Hispanic adolescents' behavioral intentions to avoid texting while driving.
Abstract: The purpose of this study was to identify factors associated with Hispanic adolescents' intentions to avoid texting while driving. Data for this study came from 135 Hispanic adolescents attending high school. Participants completed a survey based on the Theory of Planned Behavior. After controlling for gender and age, behavioral attitudes, subjective norms and perceived behavioral control were all significantly related to intentions to avoid texting while driving. Comprehensive prevention efforts targeting Hispanic adolescents should utilize accurate and relevant content knowledge to shape attitudes, while also helping adolescents to understand and appropriately address perceived social pressures to engage in texting while driving.
Article Type: Survey
Subject: Behavior modification
Authors: West, Joshua H.
Hall, P.Cougar
Thygerson, Steven M.
Edwards, Eric S.
Bennion, Stephanie R.
Bennet, Carrie
Pub Date: 01/01/2011
Publication: Name: American Journal of Health Studies Publisher: American Journal of Health Studies Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2011 American Journal of Health Studies ISSN: 1090-0500
Issue: Date: Wntr, 2011 Source Volume: 26 Source Issue: 1
Topic: Event Code: 240 Marketing procedures Advertising Code: 80 Targets & Markets Canadian Subject Form: Behaviour modification
Product: Product Code: E121930 Youth
Accession Number: 308741537
Full Text: Motor vehicle crashes are the leading cause of death among young drivers. While youth aged 1520 years old represented 6% of all licensed drivers in the U.S., they accounted for an astounding 19% of crash related fatalities (National Highway Traffic Safety Administration, 2010). For drivers under 20 years of age, the risk of fatal crashes may be attributed to developmental immaturity, risk-taking behaviors and inexperience (Hedlund, Shults, & Compton, 2003; Steinberg, 2007). Additionally, driver distraction should be considered as a risk factor in crashes among young drivers. It is estimated that driver distraction is a contributing factor in 25-30% of all crashes (Wang, Knipling, & Goodman, 1996).

Studies have shown that cell phones are a key distracter to teen drivers and greatly increase crash risk (Caird, Willness, & Steel, 2008; Redelmeier & Tibshirani, 1997). Texting while driving should be of particular concern for road safety as it not only occupies the driver's attention, but their eyes and hands also. Of teens ages 16-17 who text, 34% say they have texted while driving (Madden & Lenhart, 2009). Additionally, young drivers text and drive at a proportionally higher rate than more experienced and older drivers (AAA Foundation for Traffic Safety, 2010; McEvoy, Stevenson, & Woodward, 2006).

Nationally, motor vehicle crashes are the leading cause of death for Hispanic Americans under age 45 (Ragland, 2010). According to the National Highway Transportation Safety Administration, traffic crashes continue to be the leading cause of death for Hispanic driving-age teens (National Highway Traffic Safety Administration, 2008). Impaired and distracted driving are key contributing factors in these motor vehicle crashes.


The theory of planned behavior (TPB) provides a useful theoretical model for understanding and predicting behavior (Ajzen, 1991). According to the TPB, behavioral intention is the best predictor of an individual's behavior, which is determined by the theory's three main constructs: the individual's attitudes and beliefs toward the behavior (e.g., whether the individual believes that texting while driving is dangerous or will result in a negative outcome); the individual's subjective norm (e.g., whether the individual believes that important others approve or disapprove of texting while driving); and perceived behavioral control (e.g., whether the individual believes she has the ability to avoid texting while driving). The TPB can provide an understanding of texting and driving by measuring a driver's attitudes, beliefs, and values regarding texting while driving, their perception of subjective norms and subsequent social pressures to adhere to, or reject, texting and driving norms, and the level of confidence or self-efficacy they have with regard to their ability to abstain from texting and driving.

