The Influence of Multiple Ecological Assets on Substance Use ...

School Psychology Review, 2011, Volume 40, No. 3, pp. 386 – 404

The Influence of Multiple Ecological Assets on Substance Use Patterns of Diverse Adolescents Zhanna Shekhtmeyster and Jill Sharkey University of California, Santa Barbara Sukkyung You Hankuk University of Foreign Studies Abstract. Substance use among adolescents is a complex problem and an ongoing concern. Literature has identified a critical need to understand the role that schools can play in preventing adolescent substance use. Adopting an ecological perspective, we used multigroup logistic regression to determine the influence of school support and school meaningful participation on substance use among male and female adolescents after controlling for ethnicity, home, peer, community, and internal factors. A random sample of students in 9th and 11th grade was selected for each substance use group (abstainer, user, and polyuser; 7,642 participants) from the California Healthy Kids Survey. Results suggest that for males, perceived school support was related to significantly lower odds of belonging to the polyuser group. For females, family and peer factors largely accounted for influences on substance use. Findings bring focus to alterable factors that school professionals can influence that are associated with adolescent substance use.

Substance use among high school students is an ongoing concern. Adolescent substance use increases the risk for significant mental health problems such as conduct disorder, depression, anxiety, and substance dependence (Mayberry, Espelage, & Koenig, 2009). According to the 2008 Monitoring the Future Survey, 14.6% of 8th-, 10th-, and 12thgraders in the United States reported use of illicit drugs in the past 30 days. Alcohol (28.1%) and marijuana (12.5%) were the most commonly reported used substances, but use of other substances including inhalants

(2.6%), hallucinogens (1.4%), cocaine (1.3%), heroin (0.4%), methamphetamine (0.7%), and amphetamines (2.6%) was reported (Johnston, O’Malley, Bachman, & Schulenberg, 2009). In addition, according to the information from the National Survey on Drug Use and Health from 2006 and 2007, illicit drug use combined with alcohol use was reported by 5.6% of past 30-day alcohol users 12 years and older (Substance Abuse and Mental Health Services Administration, 2008). Despite the frequency with which teens use alcohol and drugs and numerous associ-

This work was partially supported by Hankuk University of Foreign Studies Research Fund granted to Sukkyung You. Correspondence regarding this article should be addressed to Sukkyung You, Hankuk University of Foreign Studies, College of Education, 270, Imun-Dong, Dongdaemun-gu, Seoul, Korea, 130-791; e-mail: [email protected] Copyright 2011 by the National Association of School Psychologists, ISSN 0279-6015 386

Adolescent Substance Use

ated physical and mental health problems, research to understand adolescent substance use patterns has been stymied by limited consideration of polysubstance use and significant changing group differences. In addition, relatively little attention has been given to schools as systems with the opportunity to significantly affect youth development. The goal of this study is to address these gaps and to expand knowledge on adolescent substance use. In this article, we will introduce resiliency theory within an ecological framework and describe what is known about substance use among adolescents. We will bring focus to school assets as alterable factors professionals can influence. The analysis will reveal if comprehensive sets of ecological factors are associated with substance abstention versus substance use or polysubstance use. Theoretical Orientation The concept of resilience and the ecological nature of influences on development inform interrelations between assets and substance use. A transactional-ecological framework acknowledges that development occurs within an environmental context (Bronfenbrenner, 1992). Examining individual factors and the interaction between different contexts such as school, family, peers, and community, can provide a comprehensive understanding of the factors that contribute to adolescent substance use (Mayberry et al., 2009; Suldo, Mihalas, Powell, & French, 2008). Resiliency theory extends the ecological perspective with the premise that youth who experience high levels of personal and environmental assets can develop resilience traits, connections to school, and motivation to learn that can lead to positive academic, social, and health outcomes (Hanson & Kim, 2007). Theoretically, external assets (also known as strengths or promotive factors— e.g., support from teacher, involvement in school-based activities) help meet youths’ basic developmental needs, which then promote the enhancement of internal assets (e.g., ability to problem solve and empathize with others). These internal assets are proposed to

contribute to healthy outcomes among youth (Furlong, Ritchey, & O’Brennan, 2009). Specifically, assets universally discourage risky behavior such as substance use and promote positive outcomes such as academic success (Hanson & Kim, 2007). In resilience theory, assets, which universally promote healthy outcomes, are distinguished from protective factors, which help students avoid negative outcomes when experiencing adversity (Rutter & Sroufe, 2000). Many studies have examined the association between individual factors and alcohol, marijuana, and cigarette use. However, few have employed an ecological approach (Connell, Gilreath, Aklin, & Brex, 2010; Mayberry et al., 2009; Prado et al., 2009, Suldo et al., 2008) and none have specifically examined the influence of school factors above and beyond the contribution of individual, family, peer, and community assets in understanding behavior of youth in abstinence, substance use, and polysubstance use groups. Preliminary evidence suggests that school factors can affect positive outcomes such as student engagement above and beyond the influence of internal resiliency (Sharkey, You, & Schnoebelen, 2008). Thus, our study provides a better understanding of how school-related factors, after controlling for other established predictors of adolescent substance use, can inform intervention efforts. Gender and Ethnic Considerations Research regarding adolescent substance use is complicated by significant group differences necessitating large data sets to achieve adequate power to test demographic variables as potential moderators. The rate of past month illicit substance use for youth ages 12 years and older has been reported as 4.2% among Asian Americans and Pacific Islanders, 9.5% for African Americans, 6.6% for Latino/a or Hispanic Americans, and 8.2% for European Americans ( Substance Abuse and Mental Health Services Administration, 2008). A different survey found that African American students reported lowest rates of substance use, whereas European American 387

