NHTSA reports that distracted driving accounted for 8.1% of motor vehicle fatalities in 2020 (Stewart, 2022)
2.8% of drivers nationwide were observed using their cell phone at stop lights in 2020 (National Center for Statistics and Analysis, 2021)
Typing and reading text messages decreased driver reaction time, ability to detect stimuli, and ability to maintain a consistent speed, lane position, and headway (Caird et al. 2014)
As of August 2023, all states but MT have texting bans and 28 states have handheld bans (IIHS, 2024)
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Conceptualizing Effectiveness
Methods for examining effectiveness differ
Observational Data (McCartt & Geary, 2004)
Self-Report (Qiao & Bell, 2016)
Police Citations (Rudisill & Zhu, 2016)
Crashes & Fatalities (Dong et al., 2017; Nikolaev et al., 2010)
These methods can be categorized into two different groups
Observed or “Actual”
Observational
Self-Report
Reported or “Got Caught”
Citations
Crashes
Fatalities
Hospital Data
Insurance Claims
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Moderators
State
Differences in enforcement and education of drivers (McCartt et al., 2010)
Variations in how laws are written, allocation of resources, lack of educational campaigns, differences in judges, public relations concerns may impact enforcement (Rudisill et al., 2019)
Type of Ban
Not all states have complete bans (IIHS & HLDI, 2021)
Handheld bans: prohibits picking up and holding a phone to one’s ear (McCartt & Geary, 2004)
Total bans: prohibit all use of mobile phones for any purpose (Foss et al., 2009)
Considered Moderators:
Age Groups (Teen Bans)
Time
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Hypotheses & Research Question
H1: Implementing cell phone bans for drivers decreases use.
State
RQ: What is the moderating effect of state on compliance?
Type of Ban
H2: Handheld bans will result in higher rates of phone use than total bans.
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Coding & Analytical Strategy
Coding in Excel
Type of study
State
Odds ratio
Variance of the log odds ratio
Confidence intervals (95%)
Types of Bans
Handheld
Total
Effect Sizes
Pre-law and post-law data
States w/ bans vs w/out
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PRISMAFlowchart
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Studies Included
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Results - Main Effects
15 effect sizes (N = 283, 730)
Estimate of the log odds ratio was -0.267 (p = 0.013, 95% CI = [-0.477, -0.056])
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Results - Main Effects
Significant heterogeneity across studies, Q = 299.238, p < 0.001; I2 = 96.69%
Funnel plot suggests publication bias could be an issue, but could also be due to low number of effect sizes (Sterne et al., 2011)
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Results - Moderators
State
NY, NC, CT, DC
North Carolina displayed significantly more non-compliance than the country-wide group (p = 0.022, 95% CI = [0.093, 1.223])
The three remaining states did not display a significant difference when compared to the reference category
New York (p = 0.225, 95% CI = [-0.208,0.882])
Connecticut (p = 0.981, 95% CI = [-0.837, 0.817])
D.C. (p = 0.944, 95% CI = [-0.559, 0.520])
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Results - Moderators
Type of Ban
It was predicted that total bans would results in less phone use than handheld bans
Studies with total bans displayed a higher rate of phone use than studies with only handheld bans (p = 0.047, 95% CI = [0.005, 0.835])
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Discussion
The relationship between cell phone use and bans was significant
This evidence would suggest that bans are effective even if the initial rates of compliance achieved are not maintained
North Carolina showed higher rates of phone use after ban implementation
New York, Connecticut, and D.C. showed no significant differences
Total bans less compliance than handheld
Education, enforcement, state differences etc.
Low number of studies could account for the funnel plot dispersion (Sterne et al., 2011)