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To examine the bidirectional associations between adolescent siblings' alcohol use before and during the onset of the COVID-19 pandemic in Spring 2020 and whether youths' stress about missed social connections (i.e., social disruption stress) moderated these associations.The sample consisted of 682 families (2,046 participants) with two adolescent siblings (older siblings:Accounting for younger siblings' earlier drinking and other confounders, older siblings' prepandemic drinking predicted a greater likelihood of younger siblings' drinking during the Spring 2020 pandemic shutdown. This association was not moderated by younger siblings' social disruption stress. The association between younger siblings' prepandemic drinking and older siblings' drinking during the shutdown was moderated by older siblings' social disruption stress. Specifically, younger siblings' earlier drinking was more strongly related to older siblings' drinking during the shutdown if older siblings reported more social disruption stress.Siblings are important socialization agents of alcohol use during adolescence. Sibling interventions may be particularly salient during times of stress and isolation when youths' social interactions with peers may be limited. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Minority stress theory is a model for understanding health disparities among sexual minorities, defined as those who experience a level of same-sex attraction, identity, or behavior. Methods for assessing minority stress among youth included only adult measures until the development of the Sexual Minority Adolescent Stress Inventory (SMASI). The SMASI appears to be a robust scale in its ability to measure specific stressors and global minority stress among adolescents; however, it does not measure the domain-level constructs of proximal and distal stress, which are integral features of the underlying theory. This study tests the psychometric defensibility of including these domain-level factors within an updated SMASI measurement model. Results provide evidence for the structural validity of the proximal and distal domains within the SMASI measurement model and suggest this updated model has more informational value than the original model for capturing minority stress.

The purpose of this article was to explore how family chaos, parenting processes, parent-child relationship qualities, and sibling relationship qualities changed before versus the early months of the COVID-19 pandemic. Participants included one parent and two adolescent-aged children from 682 families (2,046 participants). Parents and youth participating in an ongoing longitudinal study in five Midwestern states in the United States completed an additional web-based assessment of family processes and family relationship qualities during the May-June 2020 pandemic-related shutdowns. A series of two-wave latent change score models indicated that family chaos increased with the onset of pandemic-related shutdowns and that the level of chaos within a family during the shutdowns had implications for changes in several parenting processes and family relationship qualities. Specifically, higher levels of family chaos during the pandemic mitigated observed increases in parental knowledge and were associated with declines in parental autonomy granting. Family chaos during pandemic-related shutdowns also was associated with increases in maternal-child conflict, paternal-child conflict, and sibling conflict as well as decreases in paternal-child intimacy, sibling intimacy, and sibling disclosure. Overall, consistent with a family stress perspective, the onset of the COVID-19 pandemic was associated with increased strain and commotion within many households, and these changes had implications for multiple family relationships. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

NSSI is a robust predictor of suicidal thoughts and behaviors. Despite this close link, there is a lack of information regarding specific behavioral and psychological features of NSSI that may influence, or serve as drivers, of these relationships. Consequently, the aim of the current study was to elucidate the specific NSSI-related mechanisms that may confer risk for suicide ideation and suicide attempts. Participants having a lifetime history of NSSI (N = 659) were included in the current study and completed a series of online questionnaires. Exploratory mediation analysis was utilized to simultaneously examine 28 behavioral and psychological NSSI features as potential mechanisms of the NSSI-suicide ideation and NSSI-suicide attempt relationships. Results identified 11 NSSI features as having non-zero specific indirect effects in the NSSI-suicide ideation relationship, with the antisuicide function of NSSI having the strongest influence. In examining the NSSI-suicide attempt relationship 12 NSSI features were identified as having non-zero specific indirect effects, with endorsement of swallowing dangerous substances as an NSSI method having the strongest influence. These findings suggest psychological and behavioral features of NSSI may differentially impact risk for suicide ideation and suicide attempts, respectively.

Model fit criteria for structural equation models rely on asymptotic behavior. As such, corrections are applied when fitting models to small samples (N ≤ 100). However, these corrections are based ...

