Accident Analysis and Prevention 74 (2015) 218–228
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Gender differences in psychosocial predictors of texting while driving Cindy Struckman-Johnson *, Samuel Gaster, Dave Struckman-Johnson, Melissa Johnson, Gabby May-Shinagle University of South Dakota, 414 E. Clark St., Vermillion,SD 57069, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 23 July 2013 Received in revised form 13 June 2014 Accepted 1 October 2014 Available online 14 November 2014
A sample of 158 male and 357 female college students at a midwestern university participated in an online study of psychosocial motives for texting while driving. Men and women did not differ in selfreported ratings of how often they texted while driving. However, more women sent texts of less than a sentence while more men sent texts of 1–5 sentences. More women than men said they would quit texting while driving due to police warnings, receiving information about texting dangers, being shown graphic pictures of texting accidents, and being in a car accident. A hierarchical regression for men’s data revealed that lower levels of feeling distracted by texting while driving (20% of the variance), higher levels of cell phone dependence (11.5% of the variance), risky behavioral tendencies (6.5% of the variance) and impulsivity (2.3%) of the variance) were significantly associated with more texting while driving (total model variance = 42%). A separate regression for women revealed that higher levels of cell phone dependence (10.4% of the variance), risky behavioral tendencies (9.9% of the variance), texting distractibility (6.2%), crash risk estimates (2.2% of the variance) and driving confidence (1.3% of the variance) were significantly associated with more texting while driving (total model variance = 31%.) Friendship potential and need for intimacy were not related to men’s or women’s texting while driving. Implications of the results for gender-specific prevention strategies are discussed. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Texting while driving Gender Cell phone dependence Risk assessment
1. Introduction Distracted driving is the diversion of attention away from activities critical for safe driving toward a competing activity, which may result in insufficient or no attention to activities critical for safe driving (Hosking et al., 2009). Experts further agree that distracted driving places demands upon cognitive, auditory, vocal/ verbal, visual motoric, and other resources separately or in any combination (Foley et al., 2013). Of the many activities that comprise distracted driving, cell phone use became “emblematic” of driver distraction in public opinion and in research activity from 2000 to 2005 (AAA, 2008; p. 3). Texting while driving, defined as the use of a cell phone device to send or receive displayed or voiceactivated messages while driving a moving vehicle, has recently claimed the attention of traffic safety experts (Hosking et al., 2009; Lee, 2007; Owens et al., 2011). The expansion of cell phone ownership by Americans (33% in 1999–91% in 2008) has resulted in increasing numbers of drivers
* Corresponding author. Tel.: +1 605 677 5098; fax: +1 605 677 3195. E-mail address: [email protected] (C. Struckman-Johnson). http://dx.doi.org/10.1016/j.aap.2014.10.001 0001-4575/ ã 2014 Elsevier Ltd. All rights reserved.
who text (Wilson and Stimpson, 2010). Atchley et al. (2012) observed that as cell phone technology becomes more pervasive, overall use increases and the average age of users decreases (p. 279). By 2011, 31.2% of American drivers surveyed by the Center for Disease Control (2013) had read or sent a text or message while driving in the past 30 days. The rate reached 50% for young drivers age 18–24. CDC data for high school students for 2011 revealed that 44.5% had engaged in texting while driving (Olsen et al., 2013). Texting while driving is almost universal among college-age samples. A survey of young adult drivers revealed that 92% had read, 81% had replied to, and 70% had initiated a text while driving (Atchley et al., 2011). 1.1. Distraction effects of texting The research on effects of texting while driving has had to adapt to changing technologies that tend to be quickly embraced by young drivers (Lee, 2007). In the 1990s and early 2000s, texting while driving was accomplished with hand-held devices with keyboards that required drivers to direct their eyes away from the road and potentially to remove their hands from the steering wheel (Hosking et al., 2009). Research on texting with hand-held devices
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in driver simulators and closed-course driving showed detrimental effects on performance, including increased eyes-off-road time, lane drift, missing of lane change cues, variability in following distance to cars ahead, missing traffic signs, and failure to process traffic sign information (Hosking et al., 2009; Owens et al., 2011). In a driving simulation study, Alosco et al. (2013) found that drivers instructed to text with either keyboard or touch-screen typing had poorer performance than a control group. In recent years, texting while driving has been altered by options for in-vehicle systems that convert text to speech, thus eliminating the driver’s need to look away from the road to type. Research on texting with this advanced technology, however, continues to show detrimental effects on safe-driving behaviors. Using closed-course driving, Owens et al. (2011) determined that audio playback of received text messages produced no more driver errors than baseline, but sending of pre-programmed text messages using vehicular controls led to more driver errors than baseline. In a major study using driving simulation and instrumented vehicles, Strayer et al. (2013) found that driver interaction with a speech-to-text system produced a higher level of cognitive distraction than listening to the radio, talking to a passenger, or using a hand-held or hands-free cell phone. They concluded that even when drivers can keep eyes on the road while they text, the cognitive demands of processing a voice-activated communication interfere with performance. 1.2. Texting and crash risk According to the National Highway Traffic Safety Administration (2011) (NHTSA), about 1 in 6 fatal vehicle collisions resulted from distracted driving in 2008. Driver inattention and driver distraction were found to account for 58% of serious casualty crashes in Australia (Beanland et al., 2013). What percentage of distracted-driving crashes is due to texting is currently under investigation. Wilson and Stimpson (2010) estimated with statistical models that increasing texting volumes resulted in 16,000 additional road fatalities from 2001 to 2007. The texting crash relationship is most evident among younger drivers. Largescale naturalistic driving studies conducted by Virginia Tech Transportation Institute revealed that risk or a crash of near-crash by novice drivers increased significantly if they were texting (odds ratio, 3.87) (Klauer et al., 2013). The first nationally representative telephone survey of distracted driving (National Highway Traffic Safety Administration, 2012) found that of drivers age 18–20 who were in a crash or near-crash incident in the past year, 6% were on cell phones and 2% were texting. 1.3. Driver awareness of texting risk Research has documented a serious disconnect between perceptions of the risks of texting and the behavior of texting. In an AAA telephone survey (2008), 95% of drivers rated texting while driving as completely or somewhat unacceptable. Yet 18% admitted to have engaged in texting in the last 30 days. University students surveyed by Harrison (2011) generally agreed that texting while driving was unacceptable, dangerous, and should be illegal, although 91% had occasionally engaged in the behavior. In a study by Nemme and White (2010), young Australian respondents reported strong beliefs that texting is dangerous and the wrong thing to do, although many engaged in it regardless. Because risk assessment alone does not seem to moderate texting behavior, researchers have begun to look for explanatory factors beyond the driving situation. Bayer and Campbell (2012) (p. 2087), noting that texters’ moment-to-moment motivations play out in both conscious and unconscious ways, called for “new angles of insight” from research on psychosocial factors.
