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Pediatrics. 2012 November; 130(5): e1136–e1145.
PMCID: PMC3483890

Effects of School-Wide Positive Behavioral Interventions and Supports on Child Behavior Problems



School-Wide Positive Behavioral Interventions and Supports (SWPBIS) is a universal prevention strategy currently implemented in >16 000 schools across the United States. SWPBIS intends to reduce students’ behavior problems by altering staff behaviors and developing systems and supports to meet children’s behavioral needs. The current study reports intervention effects on child behaviors and adjustment from an effectiveness trial of SWPBIS.


The sample of 12 344 elementary school children was 52.9% male, 45.1% African American, and 46.1% Caucasian. Approximately 49% received free or reduced-priced meals, and 12.9% received special education services at baseline. The trial used a group randomized controlled effectiveness design implemented in 37 elementary schools. Multilevel analyses were conducted on teachers’ ratings of children’s behavior problems, concentration problems, social-emotional functioning, prosocial behavior, office discipline referrals, and suspensions at 5 time points over the course of 4 school years.


The multilevel results indicated significant effects of SWPBIS on children’s behavior problems, concentration problems, social-emotional functioning, and prosocial behavior. Children in SWPBIS schools also were 33% less likely to receive an office discipline referral than those in the comparison schools. The effects tended to be strongest among children who were first exposed to SWPBIS in kindergarten.


These findings provide support for the hypothesized reduction in behavior problems and improvements in prosocial behavior and effective emotion regulation after training in SWPBIS. The SWPBIS framework appears to be a promising approach for reducing problems and promoting adjustment among elementary school children.

KEY WORDS: School-wide Positive Behavioral Interventions and Supports (PBIS), aggressive and disruptive behavior, social-emotional adjustment, randomized controlled trial, schools, prevention

What’s Known on This Subject:

School-Wide Positive Behavioral Interventions and Supports (SWPBIS) is a widely used universal prevention strategy currently implemented in >16 000 schools across the United States. Previous research has shown positive effects on school climate and school-level discipline problems.

What This Study Adds:

This study reports multilevel results on data from a 4-year randomized controlled effectiveness trial of SWPBIS in 37 elementary schools. Results indicate significant impacts on children’s aggressive behavior problems, concentration problems, office discipline referrals, emotion regulation, and prosocial behavior.

Conduct and disruptive behavior problems pose a significant concern for children’s development. An onset of disruptive and aggressive behavior problems in elementary school is associated with an increased risk for academic problems, placement in special education programs, school dropout, substance abuse problems, and antisocial behavior.1,2 There is growing interest in school-wide prevention models, such as School-Wide Positive Behavioral Interventions and Supports (SWPBIS),3,4 as an approach for preventing an early-onset of behavior problems and promoting positive adjustment.

SWPBIS is a noncurricular universal prevention strategy that aims to alter the school’s organizational context to implement enhanced procedures and systems to guide data-based decisions related to student behavior problems and academics. It applies behavioral, social learning, and organizational principles to an entire student body consistently across all school contexts.5 Schools establish a set of positively stated, school-wide expectations for student behavior, which are taught to all students and staff. It aims to prevent disruptive behavior and enhance organizational climate by implementing a 3-tiered prevention framework6 in which 2 levels of selective and indicated programs are implemented to complement the universal school-wide components (for a review, see Sugai and Horner, Taylor-Greene and Kartub, and Horner et al3,4,7,8). However, most of the >16 000 schools currently implementing SWPBIS have focused on the universal components (see

Two randomized controlled trials (RCTs) were recently conducted of SWPBIS in elementary schools and indicated positive outcomes for students and staff. Specifically, a 1-year waitlist RCT indicated significant improvements in school climate and achievement.9 Previous studies reporting data from the current sample of 37 elementary schools enrolled in a 4-year RCT indicated significant improvements in the schools’ organizational health, school-level discipline data, and the implementation of classroom-based supports.1012 To date, there has been no research using longitudinal RCT designs that has examined the effect on teacher ratings of behavior problems and social-emotional adjustment.

