Suchergebnisse

Twin studies used to be almost the only way to compare the influence of genes against the environment on personality and behavior. Recent advances in genetics, however, suggest that opposing "nature" to "nurture" is misleading. Genes combine with the environment to produce complex human traits.

We review some of the work of the past ten years that applied the multilevel logit model. We attempt to provide a brief description of the hypothesis tested, the hierarchical data structure analyzed, and the multilevel data source for each piece of work we have reviewed. We have also reviewed the technical literature and worked out two examples on multilevel models for binary outcomes. The review and examples serve two purposes: First, they are designed to assist in all aspects of working with multilevel models for binary outcomes, including model conceptualization, model description for a research report, understanding of the structure of required multilevel data, estimation of the model via a generally available statistical package, and interpretation of the results. Second, our examples contribute to the evaluation of the approximation procedures for binary multilevel models that have been implemented for general public use.

Using data collected from June 1, 1995, to August 30, 1996, the researchers compared the disciplinary conviction rates of North Carolina prison inmates admitted during the study period before the truth-in-sentencing law and inmates admitted during that period under the truth-in-sentencing law. Based on deterrence theory, the researchers hypothesized that pre-truth-in-sentencing inmates, who could lose time off for good behavior and parole as results of a disciplinary conviction, would have higher disciplinary conviction rates than truth-in-sentencing inmates. Truth-in-sentencing inmates, with shorter sentences, could lose neither time off for good behavior nor parole as a result of disciplinary conviction. Cox regression and negative binomial regression procedures using control variables identified in the research literature generally supported the research hypotheses.

AbstractSelf-reported race is generally considered the basis for racial classification in social surveys, including the U.S. census. Drawing on recent advances in human molecular genetics and social science perspectives of socially constructed race, our study takes into account both genetic bio-ancestry and social context in understanding racial classification. This article accomplishes two objectives. First, our research establishes geographic genetic bio-ancestry as a component of racial classification. Second, it shows how social forces trump biology in racial classification and/or how social context interacts with bio-ancestry in shaping racial classification. The findings were replicated in two racially and ethnically diverse data sets: the College Roommate Study (N = 2,065) and the National Longitudinal Study of Adolescent Health (N = 2,281).