Developmentally appropriate sport contexts have the potential to positively influence young people's physiological, psychological, and social outcomes. However, little is known about how families returned to sport in the wake of COVID-19-related restrictions or how socioeconomic and demographic factors influenced parents' perceptions of barriers to returning. A nationally representative sample (

We examined the direct and indirect effects of older siblings' substance use behaviors (i.e., cannabis and e-cigarettes) on younger siblings' later substance use intentions via their substance use expectations.Data were collected from 682 families (N = 2,046) with two adolescent siblings (older siblings: M age = 15.67 years, 51% female; younger siblings: M age = 13.14 years, 48% female) and one parent (M age = 45.15 years; 85% female). Participants completed annual online surveys at two occasions. Older siblings reported on their cannabis and e-cigarette use frequencies (Time 1) and younger siblings reported on their substance use expectations (Time 1) and intentions (Time 1 and 2); parents reported on adolescents' sociodemographic characteristics and their own substance use (Time 2).Structural equation modeling results suggested that older siblings' cannabis and e-cigarette use was indirectly related to younger siblings' later intentions to use these substances through their positive expectations about substances, after accounting for younger siblings' earlier intentions to use substances and control variables including parents' and friends' use. There were no significant direct relations between older siblings' cannabis or e-cigarette use and younger siblings' intentions to use them.Findings indicate that older siblings are critical and unique socialization agents of younger siblings' expectations and intentions to use substances. Intervention and prevention programs that target adolescents' substance use should consider the ways in which siblings shape each other's substance use.

Structural equation model trees (SEM Trees) allow for the construction of decision trees with structural equation models fit in each of the nodes. Based on covariate information, SEM Trees can be u...

The latent change score framework allows for estimating a variety of univariate trajectory models, such as the no change, linear change, exponential forms of change, as well as multivariate traject...

Exploratory mediation analysis via regularization, or XMed, is a recently developed technique that allows one to identify potential mediators of a process of interest. However, as currently implemented, it can only be applied to continuous outcomes. We extend this method to allow application to dichotomous outcomes, including both mediators and dependent variables. Simulation results show that XMed can achieve the same sensitivity as more conventional methods for mediation analysis such as the Sobel test, percentile bootstrap, and bias-corrected bootstrap, but in general requires only half the sample size to do so. We demonstrate the implementation of this approach using an illustrative example examining the relationship between youth behavioral/emotional problems and alcohol use.

Understanding causal effects of a treatment is often of interest in the social sciences. When treatments cannot be randomly assigned, researchers must ensure that treated and untreated participants are balanced on covariates before estimating treatment effects. Conventional practices are useful in matching such that treated and untreated participants have similar average values on their covariates. However, situations arise in which a researcher may instead want to match on model parameters. We propose an algorithm, Causal Mplus Trees, which uses decision trees to match on structural equation model parameters and estimates conditional average treatment effects in each node. We provide a proof of concept using two small simulation studies and demonstrate its application using COVID-19 data.

Over the past 40 years there have been great advances in the analysis of individual change and the analyses of between-person differences in change. While conditional growth models are the dominant approach, exploratory models, such as growth mixture models and structural equation modeling trees, allow for greater flexibility in the modeling of between-person differences in change. We continue to push for greater flexibility in the modeling of individual change and its determinants by combining growth mixture modeling with structural equation modeling trees to evaluate how measured covariates predict class membership using a recursive partitioning algorithm. This approach, referred to as growth mixture modeling with membership trees, is illustrated with longitudinal reading data from the Early Childhood Longitudinal Study with the MplusTrees package in R.

