Rural Roadway Safety Perceptions Among Rural Teen Drivers Living in and Outside of Towns
ABSTRACT Purpose: To compare perceptions about rural road and general driving behaviors between teens who live in- and out-of-town from rural communities in Iowa. Methods: A cross-sectional survey was conducted with 160 teens anticipating their Intermediate License within 3 months upon enrollment into this study. Self-administered surveys were used to collect demographics and driving exposures (eg, frequency of driving, age when first drove unsupervised). Two Likert scales were included to measure agreement with safe driving behaviors on rural roads and general safe driving behaviors (eg, speeding, seat belt use). T-tests were calculated comparing mean composite scores between in- and out-of-town teens, and between mean rural road and general driving safety attitude scores. A linear regression multivariable model was constructed to identify predictors of the rural road score. Results: While the majority of teens endorsed rural road and general safe driving behaviors, up to 40% did not. Thirty-two percent did not believe the dangers of animals on rural roads, and 40% disagreed that exceeding the speed limit is dangerous. In-town teens were less safety conscious about rural road hazards with a significantly lower mean composite score (4.4) than out-of-town teens (4.6); mean scores for general driving behaviors were similar. Living out-of-town and owning one's own car were significant predictors of increased rural road safety scores. Conclusion: Rural, in-town teens have poorer safety attitudes about rural roadway hazards compared with out-of-town teens. Interventions that involve education, parental supervision, and practice on rural roads are critical for preventing teen crashes on rural roads.
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ABSTRACT: By definition, multiple regression (MR) considers more than one predictor variable, and each variable's beta will depend on both its correlation with the criterion and its correlation with the other predictor(s). Despite ad nauseam coverage of this characteristic in organizational psychology and statistical texts, researchers' applications of MR in bivariate hypothesis testing has been the subject of recent and renewed interest. Accordingly, we conducted a targeted survey of the literature by coding articles, covering a five-year span from two top-tier organizational journals, that employed MR for testing bivariate relations. The results suggest that MR coefficients, rather than correlation coefficients, were most common for testing hypotheses of bivariate relations, yet supporting theoretical rationales were rarely offered. Regarding the potential impact on scientific advancement, in almost half of the articles reviewed (44 %), at least one conclusion of each study (i.e., that the hypothesis was or was not supported) would have been different, depending on the author's use of correlation or beta to test the bivariate hypothesis. It follows that inappropriate decisions to interpret the correlation versus the beta will affect the accumulation of consistent and replicable scientific evidence. We conclude with recommendations for improving bivariate hypothesis testing.Behavior Research Methods 10/2013; 46(3). DOI:10.3758/s13428-013-0407-1 · 2.12 Impact Factor
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ABSTRACT: Purpose Although approximately one-third of agricultural equipment-related crashes occur near town, these crashes are thought to be a rural problem. This analysis examines differences between agricultural equipment-related crashes by their urban–rural distribution and distance from a town. Methods Agricultural equipment crashes were collected from nine Midwest Departments of Transportation (2005–2008). Crash zip code was assigned as urban or rural (large, small and isolated) using Rural–Urban Commuting Areas. Crash proximity to a town was estimated with ArcGIS. Multivariable logistic regression was used to estimate the odds of crashing in an urban versus rural zip codes and across rural gradients. ANOVA analysis estimated mean distance (miles) from a crash site to a town. Findings Over four years, 4444 crashes involved agricultural equipment. About 30% of crashes occurred in urban zip codes. Urban crashes were more likely to be non-collisions (aOR = 1.69[1.24–2.30]), involve ≥2 vehicles (2 vehicles: aOR = 1.58[1.14–2.20], 3+ vehicles: aOR = 1.68[0.98–2.88]), occur in a town (aOR = 2.06[1.73–2.45]) and within one mile of a town (aOR = 1.65[1.40–1.95]) than rural crashes. The proportion of crashes within a town differed significantly across rural gradients (P < 0.0001). Small rural crashes, compared to isolated rural crashes, were 1.98 (95%CI[1.28–3.06]) times more likely to be non-collisions. The distance from the crash to town differed significantly by the urban-rural distribution (P < 0.0001). Conclusions Crashes with agricultural equipment are unexpectedly common in urban areas and near towns and cities. Education among all roadway users, increased visibility of agricultural equipment and the development of complete rural roads are needed to increase road safety and prevent agricultural equipment-related crashes.Accident; analysis and prevention 09/2014; 70:8–13. DOI:10.1016/j.aap.2014.02.013 · 1.65 Impact Factor