We used data from the 4-year RCT to examine the hypothesis that children in schools implementing SWPBIS would have better teacher-rated emotion regulation and prosocial behaviors and fewer concentration problems and disruptive behaviors. We also hypothesized that children in SWPBIS schools would be less likely to be referred to the principal’s office or suspended. Given the group RCT design (ie, students nested within schools), we used a multilevel modeling approach13 and adjusted for select covariates at the school (eg, enrollment) and child levels (eg, gender).14 These findings will enhance our understanding of the effects achieved through the commonly used SWPBIS approach by determining its impact on a range of outcomes.



Data came from a group randomized controlled effectiveness trial13,15 of the universal SWPBIS model that aimed to determine the impact of SWPBIS on discipline problems and the school environment. Only public elementary schools were eligible for inclusion, and all schools approached about participation agreed to enroll. An open-cohort design was used, such that new students could enroll at each data collection; however, resources were not available to follow students who left the participating schools. Schools were matched on select baseline demographics (eg, school enrollment), of which 21 schools were randomized to the intervention condition and 16 to the comparison condition. The comparison schools refrained from implementing SWPBIS for 4 years.


The 21 schools assigned to receive SWPBIS training each formed SWPBIS teams, comprising 5 to 6 members (eg, teachers, administrators), who attended an initial 2-day summer training co-led by one of the developers of SWPBIS. To maintain consistently high levels of fidelity, the SWPBIS teams attended annual 2-day booster training events. Consistent with the effectiveness trial design,15 all initial training and booster training events were coordinated and led by the PBIS Maryland State Leadership Team and were also attended by other SWPBIS teams from across the state.16 All SWPBIS schools received at least monthly on-site support and technical assistance from a trained behavior support coach (eg, school psychologist) for the duration of the trial; these staff were trained by the state and supervised by the district. Professional development and technical assistance were provided to the behavior support coaches through state-coordinated training events conducted 4 times each year (see Barrett et al17 for additional information).


Annual assessments of SWPBIS implementation were conducted in all 37 schools by trained assessors who were unaware of the schools’ implementation status using the validated School-wide Evaluation Tool (SET)18 and staff self-reports11,12; both indicated that all SWPBIS schools reached and maintained high-fidelity implementation (eg, 80% on the School-wide Evaluation Tool)18 by the end of the trial, and no schools in the comparison condition consistently reached high fidelity; 66% of the SWPBIS schools met the 80% high-fidelity threshold18 within the first year of the trial (see means in Fig 1 and Bradshaw et al11,12,19 for additional information on the methods and fidelity in the intervention and control conditions).

Mean School-wide Evaluation Tool (SET) fidelity scores at SWPBIS and comparison schools at baseline and years 1 through 4. Analysis of the SET data suggested a significant effect (ie, intervention condition × time interaction) for the overall ...


The sample included 37 elementary schools, the size of which was determined through a power analysis. Five data points (fall and spring year 1, spring years 2-4) were collected over the 4 school years (2002–2007) on 12 334 children who were in kindergarten, first, and second grade when the study started. See the CONSORT diagram in Fig 2 and school demographics in Table 1.

CONSORT diagram for the SWPBIS RCT.
Student and School Demographic Characteristics


The Teacher Observation of Classroom Adaptation—Checklist (TOCA-C)20 was completed for each child by their primary classroom teacher. The TOCA-C is a checklist version of the TOCA (TOCA-R),21 which has been used in several previous RCTs of school-based prevention programs.2224 The TOCA-C measures each child’s level of “aggressive and disruptive behaviors” (fights; 9 items, α = .92), “concentration problems” (pays attention; 7 items, α = .96), “prosocial behaviors” (shows empathy; 5 items, α = .92), and “emotion regulation” (stops and calms down when angry or upset; 4-items, α = .89). Teachers responded to each question using a 6-point Likert scale (1 = never to 6 = almost always), which were averaged to create the 4 subscales, such that higher scores on disruptive behaviors and concentration problems indicated greater problems, whereas higher scores on the prosocial behaviors and emotion regulation scales reflected better adjustment. These scales exhibit strong internal consistency, have a consistent factor structure over time,20 relate to external criteria,25 and are sensitive to relatively modest intervention effects.26 The TOCA-C also included questions regarding each child’s receipt of an office disciplinary referral (ODR) and an out-of-school suspension during that school year using a yes/no dichotomous format. Teacher reports of these events have been shown to be a valid indicator of the child’s discipline problems.27 These items were collapsed over the 5 time points to create an aggregated dichotomous indicator for ODRs and suspensions (1 = ever, 0 = never received) over the course of the trial.


