Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement

Approaches to protocol standardization and data harmonization in the ECHO-wide cohort study

The United States (U.S.) National Institutes of Health-funded Environmental influences on Child Health Outcomes (ECHO)-wide Cohort was established to conduct high impact, transdisciplinary science to improve child health and development. The cohort is a collaborative research design in which both extant and new data are contributed by over 57,000 children across 69 cohorts. In this review article, we focus on two key challenging issues in the ECHO-wide Cohort: data collection standardization and data harmonization. Data standardization using a Common Data Model and derived analytical variables based on a team science approach should facilitate timely analyses and reduce errors due to data misuse. However, given the complexity of collaborative research designs, such as the ECHO-wide Cohort, dedicated time is needed for harmonization and derivation of analytic variables. These activities need to be done methodically and with transparency to enhance research reproducibility. IMPACT: Many collaborative research studies require data harmonization either prior to analyses or in the analyses of compiled data. The Environmental influences on Child Health Outcomes (ECHO) Cohort pools extant data with new data collection from over 57,000 children in 69 cohorts to conduct high-impact, transdisciplinary science to improve child health and development, and to provide a national database and biorepository for use by the scientific community at-large. We describe the tools, systems, and approaches we employed to facilitate harmonized data for impactful analyses of child health outcomes.

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

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

Harmonizing Social, Emotional, and Behavioral Constructs in Prevention Science: Digging into the Weeds of Aligning Disparate Measures

While integrative data analysis (IDA) presents great opportunity, it also necessitates a myriad of methodological decisions related to harmonizing disparate measures collected across multiple studies. There is a lack of step-by-step methodological guidance for harmonizing disparate measures of latent constructs differently conceptualized or operationalized across studies, such as social, emotional, and behavioral constructs often utilized in prevention science. The current paper addressed this gap by providing methodological guidance and a case illustration focused on harmonizing measures of disparately conceptualized and operationalized constructs. We do so by outlining a five-phased harmonization approach paired with an illustrative example of the approach as applied to harmonization of broadband latent emotional and behavioral health constructs assessed with different measures across studies. This approach builds on and expands upon procedures currently recommended in the IDA literature with parallels to best practices in test development procedures. The illustrative example of our phased approach is drawn from an IDA study of 11 randomized controlled trials of Coping Power (Lochman & Wells, 2004), an evidence-based preventive intervention. We demonstrate the harmonization of two constructs, internalizing and externalizing problems, as harmonized across the teacher-reported scales of the Achenbach System of Empirically Based Assessment (Achenbach, 1991a) and the Behavior Assessment System for Children (Reynolds & Kamphaus, 2004). Finally, we consider the potential strengths and limitations of this phased approach, underscoring areas for future methodological research and conclude with some recommendations.

Reexamining developmental continuity and discontinuity in the 21st century: Better aligning behaviors, functions, and mechanisms

Developmental science aims to explain development across the lifespan. Jerome Kagan observed that the same behavior can occur for different reasons, and differing behaviors can occur for the same reason. To help account for persistence, desistence, and transformation of behavior across development, Kagan introduced various types of continuity and discontinuity of forms and functions of behavior. This framework provides opportunities for identifying explanatory mechanisms in behavior development. However, misconceptions remain in applying the concepts that Kagan introduced. Much of the literature assumes developmental continuity in constructs without examining whether assumptions are supported, leading to faulty developmental inferences. For instance, the use of the same measure across time to assess development assumes that the behavior occurs for the same reason across time (homotypic continuity). In addition, just because one behavior predicts a different behavior at a later time does not necessarily indicate that age-differing behaviors occur for the same reason (heterotypic continuity). This review aims to advance conceptualizations of continuity and discontinuity from a contemporary perspective with aims to improve mechanistic understanding of behavior development across the lifespan. To better align behaviors, functions, and mechanisms, research should (a) examine (dis)continuity of individual behaviors rather than merely syndromes, (b) identify the function(s) of the given behavior(s), and (c) identify the cognitive and biological processes that underlie the behavior-function pairs. Incorporating examples from research on development of humans and nonhuman animals, I discuss challenges from work that has followed Kagan's ideas and ways to advance understanding of continuity and discontinuity across development. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

Within-dyad bidirectional relations among maternal depressive symptoms and child behavior problems from infancy through preschool

Although dyadic theory focuses on the impact of a mother's mental health on her own child and the impact of a child's mental health on their own mother, commonly used statistical approaches are incapable of distinguishing the desired within-dyad processes from between-dyad effects. Using autoregressive latent trajectory modeling with structured residuals, the current study evaluated within-dyad, bidirectional associations between maternal depressive symptoms and child behavior problems from child age 1-4.5 years among a sample of low-income, Mexican American women (N = 322, Mage = 27.8) and their children. Women reported on maternal depressive symptoms and child behavior problems during laboratory visits at child age 1, 1.5, 2, 3, and 4.5 years. Results provide novel evidence of child-driven bidirectional association between maternal depressive symptoms and child behavior problems at the within-dyad level as early as child age 1 year and within-person stability in child behavior problems emerging early in life.