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1.4. Psychosocial predictors of texting while driving To date, a limited literature on psychosocial predictors of cell phone calling and texting while driving has accumulated. Applying the theory of planned behavior (TPB), Walsh et al. (2008) found that attitudes, norms, and control factors were predictive of Australians’ intentions to use a cell phone while driving and, to a modest degree, intentions to text. Expanding the TPB model, Nemme and White (2010) documented that Australian university students’ intentions to text and actual texting were significantly influenced by combinations of personal attitudes, subjective norms, perceived control, reference group norms, and morality norms. In another approach, Feldman et al. (2011) established that higher levels of mindfulness, the tendency to intentionally attend to and accept present experiences, were related to less texting among a sample of university women. Schlehofer et al. (2010) found that participants’ illusion of control and overestimates of competence in the driving situation were predictive of cell phone use while driving. The social value of texting has been proposed to be a primary motivation for texting while driving. Atchley et al. (2011) suggested that texting among young adults is driven less by risk assessment than by the importance of peer-to-peer interactions for growth of social networks. Experimental procedures by Atchley and Warden (2012) revealed that persons’ decisions to text now or wait for a reward were significantly influenced by their social closeness to whom was texting. Similarly, Walsh et al. (2008) concluded that the benefits of cell phone use for fulfilling the need to be connected to social groups outweigh the risks of driving errors. The social value approach was supported by Nemme and White (2010) finding that reference group norms predicted texting. In an expansion of Nemme and White’s study, Bayer and Campbell (2012) also found that social norms strongly predicted texting while driving. Another benefit of cell phones and texting is that they may fulfill a psychosocial need for constant and instantaneous communication with the outside world. Aoki and Downes (2003) identified the trait of cell phone dependence in a survey of college students who, to variable degrees, felt disconnected, lost, and upset when they did not have their cell phones. In a study of psychological and demographic predictors of problematic cell phone use, Billieux et al. (2008) documented an element of cell phone dependence among Swiss community members age 20–35. Utilizing a concept of “possession attachment”, Weller et al. (2013) discovered in an on-line survey that young people who showed high cell phone dependency were more likely to use their cell phones and text on past driving trips. Harrison (2011) (p. 1518) proposed that cell phone use and texting are now such an integral part of the daily existence of college students that they allow for a “suspension of concern and consideration of consequences to ourselves and others” when driving. 1.5. The present study: gender differences in motives to text while driving While research on psychosocial motives for texting while driving has advanced, there have been few studies of how these motives may vary by gender. The literature show that male and female drivers text at about the same frequency, with some studies showing slight differences. For example, a NHTSA telephone survey (National Highway Traffic Safety Administration, 2012) found that nearly the same percentages of men (19%) and women (17%) had ever texted while driving. Nemme and White (2010) concluded that gender did not significantly predict texting frequency. We reasoned that although men and women may text while driving at the same frequency, they may differ in their motivations to text.
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The present study was undertaken to examine potential psychosocial motives for texting while driving reported by male and female college students. A second purpose was to examine gender differences in descriptive characteristics of how college students text and drive. We anticipated that knowledge of potential differences in motives and behaviors would be useful for designing gender-based prevention strategies. 1.6. Psychosocial variables of interest 1.6.1. Distractibility, crash risk, driving confidence In our reasoning, men’s and women’s decision to text while driving would be primarily affected by how dangerous and risky they believe it to be. For example, how distracted are they from driving while they text? How likely is it that they will have a crash if they text? Research has shown that women are more likely than men to perceive a wide range of behaviors as risky (Harris et al., 2006). In an on-line survey of New Zealander drivers of all ages, Hallett et al. (2011) found that half as many women as men rated cell phone use as safe. Our expectation in the present study was that the perceived distraction of texting and concerns about crash risk would play a greater role in women’s texting behavior. We also reasoned that men and women’s perceptions of their driving skills and their ability to avoid crashes while texting are related to their willingness to text on the road. Because men have been found to have higher levels of driver confidence than women (Lonczak et al., 2007), we expected in the present study that this variable would play a greater role in men’s texting behavior.
psychologists Baumeister and Leary (1995), humans have a universal need to maintain strong, stable interpersonal relationships. This motive can be expressed as a desire friends and a close romantic relationship. In the traffic safety literature, many authors have cited young people’s need for belongingness and desire to be connected to others as important motives for texting while driving (e.g., Atchley et al., 2011; Walsh et al., 2008). Based upon Baumeister and Leary (1995) suggestion that women have stronger affiliation needs than men, we expected that friendship and intimacy needs would be more closely associated with women's texting behavior. 2. Method 2.1. Participants The original sample consisted of 526 students at a midwestern university who volunteered to take an on-line survey for extra credit for classes. Data from 11 participants who timed out of the survey in less than 3 min were excluded. The final sample consisted of 515 students 158 men (M age = 21.0, SD = 4.1) and 357 women (M age = 20.1, SD = 2.30). The difference in sample sizes of men and women reflected the university sex ratio (40% men and 60% women) and the lower percentage of men than women who take psychology classes. Participants identified their ethnicity as Caucasian (92.4%), Asian (1.7%), Native American (1.2%), African American (1.0%), Hispanic (0.4%), and other (3.3%) 2.2. Procedure and data analysis
1.6.2. Cell phone dependence Men’s and women’s perceptions of the risks of texting while driving, in our opinion, may be countered by the degree to which they are dependent upon using their cell phones to communicate with others. For example, Weller et al. (2013) found that having high cell phone dependence was associated with underestimating the risks associated with cell phone talking and texting while driving. Evidence on gender differences on cell phone dependence, however, is limited. Aoki and Downes (2003) and Weller et al. (2013) did not compare cell phone dependence levels by gender. However, Billieux et al. (2008) found that dependence on mobile phone use was higher among Swiss women than men. Therefore, in the present study we anticipated that cell phone dependence would be more strongly related to women’s texting while driving. 1.6.3. Personality traits of risky behavior and impulsivity We reasoned that individuals who are prone to take risks might view texting while driving as a challenge. Recently, Olsen et al. (2013) found a relationship between frequent texting and other risky behaviors among high school students. A large literature has established that men engage in more risky behaviors than women, including speeding, running stop lights, and not wearing seatbelts (Cross et al., 2011; Harris et al., 2006). Thus, we expected a relationship between men’s risk-taking and texting on the road. Another personality trait that could lead one to text is impulsivity. While risk taking often involves planned pursuit of a reward, impulsive actions tend to be unplanned or involve failure to inhibit a response (Cross et al., 2011). Impulsivity is related to unsafe traffic behaviors such as ignoring crosswalks and receipt of speeding tickets (Sharma et al., 2013.) Because impulsivity is generally higher in men than women (Cross et al., 2011), we expected this trait to be more strongly associated with men’s texting behavior. 1.6.4. Need for affiliation motives Another psychosocial motive that may explain texting while driving is one’s need for affiliation. According to social
After approval of the University IRB, the questionnaire was posted on the psychology department’s on-line experiment scheduling system. Student volunteers from psychology and some biology courses received extra credit for taking the survey. Participants logged onto the website on their own computers and, after reading consent information, responded to questions by clicking alternatives. Once completing a page, they clicked a continue button to display the next page. Results of the survey were analyzed with Chi square tests, t tests, correlation coefficients, and hierarchical regression techniques using SPSS version 21.0 2.3. Measures The survey consisted of 135 items displayed on 12 pages of a research website. Section 1 had measures for demographic data, miles driven per week, distracted driving activities besides texting, and confidence in driving ability. Section 2 assessed perceptions and behaviors related to texting while driving. Section 3 contained standardized scales for cell phone dependence, risk-related personality traits and affiliation motives. The items sets are described below. The response categories for the items are shown in Section 3. 2.3.1. Distracting driving activities, texting level, number, and length of texts To measure all types of distracted driving activities, respondents were asked “Which of the following activities have you EVER done while driving?” The item listed eight activities that came from discussions with students about behaviors that interfered with driving. Respondents’ texting level was assessed by the question “How often do you engage in texting while driving?” followed by a 10-point semantic-differential scale ranging from “I never text while driving” to “I always text while driving”. Those who had texted while driving in the past year chose a category for “the average number of texts you send or receive in a day while
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driving” and “the average length of a text message you send while driving”. 2.3.2. Texting reasons, devices used, legality, and prevention Participants who had texted while driving in the past year were asked to check categories for reasons, type of device used, and what would make them stop texting while driving. All participants were asked if they thought texting while driving should be illegal, and if they had nearly and actually had a car accident because of texting. 2.3.3. Texting distractibility, crash risk, and driving confidence The distraction measure was “How distracting is texting while driving for you? If you have never texted while driving, how distracting do you think it would be?” The 10-point semantic differential rating scale was anchored by not distracting to extremely distracting. The crash risk item was “What do you think is the probability that you will ever have an accident if you text while driving?” The 11-point scale ranged from 0% to 100% in increments of 10%. These two questions were also answered with “other people” as the referent. Confidence in driving was assessed with eight items from a three-factor scale developed by Ho and Yong Gee (2008) to assess motives for dangerous driving among young males. Items such as “I am a skillful driver and am always in control of my driving.” are endorsed on a 6-point Likert scale, ranging from strongly disagree to strongly agree (Cronbach’s alpha = .86). Scores can range from 8 to 64, with higher scores indicating greater confidence in one’s driving skills 2.3.4. Cell phone dependence A cell phone dependence scale (CPD) was developed by Gaster, Struckman-Johnson, and Struckman-Johnson for this study. Twelve items were selected from a pool of 100 statements that reflected the importance of cell phone availability. Items were rated on a 5-point Likert scale extending from strongly disagree to strongly agree. Scores could range from 12 to 60, with higher scores reflecting greater cell phone dependence. Participants’ responses to the CPD were submitted to a principal components factor analysis with varimax rotation. Two factors were revealed, accounting for 54.7% of the total variance. Seven items in an “anxiety” factor reflected a constant need to have a cell phone available (Cronbach’s alpha = .87). Five items in a “dependence” factor reflected an unwillingness to be without a cell phone for extended periods (Cronbach’s alpha = .77). See Table 1 for the scale items by factor.
