Social Learning and Distracted Driving among Young Adults


This paper examines the relevance of Akers’ social learning theory (Akers and Jensen 2006; Akers and Jennings 2009) for the problem of distracted driving. Distracted driving is widespread and dangerous, with many drivers, particularly youthful ones, continuing to engage in such driving despite knowledge of its risks (see, e.g., Atchley et al. 2011; Prat et al. 2017). Much of the research to date is limited to texting; however, in recent years, cell phones have become major tools for entertainment and information, especially among younger people, resulting in the emergence of newer forms of distracted driving. Explanations of distracted driving from a criminological approach are limited (Quisenberry 2015 is an exception), yet criminological theory can contribute to our understanding of this relatively new and expanding form of deviance. Drawing on social learning theory, we explore the attitudes and behaviors of 935 college students regarding ‘traditional’ and newer types of phone-related distracted driving, as well as their perceptions of self and others’ cell phone use. Multivariate analysis indicates support for some of the social learning concepts, with definitions and one of the differentialreinforcement measures standing out in particular (perceived benefits of cell phone use while driving). We consider the implications of these findings for theory and policy.

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  1. 1.

    Bingham, Zakrajsek, Almani, Shope, and Sayer (2015); Fleiter et al. (2006); Scott-Parker, Watson, and King (2009); Scott-Parker, Hyde, Watson, and King (2013); Taubman-Ben-Ari, Mikulincer, and Gillath (2005).

  2. 2.

    Studies utilizing college students include Atchley et al. (2011), Beck and Watters (2016), Gauld et al. (2014), George et al. (2018), Nemme and White (2010), O’Connor et al. (2017), Panek et al. (2015), Pearson et al. (2013), Prat et al. (2015), Quisenberry (2015), and Scott-Parker et al. (2009). Further, other studies focus on samples of teens and young adults (Scott-Parker et al. 2012; Scott-Parker et al. 2013; Taubman-Ben Ari et al. 2005; Tucker et al. 2015; Weller, Shackleford, Dieckmann, and Slovic 2013).

  3. 3.

    After removing “speeders,” those who completed the survey in less than 47% of the median duration of 10 min (129 cases), there were 1092 cases. A review of the frequency distributions for all variables to be included in our statistical models revealed missing cases on seven of the eleven variables (missing values ranged from 17 to 149). No data were missing on either of the outcome variables (count of cell phone use while moving and while stopped). We employed Complete Case Analysis (CCA) for our outcomes and theoretical variables of interest (only). This resulted in a sample size of 935 cases for all variables, except for texting frequency (n = 925) and sex (n = 865), which, along with age, serve as controls in our statistical models. We compared the complete cases (n = 935) to cases with incomplete theoretical data (n = 157) on sex, age, and texting frequency. There were no significant differences by sex (X2(1, N = 943) = .001, p = .979) or age (t(1,090) = 0.06, p = .951), but there was a significant difference based on number of texts sent last month (t(1,073) = −3.13, p = .002). Subjects with complete data sent significantly more texts last month (M = 4.85, SD = 1.81) than subjects without complete data (M = 4.34, SD = 1.88). The final sample for analysis was 935.

  4. 4.

    We chose linear regression after careful consideration. We concluded that OLS Regression was the appropriate technique for the following reasons. Poisson regression is not appropriate for the data since we are not trying to estimate a rate of occurrence and we do not take into account exposure to the activity. In addition, an individual occurrence of cell phone use is not assumed to be independent from additional counts/types of cell phone use. Furthermore, our analyses indicate that there are no significant issues with overdispersion or underdispersion (Orme and Combs-Orme 2009). Skewness and kurtosis statistics indicate that our data are normally distributed (Mertler and Vannatta 2010). For both variables, skewness and kurtosis statistics are within −1 to +1. In addition, there is no indication that scores on the dependent variables are zero-inflated. Finally, there is no evidence of problematic outliers at the bivariate level. In other words, there are linear relationships between our independent and dependent variables.


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Correspondence to Pamela Tontodonato.

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Tontodonato, P., Drinkard, A. Social Learning and Distracted Driving among Young Adults. Am J Crim Just 45, 821–843 (2020).

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  • Distracted driving
  • Texting while driving
  • Cell phone use while driving
  • Social learning theory