Studying children's growth in self-regulation using changing measures to account for heterotypic continuity: A Bayesian approach to developmental scaling

Self-regulation is thought to show heterotypic continuity-its individual differences endure but its behavioral manifestations change across development. Thus, different measures across time may be necessary to account for heterotypic continuity of self-regulation. This longitudinal study examined children's (N = 108) self-regulation development using 17 measures, including 15 performance-based measures, two questionnaires, and three raters across seven time points. It is the first to use different measures of self-regulation over time to account for heterotypic continuity while using developmental scaling to link the measures onto the same scale for more accurate growth estimates. Assessed facets included inhibitory control, delayed gratification, sustained attention, and executive functions. Some measures differed across ages to retain construct validity and account for heterotypic continuity. A Bayesian longitudinal mixed model for developmental scaling was developed to link the differing measures onto the same scale. This allowed charting children's self-regulation growth across ages 3-7 years and relating it to both predictors and outcomes. Rapid growth occurred from ages 3-6. As a validation of the developmental scaling approach, greater self-regulation was associated with better school readiness (math and reading skills) and fewer externalizing problems. Our multi-wave, multi-facet, multi-method, multi-measure, multi-rater, developmental scaling approach is the most comprehensive to date for assessing the development of self-regulation. This approach demonstrates that developmental scaling may enable studying development of self-regulation across the lifespan.

Creating a developmental scale to chart the development of psychopathology with different informants and measures across time

Research Domain Criteria (RDoC) aims to advance a dimensional, multilevel understanding of psychopathology across the life span. Two key challenges exist in applying a developmental perspective to RDoC: First, the most accurate informants for assessing a person's psychopathology often differ across development (e.g., parents and teachers may be better informants of a person's externalizing problems in early childhood, whereas peer- and self-report may also be important to assess in adolescence). Second, many constructs change in their behavioral manifestation across development (i.e., heterotypic continuity). Thus, different informants and measures across time may be necessary to account for the construct's changing manifestation. The challenge of using different informants and measures of a construct across time is ensuring that the same construct is assessed in a comparable way across development. Vertical scaling creates a developmental scale to link scores from changing informants and measures to account for heterotypic continuity and study people's development of psychopathology across the life span. This is the first study that created a developmental scale to assess people's development by putting different informants and measures on the same scale. We examined the development of externalizing problems from ages 2 to 15 years (N = 1,364) using annual ratings by mothers, fathers, teachers, other caregivers, and self-report. The developmental scale linked different informants and measures on the same scale. This allowed us to chart people's growth trajectories and to identify multilevel risk factors, including poor verbal comprehension. Creating a developmental scale may be crucial to advance RDoC's goal of studying the development of psychopathology across the life span. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Review of neuropsychological outcomes in isolated methylmalonic acidemia: recommendations for assessing impact of treatments

Methylmalonic acidemia (MMA) due to methylmalonyl-CoA mutase deficiency (OMIM #251,000) is an autosomal recessive disorder of organic acid metabolism associated with life-threatening acute metabolic decompensations and significant neuropsychological deficits. "Isolated" MMA refers to the presence of excess methylmalonic acid without homocysteine elevation. Belonging to this class of disorders are those that involve complete deficiency (mut⁰) and partial deficiency (mut^-) of the methylmalonyl-CoA mutase enzyme and other disorders causing excess methylmalonic acid excretion. These other disorders include enzymatic subtypes related to cobalamin A defect (cblA) (OMIM #25,110), cobalamin B defect (cblB) (OMIM #251,110) and related conditions. Neuropsychological attributes associated with isolated MMA have become more relevant as survival rates increased following improved diagnostic and treatment strategies. Children with this disorder still are at risk for developmental delay, cognitive difficulties and progressive declines in functioning. Mean IQ for all types apart from cblA defect enzymatic subtype is rarely above 85 and much lower for mut⁰ enzymatic subtype. Identifying psychological domains responsive to improvements in biochemical status is important. This review suggests that processing speed, working memory, language, attention, and quality of life may be sensitive to fluctuations in metabolite levels while IQ and motor skills may be less amenable to change. Due to slower developmental trajectories, Growth Scale Values, Projected Retained Ability Scores and other indices of change need to be incorporated into clinical trial study protocols. Neuropsychologists are uniquely qualified to provide a differentiated picture of cognitive, behavioral and emotional consequences of MMA and analyze benefits or shortcomings of novel treatments.

Spelling out some unaddressed conceptual and methodological challenges in empirical lifespan research

The importance of taking a lifespan approach to describe and understand human development has long been acknowledged (e.g., Baltes, 1987). Nevertheless, theoretical or empirical research that actually encompasses the entire lifespan, that is, from early childhood to old age, is rare. This is not surprising given the challenges such an approach entails. Many of these challenges (e.g., establishing measurement invariance between age groups) have been addressed in the previous literature, but others have not yet been sufficiently considered. The main purpose of this article is to present several examples of such largely unaddressed conceptual and methodological challenges and reflect upon possible ways to address them. We discuss the usefulness of a lifespan approach and the generalization of the challenges to other research comparing different groups, such as gender, culture, or species.

