MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

Development and Validation of the Sexual Minority and Religious Identity Integration (SMRII) Scale

Despite the psychosocial strain of homonegative religious attitudes, many people with minoritized sexual identities also hold religious identities and benefit from integrating their sexual minority and religious identities. However, for research and clinical practice to advance, a reliable and valid measure of sexual and religious identity integration is needed. The present study reports on the development and validation of the Sexual Minority and Religious Identity Integration (SMRII) Scale. Participants were comprised of two subsamples for whom sexual and religious identity is particularly salient (Latter-day Saints and Muslims) as well as a third subsample of the general sexual minority population-totaling 1,424 individuals (39% POC, 62% cisman, 27% ciswoman, 11% trans/non-binary/genderqueer). Exploratory and confirmatory factor analyses indicated that the 5-item scale measures a single unidimensional construct. This scale demonstrated good internal consistency in the total sample (α = .80) as well as metric and scalar invariance along relevant demographics. The SMRII also evidenced strong convergent and discriminant validity, significantly correlating with other measures of religious and sexual minority identity typically between r = .2 and r = .5. Taken together, initial findings indicate that the SMRII is a psychometrically sound measure that is brief enough to be utilized in research and clinical settings.Public Significance Statement: This article introduces the Sexual Minority and Religious Identity Integration Scale (SMRII) as a reliable and valid assessment of the degree to which individuals integrate their sexual minority and religious identities. This five-item measure is brief enough to be utilized in both research and clinical settings.

Longitudinal examination of alcohol demand and alcohol-related reinforcement as predictors of heavy drinking and adverse alcohol consequences in emerging adults

BACKGROUND AND AIMS

Behavioral economic theory predicts that high alcohol demand and high proportionate alcohol-related reinforcement are important determinants of risky alcohol use in emerging adults, but the majority of research to date has been cross-sectional in nature. The present study investigated prospective and dynamic relationships between alcohol demand and proportionate alcohol-related reinforcement in relation to heavy drinking days and alcohol problems.

DESIGN

Longitudinal cohort with assessments every 4 months for 20 months.

SETTING

Ontario, Canada.

PARTICIPANTS

Emerging adults reporting regular heavy episodic drinking (n = 636, Mage = 21.44; 55.8% female).

MEASUREMENTS

Heavy drinking days (HDD; Daily Drinking Questionnaire), alcohol problems (Brief Young Adult Alcohol Consequences Questionnaire), alcohol demand (Alcohol Purchase Task) and proportionate alcohol-related reinforcement (Activity Level Questionnaire).

FINDINGS

Linear mixed effects models revealed that behavioral economic indicators and alcohol-related outcomes significantly decreased over the study, consistent with 'aging out' of risky alcohol use. Random intercept cross-lagged panel models revealed significant between-person relationships, such that higher alcohol demand and alcohol-related reinforcement were positively associated with HDD and alcohol problems (random intercepts = 0.187-0.534, Ps < 0.01). Moreover, alcohol demand indicators (particularly the rate of change in elasticity of the demand curve, as measured by α, and the maximum expenditure, Omax) and proportionate alcohol-related reinforcement significantly forecasted changes in HDD at all time points (|βs| = 0.063-0.103, Ps < 0.05) in cross-lagged relationships, with bidirectional associations noted for the rate of change in elasticity (βs = -0.085 to -0.104, Ps < 0.01). Proportionate alcohol-related reinforcement also significantly forecasted changes in alcohol problems at all time points (βs = 0.072-0.112, Ps < 0.01).

CONCLUSIONS

Multiple behavioral economic indicators (demand elasticity, maximum expenditure and reinforcement ratio) forecast changes in heavy episodic drinking and alcohol problems over the course of emerging adulthood. These results further implicate alcohol demand and proportionate alcohol-related reinforcement as etiologically and developmentally important mechanisms in alcohol use trajectories.

Applying behavioral economics to understand changes in alcohol outcomes during the transition to adulthood: Longitudinal relations and differences by sex and race

OBJECTIVE

Population drinking trends show clear developmental periodicity, with steep increases in harmful alcohol use from ages 18 to 22 followed by a gradual decline across the 20s, albeit with persistent problematic use in a subgroup of individuals. Cross-sectional studies implicate behavioral economic indicators of alcohol overvaluation (high alcohol demand) and lack of alternative substance-free reinforcers (high proportionate alcohol-related reinforcement) as potential predictors of change during this developmental window, but longitudinal evidence is sparse.

METHOD

Using a sample of emerging adults (N = 497, Mage = 22.61 years, 62% female, 48.69% White, 40.44% Black), this study examined prospective, bidirectional relations between both past-week heavy drinking days (HDD) and alcohol problems and proportionate alcohol-related reinforcement (reinforcement ratio), alcohol demand intensity (consumption at zero price), alcohol demand Omax (maximum expenditure), and change in demand elasticity (rate of change in consumption across escalating price) over five assessments (every 4 months) using random intercept cross-lagged panel models.

RESULTS

Alcohol problems and HDD decreased across assessments. Significant between-person effects indicated that each behavioral economic variable was associated with increased drinking risk. Change in reinforcement ratio was positively associated with decreases in alcohol problems. Multigroup invariance modeling revealed distinct risk pathways in that change in demand intensity and Omax predicted change in alcohol problems for male participants and change in intensity predicted change in alcohol problems for non-White participants.

CONCLUSION

The study provides consistent support for proportionate alcohol-related reinforcement and mixed support for demand as within-person predictors of reductions in drinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models

A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's information criterion (DIC), sample size adjusted BIC (SABIC), relative entropy, the integrated classification likelihood criterion (ICL-BIC), the adjusted Lo-Mendell-Rubin (LMR), and Vuong-Lo-Mendell-Rubin (VLMR). The accuracy of the fit indices was assessed for correct detection of the number of latent classes for different simulation conditions including sample size (2,500 and 5,000), test length (15, 30, and 45), mixture proportions (equal and unequal), number of latent classes (2, 3, and 4), and latent class separation (no-separation and small separation). Simulation study results indicated that as the number of examinees or number of items increased, correct identification rates also increased for most of the indices. Correct identification rates by the different fit indices, however, decreased as the number of estimated latent classes or parameters (i.e., model complexity) increased. Results were good for BIC, CAIC, DIC, SABIC, ICL-BIC, LMR, and VLMR, and the relative entropy index tended to select correct models most of the time. Consistent with previous studies, AIC and AICc showed poor performance. Most of these indices had limited utility for three-class and four-class mixture 3PL model conditions.

Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework

The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain patients undergoing interdisciplinary treatment. Questionnaire data collected at pre-treatment and 1-year follow up from 11,995 patients from the Swedish Quality Registry for Pain Rehabilitation were used. Indicators measuring pain characteristics, psychological aspects, and social functioning and general health status were used to form subgroups, and pain interference at follow-up was used for the selection and the performance evaluation of models. A nested cross-validation procedure was used for determining the number of classes (inner cross-validation) and the prediction accuracy of the selected model among unseen cases (outer cross-validation). A four-class solution was identified as the optimal model. Identified subgroups were separable on indicators, predictive of long-term outcomes, and related to background characteristics. Results are discussed in relation to previous clustering attempts of patients with diverse chronic pain conditions. Our analytical approach, as the first to combine mixture modeling with supervised, targeted learning, provides a promising framework that can be further extended and optimized for improving accurate prognosis in pain treatment and identifying clinically meaningful subgroups among chronic pain patients.

