Influence of school-level variables on aggression and associated attitudes of middle school students

Journal of School Psychology 49 (2011) 481–503

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Journal of School Psychology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j s c h p s yc

Influence of school-level variables on aggression and associated attitudes of middle school students☆ David B. Henry a,⁎, Albert D. Farrell b, Michael E. Schoeny d, Patrick H. Tolan c, Allison B. Dymnicki a a

University of Illinois at Chicago, USA Virginia Commonwealth University, USA c University of Chicago, USA d University of Virginia, USA b

a r t i c l e

i n f o

Article history: Received 3 March 2010 Received in revised form 19 April 2011 Accepted 25 April 2011 Keywords: Aggression School-level variables Norms Multi-level models

a b s t r a c t This study sought to understand school-level influences on aggressive behavior and related social cognitive variables. Participants were 5106 middle school students participating in a violence prevention project. Predictors were school-level norms opposing aggression and favoring nonviolence, interpersonal climate (positive student–teacher relationships and positive student–student relationships), and school responsiveness to violence (awareness and reporting of violence and school safety problems). Outcomes were individual-level physical aggression, beliefs supporting aggression, and self-efficacy for nonviolent responses. School norms and both interpersonal climate variables had effects on all three outcomes in theorized directions. Only one of the responsiveness measures, awareness and reporting of violence, had theoretically

☆ This research was funded by Grant # U49CE001296 from the Centers for Disease control and Prevention to the second author. The authors want to recognize the contributions of the originators of the Multisite Violence Prevention Project (MVPP). Without the collaborative work of this talented and dedicated team this project would not have been possible. The members of the MVPP project are listed below according to their original affiliation with their current affiliation noted in parentheses. The MVPP (corporate author) includes: Centers for Disease Control and Prevention, Atlanta GA: Thomas R. Simon, PhD; Robin M. Ikeda, MD, MPH (National Center for Injury Prevention and Control; Emilie Phillips Smith, PhD (Penn State University); Le'Roy E. Reese, PhD (Morehouse School of Medicine); Duke University, Durham NC: David L. Rabiner, PhD; Shari Miller-Johnson, PhD; Donna-Marie Winn, PhD (University of North Carolina—Chapel Hill); Kenneth A. Dodge, PhD (Center for Child and Family Policy); Steven R. Asher, PhD (Department of Psychology and Neuroscience); University of Georgia, Athens GA: Arthur M. Horne, PhD (Department of Counseling and Human Development Services); Pamela Orpinas, PhD (Department of Health Promotion and Behavior); Roy Martin, PhD (Dept. of Educational Psychology and Instructional Technology); William H. Quinn, PhD (Clemson University); University of Illinois at Chicago, Chicago IL: Patrick H. Tolan, PhD (University of Virginia); Deborah Gorman-Smith, PhD (University of Chicago); David B. Henry, PhD; Franklin N. Gay, MPH, Michael Schoeny, PhD; Virginia Commonwealth University, Richmond VA: Albert D. Farrell, PhD; Aleta L. Meyer, PhD (National Institute on Drug Abuse); Terri N. Sullivan, PhD; Kevin W. Allison, PhD (all Department of Psychology). ⁎ Corresponding author. Fax: + 1 312 996 2703. E-mail address: [email protected] (D.B. Henry) ACTION EDITOR: Michelle Demaray. 0022-4405/$ – see front matter © 2011 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jsp.2011.04.007

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consistent effects on all outcomes. The other, school safety problems, affected self-efficacy later in middle school. Evidence of gender moderation was generally consistent with greater influence of schoollevel factors on female adolescents. Discussion focuses on implications in light of previous research and intervention possibilities. © 2011 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

1. Introduction Aggressive behavior, such as violence and bullying, continue to be major sources of concern to administrators and impediments to student achievement and well-being (Putallaz et al., 2007). Important gains have been made in identifying individual factors that explain aggression and violence among students (e.g., Hawkins, Farrington, & Catalano, 1998; Lipsey & Derzon, 1998; Office of the Surgeon General, 2001). Alongside these gains, recent thinking has refined classifications of factors relating to risk and prevention, differentiating promotive factors that reduce risk or increase the likelihood of positive outcomes from protective factors that buffer risk (Fergus & Zimmerman, 2005). Much less progress, however, has been made in the study of risk, promotive, and protective factors beyond the individual level (Henry, 2008; Tseng & Seidman, 2007). More consideration of key factors within classroom and school settings that influence violence is needed (Farrell & Camou, 2006). Improved assessment and measurement of these influences are critical to such efforts. This paper specifically focuses on understanding how three higher-order school-level constructs, measured using individual reports on five scales, influence middle school students' aggressive behavior, beliefs supporting aggression, and selfefficacy for using nonviolent means of resolving conflicts.

1.1. Contextual influences on aggression in the middle school years Reviews of risk factors related to youth aggression and to school behavior have indicated that the middle school years may be a critical time in influencing student aggression (Dahlberg & Potter, 2001; USDHHS, 2001). Adding to its biological and developmental changes, early adolescence is a crucial social transition point. Exposure to new community and school contexts and increasing peer influence puts youth at higher risk for violence perpetration and victimization (Pellegrini & Long, 2004). Moving from self-contained elementary school classrooms, middle school students must learn to negotiate new relationships with multiple groups of peers and teachers as they change classrooms for each academic subject. Furthermore, youth find themselves in new social settings in the community as they begin to spend more time away from home and out of adult supervision (Crockett & Petersen, 1993). The effects of classroom and school climate on student behavior have been recognized as important for some time, although less attention has been paid to classroom or school factors that predict aggression (Cartland, Ruch-Ross, & Henry, 2003; Juvonen, 2006; Pianta et al., 2003). Research has found that being in a highly aggressive classroom places students at greater risk for aggressive behavior in the future (apart from the effects of pre-existing individual aggression) and aggravates risk for male students with high pre-existing aggression (Kellam, Ling, Merisca, Brown, & Ialongo, 1998). Other studies suggest that children's aggressive behavior is affected by shared norms about aggression among peers (Henry et al., 2000). A classroom-based intervention focused on having peers monitor each other's behavior appears to reduce overall classroom levels of aggression (Kellam et al., 1998) and a school-based program that implemented strategies to address classroom climate improved children's perceptions of the classroom setting, cooperation with peers, and social conversations, and reduced noncompliance and off-task behavior (Brock, Nishida, Chiong, Grimm, & Rimm-Kaufman, 2008; Rimm-Kaufman, La Paro, Downer, & Pianta, 2005). These studies suggest not only the influence of classroom and school climate on variables related to student aggression, but also the malleability of these aggression-affecting characteristics.

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1.2. Individual-level correlates of aggression Among individual variables that are related to aggressive behavior, beliefs about aggression and nonviolence and self-efficacy beliefs associated with implementing nonviolent responses in conflict situations are particularly important. There is substantial literature linking normative beliefs about aggression to aggressive behavior and as a mediator of change in aggression (Guerra, Huesmann, & Hanish, 1994; Huesmann, Guerra, Zelli, & Miller, 1992). Moreover, the direction of this mediation changes developmentally. In a study of the relation between normative beliefs about aggression and aggressive behavior, Huesmann and Guerra (1997) found that, between 1st and 3rd grades, children's aggressive behavior predicted change in normative beliefs about aggression, but in 4th to 6th grades, the direction of this relation reversed. Among older children, normative beliefs about aggression predicted change in aggressive behavior longitudinally. Like normative beliefs, self-efficacy is an important mediator of change in aggressive behavior and adoption of nonviolent alternatives to aggression. Self-efficacy beliefs may be defined as the individual's confidence in his or her ability to perform a given action. Self-efficacy is a key element in theories of motivation, social learning (Bandura, 1977), and bully–victim relationships (Gottheil & Dubow, 2001). Self-efficacy beliefs are associated with lower levels of anxiety and depression, as well as lower levels of externalizing behaviors in youth (Singh & Bussey, 2009). In addition, a recent qualitative study suggests that low self-efficacy beliefs may be one barrier to adoption of nonviolent problem-solving strategies among youth (Farrell et al., 2008). 1.3. School-level influences Based on these findings, the current study examines the effects on individual aggression and related attitudes of three school-level predictors that have received little research attention. They are (a) school norms related to aggression and nonviolence, (b) interpersonal climate (i.e., student–student relationships and student–teacher relationships), and (c) school responsiveness to violence (i.e., school safety concerns and teacher awareness and responsiveness to violence). In some school environments, informal social norms may lead to aggression being considered an appropriate method to gain social status and correct perceived injustices (Fagan & Wilkinson, 1998). For example, Espelage, Holt, and Henkel (2003) found that peer groups who favor aggressive norms influence individual aggressive behavior for middle school students and Henry et al. (2000) found that aggregate beliefs about aggression affected aggression to a greater extent than did characteristic levels of aggression in school classrooms. The importance of modifying social norms has been noted for nearly a century (e.g., Thomas, 1917). Recent studies suggest that youth may misperceive peer norms about the acceptability of bullying (Perkins & Craig, 2006). Evidence from other areas suggests that correcting such misperceptions by providing normative feedback can change individual behavior (Cunningham & Selby, 2007; Schultz, 1999). A variety of school-based efforts to affect aggression levels have focused on attempting to change norms about its commonality and its acceptability (Farrell & Camou, 2006). Although the impact of these efforts on aggressive behavior has been modest, research does suggest that norms are a malleable settinglevel characteristic (Farrell & Vulin-Reynolds, 2007) and that efforts to understand the workings and effects of school-level norms are worthwhile. Recent research has begun to distinguish between the influence of norms supporting aggression and norms supporting nonviolence because of findings that these constructs affect student behavior differently. Camou, Farrell, and Henry (2006) found that schoollevel norms for nonviolence had direct and negative effects on aggressive behavior, even after controlling for individual beliefs. Another school-level influence on student aggression can be termed interpersonal climate, which reflects the quality of relationships among students (Cartland et al., 2003), and between students and teachers (Pianta et al., 2003). Aggressive children are socially integrated with their less aggressive peers (Espelage et al., 2003; Rodkin & Hodges, 2003). Some aggressive children are unpopular, but many are socially adept, affecting and being affected by the quality of peer relationships (Estell, Cairns, Farmer, & Cairns, 2002). Additionally, the quality of interpersonal relationships is a key component of theories that describe organizations (e.g., Katz & Kahn, 1978). Research has also documented gender differences in peer relations of aggressive children. Farmer et al. (2002) found that two-thirds of aggressive boys and half of

