The Association of Group Self-Identification and Adolescent Drug Use in Three Samples Varying in Risk'

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1 The Association of Group Self-Identification and Adolescent Drug Use in Three Samples Varying in Risk' STEVE SUSS MAN,^ THOMAS R. SIMON, ALAN W. STACY, CLYDE W. DENT, ANAMARA RITT Institute for Heulth Promotion and Direore Prevention Rrsetrrch Utiiver:r.it); of Southern Culifhrnia MICtIELE D. KIPKE Division ofatlole.tcent Medicine Children i Ho.~pital. Lo.7 Angelex CA DEE BURTON School of Public Health University of Illinois at Chicago SUSANNE B. MONTGOMERY Lorna Linda Liniver.~ity School of Medicine and School of Public Health BRIAN R. FLAY Prevention Reseurch Center of llliiiois ut Chicugo This study provides a cross-sectional analysis of the relations between group self-identification and adolescent drug use in 3 samples of youth: comprehensive high-school, continuation high-school, and runawaylstreet youth. Youth identified with discrete groups in all 3 samples, and similar general groups were formed. In most comparisons, a high-risk group showed greater levels of drug use than did other groups. This is the first study to demonstrate that group self-identification (a) is a generalizable construct across different types ot adolescent samples, (b) is related to use of drugs other than tobacco, and (c) remains a significant correlate of drug use controlling for its relations with demographic variables and several other psychosocial variables. One peer-group social-influence-related variable that recently has been shown to be associated with adolescent cigarette smoking both in cross-sectional and prospective studies, even when statistically controlling for other psychosocia1 variables, is peer-group self-identification (e.g., Mosbach & Leventhal, 1988; Sussman et al., 10, 14). Since one is likely to conform to the perceived lifestyle characteristics of the peer group with which one most 'This research was supported by grants from: the National Cancer Institute (#CA44907) awarded to S. Sussman, C. W. Dent, A. Stacy. D. Burton, and B. R. Flay; the National Institute on Drug Abuse (#DA07601) awarded to S. Sussman, C. W. Dent. and A. Stacy; and the National Institute on Drug Abuse (#DA07613) awarded to M. D. Kipke and S. B. Montgomery. *Correspondence concerning this article should be addressed to Steve Sussman, Institute for Health Promotion and Disease Prevention Research and Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP 209, Los Angeles, CA Journal of Applied Social Psychology, 19, 29, 8, pp Copyright 0 19 by V. H. Winston 8 Son, Inc. All rights reserved.

2 1556 SUSSMAN ET AL identities, peer-prnup self-identi tication may increase our knouledge of lidolcscent use of other drugs in addition to cigarette smoking. Youth tend to gin, names to the groups with u hich they identify, and these discrete groups dclineatc different types of lifestyle preferences and behaviors, including d clothing styles, music preferences. and social experiences (e.g., Brown & Tru.jillo. 1985: Cohen. 1979: Eckert. 1983; Gottlieb. 1975; Hartup ; I,arkin, 1979: Poveda & Crini, 1975: Schnartz &: Mcrton. 1967: Sussman et al ; Thomas, 1973). Groups cluster into general categories based on ratings which reduce specific group names into categories (e.g.. Drown & Lohr. 1987; Appendix A). Generally. four catepories of groups are delineated (Mosbach & Le\ uittial, 1988; Sussman et al., 14). Youth \vho are physically inclined and oriented touard participation in sports are referred to as /oc,k.s. Youth nho are sociall) or academically inclined and who arc considered popular or involved in sol.ia1 or academic activities are referred to as /70/.5/70/,s. Youth who are average (e.9.. the "Charlie Rro\\n" type). and are inlolved in acti\,ities often reflecting a vocational orientation (e.g.. hall monitor). are referred to as 1qp1m:v. Finally. youth u 110 report noninvolvement in school. family conflict. a tendency toward negati\ 12 selfdescriptions (lo\\ self-esteem. depression). and a preference for risk taking, are referrcd to as high-risk ~.ori/i7. PreL ious studies haw focused primarily on cigarette smoking. While research indicates that youth M tic) smoke also tend to be involved in other risky hehviors (Donovan & Jessor, 1985). no study has investigated whether group selfidentification is associated nith multiple drug use. Mosbach and Le\t.nthal (1988) did tind a p e association betueen being a hotshot or a high-risk youth and drinking ent alcohol products. We suspect that they studied an unusual sample of self-identitied hotshots because they also tbund that hotchots reported relatively greater cigarette smoking, a result that has not been replicated with different general adolescent samples (Sussman et al., 10, 14). No study has investigated the empirical relations of group self-identification with inarijuana smoking or other illegal drug use. In the present study, group self-identitication and self-reported current cigarette smoking. current alcohol use. current marijuana use. number of types of six illegal drugs used in the last 30 days, or number of types of six illegal drugs ever used were assessed in three samples of youth. The first sample is compost.d of 14- to I 5-year-old. ninth-grade high-school youth from the comprehensive (regular) high-school system in southern California. These youth are typical ot'samples used iii most previous work in group self-identitication. The second sample are 15- to 19-year-old continuation high-school youth from southern Califivnia. Continuation versus comprehensi\.e high schools form a natural demarcation of youth \tho are at relatively high or low risk for substance use. Before reaching high school in California, youth remain at the same elementary or junior high

