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Pediatric primary care is a promising setting for reducing diversion of stimulant medications for ADHD. We tested if training pediatric primary care providers (PCPs) increased use of diversion prevention strategies with adolescents with ADHD. The study was a cluster-randomized trial in 7 pediatric primary care practices. Participants were pediatric PCPs (N = 76) at participating practices. Practices were randomized to a 1-h training in stimulant diversion prevention or treatment-as-usual. At baseline, 6 months, 12 months, and 18 months, PCPs rated how often they used four categories of strategies: patient/family education, medication management/monitoring, assessment of mental health symptoms/functioning, and assessment of risky behaviors. They completed measures of attitudes, implementation climate, knowledge/skill, and resource constraints. Generalized Estimating Equations estimated differences in outcomes by condition. Mediation analyses tested if changes in knowledge/skill mediated training effects on strategy use. PCPs in the intervention condition reported significantly greater use of patient/family education strategies at all follow-up time points. There were no differences between conditions in medication management, assessment of mental health symptoms/functioning, or assessment of risky behaviors. At 6 months, PCPs in the intervention condition reported more positive attitudes toward diversion prevention, stronger implementation climate, greater knowledge/skill, and less resource constraints. Differences in knowledge/skill persisted at 12 months and 18 months. Brief training in stimulant diversion had substantial and enduring effects on PCPs' self-reported knowledge/skill and use of patient/family education strategies to prevent diversion. Training had modest effects on attitudes, implementation climate, and resource constraints and did not change use of strategies related to medication management and assessment of mental health symptoms/functioning and risky behaviors. Changes in knowledge/skill accounted for 49% of the total effect of training on use of patient/family education strategies.

Conducting valid and reliable empirical research in the prevention sciences is an inherently difficult and challenging task. Chief among these is the need to obtain numerical scores of underlying theoretical constructs for use in subsequent analysis. This challenge is further exacerbated by the increasingly common need to consider multiple reporter assessments, particularly when using integrative data analysis to fit models to data that have been pooled across two or more independent samples. The current article uses both simulated and real data to examine the utility of a recently proposed psychometric model for multiple reporter data called the trifactor model (TFM) in settings that might be commonly found in prevention research. Results suggest that numerical scores obtained using the TFM are superior to more traditional methods, particularly when pooling samples that contribute different reporter perspectives.

Testing for differential item functioning (DIF) has undergone rapid statistical developments recently. Moderated nonlinear factor analysis (MNLFA) allows for simultaneous testing of DIF among multiple categorical and continuous covariates (e.g., sex, age, ethnicity, etc.), and regularization has shown promising results for identifying DIF among many covariates. However, computationally inefficient estimation methods have hampered practical use of the regularized MNFLA method. We develop a penalized expectation–maximization (EM) algorithm with soft- and firm-thresholding to more efficiently estimate regularized MNLFA parameters. Simulation and empirical results show that, compared to previous implementations of regularized MNFLA, the penalized EM algorithm is faster, more flexible, and more statistically principled. This method also yields similar recovery of DIF relative to previous implementations, suggesting that regularized DIF detection remains a preferred approach over traditional methods of identifying DIF. [ABSTRACT FROM AUTHOR]

Poor physiological self-regulation has been proposed as a potential biological vulnerability for adolescent suicidality. This study tested this hypothesis by examining the effect of parasympathetic stress responses on future suicide ideation. In addition, drawing from multilevel developmental psychopathology theories, the interplay between parasympathetic regulation and friendship support, conceptualized as an external source of regulation, was examined. At baseline, 132 adolescent females (M age = 14.59, SD = 1.39) with a history of mental health concerns participated in an in vivo interpersonal stressor (a laboratory speech task) and completed self-report measures of depressive symptoms and perceived support within a close same-age female friendship. Respiratory sinus arrhythmia (RSA) was measured before and during the speech task. Suicide ideation was assessed at baseline and at 3, 6, and 9 months follow-up. The results revealed that females with greater relative RSA decreases to the laboratory stressor were at higher risk for reporting suicide ideation over the subsequent 9 months. Moreover, parasympathetic responses moderated the effect of friendship support on suicide ideation; among females with mild changes or higher relative increases in RSA, but not more pronounced RSA decreases, friendship support reduced risk for future suicide ideation. Findings highlight the crucial role of physiological and external regulation sources as protective factors for youth suicidality.