The TPB has served as a theoretical framework for many studies involving risky driving behaviors. For example, Parker, Manstead, Stradling, Reason, and Baxter (1992) examined the ability of the TPB to account for a driver's speeding, close following, overtaking in risky circumstances, and driving under the influence of alcohol. Marcil, Bergeron, and Audet (2001) found that young males' intention to drink and drive was predicted by the TPB, particularly attitudes and perceived behavioral control. De Pelsmacker and Janssens (2001) employed the TPB, specifically measuring attitudes and beliefs toward speeding. Recently, Simsekoglu and Lajunen (2008) concluded that the TPB, specifically attitudes and subjective norms were positively associated with seat belt use intention. Of particular interest to the current study, Walsh, White, Hyde, and Watson (2008) applied the TPB to understand the use of mobile phones while driving. The authors reported that the TPB significantly predicted participants' intention to use a mobile phone while driving. Walsh et al. (2008) concluded that attitude was the best predictor of behavior, followed by subjective norm, and, to a much lesser extent, perceived behavioral control. Nemme and White (2010) used the TPB to explore college students' intentions to text while driving. Their study confirmed that attitudes, subjective norms and perceived behavioral control impacted intentions to engage in texting while driving. These studies suggest that the TPB can be an effective theoretical framework for understanding risky driving behaviors including texting while driving.

In recent years cell phone usage among adolescents has increased dramatically, independent of race or ethnicity (Henry J. Kaiser Family Foundation, 2010). In fact, current rates of cell phone ownership indicate that Hispanic girls in the 12- to 17-year-old age group owned cell phones at about the same rate as non-Hispanic teen girls (Isenberg School of Management, 2005). Unfortunately, there is little data on distracted driving specific to Hispanic adolescents, despite being the largest and fastest growing minority group in the United States (Pew Hispanic Center, 2010). Because of the rapid increase in the Hispanic population in recent years, there is an increasing need for culturally appropriate outreach efforts and materials. This is especially true for education efforts regarding the dangers of distracted driving. The purpose of this study was to use the TPB to better understand factors that contribute to behavioral intentions to avoid texting while driving among Hispanic adolescents.



Data for this study came from a cross-sectional survey of Hispanic students from six US Midwestern public high schools. This survey, conducted in November 2009, was the first part of an emerging effort between the county health department and the three school districts within the county to understand more about texting and driving among Hispanic adolescent students. Six of the 15 public high schools from the three districts offered an after school program for Hispanic students that focused on career development. The remaining nine high schools that did not offer the program were excluded from this study. Hispanic students accounted for 15.2% (1,371) of the total student population of the six study schools. All study participants were recruited from the after school program, which resulted in an exclusively Hispanic sample. Participants represented all three grade levels of the high schools, with approximately 24% from 10th grade, 36% from 11th grade, and 40% from 12th grade. The institutional review board for the protection of human subjects at Brigham Young University reviewed and approved this study. The school districts and each individual school also reviewed and approved the study protocol.

Two employees from the local county health department visited during the after school program to explain the purpose of the study and to distribute parental consent forms. Teachers reminded the students to return the completed forms. The county health department employees returned two weeks later to collect the completed consent forms and to administer the survey. Health department employees provided detailed instructions about how to complete the survey. They encouraged the respondents to answer each question as honestly as possible and ensured participants that all responses would remain confidential. A total of 135 students completed the survey, which constituted nearly 95% of the students participating in the career development program at the six study high schools.


Intention to avoid texting while driving was the main outcome variable for this study. The TPB was utilized to identify independent variables that were included in the analyses. Measures were developed utilizing an adapted approach suggested by Francis et al. (2004), which includes proscriptive steps for creating measures and subsequent analyses to ensure high item reliability. Indeed, Cronbach's alpha coefficients were calculated and determined to be adequate for inclusion in this study (> .9). Variable descriptions are below.


Behavioral intention to avoid texting while driving a motor vehicle was assessed by forming a composite measure using three items, "I expect to avoid texting while driving"; "I intend to avoid texting while driving"; "I want to avoid texting while driving". Each item was measured using a 7-point Likert scale from (1) strongly disagree, to (7) strongly agree. This measurement approach is consistent with other studies' measures of intentions in the TPB (e.g., Nemme & White, 2010).


Attitude toward texting and driving was assessed using a composite variable, including 5 items revolving around the same theme. Each participant was asked to respond to the following item, "I believe texting while driving is...". Response categories included a 7-point scale from (1) harmful, to (7) beneficial; (1) good, to (7) bad; (1) useful, to (7) worthless; (1) dangerous, to (7) safe; (1) unpleasant for me, to (7) pleasant for me.

Subjective norm

Two separate items assessed subjective norm, "People who are important to me want me to avoid texting while driving", and "My friends would criticize me for texting and driving". Both items were measured using a 7-point Likert scale, ranging from (1) strongly disagree, to (7) strongly agree.

