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Predicting Early Adolescent Substance Use: Do Risk Factors Differ Depending on Age of Onset?
PREDICTING EARLY ADOLESCENT SUBSTANCE USE: Do Risk Factors Differ Depending on Age of Onset? JOANNE SOBECK* ANTONIA ABBEY ELIZABETH AGIUS MONIQUE CLINTON KATHY HARRISON Wayne State University, Detroit, MI, USA ABSTRACT: This study was designed to identify different risk models associated with non-use, first use, and prior substance use among a group of early adolescents. A total of 582 students completed a self-report questionnaire at the beginning and end of sixth grade. Nine predictor variables were used in discriminant function analysis to classify adolescents into three groups. Five variables distinguished non-users (never used by the end of sixth grade) and new users (first used during sixth grade) from prior users (first used before sixth grade). Prior users were found to have weaker decision making skills, more susceptibility to peer pressure, more negative perceptions of school, less confidence in their skills, and an increased likelihood of being male. A second function indicated that new users were similar to prior users in that they had less positive peer relations, were more likely to come from single parent families, and had less knowledge about alcohol and drugs than did non-users. The similarities and differences between new and prior users have implications for future research and prevention programming.
Since the introduction of the risk factor model, lower rates of substance use have continued to be an elusive, but constant goal of prevention programs. Many risk factors for substance use have been identified including low involvement with family, *Direct all correspondence to: Dr. Joanne Sobeck, Ph.D., School of Social Work, Wayne State University, 4756 Cass Avenue, Detroit, MI 48202, USA; E-mail: [email protected] JOURNAL OF SUBSTANCE ABUSE, Volume 11, Number 1, pages 89±102. Copyright # 2000 by Elsevier Science Inc. All rights of reproduction in any form reserved. ISSN: 0899-3289
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religious, and school institutions; poor academic achievement; parental substance abuse; and association with drug using peers (Hawkins et al., 1992; Institute of Medicine, 1994; Moskowitz, 1989; Newcomb, 1992; Oetting and Beauvais, 1990). Given its potential for addressing the continuing problem of substance abuse among adolescents and its popularity among researchers as an area of study, one would expect more assimilation of risk factor knowledge into the practical real world application of prevention. Yet, many prevention programs targeted at adolescents use a general approach providing all youth with the same information and skills without regard to understanding the different types of risk factors existing within a particular group. One important area that has received increasing attention is the identification of risk factors for preventing first use separate from those risk factors predicting more problematic or heavy drug use (Conrad et al., 1992; Jackson, 1997; Wills et al., 1996). Prevention programs need to address these various risk structures for different groups of youth in order to be successful. There is a second and related reason why it is important to understand risk factors and their relationship to predicting various levels of substance use. Research shows that young people are experimenting with substances at an earlier age than they had previously (Warren et al., 1997). What are the long-term consequences of trying alcohol or cigarettes before one is 11 years old? Using substances at an early age means that the duration of risk is longer and that these early adolescent users are more likely to experience problems in later adolescence and adulthood (Dryfoos, 1990; Fegusson et al., 1996; National Research Council (U.S.), 1987; Newcomb, 1997). The study described here seeks to identify risk factors that are associated with non-use, first use, and prior substance use among sixth graders.
RISK FACTOR RESEARCH Risk factors can be identified which distinguish between first use and ongoing use. Wills et al. (1996) identified predictor variables that distinguished between five levels of use. Experimenters were found to have higher levels of stress, maladaptive coping, and deviance prone attitudes; and lower levels of parent support and self control than non-users. Predictors of use were not found to differ for various levels of users. Using a stage model of smoking and alcohol use, Jackson (1997) found different factors associated with abstinence, initiation, and experimentation stages of use. Having a best friend who had tried tobacco or alcohol and perceiving a high rate of tobacco and alcohol use among peers were both predictive of initiation and experimentation stages. Parental offers to try smoking a cigarette predicted smoking initiation and parental offers to sip alcohol predicted both initiation and experimentation stages of alcohol use. Being invited to use alcohol or tobacco by friends was related with initiation and experimentation for both substances. School adjustment and behavioral self-regulation were found to be inversely related to tobacco and alcohol use. Thus, research indicates that the influence of risk factors may vary depending on the predicted level of substance use (Beman, 1995; Thomas and Schandler, 1996). An important methodological issue in risk factor research concerns whether it is more appropriate to develop separate models to predict the use of different substances or to use
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a risk index accounting for all forms of drug use. Early risk models sought to independently explain adolescent smoking (Conrad et al., 1992; Flay, 1992; Schinke and Gilchrist, 1984;), drinking (Brook et al., 1986; Chassin et al., 1988; Jessor and Jessor, 1977) and other drug use (Botvin et al., 1989; Woolfson, 1982). Treating substances in this solitary fashion has a lengthy history in American culture and is based on legal and moral issues associated with each substance. However, the concomitant use of tobacco, alcohol, and other drugs (mainly marijuana) by most users has prompted researchers to consider a risk model that combines these various substances (Chatlos, 1996; Thomas and Schandler, 1996; Wills et al., 1996).
AGE OF ONSET OF USE With adolescents beginning to try substances, mainly alcohol and cigarettes, at an earlier age (Warren et al., 1997), the duration of risk may now be longer than in previous cohorts of adolescents. This increased exposure means that youth are more vulnerable to developing other risks for substance use and to experiencing more consequences of use (Dryfoos, 1990; Newcomb, 1997). Early substance use appears to have deleterious effects, which last into adulthood. For example, Newcomb (1997) found several direct and significant effects of teenage drug use in adulthood. Low educational achievement, family problems, relationship problems, and criminal behavior were found as teenage substance users matured into adulthood.
THEORETICAL BASIS The theoretical foundation used in this paper is based on a combination of health and psychological theories. By converging theories, we seek to utilize insights gained from both disciplines to help explain the challenges facing substance abuse prevention today. One premise underlying the risk factor model tested here is that substance use can be deterred by providing knowledge of the consequences of substance use behavior. Although information alone is not effective (Moskowitz, 1989; Tobler, 1986), it is an important part of a comprehensive substance abuse prevention program. Health knowledge is assumed to be a protective factor; knowledge provides information that triggers an internal process that motivates individuals to bolster attitudes which make them more resistant to change (McGuire, 1964). If adolescents are more knowledgeable about the consequences of substance use, they are less likely to engage to health compromising behaviors. Scheier and Botvin (1997) concluded that factual information is a strong predictor of alcohol expectancies and had a significant negative effect on use. A second theory underlying this research is the persuasion or social influence model (Bandura, 1977). In this view, substance use is learned through modeling and reinforcement. In order to withstand social and peer pressures, adolescents are expected to develop personal competency and self-efficacy skills. One of the most common applications of this theory in prevention programs is the teaching of social skills because deficiencies in these areas have been linked to substance use (Hermann and McWhirter,
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1997; Norman and Turner, 1993). In this paper, adolescents who use substances are hypothesized to have skill deficits in the domains of assertiveness, decision making, and peer pressure refusal. In addition, early users are not expected to be confident about their ability to use a variety of social skills, and this lack of confidence makes them more susceptible to peer pressure. Relationships with peers is one of the most widely used explanations for early substance use (Kandel, 1985; Needle et al., 1996; Swaim et al., 1989). Peer influences, however, are highly complex, suggesting various indirect and direct pathways to substance use. Being part of a peer group or social network is considered to be a protective factor, suggesting that adolescents who are isolated are more likely to use substances. Hawkins et al. (1992) found that adolescents who develop prosocial bonds with non-using peers are less likely to use substances. Thus, positive relations with peers are included as potential factor in understanding the onset of alcohol and cigarette use. Finally, school factors have been linked to substance use. Here, the premise is that adolescents who like school and prepare themselves for study are more likely to have a strong bond to the school institution, a commitment which makes them less likely to engage in health compromising behaviors (Hawkins et al., 1992). Using data from the National Longitudinal Study on Adolescent Health, Resnick et al. (1997) found perceived school connectedness was protective against every health risk behavior except pregnancy. In this paper, we examine a group of risk factors to explain no use, new use and prior use of alcohol and cigarettes among an early adolescent population. Using a broad conceptualization of the public health model for substance use, our premise is that adolescents with low levels of substance use knowledge, poor social skills, high peer susceptibility, poor relations with friends, and negative school perceptions will be more likely to try alcohol and tobacco at an early age.
