The More You Ask For,the More You Get: Anchoring in Personal Injury Verdicts

The ‘anchoring and adjustment’ bias was demonstrated in a personal injury case using mock jurors. In Experiment 1, the ad damnum, or requested compensation, was manipulated across participants. In Experiment 2, anchors were operationalized as the strength of the legal evidence. Both monetary and causal anchors systematically influenced judgments of the probability that the defendant caused the plaintiff's injuries, compensation awarded, and perceptions of the litigants. These results indicate that anchoring occurs in legal applications, and that plaintiffs would do well to request large compensation awards. In addition, anchors expressed on one scale affected judgments expressed on another scale. This cross-modality anchoring stands in contrast to previous studies. Finally, these anchoring effects are unlikely to be explained by either demand effects or perceived relevance of the anchor.     

BOOK REVIEWS

Book reviewed in this article: Human Information Processing: An Introduction to Psychology. By Peter H. Lindsay and Donald A. Norman . Human Memory: Research and Theory. By L. S. Cermak . Organization of Memory. Edited by Endel Tulving and Wayne Donaldson . Associations or Addresses: a Study in Serial Verbal Learning. By F. J. Strømnes . Verbal Learning Research and the Technology of Written Instruction. Edited by Ernst Z. Rothkopf and Paul E. Johnson . Inhibition and Learning. Edited by R. A. Boakes and M. S. Halliday . Purposive Explanation in Psychology. By Margaret A. Boden . The Double Brain. By S. Dimond . Limbic System Mechanisms and Autonomic Function. Edited by C. H. Hockman . Languages of the Brain: Experimental Paradoxes and Principles in Neuropsychology. By K. H. Pribram . Perception in Everyday Life. By S. Howard Bartley . Language and Social Behaviour. By W. P. Robinson . Language Development: Structure and Function. By Philip S. Dale . Language of the Mentally Retarded. Edited by Richard L. Schiefelbusch . Studies in Dyadic Communication. Edited by A. W. Siegman and B. Pope . The Social Animal. By Elliot Aronson . Moral Learning and Development, Theory and Research. By Douglas Graham . The Social Influence Processes. Edited by J. T. Tedeschi . Perception and Cognition: a Cross-Cultural Perspective. By Barbara B. Lloyd . The Psychology of Humor. Edited by J. H. Goldstein and P. E. Mc Ghee . Males and Females. By Corinne Hutt . The Interview: Research on its Anatomy and Structure. By Joseph D. Matarazzo and Arthur N. Wiens . Inquiring Man: the Theory of Personal Constructs. By D. Bannister and Fay Fransella . Change and Continuity in Infancy. By Jerome Kagan . The Psychology of Consciousness. By R. E. Ornstein . Occupational Psychology. By Donald E. Super and Martin J. Bohn , Jr .     

Value-Orientation Analysis: The Adaptation of an Anthropological Model for Counseling Research

This article proposes the adaptation of Florence Kluckhohn's theory of Variation in Value Orientations for use in counseling research. The author summarizes Kluckhohn's theory and presents guidelines for its adaptation.     

How to Assess a Model's Testability and Identifiability

Formal definitions are given of the following intuitive concepts: (a) A model is quantitatively testable if its predictions are highly precise and narrow. (b) A model is identifiable if the values of its parameters can be ascertained from empirical observations. (c) A model is redundant if the values of some parameters can be deduced from others or if the values of some observables can be deduced from others. Various rules of thumb for nonredundant models are examined. The Counting Rule states that a model is quantitatively testable if and only if it has fewer parameters than observables. This rule can be safely applied only to identifiable models. If a model is unidentifiable, one must apply a generalization of the Counting Rule known as the Jacobian Rule. This rule states that a model is quantitatively testable if and only if the maximum rank (i.e., the number of linearly independent columns) of its Jacobian matrix (i.e., the matrix of partial derivatives of the function that maps parameter values to the predicted values of observables) is smaller than the number of observables. The Identifiability Rule states that a model is identifiable if and only if the maximum rank of its Jacobian matrix equals the number of parameters. The conclusions provided by these rules are only presumptive. To reach definitive conclusions, additional analyses must be performed. To illustrate the foregoing, the quantitative testability and identifiability of linear models and of discrete-state models are analyzed. Copyright 2000 Academic Press.