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Components of Source Credibility Michael H. Birnbaum Fullerton, California, USA

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Outline I will review studies in which people aggregate evidence from sources A table of analogies can be used to link different content domains Useful to distinguish several factors describing source: expertise, bias, liking Also useful to consider “bias” of the judge, which I call point of view.

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Averaging Model A, B, and C are three sources of information. When a source’s message is not presented, its weight is assumed to be zero. The initial impression is represented by w 0 and s 0.

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Separating Weight and Value: the Zen of Weights To estimate the weight of factor A we do NOT examine the effect of A (that confounds weight and value). To estimate the weight of A, we examine the effects of B and C. To estimate the weight of A, we leave it out.

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Source Expertise and Bias Expertise refers to the validity of a source of information. An “expert” source is one who has skill and knowledge to know the right answer. Bias refers to tendency for a source to over or underestimate the “true” value. Point of View refers to the judge’s bias, produced by factors that provide asymmetric losses to the judge.

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Analogies Dependent VariableScale valueWeight Numerical predictionCue valueCue validity LikingAdjectives describing a person Length of acquaintance of source to target IQ predictionBiological and adopting parents’ IQs and SES Beliefs about heredity and environment as determinants of IQ Buying /Selling Prices of used cars Estimates of valueMechanical expertise of sources providing estimates Value of InvestmentsExperts’ predictions of future value Training and history of accuracy Buying & Selling Prices, choices among gambles Prize valuesProbabilities of winning prizes Probabilistic InferenceData: base rate, symptoms or source’s judgment Hit rate minus false alarm rate of source Medical decisionsDoctors’ opinionsTraining and bedside manner

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Analogies of Source Bias and Judge’s Viewpoint TaskBiasPoint of View Buying and Selling Prices Friend of buyer, seller, or independent Identify with buyer, seller, or “fair” judge TrialWitness for defense or prosecution Identify with defense, prosecution, or judge Political ArgumentsRepublican, DemocratIdentify with political party or independent Gambles: Estimates of ppn winning colors in urn Optimist or pessimistMood induction

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Some Results We can reject additive model for all domains in favor of averaging model. Scholars are usually more amazed by the predictions of the additive regression model than they are by the data. Expertise magnifies the effect of bias of a source; consistent with scale-adjustment model.

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Point of View Instructions to identify with the buyer, seller, or “fair” judge produce changes in rank order. Changing preference orders consistent with configural weight model and assumption of scale convergence data refute the “loss aversion” “endowment” model of Tversky & Kahneman (1991). According to that model, ratio of selling to buying price should be constant.

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Base Rate and Source Neglect Data obtained within-subjects do not support the claim of “neglect” of base rates or source credibility. Between-subjects, it has been shown that 9 is a significantly “bigger” number than 221. I remain skeptical about findings from between-Ss designs.

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Cumulative Prospect Theory Results with judged prices of gambles refute cumulative prospect theory combined with the theory of “loss aversion.” Choice data show 12 “paradoxes” of CPT which indicate that this model is not descriptive of choices between gambles.

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Summary Data accumulated over the last 25 years have favored Averaging over Additive models, consistent with claims by N. Anderson. Inconsistent with Anderson, however, data also show violations of parallel-averaging or differentially weighted models in favor of configural weighting. Effect of bias of a source is amplified by expertise, consistent with scale-adjustment model. Data violate CPT account of buying and selling prices; instead, rank orders change, consistent with configural weighting model.

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