METHOD Overview The study included sixth grade children enrolled in five school districts in suburban and semi-rural areas of a Midwestern metropolitan area. Three of the districts participated in the study during the 1995±1996 school year, the other two districts participated in the 1996 ±1997 school year. In preliminary analyses, the five districts were compared on each of the measured variables. There were no significant differences, justifying their combination for the analyses presented in the paper. The students were typical of those in many school systems in large metropolitan areas in the Midwest. They lived in areas with populations ranging from 5,000 persons (semi-rural) to 61,000 persons (suburban). The median family income was approximately $36,800. On average, 97 percent of the population in these districts was Caucasian. In terms of economic indicators, 25 percent were eligible for the free or reduced-cost lunch program based on federal guidelines. Two waves of data were collected approximately 8 months apart: when students were beginning sixth grade (Time 1, T1) and at the completion of sixth grade (Time 2, T2).
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This prospective design allowed the distinction between early users prior to the start of the sixth grade, new users who initiated use during the sixth grade, and non-users who never used by the end of sixth grade. At each point in time, two surveys were administered on different days within the same week. Two days were required because the survey was too long to administer in one class period. The sixth grade student population enrolled at the time of the initial survey was 781. There were 726 students who completed the survey the first day and 728 who completed it the second day, for a response rate of 93 percent (absences were slightly different the two days). Less than 1 percent of parents refused permission, 1 percent of the students chose not to participate, 6 percent of the students were absent, and 1 percent were unable to participate because they were non-English speaking or taken out of class due to discipline problems. The students were re-surveyed during the spring of the same school year (T2). Seventy-five percent (n = 582) of the original population completed the follow-up survey (which is 80% of the T1 participants).
Sample Description Fifty-one percent of the sample was female. Self reported ethnic/cultural descriptions were 92 percent white, 3 percent American Indian, 1 percent African American, 1 percent Hispanic, and 3 percent other ethnic groups. Over three-fourths (79%) of the students resided with two adults. Although the information available on the sample is limited, the school data reflect the general statistics available through the U.S. Census on the communities where the districts were located.
Survey Protocol A passive consent procedure was used to obtain parents' permission. Two weeks prior to survey administration, a letter was mailed to the parents of all sixth grade students describing the purpose and content of the survey. The letter also informed parents that copies of the surveys were available in the school office for review. Standard consent procedures were followed in the classroom. Students were told their participation was voluntary and they could choose to skip any or all questions. Identification numbers were used to match student responses from T1 to T2, so the surveys did not have names attached. Teachers remained in the classroom during administration, however, members of the research team read the survey's directions and answered students' questions. Students completed each survey within a 1 h class period. When students were finished, the researchers collected the surveys, which were put into a sealed envelope and taken back to the university.
Measures Measures for this study included items from standard surveys assessing alcohol and tobacco use, as well as newly developed questions designed to assess the knowledge and skills of interest. Cronbach coefficient alphas were comparable at both timepoints, so only the T1 alphas are reported below.
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Demographics In order to determine the home setting of the respondents, one question asked, ``With whom do you live?'' Adolescents were allowed to respond by using one of seven possible options. The answers were coded into a dichotomous variable, 0 = living with two parents (natural, step, or combination) or 1 = living with a single parent/guardian, or other relative. Students' gender was coded as 0 = female and 1 = male.
Substance Use Questions concerning adolescents' substance use were taken from the National Institute on Drug Abuse's Monitoring the Future Study (Johnston et al., 1993). Students were asked about cigarettes, alcohol, marijuana, sniffing glue, and smokeless tobacco. Two questions were asked about each substance. First, students were asked about lifetime use of the substance using a 5-point scale with the response options: never, once or twice, occasionally but not regularly, regularly in the past but not now, and regularly now. Then they were asked about use in the previous month using a 6-point scale with response options ranging from not at all to more than once a day.
Substance Abuse Knowledge Ten questions on alcohol and other drug facts were used to determine a score for substance abuse knowledge. Participants' responses were coded as either correct, which was scored ``1,'' or incorrect, which was scored ``0.'' A total correct score was calculated. Because part of the sample received a modified set of original questions, z scores were then computed to standardize the knowledge score across the sample.
Protective Social Skills Assertiveness was assessed with a 7-item scale measuring the frequency of confident responses given in a variety of social situations (Wills et al., 1989). Sample items include: ``I ask people to explain things when I do not understand.'' and ``I stand up for my rights when someone tries to cut in front of me in line.'' Responses were made using a 5-point scale with options which ranged from never to all the time. Cronbach's coefficient alpha was 0.66. Decision making included nine questions which assessed the extent to which adolescents utilized a comprehensive model for decision making. Students responded using a 5-point scale with options that ranged from never to all of the time. The series of questions began with the statement: ``When I have to handle a problem situation or make an important decision.'' Sample questions include, ``I try to gather all the facts.'' and ``I stop and think about solutions to the problem before I choose to act.'' Cronbach's coefficient alpha was 0.82. Because students in this age group may not be frequently exposed to situations in which they have the opportunity to use the types of skills described above, a group of
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16 items measured adolescents' confidence in their ability to use various social skills. Students answered these questions using a 5-point scale with response options which ranged from very poorly to very well. Cronbach's coefficient alpha was 0.84. Sample questions include, ``How well can you handle conflicts with friends?'' and ``How well can you make good decisions?''
Peer Susceptibility The peer susceptibility measure was developed by Dielman et al. (1987). It included six items which asked how the participant handled peer pressure situations, including general and alcohol and cigarette specific questions. Responses were made using a 4-point scale with options which ranged from definitely no to definitely yes. Sample items are ``If a friend dared you to tear a page out of a library book would you do it?'' and ``If your friends dared you to smoke a cigarette, would you smoke it?'' Cronbach's coefficient alpha was 0.84.
Relationships with Peers Three items were used to assess perceived relations with peers (e.g., ``I treat my friends with respect.''; ``I get along well with other people my age.''). Participants responded using a 4-point scale with options ranging from strongly disagree to strongly agree. Cronbach's coefficient alpha was 0.64.