3 ADOLESCENTGROUPSANDDRUGUSE 1557 school. When reaching high-school age, those youth who are unable to remain in the regularicomprehensive school system for functional reasons, including substance use, are transferred to a continuation school. Drugs are used at a much higher lcvel at continuation than regular high school (DeMoor et al., 14; Newcomb, Maddahian, Skager, & Bentler, 1987; Sussman, Dent. Burton. Stacy. & Hay, 15; Sussman, Dent. Simon, et al., 15; Sussman. Stacy, et al., 15). This population is a good one in which to study the relations of drug use with a variety of other variables. The third sample are runawayihomelessistreet youth who reside primarily in the Hollywood area of southern California. These youth range in age from approximately 13 to 23 years and have been identified as being at extreme risk for a wide range of social and mental health problems, including substance use, perpetration of and victimization from violence, and HIV infection (e.g.. Institute of Medicine, 1989; Kipke. Montgomery, Seils-Pasco, & MacKenzie. 13). These youth do self-identify with discrete groups (e.g., Kipke, Montgomery. Simon, Unger, & Johnson, 17). and it is possible that these groups can be clustered into the general categories previously presented. Thcse three samples provide a means of assessing whether or not the concept of group self-identitication is generalizable across youth from different social contexts varying in prevalence of drug use; that is, the overall level of drug use is expected to he higher among runaways compared to continuation-high-school youth which, in turn, would be expected to be higher compared to comprehensivehigh-school youth. There are at least three expectations one may hold about group self-identitication across these three samples. First, one might expect that the percentage of prosocial institution-involved youth (the hotshots and jocks) would decrease, while the percentage of high-risk youth would increase. as study contexts show greater prevalence of drug use and riskiness of litestyle (i.e., from comprehensive high school to continuation high school to runawayistreet contexts). Second, one might expect that at least some institution-involved youth still exist even among adolescents at extreme risk (e.g., some ofthe runawaykreet youth do go to school or engage in conventional employment). Finally. one might expect that the relative difference in drug use would replicate comparing different self-identified group categories within each setting. In particular. high-risk youth should show greater drug use than other self-identified groups. If one fails to replicate this pattern of data across contexts, the meaningfulness of this construct in regard to explaining drug use would be limited in generalizabilty. These three expectations were examined in the present samples. In addition, statistical redundancy of group self-identification with other psychosocial variables can be examined in these three samples. To be a robust construct across different settings, it should be related to drug use. statistically controlling for its covariation with other psychosocial variables which delineate adolescent self-identi tied groups and which also are related to drug use (e.g.,

4 1558 SUSSMAN ET AL. Sussman et al., 14). From available measures. five general types of psyhosocia1 variables were utilized here across contexts: preference for risk taking, perceived stress/life hassles, negative self-descriptions, family conflictidistrust, and latchkey status (this latter variable only regarding the comprehensive and continuation high-school samples). There was variation in the contents of these iypes of variables across samples. Still. analogous types of measures were collected across all three data sets. These five types of variables have been shown to be associated with cigarette smoking. alcohol use, and marijuana use in previous studies (e.g., Richardson et al ; Sussman et al., 10; Sussman, Dent, E3urton, et al.. 15). All five types of variables have been shown to be related to group self-identification (e.g.. Sussman et al.. 10), but these variables have riot been found to be statistically redundant uith group self-identification as prospective predictors of cigarette smoking (Sussman et al., 14). If group self-idcritification IS statistically redundant \vith some other variable as a correlate of drug use, then one may question the importance of group self-identification as a unique correlate of drug use. This is the first study to provide an empirical investigation of the relations of group self-identification with the use of different drugs, and it is the first study to examine such relations across contexts Larying in drug use. The diversity of the samples studied wilf provide an assessment of the generalizabilty of group selfidentification across adolescent populations. The investigation of' the pallern of these associations u ill pro\.ide neu information on the importance of group selfidentification and other psychosocial variables involvcd in adolescent USC ot'different drugs. Ptu-ticipcmrs Method Samples were drawn from three contexts. Demographic and drug-use data regarding all three samples are sho\\n in Table 1. Data were collected from 3,061 ninth-grade students from 34 comprehensive high schools in southern California from 1 1 to 12. Data also were collected from 803 students from 9, I continuation high schools in southern California from 14 to 15. Finally. systematic and probability sampling strategies were used to collect data from 425 homeless/street youth. These youth were included as sub,ji'cts in the project within Lvhich these data \\ere collected (Project S.U.N.S.; Kipkr et al., 13) if they were 13 to 23 years of age and homeless or near-homeless. Youth Mere considered homeless if their primary night residence was in a supt:rvised public or private shelter, a temporary shelter (e.g., motel, boarding house), or a place not usually used at night (e.g.- on the street, in an abandoned building or car), and were considered near-homeless if they were residing temporarily in a

5 ADOLESCENT GROUPS AND DRUG USE 1559 Table 1 Demographics and Drug-Use ProJile for Three Saniples of Youth Gender (YO male) Ethnicity White Latino African American Asian/Pacific islander Other Living with both parents Age (years) YO daily use Cigarettes Alcohol Marijuana % use in last 30 days Any illegal drugs 2 or more illegal drugs % ever tried Any illegal drugs 2 or more illegal drugs Mean and standard deviation Cigarettes Alcohol Marijuana Mean number of drugs used in the last 30 days (from 0 to 6) Mean number of drugs ever used (from 0 to 6) Comprehensive Continuation high-school high-school Runawayistreet students students youth (n = 3,061) (n = 803) (I? = 425) 50% 62% 66% 49% 29% 6% 8% 8% 70% M= 15.3 (SD = 0.6) 10% 6% 3 YO (SD = 1.34)a 1.79 (SD = 1.03)a 1.30 (SD = )a % 41% 6% 6% 8 Yo 42% M= 16.7 (SD = 0.8) 35% 17% 32% 77% 44% 93% 5 yo 3.36 (SD = 4.13)b 2.12 (SD = 2.56)b 3.14 (SD = 3.56)h 1.57 (SD = 1.37) 3.07 (SD = 1.82) 51% 15% '0% 0% 14% 0% M = 19.0 (SD = 7.3) 69?/0 23% - 76% 46% 93% 75% 2.33 (SD = 1.49)c 4.27 (SD = 2.25)d (SD = 1.50) 3.58 (SD=2.10) Note. -indicates no data were collected for this cell of the table. See Appendix B for drug-use response headings. ascaie ranging from 1 (no use in the last year) to 7 (daily use). bscaie ranging from 0 (no use) to 11 (91 to 100 or more times in the lasf 30 days). CFor cigarettes, scale ranging from 0 (no use in last 7 days) to 5 (smoking over I4 packs). dfor alcohol, scale ranging from 0 (never) to 5 (almost everyday).

