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.

Preventing Mental Health Problems in Children After High Conflict Parental Separation/Divorce Study: An Optimization Randomized Controlled Trial Protocol.

Parental divorce is a childhood stressor that affects approximately 1.1 million children in the U.S. annually. The children at greatest risk for deleterious mental health consequences are those exposed to high interparental conflict (IPC) following the separation/divorce. Research shows that children's emotional security and coping efficacy mediate the impact of IPC on their mental health. Interventions targeting their adaptive coping in response to IPC events may bolster their emotional security and coping efficacy. However, existing coping interventions have not been tested with children exposed to high post-separation/divorce IPC, nor has any study assessed the effects of individual intervention components on children's coping with IPC and their mental health. This intensive longitudinal intervention study examines the mechanisms through which coping intervention components impact children's responses to interactions in interparental relationships. A 23 factorial experiment will assess whether, and to what extent, three candidate intervention components demonstrate main and interactive effects on children's coping and mental health. Children aged 9-12 (target N = 144) will be randomly assigned to one of eight combinations of three components with two levels each: (1) reappraisal (present vs. absent), (2) distraction (present vs. absent), (3) relaxation (present vs. absent). The primary outcomes are child-report emotional security and coping efficacy at one-month post-intervention. Secondary outcomes include internalizing and externalizing problems at the three-month follow-up. Based on data from this optimization phase RCT, intervention components will be selected to comprise a multi-component intervention and assessed for effectiveness in a subsequent evaluation phase RCT.

Mind the context-The relevance of personality for face-to-face and computer-mediated communication.

A large body of research has examined the link between personality and face-to-face (FtF) communication knowledge, skills, abilities, and other characteristics (KSAOs). With the rise of digital media, text-based computer-mediated (CM) communication KSAOs have gained increasing attention. We conducted two studies to investigate how personality relates to KSAOs in the different contexts of FtF and CM communication. Contrasting perspectives hypothesize that the results in the FtF and CM contexts would be very similar or distinctly different. In Study 1 (n = 454), an online panel study, the Big Five personality dimensions were assessed and their relationships to FtF and CM communication KSAOs were investigated. Structural equation models and relative weight regression analyses showed that these personality dimensions, mostly extraversion and neuroticism, explained more variance in FtF as compared to CM communication KSAOs. Study 2 (n = 173), conducted in a laboratory context, showed similar results compared to Study 1. In addition, when the Big Five personality dimensions were assessed with a CM frame of reference, more variance was explained in CM than in FtF communication KSAOs. These results point to the importance of considering context effects in communication and in personality research: FtF and CM communication KSAOs need to be differentiated. If not properly contextualized, the relevance of personality and communication competencies in predicting criteria may be underestimated due to contextual mismatches.

The protective role of secure attachment in the relationship between experiences of childhood abuse, emotion dysregulation and coping, and behavioral and mental health problems among emerging adult Black women: A moderated mediation analysis.

To determine the extent to which secure attachment moderates the effects of previous child abuse history on the intermediate variables (putative mediators) of emotion dysregulation and coping, which, in turn, influence adult behavioral health and mental health problems. Black women (N = 440, M age = 20.33, SD = 1.88) were selected from the baseline data collection of a large, randomized trial. Study participants had consumed alcohol, had had unprotected sex in the last 90 days, and either reported abuse prior to age 18 or no lifetime history of abuse. Women completed measures of sociodemographics, abuse history, attachment security, coping, emotion dysregulation, psychological functioning, risky sexual behavior, and substance use problems. At low attachment security, the conditional indirect effects of childhood abuse through the intermediate variable, coping, were statistically significant for all dependent variables except proportion condom use and perceived stress. At high attachment security, none of the conditional indirect effects through coping achieved statistical significance. High attachment security also mitigated the conditional indirect effects of childhood abuse through the intermediate variable, emotion dysregulation, reducing the magnitude of the relationship with trait anger, depression, marijuana problems, and perceived stress by about 50%. These results demonstrate the potential mitigating effects of secure attachment on the relationship between childhood abuse history and select behavioral and mental health problems through the intermediate variables of coping and emotional dysregulation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Beyond Experiments.

