Safety–critical events in everyday cycling – Interviews with bicyclists and video annotation of safety–critical events in a naturalistic cycling study

Research in cycling safety seeks to better understand bicycle-related crashes and injuries. The present naturalistic cycling study contributes to this research by collecting data about bicyclists’ behavior and impressions of safety–critical situations, information unavailable in traditional data sources (e.g., accident databases, observational studies). Naturalistic data were collected from 16 bicyclists (8 female; M = 39.1 years, SD = 11.4 years) who rode instrumented bicycles for two weeks. Bicyclists were instructed to report all episodes in which they felt uncomfortable while riding (subjective risk perception), even if they didn’t fall. After data collection, the bicyclists were interviewed in detail regarding their self-reported safety–critical events. Environmental conditions were also recorded via video (e.g., road surface, weather). In total, 63 safety–critical events (56 non-crashes, 7 crashes) were reported by the bicyclists, mainly due to interactions with other road users – but also due to poorly maintained infrastructure. In low-visibility conditions, vehicle-bicycle and bicycle-bicycle events were the most uncomfortable for the bicyclists. Self-reported pedestrian–bicycle events primarily consisted of pedestrians starting to cross the bicycle path without looking. With one exception, all crashes found in the study belonged to poorly maintained road and infrastructure. In particular, construction work or obstacles in the bicycle path were reported as uncomfortable and annoying by the bicyclists. This study shows how naturalistic data and bicyclists’ interviews together can provide a more informative picture of safety–critical situations experienced by the bicyclist than traditional data sources can.     

Polytomous multilevel testlet models for testlet‐based assessments with complex sampling designs

A pplications of standard item response theory models assume local independence of items and persons. This paper presents polytomous multilevel testlet models for dual dependence due to item and person clustering in testlet‐based assessments with clustered samples. Simulation and survey data were analysed with a multilevel partial credit testlet model. This model was compared with three alternative models – a testlet partial credit model (PCM), multilevel PCM, and PCM – in terms of model parameter estimation. The results indicated that the deviance information criterion was the fit index that always correctly identified the true multilevel testlet model based on the quantified evidence in model selection, while the Akaike and Bayesian information criteria could not identify the true model. In general, the estimation model and the magnitude of item and person clustering impacted the estimation accuracy of ability parameters, while only the estimation model and the magnitude of item clustering affected the item parameter estimation accuracy. Furthermore, ignoring item clustering effects produced higher total errors in item parameter estimates but did not have much impact on the accuracy of ability parameter estimates, while ignoring person clustering effects yielded higher total errors in ability parameter estimates but did not have much effect on the accuracy of item parameter estimates. When both clustering effects were ignored in the PCM, item and ability parameter estimation accuracy was reduced.     

一种多级评分的广义认知诊断模型

认知诊断是近些年教育测量研究中的热点,大多数的认知诊断模型仅适用于0～1评分的情况.本文提出一种有多个潜变量多个滑动参数的多级评分认知诊断模型——GP-D1NA,只要由评分标准和知识状态能确定理想反应模式,就可以利用此方法进行认知诊断分析.在该方法中,我们给出项目滑动矩阵的概念,将被试的观测得分均看成由某个理想得分的滑动,并采用EM算法估计滑动矩阵.在模拟研究中,采用每掌握一个属性得1分的评分标准,结果表明线性型、收敛型、发散型、无结构型和独立型五种属性层级结构均有较高的判准率.     

Hierarchical models for relational event sequences

Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions.     

A Test of Order-Constrained Hypotheses for Circular Data With Applications to Human Movement Science

Researchers studying the movements of the human body often encounter data measured in angles (e.g., angular displacements of joints). The evaluation of these circular data requires special statistical methods. The authors introduce a new test for the analysis of order-constrained hypotheses for circular data. Through this test, researchers can evaluate their expectations regarding the outcome of an experiment directly by representing their ideas in the form of a hypothesis containing inequality constraints. The resulting data analysis is generally more powerful than one using standard null hypothesis testing. Two examples of circular data from human movement science are presented to illustrate the use of the test. Results from a simulation study show that the test performs well.     

Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation

This research is concerned with two topics in assessing model fit for categorical data analysis. The first topic involves the application of a limited-information overall test, introduced in the item response theory literature, to structural equation modeling (SEM) of categorical outcome variables. Most popular SEM test statistics assess how well the model reproduces estimated polychoric correlations. In contrast, limited-information test statistics assess how well the underlying categorical data are reproduced. Here, the recently introduced C₂ statistic of Cai and Monroe (2014) is applied. The second topic concerns how the root mean square error of approximation (RMSEA) fit index can be affected by the number of categories in the outcome variable. This relationship creates challenges for interpreting RMSEA. While the two topics initially appear unrelated, they may conveniently be studied in tandem since RMSEA is based on an overall test statistic, such as C₂. The results are illustrated with an empirical application to data from a large-scale educational survey.     

Psychometric evaluation and normative data for a Swedish version of the Patient Health Questionnaire 15‐Item Somatic Symptom Severity Scale

The lack of an available Swedish version of the Patient Health Questionnaire 15‐Item Somatic Symptom Severity Scale (PHQ‐15) motivated the present psychometric evaluation of such a version as well as providing normative data for the PHQ‐15. Data from 3,406 individuals who took part in the Västerbotten Environmental Health Study in Sweden were used. The respondents constitute a random sample, aged 18 to 79 years, stratified for age and sex. They responded to a Swedish translation of the PHQ‐15 as well as the Hospital Anxiety and Depression Scale, the Perceived Stress Scale, and the Shirom Melamed Burnout Questionnaire for assessment of convergent validity. The distribution of PHQ‐15 scores was positively skewed and mesokurtic in shape, and the internal consistency of the PHQ‐15 was satisfactory. Correlation coefficients between PHQ‐15 score and the measures of anxiety, depression, stress and mental/physical exhaustion indicate satisfactory validity. Normative data for PHQ‐15 scores as well as for categories of somatic symptom severity are provided. The favorable psychometric properties of the Swedish version of the PHQ‐15 suggest use of this instrument for quantification of somatization in Swedish and similar populations, and has the advantage of available normative data.