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This article was downloaded by: [University of Western Ontario]On: 14 November 2014, At: 08:29Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Risk and Protective Factors for Alcoholand Marijuana Use among African-American Rural and Urban AdolescentsTrenette T. Clark a , Anh B. Nguyen b & Faye Z. Belgrave ca School of Social Work, University of North Carolina , Chapel Hill,NC, USAb School of Public Health, Harvard University , Boston, MA, USAc Center for Cultural Experiences in Prevention, VirginiaCommonwealth University , Richmond, VA, USAPublished online: 29 Jun 2011.
To cite this article: Trenette T. Clark , Anh B. Nguyen & Faye Z. Belgrave (2011) Risk and ProtectiveFactors for Alcohol and Marijuana Use among African-American Rural and Urban Adolescents, Journalof Child & Adolescent Substance Abuse, 20:3, 205-220, DOI: 10.1080/1067828X.2011.581898
To link to this article: http://dx.doi.org/10.1080/1067828X.2011.581898
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Risk and Protective Factors for Alcohol andMarijuana Use among African-American
Rural and Urban Adolescents
TRENETTE T. CLARKSchool of Social Work, University of North Carolina, Chapel Hill, NC, USA
ANH B. NGUYENSchool of Public Health, Harvard University, Boston, MA, USA
FAYE Z. BELGRAVECenter for Cultural Experiences in Prevention, Virginia Commonwealth University,
Richmond, VA, USA
The purpose of this study was to examine individual, family,peer, and community risk and protective factors associatedwith past-30-days alcohol and marijuana use among African-American adolescents living in rural and urban communities.This study used data collected from 907 tenth- and twelfth-grade African-American students who completed the 2005Community Youth Survey. Peer and individual risk=protectivefactors were more influential for urban youths while familyand community risk=protective factors were more influential forrural youths. This pattern held for alcohol and marijuana use.Implications for substance use prevention programming arediscussed.
KEYWORDS rural, substance use, urban, youth
The authors wish to thank the Survey and Evaluation Research Laboratory (SERL)and the Community Research Initiative of Virginia Commonwealth University (VCU)for data collection. We also acknowledge the support of the Virginia Tobacco Settle-ment Foundation, and Drs. Rosalie Corona, Aashir Nasim, Randy Koch, and ElizabethTurf.
Address correspondence to Trenette T. Clark, PhD, LCSW, LICSW, School of Social Work,University of North Carolina, 325 Pittsboro St., CB#3550, Chapel Hill, NC 27599, USA. E-mail:[email protected]
Journal of Child & Adolescent Substance Abuse, 20:205–220, 2011Copyright # Taylor & Francis Group, LLCISSN: 1067-828X print=1547-0652 onlineDOI: 10.1080/1067828X.2011.581898
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INTRODUCTION
Alcohol, marijuana, and other drug use during adolescence is a risk factorfor drug abuse and dependence in adulthood. Although a fair amount hasbeen written on the etiology of drug use among urban African-Americanadolescents, less is known about their rural counterparts. In this study,predictors of alcohol and marijuana use among African-American urbanand rural adolescents were examined. The purpose of this study was toexamine whether different risk and protective factors for alcohol and mari-juana use emerged for African-American adolescents who lived in urbanor rural communities.
African-American adolescents tend to use alcohol at a lower rate thanmost of their ethnic group counterparts and marijuana at comparablerates. For example, in 2006, among individuals ages 12 to 20, current alco-hol use was 32.3% among whites, 31.2% among American Indians=AlaskaNatives, 25.3% among Hispanics, 19.7% among Asians, and 18.6% amongAfrican-Americans (Substance Abuse and Mental Health Services Adminis-tration [SAMHSA], 2007). Marijuana is the most commonly used illicit drugamong youths ages 12 to 17 (SAMHSA, 2007). As young as age 14, mari-juana is the most commonly used illicit drug (SAMHSA, 2006). In fact,nearly 45% of adolescents have tried marijuana at least once before theyfinish high school (Centers for Disease Control and Prevention [CDC],2006). Among those 12 to 17 years of age, 6.4% of African-Americans incomparison to 9.0% of whites reported 30-day use of marijuana. Amongthose 18 to 25, 30-day marijuana use was at 19.6% and 16.2% forAfrican-Americans and whites, respectively (SAMHSA, 2005). Marijuana isthe primary drug of abuse among African-American youths admitted forsubstance abuse treatment, presenting 66% of admissions compared to42% for whites (SAMHSA, 2002).