6 1560 SUSSMAN ET AL. residence that mas not their own (e.g., a friend s apartment). Data were collected within a 12 square mile area (approximately 19.2 square km) of Holly~ood in southern California from 14 to 15. using a systematic sampling procedure at service sites (i.e., three shelters and six drop-in centers) and natural street sites (e.g., street corners, parks, fast-food restaurants; 73 street areas ). Duration of homelessness was assessed for this sample; 3 1% reported being homeless for less than 3 months. 25% reported being homeless for 3 months to 1 year, and 44% reported being homeless for more than 1 year. The runaway sample demonstrates a higher mean level of drug use than the continuation-high-school sample \Lhich, in turn, shows a higher mean level of use than the comprehensive-high-school sample (see percentage drug use data in Table 1). Datn Collection Coniprehensive-liiglI-schoolyotith. Students were administered a 20-page self-report questionnaire. This questionnaire was composed of a core section at the front, which contained items that assessed demographic and behavioral information. followed by three sections that rotated in order on three different forms of the questionnaire. Forms were randomly administered to students within classrooms. Students were instructed that participation was voluntary, that they could withdraw from the study at any time without penalty. and that they were not expected to complete the full questionnaire. Of those approached for study participation 1 week earlier, student refusal rate was 5%, and 10% were absent on the day of assessment. Questionnaires were administered over one class period. For one cohort. a confidential data-collection procedure was used. The confidentiality of responses was emphasized in written and verbal instructions to the students. In addition, carbon monoxide (CO) and saliva measures were collected as part of a pipeline procedure to maximize the validity of self-reported tobacco use (e.g.. Stacy et al., 10). From a second cohort, an anonymous procedure was used in which no subject identifiers were placed on the questionnaires, and biochemical data were not collected (see Sussman, Dent, Burton, et al., 15, for details). Continuation-high-school youth. Students were administered a 20-page selfreport questionnaire, which was composed of a core section at the front (hehavioral and demographic information), followed by two sections that rotated on different forms of the questionnaire. Forms were randomly administered to students within classrooms. Students were instructed that participation was voluntary, that they could withdraw from the study at any time without penalty, and that they were not expected to complete the full questionnaire. Of those approached for study participation 1 week earlier, student or parental refusal rate was 3%. and 15% were absent on the day of assessment. Questionnaires were administered over one class period. Approximately 75% of the

7 ADOLESCENT GROUPS AND DRUG USE 1561 sample completed 90% of the responses. Seventy-five percent of subjects were surveyed using a confidential collection procedure; 25% were surveyed anonymously. RunaMqhtreel youth. One form of a 70-page, 60- to 75-min structured interview was administered at a nearby coffee shop or fast-food restaurant in return for a meal (worth up to $lo), bus tokens ($3 worth), and a $10 food voucher. Of those who were approached, 24.9% refused to be interviewed further after receiving a brief screening instrument. Of those who were administered a briefscreening instrument, subsequent refusers reported being relatively young in age, but not different on other demographic items. Subjects were surveyed as emancipated minors using a confidential collection procedure. Questionnaire Contents In order to allow for comparison of demographic, drug use, and psychosocial questionnaire contents assessed for each sample, a matrix of items is provided in Appendix B. Included in this matrix are examples of items and relevant response anchors for each variable for each sample. Additional information regarding the group self-identification items and other psychosocial items is provided in the following subsections. Groi~p Self-Identification A listing of the specific group names that composed each of the general group name categories is provided in Appendix A. The percentage of each of the five group name categories composing each sample is provided in Appendix C. Coiiii?rehensii/e-high-school youth. Stud en ts were asked to respond i ti a forced-choice format to the statement People often hang out in different groups at school. Which one group do you feel that you re most a part ot? A list of. I5 group names was collapsed to conform to the group typology based on Brown and Lohr (1987). Mosbach and Leventhal (1988), Sussman et al. (10), and Sussman et al. (14). Four general group categories were included in the analysis. The general names were high-risk youth, regulars/others, jocks, and hotshots. Continuation-high-school yoirfh. Students were asked to respond in a forcedchoice format to the statement People often hang out in different groups at school. Circle the letter of the one group that you feel that you re most a part of... A list of I7 group names was collapsed to conform to a similar group typology as in the comprehensive-high-school sample. Thus, the general group categories include multiple group names. Four general categories were included in the continuation-high-school sample analysis: high-risk youth, others, regulars, and jockslhotshots.