Objective: To test whether pediatrician training leads to provider utilization of stimulant diversion prevention strategies as reported by adolescent patients with ADHD. Methods: Pediatric practices received a stimulant diversion prevention workshop (SDP) or continued treatment-as-usual (TAU) in a cluster-randomized controlled trial. Surveys were completed by 341 stimulant-treated patients at baseline and three follow-up assessments. Results: In intent-to-treat analyses of patient reports, SDP adolescents reported more provider use of diversion prevention strategies compared to TAU. They also reported more parent-patient communication about diversion. Provider satisfaction with the training was strong. Conclusions: Pediatricians can make use of clinical practice strategies for the prevention of stimulant diversion following a 1-hr training; findings are novel given their reliance on confidential patient report of provider behavior and increase confidence in the results. Coupled with the positive provider satisfaction ratings, results suggest that this brief workshop may be an option for concerned providers that also has the effect of increasing discussion at home about safe use of stimulants. [ABSTRACT FROM AUTHOR]

Despite the presence of individual differences in the depressive symptom change in adults during the COVID‐19 pandemic, most studies have investigated population‐level changes in depression during the first year of the pandemic. This longitudinal repeated‐measurement study obtained 39,259 observations from 4,361 adults assessed nine times over a 24‐month period in Norway (March 2020 to March 2022). Using a Latent Change Score Mixture Model to investigate differential change patterns in depressive symptoms, five profiles were identified. Most adults revealed a consistently resilient (42.52%) or predominantly resilient pattern differentiated by an initial shock in symptomatology (13.17%). Another group exhibited consistently high depressive adversities (8.5%). One group showed mild deterioration with small increases in depressive symptomatology compared to onset levels (29.04%), and a second strong deterioration group exhibited clinically severe levels of gained symptoms over time (6.77%). Both deteriorating depressive symptom change patterns predicted the presence of a psychiatric diagnosis and treatment seeking at the end of the study period. Together, the absence of a preexisting psychiatric diagnosis at the onset of the pandemic and severe symptom increases during, combined with reports of psychiatric treatment seeking and diagnosis at the end of the study period, indicated that the strongly deteriorating subgroup represents an additional and newly emerged group of adults struggling with depressive problems. Factors related to general adverse change (lower education levels, lone residence), initial shocks prior to recovery (frequent information seeking, financial and occupational concerns), and resilience and recovery (older age, being in a relationship, physical activity) were identified. Binge drinking and belonging to an ethnic minority were influential predictors of the strongly deteriorating group. All major change patterns in depressive symptoms occurred during the first 3 months of the pandemic, suggesting this period represents a window of sensitivity for the development of long‐lasting depressive states versus patterns of recovery and resilience. These findings call for increased vigilance of psychiatric symptoms during the initial phases of infectious disease outbreaks and highlight a specific target period for the implementation of preventive measures. [ABSTRACT FROM AUTHOR]

The current study demonstrates the application of an analytic approach for incorporating multiple time trends in order to examine the impact of cohort effects on individual trajectories of eight drugs of abuse. Parallel analysis of two independent, longitudinal studies of high-risk youth that span ages 10 to 40 across 23 birth cohorts between 1968 and 1991 was conducted. The two studies include the Michigan Longitudinal Study (current analytic sample of n = 579 over 12 cohorts between 1980 and 1991 and across ages 10-27) and the Adolescent/Adult and Family Development Project (current analytic sample of n = 849 over 11 cohorts between 1968 and 1978 and across ages 10-40). A series of nonlinear, multi-level growth models controlled simultaneously for cohort and age trends in substance use trajectories. Evidence was found for both age and cohort effects across most outcomes as well as several significant age-by-cohort interactions. Findings suggest cohort trends in developmental trajectories of substance use are sample and drug-specific in the adolescent and early to mid-adult years. Thus, studies that do not control for both trends may confound cohort and developmental trends in substance use. For this reason, demonstration of one analytic approach that can be used to examine both time trends simultaneously is informative for future multi-cohort longitudinal studies where change over time is of interest.

When developing and evaluating psychometric measures, a key concern is to ensure that they accurately capture individual differences on the intended construct across the entire population of interest. Inaccurate assessments of individual differences can occur when responses to some items reflect not only the intended construct but also construct‐irrelevant characteristics, like a person's race or sex. Unaccounted for, this item bias can lead to apparent differences on the scores that do not reflect true differences, invalidating comparisons between people with different backgrounds. Accordingly, empirically identifying which items manifest bias through the evaluation of differential item functioning (DIF) has been a longstanding focus of much psychometric research. The majority of this work has focused on evaluating DIF across two (or a few) groups. Modern conceptualizations of identity, however, emphasize its multi‐determined and intersectional nature, with some aspects better represented as dimensional than categorical. Fortunately, many model‐based approaches to modelling DIF now exist that allow for simultaneous evaluation of multiple background variables, including both continuous and categorical variables, and potential interactions among background variables. This paper provides a comparative, integrative review of these new approaches to modelling DIF and clarifies both the opportunities and challenges associated with their application in psychometric research. [ABSTRACT FROM AUTHOR]

Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression. (Contains 9 tables, 4 figures, and 8 footnotes.)

Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation, though this may be a dubious assumption. Alternative model specifications have been proposed to reduce nonconvergence without imposing covariance equality constraints. These methods perform well when the correct number of classes is known, but research has not yet examined their use when the number of classes is unknown. Given the importance of selecting the number of classes, more information about class enumeration performance is crucial to assess the potential utility of these methods. We conducted an extensive simulation to explore class enumeration and classification accuracy of model specifications that are more robust to nonconvergence. Results show that the typical approach of applying covariance equality constraints performs quite poorly. Instead, we recommended covariance pattern GMMs because they (a) had the highest convergence rates, (b) were most likely to identify the correct number of classes, and (c) had the highest classification accuracy in many conditions, even with modest sample sizes. An analysis of empirical posttraumatic stress disorder (PTSD) data is provided to show that the typical four-class solution found in many empirical PTSD studies may be an artifact of the covariance equality constraint method that has permeated this literature. [ABSTRACT FROM AUTHOR]

Although numerous studies have established a link between alcohol use and partner violence in adulthood, little research has examined this relation during adolescence. The current study used multivariate growth models to examine relations between alcohol use and dating aggression across Grades 8 through 12, controlling for shared risk factors (common causes) that predict both behaviors. Associations between trajectories of alcohol use and dating aggression were reduced substantially when common causes were controlled. Concurrent associations between the two behaviors were significant across nearly all grades but no evidence was found for prospective connections from prior alcohol use to subsequent dating aggression or vice versa. Findings suggest that prevention efforts should target common causes of alcohol use and dating aggression. (Contains 2 figures and 3 tables.)

Psychologists have long been interested in characterizing individual differences in change over time. It is often plausible to assume that the distribution of these individual differences is continuous in nature, yet theory is seldom so specific as to designate its parametric form (e.g., normal). Semiparametric groups-based trajectory models (SPGMs) were thus developed to provide a discrete approximation for continuously distributed growth of unknown form. Previous research has demonstrated the adequacy of the approximation provided by SPGM but only under relatively narrow, theoretically optimal conditions. Under alternative conditions, which may be more common in practice (e.g., higher dimension random effects, smaller sample sizes), this study shows that approximation adequacy can suffer. Furthermore, this study also evaluates whether SPGM's discrete approximation is preferable to a parametric trajectory model that assumes normally distributed random effects when in fact the distribution is modestly nonnormal. The answer is shown to depend on distributional characteristics of both repeated measures (binary or continuous) and random effects (bimodal or skewed). Implications for practice are discussed in light of empirical examples on externalizing behavior. (Contains 10 figures, 11 footnotes and 6 tables.)

Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can be used to visualize and detect nonlinear relationships between latent variables. The first procedure involves fitting a linear structural equation model and then inspecting plots of factor score estimates for evidence of nonlinearity. The second procedure is to use a mixture of linear structural equation models to approximate the underlying, potentially nonlinear function. Targeted simulations indicate that the first procedure is more efficient, but that the second procedure is less biased. The mixture modeling approach is recommended, particularly with medium to large samples. (Contains 5 figures, 4 tables, and 2 footnotes.)

Previous research has compared methods of estimation for fitting multilevel models to binary data, but there are reasons to believe that the results will not always generalize to the ordinal case. This article thus evaluates (a) whether and when fitting multilevel linear models to ordinal outcome data is justified and (b) which estimator to employ when instead fitting multilevel cumulative logit models to ordinal data, maximum likelihood (ML), or penalized quasi-likelihood (PQL). ML and PQL are compared across variations in sample size, magnitude of variance components, number of outcome categories, and distribution shape. Fitting a multilevel linear model to ordinal outcomes is shown to be inferior in virtually all circumstances. PQL performance improves markedly with the number of ordinal categories, regardless of distribution shape. In contrast to binary data, PQL often performs as well as ML when used with ordinal data. Further, the performance of PQL is typically superior to ML when the data include a small to moderate number of clusters (i.e., [less than or equal to] 50 clusters). (Contains 7 tables, 8 footnotes and 7 figures.)

