Componential analysis of interpersonal perception data.

We examine the advantages and disadvantages of 2 types of analyses used in interpersonal perception studies: componential and noncomponential. Componential analysis of interpersonal perception data (Kenny, 1994) partitions a judgment into components and then estimates the variances of and the correlations between these components. A noncomponential analysis uses raw scores to analyze interpersonal perception data. Three different research areas are investigated: consensus of perceptions across social contexts, reciprocity of attraction, and individual differences in self-enhancement. Finally, we consider criticisms of componential analysis. We conclude that interpersonal perception data necessarily have components (e.g., perceiver, target, measure, and their interactions), and that the researcher needs to develop a model that best captures the researcher's questions.

Agreement in personality judgments within and between nonoverlapping social groups in collectivist cultures.

The social context hypothesis states that people behave differently in different social groups because group norms and context-specific interpersonal relationships uniquely affect behavior. Consequently, a person who is a member of different, nonoverlapping social groups (i. e., the members of different groups are unacquainted) should be judged consensually on personality traits within each group; however, between groups there should be less agreement in judgments. This research focused on cultural moderation of the social context effect in two collective cultures (China and Mexico) with different norms for interpersonal relationships. Among Chinese, there was greater consensus in trait judgments within groups than between groups, whereas in Mexico, agreement within and between groups was equivalent. Culturally based relationship norms that affect cross-context consistency of behavior and, in turn, the consistency of trait judgments across groups were described.