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Research designs to study alcohol use and abuse have included twin, adoption and family history/high risk studies. Results have consistently implied a genetic factor in the aetiology of alcohol abuse. However, less research has been conducted in search of environmental factors. This study uses kinship structure in a large national dataset (the National Longitudinal Survey of Youth) to estimate (using DeFries–Fulker analysis) the extent of the shared genetic, non-shared genetic, shared environmental and non-shared environmental influences on alcohol use. The NLSY kinship sample contained 3890 pairs of cousins, half-siblings, full-siblings and twins between the ages of 14 and 21 in the initial year of the survey (1979). Estimates of heritability (h2) and shared environment (c2) were small to moderate for the entire dataset for both light drinking and heavy drinking behaviour, with h2 estimates slightly higher in each case. Non-shared genetic measures of self-esteem and locus of control accounted for a significant portion of the remaining variance in heavy drinking behaviour. Race and gender patterns showed c2 and h2 estimates that were also small to moderate for both light and heavy drinking behaviour. Significant non-shared effects were found for the White group for heavy drinking behaviour, and for male pairs for both heavy and light drinking behaviour. Additionally, implications and future directions are discussed.

Job analytic information is central to a variety of human resource activities (e.g., selection system development, compensation system development, organizational design). It is also well understood that many jobs are changing due to forces such as technological advances, a desire for teamwork, and various organizational interventions. In addressing such changes when using job analytic information, human resource practitioners might be faced with questions about the currency, or "up-to-dateness," of their existing job analysis. Unfortunately, there is almost no guidance for researchers or professionals regarding what methods might be used to check job analyses for currency (i.e., if there are any changes to the job that might influence the job analysis, how does one systematically assess such changes). We review the scant literature in this area and offer a protocol that we developed and implemented within a litigious environment. The protocol is systematic and focused on job change. In order to be as efficient as possible (and sensitive to organizational resource expenditures), the protocol builds on prior job analytic information. Two examples of the protocol's implementation are provided. We also present a variety of lessons learned during the development and implementation of this relatively unique currency protocol, as well as a discussion of some possible variants of the method.

Combining/weighting subscores into an aggregate score involves issues that apply to many fields in the organizational sciences (e.g., weighting predictors in selection, weighting multiple performance appraisal indicators, overall evaluation of organizations). The weights that are used in practice can be different (differential weights) or equal (unit weights). Relevant literature across multiple disciplines and multiple decades is reviewed. The literature indicates that unit weights have substantial predictive validity when compared with regression weights, but there is a lack of data on how other differential weighting strategies (e.g., weights generated by subject matter experts) compare to unit weights. In response, a primary and a meta-analytic study are provided here. The recent literature also contains some potential criticisms of unit weights in regard to personnel selection and content validation—and those statements are evaluated. The data and findings indicate that unit weights can be a highly appropriate approach for weighting under many circumstances.

Fisher (1930) presented both theoretical and empirical results concerning genetic influences on fertility. Since then, only sparse research has been done on the genetics of fertility, although more sophisticated methodogy and data now exist than were available to Fisher. This paper presents a behavioural genetic analysis of age at first intercourse, accounting for genetic, shared environmental, and selected non-shared environmental influences. The data came from the nationally representative National Longitudinal Survey of Youth (NLSY). A newly developed kinship linking procedure was used that identifies links for cousins, half-siblings, full-siblings and twins in the NLSY. The results suggest a genetic influence in the overall dataset, and also among whites and in male–male and opposite-sex pairs. Genetic influences were extremely small or non-existent for blacks and for female–female pairs. Shared environmental influences were small for most subsets of the data, but moderate for female–female pairs. Two specific non-shared environmental influences – self-esteem and locus of control – were ruled out as accounting for any meaningful variance, although other general sources of non-shared environmental influence appear potentially important. Analysis of selected samples from upper and lower tails suggested that genetic influences are important in accounting for both early and late non-virginity. These findings are consistent with work reported by Miller et al. (1999), who used molecular genetic methods. Generally, these findings support the existence of genetic influences and implicate non-shared environmental influences as being important determinants of the timing of loss of virginity among US adolescents and young adults.