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aggressive girls affiliated in nonaggressive or mixed peer groups. Rodkin, Pearl, and Van Acker (2003) also found that aggressive boys had a wide base of reputational support and social status above and beyond their aggressive counterparts and that aggressive girls tended to maintain peer relationships with other aggressive girls. In terms of the influence of student–teacher relationships, several qualitative studies have found that adolescents frequently report problematic interactions with teachers as a source of considerable distress (Farrell et al., 2007; Farrell, Ampy, & Meyer, 1998). Students reported having interactions with teachers in which they felt the teachers behaved inappropriately or treated them unfairly, and the frequency of these difficult interactions was associated with levels of physical aggression (Farrell et al., 2006). Not surprisingly, improving the quality of interactions between teachers and students is a common component of interventions aimed at reducing and preventing aggression. As with the other areas noted here, there is some empirical evidence that this aspect can affect the implementation and success of preventive interventions (Gregory et al., 2007). A third school-level factor associated with students' aggressive and prosocial behavior is school responsiveness to violent incidents. Research discusses the importance of creating clear expectations about appropriate behavior and consistent enforcement (Osher, 2002). Children who are victimized report their perceptions that school personnel either do not know that bullying is occurring, or do not respond appropriately to it (Hoover, Oliver, & Hazler, 1992). Failing to intervene may inadvertently reinforce bullying if inaction is perceived as tacit approval. Furthermore, teachers often underestimate the prevalence of bullying and the role they can play to prevent it (Kallestad & Olweus, 2003), fail to intervene when it occurs, and sometimes exacerbate the problem by siding with perpetrators and blaming victims (Olweus, 1993; Pellegrini, 2002; Rodkin & Hodges, 2003). These findings suggest the hypothesis that in schools with higher levels of awareness and reporting of violence, there will be lower levels of aggression. Supporting this notion, a recent qualitative study found that middle school students indicated that they were unlikely to employ a nonviolent strategy such as seeking help from a teacher when they had a conflict with another student because they did not feel the teacher would do anything to address the situation (Farrell et al., 2008b). Another area of research describes the relation between students' safety concerns and attitudes and behaviors. Research has found that school safety relates to school connectedness and attendance (Hawkins, Guo, Hill, Battin-Pearson, & Abbott, 2001). There also seems to be an effect of being a “truly disadvantaged school” where lack of safety with a host of other school problems prevents a school from being able to adopt or implement successful reform efforts (Bryk, Sebring, Allensworth, Luppescu, & Easton, 2010). Similarly, school attention to bullying problems (i.e., whether leadership or teachers informally or formally discuss the problem of bullying) predicts how successfully a prevention program is adopted and implemented (Kallestad & Olweus, 2003). The effects of school safety concerns appear early. Hoglund and Leadbeater (2004) found that, as young as 1st grade, being in a highly aggressive classroom tends to increase aggressive behavior, but being in a classroom with high peer prosocial behavior predicts increased social competence. The importance of school safety as a school-level influence is underscored by the Safe Schools/Healthy Students Initiative, through which three Federal agencies have, for over a decade, funded integrated approaches to promote school safety, student mental and physical health, and improved academic performance (Substance Abuse and Mental Health Services Administration, 2010).

1.4. Gender moderation of school-level influences As highlighted in the findings of Farmer et al. (2002) and Rodkin and Hodges (2003), gender moderation of the relations of these variables to aggression may occur for many reasons. Because females have been shown to be more oriented toward social cues than males (Cross & Madson, 1997), and aggressive females are more orientated to aggressive peers than aggressive males, it could be expected that female aggression is more dependent on school norms and interpersonal climate than is male aggression. Yet, because males have higher levels of aggression and beliefs about aggression than females (Björkqvist, Lagerspetz, & Kaukiainen, 1992; Huesmann et al., 1992), it is also possible that school factors could exert a greater impact among males than among females. Thus, gender is a potentially important individual-level moderating factor.

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1.5. The current study With a large sample of middle school students, the present study assesses the effects on aggression of five theoretically important school-level variables (i.e., school norms for aggression and nonviolence, student–student relationships, student–teacher relationships, awareness and reporting of violence, and school safety concerns) representing three higher-order school-level theoretical constructs (i.e., norms, interpersonal climate, and responsiveness to violence). Although recent research has given increasing attention to the role of school factors in explaining student behavior, few studies have considered these constructs as measured at school level. Fewer still have been able to test the effects of multiple school-level characteristics for their combined and unique effects. Accordingly, there are many contributions of the current study to research on classroom and school-level factors. First, this study included a large and diverse sample of schools combined with an appropriate array of measures for examining contextual processes whose effects may overlap. Second, few studies have considered the potential role of school-level characteristics in promoting positive behaviors and attitudes. This study included support for nonviolence along with support for aggression in the school-level norms variable and tested the effects of school-level variables on self-efficacy for using nonviolent ways of resolving conflicts. Third, few studies have been able to examine change in the effects of school-level factors encountered upon school entry over time. The longitudinal research design included data collection at four time points during middle school. This design provides an unusual opportunity to examine change in the relations between school-level processes and individual student-level variables over time as well as the relations between school-level processes and individual variables. Finally, the sample allowed us to examine the extent to which the effects of school-level characteristics differ for males and females. As is described above, there are many reasons to expect such differences. 1.6. Study hypotheses Two overarching hypotheses, each leading to several specific ones, guided this study. The first was that, controlling for individual measures, school-level variables would influence students' behavior and related attitudes across the middle school years. Specifically, school norms less supportive of aggression and more supportive of nonviolence would be associated with lower levels of individual aggression and beliefs supporting aggression and higher self-efficacy for nonviolence. More positive interpersonal climate in schools, indicated by more positive relationships among students and between students and teachers, was expected to be associated with lower levels of aggression and beliefs supporting aggression and greater self-efficacy for nonviolence. Schools with higher awareness and reporting of violence and fewer safety problems would have students with lower levels of aggression and beliefs about aggression, and greater self-efficacy for nonviolence. The second hypothesis was that the strength of these relations would differ between females and males. Based on previous work that found stronger peer affiliations with aggressive peers for females versus males (Espelage et al., 2003; Rodkin & Hodges, 2003; Rodkin et al., 2003), school norms and positive interpersonal climate were expected to predict females' behavior but indicators of school responsiveness to violence, such as school safety problems and awareness and reporting of violence, would predict males' behavior. 2. Method This study used data from 5106 middle-school students at 37 schools in four locations (northeastern GA, n = 9 schools; Chicago, IL, n = 12 schools; Durham, NC, n = 8 schools; and Richmond, VA, n = 8 schools) who participated in the Multi-Site Violence Prevention Project (Multisite Violence Prevention Project (MVPP) (MVPP), 2004). Participating middle schools in Durham and Richmond represented nearly all middle schools in those public school systems. Middle schools in Georgia represented six school districts in Northeastern Georgia. Chicago schools served grades K-8 and were selected based on size (i.e., more than 1100 students) and residence of at least 75% of students within school district boundaries. All participating schools included a high percentage of students from low-income families based on eligibility for the federal

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free or reduced lunch program (i.e., 42% to 96% across sites). Additional details regarding school recruitment and community characteristics are reported in Henry et al. (2004b). These data were collected within the context of a Multi-Site randomized trial in which two to three schools within each site were randomly assigned to each of four intervention conditions: (a) a universal condition consisting of a social-cognitive student intervention (Meyer et al., 2004) and a teacher training and support intervention (Orpinas et al., 2004; n = 9 schools and 1305 youth), (b) a selective intervention condition consisting of a group-based family intervention (Phillips-Smith et al., 2004; n = 10 schools and 1349 youth), (c) a condition that combined both universal and selective interventions (n = 9 schools and 1241 youth), and (d) a no-intervention control condition (n = 9 schools and 1211 youth). In the universal condition, graduate students delivered a 20-session social-cognitive problem-solving curriculum focused on teaching students how to avoid dangerous situations, ignore teasing, ask for help, and defuse potentially violent situations (Meyer, Farrell, Northup, Kung, & Plybon, 2000). The intervention was designed to strengthen students' problem-solving skills, motivation, and self-efficacy as well as improve school norms against the use of violence and aggression. A two-day workshop and 10 support group meetings for teachers also attempted to increase teacher awareness of different forms of aggression, develop their strategies to prevent aggression, improve their classroom management skills, and train them in ways to aid victims of peer aggression (Orpinas et al., 2004). Students identified by teachers as having high levels of aggression and peer influence were recruited, with their parents, for the selective intervention which was designed to increase home–school partnerships; teach effective parent monitoring, supervision, and communication strategies; and improve parent and child coping, self-control, and problem-solving skills. Four to eight youth and their families participated in the 15-week intervention which began with a meal, reviewed the previous week's session and the associated homework, and introduced a new topic area through role plays and other interactive activities. Data were collected from a representative sample of students from two successive cohorts of 6th graders beginning in 2001. Data from students and teacher ratings of individual students in each cohort were collected during the fall and spring of the 6th grade and in the spring of their 7th- and 8th-grade years resulting in four time points. 2.1. Participants Participants were a random sample of approximately 80 students per cohort from the rosters of each of the larger middle schools in three sites and from all eligible students at the smaller Chicago schools. Ten students in self-contained special education classrooms (8 in cohort 1 and 2 in cohort 2) were not included in the sample. Students selected for Cohort 1 who subsequently repeated the 6th grade were not included in Cohort 2. All study procedures were approved by the institutional review boards at the four participating universities and the Centers for Disease Control and Prevention. Consent and assent letters were sent home with students. Telephone follow-ups and home visits were used to increase participation rates. Active parental consent and student assent were obtained from 5625 of the 7364 eligible students, yielding a recruitment rate of 76%. At three sites where it was permitted, students received a $5 gift card for returning the consent forms, whether or not they and their parents agreed to participate. Nine-tenths of the sample (90.7%) had sufficient data for the current analyses, resulting in a sample size of 5106. Because the focus of the study was on school-level variables, data at each wave were only obtained from students who remained in their original schools. The sample included approximately equal numbers of girls and boys. The predominant ethnic identification of participating students was African American (52.1%). Approximately one-fifth identified themselves as Hispanic (21.2%), and one-sixth identified themselves as Caucasian, with the remaining students self-identified as American Indian or Asian. Over two-thirds of participants reported that an adult male lived in their homes. Table 1 provides detailed demographic information about the sample. 2.2. Measures Drawing on data from the Multisite Project, this study used three individual-level outcome variables (i.e., physical aggression, individual beliefs supporting aggression, and self-efficacy for nonviolence) and three school-level constructs measured by five predictor variables (i.e., school norms, student–student

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Table 1 Descriptive characteristics of 5106 students.