The purpose of this study was to investigate the dimensionality of the cognitive processes related to memory capacity and language ability and to assess the magnitude of the relationships among these processes in children developing typically (TD) and children with developmental language disorder (DLD). Participants were 234 children between the ages of 7;0 and 11;11 (117 TD and 117 DLD) who were propensity matched on age, sex, mother education and family income. Latent variables created from cognitive processing tasks and standardized measures of comprehension and production of lexical and sentential aspects of language were tested with confirmatory factor analysis (CFA) and structural regression. A five-factor CFA model that included the constructs of Fluid Intelligence, Controlled Attention, Working Memory, Long-Term Memory for Language Knowledge and Language Ability yielded better fit statistics than two four-factor nested models. The four cognitive abilities accounted for more than 92% of the variance in the language measures. A structural regression model indicated that the relationship between working memory and language ability was significantly greater for the TD group than the DLD group. These results are consistent with a broad conceptualization of the nature of language impairment in older, school-age children as encompassing a dynamic system in which cognitive abilities account for nearly all of the variance in linguistic abilities.

Studies show that more positive relationship satisfaction can mitigate the effects of posttraumatic stress disorder (PTSD) and depression severity on hazardous drinking in military samples. However, past studies were not circumscribed to female service members/veterans (SM/V), who represent the fastest growing demographic in the military. Moreover, studies did not examine moderators of specific symptom clusters of PTSD and depression with hazardous drinking. Indeed, recent studies have shown that the more depressive and cognitive clusters are associated with greater dysfunction. The current study extended this literature in a convenience sample of 584 female SM/V who completed self-report measures of hazardous drinking, PTSD, depression, and relationship satisfaction. PTSD or depression severity, relationship satisfaction, and their interaction, were examined as correlates of hazardous drinking after accounting for relationship, demographic, and military characteristics. For both overall PTSD and depression severity, higher relationship satisfaction weakened their association with hazardous drinking. Such results were consistent when global scores were replaced with PTSD-related negative alterations in cognitions and mood and somatic depression symptom clusters, but not for PTSD-related dysphoric arousal, anhedonia, or non-somatic depression symptom clusters. Findings suggest that to lessen the association of PTSD or depressive symptoms with problematic drinking, interventions aimed at improving relationship satisfaction may be worth considering among women in relationships. Moreover, symptom cluster analyses show that the cognitive and depressive components of PTSD, as well as the physical symptoms of depression, are most problematic, pinpointing specific areas of function on which to intervene.

Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures heterogeneity, attributes that heterogeneity to covariates, and the kinds of research questions each can be used to address. Each approach is used to analyze data from the National Longitudinal Survey of Youth to understand the similarities and differences between methods in the context of empirical data. Specifically, changes in weight across adolescence are examined, as well as how differences in these change patterns can be explained by sex, race, and mother's education. Recommendations are provided for how to select which approach is most appropriate for analyzing one's own data.

Introduction Suicide is a leading cause of death in military service members/veterans (SM/Vs). Positive social exchanges (e.g., emotional support) buffer against suicide risk but the influence of negative social exchanges (e.g., unwanted advice) is understudied. Method The current study explored (1) the contributions of positive and negative social exchanges as simultaneous correlates of suicide ideation and risk, (2) whether positive social exchanges mitigated the association of negative social exchanges and suicide ideation and risk, and (3) if facets of social exchanges were uniquely related to suicide ideation and risk. SM/Vs men (N = 508) completed self-report questionnaires. Results Roughly 27% endorsed suicide ideation. Between 23.62 and 41.14% reported elevated suicide risk. Adjusted analyses revealed higher negative social exchanges, particularly insensitive behaviors, and lower positive social exchanges were associated with higher suicide ideation and risk. The magnitude of positive and negative social exchanges with suicide ideation or risk was statistically similar. Higher positive social exchanges did not attenuate the association of negative social exchanges and suicide ideation or risk (p > 0.05). Conclusion Higher negative social exchanges are equally strong correlates of suicide ideation and risk as positive social exchanges. Decreasing insensitive behaviors may reduce suicide ideation and risk in men SM/Vs.

