Positive student-teacher relationships and exclusionary discipline practices.

Teacher-student relationships are beneficial for students and especially important in the middle school context. Suspensions are critical to reduce as it can be detrimental to the educational trajectory of students, particularly for marginalized students in underfunded schools. This study looked at the relations between teacher-student relationships and suspensions across the academic year in a sample of students (N = 541) and teachers (N = 51) in two urban school districts in the Midwest. A brief measure of teachers' perceptions of relationships with students completed in the fall of the academic year predicted the odds of receiving in-school suspension (ISS; odds ratio [OR] = 0.65) and out-of-school suspension (OSS; OR = 0.72) by the end of the school year. Student reports of the relationships were also associated with ISS (OR = 0.94), but not OSS. On average, teachers' report on the scale improved over the course of the school year (d = 0.11); however, student report of the relationships worsened (d = 0.30). The difference in the student report of the relationships from fall to spring was also associated with the odds of receiving ISS and OSS (ORs = 0.94). These findings suggest that student and teacher perceptions together are associated with harsh discipline practices that can negatively impact students' developmental trajectories. Relationships can change but direct action is necessary to develop, maintain, and repair relationships during the academic year.

Usability and social consequences of the early identification system as a universal screener for social, emotional, and behavioral risks.

The Early Identification System (EIS) was developed to overcome many of the usability challenges of school-based behavior screeners. Several prior studies have documented the technical adequacy of the EIS. The present study expanded this work by examining the use, relevance, values implications, and social consequence of EIS implementation in a sample of 54 K-12 schools and 23,104 students in the Midwestern United States. We found that nearly all schools, teachers, and students completed the EIS as planned. Schools used the data to provide universal, selective, and indicated services to a high percentage of students identified with risks as well as to plan professional development for educators based on the screening data. Seventy-nine percent of schools implemented the EIS system with high fidelity, and fidelity was unrelated to student demographic composition within schools. These findings suggest that the EIS may overcome many of the usability barriers that plague common behavior screeners. Limitations and implications for advancing the science of social consequence evaluation are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Impact of Restorative Practices on the Use of Out-of-School Suspensions: Results from a Cluster Randomized Controlled Trial.

The overuse of exclusionary discipline practices, such as out-of-school suspensions (OSS), has consistently been documented over several decades. The resulting racial discipline disparities and the negative outcomes related to OSS have led policy makers and educators to consider other approaches to school discipline. One such approach, which has gained increasing use in the USA, is restorative practices (RP). However, despite its popularity, the experimental evidence base of the effects of RP and suspensions is extremely limited. To add to this knowledge base, we present findings from a cluster randomized controlled trial with 18 K-12 schools in an urban district in the US Northeast with 5878 students. Multilevel regression results, after 1 year of the intervention, showed no differences in the likelihood of suspension between students in the intervention and control schools and that the effects of the intervention did not vary by race/ethnicity, gender, or student disability status. However, for students in the intervention group, we show reductions in the likelihood of receiving an OSS for students who had previously been suspended.

Advanced Categorical Data Analysis in Prevention Science.

A variety of health and social problems are routinely measured in the form of categorical outcome data (such as presence/absence of a problem behavior or stages of disease progression). Therefore, proper quantitative analysis of categorical data lies at the heart of the empirical work conducted in prevention science. Categorical data analysis constitutes a broad dynamic field of methods research and data analysts in prevention science can benefit from incorporating recent advances and developments in the statistical evaluation of categorical outcomes in their methodological repertoire. The present Special Issue, Advanced Categorical Data Analysis in Prevention Science, highlights recent methods developments and illustrates their application in the context of prevention science. Contributions of the Special Issue cover a wide variety of areas ranging from statistical models for binary as well as multi-categorical data, advances in the statistical evaluation of moderation and mediation effects for categorical data, developments in model evaluation and measurement, as well as methods that integrate variable- and person-oriented categorical data analysis. The articles of this Special issue make methodological advances in these areas accessible to the audience of prevention scientists to maintain rigorous statistical practice and decision making. The current paper provides background and rationale for this Special Issue, an overview of the articles, and a brief discussion of some potential future directions for prevention research involving categorical data analysis.