8 1562 SUSSMAN ET AL. Rzi/imt.a,./strrr/r~ozith. Students were asked to respond in a multiple-choice format to the statement "We have found that there are many different groups of young people that hang out together on the streets. Of the following groups, which do you niost identify with or receive the most support from?" The rc'spondents were permitted to check all that applied from a card handed out to thcin that contained names of 23 groups. While this item had been written in a t'orcedchoice format for the other samples, it was \vritten in a multiple-choice t0rtnat within this sample. To code this list into general groups, subjects who sclf-identified with a high-risk group name, no matter \\hat other group they also mentioned. bere coded as self-identifying with a high-risk group. Next, among the remaining subjects, any subject u ho identified with a regular, jock, or hotshot group \\,as coded as B "reg~iiars~iocl\s/hotstiots." of ~ihich there were only 26 selfidentified members. The remaining sub,iects were coded as others. While the method of coding general groups. of necessity. \\as somewhat dif'ferent li.om the other samples. different orders of creating general group combinations (e.g.. code others first. then lotker risk, then high risk) produce the same general groups ( it., the same numbers of high-risk youth. others. and lower risk youth [reg.ulars.,jocks. and hotshots] result. and 96?% ofthe youth are coded into the same group). Cor~i~~~rhetai~~e-liI~~i-rc~lior~l~~oi~tli. The psychosocial indexes used here had been used in Sussman et al. ( 14). Indexes Mere constructed as the meail or the items composing each index. Three hinary Yes-No items assessed risk-taking preference (coefficient u =,661 Collins et al : Sussman et al.. 10). Perceived stressllife hassles uere assessed \\itti i of the 14-item Perceived Stress Scale items (a =.71; C'ohen. Kamarck. & Mernielstein. 1983). Negative scll-descriptions were assessed Lvith 5 items adapted from Rosenberg's ( 1973) 10-item self-esteem scale (u =.67). Family conflictidistrust was assessed with three binary items (a =.69; Sussman. Dent, Burton, et al., 15). Finally, four items measured aspects ofbeing a latchkey child (u =.60; Richardson et al ). Items were standardized before constructing the index. Coritirizicr/ion-/iig/l-.FL.hoolI.(~iit/i. Psychosocial indexes (or items) included sensation sccking (which is a similar construct to preference for risk taking), perceived stress/life hassles, depression (which is related to self-esteem), one selfesteem-related item from the depression scale, family conflictidistrust. and latchkey status. Indexes were constructed as the mean of the items composing each index. The I 1 True-False sensation-seeking items from the Impulsive Sensation Seeking subscale of the Zuckerman-Kuhlman Personality Questionnaire I\ ere included as the measure of sensation seeking (a =.88; Newcomb & McGec. 11 ; Simon, Stacy. Sussman, & Dent, 14). The same three perceived-stress items as those used with the comprehensive-high-school sample were used here (a =.68).

9 ADOLESCENTGROUPSANDDRUGUSE 1563 Depression was assessed with the 20-item Center for Epidemiological Studies- Depression Scale (CES-D; a = 34; RadlotT. 1977; Rohde. Lewinson, & Seeley. 14). This measure requested that the subject Please check a box to indicate how often you have felt this way during the past week with responses on a 4- point scale ranging from 1 (rare/!> or iione of tlie tinie. less than I da) ) to 4 (most or all of the time. 5 to 7 days). One item from that measure also was examined independently since it is nearly identical to one of Rosenberg s (1972) selfesteem items. It reads I felt that I was just as good as other people. Family conflict was measured with the same three items that were used with the comprehensive-high-school sample (cx =.76). Finally. latchkey status was measured with a single item which read My parents aren t around as much as I would like. Ruriaitwvktreet yozrth. Psychosocial indexes included preference for risk taking, perceived stredlife hassles, self-esteem, and family conflict/distrust. No latch-key index could be constructed because all of these youth were latchkey children. Indexes were constructed as the mean ofthe items composing each index. Two items assessed risk-taking preference (r = 23; similar to Collins et al., 1987; Sussman et al., 10). Perceived stress/life hassles were assessed \\ith a 15-iten1, 4-point rating scale measure (a =.87). Subjects were asked Since you ve been living on the street, how often would say that you have had problems with...? and they were provided with 15 problem areas (e.g., transportation, getting food, finding a safe place to hang out or sleep ). Self-esteem was assessed with the complete set of ten 4-point rating scale items from the Rosenberg (1972) scale (a =.84). The comprehensive-highschool sample had been administered five of these items, binary coded. Finally, family conflict/distrust was assessed with a 12-item scale (a =.87). Subjects were informed that these items requested feelings about their mothers and fathers, the other persons who acted as their mothers or fathers or, if they had multiple persons acting as their mothers or fathers. the adult persons who most influenced them. Six parallel items were addressed toward each parent. Results Prevalence of Drug Use Arnotig the Group Cakgories Least significant difference (LSD) tests were used to indicate which groups differed in level of drug use. The LSD test controls for the Type 1 comparisonwise error rate. We first calculated differences in levels of drug use across group categories. As shown in Table 2, overall the relatively highest level of cigarette smoking was reported by the high-risk youth; however, the difference from all alternative groups was significant only in the comprehensive-high-school context. High-risk youth did not differ from others in the higher risk contexts, although regulars, jocks, and hotshots showed the lowest levels of smoking. Regarding

