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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]

We investigated the relation between parental education and dementia in the United States. Participants in the Aging, Demographics, and Memory Study were included, with information regarding parental education obtained from the Health and Retirement Study. The odds of dementia in elderly Americans whose mothers had less then 8 years of schooling were twice (95% CI, 1.1-3.8) that of individuals with higher maternal education, when adjusted for paternal education. Of elderly Americans with less educated mothers, 45.4% (95% CI, 37.4-53.4%) were diagnosed with dementia or ''cognitive impairment, no dementia'' compared to 31.2% (95% CI, 25.0-37.4%) of elderly Americans whose mothers had at least an 8th grade education. The population attributable risk of dementia due to low maternal education was 18.8% (95% CI, 9.4-28.2%). The education of girls in a population may be protective of dementia in the next generation. [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 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]

The key idea in any factor analysis is that a large number of observed behaviors are a direct result of a smaller number of unobserved, or latent, sources. The purpose of this article is to give a brief overview of some new directions in the techniques and uses of factor analysis. Factor analysis has been used by psychologists for most of this century. Most researchers have used techniques now called exploratory or traditional factor analysis. However, during the past decade, there has been an increasing use of what is now termed confirmatory or structural factor analysis.

Background: Educational policy makers and test critics often assert that standardized test scores are strongly influenced by factors beyond individual differences in academic achievement such as family income and wealth. Unfortunately, few empirical studies consider the simultaneous and related influences of family income, parental education, and high school achievement on college admissions test scores. Focus Of Study: This research was animated by the nagging question of the association of family income with SAT performance. For example, is the relationship between family income and SAT performance non-linear? Does the relationship differ markedly by race? More importantly, how strong are the effects of poverty on SAT performance? Research Design: This study is a secondary analysis of a large national sample of Black and White college-bound high school students who took the SAT in 2003 (N = 781,437). Data Collection And Analysis: Employing data from the College Board's Student Descriptive Questionnaire, this study used structural equation modeling (SEM) to estimate the effects of family income on SAT scores for Black and White examinees accounting for the simultaneous effects of parental education and high school achievement. Findings/Results: Results suggest the effects of family income on SAT scores, though relatively modest in contrasts to high school achievement, are substantial, non-linear, and nearly twice as large for Black students. Moreover, the unstandardized direct effect of high school achievement on SAT performance is not enough to address the substantial effects of poverty for Black students. Conclusions/Recommendations: The findings are discussed with respect to social inequality and educational opportunity in college admissions. [ABSTRACT FROM AUTHOR]