Understanding the Impact of Child, Intervention, and Family Factors on Developmental Trajectories of Children with Hearing Loss at Preschool Age: Design of the AChild Study

Children with hearing loss and their families represent a large variety with regard to their auditory, medical, psychological, and family resource characteristics. Despite recent advances, developmental outcomes are still below average, with a significant proportion of variety remaining unexplained. Furthermore, there is a lack of studies including the whole diversity of children with hearing loss. The AChild study (Austrian Children with Hearing Impairment—Longitudinal Databank) uses an epidemiological longitudinal design including all children living in Upper and Lower Austria with a permanent uni- or bilateral hearing loss below the age of 6 years, irrespective of additional disabilities, family language, and family resources. The demographic characteristics of the first 126 children enrolled in the study showed that about half of the children are either children with additional disabilities (31%) and/or children not growing up with the majority language (31.7%) that are usually excluded from comprehensive longitudinal studies. AChild aims for a characterization of the total population of young children with hearing loss including developmental outcomes. Another goal is the identification of early predictors of developmental trajectories and family outcomes. In addition to child-related predictors the examination of family–child transactions malleable by family-centred early intervention is of particular interest. The study is designed as participatory including parent representation atall stages. Measures have been chosen, following other large population-based studies in order to gain comparability and to ensure international data pooling.

Impact of taxane-based chemotherapy among older women with breast cancer on cognition and quality of life: a longitudinal pooled analysis

PURPOSE

Older cancer patients are susceptible to long-term effects of chemotherapy, including cancer-related cognitive decline and impairments to quality of life. Taxane-based chemotherapies are associated with physical declines among older women and may negatively impact cognitive performance. We sought to examine whether changes in objective and subjective measures of cognitive performance and well-being differ among older breast cancer survivors as a function of taxane-based chemotherapy treatment regimens.

METHODS

Individual-level data were pooled and harmonized from two large prospective studies of older (greater than 60 years) breast cancer survivors. Assessments were conducted prior to systemic therapy and up to 36 months after. Cognitive performance was assessed with objective (working memory, processing speed, and executive functions) and subjective tests and physical, emotional, and functional well-being were also assessed.

RESULTS

One hundred and sixty-seven (M age = 67.3 years) women with 116 receiving chemotherapy with taxanes and 51 without taxanes contributed data. Declines in subjective cognition for both groups were significant between pre-treatment and 12-month follow-up. Significant improvements were seen on a measure of objective cognition (working memory) from 12 to 36 months. Measures of well-being improved from prior to systemic therapy to 12 months. Longitudinal changes across all measures did not vary as a function of receipt of taxane-based treatment.

CONCLUSION

Older women who received treatment with taxanes did not have greater declines in cognitive performance or well-being than women receiving other chemotherapy regimens. Despite older cancer survivors being at greater risk for negative outcomes, treatment with taxane-based chemotherapies does not appear to exacerbate these health consequences.

Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI

To improve understanding of Alzheimer’s disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.

Heterotypic continuity of inhibitory control in early childhood: Evidence from four widely used measures

Inhibitory control has been widely studied in association with social and academic adjustment. However, prior studies have generally overlooked the potential heterotypic continuity of inhibitory control and how this could affect assessment and understanding of its development. In the present study, we systematically considered heterotypic continuity in four well-established measures of inhibitory control, testing two competing hypotheses: (a) the manifestation of inhibitory control coheres within and across time in consistent, relatively simple ways, consistent with homotypic continuity. Alternatively, (b) with developmental growth, inhibitory control manifests in more complex ways with changes across development, consistent with heterotypic continuity. We also explored differences in inhibitory control as a function of the child's sex, language ability, and the family's socioeconomic status. Children (N = 513) were studied longitudinally at 30, 36, and 42 months of age. Changes in the patterns of associations within and among inhibitory control measures across ages suggest that the measures' meanings change with age, the construct manifests differently across development, and, therefore, that the construct shows heterotypic continuity. We argue that the heterotypic continuity of inhibitory control motivates the use of different combinations of inhibitory control indexes at different points in development in future research to improve validity. Confirmatory factors and growth curves also suggest that individual differences in inhibitory control endure, with convergence among inhibitory control measures by 36 months of age. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Navigating Stigma Trajectory and Mental Health Among Young Adults Living with Perinatal HIV in New York City

Perceived HIV stigma and mental health are fluid across the lifespan for people living with perinatally-acquired HIV (PHIV). The process of navigating discredited identities over time in the context of other life demands potentially exerts a toll on the mental health of adolescents and young adults living with PHIV (AYAPHIV). Based on data from a longitudinal study in New York City examining mental health and health risk behaviors among 182 AYAPHIV, we examined if increased perceived HIV stigma predicted mental health, future orientation, HIV-disclosure, and healthcare transition over time (2003-2018). Findings from linear mixed-effects modeling indicated that older age predicted poorer mental health, less future orientation, more HIV-serostatus disclosure, and adult medical services utilization. Perceived stigma was the only significant predictor of mental health and mediated the association between age and mental health-highlighting the importance of addressing stigma across development for AYAPHIV while addressing systems that perpetuate them.

You Say Tomato, I Say Radish: Can Brief Cognitive Assessments in the U.S. Health Retirement Study Be Harmonized With Its International Partner Studies?