Counterfactual Mediation Analysis with a Latent Class Exposure

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome ("one-step," "bias-adjusted three-step," "modal class assignment," "non-inclusive pseudo class draws," and "inclusive pseudo class draws") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

Exploring student teachers' latent coping profiles and their relations to perceived stress and stressors

As coping profiles can offer concrete intervention opportunities, the goal of the present study is to explore latent coping profiles in a sample of 316 student teachers at a German university, using the Brief COPE inventory (Carver, 1997). Furthermore, we aim to test the extent to which students with specific coping profiles differ in the levels of stress (PSS; Schneider et al., 2020) and stressors they perceive. Latent profile analysis (LPA) reveals that a six-profile solution shows the optimal fit, with high entropy. The six profiles include two more support-seeking coping profiles (networking and disengaged copers), withdrawn, cognitive restructuring, avoidant and repertoire-oriented copers. Both more support-seeking coping profiles used in particular strategies concerning seeking social and emotional support, and are associated with less stress. Avoidant and withdrawn copers show unfavourable perceptions of stress levels and stressors. Thus, programs to promote social-emotional support should be implemented in teacher training courses.

A person-centered approach to characterizing longitudinal ambulatory impairment in Parkinson's disease

Loss of ambulation is common and highly variable in Parkinson's disease (PD), and poorly understood from the perspectives of those with PD. Gaining insights to the anticipated perceived trajectories and their drivers, will facilitate patient-centered care. Latent class growth analysis, a person-centered mixture modelling approach, was applied to 16,863 people with PD stratified by early (N = 8612; < 3 years), mid (N = 6181; 3-10 years) and later (N = 2070; > 10 years) disease to discern clusters with similar longitudinal patterns of self-reported walking difficulty, measured by EuroQoL 5D-5L that is validated for use in PD. There were four clusters in early and mid-disease strata, with a fifth identified in later disease. Trajectories ranged from none to moderate walking difficulty, with small clusters with severe problems. The percentage of subjects with moderate (early = 17.5%, mid = 26.4%, later = 32.5%) and severe (early = 3.8%, mid = 7.4%, later = 15.4%) walking difficulty at baseline increased across disease duration groups. The trajectories tended to be stable with variability in moderate and severe groups. Across strata, clusters with moderate to severe problems were associated with more severe impairment, depression, anxiety, arthritis, higher BMI, lower income, and lower education, but no consistent race or gender differences. The findings reveal distinct longitudinal patterns in perceived difficulties in walking in PD.

Identifying the 'active ingredients' of socioeconomic disadvantage for youth outcomes in middle childhood

BACKGROUND

Youth experiencing socioeconomic deprivation may be exposed to disadvantage in multiple contexts (e.g., neighborhood, family, and school). To date, however, we know little about the underlying structure of socioeconomic disadvantage, including whether the 'active ingredients' driving its robust effects are specific to one context (e.g., neighborhood) or whether the various contexts increment one another as predictors of youth outcomes.

METHODS

The present study addressed this gap by examining the underlying structure of socioeconomic disadvantage across neighborhoods, families, and schools, as well as whether the various forms of disadvantage jointly predicted youth psychopathology and cognitive performance. Participants were 1,030 school-aged twin pairs from a subsample of the Michigan State University Twin Registry enriched for neighborhood disadvantage.

RESULTS

Two correlated factors underlay the indicators of disadvantage. Proximal disadvantage comprised familial indicators, whereas contextual disadvantage represented deprivation in the broader school and neighborhood contexts. Results from exhaustive modeling analyses indicated that proximal and contextual disadvantage incremented one another as predictors of childhood externalizing problems, disordered eating, and reading difficulties, but not internalizing symptoms.

CONCLUSIONS

Disadvantage within the family and disadvantage in the broader context, respectively, appear to represent distinct constructs with additive influence, carrying unique implications for multiple behavioral outcomes during middle childhood.

Characterizing the hierarchical depression phenotype in sexually diverse individuals

INTRODUCTION

Sexual diverse individuals are at high risk for internalizing psychopathologies, such as depression. Understanding how symptom profiles of heterogeneous psychiatric disorders such as depression differ for sexually diverse vs. heterosexual individuals is thus critical to advance precision psychiatry and maximize our ability to effectively treat members of this population. Research has failed to consider the possibility of hierarchical phenotypes, wherein sexual orientation status may be uniquely and simultaneously associated with both depression broadly and with individual symptoms.

METHOD

To address these issues, we conducted a moderated nonlinear factor analysis in Wave IV of the Add Health study, using sexual diversity status as a predictor of (a) latent depression, (b) factor loadings, and (c) individual symptoms, with and without controlling for race.

RESULTS

Sexual diversity status was positively and simultaneously associated with latent depression, concentration difficulties, and happiness.

DISCUSSION

These findings suggest that sexually diverse populations not only face greater depression, broadly defined, but are disproportionately more likely to experience concentration difficulties and be happier compared to heterosexual counterparts. Methodologically, these models indicate that the CES-D is scalar noninvariant as a function of sexual diversity status (i.e., identical scores on the CES-D may represent different manifestations of depression for sexually diverse and heterosexual participants). Studies examining disparities in depression across heterosexual and sexually diverse samples should thus consider depression broadly as well as specific symptoms. Further, it is critical to examine whether these relations function via different mechanisms.

Better, for worse, or both? Testing environmental sensitivity models with parenting at the level of individual families

According to environmental sensitivity models, children vary in responsivity to parenting. However, different models propose different patterns, with responsivity to primarily: (1) adverse parenting (adverse sensitive); or (2) supportive parenting (vantage sensitive); or (3) to both (differentially susceptible). This preregistered study tested whether these three responsivity patterns coexist. We used intensive longitudinal data of Dutch adolescents (N = 256, Mage = 14.8, 72% female) who bi-weekly reported on adverse and supportive parenting and their psychological functioning (tmean = 17.7, tmax = 26). Dynamic Structural Equation Models (DSEM) indeed revealed differential parenting effects. As hypothesized, we found that all three responsivity patterns coexisted in our sample: 5% were adverse sensitive, 3% vantage sensitive, and 26% differentially susceptible. No adolescent appeared unsusceptible, however. Instead, we labeled 28% as unperceptive, because they did not perceive any changes in parenting and scored lower on trait environmental sensitivity than others. Furthermore, unexpected patterns emerged, with 37% responding contrary to parenting theories (e.g., decreased psychological functioning after more parental support). Sensitivity analyses with concurrent effects and parent-reported parenting were performed. Overall, findings indicate that theorized responsivity-to-parenting patterns might coexist in the population, and that there are other, previously undetected patterns that go beyond environmental sensitivity models.

A novel approach to model cumulative stress: Area under the s-factor curve

OBJECTIVE

Using a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes.

METHOD

We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes.

RESULTS

The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change.

CONCLUSIONS

Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.

Comparisons of Self-Report With Objective Measurements Suggest Faster Responding but Little Change in Response Quality Over Time in Ecological Momentary Assessment Studies

Response times (RTs) to ecological momentary assessment (EMA) items often decrease after repeated EMA administration, but whether this is accompanied by lower response quality requires investigation. We examined the relationship between EMA item RTs and EMA response quality. In one data set, declining response quality was operationalized as decreasing correspondence over time between subjective and objective measures of blood glucose taken at the same time. In a second EMA study data set, declining response quality was operationalized as decreasing correspondence between subjective ratings of memory test performance and objective memory test scores. We assumed that measurement error in the objective measures did not increase across time, meaning that decreasing correspondence across days within a person could be attributed to lower response quality. RTs to EMA items decreased across study days, while no decrements in the mean response quality were observed. Decreasing EMA item RTs across study days did not appear problematic overall.

Identifying high-risk subgroups for self-harm in adolescents and young adults: A longitudinal latent class analysis of risk factors

BACKGROUND

Self-harm is a major public health concern in young people and is governed by a complex interaction of different risk factors. While many studies have identified these risk factors, less is known about how they tend to co-occur together.

