Putting the individual back into individual growth curves

Further exploration of the effects of time-varying covariate in growth mixture models with nonlinear trajectories

Growth mixture modeling (GMM) is an analytical tool for identifying multiple unobserved sub-populations in longitudinal processes. In particular, it describes change patterns within each latent sub-population and investigates between-individual differences in within-individual change for each sub-group. A key research interest in using GMMs is examining how covariates influence the heterogeneity in change patterns. Liu & Perera (2022b) extended mixture-of-experts (MoE) models, which primarily focus on time-invariant covariates, to allow covariates to account for both within-group and between-group differences and investigate the heterogeneity in nonlinear trajectories. The present study further extends Liu & Perera, 2022b by examining the effects of time-varying covariates (TVCs) on trajectory heterogeneity. Specifically, we propose methods to decompose a TVC into an initial trait (the baseline value of the TVC) and a set of temporal states (interval-specific slopes or changes of the TVC). The initial trait is allowed to account for within-group differences in growth factors of trajectories (i.e., baseline effect), while the temporal states are allowed to impact observed values of a longitudinal process (i.e., temporal effects). We evaluate the proposed models using a simulation study and real-world data analysis. The simulation study demonstrates that the proposed models are capable of separating trajectories into several clusters and generally producing unbiased and accurate estimates with target coverage probabilities. The proposed models reveal the heterogeneity in initial trait and temporal states of reading ability across latent classes of students' mathematics performance. Additionally, the baseline and temporal effects on mathematics development of reading ability are also heterogeneous across the clusters of students.

Estimating rate of change for nonlinear trajectories in the framework of individual measurement occasions: A new perspective on growth curves

Researchers are often interested in examining between-individual differences in within-individual processes. If the process under investigation is tracked for a long time, its trajectory may show a certain degree of nonlinearity, so that the rate of change is not constant. A fundamental goal of modeling such nonlinear processes is to estimate model parameters that reflect meaningful aspects of change, including the parameters related to change and other parameters that shed light on substantive hypotheses. However, if the measurement occasion is unstructured, existing models cannot simultaneously estimate these two types of parameters. This article has three goals. First, we view the change over time as the area under the curve (AUC) of the rate of change versus time ( r - t ) graph. Second, using the instantaneous rate of change midway through a time interval to approximate the average rate of change during that interval, we propose a new specification to describe longitudinal processes. In addition to obtaining the individual change-related parameters and other parameters related to specific research questions, the new specification allows for unequally spaced study waves and individual measurement occasions around each wave. Third, we derive the model-based interval-specific change and change from baseline, two common measures to evaluate change over time. We evaluate the proposed specification through a simulation study and a real-world data analysis. We also provide OpenMx and Mplus 8 code for each model with the novel specification.

Deadlines make you productive, but what do they do to your motivation? Trajectories in quantity and quality of motivation and study activities among university students as exams approach

Introduction

Recent research has emphasized that achievement motivation is context-sensitive and varies within individual students. Ubiquitous temporal landmarks such as exams or deadlines are evident contextual factors that could systematically explain variation in motivation. Indeed, research has consistently found that university students increase their study efforts as exams come closer in time, indicating increasing study motivation. However, changes in study motivation for a specific exam as it comes closer have rarely been investigated. Instead, research on developmental changes in expectancy and value beliefs has consistently founds that achievement motivation declines over a semester. Surprisingly, declining motivation thus apparently coincides with increasing study efforts for end-of-semester exams.

Methods

The present research investigates this apparent contradiction by assessing how exam-specific motivation and study behavior change under equal methodological conditions as an exam draws closer. Using parallel growth curve models, we examine changes in expectancy-value beliefs, performance approach and avoidance motivation and study behavior as well as motivational want- and should-conflicts among 96 students over eight weekly measurement points.

Results and discussion

Results show that students study more for their exam as it comes closer and increase their use of surface learning strategies more rapidly than their use of deep learning strategies. However, even exam-specific expectancy and attainment value beliefs decline while performance-avoidance motivation increases over time, indicating that students increasingly study out of fear to fail as exams come closer. Consistent with these findings, students' experience of should conflicts decreases while their want conflicts increase over time. We discuss several possible mechanisms underlying our findings in addition to potential theoretical consequences and suggest future research opportunities to better understand students' changes in situative motivation and study behavior in the context of temporal landmarks.

School Experiences and Anxiety Trajectories Among Youth with Intellectual Disabilities

This study investigated how the school experiences and personal characteristics of youth with Intellectual Disabilities (ID) contribute to their longitudinal trajectories of anxiety. To this end, we relied on a sample of 390 youth with mild (48.2%) to moderate (51.8%) levels of ID, aged from 11 to 22 (M = 15.70), and recruited in Canada (n = 140) and Australia (n = 250). Across three yearly time points, all participants completed self-report measures of anxiety, school climate, and victimization. Our results revealed a slight normative decrease in anxiety over time and showed that experiences of school victimization were associated with higher levels of anxiety (initially and momentarily) and increases in victimization were accompanied by increases in anxiety over time. Perceptions of attending a school that fosters security and promotes learning also tended to be accompanied by lower levels of anxiety (initially and momentarily). Momentary increases in perceptions of attending a school that fosters positive peer interactions were associated with momentary decreases in anxiety, whereas momentary increases in perceptions of attending a school characterized by positive teacher-student relationships and an equitable treatment of all students both led to small momentary increases in anxiety once all other components of student school experiences were considered. The theoretical and practical implications of these results are discussed.

