A Resilience Model of Adult Native Hawaiian Health Utilizing a Newly Multi-Dimensional Scale.

Resilience has conventionally focused on an individual's ability to overcome adversity. Recent research expands on this definition, making resilience a multi-dimensional construct. Native Hawaiians experience health disparities compared to the general population of Hawai'i. Despite the pressing need to address health disparities, minimal research examines resilience factors that serve as buffers for adverse experiences of Native Hawaiians. The purpose of this study was to estimate psychometric properties of scales that measured resilience-based factors through multiple levels using higher-order confirmatory factor analyses (CFA) and ascertain if this construct of resilience mediated or moderated adversity experienced by a sample of Native Hawaiians. Participants included 125 adults who participated in the Hawaiian Homestead Health Survey. Based on higher-order CFA, resilience comprised internal assets measured by hope, satisfaction with life, and environmental mastery, and external resources measured by social support and Native Hawaiian cultural identity. Results of the structural equation models were consistent with literature focusing on resiliency and health. Findings emphasized the importance of enhancing resilience by considering strengths and resources on the individual, interpersonal, and community levels. Findings also demonstrated the need to address adversity factors directly, with a specific need of addressing socio-economic status factors. According to structural equation models, resilience slightly mediated and moderated the effect of adversity related to socio-economic status. These findings have implications for future research exploring resilience as a mediator or moderator of adversity among Native Hawaiians and emphasize a multi-faceted construct of resilience to promote better health outcomes.

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion.

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.

Psychometric Testing of the Brief Coping Orientation to Problems Experienced Inventory Among Diverse Women From a Rural Community in Hawai'i.

The Brief COPE (Coping Orientation to Problems Experienced) Inventory is a standardized and widely used scale that enables researchers to measure coping responses of persons in relation to stressors. The psychometric properties of this scale, however, have not been assessed for communities in Hawai'i. This study investigated the psychometric properties of the Brief COPE for diverse women from a rural community on the island of O'ahu in Hawai'i. This study was conducted in a federally qualified health center (FQHC) with 161 women who were of a childbearing age between the ages of 18 to 38 years. Contrary to previous research, the factor structure of the final model suggested six factors: Behavioral Disengagement, Denial, Venting, and Self-Blame; Action Coping, Positive Reframing, Acceptance, and Planning; Humor; Substance Use; Social Support; and Religion. The final model demonstrated good model fit with an RMSEA of .07 and CFI of .95. The reduced factor structure may be a more robust measure of coping strategies, which may allow for better resources and interventions that adequately address the way women of childbearing ages from rural communities respond to stressful situations. Exploring coping mechanisms of diverse women will better our understanding of the way people respond to stress and develop strengths and mechanisms that mediate stressors including those that are linked to social and cultural determinants of health. Findings from this study may also inform future research and policy that aim to foster coping, and thus, resiliency of diverse women, particularly in rural settings.

Changes in PTSD symptomatology and mental health during pregnancy and postpartum.

Changes in mental health symptoms throughout pregnancy and postpartum may impact a woman's experience and adjustment during an important time. However, few studies have investigated these changes throughout the perinatal period, particularly changes in posttraumatic stress disorder (PTSD) symptoms. The purpose of this study was to examine longitudinal changes in PTSD, depression, and anxiety symptomatology during pregnancy and postpartum. Pregnant women of ethnically diverse backgrounds receiving services for prenatal care at an outpatient obstetric-gynecology clinic or private physicians' office were assessed by interview on symptoms of PTSD, depression, anxiety, and general stress up to four times, including their first, second, and third trimester, and postpartum visits. Overall, during pregnancy there was a declining trend of PTSD symptoms. For anxiety, there was no overall significant change over time; however, anxiety symptoms were individually variable in the rate of change. For both depression and general stress symptoms, there was a declining trend, which was also variable in the individual rate of change among women during their pregnancy. Visual and post hoc analyses also suggest a possible peak in PTSD symptoms in the weeks prior to delivery. While most mental health symptoms may generally decrease during pregnancy, given the individual variability among women in the rate of change in symptoms, screening and monitoring of symptom fluctuations throughout the course of pregnancy may be needed. Further studies are needed to examine potential spiking of symptoms in the perinatal period.

Social ecological determinants of youth violence among ethnically diverse Asian and Pacific Islander students.