Perceived behavioral control

PBC was assessed with one item, "I am confident that I can avoid texting while driving", scored (1) strongly disagree, to (7) strongly agree.


Pearson correlation coefficients were computed to assess the relationships among independent variables. T-test statistics were computed to compare mean scores between females and males across the various dependent variables. Backward stepwise linear regression analysis was used to determine the variables associated with intentions to text while driving.


The mean intention score to avoid texting and driving was 5.7 (SD = 1.4), with 1 being strongly disagree and 7 strongly agree. A square root transformed variable was used for analyses to correct for the negative skew. Table 1 includes characteristics of the study sample, which indicate that most respondents were juniors or seniors with more female participants than males.

While there were otherwise no differences between males and females on TPB constructs, Table 2 shows that males reported significantly less positive attitudes toward avoiding texting and driving (p < .05).

Correlations between variables in the regression model are included in Table 3. Bivariate associations reveal that high intentions to avoid texting while driving were significantly related to feeling confident that you can avoid texting while driving, reporting that people that are close to you would not want you to text while driving, having friends who would criticize you for texting while driving and less positive attitudes about texting and driving. Despite these correlations, examinations of collinearity statistics did not necessitate that any item be excluded from the regression analysis.

As shown it Table 4, a backward stepwise linear regression was conducted to predict adolescents' intention to avoid texting while driving. Behavioral intention was entered as the dependent variable. Neither year in school nor gender were related to intentions to text and drive, and were therefore removed from the model. Increased confidence in ability to avoid texting and driving, reporting that people important to him/herself would want the respondent to avoid texting and driving, having friends who would criticize him/her for texting and driving, and unfavorable attitudes toward texting and driving were all associated with an increase in behavioral intention to avoid texting while driving. These variables resulted in significantly explaining 45% (Adjusted R2 = .452) of the variance, F(4, 130) = 27.007, p < .001.


The aim of this study was to use the TPB to identify factors associated with Hispanic adolescents' intentions to avoid texting while driving. Despite rates of mobile phone usage similar to that of non-Hispanic white adolescents, there is surprisingly little information regarding factors that contribute to Hispanic adolescents' texting while driving behaviors (Juarez, Schlundt, Goldzweig, & Stinson, 2006). While recent research has explored racial and ethnic differences with respect to driving hazards (Ginsburg, Durbin, Garcia-Espana, Kalicka, & Winston, 2009), researchers have yet to explore factors associated with behavioral intentions to text and drive among Hispanic teens.

Gender has been shown to impact risky driving behaviors. Males are more likely to drive aggressively and allow emotions to impact the driving task (Arnett, Offer, & Fine, 1997), while females may be more likely to text while driving (Ivers et al., 2009). Whereas the current study found that males expressed attitudes more supportive of texting while driving, this gender disparity did not emerge in the multivariate model. There are at least two explanations for why gender was not significant in the current study. First, this may indicate the relative insignificance of gender compared to the other factors associated with behavioral intentions (e.g., social norms), for which gender was not related. Independent of gender, adolescents are motivated by social influences (West et al., 2009). For example, regardless of gender, actual and perceived social pressures to engage in risky behaviors such as sending and receiving text messages while driving are powerful predictors of these, and other, risky behaviors. Second, behavioral intentions toward texting while driving might also be functionally different from other driving related risk behaviors and risk taking which have traditionally and stereotypically helped define masculinity (e.g., driving drunk, speeding, seat belt use, and recklessness). As texting remains a relatively new technology, texting while driving appears to be free of the traditional gender-related stereotyping frequently associated with other risky driving behaviors. It has been noted that adolescents view cell phone use while driving as a more normative behavior than drinking and driving (Ginsburg et al., 2008), and perhaps a behavior that is equally acceptable for both males and females. Further research should explore these differences to determine if texting is indeed an acceptable risk while driving for women because it has not been characterized or stigmatized as a male behavior.

Attitudes, subjective norms, and perceived behavioral control were each significantly predictive of behavioral intent. Efforts to target these constructs independently are supported by these findings. Moreover, each of these theoretical constructs was also highly correlated one with another, which is largely supported by previous research (Walsh et al., 2008). This finding speaks to the interrelated nature of the factors associated with texting while driving. Meaning, changes in social norms might indirectly lead to changes in attitudes or perceived behavioral control, or vice versa. Helping adolescents form attitudes unsupportive of texting while driving may subsequently help to establish new normative behavioral beliefs supportive of avoiding texting while driving and also increase an individual's self-efficacy in avoiding this risky behavior.