Survey packets were mailed to the school and distributed to the teachers by an administrator or secretary. The packets contained a checklist in reference to each child in their classroom, and each survey had a unique identifier for each child, thereby allowing child-specific data to be tracked over the 4 years. Teachers completed a survey for each child in the class 5 times over the course of 4 years (ie, fall baseline, spring of 4 years). The project was approved by the researchers’ institutional review board; passive parental consent was used for child participants.


Longitudinal 3-level hierarchical linear models were estimated by using hierarchal linear modeling (HLM6)28 to examine the effect of SWPBIS on children’s changes in behaviors (disruptive behaviors, prosocial behaviors, concentration problems, emotion regulation) over the course of the intervention (Figs 3, ,4,4, ,5,5, and and6).6). At level 1, the within-individual time scores were entered into the model. At level 2, child characteristics (ie, special education status, race [black vs nonblack children], cohort, free or reduced meals status, gender) were included. Given the group RCT design, intervention status (SWPBIS vs control) was modeled as a school-level characteristic (level 3). Additionally, multilevel logistic regression analyses were conducted in HLM to examine the effect of SWPBIS (modeled as a school-level variable) on receiving an ODR and a suspension.

Impact of SWPBIS on disruptive behaviors.
Impact of SWPBIS on concentration problems.
Impact of SWPBIS on emotion regulation.
Impact of SWPBIS on positive behaviors.

On the basis of previous research with this data,11,12 the following school-level variables were included as covariates: student mobility, enrollment, student/teacher ratio, and faculty turnover rate. We grand-mean centered all predictor variables at levels 2 and 3, except intervention status.29 Model fit indices (Akaike information criterion [AIC] and Bayesian information criterion [BIC]) are reported in the tables and are interpreted such that smaller values indicate better fit.28

Although the participation rate was consistently high, we examined the missing data patterns but did not find evidence that missingness was problematic.30,31 For example, baseline scores on concentration problems were unrelated to subsequent missingness on this measure (adjusted odds ratio [AOR] = 1.00, 95% confidence interval = 0.96–1.04). Baseline scores on disruptive behavior were significantly associated with an increased odds of subsequent missingness (AOR = 1.05, 95% confidence interval = 1.03–1.07), yet this difference was small and likely has limited clinical significance. Neither gender nor intervention status had a significant effect on subsequent missingness on teacher ratings of behavior problems. Therefore, our analyses assumed data were missing at random, which assumes that the reason for missingness is not related to the missing value itself or is deemed random after controlling for the variables that are observed.32,33 HLM adjusts parameter estimates for attrition using full-information maximum-likelihood estimation, a widely recognized and appropriate means of handling missing data34 under the assumption that data are missing at random.35 Specifically, individuals can have missing data across any of the time points and still be included in the analyses; therefore, HLM is robust to missing data within repeated measures.35,36


The sample of 12 344 children was 52.9% male, 45.1% African American, and 46.1% Caucasian (see Table 1). Approximately 49% received free or reduced-price meals, and 12.9% received special education services. The multilevel analyses indicated a significant positive intervention effect on disruptive behaviors (γ = –0.02, t = –2.23, P < .05, effect size (ES) = 0.12), such that children in SWPBIS schools had lower levels of aggressive and disruptive behaviors compared with those in the control schools (see Table 2). A similar effect was observed on concentration problems (γ = –0.03, t = –2.08, P < .05, ES = 0.08; see Table 3). We explored for possible cross-level interaction effects between SWPBIS and grade cohort, gender, and special education status; however, none were significant.