It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.

Effectiveness and equity evaluation of an insurance-wide telephone-counseling program for self-management of chronic diseases: The Health Coach Study.

Trajectories of chronic illnesses depend on patient socioeconomic status (SES). This study examines main and equity effects (age, gender, education, region of residence) of a brief telephone self-management intervention on self-rated health and depressive symptoms of health insurance clients with chronic illnesses. Randomized invitation design (n = 2628) with predominantly male (82%) older individuals (modal age = 65-74) with one or more chronic illnesses. Primary outcomes: Self-rated health and depressive symptoms. Intervention: Brief CBT-based telephone counseling. Propensity score matching was used to equate intervention and control groups (n = 1314 pairs). Change score models were used to analyze changes in health-related outcome measures. The intervention resulted in improvements in self-rated health (d = .37) and fewer depressive symptoms (d = .17) over 4 and 6 months. There were comparable effects across education and regions, but younger and female participants profited more from the intervention compared with older and male participants. A brief telephone-based intervention led to improved self-rated health and well-being in a large sample of participants with chronic health conditions. This effect was observed over and above regular medical care. The intervention was equitable with respect to education and region, but not age and gender.

Horizons and Group Motivational Enhancement Therapy: HIV Prevention for Alcohol-Using Young Black Women, a Randomized Experiment.

INTRODUCTION: Black women are at disproportionately greater risk for HIV and sexually transmitted infections than women of other ethnic/racial backgrounds. Alcohol use may further elevate the risk of HIV/sexually transmitted infection acquisition and transmission. STUDY DESIGN: A random-assignment parallel-group comparative treatment efficacy trial was conducted with random assignment to 1 of 3 conditions. SETTING/PARTICIPANTS: The sample comprised 560 Black or African American women aged 18-24 years who reported recent unprotected vaginal or anal sex and recent alcohol use. Participants were recruited from community settings in Atlanta, Georgia, from January 2012 to February 2014. INTERVENTION: A Group Motivational Enhancement Therapy module was designed to complement a Centers for Disease Control and Prevention-designated evidence-based intervention (Horizons) to reduce sexual risk behaviors, alcohol use, and sexually transmitted infections, with 3 comparison groups: (1) Horizons + Group Motivational Enhancement Therapy intervention, (2) Horizons + General Health Promotion intervention, and (3) enhanced standard of care. MAIN OUTCOME MEASURES: Outcome measures included safe sex (abstinence or 100% condom use); condom nonuse; proportion of condom use during sexual episodes; incident chlamydia, gonorrhea, and trichomonas infections; and problematic alcohol use measured by Alcohol Use Disorders Identification Test score. Treatment effects were estimated using an intention-to-treat protocol‒generalized estimating equations with logistic regression for binomial outcomes and Poisson regression for count outcomes. Analyses were conducted between October 2018 and October 2019. RESULTS: Participants assigned to Horizons + Group Motivational Enhancement Therapy had greater odds of safe sex (AOR=1.45, 95% CI=1.04, 2.02, p=0.03), greater proportion of condom use (AOR=1.68, 95% CI=1.18, 2.41, p=0.004), and lower odds of condom nonuse (AOR=0.57, 95% CI=0.38, 0.83, p=0.004). Both interventions had lower odds of problematic alcohol use (Horizons: AOR=0.57, 95% CI=0.39, 0.85, p=0.006; Horizons + Group Motivational Enhancement Therapy: AOR=0.61, 95% CI=0.41, 0.90, p=0.01). CONCLUSIONS: Complementing an evidence-based HIV prevention intervention with Group Motivational Enhancement Therapy may increase safer sexual behaviors and concomitantly reduce alcohol use among young Black women who consume alcohol. TRIAL REGISTRATION: This study is registered at www.clinicaltrials.gov NCT01553682.