10 1564 SUSSMAN ET AL alcohol use. the high-risk youth did not she\\ a significant difference from thc others or hotshots in the continuation-liigli-school sample. but did she\\ a \igni ticantly higher mean le\ el of use troiii the rcst of the groups in the comprchi.nsi\.chigh-school and runa\\ayistreet samples. In the tuo school samples. Lvhicli cxaniined Ie\ el of. current marijuana use. the high-risk youth reported higher Ic\. els of LISC than did the rest of the groups. Finally. in the t\\o relatively high-risk s m- ples. \vhich examined number oft4pes of illegal drugs used in the last 30 days and niiniher ol'types of illegal drugs e\ cr used. the high-risk youth sho\\cd the highest mean le\el ot'drug use. The one exception is that. uithin the continuation-high-school sample. use in the last 30 days \\as significantly higher.iinong the high-risk youth only compared to the regulars (Table 2). A general linear model (CiLM) multiple regression approach (SAS, 19S7) \\:is used to ehaniine the association of' drug use \\ ith gender. cthnicity. the ps? chowcia1 uriables. and group self-identi~ic3ticin to produce the results sho\\n it1 Table -3. A parallel analysis protocol wis used ti)r each sample. This analysis protocol in- \,olved calculation of three sets of models. The tirst set of models ~ised onl? group self-identitic3tic,ri as a predictor. The second set of models used both group selfidentitication and demographics as predictors. The third set of models used group self-idcnti tication. demographics. and a single psychosocial variable as predictors. Drug use \\as considered the predicted ariable in all models. The nun1bc.r ot' models calculated M ithin each set of models \\ ithin each sample mas contingent on the different drug use measures a\-ailable in the sample. Level ot' current cigarette smoking and alcohol use data \\ere a\ailable for a11 three samples; Icvel of current iiiari-juana use \\as a\ ailable only ti)r the school samples; and diita on number of typcs of illegal drugs used in the last 30 days. or ever used, \\a\ available only for thc t\\o relatively high-risk samples. Thus. for each set ot' inodels, three models \\-ere examined among the compreliensi\,e-high-school sample, five models ivere examined among the continuation-high-school sample, and [our models \+ere examined among the runa\vay/street youth sample. The r-2 accounted for by group self-identification was noted. For the t'irst set of models. 1.2 is equal to the model R2. Next, gender and ethnicity were added as concurrent predictors of the same drug-use measures in a second set of niodels. and the model R2 accounted for by gender, ethnicity. and group seltlidentiiication was noted. In the runamayistreet sample. age and number of months on tht: street also lvere added as demographic predictors, as they are salient variable5 in this contest (Kipke et al.. 13). In addition. the group self-identitication effect Fvalues for the Type 3 sum of squares (SAS, 1987) were converted into uniqiie (partial) r.' values to detect any reduction in d for group self-identification after entering demographic variables into a model. Subsequent analyses explored all

11 ADOLESCENT GROUPS AND DRUG USE 1565 I l l I I I / I l l c c a,

12 1566 SUSSMAN E l AL. possible interactions among these variables. Demographic variables wci-e not found to interact M ith group self-identitication. A third set of GLM multiple regression analyses, M hich included psychosocia1 variables as predictors of the same drug-use measures. entered each p\ychosocial variable as a quantitative variable with one degree of freedom. alony with gender, ethnicity (and age and number of months on the street in the riin;i\+ay/ street sample). and group self-identitication (SAS, 1987). Again, the group selfidentitication effect F values mere con\,erted into partial /.? values to detcct any reduction in j.2 for group self-identification after entering demographic variables and the psychosocial variable into the model. Inter-psychosocial index correlations were not higher than 3. indicating a fair amount of nonoverlap amoiy predictors. In all three sets ofmodels. all model F\,alues \\ere significant at p c..05; specific modcl F lalues are not reported here. This analysis schcnie \\as.;imilar to that used by Sussman et al. ( 14). These results are shon n in Table 3. C'lcarly. demographics accounted tbr the largest tiaction of the variance in thesc models. Still. group selt-identiticati~,ii was as good a predictor of drug use as the other psychosocial wriables. Of 7 1 models calculated in the coinprehensi\.e-hiph-schoc,l sample ( 15 models containing an alterna t i w psych osoc i a I \,ar i able ). 40 mode Is ca I c u I a ted in the continuation -11 i g h- school sample (30 models containing an a1ternatil.e psychosocial variable), and 2-1 niodels calculated in the runanay/street youth sample ( 16 models containing an alternatii e psychosocial \anable). group self-identiticatton remained a significant predictor in all but se\.en ofthe models. and accounted tor at least IVL ofthe variance in drug use in all but six models. In the cornpreliensi\,e-high-school Sample, group self-identitication was a significant predictor in all models. In the continuation-high-school sample, the only exception to this pattern of results IS that group self-identification failed to predict alcohol use afier risk-taking prelkrence also mas entered in the model (along with demographic variables). Finally. in the runawayistreet sample. group self-identitication did not predict level of cigarette smoking in any ofti\e models in \+hich additional variables were placed in the model. In addition. group self-identi tication was a marginal predictor of number of types of illegal drugs ever used when the perceived stressilife hassles index also \vas entered as a predictor (along with demographic variables). Discussion No previous published study has determined whether or not self-identified adolescent group membership is associated with use of different types of' legal and illegal drugs. The present study addressed this issue across three adolescent samples that varied in prevalence of drug use. and also compared the predictive overlap of group self-identification with analogous psychosocial variables within each adolescent sample. The basic results may be summarized as follofic. The

13 ADOLESCENT GROUPS AND DRUG USE 1567 same types of general adolescent groups can be created in very different samplcs of adolescents: comprehensive-high-school youth, continuation-iiigli-school youth, and runawayistreet youth. The percentage of high-risk youth in the sample increases across contexts with greater levels of drug use prevalence, yet at least some youth who self-identify with lower risk groups remain even in contests of extreme risk. High-risk youth generally show a higher level of drug use across several categories of drugs. Finally, group self-identi tication remains a significant predictor of several types of drug use when compared to other psychosocial variables, such as preference for risk taking, perceived stredlife hassles. or family conflict/distrust. The findings of this study support the contention that group selfadentification is one of several variables involved in the process of adolescent drug use. The previous group self-identification literature indicates an experiential reality to the concept of discrete adolescent groups, one of which consists of high-risk youth who are those most likely to smoke cigarettes (and possibly use other drugs). I n the present study. group self-identification predicted use of several types of drugs after statistically controlling for several related psychosocial variables, taken singly. This investigation suggests that continued study of group self-identification as a predictor of drug use is warranted. Still, at least five limitations ofthis study should be mentioned. First, across the three samples there were some variations in method of sampling, data collection, coding ofgroups, general sample characteristics (e.g., age differences that might suggest developmental differences), and composition of the psychosocial variables. Thus, a few differences found across these data sets could be due to methodological differences. On the other hand, it is unclear that these sources of variation should be viewed as providing a limitation. Given these several differences, the pattern of results across samples was quite similar. Group self-identification appears to be a construct with wide generalizability. Second, these results may suggest the need to search for as yet unknown third variables. When only group is entered as a correlate, 2% to 6% of the variance in drug use is accounted for. However, the incremental variance of including demographic and psychosocial variables above and beyond including only group self-identification as correlates of drug use is 0% to 27%. mostly accounted for by a combination of group self-identification and these other variables (e.g., see Sussman et al., 14, regarding the unique variance of these other types of psychosocial variables). Future research needs to identify what these combinations reflect. Possibly some higher order construct or process explains this much larger common variance. Relatedly, it should be noted that the group self-identification and other psychosocial variables may not be best viewed as competitors for the same variance. Rather, they may form interrelated pathways leading to different substance use. Future studies should explore the nature of these pathways.

