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The authors applied linear dynamic models to longitudinal data to examine the dynamics of reading and cognition from 1st to 12th grade. They used longitudinal data (N = 445) from the Connecticut Longitudinal Study (S. E. Shaywitz, B. A. Shaywitz, J. M. Fletcher, & M. D. Escobar, 1990) to map the dynamic interrelations of various scales of the Wechsler Intelligence Scales for Children--Revised (i.e., Full, Performance, and Verbal) and specific markers of the Woodcock-Johnson Psycho-Educational Battery--Revised reading cluster (i.e., Letter-Word 1D, Decoding, and Comprehension). The results of these analyses indicate that (a) there is a positive dynamic relation between reading and cognition across the selected age range; (b) this dynamic relation is symbiotic, with positive influences in both directions; (c) the influence from cognition to reading is stronger when considering the Wechsler Intelligence Scales for Children Performance--Revised Performance scale and weaker with the Verbal scale; (d) when examining the different Reading subtests, the influences from cognition are more apparent for LetterWord ID and Comprehension and are less perceptible for Decoding; and (e) the dynamics of reading and cognition appear to be of stronger magnitude during I st to 3rd grade, less strong during 4th to 8th grade, and weaker from 9th to 12th grade. Keywords: longitudinal data analysis, dynamic modeling, reading development, cognitive development

Objectives. The purpose of this study was (a) to investigate whether attrition due to death and nonresponse leads to bias in estimated growth-decline trajectories when only complete data are used in longitudinal research, and (b) to examine the extent of the bias and possible solutions. Methods. The study sample was a subset of the Longitudinal Study of Generations and included data from 208 G1-G2 parent-child dyads and 538 G2-G3 dyads over 30 years. We used a latent growth-decline curve model based on full information maximum likelihood estimation in order to compare parents' and adult children's reports on older respondents' health and intergenerational solidarity by parents' attrition status. Results. Results indicated that attrition due to mortality biased estimates of respondents' assessments of their functional health status over time, and parents' perceptions of the quality of the parent-child relationship deteriorated more rapidly among those who died by Time 7, but nonresponse did not seriously bias estimates of these measures. Using proxies, we found that functional impairment increased more rapidly when children reported about parents, especially in advanced old age. Discussion. These results support the use of full information in estimating growth curves where mortality is present but raise concerns when using child proxies to evaluate parental health or the quality of intergenerational relationships.

Objective: Evaluate the factor structure and stability of 4 dietary items (fruit, fruit juice, vegetables, and milk) from the School Physical Activity and Nutrition questionnaire--elementary school version. Methods: Secondary analysis of intervention data from third graders measured at pre-intervention, post-intervention (10 weeks), and 3-month follow-up. The researchers conducted structural equation modeling invariance analysis to test the stability of the factor structure of the 4 items. Results: Data from 1,147 students. Fit indices revealed good fit for a single factor remaining stable across time (χ²/degrees of freedom [DF] = 59.75/59, P = .45), gender (χ²/DF = 149.72/128, P = .09), and study groups (χ²/DF = 143.04/128, P = .17). Conclusions and Implications: A healthy food factor consisting of the 4 items can be used in future data analysis. This offers several advantages in analysis, including the use of latent change scores that are more powerful, more informative, and more easily interpreted than traditional approaches. [ABSTRACT FROM AUTHOR]

Objectives. The aim of this research was to test the invariance of the cognitive variables in the Health and Retirement Study/Asset Health Dynamics Among the Oldest Old studies (HRS/AHEAD) across ethnicity, gender, and time. Method. Analyses were conducted using a selected subsample of the HRS/AHEAD data set. The cognitive performance tests measuring episodic memory and mental status were used, and invariance of a two-factor structure was tested using confirmatory factor analyses and multilevel modeling for longitudinal data. Results. Results provided some support for “strict” factorial invariance of the episodic memory and mental status measures across ethnicity and gender. Further support of weak (“metric”) measurement invariance was found across time. Discussion. Results of the research further our understanding of invariance of the HRS/AHEAD cognitive ability measures. Further implications are discussed. [ABSTRACT FROM AUTHOR]