School Psychology Review, 2011, Volume 40, No. 3

students reported highest rates of use and Latino/a American students were generally in the middle (Johnston et al., 2009). Focusing on gender, historically males have reported somewhat higher rates of illicit drug use and heavier drinking than females, with gender differences becoming more apparent as students got older. However, substance use among adolescent females has been increasing and rates of male and female use have been converging (Kumpfer, Smith, & Summerhay, 2008; Wallace et al., 2003). The current model will include gender and ethnic background variables to address potential emerging group differences in substance use patterns. School Assets School assets are of primary interest to this study, as our goal is to understand the influence of school assets above and beyond other critical factors in youths’ lives. Prior research suggests that a variety of school assets are related to substance use. In particular, school factors that repeatedly emerge in the literature as assets (i.e., factors that promote positive outcomes and reduce negative outcomes) are caring relationships and meaningful participation. Caring Relationships Research finds that perception of caring relationships at school is related to substance use. Extensive research has documented associations between perceived school social support (teachers, classmates, close friends) and students’ emotional and behavioral functioning (Mihalas, Morse, Allsopp, & Mchatton, 2009). More specifically, positive perceptions of school, perceptions of care in school, and emotional closeness between adolescents and their teachers have all been related to lower levels of problematic behavior and have been shown to offset substance use risks (Crosnoe, Erickson, & Dornbusch, 2002; Mayberry et al., 2009; Mihalas et al., 2009; Sobeck, Abbey, Agius, Clinton, & Harrison, 2000). The benefits of caring relationships in school appear to be consistent across gender and ethnic groups. For example, self-reports from adolescents of 388

various ethnicities revealed that females reported higher teacher bonding and stronger academic achievement than males, yet both factors were significantly related to substance use for males and females (Crosnoe et al., 2002). Several studies support the link between caring relationships in school and decreased substance use. Results of longitudinal study demonstrated that youth who reported low school connectedness and interpersonal conflict in early secondary school were more likely to use substances two years later (Bond et al., 2007). In another study, adolescents who reported higher teacher support and regard for student perspectives were more likely to report that their schools have respectful climates and healthy norms of drug use, which was associated with lower reported levels of cigarettes, alcohol, and marijuana use (LaRusso, Romer, & Selman, 2008). School bonding appears to have an effect that other school factors do not. For example, school connectedness (i.e., feelings that teachers treat students fairly, being close to people at the school, and feeling like a part of the school) was related to less frequent cigarette, alcohol, and marijuana use for students in Grades 7–12; whereas student prejudice, attendance, dropout rates, school type, classroom size, college plans, and parent–teacher organization were not (Resnick et al., 1997). Increased substance use may occur when caring relationships are violated or not developed (Sobeck et al., 2000). Students who reported troubled relationships with school personnel were more likely to report substance use (Suldo et al., 2008). Moreover, students who were more socially connected, but not connected to school, were more likely to become regular smokers and to use marijuana (Bond et al., 2007). In fact, connection to school appears to be one of the strongest factors associated with friendship associations, which in turn affects substance use behaviors (Crosnoe et al., 2002). This relation is supported in an investigation of 461 middle school students, for whom social support from teachers and authoritative parenting influenced choices to use substances indirectly through


links with decisions regarding peer groups (Suldo et al., 2008). The current study aims to understand the influence of school caring relationships on adolescent substance use beyond these factors. Meaningful Participation Involvement in school and enjoyment of school-related activities have been inversely related to substance. For example, in an investigation with 97 rural adolescents, participation in team and individual sports, extracurricular clubs, and other school organizations was related to lower frequency of alcohol and illicit drug use (De Haan & Trageton, 2001). Additional research with African American and European American youth suggests that among females only, participation in after school activities and school commitments was negatively associated with substance use (Abbey, Jacques, Hayman, & Sobeck, 2006). It appears that the association between school meaningful participation and substance use varies by different factors and activities. For example, Fauth, Roth, and Brooks-Gunn (2007) report that participation in arts or student government is negatively associated with substance use, while sports participation is positively associated with substance use over time. However, the outcome varied by neighborhood characteristics. Our study will reexamine the role of school meaningful participation on adolescent substance in a comprehensive ecological context. External Assets Empirical research links numerous external and internal assets to substance use. Although a full review is beyond the scope of this article, identifying some critical associations is important to contextualize the role of school assets within the ecological framework. Family Factors Clear communication and proactive family management (Mayberry et al., 2009), authoritative parenting style (Suldo et al.,

2008), high monitoring and low permissiveness (Moon, Jackson, & Hecht, 2000), and close, positive relationships with parents (Moon et al., 2000; Resnick et al., 1997) are all related to substance use. Research suggests that although parental influences may be stronger determinants of substance use for younger teens (7th grade), peer influences become more influential later (11th grade; Goldstein, Davis-Kean, & Eccles, 2005). In fact, research has found that for high school students, school factors are more significantly related to substance use patterns than family factors, a finding authors contributed to the powerful interaction between school caring relationships and the selection of positive versus delinquent peer groups (Crosnoe et al., 2002). Although it is clear that family factors play an important role in adolescent substance use, there is a limited knowledge of whether school factors are associated with substance use, beyond the influence of family factors. Peer Factors Prosocial peer relationships (Sobeck et al., 2000), peer environments that support nonuse (Valente et al., 2007), and peer support (Bryant, Schulenberg, O’Malley, Bachman, & Johnston, 2003; Mayberry et al., 2009) are associated with decreased substance use. Conversely, association with peers who display deviant behaviors (Bryant et al., 2003; Mayberry et al., 2009) and substance use (Connell et al., 2010; Prado et al., 2009) are all separately related to increased substance use. These results support the idea that substance experimentation may be perceived as normative among certain adolescent peer groups. Peer use of alcohol and tobacco, beliefs about the prevalence of use (Sobeck et al., 2000), and peer susceptibility (Abbey et al., 2006) have demonstrated strong effects separating substance users from abstainers. Overall, the literature suggests that school caring relationships connect students to school, promote prosocial peer relationships, and thereby prevent substance use involvement. What is unclear is if school caring relationships have an association with high school youth substance 389


use behaviors beyond the promotion of positive peer groups. Community Factors Community factors such as a positive sense of community and perceived neighborhood safety (Moon et al., 2000) are associated with decreased substance use. As an asset, a positive sense of community combined with a positive school climate (perceptions of getting a good education and being respected and cared for by adults in the school) have been found to moderate the relation between peer and parental influence on adolescent substance use (Mayberry et al., 2009). That is, students who felt connected to their communities and school were less likely to participate in substance use even if they had negative peer behavior and/or lacked parent support. Although the community context plays an important role in adolescent substance use, our study will examine the association of school factors and youth substance use while controlling for the community context. Internal Assets Different internal assets have also been linked to substance use. Positive behavioral coping including self-efficacy and problem solving have been inversely associated with substance use (Brady, Tschann, Pasch, Flores, & Ozer, 2009). For example, among 247 Mexican American and European American adolescents, those who engaged in higher levels of behavioral coping (i.e., adopting an active approach to information gathering, decision making, and problem solving), engaged in less alcohol and tobacco use (Brady et al., 2009). To advance knowledge of how internal assets relate to substance use, this study will examine relations between various internal assets, school assets, and substance use patterns. Measuring Substance Use Patterns Despite growing awareness of and research on adolescent substance use, there is limited consensus regarding what specific categorization is superior to others—that is, var390

