title: Componential analysis of interpersonal perception data.
abstract: 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.
authors.affiliation: Department of Psychology, University of Connecticut, Storrs, CT 06269-1020, USA. kenny@uconn.edu.
authors.forename: David A
authors.initials: DA
authors.lastname: Kenny
authors.forename: Tessa V
authors.initials: TV
authors.lastname: West
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Malloy
authors.forename: Linda
authors.initials: L
authors.lastname: Albright
mesh_terms: D006801:Humans; D007398:Interpersonal Relations; D007600:Judgment; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*; D012938:Social Perception