Gender Male Female Reported ethnic group Caucasian African American Hispanic/Latino Asian American American Indian Intervention condition Universal Selective Combined Control Participants by site Georgia Illinois North Carolina Virginia Adult male presence in the home Yes No

N

%

2510 2596

50.8% 49.2%

883 2660 1083 169 311

17.3% 52.1% 21.2% 3.3% 6.1%

1305 1349 1241 1211

25.6% 26.4% 24.3% 23.7%

1294 1541 1040 1231

25.3% 30.2% 20.4% 24.1%

3572 1534

70.0% 30.0%

relationships, student–teacher relationships, awareness and reporting of violence, and school safety concerns). Demographic data including information about participants' race, ethnicity, and family structure that had been obtained from students were included. 2.3. Individual-level outcome variables 2.3.1. Physical aggression A physical aggression composite scale composed of five student-report items from the Problem Behavior Frequency Scale (PBFS) and four teacher-report items from the Behavioral Assessment System for Children (BASC) Teacher Rating Scale, Adolescent Form (Reynolds & Kamphaus, 1998) was developed to provide a cross-informant measure of physical aggression. The PBFS was used to obtain students' reports of their frequency of aggression and included seven items representing physical aggression (e.g., “been in a fight in which someone was hit” or “shoved or pushed another kid”; Farrell, Kung, White, & Valois, 2000). Students rated how frequently each item happened in the past 30 days using a 6-point scale: 1 = Never, 2 = 1–2 times, 3 = 3–5 times, 4 = 6–9 times, 5 = 10–19 times, and 6 = 20 or more times. Construct validity of the PBFS items has been established across studies with nationally representative adolescent samples (Kolbe, Kann, & Collins, 1993). The three highest response categories were collapsed to facilitate combining the PBFS with the BASC Teacher Rating Scale (Reynolds & Kamphaus, 1998). The aggression scale of this measure was used by teachers to assess the behavioral problems and adaptive skills of students. It included items such as “bullies others” or “hits other children.” Teachers rated student behavior on a 4-point scale that ranged from Never to Almost Always. The composite scale derived from the PBFS (Farrell et al., 2000) and the BASC Teacher Rating Scale (Reynolds & Kamphaus, 1998) was the main outcome measure for the MVPP (Miller-Johnson et al., 2004), and was created using data from the pilot year of the study with single-parameter item response theory analysis of 13 items from these two scales, each tapping physical aggression (Rasch, 1966). Four items were eliminated because they did not fit a unidimensional scale or had estimated scale positions redundant with other items. The final composite scale had internal consistency ranging from 0.75 at spring of 6th grade to 0.77 at fall of 6th grade.

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2.3.2. Individual beliefs supporting aggression Eleven items tapped student's approval or disapproval of aggressive behaviors (e.g., “How would you feel if a kid hit someone who hit first?”). The items were adapted from a scale developed by Henry, Cartland, Ruch-Ross, and Monahan (2004a) to measure classroom norms. Items were rated on a 3-point scale, anchored by disapprove, neutral, and approve. Internal consistency ranged from 0.79 at 6th-grade fall to 0.86 at 8th grade spring. Validity evidence comes from significant and positive correlations of this measure with measures of physical aggression (see Table 2). In addition, the MVPP (2008) found significant effects for the universal social-cognitive intervention (described above) changing individual beliefs on this variable among youth at higher levels of individual risk, providing criterion-related validity evidence. 2.3.3. Self-efficacy for nonviolence A child's perception about his or her ability to resolve conflicts in nonviolent ways was measured using the Teen Conflict Survey (Bosworth & Espelage, 1995). This 7-item scale includes items such as “How confident are you that you can stay out of fights?”). This scale employed a 5-point Likert-type scale ranging from “not at all confident” to “very confident.” Internal consistency ranged from 0.81 at 6th-grade fall to 0.87 at 8th grade spring. Bosworth, Espelage, Daytner, DuBay, and Karageorge (2000) found that selfefficacy for nonviolence predicted aggression strongly and negatively. In the MVPP (2009) study, selfefficacy correlated significantly with beliefs about nonviolence (r = .54, p b .01) and use of nonviolent strategies (r = .68, p b .01). 2.4. School-level predictors Aggregate measures were constructed for each school-level risk or promotive factor by taking the mean score of all students within each cohort at each of the 37 schools for each wave. This method resulted in 74 school-level scores per wave. In other words, each student in a given cohort within a school had the same score on each school-level measure. 2.4.1. School norms favoring nonviolence School norms were assessed by aggregating individual reports on an expanded version of a scale developed by Henry et al. (2004a). The original scale was designed to assess students' perceptions about the extent to which students at their school approved of aggressive behaviors (e.g., “How would the kids at your school feel if a kid hit someone who hit first?”). The Multisite Violence Prevention Project (Miller-Johnson et al., 2004) expanded the scale by adding seven items tapping approval of nonviolent alternatives to aggression for solving problems (e.g. “How would kids in your school feel if a kid avoided a fight by walking down a different hall to class?). Items were rated on a 3-point scale, anchored by disapprove, neutral, and approve. Internal consistency of the individual-level scores ranged from 0.82 at 6th-grade fall to 0.88 in the spring of 8th grade. Henry et al. (2004a) found that the measure of school norms for aggression predicted individual levels of aggression in a study of rural, suburban, and urban schools. Table 2 Descriptive statistics and correlations of individual-level outcome variables. Variable

Child–Teacher Composite Aggression (CTAG) Beliefs Supporting Aggression (BSA) Self-efficacy For Nonviolence (SEN)

6th-grade fall

8th-grade spring

Mean

SD

Mean

SD

0.35 1.38 4.04

0.33 0.36 0.88

0.38 1.66 3.65

0.33 0.41 0.97

αa

ICC(1)

0.97 0.80 0.85

0.06 ⁎⁎ 0.03 ⁎⁎ 0.04 ⁎⁎

Correlations

CTAG

BSA

SEN

0.43 0.36 − 0.34

0.39 0.39 − 0.44

− 0.41 − 0.44 0.33

Note. N = 5106 students. Correlations whose absolute values are greater than 0.07 are significant at p b .01. 6th-grade fall correlations are in the subdiagonal, 8th-grade spring correlations are in the superdiagonal, and stability coefficients are on the diagonal. a Kuder–Richardson reliability for Composite Aggression and coefficient alpha for the other measures. ⁎⁎ p b .01.

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The Multisite Violence Prevention Project (Miller-Johnson et al., 2004) found that two factors fit this measure best at the individual level. However, multilevel factor analysis done for this study with an unconstrained covariance matrix at the individual level and the factor structure specified at the school level found that a single factor solution was a excellent fit to the data at 6th-grade fall, χ 2(135) = 110.31, ns, CFI = 1.0, RMSEA = 0.000, and at 8th grade spring, χ 2(135) = 58.85, ns, CFI = 1.0, RMSEA = 0.000. Based on this evidence, school norms opposing aggression and favoring nonviolence were included in a single scale. For brevity and clarity, we refer to this scale as school norms favoring nonviolence because it was scaled so that higher scores indicate less endorsement of aggression and more endorsement of nonviolent solutions to problems at the school level of analysis. 2.4.2. Interpersonal climate: positive student–student relationships and positive student–teacher relationships School means and individual scores were calculated on two measures of the interpersonal climate of the school (Vessels, 1998). Positive student–student relationships (7 items such as “Students are kind and supportive of each other;” α = 0.61) and positive student–teacher relationships (4 items such as “Teachers treat students with respect;” α = 0.66) both measured perceived quality of relationships. Response anchors ranged from strongly disagree to strongly agree on 4-point scales. Higher values indicated more positive relationships. Multilevel factor analyses similar to those conducted for school norms found excellent fit for a two-factor school-level solution at the fall of 6th grade, χ 2(43, N = 5421) = 39.34, ns, CFI = 1.0, RMSEA = 0.000, and in the spring of 8th grade, χ 2(43, N = 5421) = 55.42, ns, CFI = 1.0, RMSEA = 0.001. 2.4.3. School responsiveness to violence: teacher awareness and reporting of violence and school safety concerns The Teacher Awareness and Reporting subscale of the Vessels School Climate scale (Vessels, 1998) taps the student's view that teachers recognize violence, are receptive to student reports of such problems, and take appropriate action. It is composed of seven items such as “Teachers know when students are being picked on or being bullied.” α = 0.63). A second measure of school responsiveness was the School Safety Concerns subscale from the Department of Education School and Staffing Survey (U.S. Department of Education, 1999–2000). This 12-item self-report scale measures mostly malleable characteristics of the school that are related to violence such as “Unsafe areas in schools,” “Students carrying weapons” and “teachers ignore it when students threaten others.” Items were anchored on a 4-point scale from “Not a problem” to “Serious problem.” The scale had internal consistency of 0.89. Multilevel factor analysis found excellent fit for a two-factor school-level solution in the fall of 6th grade, χ 2(89, N = 5421) = 107.28, ns, CFI = 1.0, RMSEA b 0.01, and a reasonable fit for a two factor solution in the spring of 8th grade, χ 2(89, N = 5421) = 123.76, p b .01, CFI = 0.99, RMSEA = 0.01. 2.5. Procedures Students completed measures at school in groups of 10 to 20 using an audio-assisted computerized interview (AUDIO-CASI; Cooley et al., 1996). Students listened to recorded questions read by men and women from different ethnic groups through headphones while reading them on the computer screen and then entered their responses using the keyboard. Student behavior ratings were obtained from one teacher per student at each wave. Four waves were collected: fall and spring of 6th grade, and spring of 7th and 8th grades. The teacher who was best able to rate each student was identified by each team of teachers. As a result, several students were assessed by the same teacher at different waves (74.5% between fall and spring of 6th grade; 9.7% between spring of 6th and spring of 7th grades; and 29.1% between spring of 7th and spring of 8th grades). Teachers whose classes had many participating students were instructed to complete BASC Teacher Rating Scales on no more than five students at a time. With the permission of the test publisher, the instructions for completing the BASC Teacher Rating Scales were modified slightly for this study. Whereas the original instructions read, “Please read each phrase and mark the response that describes how this child has acted over the last six months. If the child's behavior has changed a great deal during this period, describe the child's recent behavior,” the MVPP instructions read, “Please read each phrase and mark the response that describes the child's recent behavior.” Teachers were paid $10 for each BASC Teacher Rating Scales completed.

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2.6. Data Analysis 2.6.1. Preliminary data analysis Although a measure may have internal consistency at the individual level, aggregating individual scores may not produce meaningful aggregated measures (Rousseau, 1985; Shinn, 1990; Shinn & Rapkin, 2000). Thus, the first analytic task was to assess the school-level reliability of the predictors using cohort within school as the level of aggregation.

2.6.2. Main data analysis Mixed effects regression models were used to test the effects of the school-level predictors on the outcomes. Mixed effects models are models that contain both fixed and random effects. A fixed effect is an effect that represents the best estimate for all individuals in the analysis. A random effect is a variance of individual effects or covariance between groups of individual effects. Mixed effects models allow longitudinal analyses that are much more flexible than traditional repeated measures ANOVA. Because each data point within each individual is used to estimate the group trend at the time the measure was collected, mixed effects models can include cases with missing data on one or more assessments, as are frequently encountered in longitudinal studies, without requiring imputation of missing data (Hedeker & Gibbons, 2006; Singer & Willett, 2004). All models for this study were fit using SAS PROC MIXED (SAS Institute Inc., 2004). The first set of mixed effects regression models assessed the effects of each school-level predictor on each outcome variable for the entire sample (including terms to test moderation by gender). The specific models fit were as follows: Level 1ðObservations within individualsÞ : Yijk = P0jk + P1jkðTime–W1TimeÞ + P2jkðSeasonÞ + rijk where Yijk is an outcome (individual aggression, normative beliefs for aggression, or self-efficacy for nonviolence) for person j at wave i and cohort and school k, accounting for the season in which the measurement was collected (fall = −1 versus spring = 0). Time in these analyses was centered at pretest so that all effects on the school intercepts could be interpreted as effects at the fall of 6th grade. Including the term for season allowed us to model the spring to fall decrease in aggression within an overall pattern of linear change.

