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Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model and then a linear growth curve model. A third example is also provided to demonstrate how to iteratively run WinBUGS inside SAS for Monte Carlo simulation studies. The SAS codes used in this study are easily extended to accommodate many other models with only slight modification. This interface can be of practical benefit in many aspects of Bayesian methods because it allows the SAS users to benefit from the implementation of Bayesian estimation and it also allows the WinBUGS user to benefit from the data processing routines available in SAS. [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]

The main purpose of this research is to evaluate the performance of a Bayesian approach for estimating unknown change points using Monte Carlo simulations. The univariate and bivariate unknown change point mixed models were presented and the basic idea of the Bayesian approach for estimating the models was discussed. The performance of Bayesian estimation was evaluated using simulation studies of longitudinal data with different sample sizes, varying change point values, different levels of Level-1 variances, and univariate versus bivariate outcomes. The numerical results compared the performance of the Bayesian methods with the first-order Taylor expansion method and the adaptive Gaussian quadrature method implemented in SAS PROC NLMIXED. These simulation results showed that the first-order Taylor expansion method and the adaptive Gaussian quadrature method were sensitive to the initial values, making the results somewhat unreliable. In contrast, these simulation results showed that Bayesian estimation was not sensitive to the initial values and the fixed-effects and Level-1 variance parameters can be accurately estimated in all of the conditions. One concern was that the estimates of the Level-2 covariance parameters were found to be biased when the Level-1 variance was large in the bivariate model. However, and in general, the new Bayesian approach to the estimation of turning points in longitudinal data proved to be quite robust and practically useful. [ABSTRACT FROM AUTHOR]

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 ID, 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 Letter- Word ID and Comprehension and are less perceptible for Decoding; and (e) the dynamics of reading and cognition appear to be of stronger magnitude during 1st to 3rd grade, less strong during 4th to 8th grade, and weaker from 9th to 12th grade. [ABSTRACT FROM AUTHOR]

To separate age and cohort effects on decline in normal cognitive aging, we applied growth curve models to longitudinal data from the Swedish Adoption/Twin Study of Aging. Data from up to five measurement waves covering a 16-year period were available from 806 participants (age 50 to 88 at the first wave). We divided the sample into two cohorts by birth year: 1900-1925 and 1926-1948. We generated components to tap four cognitive domains: verbal and spatial ability, memory, and speed. We tested cohort differences by using two growth models: quadratic and two linear slopes. Results indicated significant cohort differences in average performance at age 67.5 for all components except speed. When we compared linear slopes during the same age range (age 62- 78), we found no cohort differences. Trajectories of change with age in these four domains were fundamentally the same in middle-old age for individuals born during the first half of the 20th century. [ABSTRACT FROM AUTHOR]

Emerging adulthood is a period in which profound role changes take place across a number of life domains including finance, romance, and residence. On the basis of dynamic systems theory, change in one domain should be related to change in another domain, because the concept of development according to this approach is a relational one. To evaluate this hypothesis dynamic systems analysis was applied to data from narrative interviews of 200 respondents covering the years between 17 and 27 to examine how change in one domain affects change in another domain. In each dyad, the fit of the model significantly deteriorated when the coupling between domains was removed providing support for the assumption of interdependency. On average, assuming greater responsibility in one domain was associated with assuming greater responsibility in the other domain. However, imbalances were also observed in which role assumption in one domain far exceeded role assumption in another domain. These imbalances can have detrimental effects and indicate the utility of a balanced approach to development. The findings underscore the importance of studying the relational unit between domains, which is critical to understanding development over time within domains. [ABSTRACT FROM AUTHOR]

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. [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 describes the steps in latent curve modeling with longitudinal life-span data. The theoretical underpinnings of life-span developmental psychology include a life-long process, multidimensional, multi-directional and multi-determined. These principles have been fixed in modern developmental research.