Accounting for Heteroskedasticity Resulting from Between-Group Differences in Multilevel Models.

Homogeneity of variance (HOV) is a well-known but often untested assumption in the context of multilevel models (MLMs). However, depending on how large the violation is, how different group sizes are, and the variance pairing, standard errors can be over or underestimated even when using MLMs, resulting in questionable inferential tests. We evaluate several tests (e.g., the H statistic, Breusch Pagan, Levene's test) that can be used with MLMs to assess violations of HOV. Although the traditional robust standard errors used with MLMs require at least 50 clusters to be effective, we assess a robust standard error adjustment (i.e., the CR2 estimator) that can be used even with a few clusters. Findings are assessed using a Monte Carlo simulation and are further illustrated using an applied example. We show that explicitly modeling the heterogenous variance structures or using the CR2 estimator are both effective at ameliorating the issues associated with the fixed effects of the regression model related to violations of HOV resulting from between-group differences.

Alternatives to Logistic Regression Models when Analyzing Cluster Randomized Trials with Binary Outcomes.

Binary outcomes are often encountered when analyzing cluster randomized trials (CRTs). A common approach to obtaining the average treatment effect of an intervention may involve using a logistic regression model. We outline some interpretive and statistical challenges associated with using logistic regression and discuss two alternative/supplementary approaches for analyzing clustered data with binary outcomes: the linear probability model (LPM) and the modified Poisson regression model. In our simulation and applied example, all models use a standard error adjustment that is effective even if a low number of clusters is present. Simulation results show that both the LPM and modified Poisson regression models can provide unbiased point estimates with acceptable coverage and type I error rates even with as little as 20 clusters.

Evaluation of the whole school restorative practices project: One-year impact on discipline incidents.

The current study addressed the need for experimental research on Restorative Practices (RP) in its evaluation of the Morningside Center for Teaching Social Responsibility's Whole School RP Project. The study was conducted in a large Northeastern city using a cluster randomized controlled design in 18 elementary, middle, and high schools. In a single year, before the COVID-19 pandemic, and with data from 5878 students, the study found that overall, students in the RP Project schools were less likely to receive a discipline incident record (11.1%) as compared to students in the comparison schools (18.2%). However, differential treatment effects based on sex, race/ethnicity, and disability status were not found. The findings suggest prevention efforts are a cornerstone of practice/policy reforms to reduce the use of exclusionary discipline. Findings also suggest multi-year initiatives are needed to address discipline disparities.

Examining the validity of the Early Identification System - Student Version for screening in an elementary school sample.

As many as 1 in 5 youth in the United States experience social, emotional, and behavioral problems. However, many students with mental health concerns are unidentified and do not receive adequate care. The purpose of this study was to evaluate the factor structure, measurement invariance, and the concurrent and predictive validity of the Early Identification System-Student Report (EIS-SR), a screener for social, emotional, and behavioral problems, using a sample of over 5000 students from Grades 3 to 5. The EIS-SR was developed by using extant literature on the risk indicators that lead to social, emotional, and behavioral challenges among children and youth. As expected, seven subscales were identified as having adequate factor loadings. Furthermore, the measure was determined to be invariant across grade level (n = 5005), gender (n = 5005), and between Black and White students (n = 1582). The concurrent validity of the Internalizing Behavior, Attention and Academic Issues, Emotion Dysregulation, and School Disengagement subscales was supported by correlations with comparable subscales of the Behavior Assessment System for Children-3rd Edition (BASC-3; n = 382). Additionally, the EIS-SR subscales administered in the fall of the school year were predictive of important outcomes in spring, including attendance (n = 4780), disciplinary referrals (n = 4938), bully victimization (n = 4670), math academic achievement scores (n = 4736), and reading (n = 4772) academic achievement scores. The EIS-SR holds promise as a feasible and technically adequate screening tool for use in elementary schools.