Perceptions of School General school perceptions were measured with four questions. Students responded using a 4 -point scale with options ranging from strongly disagree to strongly agree. Sample questions include: ``I come to class prepared.'' and ``Getting good grades is important to me.'' Cronbach's coefficient alpha was 0.71.
DATA ANALYSIS The goal of the data analysis was to predict students' substance use categorization at T2 with the predictor variables described above measured at T1. Direct discriminant function analysis (Klecka, 1980) was conducted with the SPSS PC program. After this, analysis of variance (ANOVA) was performed, along with post-hoc comparisons (Tukey's B), to examine the pattern of means (Tabachnick and Fiddell, 1996). Students who completed only pretest surveys were compared to students who completed both pretest and posttest surveys. Attrition analysis using the predictor and outcome variables described in the Measures Section indicated that the groups were comparable on all variables (including substance use) except two. Students who remained in the study scored higher on the scale for decision making and were more likely to come from a two-parent family than were students who were only in the pretest sample.
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TABLE 1 Percent of Students Who Had Ever Used Substances at the Beginning and End of Sixth Grade ( N = 582) T1 use Substance Alcohol Cigarettes Sniffing glue Smokeless tobacco Marijuana
T2 use
N
Percent
N
Percent
170 131 43 24 18
29 22 7 4 3
253 212 67 45 51
44 36 12 8 9
RESULTS Patterns of Substance Use As expected for this age group, rates of use of all substances, except alcohol and cigarettes, were low at the beginning of the school year. As can be seen in Table 1, the percent of students who had ever tried alcohol at the beginning of sixth grade (T1) was 29 percent; in contrast, 3 percent had ever tried marijuana. Substance use increased substantially by the end of the school year (T2), highlighting that sixth grade is a crucial year for substance abuse prevention efforts. Although many students had tried alcohol and cigarettes, their use was relatively infrequent. Only 6 percent of the students reported smoking cigarettes in the previous month and only 1 percent reported smoking regularly. Similarly, 8 percent of students drank alcohol in the past month and less than 1 percent reported that they drank regularly. Thus, although interval level data were collected, for data analyses, participants' responses were collapsed into two categories: ever used versus never used. Furthermore, most of the students who had smoked cigarettes had also consumed alcohol (and vice versa). Seventy-eight percent of the students had consistent patterns of alcohol and cigarette use. Preliminary analyses were conducted in which the predictors of alcohol and cigarette use were examined separately. The models were highly comparable, further justifying combined use of both substances into a single outcome variable. Thus, adolescents were considered users if they had ever used alcohol and/or ever smoked cigarettes. This characterization of substance use delineated three groups of participants: (1) adolescents who never used at T1 or T2 (non-users), (2) adolescents who never used at T1, but used at T2 (new users), and (3) adolescents who were already users at T1 (prior users) regardless of level of use between T1 and T2.
Discriminant Function Analysis Prior to conducting multivariate analyses, 35 students' responses were dropped because of extensive missing data. Thus, there were 547 cases available for analysis. In addition, the intercorrelations among the predictor variables were examined. As can be seen from Table 2, on average, the different predictors were moderately correlated. The strongest correlations were between assertiveness and decision making (r = .50) and between confidence in using skills and peer relations (r = .48).
a
1.00 0.50** ÿ0.05 0.12** 0.18** 0.23** ÿ0.01 0.15** ÿ0.03
1 1.00 ÿ0.32** 0.33** 0.29** 0.44** ÿ0.03 0.20** ÿ0.12**
2
Response scale: 0 = Never . . . 4 = All of the time. b Response scale: 0 = No . . . 3 = Yes. c Response scale: 0 = Strongly disagree . . . 3 = Strongly agree. d Response scale: 0 = Very poorly . . . 4 = Very well. e Response scale: 0 = Two parent family; 1 = Single parent/other. f Response scale: z score of number correct. g Response scale: 0 = Female; 1 = Male. * p < .05. ** p < .01.
a
Assertiveness Decision makinga Peer susceptibilityb Perceptions of schoolc Relations with peersc Confidence in using skillsd Home settinge Substance abuse knowledgef Genderg
Notes.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Variable
1.00 ÿ0.43** ÿ0.28** ÿ0.36** 0.05 ÿ11* 0.14**
3
1.00 0.41** 0.45** ÿ0.12** 0.21** ÿ0.12**
4
1.00 0.48** 0.03 0.13** ÿ0.14**
5
1.00 ÿ0.05 0.15** ÿ0.06
6
1.00 ÿ0.02 ÿ0.01
7
1.00 ÿ0.05
8
TABLE 2 Intercorrelations Between the Predictor Variables Entered into the Discriminant Function Analysis (N = 547)
1.00
9
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TABLE 3 Results of Discriminant Function Analysis and Analyses of Variance
Variable Assertiveness Decision making Peer susceptibility Perceptions of school Relations with peers Confidence in skills Substance abuse knowledge Home setting Gender Note.
Correlations with Functions 1 2
Substance Use Group Non-users (n = 251) M (SD)
New Users (n = 110) M (SD)
Prior Users (n = 186) M (SD)
Eta Squared
0.01 ÿ0.27 0.94 ÿ0.47
0.16 0.11 0.17 0.05
2.76 (.60) 2.70a (.66) 0.26a (.30) 2.75a (.37)
2.72 (.63) 2.60a (.64) 0.36a (.38) 2.69a (.35)
2.73 (.58) 2.40b (.67) 0.98b (.70) 2.45b (.50)
0.001 0.038 0.313 0.098
ÿ0.34 ÿ0.35 ÿ0.15
0.50 0.03 0.62
2.67a (.39) 3.19a (.49) 0.22a (.97)
2.50b (.51) 3.12a (.46) ÿ0.13b (.93)
2.40b (.52) 2.93b (.49) 0.06b (.95)
0.065 0.059 0.026
0.13 0.29
ÿ0.45 0.03
0.16a (.37) 0.40a (.49)
0.27b (.45) 0.44a (.50)
0.25b (.44) 0.62b (.49)
0.014 0.040
Means with different superscripts are significantly different, p < .05.
For the discriminant function analysis, prior probabilities of group membership were calculated and accounted for: 46 percent non-users, 20 percent new users, 34 percent prior users. Two significant discriminant functions emerged. The first function had a Wilk's lambda of .63 with x 2 (8) = 246.73, p < .001; the second function has a Wilk's lambda of .96, x 2 (8) = 20.93, p < .007. The first function accounted for 93 percent of the total discriminating power for the analysis. Of the nine predictor variables, all except assertiveness significantly contributed to the discriminant functions. The first two columns in Table 3 show the magnitude of each predictor variable's correlation with the two functions. The first function includes decision making, peer susceptibility, perceptions of school, confidence in skills, and gender. The variables loading highest on the second function include positive relations with peers, substance abuse knowledge, and home setting. As noted below in the description of the mean differences found, those predictor variables which loaded highest on the first factor distinguished early (prior) users from new and non-users; those predictor variables which loaded highest on the second factor distinguished users from non-users (prior and new). ANOVA and Tukey post-hoc comparisons were conducted to examine the pattern of differences among the groups. As can be seen in Table 3, significant mean differences were found on all measures except assertiveness. Five variables distinguished non-users and new users from prior users. As compared to prior users, non- and new users had higher scores on decision making, positive perceptions of school, and confidence in using skills. Non- and new users had lower scores than prior users on susceptibility to peer pressure and they were more likely to be female. However, new users were similar to prior users with respect to peer relations, home setting, and substance use knowledge. As compared to non-users, new and prior users had less positive peer relations, less knowledge about alcohol and drugs, and were more likely to come from single parent families. Classification results indicated that the model did well at predicting non-users and those who were prior users (89% and 59%, respectively). Distinguishing new users from
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non-users was most difficult. Overall, 62.5 percent of the original cases were correctly classified. This is a significant improvement over the chance classification rate of 37 percent, z (547) = 12.52, p < .001.
