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.

BACKGROUND:Adolescence is a critical period for substance use initiation, with consequences for addiction and psychosocial problems in adulthood. However, differences in initiation by key variables such as age and racialized/ethnic group remain understudied. OBJECTIVES:This study examined age and peer influence in relation to alcohol, cannabis, and cigarette initiation across eight racialized/ethnic groups (Asian or Pacific Islanders, Black, Indigenous, Hispanic White, White, Biracial White-Asian, Biracial White-Black, and Biracial White-Indigenous) to compare biracial groups with their monoracial counterparts. METHODS:Using integrated data analysis with Add Health (n = 15,844; mean age = 15.6) and Monitoring the Future (MTF; n = 9,600; mean age 18.4), we applied discrete-time survival analysis to estimate initiation hazards during adolescence (ages 12-18), accounting for racialized/ethnic group, sex, and peer substance use. RESULTS:The hazards for new initiations of alcohol and cannabis followed a quadratic function of age, peaking around ages 16 and 18. Cigarette use initiation, in contrast, had a relatively flat hazard function. Biracial youth typically showed intermediate or higher risk of substance use initiation relative to their monoracial peers depending on the specific subgroups and substances considered. Peer substance use was associated with increased initiation across all groups, with stronger effects during peak initiation ages and variations in magnitude across racialized groups. CONCLUSION:Our findings show substance use initiation risks and susceptibility to peer influence differ between biracial and monoracial adolescents and between specific biracial subgroups, highlighting the need to consider subgroup differences when addressing adolescent substance use.

Adolescent development is increasingly shaped by social media contexts, with implications for well-being. In this commentary, we discuss and present conceptual and methodological alternatives for two persistent limitations in prior research. First, most prior work measures screen time, implicitly treating social media as a monolith. Emerging research highlights that social media are multifaceted environments where youth encounter diverse experiences. We advocate for more work taking this nuanced approach and for the development of a comprehensive taxonomic framework that categorizes specific online experiences afforded by social media features and content. To support this approach, we call for the development of psychometrically rigorous self-report scales to measure affective and cognitive social media experiences and for innovative behavioral observation techniques. Second, research that considers specific online experiences typically focuses on one in isolation. We argue that a holistic, interactionist approach to understanding human development requires integrating the numerous positive and negative online experiences that co-occur in distinct patterns for diverse adolescents. We discuss the merits of mixture models as one potential analytic solution to address configurations of online experiences and systematically model heterogeneity among youth. These conceptual and methodological shifts can lead to targeted interventions and policies that recognize the interactive effects of digital experiences.

In analyzing longitudinal data with growth curve models, a critical assumption is that changes in the observed measures reflect construct changes and not changes in the manifestation of the construct over time. However, growth curve models are often fit to a repeated measure constructed as a sum or mean of scale items, making an implicit assumption of constancy of measurement. This practice risks confounding actual construct change with changes in measurement (i.e., differential item functioning [DIF]), threatening the validity of conclusions. An improved method that avoids such confounding is the second-order growth curve (SGC) model. It specifies a measurement model at each occasion of measurement that can be evaluated for invariance over time. The applicability of the SGC model is hindered by key limitations: (a) the SGC model treats time as continuous when modeling construct growth but as discrete when modeling measurement, reducing interpretability and parsimony; (b) the evaluation of DIF becomes increasingly error-prone given multiple timepoints and groups; (c) DIF associated with continuous covariates is difficult to incorporate. Drawing on moderated nonlinear factor analysis, we propose an alternative approach that provides a parsimonious framework for including many time points and DIF from different types of covariates. We implement this model through Bayesian estimation, allowing for incorporation of regularizing priors to facilitate efficient evaluation of DIF. We demonstrate a two-step workflow of measurement evaluation and growth modeling, with an empirical example examining changes in adolescent delinquency over time. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Given the bidirectional association between psychopathology and relationship distress, an in-depth understanding of couples’ interaction processes that contribute to psychopathology is needed. This study examined the interpersonal dynamics of vocally-encoded emotional arousal (fundamental frequency, f0) during couple conversations and their associations with depressive symptoms, anxiety symptoms, and relationship distress. Data from eight samples were pooled (N = 404 couples) to examine (a) overall trajectories of f0 across the interaction and (b) moment-by-moment intraindividual changes in and interpersonal reactivity to partners’ f0. Multilevel growth models and repeated-measures actor-partner interdependence models demonstrated that individuals with more severe depression showed more synchronizing reactivity to their partners’ f0 on a moment-by-moment basis, and their overall baseline level of f0 was lower. More severe relationship distress was associated with more steeply increasing trajectories of f0 and with greater synchronizing reactivity to partners’ f0. Relative differences in depressive symptoms between the two members of a couple were associated with interpersonal dynamics of f0 as well. There were no associations with anxiety symptoms. Thus, depressive symptoms were associated with characteristic interpersonal dynamics of vocally-encoded emotional arousal; yet, most consistent associations emerged for relationship distress, which future studies on individual psychopathology should take into account.