METHODS

A latent class analysis was conducted using risk factors for self-harm from two waves at ages 13 and 17 from the Growing Up in Ireland (GUI) study their associations with self-harm were examined longitudinally at ages 17 and 20, respectively.

RESULTS

At age 13, there was a 'peer problems' group with higher probabilities of bullying, being unpopular and internalising problems and a 'school and substance use problems' group with higher probabilities of substance use, truancy or excessive absence from school and violence. Both of these two groups had over a two-fold risk of self-harm at age 17 in comparison to the low risk factor group. At age 17, there was a group with depression that was diagnosed by a medical professional with the highest relative risk (RR:13.9 (95 % CI 10.2-19.0)) of self-harm at age 20. Two other groups with undiagnosed depression had high probabilities of being bullied, and either high or low probabilities of substance use that had a 9.4 (95 % CI 6.8-13.1) and 7.4 (95 % CI 5.5-10.0) relative risk of self-harm at age 20, respectively.

CONCLUSIONS

Identifying hidden sub-groups using risk factors for self-harm in young people can inform potential public health interventions by clinicians and other professionals who work with young people.

Cognitive, emotional, and social factors promoting psychosocial adaptation: a study of latent profiles in people living in socially vulnerable contexts

Introduction

Social adaptation is a multifaceted process that encompasses cognitive, social, and affective factors. Previous research often focused on isolated variables, overlooking their interactions, especially in challenging environments. Our study addresses this by investigating how cognitive (working memory, verbal intelligence, self-regulation), social (affective empathy, family networks, loneliness), and psychological (locus of control, self-esteem, perceived stress) factors interact to influence social adaptation.

Methods

We analyzed data from 254 adults (55% female) aged 18 to 46 in economically vulnerable households in Santiago, Chile. We used Latent profile analysis (LPA) and machine learning to uncover distinct patters of socioadaptive features and identify the most discriminating features.

Results

LPA showed two distinct psychosocial adaptation profiles: one characterized by effective psychosocial adaptation and another by poor psychosocial adaptation. The adaptive profile featured individuals with strong emotional, cognitive, and behavioral self-regulation, an internal locus of control, high self-esteem, lower stress levels, reduced affective empathy, robust family support, and decreased loneliness. Conversely, the poorly adapted profile exhibited the opposite traits. Machine learning pinpointed six key differentiating factors in various adaptation pathways within the same vulnerable context: high self-esteem, cognitive and behavioral self-regulation, low stress levels, higher education, and increased social support.

Discussion

This research carries significant policy implications, highlighting the need to reinforce protective factors and psychological resources, such as self-esteem, self-regulation, and education, to foster effective adaptation in adversity. Additionally, we identified critical risk factors impacting social adaptation in vulnerable populations, advancing our understanding of this intricate phenomenon.

Systematic Integration of Multi-Informant Externalizing Ratings in Clinical Settings

Best practice clinical assessment of externalizing problems often necessitates collection of information from parents, youth themselves, and teachers. The present study tested the predictive validity of a psychometrically-driven scoring procedure to integrate multi-informant, dimensional ratings of externalizing problems. Participants were 2264 clinic-referred youth ages 6-18. Parents, teachers, and youth completed questionnaire ratings of externalizing problems (hyperactivity-inattention, conduct problems, and oppositionality-defiance) prior to an initial clinical appointment. The predictive validity of simple (highest informant rating; and all informant ratings separately) and more complex (latent S-1 bifactor model with specific informant factors; and moderated nonlinear factor analysis accounting for child age and sex) methods of informant integration was tested in predicting impairment, comorbidity, and number of clinical encounters. A simple model, in which all informant ratings were included, showed the best predictive validity across outcomes, performing as well or better than the use of the highest informant ratings or more complex latent variable models. The addition of child age and sex as moderators in the factor model did not improve predictive validity. Each informant (parent, teacher, and youth) contributes important information to the prediction of clinically-relevant outcomes. There is insufficient evidence at present to suggest that complex latent variable models should be favored over simpler models that preserve each informant's ratings.

Structural racism in primary schools and changes in epigenetic age acceleration among Black and White youth

Structural racism generates racial inequities in U.S. primary education, including segregated schools, inequitable funding and resources, racial disparities in discipline and achievement, and hostile racial climates, which are risk factors for adverse youth health and development. Black youth are disproportionately exposed to adverse school contexts that may become biologically embedded via stress-mediated epigenetic pathways. This study examined whether childhood exposure to adverse school contexts is associated with changes in epigenetic aging during adolescent development. DNA methylation-based epigenetic clocks were calculated from saliva samples at ages 9 and 15 among Black (n = 774) and White (n = 287) youth in the Future of Families and Child Wellbeing Study (2009-2015). We performed latent class analyses to identify race-specific primary school contexts using administrative data on segregation, discipline, achievement, resources, economic disadvantage, and racial harassment. We then estimated change in epigenetic age acceleration from childhood to adolescence across school typologies using GrimAge, PhenoAge, and DunedinPACE epigenetic clocks. Three distinct school contexts were identified for Black youth: segregated and highly-disadvantaged (17.0%), segregated and moderately-disadvantaged (52.1%), and integrated and moderately-disadvantaged (30.8%). Two school contexts emerged for White youth: integrated and unequal (46.5%) and predominantly White & advantaged (53.5%). At age 15, Black youth who attended segregated and highly-disadvantaged primary schools experienced increases in their speed of epigenetic aging with GrimAge and DunedinPACE. Slowed epigenetic aging with GrimAge was observed for Black youth who attended integrated and moderately-disadvantaged schools. School contexts were not associated with changes in epigenetic age acceleration for White youth. Our findings suggest that manifestations of structural racism in primary school contexts are associated with early-life epigenetic age acceleration and may forecast future health inequities.

Sexual Identity Disclosure and Alcohol Experiences Among LGBTQ+ Adolescents

Sexual and gender minority youth (SGMY) report greater alcohol use in comparison to their heterosexual counterparts. Prior research has found that elevated alcohol use among SGMY can be explained by minority stress experiences. Sexual identity outness may be another factor that drives alcohol use among SGMY, given that outness is associated with alcohol use among older sexual and gender minority samples. We examined how patterns of sexual identity outness were associated with lifetime alcohol use, past-30-day alcohol use, and past-30-day heavy episodic drinking. Data were drawn from the LGBTQ National Teen Survey (N = 8884). Participants were SGMY aged 13 to 17 (mean age = 15.59) years living in the US. Latent class analysis was used to identify sexual identity outness patterns. Multinomial regressions were used to examine the probability of class membership by alcohol use. Six outness classes were identified: out to all but teachers (n = 1033), out to siblings and peers (n = 1808), out to siblings and LGBTQ+ peers (n = 1707), out to LGBTQ+ peers (n = 1376), mostly not out (n = 1653), and very much not out (n = 1307). SGMY in classes characterized by greater outness to peers, friends, and family had greater odds of lifetime alcohol use compared with SGMY in classes characterized by lower outness. These findings suggest that SGMY with greater sexual identity outness may be a target for alcohol use prevention programming. Differences in sexual identity outness may be explained by minority stress factors.

Can you tell people's cognitive ability level from their response patterns in questionnaires?