Recent substantive research on biometric analyses of twin and family data has used both a biometric path analysis model (PAM) and a biometric variance components model (VCM). Methodological research on these same topics have suggested benefits of using linear structural equation model algorithms (SEMA) as well as mixed effect multilevel algorithms (MEMA). To better understand the potential similarities and differences among these approaches we first highlight the algebraic equivalence between the standard biometric PAM and the corresponding biometric VCM models for family data. Second, we demonstrate how several SEMA programs based on either the PAM or VCM approach produce equivalent estimates for all phenotypic and biometric parameters. Third, we show how the biometric VCM approach (but not the PAM approach) can be easily programmed using current MEMA programs (e.g., SAS PROC MIXED). We then expand the scope of these different approaches to include measured covariates, observed variable interactions and multiple relatives within each family. MEMA software is compared to SEMA software for programming complex models, including the flexibility of data input, treatment of missing data, inclusion of covariates, and ease of accommodating varying numbers of observations (per family or individual). [ABSTRACT FROM AUTHOR]

Vocabulary knowledge may not be a unidimensional construct, and the relations between vocabulary knowledge and age may depend on the aspect of vocabulary knowledge being assessed. In this study, we examined the factor structure of a vocabulary test given to a large nationally representative sample of individuals (N ∼ 20,500). Results indicated that the vocabulary test is not unidimensional but bidimensional, with Basic Vocabulary and Advanced Vocabulary factors. An analysis of age differences indicates that basic vocabulary is highest around the age of 30, with a negative relation to age in late adulthood; in contrast, advanced vocabulary is unrelated to age between ages 35 and 70. Cohort effects may explain some of the differential age trend. [ABSTRACT FROM AUTHOR]

Goals of the present study were to investigate the relationship between age changes in speed and cognition and the genetic and environmental influences on that relationship. Latent growth models and quantitative genetic methods were applied to data from the Swedish Adoption/Twin Study of Aging. The sample included 778 individuals from both complete and incomplete twin pairs who participated in at least 1 of 4 testing occasions over a 13-year-period. Four factors were constructed from 11 cognitive measures: verbal, spatial, memory, and processing speed. Results indicate that for measures of fluid abilities, the explanatory value of processing speed is paramount for both mean cognitive performance and acceleration with age. A significant proportion of the genetic influences on cognitive ability arose from genetic factors affecting processing speed. For measures of fluid abilities, it is not the linear age changes but the accelerating age changes in cognition that share genetic variance with processing speed. [ABSTRACT FROM AUTHOR]

This is an application of new longitudinal structural equation modeling techniques to time-dependent associations of memory and brain structure measurements. There were 225 participants aged 30–80 years at baseline who were measured again after a 7-year interval on both the lateral ventricular size and Wechsler memory score. Multiple regression analyses show nonlinear associations with age but no relationships among longitudinal changes. Mixed-effects latent growth curve analyses and analyses based on latent difference scores indicate that longitudinal changes in both variables are reasonably well described by an exponential or dual change model. Bivariate dynamic structural equation modeling analyses indicate age-lagged changes operate in a coupled-over-time fashion, with the brain measure (lateral ventricular size) as a leading indicator in time of memory (Wechsler memory score) declines. [ABSTRACT FROM AUTHOR]

This study examined the dynamics of cognitive abilities and academic achievement from childhood to early adulthood. Predictions about time-dependent "coupling" relations between cognition and achievement based on R. B. Cattell's (1971, 1987) investment hypothesis were evaluated using linear dynamic models applied to longitudinal data (N = 672). Contrary to Cattell's hypothesis, a first set of findings indicated that fluid and crystallized abilities, as defined by the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R; R. W. Woodcock & M. B. Johnson, 1989-1990), were not dynamically coupled with each other over time. A second set of findings provided support for the original predictions and indicated that fluid ability was a leading indicator of changes in achievement measures (i.e., quantitative ability and general academic knowledge). The findings of this study suggest that the dynamics of cognitive abilities and academic achievement follow a more complex pattern than that specified by Cattell's investment hypothesis. [ABSTRACT FROM AUTHOR]