Using cluster-robust standard errors when analyzing group-randomized trials with few clusters.

Accounting for dependent observations in cluster-randomized trials (CRTs) using nested data is necessary in order to avoid misestimated standard errors resulting in questionable inferential statistics. Cluster-robust standard errors (CRSEs) are often used to address this issue. However, CRSEs are still well-known to underestimate standard errors for group-level variables when the number of clusters is low (e.g., < 50) and with CRTs, a small number of clusters, due to logistical or financial considerations, is the norm rather than the exception. Using a simulation with various conditions, we investigate the use of a small sample correction (i.e., CR2 estimator) proposed by Bell and McCaffrey (2002) together with empirically derived degrees of freedom estimates (dofBM). Findings indicate that even with as few as 10 clusters, the CR2 estimator used with dofBM yields generally unbiased results with acceptable type I error and coverage rates. Results show that coverage and type I error rates can be largely influenced by the choice of dof, not just the standard error adjustments. An applied example is provided together with R syntax to conduct the analysis. To facilitate the use of different CRSEs, a free graphical, menu-driven SPSS add-on to compute the various cluster-robust variance estimates can be downloaded from https://github.com/flh3/CR2/tree/master/SPSS .

Individual and school predictors of teacher stress, coping, and wellness during the COVID-19 pandemic.

The Coronavirus disease 2019 (COVID-19) pandemic created unprecedented challenges for the U.S. education system and for teachers. The present study examined correlates and predictors of teacher well-being in the immediate aftermath of school closures related to the pandemic. Data were collected as part of a larger group randomized trial. Six hundred and thirty-nine teachers completed surveys about their stress, coping, health, job satisfaction, and internalizing symptoms in Fall 2019, before the pandemic, and May 2020, during the pandemic. Teachers also provided ratings during COVID-19 of their teaching, student attendance and engagement, and concern about students and families. Teachers reported lower levels of work-related stress after the pandemic's onset compared to their prepandemic levels. Multilevel regression analyses revealed teacher confidence in their ability to manage student behaviors as a consistent and robust predictor of teacher well-being outcomes. Additionally, pre-COVID-19 school-level factors measured in Fall 2019, including collegial school leadership and fair and equitable school discipline structures, also predicted aspects of teacher well-being at the onset of COVID-19. Findings suggest the importance of teacher competence and perceived efficacy in managing student behavior and engaging them in learning to help them adapt to the stressors of a pandemic. Additionally, aspects of organizational health and climate may also help facilitate or hinder teacher adjustment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The association between student socioeconomic status and student-teacher relationships on math achievement.

The importance of math achievement in the early grades has been well documented. Two variables that have independently been found to be strong predictors of math achievement are student-teacher relationships (STR) and socioeconomic status (SES). The present study investigates the interaction between STR and SES on student math achievement using the Early Childhood Longitudinal Study, a nationally representative data set. The participants included approximately 8,380 first-grade students, 2,930 teachers, and 860 schools, representing a weighted sample of approximately 3.15 million students or 90% of the population. Multilevel modeling, controlling for student-, teacher-, and school-level factors, found that both STR and SES had a strong relationship with student math achievement. There was also a statistically significant moderation effect between close STR and SES on math achievement, suggesting that close STR are beneficial for all students, but are especially beneficial for students from low SES backgrounds. Further, educational implications for research, policy, and practice are provided. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

An investigation of the psychometric properties of the early identification system-student report in a middle school sample.

Early adolescence represents a critical developmental period for the identification, prevention, and early intervention of mental health concerns. The Early Identification System-Student Report (EIS-SR) was developed as a user-friendly, accessible, and cost-efficient method for identifying youth at risk for mental health concerns. The present study examined the psychometric properties of the EIS-SR in a sample of middle school students in the Midwest. Two confirmatory factor analyses (N1 = 2,404, N2 = 2,404) supported the hypothesized seven correlated factor model showing adequate model fit and subscale reliability (range = .68-.90). Subsequent analyses found that many of the EIS-SR subscales predicted office discipline referrals, suspensions, and standardized academic achievement performance. Additionally, the subscales correlated in expected ways with subscales of the Behavior Assessment Scales for Children (3rd edition). These findings suggest that the EIS-SR is promising for identifying youth in early adolescence who are at risk for mental health concerns in addition to being a free and efficient universal screening tool. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Confirmatory Factor Structure and Predictive Validity of the Early Identification System-Student Report in a Community Sample of High School Students.

