高考数学试卷多维项目反应理论的分析及应用

高考数学学科试卷的试题综合性较强,一道试题通常考查多种能力属性,而基于单维性假设下的经典测量理论和传统的项目反应理论无法完成该种情形下试卷测量性能分析和考生作答表现分析.本文以MIRT理论为基础,使用CONQUEST软件为工具进行分析,可以获得试卷内部不同能力维度之间的相关以及考生不同维度的能力参数,为提升命题质量和改进教学提供了依据,表明MIRT具有很好的应用前景.由于MIRT理论的复杂性以至于目前分析软件的不足制约其进一步的深入应用,这是今后应该深入研究的问题.     

安乐死合法化的哲学辩护——以自由主义哲学为视角

安乐死问题一直都是一个争议性话题。从学理上,安乐死可以分为自愿安乐死和无意愿安乐死。自由主义的伦理学分为义务论和功利主义。以自由主义哲学观来审视两类安乐死合法化问题:自愿安乐死体现病人自由意志的选择,是人格尊严之自主性的结果;无意愿安乐死是由利益相关人代为选择,符合社会利益的最大化。通过严格立法来规范安乐死行为,也应成为我国法律的理性选择。     

安乐死合法化的哲学辩护——以自由主义哲学为视角

安乐死问题一直都是一个争议性话题.从学理上,安乐死可以分为自愿安乐死和无意愿安乐死.自由主义的伦理学分为义务论和功利主义.以自由主义哲学观来审视两类安乐死合法化问题:自愿安乐死体现病人自由意志的选择,是人格尊严之自主性的结果;无意愿安乐死是由利益相关人代为选择,符合社会利益的最大化.通过严格立法来规范安乐死行为,也应成为我国法律的理性选择.     

汉字字形关联对错误记忆的影响

采用DRM范式,研究汉字字形关联对错误记忆的影响。实验以占汉字主体的形声字为材料。学习阶段使用了两类字表:在一半字表中,各字表内汉字及相应的关键诱饵具有相同形旁;另一半字表中,各字表内汉字及相应关键诱饵具有相同声旁。再认测试中,被试对两类关键诱饵均表现出了明显的错误记忆,并且其效应大小与字表汉字相似度成正比。这些结果说明,汉字的字形关联可以诱发错误记忆现象,并且这种错误记忆效应由汉字间字形相似度决定。     

宗教视角下的南亚恐怖主义特征与反恐的若干思考

本文详细探究和分析了后冷战时代宗教在南亚地区恐怖主义泛滥背景下的特征,指出不同宗教间的冲突成为南亚恐怖主义最主要的特征,并分析了南亚恐怖组织的宗教背景,强调恐怖主义对南亚造成了巨大危害,加深了不同宗教间的仇视。最后,从南亚自身实际出发,提出加强不同宗教间的对话,消除彼此之间的不信任感和仇恨感是南亚地区反恐的重中之重。     

Bayesian Estimation of Categorical Dynamic Factor Models

Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations—the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model—to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with a categorical time series data set from an emotion study. To estimate the categorical dynamic factor models, a Bayesian method via Gibbs sampling is used. The results show that today's affect directly influences tomorrow's affect. The results are then validated by means of simulation studies. Differences between continuous and categorical dynamic factor models are examined.     

Structural Equation Modeling of Social Networks: Specification,Estimation, and Application

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.     

Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, U.S. Department of Labor, 1997). A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed.     

抑郁症神经成像研究的临床转化:预防、诊断和治疗的新取向

抑郁症的预防、诊断和治疗是临床所面临的主要问题,其效果的提升有待超越传统方法,寻求新的方法和技术。其中抑郁症的神经成像转化研究为此提供了一个新的突破口,该研究取向旨在将抑郁症的神经成像基础研究的发现转化为临床运用。文章综述了其在抑郁症的预防、诊断和治疗上的潜在运用价值。未来方向需要更好地促进抑郁症神经成像转化研究的双向自由流通、强调将新的方法转化到日常的临床实践和健康决策上去、促进多学科的合作和相关人才的培养,以及进一步探究转化过程中所面临的特定基础和临床主题。