DISCUSSION This longitudinal study sought to identify different risk models for early adolescent abstinence and experimentation. A total of nine predictor variables were used to classify adolescents into three groups. The results indicated that five variables helped to uniquely discriminate those students who had tried alcohol or cigarettes prior to the beginning of sixth grade from non-users and new users (those who started using during sixth grade). Prior users were found to have weaker decision making skills, more susceptibility to peer pressure, more negative perceptions of school, less confidence in their ability to use social skills, and an increased likelihood of being male. The second discriminating function separated non-users from both types of users. Non-users had better relations with peers, greater alcohol and drug knowledge, and were more likely to come from two parent homes. Some of the effect sizes were small, the largest were associated with peer susceptibility and positive perceptions of school. These findings offer support to previous research which asserts that risk factors differ depending on whether the student has been able to abstain from trying alcohol or cigarettes or can already be classified as a user. Adolescents who initiated their first alcohol or cigarette use during the sixth grade had scores on many of the risk factors that were closer to the scores of students who never used substances than to students who used substances prior to sixth grade. This effect had not been predicted. It may be that over time, these new users will begin to ``look'' more like early users; that is, their substance use may begin to impede their decision making skills and make them more negative about school. It is also possible that youth who begin using substances at a very early age (in this case, before sixth grade) have a unique combination of skill deficits which encouraged their experimentation and set them apart from their peers. There were some similarities in risk profiles between early and new users, specifically in terms of having unsatisfactory peer relations, coming from a home with one parent and having lower alcohol and drug knowledge. For students in this study, sixth grade was the first year of middle school. Students who are ignorant about the risks associated with drug use and who are unhappy with their friends may have a difficult time ignoring the pressure in middle school to act ``cool'' and may begin to use drugs to demonstrate their maturity. Special interventions at the start of middle school may decrease students' likelihood of initiating substance use (Pilgrim et al., 1998). The results of this study should be viewed with caution for several reasons. First, the study was conducted with mostly Caucasian students from semi-rural and suburban areas. Risk factors found in this study population may not generalize to urban or minority populations. Although this study reveals some of the circumstances which lead adolescents to begin using prior to the sixth grade, the antecedents of these skill deficits remain unknown. The risk factors for these early users remains puzzling and should continue to be of concern to those interested in delaying the age of onset. Another limitation was the
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attrition over time due to absences and the use of screening criteria which removed students with excessive missing data. Students excluded from the analysis had lower scores for decision making and were more likely to come from single parent households. Replication of these findings with other student populations would increase confidence in their generalizability. These findings have particular relevance for prevention programs and educational curricula. Special emphasis may be needed to help adolescents get along better with friends and classmates. The importance of these basic social skills should not be underestimated. Many middle schools utilize a team-centered approach which enables students to work cooperatively, enhance their perceptions of each other, and reduce the isolation often felt by adolescents of this age. Knowledge of the consequences of substance use continues to be a mainstay of most prevention approaches. Although it has been shown to be ineffective in reducing drug use on its own, knowledge appears to deserve more attention as a protective factor for adolescents who have yet to try alcohol or cigarettes. Finally, support may be necessary to help single parent families, whose children are more at risk for using alcohol and cigarettes. After school programs, tutoring assistance, and parenting support may help reduce the stress associated with single parenting. Future research is needed which examines the pre-adolescent phase of youth development to uncover predisposing factors for substance use. Clearly, these early users were found to have deficits in many areas, but this information does not reveal the circumstances of how or why their use was initiated. A second recommendation for research is that adolescents be followed for many years to assess changes in their substance use. Not all youth who try alcohol or cigarettes continue to use or escalate to other forms of drug use. It is important to understand what factors deter experimenters from becoming regular users. It is unclear if the risk factors studied here, particularly the social skills, are amenable to change and if changing them will alter alcohol and drug use. Given the multidimensional aspects of risk factors, ``one size fits all'' prevention cannot be expected to be successful. As prevention practitioners have observed, it is not enough to do the right thing, you have to do the right thing right. ACKNOWLEDGMENTS: This research was funded by a contract from the Skillman Foundation to
the first author. We would like to thank the staff at the participating schools for their assistance.
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Chatlos J: Recent trends and a developmental approach to substance abuse in adolescents. Child Adolesc Psychiatr Clin N Am 5: 1 ± 27, 1996. Conrad K, Flay B, Hill D: Why children start smoking cigarettes: Predictors of onset. Br J Addict 87: 1711 ± 24, 1992. Dielman T, Campanelli P, Shope J, Butchart A: Susceptibility to peer pressure, self esteem, and health locus of control as correlates of adolescent substance abuse. Health Educ Q 14: 207 ± 21, 1987. Dryfoos J: Adolescents at Risk: Prevalence and Prevention, New York, Oxford University Press, 1990. Fegusson D, Lyranskey M, Howard J: The short-term consequences of early onset cannabis use. J Abnorm Child Psychol 24: 499 ± 512, 1996. Flay B: Youth tobacco use: Risk patterns and control. In Nicotine Addiction: Principles and Management, edited by J. Slade and C. Orleans, London, Oxford University Press, 1992. Hawkins J, Catalano R, Miller J: Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse problems. Psychol Bull 112: 64 ± 105, 1992. Hermann D, McWhirter J: Refusal and resistance skills for children and adolescents: A selected review. J Counsel Dev 75: 177 ± 87, 1997. Institute of Medicine: Reducing Risks for Mental Disorders, Washington, DC, National Academy Press, 1994. Jackson C: Initial and experimental stages of tobacco and alcohol use during late childhood: Relation to peer, parent, and personal risk factors. Addict Behav 22: 685 ± 98, 1997. Jessor R, Jessor S: Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth, New York, Academic Press, 1977. Johnston L, O'Malley P, Bachman J: National Survey Results on Drug Use From Monitoring the Future Study, 1975±1992 (NIH Publication No. 93-3597), Washington, DC, U.S. Government Printing Office, 1993. Kandel D: On processes of peer influences in adolescent drug use: A developmental perspective. Adv Alcohol Subst Abuse 4: 139 ± 63, 1985. Klecka W: Discriminant Analysis, Beverly Hills, CA, Sage, 1980. McGuire W: Inducing resistance to persuasion: Some contemporary approaches. In Advances in Experimental Social Psychology vol. 1, edited by L. Berkowitz, New York, Academic Press, pp. 136 ±314, 1964. Moskowitz J: The primary prevention of alcohol problems: A critical review of the research literature. J Stud Alcohol 50: 54 ± 88, 1989. National Research Council (U.S.): Panel on adolescent pregnancy and childbearing. In Risking the Future: Adolescent Sexuality, Pregnancy, and Childbearing, edited by C. Hayes, Washington, DC, National Academy Press, 1987. Needle R, McCubin H, Wilson M, Reineck R, Lazar A, Mederer H: Interpersonal influences in adolescent drug use: The role of older siblings, parents and peers. Int J Addict 21: 739 ± 66, 1996. Newcomb M: Understanding the multidimensional nature of drug use and abuse: The role of consumption, risk factors, and protective factors. In Vulnerability to Drug Abuse, edited by M. Glantz and R. Pickens, Washington, DC, American Psychological Association, pp. 255± 98, 1992. Newcomb M: Psychosocial predictors and consequences of drug use: A developmental perspective within a prospective study. J Addict Dis 16: 51 ± 89, 1997. Norman E, Turner S: Adolescent substance abuse prevention program: Theories, models, and research in the encouraging 80's. J Prim Prev 14: 3 ± 20, 1993. Oetting E, Beauvais F: Adolescent drug use: Findings of national and local surveys. J Consult Clin Psychol 58: 385 ± 94, 1990.