Adolescent drug use typically co-occurs with other problem behaviorsand is associated with adverse individual and system-level outcomes. Theseriousness of adolescent drug use, particularly among African-Americans,who tend to be overrepresented as institutionalized adult drug abusersdespite lower drug use during youth, cannot be overstated. Drug useamong African-American adolescents is of particular concern becausethese youths tend to experience the consequences of drug use at a dispro-portionately higher rate than other ethnic groups. For example, for everyounce of alcohol consumed by African-American adolescents, they experi-ence substantially more academic and social problems compared to whiteadolescents (Barnes & Welte, 1986). African-American (compared towhite) adolescents are also at greater risk of detention and incarceration,school failure, and employment problems resulting from drug use(Wallace & Muroff, 2002).
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RISK AND PROTECTIVE FACTORS
We use Hawkins, Catalano, andMiller’s (1992) risk and protective factor frame-work to examine multiple domains affecting drug use. Risk factors are definedas an event, condition, or experience that increases the probability that a prob-lem will be created, sustained, or intensified (Fraser & Terzian, 2005). Protec-tive factors can directly influence outcomes or these factors can influenceoutcomes indirectly by buffering against risk factors (Jessor, Turbin, & Costa,1998). Ultimately, protective factors may increase adolescent resiliency andpresumably decrease adolescent drug use. This study focuses on protectiveand risk factors within the individual, family, peer, and community domains.
Individual Factors
Individual factors include demographics, biological, psychological, andsocial issues that are related to drug use. Some individual protective factorsinclude social and problem-solving skills, positive attitude, positive tempera-ment, high intelligence or academic achievement, and low childhood stress(Jenson & Fraser, 2005). By contrast, individual risk factors for adolescentdrug use may include genetics (e.g., family history of alcoholism) (Tsuang,Bar, Harley, & Lyons, 2000), sensation-seeking orientation, poor impulsecontrol, and persistent problem or risky behavior (Robbins & Bryan, 2004;Yanovitzky, 2005).
Family Factors
The family may be the most powerful protective factor among African-American adolescents. Wallace and Muroff (2002) suggested that the familymay be a stronger predictor of drug use than peer influence for African-American youths compared to white youths. Family risk factors include familyconflict, poor family management practices, dysfunctional family communi-cation patterns, parent and sibling drug use, and poor parent-child bonding(Jenson & Fraser, 2005). Protective factors include being a firstborn child,raised in a small family, low parental conflict, and high family cohesion (Bray,Adams, Greg, & Baer, 2001; Grover, 1998), caring relationships with siblingsand extended family, and attachment to parents (Boyd, Ashcraft, & Belgrave,2006). In addition, parental and sibling disapproval of adolescent drug usemay also decrease drug use initiation (Biederman, Faraone, Monuteaux, &Feighner, 2000; Hill, Shen, Lowers, & Locke, 2000; Wright & Fitzpatrick, 2004).