Level 2 ðIndividualÞ : P0jk = B00k + B01kðGender–School Mean GenderÞ + B02kðEthnicity1Þ + B03kðEthnicity2Þ + B04kðCohortÞ + B05kðAdult Male in the HomeÞ + B06kðIndividual Score on Schoollevel PredictorÞ + e0jk P1jk = B10k + B11kðGender–SchlMeanGenderÞ + B12kðEthnicity1Þ + B13kðEthnicity2Þ + B14kðCohortÞ + B15kðAdult Male in the HomeÞ + B16kðIndividual Score on Schoollevel PredictorÞ + e1jk P2jk = B20k P3jk = B30k

where P0jk is the intercept for individual j in school k, P1jk is the individual's linear slope, B00k is the intercept for school k, B01k estimates the effect of gender on the individual intercept, B02k and B03k

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estimate ethnicity effects on the intercept (African American non-Hispanic vs. Caucasian/other, and Hispanic vs. Caucasian/other), and B04 estimates effects of having an adult male in the home. Level 3 : Cohort within School B00k = G000 + G001ðSchoollevel predictorÞ + G002ðSite1Þ + G003ðSite2Þ + G004ðSite3Þ + G005ðUniversalÞ + G006ðSelectiveÞ + G007ðCombinedÞ + u00k B01k = G010 + G011ðSchoollevel predictorÞ B02k = G020 B03k = G030 B04k = G040 B05k = G050 B06k = G060 B10k = G100 + G101ðSchoollevel predictorÞ + G102ðSite1Þ + G103ðSite2Þ + G104ðSite3Þ + G105ðUniversalÞ + G106ðSelectiveÞ + G107ðCombinedÞ + u10k B11k = G110 + G111ðSchoollevel predictorÞ B12k = G120 B13k = G130 B14k = G140 B15k = G150 B16k = G160 B20k = G200 B30k = G300

Here, G000 is the overall intercept, G100 is the linear rate of growth, and G001 and G101 are the effects of the school-level predictor on the intercept and slope respectively. The parameters G011 and G111assess the impact of the school-level predictors on the effects of gender. Gender was centered within cohort and school in order to estimate the effects of individual gender independently of the effects of school gender composition. We also included Level-3 terms estimating the effects of site (G002, G003, G004, G102, G103, G104), and intervention condition (G005, G006, G007, G105, G106, G107) on intercepts and slopes. Random error terms were included for intercepts and slopes at the individual (e0jk, e1jk) and school levels (u0jk, u1jk), in addition to the residual error term at the wave of measurement level (rijk). The second type of analysis involved estimating the effects of the school-level predictors separately for males and females. Dividing the sample by gender, we fit the following model for males and females separately: Level 1ðObservations within individualsÞ :   Yijk = P0jk + P1jkðTimeÞ + P2jk Time2 + P3jkðSeasonÞ + rijk

where Yijk is an outcome (individual aggression, normative beliefs for aggression, or self-efficacy for nonviolence) for person j at wave i and cohort and school k, accounting for season (fall versus spring). Level 2ðIndividuals within Cohorts = SchoolsÞ : P0jk = B00k + B01kðEthnicity1Þ + B02kðEthnicity2Þ + B03kðCohortÞ + B04kðAdult Male in the HomeÞ + B05kðIndividual Score on School−level PredictorÞ + e0jk P1jk = B10k + B11kðEthnicity1Þ + B12kðEthnicity2Þ + B13kðCohortÞ + B14kðAdult Male in the HomeÞ + B15kðIndividual Score on School−level PredictorÞ + e1jk P2jk = B20k P3jk = B30k

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where P0jk is the intercept for individual j in school k, P1jk, B00k is the intercept for school k, B01k estimates the effect of gender on the individual intercept, B02k and B03k estimate ethnicity effects on the intercept (African American non-Hispanic vs. Caucasian/other, and Hispanic vs. Caucasian/other), and B04 estimates effects of having an adult male in the home. Level 3 : Cohort within School B00k = G000 + G001ðSchoollevel predictorÞ + G002ðSite1Þ + G003ðSite2Þ + G004ðSite3Þ + G005ðUniversalÞ + G006ðSelectiveÞ + G007ðCombinedÞ + u00k B01k = G010 B02k = G020 B03k = G030 B04k = G040 B05k = G050 B10k = G100 + G101ðSchoollevel predictorÞ + G102ðSite1Þ + G103ðSite2Þ + G104ðSite3Þ + G105ðUniversalÞ + G106ðSelectiveÞ + G107ðCombinedÞ + u10k B11k = G110 B12k = G120 B13k = G130 B14k = G140 B15k = G150 B20k = G200 B30k = G300 Here, G000 is the overall intercept, G100 is the linear rate of growth, and G001 and G101 are the effects of the school-level predictor on the intercept and slope respectively. As in the model for the total sample, we included Level-3 terms estimating the effects of site (G002, G003, G004, G102, G103, G104), and intervention condition (G005, G006, G007, G105, G106, G107) on intercepts and linear growth respectively. School-level predictor means and the individual-level scores on the school-level predictor measures were entered as time-varying covariates. Entering the predictors in this manner allowed us to assess effects at 6th grade entry and change in effects over the course of middle school. We also fit a set of models that assessed the variance attributable to the school-level predictor variables over and above the variance predicted by the individual scores on all of the school-level predictor measures, time terms (linear and season), gender, ethnicity, and site. For this purpose we used a pseudo R 2 representing the variance in school- or individual-level intercepts or slopes accounted for by addition of all of the school-level predictors simultaneously (Singer & Willett, 2004, p. 104). 3. Results Table 2 reports the descriptive statistics, internal consistency reliabilities, and correlations among the individual-level outcome variables at baseline (6th-grade fall) and 8th-grade spring. The subdiagonal contains the correlations at baseline, the superdiagonal contains the correlations at 8th grade spring, and the diagonal contains the stability correlations of each outcome over the 2.5 years of the study. Most of the bivariate correlations of individual-level outcome variables are small to moderate in magnitude (0.22 to 0.48) and in the expected directions. All were statistically significant at p b .01 due to the large sample size. Table 3 reports the descriptive statistics, Level 2 reliabilities, and correlations among the school-level risk and promotive factors used as predictors in this study. As can be seen there, all of the school-level predictors had level-2 reliabilities of at least 0.60 except for student–teacher relationships (L2Rel = 0.47)

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Table 3 Descriptive statistics, level 2 reliabilities, and school-level correlations of predictors. Predictor

6th-grade fall

8th-grade spring

Mean

Mean

SD

L2Rel Correlations

SD

School Norms Favoring Nonviolence (SN) − 0.08 0.09 − 0.32 0.12 0.81 Interpersonal Climate Student–Student Relationships (SSR) 2.67 0.09 2.64 0.10 0.63 Student–Teacher Relationships (STR) 3.12 0.10 2.80 0.19 0.47 School Responsiveness to Violence Awareness and Reporting (AAR) 2.97 0.10 2.60 0.16 0.60 School Safety Problems (SSP) 1.83 0.28 1.60 0.29 0.92

SN

SSR

STR

AAR

SSP

0.66 ⁎⁎ 0.42 ⁎⁎

0.55 ⁎⁎ 0.64 ⁎⁎ − 0.28 ⁎

0.47 ⁎⁎ 0.50 ⁎⁎ 0.40 ⁎⁎ 0.46 ⁎⁎

0.56 ⁎⁎ 0.57 ⁎⁎ 0.38 ⁎⁎ 0.75 ⁎⁎

0.56 ⁎⁎ 0.68 ⁎⁎ 0.53 ⁎⁎ 0.54 ⁎⁎ − 0.13 0.24 ⁎ − 0.12 0.21

0.03 0.16 0.04 0.86 ⁎⁎

Note. N = 74 units. Units consist of one cohort in one school. 6th-grade fall correlations are on the sub-diagonal, 8th-grade spring correlations are on the superdiagonal, and stability correlations are on the diagonal. L2Rel = Level 2 Reliability. ⁎ p b .05. ⁎⁎ p b .01.

indicating similar perceptions across students in the same schools. Examination of the intraclass correlation for this variable indicated significant clustering at the school level of analysis (ICC = 0.02, p b .01). This result confirmed that it was acceptable to use the aggregated individual-level variables as indictors of school-level constructs (Rousseau, 1985). The bivariate correlations of the predictors in Table 3 were calculated from the school-level predictors. Eight of ten bivariate correlations among the predictors were significant at baseline and seven of ten were significant at 8th grade spring. All of the stability coefficients at the school level were significant. Some conceptually related predictors such as student–student and student–teacher relationships were moderately correlated (r = .46 at baseline and r = .56 at 8th-grade spring). Moderate to strong correlations were found between student–teacher relationships and awareness and reporting (r = .53 at baseline and r = .75 at 8th-grade spring). The main results of the study are reported next. The first are those testing the hypothesis concerning the influence of school-level variables on student behaviors and attitudes at 6th grade and over time. Next results are presented that test the second hypothesis concerning moderation of these relations by gender. 3.1. Main effects and changes in effects over time The estimates of effects for testing our hypotheses are reported in Table 4. In that table the asterisks indicate that the effects to which they are attached are significantly different from zero and the superscripts indicate gender differences in the magnitude of the effects. Table 4 also reports the random effects for each outcome from the analysis with school norms as the predictor. 3.2. School norms As can be seen in the first line under each panel of Table 4, school norms favoring nonviolence was significantly related to fall 6th grade levels of all three outcomes (aggression, beliefs supporting aggression, and self-efficacy for nonviolence), with evidence of change in the effect on beliefs during middle school. In the fall of 6th grade, school norms favoring nonviolence predicted individual aggression (G001 = − 0.17, p b .01) and beliefs supporting aggression (G001 = − 0.38, p b .01) negatively. School norms was positively associated with self-efficacy for nonviolence (G001 = 0.35, p b .01) in the fall of 6th grade. The negative effect of school norms on individual beliefs favoring aggression weakened during middle school (G101 = 0.13, p b .01). 3.2.1. Interpersonal climate Both measures of interpersonal climate were significantly and negatively associated with individual aggression at 6th-grade entry (G001 = −0.12 and −0.11 respectively, p b .01). The negative relation of

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Table 4 Effect estimates from full sample and by gender. Predictor

Effect at 6th-grade fall (G001)

Change in effect per year (G101)

Main effect models

Main effect models

Moderated by gender Female

Child–teacher composite aggression School norms − 0.17 Interpersonal climate Student–student relationships − 0.12 Student–teacher relationships − 0.11 Responsiveness to violence Awareness and reporting − 0.09 School safety problems − 0.01 Random effectse School-level variance 0.0029 School-level covariance −0.0006 Individual within school 0.0610 variance Individual within school −0.0048 covariance Residual 0.0464 Beliefs favoring aggression School norms Interpersonal climate Student–student relationships Student–teacher relationships Responsiveness to violence Awareness and reporting School safety problems Random effectse School-level variance School-level covariance Individual within school variance Individual within school covariance Residual