Questionnaires are ever present in survey research. In this study, we examined whether an indirect indicator of general cognitive ability could be developed based on response patterns in questionnaires. We drew on two established phenomena characterizing connections between cognitive ability and people's performance on basic cognitive tasks, and examined whether they apply to questionnaires responses. (1) The worst performance rule (WPR) states that people's worst performance on multiple sequential tasks is more indicative of their cognitive ability than their average or best performance. (2) The task complexity hypothesis (TCH) suggests that relationships between cognitive ability and performance increase with task complexity. We conceptualized items of a questionnaire as a series of cognitively demanding tasks. A graded response model was used to estimate respondents' performance for each item based on the difference between the observed and model-predicted response ("response error" scores). Analyzing data from 102 items (21 questionnaires) collected from a large-scale nationally representative sample of people aged 50+ years, we found robust associations of cognitive ability with a person's largest but not with their smallest response error scores (supporting the WPR), and stronger associations of cognitive ability with response errors for more complex than for less complex questions (supporting the TCH). Results replicated across two independent samples and six assessment waves. A latent variable of response errors estimated for the most complex items correlated .50 with a latent cognitive ability factor, suggesting that response patterns can be utilized to extract a rough indicator of general cognitive ability in survey research.

Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects

Psychologists are increasingly interested in whether treatment effects vary in randomized controlled trials. A number of tests have been proposed in the causal inference literature to test for such heterogeneity, which differ in the sample statistic they use (either using the variance terms of the experimental and control group, their empirical distribution functions, or specific quantiles), and in whether they make distributional assumptions or are based on a Fisher randomization procedure. In this manuscript, we present the results of a simulation study in which we examine the performance of the different tests while varying the amount of treatment effect heterogeneity, the type of underlying distribution, the sample size, and whether an additional covariate is considered. Altogether, our results suggest that researchers should use a randomization test to optimally control for type 1 errors. Furthermore, all tests studied are associated with low power in case of small and moderate samples even when the heterogeneity of the treatment effect is substantial. This suggests that current tests for treatment effect heterogeneity require much larger samples than those collected in current research.

An Evaluation of Item Harmonization Strategies Between Assessment Tools of Psychopathology in Children and Adolescents

Data aggregation in mental health is complicated by using different questionnaires, and little is known about the impact of item harmonization strategies on measurement precision. Therefore, we aimed to assess the impact of various item harmonization strategies for a target and proxy questionnaire using correlated and bifactor models. Data were obtained from the Brazilian High-Risk Study for Mental Conditions (BHRCS) and the Healthy Brain Network (HBN; N = 6,140, ages 5-22 years, 39.6% females). We tested six item-wise harmonization strategies and compared them based on several indices. The one-by-one (1:1) expert-based semantic item harmonization presented the best strategy as it was the only that resulted in scalar-invariant models for both samples and factor models. The between-questionnaires factor correlation, reliability, and factor score difference in using a proxy instead of a target measure improved little when all other harmonization strategies were compared with a completely at-random strategy. However, for bifactor models, between-questionnaire specific factor correlation increased from 0.05-0.19 (random item harmonization) to 0.43-0.60 (expert-based 1:1 semantic harmonization) in BHRCS and HBN samples, respectively. Therefore, item harmonization strategies are relevant for specific factors from bifactor models and had little impact on p-factors and first-order correlated factors when the child behavior checklist (CBCL) and strengths and difficulties questionnaire (SDQ) were harmonized.

Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models?

Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parameters of affective time series and the between-person measures of psychopathology, a phenomenon known as the staging effect. However, it has been argued that such findings may represent a statistical artifact: Although common models assume normal error distributions, empirical data (for instance, measurements of negative affect among healthy individuals) often exhibit the floor effect, that is response distributions with high skewness, low mean, and low variability. In this paper, we investigated whether-and to what extent-the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals' autoregressive parameters, while also varying the number of time points and cases. Analyzing these data with the standard multilevel AR(1) model we found that positive bias only occurs when modeling with random residual variance, whereas modeling with fixed residual variance leads to negative bias. We discuss the implications of our study for data collection and modeling choices.

Paternal Activation as a Protective Factor against Problem Behaviors in Early Childhood

Activation parenting includes behaviors that challenge children to approach novel situations, explore their environments, and take physical and socioemotional risks through a balance of encouragement and limit-setting. Although components of activation parenting have been linked to lower levels of children's problem behaviors, comprehensive measures of activation parenting and longitudinal research on families from low socioeconomic backgrounds are lacking. The goal of the present study was to test associations between paternal activation parenting at age 3 and children's externalizing and internalizing problems at age 5 in a sample of low-income, ethnically diverse fathers. Participating fathers (N = 171; 9% Black, 47% white, 8% Latinx; mean household income = $25,145) and their children (51% female) were drawn from the Early Steps Multisite Study. Activation parenting during a teaching task at child age 3 was associated with lower levels of internalizing problems at age 5 and decreases in externalizing problems from baseline (age 2). Implications of the current findings are presented for future research on associations between activation parenting and child problem behaviors, including the potential for the development of prevention and intervention programs.

Can High and Consistent School-Related Protective Factors Prevent Cannabis Use Among American Indian Middle School Students?

BACKGROUND

High cannabis use rates among American Indian (AI) adolescents necessitate the identification of factors that protect against early cannabis initiation.

METHODS

Data collected from 279 AI middle school students attending reservation-based schools in 2018 and 2019 were analyzed. Three waves of data, with approximately 6 months between each, were used. A repeated measures latent class analysis examined a school-related protective factor index over three waves. The predictive power of lifetime cannabis use on school protection class membership was estimated, along with differences in past month cannabis use at follow-up 2 across school protection classes while holding baseline use constant.

RESULTS

Four school protection classes were identified: high, moderate, low, and declining protection. Abstinence at baseline was associated with an increased odds of membership in the high protection class compared to the moderate and low protection classes. Students with consistent and high school protection throughout middle school were significantly less likely to report past month cannabis use at follow-up 2 compared to other classes.

IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY

Maintaining a high level of school protection throughout middle school substantially lowers the odds of cannabis use among AI adolescents.

CONCLUSIONS

Interventions to promote school-related protective factors in this population are essential and should be designed and tested.

Life events sometimes alter the trajectory of personality development: Effect sizes for 25 life events estimated using a large, frequently assessed sample

OBJECTIVE

Personality changes across the life span. Life events, such as marriage, becoming a parent, and retirement, have been proposed as facilitating personality growth via the adoption of novel social roles. However, empirical evidence linking life events with personality development is sparse. Most studies have relied on few assessments separated by long time intervals and have focused on a single life event. In contrast, the content of life is composed of small, recurrent experiences (e.g., getting sick or practicing a hobby), with relatively few major events (e.g., childbirth). Small, frequently experienced life events may play an important and overlooked role in personality development.

METHOD

The present study examined the extent to which 25 major and minor life events alter the trajectory of personality development in a large, frequently assessed sample (Nsample  = 4904, Nassessments  = 47,814, median retest interval = 35 days).

RESULTS

Using a flexible analytic strategy to accommodate the repeated occurrence of life events, we found that the trajectory of personality development shifted in response to a single occurrence of some major life events (e.g., divorce), and recurrent, "minor" life experiences (e.g., one's partner doing something special).

CONCLUSION

Both stark role changes and frequently reinforced minor experiences can lead to personality change.

Considering between- and within-person relations in auto-regressive cross-lagged panel models for developmental data

Longitudinal data can provide inferences at both the between-person and within-person levels of analysis, but only to the extent that the statistical models chosen for data analysis are specified to adequately capture these distinct sources of association. The present work focuses on auto-regressive cross-lagged panel models, which have long been used to examine time-lagged reciprocal relations and mediation among multiple variables measured repeatedly over time. Unfortunately, many common implementations of these models fail to distinguish between-person associations among individual differences in the variables' amounts and changes over time, and thus confound between-person and within-person relations either partially or entirely, leading to inaccurate results. Furthermore, in the increasingly complex model variants that continue to be developed, what is not easily appreciated is how substantial differences in interpretation can be created by what appear to be trivial differences in model specification. In the present work, we aimed to (a) help analysts become better acquainted with the some of the more common model variants that fall under this larger umbrella, and (b) explicate what characteristics of one's data and research questions should be considered in selecting a model. Supplementary Materials include annotated model syntax and output using Mplus, lavaan in R, and sem in Stata to help translate these concepts into practice.