HLM Results for 3-Level Model Examining the Effect of SWPBIS on Disruptive Behaviors
HLM Results for 3-Level Model Examining the Effect of SWPBIS on Concentration Problems

The multilevel analyses also indicated a significant intervention effect on prosocial behavior (γ = 0.03, t = 2.11, P < .05, ES = –0.17), such that children in the intervention had higher levels of positive behaviors compared with those in the control. A similar effect was observed on emotion regulation (γ = 0.03, t = 2.30, P < .05, ES = –0.11), such that children in the intervention schools had better emotion regulation than those in the control. Again we examined for possible interaction effects of intervention status and grade cohort, gender, and special education. The results indicated that children who were in kindergarten when the trial began fared better in SWPBIS schools than in comparison schools on both prosocial behavior (γ = 0.08, t = 2.77, P < .01) and emotion regulation (γ = 0.05, t = 2.38, P < .05). No other interactions were significant (see bottom of Tables 4 and and55 for interaction terms only). We explored the fit of quadratic and cubic growth in the HLM analyses for these continuous outcomes; however, neither resulted in a significant improvement in model fit.

HLM Results for 3-Level Model Examining the Effect of SWPBIS on Prosocial Behaviors
HLM Results for 3-Level Model Examining the Effect of SWPBIS on Emotion Regulation

The final set of multilevel analyses indicated that children in SWPBIS schools were 33% less likely to receive an ODR than those in the comparison schools (AOR = 0.67, [0.57–0.79], P < .001). We also found a significant interaction between gender and intervention status (AOR = 1.27 [1.04–1.56], P < .05) in which girls in SWPBIS schools were less likely to receive an ODR than girls in comparison schools, but there was no difference for boys. With regard to suspensions, there were no significant differences between SWPBIS and comparison schools and no significant interactions (see Table 6).

HLM Results Examining the Effect of SWPBIS on Office Discipline Referrals and Suspensions


This study used data from a 4-year randomized controlled effectiveness trial to examine the hypothesis that children in schools implementing SWPBIS would experience better adjustment and fewer problem behaviors relative to their peers in comparison schools. As hypothesized, the multilevel, longitudinal analyses indicated that relative to the children in comparison schools, those in SWPBIS schools displayed lower levels of disruptive behavior problems and concentration problems, and better emotion regulation and more prosocial behavior. We also observed significant intervention effects on children’s odds of receiving an ODR. However, no significant effects were observed on suspensions. We explored for potential interactions with select demographic variables and generally found that the effects were strongest among those children who began the trial when they were in kindergarten. Interestingly, no other demographic characteristics explored were significant effect modifiers.

It was not surprising that one of the strongest effects was on ODRs, given a core component of the SWPBIS model is the establishment of a system to track, monitor, and use ODR data.4 The heightened attention to this particular source of data within SWPBIS likely contributed to the reduction in the odds of ODRs observed among children in the trained schools. Although not specifically examined within this study, it is theorized that the reduction in ODR use is mediated by reductions in children’s behavior problems. Future studies should explore the extent to which the reductions in teacher-rated behavior problems lead to the reductions in ODRs. Although the current study focused on the overall impact on receipt of ODRs, it is possible that the pattern of findings may vary by the reason for the ODR (eg, bullying, disrespect); however, data regarding these outcomes are not available for schools in both conditions. It is also possible that the more proximal impacts observed in the trial translate into longer-term effects on the need for school-based services, such as special education, and academic outcomes.

Previous studies have reported a school-level impact of SWPBIS on suspension rates9,12; therefore, we hypothesized that there would be a significant effect on students’ receipt of a suspension; however, such an effect did not reach significance. It is possible that the intervention effects would increase with longer implementation of the model. More targeted strategies, such as those used in the full 3-tiered SWPBIS model, may be necessary to reduce suspensions. Furthermore, we only explored main effects of the universal SWPBIS model and select interactions related to demographics, which indicated that the effects tended to be strongest for children who were in kindergarten when they were first exposed to SWPBIS. This suggests that the earlier the exposure to SWPBIS, the greater the potential impact of the model. From a developmental perspective, it is possible that younger children’s behaviors are more malleable and responsive to adults’ expectations and positive reinforcement for good behavior. Additional work is needed to better understand the impact of SWPBIS when implemented in middle and high schools. Future studies also should examine whether the intervention effects are greatest for children with a particular baseline risk profile.37 Although not a focus of the current study, race and special education status were significantly associated with each outcome across all models. Given the literature on disproportionality in discipline,38 future research will examine the effects on disproportionality in special education service use and discipline problems.