Peer Factors
Peer influence is a strongpredictor of adolescent druguse across all ethnic groups(Bahr, Hoffmann, & Yang, 2005; Reinherz, Giaconia, Carmola Hauf, Wasserman,
Factors Across Rural and Urban Contexts 207
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& Paradis, 2000) and typically accounts for the most variance that explains druguse (Kandel, 1996). Associating with peers who engage in risky behaviorsincreases the likelihood that an adolescent will engage in risky behaviors, suchas drug use (Beauvais & Oetting, 2002; Gil, Vega, & Turner, 2002). In a studyof 291 urban African-American adolescents, Clark, Belgrave, and Nasim (2008)found that peer drug use predicted past-30-days alcohol and marijuana use.
Community Factors
Relative to the individual, family, and peer domains, the influence of theneighborhood context on adolescent drug use has been understudied. Still,the neighborhood context is an important domain to study for minorityyouths (Gruenewald, Millar, Ponicki, & Brinkley, 2000; Treno, Alaniz, &Gruenewald, 2000). Wallace and Muroff (2002) found that contextual riskfactors, such as economic deprivation and neighborhood disorganization,were more important predictors of drug use than individual and interperso-nal risk factors among African-American adolescents. Neighborhood risk fac-tors, such as neighborhood disorganization, low neighborhood attachment,high rates of residential mobility, high rates of crime, and high populationdensity contribute to adolescent drug use (Jenson & Fraser, 2005). Con-versely, protective factors such as neighborhood cohesion, intergenerationalnetworks, and community resources are associated with lower adolescentdrug use (Plybon, Edwards, Butler, Belgrave, & Allison, 2003).
Risk and Protective Factors among Urban and Rural Youths
Less research exists on risk and protective factors among rural youths, parti-cularly rural African-American youths. The research that exists suggests thatpeer factors are a stronger predictor among urban youths and family factorsare a stronger predictor among rural youths. Wilson and Donnermeyer(2006) found that peer influence was a stronger influence on drug use amongurban-located youths than rural-located youths but did not find any differ-ences for family factors. Conversely, in a study of eleventh-grade studentsthat examined only family factors, Scheer, Borden, and Donnermeyer(2000) found similar significant findings across rural, urban, and suburbansettings. However, they also found that rural youths who reported that theirparents discussed the consequences of smoking and drinking behaviors wereless involved in drug use, unlike urban youths.
PROPOSED STUDY
The prevalence, risk and protective factors, and consequences of drug useare contingent on context and culture and the patterns of contextual factors
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likely differ according to community type, geographic locale, and popu-lation. Social disorganization theories posit that urban areas may displayweaker social control by families and neighbors (Shaw & McKay, 1942).Consequently, urban youths may be more susceptible to peer influencegiven the low levels of control. Because of geographical isolation, rural com-munities may be more family-oriented (Coleman, Ganong, Clark, & Madsen,1989). Hence, we hypothesized that individual, peer, and community factorswould be stronger predictors of drug use among urban youths and that fam-ily factors would be a stronger predictor among rural youths. The purpose ofthe present study was to examine individual, family, peer, and communityfactors associated with past-30-days alcohol and marijuana use amongAfrican-American adolescents living in rural and urban communities.
METHOD
Study Design and Participants
This study used data collected from 907 tenth- and twelfth-grade African-American students who completed the 2005 Community Youth Survey(CYS). The CYS was administered to students attending Virginia PublicSchools by the Survey and Evaluation Research Laboratory (SERL) at VirginiaCommonwealth University and combined with an additional assessment byFairfax County Public Schools. Public schools in the State of Virginia wererandomly selected using a two-stage (school- and class-level) geographicallystratified sample design.
Four hundred eighty-four participants were females (53%), 413 males(46%), and 10 provided no response to this question. Of the sample, 480(53%) were in tenth grade and 427 (47%) were in twelfth grade. In termsof community type, 855 (94%) students were from urban regions while 48(5%) were from rural settings. Additional information regarding the studydesign and sampling plan can be found elsewhere (see Moore, Honnold,Derrig, Glaze, & Ellis, 2006).