Potential Mediators of a School-Based Digital Intervention Targeting Six Lifestyle Risk Behaviours in a Cluster Randomised Controlled Trial of Australian Adolescents

Lifestyle risk behaviours-physical inactivity, poor diet, poor sleep, recreational screen time, and alcohol and tobacco use-collectively known as the "Big 6" emerge during adolescence and significantly contribute to chronic disease development into adulthood. To address this issue, the Health4Life program targeted the Big 6 risk behaviours simultaneously via a co-designed eHealth school-based multiple health behaviour change (MHBC) intervention. This study used multiple causal mediation analysis to investigate some potential mediators of Health4Life's effects on the Big 6 primary outcomes from a cluster randomised controlled trial of Health4Life among Australian school children. Mediators of knowledge, behavioural intentions, self-efficacy, and self-control were assessed. The results revealed a complex pattern of mediation effects across different outcomes. Whilst there was a direct effect of the intervention on reducing moderate-to-vigorous physical activity risk, the impact on sleep duration appeared to occur indirectly through the hypothesised mediators. Conversely, for alcohol and tobacco use, both direct and indirect effects were observed in opposite directions cancelling out the total effect (competitive partial mediation). The intervention's effects on alcohol and tobacco use highlighted complexities, suggesting the involvement of additional undetected mediators. However, little evidence supported mediation for screen time and sugar-sweetened beverage intake risk. These findings emphasise the need for tailored approaches when addressing different risk behaviours and designing effective interventions to target multiple health risk behaviours. The trial was pre-registered with the Australian and New Zealand Clinical Trials Registry: ACTRN12619000431123.

Cognitive behavioural therapy for insomnia reduces actigraphy and diary measured sleep discrepancy for individuals with comorbid insomnia and major depressive disorder: A report from the TRIAD study

OBJECTIVE/BACKGROUND

Discrepancies between sleep diaries and actigraphy occur among individuals with insomnia. Cognitive behavioural therapy for insomnia (CBT-I) improves insomnia but the impact on discrepancy is unclear. This study examined CBT-I's effects on actigraphy-diary discrepancy and explored sleep-related beliefs and attitudes as a mediator.

PATIENTS/METHODS

Participants were 108 (age M±SD = 47.23 ± 12.42, 67.60 % female) adults with insomnia and major depressive disorder from the Treatment of Insomnia and Depression study. They were randomized to 7 sessions of CBT-I or sham Quasi-Desensitization Therapy for Insomnia (DTI), plus 16 weeks of antidepressants. Two weeks of actigraphy and sleep diary were collected at baseline, mid-treatment, end-treatment. Differences between sleep diary and actigraphy total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) were calculated. Participants completed Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS) at baseline and mid-treatment.

RESULTS

At baseline, diary (versus actigraphy) TST was shorter (1.1 ± 1.41h), whilst SOL (21.64 ± 41.25min) and WASO (17.45 ± 61.99min) were longer. Mixed effects models using daily data showed that after adjusting for age and sex, participants in the CBT-I group (versus DTI) showed greater reduction in all actigraphy-diary discrepancy domains (all p-values<.01), reductions evident from mid-treatment. Group differences on actigraphy-diary discrepancy reductions in TST, SOL, and SE (not WASO) were mediated by changes in DBAS from baseline to mid-treatment (all p-values<.05). Changes in discrepancy did not mediate insomnia symptom changes (p-values>.39).

CONCLUSIONS

CBT-I reduced actigraphy-diary discrepancy in individuals with comorbid insomnia and depression; this reduction was associated with improved sleep-related attitudes, a therapeutic target of CBT-I.

CLINICAL TRIAL REGISTRATION

TRIAD (Treatment of Insomnia and Depression): Improving Depression Outcome by Adding Insomnia Therapy to Antidepressants. Prospectively registered with Clinical Trials (NCT00767624).

SUPPORT (IF ANY)

MH078924, MH078961, MH079256.

Patterns of digital health access and use among US adults: A latent class analysis

Background Digital technologies allow users to engage in health-related behaviors associated with positive outcomes. We aimed to identify classes of US adults with distinct digital technologies access and health use patterns and characterize class composition. Data came from Health Information National Trends Survey Wave 5 Cycles 1-4, a nationally representative cross-sectional survey of US adults ( N  = 13,993). We used latent class analysis to identify digital technologies access and health use patterns based on 32 behaviors and access to requisite technologies and platforms that include the internet, internet-enabled devices, health monitors, and electronic health records (EHRs). We ran a multinomial logistic regression to identify sociodemographic and health correlates of class membership ( n  = 10,734). Results Ten classes captured patterns of digital technology access and health use among US adults. This included a digitally isolated, a mobile-dependent, and a super user class, which made up 8.9%, 7.8%, and 13.6% of US adults, respectively, and captured access patterns from only basic cellphones and health monitors to near complete access to web-, mobile-, and EHR-based platforms. Half of US adults belonged to classes that lacked access to EHRs and relied on alternative web-based tools typical of patient portals. The proportion of class members who used digital technologies for health purposes varied from small to large. Older and less educated adults had lower odds of belonging to classes characterized by access or engagement in health behaviors. Hispanic and Asian adults had higher odds of belonging to the mobile-dependent class. Individuals without a regular healthcare provider and those who had not visited a provider in the past year were more likely to belong to classes with limited digital technologies access or health use. Discussion Only one third of US adults belonged to classes that had near complete access to digital technologies and whose members engaged in almost all health behaviors examined. Sex, age, and education were associated with membership in classes that lacked access to 1 + digital technologies or exhibited none to limited health uses of such technologies. Results can guide efforts to improve access and health use of digital technologies to maximize associated health benefits and minimize disparities.

Read at home to do well at school: informal reading predicts achievement and motivation in English as a foreign language

Introduction

Learning English as a foreign language is necessary for many students to actively participate in an increasingly globalized world. This study explores the role of informal English language engagement for students' reading and listening skills, as well as motivation to learn English. In an era of global interconnectedness, informal learning gains importance as a supplement to formal education.

Methods

This study extends the evidence base by analyzing extramural reading and listening activities in a large-scale longitudinal investigation involving secondary school learners (N = 1,994) in Germany.

Results

Our results show that frequent informal reading significantly relates to increases in students' English comprehension skills and their motivation for language learning, reaffirming previous cross-sectional findings.

Discussion

The results highlight the relevance of informal language activities for effective language learning and students' English as a foreign language motivation. Additionally, discrepancies between reading and listening outcomes are discussed.

Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach

Measurement models traditionally make the assumption that item responses are independent from one another, conditional upon the common factor. They typically explore for violations of this assumption using various methods, but rarely do they account for the possibility that an item predicts the next. Extending the development of auto-regressive models in the context of personality and judgment tests, we propose to extend binary item response models-using, as an example, the 2-parameter logistic (2PL) model-to include auto-regressive sequential dependencies. We motivate such models and illustrate them in the context of a publicly available progressive matrices dataset. We find an auto-regressive lag-1 2PL model to outperform a traditional 2PL model in fit as well as to provide more conservative discrimination parameters and standard errors. We conclude that sequential effects are likely overlooked in the context of cognitive ability testing in general and progressive matrices tests in particular. We discuss extensions, notably models with multiple lag effects and variable lag effects.