Using Bowen and Bok's data from 23 selective colleges, we fit multilevel logit models to test two hypotheses with implications for affirmative action and group differences in attainment of science, math, or engineering (SME) degrees. Hypothesis 1, that differences in precollege academic preparation will explain later SME graduation disparities, was fully supported with respect to the outcome gap between Whites and underrepresented minorities, partially supported for that between Asians and underrepresented minorities, and between men and women. Hypothesis 2, that college selectivity, after accounting for student characteristics, will be positively associated with SME persistence, was not supported. We demonstrate that the significance of the selectivity effect is overestimated when unilevel models are used. Admission officials are advised to carefully consider the relative academic preparedness of science-interested students, and such students choosing among colleges are advised to compare their academic qualifications to those of successful science students at each institution. [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]

Latent growth models were applied to data from the Swedish Adoption/Twin Study of Aging to discover if the rate of change in cognitive performance increased from middle age to later adulthood. The sample included 590 participants aged 44 to 88 years at first measurement. Data were gathered at 2 follow-up occasions at intervals of 3 years. Cognitive ability was assessed through 11 tests that tapped crystallized, fluid, memory, and spatial abilities and perceptual speed. Results indicated stability for measures of crystallized ability, linear age changes for many cognitive abilities, and a significant acceleration in linear decline after age 65 for measures with a large speed component. Gender differences were found only in mean level, not in rate of decline. [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]

Examines the theory of fluid and crystallized intelligence in assessing growth and decline of cognitive abilities with age. General predictions of human intellectual abilities; Correlation between age changes and cognitive variables; Definition of fluid reasoning.

This report uses different forms of latent variable structural equation models (SEMs) of group differences in self-reported Center for Epidemiological Studies Depression (CES-D) inventory results among a sample of Hawaiian and non-Hawaiian high school students. The models examined are organized progressively by increasing complexity, including mean differences models in the ANOVA tradition, mean differences combined with path analysis models, extensions to confirmatory factor analysis models, and common factor differences in multiple group models. Within each type of analysis, SEMs are defined and fitted to real CES-D data, and broad issues of group differences and invariance are considered. The substantive results indicate a high degree of invariance between Hawaiian and non-Hawaiian students within the same high schools on self-ratings of depression from the CES-D scale. These analyses illustrate theoretical and practical issues about common features and relationships among standard analysis of variance approaches and contemporary structural equation methodology. [ABSTRACT FROM AUTHOR]

This article focuses on the multilevel analyses for predicting sequence effects of financial and employment problems on the probability of arrest. The narrative in this study covers five domains of the person's life--residence, financial support, career, romantic relationships, and family formation. The interview begins with establishing the dwelling setting and behavior of the respondent within that setting with regard to concrete aspects including other occupants, routine chores, and responsibilities for household tasks. We have found that having the respondent describe and date changes over the study period one domain at a time usually works best. It also allows the interviewer to check the various dates of change in one domain against dates provided for other domains to make sure that they are consistent. Often these checks help the respondent to sort out uncertainties about the dates. In this study the researches dated changes in study variables only to the month in which they occurred. When there were multiple changes within a month the coders attributed the changes to multiple months, making sure to retain the sequence indicted by the respondent.

An abstract related to a biometric latent curve analysis of visual memory development using data from the Colorado Adoption Project (CAP) is presented.

An abstract of the article "A Note on the Computer Generation of Mean and Covariance Expectations in Latent Growth Curve Analysis," by Kevin J. Grimm and John J. McArdle is presented.