14 1568 SUSSMAN ET AL.

15 ADOLESCENTGROUPSANDDRUGUSE 1569 I I c 9-9 c? 9 I I CI N mm mln 0 - PI ci N- -- c- CI t-w mm PI c 9 c 9 0 W m 9 N PI NN N r-r lnm r-tt

16 Table 3 (Continued) Model R' with other psychosocial variablc, demographics. and/or group selfidentification as correlates Number Number of of illegal drugs illegal Name of used in drugs psychosocial Ciga- Mart- last 30 ever Sample variable rcttes Alcohol.iuana days used Kunawayistreet Preference for Unique 1.: ofgroup self-identitication in that model 0 cn C cn z D Number cn of illegal Number of' z drugs illegal rn -4 used used in drugs C'iga- Marl- last 30 ever rettes Alcohol.iuana days youth ( M risk taking.i4.i4 -.I t7.s.01 -.o age = 19.0 ~ Stredlife hasyears; N = sles.i2.i3 -.IS 28.oo. 17,s.01 -.O1.01* 425)J Negative selfdescriptions.15.i2 -.I ,t7.s.Ol Family conflictidistrust.i5.i3 -.I , ns.0i.0 I.0 1 Only demographicsa and.i2.i2 - group.i , ns.01.o I Only group I - Note. Three types of models appear: a psychosocial variable, demographic variables. and group self-identification as predictors (first five models in comprehensive-high-school sample, first six models in continuation-high-school sample, and first tour models in runaway/ street sample); only demographic variables and group self-identification (no psychosocial variable in model); and only group self-identification (no other predictor in model). Parallel sets of psychosocial items were used for each sample. All numerical values reflect a significant within-wmple effect iinless otherwise indicated. mv = not signiticant; - indicates that no data were collected for this cell of the table. Gender and rtlinicity were the demographic correlaic's in the 1iigh-sc.hooi samples; tor the runaua). sireel y w~h samplc, age arid ntimbcr of months on the street were added as demographic variables, along with gender and ethniciry. */?'.I.

17 ADOLESCENT GROUPS AND DRUG USE 1571 Third, this study was cross-sectional. While the only known prospective study indicates that group self-identification is a significant prospective predictor of cigarette smoking over a 1 -year lag (Sussman et al., 14), it is not known whether or not this construct would operate similarly with other substances. Future research is needed to address this issue. Prospective data are also needed to be able to explore plausible alternative pathways of relations of other psychosocia1 variables with group self-identification as predictors of substance use. Fourth, many youth treat their identification with a group as a transitory stage. Group self-identification shows only moderate stability in a general adolescent sample (Sussman et al., 14). As youth grow older, they may be more likely to form weak ties or liaisons than remain in integrated friendship networks (Shrum & Creek, 1987). Also, many drug users may tend to become isolated later on (Ennett & Bauman, 14; regarding cigarette smoking), rather than becoming more socialized within different groups. Still, these youths previous group selfidentification may influence their beliefs about the world and, hence, their future behavior (Cohen, 1979; Eckert. 1983). Even if group self-identification is a social-perception-type variable, as opposed to reflecting real adolescent groups and group processes, this does not preclude its importance as a psychosocial predictor of drug use (Burton, Sussman, Hansen, Johnson. & Flay, 1989; lannotti & Bush, 12). Rather. if this variable is of the social-perception type, theti how this variable is formed and what it means theoretically would be more fruitfully studied within individual-level research than with studies of groups. The fact that a greater percentage of high-risk youth exist in a higher risk context, as found in this study, does suggest social ecological validity to the use of this construct, however. Finally, while some group names have been used by teenagers for perhaps 30 years (e.g., heavy metalers), group name labels might change over long periods of time. For example, the recent widespread grunge music movement may lead some youths to self-identify as being grunge youth (Dent et al., 12). Though not yet found as a specific group name in the three samples presented here, this specific group would be coded among the regulars general group category. Thus, while the self-identification approach is useful, the specific categories which construct a group-names item need monitoring over time. Still, in every generation certain self-identified groups are likely to be at increased risk for use of tobacco and other drugs, and label modifications could be accomplished readily (Brown & Trujillo, 1985). This approach remains a promising avenue of research for exploring patterns of adolescent drug experimentation. References Brown, B. B., & Lohr, M. J. (1987). Peer-group affiliation and adolescent selfesteem: An integration of ego-identity and symbolic-interaction theories. Journal qf Personality cind Social Psjrholoa, 52,

