认知诊断缺失数据处理方法的比较：零替换、多重插补与极大似然估计法

数据缺失在测验中经常发生, 认知诊断评估也不例外, 数据缺失会导致诊断结果的偏差。首先, 通过模拟研究在多种实验条件下比较了常用的缺失数据处理方法。结果表明：(1)缺失数据导致估计精确性下降, 随着人数与题目数量减少、缺失率增大、题目质量降低, 所有方法的PCCR均下降, Bias绝对值和RMSE均上升。(2)估计题目参数时, EM法表现最好, 其次是MI, FIML和ZR法表现不稳定。(3)估计被试知识状态时, EM和FIML表现最好, MI和ZR表现不稳定。其次, 在PISA2015实证数据中进一步探索了不同方法的表现。综合模拟和实证研究结果, 推荐选用EM或FIML法进行缺失数据处理。     

Analyzing pedestrian behavior when crossing urban roads by combining RP and SP data

Considering the high crash rates involving pedestrians on urban roads, it is highly relevant to understanding pedestrian crossing behavior. This paper is the first to combine stated preference (SP) and revealed preference (RP) data to evaluate the impact that individual attributes, trip characteristics, built environment, strategies to prevent unauthorized crossing, and traffic flows have on pedestrians crossing decisions in an urban context. SP and RP surveys were designed and collected in Barranquilla (Colombia) near pedestrian bridges or signalized intersections where direct crossings and a high concentration of pedestrian fatalities related to traffic accidents exist. A logit model was estimated using the data enrichment paradigm. Results show that pedestrians weigh risks and costs when choosing how to cross the road. The trajectories observed in the RP component suggest that people prefer direct crossings; nevertheless, pedestrian bridges and signalized intersections can be attractive alternatives if their location matches the origin or destination of the crossing, and no detour is needed to use them. Waiting time; safety; the fine imposed for jaywalking; personal security, and previous decisions are also variables that influence pedestrian behavior when crossing urban roads. These results can be helpful to urban planners and decision-makers interested in proposing appropriate pedestrian infrastructure. The data pooling technique and the inclusion of a cost-related variable (i.e., fine) allowed computing the willingness to pay and marginal substitution rates for attributes of the built environment and other characteristics associated with the crossing decision. Also, the inclusion of several crossing alternatives and situations allowed assessing pedestrian crossing preferences under different scenarios.     

国内调节效应的方法学研究

在心理学和其他社科研究领域, 大量实证文章建立调节效应模型, 以分析自变量对因变量的影响是如何随着调节变量的变化而改变。过去10多年, 调节效应分析成了方法学研究的一个热点。从显变量的调节效应、潜变量的调节效应、多层数据的调节效应、基于两层回归模型的单层调节分析、纵向数据的调节效应、调节和中介的整合模型六个主题系统地总结了国内调节效应分析的方法学研究的发展历程。最后对调节效应的未来研究方向做了讨论和拓展。     

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。     

Investigating cycling kinematics and braking maneuvers in the real world: e-bikes make cyclists move faster,brake harder,and experience new conflicts

