Robust nonorthogonal analyses revisited: An update based on trimmed means

Three approaches to the analysis of main and interaction effect hypotheses in nonorthogonal designs were compared in a 2×2 design for data that was neither normal in form nor equal in variance. The approaches involved either least squares or robust estimators of central tendency and variability and/or a test statistic that either pools or does not pool sources of variance. Specifically, we compared the ANOVA F test which used trimmed means and Winsorized variances, the Welch-James test with the usual least squares estimators for central tendency and variability and the Welch-James test using trimmed means and Winsorized variances. As hypothesized, we found that the latter approach provided excellent Type I error control, whereas the former two did not.Financial support for this research was provided by grants to the first author from the National Sciences and Engineering Research Council of Canada (#OGP0015855) and the Social Sciences and Humanities Research Council (#410-95-0006). The authors would like to express their appreciation to the Associate Editor as well as the reviewers who provided valuable comments on an earlier version of this paper.     

Robust estimation of the hierarchical model for responses and response times

Van der Linden's (2007, Psychometrika, 72, 287) hierarchical model for responses and response times in tests has numerous applications in psychological assessment. The success of these applications requires the parameters of the model to have been estimated without bias. The data used for model fitting, however, are often contaminated, for example, by rapid guesses or lapses of attention. This distorts the parameter estimates. In the present paper, a novel estimation approach is proposed that is robust against contamination. The approach consists of two steps. In the first step, the response time model is fitted on the basis of a robust estimate of the covariance matrix. In the second step, the item response model is extended to a mixture model, which allows for a proportion of irregular responses in the data. The parameters of the mixture model are then estimated with a modified marginal maximum likelihood estimator. The modified marginal maximum likelihood estimator downweights responses of test-takers with unusual response time patterns. As a result, the estimator is resistant to several forms of data contamination. The robustness of the approach is investigated in a simulation study. An application of the estimator is demonstrated with real data.     

Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong

This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the spatial and temporal heterogeneity simultaneously. To achieve the objectives, geographically and temporally weighted regression (GTWR) model was proposed to deal with both spatial and temporal heterogeneity simultaneously. The pedestrian crash data of Hong Kong metropolitan area from 2008 to 2012 were collected, involving 1652 pedestrian-related injury samples. By comparing GTWR model and standard geographically weighted regression (GWR) model and temporally weighted regression (TWR) model, the proposed GTWR model showed potential benefits in modeling both spatial and temporal non-stationarity simultaneously in terms of goodness-of-fit and F statistics. Results revealed that number of vehicles, number of pedestrian-related casualties, speed limit, vehicle movement and injury location have significant influence on pedestrian injury severity in different areas. The conclusions are reached that GRWR model can address the relationship between pedestrian injury severities and influencing factors, as well as accommodating spatial and temporal heterogeneity simultaneously. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety.