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Alcohol-related predictors of adolescent driving: Gender differences in crashes and offenses
Accid. Anal. and Prev., Vol. 28, No. 6, pp. 755-764, 1996 Copyright 0 1996ElsevierScienceLtd Printed in Great Britain. All rights reserved 0001-4575/96S15.00+ 0.00
PIE sooo1-4575(%)ooo53-x
ALCOHOL-RELATED PREDICTORS OF ADOLESCENT DRIVING: GENDER DIFFERENCES IN CRASHES AND OFFENSES* JEAN T. SHOPE~,
PATRICIA F. WALLER
University of Michigan Transportation
and
SYLVIA W. LANG
Research Institute, 2901 Baxter Road, Ann Arbor, MI 48109-2150, U.S.A. (Received I2 August 1996)
Abstract-Demographic and alcohol-related data collected from eighth-grade students (age 13 years) were used in logistic regression to predict subsequent first-year driving crashes and offenses (age 17 years). For young men’s crashes and offenses, good-fitting models used living situation (both parents or not), parents’ attitude about teen drinking (negative or neutral), and the interaction term. Young men who lived with both parents and reported negative parental attitudes regarding teen drinking were less likely to have crashes and offenses. For young women’s crashes, a good-fitting model included friends’ involvement with alcohol. Young women who reported that their friends were not involved with alcohol were least likely to have crashes. No model predicting young women’s offenses emerged. Copyright 0 1996 Elsevier Science Ltd Keywords-Adolescent differences
driving, Crashes and offenses, Psychosocial and behavioral predictors, Alcohol, Gender
INTRODUCTION It has been well established that injury, and motor vehicle injury in particular, is a major cause of death and permanent disability among adolescents (Klitzner, 1989). Efforts to explain and understand this problem have been ongoing, with the ultimate goal to develop successful prevention programs and target those programs to the age groups where they can be most effective. Nearly half the adolescent traffic deaths involve alcohol (National Highway Traffic Safety Administration, 1990), making alcoholrelated crashes the leading cause of death for teenagers in the United States (Fell and Nash, 1989). Indeed, in a general population survey, a larger proportion of those under 25 years of age than those over 25 approved of and reported engaging in drinking and driving, although few experienced arrests or crashes (Cameron, 1982). Less alcohol consumption has been linked to fewer alcohol-related fatal crashes among young people (O’Malley and Wagenaar, 1991) but there are other factors to consider as well. Several studies have focused on the problem
*Paper presented at the 39th Annual Meeting of the Association for the Advancement of Automotive Medicine, October 16-18, 1995, Chicago, IL. tAuthor for correspondence.
driving behavior of young people. Pelz and Schuman interviewed young male drivers and found correlations between violations and driving after drinking, hostility, and alienation from the educational system (Pelz and Schuman, 1973). Mayer and Treat found that, compared to matched drivers with no crashes, young drivers with three or more crashes scored higher on measures of personal maladjustment, social maladjustment, and to a lesser extent impulsivity and information processing deficiency (Mayer and Treat, 1977). Farrow found young men at greater risk than young women, and that, although traffic violations and citations were correlated with risky driving behavior and alcohol and drug use, much dangerous driving and drinking happened without consequence (Farrow, 1985). Arnett (1990) found that selfreported drunk driving behavior among young men was related to sensation-seeking, thrill and adventureseeking, disinhibition, and boredom susceptibility, as well as low expectation of negative consequences as a result of drunk driving. Focused attention to the problem of high-risk driving by youth was given in two international symposia, ‘Youth at Risk for Traffic Accidents’ (Moskowitz, 1987), and ‘The Social Psychology of Risky Driving’ (Moskowitz, 1988). High-risk driving by adolescents was categorized with other risk-taking
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and sensation-seeking behavior, such as delinquency, precocious sex, drinking, and the use of drugs, all of which put adolescents at risk (Jessor, 1987a,b). In several studies, these behaviors have been correlated (Beirness and Simpson, 1988; Donovan, 1993; Jessor, 1991; Jessor et al., 1991; Wilson and Jonah, 1988) and can be considered a syndrome or constellation of problem behaviors. Gregersen and Berg also have found relationships between lifestyle and traffic crashes among young drivers (Gregersen and Berg, 1994). Efforts to understand and prevent high-risk driving behavior among young motorists can benefit from considering what has been learned in efforts to understand or prevent some of these other risky lifestyle and problem behaviors of adolescents (Jessor, 1989). Beirness and Simpson added subsequent longitudinal analyses to their work, finding that psychosocial and behavioral risk factors were found to precede crash involvement by up to three years (Beimess and Simpson, 1991; Simpson and Beirness, 1992, 1993). Because of the key role of alcohol use in adolescent problem behavior including risky driving, and the need for more longitudinal research, the current study was conducted. Extensive data collection evaluating a middle school substance abuse prevention program provided measures at early adolescence of such alcohol-related information as students’ own use and misuse as well as that of their friends, the availability of alcohol, students’ own propensity to use alcohol, the influence of their parents and friends regarding alcohol, and their own gender and living situation. Several years later, when these young people were old enough to obtain their driver’s licenses, the state’s driver history data were obtained for study subjects. The availability of previous psychosocial and behavioral measures and subsequent driving records made it possible to answer the research question: what are the early demographic and alcohol-related predictors of subsequent first-year driving crashes and offenses for adolescents by gender? METHODS Early in 1987, 1088 eighth graders (graduating class of 1991) from six school districts in one Michigan county completed self-administered questionnaires as part of a study to evaluate the effectiveness of a school-based substance abuse prevention program. Questionnaires were treated confidentially, but individually coded with an identification number. In 1993, participating students’ names were submitted to the Michigan Secretary of State’s Office, and driver history data were obtained for 865 matched names/birthdates of the original students (79.5%).
The driver history data were then merged with the previously collected questionnaire data. The last three months of the driver history data were not used, to assure that all subjects’ driver histories were equally complete (due to delays in court appeals, transmission of court abstracts, and data entry). Of the study subjects, 794 had at least one full year of licensed driving experience and no missing data on questionnaire variables needed for the analyses. This study sample was composed of 375 (47.2%) women and 419 (52.8%) men. After one year of driver licensure (usually obtained at age 16, but with some exceptions), the mean age of both the female and male subjects was 17.3 years (female range = 16.1-20.6, SD =0.59; male range= 16.0-20.9, SD =0&l). Measures The following
measures based on data from students’ eighth-grade questionnaires were used in the analyses. Demographic measures included ‘gender’ and ‘living situation’. For the latter measure, students responded to a single item asking, ‘Which of your parents do you live with most of the time?’ Responses included ‘mother and father’ and seven other living arrangements such as ‘mother only’, ‘father only’, ‘other relatives’, etc. Responses were collapsed into two categories: (0) mother and father (female responses 67.5%; male responses 66.6%) and (1) other living situations (female responses 32.5%; male responses 33.4%). Two measures reflected the influence of others on the adolescent regarding alcohol. ‘Parents’ attitude’ toward eighth graders’ drinking was assessed by a single item which asked students how their parents felt about young people’s drinking. Response choices included: (4) very good idea; (3) good idea; (2) neither good nor bad idea; ( 1) bad idea; and (0) very bad idea. Because only 0.1% of all respondents selected codes 3 and 4, the variable was collapsed into two categories: (0) very bad idea and bad idea (negative-female responses 92.5%; male responses 92.5%), and (1) neither good nor bad idea (neutralfemale responses 7.5%; male responses 11.5%) (Diehnan et al., 1993). ‘Friends’ involvement with alcohol’ (behavioral and attitudinal) was assessed with a six-item index based on the results of factor analyses (Cronbach’s Alpha = 0.9 1). The items reflected how the students’ friends felt about eighth graders’ drinking alcohol, how many of their friends drank alcohol, how often they talked about drinking and how much they drank, how often they had gotten into trouble because of drinking, and how often they had offered the student a drink. Responses were summed for the index, which could range from 0 (no involvement) to 21 (high involvement). Missing data
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were assigned the mean of the individual’s valid responses when more than half the items were answered. The mean score for the young women in the study sample was 8.6 (SD=5.3; range=O-20), and for young men was 8.1 (SD=5.2, range=O-21). One personality measure was included: ‘alcohol propensity’. This four-item index was based on the results of factor analyses and created from items reflecting how susceptible the student was to use alcohol under peer pressure and his or her intentions to use alcohol in the future (Cronbach’s Alpha= 0.9 1). Responses were summed for the index, yielding possible scores of 0 (no propensity) to 12 (high propensity). Missing data were assigned the mean of the individual’s valid responses when more than half of the items were answered. The mean score for young women in the sample was 6.2 (SD=3.7; range = O-12), and for young men was 6.0 (SD = 3.9; range=O-12). One environmental influence measure was included: a single item reflecting ‘alcohol availability’ as perceived by the student. Students were asked how easy it would be to get alcohol if they wanted it: (3) very easy, (2) pretty easy, ( 1) pretty hard, or (0) very hard. The mean score for young women in the sample was 1.7 (SD = 1.1; range=O-3), and for young men was 1.6 (SD= 1.1; range= l-3). Two alcohol behavior measures were used. An ‘alcohol use’ index (Cronbach’s Alpha = 0.77), based on self-reported frequency and quantity, was built in order to reflect the average number of alcoholic drinks per week consumed by each student (Shope et al., 1994). Separate items asked students about the frequency and quantity of beer, wine and liquor. An index score for each student was created by multiplying frequency by quantity for each of the three substances, summing the three, and dividing to reflect total number of drinks per week. This variable was then collapsed to form a seven-point scale reflecting no drinking (0) to eight or more drinks per week (6). The mean score for young women in the sample was 1.4 (SD = 1.7; range = O-6), and for young men was 1.5 (SD = 1.9; range = O-6). An ‘alcohol misuse’ index reflected the number of negative consequences of drinking experienced by a student (Shope et al., 1994). Ten items assessed the frequency of various types of negative consequences experienced as a result of alcohol misuse during the previous two months. The index included three areas of misuse: ( 1) ‘Overindulgence’: three items reflecting getting drunk, drinking more than planned, and feeling sick after drinking; (2) ‘Complaints’: three items reflecting complaints from friends and dates because of drinking, and (3) ‘Trouble’: four items reflecting trouble with friends, parents, school and police because of
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drinking. Because distributions were skewed, item responses were collapsed to never (0) and at least once (1) and summed for each of the three areas. Valid scores were then summed to produce the index. Students with missing responses on more than one item in any of the three areas were assigned missing codes for the index. If one item within any of the three sets of questions was missing, the mean value for the set was assigned to the missing datum. The possible range of scores was O-10. The mean score for young women in the sample was 1.1 (SD = 1.7; range =0-7), and for young men was 1.0 (SD = 1.8; range=O-10). Two measures of driving behavior were created from Michigan driver history records and used as dependent variables. In order to standardize driving experience across subjects, only the first year of licensed driving was used. The total number of vehicle crashes and the total number of driving violations, citations, or offenses, recorded in the first year of licensed driving, were summed for each subject. Because crashes and offenses are relatively rare events, even in the first year of driving, these count variables were collapsed to show (0) none (77.8% of women; 79.7% of men) or (1) at least one crash (20.3% of women; 22.2% of men) and (0) none (88.5% of women; 74.9% of men) or (1) at least one offense (11.5% of women; 25.1% of men). Data analysis Analyses were done using Statistical Analysis System (SAS) Version 6 for Windows (SAS Institute Inc., 1994). All analyses were done separately for young men and for young women. Sign&ant ditferences between students with and those without crashes or offenses were sought. Pearson product moment correlations among the predictors and the driving variables were calculated. Student’s t-tests compared means of the alcohol-related predictor variables across the crash and offense subgroups. The important predictors and the likelihood of having a crash (or offense) were determined using logistic regression. This multivariate modeling technique has become the accepted method for regression analysis when the dependent variable is dichotomous (Hosmer and Lemeshow, 1989). Predictor variables may be either dichotomous or continuous. Parameter and standard error estimates were calculated for each predictor variable in the model equations. The Wald test outcomes (comparison of the maximum likelihood estimate of the slope parameter to its estimated standard error) and the associated probability values show the significance of each parameter. Likelihood ratio test results describe the fit of models and provide a means for comparing nested models.
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In order to describe the likelihood of a crash (or offense) in terms of a percentage, predicted probabilities were calculated in addition to odds ratios. The parameter estimates from the final model equal the natural logarithms of the odds ratios for the predictors. The logit function was assumed in this analysis and the following formula was used to reparameterize the estimates and to calculate predicted probabilities: Prob( Y = 1) =
1
1 +(e-qj”)
where Y= 1 is the probability of having a crash (or offense), e is the natural log, and XijB is the matrix of parameter estimates multiplied by predictor codes and summed. See Ring (1989), Green (1993), and Hosmer and Lemeshow ( 1989) for further discussion of discrete regression models. RESULTS While crash rates were similar for both genders (22.2% young men, 20.3% young women), offense rates were different (25.0% young men, 11.5% young women). Different predictors of crashes and offenses were important for young men and young women. Therefore, the results will be presented separately, with young men’s crashes and offenses first, then those of young women. Young men’s crashes
Of the 419 young men, 22.2% had one or more crashes in the first year of driving and 77.8% did not have a crash. Differences were sought on the measures available between young men who had at least one crash and those who had none. Pearson productmoment correlations (Table 1) revealed that young men who in eighth grade reported that their living situation was something other than living with both mother and father were significantly more likely to have had a crash (r = 0.12, p < 0.05). Young men who in eighth grade reported that their parents were neutral, rather than negative, about young people’s drinking were also significantly more likely to have had a crash in their first year of driving (r=0.19, pcO.01). Table 1 also shows that all correlations among predictor variables were significant and in the expected, positive direction. The means on all the predictor variables for those young men with and without a crash were also compared. Results revealed that while those with a crash had higher means on all the alcohol-related predictors than those without a crash, the differences were not significant. A series of nested logistic regression models were performed using crash as the dependent variable (Table 2). The base model includes the intercept and
the seven predictor variables from Table 1. In Models 2, 3 and 4, nonsignificant parameters were deleted in order to gain a more parsimonious model. In all models, living situation and parents’ attitude continued to be significant ( Wald chi squares) predictors of having a crash. All four models were good-fitting models with significant p-values for likelihood ratio (LR) tests of the predictor model vs the interceptonly model. The LR test between Model 1 and the more parsimonious Model 4, however, showed that these two models were not significantly different (J? = 1.44, 5 d! 0.90
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Table I. Pearson product moment correlation coefficient matrix by gender Alcohol propensity
Parents’ attitude
Friends’ inv/alcohol
Alcohol availability
Alcohol USe
Alcohol misuse
Crash
Offense
Young men (N=419)
Living situation Alcohol propensity Parents’ attitude/alcohol Friends’ involvement/alcohol Alcohol availability Alcohol use Alcohol misuse Young women (N=375) Living situation Alcohol propensity Parents’ attitude/alcohol Friends’ involvement/alcohol Alcohol availability Alcohol use Alcohol misuse
0.19*
0.05
0.13* 0.30’
0.17* 0.7.5* 0.21*
0.06 0.43* 0.16* 0.46*
0.18* 0.70* 0.29* 0.64* 0.32*
0.20* 0.54* 0.23* 0.55* 0.22; 0.67*
0.12** 0.08 0.19* 0.07 0.02 0.04 0.06
0.09 0.04 0.07 0.05 0.03 0.04 0.02
-0.02 0.2@
0.07 0.77* 0.222
-0.01 0.47+ 0.15* 0.44*
0.07 0.71* 0.26* 0.71* 0.35*
0.09 0.61; 0.21* 0.64* 0.29* 0.78*
-0.01 0.09 0.01 0.11** 0.09 0.08 0.10
-0.05 0.06 0.06 0.07 0.05 -0.01 -0.02
*p=o.o1; **p
Table 2. Logistic regression parameter estimates, standard errors (in parentheses), model log likelihood (In(L)), and model likelihood ratio tests (LR) for crashes as a function of demographic and alcohol-related predictors for young men (N=419)
Models
1 Base model
2 Deleting behavior variables
4 Deleting friends’ involvement
5 Including liv. x par. interaction
- 1.680* (0.276)
- 1.627’ (264)
- 1.675* (0.242)
- 1.596* (0.162)
- 1.624* (0.169)
0.487* (0.252)
0.483* (0.251)
0.483* (0.250)
0.499* (0.247)
0.571* (0.271)
1.123’ (0.349) 0.026 (0.038)
1.081* (0.344) 0.019 (0.036)
1.058* (0.332) 0.011 (0.024)
1.092* (0.323)
(0.447)
0.013 (0.052)
- 0.006 (0.048)
-0.053 (0.126)
-0.051 (0.125)
3 Deleting personality and environment
PdiCtOrS
Intercept Demographic
Living situation Others’ inzuence
Parents’ attitude Friends’ involvement
1.287*
Personality
Alcohol propensity Environment
Alcohol availability Behavior
Alcohol use Alcohol misuse
-1.103 (0.102) 0.031 (0.088)
Interaction
Living x parent
in(L) LR Test (Model vs intercept only)
LR Test (Model vs base model) *p< =0.05.
-0.401 (0.642) 425.433 18.183 7 df p=O.Oll
426.478 17.138 5 df
426.68 1 16.935 3 df
426.877 16.738 2 df
p=o.O04
p
p
1.045
1.248
1.444
2 df 0.98
4
df
0.80
5 df
0.9O
426.489 17.127 3 df
p
J. T.
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SHOPE
et al.
Table 3. Logistic regression parameter estimates, standard errors (in parentheses), model log likelihood (In(L)), and model likelihood ratio tests (LR) for offenses as a function of demographic and alcohol-related predictors for young men (N= 419)
Models
1 Base model
2 Deleting behavior variables
- 1.410* (0.258)
- 1.386* (0.247)
0.409 (0.241)
3 Deleting personality and environment
4 Deleting friends’ involvement
5 Including liv. x par. interaction
- 1.383* (0.255)
- 1.289* (0.148)
- 1.223* (0.149)
0.393 (0.239)
0.386 (0.238)
0.406 (0.235)
0.214 (0.258)
0.369 (0.353) 0.022 (0.035)
0.364 (0.351) 0.017 (0.034)
0.346 (0.341) 0.013 (0.023)
0.386 (0.334)
-0.387 (0.568)
-0.012 (0.049)
-0.013 (0.045)
0.022 (0.118)
0.027 (0.117)
PK?diCtOrS
Intercept Demographic
Living situation Others’ Injluence
Parents’ attitude Friends’ involve. Personality
Alcohol propensity Environment
Alcohol availability Behavior
Alcohol use Alcohol misuse
-0.26 (0.097) -0.058 (0.088)
Interaction
Living x parent
in(L) LR Test (Model vs intercept only)
1.395* (0.730) 466.105 5.680 7 df p=O.578
466.546 5.239
466.668 5.117
466.982 4.804
df
5 df
3 df
2
p=O.387
p=O.163
p=o.o91
463.024 8.762 3 df p=o.o33
*p< =0.05.
were not significant parameter
estimates. The interaction term showed the most striking effect. The predicted probability of an offense for young men who lived with both parents and whose parents were negative regarding eighth graders’ drinking was 22.7%; whereas the probability of an offense for those who did not live with both parents and whose parents’ attitudes were neutral regarding eighth graders’ drinking was 50.0%. Interestingly, in contrast to the finding for crashes, living situation was more important than parents’ attitude in predicting offenses: young men who lived with both parents, yet perceived a neutral parental attitude toward their own drinking had a 16.7% probability of having an offense, whereas young men who did not live with both parents yet perceived a negative parental attitude toward their own drinking had a 26.7% probability of having an offense. Young women’s crashes
Of the 375 females, 20.3% had at least one crash and 79.7% had no crash in the first year of driving. In bivariate analyses, young women who in eighth grade scored high on the index of friends’ involvement
with alcohol were significantly more likely to have had crashes in their first year of driving (r =O.l 1, p < 0.05; Table 1) . The correlations among the predictor variables, except for living situation, were significant, and in the expected, positive direction. Means tests on the alcohol-related predictors showed that young women with a crash had higher means than those without a crash, but the differences were significant only for the alcohol involvement of friends (t= -2.22 df= 373, ~~0.03). Those young women with a crash (M = 9.81 SD = 5.60 SE = 0.64) reported higher alcohol involvement of friends than those without a crash (M=8.30 SD=5.21 SE=0.30). Table 4 presents the logistic regression results. As before, nonsignificant predictors were deleted in a series of nested models to determine a parsimonious good fit of the data. Model 4, with only the intercept and friends’ alcohol involvement included, was a good fit (ln(L)=373.171, LR=4.887, 1 df,p=O.O27) and was not significantly different from the larger base model (LR = 1.746,6 df, 0.90
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Table 4. Logistic regression parameter estimates, standard errors (in parentheses), model log likelihood (In(L)), and model likelihood ratio tests (LR) for crashes as a function of demographic and alcoholrelated predictors for young women (N= 375)
Models
1 Base model
2 Deleting behavior variables
- 1.945* (0.343)
- 1.964* (0.324)
- 1.832* (0.275)
-0.107 (0.281)
-0.095 (0.280)
-0.105 (0.279)
- 1.195 (0.503) 0.0436 (0.041)
-0.195 (0.498) 0.046 (0.038)
-0.174 (0.494) 0.056* (0.025)
0.0002 (0.061)
0.0005 (0.056)
0.124 (0.142)
0.122 (0.142)
3 Deleting personality and environment
4 Friends’ involvement only
Predictors Intercept Demographic
Living situation Others’ Injuence
Parents’ attitude Friends’ involvement Personality
Alcohol propensity
- 1.856* (0.265)
0.054* (0.024)
Environment
Alcohol availability Behavior
Alcohol use Alcohol misuse
W-4 LR Test (Model vs intercept only)
- 0.076 (0.134) 0.098 (0.116) 371.425 6.633 7 df
p=O.468
372.142 5.916
372.914 5.143
373.171 4.887
5 df p=o.315
3 df p=O.l62
p = 0.027
1df
*p< =0.05
and parents’ attitudes and living situation, but did not fit well (not shown). The predicted probability of having a crash for young women who reported low involvement of their friends with alcohol was 13.5%. The predicted probability of having a crash for young women with average friends’ alcohol involvement was 19.9%, whereas the probability of having a crash for those whose friends were highly involved with alcohol was 31.9%. oflenses Of the 375 young women, 11.5% (n = 43) had at least one offense in the first year of driving, and 88.5% had none. There were no significant bivariate predictors of offenses. There was also no good-fitting logistic regression model predicting offenses for young women (Table 5). As with crashes, additional models that included the interaction terms of friends’ alcohol involvement and living situation, friends’ alcohol involvement and parents’ attitude, and parents’ attitude and living situation did not fit well. It is possible that because the rate of offenses was very low, no good-fitting model could be found. Therefore, the probability of an offense for all young women was the same as the offense rate, or 11.5%. Young women’s
DISCUSSION While relationships between alcohol-related variables and crashes or offenses among adolescents have been reported previously, with one exception (Beirness and Simpson, 1988) the focus has been on concurrent relationships. The current study establishes relationships between demographic and alcohol-related variables of students in eighth grade with subsequent first-year driving performance several years later. Logistic regression analyses were used to predict crashes and offenses separately for young men and young women, with the following findings. In predicting both young men’s crashes and offenses, good-fitting and parsimonious models used living situation (with both parents or not) and parents’ attitude about young people’s drinking (negative or neutral), as well as the interaction term between those variables. Young men who in eighth grade lived with both parents and reported that their parents had negative attitudes regarding young people’s drinking were less likely (than those not living with both parents and whose parents had neutral attitudes) to have crashes and offenses several years later in their first year of driving. In predicting young women’s crashes, a good-fitting model included only friends’
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Table 5. Logistic regression parameter estimates, standard errors (in parentheses), mode1 log likelihood (In(L)), and mode1 likelihood ratio tests (LR) for offenses as a function of demographic and alcoholrelated predictors for young women (N= 375)
Models
1 Base mode1
2 Deleting behavior variables
3 Deleting personality and environment
4 Friends’ involvement only
-2.679* (0.444)
-2.382* (0.402)
-2.323* (0.343)
- 2.429+ (0.333)
-0.363 (0.374)
-0.401 (0.371)
-0.406 (0.371)
0.519 (0.555) 0.079 (0.052)
0.376 (0.549) 0.039 (0.048)
0.317 (0.543)
0.046 (0.075)
-0.008 (0.071)
Predictors
Intercept Demographic
Living situation Others’ Injluence
Parents’ attitude Friends’ involve.
(Z)
0.042 (0.031)
Personality
Alcohol propensity Environment
Alcohol availability (Z3)
0.067 (0.179)
Behavior
Alcohol use Alcohol misuse
In(L) LR Test (Mode1 vs intercept only)
-0.173 (0.174) -0.143 (0.164)
259.187 7.935 7 df
p=O.338
263.241 3.880 5 df
p=O.567
263.381 3.740 3 df
p=O.291
265.177 1.944
1 df
p=O.163
*p < =0.05.
involvement with alcohol. Young women who in eighth grade reported friends with low alcohol involvement were least likely to have crashes later in their first year of driving. A good model predicting young women’s offenses from eighth grade data was not found, possibly due to the low frequency of offenses. It is especially interesting that the predictors of first-year crashes and offenses differed by gender. Few studies have examined gender differences in predictors of adolescent risky behavior. For young men, parents’ attitudes and living situation were both important in predicting both crashes and offenses. Parents who communicated negative attitudes about young people’s drinking (and presumably other unacceptable behaviors) had sons who were less likely to have a crash or offense. Sons who lived with both parents probably benefited from hearing more about those negative attitudes (and presumably higher behavioral expectations in general), and/or they may have experienced less disruption in their young lives. For young women, only friends’ involvement with alcohol was important, and only for crashes. Young women whose friends in eighth grade were highly involved with alcohol (and presumably other unacceptable beha-
viors) were probably more prone than others to risktaking behavior in general at that age and subsequently. It is interesting and typical that in eighth grade, the girls who might later be regarded as more precocious or likely to take risks have advanced farther than the boys along the adolescent developmental progression from being more strongly influenced by parents to being more strongly influenced by friends (Arnett, 1990; Brook and Brook, 1988; Keefe, 1994). It is not surprising that the other eighth grade alcohol-related measures, although correlated significantly with each other, were not important predictors of first-year driving behavior. The predictors were measured as much as three years before students were eligible for driver licensure, and were developed to evaluate a substance abuse prevention program, not a driving program. Although many eighth-graders have tried alcohol, very few use or misuse it regularly. Furthermore, the important crash and offense predictors-parents’ attitude, living situation, and friends’ influence-are all predictors of future adolescent alcohol misuse as well as other risk-taking behaviors (Ary et al., 1993; Barnes and Windle, 1987; Brook and Brook, 1988; Steinberg et al., 1994; Thomas,
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1992). Other possible limitations of the study include the fact that the measures used were based on widely varying numbers of questionnaire items. Single-item measures, such as students’ perceptions of their parents’ attitudes toward young people’s drinking, are of unknown and possibly low reliability. A measure of driving exposure could not be used because questionnaire data came several years before licensed driving began. Although length of licensure was carefully standardized, wide variation in driving exposure could have occurred. Identification of variables important in predicting driving behavior adds to a knowledge base that enables prevention efforts to be appropriately developed and targeted. The findings of this study re-emphasize the power of communicating to young people parental attitudes regarding acceptable behavior (Ary et al., 1993; Barnes and Windle, 1987; Brook and Brook, 1988; Steinberg et al., 1994). Young men, particularly, can benefit from such an approach. Developers of adolescent safe-driving programs should recognize and include such a program component. Programs should involve parents and assist them in setting expectations for their children’s behavior. Programs should also be developed that acknowledge and utilize the fact that for young women, the influence of friends is very important. Norm-setting, or the establishment of peer group norms that promote safe driving, should be considered. Implications for future research include examining predictor measures that have been collected at other grade levels than eighth grade. Research that studies the progression on several measures over time among the same adolescents would be an especially valuable contribution for examining the gender differences in development related to risk-taking. Risk-taking behaviors of several types will become more prevalent as adolescents develop and statistical relationships should be easier to identify. More work is also needed to understand the mechanisms by which living with both parents or not affects adolescent behavior, particularly the driving behavior of young men. Young men’s risk-taking behavior may be a factor of their infrequently living with their father after the break-up of their parents. Understanding such a phenomenon is important because of the large numbers of mothers who are currently raising sons alone. This study has established that important predictors of adolescent high-risk driving behavior can be determined as early as eighth grade. Young men who lived with both parents and reported negative parental attitudes regarding teen drinking were least likely
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to have a crash or an offense. Young women who reported no friends’ involvement with alcohol were least likely to have a crash. These findings have valuable implications for the design of prevention programs that could be implemented before young people begin driving. study was supported in part by the National Institute on Alcohol Abuse and Alcoholism, ROl AA09026. The authors are grateful for the support and assistance of the intermediate and local school district staff, the Michigan Secretary of State’s office staff, and the research staff. Acknowledgements-This
REFERENCES Amett J. Drunk driving, sensation seeking, and egocentrism among adolescents. Personality and Individual Differences 11 (6):541-546; 1990. Ary D. V.; Tildesley B. A.; Hops H.; Andrews J. The influence of parent, sibling, and peer modeling and attitudes on adolescent use of alcohol. International Journal of the Addictions 28 (9):853-880; 1993. Barnes G. M.; Windle M. Family factors in adolescent alcohol and drug abuse. Pediatrician 14:13-18; 1987. Beimess D. J.; Simpson H. M. Lifestyle correlates of risky driving and accident involvement among youth. Alcohol, Drugs, and Driving 4 (3-4):193-2&l; 1988. Beimess, D.J.; Simpson, H.M. Predicting young driver crash involvement: the role of lifestyle factors. Presentation at the International Symposium, New to the Road. Prevention measures for young and novice drivers. Halifax, N.S., February, 1991. Brook J. E.; Brook J. S. A developmental approach examining social and personal correlates in relation to alcohol use over time. Journal of Genetic Psychology 149 (1):93-l 10; 1988. Cameron T. L. Drinking and driving among American Youth: beliefs and behaviors. Drug and Alcohol Dependence lO:l-33; 1982. Dielman T. E.; Butchart A. T.; Shope J. T. Structural equation model tests of patterns of family interaction, peer alcohol use, and intrapersonal predictors of adolescent alcohol use and misuse. Journal of Drug Education 23 (3):273-316; 1993. Donovan J. E. Young adult drinking-driving: behavioral and psychosocial correlates. Journal of Studies on Alcohol 54:600-613; 1993. Farrow J. A. Drinking and driving behaviors of 16-19 yearolds. Journal of Studies on Alcohol 46 (5):369-374; 1985. Fell J. C.; Nash C. E. The nature of the alcohol problem in US. Fatal crashes. Health Education Quarterly 16:335-343; 1989. Green, W. H. Econometric Analysis, 2nd Edition, Macmillan Publishing Company, New York; 1993. Gregersen N. P.; Berg H. Y. Lifestyle and accidents among young drivers. Accident Analysis & Prevention 26 (3):297-303; 1994. Hosmer, D. W. Jr.; Lemeshow, S. Applied Logistic Regression. John Wiley and Sons, Inc, New York; 1989. Jessor R. Risky driving and adolescent problem behavior: an extension of problem-behavior theory. Alcohol, Drugs, and Driving 3 (3-4):1-11; 1987a.
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Jessor, R. Risky driving and adolescent problem behavior: theoretical and empirical linkage. In Young Drivers Impaired by Alcohol and Other Drugs, ed. T. Benjamin 97-110. Royal Society of Medicine Services, London. Jessor R. Road safety and health behavior: some lessons for research and intervention. Health Education Research: Theory and Practice 5 (2):281-283; 1989. Jessor R. Risk behavior in adolescence: a psychosocial framework for understanding and action. Journal of Adolescent Health 12597-605; 1991. Jessor, R.; Donovan, J.E.; Costa, F.M. Beyond Adolescence. Problem Behavior and Young Adult Development. Cambridge University Press, New York; 1991. Keefe K. Perceptions of normative social pressure and attitudes toward alcohol use: changes during adolescence. Journal of Studies on Alcohol 55:46-54; 1994. King, G. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Cambridge University Press, Cambridge; 1989. Klitzner, M. Youth impaired driving: causes and countermeasures. Surgeon General’s workshop on drunk driving (background papers), pp. 192-206. US Department of Health and Human Services, Washington, DC; 1989. Mayer R. E.; Treat J. R. Psychological, social and cognitive characteristics of high-risk drivers: a pilot study. Accident Analysis & Prevention 9:1-8; 1977. Moskowitz, H. (Ed.) Youth at risk for traffic accidents. Proceedings of an international symposium, Santa Monica, CA, 1987. Alcohol, Drugs, and Driving 3(3-4); 1987. Moskowitz, H. (Ed.) The social psychology of risky driving. Proceedings of an international symposium, Santa
SHOPE et al. Monica, CA, 1988. Alcohol, Drugs, and Driving 4(3-4); 1988. National Highway Traffic Safety Administration. Drunk driving facts. NHTSA, Washington, DC; 1990. O’Malley P. M.; Wagenaar A. C. Effects of minimum drinking age laws on alcohol use, related behaviors and trafhc crash involvement among American youth: 1976-1987. Journal of Studies on Alcohol 52 (5):478-491; 1991. Pelz, D. C.; Schuman, S. H. Drinking, hostility, and alienation in driving of young men. Proc. Third Annual Alcoholism Conference National Institute on Alcohol Abuse and Alcoholism, Washington, DC; pp. 50-74; 1973. SAS Institute Inc. Statistical Analysis System (SAS) Version 6 for Windows. SAS Institute Inc., Cary, NC; 1994. Shope J. T.; Copeland L. A.; Diehnan T. E. Measurement of student alcohol use and misuse. Alcoholism: Clinical and Experimental Research. 18 (3):726-733; 1994. Simpson, H. M.; Beimess, D. J. Tralhc accidents and youth: alcohol and other lifestyle factors. Proceedings of a Symposium, A Decade of Progress, 1982-1991, Baltimore, MD, pp. 77-84; 1992. Simpson, H. M.; Beirness, D. J. Traffic accidents and youth: alcohol and other lifestyle factors. Journal of the Alcohol Beverage Medical Research Foundation 3, 77-84; 1993. Steinberg L.; Fletcher A.; Darling N. Parental monitoring and peer influences on adolescent substance use. Pediatrics 93 (6):1060-1064; 1994. Thomas B. S. Patterns of alcohol and other drug use in an Iowa community. Journal of School Health 62 (10):454-458; 1992. Wilson R. J.; Jonah B. A. The application of problem behavior theory to the understanding of risky driving. Alcohol, Drugs, and Driving 4 (3-4):173-191; 1988.
PIE sooo1-4575(%)ooo53-x
ALCOHOL-RELATED PREDICTORS OF ADOLESCENT DRIVING: GENDER DIFFERENCES IN CRASHES AND OFFENSES* JEAN T. SHOPE~,
PATRICIA F. WALLER
University of Michigan Transportation
and
SYLVIA W. LANG
Research Institute, 2901 Baxter Road, Ann Arbor, MI 48109-2150, U.S.A. (Received I2 August 1996)
Abstract-Demographic and alcohol-related data collected from eighth-grade students (age 13 years) were used in logistic regression to predict subsequent first-year driving crashes and offenses (age 17 years). For young men’s crashes and offenses, good-fitting models used living situation (both parents or not), parents’ attitude about teen drinking (negative or neutral), and the interaction term. Young men who lived with both parents and reported negative parental attitudes regarding teen drinking were less likely to have crashes and offenses. For young women’s crashes, a good-fitting model included friends’ involvement with alcohol. Young women who reported that their friends were not involved with alcohol were least likely to have crashes. No model predicting young women’s offenses emerged. Copyright 0 1996 Elsevier Science Ltd Keywords-Adolescent differences
driving, Crashes and offenses, Psychosocial and behavioral predictors, Alcohol, Gender
INTRODUCTION It has been well established that injury, and motor vehicle injury in particular, is a major cause of death and permanent disability among adolescents (Klitzner, 1989). Efforts to explain and understand this problem have been ongoing, with the ultimate goal to develop successful prevention programs and target those programs to the age groups where they can be most effective. Nearly half the adolescent traffic deaths involve alcohol (National Highway Traffic Safety Administration, 1990), making alcoholrelated crashes the leading cause of death for teenagers in the United States (Fell and Nash, 1989). Indeed, in a general population survey, a larger proportion of those under 25 years of age than those over 25 approved of and reported engaging in drinking and driving, although few experienced arrests or crashes (Cameron, 1982). Less alcohol consumption has been linked to fewer alcohol-related fatal crashes among young people (O’Malley and Wagenaar, 1991) but there are other factors to consider as well. Several studies have focused on the problem
*Paper presented at the 39th Annual Meeting of the Association for the Advancement of Automotive Medicine, October 16-18, 1995, Chicago, IL. tAuthor for correspondence.
driving behavior of young people. Pelz and Schuman interviewed young male drivers and found correlations between violations and driving after drinking, hostility, and alienation from the educational system (Pelz and Schuman, 1973). Mayer and Treat found that, compared to matched drivers with no crashes, young drivers with three or more crashes scored higher on measures of personal maladjustment, social maladjustment, and to a lesser extent impulsivity and information processing deficiency (Mayer and Treat, 1977). Farrow found young men at greater risk than young women, and that, although traffic violations and citations were correlated with risky driving behavior and alcohol and drug use, much dangerous driving and drinking happened without consequence (Farrow, 1985). Arnett (1990) found that selfreported drunk driving behavior among young men was related to sensation-seeking, thrill and adventureseeking, disinhibition, and boredom susceptibility, as well as low expectation of negative consequences as a result of drunk driving. Focused attention to the problem of high-risk driving by youth was given in two international symposia, ‘Youth at Risk for Traffic Accidents’ (Moskowitz, 1987), and ‘The Social Psychology of Risky Driving’ (Moskowitz, 1988). High-risk driving by adolescents was categorized with other risk-taking
J. T. SHOPE et al.
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and sensation-seeking behavior, such as delinquency, precocious sex, drinking, and the use of drugs, all of which put adolescents at risk (Jessor, 1987a,b). In several studies, these behaviors have been correlated (Beirness and Simpson, 1988; Donovan, 1993; Jessor, 1991; Jessor et al., 1991; Wilson and Jonah, 1988) and can be considered a syndrome or constellation of problem behaviors. Gregersen and Berg also have found relationships between lifestyle and traffic crashes among young drivers (Gregersen and Berg, 1994). Efforts to understand and prevent high-risk driving behavior among young motorists can benefit from considering what has been learned in efforts to understand or prevent some of these other risky lifestyle and problem behaviors of adolescents (Jessor, 1989). Beirness and Simpson added subsequent longitudinal analyses to their work, finding that psychosocial and behavioral risk factors were found to precede crash involvement by up to three years (Beimess and Simpson, 1991; Simpson and Beirness, 1992, 1993). Because of the key role of alcohol use in adolescent problem behavior including risky driving, and the need for more longitudinal research, the current study was conducted. Extensive data collection evaluating a middle school substance abuse prevention program provided measures at early adolescence of such alcohol-related information as students’ own use and misuse as well as that of their friends, the availability of alcohol, students’ own propensity to use alcohol, the influence of their parents and friends regarding alcohol, and their own gender and living situation. Several years later, when these young people were old enough to obtain their driver’s licenses, the state’s driver history data were obtained for study subjects. The availability of previous psychosocial and behavioral measures and subsequent driving records made it possible to answer the research question: what are the early demographic and alcohol-related predictors of subsequent first-year driving crashes and offenses for adolescents by gender? METHODS Early in 1987, 1088 eighth graders (graduating class of 1991) from six school districts in one Michigan county completed self-administered questionnaires as part of a study to evaluate the effectiveness of a school-based substance abuse prevention program. Questionnaires were treated confidentially, but individually coded with an identification number. In 1993, participating students’ names were submitted to the Michigan Secretary of State’s Office, and driver history data were obtained for 865 matched names/birthdates of the original students (79.5%).