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Pilgrim C, Abbey A, Hendrickson P, Lorenz S: Implementation and impact of a family-based substance abuse prevention program in rural communities. J Prim Prev 18: 341 ± 61, 1998. Resnick M, Bearman P, Blum R, Bauman K, Harris K, Jones J, Tabor J, Beuhring T, Sieving R, Shew M, Ireland M, Bearinger L, Udry J: Protecting adolescents from harm: Findings from the national longitudinal study on adolescent health. JAMA 278: 823 ± 32, 1997. Scheier L, Botvin G: Expectancies as mediators of the effects of social influences and alcohol knowledge on adolescent alcohol use: A prospective analysis. J Addict Behav 11: 48 ± 64, 1997. Schinke S, Gilchrist L: Preventing cigarette smoking with youth. J Prim Prev 5: 177 ± 87, 1984. Swaim R, Oetting E, Edwards R, Beauvais F: Links from emotional distress to adolescent drug use: A path model. J Consult Clin Psychol 57: 227 ± 31, 1989. Tabachnick B, Fiddell L: Using Multivariate Statistics, 3rd edn., New York, Harper Collins, 1996. Thomas C, Schandler S: Risk factors in adolescent substance abuse: Treatment and management implications. J Child Adolesc Subst Abuse 5: 1 ± 16, 1996. Tobler N: Meta-analysis of 143 adolescent drug prevention programs: Quantitative outcome results of program participants compared to control or comparison groups. J Drug Issues 16: 537 ± 67, 1986. Warren C, Kann L, Small M, Santelli J, Collins J, Kolbe L: Age of initiating selected health-risk behaviors among high school students in the United States. J Adolesc Health 21: 225 ± 31, 1997. Wills T, Baker E, Botvin G: Dimensions of assertiveness: Differential relationships to substance use in early adolescence. J Consult Clin Psychol 57: 473 ± 78, 1989. Wills T, McNamar G, Vaccaro D, Hirky A: Escalated substance use: A longitudinal grouping analysis from early to middle adolescence. J Abnorm Psychol 105: 166 ± 80, 1996. Woolfson R: Psychological correlates of solvent abuse. Br J Med Psychol 55: 63 ± 6, 1982.
Since the introduction of the risk factor model, lower rates of substance use have continued to be an elusive, but constant goal of prevention programs. Many risk factors for substance use have been identified including low involvement with family, *Direct all correspondence to: Dr. Joanne Sobeck, Ph.D., School of Social Work, Wayne State University, 4756 Cass Avenue, Detroit, MI 48202, USA; E-mail: [email protected] JOURNAL OF SUBSTANCE ABUSE, Volume 11, Number 1, pages 89±102. Copyright # 2000 by Elsevier Science Inc. All rights of reproduction in any form reserved. ISSN: 0899-3289
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religious, and school institutions; poor academic achievement; parental substance abuse; and association with drug using peers (Hawkins et al., 1992; Institute of Medicine, 1994; Moskowitz, 1989; Newcomb, 1992; Oetting and Beauvais, 1990). Given its potential for addressing the continuing problem of substance abuse among adolescents and its popularity among researchers as an area of study, one would expect more assimilation of risk factor knowledge into the practical real world application of prevention. Yet, many prevention programs targeted at adolescents use a general approach providing all youth with the same information and skills without regard to understanding the different types of risk factors existing within a particular group. One important area that has received increasing attention is the identification of risk factors for preventing first use separate from those risk factors predicting more problematic or heavy drug use (Conrad et al., 1992; Jackson, 1997; Wills et al., 1996). Prevention programs need to address these various risk structures for different groups of youth in order to be successful. There is a second and related reason why it is important to understand risk factors and their relationship to predicting various levels of substance use. Research shows that young people are experimenting with substances at an earlier age than they had previously (Warren et al., 1997). What are the long-term consequences of trying alcohol or cigarettes before one is 11 years old? Using substances at an early age means that the duration of risk is longer and that these early adolescent users are more likely to experience problems in later adolescence and adulthood (Dryfoos, 1990; Fegusson et al., 1996; National Research Council (U.S.), 1987; Newcomb, 1997). The study described here seeks to identify risk factors that are associated with non-use, first use, and prior substance use among sixth graders.
RISK FACTOR RESEARCH Risk factors can be identified which distinguish between first use and ongoing use. Wills et al. (1996) identified predictor variables that distinguished between five levels of use. Experimenters were found to have higher levels of stress, maladaptive coping, and deviance prone attitudes; and lower levels of parent support and self control than non-users. Predictors of use were not found to differ for various levels of users. Using a stage model of smoking and alcohol use, Jackson (1997) found different factors associated with abstinence, initiation, and experimentation stages of use. Having a best friend who had tried tobacco or alcohol and perceiving a high rate of tobacco and alcohol use among peers were both predictive of initiation and experimentation stages. Parental offers to try smoking a cigarette predicted smoking initiation and parental offers to sip alcohol predicted both initiation and experimentation stages of alcohol use. Being invited to use alcohol or tobacco by friends was related with initiation and experimentation for both substances. School adjustment and behavioral self-regulation were found to be inversely related to tobacco and alcohol use. Thus, research indicates that the influence of risk factors may vary depending on the predicted level of substance use (Beman, 1995; Thomas and Schandler, 1996). An important methodological issue in risk factor research concerns whether it is more appropriate to develop separate models to predict the use of different substances or to use
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a risk index accounting for all forms of drug use. Early risk models sought to independently explain adolescent smoking (Conrad et al., 1992; Flay, 1992; Schinke and Gilchrist, 1984;), drinking (Brook et al., 1986; Chassin et al., 1988; Jessor and Jessor, 1977) and other drug use (Botvin et al., 1989; Woolfson, 1982). Treating substances in this solitary fashion has a lengthy history in American culture and is based on legal and moral issues associated with each substance. However, the concomitant use of tobacco, alcohol, and other drugs (mainly marijuana) by most users has prompted researchers to consider a risk model that combines these various substances (Chatlos, 1996; Thomas and Schandler, 1996; Wills et al., 1996).