The data were obtained through teacher reports over the course of the trial because archival data (eg, ODRs) or pretraining teacher-report data are not available for analysis. Although we recognize that a teacher report of need is not equivalent to an assessment made by a clinician or a diagnostic assessment, teachers are the most common source of children’s mental health and special education referrals,39 thus their assessments are important in the context of school-based interventions and have been shown to predict mental health problems.2,20 Furthermore, the SWPBIS and control schools did not have common measurement systems for ODRs that would allow a functional comparison of these data elements; however, teacher reports of ODRs have been validated.27 Future research should examine other effects of SWPBIS, such as the impact on achievement and attendance.

The effect sizes were relatively modest; however, small effect sizes are common in longitudinal universal prevention studies15,40; we anticipate that the effects will be stronger for higher-risk students.24 Also bear in mind that the RCT was an effectiveness trial in which all the training, implementation, and support activities were led by the state and local school districts, not by the researchers. These training events also included schools not participating in the RCT, and thus we anticipate that the findings would generalize to other schools in the state who participated in these events (see Stuart et al41 for information on generalizability).


This is the first RCT to demonstrate impacts of SWPBIS on trajectories of children’s behavior problems and adjustment over multiple school years. These findings suggest that there are proximal effects of SWPBIS on a range of behavior problems, such as ODRs, concentration difficulties, and aggressive or disruptive behavior, as well as improvements in prosocial behaviors and emotion regulation. The effects of SWPBIS on prosocial behavior and emotional regulation are relatively unique in the literature. The finding that these effect sizes were as strong as or stronger than disruptive behavior is also noteworthy. Demonstrating the impact of SWPBIS on a range of early-onset behavior and social-emotional problems has important public health significance, especially in light of the wide dissemination of SWPBIS.29

These findings provide support for the hypothesized reduction in behavior problems and improvements in adaptive skills through SWPBIS. Although these effects are promising, there are some children who are not responding adequately to the universal model,4 and thus additional work is needed to identify these children so that their needs can be better met within the school. The tiered prevention model also provides an infrastructure for the delivery of more intensive services and programs for children with greater needs.4,42 Furthermore, SWPBIS holds promise for improving the organizational context to support higher-quality implementation of selective and indicated preventive interventions for nonresponders.11


We thank the PBIS Maryland State Leadership Team for their support of this project and Dr. Mary Mitchell for assistance with an earlier draft of this manuscript.


adjusted odds ratio
effect size
hierarchal linear modeling
office disciplinary referral
randomized controlled trials
School-Wide Positive Behavioral Interventions and Supports
Teacher Observation of Classroom Adaptation, Checklist


Dr Bradshaw obtained funding, made substantial contributions to study design, and made substantial contributions to drafting the manuscript, interpretation of results, and critical review and critique of each version of the manuscript. Dr Waasdorp provided assistance with manuscript preparation including substantial contributions to data analyses, interpretation of results, and critical review and critique of each version of the manuscript. Dr Leaf obtained funding, made substantial contributions to study design, and critically revised the manuscript.

The opinions expressed are those of the authors, not of the funding agencies, and such endorsements should not be inferred. Dr Bradshaw had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

This trial has been registered at (identifier NCT01583127).

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: Support for this project comes from grants from the Centers for Disease Control and Prevention (grants R49/CCR318627, 1U49CE 000728, K01CE001333-01), the National Institute of Mental Health (grant 1R01MH67948-1A), and the Institute of Education Sciences (grant R305A090307). Funded by the National Institutes of Health (NIH).