ious studies measure patterns of use in unique ways. For example, some studies separate substance use by type of substance and frequency of use (Bryant et al., 2003; Resnick et al., 1997) whereas others focus on patterns of use. For example, Connell et al., (2010) performed a latent class analysis to form four patterns (nonuse, alcohol experimenters, occasional polysubstance users, and frequent polysubstance users). Yet other research has employed the gateway hypothesis, which suggests a progression of patterns: nonuse, to alcohol use, followed by other drug use (Connell et al., 2010), although accumulating research has challenged such patterns (e.g., Wu, Temple, Shokar, Nguyen-Oghalai, & Grady, 2010). Without consensus among researchers regarding how best to define and measure adolescent substance use, studies are difficult to compare and may remain inconclusive. Thus, additional research to uncover meaningful substance use patterns is warranted. In addition, research on the simultaneous use of substances among adolescents is particularly scarce (Pape, Rossow, & Storvoll, 2009). Despite lack of clarity on specific substance use patterns, evidence clearly indicates that polysubstance use (i.e., simultaneous use of two or more substances) is prevalent because of factors such as drug availability, a presubstance cultural context, and normalization of drug use (Ives & Ghelani, 2006). Polysubstance use can be particularly dangerous because it may produce additive or synergistic effects and can increase health and safety risks (Pape et al., 2009). Unfortunately, polysubstance use is a poorly defined concept and is not well studied (Ives & Ghelani, 2006; Pape et al., 2009). Initial studies of polysubstance use have demonstrated that the use of alcohol in combination with other drugs is associated with more severe psychological and social consequences than alcohol abuse or dependence alone (Hedden et al., 2010; Kandel, Huang, & Davies, 2001). For example, the odds of mental health comorbidities such as anxiety and depression are double for individuals who report dependency on both alcohol and illicit drugs compared to individuals with single dependency (Kandel et al., 2001).


Therefore, the need to understand polysubstance use among adolescents is critical to our study. Research Question and Hypothesis The purpose of this study is to explain the interrelations between assets and adolescent substance use through an ecological approach. There is a growing interest for understanding the role schools can play to prevent adolescent antisocial behavior, including substance use (Cleveland, Feinberg, Bontempo, & Greenberg, 2008). Thus, our primary research question is to what extent school support and school meaningful participation have an association with substance use beyond their association with family, peer, community, and internal factors. We hypothesize that adolescents with higher perceived school assets will be less likely to report substance use. In designing the study, we recognized there is a need to attend to whether assets differ by gender and for youth of diverse ethnicities (Wallace et al., 2003). Given biological and socialization differences between males and females, even if substance use rates are similar, it is likely that different factors are associated with use and should be examined separately (Kumpfer et al., 2008). Thus, we examined substance use patterns separately by gender as a moderating variable. In contrast, we entered racial-ethnic background as a first step in our model to control its relation to substance use before and after entering various elements. Attending to gender and ethnicity factors, and subsequently controlling for external and internal assets in the ecological model, allowed us to isolate the association of school factors with substance use. Method Participants Participants were 7,642 9th-grade (n ⫽ 3,377; 44.2%) and 11th-grade (n ⫽ 4,263; 55.8%) students living in California. More identified as female (n ⫽ 4,253; 55.7%) than male (n ⫽ 3,389; 44.3%) and the

average age was 15.35 years (SD ⫽ 1.09). Table 1 displays detailed demographic characteristics of the sample. Measures The California Healthy Kids Survey (CHKS) is a set of assessment modules developed by WestEd’s Health and Human Development Program in collaboration with Duerr Evaluation Resources for the California Department of Education. The CHKS emphasizes that it is equally important to assess youth strengths and positive health behaviors as their risk and problem behaviors. The CHKS is administered in all California schools biannually to all students in 5th, 7th, 9th, and 11th grades. Surveys are available on the California Healthy Kids Survey Web site developed by WestEd (http://chks.wested.org/ administer) and extensive psychometric information is available (Hanson & Kim, 2007). Ethnic background. Module A requests background information from participants, including the question, “How do you describe yourself?” Students are asked to “Mark All That Apply” with seven ethnicity options. For this study, students were placed in Asian American, African American, Latino/a American, American Indian/Alaskan, and European American groups if they marked only that category. Youth who marked other ethnicities or more than one ethnicity were categorized under “other” and were therefore excluded from analysis as the group was too diverse to make meaningful conclusions. External assets. The Resilience and Youth Development Module (RYDM) measures external assets. Recent psychometric analysis, including exploratory and confirmatory factor analysis as well as internal consistency reliabilities, provided support for an eight-scale measure of external assets (Hanson & Kim, 2007). The family support scale measures supportive relationships with adults in the home environment with six items (e.g., “At home there is a parent or some other adult who listens to me when I have something to say”). Factor loadings ranged from 0.78 391


Table 1 Demographic Characteristics of the Study Compared to National and State Statistics: Percent Participation Abstainer n ⫽ 3,000

User n ⫽ 3,000

Polyuser n ⫽ 3,000

Family Background

Nationa

Stateb

Study n ⫽ 9,000

Female

Male

Female

Male

Female

Male

Asian American African American Latino/a American European American American Indian/Alaskan Other

5 17 21.5 54.9 1.2 0.5

11.7 7.3 49.0 27.9 0.7 3.4

8.2 3.8 35.5 36.4 1.0 15.1

53.6 13.4 4.7 36.0 31.9 0.6 13.4

46.4 15.1 4.5 33.4 30.7 0.9 15.4

60.3 5.0 3.0 40.3 34.9 1.0 15.8

39.7 7.7 4.4 37.0 35.4 1.4 14.1

53.3 4.0 2.6 32.4 43.7 0.9 16.4

46.7 4.5 3.9 33.3 41.8 1.4 15.1

Note. United States and California enrollment statistics retrieved on April 13, 2011, from http://nces.ed.gov/programs/ digest/d10/tables/dt10_043.asp. Differences in ethnic classification make comparisons across data sets difficult. Study participants, as a random sample of high school students in California, are representative of the state population. However, in the database used for this study, participants could mark more than one ethnic group, in which case they were included in the “other” category. This explains differences in percentages between state and study data. a United States Enrollment in Public Elementary and Secondary Schools, Fall 2008. b California Enrollment in Public Elementary and Secondary Schools, Fall 2008.

