Conjoint-measurement framework for the study of probabilistic information processing.

Thomas S. Wallsten

38 citations0/0 files storedPsychological Review

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Decision-Making and Behavioral Economics Psychometric Methodologies and Testing Bayesian Modeling and Causal Inference

Abstract

The theory of conjoint measurement described by Krantz et al. (1971) is shown to indicate how a descriptive model of human processing of probabilistic information built around Bayes' rule is to be tested and how it is to be used to obtain subjective scale values. Specific relationships concerning these scale values are shown to emerge, and the theoretical prospects resulting from this development are discussed.

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References

1971Comparison of Bayesian and regression approaches to the study of information processing in judgment 1982Conservatism in human information processing 1966Conservatism in a simple probability inference task.1965Analysis of Factorial Experiments by Estimating Monotone Transformations of the Data 1971Conjoint-measurement analysis of composition rules in psychology.1965Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing 1972Individual Decision Behavior 1965Sample size and the revision of subjective probabilities.

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1984Decompositional Multiattribute Preference Models in Spatial Choice Analysis: A Review of Some Recent Developments 1983Formal Choice Models in Marketing 1984Modeling External Auditors' Evaluations of Internal Auditing 1979Axiomatic and Numerical Conjoint Measurement: An Evaluation of Diagnostic Efficacy 1976Using conjoint-measurement models to investigate a theory about probabilistic information processing

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