The driver history data were then merged with the previously collected questionnaire data. The last three months of the driver history data were not used, to assure that all subjects’ driver histories were equally complete (due to delays in court appeals, transmission of court abstracts, and data entry). Of the study subjects, 794 had at least one full year of licensed driving experience and no missing data on questionnaire variables needed for the analyses. This study sample was composed of 375 (47.2%) women and 419 (52.8%) men. After one year of driver licensure (usually obtained at age 16, but with some exceptions), the mean age of both the female and male subjects was 17.3 years (female range = 16.1-20.6, SD =0.59; male range= 16.0-20.9, SD =0&l). Measures The following
measures based on data from students’ eighth-grade questionnaires were used in the analyses. Demographic measures included ‘gender’ and ‘living situation’. For the latter measure, students responded to a single item asking, ‘Which of your parents do you live with most of the time?’ Responses included ‘mother and father’ and seven other living arrangements such as ‘mother only’, ‘father only’, ‘other relatives’, etc. Responses were collapsed into two categories: (0) mother and father (female responses 67.5%; male responses 66.6%) and (1) other living situations (female responses 32.5%; male responses 33.4%). Two measures reflected the influence of others on the adolescent regarding alcohol. ‘Parents’ attitude’ toward eighth graders’ drinking was assessed by a single item which asked students how their parents felt about young people’s drinking. Response choices included: (4) very good idea; (3) good idea; (2) neither good nor bad idea; ( 1) bad idea; and (0) very bad idea. Because only 0.1% of all respondents selected codes 3 and 4, the variable was collapsed into two categories: (0) very bad idea and bad idea (negative-female responses 92.5%; male responses 92.5%), and (1) neither good nor bad idea (neutralfemale responses 7.5%; male responses 11.5%) (Diehnan et al., 1993). ‘Friends’ involvement with alcohol’ (behavioral and attitudinal) was assessed with a six-item index based on the results of factor analyses (Cronbach’s Alpha = 0.9 1). The items reflected how the students’ friends felt about eighth graders’ drinking alcohol, how many of their friends drank alcohol, how often they talked about drinking and how much they drank, how often they had gotten into trouble because of drinking, and how often they had offered the student a drink. Responses were summed for the index, which could range from 0 (no involvement) to 21 (high involvement). Missing data
Adolescent driving
were assigned the mean of the individual’s valid responses when more than half the items were answered. The mean score for the young women in the study sample was 8.6 (SD=5.3; range=O-20), and for young men was 8.1 (SD=5.2, range=O-21). One personality measure was included: ‘alcohol propensity’. This four-item index was based on the results of factor analyses and created from items reflecting how susceptible the student was to use alcohol under peer pressure and his or her intentions to use alcohol in the future (Cronbach’s Alpha= 0.9 1). Responses were summed for the index, yielding possible scores of 0 (no propensity) to 12 (high propensity). Missing data were assigned the mean of the individual’s valid responses when more than half of the items were answered. The mean score for young women in the sample was 6.2 (SD=3.7; range = O-12), and for young men was 6.0 (SD = 3.9; range=O-12). One environmental influence measure was included: a single item reflecting ‘alcohol availability’ as perceived by the student. Students were asked how easy it would be to get alcohol if they wanted it: (3) very easy, (2) pretty easy, ( 1) pretty hard, or (0) very hard. The mean score for young women in the sample was 1.7 (SD = 1.1; range=O-3), and for young men was 1.6 (SD= 1.1; range= l-3). Two alcohol behavior measures were used. An ‘alcohol use’ index (Cronbach’s Alpha = 0.77), based on self-reported frequency and quantity, was built in order to reflect the average number of alcoholic drinks per week consumed by each student (Shope et al., 1994). Separate items asked students about the frequency and quantity of beer, wine and liquor. An index score for each student was created by multiplying frequency by quantity for each of the three substances, summing the three, and dividing to reflect total number of drinks per week. This variable was then collapsed to form a seven-point scale reflecting no drinking (0) to eight or more drinks per week (6). The mean score for young women in the sample was 1.4 (SD = 1.7; range = O-6), and for young men was 1.5 (SD = 1.9; range = O-6). An ‘alcohol misuse’ index reflected the number of negative consequences of drinking experienced by a student (Shope et al., 1994). Ten items assessed the frequency of various types of negative consequences experienced as a result of alcohol misuse during the previous two months. The index included three areas of misuse: ( 1) ‘Overindulgence’: three items reflecting getting drunk, drinking more than planned, and feeling sick after drinking; (2) ‘Complaints’: three items reflecting complaints from friends and dates because of drinking, and (3) ‘Trouble’: four items reflecting trouble with friends, parents, school and police because of
151
drinking. Because distributions were skewed, item responses were collapsed to never (0) and at least once (1) and summed for each of the three areas. Valid scores were then summed to produce the index. Students with missing responses on more than one item in any of the three areas were assigned missing codes for the index. If one item within any of the three sets of questions was missing, the mean value for the set was assigned to the missing datum. The possible range of scores was O-10. The mean score for young women in the sample was 1.1 (SD = 1.7; range =0-7), and for young men was 1.0 (SD = 1.8; range=O-10). Two measures of driving behavior were created from Michigan driver history records and used as dependent variables. In order to standardize driving experience across subjects, only the first year of licensed driving was used. The total number of vehicle crashes and the total number of driving violations, citations, or offenses, recorded in the first year of licensed driving, were summed for each subject. Because crashes and offenses are relatively rare events, even in the first year of driving, these count variables were collapsed to show (0) none (77.8% of women; 79.7% of men) or (1) at least one crash (20.3% of women; 22.2% of men) and (0) none (88.5% of women; 74.9% of men) or (1) at least one offense (11.5% of women; 25.1% of men). Data analysis Analyses were done using Statistical Analysis System (SAS) Version 6 for Windows (SAS Institute Inc., 1994). All analyses were done separately for young men and for young women. Sign&ant ditferences between students with and those without crashes or offenses were sought. Pearson product moment correlations among the predictors and the driving variables were calculated. Student’s t-tests compared means of the alcohol-related predictor variables across the crash and offense subgroups. The important predictors and the likelihood of having a crash (or offense) were determined using logistic regression. This multivariate modeling technique has become the accepted method for regression analysis when the dependent variable is dichotomous (Hosmer and Lemeshow, 1989). Predictor variables may be either dichotomous or continuous. Parameter and standard error estimates were calculated for each predictor variable in the model equations. The Wald test outcomes (comparison of the maximum likelihood estimate of the slope parameter to its estimated standard error) and the associated probability values show the significance of each parameter. Likelihood ratio test results describe the fit of models and provide a means for comparing nested models.