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]

Previous analyses have identified a genetic contribution to the correlation between declines with age in processing speed and higher cognitive abilities. The goal of the current analysis was to apply the biometric dual change score model to consider the possibility of temporal dynamics underlying the genetic covariance between aging trajectories for processing speed and cognitive abilities. Longitudinal twin data from the Swedish Adoption/Twin Study of Aging, including up to 5 measurement occasions covering a 16-year period, were available from 806 participants ranging in age from 50 to 88 years at the 1st measurement wave. Factors were generated to tap 4 cognitive domains: verbal ability, spatial ability, memory, and processing speed. Model-fitting indicated that genetic variance for processing speed was a leading indicator of variation in age changes for spatial and memory ability, providing additional support for processing speed theories of cognitive aging. [ABSTRACT FROM AUTHOR]

Though many cognitive abilities exhibit marked decline over the adult years, individual differences in rates of change have been observed. In the current study, biometrical latent growth models were used to examine sources of variability for ability level (intercept) and change (linear and quadratic effects) for verbal, fluid, memory, and perceptual speed abilities in the Swedish Adoption/Twin Study of Aging. Genetic influences were more important for ability level at age 65 and quadratic change than for linear slope at age 65. Expected variance components indicated decreasing genetic and increasing nonshared environmental variation over age. Exceptions included one verbal and two memory measures that showed increasing genetic and nonshared environmental variance. The present findings provide support for theories of the increasing influence of the environment with age. on. cognitive abilities. [ABSTRACT FROM AUTHOR]

In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers. [ABSTRACT FROM AUTHOR]

Objective The aim of this study was to examine the putative adverse effects of ambient fine particulate matter (PM2.5: PM with aerodynamic diameters <2.5μm) on brain volumes in older women. Methods We conducted a prospective study of 1,403 community-dwelling older women without dementia enrolled in the Women's Health Initiative Memory Study, 1996-1998. Structural brain magnetic resonance imaging scans were performed at the age of 71-89 years in 2005-2006 to obtain volumetric measures of gray matter (GM) and normal-appearing white matter (WM). Given residential histories and air monitoring data, we used a spatiotemporal model to estimate cumulative PM2.5 exposure in 1999-2006. Multiple linear regression was employed to evaluate the associations between PM2.5 and brain volumes, adjusting for intracranial volumes and potential confounders. Results Older women with greater PM2.5 exposures had significantly smaller WM, but not GM, volumes, independent of geographical region, demographics, socioeconomic status, lifestyles, and clinical characteristics, including cardiovascular risk factors. For each interquartile increment (3.49μg/m3) of cumulative PM2.5 exposure, the average WM volume (WMV; 95% confidence interval) was 6.23cm3 (3.72-8.74) smaller in the total brain and 4.47cm3 (2.27-6.67) lower in the association areas, equivalent to 1 to 2 years of brain aging. The adverse PM2.5 effects on smaller WMVs were present in frontal and temporal lobes and corpus callosum (all p values <0.01). Hippocampal volumes did not differ by PM2.5 exposure. Interpretation PM2.5 exposure may contribute to WM loss in older women. Future studies are needed to determine whether exposures result in myelination disturbance, disruption of axonal integrity, damages to oligodendrocytes, or other WM neuropathologies. Ann Neurol 2015;78:466-476 [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]

This study tested whether history of depression is associated with an increased likelihood of dementia, and whether a first depressive episode earlier in life is associated with increased dementia risk, or whether only depressive episodes close in time to dementia Onset are related to dementia. Depression information came from national hospital discharge registries, medical history, and medical records. Dementia was diagnosed clinically. In case-control results, individuals with recent registry-identified depression were 3.9 times more likely than those with no registry-identified depression history to have dementia, whereas registry-identified depression earlier in life was not associated with dementia risk. Each 1-year increase in time between depression onset and dementia onset or equivalent age decreased the likelihood of dementia by 8.4%. In co-twin control analyses, twins with prior depression were 3.0 times more likely to have dementia than their nondepressed twin partners, with a similar age of depression gradient. These findings suggest that after partially controlling for genetic influences, late-life depression for many individuals may be a prodrome rather than a risk factor for dementia. [ABSTRACT FROM AUTHOR]