OBJECTIVES

To characterize the extent to which brief cognitive assessments administered in the population-representative U.S. Health and Retirement Study (HRS) and its International Partner Studies can be considered to be measuring a single, unidimensional latent cognitive function construct.

METHODS

Cognitive function assessments were administered in face-to-face interviews in 12 studies in 26 countries (N = 155,690), including the U.S. HRS and selected International Partner Studies. We used the time point of the first cognitive assessment for each study to minimize differential practice effects across studies and documented cognitive test item coverage across studies. Using confirmatory factor analysis models, we estimated single-factor general cognitive function models and bifactor models representing memory-specific and nonmemory-specific cognitive domains for each study. We evaluated model fits and factor loadings across studies.

RESULTS

Despite relatively sparse and inconsistent cognitive item coverage across studies, all studies had some cognitive test items in common with other studies. In all studies, the bifactor models with a memory-specific domain fit better than single-factor general cognitive function models. The data fit the models at reasonable thresholds for single-factor models in 6 of the 12 studies and for the bifactor models in all 12 of the 12 studies.

DISCUSSION

The cognitive assessments in the U.S. HRS and its International Partner Studies reflect comparable underlying cognitive constructs. We discuss the assumptions underlying our methods, present alternatives, and future directions for cross-national harmonization of cognitive aging data.

The Precipitous Decline in Reasoning and Other Key Abilities with Age and Its Implications for Federal Judges

U. S. Supreme Court justices and other federal judges are, effectively, appointed for life, with no built-in check on their cognitive functioning as they approach old age. There is about a century of research on aging and intelligence that shows the vulnerability of processing speed, fluid reasoning, visual-spatial processing, and working memory to normal aging for men and women at all levels of education; even the maintained ability of crystallized knowledge declines in old age. The vulnerable abilities impact a person's decision-making and problem solving; crystallized knowledge, by contrast, measures a person's general knowledge. The aging-IQ data provide a rationale for assessing the key cognitive abilities of anyone who is appointed to the federal judiciary. Theories of multiple cognitive abilities and processes, most notably the Cattell-Horn-Carroll (CHC) model, provide a well-researched blueprint for interpreting the plethora of findings from studies of IQ and aging. Sophisticated technical advances in test construction, especially in item-response theory and computerized-adaptive testing, allow for the development of reliable and valid theory-based tests of cognitive functioning. Such assessments promise to be a potentially useful tool for evaluating federal judges to assess the impact of aging on their ability to perform at a level their positions deserve, perhaps to measure their competency to serve the public intelligently. It is proposed that public funding be made available to appoint a panel of experts to develop and validate an array of computerized cognitive tests to identify those justices who are at risk of cognitive impairment.

Integrative data analysis of self-efficacy in 4 clinical trials for alcohol use disorder

BACKGROUND

Self-efficacy has been proposed as a key predictor of alcohol treatment outcomes and a potential mechanism of success in achieving abstinence or drinking reductions following alcohol treatment. Integrative data analysis, where data from multiple studies are combined for analyses, can be used to synthesize analyses across multiple alcohol treatment trials by creating a commensurate measure and controlling for differential item functioning (DIF) to determine whether alcohol treatments improve self-efficacy.

METHOD

The current study used moderated nonlinear factor analysis (MNLFA) to examine the effect of treatment on self-efficacy across four different treatment studies (N = 3720; 72.5% male, 68.4% non-Hispanic white). Self-efficacy was measured using the Alcohol Abstinence Self-Efficacy Scale (AASE) in the COMBINE Study (n = 1383) and Project MATCH (n = 1726), and the Drug Taking Confidence Questionnaire (DTCQ) in two studies of Telephone Continuing Care (TEL Study 1: n = 303; TEL Study 2: n = 212). DIF was examined across time, study, treatment condition, marital status, age, and sex.

RESULTS

We identified 12 items from the AASE and DTCQ to create a commensurate measure of self-efficacy using MNLFA. All active treatments, including cognitive-behavioral treatment, a combined behavioral intervention, medication management, motivation enhancement treatment, telephone continuing care, twelve-step facilitation, and relapse prevention, were associated with significant increases in self-efficacy from baseline to posttreatment that were maintained for up to a year. Importantly, treatment as usual in community settings, which consisted of weekly group therapy that included addiction counseling and twelve-step recovery support, was not associated with significant increases in self-efficacy.

CONCLUSIONS

Alcohol self-efficacy increases following treatment and numerous evidence-based treatments are associated with significant increases in self-efficacy, which are maintained over time. Community treatment that focuses solely on addiction counseling and twelve-step support may not promote increases in self-efficacy.

The Effects of Familial Social Support Relationships on Identity Meaning in Older Adults: A Longitudinal Investigation

This study aimed to examine whether social support promotes identity meaning among older adults. We hypothesized that when two spouses exchange social support, their sense of marital identity is enhanced. Among older adults, parental identity may be more strongly enhanced when parents provide social support to their children rather than receive social support from them. We conducted a longitudinal survey of 355 older adults (240 men and 115 women aged >60 years), who were assessed four times over 2 years. First, we confirmed the relationship between social support and identity meaning using an autoregressive path model. Second, we examined the effect of social support on the trajectory of role identities in a growth curve model. The intercepts of receiving support and providing support were significantly associated with the intercept of marital identity. In addition, the intercept of identity meaning for parents correlated with the intercept of providing support to their children but not with that of receiving support from their children. Social support between family members promotes role identities in family relationships. In particular, providing support to children correlates with parental roles which connect to subjective well-being.