The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods

Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize the promise of this wealth of information on how biology, behavior, and cognition change over time, there is a need to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmental neuroscientists. Fortunately, these models have an incredibly rich tradition in the broader developmental sciences that we can draw from. Here, we provide a primer on longitudinal models, written in a beginner-friendly (and slightly irreverent) manner, with a particular focus on selecting among different modeling frameworks (e.g., multilevel versus latent curve models) to build the theoretical model of development a researcher wishes to test. Our aims are three-fold: (1) lay out a heuristic framework for longitudinal model selection, (2) build a repository of references that ground each model in its tradition of methodological development and practical implementation with a focus on connecting researchers to resources outside traditional neuroimaging journals, and (3) provide practical resources in the form of a codebook companion demonstrating how to fit these models. These resources together aim to enhance training for the next generation of developmental neuroscientists by providing a solid foundation for future forays into advanced modeling applications.

Psychotic experiences and negative symptoms from adolescence to emerging adulthood: developmental trajectories and associations with polygenic scores and childhood characteristics

BACKGROUND

Psychotic experiences and negative symptoms (PENS) are common in non-clinical populations. PENS are associated with adverse outcomes, particularly when they persist. Little is known about the trajectories of PENS dimensions in young people, nor about the precursory factors associated with these trajectories.

METHODS

We conducted growth mixture modelling of paranoia, hallucinations, and negative symptoms across ages 16, 17, and 22 in a community sample (N = 12 049-12 652). We then described the emergent trajectory classes through their associations with genome-wide polygenic scores (GPS) for psychiatric and educational phenotypes, and earlier childhood characteristics.

RESULTS

Three trajectory classes emerged for paranoia, two for hallucinations, and two for negative symptoms. Across PENS, GPS for clinical help-seeking, major depressive disorder, and attention deficit hyperactivity disorder were associated with increased odds of being in the most elevated trajectory class (OR 1.07-1.23). Lower education GPS was associated with the most elevated trajectory class for hallucinations and negative symptoms (OR 0.77-0.91). Conversely for paranoia, higher education GPS was associated with the most elevated trajectory class (OR 1.25). Trajectory class associations were not significant for schizophrenia, obsessive-compulsive disorder, bipolar disorder, or anorexia GPS. Emotional/behaviour problems and life events in childhood were associated with increased odds of being in the most elevated trajectory class across PENS.

CONCLUSIONS

Our results suggest latent heterogeneity in the development of paranoia, hallucinations, and negative symptoms in young people that is associated with specific polygenic scores and childhood characteristics.

Assessing mediational processes using piecewise linear growth curve models with individual measurement occasions

Longitudinal processes often unfold concurrently where the growth patterns of two or more longitudinal outcomes are associated. Additionally, if the study under investigation is long, the growth curves may exhibit nonconstant change with respect to time. Multiple existing studies have developed multivariate growth models with nonlinear functional forms to explore joint development where two longitudinal records are correlated over time. However, the relationship between multiple longitudinal outcomes may also be unidirectional. Accordingly, it is of interest to estimate regression coefficients of such unidirectional paths. One statistical tool for such analyses is longitudinal mediation models. In this study, we develop two models to evaluate mediational processes where the linear-linear piecewise functional form is utilized to capture the change patterns. We define the mediational process as either the baseline covariate or the change in covariate influencing the change in the mediator, which, in turn, affects the change in the outcome. We present the proposed models through simulation studies and real-world data analyses. Our simulation studies demonstrate that the proposed mediational models can provide unbiased and accurate point estimates with target coverage probabilities with a 95% confidence interval. The empirical analyses demonstrate that the proposed models can estimate covariates' direct and indirect effects on the change in the outcome. We also provide the corresponding code for the proposed models.

Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models

We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic differentiation. Mixed response types, heteroscedasticity, and crossed random effects are naturally incorporated into the framework. The models developed were motivated by applications in cognitive neuroscience, and two case studies are presented. First, we show how GALAMMs can jointly model the complex lifespan trajectories of episodic memory, working memory, and speed/executive function, measured by the California Verbal Learning Test (CVLT), digit span tests, and Stroop tests, respectively. Next, we study the effect of socioeconomic status on brain structure, using data on education and income together with hippocampal volumes estimated by magnetic resonance imaging. By combining semiparametric estimation with latent variable modeling, GALAMMs allow a more realistic representation of how brain and cognition vary across the lifespan, while simultaneously estimating latent traits from measured items. Simulation experiments suggest that model estimates are accurate even with moderate sample sizes.

Self-esteem development during the transition to work: A 14-year longitudinal study from adolescence to young adulthood

INTRODUCTION

Previous studies examined the trajectory of self-esteem during critical developmental periods and over the life-span. However, little is known about how self-esteem changes during the school-to-work transition.

METHOD

We examined the effect of beginning a job for the first time on self-esteem development, using data from 368 adolescents assessed up to six times across a 14-year time span. Specifically, we analyzed the pattern of self-esteem change during the transition to work and whether the self-esteem trajectory varied as a function of several school- and job-related variables, while controlling for important covariates.

RESULTS

Results revealed linear increases in self-esteem across the 14-year study period, with partial support that the rate of increase slowed slightly after the school-to-work transition. We found significantly greater variability in the slopes after the transition, supporting the idea that people differ in the way they cope with the developmental tasks associated with important life transitions. We also found evidence for an interaction between college graduation and educational expectations, such that the positive effect of college graduation on self-esteem change was stronger for those who graduated with low (vs. high) educational expectations.

CONCLUSION

School-to-work transition has an effect on self-esteem development. Developmental processes of findings were discussed.

Estimating Likelihood of Dementia in the Absence of Diagnostic Data: A Latent Dementia Index in 10 Genetically Informed Studies

BACKGROUND

Epidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available.

OBJECTIVE

We apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results.

METHODS

Using 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available.

RESULTS

Results indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis.

CONCLUSION

This latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.

The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates-which are unique to BLCS models-were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a nonlinear tendency. While frequentist analysis of nonlinear mixed effects models has a long history, Bayesian analysis of the models has received comparatively little attention until the late 1980s, primarily due to the time-consuming nature of Bayesian computation. Since the early 1990s, Bayesian approaches for the models began to emerge to leverage rapid developments in computing power, and have recently received significant attention due to (1) superiority to quantify the uncertainty of parameter estimation; (2) utility to incorporate prior knowledge into the models; and (3) flexibility to match exactly the increasing complexity of scientific research arising from diverse industrial and academic fields. This review article presents an overview of modeling strategies to implement Bayesian approaches for the nonlinear mixed effects models, ranging from designing a scientific question out of real-life problems to practical computations.