18 1572 SUSSMAN ET AL. Brown, B. B., & Trujillo, C. M. (1985). Adolescents perceptiot7.~ ofpeer gt-oiip stereotypes. Unpublished manuscript, University of Wisconsin-Madison. Burton, D., Sussman, S., Hansen, W., Johnson, C. A., & Flay, B. R. ( 1989) lmage attributions and smoking: Intentions among seventh grade students. Joirrnal of Applied Social Psychologv, Cohen, J. (1979). High school subcultures and the adult world. Adolescerm, 14, Cohen, S.. Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journul of Health rind Social Behavior, 24, Collins. L. M., Sussman, S.. Rauch, J. M., Dent. C. W., Johnson, C. A., Ilansen. W. B., & Flay, B. R. ( 1987). Psychosocial predictors of young adolescent cigarette smoking: A sixteen-month, three-wave longitudinal study. Joiirnul qf Applied Social Psychology, 11, DeMoor. C., Johnston, D. A,, Werden, D. L., Elder, J. P., Senn, K., & Whitehorse, L. (14). Patterns and correlates of smoking and smokeless tobacco use among continuation high school students. Addictirv Behaviors, 19, Dent, C. W., Galaif, J., Sussman. S., Stacy. A., Burton, D.. & Flay, B. R. ( 12). The diagnostic properties of adolescent music preferences. Ariierican./ournu1 of Piihlic Health, Donovan, J. E., & Jessor, R. (I 985). Structure of problem behavior in adolescence and young adulthood. Joiirtial of Consiilting and Cliniccrl Psjriiologv, 53, Eckert, P. (1983). Beyond the statistics of adolescent smoking. American./oiirnal of Public Health, I. Ennett, S. T., & Bauman. K. E. (14). The contribution of influence and selection to adolescent peer group homogeneity: The case of adolescent cigarette smoking. Journal ofpersonali(v and Social Psychology, 61, Gottlieb, B. H. (1975). The contribution of natural support systems to primary prevention among four social subgroups of adolescent males. Adolc~.vrc~t~c~e, 10, Hartup, W. W. ( 1983). Peer relations. In E. M. Hetherington (Ed.), Handbook of child psychology: Vol. 4. Socialization. personality, and social development (pp ). New York, NY: John Wiley & Sons. Iannotti, R. J., & Bush, P. J. (12). Perceived vs. actual friends use of alcohol, cigarettes, marijuana. and cocaine: Which has the most influence? Jour.tml of Youth and Adolescence, 21, Institute of Medicine. ( 1989). Research on children and adolescents with rnentul, behavioral. and developmental disorders: Mobilizing a national itiiiitrtive. Washington, DC: National Academy Press. Kipke, M. D., Montgomery, S. B., Seils-Pasco, J., & MacKenzie, R. G. (13). Substance use among youth seen at a community-based health clinic. Joiirnal of Adolescent Health. 14,

19 ADOLESCENTGROUPSANDDRUGUSE 1573 Kipke, M. D., Montgomery, S. B., Simon, T. R., Unger. J. B., & Johnson, C..I. (17). Homeless youth: Drug use patterns and HIV risk profiles according to peer group affiliation. AIDS and Behavior, 1, Larkin, R. W. ( 1979). Siihzirhan ~ mth in cziltciral crisis. New York, NY: Oxford University Press. Mosbach, P., &: Leventhal, H. (1988). Peer group identification and smoking: Implications for intervention. Joiirnal ojahtiomol Ps~diolog~: 97, Newcomb, M. D., Maddahian, E., Skager, R., & Bentler. P. M. (1987). Substance abuse and psychosocial risk factors among teenagers: Associations v, ith ses. age. ethnicity, and type of school. American.Joiirnal of' Driig nntl Al(~)hol Ahii.ve, Newconib, M. D., & McCee, L. (11). The influence of sensation seeking on gcneral deviance and specific problem behaviors from adolescence to younp adulthood. Joiirnal cfpersoridity and Socitrl Psvcliolo~, Poveda, T. G., & Crim, D. (1975). Reputation and the adolescent girl: An analysis. Adoksrctire, 10, Radloff. L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Pyrhologic~cil bfeasiire/neri/. 1, Richardson, J. L., Dwyer, K. M.. McGuigan, K.. Hansen, W. B.. Dent, C. W.. Johnson, C. A., Sussman, S. Y., Brannon, B., & Flay, B. R. (1989). Substance use among eighth-grade students who take care of themselves after school. Pediatrics Rohde, P.. Lewinsohn, P. M., & Seeley, J. R. (14). Response of depressed adolescents to cognitive-behavioral treatment: Do differences in initial severity clarify the comparison of treatments? Jorirncrl ofc'onsiil/ing trnd C'linicrrl PsJ'- cholog?i 62, Rosenberg. M. D. (1972). Society and /he adolescent.selflimage. Princeton, NJ: Princeton University Press. SASSTAT guide for personal computers (Version 6). ( 1987). [Computer software]. Cary, NC: SAS Institute. Schwartz, G., & Merten, D. (1967). The language of adolescence: An anthropological approach to the youth culture. American Joiirnal of Sociologv, 72, Shrurn, W., & Cheek, N. H. (1987). Social structure during the school years: Onset of the degrouping process. American Sociological ReLYew, 52, Simon, T. R., Stacy, A. W., Sussman, S., & Dent, C. W. (14). Sensation seeking and drug use among high risk Latino and Anglo adolescents. Perxmdifv und Individual Dlferences, 17, Stacy, A. W., Flay, B. R., Sussman, S., Brown, K. S., Santi, S., & Best, J. A. (10). Validity of alternative self-report indices of smoking among adolescents. Psychosocial Assessment: A Joirmal of Consiilting arid Clinical Psycholoa~. 2,