The purpose of this paper is to confirm the factor structure, examine the invariance, and investigate the predictive validity using disciplinary data for 5262 high school students who completed the Early Identification System-Student Response (EIS-SR). The development and theory of the EIS-SR is discussed along with prior work. Building off of prior factor analytic work with a separate sample, it was hypothesized the items of the EIS-SR would coalesce into seven factors representing Externalizing Behavior, Internalizing Behavior, Peer Relationship Problems, School Disengagement, Emotional Dysregulation, Attention and Academic Issues, and Relational Aggression. Furthermore, it was hypothesized that EIS-SR scores would be invariant with regard to gender and grade level. Lastly, it was hypothesized that students with high EIS-SR subscale scores would be predictive of school discipline events. Our analyses indicated the EIS-SR did fit the previously observed factor structure with the items loading on seven distinct scales. Tests for measurement invariance indicated support that the EIS-SR measured the seven factors equally well regardless of both gender and grade level. Lastly, EIS-SR subscale scores predicted spring office disciplinary referrals, both in and out of school suspensions, and attendance.

The Role of the Perceptions of School Climate and Teacher Victimization by Students.

Violence directed toward teachers in schools is relatively understudied in comparison with other school-based forms of peer aggression (e.g., school bullying). Based on the nationally representative Schools and Staffing Survey (SASS) 2011-2012, approximately 10% of K-12 public school teachers in the United States, received a threat in the past 12 months and 6% reported being physically attacked. The effects of teacher-directed violence are far reaching and affect not just the victimized teacher, but the larger community itself. In the current study, we used multilevel logistic regression models with state fixed effects to analyze the SASS data set. The analytic sample consisted of 24,070 K-12 teachers in 4,610 public schools and specifically excluded special education teachers and teachers in alternative settings (i.e., online schools, special education centers, juvenile correction facilities). Guided by authoritative school climate theory, we tested for the beneficial associations of disciplinary structure and administrative support with the reduced likelihood of a teacher being threatened or physically attacked by a student, while controlling for teacher (e.g., gender, years of experience, race/ethnicity), school (e.g., school size, percent minority enrollment), and state-level factors. Results indicated that teachers who felt supported by the administration and worked with others (i.e., the principal and other teachers) who enforced the rules consistently were less likely to be victims of threats of injury or physical attacks. Although school climate has been shown to have a positive effect on student outcomes, the current study also suggests that school climate, characterized by consistent rule enforcement and supportive administrators and teachers, may play a role in reducing the likelihood of teacher victimization.

Understanding family-school engagement across and within elementary- and middle-school contexts.

Although family-school engagement is important across child and adolescent development, most research, programs, and policies have focused primarily on elementary students and contexts. The current study extends beyond elementary settings by exploring the unique and shared contributions of developmental context on family-school engagement (i.e., across and within elementary- and middle-school settings). Data were drawn from two randomized controlled trials that evaluated the efficacy of teacher training in universal classroom-management practices. Participants included 3,174 students and 207 teachers across 21 elementary and middle schools in the Midwest. Using hierarchical linear modeling, results revealed that family-school engagement was significantly higher in elementary than in middle schools. Student-level characteristics (i.e., identifying as White, participation in the free/reduced-price lunch program, and having lower levels of disruptive behavior) were also associated with higher levels of family-school engagement. In addition, student characteristics (i.e., race/ethnicity and level of disruptive behavior) moderated the relations between family-school engagement and developmental context. Regardless of developmental context, family-school engagement predicted positive end-of-year behavioral outcomes (i.e., increases in youth prosocial skills and decreases in youth concentration problems, disruptive behaviors, and emotional dysregulation). Last, moderation analyses revealed that these effects of family-school engagement were especially pronounced in middle school for concentration problems and emotional dysregulation. Overall, findings provide further support for the value of family-school engagement across development in fostering positive youth outcomes. However, it is evident that more steps must be taken to ensure family-school engagement practices are developed to support the unique needs of middle-school students and contexts. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Multilevel modeling myths.