Longitudinal associations between social media use, mental well-being and structural brain development across adolescence

Youth of today grow up in a digital social world but the effects on well-being and brain development remain debated. This study tracked longitudinal associations between structural brain development, social media use and mental well-being.

The study demonstrated two pathways of heterogeneity in brain development. First, adolescents who used social media more than their peers showed higher baseline cortical thickness in lateral prefrontal cortex (PFC) and medial PFC; and stronger decreases in the lateral PFC and temporal parietal junction. In contrast, adolescents with lower mental well-being showed lower baseline levels of surface area in the medial PFC and posterior superior temporal sulcus relative to their peers. Whereas the associations between structural brain development and well-being remained significant after correction for multiple testing, the results for social media use did not survive FDR correction.

These findings demonstrate that although social media use and mental well-being were both associated with differential trajectories of brain development, the associations we report are distinct. These results show a nuanced perspective on the presumed relations between social media use and well-being and provide a starting point to further examine neural mechanisms that could explain which adolescents thrive by social media and which might be harmed.

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Longitudinal study with three time points across adolescence on MPFC, TPJ, pSTS and LPFC development.

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Cortical thickness development differed between high vs low social media users.

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Surface area development differed between low vs high mental well-being subgroups.

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Associations with well-being remained significant after stringent FDR correction.

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Social media and well-being are distinctly associated with structural brain development.

Developmental cascades and educational attainment

Developmental cascades describe how systems of development interact and influence one another to shape human development across the lifespan. Despite its popularity, developmental cascades are commonly used to understand the developmental course of psychopathology, typically in the context of risk and resilience. Whether this framework can be useful for studying children's educational outcomes remains underexplored. Therefore, in this chapter, we provide an overview of how developmental cascades can be used to study children's academic development, with a particular focus on the biological, cognitive, and contextual pathways to educational attainment. We also provide a summary of contemporary statistical methods and highlight existing data sets that can be used to test developmental cascade models of educational attainment from birth through adulthood. We conclude the chapter by discussing the challenges of this research and explore important future directions of using developmental cascades to understand educational attainment.

Medical responsibility growth in youth with spina bifida: Neuropsychological and parenting predictors

OBJECTIVE

For youth with spina bifida (SB) there is a growing need to understand how responsibilities for health care are transferred from family- to self-management over time. The current study examined trajectories of responsibility for medical tasks in youth with SB across adolescence, as well as executive functioning/attention and parenting behaviors as predictors of growth.

METHOD

As part of a larger, longitudinal study, 140 youth with SB (ages 8-15 at time 1; Mage = 11.43) reported on their responsibility for relevant medical tasks across five time points. Attention and executive functioning were assessed via performance-based and parent/teacher-report methods. Parenting behaviors consisted of acceptance, behavioral control, and psychological control and were assessed via observational and parent-report.

RESULTS

Growth curve analyses revealed significant increases in youth medical responsibility across all SB tasks over time. Attention, executive functioning, maternal behavioral control, and paternal psychological control emerged as predictors of growth parameters in responsibility for communicating about SB and managing health care appointments.

CONCLUSION

Results indicated that youth with SB obtain increasing responsibility for their health care over time. The transfer of responsibility for SB management may differ based on individual (i.e., the child's neuropsychological abilities) and family level (i.e., parenting behaviors) factors. Further research is needed to understand how growth in medical responsibility relates to changes in other aspects of SB self-management across development, such as medical adherence. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Trajectories of Executive Functions and ADHD Symptoms in Preschoolers and the Role of Negative Parental Discipline

In the present study, we investigated whether the longitudinal growth trajectories of executive functions (EF) and Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms are related. In addition, we investigated whether negative discipline moderated these longitudinal relations. The sample consisted of predominantly clinically referred preschoolers (N = 248, age 42-66 months at Time 1; 79.0% boys). Assessment occurred three times: at baseline, at 9 months, and at 18 months. EF was assessed with five EF tasks. ADHD symptoms (Child Behavior Checklist 1.5-5) were reported by parents. Groups of medium to high and low negative discipline were based on mother- and father-reports (Parenting Practices Inventory). Growth curve models showed that EF generally increased and ADHD symptoms generally decreased over time. Parallel process models showed that there was no relation between the change in EF and the change in ADHD symptoms over time, suggesting no co-development. However, higher EF at baseline was related to lower ADHD symptoms at baseline. This was irrespective of whether children were exposed to high or low negative discipline. Overall, the results suggest that, while EF and ADHD symptoms are related, they develop independently across the preschool years.

The development of life goals over the adult lifespan

OBJECTIVES

Life goals are important organizing units for individual agency in development. On a societal level, they align with age-normative developmental tasks; on the individual level, they guide people's attempts at shaping their own development. This study investigates the development of life goals across the adult lifespan with a focus on differences regarding gender, parental status, education, and region.

METHOD

Using longitudinal data from the German Socio-Economic Panel study (SOEP, N=52,052; age range: 18-84 years), we estimated the developmental trajectories of importance ratings for nine life goals across the adult lifespan using multiple-group latent growth curve modelling.

RESULTS

Having a happy relationship or marriage, having children, and being there for others are the life goals rated as most important across almost the entire adult lifespan. Having a happy relationship or marriage differed strongly by gender. Up to middle adulthood it was more important for women but more important for men in late adulthood. Parental status amplified gender differences in the work and family domain. Low education was associated with a higher perceived importance of being there for others. The largest regional differences (East vs. West) were found for home ownership.

DISCUSSION

Although the importance of some life goal trajectories reflects typical age-grading in developmental tasks, other life goals (e.g., having children) remain important even after goal attainment or after developmental deadlines have passed.