The Importance of Childhood for Adult Health and Development-Study Protocol of the Zurich Longitudinal Studies

Evidence is accumulating that individual and environmental factors in childhood and adolescence should be considered when investigating adult health and aging-related processes. The data required for this is gathered by comprehensive long-term longitudinal studies. This article describes the protocol of the Zurich Longitudinal Studies (ZLS), a set of three comprehensive cohort studies on child growth, health, and development that are currently expanding into adulthood. Between 1954 and 1961, 445 healthy infants were enrolled in the first ZLS cohort. Their physical, motor, cognitive, and social development and their environment were assessed comprehensively across childhood, adolescence, and into young adulthood. In the 1970s, two further cohorts were added to the ZLS and assessed with largely matched study protocols: Between 1974 and 1979, the second ZLS cohort included 265 infants (103 term-born and 162 preterm infants), and between 1970 and 2002, the third ZLS cohort included 327 children of participants of the first ZLS cohort. Since 2019, the participants of the three ZLS cohorts have been traced and invited to participate in a first wave of assessments in adulthood to investigate their current health and development. This article describes the ZLS study protocol and discusses opportunities, methodological and conceptual challenges, and limitations arising from a long-term longitudinal cohort recruited from a study about development in early life. In the future, the ZLS will provide data to investigate childhood antecedents of adult health outcomes and, ultimately, will help respond to the frequent call of scientists to shift the focus of aging research into the first decades of life and, thus, to take a lifespan perspective on aging.

Creating a Developmental Scale to Account for Heterotypic Continuity in Development: A Simulation Study

Many psychological constructs show heterotypic continuity-their behavioral manifestations change with development but their meaning remains the same. However, research has paid little attention to how to account for heterotypic continuity. A promising approach to account for heterotypic continuity is creating a developmental scale using vertical scaling. A simulation was conducted to compare creating a developmental scale using vertical scaling to traditional approaches of longitudinal assessment. Traditional approaches that failed to account for heterotypic continuity resulted in less accurate growth estimates, at the person- and group level. Findings suggest that ignoring heterotypic continuity may result in faulty developmental inferences. Creating a developmental scale with vertical scaling is recommended to link different measures across time and account for heterotypic continuity.

Pre-Statistical Considerations for Harmonization of Cognitive Instruments: Harmonization of ARIC, CARDIA, CHS, FHS, MESA, and NOMAS

BACKGROUND

Meta-analyses of individuals' cognitive data are increasing to investigate the biomedical, lifestyle, and sociocultural factors that influence cognitive decline and dementia risk. Pre-statistical harmonization of cognitive instruments is a critical methodological step for accurate cognitive data harmonization, yet specific approaches for this process are unclear.

OBJECTIVE

To describe pre-statistical harmonization of cognitive instruments for an individual-level meta-analysis in the blood pressure and cognition (BP COG) study.

METHODS

We identified cognitive instruments from six cohorts (the Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, Coronary Artery Risk Development in Young Adults study, Framingham Offspring Study, Multi-Ethnic Study of Atherosclerosis, and Northern Manhattan Study) and conducted an extensive review of each item's administration and scoring procedures, and score distributions.

RESULTS

We included 153 cognitive instrument items from 34 instruments across the six cohorts. Of these items, 42%were common across ≥2 cohorts. 86%of common items showed differences across cohorts. We found administration, scoring, and coding differences for seemingly equivalent items. These differences corresponded to variability across cohorts in score distributions and ranges. We performed data augmentation to adjust for differences.

CONCLUSION

Cross-cohort administration, scoring, and procedural differences for cognitive instruments are frequent and need to be assessed to address potential impact on meta-analyses and cognitive data interpretation. Detecting and accounting for these differences is critical for accurate attributions of cognitive health across cohort studies.

Studying a Moving Target in Development: The Challenge and Opportunity of Heterotypic Continuity

Many psychological constructs show heterotypic continuity-their behavioral manifestations change with development but their meaning remains the same (e.g., externalizing problems). However, research has paid little attention to how to account for heterotypic continuity. Conceptual and methodological challenges of heterotypic continuity may prevent researchers from examining lengthy developmental spans. Developmental theory requires that measurement accommodate changes in manifestation of constructs. Simulation and empirical work demonstrate that failure to account for heterotypic continuity when collecting or analyzing longitudinal data results in faulty developmental inferences. Accounting for heterotypic continuity may require using different measures across time with approaches that link measures on a comparable scale. Creating a developmental scale (i.e., developmental scaling) is recommended to link measures across time and account for heterotypic continuity, which is crucial in understanding development across the lifespan. The current synthesized review defines heterotypic continuity, describes how to identify it, and presents solutions to account for it. We note challenges of addressing heterotypic continuity, and propose steps in leveraging opportunities it creates to advance empirical study of development.

