Document Type
Article
DOI
10.25778/SETP-H375
Abstract
Problem: Several approaches to analyze seatbelt use have been proposed in the literature. Two methods that have not been explored are the use of unweighted and weighted logistic regression models and the use of item response theory (IRT) or the Rasch model. Since accurate methods to predict seatbelt use behavior based upon observed data must include a built-in design method and model and overcome computation challenges, weighted and IRT methods deem to be other options for an observational survey of seatbelt use in the state of Virginia.
Method: The data observed from 136 sites within the Commonwealth of Virginia over two years was collected in a two-stage, systematically stratified, proportional-to-size sampling plan. The data is analyzed using a weighted Rasch model.
Results: A relationship between seatbelt use of drivers, weighted for county aggregate population size, length of the road segment observed, and the factors of vehicle type and gender standardized using a standardized scale, is confirmed using logistic regression model selection and AIC analysis. An IRT model was considered and was found highly significant.
Practical Application: The addition of socio-economic measures, measure of road and driving difficulty, and data from other states may allow the prediction of seatbelt use with a new methodology: the models provide tools for policy decision-making.
Recommended Citation
Ledbetter, M. K., N. Diawara, and B. E. Porter. 2016. Exploring New Models for Seatbelt Use in Survey Data. Virginia Journal of Science, 67(3/4): 1-24. doi: 10.25778/SETP-H375. Available at: https://digitalcommons.odu.edu/vjs/vol67/iss3/2
"online ahead of print" version
Included in
Applied Statistics Commons, Design of Experiments and Sample Surveys Commons, Statistical Models Commons
Comments
This article has been copyedited and reformatted and is now in its final version. The early “online ahead of print” version is available for reference as an "Additional File" (below).