20 1574 SUSSMAN ET AL. Sussman. S.. Dent. C. W.. Burton. D.. Stacy. A. W.. & Flay. B. R. (15). lk~ivlopit7g.sc,iiool-hrised tohacco IISE preiwitioti titid ce.r.vtifion progrmi1.s. 'Phousand Oaks. CA: Sage. Sussman. S.. Dent. C. W., McAdams. L. A,. Stacy, A. W.. Burton. D.. R Flay, B. R. (14). Group self-identification and adolescent cigarette smoking: A one-year prospectilre study. Joiit.Iid of'ilht7otmil P.ydio/o<q?: Sussinan, S., Dent. C. W.. Raynor. A,. Stacy. A,. Charlin, V., Craig, S.. tlunsen. W. B., Burton. D.. & Flay, B. R. (1988. September). The relciwnce r~f'per groiip rnsocicition to ridoie.vcwir tohricco ~I.w. Poster presentation at thc Third Behavior Therapy World Congress. Edinburgh. UK. Sussman. S.. Dent. C. W.. Simon, T. R.. Stacy. A. W.. Galaif. E. R.. Moss. $1. A,. Craig, S., & Johnson. C. A. (1905). Efftctiveness of social influence substance abuse prevention curricula in comprehensive and continuation high schools. Driig.~ cud.tocic,/>; Sussman, S.. Dent, C. W., Stacy, A. W., Burciaga. C., Raynor, A,. Turner. G. E., Charlin. V., Craig. S.. Hansen. W. B.. Burton. D.. & Flay. B. R. (10). Peer group association and adolescent tobacco use. Joi1/.17td of Ahtiotwiul P\.ir/iolom; Sussman. S.. Stacy. A. W.. Dent. C. W.. Simon. T. R.. Galaif. E. R.. Moss, M. A,, Craig. S.. & Johnson. C. A. (15). Continuation high schools: Youth at risk for drug abuse. Joiirniil of Drug Ediirntioti Thomas. L. E. ( 1973). Clothing and counterculture: An empirical study.,/c/o/cj.y- C'C'/ICC,

21 Appendix A Names Used for Derivations of General Categories for Three Samples of Youth Sample Jocks Hotshots Regulars Others High risk Comprehen- Jocks (ath- Brains (book- Actors, mods, Others, nerds, independents Stoners, heavy metalers, sive-high- letes), cheer- worms), skaters, surf- gang-bangers, skinheads school stu- leaders social (popu- ers dents lar, preppies) Continuation- Jocks (ath- Brains, popu- Regulars Others, nerds (gooties), high-school letes) lar (socials, (average), aggies (farmers, cowboys), students preppies) actors loners (wanderers), squares, (drama, independents band, musician), progressive (techno, new order), skaters, surfers (beach kids) Stoners (burnouts, druggies), heavy metalers, rappers (rap D club), taggers, gang mern- 0 ber, skinheads, hippies, P rn deadheads, punks, home- cn boys, rebels, freaks, hus- rn tiers. partier, thug, player, z --I bud smoker, tweaker. White sumpremacist ;D D Runaway/ Athletes Students Surfers, skat- Other, none, loners, male sex Punker, skinheads, dead- z street youth ers workers, female sex work- heads, gangsters, taggers, 0 ers, transgenders, drag drug users, drug dealers, al queens, gay young women, drug buyers or runners gay young men, bisexual C cn young women, bisexual rn young men, older people, -. frontiersmen, independents ul 0 e L -0 cn 0 c 0

22 Appendix B -L 8 ai Mutrix- ofrelc,\unt Qiiestiontitrire Itetns Rcqmwe Aiic.hol:s Administered to Three Sutnples of' hrtli ('omprehensive-high- Continuation-highcn c Item school students school students Runawayistreet youth cn Demographics Percentage male White. Latino, or other Gender Percentage male 1.: t hn i c i t y White or non-white Drug use Age Years Years Cigarette smoking I = t1elvt. st1loke.s I = 1) tititcs in the lust 2 = none in the last J~CWI~ 30 c k y 3 = (1 f&! tiiws this yeur 2 = I - 10 titties Alcohol use... to 3 = tirne.r 8 = u puck 01' ttiore II dq 4 = litf1c.s 5 = tin1e.s 6 = tiriicv 7 = times 8 6 I - 70 tinies... to 1 I = YI-IOO+ times in the lust 30 du,v.s I = newr drinks I = 0 times in the lust 2 = none in the Itr.st yew 30 dq:s 3 = LI f& titiws this J W I ~ 2 = 1-11) titiies... to 3 = titnes 8 = tlritiks ulcohol tiiutiy 4 = times tit11e.s elel:l. till)? 5 = titnes... to 1 I = 01 =.?OO* tinic'c. in IUSl 30 LILI&:Y I Percentage male White. Latino, African rn -I American, or other D!- Years 0 = none in the lust 7 duls 1 = 1-10 times 2 = titties 3 = I puck to 7puck.s 4 = H pucks to I4 puchs 5 = more than I4 puck.s it? the ILISI 7 dujs 1 = never 2 =.short coticvntrated perioh only 3 = less than once u nionth 4 = 1-7 lhlys u th 5 = I-.? dn\~ N wtvk 6 = 3-4 LIu,v.s u ~t'eek 7 = almost everi\n'uy in die lust 3 tiionths

23 Marijuana use 1 =never uses 1 = 0 times in the last 0 = did not use in last 30 2 = none in the lost year 30 days davs 3 = a,kw times this.war 2 = 1-10 times I = used in Imt 30 dqvs... to 3 = times 8 = uses marijuana 4 = times many times every 5 = times day 6 = times 7 = times... to 11 = times in the last 30 days Index of number of ille- - Sum of binary coded Sum of binary coded gal drugs ever used items (0 =No, 1 = Yes): use of marijuana (weed), cocaine (crack), inhalants (rush, nitrous), stimulants (ice, speed, amphetamines), hallucinogens (LSD, acid, mushrooms), and other drugs (depressants, PCP, steroids, heroin, etc.); index ranges from 0 to 6 items (0 =No, 1 = Yes): use of marijuana, cocaine (blow) or crack (rock), inhalants, speed, hallucinogens (i.e., mushrooms, LSD, mescaline, or peyote) and other drugs (i.e., other nonmedicinal drugs. opiates, ecstasy, heroin, speedball, or PCP); index ranges from 0 to 6 (appendix continiies)

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