1. Moffitt TE. Life-course-persistent versus adolescence-limited antisocial behavior. In: Cicchetti D, Cohen DJ, editors. , eds. Developmental Psychopathology. Hoboken, NJ: John Wiley & Sons; 2006:570–598
2. Schaeffer CM, Petras H, Ialongo N, Poduska J, Kellam S.. Modeling growth in boys’ aggressive behavior across elementary school: links to later criminal involvement, conduct disorder, and antisocial personality disorder. Dev Psychol. 2003;39(6):1020–1035 [PubMed]
3. Sugai G, Horner RR. Introduction to the special series on positive behavior support in schools. J Emot Behav Disord. 2002;10(3):130–135
4. Sugai G, Horner RR. A promising approach for expanding and sustaining School-Wide Positive Behavior Support. School Psych Rev. 2006;35(2):245–259
5. Lewis T, Sugai G. Effective behavior support: a systems approach to proactive schoolwide management. Focus Exceptional Child. 1999;31(6):1–24
6. Mrazek PJ, Haggerty RJ. Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press; 1994 [PubMed]
7. Taylor-Greene SJ, Brown D, Nelson L, et al. School-Wide Behavioral Support: starting the year off right. J Behav Educ. 1997;7(1):99–112
8. Taylor-Greene SJ, Kartub DT. Durable implementation of School-Wide Behavior Support: The High Five Program. J Posit Behav Interv. 2000;2(4):233–235
9. Horner RH, Sugai G, Smolkowski K, et al. A randomized, wait-list controlled effectiveness trial assessing School-Wide Positive Behavior Support in elementary schools. J Posit Behav Interv. 2009;11(3):133–144
10. Bradshaw CP, Koth CW, Bevans KB, Ialongo N, Leaf PJ. The impact of School-Wide Positive Behavioral Interventions and Supports (PBIS) on the organizational health of elementary schools. Sch Psychol Q. 2008;23(4):462–473
11. Bradshaw CP, Koth CW, Thornton LA, Leaf PJ.. Altering school climate through school-wide Positive Behavioral Interventions and Supports: findings from a group-randomized effectiveness trial. Prev Sci. 2009;10(2):100–115 [PubMed]
12. Bradshaw CP, Mitchell MM, Leaf PJ. Examining the effects of Schoolwide Positive Behavioral Interventions and Supports on student outcomes. J Posit Behav Interv. 2010;12(3):133–148
13. Murray DM. Design and Analysis of Group-randomized Trials. New York, NY: Oxford Press; 1998
14. Birnbaum AS, Lytle LA, Hannan PJ, Murray DM, Perry CL, Forster JL.. School functioning and violent behavior among young adolescents: a contextual analysis. Health Educ Res. 2003;18(3):389–403 [PubMed]
15. Flay BR, Biglan A, Boruch RF, et al. . Standards of evidence: criteria for efficacy, effectiveness and dissemination. Prev Sci. 2005;6(3):151–175 [PubMed]
16. Bradshaw CP, Pas ET. A state-wide scale-up of Positive Behavioral Interventions and Supports (PBIS): a description of the development of systems of support and analysis of adoption and implementation. School Psych Rev. 2011;40(4):530–548
17. Barrett SB, Bradshaw CP, Lewis-Palmer T. Maryland statewide PBIS initiative. J Posit Behav Interv. 2008;10(2):105–114
18. Horner RH, Todd A, Lewis-Palmer T, Irvin L, Sugai G, Boland J. The school-wide evaluation tool (SET): a research instrument for assessing School-Wide Positive Behavior Support. J Posit Behav Interv. 2004;6(1):3–12
19. Bradshaw CP, Reinke WM, Brown LD, Bevans KB, Leaf PJ. Implementation of school-wide positive behavioral interventions and supports (PBIS) in elementary schools: observations from a randomized trial. Educ Treat Child. 2008;31(1):1–26
20. Koth CW, Bradshaw CP, Leaf PJ. Teacher observation of classroom adaptation-checklist: development and factor structure. Meas Eval Couns Dev. 2009;42(1):15–30
21. Werthamer-Larsson L, Kellam S, Wheeler L.. Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. Am J Community Psychol. 1991;19(4):585–602 [PubMed]
22. The Conduct Problems Prevention Research Group . Evaluation of the first 3 years of the Fast Track prevention trial with children at high risk for adolescent conduct problems. J Abnorm Child Psychol. 2002;30(1):19–35 [PubMed]