Estimating New Quantities from Longitudinal Test Scores to Improve Forecasts of Future Performance

Psychometric models for longitudinal test scores typically estimate quantities associated with single-administration tests, like ability at each time-point. However, models for longitudinal tests have not considered opportunities to estimate new quantities that are unavailable from single-administration tests. Specifically, we discuss dynamic measurement models - which combine aspects of longitudinal IRT, nonlinear growth models, and dynamic assessment - to directly estimate capacity, defined as the expected future score once the construct has fully developed. After discussing the history and connecting these areas into a single framework, we apply the model to verbal test scores from the Intergenerational Studies, which follow 494 people from 3 to 72 years old. The goal is to predict adult verbal scores (Age ≥ 34) from adolescent scores (Age ≤ 20). We held-out the adult data for prediction and compared predictions from traditional longitudinal IRT ability scores and proposed dynamic measurement capacity scores from models fit to the adolescent data. Results showed that the R² from capacity scores were 2.5 times larger than the R² from longitudinal IRT ability scores (43% vs. 16%), providing some evidence that exploring new quantities available from longitudinal testing could be worthwhile when an interest in testing is forecasting future performance.

Extensions of Multiple-Group Item Response Theory Alignment: Application to Psychiatric Phenotypes in an International Genomics Consortium

Large-scale studies spanning diverse project sites, populations, languages, and measurements are increasingly important to relate psychological to biological variables. National and international consortia already are collecting and executing mega-analyses on aggregated data from individuals, with different measures on each person. In this research, we show that Asparouhov and Muthén's alignment method can be adapted to align data from disparate item sets and response formats. We argue that with these adaptations, the alignment method is well suited for combining data across multiple sites even when they use different measurement instruments. The approach is illustrated using data from the Whole Genome Sequencing in Psychiatric Disorders consortium and a real-data-based simulation is used to verify accurate parameter recovery. Factor alignment appears to increase precision of measurement and validity of scores with respect to external criteria. The resulting parameter estimates may further inform development of more effective and efficient methods to assess the same constructs in prospectively designed studies.

Modeling Population and Subject-Specific Growth in a Latent Trait Measured by Multiple Instruments over Time using a Hierarchical Bayesian Framework

Psychometric growth curve modeling techniques are used to describe a person's latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model's utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.

Using Integrative Data Analysis to Investigate School Climate Across Multiple Informants

Survey research frequently involves the collection of data from multiple informants. Results, however, are usually analyzed by informant group, potentially ignoring important relationships across groups. When the same construct(s) are measured, integrative data analysis (IDA) allows pooling of data from multiple sources into one data set to examine information from multiple perspectives within the same analysis. Here, the IDA procedure is demonstrated via the examination of pooled data from student and teacher school climate surveys. This study contributes to the sparse literature regarding IDA applications in the social sciences, specifically in education. It also lays the groundwork for future educational researchers interested in the practical applications of the IDA framework to empirical data sets with complex model structures.

Accounting for standard errors of measurement when modeling change

Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores can differ in terms of their precision—both within and across participants. We propose an approach to weight observed scores by their level of precision, which is estimated as the inverse of their standard error of measurement in the context of item response modeling. Thus, scores with lower standard errors of measurement have greater weight, and scores with higher standard errors of measurement are down weighted. We discuss the weighting approaches and illustrate how to apply this approach with commonly available software. We then compare this approach to modeling change without weighting based on standard errors of measurement.

Strategies for Assessing and Modeling Depressive Symptoms in Longitudinal Studies of Adolescents

Studying age-related change in psychosocial behavior is difficult because manifestation differs with development. While the use of age-appropriate measurement instruments addresses developmental differences, changes in measurement also challenge researchers' ability to study developmental trajectories. Leveraging 8-occasion data from 262 girls (baseline ages 11 and 17 years) participating in a cross-sequential study spanning childhood to adulthood, this paper (1) highlights the needs of developmental researchers seeking to measure change across large swaths of development, (2) forwards an initial formula to convert Beck Depression Inventory-II scores into Children's Depression Inventory scores and facilitate longitudinal analysis and understanding of how depression develops across adolescence, and (3) suggests collection and analysis of new data that would better facilitate researcher's linking of child-, adolescent-, and adult-oriented measurement instruments.

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Evaluating construct equivalence of youth depression measures across multiple measures and multiple studies

Construct equivalence of measures across studies is necessary for synthesizing results when combining data in meta-analysis or integrative data analysis. We discuss several assumptions required for construct equivalence, and review methods using individual-level data and item response theory (IRT) analysis for detecting or adjusting for violations of these assumptions. We apply IRT to data from 7 measures of depressive symptoms for 4,283 youth from 16 randomized prevention trials. Findings indicate that these data violate assumptions of conditional independence. Bifactor IRT models find that depression measures contain substantial reporter variance, and indicate that a single common factor model would be substantially biased. Separate analyses of ratings by youth find stronger evidence for construct equivalence, but factor invariance across sex and age does not hold. We conclude that data synthesis studies employing measures of youth depression should analyze results separately by reporter, explore more complex approaches to integrate these different perspectives, and explore methods that adjust for sex and age differences in item functioning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries.

