Factors associated with red-light running among motorcyclists at signalised junctions in Malaysia

Red-light running (RLR) among motorcyclists at signalised junctions (SJ) in Malaysia are common. This study investigates factors associated with RLR among motorcyclists at signalised junctions. The observation was conducted in 2016–2017, at 26 signalised junctions nationwide, and was analysed using the mixed-effect logistic regression. Out of the 53,475 observed vehicles, only 3.6% run the red light (by which 99% are motorcycles), and from the 19,232 observed motorcycles, 26.2% among motorcyclists run the red light while 54.1% of them, run the red-light without stopping. Factors associated with a high probability of RLR are male riders who do not wear a helmet, who approach the SJ via lane split, who cross SJ via turning, SJ with no traffic island, and SJ with the presence of vehicles waiting on one or both legs. The random parameters show that the majority (83%) of variation in the outcomes occurs among the riders (at Level 1), which suggest that motorcyclists’ characteristics and riding behaviour are still the main factors in affecting the RLR occurrences and require more in-depth study. While 1.5% of variation occurs among the type of motorcycle by region (at Level 2), it also shows that RLR occurs regardless. Other variations are from the range of median width (3.9% at Level 3) and the speed limit range along major roads (9.5% at Level 4). To reduce RLR among motorcyclists, we are recommending that SJ be equipped with traffic islands, and replace the overhead signal pole with pedestal pole.     

Does Agrammatic Speech Constitute a Regression to Child Language? A Three-Way Comparison between Agrammatic, Child, and Normal Ellipsis

When children are in the process of learning their mother tongue, they show frequent use of nonfinite clauses, even though they produce finite clauses at the same time, thereby demonstrating the availability of the functional domain associated with finiteness. In this study the hypothesis was tested that this behavior results from an overuse of the normal elliptical repertoire that has also been observed in agrammatic aphasia. The purpose of this overuse is prevention of computational overload. In support of the hypothesis it was found that children behaved very similar to aphasics and normal adults with respect to the following parameters: (a) distribution of types of ellipsis, (b) elaboration of ellipses, (c) word order, (d) subject omission, (e) frequency of weak subject pronouns, and (f) verb type (eventivity). The results also support the Jackson/Jakobson regression hypothesis, at least at the grammatical level.     

教育和心理研究中的多层线性模型

多层线性模型是分析具有层次结构数据的一种新型统计分析技术,与传统统计方法相比,具有模型假设与实际更吻合、结果解释更合理等特点。近年来这一方法的应用逐渐在社会科学的研究中受到重视。文章从多层线性模型的基本假设入手,较系统地介绍了模型参数估计和假设检验的方法,并通过一个具体例子将这一方法与传统回归分析方法相比,进一步说明了多层线性模型在分析具有层次结构数据时的优点。     

On component analyses

Principal components analysis can be redefined in terms of the regression of observed variables upon component variables. Two criteria for the adequacy of a component representation in this context are developed and are shown to lead to different component solutions. Both criteria are generalized to allow weighting, the choice of weights determining the scale invariance properties of the resulting solution. A theorem is presented giving necessary and sufficient conditions for equivalent component solutions under different choices of weighting. Applications of the theorem are discussed that involve the components analysis of linearly derived variables and of external variables.Preparation of this article was supported in part by National Institute of Aging Grant NIA-AG03164-03 to William Meredith.     

Describing the elephant: Structure and function in multivariate data

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain.     

The linear regression line as a judgmental aid in visual analysis of serially dependent A-B time-series data

The purpose of this study was to investigate the effects of different types and magnitudes of serial dependence (first-order moving average and autoregression) and of linear regression lines within experimental phases on the agreement between results of visual and results of statistical data analyses. The stimulus material consisted of computer-simulated A-B-design data graphs. The time series were generated with a constant variance, varying degrees of treatment effects (changes in level), five conditions of serial dependency, and with or without linear regression lines. The material was presented to three groups of student raters (n₁=52, n₂=14, n₃=17) who rated the treatment effect in the graphs on a five-point scale. These ratings were compared with statistical results (time-series analyses). Each group had to interpret 70 graphs, 35 of which had regression lines. Data were analyzed by means of two three-factor and one four-factor ANOVA and by graphic display. The linear regression lines generally enhanced the agreement between the raters' estimations and the statistical results. Serial dependency also increased the agreement between the two analysis methods. However, with strong autoregression processes in the data, the raters tended to overestimate treatment effects relative to time-series analysis.Parts of this study were presented at the World Congress on Behavior Therapy, Washington, DC, December 11, 1983. The authors wish to express their appreciation to Christoph Bonk and Willi Ecker for their extensive collaboration in data analysis and for their assistance in carrying out the study.     

Model modification

An analysis of empirical data often leads to a rejection of a hypothesized model, even if the researcher has spent considerable efforts in including all available information in the formulation of the model. Thus, the researcher must reformulate the model in some way, but in most instances there is, at least theoretically, an overwhelming number of possible actions that could be taken. In this paper a modification index will be discussed which should serve as a guide in the search for a better model. In statistical terms, the index measures how much we will be able to reduce the discrepancy between model and data, as defined by a general fit function, when one parameter is added or freed or when one equality constraint is relaxed. The modification index discussed in this paper is an improvement of the one incorporated in the LISREL V computer program in that it takes into account changes in all the parameters of the model when one particular parameter is freed.The research reported in this paper has been supported by The Swedish Council for Research in the Humanities and Social Sciences under Research Program Multivariate Statistical Analysis, Project Director Karl G Jöreskog.     

Joint consistency of nonparametric item characteristic curve and ability estimation

The simultaneous and nonparametric estimation of latent abilities and item characteristic curves is considered. The asymptotic properties of ordinal ability estimation and kernel smoothed nonparametric item characteristic curve estimation are investigated under very general assumptions on the underlying item response theory model as both the test length and the sample size increase. A large deviation probability inequality is stated for ordinal ability estimation. The mean squared error of kernel smoothed item characteristic curve estimates is studied and a strong consistency result is obtained showing that the worst case error in the item characteristic curve estimates over all items and ability levels converges to zero with probability equal to one.     

A new procedure for detection of crossing DIF

The purpose of this paper is to present a hypothesis testing and estimation procedure, Crossing SIBTEST, for detecting crossing DIF. Crossing DIF exists when the difference in the probabilities of a correct answer for the two examinee groups changes signs as ability level is varied. In item response theory terms, crossing DIF is indicated by two crossing item characteristic curves. Our new procedure, denoted as Crossing SIBTEST, first estimates the matching subtest score at which crossing occurs using least squares regression analysis. A Crossing SIBTEST statistic then is used to test the hypothesis of crossing DIF. The performance of Crossing SIBTEST is evaluated in this study.This research was partially supported by a grant from the Law School Admission Council and by National Science Foundation Mathematics Grant NSF-DMS-94-04327. The research reported here is collaborative in every respect and the order of authorship is alphabetical. The authors thank Jeff Douglas and Louis Roussos for their useful comments and discussions.     

Improved bounds for optimal intervals in weakly constrained regression problems

The solution of weakly constrained regression problems typically requires the iterative search, in a given interval, of a point where a certain function has a zero derivative. This note deals with improved bounds for the interval to be searched.