Journal ArticleDOI
Bayesian and regression modeling of graduate admission policy
TLDR
In this paper, four Ph.D.s in Biology made admissions judgments on 528 hypothetical applicants to their graduate program and compared Bayes' theorem and multiple regression analysis as descriptive models of the judges.About:
This article is published in Organizational Behavior and Human Performance.The article was published on 1972-10-01. It has received 27 citations till now. The article focuses on the topics: Regression analysis & Bayes' theorem.read more
Citations
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Journal ArticleDOI
The Multinomial, Multiattribute Logit Choice Model:
TL;DR: For the cross-sectional multiattribute approach to choice modeling, the multinomial logit is theoretically and empirically superior to the more commonly used regression approach as discussed by the authors, and it is shown to be better suited for cross-attribute choice modeling.
Journal ArticleDOI
Multi-Attribute Utility Models: A Review of Field and Field-Like Studies
TL;DR: This article contains a review of published field and field-like research studies concerned with the development and use of multi-attribute utility models and methods for estimating the model parameters.
Journal ArticleDOI
A comparison of holistic and decomposed judgment strategies in job analyses by job incumbents.
Journal ArticleDOI
Nonlinear and noncompensatory processes in performance evaluation
TL;DR: In this paper, two performance evaluation studies using the policy capturing (paper people) paradigm are reported, and the conclusions drawn from these studies were (a) most judges appeared to use nonlinear judgment strategies, (b) for many judges, the nonlinearity was compatible with a noncompensatory judgment strategy, and (c) regression methods are capable of detecting nonlinearness in a series of judgments, at least in the performance evaluation context.
References
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Journal ArticleDOI
Subjective Probability: A Judgment of Representativeness
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the subjective probability of an event, or a sample, is determined by the degree to which it is similar in essential characteristics to its parent population and reflects the salient features of the process by which it was generated.
Journal ArticleDOI
Comparison of Bayesian and Regression Approaches to the Study of Information Processing in Judgment.
Paul Slovic,Sarah Lichtenstein +1 more
TL;DR: This work examines the models that have been developed for describing and prescribing the use of information in decision making, the major experimental paradigms, and the major empirical results and conclusions of these two approaches.
Journal ArticleDOI
The conceptual framework of psychology
TL;DR: Brunswik as discussed by the authors reviewed the book by Egon Brunswik (see record 1952-05895-000) and described it as "a slim volume, hardly a book, rather a monograph of barely a hundred pages".
Book ChapterDOI
Conservatism in human information processing
Abstract: … An abundance of research has shown that human beings are conservative processors of fallible information. Such experiments compare human behavior with the outputs of Bayes's theorem, the formally optimal rule about how opinions (that is, probabilities) should be revised on the basis of new information. It turns out that opinion change is very orderly, and usually proportional to numbers calculated from Bayes's theorem – but it is insufficient in amount. A convenient first approximation to the data would say that it takes anywhere from two to five observations to do one observation's worth of work in inducing a subject to change his opinions. A number of experiments have been aimed at an explanation for this phenomenon. They show that a major, probably the major, cause of conservatism is human misaggregation of the data. That is, men perceive each datum accurately and are well aware of its individual diagnostic meaning, but are unable to combine its diagnostic meaning well with the diagnostic meaning of other data when revising their opinions. … Probabilities quantify uncertainty. A probability, according to Bayesians like ourselves, is simply a number between zero and one that represents the extent to which a somewhat idealized person believes a statement to be true. The reason the person is somewhat idealized is that the sum of his probabilities for two mutually exclusive events must equal his probability that either of the events will occur.
Related Papers (5)
Comparison of Bayesian and Regression Approaches to the Study of Information Processing in Judgment.
Paul Slovic,Sarah Lichtenstein +1 more