to 0.92 and internal consistency reliability was .89 in the validation study and .90 in the current study. The family meaningful participation scale measures involvement in interesting activities with opportunities for responsibility in the home with two items (e.g., “At home I help make decisions with my family”). Factor loadings were 0.85 and 0.86 for the two items on this scale and internal consistency reliability was .78 in the validation study and .82 in the current study. The peer caring relationships scale measures supportive peer relationships with three items (e.g., “I have a friend about my own age who really cares about me”). Factor loadings ranged from 0.92 to 0.94 and internal consistency reliability was .90 in the validation study and .92 in the current study. The prosocial peers scale measures affiliation with prosocial peers with two items (e.g., “My friends try to do what is right”). Factors loadings were 0.78 and 0.86 for scale items and the internal consistency reliability was .74 in the validation study and .78 in the current study. The community support scale measures supportive relationships 392

with adults in the community with six items (e.g., “Outside of my home and school, there is an adult whom I trust”). Factor loadings ranged from 0.88 to 0.95 and internal consistency reliability was .95 in the validation study and .95 in the current study. The community meaningful participation scale measures involvement in interesting activities with opportunities for responsibility in the community with two items (e.g., “Outside of my home and school I am involved in music, art, literature, sports, or a hobby”). Factor loadings were 0.86 and 0.88 and internal consistency reliability was .75 in the validation study and .75 in the current study. The school support scale measures supportive relationships with adults in school with six items (e.g., “At my school there is a teacher or some other adult who believes that I will be a success”). Factor loadings ranged from 0.79 to 0.88 and internal consistency reliability was .90 in the validation study and .90 in the current study. The school meaningful participation scale measures involvement in interesting activities such as sports or clubs (e.g., “At school I do inter-


esting activities”). Factor loadings ranged from 0.77 to 0.88 and internal consistency reliability was .78 in the validation study and .77 in the current study. All assets scales are anchored by 1 ⫽ not at all true and 4 ⫽ very much true.

or pill to get ‘high’”; and “two or more drugs at the same time (for example, alcohol with marijuana, ecstasy with mushrooms)?” Response options are 1 ⫽ 0 days, 2 ⫽ 1 day, 3 ⫽ 2 days, 4 ⫽ 3–9 days, 5 ⫽ 10 –19 days, and 6 ⫽ 20 –30 days.

Internal assets. The RYDM also measures four internal assets validated through psychometric analysis (Hanson & Kim, 2007). The self-efficacy scale measures students’ perceptions that they can succeed with four items (e.g., “I can do most things if I try”). Internal consistency reliability was .82 in the validation study and .84 in the current study. Factor loadings ranged from 0.77 to 0.84. The empathy scale measures the degree to which students can understand other people’s feelings with three items (e.g., “I feel bad when someone gets their feelings hurt”). Internal consistency reliability was .85 in the validation study and .87 in the current study. Factor loadings ranged from 0.82 to 0.91. The problem-solving scale measures if students perceive they can work out their problems with two items (e.g., “I try to work out my problems by talking or writing about them”). Internal consistency reliability was .73 in the validation study and .74 in the current study. Factor loadings were 0.80 and 0.85. The self-awareness scale measures how well students feel they understand themselves with three items (e.g., “I understand my moods and feelings”). Internal consistency reliability was .81 in the validation study and .83 in the current study. Factor loadings ranged from 0.83 to 0.84. All assets scales are anchored by 1 ⫽ not at all true and 4 ⫽ very much true.

Procedures

Substance use. Items used to create the dependent variable were “During the past 30 days, on how many days did you use …” “At least one drink of alcohol”; “marijuana (pot, weed, grass, hash, bud)”; “inhalants (things you sniff, huff, or breathe to get “high”)”; “cocaine (any form, coke, crack, rock, base, snort)”; “methamphetamine or amphetamines (meth, speed, crystal, crank, ice)”; “ecstasy, LSD or other psychedelics (acid, mescaline, peyote, mushrooms)”; “any other illegal drug

Survey procedures. A total of 92,616 high school students took the CHKS survey during the 2008 –2009 school year. Administration of the school and community asset scales of the RYDM is required by California Department of Education, whereas the rest of the module is optional. Student participation is voluntary and responses are anonymous. Negative consent procedures were conducted to gain parent permission for student participation. School personnel administered surveys during one class period of approximately 45–50 minutes. CHKS data were subjected to seven response consistency and reliability checks (e.g., giving the same response option too many times in a row; WestEd, Jerry Bailey, personal communication, May 11, 2008). This case rejection identifier was conducted in preparation of the raw data. Therefore, students whose responses did not meet case validity criteria were not included in the database. Information regarding average survey response time, number of students who declined to participate, and number of cases screened out during data cleaning procedures is not available. Study procedures. After gaining permission to access the database, we conducted several procedures to prepare the data for analysis. Students who endorsed use of Derbisol, a fictitious drug, were excluded. Standardized principal component factor scores were created for family, peer, community, internal, and school assets. All individual factor scores were standardized to have a mean of zero and a standard deviation of one. After this procedure, we randomly selected a sample of 3,000 students for each substance use group. We eliminated students who were assigned the “other” ethnicity category for final samples of 2,569 abstainers, 2,546 users, and 2,527 393