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In order to describe the likelihood of a crash (or offense) in terms of a percentage, predicted probabilities were calculated in addition to odds ratios. The parameter estimates from the final model equal the natural logarithms of the odds ratios for the predictors. The logit function was assumed in this analysis and the following formula was used to reparameterize the estimates and to calculate predicted probabilities: Prob( Y = 1) =
1
1 +(e-qj”)
where Y= 1 is the probability of having a crash (or offense), e is the natural log, and XijB is the matrix of parameter estimates multiplied by predictor codes and summed. See Ring (1989), Green (1993), and Hosmer and Lemeshow ( 1989) for further discussion of discrete regression models. RESULTS While crash rates were similar for both genders (22.2% young men, 20.3% young women), offense rates were different (25.0% young men, 11.5% young women). Different predictors of crashes and offenses were important for young men and young women. Therefore, the results will be presented separately, with young men’s crashes and offenses first, then those of young women. Young men’s crashes
Of the 419 young men, 22.2% had one or more crashes in the first year of driving and 77.8% did not have a crash. Differences were sought on the measures available between young men who had at least one crash and those who had none. Pearson productmoment correlations (Table 1) revealed that young men who in eighth grade reported that their living situation was something other than living with both mother and father were significantly more likely to have had a crash (r = 0.12, p < 0.05). Young men who in eighth grade reported that their parents were neutral, rather than negative, about young people’s drinking were also significantly more likely to have had a crash in their first year of driving (r=0.19, pcO.01). Table 1 also shows that all correlations among predictor variables were significant and in the expected, positive direction. The means on all the predictor variables for those young men with and without a crash were also compared. Results revealed that while those with a crash had higher means on all the alcohol-related predictors than those without a crash, the differences were not significant. A series of nested logistic regression models were performed using crash as the dependent variable (Table 2). The base model includes the intercept and
the seven predictor variables from Table 1. In Models 2, 3 and 4, nonsignificant parameters were deleted in order to gain a more parsimonious model. In all models, living situation and parents’ attitude continued to be significant ( Wald chi squares) predictors of having a crash. All four models were good-fitting models with significant p-values for likelihood ratio (LR) tests of the predictor model vs the interceptonly model. The LR test between Model 1 and the more parsimonious Model 4, however, showed that these two models were not significantly different (J? = 1.44, 5 d! 0.90
Adolescent driving
7.59
Table I. Pearson product moment correlation coefficient matrix by gender Alcohol propensity
Parents’ attitude
Friends’ inv/alcohol
Alcohol availability
Alcohol USe
Alcohol misuse
Crash
Offense
Young men (N=419)
Living situation Alcohol propensity Parents’ attitude/alcohol Friends’ involvement/alcohol Alcohol availability Alcohol use Alcohol misuse Young women (N=375) Living situation Alcohol propensity Parents’ attitude/alcohol Friends’ involvement/alcohol Alcohol availability Alcohol use Alcohol misuse
0.19*
0.05
0.13* 0.30’
0.17* 0.7.5* 0.21*
0.06 0.43* 0.16* 0.46*
0.18* 0.70* 0.29* 0.64* 0.32*
0.20* 0.54* 0.23* 0.55* 0.22; 0.67*
0.12** 0.08 0.19* 0.07 0.02 0.04 0.06
0.09 0.04 0.07 0.05 0.03 0.04 0.02
-0.02 0.2@
0.07 0.77* 0.222
-0.01 0.47+ 0.15* 0.44*
0.07 0.71* 0.26* 0.71* 0.35*
0.09 0.61; 0.21* 0.64* 0.29* 0.78*
-0.01 0.09 0.01 0.11** 0.09 0.08 0.10
-0.05 0.06 0.06 0.07 0.05 -0.01 -0.02
*p=o.o1; **p
Table 2. Logistic regression parameter estimates, standard errors (in parentheses), model log likelihood (In(L)), and model likelihood ratio tests (LR) for crashes as a function of demographic and alcohol-related predictors for young men (N=419)
Models
1 Base model
2 Deleting behavior variables
4 Deleting friends’ involvement
5 Including liv. x par. interaction
- 1.680* (0.276)
- 1.627’ (264)
- 1.675* (0.242)
- 1.596* (0.162)
- 1.624* (0.169)
0.487* (0.252)
0.483* (0.251)
0.483* (0.250)
0.499* (0.247)
0.571* (0.271)
1.123’ (0.349) 0.026 (0.038)
1.081* (0.344) 0.019 (0.036)
1.058* (0.332) 0.011 (0.024)
1.092* (0.323)
(0.447)
0.013 (0.052)
- 0.006 (0.048)
-0.053 (0.126)
-0.051 (0.125)
3 Deleting personality and environment
PdiCtOrS
Intercept Demographic
Living situation Others’ inzuence
Parents’ attitude Friends’ involvement
1.287*
Personality
Alcohol propensity Environment
Alcohol availability Behavior
Alcohol use Alcohol misuse
-1.103 (0.102) 0.031 (0.088)
Interaction
Living x parent
in(L) LR Test (Model vs intercept only)
LR Test (Model vs base model) *p< =0.05.
-0.401 (0.642) 425.433 18.183 7 df p=O.Oll
426.478 17.138 5 df
426.68 1 16.935 3 df
426.877 16.738 2 df
p=o.O04
p
p
1.045
1.248
1.444
2 df 0.98
4
df
0.80
5 df
0.9O
426.489 17.127 3 df
p
J. T.
760
SHOPE
et al.
Table 3. Logistic regression parameter estimates, standard errors (in parentheses), model log likelihood (In(L)), and model likelihood ratio tests (LR) for offenses as a function of demographic and alcohol-related predictors for young men (N= 419)
Models
1 Base model
2 Deleting behavior variables
- 1.410* (0.258)
- 1.386* (0.247)
0.409 (0.241)
3 Deleting personality and environment
4 Deleting friends’ involvement
5 Including liv. x par. interaction
- 1.383* (0.255)
- 1.289* (0.148)
- 1.223* (0.149)
0.393 (0.239)
0.386 (0.238)
0.406 (0.235)
0.214 (0.258)
0.369 (0.353) 0.022 (0.035)
0.364 (0.351) 0.017 (0.034)
0.346 (0.341) 0.013 (0.023)
0.386 (0.334)
-0.387 (0.568)
-0.012 (0.049)
-0.013 (0.045)
0.022 (0.118)
0.027 (0.117)
PK?diCtOrS
Intercept Demographic
Living situation Others’ Injluence
Parents’ attitude Friends’ involve. Personality
Alcohol propensity Environment
Alcohol availability Behavior
Alcohol use Alcohol misuse
-0.26 (0.097) -0.058 (0.088)
Interaction
Living x parent
in(L) LR Test (Model vs intercept only)
1.395* (0.730) 466.105 5.680 7 df p=O.578
466.546 5.239
466.668 5.117
466.982 4.804
df
5 df
3 df
2
p=O.387
p=O.163
p=o.o91
463.024 8.762 3 df p=o.o33
*p< =0.05.