Male

Female

⁎⁎ − 0.25

⁎⁎

− 0.10

⁎⁎ − 0.06 ⁎⁎ − 0.12

⁎⁎

− 0.19 − 0.10

⁎⁎ − 0.14 0.03

⁎⁎a − 0.05 − 0.03

b

⁎⁎ 0.0030 ⁎ − 0.0008 ⁎⁎ 0.0502

⁎⁎ ⁎⁎ ⁎⁎

0.0023 − 0.0004 0.0717

⁎⁎ − 0.0017

⁎⁎

− 0.0080

⁎⁎

⁎⁎

⁎⁎

0.0464

⁎⁎

0.0356

Moderated by gender

− 0.00

0.01 ⁎⁎ ⁎⁎

Male 0.03 c

0.08 0.04

d

0.03 0.04

⁎

0.01 0.04

0.01 0.04

⁎

0.02 0.03

c

0.02 0.04

d

⁎⁎

0.0006

⁎⁎

0.0004

⁎⁎

0.0007

⁎⁎

⁎⁎

0.0065

⁎⁎

0.0048

⁎⁎

0.0084

⁎⁎

− 0.38

⁎⁎ − 0.33

⁎⁎

− 0.42

⁎⁎

0.13

⁎⁎

0.12

⁎c

0.14

⁎⁎d

− 0.22 − 0.22

⁎⁎ − 0.27 ⁎⁎ − 0.23

⁎⁎ ⁎⁎

− 0.19 − 0.23

⁎ ⁎⁎

0.13 0.14

⁎⁎ ⁎⁎

0.14 0.13

⁎c ⁎⁎c

0.11 0.15

⁎d ⁎⁎d

− 0.28 − 0.06

⁎⁎ − 0.27 − 0.01

⁎⁎

− 0.30 − 0.03

⁎⁎

0.12 0.10

⁎⁎ ⁎⁎

0.10 0.07

⁎⁎c ⁎⁎c

0.14 0.09

⁎⁎d ⁎⁎d

⁎

0.0000

⁎⁎

0.0077

0.0012 0.0000 0.0596 −0.0021 0.0653

Self-efficacy for nonviolence School norms 0.35 Interpersonal climate Student–student relationships 0.14 Student–teacher relationships 0.22 Responsiveness to violence Awareness and reporting 0.24 School safety problems − 0.06 Random effectse School-level variance 0.0090 School-level covariance −0.0015 Individual within school 0.3836 variance Individual within school −0.0292 covariance Residual 0.3979

⁎⁎ ⁎⁎ ⁎

0.0004 0.0000 0.0519

⁎⁎

0.0013 0.0000 0.0677

− 0.0010

− 0.0037

⁎⁎

0.0588

0.0588

⁎⁎

0.57

⁎⁎a

0.14

⁎⁎

0.27 0.30

⁎⁎

0.11 0.13

⁎⁎

0.41 − 0.07

⁎⁎

⁎⁎a a

⁎

0.0073

0.0000 ⁎⁎

0.0080

⁎⁎

⁎

b

b b

0.0102 − 0.0017 ⁎⁎ 0.3350

⁎⁎

0.0101 ⁎ − 0.0036 0.4274 ⁎⁎

⁎⁎ − 0.0181

⁎

− 0.0386 ⁎⁎

⁎⁎

⁎⁎

0.3498

⁎⁎

0.08 − 0.07

0.0001

0.02

− 0.07

0.04 − 0.08

0.03 − 0.16

⁎⁎

0.07 0.01

− 0.05 − 0.12

− 0.14 ⁎⁎ − 0.14

⁎ ⁎⁎

0.05 − 0.08

0.0002

⁎⁎

0.0000

0.0630

⁎⁎

0.0575

0.03

0.0026 ⁎⁎

0.0664

⁎⁎

Note. Main Effect Model estimates were from the full sample with gender centered within school, and within-gender models were from separate analyses by gender. a–b Effect at 6th-grade entry differs by gender at p b .05. c–dChange in effect differs by gender at p b .05. eRandom effects for each outcome are taken from the analysis with school norms as the predictor. ⁎ p b .05. ⁎⁎ p b .01.

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student–teacher relationships and aggression was estimated to weaken significantly each year during the course of middle school (G101 = 0.04, p b .05). Both interpersonal climate variables also were negatively associated with school mean individual beliefs supporting aggression (G001 = −0.22 for student–student relationships and student–teacher relationships, p b .01), and both of these negative relations also became weaker over time (G101 = 0.13 and 0.14, p b .01). Only school-level student–teacher relationships was associated with initial levels of self-efficacy for nonviolence (G001 = 0.22, p b .01), with no significant change over time. 3.2.2. Responsiveness to violence School safety problems did not predict fall 6th grade levels of any of the three outcome variables but awareness and reporting predicted all three outcomes. Higher 6th-grade fall levels of awareness and reporting of violence were associated with lower levels of aggression, (G001 = − 0.09, p b .01), lower levels of individual beliefs supporting aggression (G001 = − 0.28, p b .01), and higher levels of self-efficacy for nonviolence (G001 = 0.24, p b .01). The negative effect of awareness and reporting on school mean individual beliefs supporting aggression was strongest in the fall of 6th grade, and became weaker each year, G101 = 0.12, p b .01. The null effect of school safety problems on beliefs supporting aggression became significantly more positive with each year, G101 = 0.10, p b .01. The null effect of school safety problems on self-efficacy for nonviolence in the fall of 6th grade became increasingly negative each year, G101 = − 0.10, p b .01. School safety problems was the only significant predictor of change in self-efficacy for nonviolence over time. 3.3. Moderation by gender 3.3.1. School norms Moderated analyses suggested that the 6th-grade fall relations between school norms and outcomes were stronger among females than among males. School norms favoring nonviolence predicted initial school mean levels of beliefs supporting aggression equally for both females (G001 = − 0.33, p b .01) and males (G001 = −0.42, p b .01). However, school norms predicted school mean individual aggression significantly for females, G001 = − 0.25, p b .01, but not for males, G001 = −0.10, ns. Norms also predicted self-efficacy more strongly among females (G001 = 0.57, p b .01) than among males (G001 = 0.14, ns). When looking at how gender moderated change in effects of school norms (or more accurately, how change in the effect of school norms was different for males and females) during the remainder of middle school, only one difference emerged. The negative effect of school norms favoring nonviolence on individual beliefs favoring aggression became weaker among males (G101 = 0.14, p b .01) and females (G101 = 0.12, p b .01), but the change in effects differed significantly by gender. 3.3.2. Interpersonal climate On effects at 6th-grade fall, none of the test for differences in effects by gender produced significant results. The effect of Student–teacher relationships on self-efficacy for nonviolence was significant among females (G001 = 0.30, p b .01) but not among males (G001 = 0.13, ns). On interpersonal climate, there was substantial evidence of gender moderation in the terms for change in effects. Among females, the null effect of student–student relationships on mean individual aggression was stable during middle school (G101 = 0.01, ns), whereas the significant negative effect of student– student relationships on aggression weakened slightly during middle school among males (G101 = 0.08, ns). Although the change in slope among males was nonsignificant, as the means and 95% confidence intervals plotted in Fig. 1 show, the gender difference in yearly change, when considered over the two and a half years of middle school, resulted in no gender difference by 8th-grade spring. The effects of both interpersonal climate variables on individual beliefs changed became weaker among both males and females during the middle school years, but the degree of change differed by gender. On student–student relationships, the rate of change was significantly greater among females (G101 = 0.14) than among males (G101 = 0.11), but on Student–Teacher Relationships the reverse was true (G101 = 0.13 vs. 0.15). Lastly, the rate of change in the effect of student–teacher relationships on self-efficacy was significant and negative among females (G101 = − 0.16, p b .05), but nonsignificant among males (G101 = 0.01, ns).

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Fig. 1. Effects of positive student–student relationships on individual aggressive behavior, by gender at 6th-grade fall and 8th-grade spring, in units of the pooled Wave 1 standard deviations, with 95% confidence intervals.

3.3.3. Responsiveness to violence Similar to the results for school norms, relations between one measure of responsiveness to violence and outcomes were significantly stronger for females than for males. The effect of awareness and reporting on individual aggression was significant and negative among females (G001 = −0.14, p b .01) but nonsignificant among males (G001 = −0.05, ns). Awareness and reporting also significantly predicted selfefficacy for nonviolence among females (G001 = 0.41, p b .01), but not among males (G001 = 0.08, ns), and the male–female difference in effects was significant (G111 = − 0.22, p b .05). The directions of the significant effects among females were as expected: higher levels of awareness and reporting were associated with lower aggression and higher self-efficacy for nonviolence. Awareness and reporting predicted beliefs supporting aggression for both females and males (G001 =− 0.27, p b .01; G001 = − 0.30, p b .01, respectively). Evidence of gender differences in the rates of change in relations between responsiveness to violence and outcomes emerged for all three outcome variables. The rate of change in the effect of school safety problems on individual aggression was significantly different for males and females, G111 = − 0.01, p b .01, and the males' rate of change was marginally different from zero, G101 = 0.04, p b .10. Among males, the effect of awareness and reporting of violence on individual beliefs favoring aggression became less negative over the course of middle school (G101 = 0.14, p b .01), which was significantly greater in magnitude than the rate of change among females (G101 = 0.10, p b .01). Among females, the effect of awareness and reporting on self-efficacy became more negative over time (G101 = −0.14, p b .05), and this was significantly different among males, for whom this effect became slightly more positive over time (G101 = 0.05, ns). 3.4. Combined effects of school-level predictors As was noted above, pseudo R 2 values index the reduction in variance of school-level intercepts when all of the school-level predictors were entered over a base model (Singer & Willett, 2004, p. 104). For physical aggression, 36.4% of the variance in school-level intercepts was accounted for by the addition of the school-level predictors taken together, χ 2(7, N = 5106) = 33.02, p b .01. On beliefs supporting aggression, 23.2% of the school-level intercept variance was accounted for by the school-level predictors together, χ 2(7, N = 5106) = 88.53, p b .01. On self-efficacy for nonviolence, 28.5% of the school-level intercept variance was accounted for by the school-level predictors taken together, which was significant, χ 2(7, N = 5106) = 29.55, p b .01. The individual-level scores on the school-level predictors, taken together, accounted for 9.2% of the individual intercept variance in physical aggression, 24.2% of the variance in beliefs supporting aggression, and 28.4% of the individual intercept variance in Self-efficacy for Nonviolence.