Delay discounting is the loss in value of an outcome as a function of its delay. The present study focused on examining a trait-like characteristic of delay discounting in a preclinical animal model. Specifically, we were interested in whether there was a positive relation between discounting of 2 different outcomes in rats. That is, would rats that discount delayed food steeply also discount delayed water steeply? In addition, we examined how session-to-session variability in discounting could be attributed to differences between subjects (trait variability) and to differences within subjects (state variability). Finally, we measured discounting from early- to mid-adulthood, allowing us to examine changes in discounting as a function of age. Overall, we found a moderate, positive correlation between discounting of food and discounting of water in rats, providing further evidence that the relative consistency with which individuals discount different outcomes is a trait-like characteristic. In addition, we found a high degree of within-subject variability in discounting, indicating strong state-like differences from session to session. Finally, overall, discounting decreased as a function of age; however, individual-subject data showed variability in how discounting changed across time. Overall, our results show that differences in delay discounting between individuals reflect variability in both trait- and state-like characteristics.

This study investigates the direct and indirect effects of maternal and sibling relational intimacy on adolescents’ volunteering behaviors via their social responsibility values. Participants included two adolescents (50% female; M age = 1 year) and one parent (85% female; M age = 45 years) from 682 families ( N = 2046) from an ongoing longitudinal study. Adolescents self-reported their intimacy with mothers and siblings (Time 1), social responsibility values (Time 1), and volunteering (Times 1 and 2); parents reported on sociodemographic characteristics (i.e., gender, birth order, and family income). Results from a structural equation model indicated that after accounting for adolescents’ earlier volunteering, both maternal and sibling intimacy were indirectly related to greater volunteering via social responsibility values. There were no significant direct effects from maternal or sibling intimacy to adolescents’ volunteering. Results indicate that both mothers and siblings are important in socializing prosocial and civic values and behaviors during adolescence.

There has been a great deal of work in the literature on the equivalence between the mixed-effects modeling and structural equation modeling (SEM) frameworks in specifying growth models (Willett & ...

Introduction Suicide is a major public health concern. One consistently cited risk factor for suicide is childhood maltreatment, which also may play a role in the transition from suicidal ideation to suicidal behavior. Method The current study aimed to examine the relationship between childhood maltreatment and suicide attempts during adolescence (N = 4834; 52.1% female; 67.5% Caucasian). Data from the U.S. National Longitudinal Study of Adolescent Health were utilized. Forty-six theoretically-relevant risk factors were explored as potential mediators of this relationship using an exploratory mediation data analytic method. Results Results demonstrated a significant childhood maltreatment - suicide attempt relationship only among females. After considering demographics and suicidal ideation, having received counseling in the previous 12 months was the most influential mediator, followed by having a friend attempt suicide in the previous 12 months. Conclusions These findings highlight potential gender differences in the relationship between childhood maltreatment and later suicide attempts, and, moreover, the importance of assessing for recent exposure to peer suicidal behavior in suicide risk assessments.

Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment.

The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and p values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have occurred during the past 50 years. There are, of course, limitations to what null hypothesis testing and p values reveal about data. But modern advances make it clear that there are serious limitations and concerns associated with conventional confidence intervals, standard Bayesian methods, and commonly used measures of effect size. Many of these concerns can be addressed using modern robust methods.

Change over time often takes on a nonlinear form. Furthermore, change patterns can be characterized by heterogeneity due to unobserved subpopulations. Nonlinear mixed-effects mixture models provide one way of addressing both of these issues. This study attempts to extend these models to accommodate time-unstructured data. We develop methods to fit these models in both the structural equation modeling framework as well as the Bayesian framework and evaluate their performance. Simulations show that the success of these methods is driven by the separation between latent classes. When classes are well separated, a sample of 200 is sufficient. Otherwise, a sample of 1,000 or more is required before parameters can be accurately recovered. Ignoring individually varying measurement occasions can also lead to substantial bias, particularly in the random-effects parameters. Finally, we demonstrate the application of these techniques to a data set involving the development of reading ability in children.

This chapter proposes to examine the performance of measures of model fit for the latent change score model in small samples with missing data. Structural equation modeling is a flexible modeling framework that encompasses a broad set of statistical models. The empirical example demonstrated that the framework within which a model is fit can lead to opposing results even when comparing models using the likelihood ratio test. The missing data correction used should be of the form such that it, in effect, deactivates itself when data are complete. Otherwise, it should adequately characterize the quality of the observed information in the data. Such a small sample corrections would be of great use to researchers with small samples wishing to evaluate how well longitudinal models fit their data. The chapter concludes with a discussion of the results accompanied by some thoughts on how future research should proceed.