Defensiveness Toward IAT Feedback Predicts Willingness to Engage in Anti-Bias Behaviors

People who are more defensive about their feedback on the Race-Attitudes Implicit Association Test (IAT) are less willing to engage in anti-bias behaviors. Extending on this work, we statistically clarified defensiveness constructs to predict willingness to engage in anti-bias behaviors among people who received pro-White versus no-bias IAT feedback. We replicated the finding that U.S. Americans are generally defensive toward pro-White IAT feedback, and that more defensiveness predicts less willingness to engage in anti-bias behaviors. However, people who believed their pro-White IAT feedback was an inaccurate reflection of their "true attitudes" were more willing to engage in anti-bias behaviors compared with people who received no-bias IAT feedback. These results better illuminate the defensiveness construct suggesting that receiving self-threatening feedback about bias may motivate people's willingness to engage in anti-bias behaviors in different ways depending on how people respond to that feedback.

Developmental trajectories of achievement emotions in mathematics during adolescence

This study examined how adolescents' emotions in mathematics develop over time. Growth curve modeling was applied to longitudinal data collected annually from 2002 to 2006 (Grades 5-9; N = 3425 German adolescents; Mage  = 11.7, 15.6 years at the first and last waves, respectively; 50.0% female). Results indicated that enjoyment and pride decreased over time (Glass's Δs = -.86, -.71). In contrast, negative emotions exhibited more complex patterns: Anger, boredom, and hopelessness increased (Δs = .52, .79, .26), shame decreased (Δ = -.12), and anxiety remained stable (Δ = .00). These change trajectories of emotions were associated with change trajectories of perceived control, intrinsic value, achievement value, and achievement in mathematics. Implications and future directions are discussed.

Genotype-environment interplay in associations between maternal drinking and offspring emotional and behavioral problems

BACKGROUND

While maternal at-risk drinking is associated with children's emotional and behavioral problems, there is a paucity of research that properly accounts for genetic confounding and gene-environment interplay. Therefore, it remains uncertain what mechanisms underlie these associations. We assess the moderation of associations between maternal at-risk drinking and childhood emotional and behavioral problems by common genetic variants linked to environmental sensitivity (genotype-by-environment [G × E] interaction) while accounting for shared genetic risk between mothers and offspring (GE correlation).

METHODS

We use data from 109 727 children born to 90 873 mothers enrolled in the Norwegian Mother, Father, and Child Cohort Study. Women self-reported alcohol consumption and reported emotional and behavioral problems when children were 1.5/3/5 years old. We included child polygenic scores (PGSs) for traits linked to environmental sensitivity as moderators.

RESULTS

Associations between maternal drinking and child emotional (β1 = 0.04 [95% confidence interval (CI) 0.03-0.05]) and behavioral (β1 = 0.07 [0.06-0.08]) outcomes attenuated after controlling for measured confounders and were almost zero when we accounted for unmeasured confounding (emotional: β1 = 0.01 [0.00-0.02]; behavioral: β1 = 0.01 [0.00-0.02]). We observed no moderation of these adjusted exposure effects by any of the PGS.

CONCLUSIONS

The lack of strong evidence for G × E interaction may indicate that the mechanism is not implicated in this kind of intergenerational association. It may also reflect insufficient power or the relatively benign nature of the exposure in this sample.

Mapping and identifying service models for community-based services for children with intellectual disabilities and behaviours that challenge in England

BACKGROUND

One in five children with an intellectual disability in the UK display behaviours that challenge. Despite associated impacts on the children themselves, their families, and services, little research has been published about how best to design, organise, and deliver health and care services to these children. The purpose of this study was to describe how services are structured and organised ("service models") in England for community-based health and care services for children with intellectual disability who display behaviours that challenge.

METHODS

Survey data about services were collected from 161 eligible community-based services in England. Staff from 60 of these services were also interviewed. A combination of latent class and descriptive analysis, coupled with consultation with family carers and professionals was used to identify and describe groupings of similar services (i.e., "service models").

RESULTS

The latent class analysis, completed as a first step in the process, supported a distinction between specialist services and non-specialist services for children who display behaviours that challenge. Planned descriptive analyses incorporating additional study variables were undertaken to further refine the service models. Five service models were identified: Child and Adolescent Mental Health Services (CAMHS) (n = 69 services), Intellectual Disability CAMHS (n = 28 services), Children and Young People Disability services (n = 25 services), Specialist services for children who display behaviours that challenge (n = 27 services), and broader age range services for children and/or adolescents and adults (n= 12 services).

CONCLUSIONS

Our analysis led to a typology of five service models for community health and care services for children with intellectual disabilities and behaviours that challenge in England. Identification of a typology of service models is a first step in building evidence about the best provision of services for children with intellectual disabilities who display behaviours that challenge. The methods used in the current study may be useful in research developing service typologies in other specialist fields of health and care.

STUDY REGISTRATION

Trial Registration: Current Controlled Trials ISRCTN88920546, Date assigned 05/07/2022.

Exploring the differentiation of behavioural and emotional problems across childhood: A prospective longitudinal cohort study

Background

An individual's overall burden of behavioural and emotional problems across childhood is associated with increased likelihood of later mental health conditions. However, the relative extent of behavioural versus emotional problems - that is, the extent to which the domains are differentiated from one another - may provide additional information about who is at risk of developing a mental health condition. Here, we seek to validate differentiation as an independent predictor of later mental health conditions, and to explore its aetiology.

Methods

We analysed data from ~79,000 children in the population-based Norwegian Mother, Father, and Child Cohort Study, and linked health-care registries. In preregistered analyses, we modelled the extent and rate of differentiation of behavioural and emotional problems between ages 1.5-5 years, and estimated associations with later symptoms (age 8) and diagnoses (after age 8). We also explored the aetiology of differentiation by estimating associations with early life exposures and, in a subset of 23,945 full siblings, assessing the impact of accounting for unobserved familial confounding.

Results

Differentiation of behavioural and emotional problems was associated with later symptoms and diagnoses of mental health conditions, independent of total problems. Maternal at-risk drinking (β = 0.04 [0.02, 0.06]) and parental relationship problems (β = 0.04 [0.02, 0.05]) were associated with higher behavioural relative to emotional problems at age 5. Maternal prenatal distress (|β| = 0.04 [0.03, 0.06]), concurrent distress (|β| = 0.04 [0.02, 0.06]) and parental education (|β| = 0.05 [0.04, 0.07]) predicted higher emotional relative to behavioural problems at age 5. Estimates for maternal prenatal distress and at-risk drinking were consistent across both unadjusted and adjusted analyses accounting for unobserved familial risk.

Conclusions

Differentiation of behavioural and emotional problems in early childhood represents a valid source of inter-individual variability linked to the later emergence of psychopathology and may be relevant for early detection and prevention strategies for mental health.

Heterogeneity in children's reading comprehension difficulties: A latent class approach

Background

Poor comprehenders are traditionally identified as having below-average reading comprehension, average-range word reading, and a discrepancy between the two. While oral language tends to be low in poor comprehenders, reading is a complex trait and heterogeneity may go undetected by group-level comparisons.

Methods

We took a preregistered data-driven approach to identify poor comprehenders and examine whether multiple distinct cognitive profiles underlie their difficulties. Latent mixture modelling identified reading profiles in 6846 children from the Avon Longitudinal Study of Parents and Children, based on reading and listening comprehension assessments at 8-9 years. A second mixture model examined variation in the cognitive profiles of weak comprehenders, using measures of reading, language, working memory, nonverbal ability, and inattention.

Results

A poor comprehender profile was not identified by the preregistered model. However, by additionally controlling for overall ability, a 6-class model emerged that incorporated a profile with relatively weak comprehension (N = 947, 13.83%). Most of these children had weak reading comprehension in the context of good passage reading, accompanied by weaknesses in vocabulary and nonverbal ability. A small subgroup showed more severe comprehension difficulties in the context of additional cognitive impairments.