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4. Discussion The results of this study present a complex picture of the association between school-level risk and promotive factors and outcomes related to aggression at the start of the 6th grade, how those relations vary by gender and change over the middle school years. First, these results affirm that significant amounts of added information can be provided by school levels of norms, interpersonal climate, and responsiveness to violence over the information provided by individual scores on these same variables. These findings also suggest that males and females respond to different risk and promotive factors in the same social environment, sometimes, in different directions, and that their responses are consistent with what is known about variation in self-concept and social cognition between males and females. Further, these results document the changes that occur between 6th and 8th grades in the relations between school-level factors and individual outcomes, and how such changes vary by gender. The following paragraphs summarize the effects of each type of process studied. 4.1. Specific processes 4.1.1. Norms Previous studies suggest that student norms are important factors in the development of aggression (Henry et al., 2000; Henry et al., 2004a; Salmivalli & Voeten, 2004). This study tested the hypothesis that stronger school-level norms against aggression and favoring nonviolence would predict lower levels of aggression and individual beliefs supporting aggression. Stronger school norms favoring nonviolence were expected to predict higher levels of self-efficacy for nonviolence. The results are generally consistent with these expectations, but the findings suggest that gender and time moderate the influence of school norms. The effects of school norms favoring nonviolence on aggression and on self-efficacy for using nonviolent responses appear to be stronger among females than among males. Males and females differed significantly in their rates of change in these effects, despite the significant and negative effects for both genders at 6th-grade entry, and the absence of either a significant overall rate of change or significance of either gender-specific rate of change. The initially negative relation for males becomes weaker each year so that by 8th grade school norms have no effect on male aggression. This finding is reminiscent of the developmental variation observed in the effects of individual normative beliefs on aggressive behavior (Huesmann & Guerra, 1997). It suggests that the maximum effect of middle school interventions to alter school norms among both males and females may be obtained by intervening in 6th grade or earlier, given previous findings about the effects of classroom norms among younger children (e.g., Henry et al., 2000). 4.1.2. Interpersonal climate Previous research led to the hypothesis that more positive interpersonal climate, represented in this study by student–student relationships and student–teacher relationships, would predict lower levels of aggression and higher levels of beliefs and self-efficacy for nonviolent alternatives to aggression. The results support the predictions. At least one of the measures tapping interpersonal climate was a significant predictor of each outcome, but there was variation by gender. Relations between the interpersonal climate variables and the outcomes of aggression and individual beliefs supporting aggression appeared to weaken over the course of middle school. Unlike the pattern for school norms, the effects of interpersonal climate weakened for both males and females over the course of middle school. 4.1.3. School responsiveness to violence Higher school levels of responsiveness to violence, indicated by higher levels of awareness and reporting of violence and lower levels of school safety problems, were expected to predict lower levels of aggression and higher levels of beliefs and self-efficacy for nonviolent responses. Awareness and reporting appeared to be associated with all three outcomes among females, but only with beliefs supporting aggression among males. There also appeared to be some developmental variation. This evidence is consistent with the notion that greater responsiveness of teachers and students to aggressive behavior may act to not only reduce aggression and violence, but also to encourage positive alternative ways of dealing with conflicts. Greater responsiveness to violence would encourage students to search for new ways to resolve conflicts.

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School safety problems, the other variable indicative of school responsiveness to violence, relates in the hypothesized directions to aggression and to each of the social-cognitive outcomes. It appears that school safety problems such as unsafe areas and weapon carrying may affect aggression directly and through increasing normative beliefs supporting aggression and decreasing self-efficacy for nonviolence. School safety problems appeared to have little effect on beliefs supporting aggression or self-efficacy for nonviolence early in middle school, but the effects on both changed with each year. By 8th grade, school safety problems were associated with beliefs more supportive of aggression and lower self-efficacy for nonviolence. 4.1.4. Gender differences In the fall of 6th grade, females appeared to be affected by school environments to a greater extent than males. Female aggression was more closely tied to school norms favoring nonviolence and awareness and reporting than was males' aggression. Among females but not males, awareness and reporting predicted self-efficacy in 6th grade. The strongest evidence that females are more responsive to school-level variables than males came from the outcome self-efficacy for nonviolence, on which three of five school-level predictors were significant for females and none were significant for males. This pattern changes somewhat when gender differences are analyzed over time. 4.2. Limitations It should be recalled that all of the effect sizes reported in this study are effects of the school-level intercepts after the variance attributable to individual scores on the same variables is accounted for. Accordingly, the effect size estimates (pseudo R 2) are estimates of the proportion of school-level variance in the outcome accounted for by the school-level predictors, which is the method Singer and Willett (2004, p. 105) recommend for calculating this type of effect size. Although the study aimed to test the effects of school-level predictors on individual level variables, the models specify the effects on individual-level outcomes as effects on their school-level intercepts. The effect sizes on each individual student and on points of measurement within individuals cannot be estimated because these levels of measurement are nested within the school level of analysis. Substantial individual-level variability in outcomes was predicted by the individual-level scores on the predictors, but individual-level variability on the predictors is not relevant to the question of the effects of school-level predictors, and was included in the models so that the school-level effects would truly be effects of the school-level variables. The models included multiple other variables that account for additional variability in aggressive behavior and attitudes, including gender, ethnicity, season, site, time, and intervention condition. A second limitation is that these data were taken from a prevention trial, in which most participants took part in one or both interventions. Thus, the results may have been affected to some extent by the interventions. Including dummy codes for intervention condition in the models with the control condition as the comparison addressed this limitation, as did conducting the analyses using control participants only to determine if the directions of effects differed substantially from those reported here. They did not, providing some reassurance that the strategy of reporting the effects from the full sample was appropriate. Another limitation related to the prevention project from which this sample was derived concerns representativeness. At the individual level, the sample was ethnically diverse and sizable enough to include substantial numbers of youth of multiple ethnic groups. The sample of schools, however, was drawn intentionally from the universe of schools serving lower income populations. This characteristic of the sample might limit the extent to which the findings can be generalized to schools and districts serving more affluent students. Related to the issue of representativeness is the slight possibility that assignment to certain teachers and classes was partly due to child behavior, and that such assignment may have affected the obtained results. A fourth set of limitations concerns the measures used in the study. All of the measures but one (composite aggression) were derived from self-reports, which may be more subject to social desirability bias than measures derived from multiple sources. Some of the items and measures were developed specifically for the MVPP study (e.g., school norms for nonviolence; cf., Miller-Johnson et al., 2004) and thus had less evidence for validity than more widely used measures. Additionally, some of the individual reports of the school-level predictors had somewhat low internal consistency (e.g., individual reports of

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student–teacher relationships had an alpha of 0.61 and individual reports of awareness and reporting had an alpha of 0.63). Evidence for the validity of the structure of these measures at the school level of analysis comes from multilevel factor analysis and intraclass correlations. Related to this limitation is the possible role of common method variance, which might have inflated the results. The role of common-method variance would likely be weaker due to the cross-level effects that were the focus of this study. 4.3. Implications for schools The results of this study highlight the importance of 6th-grade entry as a time in middle school when the potential effects of the school environment may be strongest. After 6th grade, the influence of the school environment on aggression and social cognition appears to weaken. There appears to be some developmental variation in contextual influences by gender, but its nature should be pursued in future research. These results suggest some other potentially fruitful avenues for investigation and intervention aimed at changing school-level factors to reduce violence. Four of the school-level predictor variables (i.e., school norms, student–student relationships, student–teacher relationships, and awareness and reporting) had significant effects on physically aggressive behavior for youth of both genders. Two showed no evidence of gender variation at 6th-grade entry, and two showed no evidence of deterioration in these effects over time. These results suggest that efforts to foster a culture of nonviolence in which there are clear expectations that problems can and should be solved nonviolently can contribute to lowered levels of aggression, possibly through enhancing individual beliefs and confidence in the efficacy of nonviolent solutions. Such a culture would be identifiable by students' shared beliefs in the desirability of nonviolent solutions coupled with enforcement of such normative expectations by teachers. Although other studies have suggested the importance of a school-wide culture endorsing nonviolence, this study contributes to the literature by providing recommendations about specific mechanisms of change and factors that do not seem to influence student outcomes. The findings suggest the importance of targeting teachers' beliefs and practices to promote this school-wide culture, specifically by increasing their awareness of the problem of violence in their schools, providing them with strategies to prevent bullying in their classroom and aid victims, and providing them with methods and encouragement to promptly intervene when bullying or violence occurs. Furthermore, to influence student aggression, the results imply focusing on establishing positive student–student and student–teacher relationships. The importance of school norms implies that preventing aggressive behavior requires a school working to establish a shared vision among staff and students. Instead of implementing a school-based violence prevention program with a few select teachers or in one grade level, a key component to influencing behavior may be school-wide reinforcement. A number of school-based approaches are being implemented that focus on promoting a school-wide culture of nonviolence. Several approaches aim to change school culture by changing student attitudes and behaviors. These include the Resolving Conflict Creatively program (Aber, Jones, Brown, Chaundry, & Samples, 1998), Responding in Peaceful and Positive Ways (Meyer et al., 2000), and Positive Action (Flay, Allred, & Ordway, 2001). Other approaches seek to change school culture by changing teacher behaviors. Research has shown that teachers' self-efficacy beliefs are linked to their classroom behavior and practices, improved student academic achievement, improved student attitudes towards school, and greater student self-efficacy (Brophy & Good, 1984; Goddard, Hoy, & Woolfolk-Hoy, 2000; Howard, Horne, & Jolliff, 2001; Miskel, McDonald, & Bloom, 1983; Rimm-Kaufman & Sawyer, 2004). This relation is bi-directional; teachers feel more efficacious when their students do well and students do well when teachers feel more efficacious (Ross, 1988). Some recent studies have found that teachers' beliefs and attitudes moderate school-based program implementation and student impacts. For example, the Responsive Classroom program is designed to provide a set of practices and a system of expectations that guide social and instructional interactions. Evidence exists that this program affects teacher self-efficacy, attitudes towards teaching, and beliefs about disciplinary practices (Rimm-Kaufman & Sawyer, 2004). Therefore, another place to intervene with teachers would be to increase their sense of responsibility for addressing behavioral problems. Beets et al. (2008) found that there were both direct and indirect effects of teacher attitudes and school climate that influenced the implementation of the Positive Action character education program. Their results suggest the need to ensure teachers and administration are united in their perceptions of a