Table 1 Cell phone dependence scale. Factor 1 – anxious without cell phone 1. I feel anxious at the thought of not having my cell phone with me. 2. I feel secure when I have my cell phone. 3. I check my cell phone constantly for messages and texts. 4. Without my cell phone, I would be isolated from the world. 5. I consider my cell phone an extension of myself. 6. I keep my cell phone with me at all times day and night. 7. It is hard to imagine my life without my cell phone. Factor 2 – dependent on cell phone 8. I am not dependent on my cell phone.1 9. I could easily give up my cell phone if I had to.1 10. I only use my cell phone for emergencies and on other rare occasions.1 11. I would not mind going the entire day without my cell phone.1 12. I like to turn my cell phone off so that I can concentrate on other things.1 Note: Scale responses were on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). 1 indicates item was reversed scored. Scale authors are S. Gaster, C. Struckman-Johnson, and D. Struckman-Johnson.
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2.3.5. Personality traits of risky behavior and impulsivity The trait of riskiness was measured by Campbell and Muncer (2009) 12-item “Risky Impulsiveness” scale that contains items for physical injury, health risk, and criminal risk (Cronbach’s alpha = .81). Participants rate the likelihood of engaging in behavior such as “have unprotected sex”, or “drive too fast when I am feeling upset” on a 5-point semantic differential scale anchored by very unlikely to very likely. Scores can range from 12 to 60, with higher scores reflecting greater riskiness. The trait of impulsiveness was measured by the short form of the Barratt Impulsiveness Scale (Spinella, 2007), Cronbach’s alpha = .79–.81. This 15-item scale has validity for both clinical and non-clinical populations. Items such as “I act on impulse” are rated on a 4-point semantic differential scale anchored by very unlikely and very likely. Scores can range from 15 to 60, with higher scores reflecting greater impulsivity. 2.3.6. Affiliation motives of friendship potential and need for intimacy The desire to have many friends was measured by Dawley (1980) 12-item friendship potential scale (Cronbach’s alpha was not reported). Items such as “I am the type of person who likes people” are rated on a 5-point Likert scale ranging from completely agree to completely disagree. Scores can range from 12 to 60, with higher scores reflecting a greater desire to have many friends. The need for intimacy was measured with the 9-item need for affiliation factor drawn from the sexual intimacy scale by Marelich and Lundquist, 2008 (Cronbach’s alpha = .82). Items such as “I need someone to love” are rated on a 5-point semantic differential scale extending from disagree definitely to agree definitely. Scores can range from 9 to 45 with higher scores indicating a greater need for intimacy. 3. Results Note that the total n for male and female groups varied slightly among analyses due to missing data from participants who failed to answer every question. To control for inflated alpha effects due to multiple tests, significance levels for Chi square analyses were restricted to p < .01. 3.1. Demographic data and distracting driving activities Of 515 respondents, 163 (32.4%) reported driving 25 miles or less, 205 (40.0%) drove between 25–100 miles per week, and 147 (28.6%) reported driving more than 100 miles per week. Men and women were similar for miles driven. Table 2 shows the distributions of men and women who reported “yes” to having ever engaged in various distracting driving behaviors other than texting. Talking on cell phones was the highest level distractor (reported by 98% of the sample), while watching movies was the lowest level distractor (reported by 6% of the sample). Gender differences were found for only three of the eight distracted driving categories. As expected, more women than men applied make up, x2 (1, 512) = 63.57; p < .001. More men than women had watched movies, x2 (1, 514) = 25.08, p < .001, and engaged in sexual activities, x2 (1, 514) = 42.64, p < .001. 3.2. Level of texting, devices used, length, reasons Men and women were similar in the key dependent measure of how often they engaged in texting while driving. On a 10-point semantic differential scale from never to always, the average rating was 4.97 (SD = 2.53) for men and 5.01 (SD = 2.72) for women. On average, participants rated themselves in the midlevel range. Fig. 1 shows the distributions of men and women by responses to the scale.
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Table 2 Distracted driving behaviors besides texting. Men (N = 156–158)
Women (N = 354–356)
Total (N = 507–515)
Item on Questionnaire
n
%
n
%
n
%
Talk on cell phone Apply makeup* Change clothes Hold child or pet in lap Read/study Watch movies* Take photographs Engage in sexual activity* (e.g., masturbation, oral sex)
155 2 61 38 35 22 56 65
98.1 1.3 38.6 24.2 22.2 13.9 35.9 41.1
352 121 141 125 58 9 157 53
98.6 34.0 39.9 35.0 16.4 2.5 44.2 14.9
507 123 202 163 93 31 213 118
98.4 24.0 39.5 31.7 18.2 6.0 41.7 23.0
Frequencies are the number of respondents who answered yes to having ever engaged in the activity while driving. * p < .001 for Chi square comparisons by gender.
Fig. 1. Percentage counts for frequency of engaging in texting and driving.
Of 515 participants, equal proportions of men (149 of 158 or 94.3%) and women (336 of 357 or 94.1%) reported that they had sent or read text messages while driving in the past year. As shown in Table 3, 267 (55%) of the 484 participants who had texted while driving in the past year reported sending or receiving from 1 to 5 texts per day. Although men and women were similar for number of texts sent, they differed for length of texts sent, x2 (3, 484) = 17.29; p < .001). More women reported sending texts of less than a sentence, while more men reported sending texts of 1 to 5 sentences.
Of a sub-sample of 484 participants who had texted during the past year, 40 (8.3%) used the ABC method, 215 (44.4%) used T9 or predictive texting, 224 (46.3%) used a full keypad, and 5 (1%) used a voice-activated or speech-to-text converter. The most endorsed reason for texting was to receive important information and updates such as an event cancellation or weather (375 of 483 or 77.6%). The second most endorsed reason was to keep in close touch with friends and family (359 of 480 or 74.8%), followed by to avoid boredom (236 of 478 or 49.4%) and to stay awake or alert (155 of 483 or 32.1%). Men and women did not differ for device used or reasons for texting.