Attitudes toward texting and driving were significantly related to behavioral intentions. Within the TPB, attitudes relate to an individual's belief and disposition toward a behavior (Page & Page, 2011). Educational efforts and learning objectives aimed at behavior change should include functional content knowledge that is both basic, accurate, and directly contributes to risk reducing decisions (Joint Committee on National Health Education Standards, 2007). For example, a primary goal of formal driver education is to enhance the disposition of the teen, so she can perform as a safe and competent driver, thereby contributing to the reduction of crashes (National Highway Traffic Safety Administration, 2009). Shaping attitudes and beliefs of young Hispanic drivers related to the dangers of texting and driving should be a major objective for both education and public health programs. It is likely that many teens underestimate the danger of texting while driving. Thus efforts to shape attitudes related to this behavior should help drivers to more accurately assess the danger of texting and driving by comparing the risks and consequences of this behavior with other risk behaviors.

The current study offers support for subjective norms approaches in preventing texting while driving and distracted driving crashes among teen Hispanic drivers. Interventions and programs targeting texting and driving among adolescents in general should include key referents (e.g., parents, peers, and respected community members) in establishing a subjective norm unsupportive of texting while driving. Inclusion of parents in efforts to establish and reinforce the subjective norm should not be overlooked. Ginsburg et al. (2009) found that parents can directly influence teen risky driving behavior, including cell phone usage while driving. For this reason, well documented parenting practices, including setting boundaries, monitoring behavior, and modeling appropriate behavior, found to be effective in preventing other risk taking behaviors, should be employed in preventing texting while driving. All parents should be encouraged to share their disapproval of texting while driving with their teens and to establish and enforce family rules prohibiting this risky behavior. Outreach efforts and materials targeted specifically at Hispanic teens should be culturally appropriate in their attempt to establish subjective norms. For example, these efforts should include messages from Hispanic parents and teachers, Hispanic community leaders, Hispanic celebrities and athletes, and of course, Hispanic peers, each emphasizing that texting while driving is unacceptable.

One of the strongest correlates of intentions to avoid texting and driving was perceived behavioral control, which strongly resembles self-efficacy. Increasing Hispanic teens' self-efficacy related to avoiding texting while driving can be addressed through skills based approaches. Such approaches aimed at building personal confidence and the ability to deal with social pressures and avoid or reduce risk-taking behaviors have been shown to be effective (Joint Committee on Health Education Standards, 2007). The Joint Committee on Health Education Standards (2007) has suggested the following pedagogical approach for skill development: 1) discussing the importance of the skill; 2) presenting steps for developing the skill; 3) modeling the skill; 4) practicing and rehearsing the skill by using real-life scenarios; and 5) providing feedback and reinforcement. More specifically, increasing perceived behavioral control over pressures related to texting while driving should include self-management skill development (Lorig & Holman, 2003). While the influence of social and subjective norms relating to texting and driving behaviors has been discussed, self-management of this risk behavior is vital as only the driver can ultimately decide whether to text while driving. Given this fact, both public health and education efforts should teach skills related to self-management for texting and driving behavior. For example, drivers can be taught to self-manage their texting and driving behavior using the "H.O.T. Rule" for cell phones while driving. In this approach, "H" refers to "hand it over" to a passenger when the driver is behind the wheel; "O" refers to placing the cell phone "out of sight" to minimize the temptation to text while behind the wheel; and "T" refers to "turn it off" so that the driver will not be tempted to respond to a text while driving. While just an example of a possible self-management skill to increase perceived behavioral control, the "H.O.T. Rule" is easy to remember and yet comprehensive enough to empower drivers with the confidence that they can avoid texting while driving.


This study's findings should be interpreted in the context of its limitations. All of the data for this study are based on self-report, which may introduce some bias on the part of the respondent to over report intentions to avoid texting while driving in an effort to appear more conscientious. Steps were taken to minimize this potential bias. Namely, we informed participants that their data would remain confidential and their answers anonymous.