were not significant parameter
estimates. The interaction term showed the most striking effect. The predicted probability of an offense for young men who lived with both parents and whose parents were negative regarding eighth graders’ drinking was 22.7%; whereas the probability of an offense for those who did not live with both parents and whose parents’ attitudes were neutral regarding eighth graders’ drinking was 50.0%. Interestingly, in contrast to the finding for crashes, living situation was more important than parents’ attitude in predicting offenses: young men who lived with both parents, yet perceived a neutral parental attitude toward their own drinking had a 16.7% probability of having an offense, whereas young men who did not live with both parents yet perceived a negative parental attitude toward their own drinking had a 26.7% probability of having an offense. Young women’s crashes
Of the 375 females, 20.3% had at least one crash and 79.7% had no crash in the first year of driving. In bivariate analyses, young women who in eighth grade scored high on the index of friends’ involvement
with alcohol were significantly more likely to have had crashes in their first year of driving (r =O.l 1, p < 0.05; Table 1) . The correlations among the predictor variables, except for living situation, were significant, and in the expected, positive direction. Means tests on the alcohol-related predictors showed that young women with a crash had higher means than those without a crash, but the differences were significant only for the alcohol involvement of friends (t= -2.22 df= 373, ~~0.03). Those young women with a crash (M = 9.81 SD = 5.60 SE = 0.64) reported higher alcohol involvement of friends than those without a crash (M=8.30 SD=5.21 SE=0.30). Table 4 presents the logistic regression results. As before, nonsignificant predictors were deleted in a series of nested models to determine a parsimonious good fit of the data. Model 4, with only the intercept and friends’ alcohol involvement included, was a good fit (ln(L)=373.171, LR=4.887, 1 df,p=O.O27) and was not significantly different from the larger base model (LR = 1.746,6 df, 0.90
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761
Table 4. Logistic regression parameter estimates, standard errors (in parentheses), model log likelihood (In(L)), and model likelihood ratio tests (LR) for crashes as a function of demographic and alcoholrelated predictors for young women (N= 375)
Models
1 Base model
2 Deleting behavior variables
- 1.945* (0.343)
- 1.964* (0.324)
- 1.832* (0.275)
-0.107 (0.281)
-0.095 (0.280)
-0.105 (0.279)
- 1.195 (0.503) 0.0436 (0.041)
-0.195 (0.498) 0.046 (0.038)
-0.174 (0.494) 0.056* (0.025)
0.0002 (0.061)
0.0005 (0.056)
0.124 (0.142)
0.122 (0.142)
3 Deleting personality and environment
4 Friends’ involvement only
Predictors Intercept Demographic
Living situation Others’ Injuence
Parents’ attitude Friends’ involvement Personality
Alcohol propensity
- 1.856* (0.265)
0.054* (0.024)
Environment
Alcohol availability Behavior
Alcohol use Alcohol misuse
W-4 LR Test (Model vs intercept only)
- 0.076 (0.134) 0.098 (0.116) 371.425 6.633 7 df
p=O.468
372.142 5.916
372.914 5.143
373.171 4.887
5 df p=o.315
3 df p=O.l62
p = 0.027
1df
*p< =0.05
and parents’ attitudes and living situation, but did not fit well (not shown). The predicted probability of having a crash for young women who reported low involvement of their friends with alcohol was 13.5%. The predicted probability of having a crash for young women with average friends’ alcohol involvement was 19.9%, whereas the probability of having a crash for those whose friends were highly involved with alcohol was 31.9%. oflenses Of the 375 young women, 11.5% (n = 43) had at least one offense in the first year of driving, and 88.5% had none. There were no significant bivariate predictors of offenses. There was also no good-fitting logistic regression model predicting offenses for young women (Table 5). As with crashes, additional models that included the interaction terms of friends’ alcohol involvement and living situation, friends’ alcohol involvement and parents’ attitude, and parents’ attitude and living situation did not fit well. It is possible that because the rate of offenses was very low, no good-fitting model could be found. Therefore, the probability of an offense for all young women was the same as the offense rate, or 11.5%. Young women’s
DISCUSSION While relationships between alcohol-related variables and crashes or offenses among adolescents have been reported previously, with one exception (Beirness and Simpson, 1988) the focus has been on concurrent relationships. The current study establishes relationships between demographic and alcohol-related variables of students in eighth grade with subsequent first-year driving performance several years later. Logistic regression analyses were used to predict crashes and offenses separately for young men and young women, with the following findings. In predicting both young men’s crashes and offenses, good-fitting and parsimonious models used living situation (with both parents or not) and parents’ attitude about young people’s drinking (negative or neutral), as well as the interaction term between those variables. Young men who in eighth grade lived with both parents and reported that their parents had negative attitudes regarding young people’s drinking were less likely (than those not living with both parents and whose parents had neutral attitudes) to have crashes and offenses several years later in their first year of driving. In predicting young women’s crashes, a good-fitting model included only friends’
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J. T. SHOPEet al.
Table 5. Logistic regression parameter estimates, standard errors (in parentheses), mode1 log likelihood (In(L)), and mode1 likelihood ratio tests (LR) for offenses as a function of demographic and alcoholrelated predictors for young women (N= 375)
Models
1 Base mode1
2 Deleting behavior variables
3 Deleting personality and environment
4 Friends’ involvement only
-2.679* (0.444)
-2.382* (0.402)
-2.323* (0.343)
- 2.429+ (0.333)
-0.363 (0.374)
-0.401 (0.371)
-0.406 (0.371)
0.519 (0.555) 0.079 (0.052)
0.376 (0.549) 0.039 (0.048)
0.317 (0.543)
0.046 (0.075)
-0.008 (0.071)
Predictors
Intercept Demographic
Living situation Others’ Injluence
Parents’ attitude Friends’ involve.
(Z)
0.042 (0.031)
Personality
Alcohol propensity Environment
Alcohol availability (Z3)
0.067 (0.179)
Behavior
Alcohol use Alcohol misuse
In(L) LR Test (Mode1 vs intercept only)
-0.173 (0.174) -0.143 (0.164)
259.187 7.935 7 df
p=O.338
263.241 3.880 5 df
p=O.567
263.381 3.740 3 df
p=O.291
265.177 1.944
1 df
p=O.163
*p < =0.05.
involvement with alcohol. Young women who in eighth grade reported friends with low alcohol involvement were least likely to have crashes later in their first year of driving. A good model predicting young women’s offenses from eighth grade data was not found, possibly due to the low frequency of offenses. It is especially interesting that the predictors of first-year crashes and offenses differed by gender. Few studies have examined gender differences in predictors of adolescent risky behavior. For young men, parents’ attitudes and living situation were both important in predicting both crashes and offenses. Parents who communicated negative attitudes about young people’s drinking (and presumably other unacceptable behaviors) had sons who were less likely to have a crash or offense. Sons who lived with both parents probably benefited from hearing more about those negative attitudes (and presumably higher behavioral expectations in general), and/or they may have experienced less disruption in their young lives. For young women, only friends’ involvement with alcohol was important, and only for crashes. Young women whose friends in eighth grade were highly involved with alcohol (and presumably other unacceptable beha-
viors) were probably more prone than others to risktaking behavior in general at that age and subsequently. It is interesting and typical that in eighth grade, the girls who might later be regarded as more precocious or likely to take risks have advanced farther than the boys along the adolescent developmental progression from being more strongly influenced by parents to being more strongly influenced by friends (Arnett, 1990; Brook and Brook, 1988; Keefe, 1994). It is not surprising that the other eighth grade alcohol-related measures, although correlated significantly with each other, were not important predictors of first-year driving behavior. The predictors were measured as much as three years before students were eligible for driver licensure, and were developed to evaluate a substance abuse prevention program, not a driving program. Although many eighth-graders have tried alcohol, very few use or misuse it regularly. Furthermore, the important crash and offense predictors-parents’ attitude, living situation, and friends’ influence-are all predictors of future adolescent alcohol misuse as well as other risk-taking behaviors (Ary et al., 1993; Barnes and Windle, 1987; Brook and Brook, 1988; Steinberg et al., 1994; Thomas,
Adolescent driving
1992). Other possible limitations of the study include the fact that the measures used were based on widely varying numbers of questionnaire items. Single-item measures, such as students’ perceptions of their parents’ attitudes toward young people’s drinking, are of unknown and possibly low reliability. A measure of driving exposure could not be used because questionnaire data came several years before licensed driving began. Although length of licensure was carefully standardized, wide variation in driving exposure could have occurred. Identification of variables important in predicting driving behavior adds to a knowledge base that enables prevention efforts to be appropriately developed and targeted. The findings of this study re-emphasize the power of communicating to young people parental attitudes regarding acceptable behavior (Ary et al., 1993; Barnes and Windle, 1987; Brook and Brook, 1988; Steinberg et al., 1994). Young men, particularly, can benefit from such an approach. Developers of adolescent safe-driving programs should recognize and include such a program component. Programs should involve parents and assist them in setting expectations for their children’s behavior. Programs should also be developed that acknowledge and utilize the fact that for young women, the influence of friends is very important. Norm-setting, or the establishment of peer group norms that promote safe driving, should be considered. Implications for future research include examining predictor measures that have been collected at other grade levels than eighth grade. Research that studies the progression on several measures over time among the same adolescents would be an especially valuable contribution for examining the gender differences in development related to risk-taking. Risk-taking behaviors of several types will become more prevalent as adolescents develop and statistical relationships should be easier to identify. More work is also needed to understand the mechanisms by which living with both parents or not affects adolescent behavior, particularly the driving behavior of young men. Young men’s risk-taking behavior may be a factor of their infrequently living with their father after the break-up of their parents. Understanding such a phenomenon is important because of the large numbers of mothers who are currently raising sons alone. This study has established that important predictors of adolescent high-risk driving behavior can be determined as early as eighth grade. Young men who lived with both parents and reported negative parental attitudes regarding teen drinking were least likely
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to have a crash or an offense. Young women who reported no friends’ involvement with alcohol were least likely to have a crash. These findings have valuable implications for the design of prevention programs that could be implemented before young people begin driving. study was supported in part by the National Institute on Alcohol Abuse and Alcoholism, ROl AA09026. The authors are grateful for the support and assistance of the intermediate and local school district staff, the Michigan Secretary of State’s office staff, and the research staff. Acknowledgements-This
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