Table 3 Frequency and extent of text-messaging while driving. Item
Women (N = 335)
Total (N = 484)
n
%
n
%
n
%
What is the average number of texts you send or receive in a day while driving? 1–5 6–10 11–20 21–50 51–100 100+
83 39 17 9 1 1
55.3 26.0 11.3 6.0 0.7 0.7
184 76 44 25 4 2
54.9 22.7 13.1 7.5 1.2 0.6
267 61 34 5 3
54.9 23.9 12.6 7.0 1.0 0.6
What is the average length of a text message you send while driving?* Less than a sentence or phrase 1–5 sentences or phrases 6–10 sentences or phrases More than 10 sentences or phrases
64 82 2 2
42.7 54.7 1.3 1.3
201 128 6 0
60.0 38.2 1.8 0.0
265 210 8 3
54.5 43.2 1.6 0.6
*
p < .01 for Chi square comparisons by gender.
Men (N = 1)
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Table 4 What would make you stop texting while driving? Men (N = 145–149)
Women (N = 328–333)
Total (N = 473–482)
Item on questionnaire
n
%
n
%
n
%
Friends telling you not to do it Parents telling you not to do it Police warning not to do it** Police arrest if illegal Receiving information about how dangerous it is** Watching graphic pictures of accidents caused by it** Signing a pledge to stop doing it Getting lower car insurance not to do it Being in a car accident because of it* Nothing would make me stop
45 45 69 126 38 59 38 122 129 8
30.2 30.2 46.6 84.6 25.7 39.6 25.7 84.1 88.4 5.4
125 137 214 304 138 239 123 286 315 8
37.5 41.3 65.2 91.6 41.8 72.2 37.3 87.2 95.7 2.4
170 182 283 430 176 298 161 408 444 16
35.3 37.8 59.5 89.4 36.8 62.1 33.7 86.3 93.5 3.3
Note: Frequencies are the number of respondents who answered “yes” to the question “What would it take to get you to stop texting while driving?” * p < .01. ** p < .001 for Chi square comparisons by gender.
3.3. Intervention opinions and accidents All 515 participants were asked whether texting while driving should be illegal. A significantly greater proportion of women (129 of 353 or 63.5%) than men (83 of 158 or 52.5%) said yes, x2 (1, 476) = 14.67, p < .001. The sub-sample of 484 participants who had texted while driving in the past year answered yes or no to what interventions would make them stop texting. As shown in Table 4, the most endorsed interventions were being in an accident because of texting, being arrested by the police if texting were illegal, and getting lower car insurance. Least endorsed were being told by friends or parents not to do it, receiving information about texting dangers, and signing a pledge not to text. There were gender differences for 4 of the 10 categories. More women than men said they would quit due to police warnings, x2 (1, 476) = 14.67, p < .001; receiving information about texting dangers, x2 (1, 478) = 11.44, p < .001; being shown graphic pictures of texting accidents, x2 (1, 480) = 46.41, p < .001; and being in a car accident, x2 (1, 475) = 9.05, p < .01. All 515 participants were asked to report their car accident history with texting. Of 509 respondents, 112 (22%) had nearly had a car accident because of texting. Of 505 respondents, 20 (4%) had actually had a car accident because of texting. Men and women had similar car accident histories. 3.4. Psychosocial variables
Men scored higher than women on the driving confidence scale, t(1, 513) = 4.21; p < = .001. Men moderately agreed that they had good driving skills, while women were just above “barely agree”. 3.4.2. Cell phone dependence Women rated themselves as having more cell phone dependence than men, t(1, 496) = 3.40, p < .001. On average, men were close to the midpoint and women were between the midpoint and moderately agree to statements reflecting cell phone dependence. 3.4.3. Risky behavior and impulsiveness Men rated themselves as more likely to engage in risky behavior than did women, t(1, 506) = 6.14; p < .001, although both were in the low-risk portion of the scale. On average, men rated themselves between very unlikely and the midpoint to engage in risky acts, while women rated themselves in the very unlikely range. Men rated themselves as slightly more impulsive than women, t (1, 474) = 5.31; p < .01. On average, both men and women rated themselves just under the midpoint in the rarely/never portion of the scale.
Table 5 Means and standard deviations of psychosocial variables by gender. Men
All 515 respondents were asked to complete the psychosocial measures. Means, standard deviations, and Cronbach’s alphas for scales are shown in Table 5. 3.4.1. Texting distractibility, crash risk, and driving confidence Women rated texting while driving as more distracting than did men, t(1, 512) = 3.41; p < .001. Women’s ratings were just above the midpoint, falling into the distracted range of the scale, whereas men’s ratings were just below the midpoint, falling into the notdistracted range. Men’s self ratings of distractibility (M = 5.16) were significantly lower than their rating of other people’s distractibility: “other” M = 7.46, SD = 1.98, t(1, 157) = 9.88; p < .001. A similar difference was found for women (M = 5.96): “other” M = 7.02, SD = 1.66, t(1, 356) = 14.30; p < .001. Both men and women rated other people as being in the distracted range of the scale. Women, compared to men, rated themselves as more likely to ever have an accident due to texting while driving, t(1, 512) = 5.31; p < .001. On average, women rated their probability of having a texting accident at 53%, while men rated their probability at 40%.
Driving assessments Distractibility** Crash probability*** Driving confidence*** Cell phone factor Cell phone depend.** Risk-related personality Risky behavior*** Impulsivity* Affiliation motives Friendship potential Need for intimacy**
Women Cron-bach’s a
N = 156– 158
SD
5.16 4.99 40.63
2.52 5.96 2.52 6.25 7.49 37.53
2.45 – 2.46 – 6.62 –
38.31 traits 29.70 34.88
8.84 41.26
9.06 .77, .87
9.42 24.74 6.67 33.14
7.34 .8 6.67 –
41.93 32.00
6.88 43.03 6.16 33.62
6.83 .42 5.74 –
N = 352– 357
SD
Note: Higher means reflect higher levels of every factor. The range of scores for factors were: Distractibility (1–10), crash probability (1–11), driving confidence (8– 64), cell phone dependence (12–60), risky behavior (12–60), impulsivity (15–60), friendship potential (12–60), and need for intimacy (9–45). * p < .05. ** p < .01. *** p < .001.
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Table 6 Bivariate correlations for texting while driving regression analysis for men (N = 154). Variable
1 Texting level
– 1. Texting level 2.Texting distractibility 3. Crash risk 4. Driving confidence 5. Cell phone dependency 6. Risky behavior 7. Impulsivity 8. Friendship potential 9. Need for intimacy