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program before it is implemented and that the program supports the school's core values and mission. It also suggests that a positive climate encouraging nonviolence supports the implementation and adoption of these types of school-based programs which require teacher buy-in and support. The gender findings, specifically that the influence of some school-level predictors is weaker among males, also have implications for schools and school-based programming. First, it seems important to build on the influence of school-level factors, particularly interpersonal climate, on males' attitudes and behaviors early in middle school. Schools and researchers should work to understand the weaker school-level effects on selfefficacy for nonviolence among males, and focus on those school-level characteristics that appear to have effects on males as well as females. One hint from this investigation is that student–student and student– teacher relationships appear to positively affect aggression and beliefs favoring aggression among males, and these effects does not appear to deteriorate substantially over the course of middle school. More generally, this study's findings are consistent with research that has found evidence of students' disengagement from the school community during adolescence (Fleming, Catalano, Haggerty, & Abbott, 2010), but they also point to specific factors within the school community that may be associated with such disengagement. Despite evidence of increasing peer effects on aggression during adolescence, both student–student and student–teacher relationships seem to decrease in their influence on aggressive behavior during middle school, suggesting, perhaps, that neighborhood peers who may or may not be part of the child's school grow in influence during early adolescence (Hill, Howell, Hawkins, & Battin-Pearson, 1999). School-based programs that target school bonding have found that improvements in bonding led to other positive student outcomes (Hawkins et al., 2001). 4.4. Summary and conclusion This study demonstrates the value of considering school level variables related to aggressive behavior such as norms, interpersonal climate, and responsiveness to violence. It also suggests the potential utility of strategies aimed at changing these school-level predictors for addressing the problem of violence. Although normative feedback interventions have shown some ability to change social setting norms related to substance use (Borsari & Carey, 2000; Larimer et al., 2001; Neal & Carey, 2004), norms for aggression and norms for nonviolent alternatives to aggression may be more resistant to change (MVPP, 2008). Future research is needed to design interventions that can shift school norms about violence. Of the school-level risk and promotive factors studied in these analyses, those related to school norms favoring nonviolence and awareness and reporting appear to be mostly positive in their effects on outcomes related to aggression. Although norms favoring aggression have been the subject of several studies, this study is one of the first to examine the full range of school norms about aggression by including questions about norms favoring nonviolence. There appears to be considerable overlap in the effects of these school-level variables. When combined, the effects of the school-level predictors on school-level intercepts are substantial in magnitude, suggesting, perhaps, that schools develop distinctive cultures that may promote or prevent violence, composed of norms, interpersonal climate, and responsiveness. Future research and prevention efforts should attend to such setting-level characteristics (cf., Shinn & Yoshikawa, 2008). References Aber, J. L., Jones, S. M., Brown, J. L., Chaundry, N., & Samples, F. (1998). Resolving conflict creatively: Evaluating the developmental effects of a school-based violence prevention program in neighborhood and classroom context. Development and Psychopathology, 10, 187–214. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. Beets, M. W., Flay, B. R., Vuchinich, S., Acock, A. C., Li, K. K., & Allred, C. (2008). School climate and teachers' beliefs and attitudes associated with implementation of the positive action program: A diffusion of innovations model. Prevention Science, 9, 264–275. Björkqvist, K., Lagerspetz, K. M., & Kaukiainen, A. (1992). Do girls manipulate and boys fight? Developmental trends in regard to direct and indirect aggression. Aggressive Behavior, 18, 117–127. Borsari, B., & Carey, K. B. (2000). Effects of a brief motivational intervention with college student drinkers. Journal of Consulting and Clinical Psychology, 68, 728–733. Bosworth, K., & Espelage, D. (1995). Teen conflict survey. Bloomington, IN: Center for Adolescent Studies. Bosworth, K., Espelage, D. L., Daytner, G., DuBay, T., & Karageorge, K. (2000). The effectiveness of a multimedia violence prevention program for early adolescents. American Journal of Health Behavior, 24, 268–280.

D.B. Henry et al. / Journal of School Psychology 49 (2011) 481–503

501

Brock, L. L., Nishida, T. K., Chiong, C., Grimm, K. J., & Rimm-Kaufman, S. E. (2008). Children's perceptions of the classroom environment and social and academic performance: A longitudinal analysis of the contribution of the Responsive Classroom approach. Journal of School Psychology, 46, 129–149. Brophy, J., & Good, L. E. (1984). Teacher behavior and student achievement. (Occasional Paper No. 73). Ann Arbor, MI: Michigan State University, The Institute for Research on Teaching. Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2010). Organizing schools for improvement: Lessons from Chicago. Chicago, IL: University of Chicago Press. Camou, S., Farrell, A., & Henry, D. (2006). Parents and peers as moderators of the impact of school environment on the development of aggression in middle school. Paper presented at the Annual Convention of The Association for Behavioral and Cognitive Therapies., Chicago, IL. Cartland, J., Ruch-Ross, H., & Henry, D. (2003). Feeling at home in one's school: A first look at a new measure. Adolescence, 38, 305–319. Cooley, P. C., Turner, C. F., O'Reilly, J. M., Allen, D. R., Hamill, D. N., & Paddock, R. E. (1996). Hardware and software considerations in adding sound to a computer-assisted interviewing system. Social Science Computer Review, 14(2), 197–204. doi: 10.1177/089443939601400208. Crockett, L. J., & Petersen, A. C. (1993). Adolescent development: Health risks and opportunities for health promotion. In S. G. Millstein, A. C. Petersen & E. O. Nightingale (Eds.), Promoting the health of adolescents: New directions for the twenty-first century (pp. 13–37). New York, NY: Oxford University Press. Cross, S. E., & Madson, L. (1997). Models of the self: Self-construals and gender. Psychological Bulletin, 122, 5–37. Cunningham, J. A., & Selby, P. L. (2007). Implications of the normative fallacy in young adult smokers aged 19–24 years. American Journal of Public Health, 97, 1399–1400. Dahlberg, L. L., & Potter, L. B. (2001). Youth violence: Developmental pathways and prevention challenges. American Journal of Preventive Medicine, 20(Suppl. 1), 3–14. Espelage, D. L., Holt, M. K., & Henkel, R. R. (2003). Examination of peer-group contextual effects on aggression during early adolescence. Child Development, 74, 205–220. Estell, D. B., Cairns, R. B., Farmer, T. W., & Cairns, B. D. (2002). Aggression in inner-city early elementary classrooms: Individual and peer-group configurations. Merrill–Palmer Quarterly: Journal of Developmental Psychology, 48, 52–76. Fagan, J., & Wilkinson, D. L. (1998). Social contexts and functions of adolescent violence. In D. S. Elliott, B. A. Hamburg & K. R. Williams (Eds.), Violence in American schools: A new perspective (pp. 55–93). New York, NY, US: Cambridge University Press. Farmer, T. W., Leung, M. -C., Pearl, R., Rodkin, P. C., Cadwallader, T. W., & Acker, R. V. (2002). Deviant or diverse peer groups? The peer affiliations of aggressive elementary students. Journal of Educational Psychology, 94, 611–620. Farrell, A. D., Ampy, L. A., & Meyer, A. L. (1998). Identification and assessment of problematic interpersonal situations for urban adolescents. Journal of Clinical Child Psychology, 27, 293–305. Farrell, A. D., Erwin, E. H., Bettencourt, A., Mays, S., Vulin-Reynolds, M., & Sullivan, T. N. (2008). Individual factors influencing effective nonviolent behavior and fighting in peer situations: A qualitative study with urban African American adolescents. Journal of Clinical Child and Adolescent Psychology, 37, 397–411. Farrell, A. D., & Camou, S. (2006). School-based interventions for youth violence prevention. In J. R. Lutzker (Ed.), Preventing violence: Research and evidence-based intervention strategies (pp. 125–145). Washington, DC, US: American Psychological Association. Farrell, A. D., Erwin, E. H., Allison, K., Meyer, A. L., Sullivan, T. N., Camou, S., et al. (2007). Problematic situations in the lives of urban African American middle school students: A qualitative study. Journal of Research on Adolescence, 17, 413–454. Farrell, A. D., Erwin, E. H., Bettencourt, A., Mays, S., Vulin-Reynolds, M., Sullivan, T. N., et al. (2008a). Individual factors influencing effective nonviolent behavior and fighting in peer situations: A qualitative study with urban African American adolescents. Journal of Clinical Child and Adolescent Psychology, 37, 397–411. Farrell, A. D., Kung, E. M., White, K. S., & Valois, R. F. (2000). The structure of self-reported aggression, drug use, and delinquent behaviors during early adolescence. Journal of Clinical Child Psychology, 29, 282–292. Farrell, A. D., Sullivan, T. N., Kliewer, W., Allison, K., Erwin, E. H., Meyer, A. L., et al. (2006). Peer and school problems in the lives of urban adolescents: Frequency, difficulty, and relation to adjustment. Journal of School Psychology, 44, 169–190. Farrell, A. D., & Vulin-Reynolds, M. (2007). Violent behavior and the science of prevention. In D. J. Flannery, A. T. Vazsonyi & I. D. Waldman (Eds.), The Cambridge handbook of violent behavior and aggression (pp. 767–786). New York, NY, US: Cambridge University Press. Fergus, S., & Zimmerman, M. A. (2005). Adolescent resilience: A framework for understanding healthy development in the face of adversity. Annual Review of Public Health, 26, 399–419. Flay, B. R., Allred, C. G., & Ordway, N. (2001). Effects of the Positive Action program on achievement and discipline: Two matchedcontrol comparisons. Prevention Science, 2, 71–89. Fleming, C. B., Catalano, R. F., Haggerty, K. P., & Abbott, R. D. (2010). Relationships between level and change in family, school, and peer factors during two periods of adolescence and problem behavior at age 19. Journal of Youth and Adolescence, 39(6), 670–682. Goddard, R. D., Hoy, W. K., & Woolfolk-Hoy, A. (2000). Collective teacher efficacy: Its meaning, measure, and impact on student achievement. American Educational Research Journal, 37, 479–507. Gottheil, N. F., & Dubow, E. F. (2001). Tripartite beliefs models of bully and victim behavior. Journal of Emotional Abuse, 2(2–3), 25–47. Gregory, A., Henry, D., & Schoeny, M.The Metropolitan Area Child Study Research Group. (2007). School climate and implementation of a preventive intervention. American Journal of Community Psychology, 40, 250–260. Guerra, N. G., Huesmann, L. R., & Hanish, L. (1994). The role of normative beliefs in children's social behavior. In N. Eisenberg (Ed.), Review of personality and social psychology, development and social psychology: The interface. Beverley Hills, CA: Sage. Hawkins, J. D., Farrington, D. P., & Catalano, R. F. (1998). Reducing violence through the schools. In D. S. Elliott, B. A. Hamburg & K. R. Williams (Eds.), Violence in American schools: A new perspective (pp. 188–216). New York, NY: Cambridge University Press. Hawkins, J. D., Guo, J., Hill, K. G., Battin-Pearson, S., & Abbott, R. D. (2001). Long-term effects of the Seattle Social Development Intervention on school bonding trajectories. Applied Developmental Science, 5(4), 225–236. Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. New York, NY: Wiley Blackwell. Henry, D. (2008). Changing classroom social settings through attention to norms. In B. Shinn & H. Yoshikawa (Eds.), Changing schools and community organizations to foster positive youth development (pp. 40–57). New York, NY: Oxford University Press. Henry, D., Cartland, J., Ruch-Ross, H., & Monahan, K. (2004a). A return potential model of setting norms for aggression. American Journal of Community Psychology, 33, 131–149.

