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The extent to which perceived social support reflects characteristics of the environment, the personality of the perceiver, and their interaction is unknown. This article shows how the methods of generalizability theory can be used to address these questions. When participants rate the same targets on the targets' supportiveness, generalizability theory provides methods for determining the extent to which support judgments are determined by effects due to targets (supporters), perceivers, and their interaction. In 3 studies, each source of variance made significant contributions to support judgments, with the Perceivers x Supporters interaction, characteristics of supporters, and biases of perceivers making the largest contributions, respectively. The implications for theoretical models of perceived support are discussed.

The appropriateness of a traditional correla tional measure of halo error (the difference between dimensional rating intercorrelations and dimensional true score intercorrelations) is reexamined in the context of three causal models of halo error. Mathematical derivations indicate that the traditional correlational measure typically will underestimate halo error in ratings and can suggest no halo error or even "negative" halo error when positive halo error actually occurs. A corrected correlational measure is derived that avoids these problems, and the traditional and corrected measures are compared empirically. Results suggest that use of the traditional correlational measure of halo error be dis continued.

The traditional assumption has been that halo error is negatively related to accuracy of ratings. W. H. Cooper (1981) evaluated this assumption by examining correlation coefficients between measures of accuracy and halo error from five earlier studies of performance and trait ratings. Because the correlation coefficients were typically positive. Cooper concluded that a "paradoxical" positive relation exists between halo error and accuracy. However, there is no paradox; some of these positive correlation coefficients were between halo error and inaccuracy, whereas others were based on analyses that did not take into consideration negative halo errors. When analyses that correct these problems were performed on two sets of data (R. Tallarigo, 1986, n = 107; R.}. Vance, K. W. Kuhnert, & 3. L. Farr, 1978, n = 112), all significant (p < .05) correlation coefficients between measures of accuracy and halo error were negative. The use of halo error measures, the possibility of negative halo errors, and implications of the results for rater training are discussed.

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