2 Texting distractibility .447*** –
3 Crash risk
4 Driving confidence
5 Cell phone depend.
6 Risky behavior
7 8 Impulsivity Friendship potential
9 Need for intimacy
.204** .384***
.152* .078
.435*** .240**
.391*** .188**
.366*** .119
.114 .226**
.083 .004
.142*
.261** .020 –
.071 .097 .190**
.010 .014 .287***
.007 .167* .208**
.066 .047 .213**
–
.400*** –
.096 .040 –
.002 .133** .193** –
– –
Note: Higher scores on all measures indicated higher levels of the variable. Texting level reflects how often respondents text while driving from never to always. Distractibility is how distracted one feels when texting while driving. Crash risk is the perceived probability that one will get in a car crash because of texting. Confidence in driving reflects one’s view of his/her driving skills. Cell phone dependence reflects the need to have constant communication by cell phone. Risky behavior is the propensity to engage in risky and dangerous behaviors. Impulsivity is the propensity to act without thinking. Friendship potential is the desire to have many friends. Need for intimacy reflects a desire to have a close relationship with another. * p < .05. ** p
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
Gender differences in psychosocial predictors of texting while driving Cindy Struckman-Johnson *, Samuel Gaster, Dave Struckman-Johnson, Melissa Johnson, Gabby May-Shinagle University of South Dakota, 414 E. Clark St., Vermillion,SD 57069, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 23 July 2013 Received in revised form 13 June 2014 Accepted 1 October 2014 Available online 14 November 2014
A sample of 158 male and 357 female college students at a midwestern university participated in an online study of psychosocial motives for texting while driving. Men and women did not differ in selfreported ratings of how often they texted while driving. However, more women sent texts of less than a sentence while more men sent texts of 1–5 sentences. More women than men said they would quit texting while driving due to police warnings, receiving information about texting dangers, being shown graphic pictures of texting accidents, and being in a car accident. A hierarchical regression for men’s data revealed that lower levels of feeling distracted by texting while driving (20% of the variance), higher levels of cell phone dependence (11.5% of the variance), risky behavioral tendencies (6.5% of the variance) and impulsivity (2.3%) of the variance) were significantly associated with more texting while driving (total model variance = 42%). A separate regression for women revealed that higher levels of cell phone dependence (10.4% of the variance), risky behavioral tendencies (9.9% of the variance), texting distractibility (6.2%), crash risk estimates (2.2% of the variance) and driving confidence (1.3% of the variance) were significantly associated with more texting while driving (total model variance = 31%.) Friendship potential and need for intimacy were not related to men’s or women’s texting while driving. Implications of the results for gender-specific prevention strategies are discussed. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Texting while driving Gender Cell phone dependence Risk assessment
1. Introduction Distracted driving is the diversion of attention away from activities critical for safe driving toward a competing activity, which may result in insufficient or no attention to activities critical for safe driving (Hosking et al., 2009). Experts further agree that distracted driving places demands upon cognitive, auditory, vocal/ verbal, visual motoric, and other resources separately or in any combination (Foley et al., 2013). Of the many activities that comprise distracted driving, cell phone use became “emblematic” of driver distraction in public opinion and in research activity from 2000 to 2005 (AAA, 2008; p. 3). Texting while driving, defined as the use of a cell phone device to send or receive displayed or voiceactivated messages while driving a moving vehicle, has recently claimed the attention of traffic safety experts (Hosking et al., 2009; Lee, 2007; Owens et al., 2011). The expansion of cell phone ownership by Americans (33% in 1999–91% in 2008) has resulted in increasing numbers of drivers
* Corresponding author. Tel.: +1 605 677 5098; fax: +1 605 677 3195. E-mail address: [email protected] (C. Struckman-Johnson). http://dx.doi.org/10.1016/j.aap.2014.10.001 0001-4575/ ã 2014 Elsevier Ltd. All rights reserved.
who text (Wilson and Stimpson, 2010). Atchley et al. (2012) observed that as cell phone technology becomes more pervasive, overall use increases and the average age of users decreases (p. 279). By 2011, 31.2% of American drivers surveyed by the Center for Disease Control (2013) had read or sent a text or message while driving in the past 30 days. The rate reached 50% for young drivers age 18–24. CDC data for high school students for 2011 revealed that 44.5% had engaged in texting while driving (Olsen et al., 2013). Texting while driving is almost universal among college-age samples. A survey of young adult drivers revealed that 92% had read, 81% had replied to, and 70% had initiated a text while driving (Atchley et al., 2011). 1.1. Distraction effects of texting The research on effects of texting while driving has had to adapt to changing technologies that tend to be quickly embraced by young drivers (Lee, 2007). In the 1990s and early 2000s, texting while driving was accomplished with hand-held devices with keyboards that required drivers to direct their eyes away from the road and potentially to remove their hands from the steering wheel (Hosking et al., 2009). Research on texting with hand-held devices
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in driver simulators and closed-course driving showed detrimental effects on performance, including increased eyes-off-road time, lane drift, missing of lane change cues, variability in following distance to cars ahead, missing traffic signs, and failure to process traffic sign information (Hosking et al., 2009; Owens et al., 2011). In a driving simulation study, Alosco et al. (2013) found that drivers instructed to text with either keyboard or touch-screen typing had poorer performance than a control group. In recent years, texting while driving has been altered by options for in-vehicle systems that convert text to speech, thus eliminating the driver’s need to look away from the road to type. Research on texting with this advanced technology, however, continues to show detrimental effects on safe-driving behaviors. Using closed-course driving, Owens et al. (2011) determined that audio playback of received text messages produced no more driver errors than baseline, but sending of pre-programmed text messages using vehicular controls led to more driver errors than baseline. In a major study using driving simulation and instrumented vehicles, Strayer et al. (2013) found that driver interaction with a speech-to-text system produced a higher level of cognitive distraction than listening to the radio, talking to a passenger, or using a hand-held or hands-free cell phone. They concluded that even when drivers can keep eyes on the road while they text, the cognitive demands of processing a voice-activated communication interfere with performance. 1.2. Texting and crash risk According to the National Highway Traffic Safety Administration (2011) (NHTSA), about 1 in 6 fatal vehicle collisions resulted from distracted driving in 2008. Driver inattention and driver distraction were found to account for 58% of serious casualty crashes in Australia (Beanland et al., 2013). What percentage of distracted-driving crashes is due to texting is currently under investigation. Wilson and Stimpson (2010) estimated with statistical models that increasing texting volumes resulted in 16,000 additional road fatalities from 2001 to 2007. The texting crash relationship is most evident among younger drivers. Largescale naturalistic driving studies conducted by Virginia Tech Transportation Institute revealed that risk or a crash of near-crash by novice drivers increased significantly if they were texting (odds ratio, 3.87) (Klauer et al., 2013). The first nationally representative telephone survey of distracted driving (National Highway Traffic Safety Administration, 2012) found that of drivers age 18–20 who were in a crash or near-crash incident in the past year, 6% were on cell phones and 2% were texting. 1.3. Driver awareness of texting risk Research has documented a serious disconnect between perceptions of the risks of texting and the behavior of texting. In an AAA telephone survey (2008), 95% of drivers rated texting while driving as completely or somewhat unacceptable. Yet 18% admitted to have engaged in texting in the last 30 days. University students surveyed by Harrison (2011) generally agreed that texting while driving was unacceptable, dangerous, and should be illegal, although 91% had occasionally engaged in the behavior. In a study by Nemme and White (2010), young Australian respondents reported strong beliefs that texting is dangerous and the wrong thing to do, although many engaged in it regardless. Because risk assessment alone does not seem to moderate texting behavior, researchers have begun to look for explanatory factors beyond the driving situation. Bayer and Campbell (2012) (p. 2087), noting that texters’ moment-to-moment motivations play out in both conscious and unconscious ways, called for “new angles of insight” from research on psychosocial factors.