Measures
The CYS was based on the Communities That Care (CTC) Youth Survey ofrisk and protective factors for alcohol and other drug use (Arthur, Hawkins,Pollard, Catalano, & Baglioni, 2002). Students are asked to respond to demo-graphic items and measures of 24 risk and 10 protective factors (Moore et al.,2006). Due to the small sample of rural students, the data were weighted toreflect full population numbers. The weighting procedure adjusted theweight for specific data points (in this case, urban and rural samples), andaccounts for the probability of selection within our sample population.
Factors Across Rural and Urban Contexts 209
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Although risk and protective factor scales had been created for the lar-ger sample, we created subscales for the sample by running a factor analy-sis. To maximize the amount of items that would load onto one singlefactor, the factor analyses did not use varimax or other rotations. Three cri-teria were used to determine inclusion of a factor: (1) the item must have aloading of .50 with its primary factor; (2) a minimum of five items mustload on the factor; and (3) items that loaded highly on two or more factorswere dropped from the analyses. The peer risk subscale had 8 items thatloaded onto the factor (a¼ .82). A sample item is ‘‘In the past year, howmany of your four best friends have been suspended from school?’’ The indi-vidual risk subscale had 7 items (a¼ .74), (e.g., ‘‘How old were you whenyou first had a sip of beer, wine, or hard liquor?’’). The individual protec-tive subscale had 7 items (a¼ .87) (e.g., ‘‘How wrong do you think it wouldbe for someone your age to skip school?’’). The family risk sub-scale had 5items (a¼ .72) (e.g., ‘‘Have any of your brothers or sisters ever smokedmarijuana?). The family protective subscale had 16 items (a¼ .89) (e.g.,‘‘My parents want me to call if I am going to be late getting home.’’).The community risk subscale had 18 items (a¼ .86) (e.g., ‘‘If you wantedto get some illegal drugs, how easy would it be for you to get some?’’).The community protective subscale had 7 items (a¼ .80) (e.g., ‘‘If I hadto move I would miss the neighborhood I now live in.’’). Refer to Figure 1for factors and respective items.
Past 30 day alcohol use was assessed by the question ‘‘On how manyoccasions (if any) have you had beer, wine, or hard liquor during the past30 days?’’ Past-30-day marijuana use was assessed by the question ‘‘Onhow many occasions (if any) have you used marijuana during the past 30days?’’ Participants could provide a range of 7 responses that include 0 occa-sions, 1–2 occasions, 3–5 occasions, 6–9 occasions, 10–19 occasions, 20–39occasions, and 40 or more occasions.
RESULTS
Bivariate associations were computed among past 30 day alcohol use, past30 day marijuana use, peer risk factors, individual risk factors, individualprotective factors, family risk factors, family protective factors, communityrisk factors, and community protective factors. The results of the correla-tional analyses are presented in Table 1. Peer, individual, and communityrisk factors were associated with higher levels of alcohol and marijuanause, providing moderate effect sizes. Individual and family protective fac-tors were associated with lower levels of alcohol and marijuana use, pro-viding moderate effect sizes. Since none of the associations among riskand protective factors were above .70, there were no concerns regardingmulticollinearity.
210 T. T. Clark et al.
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FIGURE 1 Risk and protective factors.
Factors Across Rural and Urban Contexts 211
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TABLE1
Associationsam
ongAlcoholUse,Mariju
anaUse,an
dRiskan
dProtectiveFactors
12
34
56
78
9
1.AlcoholUse
1.44��
.25
.42��
�.40�
�.06��
�.12��
�.22��
.01
2.Mariju
anaUse
1.30��
.36��
�.36��
�.09��
�.23��
.31��
�.03��
3.PeerRisk
1.47��
�.37��
�.02��
�.25��
.40��
�.10��
4.Individual
Risk
1�.53��
�.20��
�.37��
.43��
�.08��
5.Individual
Protective
1.14��
.31��
�.40��
.12��
6.Fam
ilyRisk
1.06��
�.23��
.14��
7.Fam
ilyProtective
1�.13��
.22��
8.CommunityRisk
1�.01��
9.CommunityProtective
1
��denotessignifican
ceat
p<.01level.
