful comments.The analysis of the data discussed in this report was part of a study funded jointly by the Graduate Management Admission Council and Educational Testing Service.     

The effects of instructions and calculation procedures on observers' accuracy, agreement, and calculation correctness

Although the quality of observational data is generally evaluated by observer agreement, measures of both observer agreement and accuracy were available in the present study. Videotapes with a criterion protocol were coded by 16 observers. All observers calculated agreement scores both on their own and their partner's data and on a contrived data set misrepresented as data collected by other observers. Compared with agreement scores calculated by the experimenter, observers erroneously inflated their own agreement scores and deflated the agreement scores on the contrived data. Half of the observers (n = 8) had been given instructions emphasizing the importance of accuracy during observation while the other half had been given instructions emphasizing interobserver agreement. Accuracy exceeded agreement for the former group, whereas agreement exceeded accuracy for the latter group. The implications are that agreement should be calculated by the experimenter and that the accuracy-agreement relationship can be altered by differential observer instructions.     

Statistics and ethics in medical research

Ethical conduct is an essential component in research, especially in medical research. Statistical methods for design and analysis are powerful research tools if used properly. Abuse of these principles and methods are just as unethical as other laboratory or clinical misconduct. Inadequate research design can produce worthless results and thus wastes effort and valuable resources. For clinical research, patient resources are wasted. Inappropriate analysis of data can also produce misleading results and conclusions. For clinical research, inferior therapy might be given to patients as a consequence. These ethical concerns can have implications for and affect the individuals responsible for the statistical design and analysis. Examples are provided which illustrate some possible abuses and inappropriate pressures.