polysubstance users. The substance use dependent variable was created by examining responses to the substance use questions detailed in the measures section. An abstainer reference group was created from students who reported no alcohol, marijuana, inhalants, cocaine, methamphetamine, psychedelics, or any other illegal drug in the past 30 days. A substance use comparison group was created from youth who reported more than zero times use of one or more of these same substances, excluding youth who endorsed using two or more drugs at the same time. Finally, a polysubstance use comparison group was created of youth who endorsed the polysubstance use item (i.e., using two or more drugs at the same time), excluding youth who only endorsed use of cigarettes and alcohol. Analysis An additive linear model was indicated to address the study purpose. The most popular of the linear models is ordinary leastsquares regression, which allows for estimates of the effect of predictor variables in terms of their unique contributions to the variability of an outcome (Gottfredson & Gottfredson, 1988). Logistical regression is a special case of regression that allows for categorical variables and does not require strict adherence to assumptions of distribution (i.e., linearity, normality, and continuity). Thus, logistic regression was the appropriate statistical technique to examine our categorical dependent variable (i.e., abstainer, substance user, polysubstance user). We used multigroup logistic regression to examine the relations between family background, internal assets, school assets, and substance use patterns across gender groups. Together, the models allowed us to estimate the size and statistical significance of several predictors simultaneously. In other words, we were able to determine the unique contribution of each variable in the model while controlling for the effects of the other variables in the model. The models predicted the change in odds of belonging to substance use pattern groups (i.e., user or polyuser groups) versus 394

belonging to the reference group (i.e., abstainer group). The change in odds is expressed as a ratio that can vary from less than one to greater than one. A value of one signifies no significant change in the likelihood of being in the substance use group compared to the abstainer group, whereas a value greater than one indicates an increased likelihood, and a value less than one indicates a decreased likelihood of being in the substance use group. The model building process is broken down into multiple steps. Model 1 contained only student ethnic background. With ethnic background controlled for, Model 2 introduced family, peer, and community, and internal factors. With all other variables controlled for, Model 3 added school factors, allowing us to assess their unique contributions. Finally, multigroup testing was conducted to investigate the significance of differences in coefficients across the female and male models using Wald test of parameter constraints available in Mplus (Muthen & Muthen, 2006). The Wald test is only necessary when coefficients are significant for both males and females to test whether one is stronger than the other. Results Descriptive Characteristics of Substance Use Patterns Within the substance use group, 87.5% reported alcohol use, 27.8% reported marijuana use, 6.6% reported inhalant use, 2.0% reported cocaine use, 1.1% reported methamphetamine use, 2.1% reported psychedelic use, and 3.7% reported using other illegal drugs. Yet, none of the students in the substance use group reported using any of these substances simultaneously. Within the polysubstance use group, 93.9% reported alcohol use, 93% reported marijuana use, 25.5% reported inhalant use, 12.5% reported methamphetamine use, 27.2% reported psychedelic use, and 33.4% reported using other illegal drugs. All of these students reported using two or more of these substances simultaneously. Table 2 provides the mean and standard deviations of all factor scores for each asset. An analysis of variance was run to examine

Females M(SD)

⫺.15b (.90)

⫺.07 (.91) .12b (.85) .08b (.80) ⫺.12b (.94)

⫺.02 (.92)

⫺.10b (.87)

⫺.08 (.88)

⫺.06 (.94) ⫺.06 (.98) ⫺.07 (.87) ⫺.09 (.95)

⫺.07 (.94)

⫺.09 (.98)

⫺.07b (.88)

⫺.14 (.96)

⫺.05 (1.00) ⫺.30 (1.07) ⫺.26b (.92) ⫺.04b (.96)

.01b (.85)

.04b (.91)

⫺.11b (1.01)

⫺.02 (.96) .09b (.84)

.04a (.91) .04 (.90)

.06 (.91)

.01b (.93)

.06 (.86) .06 (.92) .24b (.77) ⫺.18b (1.02) .20b (.75) ⫺.16b (.90) .04 (.87) .08 (.89)

.04a (.93) .08b (.93)

.06a (.89) .04a (.92) .04a (.84) .06a (.88)

.05a (.86) .01b (.86)

.01a (.98) .09b (.91) ⫺.10b (1.04) .06b (.88)

⫺.09a (.86) ⫺.11 (.86)

⫺.08 (.87)

⫺.09b (.91)

⫺.03a (.89)

.01b (.88)

⫺.01b (.95) ⫺.24b (1.03) ⫺.25b (.93) ⫺.01b (.94)

.07b (.83)

⫺.03b (.96)

⫺.06a (.91) ⫺.09b (.88) ⫺.04a (.94) .10b (.83) ⫺.05a (.86) .08b (.78) ⫺.09 (.93) ⫺.14b (.92)

⫺.06a (.88) ⫺.15b (.90)

.02 (.91)

⫺.06 (.89)

⫺.26b (.90) ⫺.02a (.96) .22b (.83) ⫺.31b (1.00) .09a (.90) ⫺.06b (.79) ⫺.23b (.87) ⫺.28b (1.04) .09a (.85) .22b (.82) ⫺.04b (.86) ⫺.12a (.83) ⫺.23b (1.00) ⫺.30b (.74)

⫺.10a (.89) ⫺.13 (.89)

⫺.04b (.85) ⫺.26b (.79)

.02b (.89)

.02 (.95) ⫺.14 (.86)

.07a (.88) .12b (.87)

⫺.11 (.92)

⫺.11 (.91)

⫺.08 (.98)

Males M(SD)

⫺.11 (.91)

⫺.10a (.96) ⫺.12 (.95)

All M(SD)

⫺.13 (1.00) .06 (.91)

Males M(SD)

User

⫺.12 (1.00) ⫺.13 (1.00) .08a (.90) .09 (.88)

All M(SD)

Females M(SD)

Males M(SD)

Females M(SD)

Note. All individual factor scores were standardized to have a mean of near zero and a standard deviation of one. a The group mean differences among substance use groups is significant at ␣ ⬍ .05. b The group mean differences between gender is significant at ␣ ⬍ .05.

Family Assets Support Meaningful Part. Peer assets Pro-Social Peers Caring Relations Community Assets Support Meaningful Part. Internal Assets Self-Efficacy Empathy Problem-Solving Self-Awareness School Assets Support Meaningful Part.