Hidden framings and hidden asymmetries in the measurement of personality--A combined lens-model and frame-of-reference perspective.

OBJECTIVE: The symmetry principle and the frame-of-reference perspective have each made contributions to improving the measurement of personality. Although each perspective is valuable in its own right, we argue that even greater improvement can be achieved through the combination of both. Therefore, the goal of the current article was to show the value of a combined lens-model and frame-of-reference perspective. METHOD: We conducted a literature review to summarize relevant research findings that shed light on the interplay of both perspectives and developed an integrative model. RESULTS: Based on the literature review and on theoretical grounds, we argue that a basic premise of the frame-of-reference literature--that personality items are open to interpretation and allow individuals to impose their own contextual framings--should be considered from a symmetry perspective. Unintended context-specificity in items may "spread" to personality facets and domains, and thus, impact the symmetry of personality measures with other criteria. As the individuals´ frames-of-reference and (a)symmetric relationships are not always apparent, we term them as "hidden." CONCLUSIONS: The proposed combination of lens-model and frame-of-reference perspectives provides further insights into current issues in personality research and uncovers important avenues for future research.

Forecasting Causal Effects of Interventions versus Predicting Future Outcomes.

The present article provides a didactic presentation and extension of selected features of Pearl's DAG-based approach to causal inference for researchers familiar with structural equation modeling. We illustrate key concepts using a cross-lagged panel design. We distinguish between (a) forecasts of the value of an outcome variable after an intervention and (b) predictions of future values of an outcome variable. We consider the mean level and variance of the outcome variable as well as the probability that the outcome will fall within an acceptable range. We extend this basic approach to include additive random effects, allowing us to distinguish between average effects of interventions and person-specific effects of interventions. We derive optimal person-specific treatment levels and show that optimal treatment levels may differ across individuals. We present worked examples using simulated data based on the results of a prior empirical study of the relationship between blood insulin and glucose levels.

A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer.

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.

Depression in mothers and the externalizing and internalizing behavior of children: An attempt to go beyond association.

Hundreds of studies have documented an association between depression in mothers and behavior problems in children. Theory and empirical findings suggest this association may be confounded by other factors, but little attention has been paid to this issue. We used propensity score methods in a sample of 731 low-income families assessed repeatedly from child age 2 through 14 years to produce a weighted sample of families that were similar at child age 3 years except for mothers' depression. Depressive symptomatology was measured via self-report rating scale. Mothers were categorized as having clinically-elevated versus non-clinically-elevated scores based on an established threshold. Mothers with elevated versus nonelevated scores were equated on 89 other relevant characteristics (e.g., SES, child behavior, marital conflict). We then compared the equated groups on mother, secondary caregiver, and teacher ratings of child externalizing and internalizing behavior from child ages 4 to 14 years. Prior to equating, the mean prima facie effect of exposure to clinically-elevated mothers' depression scores at child age 3 years was d = 0.45 per mothers, d = 0.26 per secondary caregivers, and d = 0.13 per teachers. After equating, the mean adjusted effect was d = 0.07 per mothers, d = 0.01 per secondary caregivers, and d = 0.03 per teachers. Findings suggest that a substantial portion of the prima facie association between mothers' depression and later child behavior problems is accounted for by confounding variables rather than a causal effect of depressive symptoms per se. To fully understand why children of depressed mothers exhibit more behavior problems, a multicausal theory is needed that jointly considers the cluster of co-occurring clinical features that often accompany maternal depression. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Regression models involving nonlinear effects with missing data: A sequential modeling approach using Bayesian estimation.

