东西相遇：中德双校园学习差异性比较分析——基于Z大学中德合作2＋3项目学生的访谈

在对Z大学中德合作2＋3项目留德学生访谈的基础上，以差异性分析为视角，从教学组织、专业学习和校园生活三个纬度，分析了项目学生对两校园学习的差异性体验，并从文化价值、教育体制、教育投入方面探究其原因，提出了强化国际合作、提升师资能力水平、加大工程实训领域投入等对策。     

区间型符号数据的因子分析及其应用

符号数据分析是一种新兴的数据挖掘技术,区间数是最常用的一种符号数据。基于误差分析理论,研究针对区间数据的因子分析方法。将区间数看作一个由中点和半径构成的有序偶,并将半径视为区间数的极限误差。对中点样本阵进行因子分析,得到因子得分的中点值。然后将半径样本阵按照误差传递公式,得到因子得分的极限误差。由因子得分的中点值和极限误差最终得到因子得分的区间值。最后以股票的市场综合表现评价问题为案例,进行了应用研究。     

Evaluating Item Discrimination Power of WHOQOL-BREF from an Item Response Model Perspectives

Quality of life (QOL) has become an important component of health. By using the methodology of psychometric theory, we examine the item properties of the WHOQOL-BRIEF. Samejima’s graded response model with natural metrics of the logistic response function was fitted. The results showed items with negative natures were less discriminating. Items written in a specific way were more suitable to assess certain subgroups. The national items showed variation in discriminatory power. Questions measuring objective and specific issues performed worse than items assessing general aspects of the QOL.     

从马克思到贝克：因“剩女”危机引发性别冲突的多维度比较分析

在社会转型与全球化语境下，“剩女”时代已经悄悄来临，性别冲突再次成为后现代社会人们热论的焦点话题。对于性别冲突问题的探究，同一国度的卡尔·马克思与乌尔里希·贝克皆有深刻的阐述。本文以“剩女”危机作为个案，阐述两位理论大师在性别冲突关注上的差异性，这对我们认识“剩女”危机有着重要的启迪。     

Incorporating quality into data envelopment analysis of nursing home performance: A case study

When using data envelopment analysis (DEA) as a benchmarking technique for nursing homes, it is essential to include measures of the quality of care. We survey applications where quality has been incorporated into DEA models and consider the concerns that arise when the results show that quality measures have been effectively ignored. Three modeling techniques are identified that address these concerns. Each of these techniques requires some input from management as to the proper emphasis to be placed on the quality aspect of performance. We report the results of a case study in which we apply these techniques to a DEA model of nursing home performance. We examine in depth not only the resulting efficiency scores, but also the benchmark sets and the weights given to the input and output measures. We find that two of the techniques are effective in insuring that DEA results discriminate between high and low quality performance.     

Multiple deletion measures and conditional influence in regression model

The joint effect of the deletion of the ith and jih cases is given by Gray and Ling (1984), they discussed the influence measures for influential subsets in linear regression analysis. The present paper is concerned with multiple sets of deletion measures in the linear regression model. In particular we are interested in the effects of the jointly and conditional influence analysis for the detection of two influential subsets.     

"Chain pooling" model selection for two-level fixed effects factorial experiments

As many as three iterated statistical model deletion procedures are considered for an experiment.Population model coeff cients were chosen to simulate a saturated 2⁴experiment having an unfavorable distribution of parameter values.Using random number studies, three model selection strategies were developed, namely, (1) a strategy to be used in anticipation of large coefficients of variation (neighborhood of 65 percent), (2) strategy to be used in anticipation of small coefficients of variation (4 percent or less), and (3) a security regret strategy to be used in the absence of such prior knowledge     

A generalized scheffé method of multiple comparisons

Standard resulrs on the extrema of quotients of quadratic forms are extended to the non-negative definite case. The maximum and the set over which it is achieved are characterized explicitly both in terms of generalized inverse matrices and generalized eigenvalues. These results become the basis of Scheffe type multiple comparisons in the usual way. To demonstrate their application to statistics with singular covariance matrices, the method is detailed for Mantel-Haenszel, Breslow, and Cox statistics. An example is presented illustrating a situation where the proposed Scheffe type comparisons may be better than the pairwise method.     

Caregivers' perspectives on the SafeCare® programme: Implementing an evidence‐based intervention for child neglect

This qualitative study examined caregivers' experiences with SafeCare®, an evidence‐based programme that focuses on child neglect through modules on health, safety, and parenting. Shortly after completing SafeCare, 30 caregivers participated in a semi‐structured interview about their experiences with the programme. Overall, caregivers indicated that the programme helped with improvements in their parenting skills. Among the factors that contributed to a positive experience were the simplicity of language, the skills‐based approach, and the quality of the relationship with the SafeCare provider. Caregivers also noted several factors that made it difficult to fully benefit from the programme, including financial constraints, removal of their child from the home, and general distrust towards the child welfare system. Findings provide relevant information for SafeCare providers in terms of identifying areas that work well for caregivers completing the programme, as well as areas that might serve as impediments. Implications for contemporary child welfare practice are also considered.     

Reliability of global sensitivity indices

Uncertainty and sensitivity analysis is an essential ingredient of model development and applications. For many uncertainty and sensitivity analysis techniques, sensitivity indices are calculated based on a relatively large sample to measure the importance of parameters in their contributions to uncertainties in model outputs. To statistically compare their importance, it is necessary that uncertainty and sensitivity analysis techniques provide standard errors of estimated sensitivity indices. In this paper, a delta method is used to analytically approximate standard errors of estimated sensitivity indices for a popular sensitivity analysis method, the Fourier amplitude sensitivity test (FAST). Standard errors estimated based on the delta method were compared with those estimated based on 20 sample replicates. We found that the delta method can provide a good approximation for the standard errors of both first-order and higher-order sensitivity indices. Finally, based on the standard error approximation, we also proposed a method to determine a minimum sample size to achieve the desired estimation precision for a specified sensitivity index. The standard error estimation method presented in this paper can make the FAST analysis computationally much more efficient for complex models.