Another limitation of this study is the participants' age. Juniors and seniors in this study were almost certainly of driving age. Indeed, 16 years old was the age of driver licensure in the state where the data were collected. Nevertheless, this study also included responses from sophomores, which may or may not be of driving age. The program was comprised of mixed grades (e.g., juniors, seniors, and sophomores) and we included all sophomores to avoid excluding students, since participants responded during the after school program. We do not view this as serious limitation for two reasons. First, sophomore respondents comprised the age category with the fewest responses. Second, the outcome measure was based on behavioral intentions, which inherently measure future occurrences of behavior. Behavioral intention is the premier construct in the TPB, which has been used extensively in research related to risky driving. Theoretical constructs are hypothesized to be causally related to behavior are therefore appropriately used under circumstances such as the current study (Michie & Prestwich, 2010).

Lastly, data were collected from students who elected to participate in the career development program. Hispanic students comprised approximately 17% of the collective student body from the six participating schools, and approximately 15% of Hispanic students participated in this study. Given our modest sample size (n = 135), it was not possible to determine how closely our study sample reflected the total population of Hispanic students at the schools. While we have no evidence to the contrary, we were unable to estimate the extent to which this study's findings pertain generally to Hispanic adolescents. It should be noted however, comparable studies examining risky driving behavior among adolescents, including texting and driving, have relied upon similar sample sizes (Arnett, Offer & Fine, 1997; Nemme & White, 2010).


While approaches to address each construct independently may yield positive results in reducing rates of texting while driving among Hispanic adolescents, findings from this study support more comprehensive approaches. Indeed, Juarez et al. (2006) presented a conceptual framework for reducing risky driving behaviors among minority youth in which they propose a multileveled approach which may involve social contexts as well as individual characteristics (e.g., attitudes). Given the interrelated nature of the constructs and the overwhelming amount of variance explained by the multivariate model using the TPB, such an approach seems warranted.


AAA Foundation for Traffic Safety. (2010). Text messaging and cellphone use while driving. Retrieved June 10, 2010, from: www.aaafoundation.org

Ajzen, I. (1991). The theory of planned behavior. Organizational Theory and Human Decision Processes, 50, 179-211.

Arnett, J., Offer, D., & Fine, M. (1997). Reckless driving in adolescence: 'state' and 'trait' factors. Accident Analysis and Prevention, 29, 57-63.

Caird, J. K., Willness, C. R., & Steel, P. (2008). A meta-analysis of the effects of cell phone on driver performance. Accident Analysis and Prevention, 40, 1282-1293.

De Plesmacker, P., & Janssesn, W. (2001). The effect of norms, attitudes and habits on speeding behavior: Scale development and model building and estimation. Accident Analysis and Prevention, 39, 6-15.

Francis, J., Eccles, M., Johnston, M., Walker, A., Grimshaw, J., Foy, R., Kaner, E., Smith, L., & Bonetti, D. (2004). Constructing questionnaires based on the theory of planned behaviour: A manualfor health services researchers. Centre for Health Services Research: University of Newcastle. Newcastle upon Tyne, UK.

Ginsburg, K. R., Durbin, D. R., Garcia-Espana, J. F., Kalicka, E. A., Winston, F. K., (2009). Associations between parenting styles and teen driving, safety-related behaviors and attitudes. Pediatrics 124, 1040-1051

Ginsburg, K., Winston, F. Senserrick, T., Garcia-Espana, F., Kinsman, S., Quistberg, D., Ross, J., & Elliot, M. (2008). National young-driver survey: Teen perspective and experience with factors that affect driving safety. Pediatrics 121, 1391-1403.

Hedlund, J., Shults, R. A., & Compton, R. (2003). What we know, what we don't know, and what we need to know about graduated driver licensing. Journal of Safety Research, 34, 107-115.

Henry J. Kaiser Family Foundation. (2010). Daily media use among children and teens up dramatically from five years ago. Retrieved January 20, 2010, from: http://www.kff.org.

Isenberg School of Management. (2005). The U.S. Hispanic market. Packaged facts. 2005. Retrieved January 20, 2010, from: http://intra.som.umass.edu/furnia/Research%20Papers/Research_%20Hispanic%20Market_Market%20Research.com.pdf.

Ivers, R., Senserrick, T., & Boufous, S. (2009). Novice drivers' risky driving behavior, risk perception, and crash risk: Findings from the DRIVE study. Research and Practice, 99, 1638-1644.