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a joint normal distribution, the default in many statistical software packages. This distribution will in general be misspecified if the predictors with missing data have nonlinear effects (e.g., x2) or are included in interaction terms (e.g., x·z). In the present article, we discuss a sequential modeling approach that can be applied to decompose the joint distribution of the variables into 2 parts: (a) a part that is due to the model of interest and (b) a part that is due to the model for the incomplete predictors. We demonstrate how the sequential modeling approach can be used to implement a multiple imputation strategy based on Bayesian estimation techniques that can accommodate rather complex substantive regression models with nonlinear effects and also allows a flexible treatment of auxiliary variables. In 4 simulation studies, we showed that the sequential modeling approach can be applied to estimate nonlinear effects in regression models with missing values on continuous, categorical, or skewed predictor variables under a broad range of conditions and investigated the robustness of the proposed approach against distributional misspecifications. We developed the R package mdmb, which facilitates a user-friendly application of the sequential modeling approach, and we present a real-data example that illustrates the flexibility of the software. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation.

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x2) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.

Nonlinear growth curve modeling using penalized spline models: A gentle introduction.

This didactic article aims to provide a gentle introduction to penalized splines as a way of estimating nonlinear growth curves in which many observations are collected over time on a single or multiple individuals. We begin by presenting piecewise linear models in which the time domain of the data is divided into consecutive phases and a separate linear regression line is fitted in each phase. Linear splines add the feature that the regression lines fitted in adjacent phases are always joined at the boundary so there is no discontinuity in level between phases. Splines are highly flexible raising the fundamental tradeoff between model fit and smoothness of the curve. Penalized spline models address this tradeoff by introducing a penalty term to achieve balance between fit and smoothness. The linear mixed-effects model, familiar from multilevel analysis, is introduced as a method for estimating penalized spline models. Higher order spline models using quadratic or cubic functions which further enhance a smooth fit are introduced. Technical issues in estimation, hypothesis testing, and constructing confidence intervals for higher order penalized spline models are considered. We then use data from the Early Childhood Longitudinal Study to illustrate each step in fitting a higher order penalized spline model, and to illustrate hypothesis testing, the construction of confidence intervals, and the comparison of the functions in 2 groups (boys and girls). Extensive graphical illustrations are provided throughout. Annotated computer scripts using the R package nlme are provided in online supplemental materials. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Opportunities and Issues in Modeling Intensive Longitudinal Data: Learning from the COGITO Project.

Technological developments increasingly permit the collection of longitudinal data sets in which the data structure contains a large number of participants N and a large number of measurement occasions T. Promising new dynamical systems approaches to the analysis of large N, large T data sets have been proposed that utilize both between-subjects and within-subjects information. The COGITO project, begun over a decade ago, is an early large N = 204, large T = 100 study that collected high quality cognitive and psychosocial data. In this introduction, I describe the COGITO project and conceptual and statistical issues that arise in the analysis of large N, large T data sets. I provide a brief overview of the five papers in the special section which include conceptual pieces, a didactic presentation of a dynamic structural equation approach, and papers reporting new statistical analyses of the COGITO data set to answer substantive questions. Although many challenges remain, these new approaches offer the promise of improving scientific inquiry in the behavioral sciences.

Effect of Early Grade Retention on School Completion: A Prospective Study.

This 14 year prospective study investigated the effect of retention in grades 1-5 on high school completion (diploma, GED, or drop out). Participants were 734 (52.7% males) ethnically diverse, academically at-risk students recruited from Texas schools into the study when they were in first grade (mean age = 6.57). Propensity score weighting successfully equated the 256 retained students and the 478 students continuously promoted students on 65 covariates assessed in grade 1. At the end of 14 years, 477 had earned a diploma, 21 had obtained a GED, 110 had dropped out, and 126 were missing school completion status. Using multinomial logistic regression with high school graduation as the reference outcome, retention led to a significant increase in the likelihood of dropping out of high school (odds ratio = 2.61), above students' propensity to be retained and additional covariates. The contrast between graduation and GED outcomes was not significant. A significant Retention X Ethnicity X Gender interaction was obtained: The negative effect of retention was strongest for African American and Hispanic girls. Even though grade retention in the elementary grades does not harm students in terms of their academic achievement or educational motivation at the transition to high school, retention increases the odds that a student will drop out of school before obtaining a high school diploma.