There have been long and bitter debates between those who advocate for the use of residualized change (regressing a variable on itself measured at some time lag prior) as the foundation of longitudinal models versus those who utilize difference scores (subtracting prior from current status). However, most of the methodological work on this topic has focused on the outcome variable in different models. Here, we extend these same issues to the covariates -- or predictors -- in longitudinal models of change and find that similar issues arise when using lagged versus difference score predictors. We show how apparently distinct models using different versions of time-varying covariates are, in fact, simply repackaged versions of the same predictive information and are related through a set of equations that we lay out. We then work through several applied examples across traditional and multilevel regression models. We conclude by considering the issues that arise where a time-varying variable acts as both outcome and predictor - with a specific focus on mediation analysis within multivariate longitudinal models. Our results suggest that users should exercise caution when using change scores as time-varying covariates – not because they are wrong per se, but because they can introduce apparent inferential inversions that can mislead researchers when drawing substantive conclusions.

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.

Background: Decades of evidence have elucidated associations between early adversity and risk for negative outcomes. However, traditional conceptualizations of the biologic embedding of adversity ignore neuroscientific principles which emphasize developmental plasticity. Dimensional models suggest that separate dimensions of experiences shape behavioral development differentially. We hypothesized that deprivation would be associated with higher psychopathology and lower academic achievement through executive function and effortful control, while threat would do so through observed, and parent reported emotional reactivity.Methods: In this longitudinal study of 206 mother-child dyads, we test these theories across the first 7 years of life. Threat was measured by the presence of domestic violence, and deprivation by the lack of cognitive stimulation within the parent-child interaction. We used path analyses to test associations between deprivation and threat with psychopathology and school outcomes through cognition and emotional reactivity.Results: We show that children who experienced more deprivation showed poor academic achievement through difficulties with executive function, while children who experienced more threat had higher levels of psychopathology through increased emotional reactivity.Conclusion: These observations are consistent with work in adolescence and reflect how unique adverse experiences have differential effects on children's behavior and subsequently long-term outcomes.

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.

Methodology serves an essential role in advancing psychological science. However, meta-science research points to a leaky translational pipeline in which substantive research often fails to utilize recommended methodological practices. Various explanations for this problem include valuing the development of methods over methodology (making tools over using tools), incentives for methodological research, incentives for promoting pedagogy in methodology, an insufficient number of quantitative methodologists in the discipline, and scarcity of resources for substantive researchers seeking more advanced methodological training. Policy makers might consider several recommendations that could mitigate extant leaks in the translational pipeline.

Measurement invariance (MI) is one of the main psychometric requirements for analyses that focus on potentially heterogeneous populations. MI allows researchers to compare latent factor scores across persons from different subgroups, whereas if a measure is not invariant across all items and persons then such comparisons may be misleading. If full MI does not hold further testing may identify problematic items showing differential item functioning (DIF). Most methods developed to test DIF focused on simple scenarios often with comparisons across two groups. In practical applications, this is an oversimplification if many grouping variables (e.g., gender, race) or continuous covariates (e.g., age) exist that might influence the measurement properties of items; these variables are often correlated, making traditional tests that consider each variable separately less useful. Here, we propose the application of Bayesian Moderated Nonlinear Factor Analysis to overcome limitations of traditional approaches to detect DIF. We investigate how modern Bayesian shrinkage priors can be used to identify DIF items in situations with many groups and continuous covariates. We compare the performance of lasso-type, spike-and-slab, and global-local shrinkage priors (e.g., horseshoe) to standard normal and small variance priors. Results indicate that spike-and-slab and lasso priors outperform the other priors. Horseshoe priors provide slightly lower power compared to lasso and spike-and-slab priors. Small variance priors result in very low power to detect DIF with sample sizes below 800, and normal priors may produce severely inflated type I error rates. We illustrate the approach with data from the PISA 2018 study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Despite the presence of individual differences in the depressive response patterns of adults during the COVID-19 pandemic, most studies have investigated overall population-level changes in depressive symptoms during the first year of the pandemic. This longitudinal repeated-measurement observational study obtained 39,259 observations from 4,361 adults assessed nine times over a 24-month period, spanning from the onset of the pandemic to its proclaimed termination. Using a Latent Change Score Mixture Model to investigate differential change patterns in depressive symptoms over time, 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 substantial and clinically severe levels of gained symptoms over time during the pandemic (6.77%). Both deteriorating depressive response patterns predicted the presence of a psychiatric diagnosis and treatment-seeking behavior at the end of the pandemic. Together, the absence of a preexisting psychiatric diagnosis at the onset of the pandemic, severe symptom increases during, combined with reports of psychiatric treatment-seeking and diagnosis at its termination, indicated that the strongly deteriorating subgroup represents an additional 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, and financial and occupational concerns at the onset of the pandemic), 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. Major changes in response patterns occurred during the first three months of the pandemic, suggesting this period represents a window of sensitivity for the development 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.