� denotessignifican
ceat
p<.05.
212
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Past-30-days Alcohol Use
A hierarchical multiple regression analysis was conducted to predict how fre-quently a student drank alcohol in the past 30 days. Gender (dummy coded;0¼male, 1¼ female) was controlled for and entered in the first step of theregression. Community type (dummy coded; 0¼ rural; 1¼urban), peer riskfactors, individual risk factors, individual protective factors, family risk fac-tors, family protective factors, community risk factors, and community pro-tective factors were entered into the second step. The results indicated thatthe predictors accounted for a significant amount of variance in alcoholuse, R2¼ .22, F (9, 26099)¼ 807.82, p� .01. Community type, peer risk fac-tors, individual risk and protective factors, family risk and protective factors,and community risk and protective factors significantly predicted drinking inthe first step of the analysis (p� .01).
Following guidelines by Jaccard, Wan, and Turrisi (1990), we tested forinteraction effects between community type and risk and protective factors tosee if different patterns emerged for urban versus rural youths. We createdinteraction effects by multiplying community type (dummy coded; 0¼ rural,rural, 1¼urban) with each respective domain’s risk and protective factors.These interaction effects were entered in the third step of the regressionequation, and the R2 change was significant, R2¼ .02, F (7, 26092)¼ 77.24,p� .01. Specifically, peer risk, individual protective, family risk, family pro-tective, community risk, and community protective factors interacted withcommunity type when predicting alcohol use. Refer to Table 2 for other pre-dictors and their respective contributions to the equation.
TABLE 2 Predictor Coefficients for 30-Day Alcohol Use
Predictors b Beta t Sig.
Gender .003 .002 .27 .79Regional Setting �.01 �.003 �.50 .62Peer Risk �.02 �.10 �3.82 .00Individual Risk .02 .23 9.12 .00Individual Protective �.04 �.21 �9.6 .00Family Risk �.05 �.07 �3.42 .00Family Protective �.01 �.07 �3.67 .00Community Risk .03 .24 9.2 .00Community Protective �.04 �.15 �8.9 .00Region� Peer Risk .04 .18 6.66 .00Region� Individual Risk .004 .04 1.6 .109Region� Individual Protective �.01 �.07 �3.12 .002Region� Family Risk .04 .06 2.86 .004Region� Family Protective .02 .16 8.3 .00Region�Community Risk �.03 �.29 �11.09 .00Region�Community Protective .06 .24 14.51 .00
Factors Across Rural and Urban Contexts 213
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After assessing the significant moderating effect of community type, weconducted separate regression analyses for urban and rural populations toexamine differences in the standardized coefficients of the predictors. Thepredictors were initially centered, so the comparison of beta weights wasappropriate when assessing strength of predictors. Overall, the results sug-gest that individual and peer risk and protective factors are more influentialto urban youths’ drinking behavior while family and community risk andprotective factors are more influential to rural youths’ drinking behavior.Specifically, peer risk factors more strongly influenced urban youths(b¼ .09) as compared to rural youths (b¼ .00), which led to increased drink-ing for urban youths. Individual protective factors more strongly influencedurban youths (b¼�.30) as compared to rural youths (b¼�.12), which led todecreased drinking behaviors. Family protective factors more strongly influ-enced rural youths (b¼�.04) as compared to urban youths (b¼ .00), whichled to decreased drinking behaviors for rural youths. Community risk factorsmore strongly influenced rural youths (b¼ .29) as compared to urban youths(b¼�.05), which led to increased drinking behavior among rural youths.Community protective factors more strongly influenced rural youths (b¼�.34) as compared to urban youths (b¼ .10), which led to decreased drink-ing behavior for rural youths.