Beyond the average brain: individual differences in social brain development are associated with friendship quality

We tested whether adolescents differ from each other in the structural development of the social brain and whether individual differences in social brain development predicted variability in friendship quality development. Adolescents (N = 299, M_age T1 =13.98 years) were followed across three biannual waves. We analysed self-reported friendship quality with the best friend at T1 and T3, and bilateral measures of surface area and cortical thickness of the medial prefrontal cortex (mPFC), posterior superior temporal sulcus (pSTS), temporoparietal junction (TPJ) and precuneus across all waves. At the group level, growth curve models confirmed non-linear decreases of surface area and cortical thickness in social brain regions. We identified substantial individual differences in levels and change rates of social brain regions, especially for surface area of the mPFC, pSTS and TPJ. Change rates of cortical thickness varied less between persons. Higher levels of mPFC surface area and cortical thickness predicted stronger increases in friendship quality over time. Moreover, faster cortical thinning of mPFC surface area predicted a stronger increase in friendship quality. Higher levels of TPJ cortical thickness predicted lower friendship quality. Together, our results indicate heterogeneity in social brain development and how this variability uniquely predicts friendship quality development.

Peer Effects on Early Language Development in Dual Language Learners

This study investigated the vocabulary development of children (N = 547) from linguistically and socioeconomically diverse classrooms in Germany from age 3 in preschool to age 7 in Grade 1. The results showed that for dual language learners (DLLs, n = 107) growth rates in their German majority language skills varied over classrooms. Compared to monolingual children, DLLs improved faster in classrooms with higher peer-level skills in the majority language than DLLs in classrooms with lower peer-level skills (controlling for socioeconomic status and classroom quality). DLLs showed stronger growth dynamics than monolingual children during later preschool stages. The findings highlight the role of preschool peers in DLLs' acquisition of the majority language before entering elementary school.

Personality change and sustainability attitudes and behaviors

Withstanding the climate crisis will depend in part on individuals behaving in a more environmentally sustainable manner. However, relatively little is known about the individual factors that promote sustainable attitudes and behaviors (SABs). Although there are established cross-sectional associations between personality traits and SABs, it is unclear whether changes in personality are related to increases in SABs over time, and how personality is differentially related to specific SABs. Using data from 61,479 participants in New Zealand, we tested preregistered hypotheses about how personality codevelops with valuing the environment, believing in climate change, concern about climate change, personal environmental efficacy, personal environmental sacrifice, and support for the Green Party. We found that SABs generally increased from 2009 to 2017, although there was variation across age cohorts, SAB variables, and samples. We replicated concurrent correlations between broad personality traits—particularly Agreeableness, Openness, and Honesty/Humility—and SABs and present novel evidence that increases in SAB are related to changes in traits, particularly Agreeableness. These findings have implications for both understanding the factors associated with changes in SABs over time and understanding the factors that drive personality change.

Shift-&-Persist and discrimination predicting depression across the life course: An accelerated longitudinal design using MIDUSI-III

Life course theorists posit that sensitive periods exist during life span development where risk and protective factors may be particularly predictive of psychological outcomes relative to other periods in life. While there have been between-cohort studies trying to examine differences in discrimination and depressive symptoms, these studies have not been designed to identify these sensitive periods, which are best modeled by examining intra-individual change across time. To identify sensitive periods where discrimination and shift-&-persist (S&P) - a coping strategy that may protect against the negative impact of discrimination - are most strongly predictive of depressive symptoms, we employed latent growth curve modeling using an accelerated longitudinal design to track intra-individual change in depressive symptoms from ages 20-69. Participants were 3,685 adults measured at three time points ~10 years apart from the Midlife in the United States study (Mage = 37.93, SD = 6.948 at Wave I). Results identified two sensitive periods in development where high levels of S&P interacted with discrimination to protect against depressive symptoms; during the 30s and a lagged effect where 40's S&P protected against depressive symptoms when participants were in their 50s. Implications for the life course study of discrimination, coping, and depression are discussed.

Longitudinal network re-organization across learning and development

While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8-29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naïve subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals.

A dynamical systems approach to examining the efficacy of suicide-focused interventions

OBJECTIVE

Suicide risk is a nonlinear temporal process, but the ways in which suicide-focused interventions have statistically examined risk effects have ignored these nonlinearities. This paper highlights the potential benefits of using data analytic methods that account for nonlinear change patterns.

METHOD

Using a dynamical systems perspective, interventions are framed in terms of attractor dynamics. An attractor has three primary qualities where an intervention can have an effect. These correspond to contextual differences, shifts in the underlying temporal patterns, and changes in the stability of the temporal pattern.

RESULTS/CONCLUSIONS

It is argued that the ideal effect is one in which there is both an observed change in stability and a shift in the underlying temporal pattern toward less risk. Other types of intervention effects can have alternate explanations that are less desirable. Mean, variance, and growth differences are discussed within a systems context, and an example model is provided using Latent Change Score Modeling (McArdle, Annual Review of Psychology, 60, 2009, 577-605).

Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R-squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies

Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R² ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random intercepts and/or slopes was recently introduced by Rights and Sterba and it subsumed pre-existing MLM R-squareds as special cases. However, this work focused on cross-sectional applications, and hence did not address how the computation and interpretation of MLM R-squareds are affected by modeling considerations typically arising in longitudinal settings: (a) alternative centering choices for time (e.g., centering-at-a-constant vs. person-mean-centering), (b) nonlinear effects of predictors such as time, (c) heteroscedastic level-1 errors and/or (d) autocorrelated level-1 errors. This paper addresses these gaps by extending the Rights and Sterba R-squared framework to longitudinal contexts. We: (a) provide a full framework of total and level-specific R-squared measures for MLMs that utilize any type of centering, and contrast these with Rights and Sterba's measures assuming cluster-mean-centering, (b) explain and derive which measures are applicable for MLMs with nonlinear terms, and extend the R-squared computation to accommodate (c) heteroscedastic and/or (d) autocorrelated errors. Additionally, we show how to use differences in R-squared (ΔR² ) measures between growth models (adding, for instance, time-varying covariates as level-1 predictors or time-invariant covariates as level-2 predictors) to obtain effects sizes for individual terms. We provide R software (r2MLMlong) and a running pedagogical example analyzing growth in adolescent self-efficacy to illustrate these methodological developments. With these developments, researchers will have greater ability to consider effect size when analyzing and predicting change using MLMs.

