Do physical and relational aggression explain adolescents' friendship selection? The competing roles of network characteristics,gender, and social status

The role of physical and relational aggression in adolescents' friendship selection was examined in a longitudinal sample of 274 Chilean students from 5th and 6th grade followed over 1 year. Longitudinal social network modeling (SIENA) was used to study selection processes for aggression while influence processes were controlled for. Furthermore, the effects of network characteristics (i.e., reciprocity and transitivity), gender, and social status on friendship selection were examined. The starting assumption of this study was that selection effects based on aggression might have been overestimated in previous research as a result of failing to consider influence processes and alternative characteristics that steer friendship formation. The results show that selection effects of both physical and relational aggression disappeared when network effects, gender, and social status were taken into account. Particularly gender and perceived popularity appeared to be far more important determinants of friendship selection over time than aggression. Moreover, a peer influence effect was only found for relational aggression, and not for physical aggression. These findings suggest that similarity in aggression among befriended adolescents can be considered to be mainly a by-product rather than a leading dimension in friendship selection.     

Supervisory interpretation of safety climate versus employee safety climate perception: Association with safety behavior and outcomes for lone workers

Research has shown that safety climate predicts safety behavior and safety outcomes in a variety of settings. Prior studies have focused on traditional work environments in which employees and supervisors work in the same location and the mechanisms through which safety climate affects behavior are largely understood. However, the nascent research examining safety climate among lone workers suggests that safety climate may have some uniqueness in this context. Based on leadership theories and utilizing an exploratory approach, this study increases our understanding of the lone worker context by examining employee perception of safety climate and supervisory interpretation of safety climate; how similar or different they are, and how they are related to important safety outcomes. Surveys were administered to a matched sample of 1831 truck drivers and their 219 supervisors at four different trucking companies. Objective data on employee injuries were collected six months after survey administration. The results provided support for the measurement equivalence of the Trucking Safety Climate Scale at the organization level for both employee and supervisor respondents. For both organization- and group-level safety climate, employee perceptions of safety climate and supervisory interpretation of safety climate were significantly different, such that supervisors provided higher ratings for both safety climate sub-scales. Further, only employee safety climate perceptions significantly predicted self-reported safety behavior (directly) and objective injury outcomes (indirectly). This suggests that when trying to gauge and improve upon a trucking company’s safety climate, we should rely on employee perspectives, rather than supervisory interpretation, of safety climate.     

有时间压力下学习率对学习时间分配议程的影响:来自眼动的证据

利用眼动分析技术,通过设置不同难度和分值的计算项目,探讨了在有时间压力下学习者的学习时间分配。结果发现:(1)在首次项目选择上,学习者优先选择学习率最高的项目;(2)在学习过程中,学习者的项目选择次序也是根据学习率由高到低。总之,在学习时间分配上,如果需要权衡难度与分值,学习者会考虑单位时间的获益,也就是以学习率为议程的主要依据。研究不仅利用眼动分析技术证实了基于议程的学习时间分配模型,而且进一步揭示了学习率是设置学习时间分配议程的重要依据。     

项目反应理论观察分数核等值的影响因素

探究带宽选择方法、样本量、题目数量、等值设计、数据模拟方式对项目反应理论观察分数核等值的影响。通过两种数据模拟方式,获得研究数据,并计算局部与全域评价指标。研究发现,在随机组设计中,带宽选择方法表现相似;考生样本量和题目数量影响甚微。在非等组设计中,惩罚法与Silverman经验准则表现优异;增加题目量可降低百分相对误差和随机误差;增加样本量导致百分相对误差变大,随机误差减小。数据模拟方式可影响等值评价。未来应重点关注等值系统评估。     

基于项目区分度的双目标CD-CAT选题策略

针对双目标CD-CAT,将六种项目区分度（鉴别力D、一般区分度GDI、优势比OR、2PL的区分度a、属性区分度ADI、认知诊断区分度CDI）分别与IPA方法结合,得到新的选题策略。模拟研究比较了它们的表现,还考察了区分度分层在控制项目曝光的表现。结果发现：新方法都能明显提高知识状态的判准率和能力估计精度;分层选题均能很好地提高题库利用率。总体上,OR加权能显著提高测量精度;OR分层选题在保证测量精度条件下显著提高项目曝光均匀性。     

The influence of item order on intentional response distortion in the assessment of high potentials: Assessing pilot applicants

An experiment was conducted to investigate the effects of item order and questionnaire content on faking good or intentional response distortion. It was hypothesized that intentional response distortion would either increase towards the end of a long questionnaire, as learning effects might make it easier to adjust responses to a faking good schema, or decrease because applicants' will to distort responses is reduced if the questionnaire lasts long enough. Furthermore, it was hypothesized that certain types of questionnaire content are especially vulnerable to response distortion. Eighty‐four pre‐selected pilot applicants filled out a questionnaire consisting of 516 items including items from the NEO five factor inventory (NEO FFI), NEO personality inventory revised (NEO PI‐R) and business‐focused inventory of personality (BIP). The positions of the items were varied within the applicant sample to test if responses are affected by item order, and applicants' response behaviour was additionally compared to that of volunteers. Applicants reported significantly higher mean scores than volunteers, and results provide some evidence of decreased faking tendencies towards the end of the questionnaire. Furthermore, it could be demonstrated that lower variances or standard deviations in combination with appropriate (often higher) mean scores can serve as an indicator for faking tendencies in group comparisons, even if effects are not significant.     

CAT选题策略分类概述

选题是计算机化自适应测验（CAT）测试过程的关键环节,选题策略的目标是要达到较高的测量精度,同时也实现试题曝光率控制及其他测验目标的实现.本文根据选题策略的基本原理和衍生发展,将众多CAT选题策略分为五大选题策略系列：Fisher函数系列、K-LI函数系列、α分层系列、贝叶斯系列、b匹配系列;并根据测验目标（测验精度、试题曝光率控制、内容平衡、多条件约束）对这些选题策略进行了细分,并对CAT选题策略的选择思路进行归纳.     

Bidirectional feature selection with global and local structure preservation for small size samples

Collecting samples is a challenging task for face recognition, especially for some real-world applications such as law enhancement and ID card identification, where there is usually single sample per person (SSPS) used to train a face recognition system. To extract discriminative features from the small size samples, in this paper we propose virtual samples via bidirectional feature selection with global and local structure preservation (VS-BFS-GL) to augment the number of training samples. In VS-BFS-GL, bidirectional feature selection is developed, which introduces L_2,1 norm to explore the face variations from both horizontal and vertical directions. Further, to include more variations in the virtual images, the global structure information and sample-specified local structure information of the SSPP training set are considered. By integrating bidirectional feature selection, global and local structure, the limited training samples are fully utilized and more knowledge are mined. To further improve the effectiveness of VS-BFS-GL, an auxiliary database containing different face variations can be used to explore the local structure information. We extensively evaluated the proposed approach on AR and FERET database. The promising recognition results demonstrate that VS-BFS-GL is robust to expression, pose and partial occlusion variations in the faces.