This study assesses the relative fit of risk/protective and social ecological models of youth violence among predominantly Asian and Pacific Islander students. Data from a 2007 survey of two multi-ethnic high schools in Hawai'i were used. The survey assessed interpersonal youth violence, suicidality and risk and protective factors. Two models of youth violence (risk/protective and social ecological) were tested using structural equation modeling. We found good fits for the risk/protective model (χ(2) = 369.42, df = 77, P < .0001; CFI = .580; RMSEA = .066) and the ecological model (χ(2) = 1763.65, df = 292, P < .0001; CFI = .636; RMSEA = .076). The risk/protective model showed the importance of coping skills. However, the ecological model allowed examination of the interconnectivity among factors. Peer exposure to violence had no direct influence on individuals and peer influence was fully mediated by school climate. Furthermore, family factors directly contributed to peer exposure, community, and individual risk/protection. These findings have significant implications for intervention and prevention efforts and for the promotion of positive, competent, and healthy youth development. While few family and school-based programs have been developed and evaluated for adolescents, they have the greatest potential for success.

Nonlinear growth curves in developmental research.

Developmentalists are often interested in understanding change processes, and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood.

Genetic variance in processing speed drives variation in aging of spatial and memory abilities.

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.

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

The authors use multiple-sample longitudinal data from different test batteries to examine propositions about changes in constructs over the life span. The data come from 3 classic studies on intellectual abilities in which, in combination, 441 persons were repeatedly measured as many as 16 times over 70 years. They measured cognitive constructs of vocabulary and memory using 8 age-appropriate intelligence test batteries and explore possible linkage of these scales using item response theory (IRT). They simultaneously estimated the parameters of both IRT and latent curve models based on a joint model likelihood approach (i.e., NLMIXED and WINBUGS). They included group differences in the model to examine potential interindividual differences in levels and change. The resulting longitudinal invariant Rasch test analyses lead to a few new methodological suggestions for dealing with repeated constructs based on changing measurements in developmental studies.

Change Pathways in Indigenous and Nonindigenous Youth Suicide.

Multivariate dynamic relationships among suicide attempts, anxiety and/or depressive symptoms, hope, and help-seeking were examined across time in Native Hawaiian and non-Hawaiian adolescents, using data from a 5-year longitudinal cohort study (N = 7,317). The rate of suicide attempts decreased over time, but this reduction was significantly less among Native Hawaiian youth than their non-Hawaiian peers. There were also significant differences between groups in hope and help-seeking, with Native Hawaiian youth increasing help-seeking and decreasing hope to a greater degree. Youth-centered, cultural approaches to suicide prevention are essential in enhancing well-being in indigenous communities.

Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span.

Latent growth curve techniques and longitudinal data are used to examine predictions from the theory of fluid and crystallized intelligence (Gf-Gc theory; J. L. Horn & R. B. Cattell, 1966, 1967). The data examined are from a sample (N approximately 1,200) measured on the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R). The longitudinal structural equation models used are based on latent growth models of age using two-occasion "accelerated" data (e.g., J. J. McArdle & R. Q. Bell, 2000; J. J. McArdle & R. W. Woodcock, 1997). Nonlinear mixed-effects growth models based on a dual exponential rate yield a reasonable fit to all life span cognitive data. These results suggest that most broad cognitive functions fit a generalized curve that rises and falls. Novel multilevel models directly comparing growth curves show that broad fluid reasoning (Gf) and acculturated crystallized knowledge (Gc) have different growth patterns. In all comparisons, any model of cognitive age changes with only a single g factor yields an overly simplistic view of growth and change over age.

Longitudinal Dynamic Analyses of Depression and Academic Achievement in the Hawaiian High Schools Health Survey using Contemporary Latent Variable Change Models.

The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this paper we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai'i (following McArdle, 2009; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply three recently available forms of longitudinal data analysis: (1) Dealing with Incomplete Data -- We apply these methods to cohort-sequential data with relatively large blocks of data which are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (2) Ordinal Measurement Models (Muthén & Muthén, 2006) -- We use a variety of statistical and psychometric measurement models, including ordinal measurement models to help clarify the strongest patterns of influence. (3) Dynamic Structural Equation Models (DSEMs; McArdle, 2009). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study.

Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories.

We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of rates of change and acceleration in latent growth curves-information available indirectly through traditional growth curve models when change patterns are nonlinear with respect to time. To illustrate this approach, exponential growth models in the three frameworks are fit to longitudinal response time data from the Math Skills Development Project ( Mazzocco & Meyers, 2002 , 2003 ). We highlight the additional information gained from fitting growth curves in these frameworks as well as limitations and extensions of these approaches.

Potential causal relationship between depressive symptoms and academic achievement in the Hawaiian high schools health survey using contemporary longitudinal latent variable change models.

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.

The Dynamic Interdependence of Developmental Domains Across Emerging Adulthood.

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.

Structural equation models for evaluating dynamic concepts within longitudinal twin analyses.

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.