All M(SD)

Abstainer

Total

Table 2 Descriptive Statistics for Key Variables

Females M(SD)

⫺.33b (.89)

⫺.03b (.92)

⫺.49b (.89) ⫺.29b (.94)

⫺.33 (.92)

⫺.21a (.84)

⫺.23a (.98)

⫺.23 (.83)

⫺.14b (.95)

⫺.19a (1.01) ⫺.18 (.95) ⫺.22a (1.07) .02b (.93) ⫺.20a (.90) ⫺.06b (.83) ⫺.24a (1.01) ⫺.27b (1.00)

⫺.24a (.88)

⫺.10a (.98)

.01a (.97) ⫺.38a (.88)

⫺.32a (.93)

⫺.36a (1.09) ⫺.36 (1.00)

All M(SD)

Polyuser

⫺.18 (.85)

⫺.32b (1.00)

⫺.20b (1.00) ⫺.47b (1.01) ⫺.37b (.94) ⫺.19b (1.03)

⫺.14b (.85)

⫺.18b (1.03)

⫺.30b (.85) ⫺.33b (.92)

⫺.30 (.94)

⫺.35 (1.10)

Males M(SD)


395


mean score differences using Tukey post hoc comparisons. Group mean differences were significant for most variables across substance use groups, confirming that youths with different substance use behaviors have different levels of assets. For females, all mean assets fell in the expected pattern, with the highest level of assets for the abstainer group and the lowest level of assets for the polyuser group. For males, although most assets fell in the expected pattern, prosocial peers and community support were highest for the user group compared to the abstainer and polyuser groups. Mean levels of internal and external assets were often different between females and males. For example, compared to males, females had significantly higher scores in empathy and problem solving across all substance use groups. Gender Differences in Variables Associated with Group Membership Table 3 presents the three models that, together, examined the role of school assets in explaining substance use membership. When odds ratios (ORs) across gender groups were both significant, Wald tests of parameter constraints were performed to detect gender differences. No pairs of significant ORs were found to differ significantly by gender. Specific gender differences will be presented at each model step. Model 1: Ethnic Background Table 3 summarizes the relative odds of substance use behaviors across males and females at each step of the model. We used the abstainer group as reference. Model 1 includes only the ethnic background characteristics, with European American ethnicity selected as the reference group because it represents the ethnic majority. Asian Americans and African Americans were less likely to belong to the substance use groups than European Americans. For example, the odds of belonging to the polyuser group were 77% lower for female Asian Americans (OR ⫽ 0.23) and 57% lower for female African Americans (OR ⫽ 0.43) than for female European Americans. Latino/a 396

Americans were less likely to belong to the polyuser group than European Americans (OR ⫽ 0.68 for females; 0.81 for males) but as likely to belong to the user group. American Indians were more likely to belong to substance user groups than European Americans, although these differences were not significant because of a small number of participants. For example, the odds of belonging to the polyuser group were 27% higher for female American Indians (OR ⫽ 1.27) than European Americans. Model 2: Adding Family, Peer, Community, and Internal Assets Adding the family, peer, community, and internal factors decreased the odds that a member of an ethnic minority group would belong to one of the substance use groups. For example, the odds of African American males belonging to the polyuser group decreased from a nonsignificant 0.70 to a significant 0.55. Family support, prosocial peers, and peer caring relationships all had small but significant associations with the substance use group for both females and males, with ORs statistically equivalent across the two groups. However, family support and prosocial peers decreased the odds whereas peer caring relationships increased the odds of belonging to a substance use group. For example, for every unit increase in family support, the odds of a female belonging to the user group decreased by 14% (OR ⫽ 0.86) and the odds of a female belonging to the polyuser group decreased by 29% (OR ⫽ 0.71). On the other hand, for every unit increase in peer caring relationships, the odds of a female belonging to the user group increased by 50% (OR ⫽ 1.50) and the odds of a female belonging to the polyuser group increased by 72% (OR ⫽ 1.72). Other variables added to Model 2 had mixed or no effect on substance use group. Family meaningful participation was related to decreased odds of being in user and polyuser groups for females (OR ⫽ 0.87, 0.88) and being in the polyuser group for males (OR ⫽ 0.88) but not for being in the male user group (OR ⫽ 0.99). Community meaningful

0.34 * 0.59 * 1.00 1.59

* *

0.69 1.50

1.03 0.96 0.88 0.95

1.03 0.96 0.88 0.95

1.01 0.98

1.08 0.91

1.07 0.91

0.69 1.50

0.85 0.86

* *

0.86 0.87

*

* *

* *

0.35 * 0.46 * 0.89 1.29

Model 3

0.35 * 0.46 * 0.88 1.29

Model 2

Note. The abstainer group was used as reference. * p ⬍ .05.

Ethnicity Asian African Latino/a Indian/Alaskan Family Assets Support Mean. Part. Peer Assets Prosocial Caring Community Support Mean. Part. Internal Assets Self-Efficacy Empathy Prob.-Solving Self-Awaren. School Assets Support Mean. Part.

Model 1

User

0.23 * 0.43 * 0.68 * 1.27

Model 1

Females

1.21 0.99 0.79 0.91

1.11 0.74

0.80 1.72

0.71 0.88

*

*

*

* *

* *

0.22 * 0.33 * 0.47 * 1.09

Model 2

Polyuser

0.94 1.00

1.21 1.00 0.79 0.91

1.12 0.75

0.81 1.74

0.71 0.81

* *

*

*

* *

* *

0.22 * 0.33 * 0.47 * 1.08

Model 3

0.47 * 0.89 * 1.03 1.54

Model 1

1.00 1.08 0.88 0.96

1.16 0.99

0.75 1.24

0.81 0.99

*

* *

* *

0.45 * 0.77 * 0.93 1.47

Model 2

User

0.91 0.84

1.01 1.09 0.89 0.96

1.21 1.04

0.76 * 1.27 *

0.80 * 1.06

0.44 * 0.78 * 0.92 1.51

0.24 * 0.70 0.81 * 1.34

Model 1

Males

Model 3

Table 3. Odds Ratios of Variables Related to Use and Polysubstance Use

* *

* *

1.00 0.93 1.00 1.02

1.16 0.80 *

0.57 1.55

0.70 0.88

0.24 * 0.55 * 0.65 * 1.06

Model 2

Polyuser

* *

*

0.78 * 0.91

1.00 0.94 1.01 1.01

1.24 0.84 *

0.58 1.60

0.69 0.93

0.24 * 0.56 * 0.64 * 1.11

Model 3


397


participation was related to decreased odds of being in the polyuser group (OR ⫽ 0.74, 0.80) but not in the user group (OR ⫽ 0.91, 0.99) for both females and males. Self-efficacy was associated with increased odds of females being in the polyuser group (OR ⫽ 1.21) but not the user group (OR ⫽ 1.03). Self-efficacy was not related to substance use category for males. Problem solving was related to decreased odds of females being in the polyuser group (OR ⫽ 0.79) but not the user group (OR ⫽ 0.88), and males being in the user group (OR ⫽ 0.88) but not the polyuser group (OR ⫽ 1.00). Community support, empathy, and self-awareness were not related to substance use group for either male or females at this step.

factors across multiple spheres of influence (Mayberry et al., 2009; Suldo et al., 2008). Prior studies have identified the association between school factors and substance use. Our primary goal was to extend this research by identifying the associations of school support and school meaningful participation with use and polysubstance use above and beyond the influence of other critical ecological factors. Thus, we employed a multigroup logistic regression model analysis to examine relations between school assets and substance use among male and female adolescents while controlling for ethnicity variables and a comprehensive set of internal and external assets.