Conclusions

Isolated impairments in specific components of reading are rare, yet a data-driven approach can be used to identify children with relatively weak comprehension. Vocabulary and nonverbal ability were most consistently weak within this group, with broader cognitive difficulties also apparent for a subset of children. These findings suggest that poor comprehension is best characterised along a continuum, and considered in light of multiple risks that influence severity.

An Initial examination of fear of negative and positive evaluation in youth

Fear of negative evaluation (FNE) and fear of positive evaluation (FPE) are both core features of social anxiety. The majority of research with these constructs has been done with older adolescents and adults, with only one previous study examining FNE and FPE in childhood. However, this previous work relied exclusively on parent-report of youth FNE and FPE. Here, we examined the factor structure of FNE and FPE using youth self-reports. Moreover, we examined the associations with dimensions of internalizing and externalizing problems. We found that two-factor structure of FNE and FPE was a marginal fit to the data. Exploratory models identified three items that showed significant cross-loadings on non-target factors. Overall, we found that FNE was associated with dimensions of internalizing problems reported by youth and their mothers. FPE was associated with internalizing problems reported by youth, but not parents. Associations between FNE and clinical outcomes were stronger than those for FPE. This study demonstrates promise of FNE and FPE in youth and highlights important directions for future research.

When is Solitude Maladaptive for Adolescents? A Comprehensive Study of Sociability and Characteristics of Solitude

Research examining the link between solitude and psychosocial adjustment among adolescents has lacked a comprehensive, person-centered examination of differential patterns of both solitude and sociability. The current study surveyed 1071 adolescents (Mage = 12.48, SD = 1.71, 49.86% female, age range = 10-16 years). Using latent-profile analysis, four groups were identified with differential patterns of characteristics of solitude (i.e., enjoyment, motivations, preference, frequency) and sociability. Results indicated that worse psychosocial adjustment across time points was associated with membership in the PFS-NonSociable group (characterized by high enjoyment, preference, and frequency of solitude; low sociability) compared to all other groups. Findings suggest that solitude for adolescents appears to be linked to worse psychosocial adjustment only if accompanied by a lack of sociability.

Sexual Minority Identity Development: Latent Profiles of Developmental Milestones in a National Probability Sample

Sexual identity development milestones mark the ages at which sexual minority people first experience key developmental events including same-sex attraction, self-realization of a sexual minority identity, same-sex sexual behavior and romantic relationships, and sexual identity disclosure. Most studies of milestones use variable-centered, rather than person-centered approaches, potentially obscuring diversity in patterns across milestones. Using data from The Generations Study, the first national probability sample of White, Black, and Latinx sexual minority adults in the United States (n = 1,492), we examined variability in milestone timing and patterning using a latent profile analysis approach. We identified four distinct profiles, characterized by variability in milestone mean ages, pacing, and sequences: an early adolescence profile (22.9%), a middle adolescence profile (33.6%), a late adolescence profile (27.6%), and an adulthood profile (15.9%). Profiles were demographically distinct, varying by birth cohort, sexual identity, race/ethnicity, gender identity, and childhood gender nonconformity. Results suggest developmental and demographic diversity in the emergence of sexual identity development across the life course, with implications for sexual minority health and thriving.

Alzheimer's Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe Atrophy

Exposure to ambient air pollution, especially particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2), are environmental risk factors for Alzheimer's disease and related dementia. The medial temporal lobe (MTL) is an important brain region subserving episodic memory that atrophies with age, during the Alzheimer's disease continuum, and is vulnerable to the effects of cerebrovascular disease. Despite the importance of air pollution it is unclear whether exposure leads to atrophy of the MTL and by what pathways. Here we conducted a longitudinal study examining associations between ambient air pollution exposure and MTL atrophy and whether putative air pollution exposure effects resembled Alzheimer's disease-related neurodegeneration or cerebrovascular disease-related neurodegeneration. Participants included older women (n = 627; aged 71-87) who underwent two structural brain MRI scans (MRI-1: 2005-6; MRI-2: 2009-10) as part of the Women's Health Initiative Memory Study of Magnetic Resonance Imaging. Regionalized universal kriging was used to estimate annual concentrations of PM2.5 and NO2 at residential locations aggregated to 3-year averages prior to MRI-1. The outcome was 5-year standardized change in MTL volumes. Mediators included voxel-based MRI measures of the spatial pattern of neurodegeneration of Alzheimer's disease (Alzheimer's disease pattern similarity scores [AD-PS]) and whole-brain white matter small-vessel ischemic disease (WM-SVID) volume as a proxy of global cerebrovascular damage. Structural equation models were constructed to examine whether the associations between exposures with MTL atrophy were mediated by the initial level or concurrent change in AD-PS score or WM-SVID while adjusting for sociodemographic, lifestyle, clinical characteristics, and intracranial volume. Living in locations with higher PM2.5 (per interquartile range [IQR]=3.17μg/m3) or NO2 (per IQR=6.63ppb) was associated with greater MTL atrophy (βPM2.5 = -0.29, 95% confidence interval [CI]=[-0.41,-0.18]; βNO2 =-0.12, 95%CI=[-0.23,-0.02]). Greater PM2.5 was associated with larger increases in AD-PS (βPM2.5 = 0.23, 95%CI=[0.12,0.33]) over time, which partially mediated associations with MTL atrophy (indirect effect= -0.10; 95%CI=[-0.15, -0.05]), explaining approximately 32% of the total effect. NO2 was positively associated with AD-PS at MRI-1 (βNO2=0.13, 95%CI=[0.03,0.24]), which partially mediated the association with MTL atrophy (indirect effect= -0.01, 95% CI=[-0.03,-0.001]). Global WM-SVID at MRI-1 or concurrent change were not significant mediators between exposures and MTL atrophy. Findings support the mediating role of Alzheimer's disease-related neurodegeneration contributing to MTL atrophy associated with late-life exposures to air pollutants. Alzheimer's disease-related neurodegeneration only partially explained associations between exposure and MTL atrophy suggesting the role of multiple neuropathological processes underlying air pollution neurotoxicity on brain aging.

Extraverts Reap Greater Social Rewards From Passion Because They Express Passion More Frequently and More Diversely

Passion is stereotypically expressed through animated facial expressions, energetic body movements, varied tone, and pitch-and met with interpersonal benefits. However, these capture only a subset of passion expressions that are more common for extraverts. Indeed, in an initial dyadic study of supervisors and their subordinates (N = 330), extraverts expressed their passion more strongly through these stereotypical expressions of passion, and observers perceived extraverts as more passionate than introverts. Across three studies (Ntotal = 1,373), we subsequently developed a more comprehensive passion expressions and behaviors scale (PEBS). Using this measure in a daily diary study (N = 206, k = 1,862), we found that extraverts not only expressed their passion in more stereotypical ways, but through a broader variety of expressions in general. Extraverts are perceived as more passionate because they have a broader behavioral repertoire, express their passion more frequently and diversely, and thereby attain greater interpersonal rewards.

Common Factor Structure of the Ten Subtest Wechsler Adult Intelligence Scale-Fourth Edition in a Clinical Sample and 15 Subtest Version in the Standardization Sample

OBJECTIVE

The 10 core subtests of the Wechsler Adult Intelligence Scale-IV (WAIS-IV) suffice to produce the 4 index scores for clinical assessments. Factor analytic studies with the full complement of 15 subtests reveal a 5-factor structure that aligns with Cattell-Horn-Carroll taxonomy of cognitive abilities. The current study investigates the validity of 5-factor structure in a clinical setting with reduced number of 10 subtests.