AGE OF ONSET OF USE With adolescents beginning to try substances, mainly alcohol and cigarettes, at an earlier age (Warren et al., 1997), the duration of risk may now be longer than in previous cohorts of adolescents. This increased exposure means that youth are more vulnerable to developing other risks for substance use and to experiencing more consequences of use (Dryfoos, 1990; Newcomb, 1997). Early substance use appears to have deleterious effects, which last into adulthood. For example, Newcomb (1997) found several direct and significant effects of teenage drug use in adulthood. Low educational achievement, family problems, relationship problems, and criminal behavior were found as teenage substance users matured into adulthood.
THEORETICAL BASIS The theoretical foundation used in this paper is based on a combination of health and psychological theories. By converging theories, we seek to utilize insights gained from both disciplines to help explain the challenges facing substance abuse prevention today. One premise underlying the risk factor model tested here is that substance use can be deterred by providing knowledge of the consequences of substance use behavior. Although information alone is not effective (Moskowitz, 1989; Tobler, 1986), it is an important part of a comprehensive substance abuse prevention program. Health knowledge is assumed to be a protective factor; knowledge provides information that triggers an internal process that motivates individuals to bolster attitudes which make them more resistant to change (McGuire, 1964). If adolescents are more knowledgeable about the consequences of substance use, they are less likely to engage to health compromising behaviors. Scheier and Botvin (1997) concluded that factual information is a strong predictor of alcohol expectancies and had a significant negative effect on use. A second theory underlying this research is the persuasion or social influence model (Bandura, 1977). In this view, substance use is learned through modeling and reinforcement. In order to withstand social and peer pressures, adolescents are expected to develop personal competency and self-efficacy skills. One of the most common applications of this theory in prevention programs is the teaching of social skills because deficiencies in these areas have been linked to substance use (Hermann and McWhirter,
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1997; Norman and Turner, 1993). In this paper, adolescents who use substances are hypothesized to have skill deficits in the domains of assertiveness, decision making, and peer pressure refusal. In addition, early users are not expected to be confident about their ability to use a variety of social skills, and this lack of confidence makes them more susceptible to peer pressure. Relationships with peers is one of the most widely used explanations for early substance use (Kandel, 1985; Needle et al., 1996; Swaim et al., 1989). Peer influences, however, are highly complex, suggesting various indirect and direct pathways to substance use. Being part of a peer group or social network is considered to be a protective factor, suggesting that adolescents who are isolated are more likely to use substances. Hawkins et al. (1992) found that adolescents who develop prosocial bonds with non-using peers are less likely to use substances. Thus, positive relations with peers are included as potential factor in understanding the onset of alcohol and cigarette use. Finally, school factors have been linked to substance use. Here, the premise is that adolescents who like school and prepare themselves for study are more likely to have a strong bond to the school institution, a commitment which makes them less likely to engage in health compromising behaviors (Hawkins et al., 1992). Using data from the National Longitudinal Study on Adolescent Health, Resnick et al. (1997) found perceived school connectedness was protective against every health risk behavior except pregnancy. In this paper, we examine a group of risk factors to explain no use, new use and prior use of alcohol and cigarettes among an early adolescent population. Using a broad conceptualization of the public health model for substance use, our premise is that adolescents with low levels of substance use knowledge, poor social skills, high peer susceptibility, poor relations with friends, and negative school perceptions will be more likely to try alcohol and tobacco at an early age.
METHOD Overview The study included sixth grade children enrolled in five school districts in suburban and semi-rural areas of a Midwestern metropolitan area. Three of the districts participated in the study during the 1995±1996 school year, the other two districts participated in the 1996 ±1997 school year. In preliminary analyses, the five districts were compared on each of the measured variables. There were no significant differences, justifying their combination for the analyses presented in the paper. The students were typical of those in many school systems in large metropolitan areas in the Midwest. They lived in areas with populations ranging from 5,000 persons (semi-rural) to 61,000 persons (suburban). The median family income was approximately $36,800. On average, 97 percent of the population in these districts was Caucasian. In terms of economic indicators, 25 percent were eligible for the free or reduced-cost lunch program based on federal guidelines. Two waves of data were collected approximately 8 months apart: when students were beginning sixth grade (Time 1, T1) and at the completion of sixth grade (Time 2, T2).
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This prospective design allowed the distinction between early users prior to the start of the sixth grade, new users who initiated use during the sixth grade, and non-users who never used by the end of sixth grade. At each point in time, two surveys were administered on different days within the same week. Two days were required because the survey was too long to administer in one class period. The sixth grade student population enrolled at the time of the initial survey was 781. There were 726 students who completed the survey the first day and 728 who completed it the second day, for a response rate of 93 percent (absences were slightly different the two days). Less than 1 percent of parents refused permission, 1 percent of the students chose not to participate, 6 percent of the students were absent, and 1 percent were unable to participate because they were non-English speaking or taken out of class due to discipline problems. The students were re-surveyed during the spring of the same school year (T2). Seventy-five percent (n = 582) of the original population completed the follow-up survey (which is 80% of the T1 participants).
Sample Description Fifty-one percent of the sample was female. Self reported ethnic/cultural descriptions were 92 percent white, 3 percent American Indian, 1 percent African American, 1 percent Hispanic, and 3 percent other ethnic groups. Over three-fourths (79%) of the students resided with two adults. Although the information available on the sample is limited, the school data reflect the general statistics available through the U.S. Census on the communities where the districts were located.
Survey Protocol A passive consent procedure was used to obtain parents' permission. Two weeks prior to survey administration, a letter was mailed to the parents of all sixth grade students describing the purpose and content of the survey. The letter also informed parents that copies of the surveys were available in the school office for review. Standard consent procedures were followed in the classroom. Students were told their participation was voluntary and they could choose to skip any or all questions. Identification numbers were used to match student responses from T1 to T2, so the surveys did not have names attached. Teachers remained in the classroom during administration, however, members of the research team read the survey's directions and answered students' questions. Students completed each survey within a 1 h class period. When students were finished, the researchers collected the surveys, which were put into a sealed envelope and taken back to the university.
Measures Measures for this study included items from standard surveys assessing alcohol and tobacco use, as well as newly developed questions designed to assess the knowledge and skills of interest. Cronbach coefficient alphas were comparable at both timepoints, so only the T1 alphas are reported below.
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Demographics In order to determine the home setting of the respondents, one question asked, ``With whom do you live?'' Adolescents were allowed to respond by using one of seven possible options. The answers were coded into a dichotomous variable, 0 = living with two parents (natural, step, or combination) or 1 = living with a single parent/guardian, or other relative. Students' gender was coded as 0 = female and 1 = male.
Substance Use Questions concerning adolescents' substance use were taken from the National Institute on Drug Abuse's Monitoring the Future Study (Johnston et al., 1993). Students were asked about cigarettes, alcohol, marijuana, sniffing glue, and smokeless tobacco. Two questions were asked about each substance. First, students were asked about lifetime use of the substance using a 5-point scale with the response options: never, once or twice, occasionally but not regularly, regularly in the past but not now, and regularly now. Then they were asked about use in the previous month using a 6-point scale with response options ranging from not at all to more than once a day.