Model 3: Adding School Factors

The findings demonstrate that males with high perceived school support, defined as having caring and supportive adults in the school, were significantly less likely to belong to the polyuser group. Thus, despite other factors, school support appears to be associated with reduced odds of males engaging in harmful substance use. This finding is consistent with previous research in that higher perceived teacher support and expectations are linked with decreased reports of substance use (Suldo et al., 2008). Also, school connectedness and teacher bonding have been related to less substance use among high school students, and the results were especially strong for males (Crosnoe et al., 2002). The finding that school support emerged as significant above and beyond the influences of other variables is important because prior research has indicated that other factors such as peer relationships may explain the association between teacher caring relationships and substance use (Crosnoe, 2002; Suldo, 2008). For males, when school factors were added to a comprehensive ecological model, the associations between other contextual factors and substance use group remained stable with one exception. Family meaningful participation was no longer significantly related to polyuse. In the literature, family-related activities have been shown to strengthen relationships and reduce delinquency (Barnes, Hoffman, Welte,

Adding school variables did not affect most associations between previously entered factors and substance use category. One noteworthy effect was that for males the association between family meaningful participation and polysubstance use group membership shifted from significant (OR ⫽ 0.88) to not significant (OR ⫽ 0.93) with the addition of school factors. The relation between school factors and substance use was different for males and females. Males with stronger school support were less likely to belong to the polyuser group than the abstainer group (OR ⫽ 0.78). This trend was not statistically significant for females (OR ⫽ 0.94). School meaningful participation did not contribute to substance use category beyond the consideration of other variables in the model for females or males. Discussion Given high levels of comorbid substance use among teens, there is a critical need to examine a comprehensive multisubstance approach to prevention. This study examined the interrelations between home, peer, community, school, and internal assets, and adolescent substance, use within an ecological framework. The ecological model posits that adolescent substance use is associated with 398

School Support


Farrell, & Dintcheff, 2007). Meaningful youth participation in the home can enhance an adolescent’s sense of connectedness, belonging, and help them feel valued, which affects their mental health and well-being (Oliver, Collin, Burns, & Nicholas, 2006). In light of our research, however, school support appears to be an important area to focus on to reduce polysubstance use. Future research could extend this finding by investigating the protective role of teacher caring relationships against polysubstance use for males involved with antisocial peers or other risk behaviors. School caring relationships, however, did not significantly affect the odds of belonging to a substance use group for females. Moreover, adding school factors to the model did not substantially alter the associations between contextual factors and substance use group. It appears that for females, family and peer factors largely accounted for influences on substance use group membership. In addition to family influences on adolescent substance use, research has demonstrated that peers are influential in the substance use process (Valente et al., 2007). Given females’ overall reported higher mean levels of prosocial peer involvement than males, this may be a more influential asset for them. It also appears that family factors are more salient for females at this age than for males, as the association of family participation to substance use group did not change with the addition of assets for females. Future research may need to consider gender as a moderating influence on the associations between school support, prosocial peers, family factors, and substance use group.

mote substance use. For example, sports participation was associated with increased substance use over time, whereas participation in the arts or student government was negatively associated with average substance use, although outcomes varied by neighborhood characteristics (Fauth et al., 2007). Failing to consider the type of involvement in our study may have canceled out effects. Thus, it is unclear if meaningful participation truly does not have a relation with substance use behavior beyond other contextual influences. Future research is required to understand the components of school-based participation that are assets versus risks for substance use. Contextual Factors, Ethnic Background, and Substance Use Patterns Results are consistent with previous studies on the differences in substance use patterns among different ethnic groups. In our study, a variety of contextual factors such as family support and prosocial peers affected the odds of being in a substance use group for participants of ethnic minority backgrounds compared to European American participants. Wu et al. (2010) found that the gateway hypothesis, in which later substance use is predicted by early initiation of tobacco and alcohol use, better explained substance use patterns for European American participants than for ethnic minority participants for whom the researchers hypothesized other factors must be important. It is possible that a focus on contextual factors would be particularly beneficial for ethnic minority students. Further research is needed to investigate this hypothesis. Limitations and Future Directions

School Meaningful Participation Although many studies suggest that adolescents who engage in organized school activities are less likely to engage in substance use (Elder, Leaver-Dunn, Wang, Nagy, & Green, 2000), these effects were not significant in our study beyond other factors. Prior investigations suggest that although prosocial activity participation can predict lower substance use, other specific activities can pro-

Several limitations of this study should be noted. First, the study design was correlational, which limits understanding the direction of associations. Also, the respondents were students attending school and information from youth who had dropped out of high school would have provided valuable information regarding substance use. Relations between dropping out of high school and substance use exist (Townsend, Flisher, & King, 399