502

D.B. Henry et al. / Journal of School Psychology 49 (2011) 481–503

Henry, D., & Farrell, A. D.The Multisite Violence Prevention Project. (2004b). The study designed by a committee: Design of the Multisite Violence Prevention Project. American Journal of Preventive Medicine, 26, 12–19. Henry, D., Guerra, N. G., Huesmann, L. R., Tolan, P. H., VanAcker, R., & Eron, L. D. (2000). Normative influences on aggression in urban elementary school classrooms. American Journal of Community Psychology, 28, 59–81. Hill, K. G., Howell, J. C., Hawkins, J. D., & Battin-Pearson, S. R. (1999). Childhood risk factors for adolescent gang membership: Results from the Seattle Social Development Project. Journal of Research in Crime and Delinquency, 36, 300–322. Hoglund, W. L., & Leadbeater, B. J. (2004). The effects of family, school, and classroom ecologies on changes in children's social competence and emotional and behavioral problems in first grade. Developmental Psychology, 40, 533–544. Hoover, J. H., Oliver, R., & Hazler, R. J. (1992). Bullying: Perceptions of adolescent victims in the Midwestern USA. School Psychology International, 13, 5–16. Howard, N. M., Horne, A. M., & Jolliff, D. (2001). Self-efficacy in a new training model for the prevention of bullying in schools. Journal of Emotional Abuse, 2, 181–191. Huesmann, L. R., & Guerra, N. G. (1997). Children's normative beliefs about aggression and aggressive behavior. Journal of Personality and Social Psychology, 72, 408–419. Huesmann, L. R., Guerra, N. G., Zelli, A., & Miller, L. (1992). Differing normative beliefs about aggression for boys and girls. In K. Bjorkqvist & P. Niemela (Eds.), Of mice and women: Aspects of female aggression. Orlando, Florida: Academic Press. Juvonen, J. (2006). Sense of belonging, social bonds, and school functioning. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (pp. 655–674). Mahwah, NJ, US: Erlbaum. Kallestad, J. H., & Olweus, D. (2003). Predicting teachers' and school's implementation of the Olweus Bullying Prevention Program: A multilevel study. Prevention & Treatment, 6, Article 21, posted October 1. at http://journals.apa.org/prevention Kellam, S. G., Ling, X., Merisca, R., Brown, C. H., & Ialongo, N. (1998). The effect of the level of aggression in the first grade classroom on the course and malleability of aggressive behavior into middle school. Development and Psychopathology, 10, 165–185. Katz, R., & Kahn, D. (1978). The social psychology of organizations. New York, NY, US: Wiley. Kolbe, L. J., Kann, L., & Collins, J. L. (1993). Overview of the youth risk behavior surveillance system. Public Health Reports, 108(Suppl 1), 2–10. Larimer, M. E., Turner, A. P., Anderson, B. K., Fader, J. S., Kilmer, J. R., Palmer, R. S., et al. (2001). Evaluating a brief intervention with fraternities. Journal of Studies on Alcohol, 62, 370–380. Lipsey, M. W., & Derzon, J. H. (1998). Predictors of violent or serious delinquency in adolescence and early adulthood: A synthesis of longitudinal research. In R. Loeber & D. P. Farrington (Eds.), Serious & violent juvenile offenders: Risk factors and successful interventions (pp. 86–105). Thousand Oaks, CA, US: Sage. Meyer, A. L., Allison, K. W., Reese, L. E., & Gay, F. N.The Multisite Violence Prevention, Project. (2004). Choosing to be violence free in middle school: The student component of the GREAT Schools and Families universal program. American Journal of Preventive Medicine, 26, 20–29. Meyer, A. L., Farrell, A. D., Northup, W. B., Kung, E. M., & Plybon, L. (2000). Promoting nonviolence in early adolescence: Responding in peaceful and positive ways. Dordrecht, Netherlands: Kluwer Academic. Miller-Johnson, S., Sullivan, T. N., & Simon, T. R.The Multisite Violence Prevention Project. (2004). Evaluating the impact of interventions in the Multisite Violence Prevention Study: Samples, procedures, and measures. American Journal of Preventive Medicine, 26, 48–61. Miskel, C., McDonald, D., & Bloom, S. (1983). Structural and expectancy linkages within schools and organizational effectiveness. Educational Administration Quarterly, 19, 49–82. Multisite Violence Prevention Project (MVPP) (2004). The Multisite Violence Prevention Project: Background and overview. American Journal of Preventive Medicine, 26, 3–11. Neal, D. J., & Carey, K. B. (2004). Developing discrepancy within self-regulation theory: Use of personalized normative feedback and personal strivings with heavy-drinking college students. Addictive Behaviors, 29, 281–297. Office of the Surgeon General (2001). Youth violence: A report of the Surgeon General. Rockville, MD: US Department of Health and Human Services. Olweus, D. (1993). Bullying at school: What we know and what we can do. Oxford, UK: Blackwell. Orpinas, P., & Horne, A. M.The Multisite Violence Prevention Project. (2004). A teacher-focused approach to prevent and reduce students? Aggressive behavior: The GREAT teacher program. American Journal of Preventive Medicine, 26, 29–38. Osher, D. M. (2002). Creating comprehensive and collaborative systems. Journal of Child and Family Studies, 11(1), 91–99. Pellegrini, A. D. (2002). Perceptions of playfighting and real fighting: Effects of sex and participant status. In J. L. Roopnarine (Ed.), Conceptual, social-cognitive, and contextual issues in the field of play: Play and culture studies, Vol. 4. (pp. 223–233)Westport, CT: Ablex. Pellegrini, A. D., & Long, J. D. (2004). Part of the solution and part of the problem: The role of peers in bullying, dominance, and victimization during the transition from primary school through secondary school. In D. L. Espelage & S. M. Swearer (Eds.), Bullying in American schools: A social-ecological perspective on prevention and intervention (pp. 107–117). Mahwah, NJ: Erlbaum. Perkins, H. W., & Craig, D. W. (2006). Assessing bullying in secondary school with a new online survey: Applying the social norms model to adolescent violence. Program Presentation for the 2006 National Social Norms Conference. from http://www.youthhealthsafety.org/ BullyhandoutNSNC06.pdf Phillips-Smith, E., Gorman-Smith, D., Quinn, W. H., Rabiner, D. L., Tolan, P. H., Winn, D., et al. (2004). Community-based multiple family groups to prevent and reduce violent and aggressive behavior: The GREAT Families Program. American Journal of Preventive Medicine, 26(Supplement 1), 39–47. Pianta, R. C., Hamre, B., Stuhlman, M., Reynolds, W. M., & Miller, G. E. (2003). Relationships between teachers and children. In I. B. Weiner (Series Ed.) and W. M. Reynolds & G. E Miller (Volume Eds.), Handbook of psychology: Educational psychology, Vol. 7 (pp. 199–234). Hoboken, NJ, US: Wiley. Putallaz, M., Grimes, C. L., Foster, K. J., Kupersmidt, J. B., Coie, J. D., & Dearing, K. (2007). Overt and relational aggression and victimization: Multiple perspectives within the school setting. Journal of School Psychology, 45(5), 523–547. Rasch, G. (1966). An item analysis which takes individual differences into account. The British Journal of Mathematical and Statistical Psychology, 19, 49–57.

D.B. Henry et al. / Journal of School Psychology 49 (2011) 481–503

503

Reynolds, C. R., & Kamphaus, R. W. (1998). Behavioral assessment system for children—Manual. Circle Pines, MN, US: American Guidance Service. Rimm-Kaufman, S. E., La Paro, K. M., Downer, J. T., & Pianta, R. C. (2005). The contribution of classroom setting and quality of instruction to children's behavior in kindergarten classrooms. The Elementary School Journal, 105(4), 377–394. Rimm-Kaufman, S. E., & Sawyer, B. E. (2004). Primary-grade teachers' self-efficacy beliefs, attitudes toward teaching, and discipline and teaching practice priorities in relation to the “Responsive Classroom” approach. The Elementary School Journal, 104, 321–341. Rodkin, P. C., & Hodges, E. V. E. (2003). Bullies and victims in the peer ecology: Four questions for psychologists and school professionals. School Psychology Review, 32, 384–400. Rodkin, P. C., Pearl, R., & Van Acker, R. (2003, Aprill). They're cool: Ethnic and peer group supports for aggressive boys and girls. Paper presented at the Biennial Meeting of the Society for Research in Child Development, Tampa, FL. Ross, E. W. (1988). Preservice teachers' responses to institutional constraints: The active role of the individual in teacher socialization. Educational Foundations, 2(1), 77–92. Rousseau, D. M. (1985). Issues of level in organizational research: Multi-level and cross-level perspectives. Research in Organizational Behavior, 7, 1–37. Salmivalli, C., & Voeten, M. (2004). Connections between attitudes, group norms, and behaviour in bullying situations. International Journal of Behavioral Development, 28, 246–258. SAS Institute Inc. (2004). SAS® Online Documentation, version 9.1.3. Cary, NC: SAS Institute Inc. Schultz, P. W. (1999). Changing behavior with normative feedback interventions: A field experiment on curbside recycling. Basic and Applied Social Psychology, 2, 25–36. Shinn, M. (1990). Mixing and matching: Levels of conceptualization, measurement, and statistical analysis in community psychology. In P. H. Tolan, C. B. Keys, F. Chertok & L. Jason (Eds.), Researching community psychology: Issues of theory and methods (pp. 111–126). Washington, DC: American Psychological Association. Shinn, M., & Rapkin, B. D. (2000). Cross-level research without cross-ups in community psychology. In J. Rappaport & E. Seidman (Eds.), Handbook of community psychology (pp. 669–695). Dordrecht, Netherlands: Kluwer Academic. Shinn, M., & Yoshikawa, H. (2008). Toward positive youth development: transforming schools and community programs. New York, N. Y.: Oxford University Press. Singer, J., & Willett, J. (2004). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press. Singh, P., & Bussey, K. (2009). The development of a Peer Aggression Coping Self-efficacy Scale for adolescents. The British Journal of Developmental Psychology, 27, 971–992. Substance Abuse and Mental Health Services Administration (2010). About the Safe Schools Healthy Students (SS/HS) Initiative. from. http://www.sshs.samhsa.gov/initiative/about.aspx The Multisite Violence Prevention Project (2008). Impact of a universal school-based violence prevention program on socialcognitive outcomes. Prevention Science, 9, 231–244. The Multisite Violence Prevention Project (2009). The ecological effects of universal and selective violence prevention programs for middle school students: A randomized trial. Journal of Consulting and Clinical Psychology, 77, 526–542. Thomas, W. (1917). The persistence of primary-group norms in present-day society and their influence in our educational system. In H. S. Jennings, J. B. Watson, A. Meyer & W. Thomas (Eds.), Suggestions of modern science concerning education (pp. 157–197). New York, NY, US: MacMillan. Tseng, V., & Seidman, E. (2007). A systems framework for understanding social settings. American Journal of Community Psychology. Special Issue: Systems change, 39, 217–228. U.S. Department of Education (1999–2000). Public school teacher questionnaire: Schools and staffing survey—1999–2000 School year. from. http://nces.ed.gov/surveys/sass/ U.S. Department of Health and Human Services (2000). Healthy People 2010: Understanding and Improving Health. Washington, DC: U.S. Government Printing Office. Retrieved from. http://www.healthypeople.gov/Document/tableofcontents.htm#volume1 Vessels, G. (1998). Character and community development: A school planning and teacher training handbook. Westport, CT: Praeger.