Partially clustered designs, where clustering occurs in some conditions and not others, are common in psychology, particularly in prevention and intervention trials. This article reports results from a simulation comparing 5 approaches to analyzing partially clustered data, including Type I errors, parameter bias, efficiency, and power. Results indicate that multilevel models adapted for partially clustered data are relatively unbiased and efficient and consistently maintain the nominal Type I error rate when using appropriate degrees of freedom. To attain sufficient power in partially clustered designs, researchers should attend primarily to the number of clusters in the study. An illustration using data from a partially clustered eating disorder prevention trial is provided. (Contains 6 figures, 3 tables and 2 footnotes.)

The current study examined the role of heavy alcohol use in the developmental process of desistance in physical dating aggression during adolescence. Using longitudinal data spanning grades 8 through 12 we tested the hypotheses that (a) higher levels of early heavy alcohol use would be associated with decreased deceleration from dating aggression during late adolescence and (b) higher levels of heavy alcohol use during time-points in late adolescence would be contemporaneously associated with elevated levels of dating aggression at those same time points. Contrary to expectations, findings indicate that the effects of both early and continuing heavy alcohol use on dating aggression were strong during early adolescence but tended to diminish over time. Unexpectedly, the contemporaneous effects of alcohol use on dating aggression were stronger in the spring than in the fall semesters. Implications for prevention and for understanding developmental relations between the two behaviors are discussed.

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the service of more flexibly recovering characteristics of the latent aggregate regression function. This article develops and evaluates delta method and parametric bootstrap approaches for obtaining approximate confidence intervals for Bauer's semiparametric approach to modeling latent nonlinear functions. Coverage rates of these approximate point-wise confidence intervals or nonsimultaneous confidence bands are evaluated by Monte Carlo and recommendations for their use are suggested. (Contains 8 figures and 1 footnote.)

When conducting an integrative analysis of data obtained from multiple independent studies, a fundamental problem is to establish commensurate measures for the constructs of interest. Fortunately, procedures for evaluating and establishing measurement equivalence across samples are well developed for the linear factor model and commonly used item response theory models. A newly proposed moderated nonlinear factor analysis model generalizes these models and procedures, allowing for items of different scale types (continuous or discrete) and differential item functioning across levels of categorical and/or continuous variables. The potential of this new model to resolve the problem of measurement in integrative data analysis is shown via an empirical example examining changes in alcohol involvement from ages 10 to 22 years across 2 longitudinal studies. (Contains 3 tables, 12 figures, and 6 footnotes.)

When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for binary or ordered-categorical outcomes, however, comparisons between such models are impeded by the implicit rescaling of the model estimates that takes place with the inclusion of new predictors and/or random effects. This paper presents an approach for putting the estimates on a common scale to facilitate relative comparisons between models fit to binary or ordinal outcomes. The approach is developed for both population-average and unit-specific models.

Applications of multilevel models have increased markedly during the past decade. In incorporating lower-level predictors into multilevel models, a key interest is often whether or not a given predictor requires a random slope, that is, whether the effect of the predictor varies over upper-level units. If the variance of a random slope significantly differs from zero, the focus of the analysis may then shift to explaining this heterogeneity with upper-level predictors through the testing of cross-level interactions. As shown in this article, however, both the variance of the random slope and the cross-level interaction effects may be entirely spurious if the relationship between the lower-level predictor and the outcome is nonlinear in form but is not modeled as such. The importance of conducting diagnostics to detect nonlinear effects is discussed and demonstrated via an empirical example. (Contains 9 notes, 3 tables, and 6 figures.)

The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixtures of linear structural equations. Second, it provides several examples showing how the method works and how it is implemented and interpreted in practical applications. In particular, this article examines the potentially nonlinear relationships between positive and negative affect and cognitive processing. Third, a recommended display format for illustrating latent bivariate relationships is demonstrated. Finally, the article describes an R package and an online utility that generate these displays automatically. (Contains 10 figures, 1 table, and 4 footnotes.)

Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as independent, given that these individuals are not placed into groups. Although this kind of design is common in intervention research, the statistical models applied to evaluate the treatment effects are usually inconsistent with the resulting data structure, potentially leading to biased inferences. This article presents an alternative model that explicitly accounts for the fact that only treated participants are grouped. In addition to providing a useful test of the overall treatment effect, this approach also permits one to formally determine the extent to which treatment effects vary over treatment groups and whether there is evidence that individuals within treatment groups become similar to one another. This strategy is demonstrated with data from the Reconnecting Youth program for high school students at risk of school failure and behavioral disorders. (Contains 1 table and 8 footnotes.)

The relations among several alternative parameterizations of the binary factor analysis model and the 2-parameter item response theory model are discussed. It is pointed out that different parameterizations of factor analysis model parameters can be transformed into item response model theory parameters, and general formulas are provided. Illustrative data analysis is provided to demonstrate the transformations. (Contains 5 tables, 1 figure, and 5 footnotes.)