Intergenerational longitudinal studies over the lifespan provide valuable information for understanding the contexts and dynamic relations among cognition, family and health in adults and the elderly. The Hawai‘i Family Study of Cognition (HFSC), initiated in the early 1970s, included a cohort of over 6500 individuals representing over 1800 families of parents and their offspring. The HFSC gathered data on cognitive, personality, biological and other psychosocial variables, and provided novel information on the nature of cognitive abilities, especially on family issues. Some families were reassessed with short-term retesting in the 1970s. A select sample of offspring and their siblings and spouses were re-measured in the 1980s. Decades later, a 40-year follow-up of the original HFSC cohort was facilitated by the availability of contemporary tracking and tracing methods and internet-based testing. A subgroup of the original HFSC participants was re-contacted and retested on contemporary cognitive as well as socio-demographic and health measures. In this paper, we describe the original HFSC cohort and the design and methodology of the re-contact and retest studies of the HFSC, plans for expanding the re-contact and retesting, as well as directions for future research and collaborations. The Principal Investigator may be contacted for more information regarding the application, review and approval process for data access requests from qualified individuals outside the project. [ABSTRACT FROM AUTHOR]

Mixture structural equation model with regime switching (MSEM-RS) provides one possible way of representing over-time heterogeneities in dynamic processes by allowing a system to manifest qualitatively or quantitatively distinct change processes conditional on the latent “regime” the system is in at a particular time point. Unlike standard mixture structural equation models such as growth mixture models, MSEM-RS allows individuals to transition between latent classes over time. This class of models, often referred to as regime-switching models in the time series and econometric applications, can be specified as regime-switching mixture structural equation models when the number of repeated measures involved is not large. We illustrate the empirical utility of such models using one special case—a regime-switching bivariate dual change score model in which two growth processes are allowed to manifest regime-dependent coupling relations with one another. The proposed model is illustrated using a set of longitudinal reading and arithmetic performance data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 study (ECLS-K; U.S. Department of Education, National Center for Education Statistics, 2010). [ABSTRACT FROM PUBLISHER]

Project TALENT is a US national longitudinal study of about 377,000 individuals born in 1942–1946, first assessed in 1960. Students in about 1,200 schools participated in a 2-day battery covering aptitudes, abilities, interests, and individual and family characteristics (Flanagan, 1962; www.projectTALENT.org). Follow-up assessments 1, 5, and 11 years later assessed educational and occupational outcomes. The sample includes approximately 92,000 siblings from 40,000 families, including 2,500 twin pairs and 1,200 other siblings of twins. Until recently, almost no behavior genetic research has been conducted with the sample. In the original data collection information was not collected with the intent to link family members. Recently, we developed algorithms using names, addresses, birthdates, and information about family structure to link siblings and identify twins. We are testing several methods to determine zygosity, including use of yearbook photographs. In this paper, we summarize the design and measures in Project TALENT, describe the Twin and Sibling sample, and present our twin-sib-classmate model. In most twin and family designs, the ‘shared environment’ includes factors specific to the family combined with between-family differences associated with macro-level variables such as socioeconomic status. The school-based sampling design used in Project TALENT provides a unique opportunity to partition the shared environment into variation shared by siblings, specific to twins, and associated with school- and community-level factors. The availability of many measured characteristics on the family, schools, and neighborhoods enhances the ability to study the impact of specific factors on behavioral variation. [ABSTRACT FROM AUTHOR]

Latent difference score models (e.g., McArdle & Hamagami, 2001) are extended to include effects from prior changes to subsequent changes. This extension of latent difference scores allows for testing hypotheses where recent changes, as opposed to recent levels, are a primary predictor of subsequent changes. These models are applied to bivariate longitudinal data collected as part of the Baltimore Longitudinal Study of Aging on memory performance, measured by the California Verbal Learning Test, and lateral ventricle size, measured by structural MRIs. Results indicate that recent increases in the lateral ventricle size were a leading indicator of subsequent declines in memory performance from age 60 to 90. [ABSTRACT FROM PUBLISHER]