Joint Committee on National Health Education Standards. (2007). National Health Education Standards, second edition. Athens, GA: The American Cancer Society.

Juarez, P., Schlundt, D., Goldzweig, I., & Stinson, N. (2006). A conceptual framework for reducing risky teen driving behaviors among minority youth. Injury Prevention, 12, 49-55.

Lorig, K., & Holman, H. (2003). Self-management education: History, definition, outcomes, and mechanisms. Annals of Behavioral Medicine, 26, 1-7.

Madden, M., & Lenhart, A. (2009). Teens and distracted driving. Pew Internet & American Life Project. Retrieved July 21, 2009, from: http://pewinternet.org/Reports/2009/Teens-and-distracted-driving.aspx

Marcil, I., Bergeron, J., & Audet, T. (2001). Motivational factors underlying the intention to drink and drive in young male drivers. Journal of Safety Research, 32, 363-376.

McEvoy, S. P., Stevenson, M. R., & Woodward, M. (2006). The impact of driver distraction on road safety: Results from a representative survey in two Australian states. Injury Prevention, 12, 242-247.

Michie, S., & Prestwich, A. (2010). Are interventions theory-based? Development of a theory coding scheme. Health Psychology, 29, 1-8.

National Highway Traffic Safety Administration. (2008). Motor vehicle traffic crashes as a leading cause of death in the U.S., 2002--A demographic perspective. Washington, DC: U.S. Department of Transportation. Retrieved June 10, 2010 from: http://www-nrd.nhtsa.dot.gov/pubs/809843.pdf.

National Highway Traffic Safety Administration. (2009). Novice teen driver education and training administrative standards. Washington, DC: U.S. Department of Transportation, 2009. Retrieved January 20, 2009, from: http://www.adtsea.org/adtsea/pdf/NTDETAS%20-%20Final%20Draft.pdf.

National Highway Traffic Safety Administration. (2010). Traffic safety facts 2008 data. Washington, DC: U.S. Department of Transportation, 2009. Retrieved June 10, 2010, from: http://www.nrd.nhtsa.dot.gov/Pubs/811162.pdf.

Nemme, H. E., & White, K. M. (2010). Texting while driving: Psychosocial influences on young people's texting intentions and behavior. Accident Analysis and Prevention, 42, 1257-1265.

Page, R., & Page, T. (2011). Promoting health and emotional well-being in your classroom (Fifth ed.). Boston, MA: Jones and Bartlett.

Parker, D., Manstead, A. S. R., Stradling, S. G., Reason, J.T., & Baxter, J.S. (1992). Intention to commit driving violations: An application of the theory of planned behavior. Journal of Applied Psychology, 77, 94-101.

Pew Hispanic Center. (2010). Statistical portrait of Hispanics in the United States, 2008. Retrieved July 26, 2010, from: http://pewhispanic.org/files/factsheets/hispanics2008/Table%201.pdf

Ragland, D. Traffic safety and Latino youth: Patterns, factors, and solutions: General statistics, patterns and trends. UC Berkeley Traffic Safety Center. California Childhood Injury Prevention Conference, Retrieved June 10, 2010 from: http://www.escholarship.org/uc/item/1fx5g427.

Redelmeier, D. A., & Tibshirani, R. J. (1997). Association between cellular-telephone calls and motor vehicle collisions. New England Journal of Medicine, 336, 453-458.

Simsekoglu, O., & Lajunen, T. (2008). Social psychology of seat belt use: A comparison of theory of planned behavior and health belief model. Transportation Research Part F: Traffic Psychology and Behavior, 11, 181-191.

Steinberg, L. (2007). Risk taking in adolescence--new perspectives from brain and behavioral science. Current Directions in Psychological Science, 16, 55-59.

Walsh, S. P., White, K. M., Hyde, M. K., & Watson, B. (2008). Dialing and driving: Factors influencing intentions to use a mobile phone while driving. Accident Analysis and Prevention, 40, 1893-1900.

Wang, J. S., Knipling, R. R., & Goodman, M. J. (1996). The role of driver inattention in crashes: New statistics from the 1995 crashworthiness data system. In: Fortieth annual proceedings of the association for the advancement of automotive medicine, Vancouver, BC, pp. 377-392.