Covariance pattern mixture models: Eliminating random effects to improve convergence and performance

Growth mixture models (GMMs) are prevalent for modeling unknown population heterogeneity via distinct latent classes. However, GMMs are riddled with convergence issues, often requiring researchers to atheoretically alter the model with cross-class constraints simply to obtain convergence. We discuss how within-class random effects in GMMs exacerbate convergence issues, even though these random effects rarely help answer typical research questions. That is, latent classes provide a discretization of continuous random effects, so including additional random effects within latent classes can unnecessarily complicate the model. These random effects are commonly included in order to properly specify the marginal covariance; however, random effects are inefficient for patterning a covariance matrix, resulting in estimation issues. Such a goal can be achieved more simply through covariance pattern models, which we extend to the mixture model context in this article (covariance pattern mixture models, or CPMMs). We provide evidence from theory, simulation, and an empirical example showing that employing CPMMs (even if they are misspecified) instead of GMMs can circumvent the computational difficulties that can plague GMMs, without sacrificing the ability to answer the types of questions commonly asked in empirical studies. Our results show the advantages of CPMMs with respect to improved class enumeration and less biased class-specific growth trajectories, in addition to their vastly improved convergence rates. The results also show that constraining the covariance parameters across classes in order to bypass convergence issues with GMMs leads to poor results. An extensive software appendix is included to assist researchers in running CPMMs in Mplus.

Adolescents' music preferences and personality characteristics

The present paper examined the structure of Dutch adolescents' music preferences, the stability of music preferences and the relations between Big‐Five personality characteristics and (changes in) music preferences. Exploratory and confirmatory factor analyses of music‐preference data from 2334 adolescents aged 12–19 revealed four clearly interpretable music‐preference dimensions: Rock, Elite, Urban and Pop/Dance. One thousand and forty‐four randomly selected adolescents from the original sample filled out questionnaires on music preferences and personality at three follow‐up measurements. In addition to being relatively stable over 1, 2 and 3‐year intervals, music preferences were found to be consistently related to personality characteristics, generally confirming prior research in the United States. Personality characteristics were also found to predict changes in music preferences over a 3‐year interval. Copyright © 2007 John Wiley & Sons, Ltd.

Big Five personality development in adolescence and adulthood

The present article examines Big Five personality development across adolescence and middle adulthood. Two adolescents and their fathers and mothers from 285 Dutch families rated their own and their family members' personality. Using accelerated longitudinal growth curve analyses, mean level change in Big Five factors was estimated. For boys, Extraversion and Openness decreased and for girls, Extraversion, Agreeableness, Conscientiousness, and Openness increased. Whereas mothers' Emotional Stability and Conscientiousness increased, fathers' Extraversion, Agreeableness, and Emotional Stability decreased. Differences in self‐ and other‐reported personality change were found, as well as interindividual differences in personality change. Results confirm that personality change is possible across the life course but these changes are not similar for all individuals and depend on the type of observer. Copyright © 2006 John Wiley & Sons, Ltd.

A recipe for accurate estimation of lifespan brain trajectories, distinguishing longitudinal and cohort effects

We address the problem of estimating how different parts of the brain develop and change throughout the lifespan, and how these trajectories are affected by genetic and environmental factors. Estimation of these lifespan trajectories is statistically challenging, since their shapes are typically highly nonlinear, and although true change can only be quantified by longitudinal examinations, as follow-up intervals in neuroimaging studies typically cover less than 10% of the lifespan, use of cross-sectional information is necessary. Linear mixed models (LMMs) and structural equation models (SEMs) commonly used in longitudinal analysis rely on assumptions which are typically not met with lifespan data, in particular when the data consist of observations combined from multiple studies. While LMMs require a priori specification of a polynomial functional form, SEMs do not easily handle data with unstructured time intervals between measurements. Generalized additive mixed models (GAMMs) offer an attractive alternative, and in this paper we propose various ways of formulating GAMMs for estimation of lifespan trajectories of 12 brain regions, using a large longitudinal dataset and realistic simulation experiments. We show that GAMMs are able to more accurately fit lifespan trajectories, distinguish longitudinal and cross-sectional effects, and estimate effects of genetic and environmental exposures. Finally, we discuss and contrast questions related to lifespan research which strictly require repeated measures data and questions which can be answered with a single measurement per participant, and in the latter case, which simplifying assumptions that need to be made. The examples are accompanied with R code, providing a tutorial for researchers interested in using GAMMs.

Longitudinal associations between structural prefrontal cortex and nucleus accumbens development and daily identity formation processes across adolescence

We tested whether adolescents with daily high identity uncertainty showed differential structural brain development across adolescence and young adulthood. Participants (N = 150, M_ageT1 15.92 years) were followed across three waves, covering 4 years. Self-reported daily educational identity and structural brain data of lateral prefrontal cortex (lPFC)/anterior cingulate cortex (ACC), medial PFC, and nucleus accumbens (NAcc) was collected across three waves. All hypotheses were pre-registered. Latent class growth analyses confirmed 2 identity subgroups: an identity synthesis class (characterized by strong commitments, and low uncertainty), and an identity moratorium class (high daily identity uncertainty). Latent growth curve models revealed, on average, delayed maturation of the lateral PFC/ACC and medial PFC and stable NAcc. Yet, adolescents in identity moratorium showed lower levels and less decline in NAcc gray matter volume. Lateral PFC/ACC and medial PFC trajectories did not differ between identity subgroups. Exploratory analyses revealed that adolescents with higher baseline levels and delayed maturation of lateral PFC/ACC and medial PFC gray matter volume, surface area, and cortical thickness reported higher baseline levels and stronger increases of in-depth exploration. These results provide insight into how individual differences in brain development relate to fluctuations in educational identity development across adolescence and young adulthood.