Effect of retention in elementary grades on dropping out of school early.

This study investigated the effect of grade retention in elementary school on dropping out of school by age 16. Participants were 538 (54% males) ethnically diverse, academically at-risk students recruited from Texas schools into a longitudinal study when they were in first grade (mean age=6.58). Propensity score weighting successfully equated the 171 retained students and the 367 continuously promoted students on 65 covariates assessed in grade 1. Fifty-one students dropped out of school by age 16 and 487 persisted. Retention (vs. promotion) led to an increased early dropout rate (odds ratio=1.68), even after controlling for 65 covariates associated with school achievement, retention, or both. Implications of findings for dropout prevention and grade retention policies are discussed.

Tests of Simple Slopes in Multiple Regression Models with an Interaction: Comparison of Four Approaches.

In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional variance expression obtained when the values of X and Z are fixed. A simulation study using randomly sampled predictors compared four approaches: (a) the Aiken and West ( 1991 ) test of simple slopes at fixed population values of Z, (b) the Aiken and West test at sample-estimated values of Z, (c) a 95% percentile bootstrap confidence interval approach, and (d) a fully Bayesian approach with diffuse priors. The results showed that approach (b) led to inflated Type 1 error rates and 95% confidence intervals with inadequate coverage rates, whereas other approaches maintained acceptable Type 1 error rates and adequate coverage of confidence intervals. Approach (c) had asymmetric rejection rates at small sample sizes. We used an empirical data set to illustrate these approaches.

Testing measurement invariance in longitudinal data with ordered-categorical measures.

A goal of developmental research is to examine individual changes in constructs over time. The accuracy of the models answering such research questions hinges on the assumption of longitudinal measurement invariance: The repeatedly measured variables need to represent the same construct in the same metric over time. Measurement invariance can be studied through factor models examining the relations between the observed indicators and the latent constructs. In longitudinal research, ordered-categorical indicators such as self- or observer-report Likert scales are commonly used, and these measures often do not approximate continuous normal distributions. The present didactic article extends previous work on measurement invariance to the longitudinal case for ordered-categorical indicators. We address a number of problems that commonly arise in testing measurement invariance with longitudinal data, including model identification and interpretation, sparse data, missing data, and estimation issues. We also develop a procedure and associated R program for gauging the practical significance of the violations of invariance. We illustrate these issues with an empirical example using a subscale from the Mexican American Cultural Values scale. Finally, we provide comparisons of the current capabilities of 3 major latent variable programs (lavaan, Mplus, OpenMx) and computer scripts for addressing longitudinal measurement invariance. (PsycINFO Database Record

An experimental test of the Bridges to High School intervention on harsh parenting and early age intercourse among Mexican American adolescents.

OBJECTIVE: Can an intervention that contained no content on sex or contraception reduce rates of early-age intercourse among Mexican American adolescents? The current study examined whether the Bridges to High School intervention designed, in part, to decrease harsh parenting, had a longitudinal effect on decreasing rates of early-age intercourse in the treatment versus control groups, as well as the moderating role of gender and linguistic acculturation. METHOD: The sample consisted of 516 Mexican American adolescents (Mage = 12.31 years; 50.8% female) and their mothers who participated in a randomized, intervention trial. A series of longitudinal, meditational path models were used to examine the effects of the intervention on harsh parenting practices and early-age intercourse. RESULTS: Our findings revealed that participation in the treatment versus control group was indirectly linked to a lower likelihood of early-age intercourse through decreased maternal harsh parenting. Tests of mediation were significant. These findings did not vary across gender and linguistic acculturation. CONCLUSION: Results suggest that the Bridges to High School intervention successfully decreased early-age intercourse among Mexican American adolescents through reduced harsh parenting among mothers. This finding is consistent with positive youth development programs that have been found to have broad, and sometimes nontargeted, effects on adolescent sexual behaviors. (PsycINFO Database Record