Data Harmonization: Establishing Measurement Invariance across Different Assessments of the Same Construct across Adolescence

Longitudinal measurement invariance is a major concern for developmental scholars who seek to evaluate the same underlying construct across time. Unfortunately, discontinuities in the expression of various psychological constructs, as well as essential changes in measurement that are necessitated by shifting developmental capacities and practice effects over time, make the task of establishing longitudinal invariance extremely difficult. Drawing on 5 waves of longitudinal data from 392 families (52% female; M_{age_W1} = 12.89, SD = .48; M_{age_W5} = 21.95, SD = .77; 199 European American and 193 Mexican American families), the current investigation sought to establish measurement invariance across developmentally appropriate changes in measures of depressive symptomatology from early adolescence through early adulthood. Using a combination of item parceling and the common and unique items from 2 assessment instruments for depressive symptoms, the data supported strong factorial invariance in youth's depressive symptoms across 5 waves of measurement. Findings suggest that traditional invariance approaches can be adapted to determine whether the same construct underlies different measurement instruments across time. This analytic strategy can allow researchers and clinicians to use more sophisticated techniques to understand changes in symptomatology regardless of changes in measurement or developmental capacity. Applying this approach to model patterns of depressive symptomatology from early adolescence to early adulthood has important clinical implications for elucidating periods when youth experience elevations in depressive symptoms and heightened needs for intervention services.

How Openness Enriches the Environment: Read More

The recently proposed OFCI model and specifically the Environmental Enrichment Hypothesis state that Openness positively influences the development of cognitive abilities (Ziegler et al., 2012). It is assumed that Openness leads to engagement in more learning activities through creating an enriched environment (e.g., reading). However, despite positive evaluations of the OFCI model in general, there is little empirical research on this specific hypothesis. The current paper used a longitudinal design to test the positive impact of Openness on the frequency of reading activities in general and in the specific case of periods of unemployment. PIAAC (Programme for the International Assessment of Adult Competencies) data were used to fit structural equation models. The results show that Openness fosters greater engagement in reading activities over 3 years; a buffering function in case of unemployment could not be found. Theoretical and practical implications are discussed.

Trajectories of Relative Performance with 2 Measures of Global Cognitive Function

OBJECTIVES

To examine whether trajectories of global cognitive function over time in studies that change assessment protocols may be modeled based on an individual's performance relative to others in the study cohort.

DESIGN

Extended follow-up of a cohort originally enrolled in a clinical trial of postmenopausal hormone therapy.

SETTING

The Women's Health Initiative Memory Study switched from an in-person interview with the Modified Mini-Mental State Examination to a telephone-based interview with the modified Telephone Interview for Cognitive Status to assess global cognitive function over long-term follow-up.

PARTICIPANTS

Women aged 75 to 92 (N=2,561).

MEASUREMENTS

Annual cognitive assessments from participants, ranked according to age-, race- and ethnicity-adjusted performance levels, were used to identify distinct trajectories. Participants assigned to the resulting trajectories were compared for selected risk factor profiles.

RESULTS

Our approach grouped participants into five trajectories according to relative cognitive performance over time. These groups differed significantly according to 3 known risk factors for cognitive decline-education level, apolipoprotein E-ϵ4 genotype, and type 2 diabetes mellitus-and a biomarker based on brain structure that has been linked to cognitive decline and Alzheimer's disease. Participants with consistently low relative levels of cognitive function over time and those whose relative performance over time declined to these levels tended to have poorer risk factor profiles.

CONCLUSION

Longitudinal measures of an individual's relative performance on different assessment protocols for global cognitive function can be used to identify trajectories of change over time that appear to have internal validity with respect to known risk factors.

The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test

The Openness-Fluid-Crystallized-Intelligence (OFCI) model posits long-term relations between Openness and cognitive abilities and has been successfully tested with longitudinal data. However, research on the developmental interplay between cognitive abilities and personality exists only sparsely. The current paper focuses on a theoretical development of the OFCI model which suggests micro-level mechanisms underlying the long-term development. Specifically, within-situation relations between Openness, interests, situational perception, cognitive abilities, and emotions are proposed to explain longitudinal relations between Openness and cognitive abilities. Using experience sampling, selected parts of this elaboration were empirically scrutinized in a first test of the proposed ideas. Openness and specific interest both varied substantially across situations and covaried systematically. In interaction with an indicator of fluid intelligence, this covariation was related to an indicator of crystallized intelligence. The paper contributes to theorizing the intertwined development of personality and cognitive abilities, and highlights the importance of within-situation research for explaining long-term development.

Advancing the Study of Adolescent Substance Use Through the Use of Integrative Data Analysis

A wealth of information is currently known about the epidemiology, etiology, and evaluation of drug and alcohol use across the life span. Despite this corpus of knowledge, much has yet to be learned. Many factors conspire to slow the pace of future advances in the field of substance use including the need for long-term longitudinal studies of often hard-to-reach subjects who are reporting rare and episodic behaviors. One promising option that might help move the field forward is integrative data analysis (IDA). IDA is a principled set of methodologies and statistical techniques that allow for the fitting of statistical models to data that have been pooled across multiple, independent samples. IDA offers a myriad of potential advantages including increased power, greater coverage of rare behaviors, more rigorous psychometric assessment of theoretical constructs, accelerated developmental time period under study, and enhanced reproducibility. However, IDA is not without limitations and may not be useful in a given application for a variety of reasons. The goal of this article is to describe the advantages and limitations of IDA in the study of individual development over time, particularly as it relates to trajectories of substance use. An empirical example of the measurement of polysubstance use is presented and this article concludes with recommendations for practice.

