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Many studies have dealt with the relative impact of parents and peers on adolescent substance use. However, only a few studies have explored the role of adolescents themselves in social relationships. Homogeneity in behavioral patterns within friendships can also be generated by selective association. Acknowledgment of selection processes might shed new light on the debate on the influences of parents and friends. This paper examines the impact of parental and best friends' smoking and drinking on adolescents' use, using data (N = 1,063) from a 3-wave, 5-year longitudinal study.The results show that friends' smoking did not affect adolescent smoking over time. Only in early adolescence did friends' drinking exert an influence on subjects' alcohol use. On the other hand, parental use had a small but significant impact on their offspring. Furthermore, support was found for processes of selective association.

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This article describes a test of Karasek's Job Demand-Control (JD-C) Model using both group and individual level assessments of job characteristics. By group assessments, we mean aggregated individual data. A random sample from general hospitals and nursing homes included 16 institutions, 64 units, and 1489 health care workers (82% response). Because of the hierarchically nested data structure (i.e., institutions, units, and individuals) the research questions and hypothesis were tested in multilevel regression analyses (VARCL). The results revealed both group level and individual level effects with regard to psychological outcomes, and stressed the usefulness of multilevel techniques. Karasek's JID-C Model was partly confirmed by finding two interaction effects at group level and at individual level with regard to job satisfaction and work motivation, respectively. The discussion focuses on theoretical, methodological, and practical implications of multilevel modeling with respect to the JD-C Model.

When designing experiments in multilevel populations the following questions arise: what is the optimal level of randomization, and what is the optimal allocation of units? In this paper these questions will be dealt with for populations with two levels of nesting and binary outcomes. The multilevel logistic model, which is used to describe the relationship between treatment condition and outcome, is linearized. The variance of the regression coef®cient associated with treatment condition in the linearized model is used to ®nd the optimal level of randomization and the optimal allocation of units. An analytical expression for this variance can only be obtained for the ®rst-order marginal quasi-likelihood linearization method, which is known to be biased. A simulation study shows that penalized quasi-likelihood linearization and numerical integration of the likelihood lead to conclusions about the optimal design that are similar to those from the analytical derivations for ®rst-order marginal quasi-likelihood.

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