Impulsivity profiles across five harmonized longitudinal childhood preventive interventions and associations with adult outcomes.

This study aimed to parse between-person heterogeneity in growth of impulsivity across childhood and adolescence among participants enrolled in five childhood preventive intervention trials targeting conduct problems. In addition, we aimed to test profile membership in relation to adult psychopathologies. Measurement items representing impulsive behavior across grades 2, 4, 5, 7, 8, and 10, and aggression, substance use, suicidal ideation/attempts, and anxiety/depression in adulthood were integrated from the five trials (N = 4,975). We applied latent class growth analysis to this sample, as well as samples separated into nonintervention (n = 2,492) and intervention (n = 2,483) participants. Across all samples, profiles were characterized by high, moderate, low, and low-increasing impulsive levels. Regarding adult outcomes, in all samples, the high, moderate, and low profiles endorsed greater levels of aggression compared to the low-increasing profile. There were nuanced differences across samples and profiles on suicidal ideation/attempts and anxiety/depression. Across samples, there were no significant differences between profiles on substance use. Overall, our study helps to inform understanding of the developmental course and prognosis of impulsivity, as well as adding to collaborative efforts linking data across multiple studies to better inform understanding of developmental processes.

Psychosis Symptom Trajectories Across Childhood and Adolescence in Three Longitudinal Studies: An Integrative Data Analysis with Mixture Modeling.

Psychotic-like experiences (PLEs) are common throughout childhood, and the presence of these experiences is a significant risk factor for poor mental health later in development. Given the association of PLEs with a broad number of mental health diagnoses, these experiences serve as an important malleable target for early preventive interventions. However, little is known about these experiences across childhood. While these experiences may be common, longitudinal measurement in non-clinical settings is not. Therefore, in order to explore longitudinal trajectories of PLEs in childhood, we harmonized three school-based randomized control trials with longitudinal follow-up to identify heterogeneity in trajectories of these experiences. In an integrative data analysis (IDA) using growth mixture modeling, we identified three latent trajectory classes. One trajectory class was characterized by persistent PLEs, one was characterized by high initial probabilities but improving across the analytic period, and one was characterized by no reports of PLEs. Compared to the class without PLEs, those in the improving class were more likely to be male and have higher levels of aggressive and disruptive behavior at baseline. In addition to the substantive impact this work has on PLE research, we also discuss the methodological innovation as it relates to IDA. This IDA demonstrates the complexity of pooling data across multiple studies to estimate longitudinal mixture models.

The discipline gap in context: The role of school racial and ethnic diversity and within school positionality on out-of-school suspensions.

Disparities in exclusionary discipline practices are well-documented; however, variation in Black students' disciplinary experiences across different racial and ethnic school compositions remains understudied. Utilizing a state-wide dataset (N = 769,050 students in J = 1296 schools), we examined student- and school-level factors that contribute to suspensions for Black students across schools with varying racial and ethnic diversity. Consistent with prior research, we found that Black students were disproportionately suspended more often, for more days, and more likely for soft offenses. We also found that students in majority Black schools (i.e., those where >50% of the students were Black) had the highest unadjusted rates of suspension. However, when controlling for multiple other student- and school-level characteristics, including overall suspension rates, we found that Black students attending majority White schools had a higher adjusted risk of suspension than in majority Black or heterogenous diverse schools, suggesting higher rates of differential treatment in White majority schools. We discuss the implications of these results and the role school psychologists play in supporting professional development, training, and data-based decision making to reduce disproportionality.

Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial.

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through implementation of moderated nonlinear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex modeling building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.

The Sampling Ratio in Multilevel Structural Equation Models: Considerations to Inform Study Design.

Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research.

Predictors of suicidal ideation trajectories in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study.

A better understanding of suicidal ideation (SI), including patterns of SI, may help elucidate links between depression, SI, and suicidal behavior. This study sought to identify trajectories of SI in a large, community-based clinical trial of participants with major depressive disorder (MDD) and to investigate the relationships between these trajectories and predictors of interest, including anxiety and anhedonia. A longitudinal latent class analysis was conducted in 3923 participants enrolled in Level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study of citalopram for the treatment of MDD. An unconditional latent class analysis was conducted using SI at study weeks 0, 2, 4, 6, and 9 as the indicators. A multinomial regression was then conducted with SI trajectory as the outcome and anhedonia, severity of depressive symptoms, atypical depression, anxiety, history of suicide attempt, history of substance abuse, history of trauma, and other covariates as the predictors. Four SI trajectories were identified: 1) variable SI; 2) little-to-no SI; 3) persistent SI; and 4) improving SI. Compared to the little-to-no SI trajectory, those with more severe anhedonia were more likely to experience persistent SI, while those with more severe anxiety were more likely to experience improving SI. Factors that distinguish SI trajectories, such as anxiety and anhedonia, may be critical targets for intervention or profiles for prognosis.

