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.

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.

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.

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.

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).

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.

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

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.

The utility of single-item readiness screeners in middle school.

This study examined the benefit of utilizing one-item academic and one-item behavior readiness teacher-rated screeners at the beginning of the school year to predict end-of-school year outcomes for middle school students. The Middle School Academic and Behavior Readiness (M-ABR) screeners were developed to provide an efficient and effective way to assess readiness in students. Participants included 889 students in 62 middle school classrooms in an urban Missouri school district. Concurrent validity with the M-ABR items and other indicators of readiness in the fall were evaluated using Pearson product-moment correlation coefficients, with the academic readiness item having medium to strong correlations with other baseline academic indicators (r=±0.56 to 0.91) and the behavior readiness item having low to strong correlations with baseline behavior items (r=±0.20 to 0.79). Next, the predictive validity of the M-ABR items was analyzed with hierarchical linear regressions using end-of-year outcomes as the dependent variable. The academic and behavior readiness items demonstrated adequate validity for all outcomes with moderate effects (ß=±0.31 to 0.73 for academic outcomes and ß=±0.24 to 0.59 for behavioral outcomes) after controlling for baseline demographics. Even after controlling for baseline scores, the M-ABR items predicted unique variance in almost all outcome variables. Four conditional probability indices were calculated to obtain an optimal cut score, to determine ready vs. not ready, for both single-item M-ABR scales. The cut point of "fair" yielded the most acceptable values for the indices. The odd ratios (OR) of experiencing negative outcomes given a "fair" or lower readiness rating (2 or below on the M-ABR screeners) at the beginning of the year were significant and strong for all outcomes (OR=2.29 to OR=14.46), except for internalizing problems. These findings suggest promise for using single readiness items to screen for varying negative end-of-year student outcomes.

Authoritative school climate, number of parents at home, and academic achievement.

School climate is widely recognized as an important factor in promoting student academic achievement. The current study investigated the hypothesis that a demanding and supportive school climate, based on authoritative school climate theory, would serve as a protective factor for students living with 1 or no parents at home. Using a statewide sample of 56,508 middle school students from 415 public schools in 1 state, results indicated that student perceptions of disciplinary structure, academic demandingness, and student support all had positive associations with student self-reported grade point average (GPA). In addition, findings showed that academic expectations and student support were more highly associated with GPA for students not living with any parent. Implications for policy and practice are discussed. (PsycINFO Database Record

Question Order Affects the Measurement of Bullying Victimization Among Middle School Students.

Bullying among youth is recognized as a serious student problem, especially in middle school. The most common approach to measuring bullying is through student self-report surveys that ask questions about different types of bullying victimization. Although prior studies have shown that question-order effects may influence participant responses, no study has examined these effects with middle school students. A randomized experiment (n = 5,951 middle school students) testing the question-order effect found that changing the sequence of questions can result in 45% higher prevalence rates. These findings raise questions about the accuracy of several widely used bullying surveys.

Multilevel Factor Structure and Concurrent Validity of the Teacher Version of the Authoritative School Climate Survey.

BACKGROUND: School climate is well recognized as an important influence on student behavior and adjustment to school, but there is a need for theory-guided measures that make use of teacher perspectives. Authoritative school climate theory hypothesizes that a positive school climate is characterized by high levels of disciplinary structure and student support. METHODS: A teacher version of the Authoritative School Climate Survey (ASCS) was administered to a statewide sample of 9099 7th- and 8th-grade teachers from 366 schools. The study used exploratory and multilevel confirmatory factor analyses (MCFA) that accounted for the nested data structure and allowed for the modeling of the factor structures at 2 levels. RESULTS: Multilevel confirmatory factor analyses conducted on both an exploratory (N = 4422) and a confirmatory sample (N = 4677) showed good support for the factor structures investigated. Factor correlations at 2 levels indicated that schools with greater levels of disciplinary structure and student support had higher student engagement, less teasing and bullying, and lower student aggression toward teachers. CONCLUSIONS: The teacher version of the ASCS can be used to assess 2 key domains of school climate and associated measures of student engagement and aggression toward peers and teachers.

The impact of definition and question order on the prevalence of bullying victimization using student self-reports.

Accurate measurement is essential to determining the prevalence of bullying and evaluating the effectiveness of intervention efforts. The most common measurement approach is through anonymous self-report surveys, but previous studies have suggested that students do not adhere to standard definitions of bullying and may be influenced by the order of questions about types of victimization. In the current study, we have presented findings from 2 randomized experiments designed to determine (a) the impact of using or not using a definition of bullying and (b) asking about general versus specific types of bullying victimization and how the order of these questions affects victimization-prevalence rates. The study was conducted using a sample of 17,301 students attending 119 high schools. Findings indicate that the use of a definition had no impact on prevalence rates, but asking specific bullying-victimization questions (e.g., "I have been verbally bullied at school") prior to general bullying-victimization questions (e.g., "I have been bullied at school"), resulted in a 29-76% increase in victimization-prevalence rates. Results suggest that surveys that ask general-to-specific bullying-victimization questions, such as those found in national and international surveys, may be underreporting bullying victimization.

Aggressive Attitudes in Middle Schools: A Factor Structure and Criterion-Related Validity Study.

Student attitudes toward aggression have been linked to individual aggressive behavior, but the relationship between school-wide normative beliefs about aggression and aggressive behavior poses some important measurement challenges that have not been adequately examined. The current study investigated the factor structure, measurement invariance, and criterion-related validity of a six-item Aggressive Attitudes scale using a large sample of seventh- and eighth-grade students (n = 39,364) from 423 schools. Analytic procedures accounted for the frequently ignored modeling problems of clustered and ordinal data to provide more reliable and accurate model estimates and standard errors. The resulting second-order factor structure of the Aggressive Attitudes scale demonstrated measurement invariance across gender, grade, and race/ethnicity groups. Criterion-related validity was supported with eight student- and school-level indices of aggressive behavior.