2007). Future studies can focus on the risks and resilience of high school dropouts and their substance use prevalence. Another limitation concerns the use of self-reports of drug use and the extent to which they are equally valid and reliable across ethnicity groups (Wallace et al., 2003). Regarding the data, variables were limited to what was available in the extant data set. Although the RYDM model was an excellent fit for the study purpose, it did not cover the breadth of variables likely related to substance use nor did we achieve the golden standard for each construct. Another limitation is related to the substance use categorization procedure, as it appears that no one categorization has been identified as superior. Future studies can aim to determine validity and reliability of substance use categories and to incorporate multiple measures of substance use. Finally, polysubstance use, which is an important component of substance use, was not separated by specific type of use. Future research should ask about what types of substances were used simultaneously (Pape et al., 2009). Implications for Practice Overall, the study results confirm that adolescent substance use is a complex problem influenced by numerous factors in the ecological context. Although one of two school assets, school support, has a significant association with substance use for males above and beyond the influence of other factors, our results indicate that a comprehensive prevention approach should focus on the interrelations between peer, family, and school assets. Internal assets, which are often focused on in substance abuse treatment, were found to have little or even a negative effect on the odds of belonging to a substance use group. Given the importance of school as a social and community system, strengthening adolescent ties to school allows them access to prevention and intervention efforts targeting the broader ecological context. Identifying youth who have not used, occasionally used, and used multiple substances simultaneously is an important first step in developing appropriate 400

programs. For example, for youth who engage in occasional use, prevention efforts can focus on shaping attitudes and providing appropriate supports, while frequent polysubstance users may require targeted or intensive interventions aimed at significantly reducing substance use behaviors (Connell et al., 2010). Literature suggests that substance use typically increases during adolescence and can takes place in the context of normalized socializing behavior with peers and continue within the context of meaningful community participation (Fleming, Catalano, Haggerty, & Abbott, 2010). Studies suggest that the school’s role may be to target health behavior thoughts early as they are most modifiable, and to prevent substance use by changing perceptions of social images in which substance use is positive and reidentifying perceptions that substance use is normative (e.g., Andrews, Hampson, & Peterson, 2010). For example, one technique is changing social images of cigarette smokers by illustrating that smokers are uncool (Andrews et al., 2010). School psychologists have an important role in working with teachers and other school-based professionals to encourage them to show youth care, support, and positive reinforcement. Teacher awareness of the importance of fostering positive student–teacher relations may help decrease the frequency of substance use and indirectly influence academic problems associated with substance use such as diminished school attendance, interest in school, and academic achievement (Bryant at al., 2003; Suldo et al., 2008). Literature suggests that school psychologists can help educate teachers about the positive outcomes associated with providing students with information and advice (informal support), evaluative feedback on behavior and performance (appraisal support), needed resources (instrumental support), and actions and words that convey care and trust (emotional support; Suldo et al., 2008). Many prevention programs seek to reduce negative peer influences to decrease substance use, which may be particularly important for youth who are increasing their involvement with negative peers (Fleming et al.,


2010). Studies suggest that youth who are more socially connected, but not connected to school, are more likely to become substance users in the future (Bond et al., 2007). Even though adolescents spend more time with peers, positive family factors such as shared family time have been linked to reduced substance use (Barnes et al., 2007). Because the importance of peer relationships on substance use cannot be overlooked, Barnes et al. (2007) suggest that social policies that encourage parents and adolescents to spend positive social time together may facilitate prevention efforts. Our finding supports the need to understand diverse internal assets related to substance use within the context of different ethnicity and ecological factors. Internal assets had little or even a negative effect on substance use group, particularly for males. Specifically, females with problem solving and self-awareness were more likely to belong to the abstainer than polyuser groups. For males, problem solving was related to a small but significant decreased likelihood of being in the user group. Neuro-scientific evidence suggests that there is a relation between self-awareness and drug abuse (Verdejo-García & Pe´rezGarcía, 2008) and that low self concept and self-esteem are related to alcohol and drug abuse (Keen, 2004). The empowerment theory suggests increasing self-awareness skills of females to reduce feelings of isolation and decrease substance use (Wald, Harvey, & Hibbard, 1995). Research suggests that problem solving is a multidimensional concept, can be measured in several different ways, and has been associated with decreased substance use (Jaffee & D’Zurilla, 2009). It is possible that promotion of factors such as problem solving and self-awareness through school-based groups can help contribute to decreased substance use among youth. Research suggests that school-based groups can expand adolescent awareness and help them make better critical choices and change dysfunctional patterns (Gance-Cleveland, 2004) and that training in rational problem solving skills may be helpful (Jaffee & D’Zurilla, 2009). An interesting finding emerged, that perceived self-efficacy among females was asso-

ciated with increased polysubstance use. This was surprising in that research frequently suggests that self-efficacy is negatively related to substance use (Robinson & Walsh, 1994). It is possible that general self-efficacy, as measured in this study, is related to increased substance use while domain-specific substance use, such as the ability to resist peer pressure, is related to decreased substance use (Caprara et al., 1998). It will be important to further investigate this explanation and also monitor the effectiveness of interventions that target self-efficacy for females involved with substance use to make sure they do not have iatrogenic effects. Conclusion Literature has identified a need to understand the role schools can play to prevent adolescent substance use (Cleveland et al., 2008). This study advances the literature by providing an ecological perspective to the topic of substance use by focusing on the important influence of school factors beyond the influence of home, peer, community and internal factors. The results of our study suggest that school support is influential beyond the influence of other factors for males. From our findings and the results of others it is clear that school psychologists have an important role educating students (Andrews et al., 2010) and school-based personnel about substance use. Future research should continue to focus on the important influences of school factors on adolescent substance use and the roles of school psychologists in decreasing substance use. In developing appropriate school based substance use reduction programs, it is important to tailor prevention efforts to meet the specific needs of the population. It will be important to differentiate racial/ethnic patterns in substance use (Wu et al., 2010) and to address substance use and other risk behaviors simultaneously (Wiefferink et al., 2006). References Abbey, A., Jacques, A. J., Hayman, L. W., & Sobeck, J. (2006). Predictors of early substance use among African American and Caucasian youth from urban and 401


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Date Received: September 13, 2010 Date Accepted: July 20, 2011 Action Editor: Shannon Suldo 䡲

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Zhanna Shekhtmeyster is a graduate student in the Department of Counseling, Clinical, and School Psychology at the University of California, Santa Barbara. Jill D. Sharkey, PhD, is an academic coordinator in the Department of Counseling, Clinical, and School Psychology at the University of California, Santa Barbara. She conducts her research at the Center for School-Based Youth Development. Her interests include gender and ethnic differences in emotional and behavioral problems, student engagement, risk and resilience, school discipline, school safety and violence, and screening and assessment for antisocial behavior. Sukkyung You, PhD, is a professor in the College of Education at Hankuk University of Foreign Studies (Seoul, Korea). Her recent scholarly publications and conference presentations have focused on students’ achievement, motivation, risk and resilience, school violence, social context of education, and gender and ethnic differences in emotional and behavioral problems. She is the recipient of numerous research grants including awards from the Association for Institutional Research, the American Educational Research Association, the Spencer foundation, and the National Institute of Mental Health.

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