Latent Growth and Dynamic Structural Equation Models

Latent growth models make up a class of methods to study within-person change—how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

V. DESIGN-BASED APPROACHES FOR IMPROVING MEASUREMENT IN DEVELOPMENTAL SCIENCE

The study of change and variation within individuals, and the relative comparison of changes across individuals, relies on the assumption that observed measurements reflect true change in the construct being measured. Measurement properties that change over time, contexts, or people pose a fundamental threat to validity and lead to ambiguous conclusions about change and variation. We highlight such measurement issues from a within-person perspective and discuss the merits of measurement-intensive research designs for improving precision of both within-person and between-person analysis. In general, intensive measurement designs, potentially embedded within long-term longitudinal studies, provide developmental researchers an opportunity to more optimally capture within-person change and variation as well as provide a basis to understand changes in dynamic processes and determinants of these changes over time.

IV. DEVELOPMENTS IN THE ANALYSIS OF LONGITUDINAL DATA

Longitudinal data analytic techniques include a complex array of statistical techniques from repeated-measures analysis of variance, mixed-effects models, and time-series analysis, to longitudinal latent variable models (e.g., growth models, dynamic factor models) and mixture models (longitudinal latent profile analysis, growth mixture models). In this article, we focus our attention on the rationales of longitudinal research laid out by Baltes and Nesselroade (1979) and discuss the advancements in the analysis of longitudinal data since their landmark paper. We highlight the developments in growth and change analysis and its derivatives because these models best capture the rationales for conducting longitudinal research. We conclude with additional rationales of longitudinal research brought about by the development of new analytic techniques.

Data Integration Approaches to Longitudinal Growth Modeling

Synthesizing results from multiple studies is a daunting task during which researchers must tackle a variety of challenges. The task is even more demanding when studying developmental processes longitudinally and when different instruments are used to measure constructs. Data integration methodology is an emerging field that enables researchers to pool data drawn from multiple existing studies. To date, these methods are not commonly utilized in the social and behavioral sciences, even though they can be very useful for studying various complex developmental processes. This article illustrates the use of two data integration methods, the data fusion and the parallel analysis approaches. The illustration makes use of six longitudinal studies of mathematics ability in children with a goal of examining individual changes in mathematics ability and determining differences in the trajectories based on sex and socioeconomic status. The studies vary in their assessment of mathematics ability and in the timing and number of measurement occasions. The advantages of using a data fusion approach, which can allow for the fitting of more complex growth models that might not otherwise have been possible to fit in a single data set, are emphasized. The article concludes with a discussion of the limitations and benefits of these approaches for research synthesis.

Development of internalizing problems from adolescence to emerging adulthood: Accounting for heterotypic continuity with vertical scaling

Manifestations of internalizing problems, such as specific symptoms of anxiety and depression, can change across development, even if individuals show strong continuity in rank-order levels of internalizing problems. This illustrates the concept of heterotypic continuity, and raises the question of whether common measures might be construct-valid for one age but not another. This study examines mean-level changes in internalizing problems across a long span of development at the same time as accounting for heterotypic continuity by using age-appropriate, changing measures. Internalizing problems from age 14-24 were studied longitudinally in a community sample (N = 585), using Achenbach's Youth Self-Report (YSR) and Young Adult Self-Report (YASR). Heterotypic continuity was evaluated with an item response theory (IRT) approach to vertical scaling, linking different measures over time to be on the same scale, as well as with a Thurstone scaling approach. With vertical scaling, internalizing problems peaked in mid-to-late adolescence and showed a group-level decrease from adolescence to early adulthood, a change that would not have been seen with the approach of using only age-common items. Individuals' trajectories were sometimes different than would have been seen with the common-items approach. Findings support the importance of considering heterotypic continuity when examining development and vertical scaling to account for heterotypic continuity with changing measures. (PsycINFO Database Record

CBM maze-scores as indicators of reading level and growth for seventh-grade students

The technical adequacy of CBM maze-scores as indicators of reading level and growth for seventh-grade secondary-school students was examined. Participants were 452 Dutch students who completed weekly maze measures over a period of 23 weeks. Criterion measures were school level, dyslexia status, scores and growth on a standardized reading test. Results supported the technical adequacy of maze scores as indicators of reading level and growth. Alternate-form reliability coefficients were significant and intermediate to high. Mean maze scores showed significant increase over time, students' growth trajectories differed, and students' initial performance levels (intercepts) and growth rates (slopes) were not correlated. Maze reading level and growth were related to reading level and/or growth on criterion measures. A nonlinear model provided a better fit for the data than a linear model. Implications for use of CBM maze-scores for data-based decision-making are discussed.