The structure, development, and correlates of individual differences in human personality

textHuman personality possesses a complex psychometric structure that changes with maturation and predicts a number of important life outcomes. The current dissertation presents four studies that track the structure, development, and correlates of personality, primarily through a behavioral genetic lens. Study 1 finds that the genetic and environmental structure of personality domains residing over more narrow facets is more complex than can be accounted for by simple, broad domains. Study 2 presents meta-analytic evidence for the differential stability of personality traits across the lifespan, as well as genetic and environmental contributions to stability. Personality traits become more stable with age, and this trend is primarily driven by increasing environmental sources of stability. Study 3 details a transactional model of parental educational expectations, child academic behaviors, and child academic achievement across early development. Finally, Study 4 presents associations between state-level aggregates of personality and the level, timing, and context of fertility across the United States. Together, these studies indicate the importance of understanding personality for individual-level and population-level processes, as well as, the complexities of this goal due to the dynamic nature of gene-environment dependencies that undergird personality development.Psycholog

Structure and correlates of cognitive aging in a narrow age cohort

Aging-related changes occur for multiple domains of cognitive functioning. An accumulating body of research indicates that, rather than representing statistically independent phenomena, aging-related cognitive changes are moderately to strongly correlated across domains. However, previous studies have typically been conducted in age-heterogeneous samples over longitudinal time lags of 6 or more years, and have failed to consider whether results are robust to a comprehensive set of controls. Capitalizing on 3-year longitudinal data from the Lothian Birth Cohort of 1936, we took a longitudinal narrow age cohort approach to examine cross-domain cognitive change interrelations from ages 70 to 73 years. We fit multivariate latent difference score models to factors representing visuospatial ability, processing speed, memory, and crystallized ability. Changes were moderately interrelated, with a general factor of change accounting for 47% of the variance in changes across domains. Change interrelations persisted at close to full strength after controlling for a comprehensive set of demographic, physical, and medical factors including educational attainment, childhood intelligence, physical function, APOE genotype, smoking status, diagnosis of hypertension, diagnosis of cardiovascular disease, and diagnosis of diabetes. Thus, the positive manifold of aging-related cognitive changes is highly robust in that it can be detected in a narrow age cohort followed over a relatively brief longitudinal period, and persists even after controlling for many potential confounders

Behavior Genetic Frameworks of Causal Reasoning for Personality Psychology

Identifying causal relations from correlational data is a fundamental challenge in personality psychology. In most cases, random assignment is not feasible, leaving observational studies as the primary methodological tool. Here, we document several techniques from behavior genetics that attempt to demonstrate causality. Although no one method is conclusive at ruling out all possible confounds, combining techniques can triangulate on causal relations. Behavior genetic tools leverage information gained by sampling pairs of individuals with assumed genetic and environmental relatedness or by measuring genetic variants in unrelated individuals. These designs can find evidence consistent with causality, while simultaneously providing strong controls against common confounds. We conclude by discussing several potential problems that may limit the utility of these techniques when applied to personality. Ultimately, genetically informative designs can aid in drawing causal conclusions from correlational studies

Behavior Genetic Frameworks of Causal Reasoning for Personality Psychology

Identifying causal relations from correlational data is a fundamental challenge in personality psychology. In most cases, random assignment is not feasible, leaving observational studies as the primary methodological tool. Here, we document several techniques from behavior genetics that attempt to demonstrate causality. Although no one method is conclusive at ruling out all possible confounds, combining techniques can triangulate on causal relations. Behavior genetic tools leverage information gained by sampling pairs of individuals with assumed genetic and environmental relatedness or by measuring genetic variants in unrelated individuals. These designs can find evidence consistent with causality, while simultaneously providing strong controls against common confounds. We conclude by discussing several potential problems that may limit the utility of these techniques when applied to personality. Ultimately, genetically informative designs can aid in drawing causal conclusions from correlational studies

What explains the heritability of completed fertility? Evidence from two large twin studies.

In modern societies, individual differences in completed fertility are linked with genotypic differences between individuals. Explaining the heritability of completed fertility has been inconclusive, with alternative explanations centering on family formation timing, pursuit of education, or other psychological traits. We use the twin subsample from the Midlife Development in the United States study and the TwinsUK study to examine these issues. In total, 2606 adult twin pairs reported on their completed fertility, age at first birth and marriage, level of education, Big Five personality traits, and cognitive ability. Quantitative genetic Cholesky models were used to partition the variance in completed fertility into genetic and environmental variance that is shared with other phenotypes and residual variance. Genetic influences on completed fertility are strongly related to family formation timing and less strongly, but significantly, with psychological traits. Multivariate models indicate that family formation, demographic, and psychological phenotypes leave no residual genetic variance in completed fertility in either dataset. Results are largely consistent across U.S. and U.K. sociocultural contexts

Investigation of the Jahn-Teller Transition in TiF3 using Density Functional Theory

We use first principles density functional theory to calculate electronic and magnetic properties of TiF3 using the full potential linearized augmented plane wave method. The LDA approximation predicts a fully saturated ferromagnetic metal and finds degenerate energy minima for high and low symmetry structures. The experimentally observed Jahn-Teller phase transition at Tc=370K can not be driven by the electron-phonon interaction alone, which is usually described accurately by LDA. Electron correlations beyond LDA are essential to lift the degeneracy of the singly occupied Ti t2g orbital. Although the on-site Coulomb correlations are important, the direction of the t2g-level splitting is determined by the dipole-dipole interactions. The LDA+U functional predicts an aniferromagnetic insulator with an orbitally ordered ground state. The input parameters U=8.1 eV and J=0.9 eV for the Ti 3d orbital were found by varying the total charge on the TiF$_6^{2-}$ ion using the molecular NRLMOL code. We estimate the Heisenberg exchange constant for spin-1/2 on a cubic lattice to be approximately 24 K. The symmetry lowering energy in LDA+U is about 900 K per TiF3 formula unit.Comment: 7 pages, 9 figures, to appear in Phys. Rev.

Nonparametric estimates of gene x environment interaction using local structural equation modeling

Gene × Environment (G×E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G×E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G×E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status (SES) interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G×E interaction with sufficient power to detect statistically significant G×E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R