Doctor of Philosophy (PhD)
Civil and Environmental Engineering
Distracted driving continues to remain a cause of concern for a number of bodies, including government agencies, traffic safety advocacy groups and law enforcement agencies, because of its traffic safety risks. The driving simulator continues to be popular with researchers in collecting data on performance variables that provide scientific knowledge of the effects of distracted driving. Several of these performance variables can be used to quantify a single distracting effect, resulting in a multivariate dataset. A literature review of related studies revealed that researchers overwhelmingly use univariate (single and multiple) tests to analyze the resulting dataset. Performing multiple univariate tests on a multivariate dataset results in inflated Type-I error rates, and could result in inaccurately concluding that there is a distracting effect when there may not be. Researchers also provided very little or no justification for the selection of variables that were used for the univariate analysis. Being able to correctly identify a set of variables to be used to research a single distracting effect is critical in that different variables may lead to different conclusions of significant findings or not. The primary objective of this dissertation was to develop a sound statistical basis for correctly identifying a set of variables and also to demonstrate the benefits of adopting a multivariate gate-keeper test in distracted driving studies. This was demonstrated with an experiment where 67 drivers participated in a repeated measures driving simulator experiment. 14 commonly used performance variables were used as the multivariate response variables. The corresponding data were analyzed using univariate tests, and multivariate gate-keeper tests. The results indicate that ignoring the multivariate structure and performing multiple univariate tests, as has been found to be prevalent in past studies, will lead to inflated Type-I error rates and potentially misleading conclusions. The procedure developed in this study also led to the development of sound statistical basis for the selection of variables that can be best used to account for the distracting effect of the texting and phone call activities that were investigated. The findings of this study have significant educational value to the body of knowledge on distracted driving studies and any other studies that analyze multiple dependent variables for a single factor.
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Codjoe, Julius A., "The Effects of Concurrent Driving and In-Vehicle Tasks: A Multivariate Statistical Analysis of Driver Distraction in a High-Fidelity Driving Simulator" (2014). LSU Doctoral Dissertations. 2366.