Substance Abuse Knowledge Ten questions on alcohol and other drug facts were used to determine a score for substance abuse knowledge. Participants' responses were coded as either correct, which was scored ``1,'' or incorrect, which was scored ``0.'' A total correct score was calculated. Because part of the sample received a modified set of original questions, z scores were then computed to standardize the knowledge score across the sample.
Protective Social Skills Assertiveness was assessed with a 7-item scale measuring the frequency of confident responses given in a variety of social situations (Wills et al., 1989). Sample items include: ``I ask people to explain things when I do not understand.'' and ``I stand up for my rights when someone tries to cut in front of me in line.'' Responses were made using a 5-point scale with options which ranged from never to all the time. Cronbach's coefficient alpha was 0.66. Decision making included nine questions which assessed the extent to which adolescents utilized a comprehensive model for decision making. Students responded using a 5-point scale with options that ranged from never to all of the time. The series of questions began with the statement: ``When I have to handle a problem situation or make an important decision.'' Sample questions include, ``I try to gather all the facts.'' and ``I stop and think about solutions to the problem before I choose to act.'' Cronbach's coefficient alpha was 0.82. Because students in this age group may not be frequently exposed to situations in which they have the opportunity to use the types of skills described above, a group of
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16 items measured adolescents' confidence in their ability to use various social skills. Students answered these questions using a 5-point scale with response options which ranged from very poorly to very well. Cronbach's coefficient alpha was 0.84. Sample questions include, ``How well can you handle conflicts with friends?'' and ``How well can you make good decisions?''
Peer Susceptibility The peer susceptibility measure was developed by Dielman et al. (1987). It included six items which asked how the participant handled peer pressure situations, including general and alcohol and cigarette specific questions. Responses were made using a 4-point scale with options which ranged from definitely no to definitely yes. Sample items are ``If a friend dared you to tear a page out of a library book would you do it?'' and ``If your friends dared you to smoke a cigarette, would you smoke it?'' Cronbach's coefficient alpha was 0.84.
Relationships with Peers Three items were used to assess perceived relations with peers (e.g., ``I treat my friends with respect.''; ``I get along well with other people my age.''). Participants responded using a 4-point scale with options ranging from strongly disagree to strongly agree. Cronbach's coefficient alpha was 0.64.
Perceptions of School General school perceptions were measured with four questions. Students responded using a 4 -point scale with options ranging from strongly disagree to strongly agree. Sample questions include: ``I come to class prepared.'' and ``Getting good grades is important to me.'' Cronbach's coefficient alpha was 0.71.
DATA ANALYSIS The goal of the data analysis was to predict students' substance use categorization at T2 with the predictor variables described above measured at T1. Direct discriminant function analysis (Klecka, 1980) was conducted with the SPSS PC program. After this, analysis of variance (ANOVA) was performed, along with post-hoc comparisons (Tukey's B), to examine the pattern of means (Tabachnick and Fiddell, 1996). Students who completed only pretest surveys were compared to students who completed both pretest and posttest surveys. Attrition analysis using the predictor and outcome variables described in the Measures Section indicated that the groups were comparable on all variables (including substance use) except two. Students who remained in the study scored higher on the scale for decision making and were more likely to come from a two-parent family than were students who were only in the pretest sample.
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TABLE 1 Percent of Students Who Had Ever Used Substances at the Beginning and End of Sixth Grade ( N = 582) T1 use Substance Alcohol Cigarettes Sniffing glue Smokeless tobacco Marijuana
T2 use
N
Percent
N
Percent
170 131 43 24 18
29 22 7 4 3
253 212 67 45 51
44 36 12 8 9
RESULTS Patterns of Substance Use As expected for this age group, rates of use of all substances, except alcohol and cigarettes, were low at the beginning of the school year. As can be seen in Table 1, the percent of students who had ever tried alcohol at the beginning of sixth grade (T1) was 29 percent; in contrast, 3 percent had ever tried marijuana. Substance use increased substantially by the end of the school year (T2), highlighting that sixth grade is a crucial year for substance abuse prevention efforts. Although many students had tried alcohol and cigarettes, their use was relatively infrequent. Only 6 percent of the students reported smoking cigarettes in the previous month and only 1 percent reported smoking regularly. Similarly, 8 percent of students drank alcohol in the past month and less than 1 percent reported that they drank regularly. Thus, although interval level data were collected, for data analyses, participants' responses were collapsed into two categories: ever used versus never used. Furthermore, most of the students who had smoked cigarettes had also consumed alcohol (and vice versa). Seventy-eight percent of the students had consistent patterns of alcohol and cigarette use. Preliminary analyses were conducted in which the predictors of alcohol and cigarette use were examined separately. The models were highly comparable, further justifying combined use of both substances into a single outcome variable. Thus, adolescents were considered users if they had ever used alcohol and/or ever smoked cigarettes. This characterization of substance use delineated three groups of participants: (1) adolescents who never used at T1 or T2 (non-users), (2) adolescents who never used at T1, but used at T2 (new users), and (3) adolescents who were already users at T1 (prior users) regardless of level of use between T1 and T2.
Discriminant Function Analysis Prior to conducting multivariate analyses, 35 students' responses were dropped because of extensive missing data. Thus, there were 547 cases available for analysis. In addition, the intercorrelations among the predictor variables were examined. As can be seen from Table 2, on average, the different predictors were moderately correlated. The strongest correlations were between assertiveness and decision making (r = .50) and between confidence in using skills and peer relations (r = .48).
a
1.00 0.50** ÿ0.05 0.12** 0.18** 0.23** ÿ0.01 0.15** ÿ0.03
1 1.00 ÿ0.32** 0.33** 0.29** 0.44** ÿ0.03 0.20** ÿ0.12**
2
Response scale: 0 = Never . . . 4 = All of the time. b Response scale: 0 = No . . . 3 = Yes. c Response scale: 0 = Strongly disagree . . . 3 = Strongly agree. d Response scale: 0 = Very poorly . . . 4 = Very well. e Response scale: 0 = Two parent family; 1 = Single parent/other. f Response scale: z score of number correct. g Response scale: 0 = Female; 1 = Male. * p < .05. ** p < .01.
a
Assertiveness Decision makinga Peer susceptibilityb Perceptions of schoolc Relations with peersc Confidence in using skillsd Home settinge Substance abuse knowledgef Genderg
Notes.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Variable
1.00 ÿ0.43** ÿ0.28** ÿ0.36** 0.05 ÿ11* 0.14**
3
1.00 0.41** 0.45** ÿ0.12** 0.21** ÿ0.12**
4
1.00 0.48** 0.03 0.13** ÿ0.14**
5
1.00 ÿ0.05 0.15** ÿ0.06
6
1.00 ÿ0.02 ÿ0.01
7
1.00 ÿ0.05
8
TABLE 2 Intercorrelations Between the Predictor Variables Entered into the Discriminant Function Analysis (N = 547)
1.00
9
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TABLE 3 Results of Discriminant Function Analysis and Analyses of Variance
Variable Assertiveness Decision making Peer susceptibility Perceptions of school Relations with peers Confidence in skills Substance abuse knowledge Home setting Gender Note.