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1.4. Psychosocial predictors of texting while driving To date, a limited literature on psychosocial predictors of cell phone calling and texting while driving has accumulated. Applying the theory of planned behavior (TPB), Walsh et al. (2008) found that attitudes, norms, and control factors were predictive of Australians’ intentions to use a cell phone while driving and, to a modest degree, intentions to text. Expanding the TPB model, Nemme and White (2010) documented that Australian university students’ intentions to text and actual texting were significantly influenced by combinations of personal attitudes, subjective norms, perceived control, reference group norms, and morality norms. In another approach, Feldman et al. (2011) established that higher levels of mindfulness, the tendency to intentionally attend to and accept present experiences, were related to less texting among a sample of university women. Schlehofer et al. (2010) found that participants’ illusion of control and overestimates of competence in the driving situation were predictive of cell phone use while driving. The social value of texting has been proposed to be a primary motivation for texting while driving. Atchley et al. (2011) suggested that texting among young adults is driven less by risk assessment than by the importance of peer-to-peer interactions for growth of social networks. Experimental procedures by Atchley and Warden (2012) revealed that persons’ decisions to text now or wait for a reward were significantly influenced by their social closeness to whom was texting. Similarly, Walsh et al. (2008) concluded that the benefits of cell phone use for fulfilling the need to be connected to social groups outweigh the risks of driving errors. The social value approach was supported by Nemme and White (2010) finding that reference group norms predicted texting. In an expansion of Nemme and White’s study, Bayer and Campbell (2012) also found that social norms strongly predicted texting while driving. Another benefit of cell phones and texting is that they may fulfill a psychosocial need for constant and instantaneous communication with the outside world. Aoki and Downes (2003) identified the trait of cell phone dependence in a survey of college students who, to variable degrees, felt disconnected, lost, and upset when they did not have their cell phones. In a study of psychological and demographic predictors of problematic cell phone use, Billieux et al. (2008) documented an element of cell phone dependence among Swiss community members age 20–35. Utilizing a concept of “possession attachment”, Weller et al. (2013) discovered in an on-line survey that young people who showed high cell phone dependency were more likely to use their cell phones and text on past driving trips. Harrison (2011) (p. 1518) proposed that cell phone use and texting are now such an integral part of the daily existence of college students that they allow for a “suspension of concern and consideration of consequences to ourselves and others” when driving. 1.5. The present study: gender differences in motives to text while driving While research on psychosocial motives for texting while driving has advanced, there have been few studies of how these motives may vary by gender. The literature show that male and female drivers text at about the same frequency, with some studies showing slight differences. For example, a NHTSA telephone survey (National Highway Traffic Safety Administration, 2012) found that nearly the same percentages of men (19%) and women (17%) had ever texted while driving. Nemme and White (2010) concluded that gender did not significantly predict texting frequency. We reasoned that although men and women may text while driving at the same frequency, they may differ in their motivations to text.
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The present study was undertaken to examine potential psychosocial motives for texting while driving reported by male and female college students. A second purpose was to examine gender differences in descriptive characteristics of how college students text and drive. We anticipated that knowledge of potential differences in motives and behaviors would be useful for designing gender-based prevention strategies. 1.6. Psychosocial variables of interest 1.6.1. Distractibility, crash risk, driving confidence In our reasoning, men’s and women’s decision to text while driving would be primarily affected by how dangerous and risky they believe it to be. For example, how distracted are they from driving while they text? How likely is it that they will have a crash if they text? Research has shown that women are more likely than men to perceive a wide range of behaviors as risky (Harris et al., 2006). In an on-line survey of New Zealander drivers of all ages, Hallett et al. (2011) found that half as many women as men rated cell phone use as safe. Our expectation in the present study was that the perceived distraction of texting and concerns about crash risk would play a greater role in women’s texting behavior. We also reasoned that men and women’s perceptions of their driving skills and their ability to avoid crashes while texting are related to their willingness to text on the road. Because men have been found to have higher levels of driver confidence than women (Lonczak et al., 2007), we expected in the present study that this variable would play a greater role in men’s texting behavior.
psychologists Baumeister and Leary (1995), humans have a universal need to maintain strong, stable interpersonal relationships. This motive can be expressed as a desire friends and a close romantic relationship. In the traffic safety literature, many authors have cited young people’s need for belongingness and desire to be connected to others as important motives for texting while driving (e.g., Atchley et al., 2011; Walsh et al., 2008). Based upon Baumeister and Leary (1995) suggestion that women have stronger affiliation needs than men, we expected that friendship and intimacy needs would be more closely associated with women's texting behavior. 2. Method 2.1. Participants The original sample consisted of 526 students at a midwestern university who volunteered to take an on-line survey for extra credit for classes. Data from 11 participants who timed out of the survey in less than 3 min were excluded. The final sample consisted of 515 students 158 men (M age = 21.0, SD = 4.1) and 357 women (M age = 20.1, SD = 2.30). The difference in sample sizes of men and women reflected the university sex ratio (40% men and 60% women) and the lower percentage of men than women who take psychology classes. Participants identified their ethnicity as Caucasian (92.4%), Asian (1.7%), Native American (1.2%), African American (1.0%), Hispanic (0.4%), and other (3.3%) 2.2. Procedure and data analysis
1.6.2. Cell phone dependence Men’s and women’s perceptions of the risks of texting while driving, in our opinion, may be countered by the degree to which they are dependent upon using their cell phones to communicate with others. For example, Weller et al. (2013) found that having high cell phone dependence was associated with underestimating the risks associated with cell phone talking and texting while driving. Evidence on gender differences on cell phone dependence, however, is limited. Aoki and Downes (2003) and Weller et al. (2013) did not compare cell phone dependence levels by gender. However, Billieux et al. (2008) found that dependence on mobile phone use was higher among Swiss women than men. Therefore, in the present study we anticipated that cell phone dependence would be more strongly related to women’s texting while driving. 1.6.3. Personality traits of risky behavior and impulsivity We reasoned that individuals who are prone to take risks might view texting while driving as a challenge. Recently, Olsen et al. (2013) found a relationship between frequent texting and other risky behaviors among high school students. A large literature has established that men engage in more risky behaviors than women, including speeding, running stop lights, and not wearing seatbelts (Cross et al., 2011; Harris et al., 2006). Thus, we expected a relationship between men’s risk-taking and texting on the road. Another personality trait that could lead one to text is impulsivity. While risk taking often involves planned pursuit of a reward, impulsive actions tend to be unplanned or involve failure to inhibit a response (Cross et al., 2011). Impulsivity is related to unsafe traffic behaviors such as ignoring crosswalks and receipt of speeding tickets (Sharma et al., 2013.) Because impulsivity is generally higher in men than women (Cross et al., 2011), we expected this trait to be more strongly associated with men’s texting behavior. 1.6.4. Need for affiliation motives Another psychosocial motive that may explain texting while driving is one’s need for affiliation. According to social
After approval of the University IRB, the questionnaire was posted on the psychology department’s on-line experiment scheduling system. Student volunteers from psychology and some biology courses received extra credit for taking the survey. Participants logged onto the website on their own computers and, after reading consent information, responded to questions by clicking alternatives. Once completing a page, they clicked a continue button to display the next page. Results of the survey were analyzed with Chi square tests, t tests, correlation coefficients, and hierarchical regression techniques using SPSS version 21.0 2.3. Measures The survey consisted of 135 items displayed on 12 pages of a research website. Section 1 had measures for demographic data, miles driven per week, distracted driving activities besides texting, and confidence in driving ability. Section 2 assessed perceptions and behaviors related to texting while driving. Section 3 contained standardized scales for cell phone dependence, risk-related personality traits and affiliation motives. The items sets are described below. The response categories for the items are shown in Section 3. 2.3.1. Distracting driving activities, texting level, number, and length of texts To measure all types of distracted driving activities, respondents were asked “Which of the following activities have you EVER done while driving?” The item listed eight activities that came from discussions with students about behaviors that interfered with driving. Respondents’ texting level was assessed by the question “How often do you engage in texting while driving?” followed by a 10-point semantic-differential scale ranging from “I never text while driving” to “I always text while driving”. Those who had texted while driving in the past year chose a category for “the average number of texts you send or receive in a day while
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driving” and “the average length of a text message you send while driving”. 2.3.2. Texting reasons, devices used, legality, and prevention Participants who had texted while driving in the past year were asked to check categories for reasons, type of device used, and what would make them stop texting while driving. All participants were asked if they thought texting while driving should be illegal, and if they had nearly and actually had a car accident because of texting. 2.3.3. Texting distractibility, crash risk, and driving confidence The distraction measure was “How distracting is texting while driving for you? If you have never texted while driving, how distracting do you think it would be?” The 10-point semantic differential rating scale was anchored by not distracting to extremely distracting. The crash risk item was “What do you think is the probability that you will ever have an accident if you text while driving?” The 11-point scale ranged from 0% to 100% in increments of 10%. These two questions were also answered with “other people” as the referent. Confidence in driving was assessed with eight items from a three-factor scale developed by Ho and Yong Gee (2008) to assess motives for dangerous driving among young males. Items such as “I am a skillful driver and am always in control of my driving.” are endorsed on a 6-point Likert scale, ranging from strongly disagree to strongly agree (Cronbach’s alpha = .86). Scores can range from 8 to 64, with higher scores indicating greater confidence in one’s driving skills 2.3.4. Cell phone dependence A cell phone dependence scale (CPD) was developed by Gaster, Struckman-Johnson, and Struckman-Johnson for this study. Twelve items were selected from a pool of 100 statements that reflected the importance of cell phone availability. Items were rated on a 5-point Likert scale extending from strongly disagree to strongly agree. Scores could range from 12 to 60, with higher scores reflecting greater cell phone dependence. Participants’ responses to the CPD were submitted to a principal components factor analysis with varimax rotation. Two factors were revealed, accounting for 54.7% of the total variance. Seven items in an “anxiety” factor reflected a constant need to have a cell phone available (Cronbach’s alpha = .87). Five items in a “dependence” factor reflected an unwillingness to be without a cell phone for extended periods (Cronbach’s alpha = .77). See Table 1 for the scale items by factor.