The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their origin, and suggest approaches to dealing with certain issues. Misunderstandings related to the use of the intraclass correlation, design effects, minimum sample size, multilevel factor structures, model R², and the misestimation of standard errors are reviewed. Many of the cited myths have much truth in them-though at times, researchers may not be aware of the exceptions to the rules that prevent their overall generalization. Although nesting should be accounted for, researchers should realize that MLM, which is a powerful and flexible technique, is not the only method that can be used to account for the clustering effect. (PsycINFO Database Record

Bullying involvement, teacher-student relationships, and psychosocial outcomes.

Students involved in bullying experience mental health issues and negative psychosocial outcomes. Few studies have investigated how teacher-student relationships (TSRs) may buffer the negative outcomes experienced by students involved in bullying. To investigate the moderating role of TSRs with bullying involvement status and psychosocial outcomes, we used data from 691 middle school students, 85 teachers, and 6 schools in one urban district. We used both student- and teacher-reported outcomes and regression models included baseline measures (i.e., depression, concentration problems, emotional regulation problems, behavioral engagement) taken 8 months earlier. Regardless of bullying involvement, student-reported TSR had a beneficial association for all outcomes controlling for baseline measures and student demographic variables. However, bully/victims with low TSRs experienced a heightened risk for depressive symptoms suggesting increased attention to this subgroup of students. (PsycINFO Database Record

The Impact of Validity Screening on Associations Between Self-Reports of Bullying Victimization and Student Outcomes.

Self-report surveys are widely used to measure adolescent risk behavior and academic adjustment, with results having an impact on national policy, assessment of school quality, and evaluation of school interventions. However, data obtained from self-reports can be distorted when adolescents intentionally provide inaccurate or careless responses. The current study illustrates the problem of invalid respondents in a sample (N = 52,012) from 323 high schools that responded to a statewide assessment of school climate. Two approaches for identifying invalid respondents were applied, and contrasts between the valid and invalid responses revealed differences in means, prevalence rates of student adjustment, and associations among reports of bullying victimization and student adjustment outcomes. The results lend additional support for the need to screen for invalid responders in adolescent samples.

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Student threat assessment as a standard school safety practice: Results from a statewide implementation study.

Threat assessment has been widely endorsed as a school safety practice, but there is little research on its implementation. In 2013, Virginia became the first state to mandate student threat assessment in its public schools. The purpose of this study was to examine the statewide implementation of threat assessment and to identify how threat assessment teams distinguish serious from nonserious threats. The sample consisted of 1,865 threat assessment cases reported by 785 elementary, middle, and high schools. Students ranged from pre-K to Grade 12, including 74.4% male, 34.6% receiving special education services, 51.2% White, 30.2% Black, 6.8% Hispanic, and 2.7% Asian. Survey data were collected from school-based teams to measure student demographics, threat characteristics, and assessment results. Logistic regression indicated that threat assessment teams were more likely to identify a threat as serious if it was made by a student above the elementary grades (odds ratio 0.57; 95% lower and upper bound 0.42-0.78), a student receiving special education services (1.27; 1.00-1.60), involved battery (1.61; 1.20-2.15), homicide (1.40; 1.07-1.82), or weapon possession (4.41; 2.80-6.96), or targeted an administrator (3.55; 1.73-7.30). Student race and gender were not significantly associated with a serious threat determination. The odds ratio that a student would attempt to carry out a threat classified as serious was 12.48 (5.15-30.22). These results provide new information on the nature and prevalence of threats in schools using threat assessment that can guide further work to develop this emerging school safety practice. (PsycINFO Database Record