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Previous research has shown cognitive abilities to have different biometric patterns of age-changes. We examined the variation in episodic memory (word recall task) for over 6,000 twin pairs who were initially aged 59–75, and were subsequently re-assessed up to three more times over 12 years. In cross-sectional analyses, variation in the number of words recalled independent of age was explained largely by non-shared influences (65–72%), with clear additive genetic influences (12–32%), and marginal shared family influences (1–18%). The longitudinal phenotypic analysis of the word recall task showed systematic linear declines over age, but several nonlinear models with more dramatic changes at later ages, improved the overall fit. A two-part spline model for the longitudinal twin data with an optimal turning point at age 74 led to: (a) a separation of non-shared environmental influences and transient measurement error (~50%); (b) strong additive genetic components of this latent curve (~44% at age 60) with increases (over 50%) up to age 74, but with no additional genetic variation after age 74; (c) the smaller influences of shared family environment (~15% at age 74) were constant over all ages; (d) non-shared effects play an important role over most of the life-span but diminish after age 74. [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]

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]

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]

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]

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]

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.