Table 1 Cell phone dependence scale. Factor 1 – anxious without cell phone 1. I feel anxious at the thought of not having my cell phone with me. 2. I feel secure when I have my cell phone. 3. I check my cell phone constantly for messages and texts. 4. Without my cell phone, I would be isolated from the world. 5. I consider my cell phone an extension of myself. 6. I keep my cell phone with me at all times day and night. 7. It is hard to imagine my life without my cell phone. Factor 2 – dependent on cell phone 8. I am not dependent on my cell phone.1 9. I could easily give up my cell phone if I had to.1 10. I only use my cell phone for emergencies and on other rare occasions.1 11. I would not mind going the entire day without my cell phone.1 12. I like to turn my cell phone off so that I can concentrate on other things.1 Note: Scale responses were on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). 1 indicates item was reversed scored. Scale authors are S. Gaster, C. Struckman-Johnson, and D. Struckman-Johnson.
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2.3.5. Personality traits of risky behavior and impulsivity The trait of riskiness was measured by Campbell and Muncer (2009) 12-item “Risky Impulsiveness” scale that contains items for physical injury, health risk, and criminal risk (Cronbach’s alpha = .81). Participants rate the likelihood of engaging in behavior such as “have unprotected sex”, or “drive too fast when I am feeling upset” on a 5-point semantic differential scale anchored by very unlikely to very likely. Scores can range from 12 to 60, with higher scores reflecting greater riskiness. The trait of impulsiveness was measured by the short form of the Barratt Impulsiveness Scale (Spinella, 2007), Cronbach’s alpha = .79–.81. This 15-item scale has validity for both clinical and non-clinical populations. Items such as “I act on impulse” are rated on a 4-point semantic differential scale anchored by very unlikely and very likely. Scores can range from 15 to 60, with higher scores reflecting greater impulsivity. 2.3.6. Affiliation motives of friendship potential and need for intimacy The desire to have many friends was measured by Dawley (1980) 12-item friendship potential scale (Cronbach’s alpha was not reported). Items such as “I am the type of person who likes people” are rated on a 5-point Likert scale ranging from completely agree to completely disagree. Scores can range from 12 to 60, with higher scores reflecting a greater desire to have many friends. The need for intimacy was measured with the 9-item need for affiliation factor drawn from the sexual intimacy scale by Marelich and Lundquist, 2008 (Cronbach’s alpha = .82). Items such as “I need someone to love” are rated on a 5-point semantic differential scale extending from disagree definitely to agree definitely. Scores can range from 9 to 45 with higher scores indicating a greater need for intimacy. 3. Results Note that the total n for male and female groups varied slightly among analyses due to missing data from participants who failed to answer every question. To control for inflated alpha effects due to multiple tests, significance levels for Chi square analyses were restricted to p < .01. 3.1. Demographic data and distracting driving activities Of 515 respondents, 163 (32.4%) reported driving 25 miles or less, 205 (40.0%) drove between 25–100 miles per week, and 147 (28.6%) reported driving more than 100 miles per week. Men and women were similar for miles driven. Table 2 shows the distributions of men and women who reported “yes” to having ever engaged in various distracting driving behaviors other than texting. Talking on cell phones was the highest level distractor (reported by 98% of the sample), while watching movies was the lowest level distractor (reported by 6% of the sample). Gender differences were found for only three of the eight distracted driving categories. As expected, more women than men applied make up, x2 (1, 512) = 63.57; p < .001. More men than women had watched movies, x2 (1, 514) = 25.08, p < .001, and engaged in sexual activities, x2 (1, 514) = 42.64, p < .001. 3.2. Level of texting, devices used, length, reasons Men and women were similar in the key dependent measure of how often they engaged in texting while driving. On a 10-point semantic differential scale from never to always, the average rating was 4.97 (SD = 2.53) for men and 5.01 (SD = 2.72) for women. On average, participants rated themselves in the midlevel range. Fig. 1 shows the distributions of men and women by responses to the scale.
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Table 2 Distracted driving behaviors besides texting. Men (N = 156–158)
Women (N = 354–356)
Total (N = 507–515)
Item on Questionnaire
n
%
n
%
n
%
Talk on cell phone Apply makeup* Change clothes Hold child or pet in lap Read/study Watch movies* Take photographs Engage in sexual activity* (e.g., masturbation, oral sex)
155 2 61 38 35 22 56 65
98.1 1.3 38.6 24.2 22.2 13.9 35.9 41.1
352 121 141 125 58 9 157 53
98.6 34.0 39.9 35.0 16.4 2.5 44.2 14.9
507 123 202 163 93 31 213 118
98.4 24.0 39.5 31.7 18.2 6.0 41.7 23.0
Frequencies are the number of respondents who answered yes to having ever engaged in the activity while driving. * p < .001 for Chi square comparisons by gender.
Fig. 1. Percentage counts for frequency of engaging in texting and driving.
Of 515 participants, equal proportions of men (149 of 158 or 94.3%) and women (336 of 357 or 94.1%) reported that they had sent or read text messages while driving in the past year. As shown in Table 3, 267 (55%) of the 484 participants who had texted while driving in the past year reported sending or receiving from 1 to 5 texts per day. Although men and women were similar for number of texts sent, they differed for length of texts sent, x2 (3, 484) = 17.29; p < .001). More women reported sending texts of less than a sentence, while more men reported sending texts of 1 to 5 sentences.
Of a sub-sample of 484 participants who had texted during the past year, 40 (8.3%) used the ABC method, 215 (44.4%) used T9 or predictive texting, 224 (46.3%) used a full keypad, and 5 (1%) used a voice-activated or speech-to-text converter. The most endorsed reason for texting was to receive important information and updates such as an event cancellation or weather (375 of 483 or 77.6%). The second most endorsed reason was to keep in close touch with friends and family (359 of 480 or 74.8%), followed by to avoid boredom (236 of 478 or 49.4%) and to stay awake or alert (155 of 483 or 32.1%). Men and women did not differ for device used or reasons for texting.