Alcohol use among Biracial adolescents remains understudied. This study examined how parenting and peer factors relate to age of alcohol use onset among Black, White, and Biracial Black-White adolescents and emerging adults. We used Add Health data to produce a final analytic sample of 13,528 adolescents who self-identified as White, Black, or Biracial Black-White. Discrete-time survival analysis implemented within logistic regression indicated Black adolescents showed the lowest probability of alcohol use onset by age 18, followed by Biracial adolescents, and White adolescents. The probability of alcohol use onset increased for Monoracial Black and White adolescents at ages 16, 18, and 21. Descriptively our model suggest that Biracial adolescents exhibit a sharp decline in their probability of alcohol use onset at age 16 and a sharp increase at age 21. However, this trend did not differ significantly from the other racial groups. Consistent with social control and learning theories, low parental acceptance, high parental control, and peer substance use were associated with alcohol use onset. Alcohol use onset trajectories differed for Monoracial and Biracial adolescents with Biracial individuals reporting greater alcohol onset in adulthood. Prevention efforts should continue to target parental acceptance, parental control, and peer substance use.

"Modeling Growth in the Presence of Changing Measurement Properties between Persons and within Persons over Time: A Bayesian Regularized Second-Order Growth Curve Model." Multivariate Behavioral Research, 58(1), pp. 150–151 Article informationConflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.Ethical principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.Funding: This work was not supported.Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.Acknowledgment: I would like to express my appreciation to my SMEP sponsor, Daniel Bauer.

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.

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. Trial registration This trial is registered on ClinicalTrials.gov (NCT03080259). Posted March 15, 2017.

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. This approach is criticized because it introduces a dubious homoskedasticity assumption across classes. Alternative methods have been proposed to reduce nonconvergence without imposing covariance equality constraints, and though studies have shown that these methods perform well when the correct number of classes is known, research has not examined whether they can accurately identify the number of classes. Given that selecting the number of classes tends to be the most difficult aspect of GMMs, more information about class enumeration performance is crucial to assess the potential utility of these methods. We conduct an extensive simulation based on model characteristics from studies in the PTSD literature to explore class enumeration and classification accuracy of methods for improving nonconvergence. Despite its popularity, results showed that typical approach of applying covariance equality constraints performs quite poorly and is not recommended. However, 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 PTSD data is provided to show that the typical 4-Class solution found in many empirical PTSD studies may be an artefact of the covariance equality constraint method that has permeated this literature.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

This 17-month longitudinal study on a representative sample of 4,361 Norwegian adults employs an observational ABAB design across 6 repeated assessments and 3 pandemic waves to systematically investigate the evolution of depressive symptomatology across all modifications of social distancing protocols (SDPs) from their onset to termination. Using Latent Change Score Models to analyze 26,166 observations, the study empirically corroborates that critical fluctuations in depressive symptomatology within and across individuals occur during the first 3 months of the pandemic, after which symptom profiles are predominantly consolidated throughout the pandemic period. Contrary to established belief, female sex, young age, lower education and preexisting psychiatric diagnosis only served as adequate predictors of the initial shocks to symptomatology observed during the onset of the pandemic and did not adequately predict subsequent change observed in symptoms within and across individuals. Population-level analyses demonstrated that symptom levels strongly covaried with the presence and strictness of SDPs and were unrelated to COVID-19 incidence rates. Upon predominant termination of SDPs, population-level symptoms began declining, while large heterogeneity was present across the adult population. Detrimental long-term adversities were revealed by 10% of the adults. These individuals displayed chaotic adaptation to the pandemic and its SDPs, exhibiting substantial increases in clinical levels of symptomatology ensuing partial reopening of society and through the remainder of the pandemic, with these deleterious symptoms projected to remain heightened ahead. Frequency of quarantine exposure was incrementally tied with increases in contemporaneously experienced and long-term depressive adversities, with information obtainment through unmonitored sources further associated with contemporaneous and long-term states of heightened symptomatology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The social distancing protocols (SDPs) implemented as a response to the COVID-19 pandemic may seriously influence peoples' mental health. We used a sample of 4361 Norwegian adults recruited online and stratified to be nationally representative to investigate the evolution of anxiety following each modification in national SDPs across a 20-month period from the onset of the pandemic to the reopening of society and discontinuation of SDPs. The mean anxiety level fluctuated throughout the observation period and these fluctuations were related to the stringency of the modified SDPs. Those with a high initial level almost in unison showed a substantial and lasting decrease of anxiety after the first lifting of SDPs. A sub-group of 9% had developed a persistent anxiety state during the first 3 months. Younger age, pre-existing psychiatric diagnosis, and use of unverified information platforms proved to predict marked higher anxiety in the long run. In conclusion, individuals with a high level of anxiety at the outbreak of the pandemic improved when the social distancing protocols were lifted. By contrast, a sizeable subgroup developed lasting clinical levels of anxiety during the first 3 months of the pandemic and is vulnerable to prolonged anxiety beyond the pandemic period.