23. Ialongo NS, Werthamer L, Kellam SG, Brown CH, Wang S, Lin Y.. Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression, and antisocial behavior. Am J Community Psychol. 1999;27(5):599–641 [PubMed]
24. Kellam SG, Ling X, Merisca R, Brown CH, Ialongo N.. The effect of the level of aggression in the first grade classroom on the course and malleability of aggressive behavior into middle school. Dev Psychopathol. 1998;10(2):165–185 [PubMed]
25. Stormshak EA, Bierman KL, Bruschi C, Dodge KA, Coie JD, Conduct Problems Prevention Research Group . The relation between behavior problems and peer preference in different classroom contexts. Child Dev. 1999;70(1):169–182 [PMC free article] [PubMed]
26. Ialongo NS, Poduska J, Werthamer L, Kellam S. The distal impact of two first-grade preventive interventions on conduct problems and disorder in early adolescence. J Emot Behav Disord. 2001;9(3):146–160
27. Pas ET, Bradshaw CP, Mitchell MM. Examining the validity of office discipline referrals as an indicator of student behavior problems. Psychol Sch. 2011;48(6):541–555
28. Raudenbush SW, Bryk AS, Cheong YF, Congdon RT, Jr, du Toit M. Hierarchical Linear and Nonlinear Modeling (HLM7). Lincolnwood, IL: Scientific Software International, Inc.; 2011
29. Enders CK, Tofighi D.. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychol Methods. 2007;12(2):121–138 [PubMed]
30. Schlomer GL, Bauman S, Card NA.. Best practices for missing data management in counseling psychology. J Couns Psychol. 2010;57(1):1–10 [PubMed]
31. Wothke W. Longitudinal and multigroup modeling with missing data. In: Little TD, Schnabel KU, Baumert J, editors. , eds. Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples. Mahwah, NJ: Lawrence Erlbaum Associates; 2000:219–240
32. Arbuckle JL. Full information estimation in the presence of incomplete data. In: Marcoulides GA, Schumacker RE, eds. Advanced Structural Equation Modeling: Issues and techniques. Mahwah, NJ: Lawrence Erlbaum Associates; 1996
33. Little RJ. Modeling the dropout mechanism in repeated-measures studies. J Am Stat Assoc. 1995;90(431):1112–1121
34. Schafer JL, Graham JW.. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147–177 [PubMed]
35. Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications and Data Analysis Methods. 2nd ed. Thousand Oaks, CA: Sage; 2002
36. Rumberger RW, Palardy GJ. Multilevel models for school effectiveness research. In: Kaplan D, editor. , ed. Handbook of Quantitative Methodology for the Social Sciences. Thousand Oaks, CA: Sage; 2004
37. van Lier PAC, Verhulst FC, Crijnen AAM.. Screening for disruptive behavior syndromes in children: the application of latent class analyses and implications for prevention programs. J Consult Clin Psychol. 2003;71(2):353–363 [PubMed]
38. Bradshaw CP, Mitchell MM, O'Brennan LM, Leaf PJ. Multilevel exploration of factors contributing to the overrepresentation of black students in office disciplinary referrals. J Educ Psychol. 2010;102(2):508–520
39. Zima BT, Hurlburt MS, Knapp P, et al. . Quality of publicly-funded outpatient specialty mental health care for common childhood psychiatric disorders in California. J Am Acad Child Adolesc Psychiatry. 2005;44(2):130–144 [PubMed]
40. Durlak JA, Weissberg RP, Dymnicki AB, Taylor RD, Schellinger KB.. The impact of enhancing students’ social and emotional learning: a meta-analysis of school-based universal interventions. Child Dev. 2011;82(1):405–432 [PubMed]
41. Stuart EA, Cole SR, Bradshaw CP, Leaf PJ. The use of propensity scores to assess the generalizability of results from randomized trials. J R Stat Soc A. 2011;174(2):369–386 [PMC free article] [PubMed]
42. Hawken LS, Vincent CG, Schumann J. Response to intervention for social behavior: challenges and opportunities. J Emot Behav Disord. 2008;16(4):213–225

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