Table 3 Frequency and extent of text-messaging while driving. Item
Women (N = 335)
Total (N = 484)
n
%
n
%
n
%
What is the average number of texts you send or receive in a day while driving? 1–5 6–10 11–20 21–50 51–100 100+
83 39 17 9 1 1
55.3 26.0 11.3 6.0 0.7 0.7
184 76 44 25 4 2
54.9 22.7 13.1 7.5 1.2 0.6
267 61 34 5 3
54.9 23.9 12.6 7.0 1.0 0.6
What is the average length of a text message you send while driving?* Less than a sentence or phrase 1–5 sentences or phrases 6–10 sentences or phrases More than 10 sentences or phrases
64 82 2 2
42.7 54.7 1.3 1.3
201 128 6 0
60.0 38.2 1.8 0.0
265 210 8 3
54.5 43.2 1.6 0.6
*
p < .01 for Chi square comparisons by gender.
Men (N = 1)
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Table 4 What would make you stop texting while driving? Men (N = 145–149)
Women (N = 328–333)
Total (N = 473–482)
Item on questionnaire
n
%
n
%
n
%
Friends telling you not to do it Parents telling you not to do it Police warning not to do it** Police arrest if illegal Receiving information about how dangerous it is** Watching graphic pictures of accidents caused by it** Signing a pledge to stop doing it Getting lower car insurance not to do it Being in a car accident because of it* Nothing would make me stop
45 45 69 126 38 59 38 122 129 8
30.2 30.2 46.6 84.6 25.7 39.6 25.7 84.1 88.4 5.4
125 137 214 304 138 239 123 286 315 8
37.5 41.3 65.2 91.6 41.8 72.2 37.3 87.2 95.7 2.4
170 182 283 430 176 298 161 408 444 16
35.3 37.8 59.5 89.4 36.8 62.1 33.7 86.3 93.5 3.3
Note: Frequencies are the number of respondents who answered “yes” to the question “What would it take to get you to stop texting while driving?” * p < .01. ** p < .001 for Chi square comparisons by gender.
3.3. Intervention opinions and accidents All 515 participants were asked whether texting while driving should be illegal. A significantly greater proportion of women (129 of 353 or 63.5%) than men (83 of 158 or 52.5%) said yes, x2 (1, 476) = 14.67, p < .001. The sub-sample of 484 participants who had texted while driving in the past year answered yes or no to what interventions would make them stop texting. As shown in Table 4, the most endorsed interventions were being in an accident because of texting, being arrested by the police if texting were illegal, and getting lower car insurance. Least endorsed were being told by friends or parents not to do it, receiving information about texting dangers, and signing a pledge not to text. There were gender differences for 4 of the 10 categories. More women than men said they would quit due to police warnings, x2 (1, 476) = 14.67, p < .001; receiving information about texting dangers, x2 (1, 478) = 11.44, p < .001; being shown graphic pictures of texting accidents, x2 (1, 480) = 46.41, p < .001; and being in a car accident, x2 (1, 475) = 9.05, p < .01. All 515 participants were asked to report their car accident history with texting. Of 509 respondents, 112 (22%) had nearly had a car accident because of texting. Of 505 respondents, 20 (4%) had actually had a car accident because of texting. Men and women had similar car accident histories. 3.4. Psychosocial variables
Men scored higher than women on the driving confidence scale, t(1, 513) = 4.21; p < = .001. Men moderately agreed that they had good driving skills, while women were just above “barely agree”. 3.4.2. Cell phone dependence Women rated themselves as having more cell phone dependence than men, t(1, 496) = 3.40, p < .001. On average, men were close to the midpoint and women were between the midpoint and moderately agree to statements reflecting cell phone dependence. 3.4.3. Risky behavior and impulsiveness Men rated themselves as more likely to engage in risky behavior than did women, t(1, 506) = 6.14; p < .001, although both were in the low-risk portion of the scale. On average, men rated themselves between very unlikely and the midpoint to engage in risky acts, while women rated themselves in the very unlikely range. Men rated themselves as slightly more impulsive than women, t (1, 474) = 5.31; p < .01. On average, both men and women rated themselves just under the midpoint in the rarely/never portion of the scale.
Table 5 Means and standard deviations of psychosocial variables by gender. Men
All 515 respondents were asked to complete the psychosocial measures. Means, standard deviations, and Cronbach’s alphas for scales are shown in Table 5. 3.4.1. Texting distractibility, crash risk, and driving confidence Women rated texting while driving as more distracting than did men, t(1, 512) = 3.41; p < .001. Women’s ratings were just above the midpoint, falling into the distracted range of the scale, whereas men’s ratings were just below the midpoint, falling into the notdistracted range. Men’s self ratings of distractibility (M = 5.16) were significantly lower than their rating of other people’s distractibility: “other” M = 7.46, SD = 1.98, t(1, 157) = 9.88; p < .001. A similar difference was found for women (M = 5.96): “other” M = 7.02, SD = 1.66, t(1, 356) = 14.30; p < .001. Both men and women rated other people as being in the distracted range of the scale. Women, compared to men, rated themselves as more likely to ever have an accident due to texting while driving, t(1, 512) = 5.31; p < .001. On average, women rated their probability of having a texting accident at 53%, while men rated their probability at 40%.
Driving assessments Distractibility** Crash probability*** Driving confidence*** Cell phone factor Cell phone depend.** Risk-related personality Risky behavior*** Impulsivity* Affiliation motives Friendship potential Need for intimacy**
Women Cron-bach’s a
N = 156– 158
SD
5.16 4.99 40.63
2.52 5.96 2.52 6.25 7.49 37.53
2.45 – 2.46 – 6.62 –
38.31 traits 29.70 34.88
8.84 41.26
9.06 .77, .87
9.42 24.74 6.67 33.14
7.34 .8 6.67 –
41.93 32.00
6.88 43.03 6.16 33.62
6.83 .42 5.74 –
N = 352– 357
SD
Note: Higher means reflect higher levels of every factor. The range of scores for factors were: Distractibility (1–10), crash probability (1–11), driving confidence (8– 64), cell phone dependence (12–60), risky behavior (12–60), impulsivity (15–60), friendship potential (12–60), and need for intimacy (9–45). * p < .05. ** p < .01. *** p < .001.
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C. Struckman-Johnson et al. / Accident Analysis and Prevention 74 (2015) 218–228
Table 6 Bivariate correlations for texting while driving regression analysis for men (N = 154). Variable
1 Texting level
– 1. Texting level 2.Texting distractibility 3. Crash risk 4. Driving confidence 5. Cell phone dependency 6. Risky behavior 7. Impulsivity 8. Friendship potential 9. Need for intimacy
2 Texting distractibility .447*** –
3 Crash risk
4 Driving confidence
5 Cell phone depend.
6 Risky behavior
7 8 Impulsivity Friendship potential
9 Need for intimacy
.204** .384***
.152* .078
.435*** .240**
.391*** .188**
.366*** .119
.114 .226**
.083 .004
.142*
.261** .020 –
.071 .097 .190**
.010 .014 .287***
.007 .167* .208**
.066 .047 .213**
–
.400*** –
.096 .040 –
.002 .133** .193** –
– –
Note: Higher scores on all measures indicated higher levels of the variable. Texting level reflects how often respondents text while driving from never to always. Distractibility is how distracted one feels when texting while driving. Crash risk is the perceived probability that one will get in a car crash because of texting. Confidence in driving reflects one’s view of his/her driving skills. Cell phone dependence reflects the need to have constant communication by cell phone. Risky behavior is the propensity to engage in risky and dangerous behaviors. Impulsivity is the propensity to act without thinking. Friendship potential is the desire to have many friends. Need for intimacy reflects a desire to have a close relationship with another. * p < .05. ** p