The Dynamic Associations Between Cortical Thickness and General Intelligence are Genetically Mediated

The neural substrates of intelligence represent a fundamental but largely uncharted topic in human developmental neuroscience. Prior neuroimaging studies have identified modest but highly dynamic associations between intelligence and cortical thickness (CT) in childhood and adolescence. In a separate thread of research, quantitative genetic studies have repeatedly demonstrated that most measures of intelligence are highly heritable, as are many brain regions associated with intelligence. In the current study, we integrate these 2 streams of prior work by examining the genetic contributions to CT-intelligence relationships using a genetically informative longitudinal sample of 813 typically developing youth, imaged with high-resolution MRI and assessed with Wechsler Intelligence Scales (IQ). In addition to replicating the phenotypic association between multimodal association cortex and language centers with IQ, we find that CT-IQ covariance is nearly entirely genetically mediated. Moreover, shared genetic factors drive the rapidly evolving landscape of CT-IQ relationships in the developing brain.

Depression and ADHD-Related Risk for Substance Use in Adolescence and Early Adulthood: Concurrent and Prospective Associations in the MTA

Childhood attention-deficit/hyperactivity disorder (ADHD) is prospectively linked to substance use and disorder. Depression emerging in adolescence is an understudied risk factor that may explain some of this risk. In the present study, we considered mediating and moderating roles of adolescent depression in explaining this association by using longitudinal data from the prospective 16-year follow-up of the Multimodal Treatment Study of ADHD (MTA). Participants were 547 children diagnosed with DSM-IV ADHD Combined Type, and 258 age- and sex-matched comparison children. In adolescence, depressive symptoms did not exacerbate effects of childhood ADHD on any substance use. For both groups, time-varying and average depressive symptoms were associated with more frequent use of all substances. Prospectively, we found no evidence of depression mediation to adult substance use. However, adolescent depression moderated the association between childhood ADHD and adult marijuana use. Although adults without ADHD histories used marijuana more frequently if they had elevated depressive symptoms in adolescence, marijuana use by adults with ADHD histories was independent of their adolescent depression. In adulthood, depression diagnoses and ADHD persistence continued to operate as independent, additive correlates of substance use risk. Our findings suggest a circumscribed role for depression in substance use risk that adds to, but does not alter or explain, ADHD-related risk.

Inhibitory Control Across the Preschool Years: Developmental Changes and Associations with Parenting

The normative developmental course of inhibitory control between 2.5 and 6.5 years, and associations with maternal and paternal sensitivity and intrusiveness were tested. The sample consisted of 383 children (52.5% boys). During four annual waves, mothers and fathers reported on their children’s inhibitory control using the Children's Behavior Questionnaire. During the first wave, mothers’ and fathers’ sensitivity and intrusiveness were observed and coded with the Emotional Availability Scales. Inhibitory control exhibited partial scalar invariance over time, and increased in a decelerating rate. For both mothers and fathers, higher levels of sensitivity were associated with a higher initial level of children's inhibitory control, whereas higher levels of intrusiveness predicted a slower increase in children's inhibitory control.

Adolescent E-cigarette use trajectories and subsequent alcohol and marijuana use

INTRODUCTION

As electronic cigarette (e-cigarette) use has become more prevalent among adolescents, there is a growing body of evidence linking e-cigarette use to the initiation of other substances. Whether there is a threshold level of e-cigarette use that is predictive of other substance use is unknown. The current study examines patterns of e-cigarette use over time and determines whether different patterns of early adolescent e-cigarette use are concurrently and prospectively associated with alcohol and marijuana use in late adolescence.

METHOD

Eight hundred and one adolescents (13-15 years old at baseline recruitment) completed five on-line surveys over a two-year period. Latent class growth analysis was used to model different developmental courses of e-cigarette, alcohol (drinking to intoxication), and marijuana use. Logistic regression was used to test the association between e-cigarette use trajectory patterns and alcohol and marijuana use trajectories.

RESULTS

Three developmental courses of e-cigarette use were identified: 1) high and increasing, 2) low and increasing, and 3) never. Compared to adolescents who had never used e-cigarettes, those in the other two groups were more likely to have been intoxicated and to be in the moderate and increasing marijuana use group.

CONCLUSION

Both high and low levels of e-cigarette use patterns are associated with increasing use of other substances (alcohol and marijuana use) over time. Findings highlight the need for early intervention and prevention of e-cigarette use among adolescents.

Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

Analysing trajectories of a longitudinal exposure: A causal perspective on common methods in lifecourse research

Longitudinal data is commonly analysed to inform prevention policies for diseases that may develop throughout life. Commonly methods interpret the longitudinal data as a series of discrete measurements or as continuous patterns. Some of the latter methods condition on the outcome, aiming to capture ‘average’ patterns within outcome groups, while others capture individual-level pattern features before relating these to the outcome. Conditioning on the outcome may prevent meaningful interpretation. Repeated measurements of a longitudinal exposure (weight) and later outcome (glycated haemoglobin levels) were simulated to match three scenarios: one with no causal relationship between growth rate and glycated haemoglobin; two with a positive causal effect of growth rate on glycated haemoglobin. Two methods that condition on the outcome and one that did not were applied to the data in 1000 simulations. The interpretation of the two-step method matched the simulation in all causal scenarios, but that of the methods conditioning on the outcome did not. Methods that condition on the outcome do not accurately represent a causal relationship between a longitudinal pattern and outcome. Researchers considering longitudinal data should carefully determine if they wish to analyse longitudinal data as a series of discrete time points or by extracting pattern features.