Correlations with Functions 1 2
Substance Use Group Non-users (n = 251) M (SD)
New Users (n = 110) M (SD)
Prior Users (n = 186) M (SD)
Eta Squared
0.01 ÿ0.27 0.94 ÿ0.47
0.16 0.11 0.17 0.05
2.76 (.60) 2.70a (.66) 0.26a (.30) 2.75a (.37)
2.72 (.63) 2.60a (.64) 0.36a (.38) 2.69a (.35)
2.73 (.58) 2.40b (.67) 0.98b (.70) 2.45b (.50)
0.001 0.038 0.313 0.098
ÿ0.34 ÿ0.35 ÿ0.15
0.50 0.03 0.62
2.67a (.39) 3.19a (.49) 0.22a (.97)
2.50b (.51) 3.12a (.46) ÿ0.13b (.93)
2.40b (.52) 2.93b (.49) 0.06b (.95)
0.065 0.059 0.026
0.13 0.29
ÿ0.45 0.03
0.16a (.37) 0.40a (.49)
0.27b (.45) 0.44a (.50)
0.25b (.44) 0.62b (.49)
0.014 0.040
Means with different superscripts are significantly different, p < .05.
For the discriminant function analysis, prior probabilities of group membership were calculated and accounted for: 46 percent non-users, 20 percent new users, 34 percent prior users. Two significant discriminant functions emerged. The first function had a Wilk's lambda of .63 with x 2 (8) = 246.73, p < .001; the second function has a Wilk's lambda of .96, x 2 (8) = 20.93, p < .007. The first function accounted for 93 percent of the total discriminating power for the analysis. Of the nine predictor variables, all except assertiveness significantly contributed to the discriminant functions. The first two columns in Table 3 show the magnitude of each predictor variable's correlation with the two functions. The first function includes decision making, peer susceptibility, perceptions of school, confidence in skills, and gender. The variables loading highest on the second function include positive relations with peers, substance abuse knowledge, and home setting. As noted below in the description of the mean differences found, those predictor variables which loaded highest on the first factor distinguished early (prior) users from new and non-users; those predictor variables which loaded highest on the second factor distinguished users from non-users (prior and new). ANOVA and Tukey post-hoc comparisons were conducted to examine the pattern of differences among the groups. As can be seen in Table 3, significant mean differences were found on all measures except assertiveness. Five variables distinguished non-users and new users from prior users. As compared to prior users, non- and new users had higher scores on decision making, positive perceptions of school, and confidence in using skills. Non- and new users had lower scores than prior users on susceptibility to peer pressure and they were more likely to be female. However, new users were similar to prior users with respect to peer relations, home setting, and substance use knowledge. As compared to non-users, new and prior users had less positive peer relations, less knowledge about alcohol and drugs, and were more likely to come from single parent families. Classification results indicated that the model did well at predicting non-users and those who were prior users (89% and 59%, respectively). Distinguishing new users from
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non-users was most difficult. Overall, 62.5 percent of the original cases were correctly classified. This is a significant improvement over the chance classification rate of 37 percent, z (547) = 12.52, p < .001.
DISCUSSION This longitudinal study sought to identify different risk models for early adolescent abstinence and experimentation. A total of nine predictor variables were used to classify adolescents into three groups. The results indicated that five variables helped to uniquely discriminate those students who had tried alcohol or cigarettes prior to the beginning of sixth grade from non-users and new users (those who started using during sixth grade). Prior users were found to have weaker decision making skills, more susceptibility to peer pressure, more negative perceptions of school, less confidence in their ability to use social skills, and an increased likelihood of being male. The second discriminating function separated non-users from both types of users. Non-users had better relations with peers, greater alcohol and drug knowledge, and were more likely to come from two parent homes. Some of the effect sizes were small, the largest were associated with peer susceptibility and positive perceptions of school. These findings offer support to previous research which asserts that risk factors differ depending on whether the student has been able to abstain from trying alcohol or cigarettes or can already be classified as a user. Adolescents who initiated their first alcohol or cigarette use during the sixth grade had scores on many of the risk factors that were closer to the scores of students who never used substances than to students who used substances prior to sixth grade. This effect had not been predicted. It may be that over time, these new users will begin to ``look'' more like early users; that is, their substance use may begin to impede their decision making skills and make them more negative about school. It is also possible that youth who begin using substances at a very early age (in this case, before sixth grade) have a unique combination of skill deficits which encouraged their experimentation and set them apart from their peers. There were some similarities in risk profiles between early and new users, specifically in terms of having unsatisfactory peer relations, coming from a home with one parent and having lower alcohol and drug knowledge. For students in this study, sixth grade was the first year of middle school. Students who are ignorant about the risks associated with drug use and who are unhappy with their friends may have a difficult time ignoring the pressure in middle school to act ``cool'' and may begin to use drugs to demonstrate their maturity. Special interventions at the start of middle school may decrease students' likelihood of initiating substance use (Pilgrim et al., 1998). The results of this study should be viewed with caution for several reasons. First, the study was conducted with mostly Caucasian students from semi-rural and suburban areas. Risk factors found in this study population may not generalize to urban or minority populations. Although this study reveals some of the circumstances which lead adolescents to begin using prior to the sixth grade, the antecedents of these skill deficits remain unknown. The risk factors for these early users remains puzzling and should continue to be of concern to those interested in delaying the age of onset. Another limitation was the
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attrition over time due to absences and the use of screening criteria which removed students with excessive missing data. Students excluded from the analysis had lower scores for decision making and were more likely to come from single parent households. Replication of these findings with other student populations would increase confidence in their generalizability. These findings have particular relevance for prevention programs and educational curricula. Special emphasis may be needed to help adolescents get along better with friends and classmates. The importance of these basic social skills should not be underestimated. Many middle schools utilize a team-centered approach which enables students to work cooperatively, enhance their perceptions of each other, and reduce the isolation often felt by adolescents of this age. Knowledge of the consequences of substance use continues to be a mainstay of most prevention approaches. Although it has been shown to be ineffective in reducing drug use on its own, knowledge appears to deserve more attention as a protective factor for adolescents who have yet to try alcohol or cigarettes. Finally, support may be necessary to help single parent families, whose children are more at risk for using alcohol and cigarettes. After school programs, tutoring assistance, and parenting support may help reduce the stress associated with single parenting. Future research is needed which examines the pre-adolescent phase of youth development to uncover predisposing factors for substance use. Clearly, these early users were found to have deficits in many areas, but this information does not reveal the circumstances of how or why their use was initiated. A second recommendation for research is that adolescents be followed for many years to assess changes in their substance use. Not all youth who try alcohol or cigarettes continue to use or escalate to other forms of drug use. It is important to understand what factors deter experimenters from becoming regular users. It is unclear if the risk factors studied here, particularly the social skills, are amenable to change and if changing them will alter alcohol and drug use. Given the multidimensional aspects of risk factors, ``one size fits all'' prevention cannot be expected to be successful. As prevention practitioners have observed, it is not enough to do the right thing, you have to do the right thing right. ACKNOWLEDGMENTS: This research was funded by a contract from the Skillman Foundation to
the first author. We would like to thank the staff at the participating schools for their assistance.
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