There is a great deal of interest in the analysis of Genotype × Environment interactions (G×E). There are some limitations in the typical models for the analysis of G×E, including well-known statistical problems in identifying interactions and unobserved heterogeneity of persons across groups. The impact of a treatment may depend on the level of an unobserved variable, and this variation may dampen the estimated impact of treatment. Some researchers have noted that genetic variation may sometimes account for unobserved, and hence unaccounted for, heterogeneity. The statistical power associated with the G×E design has been studied in many different ways, and most results show that the small effects expected require relatively large or nonrepresentative samples (i.e., extreme groups). In this article, we describe some alternative approaches, such as randomized designs with multiple measures, multiple groups, multiple occasions, and analyses, to identify latent (unobserved) classes of people. These approaches are illustrated with data from the Aging, Demographics, and Memory Study (part of the Health and Retirement Study) examining the relations among episodic memory (based on word recall), APOE4 genotype, and educational attainment (as a proxy for an environmental exposure). Randomized clinical trials (RCTs) and randomized field trials (RFTs) have multiple strengths in the estimation of causal influences, and we discuss how measured genotypes can be incorporated into these designs. Use of these contemporary modeling techniques often requires different kinds of data be collected and encourages the formation of parsimonious models with fewer overall parameters, allowing specific G×E hypotheses to be investigated with a reasonable statistical foundation. [ABSTRACT FROM PUBLISHER]

In this article we provide a review of recent advances in longitudinal models for multivariate change. We first claim the need for dynamic modeling approaches as a way to evaluate psychological theories. We then describe one such approach, latent change score (LCS) models, and illustrate their utility with a summary of research findings in various areas of psychological science. We then highlight the most prominent features of LCS models. We conclude the article with suggestions for future research on multivariate models of change that can enhance our understanding of psychological science. [ABSTRACT FROM AUTHOR]

Score limitation at the top of a scale is commonly termed “ceiling effect.” Ceiling effects can lead to serious artifactual parameter estimates in most data analysis. This study examines the consequences of ceiling effects in longitudinal data analysis and investigates several methods of dealing with ceiling effects through Monte Carlo simulations and empirical data analyses. Data were simulated based on a latent growth curve model with T = 5 occasions. The proportion of the ceiling data [10%-40%] was manipulated by using different thresholds, and estimated parameters were examined for R = 500 replications. The results showed that ceiling effects led to incorrect model selection and biased parameter estimation (shape of the curve and magnitude of the changes) when regular growth curve models were applied. The Tobit growth curve model, instead, performed very well in dealing with ceiling effects in longitudinal data analysis. The Tobit growth curve model was then applied in an empirical cognitive aging study and the results were discussed. [ABSTRACT FROM AUTHOR]

In this article, the authors introduce a latent difference score (LDS) approach to analyzing longitudinal data in trauma research. The LDS approach accounts for internal sources of change in an outcome variable, including the influence of prior status on subsequent levels of that variable and the tendency for individuals to experience natural change (e.g., a natural decrease in posttraumatic stress disorder [PTSD] symptoms over time). Under traditional model assumptions, the LDSs are maximally reliable and therefore less likely to introduce biases into model testing. The authors illustrate the method using a sample of children who experienced significant burns or other injuries to examine potential influences (i.e., age of child–adolescent at time of trauma and ongoing family strains) on PTSD symptom severity over time. [ABSTRACT FROM AUTHOR]

Despite the proliferation of longitudinal trauma research, careful attention to timing of assessments is often lacking. Patterns in timing of assessments, alternative time structures, and the treatment of time as an outcome are discussed and illustrated using trauma data. [ABSTRACT FROM AUTHOR]