Gradual Change, Homeostasis, and Punctuated Equilibrium: Reconsidering Patterns of Health in Later Life

Longitudinal methods aggregate individual health histories to produce inferences about aging populations, but to what extent do these summaries reflect the experiences of older adults? We describe the assumption of gradual change built into several influential statistical models and draw on widely used, nationally representative survey data to empirically compare the conclusions drawn from mixed-regression methods (growth curve models and latent class growth analysis) designed to capture trajectories with key descriptive statistics and methods (multistate life tables and sequence analysis) that depict discrete states and transitions. We show that individual-level data record stasis irregularly punctuated by relatively sudden change in health status or mortality. Although change is prevalent in the sample, for individuals it occurs rarely, at irregular times and intervals, and in a nonlinear and multidirectional fashion. We conclude by discussing the implications of this punctuated equilibrium pattern for understanding health changes in individuals and the dynamics of inequality in aging populations.

The Genetic Contributions to Maturational Coupling in the Human Cerebrum: A Longitudinal Pediatric Twin Imaging Study

Although prior studies have demonstrated that genetic factors play the dominant role in the patterning of the pediatric brain, it remains unclear how these patterns change over time. Using 1748 longitudinal anatomic MRI scans from 792 healthy twins and siblings, we quantified how genetically mediated inter-regional associations change over time via multivariate longitudinal structural equation modeling. These analyses found that genetic correlations for both lobar volumes and cortical thickness are dynamic, with relatively static effects on surface area. While genetic correlations for lobar volumes decrease over childhood and adolescence, in general they increase for cortical thickness in the second decade of life. Quantification of how genetic factors influence maturational coupling improves our understanding of typical neurodevelopment and informs future molecular genetic analyses.

Growth in Math Computation among Monolingual and English Language Learners:Does the Executive System have a Role?

This cohort-sequential study explored the components of working memory that underlie math calculation in elementary school children who are monolingual (English) or English language learners (ELLs) whose first language is Spanish. To this end, children (N = 789) in grades 1, 2, and 3 at wave 1 were administered a battery of math, vocabulary, reading and cognitive (short-term memory [STM], working memory [WM], rapid naming, and inhibition) measures. The battery of tests was administered again one year and two years later to the same participants. Three important findings emerged. First, along with naming speed, the results suggest that growth in the executive component of WM was significantly related to growth in calculation performance. Second, performance on measures of reading, fluid intelligence, naming speed and executive processes in wave 1 were significantly related to wave 3 math calculation performance. Finally, the full latent growth model showed that monolingual and ELL children were statistically comparable in computation at wave 3. Thus, strong support was found for the notion that the executive component of WM was related to math computation but weak support for the notion ELL children experienced a math achievement gap.

Developing a Longitudinal Scale for Language: Linking Across Developmentally Different Versions of the Same Test

Purpose Many language tests use different versions that are not statistically linked or do not have a developmental scaled score. The current article illustrates the problems of scores that are not linked or equated, followed by a statistical model to derive a developmental scaled score. Method Using an accelerated cohort design of 890 students in Grades 1-5, a confirmatory factor model was fit to 6 subtests of the Test of Language Development-Primary and Intermediate: Fourth Edition ( Hammill & Newcomer, 2008a , 2008b ). The model allowed for linking the subtests to a general factor of language and equating their measurement characteristics across grades and cohorts of children. A sequence of models was fit to evaluate the appropriateness of the linking assumptions. Results The models fit well, with reasonable support for the validity of the tests to measure a general factor of language on a longitudinally consistent scale. Conclusion Although total and standard scores were problematic for longitudinal relations, the results of the model suggest that language grows in a relatively linear manner among these children, regardless of which set of subtests they received. Researchers and clinicians interested in longitudinal inferences are advised to design research or choose tests that can provide a developmental scaled score.

On the use of growth models to study normal cognitive aging

Growth models (GM) of the mixed-effects and latent curve varieties have become popular methodological tools in lifespan research. One of the major advantages of GM is their flexibility in studying individual differences in change. We scrutinized the change functions of GM used in five years of publications on cognitive aging. Of the 162 publications that we identified, 88% test linear or quadratic polynomials, and fewer than 5% apply functions that are nonlinear in their parameters, such as exponential decline. This apparent bias in favor of polynomial decomposition calls for exploring what conclusions about individual differences in change are likely to be drawn if one applies linear or quadratic GMs to data simulated under a conceptually and empirically plausible model of exponential cognitive decline from adulthood to old age. Hence, we set up a simulation that manipulated the rate of exponential decline, measurement reliability, number of occasions, interval width, and sample size. True rate of decline and interval width influenced results strongly, number of occasions and measurement reliability exerted a moderate effect, and the effects of sample size appeared relatively minor. Critically, our results show that fit statistics generally do not differentiate misspecified linear or quadratic models from the true exponential model. Moreover, power to detect variance in change for the linear and quadratic GMs is low, and estimates of individual differences in level and change can be highly biased by model misspecification. We encourage researchers to also consider plausible nonlinear change functions when studying behavioral development across the lifespan.

Historical and Developmental Changes in Condom Use Among Young Men Who Have Sex with Men Using a Multiple-Cohort, Accelerated Longitudinal Design

Young men who have sex with men (YMSM) have the highest HIV incidence in the U.S. The last 5 years has seen emergence of new methods for HIV prevention and societal shifts in gay rights. It is important to understand if there have been generational shifts in condom use during the developmental transition from adolescents to young adulthood. To disentangle history from development, we require a multiple-cohort, longitudinal design-a methodology never before applied to study YMSM. We followed three cohorts of YMSM recruited in 2007, 2010, and 2015 (N = 1141) from the ages of 17-26 years and modeled their longitudinal change over time in counts of anal sex acts and the ratio of condomless anal sex (CAS) acts to anal sex acts using latent curve growth modeling. We found that there was no significant developmental change in raw counts of anal sex acts, but there was a significant decline in the ratio of anal sex acts that were condomless. We also found significantly different patterns for ratio of CAS acts for the 2015 cohort. The 2015 cohort reported a significantly lower ratio of CAS acts at age 17, but significantly higher growth in ratio of CAS acts over development. The present study suggests that YMSM recruited in 2015 have very different trajectories of CAS compared to previous cohorts, including lower risk in late adolescence, but with the potential for higher risk after the transition into adulthood.