METHOD

Confirmatory factor analytic models were fitted to a clinical neurosciences archival data set (n_Male = 166, n_Female = 155) and to 9 age-group samples of the WAIS-IV standardization data (n = 200 for each group). The clinical and the standardization samples differed as (a) the former comprised scores from patients, aged 16 to 91, with disparate neurological diagnosis whereas the latter was demographically stratified, (b) only the 10 core subtests in the former but all 15 subtests in the latter were administered, and (c) the former had missing data, but the latter was complete.

RESULT

Despite empirical constraints to eliciting 5 factors with only 10 indicators, the well-fitting, 5-factor (acquired knowledge, fluid intelligence, short-term memory, visual processing, and processing speed) measurement model evinced metric invariance between the clinical and standardization samples.

CONCLUSION

The same cognitive constructs are measured on the same metrics in every sample examined and provide no reason to reject the assumption that the 5 underlying latent abilities of the 15 subtest version in the standardization samples can also be inferred from the 10 subtest version in clinical populations.

Patients with AUD exhibit dampened heart rate variability during sleep as compared to social drinkers

Chronic heavy alcohol use profoundly affects the cardiovascular system, contributing to several life-threatening cardiovascular diseases. Heart rate variability (HRV), or the fluctuations in heart rate, reflects dynamic autonomic nervous system processes that change to meet biological demands and environmental challenges. In the current study, we examined whether HRV metrics are altered in alcohol use disorder (AUD) during waking and sleeping with passive biomonitoring as participants went about their daily lives. Social drinkers (standard deviation: n = 10, 5 female) and treatment-seeking individuals with moderate to severe AUD (n = 16, 7 female) provided continuous, real-world heart rate monitoring for 5 days of monitoring on average (M = 5.27 ± 2.22). Five indices of respiration and HRV-respiratory sinus arrhythmia (RSA) amplitude, high frequency (HF), low frequency (LF), HF/LF ratio, root-mean-square standard deviation (RMSSD), and standard deviation of the N-N intervals (SDNN)-were analyzed separately for waking and sleeping hours. Both RMSSD and SDNN decreased the longer the participants were awake (Ps < .013). During sleeping hours, HF, RSA amplitude, RMSSD, and SDNN were significantly higher in light social drinkers as compared to patients with AUD (all Ps < .009), indicating higher parasympathetic activation during sleep in the SD versus AUD group. Sleep and waking HRV measures were significantly correlated with patient-reported symptoms of depression and sleep difficulties in the AUD group (Ps < .05). This natural observational study utilizing continuous autonomic biomonitoring in the real world indicates parasympathetic dysfunction that is clearly detectable during sleep in AUD and HRV measures, which are also related to clinical, patient-related symptoms of AUD.

Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse

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.

The event-driven nature of online political hostility: How offline political events make online interactions more hostile

Hostile interactions permeate political debates on social media, but what is driving the long-term developments in online political hostility? Prior research focuses on individual-level factors such as the dispositions of users or network-level factors such as echo chambers. Moving beyond these accounts, we develop and test an event-oriented explanation and demonstrate that over the course of the 2020 election year in the United States, all major shifts in political hostility on the social media platform Twitter were driven by external offline events. Importantly, these events were magnified by Twitter users within the most politically hostile and most ideologically homogeneous networks. Further contributing to the individual and network-oriented accounts, we show that divisive offline events mobilized individual users not already disposed for hostility and may have helped facilitate the formation of echo chambers. The dynamics of online interactions-including their level of hostility-seem crucially dependent on developments in the offline world.

Mitigating Multiple Sources of Bias in a Quasi-Experimental Integrative Data Analysis: Does Treating Childhood Anxiety Prevent Substance Use Disorders in Late Adolescence/Young Adulthood?

Psychiatric epidemiologists, developmental psychopathologists, prevention scientists, and treatment researchers have long speculated that treating child anxiety disorders could prevent alcohol and other drug use disorders in young adulthood. A primary challenge in examining long-term effects of anxiety disorder treatment from randomized controlled trials is that all participants receive an immediate or delayed study-related treatment prior to long-term follow-up assessment. Thus, if a long-term follow-up is conducted, a comparison condition no longer exists within the trial. Quasi-experimental designs (QEDs) pairing such clinical samples with comparable untreated epidemiological samples offer a method of addressing this challenge. Selection bias, often a concern in QEDs, can be mitigated by propensity score weighting. A second challenge may arise because the clinical and epidemiological studies may not have used identical measures, necessitating Integrative Data Analysis (IDA) for measure harmonization and scale score estimation. The present study uses a combination of propensity score weighting, zero-inflated mixture moderated nonlinear factor analysis (ZIM-MNLFA), and potential outcomes mediation in a child anxiety treatment QED/IDA (n = 396). Under propensity score-weighted potential outcomes mediation, CBT led to reductions in substance use disorder severity, the effects of which were mediated by reductions in anxiety severity in young adulthood. Sensitivity analyses highlighted the importance of attending to multiple types of bias. This study illustrates how hybrid QED/IDAs can be used in secondary prevention contexts for improved measurement and causal inference, particularly when control participants in clinical trials receive study-related treatment prior to long-term assessment.

The Tripartite Model of Depression in Schizophrenia and Bipolar Disorder: A Secondary Analysis

ABSTRACT

Models of affect, like the tripartite model, suggest that positive affect (PA) and negative affect (NA) are independent between subjects and negatively correlated within. Correlations may differ in bipolar disorder (BD) and schizophrenia. Using ecological momentary assessment (EMA) and clinical ratings, this secondary analysis evaluated the tripartite model by examining PA and NA. Two hundred eighty-one participants with BD or a psychotic disorder completed 30 days of EMA of PA and NA, and clinical raters assessed depression. PA and NA were more related between subjects and less related within subjects among participants with schizophrenia. In BD, lower momentary PA was positively associated with clinical ratings of depression, although greater momentary NA was not significantly associated with clinical ratings. In schizophrenia, the inverse was found. These results suggest that the tripartite model was not confirmed in people with schizophrenia or BD. However, PA and NA manifested associations in BD that were more congruent with population studies than in schizophrenia. These findings may have implications for clinical interventions targeting depression, PA, and NA in these populations.

Profiles of epistemological beliefs, knowledge about explanation norms, and explanation skills: changes after an intervention

In this study, we exploratively investigate the relation between students' epistemological beliefs and their declarative knowledge about scientific explanations and their practical skills to explain psychological phenomena drawing on scientific theories before and after a training intervention using a person-centered approach. We theoretically derive profiles of epistemological beliefs that should be beneficial for constructing scientific explanations. We those having higher explanation skills show a profile of epistemological beliefs that is beneficial for explanations skills. Using a latent profile transition analysis and a sample with N = 108 students, we explore which profiles of epistemological beliefs, declarative knowledge about explanations, and explanation skills empirically emerge before and after an intervention that aimed and fostering students' skills to construct scientific explanations. Before the intervention, two profiles emerged that differed in epistemological beliefs and explanation skills, but both did not in declarative knowledge about explanation. The intervention, in general, yielded a gain in declarative knowledge about explanations and explanation skills. After the intervention, again, two profiles emerged. However, these profiles did not differ in their epistemological beliefs but only in declarative knowledge about explanations and explanation skills. Thus, the intervention seems to level out the effects of epistemological beliefs. Additionally, the pattern of change in epistemological beliefs is consistent with theoretical expectations about which epistemological beliefs are beneficial for explanations. We discuss the results and their implications, as well as their limitations. Finally, we provide an outlook of using the person-oriented approach and this study's type of intervention in the research on changing epistemological beliefs.