Teachers' Mental Health During the COVID-19 Pandemic.

With an emergence of research investigating the educational impacts of the COVID-19 pandemic, empirical studies assessing teachers' mental health throughout the pandemic have been scarce. Using a large national data set, the current study compares mental health outcomes during the pandemic between pre-K-12 teachers and professionals in other occupations. Further, we compare the prevalence of mental health outcomes between in-person and remote teachers (N = 134,693). Findings indicate that teachers reported a greater prevalence of anxiety symptoms than did those in other professions and that remote teachers reported significantly higher levels of distress than did those teaching in person. We summarize the policy implications of these results.

Can Policy Promote Adoption or Outcomes of Evidence-based Prevention Programming?: a Case Illustration of Positive Behavioral Interventions and Supports.

This study examined the impact of a state policy requiring that any school with a habitual truancy rate of 8% or higher to be trained in Tier 1 school-wide Positive Behavioral Interventions and Supports (SW-PBIS). A regression discontinuity (RD) design was used to examine how the schools' mandate status related to SW-PBIS training as well as student suspensions, truancy, and achievement in 410 public middle and high schools, of which 261 were affected by the mandate. We further examined the growth trajectories (i.e., improvement) of implementation fidelity over time using growth mixture modeling (GMM). Contrary to the intent of the policy to improve student outcomes, the RD results suggested that the mandate did not significantly impact reading and math achievement, truancy rates, or SW-PBIS training in 2010-2011 through 2013-2014. Mandated schools had higher suspension rates in 2010-2011 through 2013-2014 than the non-mandated schools; however, these differences in the suspension rates appear to have persisted from years prior to the mandate. Descriptive analyses suggested that mandated schools had statistically significantly higher rates of training, and the GMM analyses on the fidelity data indicated that mandated schools were significantly more likely to be in an improving implementation growth trajectory over time. Taken together, results suggested that the policy showed some promise for improving SW-PBIS training and fidelity over time, but it had little to no impact on student outcomes.

A multilevel analysis of racial discipline disproportionality: A focus on student perceptions of academic engagement and disciplinary environment.

Excessive use of exclusionary school discipline with Black students is a persistent, systemic problem in U.S. schools with potential to affect students' perceptions of their school. For example, students may notice racial differences in out-of-school suspensions, which may relate to how academically engaged they feel and the extent to which they view the school's disciplinary environment as positive. The current study investigated school-level racial discipline disproportionality and observed classroom-level, positive behavior supports in relation to student perceptions of academic engagement and school disciplinary environment by fitting a series of three-level models, which included data on students (N = 17,115), classrooms (J = 310), and schools (K = 53). Two metrics of discipline disproportionality were used (i.e., the risk ratio and the risk difference) and moderation was examined through cross-level interactions. Results indicated that, regardless of race, students perceived the disciplinary environment as significantly less favorable in schools with greater racial discipline disproportionality when measured by the risk ratio, but not when measured by the risk difference. Using different disproportionality metrics in education research has important implications for policies and practices to identify and address the issue. How discipline disparities relate to the way that students perceive the disciplinary environment will likely inform intervention efforts for school psychologists.

Adjustment outcomes of victims of cyberbullying: The role of personal and contextual factors.

With many of today's youth utilizing technology to bully their peers, there is a need to better understand both predictors and consequences of cybervictimization. However, few researchers have employed a multi-level approach to jointly identify potential individual (e.g., gender) and school-level (e.g., urbanicity) predictors of cybervictimization, or examined a range of psycho-social and adjustment outcomes. The current study used survey data from 28,583 students from 58 high schools to explore the risk factors associated with cybervictimization. We also examined the association between cybervictimization and adjustment outcomes (e.g., psychological, academic), as well as a possible moderators (e.g., student connectedness) that may buffer youth from these negative outcomes. Self-report measures assessed experiences with cybervictimization, adjustment problems, and student connectedness using previously validated measures. A series of two-level hierarchical linear modeling analyses revealed that females, underclassman, and those who are traditionally victimized or were perpetrators of cyberbullying were at significantly increased risk of cybervictimization. Cybervictimization was also associated with an increased risk of psychological (internalizing problems, sleep problems, stress problems) and academic (truancy, poor grades) adjustment problems. However, student connectedness buffered the internalizing problems experienced by victims of cyberbullying. These findings extend prior research on cybervictimization predictors, outcomes, and buffers, and in turn inform the potential use of school-based efforts aimed at preventing cyberbullying.