Multivariate Analysis of Distracted Driving
Over the past decade, a number of bodies, including government agencies, traffic safety advocacy groups and law enforcement agencies, have successfully increased the public awareness level of traffic safety risks from distracted driving. The driving simulator continues to be popular with researchers in collecting data on dependent variables that provide scientific knowledge of the effects of distracted driving. Several of these dependent variables can be used to quantify a single distracting effect, resulting in a multivariate dataset. A literature review of current 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 erroneously concluding that there is a distracting effect when there may not be. The primary objective of this research study is to show that data analysis techniques that ignore the multivariate structure of the data may lead to inaccurate estimates and erroneous conclusions. This is demonstrated with a pilot study where 13 drivers participated in a 2 (drive) x 1 (operating a navigation device) repeated measures driving simulator experiment. Six commonly used dependent variables that are often used to quantify lateral and longitudinal control were used as the multivariate response variables. The corresponding data were analyzed using univariate tests, Bonferroni adjustment 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.
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Supplemental Notes:
- This paper was sponsored by TRB committee AND10 Vehicle User Characteristics.
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Corporate Authors:
500 Fifth Street, NW
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Authors:
- Codjoe, Julius
- McCarter, Kevin
- Ishak, Sherif
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Conference:
- Transportation Research Board 94th Annual Meeting
- Location: Washington DC, United States
- Date: 2015-1-11 to 2015-1-15
- Date: 2015
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 18p
- Monograph Title: TRB 94th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Data analysis; Data quality; Distraction; Drivers; Driving simulators; Highway safety; Methodology
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I70: Traffic and Transport; I83: Accidents and the Human Factor;
Filing Info
- Accession Number: 01550180
- Record Type: Publication
- Report/Paper Numbers: 15-2246
- Files: TRIS, TRB, ATRI
- Created Date: Jan 16 2015 8:29AM