This article explores new statistical methodologies for using longitudinal data in the early prediction of Alzheimer's disease (AD). Specifically, the authors examine some new techniques that allow the joint or "shared" estimation of longitudinal components based on both duration (survival) and quantitative changes (growth curves). These new shared growth-survival parameter models may be used to characterize the declining functions that anticipate the onset of AD. The authors apply these models to data from the Kungsholmen Project, a longitudinal study of aging in Stockholm, Sweden. They examine age-based survival-frailty models for the onset of AD, latent growth-decline curve models for changes in cognition over age, and 3 alternative forms of models for the shared relationships of survival and early cognitive decline. The accuracy and reliability of this approach is considered for a better understanding of the developmental course of AD in these data, including the potential removal of biases due to subject selection. [ABSTRACT FROM AUTHOR]

This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results. even in the ease of models of latent growth fitted to incomplete data. The general purpose of this article is to provide a demonstration that integrates programming features from different software. The most immediate coal is to help researchers implement these LGC models as a useful way to test hypotheses of growth. [ABSTRACT FROM AUTHOR]

Structural equation models are presented as alternative models for examining longitudinal data. The models include (a) a cross-lagged regression model, (b) a factor model based on latent growth curves, and (c) a dynamic model based on latent difference scores. The illustrative data are on motivation and perceived competence of students during their first semester in high school. The 3 models yielded different results and such differences were discussed in terms of the conceptualization of change underlying each model. The last model was defended as the most reasonable for these data because it captured the dynamic interrelations between the examined constructs and, at the same time, identified potential growth in the variables. [ABSTRACT FROM AUTHOR]

A great deal of prior research using structural equation models has focused on longitudinal analyses and biometric analyses. Some of this research has even considered the simultaneous analysis of both kinds of analytic problems. The key benefits of these kinds of analyses come from the estimation of novel parameters, such as the heritability of changes. This paper discusses some recent extensions of longitudinal multivariate models that can be informative within biometric designs. In the methods section we review a previous latent growth structural equation analysis of the New York Twin (NYT) longitudinal data (from McArdle et al., 1998). In the models section we recast this growth model in terms of latent difference scores, add several new dynamic components, including coupling parameters, and consider biometric components and examine model stability. In the results section we present new univariate and bivariate dynamic estimates and tests of various dynamic hypotheses for the NYT data, and we consider a few ways to interpret the age-related biometric components of these models. In the discussion we consider our limitations and present suggestions for future dynamic-genetic research. [ABSTRACT FROM AUTHOR]

Longitudinal multivariate mixed models were used to examine the correlates of change between memory and processing speed and the contribution of age and retest to such change correlates. Various age- and occasion-mixed models were fitted to 2 longitudinal data sets of adult individuals (N>1,200). For both data sets, the results indicated that the correlation between the age slopes of memory and processing speed decreased when retest effects were included in the model. If retest effects existed in the data but were not modeled, the correlation between the age slopes was positively biased. The authors suggest that although the changes in memory and processing speed may be correlated over time, age alone does not capture such a covariation. [ABSTRACT FROM AUTHOR]

This study examined dyadic interrelations between episodic memory and depressive symptom trajectories of change in old and advanced old age. The authors applied dynamic models to 10-year incomplete longitudinal data of initially 1,599 married couples from the study of Asset and Health Dynamics Among the Oldest Old (Mage = 75 years at Time 1). The authors found domain-specific lead-lag associations (time lags of 2 years) among wives and husbands as well as between spouses. For memory, better performance among husbands protected against subsequent memory decline among wives, with no evidence of a directed effect in the other direction. For depressive symptoms, wives' scores predicted subsequent depression increase and memory decline among husbands. Possible individual covariates (age, education, functional limitations) and spousal covariates (length of marriage, number of children, and whether the couple remained intact over the study period) did not account for differential lead-lag associations. The findings of antecedent-consequent relations between wives and husbands are consistent with life-span notions that individual development both influences and is influenced by contextual factors such as close social relationships. [ABSTRACT FROM AUTHOR]