Past-30-days Marijuana Use
The statistical analyses used for past 30 day marijuana use were the same asthose used for past 30 day alcohol use. The results indicated that the predic-tors accounted for a significant amount of variance in marijuana use, R2¼ .28,F (9, 25801)¼ 1121.41, p� .01. Community type, peer risk factors, individualrisk and protective factors, family risk and protective factors, and communityrisk and protective factors significantly predicted marijuana use in the firststep of the analysis (p� .01).
Again, interaction effects between community type and the previouslymentioned risk and protective factors were computed to see if different pat-terns emerged for urban versus rural youths. These interaction effects wereentered in the third step of the regression equation, and the R2 changewas significant, R2¼ .01, F (7, 25794)¼ 36.99, p� .01. Specifically, peer risk,individual protective, family risk, family protective, and community protec-tive factors interacted with community type when predicting marijuana use(see Table 3).
Again to examine predictors of marijuana use for each community type,separate models were computed for urban and rural populations. Overall,the results suggest that individual and peer risk and protective factors aremore influential to urban youth marijuana use while family and communityrisk and protective factors are more influential to rural youth smoking beha-vior. Specifically, we found that peer risk factors more strongly influenced
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urban youths (b¼ .13) as compared to rural youths (b¼�.01), which led toincreased marijuana use among urban youths. Individual protective factorsmore strongly influenced urban youths (b¼�.16) as compared to ruralyouths (b¼�.05), and led to decreased smoking behavior for urban andrural youths. Family risk factors more strongly influenced rural youths(b¼ .28) as compared to urban youths (b¼�.01), which led to increasedsmoking behavior for rural youths. Family protective factors more stronglyinfluenced rural youths (b¼�.27) as compared to urban youths (b¼�.07), which led to decreased smoking behaviors for urban and rural youths.Community protective factors more strongly influenced rural youths (b¼�.03) as compared to urban youths (b¼ .00), which led to decreased mari-juana use behavior for rural youths.
DISCUSSION
The objective of the current study was to examine whether different riskand protective factors for alcohol and marijuana use emerged forAfrican-American youths living in urban or rural communities. The resultsof the study generally supported our hypotheses (with one exception). Ingeneral, peer and individual risk=protective factors were more influentialfor urban youths while family and community risk=protective factors weremore influential for rural youths. This pattern held for both alcohol and mari-juana use.
Regarding alcohol use, peer risk more strongly predicted increased past30 day alcohol consumption for urban than rural youths, while the individual
TABLE 3 Predictor Coefficients for 30-Day Marijuana Use
Predictors b Beta t Sig.
Gender �.07 �.03 �4.88 .00Regional Setting .28 .06 10.40 .00Peer Risk .00 .00 .00 .99Individual Risk .04 .30 11.89 .00Individual Protective .00 .01 .22 .83Family Risk .11 .14 6.70 .00Family Protective �.02 �.14 �7.34 .00Community Risk .01 .08 3.27 .00Community Protective �.02 �.06 �3.89 .00Region� Peer Risk .04 .13 5.02 .00Region� Individual Risk �.004 �.03 �1.34 .18Region� Individual Protective �.04 �.16 �7.48 .00Region� Family Risk �.12 �.15 �7.12 .00Region� Family Protective .01 .06 3.18 .00Region�Community Risk .003 .02 .78 .44Region�Community Protective .04 .12 7.48 .00
Factors Across Rural and Urban Contexts 215
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protective factor more strongly predicted decreased alcohol consumption forurban youths than rural youths. Family protective factors and community pro-tective factors more strongly predicted decreased alcohol consumption forrural than urban youths. In addition, community risk factors were more influ-ential for the rural youth population and led to increased drinking behavior.