Objectives: The prevalence rates of disorders among a community-based sample of Hawaiian youths were determined and compared to previously published epidemiological studies.Method: Using a two-phase design, 7,317 adolescents were surveyed (60% participation rate), from which 619 were selected in a modified random sample during the 1992-1993 to 1995-1996 school years: 590 selected randomly and 29 at risk (i.e., Center for Epidemiologic Studies-Depression score of >or=35 and suicidal risk) from grades 9-12. The Diagnostic Interview Schedule for Children-Version 2.3, was used to determine DSM-III-R diagnoses. Prevalence rates, weighted for ethnicity, Center for Epidemiologic Studies-Depression scores, and suicide attempts, were calculated for any diagnosis and various disorders. Meta-analyses compared the Hawai'i sample to four community-based studies (randomly selected youths from community populations) and two high-risk studies (homeless, low-income, or high unemployment communities).Results: Hawaiian females had the highest rate for any diagnosis (37.7%; 95% confidence interval [CI] 28.4%-48.0%) and non-Hawaiian males had the lowest rate (19.6%; 95% CI 14.8%-25.5%). Hawaiian males (26.8%; 95% CI 18.2%-37.5%) and non-Hawaiian females (27.9%; 95% CI 22.2%-34.4%) had intermediate and comparable rates. Overall, Hawaiians had significantly higher rates (32.7%; 95% CI 26.1%-40.1%) than non-Hawaiians (23.7%; 95% CI 19.9%-28.0%) when controlling for gender, and girls had significantly higher rates (30.8%; 95% CI 25.8%-36.3%) than boys (21.1%; 95% CI 16.8%-26.1%) when controlling for ethnicity. These findings were primarily the result of the significant differences in rates regarding anxiety disorders. Meta-analyses showed the Hawaiian youth rate for any diagnosis was comparable to high-risk studies and nearly three times higher than the community studies.Conclusions: Hawaiian youths, especially females, are at high risk. Research on the sociocultural factors that underpin both the genesis of and protection from psychopathology is imperative for Hawaiian and non-Hawaiian mixed-ethnicity youths. [ABSTRACT FROM AUTHOR]

Classification and Regression Trees (CART), and their successors-bagging and random forests, are statistical learning tools that are receiving increasing attention. However, due to characteristics of censored data collection, standard CART algorithms are not immediately transferable to the context of survival analysis. Questions about the occurrence and timing of events arise throughout psychological and behavioral sciences, especially in longitudinal studies. The prediction power and other key features of tree-based methods are promising in studies where an event occurrence is the outcome of interest. This article reviews existing tree algorithms designed specifically for censored responses as well as recently developed survival ensemble methods, and introduces available computer software. Through simulations and a practical example, merits and limitations of these methods are discussed. Suggestions are provided for practical use.

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 (χ(2)/degrees of freedom [DF] = 59.75/59, P = .45), gender (χ(2)/DF = 149.72/128, P = .09), and study groups (χ(2)/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., (Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.)

Objective: To clarify the underlying relationship between nutrition self-efficacy and outcome expectations because the direction of the relationship (unidirectional vs bidirectional) is debated in the literature., Methods: Secondary data analysis of a 10-week, 10-lesson school-based nutrition education intervention among 3rd grade students (N  =  952). Nutrition self-efficacy (7 items) and nutrition outcome expectations (9 items) were measured through student self-report at intervention pre- (time 1) and post- (time 2) assessments. A series of two time point, multi-group cross-lagged bivariate change score models were used to determine the direction of the relationship., Results: A cross lag from nutrition self-efficacy at time 1 predicting changes in nutrition outcome expectations at time 2 significantly improved the fit of the model (Model 3), whereas a cross lag from nutrition outcome expectations at time 1 to changes in nutrition self-efficacy at time 2 only slightly improved the fit of the model (Model 2). Furthermore, adding both cross lags (Model 4) did not improve model fit compared to the model with only the self-efficacy cross lag (Model 3). Lastly, the nutrition outcome expectations cross lag did not significantly predict changes in nutrition self-efficacy in any of the models., Conclusions: Data suggest that there is a unidirectional relationship between nutrition self-efficacy and outcome expectations, in which self-efficacy predicts outcome expectations. Therefore, theory-based nutrition interventions may consider focusing more resources on changing self-efficacy because it may also lead to changes in outcome expectations as well., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

