Models of group process propose that stressful social environments develop when there is a lack of consensus among group members about issues of relevance to the group. Based on these models, we expected that levels of consensus would be positively related to the average levels of psychological well‐being in naturally occurring work groups. An examination of data from 3,546 respondents within 73 work groups revealed that levels of consensus about leadership and peer relations were positively related to the average psychological well‐being of the group members, even after controlling for absolute level effects and covariates. In contrast, levels of consensus were not related to the average psychological well‐being of group members when identical analyses were conducted using pseudogroups.

Many models of job stress are implicitly based on the assumption that there is considerable variability in how individuals perceive and respond to their environments. In this paper, we introduce a nomothetic perspective of job stress. The nomothetic perspective assumes that despite individual differences there will be consistencies in how groups of individuals perceive and respond to similar work environments. To contrast the individual and nomothetic perspectives, we analyzed data from 7,382 respondents from 99 groups. In the analyses, we examined individual‐and group‐level relationships using both real groups and randomly formed groups. The results revealed that respondents from the same work group agreed about perceptions of the work climate. The results also revealed that both individual and nomothetic perspectives were useful in describing the relationship between cohesion and psychological well‐being. The relationship between work hours and psychological well‐being, however, was best modeled from a nomothetic perspective.

The eta-squared (TI 2) from a one-way random effects ANOVA is an index commonly used to estimate group-level properties of data in multilevel research. Under some circumstances, however, TI 2 values provide biased estimates of the group-level properties. Biased estimates occur because the magnitude of the 1.12 in a one-way rando m effects ANOVA is partially a function of group size. In this paper, the relationship between group size and ~2 is described, and a simulation verifying the relationship between group size and 112 is conducted. The simulation demonstrates the conditions under which T] 2 does and does not provide a biased estimate of group-level properties. The paper concludes by (a) discussing corrections for 1"12, and (b) providing guidelines for calculating estimates of group-level properties in samples having unequal group sizes. The analysis of group-level data has received considerable attention in orgaA key concern in group-level analyses is the need to determine how much (if any) of a variable's total variance is due to the group-level properties of the data. This issue is important because the amount of the total variance that is due to the group-level properties of the data has theoretical implications about underlying group processes. Strong Underlying group processes are assumed to be reflected in data that have strong group-level properties, while weak underlying group processes are assumed to correspond to weak group-level properties (Dansereau, et al., 1984;James, 1982).One approach commonly used to estimate the magnitude of the group-level properties of data is based on a one-way random effects ANOVA model. In the ANOVA model, the variable of interest is predicted from a group membership factor. For example, one might conduct an analysis in which individual percep-

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