Pedelecs (e-bikes), which facilitate higher speeds with less effort in comparison to traditional bicycles (t-bikes), have grown considerably in popularity in recent years. Despite the large expansion of this new transportation mode, little is known about the behavior of e-cyclists, or whether cycling an e-bike increases crash risk and the likelihood of conflicts with other road users, compared to cycling on t-bikes. In order to support the design of safety measures and to maximize the benefits of e-bike use, it is critical to investigate the real-world behavior of riders as a result of switching from t-bikes to e-bikes.Naturalistic studies provide an unequaled method for investigating rider cycling behavior and bicycle kinematics in the real world in which the cyclist regularly experiences traffic conflicts and may need to perform avoidance maneuvers, such as hard braking, to avoid crashing. In this paper we investigate cycling kinematics and braking events from naturalistic data to determine the extent to which cyclist behavior changes as a result of transferring from t-bikes to e-bikes, and whether such change influences cycling safety.Data from the BikeSAFE and E-bikeSAFE naturalistic studies were used in this investigation to evaluate possible changes in the behavior of six cyclists riding t-bikes in the first study and e-bikes in the second one. Individual cyclists’ kinematics were compared between bicycle types. In addition, a total of 5092 braking events were automatically extracted after identification of dynamic triggers. The 286 harshest braking events (136 cases for t-bike and 150 for e-bike) were then validated and coded via video inspection.Results revealed that each of the cyclists rode faster on the e-bike than on the t-bike, increasing his/her average speed by 2.9–5.0 km/h. Riding an e-bike also increased the probability to unexpectedly have to brake hard (odds ratio = 1.72). In addition, the risk of confronting abrupt braking and sharp deceleration were higher when riding an e-bike than when riding a t-bike.Our findings provide evidence that cyclists’ behavior and the way cyclists interact with other road users change when cyclists switch from t-bikes to e-bikes. Because of the higher velocity, when on e-bikes, cyclists appear to have harder time predicting movements within the traffic environment and, as a result, they need to brake abruptly more often to avoid collisions, compared with cycling on t-bikes. This study provides new insights into the potential impact on safety that a cycling society moving to e-bikes may have, indicating that e-cycling requires more reactive maneuvers than does cycling traditional bicycles and suggesting that any distractive activity may be more critical when riding e-bikes compared to traditional bikes.     

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques

Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such as the 1/T approximation method and the Bartlett's formula method may fail in finite samples and are vulnerable to non-normality. Resampling techniques such as the moving block bootstrap and the surrogate data method are competitive alternatives. In this study, we use a Monte Carlo simulation study and a real data example to compare asymptotic methods with the aforementioned resampling techniques. For each resampling technique, we consider both the percentile method and the bias-corrected and accelerated method for interval construction. Simulation results show that the surrogate data method with percentile intervals yields better performance than the other methods. An R package pautocorr is used to carry out tests evaluated in this study.     

Panning for the gold in health research: Incorporating studies’ methodological quality in meta-analysis

Background: Systematic reviews now routinely assess methodological quality to gauge the validity of the included studies and of the synthesis as a whole. Although trends from higher quality studies should be clearer, it is uncertain how often meta-analyses incorporate methodological quality in models of study results either as predictors, or, more interestingly, in interactions with theoretical moderators.

Objective and methods: We survey 200 meta-analyses in three health promotion domains to examine when and how meta-analyses incorporate methodological quality.

Results: Although methodological quality assessments commonly appear in contemporary meta-analyses (usually as scales), they are rarely incorporated in analyses, and still more rarely analysed in interaction with theoretical determinants of the success of health promotions. The few meta-analyses (2.5%) that did include such an interaction analysis showed that moderator results remained significant in higher quality studies or were present only among higher quality studies. We describe how to model quality interactively with theoretically derived moderators and discuss strengths and weaknesses of this approach and in relation to current meta-analytic practice.

Conclusions: In large literatures exhibiting heterogeneous effects, meta-analyses can incorporate methodological quality and generate conclusions that enable greater confidence not only about the substantive phenomenon but also about the role that methodological quality itself plays.     

Do Bettors Correctly Perceive Odds? Three Studies of How Bettors Interpret Betting Odds as Probabilistic Information

This paper reports on three studies investigating how accurately bettors (=people who regularly bet on sports events) interpret the probabilistic information implied by betting odds. All studies were based on data collected by web surveys prompting a total of 186 experienced bettors to convert sets of representative odds into frequency judgments. Bayesian statistical methods were used to analyze the data. From the results, the following conclusions were made: (i) On the whole, the bettors produced well‐calibrated judgments, indicating that they have realistic perceptions of odds. (ii) Bettors were unable to consciously adjust judgments for different margins. (iii) Although their interval judgments often covered the estimates implied by the odds, the bettors tended to overestimate the variation of expected profitable bets between months. The results are consistent with prior research showing that people tend to make accurate probability judgments when faced with tasks characterized by constant and clear feedback. Copyright © 2015 John Wiley & Sons, Ltd.