Survey of New Zealand truck driver fatigue and fitness for duty

This paper presents recent research into compliance with current driving hours regulations, the effectiveness of using driving hours to predict fatigue, and alternative compliance and enforcement options. The paper describes results of a major survey of truck driver fatigue in New Zealand, a review of international compliance and enforcement procedures, and research focussing on the social forces and influences that affect truck drivers. The survey of truck drivers was based on interviews and performance tests collected from 600 truck drivers at depots, wharves, markets, and other locations throughout the North Island of New Zealand. The interviews included: questions on driver demographic and work/rest patterns, drivers' attitudes towards fatigue, propensity towards daytime sleepiness, and a self-assessment of the driver's momentary level of fatigue. In addition, a simulator-based performance test of driving was undertaken. The performance test included a combination of a standard driving task, a dual-axis sub-critical tracking task (maintaining speed and steering in a controlled but unstable environment, a virtual roadway affected by the appearance of random wind gusts requiring steering correction), and a tertiary or side-task requiring driver monitoring and periodic responses. The initial results from the first 100 drivers have found a sizable number of drivers exceeding the allowable driving hours, high levels of fatigue and sleepiness, and interesting differences between line-haul and local delivery drivers. A related research project into the social processes and relationships that affect truck drivers has resulted in a good understanding of the social conditions that influence cultural change and the actions of truck drivers and fleet managers. In this paper we will have particular regard to these processes in the construction of ideas concerning safety. This includes an understanding of the role of major stakeholders, such as freight forwarders and the enforcement agencies with respect to drivers and their conditions, actions and understanding of the road transport industry. This knowledge coupled with the survey results and an understanding of compliance and enforcement alternatives will be used to explore potential fatigue management options.     

中文版过剩适应量表在大学生群体中的效度和信度

为修订中文版过剩适应量表(OAS-C),并检验其在中国大学生群体中的信效度,抽取589名大学生(样本1),278名大学生(样本2)和174名大学生(样本3)进行施测.效度分析结果表明,OAS-C为二因子结构,包括外部适应过剩和内部适应匮乏.该二因子模型拟合良好,且与各效标间呈显著正相关.信度分析结果显示,内部一致性系数...     

大学生职业决策自我效能测评的研究

本研究参照Betz和Taylor"职业生涯决策自我效能量表",依据对学生进行的访谈资料和学生开放式问卷调查结果,编制出"大学生职业决策自我效能量表".本研究抽取武汉地区14所大学30个专业1000名毕业年级的大学生作为研究样本,进行正式施测.统计分析结果表明(1)该量表的项目特性良好;(2)该量表都具有较好的内部一致性信度;(3)该量表均具有较强的辨别效度;(4)该量表作为整体判断使用较好.     

自我职业选择测验(SDS)的试用报告

本研究对自我职业选择测验 (SDS) 1 985年版进行了修订 ,并在武汉市中学生中进行了适用性的验证。在原测验中译本基础上 ,进行了项目修改、项目分析、信效度检验等标准化工作。结果表明 :①该测验具有良好的项目特性 ;②该测验同质性信度、分半信度均达到一般心理测验要求标准 ;③该测验结构效度与效标关联效度亦较为理想 ;④个别项目仍有待于进一步修改 ,取样还应面向全国 ,以利于进一步的推广作用。在武汉市中学生中的试用结果表明 :①该测验可以作为中学生职业辅导的选用工具 ;②在该测验中使用标准分代替粗分更具科学性。     

城市中老年人心理健康量表的编制

本研究以来自全国7 个代表性城市的1373 名中老年人为被试,在文献研究、咨询案例分析、专家访谈和讨论、开放式问卷调查的基础上,从实证研究的角度出发,编制了城市中老年人心理健康量表,并对其信度和效度进行了检验。结果表明:该量表具有良好的信度与效度,可以用于对城市中老年人心理健康水平的分析和诊断。     

《儿童元认知问卷》的编制与修订

通过对现有元认知概念体系的分析与整合,初步确立《儿童元认知问卷》的六个基本维度:自我认知、动机信念、策略、计划、监控、调节。以此为基础编制原始问卷,对小学四~六年级学生取样测试。结果表明:修订后的问卷各题目在对应潜变量上有较高的负荷,数据与理论模型拟合良好,问卷具有较好的信度和效度。     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

Maybe not so independent after all: The possibility,prevalence, and consequences of violating the independence assumptions in psychometric meta-analysis

Psychometric meta-analysis assumes that moderators are unrelated to study artifacts (e.g., criterion reliability), and that study artifacts are independent of true validities. Meeting these assumptions is important for researchers seeking to accurately partition the variance in effect sizes due to study artifacts from the variance due to meaningful moderators. Despite the critical role of these assumptions, we know very little about their tenability. To address this basic gap in the literature, we conducted three studies to determine if there are potential violations of the independence assumptions (Study 1), the prevalence of such violations (Study 2), and the consequences of violating the independence assumptions via a series of Monte Carlo simulations (Study 3). We found that violations of the independence assumptions are not only plausible but also routinely detected across a wide array of topics. Simulation results indicate that violating the independence assumptions can result in biases under certain circumstances, which are further accentuated due to the lack of stability in the estimators. We offer suggestions for the future use of psychometric meta-analysis and discuss the implications for research focused on refining psychometric meta-analysis.     

Accounting for auto-dependency in binary dyadic time series data: A comparison of model- and permutation-based approaches for testing pairwise associations

Many theories have been put forward on how people become synchronized or co-regulate each other in daily interactions. These theories are often tested by observing a dyad and coding the presence of multiple target behaviours in small time intervals. The sequencing and co-occurrence of the partners’ behaviours across time are then quantified by means of association measures (e.g., kappa coefficient, Jaccard similarity index, proportion of agreement). We demonstrate that the association values obtained are not easy to interpret, because they depend on the marginal frequencies and the amount of auto-dependency in the data. Moreover, often no inferential framework is available to test the significance of the association. Even if a significance test exists (e.g., kappa coefficient) auto-dependencies are not taken into account, which, as we will show, can seriously inflate the Type I error rate. We compare the effectiveness of a model- and a permutation-based framework for significance testing. Results of two simulation studies show that within both frameworks test variants exist that successfully account for auto-dependency, as the Type I error rate is under control, while power is good.     

Comparing the effects of generating questions,testing, and restudying on students' long-term recall in university learning

We compared the long-term effects of generating questions by learners with answering questions (i.e., testing) and restudying in the context of a university lecture. In contrast to previous studies, students were not prepared for the learning strategies, learning content was experimentally controlled, and effects on factual and transfer knowledge were examined. Students' overall recall performance after one week profited from generating questions and testing but not from restudying. When analyzing the effects on both knowledge types separately, traditional analyses revealed that only factual knowledge appeared to benefit from testing. However, additional Bayesian analyses suggested that generating questions and testing similarly benefit factual and transfer knowledge compared with restudying. The generation of questions thus seems to be another powerful learning strategy, yielding similar effects as testing on long-term retention of coherent learning content in educational contexts, and these effects emerge for factual and transfer knowledge.