The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and p values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have occurred during the past 50 years. There are, of course, limitations to what null hypothesis testing and p values reveal about data. But modern advances make it clear that there are serious limitations and concerns associated with conventional confidence intervals, standard Bayesian methods, and commonly used measures of effect size. Many of these concerns can be addressed using modern robust methods.

In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear longitudinal trajectories. At each node, a user-specified linear or nonlinear mixed-effects model is estimated. This method is an extension of Abdolell et al.'s (2002) longitudinal recursive partitioning while permitting a nonlinear mixed-effects model in addition to a linear mixed-effects model in each node. We give an overview of recursive partitioning, nonlinear mixed-effects models for longitudinal data, describe nLRP, and illustrate its use with empirical data from the Early Childhood Longitudinal Study-Kindergarten Cohort.

The growth mixture model has become increasingly popular, given the willingness to acknowledge developmental heterogeneity in populations. Typically, linear growth mixture models, based on polynomials or piecewise functions, are used in substantive applications and evaluated quantitatively through simulation. Growth mixture models that follow inherently nonlinear trajectories, referred to as nonlinear mixed-effects mixture models, have received comparatively little attention—likely due to estimation complexity. Previous work on the estimation of these models has involved multistep routines (Kelley, 2008), maximum likelihood estimation (MLE) via the E-M algorithm (Harring, 2005, 2012), Taylor series expansion and MLE within the structural equation modeling framework (Grimm, Ram, & Estabrook, 2010), and MLE by adaptive Gauss–Hermite quadrature (Codd & Cudeck, 2014). This article proposes and evaluates the use of Bayesian estimation with OpenBUGS (Lunn, Spiegelhalter, Thomas, & Best, 2009), a free program, a...

age was 25.5 and 53.8% were females. The average “Tubes” score for the dV-Trainer and dVSSS were 10/100 and 48.5/100 respectively. Scores of MS and JR were similar (p1⁄40.36). GEARS scores of participants who initially used the dVSSS compared to the dV-Trainer were significantly higher (21/25 vs. 17.2/25, p1⁄40.04). Similarly, RACE scores of participants who used the dVSSS were also significantly higher compared to the dV-Trainer (23.2/25 vs. 17.8/25, p1⁄40.02). Scores of MS and JR were similar for GEARS (p1⁄40.50) and RACE score (p1⁄40.57). Intraclass correlation coefficient for the GEARS and RACE scoring were 72.6 and 89.3 respectively. CONCLUSIONS: The dVSSS trainer lead to superior scores in performing UVA in the OR for both MS and JR compared to the dVTrainer. The dVSSS can be used to improve teaching in surgical trainees in a safe and effective manner.

We evaluated feasibility and benefit of implementing structured learning in a robotics program. Furthermore, we assessed validity of a proficiency assessment tool for stepwise graduation. Teaching cases included robotic radical prostatectomy and partial nephrectomy. Procedure steps were categorized: basic, intermediate, and advanced. An assessment tool [“proficiency score” (PS)] was developed to evaluate ability to safely and autonomously complete a step. Graduation required a passing PS (PS ≥ 3) on three consecutive attempts. PS and validated global evaluative assessment of robotic skills (GEARS) were evaluated for completed steps. Linear regression was utilized to determine postgraduate year/PS relationship (construct validity). Spearman’s rank correlation coefficient measured correlation between PS and GEARS evaluations (concurrent validity). Intraclass correlation (ICC) evaluated PS agreement between evaluator classes. Twenty-one robotic trainees participated within the pilot program, completing a median of 14 (2–69) cases each. Twenty-three study evaluators scored 14 (1–60) cases. Over 4 months, 229/294 (78 %) cases were designated “teaching” cases. Residents completed 91 % of possible evaluations; faculty completed 78 %. Verbal and quantitative feedback received by trainees increased significantly (p = 0.002, p