Growth Curves for Headache Research: A Multilevel Modeling Perspective

OBJECTIVE

To introduce growth curve modeling for longitudinal headache research.

BACKGROUND

Longitudinal data play an important role in the study of headache-related outcomes by allowing researchers to test hypotheses about change over time. However, headache researchers are often unfamiliar with the flexibility and power that growth curves can offer in analyzing longitudinal data. The goals of this paper are to introduce growth curve models within the multilevel modeling framework for analyzing longitudinal headache-related data and to show how these models can be applied in practice.

METHODS

Longitudinal data for the empirical example came from publicly available data from Wave I to Wave IV of the National Longitudinal Study of Adolescent to Adult Health. In total, 5608 individuals were included in the study and multilevel models were fit to examine, for individuals with and without adolescent migraine, longitudinal changes in depression from age 13 to 27 years old.

RESULTS

Findings showed that individuals varied in their longitudinal depression trajectories. A cubic time trend best approximated the data with depression increasing through adolescence, decreasing during young adulthood, and then beginning to increase again in adulthood. Further, results also indicated that individuals with adolescent migraine had higher levels of depression throughout the age span compared those without adolescent migraine, but the shape of change did not differ across the groups.

CONCLUSION

Growth curve models offer a flexible alternative to traditional statistical methods and can rigorously evaluate a wide array of headache-related hypotheses.

Trajectories of Depressive Symptomatology and Loneliness in Older Adult Sexual Minorities and Heterosexual Groups

OBJECTIVES

This article examines whether sexual minority men and women experience greater increases in depressive symptoms and loneliness with age compared to heterosexual men and women.

METHODS

Using three waves of data from sexual minority (n_Men = 87 and n_Women = 62) and heterosexual (n_Men = 1,297 and n_Women = 1,362) older adults in the National Social Life, Health, and Aging Project, we used latent growth curve modeling to test whether change in depressive symptoms and loneliness varies across sexual orientation and whether annual household income and family support accounted for this change.

RESULTS

Although differences in the growth trajectories of depressive symptoms and loneliness across sexual orientation were not observed, gender differences were. Annual household income and family support more strongly influenced initial depressive symptoms and loneliness in sexual minority men and women than in heterosexual men and women.

CONCLUSIONS

Trajectories of depressive symptoms and loneliness in older adulthood do not vary by sexual orientation. Economic and family resources may allow sexual minorities to cope effectively with depressive symptoms and loneliness.

CLINICAL IMPLICATIONS

Clinicians should be cautious about assuming that older sexual minority group members are more susceptible to depressive symptoms and loneliness than heterosexual groups by virtue of their sexual preference.

Perinatal depression among a global sample of Spanish-speaking women: A sequential-process latent growth-curve analysis

BACKGROUND

Despite high rates of perinatal depression among women from diverse backgrounds, the understanding of the trajectory of depressive symptoms is limited. The aim of this study was to investigate the trajectories of depressive symptoms from pregnancy to postpartum among an international sample of pregnant women.

METHODS

Hispanic/Latina (79.2%), Spanish-speaking (81%) pregnant women (N = 1796; Mean age = 28.32, SD = 5.51) representing 78 unique countries/territories participated in this study. A sequential-process latent growth-curve model was estimated to examine general trajectories of depression as well as risk and protective factors that may impact depression levels throughout both the prenatal and postpartum periods.

RESULTS

Overall, depression levels decreased significantly across the entire perinatal period, but this decrease slowed over time within both the prenatal and postpartum periods. Spanish-speaking women, those who were partnered, and those with no history of depression reported lower levels of depression during early pregnancy, but this buffer effect reduced over time. Depression levels at delivery best predicted postpartum depression trajectories (i.e., women with higher levels of depression at delivery were at greater risk for depression postpartum).

LIMITATIONS

Given the emphasis on language and not country or culture of origin this study was limited in its ability to examine the impact of specific cultural norms and expectations on perinatal depression.

CONCLUSIONS

Given these findings, it is imperative that providers pay attention to, and assess for, depressive symptoms and identified buffers for depression, especially when working with women from diverse communities.

Longitudinal effects of acculturation and enculturation on mental health: Does the measure of matter?

A great deal of research has focused on acculturation and enculturation, which represent the processes of adapting to a new culture. Despite this growing literature, results have produced inconsistent findings that may be attributable to differences in terms of the instruments used to assess acculturation and enculturation. Utilizing a 3-year longitudinal data set (with 1-year lags between assessments), the present study explored the psychometric properties of the Bicultural Involvement Questionnaire—Short Version (BIQ-S) and the Acculturation Rating Scale for Mexican Americans II (ARSMA-II) and examined the overlap between changes in these measures as they relate to internalizing and externalizing problem behavior. The present sample consisted of 216 immigrant Latino youth (43% boys; mean age 13.6 years at baseline;SD= 1.44 years, range 10 to 17). Exploratory structural equation modeling identified factor structures for the BIQ-S and ARSMA-II that diverged from their hypothesized structure. Growth curve models also indicate divergence between the BIQ-S and ARSMA-II in terms ofchangein acculturation and enculturation processes. Finally, the present findings emphasized that measures of acculturation and enculturation are not equivalent in terms of their effects on internalizing and externalizing problems.

Developmental cognitive neuroscience using latent change score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N = 204 (N = 32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N = 176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and Ωnyx).

Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology

Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.