The same patterns were found for past 30 day marijuana use. Resultssuggest that individual and peer risk=protective factors are more influentialfor marijuana use for urban than rural youths. Peer risk more strongly pre-dicted increased past 30 day marijuana consumption for urban than ruralyouths. The individual protective factor was a better predictor of decreasedmarijuana use for urban rather than rural youths. Family protective factorsand community protective factors were stronger predictors of decreasedmarijuana use for rural than urban youths. In addition, family risks weremore influential for the rural than urban youths and led to increased mari-juana smoking.
Peer and individual factors may be more influential for drug use amongurban African-American youths and family factors for rural African-Americanyouths for several reasons. Peers are central in day-to-day activities for urbanyouths. Adolescents in urban communities are likely to be in interactions andcontact with peers in many settings and contexts (e.g., outdoor space, rec-reational centers, schools). In addition, for rural youths, families may bemore influential because of the proximal influence of the family. In ruralcommunities, where neighborhoods are sparser, youths tend to spend moretime interacting with their parents and other family members. Interactionswith peers may be limited to schools and=or religious institutions. The litera-ture supports the notion that rural families tend to be more involved withtheir children and place more emphasis on conventional behavior (Scheeret al., 2000). Given the proximal influences of peers in urban communitiesand families in rural communities, peer factors are more influential in urbancommunities while family factors are more important in rural communities.
Individual risk and protective factors such as school performance, delin-quency, and early drug use may be more influential in urban environmentsthat tend to be less predictable and perhaps more chaotic. Urban youths aremore likely than rural youths to observe deviant behavior, and thus, theymay be more likely to retain knowledge and reproduce such behavior. Inaddition, they may be more likely to perceive such behavior as normativeand possibly conventional compared to their rural counterparts.
We had expected community factors to be more influential to alcoholand marijuana use among urban compared to rural youths (Nasim, Belgrave,Corona, & Townsend, 2008), and this was not the case. Both community riskand protective factors were influential factors for alcohol use, and com-munity protective factors were influential for marijuana use among ruralyouths. Community protective factors may be more influential in rural com-munities for a couple of reasons. First, as noted in the Introduction, rural
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communities have less residential mobility, and as a result, neighbors tend tointeract more frequently. Furthermore, because rural communities are per-ceived as relatively safe, parents tend to encourage their children to interactwith their adult and peer neighbors (Coleman et al., 1989). As a result, strongcommunity ties and adult intergenerational networks may be more prevalentin rural communities than urban communities. Consequently, adolescents aremore likely to be monitored, which limits the opportunity for adolescentdeviant behavior. Second, rural families tend to be more religious (Weinert& Long, 1990). As a result, many youths may participate in organizedreligious activities through their church, synagogue, or other religious insti-tution that fosters social control by providing supervised activities, which lim-its unsupervised time and opportunities to engage in risky behavior.
We found support for our hypotheses, but this study does have limita-tions. Although the sample was weighted, the number of participants fromrural communities was relatively small. Given the small rural sample, thisstudy’s results should be interpreted with caution. Also, a large percentageof participants were recruited from one geographical area of the state. Thesefactors may limit the generalizability of the study, and future studies withother urban and rural samples are necessary.
Despite limitations, this study contributes to our understanding of alco-hol and marijuana use among African-American youths. Our findings areconsistent with previous studies that indicate that contextual factors suchas community and neighborhood type are related to adolescent drug use.However, few studies have examined neighborhood factors as predictorsof African-American adolescent drug use in both urban and rural communi-ties, as this study did. Also, few studies have examined whether risk and pro-tective factors differ according to community type, and even fewer haveexamined risk and protective factors across rural and urban settings inrelation to drug use among African-American adolescents.
This exploratory research introduces several areas to address in futureresearch. Given our findings, a logical extension would be to examinehow risk and protective factors differ according to community type, toinclude suburban communities; type of drug(s) used, to include additionallicit and illicit drugs; race=ethnicity; gender; and age. Another area ofresearch inquiry may be to examine the mechanism or how risk and protec-tive factors vary according to geographic and spatial location.
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