West, J. H., Blumberg, E. J., Kelley, N.J., Hill, L., Sipan, C.L., Schmitz, K.E., Ryan, S., Clapp, J.D., & Hovell, M.F. (2010). Does proximity to retailers influence alcohol and tobacco use among Latino adolescents? Journal of Immigrant and Minority Health, 12, 626-633.

Joshua H. West, PhD, MPH

P.Cougar Hall, PhD

Steven M. Thygerson, PhD, MSPH

Eric S. Edwards, MPH

Stephanie R. Bennion, BA

Carrie Bennet, BA

Joshua H. West, PhD, MPH, is affiliated with Brigham Young University, P.Cougar Hall, PhD, is affiliated with Brigham Young University, Steven M. Thygerson, PhD, MSPH, is affiliated with Brigham Young University, Eric S. Edwards, MPH, is affiliated with Utah County Health Department, Stephanie R. Bennion, BA, is affiliated with Utah County Health Department, Carrie Bennet, BA, is affiliated with Utah County Health Department. Corresponding Author: Joshua H. West, 229L Richards Building, Provo, UT, 84602, USA, Phone: (801) 422-3444, Fax: (801) 422-0273, E-mail: josh.west@byu.edu
Table 1. Characteristics of the study sample (n=135)

      Grade in School                 Gender

(10th) Sophomore 33 (24.4%)    Female (69.6%)
   (11th) Junior 49 (36.3%)      Male  40 (29.6%)
   (12th) Senior 53 (39.3%)   Missing  1 (0.8%)

 Hours Studying per Week      Academic Performance

             0-1 23 (17.0%)   Mostly As 16 (11.9%)
             1-2 37 (27.4%)     As & Bs 58 (43.0%)
             2-3 31 (23.0%)   Mostly Bs 17 (12.6%)
             3-4 24 (17.8%)     Bs & Cs 36 (26.7%)
             4-5 5 (3.7%)     Mostly Cs 4 (3.0%)
             5+ 15 (11.1%)      Cs & Ds 4 (3.0%)

Table 2 Attitudes towards texting and driving

                           Total        Females        Males

I am confident that     5.80 (1.56)   5.90 (1.53)   5.58 (1.65)
  I can avoid texting
  while driving
People who are          5.80 (1.51)   5.93 (1.35)   5.48 (1.81)
  important to me
  want me to avoid
  texting while
My friends would        4.15 (2.05)   4.13 (2.14)   4.20 (1.87)
  criticize me for
  texting while
  driving a motor
Attitudes (a) **        1.81 (1.14)   1.61 (0.92)   2.30 (1.44)

Note: Each item based on a scale, 1-7: (1) Strongly disagree,
(2) Disagree, (3) Somewhat disagree, (4) Neither disagree nor
agree, (5) Somewhat agree, (6) Agree, (7) Strongly agree;
One-way ANOVA was used to compare mean scores for females and
males; (a) Represents a composite scale; ** p < .01

Table 3 Correlations of variables in the regression analysis
(n = 135)

Variables            1         2        3       4      5     6

1. Behavioral       --
2. I am            .48 **     --
  that I can
  avoid texting
  while driving
3. People who      .41 **    .50 **    --
  are important
  to me want
  me to avoid
  texting while
4. Grade in        .06       .14      .02       --
5. My friends      .21 *     .07      .17      -.09     --
  criticize me
  for texting
  while driving
  a motor
6. Attitudes       .52 **   -.35 **   -.19 *   -.02   -.10   --

Note: Correlations were evaluated using Spearman correlation
coefficient; * p < .05; ** p < .01

Table 4. Predictors of Intentions to Text and Drive (N = 135)

Variable                           B        SE B     [beta]

I am confident that I can        .068       .017    .330 ***
  avoid texting while driving
People who are important to      .039       .016    .187 *
  me want me to avoid
  texting while driving
My friends would criticize       .021       .010    .134 *
  me for texting while
  driving a motor vehicle
Attitudes                       -.092       .020   -.335 ***
Adjusted [R.sup.2]                      .452 ***

Notes: Backward stepwise linear regression was used, which resulted
in age and grade in school being removed because they were not
significant; [R.sup.2] refers to the adjusted regression equation
after each variable has been entered into the model; * p < .05;
** p < .01; *** p < .001
Gale Copyright: Copyright 2011 Gale, Cengage Learning. All rights reserved.