This is a short story about John C. Loehlin who is now at the University of Texas at Austin, dealing with his original simulation models and developments, which led to his current latent variable models. This talk was initially presented at a special meeting for John before the BGA in Rhode Island, and I was very pleased to contribute. It probably goes without saying, but John helped create this important society, has been a key contributor to this journal for several decades, and he deserves a lot for this leadership.

There is a relatively consistent negative relationship between adolescent depressive symptoms and educational achievement (e.g., grade point average [GPA]). However, the causal direction for this association is less certain due to the lack of longitudinal data with both indicators measured across at least 2 time periods and due to the lack of application of more sophisticated contemporary statistical techniques. We present multivariate results from a large longitudinal cohort-sequential study of high school students (N = 7,317) with measures of self-reported depressive symptoms and self-reported GPAs across multiple time points (following McArdle, 2009, and McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001) using an ethnically diverse sample from Hawai'i. Contemporary statistical techniques included bivariate dynamic structural equation modeling (DSEM), multigroup ethnic and gender DSEMs, ordinal scale measurement of key outcomes, and imputation for incomplete longitudinal data. The findings suggest that depressive symptoms affect subsequent academic achievement and not the other way around, especially for Native Hawaiians compared with female non-Hawaiians. We further discuss the scientific, applied, and methodological-statistical implications of the results, including the need for further theorizing and research on mediating variables. We also discuss the need for increased prevention, early intervention, screening, identification, and treatment of depressive symptoms and disorders. Finally, we argue for utilization of more contemporary methodological-statistical techniques, especially when violating parametric test assumptions., (PsycINFO Database Record (c) 2012 APA, all rights reserved.)

Objective: Do lifestyle activities buffer normal aging-related declines in cognitive performance? The emerging literature will benefit from theoretically broader measurement of both lifestyle activities and cognitive performance, and longer-term longitudinal designs complemented with dynamic statistical analyses. We examine the temporal ordering of changes in lifestyle activities and changes in cognitive neuropsychological performance in older adults., Method: We assembled data (n = 952) across a 12-year (5-wave) period from the Victoria Longitudinal Study. Latent change score models were applied to examine whether (and in which temporal order) changes in physical, social, or cognitive lifestyle activities were related to changes in three domains of cognitive performance., Results: Two main results reflect the dynamic coupling among changes in lifestyle activities and cognition. First, reductions in cognitive lifestyle activities were associated with subsequent declines in measures of verbal speed, episodic memory, and semantic memory. Second, poorer cognitive functioning was related to subsequent decrements in lifestyle engagement, especially in social activities., Conclusions: The results support the dual contention that (a) lifestyle engagement may buffer some of the cognitive changes observed in late life, and (b) persons who are exhibiting poorer cognitive performance may also relinquish some lifestyle activities.

In recent years the use of the Latent Curve Model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and to the availability of specialized literature. Extensions of the LCM, like the the Latent Change Score Model (LCSM), have also increased in popularity. At the same time, the R statistical language and environment, which is open source and runs on several operating systems, is becoming a leading software for applied statistics. We show how to estimate both the LCM and LCSM with the sem, lavaan, and OpenMx packages of the R software. We also illustrate how to read in, summarize, and plot data prior to analyses. Examples are provided on data previously illustrated by Ferrer, Hamagami, & McArdle, 2004. The data and all scripts used here are available on the first author's website.

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.

Children of persons with Alzheimer disease (AD), as a group, face an increased risk of developing AD. Many of them, throughout their adult lives, seek input on how to reduce their chances of one day suffering their parent's fate. We examine the state of knowledge with respect to risk and protective factors for AD and recommend a research agenda with special emphasis on AD offspring.

Objective: 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., Result: 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.